Imaging optimization method and device of 3D printer and 3D printer

Abstract

The invention provides an imaging optimization method and device of a 3D printer and the 3D printer, is applied to the technical field of 3D printing, and utilizes an image acquisition module to obtain a plurality of sub-region pixel images of a plurality of preset heights. The pixel images are images of the ray machine on the image acquisition module when the image acquisition module is located on imaging surfaces of multiple preset heights, and the pixel images of the multiple sub-regions of each preset height correspond to different shooting regions of the imaging surfaces of the preset heights. And for each shooting area, determining a plurality of same-area pixel images corresponding to the shooting area from a plurality of obtained sub-area pixel images corresponding to each preset height. And based on the same-region pixel image, determining the gray variance of the same-region pixel image. And for each shooting area, determining the same-area image with the maximum gray variance as an optimal pixel image. And determining the spatial position of the ray machine based on the optimal pixel image corresponding to each shooting area. And the printing precision of the 3D printer can be improved.

Description

Technical Field

[0001] The present application relates to the field of 3D printing technology, and in particular to an imaging optimization method and device for a 3D printer, and a 3D printer. Background Art

[0002] DLP (digital light processing, DLP) technology is a rapid prototyping technology, commonly used in the field of 3D printing. DLP technology uses a digital micromirror device (DMD) to project a high-intensity ultraviolet beam, and solidifies liquid photosensitive resin layer by layer through photolithography to form a solid object.

[0003] To adjust the clarity of optical machine imaging, most methods are to place white paper or a uniform light film on the imaging surface, project a test image, and use human eyes to visually determine whether the image is clear. This method can only adjust the clarity of the optical machine from the macroscopic level of the naked eye. For high-precision 3D printing applications, the clarity adjusted by this method cannot meet the requirements. Another method is to use a camera to shoot the image projected on white paper from a distance. This method is also macroscopic, and the larger the format of the DLP light-curing 3D printer, the worse the detection and debugging effect.

[0004] Based on this, the present application provides an imaging optimization method for a 3D printer, which can improve the printing accuracy of the printer. Summary of the invention

[0005] The purpose of this application is to provide an imaging optimization method and device for a 3D printer, and a 3D printer, so as to improve the printing accuracy of the 3D printer.

[0006] In a first aspect, an embodiment of the present application provides an imaging optimization method for a 3D printer, which is applied to a 3D printer, wherein the 3D printer includes an optical machine. The method includes: using the image acquisition module to obtain multiple sub-region pixel images at multiple preset heights. The pixel image is the imaging of the optical machine on the image acquisition module when the image acquisition module is at the imaging surface of the multiple preset heights, and the multiple sub-region pixel images at each preset height correspond to different shooting areas of the imaging surface of the preset height. For each shooting area, multiple pixel images of the same area corresponding to the shooting area are determined from the multiple sub-region pixel images corresponding to each preset height. Based on the pixel images of the same area, the grayscale variance of the pixel images of the same area is determined. For each shooting area, the image of the same area with the largest grayscale variance is determined as the optimal pixel image. Based on the optimal pixel images corresponding to each shooting area, the spatial position of the optical machine is determined.

[0007] The imaging optimization method and device of the 3D printer and the 3D printer provided in the embodiment of the present application obtain multiple sub-region pixel images of multiple preset heights by using an image acquisition module. For each shooting area, multiple pixel images of the same area corresponding to the shooting area are determined from the multiple sub-region pixel images corresponding to each preset height, and then the grayscale variance of the pixel images of the same area is determined based on the grayscale of each pixel in the pixel images of the same area and the pixel images. For each shooting area, the image of the same area with the largest grayscale variance is determined as the optimal pixel image, and the spatial position of the optical machine is determined based on the optimal pixel images corresponding to each shooting area. By automatically finding the optimal imaging surface of the 3D printer at the pixel level, the spatial position of the 3D printer is adjusted, which can reduce manual intervention and improve work efficiency while ensuring the accuracy of each adjustment and improving the printing accuracy of the 3D printer.

[0008] A possible implementation method is that determining the grayscale variance of the pixel image in the same area based on the pixel image in the same area includes: determining the grayscale variance of the pixel image according to the number of pixels in the horizontal direction, the number of pixels in the vertical direction, and the position information of each pixel point in the pixel image.

[0009] In a possible implementation, the grayscale variance of the pixel image is determined according to the number of pixels in the horizontal direction, the number of pixels in the vertical direction, and the position information of each pixel in the pixel image, including: determining the grayscale value of each pixel according to the number of pixels in the horizontal direction, the number of pixels in the vertical direction, and the position information of each pixel in the pixel image. Determining the grayscale average of the pixel image according to the number of pixels in the horizontal direction, the number of pixels in the vertical direction, and the grayscale value of each pixel. Determining the grayscale variance of the pixel image according to the number of pixels in the horizontal direction, the number of pixels in the vertical direction, the grayscale value of each pixel, and the grayscale average of the pixel image.

[0010] In a possible implementation, determining the grayscale average value of the pixel image according to the number of pixels in the horizontal direction, the number of pixels in the vertical direction, and the grayscale value of each pixel point of the pixel image includes:

[0011]

[0012] in, is the grayscale average value of the pixel image, N x is the number of pixels in the horizontal direction of the pixel image, N yis the number of pixels in the vertical direction of the pixel image, x represents the serial number of the pixel in the horizontal direction of the pixel image, y represents the serial number of the pixel in the vertical direction of the pixel image, and f(x, y) represents the grayscale value of the pixel point at (x, y) in the pixel image.

[0013] In a possible implementation, determining the grayscale variance of the pixel image according to the number of pixels in the horizontal direction, the number of pixels in the vertical direction, the grayscale value of each pixel point, and the grayscale average value of the pixel image includes:

[0014]

[0015] Wherein, F represents the grayscale variance of the pixel image.

[0016] In a possible implementation, the determining the spatial position of the optical machine based on the optimal pixel image corresponding to each shooting area includes: determining the center position of the optimal pixel image according to the optimal pixel image. Fitting the center position of the pixel image corresponding to each shooting area using the least squares method to obtain the target imaging surface of the optical machine. Determining the spatial position of the optical machine according to the target imaging surface.

[0017] In a possible implementation, determining the spatial position of the optical machine according to the target imaging plane includes: determining an optimal imaging plane of the optical machine based on the target imaging plane, and adjusting the spatial position of the optical machine using the optimal imaging plane of the optical machine.

[0018] A possible implementation method, wherein the use of the image acquisition module to obtain multiple pixel images at multiple preset heights includes: using the image acquisition module to obtain an imaging surface when the image acquisition module is at the multiple preset heights, the optical machine images the image of the image acquisition module to obtain the multiple sub-area pixel images.

[0019] In a second aspect, the embodiment of the present application provides an imaging optimization device for a 3D printer, including an image acquisition module, applied to a 3D printer, and the 3D printer optical machine. The device also includes: an acquisition module and a processing module.

[0020] The acquisition module is used to acquire multiple sub-region pixel images at multiple preset heights using the image acquisition module. The sub-region pixel images are images of the optical machine on the image acquisition module when the image acquisition module is at the imaging surface of the multiple preset heights, and the multiple sub-region pixel images at each preset height correspond to different shooting areas of the imaging surface at the preset height.

[0021] The processing module is used to determine, for each shooting area, a plurality of pixel images corresponding to the shooting area from the plurality of pixel images corresponding to the preset heights. Based on the pixel images in the same area, determine the grayscale variance of the pixel images in the same area. For each shooting area, determine the image in the same area with the largest grayscale variance as the optimal pixel image. Based on the optimal pixel images corresponding to each shooting area, determine the spatial position of the optical machine.

[0022] In a third aspect, the present application provides a 3D printer having the function of implementing the imaging optimization method of the 3D printer of the first aspect or any possible implementation. The function can be implemented by hardware, or by hardware executing corresponding software. The hardware or software includes one or more modules corresponding to the above functions.

[0023] In a fourth aspect, a computer-readable storage medium is provided, wherein instructions are stored in the computer-readable storage medium, and when the computer-readable storage medium is run on a computer, the computer can execute the imaging optimization method of a 3D printer according to the first aspect or any possible implementation manner.

[0024] In a fifth aspect, a computer program product comprising instructions is provided, which, when executed on a computer, enables the computer to execute the imaging optimization method of a 3D printer according to the first aspect or any possible implementation manner.

[0025] Among them, the technical effects brought about by any design method in the second to fifth aspects can refer to the technical effects brought about by different possible implementation methods in the first aspect, and will not be repeated here. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] In order to more clearly illustrate the specific implementation methods of the present application or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some implementation methods of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0027] Figure 1 A structural schematic diagram of a 3D printer provided in an embodiment of the present application; at this time, the image acquisition module is located on the initial imaging surface;

[0028] Figure 2 A structural schematic diagram of an imaging calibration device provided in an embodiment of the present application;

[0029] Figure 3 A schematic flow chart of an imaging optimization method for a 3D printer provided in an embodiment of the present application;

[0030] Figure 4 Another schematic diagram of an imaging optimization method for a 3D printer provided in an embodiment of the present application;

[0031] Figure 5 A structural schematic diagram of an imaging optimization device for a 3D printer provided in an embodiment of the present application;

[0032] Figure 6 A structural schematic diagram of an imaging optimization system for a 3D printer provided in an embodiment of the present application. DETAILED DESCRIPTION

[0033] In order to make the purpose, technical solution and advantages of the embodiments of the present application clearer, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, rather than all the embodiments. The components of the embodiments of the present application described and shown in the drawings here can be arranged and designed in various different configurations.

[0034] Therefore, the following detailed description of the embodiments of the present application provided in the accompanying drawings is not intended to limit the scope of the present application for which protection is sought, but merely represents selected embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in the field without creative work are within the scope of protection of the present application.

[0035] At present, most of the ways to adjust the pixel clarity of optical machine imaging are to place white paper or light-homogenizing film on the imaging surface, project a test image, and visually determine whether the image is clear by human eyes. This method can only adjust the pixel clarity of the optical machine from the macroscopic level of the naked eye. For high-precision 3D printing applications, the pixel clarity adjusted by this method cannot meet the requirements. Another method is to use a camera to shoot the image projected on the white paper from a distance. This method is also macroscopic, and the larger the format of the DLP light-curing 3D printer, the worse the detection and debugging effect.

[0036] Based on this, the embodiment of the present application provides an imaging optimization method and device of a 3D printer, and a 3D printer, which are applied to a 3D printer, and the 3D printer may include an optical machine. The method includes: using an image acquisition module to obtain multiple sub-region pixel images at multiple preset heights. When the pixel image is an imaging surface of the image acquisition module at multiple preset heights, the optical machine images at the image acquisition module, and the multiple sub-region pixel images at each preset height correspond to different shooting areas of the imaging surface at the preset height. For each shooting area, multiple pixel images of the same area corresponding to the shooting area are determined from the multiple sub-region pixel images corresponding to each preset height. Based on the pixel images of the same area, the grayscale variance of the pixel images of the same area is determined. For each shooting area, the image of the same area with the largest grayscale variance is determined as the optimal pixel image. Based on the optimal pixel images corresponding to each shooting area, the spatial position of the optical machine is determined.

[0037] The imaging optimization method and device of the 3D printer and the 3D printer provided in the embodiment of the present application use an image acquisition module to obtain multiple sub-region pixel images of multiple preset heights. For each shooting area, multiple pixel images of the same region corresponding to the shooting area are determined from the multiple sub-region pixel images corresponding to each preset height, and then the grayscale variance of the pixel images of the same region is determined based on the pixel images of the same region. For each shooting area, the image of the same region with the largest grayscale variance is determined as the optimal pixel image, and the spatial position of the optical machine is determined based on the optimal pixel images corresponding to each shooting area. By automatically finding the optimal imaging surface of the 3D printer at the pixel level, the spatial position of the 3D printer is adjusted, which can reduce manual intervention and improve work efficiency while ensuring the accuracy of each adjustment and improving the printing accuracy of the 3D printer.

[0038] The implementation of the embodiments of the present application will be described in detail below with reference to the accompanying drawings.

[0039] On the one hand, the imaging calibration method of a 3D printer provided in the embodiment of the present application can be executed by a 3D printer. Figure 1 As shown, the 3D printer 100 may include: an optical machine 102 and an optical machine adjustment device 103 .

[0040] Among them, Figure 2As shown, the imaging calibration device 101 applied to the 3D printer 100 may include: an image acquisition module 1011 and a positioning fixture 1012. The image acquisition module 1011 may be a charge-coupled device (CCD) camera. The imaging sensor in the image acquisition module 1011 captures light and converts the light signal into an electrical signal, thereby forming a pixel image on its imaging surface. In this process, the pixel image is decomposed into many tiny elements, namely pixels, each of which has its corresponding brightness and color information. These pixels are combined to form a pixel image.

[0041] The image acquisition module 1011 may be equipped with a light-transmitting structure similar to the resin tank. The image acquisition module 1011 may also be equipped with an automatic lifting device for realizing the up and down movement of the image acquisition module 1011 relative to the positioning tool 1012.

[0042] The positioning fixture 1012 is used to fix the image acquisition module 1011. The positioning fixture 1012 may include a plurality of fixing areas, and an image acquisition module 1011 may be deployed in each fixing area.

[0043] It should be noted that the positioning tool 1012 can be deployed in the original resin tank area of ​​the light-curing 3D printer, and can also be deployed at a relative position of the original resin tank area, which is not limited in this application.

[0044] The optical machine 102 can be an ultraviolet light source and an optical projection system. The optical machine 102 can be used to irradiate the photosensitive resin material, and accurately project a light spot or light pattern on the imaging surface of the light-curing 3D printer (specifically, the release film of the resin tank) according to each layer of the slice information of the digital model, so that the resin layer undergoes a photopolymerization reaction and solidifies, and is stacked layer by layer to form a three-dimensional object. The optical machine 102 can also be used to irradiate the image acquisition module 1011, so that the image acquisition module 1011 captures light to form a pixel image on the imaging surface of the image acquisition module 1011. During imaging calibration, the resin tank originally installed on the middle plate (part of the mechanical mechanism of the 3D printer) is removed and the positioning fixture is installed on the middle plate. The positioning fixture 1012 is also used to align the initial imaging surface of the image acquisition module 1011 with the imaging surface of the original light-curing 3D printer. After imaging calibration, the positioning fixture 1012 is removed and the resin tank is installed back on the middle plate. The position of the middle plate is always fixed.

[0045] The optical machine adjusting device 103 is used to deploy the optical machine 102. The optical machine adjusting device 103 can be adjusted to adjust the spatial position of the optical machine.

[0046] On the one hand, the present application embodiment discloses a 3D printer imaging optimization method, which can be Figure 1The 3D printer 100 shown in FIG. Figure 3 As shown, the method may include:

[0047] S301, using an image acquisition module to acquire a plurality of sub-region pixel images at a plurality of preset heights.

[0048] Among them, the pixel image is the imaging of the optical machine in the image acquisition module when the image acquisition module is at an imaging surface at multiple preset heights, and the multiple sub-area pixel images at each preset height correspond to different shooting areas of the imaging surface at the preset height.

[0049] A possible implementation method is to adjust the height of the automatic lifting device to adjust the image acquisition module to multiple preset heights. When the image acquisition module is used to obtain the imaging surface of the image acquisition module at multiple preset heights, the optical machine images the image acquisition module to obtain multiple sub-region pixel images.

[0050] For example, Figure 4 As shown, the shooting area of ​​the pixel image may include a1, a2, a3, ..., a n .

[0051] By adjusting the height of the automatic lifting device, the image acquisition module can be adjusted to a plurality of preset heights z1, z2, z3...z n When the image acquisition module is used to acquire the imaging surface of the image acquisition module at multiple preset heights, the optical machine images the image acquisition module to obtain multiple sub-region pixel images p1, p2, p3...p n .

[0052] S302: For each shooting area, determine a plurality of same-area pixel images corresponding to the shooting area from a plurality of sub-area pixel images corresponding to respective preset heights.

[0053] For example, for a shooting area a1 in each shooting area, a plurality of preset heights z1, z2, z3, ... z n Among the corresponding multiple pixel images, the multiple pixel images corresponding to the imaging area a1 are determined.

[0054] S303: Determine the grayscale variance of the pixel images in the same region based on the pixel images in the same region.

[0055] In a possible implementation, the grayscale variance of the pixel image is determined according to the number of pixels in the horizontal direction, the number of pixels in the vertical direction, and the position information of each pixel in the pixel image.

[0056] A possible implementation method is to determine the grayscale variance of the pixel image according to the number of pixels in the horizontal direction, the number of pixels in the vertical direction, and the position information of each pixel in the pixel image, including: determining the grayscale value of each pixel according to the number of pixels in the horizontal direction, the number of pixels in the vertical direction, and the position information of each pixel in the pixel image. Determine the grayscale average of the pixel image according to the number of pixels in the horizontal direction, the number of pixels in the vertical direction, and the grayscale value of each pixel. Determine the grayscale average of the pixel image according to the number of pixels in the horizontal direction, the number of pixels in the vertical direction, and the grayscale value of each pixel, and determine the grayscale variance of the pixel image.

[0057] For example,

[0058]

[0059] in, is the grayscale average of the pixel image, N x is the number of pixels in the horizontal direction of the pixel image, N y is the number of pixels in the vertical direction of the pixel image, x represents the serial number of the pixel in the horizontal direction of the pixel image, y represents the serial number of the pixel in the vertical direction of the pixel image, and f(x,y) represents the grayscale value of the pixel at (x,y) in the pixel image.

[0060]

[0061] Where F represents the grayscale variance of the pixel image.

[0062] Further, for each shooting area.

[0063]

[0064] Among them, F n Represents the grayscale variance of each shooting area, f(x,y) n Represents the grayscale value of the pixel at (x, y) in the pixel image of each captured area, Represents the grayscale variance of the pixel image in each captured area.

[0065] S304: For each shooting area, determine the image in the same area with the largest grayscale variance as the optimal pixel image.

[0066] Among them, the grayscale variance represents the average degree of grayscale change of the pixel image. The larger the average degree of grayscale change, the clearer the image is, and the smaller the average degree of grayscale change, the blurrier the image is.

[0067] For example, the image acquisition module is moved up and down by an automatic lifting device to test the F of each sub-area.n The maximum value of F n is the optimal value.

[0068] S305: Determine the spatial position of the optical machine based on the optimal pixel image corresponding to each shooting area.

[0069] One possible implementation method is to determine the center position of the optimal pixel image based on the optimal pixel image. The center position of the pixel image corresponding to each shooting area is fitted using the least square method to obtain the target imaging surface of the optical machine. The spatial position of the optical machine is determined based on the target imaging surface.

[0070] Exemplarily, each shooting area obtains the F n Optimal detection height z n . Get the center position of each shooting area (x n ,y n ), the three-dimensional coordinates of the best imaging plane of each shooting area are (x1, y1, z1), (x2, y2, z2), ..., (x n ,y n , z n ).

[0071] Since (x1, y1, z1), (x2, y2, z2), ..., (x n ,y n , z n )These points are not actually coplanar, so the least squares method is needed to fit the plane.

[0072] The calculation method of the fitted plane is as follows. The general formula of the plane equation is:

[0073] Ax+By+Cz+D=0.

[0074] Among them, A, B, and C are not all zero.

[0075]

[0076] make

[0077] The plane equation is transformed into:

[0078] z=a0x+a1y+a2

[0079] Matrix formula: Ax = b;

[0080]

[0081] (x1, y1, z1), (x2, y2, z2), ..., (x n ,y n, z n ) are the three-dimensional point coordinates of the optimal imaging surface of each shooting area.

[0082] According to the least squares formula, the solution of the normal equations is:

[0083] x=(A T A) -1 A T b

[0084] Using the solution formula of the normal equations, we can obtain (a0, a1, a2);

[0085] The final fitted imaging plane equation is z = a0x + a1y + a2

[0086] One possible implementation method is to determine the optimal imaging plane of the optical machine based on the target imaging plane, and to adjust the spatial position of the optical machine using the optimal imaging plane of the optical machine.

[0087] Exemplarily, the imaging plane equation of the target imaging surface is used to perform reverse adjustment to obtain the optimal imaging plane z=-(a0x+a1y+a2) of the optical machine. The spatial position of the optical machine is adjusted using the optimal imaging plane. After adjusting the spatial position of the optical machine, the imaging calibration is completed, the positioning tool is removed, and the resin tank is installed to the initial position.

[0088] The imaging optimization method and device of the 3D printer and the 3D printer provided in the embodiment of the present application use an image acquisition module to obtain multiple sub-region pixel images of multiple preset heights. For each shooting area, multiple pixel images of the same region corresponding to the shooting area are determined from the multiple sub-region pixel images corresponding to each preset height, and then the grayscale variance of the pixel images of the same region is determined based on the pixel images of the same region. For each shooting area, the image of the same region with the largest grayscale variance is determined as the optimal pixel image, and the spatial position of the optical machine is determined based on the optimal pixel images corresponding to each shooting area. By automatically finding the optimal imaging surface of the 3D printer at the pixel level, the spatial position of the 3D printer is adjusted, which can reduce manual intervention and improve work efficiency while ensuring the accuracy of each adjustment and improving the printing accuracy of the 3D printer.

[0089] The above mainly introduces the solution provided by the embodiment of the present application from the perspective of the working principle of the device. It can be understood that in order to realize the above functions, the imaging optimization device of the 3D printer includes hardware structures and / or software modules corresponding to the execution of each function. Those skilled in the art should easily realize that, in combination with the algorithm steps of each example described in the embodiments disclosed in this article, the present application can be implemented in the form of hardware or a combination of hardware and computer software. Whether a function is executed in the form of hardware or computer software driving hardware depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0090] The embodiment of the present application can divide the functional modules of the imaging optimization device of the 3D printer according to the above method example. For example, each functional module can be divided according to each function, or two or more functions can be integrated into one processing module. The above integrated module can be implemented in the form of hardware or software functional module.

[0091] It should be noted that the division of modules in the embodiments of the present application is schematic and is only a logical function division. There may be other division methods in actual implementation. Figure 5 A possible schematic diagram of the composition of the imaging optimization device of the 3D printer involved in the above and embodiments is shown. Figure 5 As shown, the imaging optimization device 500 of the 3D printer may include: an acquisition module 501 and a processing module 502.

[0092] The acquisition module 501 is used to support the imaging optimization device 500 of the 3D printer to execute Figure 2 S201 in the imaging optimization method of the 3D printer shown.

[0093] Processing module 502, used to support the imaging optimization device 500 of the 3D printer to execute Figure 2 S202 or S203 or S204 or S205 in the imaging optimization method of the 3D printer shown.

[0094] It should be noted that all relevant contents of each step involved in the above method embodiment can be referred to the functional description of the corresponding functional module and will not be repeated here.

[0095] The imaging optimization device 500 of a 3D printer provided in the embodiment of the present application is used to perform the above Figure 2 Or the imaging optimization method of the 3D printer shown in 3, so it can achieve the same effect as the imaging optimization method of the 3D printer mentioned above.

[0096] The present application also provides a 3D printer, which can be the 3D printer described in the above embodiment. Figure 1 The 3D printer 100 shown can execute the imaging optimization method and related steps of the 3D printer in the above method embodiment.

[0097] The embodiment of the present application also provides a computer-readable storage medium on which instructions are stored. When the instructions are executed, the imaging optimization method and related steps of the 3D printer in the above method embodiment are executed.

[0098] The embodiment of the present application also provides a computer program product. When the computer program product is run on a computer, the computer executes the imaging optimization method and related steps of the 3D printer in the above method embodiment.

[0099] In some embodiments, the methods described herein may be implemented as computer program instructions encoded in a machine-readable format on a computer-readable storage medium or encoded on other non-transitory media or articles of manufacture.

[0100] The present application also provides an imaging optimization system 600 for a 3D printer. Figure 6 As shown, the imaging optimization system 600 of the 3D printer includes at least one processor 601 and at least one interface circuit 602 .

[0101] As an example, when the imaging optimization system 600 of the 3D printer includes a processor and an interface circuit, the processor may be Figure 6 The processor 601 shown in the solid line frame (or the processor 601 shown in the dotted line frame) may be Figure 6 The interface circuit 602 shown in the solid line frame (or the interface circuit 602 shown in the dotted line frame) is shown in the solid line frame. When the chip system 600 includes two processors and two interface circuits, the two processors include Figure 6 The processor 601 shown in the solid line frame and the processor 601 shown in the dotted line frame, the two interface circuits include Figure 6 The interface circuit 602 shown in the solid line frame and the interface circuit 602 shown in the dotted line frame are not limited to this.

[0102] The processor 601 and the interface circuit 602 can be interconnected via a line. For example, the interface circuit 602 can be used to receive a signal. For another example, the interface circuit 602 can be used to send a signal to another device (such as the processor 601). For example, the interface circuit 602 can read a computer instruction stored in a memory and send the computer instruction to the processor 601. The processor 601 executes the instruction and implements the various steps in the above embodiments in combination with the input and output devices, such as implementing Figure 3 or Figure 4 The various steps performed in the method embodiment shown. Of course, the chip system may also include other discrete devices, which are not specifically limited in the embodiment of the present application.

[0103] Through the description of the above implementation methods, technical personnel in the relevant field can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional modules is used as an example. In actual applications, the above-mentioned functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above.

[0104] In the several embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the modules or units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another device, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0105] The units described as separate components may or may not be physically separated, and the components shown as units may be one physical unit or multiple physical units, that is, they may be located in one place or distributed in multiple different places. Some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0106] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0107] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a readable storage medium. Based on this understanding, the technical solution of the embodiment of the present application is essentially or the part that contributes or all or part of the technical solution can be embodied in the form of a software product, which is stored in a storage medium, including several instructions to enable a device (which can be a single-chip microcomputer, chip, etc.) or a processor (processor) to perform all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read only memory (ROM), random access memory (RAM), disk or optical disk and other media that can store program code.

[0108] The above contents are only specific implementation methods of the present application, but the protection scope of the present application is not limited thereto. Any changes or substitutions within the technical scope disclosed in the present application shall be included in the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.

Claims

1. A 3D printer imaging optimization method, characterized in that: Applied to a 3D printer, the 3D printer includes an optical machine; the method includes: The image acquisition module is used to acquire a plurality of sub-region pixel images at a plurality of preset heights; the pixel images are images of the optical machine on the image acquisition module when the image acquisition module is at an imaging surface at the plurality of preset heights, and the plurality of sub-region pixel images at the preset heights correspond to different shooting areas of the imaging surface at the preset heights; For each shooting area, determining a plurality of pixel images of the same area corresponding to the shooting area from the obtained plurality of sub-area pixel images corresponding to each preset height; Based on the same-region pixel image, determining the grayscale variance of the same-region pixel image; For each shooting area, determining the image in the same area with the largest grayscale variance as the optimal pixel image; Based on the optimal pixel image corresponding to each shooting area, the spatial position of the optical machine is determined.

2. The method according to claim 1, characterized in that The step of determining the grayscale variance of the pixel image in the same region based on the pixel image in the same region includes: The grayscale variance of the pixel image is determined according to the number of pixels in the horizontal direction, the number of pixels in the vertical direction, and the position information of each pixel point in the pixel image.

3. The method according to claim 2, characterized in that Determining the grayscale variance of the pixel image according to the number of pixels in the horizontal direction, the number of pixels in the vertical direction, and the position information of each pixel point in the pixel image includes: Determine the grayscale value of each pixel point according to the number of pixels in the horizontal direction, the number of pixels in the vertical direction, and the position information of each pixel point in the pixel image; Determine the grayscale average value of the pixel image according to the number of pixels in the horizontal direction, the number of pixels in the vertical direction, and the grayscale value of each pixel point of the pixel image; The grayscale variance of the pixel image is determined according to the number of pixels in the horizontal direction, the number of pixels in the vertical direction, the grayscale value of each pixel point and the grayscale average value of the pixel image.

4. The method according to claim 3, characterized in that Determining the grayscale average value of the pixel image according to the number of pixels in the horizontal direction, the number of pixels in the vertical direction, and the grayscale value of each pixel point of the pixel image includes: in, is the grayscale average value of the pixel image, N x is the number of pixels in the horizontal direction of the pixel image, N y is the number of pixels in the vertical direction of the pixel image, x represents the serial number of the pixel in the horizontal direction of the pixel image, y represents the serial number of the pixel in the vertical direction of the pixel image, and f(x, y) represents the grayscale value of the pixel point at (x, y) in the pixel image.

5. The method according to claim 4, characterized in that Determining the grayscale variance of the pixel image according to the number of pixels in the horizontal direction, the number of pixels in the vertical direction, the grayscale value of each pixel point, and the grayscale average value of the pixel image includes: Wherein, F represents the grayscale variance of the pixel image.

6. The method according to claim 1, characterized in that The determining the spatial position of the optical machine based on the optimal pixel image corresponding to each shooting area includes: Determining a center position of the optimal pixel image according to the optimal pixel image; Fitting the center position of the pixel image corresponding to each shooting area by the least square method to obtain the target imaging surface of the optical machine; The spatial position of the optical machine is determined according to the target imaging plane.

7. The method according to claim 6, characterized in that Determining the spatial position of the optical machine according to the target imaging plane includes: Based on the target imaging plane, determining the optimal imaging plane of the optical machine; The spatial position of the optical machine is adjusted by utilizing the optimal imaging plane of the optical machine.

8. The method according to claim 1, characterized in that The method of using the image acquisition module to acquire a plurality of sub-region pixel images at a plurality of preset heights includes: When the image acquisition module is used to acquire an imaging surface of the image acquisition module at the plurality of preset heights, the optical machine forms an image on the image acquisition module to obtain the plurality of sub-region pixel images.

9. An imaging optimization device for a 3D printer, comprising an image acquisition module, characterized in that: Applied to a 3D printer, the 3D printer includes an optical machine; the device also includes: An acquisition module, used to acquire a plurality of sub-region pixel images at a plurality of preset heights by using the image acquisition module; the sub-region pixel images are images of the optical machine on the image acquisition module when the image acquisition module is at an imaging surface at the plurality of preset heights, and the plurality of sub-region pixel images at each preset height correspond to different shooting areas of the imaging surface at the preset height; A processing module is used to determine, for each shooting area, a plurality of same-area pixel images corresponding to the shooting area from a plurality of pixel images corresponding to each preset height; determine the grayscale variance of the same-area pixel images based on the same-area pixel images; for each shooting area, determine the same-area image with the largest grayscale variance as the optimal pixel image; and determine the spatial position of the optical machine based on the optimal pixel images corresponding to each shooting area.

10. A 3D printer, characterized in that: The 3D printer includes a processor and a memory, wherein the memory stores machine executable instructions that can be executed by the processor, and the processor executes the machine executable instructions to implement the imaging optimization method of the 3D printer according to any one of claims 1 to 8.

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