CN117853343B - Heterogeneous lunar soil random modeling method, equipment and medium based on fractal noise - Google Patents
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Abstract
The application provides a heterogeneous lunar soil random modeling method based on fractal noise, which relates to the technical field of lunar soil random modeling and comprises the following steps: determining a first range of lunar soil relative dielectric constants; generating a two-dimensional random number matrix which obeys standard Gaussian normal distribution, and obtaining a two-dimensional white noise image of a space domain; transforming the two-dimensional white noise image into a frequency domain through Fourier transformation to obtain a frequency domain image; calculating frequencies of the two-dimensional white noise images in the X and Y directions, and constructing a filter; multiplying the filter with the frequency domain image for filtering; converting the filtered frequency domain image into a spatial domain to obtain a spatial domain image; the range of the spatial domain image is adjusted to the first range by overall scaling and translation, and the value of each point of the spatial domain image is updated and used as the lunar soil relative dielectric constant, thereby creating a heterogeneous lunar soil model. A large number of lunar soil models close to the real situation can be generated rapidly, and a large number of labeled training data are provided for inverting lunar radar data.
Description
Technical Field
The application relates to the technical field of lunar soil random modeling, in particular to a fractal noise-based heterogeneous lunar soil random modeling method, a fractal noise-based heterogeneous lunar soil random modeling equipment and a fractal noise-based heterogeneous lunar soil random modeling medium.
Background
Since ancient times, people have infinite curiosity and directions in outer space, as a celestial body closest to the earth, the moon naturally becomes the first step of the human to the universe, and exploration of the moon has important significance for people to know life, earth and solar system and the whole universe origin and evolution. By detecting the internal structure of the moon, one can understand the evolution history, geologic structure, and geologic motion of the moon. The radar technology is an important means for detecting moon, and the moon detection radar carried by 'Chang' III and 'Chang' IV in China realizes the detection of the lunar surface, thereby providing possibility for researching lunar soil and structures below the lunar soil.
At present, dielectric constant inversion is mostly carried out by a hyperbola fitting algorithm in research of lunar exploration radar data, however, the method is still limited to a relatively simple underground environment, and the accuracy of the inverted dielectric constant is not ideal. In recent years, with the increase of the computational power level, the deep learning is rapidly developed, and a lot of places are available in various fields, however, the lack of a large amount of marked data is the biggest obstacle for applying the deep learning technology to the inversion of lunar exploration radar data.
Disclosure of Invention
The invention aims at: in order to solve the problems mentioned in the background art, a heterogeneous lunar soil random modeling method, equipment and medium based on fractal noise, which can generate a large amount of tag data required by lunar exploration radar data, are provided.
The above object of the present application is achieved by the following technical solutions:
s1: determining a first range of relative dielectric constants of lunar soil by referring to a test result of the lunar soil sample;
s2: generating a two-dimensional random number matrix with noise amplitude values conforming to standard Gaussian normal distribution based on a fractal noise method to obtain a two-dimensional white noise image of a spatial domain;
s3: transforming the two-dimensional white noise image in the space domain into the frequency domain through Fourier transformation to obtain a frequency domain image;
s4: calculating the frequency of the space domain in the X direction and the frequency of the space domain in the Y direction of the two-dimensional white noise image, and constructing a filter through the frequency and the fractal dimension D in the fractal noise method; multiplying the filter with the frequency domain image for filtering;
s5: performing inverse Fourier transform on the filtered frequency domain image, and converting the frequency domain image into a spatial domain to obtain a spatial domain image;
S6: and adjusting the range of the spatial domain image to a first range through integral scaling and translation, updating the value of each point of the spatial domain image and taking the value as the lunar soil relative dielectric constant, and completing the creation of the heterogeneous lunar soil model.
Optionally, step S2 includes:
generating a two-dimensional white noise image with constant power density, namely, generating a noise amplitude Z value of the two-dimensional white noise image to obey Gaussian distribution, wherein the noise amplitude Z value is as follows:
Z～N(μ，σ2)
the probability density function of the noise amplitude Z value of the two-dimensional white noise image is:
Wherein sigma is the total standard deviation, and the value of sigma is 1; mu is the overall average value, and the value of mu is 0; pi is 3.14159 and e is 2.71828; z represents a part of the Z value, and is a point; the Z values may be a two-dimensional array or matrix.
Optionally, step S3 includes:
transforming the two-dimensional white noise image from a space domain to a frequency domain by using a two-dimensional Fourier transform to obtain a frequency domain image, wherein the calculation formula is as follows:
Wherein M is the length of the two-dimensional white noise image in the spatial domain, N is the height of the two-dimensional white noise image in the spatial domain, F (u, v) represents the frequency domain image, u is the spatial frequency in the X direction in the spatial domain, v is the spatial frequency in the Y direction in the spatial domain, the range of u is [0, M-1], and the range of v is [0, N-1]; f (x, y) represents a two-dimensional white noise image of the spatial domain, and x represents the x-axis coordinate of the spatial domain image; y represents the y-axis coordinates of the spatial domain image, the range of x is [0, M-1], and the range of y is [0, N-1].
Optionally, step S4 includes:
The filter construction formula is:
Wherein X freq represents the frequency in the X direction of the two-dimensional white noise image of the spatial domain, and W x represents the weight in the X direction; y freq denotes the frequency in the Y direction of the two-dimensional white noise image of the spatial domain, and W y denotes the weight in the Y direction; b is a constant related to the fractal dimension D
The filter is multiplied by the frequency domain image to filter, and the expression is as follows:
F′(u，v)＝F(u，v)*filter
Where F (u, v) represents a frequency domain image and F' (u, v) represents a filtered frequency domain image.
Optionally, step S5 includes:
And carrying out inverse Fourier transform on the filtered frequency domain image, and converting the frequency domain image back to a space domain, wherein the calculation formula is as follows:
Wherein F' (u, v) represents the filtered frequency domain image, u is the spatial frequency in the spatial domain X direction, v is the spatial frequency in the spatial domain Y direction, the range of u is [0, M-1], and the range of v is [0, N-1]; f' (x, y) represents the filtered spatial domain image, x represents the spatial domain image x-axis coordinates; y represents the y-axis coordinate of the spatial domain image, the range of x is [0, M-1], and the range of y is [0, N-1]; m is the length of the spatial domain image and N is the height of the spatial domain image.
Optionally, step S6 includes:
The lunar soil relative dielectric constant of each point of the spatial domain image is updated by adjusting the whole data range [ min, max ] of the spatial domain image, and the calculation formula is as follows:
V i is the original value at the ith point of the spatial domain image, and v' i is the updated lunar soil relative dielectric constant at the ith point of the spatial domain image; v min denotes the minimum value of the spatial domain image; v max denotes the maximum value of the spatial domain image.
An electronic device comprising a processor, a memory, a user interface and a network interface, the memory for storing instructions, the user interface and the network interface for communicating to other devices, the processor for executing the instructions stored in the memory to cause the electronic device to perform a fractal noise based heterogeneous lunar soil random modeling method.
A computer readable storage medium storing instructions that, when executed, perform a method of heterogeneous lunar soil stochastic modeling based on fractal noise.
The technical scheme provided by the application has the beneficial effects that:
determining a first range of relative dielectric constants of lunar soil by referring to a test result of the lunar soil sample; generating a two-dimensional white noise image based on a fractal noise method, and constructing a filter; the method comprises the steps of filtering a two-dimensional white noise image after Fourier transformation through a filter, performing inverse Fourier transformation on the frequency domain image after Fourier transformation, converting the frequency domain image into a spatial domain, obtaining the spatial domain image, adjusting the range of the spatial domain image to a first range through integral scaling and translation, updating the value of each point of the spatial domain image and taking the value as lunar soil relative permittivity, wherein the lunar soil relative permittivity is a heterogeneous lunar soil model, and is tag data required by lunar exploration radar training data. The method is based on fractal noise, the first range of lunar soil relative dielectric constant is determined to be an accurate constant, a large number of lunar soil models close to real conditions can be rapidly generated, and a large number of labeled training data are provided for deep learning inversion lunar radar data.
Drawings
The application will be further described with reference to the accompanying drawings and examples, in which:
FIG. 1 is a step diagram of a fractal noise based heterogeneous lunar soil stochastic modeling method in an embodiment of the application;
FIG. 2 is an exemplary schematic diagram of white noise in an embodiment of the invention;
FIG. 3 is an exemplary diagram of frequency domain white noise in an embodiment of the invention;
FIG. 4 is an exemplary schematic diagram of a filter in an embodiment of the invention;
FIG. 5 is an exemplary diagram of white noise frequency domain filtering results in an embodiment of the invention;
FIG. 6 is an exemplary diagram of white noise spatial domain filtering results in an embodiment of the invention;
FIG. 7 is a diagram of a heterogeneous lunar soil model for random modeling of heterogeneous lunar soil in an embodiment of the present invention;
fig. 8 is a schematic diagram of an electronic device structure of a fractal noise-based heterogeneous lunar soil random modeling method in an embodiment of the application.
Detailed Description
For a clearer understanding of technical features, objects and effects of the present application, a detailed description of embodiments of the present application will be made with reference to the accompanying drawings.
The embodiment of the application provides a heterogeneous lunar soil random modeling method based on fractal noise.
Referring to fig. 1, fig. 1 is a step diagram of a fractal noise-based heterogeneous lunar soil random modeling method in an embodiment of the present application, including:
s1: determining a first range of relative dielectric constants of lunar soil by referring to a test result of the lunar soil sample;
s2: generating a two-dimensional random number matrix with noise amplitude values conforming to standard Gaussian normal distribution based on a fractal noise method to obtain a two-dimensional white noise image of a spatial domain;
s3: transforming the two-dimensional white noise image in the space domain into the frequency domain through Fourier transformation to obtain a frequency domain image;
s4: calculating the frequency of the space domain in the X direction and the frequency of the space domain in the Y direction of the two-dimensional white noise image, and constructing a filter through the frequency and the fractal dimension D in the fractal noise method; multiplying the filter with the frequency domain image for filtering;
s5: performing inverse Fourier transform on the filtered frequency domain image, and converting the frequency domain image into a spatial domain to obtain a spatial domain image;
S6: and adjusting the range of the spatial domain image to a first range through integral scaling and translation, updating the value of each point of the spatial domain image and taking the value as the lunar soil relative dielectric constant, and completing the creation of the heterogeneous lunar soil model.
Specifically, the first range of lunar soil relative permittivity may be [2, 10]. The test result of the lunar soil sample is derived from the test results of Apollo and the sample of Chang No. 5.
Specifically, a standard_normal method in numpy is used to directly generate a two-dimensional random number matrix that obeys a standard gaussian normal distribution.
As shown in fig. 2, step S2 includes:
generating a two-dimensional white noise image with constant power density, namely, generating a noise amplitude Z value of the two-dimensional white noise image to obey Gaussian distribution, wherein the noise amplitude Z value is as follows:
Z～N(μ，σ2)
the probability density function of the noise amplitude Z value of the two-dimensional white noise image is:
Wherein sigma is the total standard deviation, and the value of sigma is 1; mu is the overall average value, and the value of mu is 0; pi is 3.14159 and e is 2.71828; z represents a part of the Z value, and is a point; the Z values may be a two-dimensional array or matrix.
As shown in fig. 3, step S3 includes:
transforming the two-dimensional white noise image from a space domain to a frequency domain by using a two-dimensional Fourier transform to obtain a frequency domain image, wherein the calculation formula is as follows:
Wherein M is the length of the two-dimensional white noise image in the spatial domain, N is the height of the two-dimensional white noise image in the spatial domain, F (u, v) represents the frequency domain image, u is the spatial frequency in the X direction in the spatial domain, v is the spatial frequency in the Y direction in the spatial domain, the range of u is [0, M-1], and the range of v is [0, N-1]; f (x, y) represents a two-dimensional white noise image of the spatial domain, and x represents the x-axis coordinate of the spatial domain image; y represents the y-axis coordinates of the spatial domain image, the range of x is [0, M-1], and the range of y is [0, N-1].
As shown in fig. 4, step S4 includes:
The filter construction formula is:
Wherein X freq represents the frequency in the X direction of the two-dimensional white noise image of the spatial domain, and W x represents the weight in the X direction; y freq denotes the frequency in the Y direction of the two-dimensional white noise image of the spatial domain, and W y denotes the weight in the Y direction; b is a constant related to the fractal dimension D
As shown in fig. 5, the filter is multiplied by the frequency domain image to perform filtering, and the expression is as follows:
F′(u，v)＝F(u，v)*filter
Where F (u, v) represents a frequency domain image and F' (u, v) represents a filtered frequency domain image.
It will be appreciated by those skilled in the art that the values of the weights W x、Wy and D are merely illustrative of examples in step S4 in the heterogeneous lunar soil model random modeling method of the present invention, and the method of the present invention is not limited thereto.
As shown in fig. 6, step S5 includes:
And carrying out inverse Fourier transform on the filtered frequency domain image, and converting the frequency domain image back to a space domain, wherein the calculation formula is as follows:
Wherein F' (u, v) represents the filtered frequency domain image, u is the spatial frequency in the spatial domain X direction, v is the spatial frequency in the spatial domain Y direction, the range of u is [0, M-1], and the range of v is [0, N-1]; f' (x, y) represents the filtered spatial domain image, x represents the spatial domain image x-axis coordinates; y represents the y-axis coordinate of the spatial domain image, the range of x is [0, M-1], and the range of y is [0, N-1]; m is the length of the spatial domain image and N is the height of the spatial domain image.
As shown in fig. 7, step S6 includes:
The lunar soil relative dielectric constant of each point of the spatial domain image is updated by adjusting the whole data range [ min, max ] of the spatial domain image, and the calculation formula is as follows:
V i is the original value at the ith point of the spatial domain image, and v' i is the updated lunar soil relative dielectric constant at the ith point of the spatial domain image; v min denotes the minimum value of the spatial domain image; v max denotes the maximum value of the spatial domain image.
It should be noted that: in the device provided in the above embodiment, when implementing the functions thereof, only the division of the above functional modules is used as an example, in practical application, the above functional allocation may be implemented by different functional modules according to needs, that is, the internal structure of the device is divided into different functional modules, so as to implement all or part of the functions described above. In addition, the embodiments of the apparatus and the method provided in the foregoing embodiments belong to the same concept, and specific implementation processes of the embodiments of the method are detailed in the method embodiments, which are not repeated herein.
The application also discloses electronic equipment. Referring to fig. 8, fig. 8 is a schematic structural diagram of an electronic device according to an embodiment of the present disclosure. The electronic device 500 may include: at least one processor 501, at least one network interface 504, a user interface 503, a memory 505, at least one communication bus 502.
Wherein a communication bus 502 is used to enable connected communications between these components.
The user interface 503 may include a Display screen (Display) and a Camera (Camera), and the optional user interface 503 may further include a standard wired interface and a standard wireless interface.
The network interface 504 may optionally include a standard wired interface, a wireless interface (e.g., WI-FI interface), among others.
Wherein the processor 501 may include one or more processing cores. The processor 501 connects various parts throughout the server using various interfaces and lines, performs various functions of the server and processes data by executing or executing instructions, programs, code sets, or instruction sets stored in the memory 505, and invoking data stored in the memory 505. Alternatively, the processor 501 may be implemented in at least one hardware form of digital signal Processing (DIGITAL SIGNAL Processing, DSP), field-Programmable gate array (Field-Programmable GATE ARRAY, FPGA), programmable logic array (Programmable Logic Array, PLA). The processor 501 may integrate one or a combination of several of a central processing unit (Central Processing Unit, CPU), an image processor (Graphics Processing Unit, GPU), and a modem, etc. The CPU mainly processes an operating system, a user interface, an application program and the like; the GPU is used for rendering and drawing the content required to be displayed by the display screen; the modem is used to handle wireless communications. It will be appreciated that the modem may not be integrated into the processor 501 and may be implemented by a single chip.
The Memory 505 may include a random access Memory (Random Access Memory, RAM) or a Read-Only Memory (Read-Only Memory).
Optionally, the memory 505 comprises a non-transitory computer readable medium (non-transitory computer-readable storage medium). Memory 505 may be used to store instructions, programs, code sets, or instruction sets. The memory 505 may include a stored program area and a stored data area, wherein the stored program area may store instructions for implementing an operating system, instructions for at least one function (such as a touch function, a sound playing function, an image playing function, etc.), instructions for implementing the above-described various method embodiments, etc.; the storage data area may store data or the like involved in the above respective method embodiments. The memory 505 optionally also includes, but is not limited to, at least one storage device located remotely from the aforementioned processor 501. Referring to fig. 8, an operating system, a network communication module, a user interface module, and an application program of a heterogeneous lunar soil random modeling method based on fractal noise may be included in a memory 505 as a computer storage medium.
In the electronic device 500 shown in fig. 8, the user interface 503 is mainly used for providing an input interface for a user, and acquiring data input by the user; and the processor 501 may be configured to invoke an application in the memory 505 that stores a fractal noise based heterogeneous lunar soil stochastic modeling method that, when executed by the one or more processors 501, causes the electronic device 500 to perform the method as in one or more of the embodiments described above. It should be noted that, for simplicity of description, the foregoing method embodiments are all described as a series of acts, but it should be understood by those skilled in the art that the present application is not limited by the order of acts described, as some steps may be performed in other orders or concurrently in accordance with the present application. Further, those skilled in the art will also appreciate that the embodiments described in the specification are all of the preferred embodiments, and that the acts and modules referred to are not necessarily required for the present application.
In the foregoing embodiments, the descriptions of the embodiments are emphasized, and for parts of one embodiment that are not described in detail, reference may be made to related descriptions of other embodiments.
In the several embodiments provided by the present application, it should be understood that the disclosed apparatus may be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative, such as a division of units, merely a division of logic functions, and there may be additional divisions in actual implementation, such as multiple units or components may be combined or integrated into another system, or some features may be omitted, or not performed. In addition, the coupling or direct coupling or communication connection shown or discussed with each other includes, but is not limited to, an indirect coupling or communication connection via some service interface, device or unit, including but not limited to electrical or other forms.
Elements illustrated as separate elements include, but are not limited to, or may not be physically separate, and elements shown as elements include, but are not limited to, or may not be physical elements, may be located in one place, or may be distributed over a plurality of network elements. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, functional units in various embodiments of the application may be integrated in one processing unit, including but not limited to individual units physically present alone, or two or more units may be integrated in one unit. The integrated units may be implemented in hardware or in software functional units.
The integrated units, if implemented in the form of software functional units and sold or used as stand-alone goods, may be stored in a computer readable memory. Based on such understanding, the technical solution of the present application may be embodied in essence or a part contributing to the prior art or all or part of the technical solution in the form of a software product stored in a memory, comprising several instructions for causing a computer device (which may be a personal computer, a server or a network device, etc.) to perform all or part of the steps of the method of the various embodiments of the present application. And the aforementioned memory includes: various media capable of storing program codes, such as a U disk, a mobile hard disk, a magnetic disk or an optical disk.
The above are merely exemplary embodiments of the present disclosure and are not intended to limit the scope of the present disclosure. That is, equivalent changes and modifications are contemplated by the teachings of this disclosure, which fall within the scope of the present disclosure. Other embodiments of the disclosure will be apparent to those skilled in the art from consideration of the specification and practice of the disclosure.
This application is intended to cover any variations, uses, or adaptations of the disclosure following, in general, the principles of the disclosure and including such departures from the present disclosure as come within known or customary practice within the art to which the disclosure pertains. It is intended that the specification and examples be considered as exemplary only, with a scope and spirit of the disclosure being indicated by the claims.
Claims (7)
1. The heterogeneous lunar soil random modeling method based on the fractal noise is characterized by comprising the following steps of:
s1: determining a first range of relative dielectric constants of lunar soil by referring to a test result of the lunar soil sample;
s2: generating a two-dimensional random number matrix with noise amplitude values conforming to standard Gaussian normal distribution based on a fractal noise method to obtain a two-dimensional white noise image of a spatial domain;
s3: transforming the two-dimensional white noise image in the space domain into the frequency domain through Fourier transformation to obtain a frequency domain image;
s4: computing a two-dimensional white noise image of a spatial domain Frequency sum of directionsFrequency of direction, by the frequency and fractal dimension in the method of fractal noiseThe filter is constructed as follows:
The filter construction formula is:
Wherein, Two-dimensional white noise image representing spatial domainThe frequency in the direction of the wave is,Representation ofWeights in the direction; two-dimensional white noise image representing spatial domain The frequency in the direction of the wave is,Representation ofWeights in the direction; For and fractal dimension Constant of correlation；
The filter is multiplied by the frequency domain image to filter, and the expression is as follows:
Wherein, A frequency domain image is represented and,Representing the filtered frequency domain image;
multiplying the filter with the frequency domain image for filtering;
s5: performing inverse Fourier transform on the filtered frequency domain image, and converting the frequency domain image into a spatial domain to obtain a spatial domain image;
S6: and adjusting the range of the spatial domain image to a first range through integral scaling and translation, updating the value of each point of the spatial domain image and taking the value as the lunar soil relative dielectric constant, and completing the creation of the heterogeneous lunar soil model.
2. The heterogeneous lunar soil random modeling method based on fractal noise as claimed in claim 1, wherein the step S2 comprises:
generating a two-dimensional white noise image with constant power density, namely, generating a noise amplitude Z value of the two-dimensional white noise image to obey Gaussian distribution, wherein the noise amplitude Z value is as follows:
the probability density function of the noise amplitude Z value of the two-dimensional white noise image is:
Wherein, Is the total standard deviation, and the value is 1; Is the overall average value, and the value is 0; Is that 3.14159 of the total number of the components, 2.71828; Z represents a part of the Z value, and is a point; the Z values are two-dimensional arrays or matrices.
3. The heterogeneous lunar soil random modeling method based on fractal noise as claimed in claim 1, wherein the step S3 comprises:
transforming the two-dimensional white noise image from a space domain to a frequency domain by using a two-dimensional Fourier transform to obtain a frequency domain image, wherein the calculation formula is as follows:
Wherein, Is the length of a two-dimensional white noise image in the spatial domain,Is the high of the two-dimensional white noise image in the spatial domain,A frequency domain image is represented and,Is the spatial frequency in the direction of the spatial domain X,Is the spatial frequency in the Y direction of the spatial domain,Is within the range of，Is within the range of；A two-dimensional white noise image representing the spatial domain,Representing spatial domain imagesAn axis coordinate; Representing spatial domain images The axis of the rotation is set to be at the same position,Is within the range of，Is within the range of。
4. The heterogeneous lunar soil random modeling method based on fractal noise as claimed in claim 1, wherein the step S5 comprises:
And carrying out inverse Fourier transform on the filtered frequency domain image, and converting the frequency domain image back to a space domain, wherein the calculation formula is as follows:
Wherein, Representing the filtered frequency domain image,Is the spatial frequency in the direction of the spatial domain X,Is the spatial frequency in the Y direction of the spatial domain,Is within the range of，Is within the range of；Representing the filtered spatial domain image(s),Representing spatial domain imagesAn axis coordinate; Representing spatial domain images The axis of the rotation is set to be at the same position,Is within the range of，Is within the range of；For the length of the spatial domain image,Is the high of the spatial domain image.
5. The heterogeneous lunar soil random modeling method based on fractal noise as claimed in claim 1, wherein the step S6 comprises:
By adjusting the overall data range of the spatial domain image Updating the lunar soil relative dielectric constant of each point of the space domain image, wherein the calculation formula is as follows:
Wherein, Is the first of the spatial domain imageThe original value at the point is the value,Is the first of the spatial domain imageThe updated lunar soil relative dielectric constant at the point; representing a minimum value of the spatial domain image; representing the maximum value of the spatial domain image.
6. An electronic device comprising a processor (501), a memory (505), a user interface (503) and a network interface (504), the memory (505) being configured to store instructions, the user interface (503) and the network interface (504) being configured to communicate to other devices, the processor (501) being configured to execute the instructions stored in the memory (505) to cause the electronic device (500) to perform the method according to any of claims 1-5.
7. A computer readable storage medium storing instructions which, when executed, perform the method steps of any one of claims 1-5.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN102017147A (en) * | 2007-04-18 | 2011-04-13 | 因维萨热技术公司 | Materials systems and methods for optoelectronic devices |
| CN116091529A (en) * | 2023-02-06 | 2023-05-09 | 杭州集视智能科技有限公司 | Amblyopia real-time image processing method and device suitable for portable equipment |
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102017147A (en) * | 2007-04-18 | 2011-04-13 | 因维萨热技术公司 | Materials systems and methods for optoelectronic devices |
| CN116091529A (en) * | 2023-02-06 | 2023-05-09 | 杭州集视智能科技有限公司 | Amblyopia real-time image processing method and device suitable for portable equipment |
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