Method and system for curved surface fitting imaging of a gene chip with deformation

Abstract

The present invention provides a method and system for curved surface fitting imaging of a deformed gene chip. The curved surface fitting imaging method includes: collecting cross marks at four corners of the deformed gene chip and placing them in a coordinate system, calculating the positions of the cross marks in the coordinate system, generating pre-sampling points evenly distributed on the projection plane of the gene chip at equal intervals according to their coordinates, and obtaining the X-axis and Y-axis coordinate positions corresponding to all pre-sampling points; for any point to be evaluated on the surface of the gene chip and several pre-sampling points within its neighborhood range, using Gaussian curvature to estimate the local curvature at the point to be evaluated; according to the sampling points and their X-axis, Y-axis, and Z-axis coordinate positions, using radial basis functions for surface fitting to obtain the fitted surface equation, and subsequently, by inputting the X-axis coordinate and Y-axis coordinate of any point into the surface equation, the corresponding Z-axis coordinate can be calculated. After adopting the above technical solution, it is possible to perform rapid curved surface fitting imaging on the deformed gene chip.

Description

Technical Field

[0001] The present invention relates to the technical field of electronic digital data processing, and particularly to the surface fitting technology in the imaging process of gene chips. Background Art

[0002] A gene chip (also known as a DNA chip) is fabricated by microfabrication technology to regularly arrange and fix tens of thousands or even millions of DNA fragments (gene probes) with specific sequences on a carrier plane such as a silicon wafer, forming a two-dimensional DNA probe array, which is very similar to the electronic chip of a computer, so it is called a gene chip. The sequencing principle of a gene chip is the hybridization sequencing method.

[0003] In the production and preparation process of gene chips, it is necessary to first use a gene chip scanner to collect the image of the gene chip, and then locate and decode the holes on the image before it can be used for sample sequence detection. However, during the process of obtaining the image using the scanner, it is necessary to hold the gene chip. Especially for gene chips with a silicon wafer carrier, the silicon wafer carrier will deform due to the external clamping force. In the prior art, the method for fitting and imaging a deformed gene chip is to divide the curved image into multiple local small regions, then focus on 3 - 5 data points, and finally perform plane fitting. However, this method of fitting and imaging has low efficiency and takes a long time. Summary of the Invention

[0004] To overcome the above technical defects, the first aspect of the present invention provides a method for surface fitting and imaging of a deformed gene chip, which includes:

[0005] Step S1: Collect the cross marks at the four corners of the deformed gene chip and place them in a coordinate system, calculate the positions of the cross marks in the coordinate system, generate uniformly distributed pre-sampling points on the projection plane of the gene chip at equal intervals according to their coordinates, and obtain the X-axis and Y-axis coordinate positions corresponding to all pre-sampling points;

[0006] Step S2: For any point to be evaluated on the surface of the gene chip and several pre-sampling points within its neighborhood range, use Gaussian curvature to estimate the local curvature at the point to be evaluated, and this curvature determines the density and direction of subsequent sampling points;

[0007] Step S3: According to the several non-uniformly discretely distributed sampling points obtained by sampling and their X-axis, Y-axis, and Z-axis coordinate positions, use radial basis functions to perform surface fitting to obtain the fitted surface equation, and then input the X-axis coordinate and Y-axis coordinate of any point into this surface equation to calculate the corresponding Z-axis coordinate.

[0008] Further, the step S2 includes:

[0009] Step S2.1: Construct a Delaunay triangular mesh with the current point to be evaluated as the center and the four pre-sampling points with the farthest distances from the current point to be evaluated within its neighborhood range;

[0010] Step S2.2: For each point to be evaluated, calculate the Gaussian curvature at this point to be evaluated , and the calculation formula is:

[0011]

[0012] where, is the sum of all the interior angles of the triangles around the point to be evaluated;

[0013] Step S2.3: Calculate the change rates of the Z-axis coordinate values of the current point to be evaluated and the four pre-sampling points with the farthest distances from the point to be evaluated within its neighborhood range in their respective connection directions, compare the change rates of the four pre-sampling points in the Z-axis direction, and the largest one is the sampling direction of the next sampling point;

[0014] Step S2.4: Adjust the next sampling step size according to the relationship between the Gaussian curvature and the threshold.

[0015] Furthermore, in Step S2.4,

[0016] (1) If ,

[0017]

[0018] (2) If ,

[0019]

[0020] (3) If ,

[0021]

[0022] where, the initial sampling step size is , the current step size is , adjust the next sampling step size according to the relationship between the Gaussian curvature and the threshold , is the high curvature threshold, is the low curvature threshold, is the step size adjustment coefficient corresponding to the high curvature threshold, is the step size adjustment coefficient corresponding to the low curvature threshold, and are the minimum and maximum step sizes allowed for sampling respectively.

[0023] Further, in the step S3, assuming there are N sampling points , the obtained fitting surface is expressed as:

[0024]

[0025] wherein,

[0026] wherein,

[0027] wherein, is the weight coefficient of the basis function, is the low-order polynomial term.

[0028] The second aspect of the present application provides a surface fitting imaging system for a deformed gene chip, which includes:

[0029] A pre-sampling point generation module, which is used to collect the cross marks at the four corners of the deformed gene chip and place them in a coordinate system, calculate the positions of the cross marks in the coordinate system, generate uniformly distributed pre-sampling points on the projection plane of the gene chip at equal intervals according to their coordinates, and obtain the X-axis and Y-axis coordinate positions corresponding to all pre-sampling points;

[0030] A Gaussian curvature evaluation module, which is used to estimate the local curvature at a point to be evaluated on the surface of the gene chip and several pre-sampling points within its neighborhood range by using Gaussian curvature, and this curvature determines the density and direction of subsequent sampling points;

[0031] A surface fitting module, which is used to perform surface fitting on several non-uniformly discretely distributed sampling points obtained by sampling and their X-axis, Y-axis, and Z-axis coordinate positions by using a radial basis function to obtain a fitted surface equation, and then input the X-axis coordinate and Y-axis coordinate of any point into this surface equation to calculate the corresponding Z-axis coordinate.

[0032] Further, the Gaussian curvature evaluation module is used for:

[0033] Step S2.1: Construct a Delaunay triangular mesh with the current point to be evaluated as the center and the four pre-sampling points farthest from the current point to be evaluated within its neighborhood range;

[0034] Step S2.2: For each point to be evaluated, calculate the Gaussian curvature at this point to be evaluated , and the calculation formula is:

[0035]

[0036] wherein, is the sum of the interior angles of all triangles around the point to be evaluated;

[0037] Step S2.3: Calculate the change rates of the Z-axis coordinate values of the current point to be evaluated and the four pre-sampling points with the farthest distances from the point to be evaluated within its neighborhood range in their respective connection directions, and compare the change rates of the four pre-sampling points in the Z-axis direction. The largest one is the sampling direction of the next sampling point;

[0038] Step S2.4: Adjust the sampling step size for the next time according to the relationship between the Gaussian curvature and the threshold.

[0039] Furthermore, in step S2.4,

[0040] (1) If ,

[0041]

[0042] (2) If ,

[0043]

[0044] (3) If ,

[0045]

[0046] where the initial sampling step size is , the current step size is , and the sampling step size for the next time is adjusted according to the relationship between the Gaussian curvature and the threshold , is the high curvature threshold, is the low curvature threshold, is the step size adjustment coefficient corresponding to the high curvature threshold, is the step size adjustment coefficient corresponding to the low curvature threshold, and are the minimum and maximum step sizes allowed for sampling respectively.

[0047] Furthermore, the surface fitting module is used for: assuming there are N sampling points , then the obtained fitting surface is expressed as:

[0048]

[0049] where,

[0050] where,

[0051] where, is the weight coefficient of the basis function, is a low-order polynomial term.

[0052] The third aspect of the present application provides an electronic device, which includes: a memory, a processor, and a computer program stored on the memory and executable on the processor. When the computer program is executed by the processor, the steps in the curved surface fitting imaging method of the deformed gene chip as described above are implemented.

[0053] The fourth aspect of the present application provides a computer-readable storage medium, on which a computer program is stored. The computer program is characterized in that when it is executed by a processor, the steps in the curved surface fitting imaging method of the deformed gene chip as described above are implemented.

[0054] After adopting the above technical solutions, compared with the prior art, the following beneficial effects are obtained:

[0055] The technical solution of the present application can perform curved surface fitting on the entire deformed gene chip, with a short time and high efficiency, and can achieve fast and accurate imaging of the gene chip, which is beneficial to the accuracy and efficiency of the subsequent decoding process. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] Figure 1 is a schematic diagram of a fluorescence curved surface image of a deformed gene chip in a coordinate system and several generated pre-sampling points;

[0057] Figure 2 is a schematic diagram of a Delaunay triangular mesh;

[0058] Figure 3 is a schematic diagram of a fitting image obtained by performing curved surface fitting on non-uniformly distributed discrete data points obtained by sampling. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0059] The advantages of the present invention are further elaborated below in conjunction with the accompanying drawings and specific embodiments. Those skilled in the art should understand that the content specifically described below is illustrative rather than restrictive, and should not be used to limit the protection scope of the present invention.

[0060] Embodiment 1

[0061] This embodiment provides a curved surface fitting imaging method for a deformed gene chip, which includes:

[0062] Step S1: Collect the cross marks at the four corners of the deformed gene chip and place them in a coordinate system, calculate the positions of the cross marks in the coordinate system, generate uniformly distributed pre-sampling points on the projection plane of the gene chip at equal intervals according to their coordinates, and obtain the X-axis and Y-axis coordinate positions corresponding to all pre-sampling points.

[0063] Using the conventional positioning method in the art, the entire chip is positioned according to the cross mark on the chip, and the coordinates of the four corner points and the geometric center point of the chip are obtained (see Figure 1 for the red dots). Pre-sampling points evenly distributed on the chip projection plane are generated at equal intervals from the four corner points (such as Figure 1 for the green dots on the X coordinate system and Y coordinate system), and the minimum sampling interval is set according to the accuracy of the sampling device . For example, if the coordinates of corner point A are , and the coordinates of corner point B are , then the number of pre-sampling points of the chip in the X-axis direction is .

[0064] Step S2: For any point to be evaluated on the surface of the gene chip and several pre-sampling points within its neighborhood range, the Gaussian curvature is used to estimate the local curvature at the point to be evaluated, and this curvature determines the density and direction of the subsequent sampling points.

[0065] Step S2 includes:

[0066] Step S2.1: As shown in Figure 2 , a Delaunay triangular mesh is constructed with the current point to be evaluated as the center and the four pre-sampling points farthest from the current point to be evaluated within its neighborhood (such as 5x5);

[0067] Step S2.2: For each point to be evaluated, calculate the Gaussian curvature at the point to be evaluated, and the calculation formula is:

[0068]

[0069] where is the sum of all the interior angles of the triangles around the point to be evaluated in the Delaunay triangular mesh;

[0070] Step S2.3: Calculate the change rates of the Z-axis coordinates of the current point to be evaluated and the four pre-sampling points farthest from the point to be evaluated within its neighborhood range in their respective connection directions, compare the change rates of the four pre-sampling points in the Z-axis direction, and the largest one is the sampling direction of the next sampling point.

[0071] Calculate the change rates of z in their respective connection directions of the current point to be evaluated and the 4 pre-sampling points farthest from the point to be evaluated within its 5x5 neighborhood range. Assume the coordinates of the point to be evaluated are , and the coordinates of a pre-sampling point in the neighborhood are , and a unit vector u is defined as the direction from P2 to P1.

[0072]

[0073] Among them,

[0074] in this embodiment, only the change rate in the z direction is considered, that is, , compare the change rates of 4 pre-sampling points in the Z direction, and the largest one is the sampling direction of the next sampling point.

[0075] Step S2.4: Adjust the next sampling step according to the relationship between the Gaussian curvature and the threshold.

[0076] When the curvature is high, reduce the step size to increase the sampling density; when the curvature is low, increase the step size to reduce the sampling density. Define the initial sampling step size as , and the current step size is , adjust the next sampling step according to the relationship between the Gaussian curvature and the threshold .

[0077] (1) If ,

[0078]

[0079] (2) If ,

[0080]

[0081] (3) If ,

[0082]

[0083] Among them, is the high curvature threshold, is the low curvature threshold, is the step size adjustment coefficient corresponding to the high curvature threshold, is the step size adjustment coefficient corresponding to the low curvature threshold, and are the minimum and maximum step sizes allowed for sampling respectively.

[0084] Step S3: According to a number of non-uniformly discretely distributed sampling points obtained by sampling and their X-axis, Y-axis, and Z-axis coordinate positions, use radial basis functions to perform surface fitting to obtain the fitted surface equation. Subsequently, input the X-axis coordinate and Y-axis coordinate of any point into this surface equation to calculate the corresponding Z-axis coordinate. As Figure 3 shown.

[0085] Perform surface fitting on the non-uniformly distributed data points obtained by sampling using radial basis functions. Here, select the multiquadric function as the basis function, and its form is , where r is the distance from the center point to any point, is a scale parameter used to control the width and smoothness of the basis function.

[0086] Assume there are N sampling points , then the obtained fitting surface is expressed as:

[0087]

[0088] where

[0089] where is the weight coefficient of the basis function, is the low-order polynomial term used to ensure the uniqueness and stability of interpolation.

[0090] Embodiment 2

[0091] This embodiment provides a surface fitting imaging system for a deformed gene chip, which includes: a pre-sampling point generation module, a Gaussian curvature evaluation module, and a surface fitting module.

[0092] The pre-sampling point generation module is used to collect the cross marks at the four corners of the deformed gene chip and place them in a coordinate system, calculate the positions of the cross marks in the coordinate system, generate uniformly distributed pre-sampling points on the projection plane of the gene chip at equal intervals according to their coordinates, and obtain the X-axis and Y-axis coordinate positions corresponding to all pre-sampling points.

[0093] The Gaussian curvature evaluation module is used to estimate the local curvature at a point to be evaluated on the surface of the gene chip and several pre-sampling points within its neighborhood range by using Gaussian curvature, and this curvature determines the density and direction of subsequent sampling points.

[0094] The Gaussian curvature evaluation module is used for:

[0095] Step S2.1: Construct a Delaunay triangular mesh with the current point to be evaluated as the center and the four pre-sampling points farthest from the current point to be evaluated within its neighborhood range;

[0096] Step S2.2: For each point to be evaluated, calculate the Gaussian curvature at this point to be evaluated , and the calculation formula is:

[0097]

[0098] where is the sum of all the interior angles of the triangles around the point to be evaluated;

[0099] Step S2.3: Calculate the change rates of the Z-axis coordinates of the current point to be evaluated and the four pre-sampling points with the farthest distances from the point to be evaluated within its neighborhood range in their respective connection directions, and compare the change rates of the four pre-sampling points in the Z-axis direction. The largest one is the sampling direction of the next sampling point;

[0100] Step S2.4: Adjust the next sampling step size according to the relationship between the Gaussian curvature and the threshold.

[0101] (1) If ,

[0102]

[0103] (2) If ,

[0104]

[0105] (3) If ,

[0106]

[0107] where, is the high-curvature threshold, is the low-curvature threshold, is the step-size adjustment coefficient corresponding to the high-curvature threshold, is the step-size adjustment coefficient corresponding to the low-curvature threshold, and are the minimum and maximum step sizes allowed for sampling, respectively.

[0108] The surface fitting module is used to perform surface fitting on the basis of a number of non-uniformly discretely distributed sampling points obtained by sampling and their X-axis, Y-axis, and Z-axis coordinate positions, and obtain the fitted surface equation by using the radial basis function. Subsequently, by inputting the X-axis coordinate and Y-axis coordinate of any point into this surface equation, the corresponding Z-axis coordinate can be calculated. Suppose there are N sampling points , then the obtained fitted surface equation is expressed as:

[0109]

[0110] where,

[0111] where,

[0112] where, is the weight coefficient of the basis function, is the low-order polynomial term.

[0113] Example 3

[0114] This embodiment provides an electronic device, which includes: a memory, a processor, and a computer program stored on the memory and executable on the processor. When the computer program is executed by the processor, it implements the steps in the curved surface fitting imaging method of the deformed gene chip in the above-mentioned Embodiment 1. For the sake of brevity, it will not be repeated here. It should be understood that in this embodiment, the processor may be a central processing unit (CPU), and the processor may also be other general-purpose processors, digital signal processors (DSPs), application specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc. The memory may include a read-only memory and a random access memory, and provide instructions and data to the processor. A part of the memory may also include a non-volatile random access memory. For example, the memory may also store information about the device type. The electronic device includes, but is not limited to, a user device, a network device, or a device formed by integrating the user device and the network device through a network. The user device includes, but is not limited to, any mobile electronic product that can perform human-computer interaction with the user through a touchpad, such as a smart phone, a tablet computer, etc. The mobile electronic product may adopt any operating system, such as the Android operating system, the iOS operating system, etc. Among them, the network device includes an electronic device that can automatically perform numerical calculations and information processing according to pre-set or stored instructions. Its hardware includes, but is not limited to, a microprocessor, an application specific integrated circuit (ASIC), a field-programmable gate array (FPGA), a digital signal processor (DSP), an embedded device, etc. The network device includes, but is not limited to, a computer, a network host, a single network server, a set of multiple network servers, or a cloud composed of multiple servers; here, the cloud is composed of a large number of computers or network servers based on cloud computing (Cloud Computing). Among them, cloud computing is a type of distributed computing, consisting of a virtual supercomputer composed of a group of loosely coupled computer sets.

[0115] Embodiment 4

[0116] This embodiment provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, it implements the steps in the curved surface fitting imaging method of the deformed gene chip in the above-mentioned Embodiment 1. For the sake of brevity, it will not be repeated here. A computer-readable storage medium can be a tangible device that can hold and store instructions used by an instruction execution device. A computer-readable storage medium can be, for example, but not limited to, an electrical storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the above. More specific examples (non-exhaustive list) of a computer-readable storage medium include: a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a static random access memory (SRAM), a portable compact disk read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanical encoding device, such as a punched card or a raised structure in a groove storing instructions thereon, and any suitable combination of the above. The computer-readable program instructions described herein can be downloaded from the computer-readable storage medium to various computing / processing devices, or downloaded to an external computer or an external storage device through a network, such as the Internet, a local area network, a wide area network, and / or a wireless network. The network can include a copper transmission cable, an optical fiber transmission, a wireless transmission, a router, a firewall, a switch, a gateway computer, and / or an edge server. The network adapter or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards the computer-readable program instructions for storage in the computer-readable storage medium in each computing / processing device.

[0117] It should be noted that the embodiments of the present invention have good implementability and do not impose any form of limitation on the present invention. Any person skilled in the art may use the disclosed technical content to modify or transform it into an equivalent effective embodiment. However, as long as the content of the technical solution of the present invention is not departed from, any modification, equivalent change, or modification made to the above embodiments according to the technical essence of the present invention still falls within the scope of the technical solution of the present invention.

Claims

1. A method for curved surface fitting imaging of a gene chip with deformation, characterized in that, Including: Step S1: Collect the cross marks at the four corners of the deformed gene chip and place them in a coordinate system. Calculate the positions of the cross marks in the coordinate system, generate pre-sampling points evenly distributed on the projection plane of the gene chip at equal intervals according to their coordinates, and obtain the X-axis and Y-axis coordinate positions corresponding to all pre-sampling points. Step S2: For any point to be evaluated on the surface of the gene chip and several pre-sampling points within its neighborhood range, use Gaussian curvature to estimate the local curvature at the point to be evaluated. This curvature determines the density and direction of subsequent sampling points. Step S3: According to a number of sampled points with non-uniform discrete distribution and their X-axis, Y-axis, and Z-axis coordinate positions, a radial basis function is used for surface fitting to obtain the surface equation after fitting. Subsequently, by inputting the X-axis coordinate and Y-axis coordinate of any point into this surface equation, the corresponding Z-axis coordinate can be calculated. The said step S2 includes: Step S2.1: Construct a Delaunay triangular mesh with the current point to be evaluated as the center and the four pre-sampling points farthest from the current point to be evaluated within its neighborhood range. Step S2.2: For each point to be evaluated, calculate the Gaussian curvature Kv at this point to be evaluated. The calculation formula is: Kv = 2π - ∑θi; where, ∑θi is the sum of all the interior angles of the triangles around the point to be evaluated. Step S2.3: Calculate the change rates of the Z-axis coordinate values of the current point to be evaluated and the four pre-sampling points farthest from the point to be evaluated within its neighborhood range in their respective connection directions. Compare the change rates of the four pre-sampling points in the Z-axis direction, and the largest one is the sampling direction of the next sampling point. Step S2.4: Adjust the sampling step size for the next time according to the relationship between the Gaussian curvature Kv and the threshold. In step S2.4, (1) If K v > T high , t n+1 = max(t n × k low , t min ); (2) If K v <T low , t n+1 = min(t n × k high , t max ); (3) If T low < K v < T high , t n+1 = t n ; Among them, the initial sampling step size is t0, and the current step size is t n , and the next sampling step size t is adjusted according to the relationship between the Gaussian curvature and the threshold n+1 , T high is the high curvature threshold, T low is the low curvature threshold, k low is the step size adjustment coefficient corresponding to the high curvature threshold, k high is the step size adjustment coefficient corresponding to the low curvature threshold, t min and t max are the minimum and maximum step sizes allowed for sampling, respectively.

2. The curved surface fitting imaging method of a deformed gene chip according to claim 1, characterized in that, In the step S3, assuming that there are N sampling points (x i , y i , z i ), and i = 1, 2,..., N, the obtained fitting surface is expressed as: Among them, where r = ||(x, y) - (x i , y i )||; ε is a scale parameter used to control the width and smoothness of the basis function; where, w i is the weight coefficient of the basis function, and p(x, y) is the low-order polynomial term.

3. A curved surface fitting imaging system for a gene chip with deformation, characterized in that, Including: A pre-sampling point generation module, which is used to collect the cross marks at the four corners of the deformed gene chip and place them in a coordinate system. Calculate the positions of the cross marks in the coordinate system, generate pre-sampling points evenly distributed on the projection plane of the gene chip at equal intervals according to their coordinates, and obtain the X-axis and Y-axis coordinate positions corresponding to all pre-sampling points. A Gaussian curvature evaluation module, which is used to use Gaussian curvature to estimate the local curvature at any point to be evaluated on the surface of the gene chip and several pre-sampling points within its neighborhood range. This curvature determines the density and direction of subsequent sampling points. A surface fitting module, which is used to perform surface fitting using radial basis functions based on several non-uniformly discretely distributed sampling points obtained by sampling and their X-axis, Y-axis, and Z-axis coordinate positions to obtain the fitted surface equation. Subsequently, inputting the X-axis coordinate and Y-axis coordinate of any point into this surface equation can calculate the corresponding Z-axis coordinate. The said Gaussian curvature evaluation module is used for: Step S2.1: Construct a Delaunay triangular mesh with the current point to be evaluated as the center and the four pre-sampling points farthest from the current point to be evaluated within its neighborhood range. Step S2.2: For each point to be evaluated, calculate the Gaussian curvature Kv at this point to be evaluated. The calculation formula is: Kv = 2π - ∑θi; where, ∑θi is the sum of all the interior angles of the triangles around the point to be evaluated. Step S2.3: Calculate the change rates of the Z-axis coordinates of the current point to be evaluated and the four pre-sampling points that are farthest from the point to be evaluated within its neighborhood in their respective connection directions, and compare the change rates of the four pre-sampling points in the Z-axis direction. The largest one is the sampling direction of the next sampling point. Step S2.4: Adjust the sampling step size for the next time according to the relationship between the Gaussian curvature Kv and the threshold. In step S2.4, (1) If K v > T high , t n+1 = max(t n × k low , t min ); (2) If K v < T low , t n+1 = min(t n × k high , t max ); (3) If T low <K v <T high , t n+1 = t n ; Among them, the initial sampling step size is t0, and the current step size is t n , adjust the next sampling step size t according to the relationship between the Gaussian curvature and the threshold n+1 , T high is the high curvature threshold, T low is the low curvature threshold, k low is the step size adjustment coefficient corresponding to the high curvature threshold, k high is the step size adjustment coefficient corresponding to the low curvature threshold, t min and t max are the minimum and maximum step sizes allowed for sampling respectively.

4. The curved surface fitting imaging system for a deformed gene chip as described in claim 3, characterized in that, The surface fitting module is used for: Assume there are N sampling points (x i , y i , z i ), where i = 1, 2,..., N, then the obtained fitting surface is expressed as: Among them, where r = ||(x, y) - (x i , y i )||; ε is a scale parameter used to control the width and smoothness of the basis function; where w i is the weight coefficient of the basis function, and p(x, y) is the low-order polynomial term.

5. An electronic device, characterized in that, including: A memory, a processor, and a computer program stored on the memory and executable on the processor. When the computer program is executed by the processor, it implements the steps in the method for surface fitting imaging of a deformed gene chip as described in any one of claims 1 to 2.

6. A computer-readable storage medium, on which a computer program is stored, characterized in that, When the computer program is executed by the processor, it implements the steps in the method for surface fitting imaging of a deformed gene chip as described in any one of claims 1 to 2.

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