A system and method for sld particle phase identification

Abstract

The present application relates to a system and method for SLD particle phase identification. In the technical solution of the present application, a feature matrix of cloud particle image data can be first constructed based on CIP probe data, and image binarization is performed on the constructed feature matrix and a binary image of the cloud particle is extracted. Subsequently, a circular proximity calculation can be performed on the binary image to determine whether the cloud particle is an SLD particle.

Description

Technical Field

[0001] This invention relates to the field of image recognition, and more specifically to a system and method for SLD particle phase recognition. Background Technology

[0002] The aircraft encountered a new challenge during EASA certification: it needed to verify its performance in supercooled large water droplet (SLD) icing environments. When the C919 aircraft conducted SLD test flights, it was necessary to perform phase identification on the raw cloud particle data obtained from CIP detection in potential SLD environments in order to accurately obtain the SLD particles and thus carry out EASA compliance calculations for SLD environmental conditions.

[0003] Therefore, there is an urgent need for a SLD particle phase identification scheme that further improves upon existing technologies. Summary of the Invention

[0004] This summary is provided to introduce, in a simplified form, some concepts that will be further described in the following detailed description section. This summary is not intended to identify key or essential features of the claimed subject matter, nor is it intended to help determine the scope of the claimed subject matter.

[0005] To address the problems in existing technologies, this invention proposes a system and method for SLD particle phase identification. In the technical solution of this invention, a feature matrix of cloud particle image data is first constructed based on CIP detection data. Image binarization is then performed on the constructed feature matrix to extract the binarized image of the cloud particle. Subsequently, a circular proximity calculation is performed on the binarized image to determine whether the cloud particle is an SLD particle. This circular proximity calculation may include roundness calculation, shape factor calculation, and the ratio of the circumscribed circle to the contour area. The method and system of this invention are applicable to EASA airworthiness certification.

[0006] Specifically, in one embodiment of the present invention, a system for SLD particle phase identification is disclosed, the system comprising:

[0007] The CIP detection data decoding module is configured to perform data decoding on CIP detection data to obtain particle image data;

[0008] The particle feature matrix construction module is configured to construct a two-dimensional feature matrix of cloud particles based on the particle image data.

[0009] A particle binarization image extraction module is configured to extract a binarized image of cloud particles from the two-dimensional feature matrix; and

[0010] The SLD particle phase recognition module is configured to perform SLD particle phase recognition by performing circular proximity calculation on the binarized image.

[0011] In one embodiment of the invention, the CIP probe data is binary, and the CIP probe data decoding module is further configured to perform the data decoding by:

[0012] Convert the CIP probe data into hexadecimal;

[0013] Determine particle boundaries from converted CIP probe data;

[0014] Analyze the particle head data beyond the particle boundary to obtain particle information; and

[0015] The particle image data following the particle head data is rearranged.

[0016] In the above embodiments of the present invention, the particle information includes the number of particle slices and the particle timestamp. The particle slice includes particle image data with a fixed number of bits. The particle feature matrix construction module is further configured to construct a two-dimensional feature matrix of the particle image data by using the number of particle slices as the number of rows and the fixed number of bits as the number of columns.

[0017] In the above embodiments of the present invention, the fixed number of bits corresponds to the number of channels associated with the diode array in the CIP probe.

[0018] In one embodiment of the present invention, the particle binarization image extraction module is further configured to:

[0019] The pixel points of the particle image data in the two-dimensional feature matrix are represented by a plurality of digital value points corresponding to the light blocking amplitude, wherein the plurality of digital value points include a zero value point corresponding to no light blocking and one or more value points corresponding to one or more light blocking levels; and

[0020] Extract the connected regions of pixels including one or more valuable points from the two-dimensional feature matrix to obtain the binarized image of the cloud particle.

[0021] In an optional embodiment of the invention, the circular proximity calculation includes a circularity calculation, and the SLD particle phase recognition module is further configured to determine whether the cloud particle is an SLD particle by performing the circularity calculation on the binarized image.

[0022] In another optional embodiment of the invention, the circular proximity calculation includes shape factor calculation, and the SLD particle phase recognition module is further configured to determine whether the cloud particle is an SLD particle by performing the shape factor calculation on the binarized image.

[0023] In another optional embodiment of the present invention, the circular proximity calculation includes the calculation of the ratio of the circumscribed circle to the contour area, and the SLD particle phase recognition module is further configured to determine whether the cloud particle is an SLD particle by performing the calculation of the ratio of the circumscribed circle to the contour area on the binarized image.

[0024] In another embodiment of the present invention, a method for SLD particle phase identification is disclosed, the method comprising:

[0025] Perform data decoding on the CIP probe data to obtain particle image data;

[0026] A two-dimensional feature matrix of cloud particles is constructed based on the particle image data;

[0027] Extract the binarized image of cloud particles from the two-dimensional feature matrix; and

[0028] SLD particle phase recognition is performed by performing circular proximity calculation on the binarized image.

[0029] In one embodiment of the invention, the CIP probe data is binary, and the data decoding further includes:

[0030] Convert the CIP probe data into hexadecimal;

[0031] Determine particle boundaries from converted CIP probe data;

[0032] Analyze the particle head data beyond the particle boundary to obtain particle information; and

[0033] The particle image data following the particle head data is rearranged.

[0034] In the above embodiments of the present invention, the particle information includes the number of particle slices and the particle timestamp. The particle slice includes particle image data with a fixed number of bits. Constructing the two-dimensional feature matrix further includes constructing a two-dimensional feature matrix of the particle image data by using the number of particle slices as the number of rows and the fixed number of bits as the number of columns.

[0035] In one embodiment of the present invention, extracting the binarized image of cloud particles from the two-dimensional feature matrix further includes:

[0036] The pixel points of the particle image data in the two-dimensional feature matrix are represented by a plurality of digital value points corresponding to the light blocking amplitude, wherein the plurality of digital value points include a zero value point corresponding to no light blocking and one or more value points corresponding to one or more light blocking levels; and

[0037] Extract the connected regions of pixels including one or more valuable points from the two-dimensional feature matrix to obtain the binarized image of the cloud particle.

[0038] In one embodiment of the present invention, the circular proximity calculation includes a circularity calculation, and performing the circular proximity calculation on the binarized image to perform SLD particle phase recognition further includes performing the circularity calculation on the binarized image to determine whether the cloud particle is an SLD particle.

[0039] In one embodiment of the present invention, the circular proximity calculation includes shape factor calculation, and performing SLD particle phase recognition by performing the circular proximity calculation on the binarized image further includes determining whether the cloud particle is an SLD particle by performing the shape factor calculation on the binarized image.

[0040] In one embodiment of the present invention, the circular proximity calculation includes calculating the ratio of the circumscribed circle to the contour area, and performing the circular proximity calculation on the binarized image to perform SLD particle phase recognition further includes determining whether the cloud particle is an SLD particle by performing the circumscribed circle to the contour area ratio calculation on the binarized image.

[0041] Other aspects, features, and embodiments of the invention will become apparent to those skilled in the art after reading the following description of specific exemplary embodiments of the invention in conjunction with the accompanying drawings. Although features of the invention may be discussed below with reference to certain embodiments and drawings, all embodiments of the invention may include one or more of the advantageous features discussed herein. In other words, while one or more embodiments may be discussed having certain advantageous features, one or more of such features may also be used according to the various embodiments of the invention discussed herein. Similarly, although exemplary embodiments may be discussed below as embodiments of devices, systems, or methods, it should be understood that such exemplary embodiments may be implemented in various devices, systems, and methods. Attached Figure Description

[0042] To gain a more detailed understanding of the features described above in this disclosure, reference can be made to a more specific description of the above-briefly summarized aspects, some of which are illustrated in the accompanying drawings. However, it should be noted that the drawings illustrate only certain typical aspects of this disclosure and should not be considered as limiting its scope, as other equivalent aspects are permissible in this description.

[0043] Figure 1 This is a schematic block diagram of a system for SLD particle phase identification according to an embodiment of the present disclosure.

[0044] Figure 2This is a schematic diagram of a cloud particle feature matrix according to an embodiment of the present disclosure.

[0045] Figure 3 This is a schematic diagram of a cloud particle binarized image according to an embodiment of the present disclosure.

[0046] Figure 4 This is a schematic diagram of the roundness calculation of a cloud particle binarized image according to an embodiment of the present disclosure.

[0047] Figure 5 This is a schematic diagram illustrating the calculation of the shape factor of a binarized image of cloud particles according to an embodiment of the present disclosure.

[0048] Figure 6 This is a schematic diagram illustrating the calculation of the ratio of the circumcircle to the contour area of ​​a cloud particle binarized image according to an embodiment of the present disclosure.

[0049] Figure 7 This is a flowchart of a method for SLD particle phase identification according to an embodiment of the present disclosure. Detailed Implementation

[0050] The various embodiments will now be described in more detail with reference to the accompanying drawings, which form part of this invention and illustrate specific exemplary embodiments. However, the embodiments may be implemented in many different forms and should not be construed as limiting the embodiments set forth herein; rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of these embodiments to those skilled in the art. The embodiments may be implemented as methods, systems, or devices. Therefore, these embodiments may be implemented in hardware, entirely in software, or in a combination of software and hardware aspects. Therefore, the following detailed description is not intended to be limiting.

[0051] The steps in each flowchart can be performed by hardware (e.g., processor, engine, memory, circuitry), software (e.g., operating system, application, driver, machine / processor executable instructions), or a combination thereof. As will be understood by those skilled in the art, the methods involved in each embodiment may include more or fewer steps than shown.

[0052] Currently, there is no SLD particle phase identification method for airworthiness certification of civil aircraft. Therefore, this invention discloses an SLD particle phase identification method and system suitable for EASA airworthiness certification. The method involves binary image processing of the original cloud particle feature matrix, calculating the circular proximity of each cloud particle image (including but not limited to roundness, shape factor, and the ratio of the circumscribed circle to the contour area), and setting a threshold range for SLD particles. It then determines whether the circular proximity of each particle falls within the threshold range, thereby accurately identifying the SLD particles.

[0053] The various aspects of the present invention will now be described in detail.

[0054] Figure 1 This is a schematic block diagram of a system 100 for SLD particle phase identification according to an embodiment of the present disclosure.

[0055] In one embodiment of the present invention, such as Figure 1 As shown, system 100 may include a CIP detection data decoding module 102, a particle feature matrix construction module 104, a particle binarized image extraction module 106, and an SLD particle phase recognition module 108. The following will combine... Figure 2-6 Describe these modules in detail.

[0056] In one embodiment of the present invention, the CIP probe data decoding module 102 can be configured to perform data decoding on the CIP probe data acquired by the CIP probe to obtain particle image data. In this embodiment, the CIP probe data provided by the CIP probe can be binary (binary compression). In an exemplary embodiment, a run-length encoding algorithm can be used to encode (compress) the CIP probe data. The compressed CIP probe data may begin with a run-length encoded header byte (RLEHB), and, exemplaryly, the format of the RLEHB may be as follows:

[0057] Bit 7 Bit 6 Bit 5 Bit 4:0 Z O D Count

[0058] Z can be a 0-bit flag. Z=1 indicates that all encoded bytes after RLEHB are 0, and there will be [Count+1] bytes of all 0 after RLEHB.

[0059] O can be a 1-bit flag. O=1 indicates that all encoded bytes after RLEHB are 1, and there will be [Count+1] bytes of all 1 after RLEHB.

[0060] Where D can be a virtual flag, D=1 indicates that the current RLEHB is a virtual byte and should be ignored, and the next byte will be a valid RLEHB;

[0061] The Count variable stores the number of non-header bytes following RLEHB minus one; for example, 0 means there is one byte following.

[0062] As will be understood by those skilled in the art, the above-described CIP probe data compression (encoding) format is exemplary and not restrictive.

[0063] In one embodiment of the invention, the CIP probe data decoding module 102 may be further configured to convert the CIP probe data into hexadecimal, thereby saving storage space and improving clarity. In this embodiment, the CIP probe data decoding module 102 may also be further configured to determine particle boundaries from the converted CIP probe data. In an exemplary embodiment, the converted hexadecimal CIP probe data may be C0 FF FF FF FF 01 00 00F0 FF FFAA AA AA AA AA AA AA AA 89 9E 91AA 3C 66 6C 67FF FF 7F 00FC FF FF FF FF FF, wherein the CIP probe data decoding module 104 may treat 64 bits of alternating 1s and 0s in the CIP probe data as particle boundaries, that is, treat 8 bytes of AA in the converted CIP probe data as particle boundaries.

[0064] Subsequently, the CIP probe data decoding module 102 can be further configured to parse the particle head data after the particle boundary to obtain particle information. In an exemplary embodiment, the particle information may include the number of particle slices and particle timestamps. In this embodiment, the particle slices may include particle image data of a fixed number of bits (i.e., a fixed number of pixels), which may correspond to the number of channels associated with the diode array in the CIP probe. For example, if there are 64 channels in the CIP probe, the fixed number of bits is 64.

[0065] Figure 2 This is a schematic diagram of a cloud particle feature matrix according to an embodiment of the present disclosure. Figure 2 As shown, a particle slice can refer to a row (a row composed of individual pixel cells) in this two-dimensional feature matrix, with each row containing 64 bits of particle image data. The number of slices refers to how many rows of particle image data are included in this two-dimensional matrix.

[0066] In one embodiment of the present invention, the CIP detection data decoding module 102 may be further configured to rearrange the particle image data following the particle head data. In an exemplary embodiment, if each slice of the particle image data can be rearranged, such as reversed. For example, if the bytes are indexed according to the first received byte being b[0] and the last byte being b[7], i.e. b[0], b[1], b[2], b[3], b[4], b[5], b[6], b[7], then the rearranged bytes may be b[7], b[6], b[5], b[4], b[3], b[2], b[1], b[0]. In another exemplary embodiment, the particle information may also include a particle sequence number and a particle depth state.

[0067] In one embodiment of the present invention, the particle feature matrix construction module 104 may be configured to construct a two-dimensional feature matrix of cloud particles based on the acquired particle image data. This two-dimensional feature matrix may, for example, be... Figure 2 The two-dimensional feature matrix shown can also be any other suitable feature matrix. For example... Figure 2 As shown, in one embodiment, the particle feature matrix construction module 104 can be further configured to increase the number of particle slices ( Figure 2 The 36 slices in the middle) are used as the row number (36 rows) and the fixed number of bits ( Figure 2 Using 64 bits (of the data) as the column number (64 columns), a two-dimensional feature matrix of the particle image data is constructed. Figure 2 The two-dimensional feature matrix shown can be composed of individual cells representing pixels. In other embodiments, the number of slices can be any other suitable number, and is not limited to any particular number. As mentioned above, the number of bits per row (number of columns) can be associated with the number of channels in the CIP probe.

[0068] In one embodiment of the present invention, the particle binarization image extraction module 106 can be configured to extract a binarized image of cloud particles from the two-dimensional feature matrix. In this embodiment, the particle binarization image extraction module 106 can be further configured to represent the pixels of the particle image data in the two-dimensional feature matrix using a plurality of digital value points corresponding to the light blocking amplitude. In other words, the particle binarization image extraction module 106 can use a plurality of digital value points corresponding to the light blocking amplitude to fill each cell in the two-dimensional feature matrix corresponding to the particle pixels. In this embodiment, the plurality of digital value points may include a zero value point corresponding to no light blocking and one or more value points corresponding to one or more light blocking levels. Figure 2 As shown, for example, the particle image data in each row and column of the two-dimensional feature matrix can be represented by the four numbers 0, 1, 2, and 3, that is, Figure 2Each data cell shown may include one of the four numbers 0, 1, 2, and 3. A cell with a value of 0 represents a dark blue pixel, a cell with a value of 1 represents a light blue pixel, a cell with a value of 2 represents a gray pixel, and a cell with a value of 3 represents a white pixel with no particle features. Here, 3 can correspond to no light blocking (no particle features) and be considered a zero-value point, while 0, 1, and 2 can be considered value points, with 2 corresponding to level one light blocking, 1 to level two light blocking, and 0 to level three light blocking. In an exemplary embodiment, level one light blocking can be 30%-50% of the light blocking range, level two light blocking can be 50%-70% of the light blocking range, and level three light blocking can be 70%-100% of the light blocking range. As those skilled in the art will understand, the above light blocking levels, the numbers representing the light blocking levels, and the percentages are exemplary and not limiting. Any other suitable light blocking levels, numbers, and percentages may be used in other embodiments of the present invention without departing from the spirit and scope of the invention.

[0069] In the above embodiments, the particle binarization image extraction module 106 can be further configured to extract the connected region of pixel points including one or more valuable points from the two-dimensional feature matrix as the binarization image of the cloud particle.

[0070] Figure 3 This is a schematic diagram of a cloud particle binarized image according to an embodiment of the present disclosure. Figure 3 As shown, the binarized image can be from... Figure 2 The particle binarized image, including the pixel connected regions with values, is extracted from the two-dimensional feature matrix shown. In another embodiment, if the two-dimensional feature matrix of the cloud particles includes multiple pixel connected regions, the particle binarization image extraction module 106 can extract the largest pixel connected region as the binarized image of the cloud particles. In yet another embodiment, since phase recognition cannot be performed based on the binary image when the number of particle pixels is too small, the particle binarization image extraction module 106 can extract the binarized image of the cloud particles from a two-dimensional matrix where the number of slices (i.e., the number of rows in the matrix) reaches a threshold (e.g., 5) for subsequent SLD particle phase recognition.

[0071] In one embodiment of the present invention, the SLD particle phase recognition module 108 may be configured to perform SLD particle phase recognition by performing a circular proximity calculation on the binarized image. As will be understood by those skilled in the art, any suitable circular proximity calculation may be performed to perform SLD particle phase recognition, and is not limited to any particular circular proximity calculation method.

[0072] In an optional embodiment of the present invention, the circular proximity calculation may include a circularity calculation, and the SLD particle phase recognition module 108 may be further configured to determine whether the cloud particle is an SLD particle by performing the circularity calculation on the binarized image.

[0073] Figure 4 This is a schematic diagram illustrating the roundness calculation of a cloud particle binarized image according to an exemplary embodiment of the present disclosure. In this exemplary embodiment, the C value can be defined as roundness; the closer the C value is to 1, the closer the image is to a circle. The formula can be as follows:

[0074]

[0075] Where L is the circumference of the circumcircle of the binarized cloud particle image (in pixels), and S is the pixel area of ​​the binarized cloud particle image (in pixels). 2 For example, the circumcircle can be obtained by connecting the center points of the outer perimeter pixels of the binarized image and fitting a circle (or ellipse) or any other suitable method, and the circumference of the circumcircle (and the area described below) can be obtained by using the circumference formula or by using a contour line obtained by connecting the center points of the outer perimeter pixels (such as...). Figure 4 The length of the circumscribed circle (e.g., the red outline shown) or any other suitable calculation method is used to determine it. As will be understood by those skilled in the art, the circumscribed circle and its circumference or area are not limited to any particular calculation method, but can be calculated using any suitable method.

[0076] By way of example, and not limitation, a roundness threshold range of 0.9 ≤ C < 1.2 can be set to determine whether the C value of each cloud particle is within this roundness threshold range, thereby identifying SLD particles. Figure 4 In the exemplary embodiment shown, the circumference of the outer circle L = 2722.87 pixels, and the pixel area S = 155727 pixels. 2 The C value obtained by formula (1) is approximately 3.7886, which does not fall within the threshold range of 0.9 ≤ C < 1.2. Therefore, it is determined that the particle does not belong to the SLD particle. As those skilled in the art will understand, the above threshold range of C value is merely exemplary and not limiting, and any other suitable threshold range may be used in other embodiments.

[0077] As will be understood by those skilled in the art, any other suitable method may be used in other embodiments to calculate the roundness of the binarized image, and is not limited to the roundness calculation method described above.

[0078] In another optional embodiment of the present invention, the circular proximity calculation may further include shape factor calculation, and the SLD particle phase recognition module 108 may be further configured to determine whether the cloud particle is an SLD particle by performing the shape factor calculation on the binarized image.

[0079] Figure 5 This is a schematic diagram illustrating the shape factor calculation of a cloud particle binarized image according to an exemplary embodiment of the present disclosure. In this exemplary embodiment, the SLD particle phase recognition module 108 can first determine the principal axis (ma) length MA and minor axis length ma of the cloud particle binarized image by connecting the center points of the peripheral pixels of the binarized image and fitting an ellipse. Figure 5 As shown, the principal axis length MA = 567.64 pixels, and the minor axis length ma = 366.70 pixels. Subsequently, the SLD particle phase recognition module 108 can determine the particle axis ratio, that is, calculate the ratio of the principal axis length to the minor axis length. This ratio Y can be defined as a shape factor; the closer Y is to 1, the more circular the particle shape. The formula is as follows:

[0080]

[0081] By way of example, and not limitation, a shape factor threshold range of 0.9 ≤ Y ≤ 1 can be set to determine whether the Y value of each cloud particle is within the threshold range, thereby identifying SLD particles. Figure 5 In the exemplary embodiment shown, the Y value is approximately 1.55 calculated according to formula (2), therefore it is determined that the cloud particle does not belong to the SLD particle.

[0082] As will be understood by those skilled in the art, any other suitable method may be used in other embodiments to calculate the shape factor of the binarized image, and is not limited to the shape factor calculation method described above.

[0083] In another optional embodiment of the present invention, the circular proximity calculation may further include the calculation of the ratio of the circumscribed circle to the contour area, and the SLD particle phase recognition module is further configured to determine whether the cloud particle is an SLD particle by performing the calculation of the ratio of the circumscribed circle to the contour area on the binarized image.

[0084] Figure 6 This is a schematic diagram illustrating the calculation of the ratio of the circumcircle to the contour area of ​​a binarized cloud particle image according to an exemplary embodiment of this disclosure. In this exemplary embodiment, the SLD particle phase recognition module 108 can first, as follows: Figure 4 As described in [the text], the pixel area S (also known as the contour area) of the extracted binarized image can be determined, and it can be determined as follows: Figure 5As described in the text, the circumcircle (ellipse) of the binary image of the cloud particle is obtained by connecting the center points of the outer pixels of the binary image and fitting an ellipse, and its principal axis (maro axis) length MA and minor axis length ma are determined. Then, the SLD particle phase recognition module 108 can determine whether the cloud particle is an SLD particle by calculating the ratio of the pixel area S of the cloud particle to the area of ​​its circumcircle (ellipse) (π×1 / 2MA×1 / 2ma). This ratio can be defined as T. A T value close to 1 indicates that the particle's shape is close to a circle, as shown in the following formula:

[0085]

[0086] By way of example, and not limitation, a threshold range for the ratio of pixel area to circumscribed circle area can be set to 0.9 ≤ T ≤ 1. The T value of each cloud particle can be determined to be within this threshold range to identify SLD particles. Figure 6 In the exemplary embodiment shown, the area of ​​the particle's circumscribed circle is approximately 163,446 pixels. 2 The particle pixel area is S = 155727 pixels. 2 Therefore, according to formula (3), the ratio of the pixel area to the circumcircle area of ​​the particle is T = 0.95, so it is determined to be a potential SLD particle.

[0087] In one embodiment of the present invention, when the results of the above-mentioned roundness calculation, shape factor calculation, and pixel area to circumcircle area ratio calculation all fall within the threshold range, it can be determined as an SLD particle. Figure 4-6 In the exemplary embodiment shown, the particle only satisfies the ratio of the circumscribed circle to the contour area, but does not satisfy the roundness calculation and shape factor calculation, therefore it is determined that it is not an SLD particle.

[0088] As those skilled in the art will understand, the above three circular proximity calculation methods (i.e., roundness calculation, shape factor calculation, and pixel area to circumcircle area ratio calculation) are merely exemplary and not limiting. In other embodiments of the present invention, any other suitable circular proximity calculation method may be used to determine SLD particles, and is not limited to any specific circular proximity calculation method or combination thereof.

[0089] In other embodiments of the present invention, in addition to satisfying the above three calculations simultaneously, any other suitable SLD particle determination method may be adopted, such as satisfying any one or two of the calculations simultaneously, or satisfying one or more other circular proximity calculations other than the above three calculations simultaneously, or satisfying one or more of the above three calculations in combination with one or more other circular proximity calculations, etc.

[0090] Figure 7This is a flowchart of a method 700 for SLD particle phase identification according to an embodiment of the present disclosure.

[0091] like Figure 7 As shown, method 700 begins at step 702, performing data decoding on the CIP probe data to obtain particle image data. In one embodiment of the invention, the CIP probe data may be binary, and this step may further include: converting the CIP probe data to hexadecimal; determining particle boundaries from the converted CIP probe data; parsing particle head data after the particle boundaries to obtain particle information; and rearranging the particle image data after the particle head data. In the above embodiment of the invention, the particle information includes the number of particle slices and particle timestamps, the particle slices comprising a fixed number of bits of particle image data.

[0092] Next, method 700 can continue to step 704, constructing a two-dimensional feature matrix of cloud particles based on the particle image data. In one embodiment of the invention, this step may further include: constructing a two-dimensional feature matrix of the particle image data by using the number of particle slices as the number of rows and the fixed number of bits as the number of columns.

[0093] Subsequently, method 700 may continue to step 706, extracting a binarized image of cloud particles from the two-dimensional feature matrix. In one embodiment of the invention, this step may further include: representing the pixels of the particle image data in the two-dimensional feature matrix with a plurality of digital value points corresponding to the light blocking amplitude, the plurality of digital value points including a zero value point corresponding to no light blocking and one or more value points corresponding to one or more light blocking levels; and extracting the connected region of the pixels including the one or more value points from the two-dimensional feature matrix as the binarized image of the cloud particles.

[0094] Finally, method 700 may continue to step 708, performing SLD particle phase recognition by performing a circular proximity calculation on the binarized image. In an optional embodiment of the invention, the circular proximity calculation may include a circularity calculation, and this step may further include determining whether the cloud particle is an SLD particle by performing the circularity calculation on the binarized image. In another optional embodiment of the invention, the circular proximity calculation may include a shape factor calculation, and this step may further include determining whether the cloud particle is an SLD particle by performing the shape factor calculation on the binarized image. In yet another optional embodiment of the invention, the circular proximity calculation may include a circumcircle-to-contour area ratio calculation, and this step may further include determining whether the cloud particle is an SLD particle by performing the circumcircle-to-contour area ratio calculation on the binarized image. In an exemplary embodiment, this step may further include determining that the cloud particle is an SLD particle if the results of the circularity calculation, the shape factor calculation, and the circumcircle-to-contour area ratio calculation all fall within a threshold range.

[0095] In summary, the SLD particle phase recognition method proposed in this invention can accurately identify SLD particles by calculating the circular proximity of the binarized image of cloud particles.

[0096] The embodiments of the present invention have been described above with reference to block diagrams and / or operational descriptions of methods, systems, and computer program products according to embodiments of the present invention. The functions / actions indicated in the blocks may appear in a different order than shown in any flowchart. For example, depending on the functions / actions involved, two blocks shown consecutively may actually be executed substantially simultaneously, or these blocks may sometimes be executed in reverse order.

[0097] The above description is merely a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A system for SLD particle phase state identification, the system comprising: The CIP detection data decoding module is configured to perform data decoding on CIP detection data to obtain particle image data; A particle feature matrix construction module is configured to construct a two-dimensional feature matrix of cloud particles based on the particle image data. A particle binarization image extraction module, configured to extract a binarized image of cloud particles from the two-dimensional feature matrix; and The SLD particle phase recognition module is configured to perform SLD particle phase recognition by performing circular proximity calculation on the binarized image.

2. The system as described in claim 1, characterized in that, The CIP probe data is binary, and the CIP probe data decoding module is further configured to perform the data decoding by: Convert the CIP detection data into hexadecimal; Determine particle boundaries from converted CIP probe data; Parse the particle head data after the particle boundary to obtain particle information; and The particle image data following the particle head data is rearranged.

3. The system as described in claim 2, characterized in that, The particle information includes the number of particle slices and the particle timestamp. The particle slices include particle image data with a fixed number of bits. The particle feature matrix construction module is further configured to construct a two-dimensional feature matrix of the particle image data by using the number of particle slices as the number of rows and the fixed number of bits as the number of columns.

4. The system as described in claim 3, characterized in that, The fixed number of bits corresponds to the number of channels associated with the diode array in the CIP probe.

5. The system as described in claim 1, characterized in that, The particle binarization image extraction module is further configured to: The pixel points of the particle image data in the two-dimensional feature matrix are represented by a plurality of digital value points corresponding to the light blocking amplitude. The plurality of digital value points include a zero value point corresponding to no light blocking and one or more value points corresponding to one or more light blocking levels. as well as The connected regions of pixels including one or more valuable points are extracted from the two-dimensional feature matrix to form the binarized image of the cloud particles.

6. The system as described in claim 1, characterized in that, The circular proximity calculation includes a circularity calculation, and the SLD particle phase recognition module is further configured to determine whether the cloud particle is an SLD particle by performing the circularity calculation on the binarized image.

7. The system as described in claim 1, characterized in that, The circular proximity calculation includes shape factor calculation, and the SLD particle phase recognition module is further configured to determine whether the cloud particle is an SLD particle by performing the shape factor calculation on the binarized image.

8. The system as described in claim 1, characterized in that, The circular proximity calculation includes the calculation of the ratio of the circumscribed circle to the contour area, and the SLD particle phase recognition module is further configured to determine whether the cloud particle is an SLD particle by performing the calculation of the ratio of the circumscribed circle to the contour area on the binarized image.

9. A method for SLD particle phase state identification, the method comprising: Perform data decoding on the CIP probe data to obtain particle image data; A two-dimensional feature matrix of cloud particles is constructed based on the particle image data; Extracting binarized images of cloud particles from the two-dimensional feature matrix; and SLD particle phase recognition is performed by performing circular proximity calculation on the binarized image.

10. The method as described in claim 9, characterized in that, The CIP probe data is binary, and the data decoding further includes: Convert the CIP detection data into hexadecimal; Determine particle boundaries from converted CIP probe data; Parse the particle head data after the particle boundary to obtain particle information; and The particle image data following the particle head data is rearranged.

11. The method as described in claim 10, characterized in that, The particle information includes the number of particle slices and the particle timestamp. The particle slices include particle image data with a fixed number of bits. Constructing the two-dimensional feature matrix further includes constructing a two-dimensional feature matrix of the particle image data by using the number of particle slices as the number of rows and the fixed number of bits as the number of columns.

12. The method as described in claim 9, characterized in that, Extracting the binarized image of cloud particles from the two-dimensional feature matrix further includes: The pixel points of the particle image data in the two-dimensional feature matrix are represented by a plurality of digital value points corresponding to the light blocking amplitude, wherein the plurality of digital value points include a zero value point corresponding to no light blocking and one or more value points corresponding to one or more light blocking levels; and The connected regions of pixels including one or more valuable points are extracted from the two-dimensional feature matrix to form the binarized image of the cloud particles.

13. The method as described in claim 9, characterized in that, The circular proximity calculation includes circularity calculation, and the SLD particle phase recognition by performing the circular proximity calculation on the binarized image further includes determining whether the cloud particle is an SLD particle by performing the circularity calculation on the binarized image.

14. The method as described in claim 9, characterized in that, The circular proximity calculation includes shape factor calculation, and the SLD particle phase recognition by performing the circular proximity calculation on the binarized image further includes determining whether the cloud particle is an SLD particle by performing the shape factor calculation on the binarized image.

15. The method as described in claim 9, characterized in that, The circular proximity calculation includes calculating the ratio of the circumscribed circle to the contour area, and the SLD particle phase recognition by performing the circular proximity calculation on the binarized image further includes determining whether the cloud particle is an SLD particle by performing the circumscribed circle to the contour area ratio calculation on the binarized image.

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