Icing thickness detection method and device

Abstract

The invention relates to an icing thickness detection method and device, relates to the technical field of aircraft icing thickness data processing, solves the problem that the thickness of a thin ice layer is difficult to effectively identify, and realizes high-sensitivity identification in the initial stage of icing and high-precision quantitative detection in the whole process of icing. The method comprises the following steps: receiving a plurality of first reflection signals after ultrasonic waves penetrate through an aluminum layer and an ice layer and second reflection signals after the ultrasonic waves penetrate through the aluminum layer; removing overlapped signals among the plurality of first reflection signals to obtain a plurality of third reflection signals which are independent from each other; and calculating the second reflection signal and the third reflection signal to obtain the icing thickness.

Description

Technical Field

[0001] The present application relates to aircraft ice thickness data processing, and in particular to an ice thickness detection method and device. Background Art

[0002] When an aircraft passes through low-temperature clouds, liquid water will quickly freeze on the fuselage to form ice. Icing problems during flight will seriously threaten the flight safety of the aircraft and cause aviation accidents. To date, aircraft icing has caused many flight accidents around the world. Installing aircraft ice detection devices can provide real-time icing information to the aircraft anti-icing system and pilots so that they can take correct operating steps. However, current aircraft ice detection devices are difficult to effectively identify thin ice layers within 1 mm, and the thickness identification accuracy needs to be improved. Summary of the invention

[0003] An ice thickness detection method and device proposed in the embodiments of the present application solve the problem of difficulty in effectively identifying the thickness of a thin ice layer, achieve high-sensitivity identification of the initial stage of ice formation, and high-precision quantitative detection of the entire ice formation process.

[0004] In a first aspect, an embodiment provides an ice thickness detection method, the method comprising: receiving a plurality of first reflection signals after ultrasonic waves pass through an aluminum layer and an ice layer, and a second reflection signal after ultrasonic waves pass through the aluminum layer; removing overlapping signals between the plurality of first reflection signals to obtain a plurality of independent third reflection signals; and calculating the second reflection signal and the third reflection signal to obtain the ice thickness.

[0005] In one embodiment, removing overlapping signals between the plurality of first reflected signals to obtain a plurality of independent third reflected signals includes: Calculating eigenvectors and eigenvalues ​​of a signal matrix; the signal matrix is ​​formed by the plurality of first reflection signals according to corresponding acquisition times; Based on the product calculation of the eigenvectors, a transformation coordinate system is established in the specified space; Performing a product operation on the signal matrix with the eigenvector, and projecting the eigenvector elements in the signal matrix to the transformation coordinate system of the specified space; Based on the expression of the characteristic vector elements in the transformed coordinate system, the third reflection signals which are independent of each other are obtained.

[0006] In one embodiment, the eigenvalue is multiple and each eigenvalue is different, and the method further includes: Different eigenvalues ​​are used as weights of corresponding independent signal vectors, and weights are superimposed on each of the independent signal vectors to obtain the normalized third reflection signal.

[0007] In one embodiment, before removing overlapping signals between the plurality of first reflected signals to obtain a plurality of independent third reflected signals, the method further includes: Arranging different first reflection signals according to the acquisition time of different first reflection signals to form a signal matrix; Calculating the covariance of the signal matrix to determine whether the signal matrix is ​​a correlation matrix; Removing overlapping signals between the multiple first reflected signals to obtain multiple independent third reflected signals, including: When the signal matrix is ​​a correlation matrix, overlapping signals between the multiple first reflection signals are removed to obtain multiple independent third reflection signals.

[0008] In one embodiment, after obtaining a plurality of independent third reflected signals, the method further includes: Using the unmixing matrix obtained by pre-training, non-Gaussianity maximization is performed on each third reflection signal to obtain an optimized third reflection signal; non-Gaussianity maximization refers to screening the optimal value of the coefficient representing the property of the third reflection signal; Calculating the first reflection signal and the third reflection signal to obtain ice thickness includes: The second reflection signal and the optimized third reflection signal are calculated to obtain ice thickness.

[0009] In one embodiment, the coefficient representing the property of the third reflection signal includes the kurtosis of the third reflection signal, and the unmixing matrix obtained by pre-training is used to maximize the non-Gaussianity of each third reflection signal to obtain an optimized third reflection signal, including: The unmixing matrix obtained by pre-training is used to maximize the kurtosis of the third reflected signal, and the kurtosis maximization calculation is performed on each third reflected signal.

[0010] In one embodiment, the coefficient representing the property of the third reflected signal includes the negative entropy of the third reflected signal, and the unmixing matrix obtained by pre-training is used to maximize the non-Gaussianity of each third reflected signal to obtain an optimized third reflected signal, including: The unmixing matrix obtained by pre-training is used to maximize the negative entropy of the third reflected signal, and the negative entropy maximization calculation is performed on each third reflected signal.

[0011] In a second aspect, an embodiment provides an ice thickness detection device, the device comprising: A signal receiving module, used for receiving a plurality of first reflected signals after the ultrasonic wave passes through the aluminum layer and the ice layer, and a second reflected signal after the ultrasonic wave passes through the aluminum layer; A signal removal module, used to remove overlapping signals between the multiple first reflected signals to obtain multiple independent third reflected signals; The signal calculation module is used to calculate the second reflection signal and the third reflection signal to obtain the ice thickness.

[0012] The beneficial effects of this application are: In view of the discovery that the reason why it is difficult to detect the thickness of a thin ice layer is that there is excessive overlap in the signal after being reflected by the ice layer, the above-mentioned ice thickness detection method performs a deduplication operation on the signal reflected by the ice layer to obtain independent signals. In addition, before the deduplication operation is performed on the signal reflected by the ice layer, it is also determined whether there is a correlation between the signals reflected by the ice layer by calculating the covariance matrix, thereby improving the efficiency of the deduplication operation; during the deduplication operation, the signal reflected by the ice layer is projected to the conversion coordinate system of the PCA space to further amplify the difference between the signals reflected by different ice layers, thereby ensuring that the ice thickness calculation can be based on signals with obvious differences, thereby improving the accuracy of ice thickness detection, wherein the signal reflected by the ice layer is projected to the conversion coordinate system in the following manner: the matrix formed by the signal reflected by the ice layer is multiplied by the eigenvalue to obtain an uncorrelated matrix, and at the same time, the elements in the uncorrelated matrix have their coordinates converted. The deconfusion matrix is ​​also used to de-Gaussianize the signal reflected by the ice layer, thereby enhancing the difference between the signals reflected by the ice layer, optimizing the blurred reflection signal, and further improving the accuracy of ice thickness detection. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] Figure 1 is a flowchart of the steps of the ice thickness detection method proposed in the embodiment of the present application; Figure 2 It is a schematic diagram of the principle of implementing the ice thickness detection method to measure the ice thickness in the embodiment of the present application; Figure 3 It is a flowchart of another ice thickness detection method proposed in an embodiment of the present application; Figure 4 It is a functional module diagram of the ice thickness detection device proposed in the embodiment of the present application. DETAILED DESCRIPTION

[0014] 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.

[0015] 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.

[0016] 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).

[0017] Current aircraft ice detection devices cannot effectively identify thin ice layers less than 1 mm, so it is difficult to promptly detect the thickness of the ice layer when the fuselage is initially frozen, resulting in data missing in the monitoring and analysis of the entire icing process, making it difficult to achieve high-precision quantitative detection of the entire icing process.

[0018] The applicant has found that it is difficult to detect the thickness of a thin ice layer under current technology because: if the ice layer is too thin, it will cause signal overlap, and there is no obvious difference between the overlapping signals. Therefore, the thickness of the ice layer cannot be estimated based on the overlapping signals. In view of the above findings, an embodiment of the present application proposes an ice thickness detection method, which processes the overlapping parts of the signals to obtain independent signals, calculates the independent signals, and realizes accurate detection of the thickness of the thin ice layer.

[0019] The ice thickness detection method proposed in the embodiment of the present application can be applied to electronic devices, such as computers, servers, etc., and the electronic devices are connected to an aircraft ice detection device.

[0020] Figure 1 is a flowchart of the ice thickness detection method proposed in the embodiment of the present application. Figure 1As shown, the ice thickness detection method includes the steps of: Step S100: receiving a plurality of first reflection signals after the ultrasonic wave passes through the aluminum layer and the ice layer, and a second reflection signal after the ultrasonic wave passes through the aluminum layer.

[0021] Figure 2 The schematic diagram of the principle of implementing the ice thickness detection method to measure the ice thickness in the embodiment of the present application is as follows Figure 2 As shown, the aircraft ice detection device for measuring ice thickness includes an ultrasonic pulse transmitting and receiving system, an ultrasonic probe, and an aluminum layer. After the fuselage is frozen, the ice layer is closely attached to the aluminum layer.

[0022] The ultrasonic pulse transmitting and receiving system transmits an electrical signal, and the ultrasonic probe transmits an ultrasonic signal. After passing through the aluminum layer, the ultrasonic signal is reflected at the aluminum-ice interface (the interface where the aluminum layer and the ice layer meet) to obtain a second 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) to obtain a first reflected signal. The ultrasonic probe collects the first reflected signal and the second reflected signal and returns them to the ultrasonic pulse transmitting and receiving system. The ultrasonic pulse transmitting and receiving system can upload the signal to the electronic device for signal calculation and processing.

[0023] S110: removing overlapping signals between the multiple first reflected signals to obtain multiple independent third reflected signals.

[0024] When the ice thickness is less than 1 mm, the time for the ultrasonic signal to pass through the ice layer is too short, and the time interval for the ultrasonic signals emitted at different times to reach the ice-air interface is too short, resulting in too much overlap between the peaks and troughs of the first reflected signals obtained by reflecting the ultrasonic information signal at the ice-air interface.

[0025] Therefore, the embodiment of the present application separates the multiple first reflection signals obtained by reflecting the ultrasonic signals corresponding to different times on the empty interface, obtains multiple independent third reflection signals, and then calculates the ice thickness of the body based on the multiple independent third reflection signals.

[0026] S120: Calculate the first reflection signal and the third reflection signal to obtain ice thickness.

[0027] According to the above analysis, the embodiment of the present application found that the reason why it is difficult to achieve accurate measurement of thin ice layers is that the ultrasonic signals are excessively overlapped after being reflected by the ice layer, resulting in a delay in ice thickness detection. Based on the above-mentioned problems, it is proposed to optimize the signals collected by the aircraft ice detection device, remove the overlapping signals, and solve the root cause of the problem of ice thickness measurement, thereby achieving accurate measurement of the thickness of thin ice layers less than 1 mm.

[0028] The embodiment of the present application also proposes a specific method for executing S110: S111: Calculate the eigenvectors and eigenvalues ​​of a signal matrix; the signal matrix is ​​formed by the multiple first reflection signals according to corresponding acquisition times.

[0029] Before executing step S110 to remove overlapping signals between the first reflected signals, a step of determining whether step S110 needs to be executed can be added to calculate the correlation between different first reflected signals, and screen the reflected signals whose overlapping signals need to be removed. If the correlation between different first reflected signals is greater than a threshold, the process of removing overlapping signals is executed on the first reflected signals. If the correlation between different first reflected signals is less than the threshold, the process of removing overlapping signals is not executed, thereby saving computing resources. At the same time, the first reflected signal for which the process of removing overlapping signals needs to be executed is accurately selected through screening.

[0030] The step of determining whether step S110 needs to be executed includes: S200: Arrange different first reflected signals according to the acquisition time of different first reflected signals to form a signal matrix.

[0031] For example, the plurality of first reflected signals include , , ,collection The time is ,collection The time is ,collection The time is ,in < < , sorted by collection time , , Get the signal matrix { , , }.

[0032] In one example of the present application, before calculating the eigenvectors and eigenvalues ​​of the signal matrix, the signal matrix may be centered to process different first reflection signals into data of the same order of magnitude. For example, the signal matrix may be centered by calculating the average value μ of the first reflection signal. , Indicates The first reflected signal, It is The first reflected signals after centralization are used to form a signal matrix D including the first reflected signals after centralization.

[0033] S210: Calculate the covariance of the signal matrix and determine whether the signal matrix is ​​a correlation matrix.

[0034] Calculate the covariance matrix of the signal matrix D as , where Σ represents the covariance matrix of the signal matrix, represents the expectation of the signal matrix, Indicates that the signal matrix is ​​transposed. The covariance matrix itself can reflect the similarity between two different elements in the matrix. Therefore, the present application first forms a signal matrix composed of the first reflection signal, and detects whether the signal matrix is ​​a correlation matrix by calculating the covariance matrix of the signal matrix. If the signal matrix is ​​a correlation matrix, then the different elements therein, that is, the different first reflection signals have overlapping signals, and the operation of removing the overlapping signals is performed on the first reflection signal. If the signal matrix is ​​not a correlation matrix, the operation of removing the overlapping signals is not performed on the first reflection signal.

[0035] S220: When the signal matrix is ​​a correlation matrix, remove overlapping signals between the multiple first reflection signals to obtain multiple independent third reflection signals.

[0036] S230: When the signal matrix is ​​not a correlation matrix, the operation of removing overlapping signals between the multiple first reflection signals is not performed.

[0037] In one example of the present application, step S111 is performed: the eigenvector V and eigenvalue of the signal matrix D are calculated to obtain the eigenvalue , n is an integer greater than 2, indicating the number of eigenvalues.

[0038] S112: Based on the product calculation of the eigenvectors, a transformation coordinate system is established in a specified space.

[0039] S113: Projecting the eigenvector elements in the signal matrix to the transformation coordinate system of the specified space.

[0040] S114: Based on the expression of the characteristic vector elements in the transformed coordinate system, obtain the third reflection signals which are independent of each other.

[0041] An example of the present application adopts the principal component analysis space (Principal Component Analysis, PCA) to calculate the signal matrix with the eigenvector, which can project multiple related first reflection signals in the signal matrix to the transformation coordinate system to obtain unrelated signals. At the same time, PCA can convert the original data set into a new coordinate system through linear transformation. The maximum variance of these new coordinate systems corresponds to the principal components of the data, amplifying the principal components in the unrelated signals to obtain unrelated third reflection signals.

[0042] Calculate the matrix formed by the third reflected signal , the matrix formed by the third reflected signal The elements in are { , ,…… }, express The nth third reflected signal in .

[0043] An example of the present application further proposes that a normalization operation may be performed on the third reflection signal to amplify an irrelevant part of the third reflection signal; Step S400 may be included between S110 and S120: using different eigenvalues ​​as weights corresponding to the third reflected signals, and superimposing the weights on each of the third reflected signals to obtain the third reflected signals.

[0044] The matrix formed after normalizing the third reflected signal is , Represents the matrix formed by permuting eigenvalues , is the matrix formed by the third reflected signal.

[0045] The embodiment of the present application also proposes another ice thickness detection method. After obtaining the third reflection signal, the matrix Z formed by normalizing the third reflection signal is subjected to a non-Gaussian operation using an unmixing matrix to enhance the difference between different third reflection signals.

[0046] Figure 3 is a flowchart of another ice thickness detection method proposed in an embodiment of the present application. Figure 3 As shown, another ice thickness detection method includes the steps of: Step S300: receiving a plurality of first reflection signals after the ultrasonic wave passes through the aluminum layer and the ice layer, and a second reflection signal after the ultrasonic wave passes through the aluminum layer.

[0047] Step S310: removing overlapping signals between the multiple first reflected signals to obtain multiple independent third reflected signals.

[0048] Step S320: using the unmixing matrix obtained by pre-training, maximizing the non-Gaussianity of each third reflection signal to obtain an optimized third reflection signal; maximizing the non-Gaussianity refers to screening the optimal value of the coefficient representing the property of the third reflection signal.

[0049] The coefficients representing the properties of the third reflected signal include negative entropy, kurtosis, and the like.

[0050] The initial expression of the unmixing matrix is ​​randomly generated and is updated using the Newton iteration method during the training process. The trained unmixing matrix has the ability to screen out the third reflection signal with the most stable difference from multiple third reflection signals.

[0051] In one example of the present application, a pre-trained unmixing matrix is ​​used to maximize the non-Gaussian property of each third reflected signal using the formula: , where is the matrix formed by the optimized third reflection signal, is the unmixing matrix. , Both are optimized third reflection signals.

[0052] During the training process, in the case where the coefficient of the property of the third reflection signal is kurtosis, the pre-trained unmixing matrix is ​​used to maximize the kurtosis of the third reflection signal, and the kurtosis maximization calculation is performed on each third reflection signal. The objective function of training the unmixing matrix can be expressed by formula (1): (1) represents the unmixing matrix, represents the matrix formed by the third reflected signal, is the expectation of the unmixing matrix, It represents the matrix formed by the optimized third reflected signal.

[0053] Formula (1) is used as the objective function of the Newton iteration method to update the unmixing matrix, and the independent variable controlling the unmixing matrix is Substitute into formula 1 and update The kurtosis of the elements until The kurtosis of the elements is maximized, and the trained objective function can amplify the difference of the input third reflection signal, thereby enhancing the difference between different third reflection signals and achieving the purpose of maximizing the non-Gaussianity of each third reflection signal.

[0054] In the case where the coefficient of the property of the third reflected signal is negative entropy, the pre-trained unmixing matrix is ​​used to maximize the negative entropy of the third reflected signal, and the negative entropy maximization calculation is performed on each third reflected signal. The objective function of training the unmixing matrix can be expressed by formula (2): (2) Formula (2) is used as the objective function of the Newton iteration method to update the unmixing matrix. The update objective is to control the unmixing matrix to After calculation, we get The negative entropy of the middle element is maximized, thereby enhancing the difference between different third reflection signals, and achieving the purpose of maximizing the non-Gaussianity of each third reflection signal.

[0055] Step S330: Calculate the second reflection signal and the optimized third reflection signal to obtain ice thickness.

[0056] This application example provides an implementation method for calculating the second reflection signal and the optimized third reflection signal to obtain the ice thickness: The different reflection signals of the aluminum-ice interface and the ice-air interface can be used to identify the transit time (ToF) by the peak value of the signal. The transit time refers to the time interval between the first signal peak in the second reflection signal and the first signal peak in the third reflection signal. The process of calculating the transit time can be expressed by formula (3): (3) in, Indicates The second reflected signal, represents the signal sequence width of the second reflected signal, Indicates The optimized third reflection signal, represents the signal sequence width of the optimized third reflection signal, Indicates that the sampling frequency can be set to 100MHz.

[0057] The formula for calculating ice thickness based on transit time can be expressed as formula (4): (4) in Indicates the thickness of the ice layer, represents the speed of sound in the ice layer and can be measured experimentally.

[0058] The ice thickness detection method proposed in the embodiment of the present application is based on the discovery that the reason why it is difficult to detect the thickness of a thin ice layer is that there is excessive overlap in the signal after being reflected by the ice layer. The signal reflected by the ice layer is deduplicated to obtain independent signals. In addition, before the signal reflected by the ice layer is deduplicated, it is determined whether there is a correlation between the signals reflected by the ice layer by calculating the covariance matrix, thereby improving the efficiency of the deduplication operation. During the deduplication operation, the signal reflected by the ice layer is projected to the conversion coordinate system of the PCA space to further amplify the difference between the signals reflected by different ice layers, thereby ensuring that the ice thickness calculation can be based on signals with obvious differences, thereby improving the accuracy of ice thickness detection. The signal reflected by the ice layer is projected to the conversion coordinate system in the following manner: the matrix formed by the signal reflected by the ice layer is multiplied by the eigenvalue to obtain an uncorrelated matrix, and the elements in the uncorrelated matrix are converted at the same time. The deconfusion matrix is ​​also used to de-Gaussianize the signal reflected by the ice layer, enhance the difference between the signals reflected by the ice layer, optimize the blurred reflection signal, and further improve the accuracy of ice thickness detection.

[0059] Figure 4 is a functional module diagram of the ice thickness detection device proposed in the embodiment of the present application, such as Figure 4 As shown, the ice thickness detection device includes a signal receiving module 41, a signal removal module 42, and a signal calculation module 43; wherein, The signal receiving module 41 is used to receive a plurality of first reflection signals after the ultrasonic wave passes through the aluminum layer and the ice layer, and a second reflection signal after the ultrasonic wave passes through the aluminum layer; The signal removal module 42 is used to remove the overlapping signals between the multiple first reflection signals to obtain multiple independent third reflection signals; The signal calculation module 43 is used to calculate the second reflection signal and the third reflection signal to obtain the ice thickness.

[0060] Figure 4 The implementation principle and technical effect of the ice thickness detection device provided in the illustrated embodiment can be further referred to the relevant description in the ice thickness detection method embodiment.

[0061] Optionally, the signal removal module includes: An eigenvalue calculation submodule, used to calculate the eigenvectors and eigenvalues ​​of a signal matrix; the signal matrix is ​​formed by the plurality of first reflection signals according to corresponding acquisition times; A coordinate system establishment submodule, used to establish a transformation coordinate system in a specified space based on the product calculation of the feature vectors; A projection submodule, used for performing a product operation on the signal matrix with the eigenvector, and projecting the eigenvector elements in the signal matrix to the transformation coordinate system of the specified space; The signal acquisition submodule is used to obtain the third reflection signals which are independent of each other based on the expression of the characteristic vector elements in the transformed coordinate system.

[0062] Optionally, the device further comprises: The normalization module is used to use different eigenvalues ​​as weights of corresponding independent signal vectors, and to superimpose weights on each of the independent signal vectors to obtain the normalized third reflection signal.

[0063] Optionally, the device further comprises: A matrix forming module, used for arranging different first reflected signals according to the acquisition time of different first reflected signals to form a signal matrix; A correlation determination module, used to calculate the covariance of the signal matrix and determine whether the signal matrix is ​​a correlation matrix; The signal removal module is specifically used to remove overlapping signals between the multiple first reflection signals when the signal matrix is ​​a correlation matrix, so as to obtain multiple independent third reflection signals.

[0064] Optionally, the device further comprises: An unmixing module is used to use the unmixing matrix obtained by pre-training to maximize the non-Gaussianity of each third reflection signal to obtain an optimized third reflection signal; the non-Gaussianity maximization refers to screening the optimal value of the coefficient representing the property of the third reflection signal; The signal calculation module is specifically used to calculate the second reflection signal and the optimized third reflection signal to obtain the ice thickness.

[0065] Optionally, the coefficient representing the property of the third reflected signal includes the kurtosis of the third reflected signal, and the unmixing module is specifically used to adopt a pre-trained unmixing matrix to maximize the kurtosis of the third reflected signal, and perform kurtosis maximization calculation on each third reflected signal.

[0066] Optionally, the coefficient representing the property of the third reflected signal includes the negative entropy of the third reflected signal, and the unmixing module is specifically used to adopt a pre-trained unmixing matrix to maximize the negative entropy of the third reflected signal, and perform negative entropy maximization calculation on each third reflected signal.

[0067] Optionally, the device further comprises a training module, configured to train the demixing matrix using the objective function shown in formula (1).

[0068] Optionally, the training module is further configured to train the demixing matrix using the objective function shown in formula (2).

[0069] 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.

[0070] 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 one of the above embodiments.

[0071] 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.

[0072] 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 detecting ice thickness, characterized in that: The method comprises: receiving a plurality of first reflection signals after the ultrasonic wave passes through the aluminum layer and the ice layer, and a second reflection signal after the ultrasonic wave passes through the aluminum layer; removing overlapping signals between the plurality of first reflected signals to obtain a plurality of independent third reflected signals; The second reflection signal and the third reflection signal are calculated to obtain ice thickness.

2. The ice thickness detection method according to claim 1, characterized in that: Removing overlapping signals between the multiple first reflected signals to obtain multiple independent third reflected signals, including: Calculating eigenvectors and eigenvalues ​​of a signal matrix; the signal matrix is ​​formed by the plurality of first reflection signals according to corresponding acquisition times; Based on the product calculation of the eigenvectors, a transformation coordinate system is established in the specified space; Performing a product operation on the signal matrix with the eigenvector, and projecting the eigenvector elements in the signal matrix to the transformation coordinate system of the specified space; Based on the expression of the characteristic vector elements in the transformed coordinate system, the third reflection signals which are independent of each other are obtained.

3. The ice thickness detection method according to claim 2, characterized in that: The eigenvalues ​​are multiple and each eigenvalue is different, and the method further includes: Different eigenvalues ​​are used as weights of corresponding independent signal vectors, and weights are superimposed on each of the independent signal vectors to obtain the normalized third reflection signal.

4. The ice thickness detection method according to claim 1, characterized in that: Before removing overlapping signals between the plurality of first reflected signals to obtain a plurality of independent third reflected signals, the method further includes: Arranging different first reflection signals according to the acquisition time of different first reflection signals to form a signal matrix; Calculating the covariance of the signal matrix to determine whether the signal matrix is ​​a correlation matrix; Removing overlapping signals between the multiple first reflected signals to obtain multiple independent third reflected signals, including: When the signal matrix is ​​a correlation matrix, overlapping signals between the multiple first reflection signals are removed to obtain multiple independent third reflection signals.

5. The ice thickness detection method according to claim 1, characterized in that: After obtaining a plurality of independent third reflected signals, the method further includes: Using the unmixing matrix obtained by pre-training, non-Gaussianity maximization is performed on each third reflection signal to obtain an optimized third reflection signal; non-Gaussianity maximization refers to screening the optimal value of the coefficient representing the property of the third reflection signal; Calculating the first reflection signal and the third reflection signal to obtain ice thickness includes: The second reflection signal and the optimized third reflection signal are calculated to obtain ice thickness.

6. The ice thickness detection method according to claim 5, characterized in that: The coefficient representing the property of the third reflection signal includes the kurtosis of the third reflection signal. The unmixing matrix obtained by pre-training is used to maximize the non-Gaussianity of each third reflection signal to obtain an optimized third reflection signal, including: The unmixing matrix obtained by pre-training is used to maximize the kurtosis of the third reflected signal, and the kurtosis maximization calculation is performed on each third reflected signal.

7. The ice thickness detection method according to claim 5, characterized in that: The coefficient representing the property of the third reflected signal includes the negative entropy of the third reflected signal. The unmixing matrix obtained by pre-training is used to maximize the non-Gaussianity of each third reflected signal to obtain an optimized third reflected signal, including: The unmixing matrix obtained by pre-training is used to maximize the negative entropy of the third reflected signal, and the negative entropy maximization calculation is performed on each third reflected signal.

8. The ice thickness detection method according to claim 6, characterized in that: The objective function for training the unmixing matrix is: （1） in, represents the unmixing matrix, represents the matrix formed by the third reflected signal, is the expectation of the unmixing matrix, It represents the matrix formed by the optimized third reflected signal.

9. The ice thickness detection method according to claim 7, characterized in that: The objective function for training the unmixing matrix is: （2）。 10. An ice thickness detection device, characterized in that: The device comprises: A signal receiving module, used for receiving a plurality of first reflected signals after the ultrasonic wave passes through the aluminum layer and the ice layer, and a second reflected signal after the ultrasonic wave passes through the aluminum layer; A signal removal module, used to remove overlapping signals between the multiple first reflected signals to obtain multiple independent third reflected signals; The signal calculation module is used to calculate the second reflection signal and the third reflection signal to obtain the ice thickness.

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