3D printing platform leveling method and system based on point map analysis

Abstract

The application discloses a 3D printing platform leveling method and system based on point position map analysis, and the method comprises the following steps: acquiring height parameters corresponding to a plurality of platform points of a 3D printing platform; generating a height point position map of the 3D printing platform according to the height parameters corresponding to all the platform points; determining at least one abnormal point position of the 3D printing platform according to the height point position map based on an image analysis algorithm; determining leveling control instructions corresponding to a leveling mechanism of the 3D printing platform based on a leveling control algorithm according to the abnormal point position; and the leveling control instructions are used for controlling the leveling mechanism to level the 3D printing platform. It can be seen that the application can realize accurate platform leveling based on height visualization and abnormal identification, improve the flatness and printing precision stability of the 3D printing platform, and reduce the risk of printing defects caused by uneven platform.

Description

Technical Field

[0001] This invention relates to the field of data processing technology, and in particular to a 3D printing platform leveling method and system based on point map analysis. Background Technology

[0002] With the rapid popularization of 3D printing technology in high-precision manufacturing, enterprises and users are increasingly emphasizing the improvement of printing stability and finished product quality through precise platform leveling. Among these challenges, achieving efficient leveling control to reduce the risk of printing defects has become a key technical issue. Existing technologies typically collect height parameters from a small number of points on the 3D printing platform, use simple threshold judgments or averaging calculations to assess platform flatness, and generate leveling commands based on fixed rules to control the leveling mechanism to maintain the printing platform's level. However, existing solutions lack image processing of multi-point height data and intelligent identification of abnormal points, making it difficult to accurately generate targeted leveling control commands. This results in insufficient precision in platform flatness control, making it prone to printing defects or layer shifts due to localized unevenness, thus limiting the stability and reliability of the 3D printing process. Therefore, existing technologies have shortcomings that urgently need to be addressed. Summary of the Invention

[0003] The technical problem to be solved by the present invention is to provide a 3D printing platform leveling method and system based on point map analysis, which can realize accurate platform leveling based on high visualization and anomaly identification, improve the flatness and printing accuracy stability of the 3D printing platform, and reduce the risk of printing defects caused by platform unevenness.

[0004] To address the aforementioned technical problems, the first aspect of this invention discloses a 3D printing platform leveling method based on point map analysis, the method comprising: Obtain the height parameters corresponding to multiple platform points of the 3D printing platform; Based on the height parameters corresponding to all the platform points, generate a height point map of the 3D printing platform; Based on image analysis algorithms, at least one abnormal point of the 3D printing platform is determined according to the height point map; Based on the abnormal points, a leveling control algorithm is used to determine the leveling control command corresponding to the leveling mechanism of the 3D printing platform; the leveling control command is used to control the leveling mechanism to level the 3D printing platform.

[0005] As an optional implementation, in the first aspect of the present invention, the platform point is the corner or center position above or below the base of the 3D printing platform.

[0006] As an optional implementation, in the first aspect of the invention, generating a height point map of the 3D printing platform based on the height parameters corresponding to all the platform points includes: For each platform point, determine the color range corresponding to that platform point; Based on the height parameters corresponding to the platform location, and based on the preset parameter correspondence, the color parameters corresponding to the platform location in the color range are determined. Based on the color parameters corresponding to all the platform points, a height point map of the 3D printing platform is generated.

[0007] As an optional implementation, in the first aspect of the present invention, determining the color range corresponding to the platform location includes: Determine the positional distance and relative direction between the platform location and the preset center position of the 3D printing platform base; Based on the location distance and relative direction, the color range corresponding to the platform location is determined in a preset color distribution reference map; the color difference between adjacent colors in the color distribution reference map is less than a preset color difference threshold.

[0008] As an optional implementation, in the first aspect of the invention, generating the height point map of the 3D printing platform based on the color parameters corresponding to all the platform points includes: Generate a scaled-down base drawing with the same shape as the base of the 3D printing platform; At each platform point on the base map, a circular pattern is generated using the corresponding color parameter to generate the height point map of the 3D printing platform.

[0009] As an optional implementation, in the first aspect of the invention, determining at least one abnormal point of the 3D printing platform based on the image analysis algorithm and the height point map includes: The elevation point map is input into the trained image recognition model to obtain multiple predicted anomaly points. For each of the predicted outlier points, calculate the average point distance between that predicted outlier point and all other predicted outlier points; By filtering out the predicted outliers whose average value is less than a preset threshold, at least one outlier of the 3D printing platform is obtained.

[0010] As an optional implementation, in the first aspect of the present invention, the image recognition model is trained using a training dataset that includes training height point maps and outlier point annotations corresponding to multiple 3D printing platforms.

[0011] As an optional implementation, in the first aspect of the present invention, determining the leveling control command corresponding to the leveling mechanism of the 3D printing platform based on the abnormal point location and a leveling control algorithm includes: Calculate the difference between the height parameter corresponding to each of the abnormal points and the reference height of the abnormal point to obtain the height difference corresponding to each of the abnormal points; The height difference corresponding to all the abnormal points is input into the leveling parameter prediction model to obtain the working parameters corresponding to the leveling mechanism of the 3D printing platform. Generate leveling control instructions that include the aforementioned operating parameters.

[0012] A second aspect of this invention discloses a 3D printing platform leveling system based on point map analysis, the system comprising: The acquisition module is used to acquire the height parameters corresponding to multiple platform points of the 3D printing platform; The generation module is used to generate a height point map of the 3D printing platform based on the height parameters corresponding to all the platform points. An analysis module is used to determine at least one abnormal point of the 3D printing platform based on the height point map using an image analysis algorithm. The control module is used to determine the leveling control command corresponding to the leveling mechanism of the 3D printing platform based on the abnormal point location and the leveling control algorithm; the leveling control command is used to control the leveling mechanism to level the 3D printing platform.

[0013] As an optional implementation, in a second aspect of the invention, the platform point is a corner or center position above or below the base of the 3D printing platform.

[0014] As an optional implementation, in a second aspect of the invention, the specific method by which the generation module generates the height point map of the 3D printing platform based on the height parameters corresponding to all the platform points includes: For each platform point, determine the color range corresponding to that platform point; Based on the height parameters corresponding to the platform location, and based on the preset parameter correspondence, the color parameters corresponding to the platform location in the color range are determined. Based on the color parameters corresponding to all the platform points, a height point map of the 3D printing platform is generated.

[0015] As an optional implementation, in the second aspect of the invention, the specific method by which the generation module determines the color range corresponding to the platform point includes: Determine the positional distance and relative direction between the platform location and the preset center position of the 3D printing platform base; Based on the location distance and relative direction, the color range corresponding to the platform location is determined in a preset color distribution reference map; the color difference between adjacent colors in the color distribution reference map is less than a preset color difference threshold.

[0016] As an optional implementation, in the second aspect of the invention, the specific method by which the generation module generates the height point map of the 3D printing platform based on the color parameters corresponding to all the platform points includes: Generate a scaled-down base drawing with the same shape as the base of the 3D printing platform; At each platform point on the base map, a circular pattern is generated using the corresponding color parameter to generate the height point map of the 3D printing platform.

[0017] As an optional implementation, in a second aspect of the invention, the analysis module determines at least one abnormal point of the 3D printing platform based on an image analysis algorithm and the elevation point map, including: The elevation point map is input into the trained image recognition model to obtain multiple predicted anomaly points. For each of the predicted outlier points, calculate the average point distance between that predicted outlier point and all other predicted outlier points; By filtering out the predicted outliers whose average value is less than a preset threshold, at least one outlier of the 3D printing platform is obtained.

[0018] As an optional implementation, in a second aspect of the invention, the image recognition model is trained using a training dataset that includes training height point maps and outlier point annotations corresponding to multiple 3D printing platforms.

[0019] As an optional implementation, in a second aspect of the invention, the control module determines the specific method of the leveling control command corresponding to the leveling mechanism of the 3D printing platform based on the abnormal point location and a leveling control algorithm, including: Calculate the difference between the height parameter corresponding to each of the abnormal points and the reference height of the abnormal point to obtain the height difference corresponding to each of the abnormal points; The height difference corresponding to all the abnormal points is input into the leveling parameter prediction model to obtain the working parameters corresponding to the leveling mechanism of the 3D printing platform. Generate leveling control instructions that include the aforementioned operating parameters.

[0020] A third aspect of this invention discloses another 3D printing platform leveling system based on point map analysis, the system comprising: Memory containing executable program code; A processor coupled to the memory; The processor calls the executable program code stored in the memory to execute some or all of the steps in the 3D printing platform leveling method based on bitmap analysis disclosed in the first aspect of the present invention.

[0021] The fourth aspect of the present invention discloses a computer storage medium storing computer instructions, which, when invoked, are used to execute some or all of the steps in the 3D printing platform leveling method based on bitmap analysis disclosed in the first aspect of the present invention.

[0022] Compared with the prior art, the embodiments of the present invention have the following beneficial effects: This invention generates a height point map by acquiring the height parameters of multiple platform points on a 3D printing platform, identifies abnormal points based on image analysis algorithms, and generates leveling control commands using a leveling control algorithm to control the leveling mechanism. This enables precise platform leveling based on height visualization and anomaly identification, improving the flatness and printing accuracy stability of the 3D printing platform and reducing the risk of printing defects caused by platform unevenness. Attached Figure Description

[0023] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0024] Figure 1 This is a flowchart illustrating a 3D printing platform leveling method based on point map analysis disclosed in an embodiment of the present invention.

[0025] Figure 2 This is a schematic diagram of the structure of a 3D printing platform leveling system based on point map analysis disclosed in an embodiment of the present invention.

[0026] Figure 3 This is a schematic diagram of another 3D printing platform leveling system based on point map analysis disclosed in an embodiment of the present invention. Detailed Implementation

[0027] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0028] The terms "first," "second," etc., used in the specification, claims, and accompanying drawings of this invention are used to distinguish different objects, not to describe a specific order. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, apparatus, product, or device that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to these processes, methods, products, or devices.

[0029] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of the invention. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0030] This invention discloses a 3D printing platform leveling method and system based on point map analysis. It generates a height point map by acquiring height parameters of multiple platform points on the 3D printing platform, identifies abnormal points based on image analysis algorithms, and generates leveling control commands using a leveling control algorithm to control the leveling mechanism. This enables precise platform leveling based on height visualization and anomaly identification, improving the flatness and printing accuracy stability of the 3D printing platform and reducing the risk of printing defects caused by platform unevenness. Detailed explanations follow.

[0031] Example 1 Please see Figure 1 , Figure 1 This is a flowchart illustrating a 3D printing platform leveling method based on point map analysis disclosed in an embodiment of the present invention. Figure 1 The described point-map-based 3D printing platform leveling method can be applied to data processing systems / data processing equipment / data processing servers (including local processing servers or cloud processing servers). For example... Figure 1 As shown, the 3D printing platform leveling method based on point map analysis may include the following operations: 101. Obtain the height parameters corresponding to multiple platform points of the 3D printing platform.

[0032] Optionally, the height parameter can be the original sensor reading, the filtered height value, the deviation value relative to the reference plane, or the normalized height data; this invention does not limit the type of height parameter.

[0033] Optionally, the acquisition process can be implemented based on a contact probe, capacitive sensor, laser rangefinder or vision measurement system, and the present invention is not limited thereto.

[0034] 102. Generate a height point map of the 3D printing platform based on the height parameters corresponding to all platform points. Optionally, the elevation point map can be a pseudo-color elevation map, a heat map, or a three-dimensional surface map; the present invention does not impose any limitations on this.

[0035] Optionally, the generation process can be implemented based on color mapping, interpolation algorithms, or image rendering techniques, and this invention does not limit it.

[0036] Optionally, the elevation point map can support interactive zooming, dynamic updates, or real-time display; this invention does not impose any limitations on this.

[0037] 103. Based on image analysis algorithms, determine at least one abnormal point on the 3D printing platform according to the height point map. Optionally, the image analysis algorithm can be a convolutional neural network, an edge detection algorithm, a clustering algorithm, or a statistical anomaly detection algorithm; this invention does not limit the algorithm.

[0038] Optionally, the abnormal point can be a height change point, a local depression point, the center of a warped area, or a sensor failure point; the present invention does not limit this.

[0039] 104. Based on the abnormal points and the leveling control algorithm, determine the leveling control command corresponding to the leveling mechanism of the 3D printing platform.

[0040] Optionally, leveling control commands are used to control the leveling mechanism to level the 3D printing platform.

[0041] Optionally, the leveling control command can be a motor step count command, a Z-axis compensation value, an independent screw adjustment command, or a multi-axis linkage command; the present invention does not impose any limitations on this.

[0042] Optionally, the balancing control algorithm can be PID control, model predictive control, reinforcement learning control, or neural network-based regression control; this invention does not limit the specific implementation.

[0043] Optionally, the generation of this instruction can be optimized by taking into account the kinematic constraints of the device, the leveling accuracy requirements, or the execution efficiency; this invention does not impose any limitations on this.

[0044] As can be seen, the above-described embodiments of the invention generate a height point map by acquiring the height parameters of multiple platform points of the 3D printing platform, determine abnormal points based on image analysis algorithms, and generate leveling control commands using leveling control algorithms to control the leveling mechanism. This enables precise platform leveling based on height visualization and anomaly identification, improving the flatness and printing accuracy stability of the 3D printing platform and reducing the risk of printing defects caused by platform unevenness.

[0045] As an optional embodiment, in the above steps, the platform point is the corner or center position above or below the base of the 3D printing platform.

[0046] As can be seen, the above optional embodiments define the position type of the platform points to comprehensively characterize the height characteristics of the points when the printing platform is working, assisting in achieving accurate platform leveling based on height visualization and anomaly identification, improving the flatness and printing accuracy stability of the 3D printing platform, and reducing the risk of printing defects caused by platform unevenness.

[0047] As an optional embodiment, the step of generating a height point map of the 3D printing platform based on the height parameters corresponding to all platform points in the above steps includes: For each platform location, determine the corresponding color range; Based on the height parameters corresponding to the platform location, and based on the preset parameter correspondence, determine the color parameters corresponding to the platform location in the color range; Generate a height point map of the 3D printing platform based on the color parameters corresponding to all platform points.

[0048] Optionally, the color range can be a height-segmented color range, a continuous gradient range, or a custom mapping range; this invention does not impose any limitations.

[0049] Optionally, the process of determining the color range can be based on position coordinates, polar coordinate mapping, or preset partitioning rules, and this invention does not limit it.

[0050] Optionally, the division of the color range can be optimized by combining visual perception uniformity or abnormal prominence, and this invention does not limit it.

[0051] Optionally, the correspondence between these parameters can be a linear mapping, a piecewise linear mapping, or a nonlinear mapping function; this invention does not impose any limitations on this.

[0052] Optionally, the color parameter can be an RGB value, an HSV value, a Lab color space value, or a grayscale value; this invention does not impose any limitations on it.

[0053] As can be seen, through the above optional embodiments, by determining the color range for each platform point and generating a height point map based on the height parameter mapping color parameter, the intuitiveness and analysis efficiency of the height distribution are improved through color visualization on the basis of accurate platform leveling, providing high-quality image support for abnormal point identification and reducing the risk of abnormal misjudgment caused by height data abstraction.

[0054] As an optional embodiment, the step of determining the color range corresponding to the platform location in the above steps includes: Determine the positional distance and relative direction between the platform location and the center position of the base of the preset 3D printing platform; Based on the location distance and relative direction, the color range corresponding to the platform location is determined in the preset color distribution reference map; the color difference between adjacent colors in the color distribution reference map is less than the preset color difference threshold.

[0055] Optionally, the center position can be the geometric center, the centroid, or the leveling reference origin; this invention does not limit this.

[0056] Optionally, the location distance can be Euclidean distance, Manhattan distance, or weighted distance; this invention does not limit the distance.

[0057] Optionally, the relative direction can be an angle value, a direction vector, or a polar coordinate angle; this invention does not limit this.

[0058] Optionally, the color distribution reference map can be a polar coordinate color map, a ring gradient map, or a spatial partition map; the present invention does not limit this.

[0059] Optionally, the process of determining the color range can be based on coordinate transformation, interpolation query, or nearest neighbor search, and this invention does not limit it.

[0060] Optionally, the color difference threshold can be a fixed threshold, a dynamic threshold, or a threshold adjusted based on human eye perception; the present invention does not impose any limitations on this.

[0061] Optionally, the color difference calculation can be based on the CIE76, CIE94, or CIE2000 color difference formulas, and this invention does not limit it.

[0062] Optionally, the design of this reference diagram can be optimized by combining color theory, visual comfort, or anomaly detection requirements; this invention does not impose any limitations on this.

[0063] As can be seen, through the above optional embodiments, by determining the color range in the color distribution reference map based on the positional distance and relative direction between the platform point and the center of the base, the scientificity and consistency of the visualization map are improved by position-related color allocation on the basis of generating a precise height point map, providing optimized image data for image analysis and reducing the risk of map deviation caused by improper color allocation.

[0064] As an optional embodiment, the step above, generating a height point map of the 3D printing platform based on the color parameters corresponding to all platform points, includes: Generate a scaled-down base map that matches the shape of the base of the 3D printing platform; For each platform point on the base map, a circular pattern is generated at the corresponding color parameter to produce a height point map of the 3D printing platform.

[0065] Optionally, the ratio can be a 1:1 mapping, a scaling mapping, or a normalized mapping; this invention does not impose any limitations.

[0066] Optionally, the circular pattern can be a solid circle, a gradient circle, a circle with a border, or a semi-transparent circle; this invention does not impose any limitations on this.

[0067] Optionally, the circular pattern can be generated based on pixel drawing, vector filling, or layer overlay; this invention does not limit the method.

[0068] Optionally, the pattern design can be optimized by combining dot density, visual clarity, or unusual prominence; this invention does not limit this.

[0069] As can be seen, through the above optional embodiments, by generating a base map proportional to the shape of the base and drawing a circular pattern with color parameters to generate a height point map, the accuracy and readability of the point map are improved through shape matching and pattern rendering on the basis of accurate visualization construction, providing clear image evidence for the identification of abnormal points and reducing the risk of analysis error caused by pattern rendering errors.

[0070] As an optional embodiment, the step described above, determining at least one abnormal point on the 3D printing platform based on an image analysis algorithm and a height point map, includes: The elevation point map is input into the trained image recognition model to obtain multiple predicted outlier points. For each predicted outlier point, calculate the average point distance between that predicted outlier point and all other predicted outlier points; By filtering out predicted outlier points whose average value is less than a preset threshold, at least one outlier point of the 3D printing platform is obtained.

[0071] Optionally, the image recognition model can be a CNN model, a U-Net model, a YOLO model, or a Transformer vision model; this invention does not impose any limitations.

[0072] Optionally, the training process can be implemented based on a labeled outlier dataset, weakly supervised learning, or self-supervised pre-training; this invention does not impose any limitations.

[0073] Optionally, the distance between points can be pixel distance, actual physical distance, or normalized distance; this invention does not impose any limitation.

[0074] Optionally, the preset threshold can be a fixed threshold, a dynamic threshold, or a threshold adjusted based on point density; the present invention does not impose any limitations on this.

[0075] As can be seen, through the above optional embodiments, by inputting the height point map into the image recognition model to obtain predicted abnormal points and filtering out the smallest average point distance as abnormal points, the accuracy and robustness of abnormal identification are improved by model prediction and distance clustering on the basis of accurate abnormal point location determination. This provides reliable point data for leveling control and reduces the risk of leveling failure due to missed abnormality detection.

[0076] As an optional embodiment, in the above steps, the image recognition model is trained using a training dataset that includes training height point maps and outlier point annotations corresponding to multiple 3D printing platforms.

[0077] As can be seen, the training details of the image recognition model are defined through the above optional embodiments, so that it can more accurately and efficiently identify abnormal points in the image, assist in achieving accurate platform leveling based on high visualization and anomaly recognition, improve the flatness and printing accuracy stability of the 3D printing platform, and reduce the risk of printing defects caused by platform unevenness.

[0078] As an optional embodiment, the step above, determining the leveling control command corresponding to the leveling mechanism of the 3D printing platform based on the abnormal point location and the leveling control algorithm, includes: Calculate the difference between the height parameter corresponding to each abnormal point and the reference height of that abnormal point to obtain the height difference corresponding to each abnormal point; The height differences corresponding to all abnormal points are input into the leveling parameter prediction model to obtain the working parameters of the leveling mechanism of the 3D printing platform. Generate leveling control commands that include operating parameters.

[0079] Optionally, the reference height can be the global average height, the local plane fitting height, or the theoretical zero plane height; this invention does not limit the reference height.

[0080] Optionally, the height difference can be an absolute difference, a signed difference, or a normalized difference; this invention does not impose any limitations on it.

[0081] Optionally, the leveling parameter prediction model can be a fully connected neural network, a regression tree model, or a physical constraint model; this invention does not impose any limitations.

[0082] Optionally, the operating parameters can be the number of motor rotation steps, the amount of screw extension and retraction, the torque value, or the execution time; this invention does not impose any limitations.

[0083] Optionally, the leveling control command can be a G-code command, a CAN bus command, or a dedicated protocol command; this invention does not impose any limitations.

[0084] Optionally, the generation process of the leveling control command can be based on the command template, parameter filling or encrypted signature, and the present invention does not limit it.

[0085] As can be seen, through the above optional embodiments, by calculating the height difference between the abnormal point height parameter and the reference height and inputting it into the leveling parameter prediction model to generate working parameters to form leveling control commands, the targeting and accuracy of leveling commands are improved through height difference analysis and model prediction on the basis of precise leveling control, providing efficient support for leveling of 3D printing platforms and reducing the risk of insufficient leveling effect caused by parameter prediction deviation.

[0086] Example 2 Please see Figure 2 , Figure 2 This is a schematic diagram of a 3D printing platform leveling system based on point map analysis, as disclosed in an embodiment of the present invention. Figure 2 The described point-map-based 3D printing platform leveling system can be applied to data processing systems / data processing equipment / data processing servers (including local processing servers or cloud processing servers). For example... Figure 2 As shown, the 3D printing platform leveling system based on point map analysis may include: The acquisition module 201 is used to acquire the height parameters corresponding to multiple platform points of the 3D printing platform.

[0087] The generation module 202 is used to generate a height point map of the 3D printing platform based on the height parameters corresponding to all platform points. Analysis module 203 is used to determine at least one abnormal point on the 3D printing platform based on an image analysis algorithm and a height point map. The control module 204 is used to determine the leveling control command corresponding to the leveling mechanism of the 3D printing platform based on the abnormal point location and the leveling control algorithm.

[0088] Optionally, leveling control commands are used to control the leveling mechanism to level the 3D printing platform.

[0089] As can be seen, the above-described embodiments of the invention generate a height point map by acquiring the height parameters of multiple platform points of the 3D printing platform, determine abnormal points based on image analysis algorithms, and generate leveling control commands using leveling control algorithms to control the leveling mechanism. This enables precise platform leveling based on height visualization and anomaly identification, improving the flatness and printing accuracy stability of the 3D printing platform and reducing the risk of printing defects caused by platform unevenness.

[0090] As an optional embodiment, the platform location is the corner or center position above or below the base of the 3D printing platform.

[0091] As can be seen, the above optional embodiments define the position type of the platform points to comprehensively characterize the height characteristics of the points when the printing platform is working, assisting in achieving accurate platform leveling based on height visualization and anomaly identification, improving the flatness and printing accuracy stability of the 3D printing platform, and reducing the risk of printing defects caused by platform unevenness.

[0092] As an optional embodiment, the specific method by which the generation module generates the height point map of the 3D printing platform based on the height parameters corresponding to all platform points includes: For each platform location, determine the corresponding color range; Based on the height parameters corresponding to the platform location, and based on the preset parameter correspondence, determine the color parameters corresponding to the platform location in the color range; Generate a height point map of the 3D printing platform based on the color parameters corresponding to all platform points.

[0093] As can be seen, through the above optional embodiments, by determining the color range for each platform point and generating a height point map based on the height parameter mapping color parameter, the intuitiveness and analysis efficiency of the height distribution are improved through color visualization on the basis of accurate platform leveling, providing high-quality image support for abnormal point identification and reducing the risk of abnormal misjudgment caused by height data abstraction.

[0094] As an optional embodiment, the specific method by which the generation module determines the color range corresponding to the platform location includes: Determine the positional distance and relative direction between the platform location and the center position of the base of the preset 3D printing platform; Based on the location distance and relative direction, the color range corresponding to the platform location is determined in the preset color distribution reference map; the color difference between adjacent colors in the color distribution reference map is less than the preset color difference threshold.

[0095] As can be seen, through the above optional embodiments, by determining the color range in the color distribution reference map based on the positional distance and relative direction between the platform point and the center of the base, the scientificity and consistency of the visualization map are improved by position-related color allocation on the basis of generating a precise height point map, providing optimized image data for image analysis and reducing the risk of map deviation caused by improper color allocation.

[0096] As an optional embodiment, the specific method by which the generation module generates the height point map of the 3D printing platform based on the color parameters corresponding to all platform points includes: Generate a scaled-down base map that matches the shape of the base of the 3D printing platform; For each platform point on the base map, a circular pattern is generated at the corresponding color parameter to produce a height point map of the 3D printing platform.

[0097] As can be seen, through the above optional embodiments, by generating a base map proportional to the shape of the base and drawing a circular pattern with color parameters to generate a height point map, the accuracy and readability of the point map are improved through shape matching and pattern rendering on the basis of accurate visualization construction, providing clear image evidence for the identification of abnormal points and reducing the risk of analysis error caused by pattern rendering errors.

[0098] As an optional embodiment, the analysis module, based on image analysis algorithms and according to the height point map, determines at least one abnormal point of the 3D printing platform in the following specific ways: The elevation point map is input into the trained image recognition model to obtain multiple predicted outlier points. For each predicted outlier point, calculate the average point distance between that predicted outlier point and all other predicted outlier points; By filtering out predicted outlier points whose average value is less than a preset threshold, at least one outlier point of the 3D printing platform is obtained.

[0099] As can be seen, through the above optional embodiments, by inputting the height point map into the image recognition model to obtain predicted abnormal points and filtering out the smallest average point distance as abnormal points, the accuracy and robustness of abnormal identification are improved by model prediction and distance clustering on the basis of accurate abnormal point location determination. This provides reliable point data for leveling control and reduces the risk of leveling failure due to missed abnormality detection.

[0100] As an optional embodiment, the image recognition model is trained using a training dataset that includes training height point maps and outlier point annotations corresponding to multiple 3D printing platforms.

[0101] As can be seen, the training details of the image recognition model are defined through the above optional embodiments, so that it can more accurately and efficiently identify abnormal points in the image, assist in achieving accurate platform leveling based on high visualization and anomaly recognition, improve the flatness and printing accuracy stability of the 3D printing platform, and reduce the risk of printing defects caused by platform unevenness.

[0102] As an optional embodiment, the control module determines the specific method of the leveling control command corresponding to the leveling mechanism of the 3D printing platform based on the abnormal point location and the leveling control algorithm, including: Calculate the difference between the height parameter corresponding to each abnormal point and the reference height of that abnormal point to obtain the height difference corresponding to each abnormal point; The height differences corresponding to all abnormal points are input into the leveling parameter prediction model to obtain the working parameters of the leveling mechanism of the 3D printing platform. Generate leveling control commands that include operating parameters.

[0103] As can be seen, through the above optional embodiments, by calculating the height difference between the abnormal point height parameter and the reference height and inputting it into the leveling parameter prediction model to generate working parameters to form leveling control commands, the targeting and accuracy of leveling commands are improved through height difference analysis and model prediction on the basis of precise leveling control, providing efficient support for leveling of 3D printing platforms and reducing the risk of insufficient leveling effect caused by parameter prediction deviation.

[0104] Example 3 Please see Figure 3 , Figure 3 This is another 3D printing platform leveling system based on point map analysis disclosed in the embodiments of the present invention. Figure 3 The described point-map-based 3D printing platform leveling system is applied in data processing systems / data processing equipment / data processing servers (wherein, the server includes a local processing server or a cloud processing server). For example... Figure 3 As shown, the 3D printing platform leveling system based on point map analysis may include: Memory 301 storing executable program code; Processor 302 coupled to memory 301; The processor 302 calls the executable program code stored in the memory 301 to execute the steps of the 3D printing platform leveling method based on bitmap analysis described in Embodiment 1.

[0105] Example 4 This invention discloses a computer read storage medium that stores a computer program for electronic data interchange, wherein the computer program causes a computer to execute the steps of the 3D printing platform leveling method based on bitmap analysis described in Embodiment 1.

[0106] Example 5 This invention discloses a computer program product, which includes a non-transitory computer-readable storage medium storing a computer program, and the computer program is operable to cause a computer to perform the steps of the 3D printing platform leveling method based on bitmap analysis described in Embodiment 1.

[0107] The foregoing has described specific embodiments of this specification; other embodiments are within the scope of the appended claims. In some cases, the actions or steps described in the claims may be performed in a different order than those shown in the embodiments and may still achieve the desired result. Furthermore, the processes depicted in the drawings do not necessarily have to follow the specific or sequential order shown to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0108] The systems, devices, modules, or units described in the above embodiments can be implemented by computer chips or entities, or by products with certain functions. A typical implementation device is a computer. Specifically, a computer can be, for example, a personal computer, laptop computer, cellular phone, camera phone, smartphone, personal digital assistant, media player, navigation device, email device, game console, tablet computer, wearable device, or any combination of these devices.

[0109] For ease of description, the above devices are described in terms of function, divided into various units. Of course, in implementing this specification, the functions of each unit can be implemented in one or more software and / or hardware components.

[0110] Those skilled in the art will understand that the embodiments of this specification can be provided as methods, systems, or computer program products. Therefore, the embodiments of this specification can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the embodiments of this specification can take the form of a computer program product implemented on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0111] This specification is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this specification. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create a machine for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0112] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0113] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0114] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0115] Memory may include non-persistent storage in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0116] Computer-readable media includes both permanent and non-permanent, removable and non-removable media that can store information using any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0117] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0118] This specification can be described in the general context of computer-executable instructions that are executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc., that perform a specific task or implement a specific abstract data type. This specification can also be practiced in distributed computing environments, where tasks are performed by remote processing devices connected via a communication network. In distributed computing environments, program modules can reside in local and remote computer storage media, including storage devices.

[0119] The various embodiments in this specification are described in a progressive manner. Similar or identical parts between embodiments can be referred to interchangeably. Each embodiment focuses on describing the differences from other embodiments. In particular, the system embodiments are basically similar to the method embodiments, so the description is relatively simple; relevant parts can be referred to the descriptions in the method embodiments.

[0120] Finally, it should be noted that the 3D printing platform leveling method and system based on point map analysis disclosed in the embodiments of the present invention are merely preferred embodiments of the present invention and are only used to illustrate the technical solutions of the present invention, not to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A 3D printing platform leveling method based on point map analysis, characterized in that, The method includes: Obtain the height parameters corresponding to multiple platform points of the 3D printing platform; Based on the height parameters corresponding to all the platform points, generate a height point map of the 3D printing platform; Based on image analysis algorithms and the elevation point map, at least one abnormal point of the 3D printing platform is determined, including: The elevation point map is input into the trained image recognition model to obtain multiple predicted anomaly points. For each of the predicted outlier points, calculate the average point distance between that predicted outlier point and all other predicted outlier points; Filter out the predicted outlier points whose average value is less than a preset threshold to obtain at least one outlier point of the 3D printing platform; Based on the abnormal points, and using a leveling control algorithm, a leveling control command corresponding to the leveling mechanism of the 3D printing platform is determined. The leveling control command is used to control the leveling mechanism to level the 3D printing platform, including: Calculate the difference between the height parameter corresponding to each of the abnormal points and the reference height of the abnormal point to obtain the height difference corresponding to each of the abnormal points; The height difference corresponding to all the abnormal points is input into the leveling parameter prediction model to obtain the working parameters corresponding to the leveling mechanism of the 3D printing platform. Generate leveling control instructions that include the aforementioned operating parameters.

2. The 3D printing platform leveling method based on point map analysis according to claim 1, characterized in that, The platform location refers to the corner or center position above or below the base of the 3D printing platform.

3. The 3D printing platform leveling method based on point map analysis according to claim 1, characterized in that, The step of generating a height point map of the 3D printing platform based on the height parameters corresponding to all the platform points includes: For each platform point, determine the color range corresponding to that platform point; Based on the height parameters corresponding to the platform location, and based on the preset parameter correspondence, the color parameters corresponding to the platform location in the color range are determined. Based on the color parameters corresponding to all the platform points, a height point map of the 3D printing platform is generated.

4. The 3D printing platform leveling method based on point map analysis according to claim 3, characterized in that, Determining the color range corresponding to the platform location includes: Determine the positional distance and relative direction between the platform location and the preset center position of the 3D printing platform base; Based on the location distance and relative direction, the color range corresponding to the platform point is determined in a preset color distribution reference map; the color difference between adjacent colors in the color distribution reference map is less than a preset color difference threshold.

5. The 3D printing platform leveling method based on point map analysis according to claim 3, characterized in that, The step of generating a height point map of the 3D printing platform based on the color parameters corresponding to all the platform points includes: Generate a scaled-down base map with the same shape as the base of the 3D printing platform; At each platform point on the base map, a circular pattern is generated using the color parameter corresponding to the platform point to generate a height point map of the 3D printing platform.

6. The 3D printing platform leveling method based on point map analysis according to claim 1, characterized in that, The image recognition model is trained using a training dataset that includes training height point maps and anomaly point annotations corresponding to multiple 3D printing platforms.

7. A 3D printing platform leveling system based on point map analysis, characterized in that, The system executes the 3D printing platform leveling method based on point map analysis as described in any one of claims 1-6, and the system comprises: The acquisition module is used to acquire the height parameters corresponding to multiple platform points of the 3D printing platform; The generation module is used to generate a height point map of the 3D printing platform based on the height parameters corresponding to all the platform points. An analysis module is used to determine at least one abnormal point of the 3D printing platform based on the height point map using an image analysis algorithm. The control module is used to determine the leveling control command corresponding to the leveling mechanism of the 3D printing platform based on the abnormal point location and the leveling control algorithm; the leveling control command is used to control the leveling mechanism to level the 3D printing platform.

8. A 3D printing platform leveling system based on point map analysis, characterized in that, The system includes: Memory containing executable program code; A processor coupled to the memory; The processor calls the executable program code stored in the memory to execute the 3D printing platform leveling method based on bitmap analysis as described in any one of claims 1-6.

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