A method and system for dynamically collecting ice layer thickness data

Abstract

The present application relates to a method and system for dynamically collecting ice layer thickness data, belonging to the field of icing sensing technology, and solves the problem of difficulty in monitoring the entire process of airframe icing. The method includes: obtaining the current ice layer thickness value output after the target device currently in use collects the ice layer thickness; the target device is one of the first data detection device, the second data detection device, or the dynamic thickness prediction model; the first data detection device is used to output the current ice layer thickness value when the ice layer thickness is lower than the first critical value; the second data detection device is used to output the current ice layer thickness value when the ice layer thickness is higher than the second critical value; the dynamic thickness prediction model is used to output the current ice layer thickness value when the ice layer thickness is within the target range; the target range is a range greater than or equal to the first critical value and less than or equal to the second critical value; determining the target device for outputting the ice layer thickness next time according to the current ice layer thickness.

Description

Technical Field

[0001] This application relates to the field of icing sensing technology, and particularly relates to a method and system for dynamically collecting ice layer thickness data. Background Art

[0002] During aviation operations, adverse atmospheric conditions such as cold and convection cause moisture in the air to condense on the aircraft surface, posing potential hazards to aircraft operation. To take timely measures to deal with the icing problem on the fuselage surface, some methods for detecting the icing thickness on the aircraft surface have been studied in this field. However, due to the different applicable ranges of different detection methods and the limited applicable ranges of different detection methods, and since the ice layer on the fuselage is changing, it is difficult to monitor the entire process of icing on the fuselage, there are blank areas in the detection data, which brings inconvenience to taking measures to deal with the icing problem on the fuselage surface. Summary of the Invention

[0003] A method and system for dynamically collecting ice layer thickness data proposed in an embodiment of this application solve the problem of difficult to monitor the entire process of icing on the fuselage, dynamically select detection methods suitable for different icing extremes, and for the blank areas in the detection data, use a dynamic thickness prediction model to dynamically predict the icing thickness to achieve the full-process detection of ice growth.

[0004] In a first aspect, in an embodiment, a method for dynamically collecting ice layer thickness data is provided, which is applied to a system for dynamically collecting ice layer thickness data. The system for dynamically collecting ice layer thickness data includes a first data detection device, a second data detection device, and a dynamic thickness prediction model. The method includes: obtaining a current ice layer thickness value output after collecting the ice layer thickness by a target device currently in use; the target device is one of the first data detection device, the second data detection device, or the dynamic thickness prediction model; the first data detection device is used to output the current ice layer thickness value when the ice layer thickness is lower than a first critical value; the second data detection device is used to output the current ice layer thickness value when the ice layer thickness is higher than a second critical value; the dynamic thickness prediction model is used to output the current ice layer thickness value when the ice layer thickness is within a target range; the target range is a range greater than or equal to the first critical value and less than or equal to the second critical value; determining a target device for outputting the ice layer thickness next time according to the current ice layer thickness.

[0005] In an embodiment, if the target device currently in use is the first data detection device, determining a target device for outputting the ice layer thickness next time according to the current ice layer thickness includes:

[0006] When the current ice layer thickness reaches the first critical value, reset the first data detection device, call the dynamic thickness prediction model, use the dynamic thickness prediction model to predict the dynamic change of the ice layer thickness based on the current ice layer thickness, obtain the estimated value of the ice layer thickness, and output the estimated value of the ice layer thickness.

[0007] In one embodiment, if the target device currently in use is the second data detection device, determining the target device for outputting the ice layer thickness next time according to the current ice layer thickness includes:

[0008] When the current ice layer thickness decreases to the second critical value, call the dynamic thickness prediction model, use the dynamic thickness prediction model to predict the dynamic change of the ice layer thickness based on the current ice layer thickness, obtain the estimated value of the ice layer thickness, and output the estimated value of the ice layer thickness.

[0009] In one embodiment, the second data detection device is further configured to collect the ice layer thickness as the measurement comparison value of the ice layer thickness when the ice layer thickness is within the target range; after using the dynamic thickness prediction model to predict the dynamic change of the ice layer thickness based on the current ice layer thickness, the method further includes:

[0010] Adjust the output of the dynamic thickness prediction model according to the measurement comparison value and the estimated value of the ice layer thickness to obtain the corrected ice layer thickness;

[0011] Output the corrected ice layer thickness.

[0012] In a second aspect, in one embodiment, a dynamic data acquisition system for ice layer thickness is provided, and the dynamic data acquisition system for ice layer thickness includes: a first data detection device, a second data detection device, a dynamic thickness prediction model, and a detection and calculation device;

[0013] The detection and calculation device is configured to obtain the current ice layer thickness value output after the target device currently in use collects the ice layer thickness, and determine the target device for outputting the ice layer thickness next time according to the current ice layer thickness.

[0014] The first data detection device is used as the target device when the ice layer thickness is lower than the first critical value to detect the current ice layer thickness value;

[0015] The second data detection device is used as the target device when the ice layer thickness is higher than the second critical value to detect the current ice layer thickness value;

[0016] The dynamic thickness prediction model is used as the target device when the ice layer thickness is within the target range to detect the current ice layer thickness value; the target range is a range greater than or equal to the first critical value and less than or equal to the second critical value.

[0017] The beneficial effects of this application are:

[0018] The above dynamic thickness prediction model predicts the ice layer state at the current time after a preset time step ΔT according to the previous ice layer thickness state and the ice layer growth law. Since the dynamic thickness prediction model can predict the ice layer thickness from 1 mm to 1.5 mm, it fills the blank area of ice layer thickness measurement. In the embodiment of the present application, a first data detection device is also used for the ice layer range less than 1 mm to solve the problem of signal overlap caused by thin ice layers. For the ice layer range greater than 1.5 mm, the transit time of the ultrasonic reflection signal collected by the ultrasonic pulse echo icing sensor is calculated, and then the ice layer thickness is calculated according to the transit time. Starting from the objective fact that the ice thickness on the fuselage is dynamically changing, different detection methods are dynamically selected for the ice thickness at different stages, which is more targeted, improves the detection accuracy, and realizes the whole process detection of ice growth. Description of the Drawings

[0019] Figure 1 is a flowchart of the steps of the ice layer thickness dynamic data acquisition method proposed in the embodiment of the present application;

[0020] Figure 2 is a schematic diagram of the ice layer thickness dynamic data acquisition system in the embodiment of the present application;

[0021] Figure 3 is a schematic diagram of the ultrasonic pulse echo icing sensor;

[0022] Figure 4 is a schematic diagram of another ice layer thickness dynamic data acquisition system proposed in the embodiment of the present application. Detailed Embodiments

[0023] The present application will be further described in detail below in conjunction with the accompanying drawings through specific embodiments. Similar elements in different embodiments are labeled with related similar element numbers. In the following embodiments, many details are described to make the present application better understood. However, those skilled in the art can easily recognize that some of the features can be omitted in different situations, or can be replaced by other elements, materials, and methods. In some cases, some operations related to the present application are not shown or described in the specification to avoid the core part of the present application being overwhelmed by excessive description. For those skilled in the art, it is not necessary to describe these related operations in detail, and they can fully understand the related operations according to the description in the specification and the general technical knowledge in the art.

[0024] In addition, the features, operations, or characteristics described in the specification can be combined in any suitable manner to form various embodiments. At the same time, the steps or actions in the method description can also be reordered or adjusted in a manner obvious to those skilled in the art. Therefore, the various sequences in the specification and drawings are only for clearly describing a certain embodiment and do not mean a necessary sequence, unless it is stated otherwise that a certain sequence must be followed.

[0025] The serial numbers assigned to the components herein, such as "first", "second", etc., are only used to distinguish the objects described and do not have any sequential or technical meaning. And as used in this application, "connection" and "coupling", unless otherwise specified, both include direct and indirect connection (coupling).

[0026] In view of the problem that there are blank areas in the detection data in the prior art for detecting the icing thickness of an aircraft, the embodiments of this application set corresponding icing thickness detection devices for different icing thickness ranges. For thin ice layers with an ice thickness within 1 mm, a first data detection device that can achieve signal separation and calculate the icing thickness based on the separated signals is set; for ice layers with an ice thickness above 1.5 mm, a first data detection device that measures the ice thickness based on ultrasonic pulse echo is set; for the transition part between 1 mm and 1.5 mm, a dynamic thickness prediction model is set to dynamically estimate the growth of the ice thickness, improve the detection accuracy, and achieve the full-process detection of ice growth.

[0027] Figure 1 is a flowchart of the steps of the method for dynamically collecting ice layer thickness data proposed by the embodiments of this application, which is applied to the system for dynamically collecting ice layer thickness data. Figure 2 is a schematic diagram of the system for dynamically collecting ice layer thickness data in the embodiments of this application. As Figure 2 shown, the system for dynamically collecting ice layer thickness data includes a first data detection device 11, a second data detection device 12, and a dynamic thickness prediction model 13.

[0028] As Figure 1 shown, the method includes:

[0029] S100: Obtain the current ice layer thickness value output after collecting the ice layer thickness by the target device currently in use.

[0030] The target device is one of the first data detection device, the second data detection device, or the dynamic thickness prediction model.

[0031] The first data detection device is used to output the current ice layer thickness value when the ice layer thickness is lower than the first critical value; the second data detection device is used to output the current ice layer thickness value when the ice layer thickness is higher than the second critical value; the dynamic thickness prediction model is used to output the current ice layer thickness value when the ice layer thickness is within the target range; the target range is a range greater than or equal to the first critical value and less than or equal to the second critical value.

[0032] The first critical value can be set to 1 mm, and the second critical value can be set to 1.5 mm.

[0033] Exemplarily, after the aircraft flies, the target device can be set to the first data detection device first. When it is detected that the current ice layer thickness value output by the first data detection device reaches 1 mm, the first data detection device is reset, and the dynamic thickness prediction model is called to predict the dynamic change of the ice layer thickness based on the current ice layer thickness, and an estimated ice layer thickness value is obtained and the estimated ice layer thickness value is output. At the same time, the second data detection device can also be called, and the data output by the second data detection device is used for comparison, and it can be mutually verified with the predicted value output by the dynamic thickness prediction model to determine the accuracy of outputting the current ice layer thickness; when the current ice layer thickness output by the dynamic thickness prediction model increases to 1.5 mm, the dynamic thickness prediction model is no longer called, and the measured value of the ice layer thickness by the second data detection device is directly output.

[0034] S110: Determine the target device for outputting the ice layer thickness next time according to the current ice layer thickness.

[0035] The ways of executing step S110 include:

[0036] If the currently used target device is the first data detection device, determining the target device for outputting the ice layer thickness next time according to the current ice layer thickness includes:

[0037] When the current ice layer thickness reaches the first critical value, reset the first data detection device, call the dynamic thickness prediction model, and use the dynamic thickness prediction model to predict the dynamic change of the ice layer thickness based on the current ice layer thickness to obtain an estimated ice layer thickness value, and output the estimated ice layer thickness value.

[0038] If the currently used target device is the first data detection device and the current ice layer thickness output by the first data detection device is still less than 1 mm, continue to call the first data detection device to measure the ice layer thickness. If the current ice layer thickness output by the first data detection device exceeds 1 mm, call the dynamic thickness prediction model with an applicable ice layer thickness range of 1 mm to 1.5 mm to estimate the ice layer thickness.

[0039] One example of the present application gives the principle of the first data detection device detecting a thin ice layer with a thickness below 1 mm:

[0040] The first data detection device is based on Figure 3The ultrasonic pulse echo icing sensor setup shown Figure 3 is a schematic diagram of an ultrasonic pulse echo icing sensor. The ultrasonic pulse echo icing sensor includes an ultrasonic pulse transmitting and receiving system, an ultrasonic probe, and an aluminum layer. After the fuselage is iced, the ice layer adheres closely to the aluminum layer. The ultrasonic pulse transmitting and receiving system emits an electrical signal, the ultrasonic probe emits an ultrasonic signal. After the ultrasonic signal passes through the aluminum layer, it is reflected at the aluminum-ice interface (the interface where the aluminum layer and the ice layer meet) to obtain a reflected signal , after the ultrasonic signal passes through the aluminum layer and the ice layer, it is reflected at the ice-air interface (the interface where the ice layer and the air layer meet) to obtain , and the ultrasonic probe collects and and then returns them to the ultrasonic pulse transmitting and receiving system. The ultrasonic pulse transmitting and receiving system can upload them to an electronic device for signal calculation and processing. , is an integer greater than 0, represents the width of the reflected signal signal sequence.

[0041] The first data detection device is arranged in the electronic device to perform signal separation processing on , and then uses the separated signal to calculate the ice layer thickness, solving the problem that when the ice layer is too thin, there are overlapping signals between two different , for example and .

[0042] The process of the first data detection device performing signal separation on includes:

[0043] Arrange according to the acquisition time of to form a signal matrix D, calculate the covariance matrix of the signal matrix, and determine whether the signal matrix is a correlation matrix. If the signal matrix is a correlation matrix, there is an associated part between different elements , and proceed to the next step. Calculate the eigenvalues and eigenvectors V of the signal matrix, perform a product operation on the signal matrix and the eigenvectors U = VD, convert the signal matrix into an uncorrelated matrix, thereby obtaining the independent elements { } in the uncorrelated matrix, and project the independent elements { , } into the Principal Component Analysis (PCA) space, amplify the principal components of each element in { }, and enhance the difference between signals.

[0044] Furthermore, a demixing matrix can also be used for { } to perform non-Gaussianization on its elements, clarify the different parts of different signals, establish an objective function based on characterizations such as maximizing negative entropy, maximizing kurtosis, maximizing likelihood, or minimizing mutual information, and use the Newton iteration method to update the demixing matrix with the objective function. The trained demixing matrix can calculate to make the different elements in { } have obvious difference characteristics.

[0045] After performing signal separation processing on , the method of calculating the ice layer thickness using the separated signals can adopt the time-of-flight (ToF, Time of Flight) algorithm. The process of the time-of-flight algorithm includes:

[0046] The time of flight refers to the time interval between the first signal peak in the reflected signal and the first signal peak in the reflected signal . The process of calculating the time of flight can be expressed by the following formula:

[0047] ,

[0048] where represents the signal sequence width of the reflected signal , represents the signal sequence width of the optimized , represents the sampling frequency that can be set to 100 MHz.

[0049] The formula for calculating the ice layer thickness based on the time of flight can be expressed by formula (5):

[0050] (5)

[0051] where represents the ice layer thickness, represents the sound speed in the ice layer, which can be obtained through experimental measurement.

[0052] The second data detection device can also calculate the ice layer thickness using the above time-of-flight (ToF, Time of Flight) algorithm, directly calculate the collected reflected signal and the reflected signal , and does not need to perform the operation of signal separation.

[0053] During the flight of the aircraft for a period of time, the ice thickness may continue to increase, or may decrease due to the effect of the deicing measures taken. Therefore, in the embodiments of the present application, the method of calculating the ice layer thickness will be dynamically adjusted according to the previous ice layer thickness throughout the monitoring process.

[0054] If the currently used target device is the second data detection device, determining the target device for outputting the ice layer thickness next time according to the current ice layer thickness includes:

[0055] When the current ice layer thickness decreases to the second critical value, call the dynamic thickness prediction model, and use the dynamic thickness prediction model to predict the dynamic change of the ice layer thickness based on the current ice layer thickness to obtain an estimated value of the ice layer thickness, and output the estimated value of the ice layer thickness.

[0056] If the currently used target device is the dynamic thickness prediction model, determining the target device for outputting the ice layer thickness next time according to the current ice layer thickness includes:

[0057] When the current ice layer thickness reaches the second critical value, output the ice layer thickness detection value collected by the second data detection device;

[0058] When the current ice layer thickness decreases to the first critical value, clear the historical data of the dynamic thickness prediction model, call the first data detection device to detect the ice layer thickness, and output the ice layer thickness detection value collected by the first data detection device.

[0059] Exemplarily, a time step ΔT for detecting the ice layer thickness can be set, ultrasonic data is collected periodically, and the current ice layer thickness is calculated. The dynamic thickness prediction model is used for the nth detection of the ice layer thickness by the ice layer thickness dynamic data acquisition system, and the current ice layer thickness output by the dynamic thickness prediction model is , if 1mm < < 1.5mm, the dynamic thickness prediction model is still used for the (n + 1)th detection of the ice layer thickness; if > 1.5mm, the second data detection device is used for the (n + 1)th detection of the ice layer thickness; if < 1mm, the second data detection device is used for the (n + 1)th detection of the ice layer thickness, and the ice layer thickness detected by the second data detection device for the (n + 1)th time is 1mm < < 1.5mm, the dynamic thickness prediction model is used for the (n + 2)th detection of the ice layer thickness.

[0060] The embodiment of the present application also proposes that the second data detection device is further used to collect the ice layer thickness as a measurement comparison value of the ice layer thickness when the ice layer thickness is within the target range;

[0061] The process of detecting the ice layer thickness using the dynamic thickness prediction model may include:

[0062] S201: If the current ice layer thickness decreases to the second critical value, call the dynamic thickness prediction model, and use the dynamic thickness prediction model to predict the dynamic change of the ice layer thickness based on the current ice layer thickness to obtain an estimated value of the ice layer thickness.

[0063] A dynamic thickness prediction model in this application predicts the dynamic change of ice layer thickness through a calculation model with formula (1) to obtain an estimated value of ice layer thickness:

[0064] (1)

[0065] Wherein, represents the current ice layer thickness state matrix, represents the state matrix of the estimated value of ice layer thickness at the next moment after the current time, is a time step matrix preset according to the icing conditions, is a constant matrix, which is adjusted by matrix Q and matrix R;

[0066] can be set to , is the time step;

[0067] Output the estimated value of the ice layer thickness based on the current ice layer thickness state matrix.

[0068] Alternatively, steps S202 to S204 can be continued to correct the current ice layer thickness state by using the measurement comparison value output by the second data detection device, and then obtain the ice layer thickness based on the corrected current ice layer thickness state.

[0069] S202: Use the second data detection device to collect the measurement comparison value of the ice layer thickness.

[0070] Establish a measurement comparison value matrix of the measurement comparison value to obtain , and substitute the measurement comparison value matrix into formula (2) to correct the measurement comparison value:

[0071] (2)

[0072] represents the corrected measurement comparison value matrix, represents the confidence level of the measurement comparison value, represents the measurement noise; is an identity matrix, which can be expressed as .

[0073] S203: Adjust the output of the dynamic thickness prediction model according to the measurement comparison value and the estimated value of the ice layer thickness to obtain the corrected ice layer thickness.

[0074] Substitute the corrected measurement comparison matrix and the ice layer thickness estimated value state matrix into formula (3) to adjust the estimated value of the ice layer thickness to obtain the corrected ice layer thickness matrix;

[0075] (3)

[0076] Among them, represents the corrected ice thickness matrix, is the adjustment parameter;

[0077] can be calculated by formula (4).

[0078] S204: Output the corrected ice thickness value.

[0079] Based on the corrected ice thickness matrix, calculate the corrected ice thickness value and output the corrected ice thickness value.

[0080] In one embodiment, when the ice thickness dynamic data acquisition method is executed for the nth time, the corrected ice thickness value is output by using the dynamic thickness prediction model. If the corrected ice thickness value is within the target range of 1 mm to 1.5 mm, when the ice thickness dynamic data acquisition method is executed for the (n + 1)th time, the dynamic thickness prediction model is still used. The dynamic thickness prediction model can directly input the corrected ice thickness matrix into formula (1) as the current ice thickness state matrix for predicting the ice thickness for the (n + 1)th time.

[0081] When the corrected ice thickness is within the target range, substitute the corrected ice thickness matrix as the current ice thickness state matrix at the current moment into formula (1) to calculate the ice thickness estimated value state matrix at the next moment.

[0082] The embodiment of the present application also proposes a method for optimizing the adjustment parameter:

[0083] Adjust the adjustment parameter according to formula (4):

[0084] (4)

[0085] Among them, is the posterior estimation covariance matrix, , is the prior estimation covariance matrix, , is the process noise covariance, which can be calculated from the variance of the process data or adjusted through artificial experience, is the measurement noise covariance, which can be calculated from the variance of the measurement data or adjusted through artificial experience.

[0086] .

[0087] The dynamic thickness prediction model proposed in the embodiments of the present application predicts the ice layer state at the current time after a preset time step ΔT according to the previous ice layer thickness state and the ice layer growth law. Since the dynamic thickness prediction model can predict the ice layer thickness of 1 mm to 1.5 mm, it fills the blank area of ice layer thickness measurement; the embodiments of the present application also adopt a first data detection device for the ice layer range less than 1 mm to solve the problem of signal overlap caused by thin ice layers. For the ice layer range greater than 1.5 mm, the transit time of the ultrasonic reflection signal collected by the ultrasonic pulse echo icing sensor is calculated, and then the ice layer thickness is calculated according to the transit time. Starting from the objective fact that the icing thickness of the fuselage is dynamically changing, different detection methods are dynamically selected for the icing thickness at different stages, which is more targeted, improves the detection accuracy, and realizes the whole process detection of ice growth.

[0088] Figure 4 FIG. is a schematic diagram of another ice layer thickness dynamic data acquisition system proposed in the embodiments of the present application, as Figure 4 shown, another ice layer thickness dynamic data acquisition system includes: a first data detection device 11, a second data detection device 12, a dynamic thickness prediction model 13, and a detection and calculation device 14;

[0089] The detection and calculation device 14 is used to obtain the current ice layer thickness value output after the target device currently in use collects the ice layer thickness, and determine the target device for outputting the ice layer thickness next time according to the current ice layer thickness.

[0090] The first data detection device 11 is used as the target device when the ice layer thickness is lower than the first critical value to detect the current ice layer thickness value;

[0091] The second data detection device 12 is used as the target device when the ice layer thickness is higher than the second critical value to detect the current ice layer thickness value;

[0092] The dynamic thickness prediction model 13 is used as the target device when the ice layer thickness is within the target range to detect the current ice layer thickness value; the target range is a range greater than or equal to the first critical value and less than or equal to the second critical value.

[0093] Figure 4 For the ice layer thickness dynamic data acquisition system provided in the shown embodiment, its implementation principle and technical effects can be further referred to the relevant descriptions in the embodiments of the ice layer thickness dynamic data acquisition method.

[0094] Optionally, the second data detection device is further used to collect the ice layer thickness as the measurement comparison value of the ice layer thickness when the ice layer thickness is within the target range.

[0095] Optionally, the detection computing device includes a target device selection module, which is configured to reset the first data detection device when the current ice layer thickness reaches a first critical value, call the dynamic thickness prediction model, use the dynamic thickness prediction model to predict the dynamic change of the ice layer thickness based on the current ice layer thickness, obtain an estimated value of the ice layer thickness, and output the estimated value of the ice layer thickness.

[0096] Optionally, the target device selection module is further configured to call the dynamic thickness prediction model when the current ice layer thickness decreases to a second critical value, use the dynamic thickness prediction model to predict the dynamic change of the ice layer thickness based on the current ice layer thickness, obtain an estimated value of the ice layer thickness, and output the estimated value of the ice layer thickness.

[0097] Optionally, the target device selection module is further configured to output the ice layer thickness detection value collected by the second data detection device when the current ice layer thickness reaches the second critical value; when the current ice layer thickness decreases to the first critical value, clear the historical data of the dynamic thickness prediction model, call the first data detection device to detect the ice layer thickness, and output the ice layer thickness detection value collected by the first data detection device.

[0098] Optionally, the detection computing device includes a calculation output module, and the output module is configured to adjust the output of the dynamic thickness prediction model according to the measurement comparison value and the estimated value of the ice layer thickness to obtain a corrected ice layer thickness; output the corrected ice layer thickness.

[0099] Optionally, the dynamic thickness prediction model is specifically configured to predict the state matrix of the estimated value of the ice layer thickness through formula (1) when the ice layer thickness is within a target range;

[0100] The calculation output module is specifically configured to output the estimated value of the ice layer thickness based on the current ice layer thickness state matrix.

[0101] Optionally, the calculation output module is further specifically configured to correct the measurement comparison value collected by the second data detection device through formula (2), and then input the corrected measurement comparison value matrix and the estimated value of the ice layer thickness into formula (3) to optimize the state matrix of the estimated value of the ice layer thickness.

[0102] The calculation output module is further specifically configured to output the corrected ice layer thickness value based on the corrected ice layer thickness matrix.

[0103] Optionally, the calculation output module is further specifically configured to adjust the adjustment parameter according to formula (4).

[0104] Regarding each module / unit included in each device described in the above embodiments, it can be a software module / unit, a hardware module / unit, or it can be partially a software module / unit and partially a hardware module / unit. For example, for each device applied to or integrated into a chip, each module / unit included therein can all be implemented in the form of hardware such as circuits. Or, at least some of the modules / units can be implemented in the form of a software program that runs on a processor integrated inside the chip, and the remaining part of the modules / units can be implemented in the form of hardware such as circuits. For each device applied to or integrated into a chip module, each module / unit included therein can all be implemented in the form of hardware such as circuits. Different modules / units can be located in the same component (such as a chip, a circuit module, etc.) or different components of the chip module. Or, at least some of the modules / units can be implemented in the form of a software program that runs on a processor integrated inside the chip module, and the remaining part of the modules / units can be implemented in the form of hardware such as circuits. For each device applied to or integrated into an electronic terminal device, each module / unit included therein can all be implemented in the form of hardware such as circuits. Different modules / units can be located in the same component (such as a chip, a circuit module, etc.) or different components inside the electronic terminal device. Or, at least some of the modules / units can be implemented in the form of a software program that runs on a processor integrated inside the electronic terminal device, and the remaining (if any) part of the modules / units can be implemented in the form of hardware such as circuits.

[0105] In an embodiment of the present application, a computer-readable storage medium is provided. A program is stored on the storage medium, and the stored program includes a method that can be loaded and processed by a processor in any of the above embodiments.

[0106] Those skilled in the art can understand that all or part of the functions of the various methods in the above embodiments can be implemented in the form of hardware or in the form of a computer program. When all or part of the functions in the above embodiments are implemented in the form of a computer program, the program can be stored in a computer-readable storage medium. The storage medium can include: read-only memory, random access memory, magnetic disks, optical disks, hard disks, etc. The above functions are implemented by a computer executing the program. For example, the program is stored in the memory of a device. When the program stored in the memory is executed by a processor, all or part of the above functions can be achieved. In addition, when all or part of the functions in the above embodiments are implemented in the form of a computer program, the program can also be stored in a storage medium such as a server, another computer, a magnetic disk, an optical disk, a flash drive, or a mobile hard disk. It is saved to the memory of a local device by downloading or copying, or the system of the local device is updated. When the program stored in the memory is executed by a processor, all or part of the functions in the above embodiments can be achieved.

[0107] The above uses specific examples to elaborate on this application, which is only used to help understand this application and is not intended to limit this application. For those skilled in the technical field to which this application pertains, based on the idea of this application, several simple deductions, deformations, or substitutions can also be made.

Claims

1. A method for collecting dynamic data of ice thickness, characterized in that: Applied to an ice thickness dynamic data acquisition system, the ice thickness dynamic data acquisition system includes a first data detection device, a second data detection device and a dynamic thickness prediction model, the method includes: Obtaining a current ice thickness value outputted by a target device currently in use after collecting ice thickness; the target device is one of the first data detection device, the second data detection device or the dynamic thickness prediction model; the first data detection device is used to detect the current ice thickness value when the ice thickness is lower than a first critical value; the second data detection device is used to detect the current ice thickness value when the ice thickness is higher than a second critical value; the dynamic thickness prediction model is used to detect the current ice thickness value when the ice thickness is within a target range; the target range is a range greater than or equal to the first critical value and less than or equal to the second critical value; Determining a target device for outputting the ice thickness next time according to the current ice thickness; The first data detection device separates and processes the reflected signal, calculates the ice thickness using the separated signal, and collects ice data below 1 mm. The dynamic thickness prediction model predicts the dynamic change of ice thickness through the calculation model of formula (1) to obtain the estimated value of ice thickness: （1） in, represents the current ice thickness state matrix, Represents the state matrix of the estimated ice thickness at the next tick after the current time. is the time step matrix pre-set according to the icing conditions, is a constant matrix; Outputting the ice thickness estimate based on the current ice thickness state matrix; The current ice thickness state is corrected using the measurement comparison value output by the second data detection device, and then the ice thickness is obtained based on the corrected current ice thickness state: a measurement comparison value matrix of the measurement comparison value is established to obtain X k , put the measurement comparison value matrix into formula (2) （2） in, represents the corrected measurement comparison value matrix, Indicates the confidence level of the measured comparison value, represents the measured comparison value, represents the measurement noise; Substituting the corrected measurement comparison value matrix and the ice thickness estimation value state matrix into formula (3), adjusting the ice thickness estimation value matrix, and obtaining a corrected ice thickness matrix; （3） in, represents the modified ice thickness matrix, is the adjustment parameter; The corrected ice thickness value is output based on the corrected ice thickness matrix, and the ice thickness of 1-1.5 mm is estimated.

2. The ice thickness dynamic data acquisition method according to claim 1, characterized in that: If the target device currently in use is the first data detection device, determining the target device for outputting the ice layer thickness next time according to the current ice layer thickness includes: When the current ice layer thickness reaches a first critical value, the first data detection device is reset, the dynamic thickness prediction model is called, and the dynamic thickness prediction model is used to predict the dynamic change of the ice layer thickness based on the current ice layer thickness to obtain an estimated value of the ice layer thickness, and the estimated value of the ice layer thickness is output.

3. The ice thickness dynamic data acquisition method according to claim 1, characterized in that: If the target device currently in use is the second data detection device, determining the target device for outputting the ice layer thickness next time according to the current ice layer thickness includes: When the current ice layer thickness drops to a second critical value, the dynamic thickness prediction model is called, and the dynamic thickness prediction model is used to predict the dynamic change of ice layer thickness based on the current ice layer thickness to obtain an estimated value of ice layer thickness, and the estimated value of ice layer thickness is output.

4. The ice thickness dynamic data acquisition method according to claim 2 or 3, characterized in that: The second data detection device is also used to collect the ice thickness as the ice thickness measurement comparison value when the ice thickness is within the target range; after using the dynamic thickness prediction model to predict the dynamic change of the ice thickness based on the current ice thickness, the method also includes: Adjust the output of the dynamic thickness prediction model according to the measurement comparison value and the ice layer thickness estimation value to obtain a corrected ice layer thickness; The corrected ice thickness is output.

5. The ice thickness dynamic data acquisition method according to claim 1, characterized in that: Substituting the current ice thickness into formula (1) obtains the current ice thickness state matrix, including: When the corrected ice thickness is within the target range, the corrected ice thickness matrix is ​​substituted into formula (1) as the current ice thickness state matrix at the current moment to calculate the ice thickness estimation state matrix at the next moment.

6. The ice thickness dynamic data acquisition method according to claim 5, characterized in that: The method further comprises: Adjust the adjustment parameters according to formula (4): （4） in, is the posterior estimated covariance matrix, , is the a priori estimated covariance matrix, , is the process noise covariance, is the measurement noise covariance.

7. The ice thickness dynamic data acquisition method according to claim 1, characterized in that: If the target device currently used is a dynamic thickness prediction model, determining the target device for outputting the ice thickness next time according to the current ice thickness includes: When the current ice thickness reaches a second critical value, outputting the ice thickness detection value collected by the second data detection device; When the current ice thickness decreases to a first critical value, the historical data of the dynamic thickness prediction model is cleared, the first data detection device is called to detect the ice thickness, and the ice thickness detection value collected by the first data detection device is output.

8. An ice thickness dynamic data acquisition system using the ice thickness dynamic data acquisition method according to any one of claims 1 to 7, characterized in that: The ice thickness dynamic data acquisition system comprises: a first data detection device, a second data detection device, a dynamic thickness prediction model and a detection calculation device; The detection and calculation device is used to obtain the current ice thickness value output by the target device currently in use after collecting the ice thickness, and determine the target device for outputting the ice thickness next time according to the current ice thickness; The first data detection device is used to detect the current ice thickness value as a target device when the ice thickness is lower than a first critical value; The second data detection device is used as a target device to detect the current ice thickness value when the ice thickness is higher than the second critical value; The dynamic thickness prediction model is used as a target device to detect the current ice thickness value when the ice thickness is within a target range; the target range is a range greater than or equal to a first critical value and less than or equal to a second critical value.

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