Ice layer thickness dynamic data acquisition method and system

Abstract

The invention relates to an ice layer thickness dynamic data acquisition method and system, relates to the technical field of icing sensing, and solves the problem that the whole icing process of a fuselage is difficult to monitor. The method comprises the following steps: acquiring a current ice layer thickness value output by a currently used target device after acquiring 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 for outputting a current ice layer thickness value when the ice layer thickness is lower than a first critical value; the second data detection device is used for outputting a current ice layer thickness value when the ice layer thickness is higher than a second critical value; the dynamic thickness prediction model is used for outputting a current ice layer thickness value when the ice layer thickness is in a target range; the target range is larger than or equal to a first critical value and smaller than or equal to a second critical value; and determining a target device for outputting the ice layer thickness next time according to the current ice layer thickness.

Description

Technical Field

[0001] The present application relates to the field of ice sensing technology, and in particular to a method and system for collecting dynamic data on ice thickness. Background Art

[0002] During aviation operations, unfavorable atmospheric conditions such as cold and convection cause moisture in the air to condense on the aircraft surface, causing potential hazards in operating the aircraft. In order to take timely measures to deal with the problem of icing on the fuselage surface, some methods for detecting the thickness of ice on the aircraft surface have been studied in this field. However, due to the different scopes of application of different detection methods, and the limited scope of application of different detection methods, and the fact that the ice layer on the fuselage is changing, it is difficult to monitor the entire process of fuselage icing, and there are blank areas in the detection data, which brings inconvenience to taking measures to deal with the problem of icing on the fuselage surface. Summary of the invention

[0003] The embodiments of the present application propose a method and system for collecting dynamic data on ice thickness, which solves the problem that it is difficult to monitor the entire icing process of the fuselage. It dynamically selects detection methods suitable for different icing extremes, and uses a dynamic thickness prediction model to dynamically predict the ice thickness in blank areas where there are detection data, thereby realizing full process detection of ice growth.

[0004] In a first aspect, an embodiment provides a method for dynamic data collection of ice layer thickness, which is applied to a dynamic data collection system for ice layer thickness, wherein the dynamic data collection system for ice layer thickness includes a first data detection device, a second data detection device, and a dynamic thickness prediction model, and the method includes: obtaining a current ice layer thickness value outputted after a target device currently in use collects 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 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; and according to the current ice layer thickness, determining a target device for outputting the ice layer thickness next time.

[0005] In one embodiment, 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.

[0006] 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: 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.

[0007] In one embodiment, the second data detection device is further used to collect the ice thickness as a measurement comparison value of the ice thickness 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 further 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.

[0008] In a second aspect, an embodiment provides an ice thickness dynamic data acquisition system, the ice thickness dynamic data acquisition system comprising: 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 currently used target device after collecting the ice thickness, and determine the target device for outputting the ice thickness next time according to the current ice thickness.

[0009] 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.

[0010] The beneficial effects of this application are: The above-mentioned dynamic thickness prediction model predicts the current ice state after a preset time step ΔT based on the ice thickness state of the previous time and the ice growth law. Since the dynamic thickness prediction model can predict the ice thickness of 1mm to 1.5mm, it fills the blank area of ​​ice thickness measurement; the embodiment of the present application also adopts a first data detection device for the ice range less than 1mm to solve the signal overlap problem caused by thin ice layers. For the ice range greater than 1.5mm, the ultrasonic reflection signal collected by the ultrasonic pulse echo icing sensor is calculated for the transit time, and then the ice thickness is calculated based on the transit time. Starting from the objective fact that the ice thickness of 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 detection of the entire process of ice growth. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] Figure 1 It is a flowchart of the steps of the ice thickness dynamic data collection method proposed in the embodiment of the present application; Figure 2 is a schematic diagram of an ice thickness dynamic data acquisition system in an embodiment of the present application; Figure 3 is a schematic diagram of an ultrasonic pulse-echo icing sensor; Figure 4 It is a schematic diagram of another ice thickness dynamic data acquisition system proposed in an embodiment of the present application. DETAILED DESCRIPTION

[0012] The present application is further described in detail below by specific embodiments in conjunction with the accompanying drawings. Wherein similar elements in different embodiments adopt associated similar element numbers. In the following embodiments, many detailed descriptions are intended to enable the present application to be 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, in order to avoid the core part of the present application being overwhelmed by too much description, and 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 based on the description in the specification and the general technical knowledge in the art.

[0013] In addition, the features, operations or characteristics described in the specification can be combined in any appropriate manner to form various implementations. At the same time, the steps or actions in the method description can also be interchanged or adjusted in a manner that is obvious to those skilled in the art. Therefore, the various sequences in the specification and the drawings are only for the purpose of clearly describing a certain embodiment and are not meant to be a required sequence, unless otherwise specified that a certain sequence must be followed.

[0014] 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 order or technical meaning. The "connection" and "coupling" mentioned in this application, unless otherwise specified, include direct and indirect connections (couplings).

[0015] In view of the problem of blank areas in the detection data of the existing technology for detecting the ice thickness of aircraft, the embodiment of the present application sets corresponding ice thickness detection devices for different ice thickness ranges. For thin ice layers with a thickness of less than 1 mm, a first data detection device is set up to realize signal separation and calculate the ice thickness based on the separated signal; for ice layers with a thickness of more than 1.5 mm, a first data detection device based on ultrasonic pulse echo measurement of ice thickness is set up; for the transition part of 1 mm to 1.5 mm, a dynamic thickness prediction model is set up to dynamically estimate the growth of ice thickness, improve detection accuracy, and realize full process detection of ice growth.

[0016] Figure 1 This is a flowchart of the steps of the ice thickness dynamic data collection method proposed in the embodiment of the present application, which is applied to the ice thickness dynamic data collection system. Figure 2 is a schematic diagram of the ice thickness dynamic data acquisition system in the embodiment of the present application, Figure 2 As shown, the ice thickness dynamic data acquisition system includes a first data detection device 11, a second data detection device 12 and a dynamic thickness prediction model 13.

[0017] like Figure 1 As shown, the method includes: S100: Obtaining a current ice thickness value output by a target device currently in use after collecting ice thickness.

[0018] The target device is one of the first data detection device, the second data detection device or the dynamic thickness prediction model.

[0019] The first data detection device is used to output the current ice thickness value when the ice thickness is lower than a first critical value; the second data detection device is used to output the current ice thickness value when the ice thickness is higher than a second critical value; the dynamic thickness prediction model is used to output 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.

[0020] The first critical value may be set to 1 mm, and the second critical value may be set to 1.5 mm.

[0021] For example, after the aircraft takes off, the target device can be set to the first data detection device. When it is detected that the current ice 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 ice thickness. At the same time, the second data detection device can also be called to compare the data output by the second data detection device. The data can be mutually verified with the predicted value output by the dynamic thickness prediction model to determine the accuracy of the output current ice thickness. When the current ice thickness output by the dynamic thickness prediction model increases to 1.5 mm, the dynamic thickness prediction model is no longer called, and the measurement value of the ice thickness by the second data detection device is directly output.

[0022] S110: Determine a target device for outputting the ice layer thickness next time according to the current ice layer thickness.

[0023] The method of executing step S110 includes: 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.

[0024] If the target device currently in use is the first data detection device, and the current ice 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 thickness; if the current ice thickness output by the first data detection device exceeds 1 mm, call a dynamic thickness prediction model applicable to the ice thickness range of 1 mm to 1.5 mm to estimate the ice thickness.

[0025] An example of the present application provides the principle of the first data detection device detecting a thin ice layer with a thickness of less than 1 mm: The first data detection device is based on Figure 3 The ultrasonic pulse echo ice sensor setup shown, Figure 3 This 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 frozen, the ice layer is close to the aluminum layer; the ultrasonic pulse transmitting and receiving system transmits an electrical signal, and the ultrasonic probe transmits 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 passing through the aluminum layer and the ice layer, the ultrasonic signal is reflected at the ice-air interface (the interface where the ice layer and the air layer meet), and the ultrasonic signal is obtained. , ultrasonic probe acquisition and The signal is then returned to the ultrasonic pulse transmitting and receiving system, which can upload it to the electronic equipment for signal calculation and processing. , is an integer greater than 0, Reflection signal Width of the signal sequence.

[0026] The first data detection device is arranged in the electronic device, The signal is separated and processed, and then the separated signal is used to calculate the thickness of the ice layer to solve the problem of two different ,For example and There is a problem of overlapping signals.

[0027] The first data detection device is The process of signal separation includes: according to The collection time arrangement , form the 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, the different elements There is a correlation between the two parts, and the next step is to proceed. Calculate the eigenvalue and eigenvector V of the signal matrix, perform the product operation U=VD on the signal matrix and the eigenvector, and convert the signal matrix into an uncorrelated matrix, so as to obtain the independent elements in the uncorrelated matrix { }, and separate the independent elements { , } projected into the principal component analysis space (PrincipalComponentAnalysis, PCA) space, zooming in on { } to enhance the differences between signals.

[0028] Furthermore, the unmixing matrix can also be used to } elements are non-Gaussianized to clarify the differences between different signals. The objective function is established based on the representation of maximizing negative entropy, maximizing kurtosis, maximizing likelihood or minimizing mutual information. The Newton iteration method is used to update the unmixing matrix with the objective function. The trained unmixing matrix can be calculated to make { The different characteristics of different elements in the} are made obvious.

[0029] right After signal separation, the ice thickness can be calculated using the separated signal using a time of flight (ToF) algorithm. The process of the ToF algorithm includes: The transit time refers to the reflected signal The first signal peak and reflected signal The time interval of the first signal peak, the process of calculating the transit time can be expressed by the following formula: , in, Represents the reflected signal The signal sequence width, Represents the optimized The signal sequence width, Indicates that the sampling frequency can be set to 100MHz.

[0030] The formula for calculating ice thickness based on transit time can be expressed as formula (5): (5) in Indicates the thickness of the ice layer, represents the speed of sound in the ice layer and can be measured experimentally.

[0031] The second data detection device can also use the above-mentioned time of flight (ToF, Time of Flight) algorithm to calculate the thickness of the ice layer, directly collecting the reflected signal and reflected signal For calculation, no signal separation operation needs to be performed.

[0032] The thickness of ice may continue to increase after the aircraft flies for a period of time, or the thickness of the ice layer may decrease due to the effect of de-icing measures taken. Therefore, the embodiment of the present application dynamically adjusts the method of calculating the ice layer thickness according to the previous ice layer thickness during the entire monitoring process.

[0033] If the target device currently in use is the second data detection device, determining the target device for outputting the ice thickness next time according to the current ice 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.

[0034] 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.

[0035] For example, the time step ΔT for detecting ice thickness can be set, ultrasonic data can be collected periodically, and the current ice thickness can be calculated. The dynamic thickness prediction model is used for the nth detection of ice thickness by the dynamic thickness prediction model. The current ice thickness is , if 1mm< <1.5mm, the dynamic thickness prediction model is still used to detect the ice thickness for the n+1th time; if >1.5mm, the second data detection device is used for the n+1th detection of ice thickness; if <1mm, the second data detection device is used to detect the ice thickness for the n+1th time, and the ice thickness is detected by the second data detection device for the n+1th time 1mm< <1.5mm, the ice thickness is detected for the n+2th time using a dynamic thickness prediction model.

[0036] The embodiment of the present application further proposes that the second data detection device is also used to collect the ice thickness as a measurement comparison value of the ice thickness when the ice thickness is within the target range; The process of detecting ice thickness using a dynamic thickness prediction model may include: S201: If the current ice layer thickness decreases to a second critical value, a 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.

[0037] The present invention provides a dynamic thickness prediction model, which predicts the dynamic change of ice thickness through a calculation model whose calculation process is formula (1), and obtains an 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, obtained by adjusting the matrix Q and the matrix R; Can be set to , is the time step; The ice thickness estimation value is output based on the current ice thickness state matrix.

[0038] Alternatively, step S202 to step S204 may be continued to be executed, the current ice layer thickness state may be corrected using the measurement comparison value output by the second data detection device, and then the ice layer thickness may be obtained based on the corrected current ice layer thickness state.

[0039] S202: Using a second data detection device to collect measurement comparison values ​​of ice layer thickness.

[0040] Establish the measurement comparison value matrix of the measurement comparison value to obtain , bring the measurement comparison value matrix into formula (2) and correct the measurement comparison value: (2) represents the corrected measurement comparison value matrix, Indicates the confidence level of the measured comparison value, represents the measurement noise; is the identity matrix and can be expressed as .

[0041] S203: adjusting 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.

[0042] Substituting the corrected measurement comparison matrix and the ice thickness estimation value state matrix into formula (3), adjusting the ice thickness estimation value, and obtaining the corrected ice thickness matrix; (3) in, represents the modified ice thickness matrix, is the adjustment parameter; It can be calculated by formula (4).

[0043] S204: Outputting the corrected ice thickness value.

[0044] A corrected ice thickness value is calculated based on the corrected ice thickness matrix, and the corrected ice thickness value is output.

[0045] In one embodiment, the ice layer thickness dynamic data collection method is executed for the nth time, and a dynamic thickness prediction model is used to output a corrected ice layer thickness value. If the corrected ice layer thickness value is within a target range of 1 mm to 1.5 mm, the ice layer thickness dynamic data collection method is executed for the n+1th time, and the dynamic thickness prediction model is still used. The dynamic thickness prediction model can directly input the corrected ice layer thickness matrix into formula (1) as the current ice layer thickness state matrix for predicting the ice layer thickness for the n+1th time.

[0046] 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.

[0047] The present application also proposes a method for optimizing and adjusting parameters: 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, which can be obtained by calculating the variance of the process data or adjusted through manual experience. is the measurement noise covariance, which can be obtained by calculating the variance of the measured data or adjusted through manual experience.

[0048] .

[0049] The dynamic thickness prediction model proposed in the embodiment of the present application predicts the current ice state after a preset time step ΔT based on the ice thickness state of the last time and the ice growth law. Since the dynamic thickness prediction model can predict the ice thickness of 1mm to 1.5mm, it fills the blank area of ​​ice thickness measurement; the embodiment of the present application also adopts a first data detection device for the ice range less than 1mm to solve the signal overlap problem caused by thin ice layers, and for the ice range greater than 1.5mm, the ultrasonic reflection signal collected by the ultrasonic pulse echo icing sensor is calculated for the transit time, and then the ice thickness is calculated based on the transit time. Starting from the objective fact that the ice thickness of 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 detection of the entire process of ice growth.

[0050] Figure 4 Schematic diagram of another ice thickness dynamic data acquisition system proposed in the embodiment of the present application. Figure 4 As shown, another ice 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 calculation device 14; The detection and calculation device 14 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.

[0051] The first data detection device 11 is used as a target device to detect the current ice thickness value when the ice thickness is lower than the first critical value; The second data detection device 12 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 13 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.

[0052] Figure 4 The implementation principle and technical effects of the ice thickness dynamic data collection system provided in the illustrated embodiment may be further referred to the relevant description in the ice thickness dynamic data collection method embodiment.

[0053] Optionally, 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.

[0054] Optionally, the detection and calculation device includes a target device selection module, which is used 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 the ice layer thickness estimate, and output the ice layer thickness estimate.

[0055] Optionally, the target device selection module is also used to call the dynamic thickness prediction model when the current ice layer thickness drops 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 the ice layer thickness estimate, and output the ice layer thickness estimate.

[0056] Optionally, the target device selection module is also used to output the ice thickness detection value collected by the second data detection device when the current ice layer thickness reaches a second critical value; when the current ice layer thickness drops to a 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.

[0057] Optionally, the detection calculation device includes a calculation output module, and the output module is used to 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; and output the corrected ice layer thickness.

[0058] Optionally, the dynamic thickness prediction model is specifically used to predict the ice thickness estimation value state matrix by formula (1) when the ice thickness is within the target range; The calculation output module is specifically used to output the ice layer thickness estimation value based on the current ice layer thickness state matrix.

[0059] Optionally, the calculation output module is further used 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 ice layer thickness estimation value into formula (3) to optimize the ice layer thickness estimation value state matrix.

[0060] The calculation output module is specifically used to output the corrected ice layer thickness value based on the corrected ice layer thickness matrix.

[0061] Optionally, the calculation output module is further configured to adjust the adjustment parameter according to formula (4).

[0062] Regarding the various modules / units included in the various devices described in the above embodiments, they can be software modules / units, or hardware modules / units, or they can be partially software modules / units and partially hardware modules / units. For example, for various devices applied to or integrated in a chip, the various modules / units 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 software programs, which run on a processor integrated inside the chip, and the remaining modules / units can be implemented in the form of hardware such as circuits; for various devices applied to or integrated in a chip module, the various modules / units included therein can all be implemented in the form of hardware such as circuits, and different modules / units can be located in the same component of the chip module (such as a chip, circuit module, etc.) or in different components, or at least some of the modules / units can be implemented in the form of software programs. The software program may be implemented in the form of a program, which runs on a processor integrated in the chip module, and the remaining modules / units may be implemented in the form of hardware such as circuits; for each device applied to or integrated in the electronic terminal device, each module / unit contained therein may be implemented in the form of hardware such as circuits, and different modules / units may be located in the same component (for example, a chip, a circuit module, etc.) or in different components in the electronic terminal device, or at least some modules / units may be implemented in the form of a software program, which runs on a processor integrated in the electronic terminal device, and the remaining (if any) modules / units may be implemented in the form of hardware such as circuits.

[0063] In one embodiment of the present application, a computer-readable storage medium is provided, on which a program is stored. The stored program includes a method that can be loaded by a processor and process any of the above embodiments.

[0064] Those skilled in the art will appreciate that all or part of the functions of the various methods in the above-mentioned embodiments can be implemented by hardware or by computer programs. When all or part of the functions in the above-mentioned embodiments are implemented by computer programs, the program can be stored in a computer-readable storage medium, and the storage medium can include: read-only memory, random access memory, disk, optical disk, hard disk, etc., and the program is executed by a computer to implement the above-mentioned functions. For example, the program is stored in the memory of the device, and when the program in the memory is executed by the processor, all or part of the above-mentioned functions can be implemented. In addition, when all or part of the functions in the above-mentioned embodiments are implemented by computer programs, the program can also be stored in a storage medium such as a server, another computer, disk, optical disk, flash disk or mobile hard disk, and can be downloaded or copied and saved in the memory of the local device, or the system of the local device is updated, and when the program in the memory is executed by the processor, all or part of the functions in the above-mentioned embodiments can be implemented.

[0065] The above specific examples are used to illustrate the present application, which is only used to help understand the present application and is not intended to limit the present application. For technicians in the technical field to which the present application belongs, they can also make some simple deductions, deformations or substitutions based on the ideas of the present application.

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; A target device for outputting the ice layer thickness next time is determined according to the current ice layer thickness.

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 4, characterized in that: The dynamic thickness prediction model is used to predict the dynamic change of ice thickness based on the current ice thickness to obtain an estimated value of ice thickness, including: Substitute the current ice thickness into formula (1) to obtain the ice thickness estimation value state matrix: （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; The ice thickness estimation value is output based on the current ice thickness state matrix.

6. The ice thickness dynamic data acquisition method according to claim 5, characterized in that: The output of the dynamic thickness prediction model is adjusted according to the measurement comparison value and the ice layer thickness estimation value to obtain a corrected ice layer thickness, including: Substitute the measurement comparison value matrix into formula (2) to correct the measurement comparison value; （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 the 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.

7. The ice thickness dynamic data acquisition method according to claim 6, 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.

8. The ice thickness dynamic data acquisition method according to claim 6, 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.

9. 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.

10. An ice thickness dynamic data acquisition system, 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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