3D printing platform leveling method and system based on dynamic analysis of discharge head

Abstract

The present invention discloses a 3D printing platform leveling method and system based on dynamic analysis of a discharge head. The method comprises: acquiring image data of the discharge head of the 3D printing platform in real time when executing a 3D printing task; determining the relative position and movement speed of the discharge head in the working area based on the image data and an image recognition algorithm; analyzing the tilt parameters corresponding to the 3D printing platform based on the relative position and the movement speed; determining the leveling control instructions corresponding to the leveling mechanism of the 3D printing platform based on the tilt parameters and a preset leveling control rule; the leveling control instructions are used to control the leveling mechanism to level the 3D printing platform to reduce the tilt parameters. It can be seen that the present invention can achieve precise tilt correction based on the dynamic image of the discharge head, improve the stability and printing accuracy of the 3D printing platform, and reduce the risk of printing failure caused by tilt.

Description

Technical Field

[0001] The present invention relates to the field of data processing technology, and in particular to a 3D printing platform leveling method and system based on dynamic analysis of a discharge head. Background Art

[0002] With the widespread application of 3D printing technology in the field of high-precision manufacturing, companies and users are paying more and more attention to improving printing stability and success rate through precise platform leveling. Existing technologies usually collect mechanical sensor data or static images of the 3D printing platform, use fixed threshold analysis or manual calibration methods to evaluate the platform tilt state, and adjust the platform position based on standard leveling rules to ensure printing quality. Because existing solutions do not utilize the impact of the platform tilt state on the movement of the discharge head, they lack dynamic analysis of the real-time image data of the discharge head and accurate calculation of the tilt parameters. It is difficult to accurately determine the relative position and movement speed of the discharge head and optimize the leveling control. The commonly used static leveling strategy cannot adapt to complex printing scenarios, resulting in insufficient accuracy of tilt correction, which can easily cause platform instability or printing failure, limiting the stability and printing accuracy of the 3D printing system. It can be seen that the existing technology has defects that need to be solved urgently. 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 dynamic analysis of the discharge head, which can realize precise tilt correction based on the dynamic image of the discharge head, improve the stability and printing accuracy of the 3D printing platform, and reduce the risk of printing failure caused by tilt.

[0004] In order to solve the above technical problems, the first aspect of the present invention discloses a 3D printing platform leveling method based on dynamic analysis of the discharge head, the method comprising:

[0005] When executing 3D printing tasks, real-time image data of the discharge head of the 3D printing platform is obtained;

[0006] Determine the relative position and movement speed of the discharge head in the working area based on the image data and an image recognition algorithm;

[0007] Analyzing a tilt parameter corresponding to the 3D printing platform according to the relative position and the movement speed;

[0008] According to the tilt parameter and based on a preset leveling control rule, a leveling control instruction corresponding to the leveling mechanism of the 3D printing platform is determined; the leveling control instruction is used to control the leveling mechanism to level the 3D printing platform to reduce the tilt parameter.

[0009] As an optional embodiment, in the first aspect of the present invention, determining the relative position and movement speed of the discharge head in the working area based on the image data and an image recognition algorithm includes:

[0010] Inputting each of the image data into a trained image segmentation model to obtain a discharge head region in each of the image data; the image segmentation model is trained using a training data set comprising a plurality of training images and corresponding discharge head region annotations;

[0011] Determining an acquisition time point corresponding to each of the image data;

[0012] The relative position and movement speed of the discharging head in the working area are determined according to the acquisition time points and the discharging head areas corresponding to all the image data.

[0013] As an optional embodiment, in the first aspect of the present invention, determining the relative position and movement speed of the discharge head in the working area according to the acquisition time points and the discharge head areas corresponding to all the image data includes:

[0014] Calculating the geometric center point corresponding to the discharge head area in each of the image data;

[0015] Connecting the geometric center points of all the image data from early to late based on the acquisition time points to obtain a movement trajectory of the discharge head;

[0016] Calculating the ratio of the length of the moving track of the discharging head to the longest time interval corresponding to all the acquisition time points to obtain the corresponding movement speed of the discharging head;

[0017] The relative position of the discharge head in the working area is calculated based on all the geometric center points.

[0018] As an optional embodiment, in the first aspect of the present invention, the calculating the relative position of the discharge head in the working area based on all the geometric center points includes:

[0019] Inputting each of the image data into a trained work area recognition model to obtain work area feature points in each of the image data; the work area recognition model is trained by a training data set including a plurality of training work images and corresponding work area annotations;

[0020] Calculating feature similarity between any two feature points in the working area of ​​the image data;

[0021] Calculating an average value of all the feature similarities corresponding to each of the image data, and screening out image data having the average value greater than a preset threshold, to obtain a plurality of preferred images;

[0022] Calculating the average coordinates of the feature points of the working area in all the preferred images to obtain a representative position of the working area;

[0023] Calculate the average coordinates of all the geometric center points to obtain the position of the discharge head;

[0024] The relative position relationship between the position of the discharge head and the representative position of the working area is calculated to obtain the relative position of the discharge head in the working area.

[0025] As an optional embodiment, in the first aspect of the present invention, analyzing the tilt parameter corresponding to the 3D printing platform according to the relative position and the movement speed includes:

[0026] Obtaining the printing operation of the 3D printing task in the current time period;

[0027] Determining a reference position and a reference movement speed of a discharge head corresponding to the printing operation in a preset database;

[0028] The tilt parameter corresponding to the 3D printing platform is analyzed according to the reference position and the reference movement speed, as well as the relative position and the movement speed.

[0029] As an optional embodiment, in the first aspect of the present invention, analyzing the tilt parameter corresponding to the 3D printing platform based on the reference position and the reference movement speed, and the relative position and the movement speed includes:

[0030] Calculating a position difference between the relative position and the reference position;

[0031] Calculating a speed difference between the movement speed and the reference movement speed;

[0032] The difference between the weighted sum of the position difference and the speed difference and a preset reference threshold is calculated to obtain a tilt parameter corresponding to the 3D printing platform.

[0033] As an optional embodiment, in the first aspect of the present invention, determining, according to the tilt parameter and based on a preset leveling control rule, a leveling control instruction corresponding to the leveling mechanism of the 3D printing platform includes:

[0034] Determining whether the tilt parameter is greater than a preset parameter threshold and obtaining a determination result;

[0035] When the judgment result is no, no further operation is performed;

[0036] When the judgment result is yes, a leveling control instruction corresponding to the leveling mechanism of the 3D printing platform is determined based on the position difference and the speed difference.

[0037] As an optional embodiment, in the first aspect of the present invention, determining a leveling control instruction corresponding to a leveling mechanism of the 3D printing platform based on the position difference and the speed difference includes:

[0038] According to a preset correspondence between the position difference and the tilt state, and based on the position difference, determining a corresponding first tilt state parameter;

[0039] According to a preset correspondence between the speed difference and the tilt state, determining a corresponding second tilt state parameter based on the speed difference;

[0040] Calculating a weighted sum of the first tilt state parameter and the second tilt state parameter to obtain a tilt state of the 3D printing platform;

[0041] According to the tilt state, based on a preset mathematical correspondence between the tilt state and the leveling instruction, a leveling control instruction corresponding to the leveling mechanism of the 3D printing platform is determined.

[0042] A second aspect of an embodiment of the present invention discloses a 3D printing platform leveling system based on dynamic analysis of a discharge head, the system comprising:

[0043] An acquisition module is used to acquire image data of the discharge head of the 3D printing platform in real time when executing a 3D printing task;

[0044] a determination module, configured to determine the relative position and movement speed of the discharge head in the working area based on the image data and an image recognition algorithm;

[0045] an analysis module, configured to analyze a tilt parameter corresponding to the 3D printing platform according to the relative position and the movement speed;

[0046] A control module is used to determine a leveling control instruction corresponding to the leveling mechanism of the 3D printing platform according to the tilt parameter and based on a preset leveling control rule; the leveling control instruction is used to control the leveling mechanism to level the 3D printing platform to reduce the tilt parameter.

[0047] As an optional embodiment, in the second aspect of the present invention, the specific manner in which the determination module determines the relative position and movement speed of the discharge head in the working area based on the image data and the image recognition algorithm includes:

[0048] Inputting each of the image data into a trained image segmentation model to obtain a discharge head region in each of the image data; the image segmentation model is trained using a training data set comprising a plurality of training images and corresponding discharge head region annotations;

[0049] Determining an acquisition time point corresponding to each of the image data;

[0050] The relative position and movement speed of the discharging head in the working area are determined according to the acquisition time points and the discharging head areas corresponding to all the image data.

[0051] As an optional embodiment, in the second aspect of the present invention, the specific manner in which the determination module determines the relative position and movement speed of the discharge head in the working area based on the acquisition time points and the discharge head area corresponding to all the image data includes:

[0052] Calculating the geometric center point corresponding to the discharge head area in each of the image data;

[0053] Connecting the geometric center points of all the image data from early to late based on the acquisition time points to obtain a movement trajectory of the discharge head;

[0054] Calculating the ratio of the length of the moving track of the discharging head to the longest time interval corresponding to all the acquisition time points to obtain the corresponding movement speed of the discharging head;

[0055] The relative position of the discharge head in the working area is calculated based on all the geometric center points.

[0056] As an optional embodiment, in the second aspect of the present invention, the specific manner in which the determination module calculates the relative position of the discharge head in the working area based on all the geometric center points includes:

[0057] Inputting each of the image data into a trained work area recognition model to obtain work area feature points in each of the image data; the work area recognition model is trained by a training data set including a plurality of training work images and corresponding work area annotations;

[0058] Calculating feature similarity between any two feature points in the working area of ​​the image data;

[0059] Calculating an average value of all the feature similarities corresponding to each of the image data, and screening out image data having the average value greater than a preset threshold, to obtain a plurality of preferred images;

[0060] Calculating the average coordinates of the feature points of the working area in all the preferred images to obtain a representative position of the working area;

[0061] Calculate the average coordinates of all the geometric center points to obtain the position of the discharge head;

[0062] The relative position relationship between the position of the discharge head and the representative position of the working area is calculated to obtain the relative position of the discharge head in the working area.

[0063] As an optional embodiment, in the second aspect of the present invention, the specific manner in which the analysis module analyzes the tilt parameter corresponding to the 3D printing platform according to the relative position and the movement speed includes:

[0064] Obtaining the printing operation of the 3D printing task in the current time period;

[0065] Determining a reference position and a reference movement speed of a discharge head corresponding to the printing operation in a preset database;

[0066] The tilt parameter corresponding to the 3D printing platform is analyzed according to the reference position and the reference movement speed, as well as the relative position and the movement speed.

[0067] As an optional embodiment, in the second aspect of the present invention, the specific manner in which the analysis module analyzes the tilt parameter corresponding to the 3D printing platform based on the reference position and the reference movement speed, as well as the relative position and the movement speed, includes:

[0068] Calculating a position difference between the relative position and the reference position;

[0069] Calculating a speed difference between the movement speed and the reference movement speed;

[0070] The difference between the weighted sum of the position difference and the speed difference and a preset reference threshold is calculated to obtain a tilt parameter corresponding to the 3D printing platform.

[0071] As an optional embodiment, in the second aspect of the present invention, the control module determines the specific manner of the leveling control instruction corresponding to the leveling mechanism of the 3D printing platform according to the tilt parameter and based on a preset leveling control rule, including:

[0072] Determining whether the tilt parameter is greater than a preset parameter threshold and obtaining a determination result;

[0073] When the judgment result is no, no further operation is performed;

[0074] When the judgment result is yes, a leveling control instruction corresponding to the leveling mechanism of the 3D printing platform is determined based on the position difference and the speed difference.

[0075] As an optional embodiment, in the second aspect of the present invention, the control module determines a specific manner of the leveling control instruction corresponding to the leveling mechanism of the 3D printing platform based on the position difference and the speed difference, including:

[0076] According to a preset correspondence between the position difference and the tilt state, and based on the position difference, determining a corresponding first tilt state parameter;

[0077] According to a preset correspondence between the speed difference and the tilt state, determining a corresponding second tilt state parameter based on the speed difference;

[0078] Calculating a weighted sum of the first tilt state parameter and the second tilt state parameter to obtain a tilt state of the 3D printing platform;

[0079] According to the tilt state, based on a preset mathematical correspondence between the tilt state and the leveling instruction, a leveling control instruction corresponding to the leveling mechanism of the 3D printing platform is determined.

[0080] The third aspect of the present invention discloses another 3D printing platform leveling system based on dynamic analysis of a discharge head, the system comprising:

[0081] a memory storing executable program code;

[0082] a processor coupled to the memory;

[0083] The processor calls the executable program code stored in the memory to execute part or all of the steps in the 3D printing platform leveling method based on discharge head dynamic analysis disclosed in the first aspect of the present invention.

[0084] The fourth aspect of the present invention discloses a computer storage medium, which stores computer instructions. When the computer instructions are called, they are used to execute some or all of the steps in the 3D printing platform leveling method based on discharge head dynamic analysis disclosed in the first aspect of the present invention.

[0085] Compared with the prior art, the embodiments of the present invention have the following beneficial effects:

[0086] The present invention obtains image data of the discharge head of the 3D printing platform in real time and determines its relative position and movement speed in the working area based on an image recognition algorithm, analyzes the tilt parameters of the 3D printing platform, and generates leveling control instructions for the leveling mechanism in combination with preset leveling control rules to reduce the tilt parameters. This can achieve precise tilt correction based on the dynamic image of the discharge head, improve the stability and printing accuracy of the 3D printing platform, and reduce the risk of printing failure caused by tilt. BRIEF DESCRIPTION OF THE DRAWINGS

[0087] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0088] Figure 1 This is a flow chart of a 3D printing platform leveling method based on dynamic analysis of a discharge head disclosed in an embodiment of the present invention.

[0089] Figure 2 This is a structural schematic diagram of a 3D printing platform leveling system based on dynamic analysis of a discharge head disclosed in an embodiment of the present invention.

[0090] Figure 3 This is a structural schematic diagram of another 3D printing platform leveling system based on dynamic analysis of the discharge head disclosed in an embodiment of the present invention. DETAILED DESCRIPTION

[0091] In order to enable those skilled in the art to better understand the solutions of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0092] The terms "first," "second," and so on, in the description and claims of the present invention and the accompanying drawings are used to distinguish between different objects, not to describe a specific order. Furthermore, the terms "including," "having," and any variations thereof, are intended to cover non-exclusive inclusions. For example, a process, method, apparatus, product, or device comprising a series of steps or elements is not limited to the listed steps or elements but may optionally include steps or elements not listed, or may optionally include other steps or elements inherent to the process, method, product, or device.

[0093] References herein to "embodiments" mean that a particular feature, structure, or characteristic described in connection with the embodiments may be included in at least one embodiment of the present invention. The appearance of this phrase in various places in the specification does not necessarily refer to the same embodiment, nor does it constitute a separate or alternative embodiment that is mutually exclusive of other embodiments. It is understood, both explicitly and implicitly, by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0094] The present invention discloses a 3D printing platform leveling method and system based on dynamic analysis of the discharge head. By acquiring real-time image data of the 3D printing platform's discharge head and determining its relative position and motion speed within the working area using an image recognition algorithm, the system analyzes the 3D printing platform's tilt parameters. Based on preset leveling control rules, the system generates leveling control instructions for the leveling mechanism to reduce the tilt parameters. This enables precise tilt correction based on the dynamic image of the discharge head, improving the stability and printing accuracy of the 3D printing platform and reducing the risk of printing failures caused by tilt. These are described in detail below.

[0095] Example 1

[0096] See also Figure 1 , Figure 1 This is a flow chart of a 3D printing platform leveling method based on dynamic analysis of the discharge head disclosed in an embodiment of the present invention. Figure 1 The 3D printing platform leveling method based on dynamic analysis of the discharge head described can be applied to a data processing system / data processing device / data processing server (wherein the server includes a local processing server or a cloud processing server). Figure 1 As shown, the 3D printing platform leveling method based on the dynamic analysis of the discharge head may include the following operations:

[0097] 101. When executing a 3D printing task, obtain image data of the discharge head of the 3D printing platform in real time.

[0098] 102. According to the image data and based on the image recognition algorithm, the relative position and movement speed of the discharge head in the working area are determined.

[0099] 103. Analyze the tilt parameters corresponding to the 3D printing platform based on the relative position and movement speed.

[0100] 104. According to the tilt parameter and based on a preset leveling control rule, determine a leveling control instruction corresponding to the leveling mechanism of the 3D printing platform.

[0101] Optionally, the leveling control instruction is used to control the leveling mechanism to level the 3D printing platform to reduce the tilt parameter.

[0102] It can be seen that the above-mentioned embodiment of the invention obtains the image data of the discharge head of the 3D printing platform in real time and determines its relative position and movement speed in the working area based on the image recognition algorithm, analyzes the tilt parameters of the 3D printing platform, and generates leveling control instructions for the leveling mechanism in combination with preset leveling control rules to reduce the tilt parameters, thereby realizing precise tilt correction based on the dynamic image of the discharge head, improving the stability and printing accuracy of the 3D printing platform, and reducing the risk of printing failure caused by tilt.

[0103] As an optional embodiment, in the above steps, determining the relative position and movement speed of the discharge head in the working area based on the image data and the image recognition algorithm includes:

[0104] Input each image data into a trained image segmentation model to obtain the discharge head region in each image data; optionally, the image segmentation model is trained using a training data set including a plurality of training images and corresponding discharge head region annotations;

[0105] Determine the acquisition time point corresponding to each image data;

[0106] According to the acquisition time points and the discharge head areas corresponding to all the image data, the relative position and movement speed of the discharge head in the working area are determined.

[0107] Optionally, the image segmentation model can be a convolutional neural network, a U-Net model, a Transformer model or a deep residual network, which is not limited in the present invention.

[0108] Optionally, the image data may be an RGB image, a grayscale image, a depth image, or an infrared image, which is not limited in the present invention.

[0109] Optionally, the discharge head area may be a contour area, a boundary box area, a pixel-level segmentation area or a three-dimensional point cloud area of ​​the discharge head, which is not limited in the present invention.

[0110] Optionally, the acquisition time point may be an image capture timestamp, a device recording time, or a system synchronization time, which is not limited in the present invention.

[0111] Optionally, the acquisition time point may be determined based on a camera trigger signal, a sensor log, or external clock synchronization, which is not limited in the present invention.

[0112] Optionally, the relative position may be a three-dimensional space coordinate, a two-dimensional plane coordinate, or an offset relative to a reference point of the working area, which is not limited in the present invention.

[0113] Optionally, the motion speed may be a linear velocity, an angular velocity, or a trajectory velocity vector, which is not limited in the present invention.

[0114] Optionally, the determination process may be based on optical flow analysis, a trajectory fitting algorithm, or a motion estimation model, which is not limited in the present invention.

[0115] It can be seen that through the above optional embodiments, by inputting each frame of image data into the trained image segmentation model to identify the discharge head area, combining the acquisition time point of each frame of image, analyzing the discharge head area and time point data to determine the relative position and movement speed of the discharge head in the working area, thereby realizing accurate dynamic tracking of the discharge head based on image segmentation and timing analysis, improving the accuracy of the tilt parameter evaluation of the 3D printing platform and the leveling control efficiency, and reducing the risk of printing failure.

[0116] As an optional embodiment, in the above steps, determining the relative position and movement speed of the discharge head in the working area according to the acquisition time points and discharge head areas corresponding to all image data includes:

[0117] Calculate the geometric center point corresponding to the discharge head area in each image data;

[0118] Connect the geometric center points of all image data from early to late based on the acquisition time point to obtain the movement trajectory of the discharge head;

[0119] Calculate the ratio of the length of the discharging head's moving trajectory to the longest time interval corresponding to all acquired time points to obtain the corresponding movement speed of the discharging head;

[0120] Based on all geometric center points, the relative position of the discharge head in the working area is calculated.

[0121] Optionally, the geometric center point may be a region centroid, a bounding box center, or a pixel weighted average center, which is not limited in the present invention.

[0122] Optionally, the calculation of the geometric center point can be based on an image processing algorithm, a geometric algorithm, or a deep learning model, which is not limited in the present invention.

[0123] Optionally, the calculation process may be optimized in combination with image resolution or shape features of the discharge head area, which is not limited in the present invention.

[0124] Optionally, the connection process of the discharging head movement trajectory can be based on linear interpolation, spline interpolation or Bezier curve algorithm, which is not limited in the present invention.

[0125] It can be seen that through the above optional embodiments, by calculating the geometric center point of the discharge head area in each frame of image data and connecting it from early to late according to the acquisition time point to form a discharge head movement trajectory, the ratio of the trajectory length to the longest time interval is calculated to obtain the discharge head movement speed, and the relative position of the discharge head in the working area is determined according to the geometric center point, thereby realizing accurate discharge head position and speed evaluation based on geometric center and trajectory analysis, improving the accuracy of the tilt parameter analysis of the 3D printing platform and the leveling control efficiency, and reducing the risk of printing failure.

[0126] As an optional embodiment, in the above step, calculating the relative position of the discharge head in the working area based on all geometric center points includes:

[0127] Input each image data into a trained work area recognition model to obtain the work area feature points in each image data; optionally, the work area recognition model is trained by a training data set including a plurality of training work images and corresponding work area annotations;

[0128] Calculate the feature similarity between the feature points of the working area of ​​any two image data;

[0129] Calculate the average value of all feature similarities corresponding to each image data, and filter out image data with an average value greater than a preset threshold to obtain multiple preferred images;

[0130] Calculate the average coordinates of the feature points in the working area of ​​all the preferred images to obtain the representative position of the working area;

[0131] Calculate the average coordinates of all geometric center points to obtain the position of the discharge head;

[0132] The relative position relationship between the discharge head position and the representative position of the working area is calculated to obtain the relative position of the discharge head in the working area.

[0133] Optionally, the working area recognition model can be a convolutional neural network, a region proposal network or a feature point detection model, which is not limited in the present invention.

[0134] Optionally, the feature point of the working area may be a corner point, an edge point, a key point or a center point of the area, which is not limited in the present invention.

[0135] Optionally, the training data set may include simulated images, historical images, or real-time acquired images, which is not limited in the present invention.

[0136] Optionally, the feature similarity may be calculated based on Euclidean distance, cosine similarity, or feature vector matching, which is not limited in the present invention.

[0137] Optionally, the calculation of the feature similarity may be combined with the spatial distribution of feature points or image texture features, which is not limited in the present invention.

[0138] Optionally, the preset threshold may be a fixed threshold, a dynamic threshold, or a threshold adaptively adjusted based on a scenario, which is not limited in the present invention.

[0139] Optionally, the relative position relationship may be a distance vector, an angle offset, or a spatial angle, which is not limited in the present invention.

[0140] It can be seen that through the above optional embodiments, by inputting each frame of image data into the trained working area recognition model to extract the working area feature points, calculating the feature similarity between the feature points of any two frames of images and taking the average value, screening the preferred images whose average value exceeds the threshold, calculating the coordinate average value of the working area feature points in the preferred image to obtain the working area representation position and the geometric center point coordinate average value to obtain the discharge head position, and then calculating the relative position relationship between the two to determine the relative position of the discharge head in the working area, thereby realizing precise discharge head positioning based on feature points and image screening, improving the accuracy of the tilt parameter analysis of the 3D printing platform and the leveling control efficiency, and reducing the risk of printing failure.

[0141] As an optional embodiment, in the above step, analyzing the tilt parameters corresponding to the 3D printing platform according to the relative position and movement speed includes:

[0142] Get the printing operations of the 3D printing task in the current time period;

[0143] Determine the reference position and reference movement speed of the discharge head corresponding to the printing operation in a preset database;

[0144] According to the reference position and reference movement speed, as well as the relative position and movement speed, the tilt parameters corresponding to the 3D printing platform are analyzed.

[0145] Optionally, the printing operation may be a discharge head movement operation, a material ejection operation, or a platform adjustment operation, which is not limited in the present invention.

[0146] Optionally, the acquisition of the printing operation may be based on a task log, real-time sensor data, or a control instruction, which is not limited in the present invention.

[0147] Optionally, the analysis process of the tilt parameter may be based on deviation analysis, a regression model, or a machine learning algorithm, which is not limited in the present invention.

[0148] Optionally, the analysis of the tilt parameter may be optimized in combination with the physical properties of the printing material or the environmental conditions, which is not limited in the present invention.

[0149] It can be seen that through the above optional embodiments, by obtaining the printing operation of the 3D printing task in the current time period and determining the reference position and reference movement speed of the corresponding discharge head from the preset database, the tilt parameters of the 3D printing platform are analyzed in combination with the real-time acquired relative position and movement speed of the discharge head, thereby realizing accurate tilt parameter evaluation based on reference data and actual data, improving the accuracy of platform leveling control and printing stability, and reducing the risk of printing failure caused by tilt.

[0150] As an optional embodiment, in the above step, analyzing the tilt parameters corresponding to the 3D printing platform according to the reference position and reference movement speed, as well as the relative position and movement speed, includes:

[0151] Calculating the position difference between the relative position and the reference position;

[0152] Calculating the speed difference between the movement speed and the reference movement speed;

[0153] The difference between the weighted sum of the position difference and the speed difference and a preset reference threshold is calculated to obtain the tilt parameter corresponding to the 3D printing platform.

[0154] Optionally, the position difference may be Euclidean distance, Manhattan distance, or weighted distance, which is not limited in the present invention.

[0155] It can be seen that through the above optional embodiments, by calculating the position difference between the relative position of the discharge head and the reference position and the speed difference between the movement speed and the reference movement speed, the difference between the weighted sum of the two and the preset reference threshold is calculated as the tilt parameter of the 3D printing platform, thereby realizing accurate tilt parameter evaluation based on position and speed differences, improving the accuracy of platform leveling control and printing stability, and reducing the risk of printing failure caused by tilt.

[0156] As an optional embodiment, in the above step, determining the leveling control instruction corresponding to the leveling mechanism of the 3D printing platform according to the tilt parameter and based on a preset leveling control rule includes:

[0157] Determine whether the tilt parameter is greater than a preset parameter threshold and obtain a determination result;

[0158] When the judgment result is no, no further operation is performed;

[0159] When the judgment result is yes, a leveling control instruction corresponding to the leveling mechanism of the 3D printing platform is determined based on the position difference and the speed difference.

[0160] Optionally, the parameter threshold may be a fixed threshold, a dynamic threshold, or a threshold adjusted based on task requirements, which is not limited in the present invention.

[0161] It can be seen that through the above optional embodiments, by judging whether the tilt parameter of the 3D printing platform exceeds the preset parameter threshold, if it does not exceed the threshold, no operation is performed; if it exceeds the threshold, a leveling control instruction of the leveling mechanism is generated based on the position difference and the speed difference, thereby realizing precise leveling control based on the degree of tilt, improving the stability and printing accuracy of the 3D printing platform, and reducing the risk of printing failure caused by tilt.

[0162] As an optional embodiment, in the above step, determining the leveling control instruction corresponding to the leveling mechanism of the 3D printing platform based on the position difference and the speed difference includes:

[0163] According to a preset correspondence between the position difference and the tilt state, a corresponding first tilt state parameter is determined based on the position difference degree;

[0164] According to a preset correspondence between the speed difference and the tilt state, a corresponding second tilt state parameter is determined based on the speed difference;

[0165] Calculating a weighted sum of the first tilt state parameter and the second tilt state parameter to obtain a tilt state of the 3D printing platform;

[0166] According to the tilt state, based on a preset mathematical correspondence between the tilt state and the leveling instruction, a leveling control instruction corresponding to the leveling mechanism of the 3D printing platform is determined.

[0167] Optionally, the corresponding relationship may be a lookup table, a mathematical function, or a machine learning mapping model, which is not limited in the present invention.

[0168] Optionally, the tilt state parameter may be a tilt angle, an offset, or a state score, which is not limited in the present invention.

[0169] Optionally, the leveling control instruction may include an angle adjustment amount, a speed correction amount, or a motion path correction, which is not limited in the present invention.

[0170] Optionally, the process of determining the leveling control instruction may be implemented based on real-time feedback, predictive control, or an optimization algorithm, which is not limited in the present invention.

[0171] It can be seen that through the above optional embodiments, the first tilt state parameter corresponding to the position difference is determined based on the preset correspondence between the position difference and the tilt state, and the second tilt state parameter corresponding to the speed difference is determined based on the correspondence between the speed difference and the tilt state. The weighted sum of the two is calculated to obtain the tilt state of the 3D printing platform, and the leveling control instruction of the leveling mechanism is generated according to the preset mathematical correspondence between the tilt state and the leveling instruction, thereby realizing accurate tilt state evaluation and leveling control based on multi-dimensional difference analysis, improving the stability and printing accuracy of the 3D printing platform, and reducing the risk of printing failure caused by tilt.

[0172] Example 2

[0173] See also Figure 2 , Figure 2 This is a schematic diagram of the structure of a 3D printing platform leveling system based on dynamic analysis of the discharge head disclosed in an embodiment of the present invention. Figure 2 The 3D printing platform leveling system based on dynamic analysis of the discharge head described can be applied to a data processing system / data processing device / data processing server (wherein the server includes a local processing server or a cloud processing server). Figure 2 As shown, the 3D printing platform leveling system based on the dynamic analysis of the discharge head may include:

[0174] The acquisition module 201 is used to acquire image data of the discharge head of the 3D printing platform in real time when executing a 3D printing task.

[0175] The determination module 202 is used to determine the relative position and movement speed of the discharge head in the working area based on the image data and the image recognition algorithm.

[0176] The analysis module 203 is used to analyze the tilt parameters corresponding to the 3D printing platform according to the relative position and movement speed.

[0177] The control module 204 is configured to determine a leveling control instruction corresponding to the leveling mechanism of the 3D printing platform according to the tilt parameter and based on a preset leveling control rule.

[0178] Optionally, the leveling control instruction is used to control the leveling mechanism to level the 3D printing platform to reduce the tilt parameter.

[0179] It can be seen that the above-mentioned embodiment of the invention obtains the image data of the discharge head of the 3D printing platform in real time and determines its relative position and movement speed in the working area based on the image recognition algorithm, analyzes the tilt parameters of the 3D printing platform, and generates leveling control instructions for the leveling mechanism in combination with preset leveling control rules to reduce the tilt parameters, thereby realizing precise tilt correction based on the dynamic image of the discharge head, improving the stability and printing accuracy of the 3D printing platform, and reducing the risk of printing failure caused by tilt.

[0180] As an optional embodiment, the specific manner in which the determination module determines the relative position and movement speed of the discharge head in the working area based on the image data and the image recognition algorithm includes:

[0181] Input each image data into a trained image segmentation model to obtain the discharge head region in each image data; optionally, the image segmentation model is trained using a training data set including a plurality of training images and corresponding discharge head region annotations;

[0182] Determine the acquisition time point corresponding to each image data;

[0183] According to the acquisition time points and the discharge head areas corresponding to all the image data, the relative position and movement speed of the discharge head in the working area are determined.

[0184] It can be seen that through the above optional embodiments, by inputting each frame of image data into the trained image segmentation model to identify the discharge head area, combining the acquisition time point of each frame of image, analyzing the discharge head area and time point data to determine the relative position and movement speed of the discharge head in the working area, thereby realizing accurate dynamic tracking of the discharge head based on image segmentation and timing analysis, improving the accuracy of the tilt parameter evaluation of the 3D printing platform and the leveling control efficiency, and reducing the risk of printing failure.

[0185] As an optional embodiment, the specific method of determining the relative position and movement speed of the discharge head in the working area according to the acquisition time point and the discharge head area corresponding to all image data includes:

[0186] Calculate the geometric center point corresponding to the discharge head area in each image data;

[0187] Connect the geometric center points of all image data from early to late based on the acquisition time point to obtain the movement trajectory of the discharge head;

[0188] Calculate the ratio of the length of the discharging head's moving trajectory to the longest time interval corresponding to all acquired time points to obtain the corresponding movement speed of the discharging head;

[0189] Based on all geometric center points, the relative position of the discharge head in the working area is calculated.

[0190] It can be seen that through the above optional embodiments, by calculating the geometric center point of the discharge head area in each frame of image data and connecting it from early to late according to the acquisition time point to form a discharge head movement trajectory, the ratio of the trajectory length to the longest time interval is calculated to obtain the discharge head movement speed, and the relative position of the discharge head in the working area is determined according to the geometric center point, thereby realizing accurate discharge head position and speed evaluation based on geometric center and trajectory analysis, improving the accuracy of the tilt parameter analysis of the 3D printing platform and the leveling control efficiency, and reducing the risk of printing failure.

[0191] As an optional embodiment, the specific method for the determination module to calculate the relative position of the discharge head in the working area based on all geometric center points includes:

[0192] Input each image data into a trained work area recognition model to obtain the work area feature points in each image data; optionally, the work area recognition model is trained by a training data set including a plurality of training work images and corresponding work area annotations;

[0193] Calculate the feature similarity between the feature points of the working area of ​​any two image data;

[0194] Calculate the average value of all feature similarities corresponding to each image data, and filter out image data with an average value greater than a preset threshold to obtain multiple preferred images;

[0195] Calculate the average coordinates of the feature points in the working area of ​​all the preferred images to obtain the representative position of the working area;

[0196] Calculate the average coordinates of all geometric center points to obtain the position of the discharge head;

[0197] The relative position relationship between the discharge head position and the representative position of the working area is calculated to obtain the relative position of the discharge head in the working area.

[0198] It can be seen that through the above optional embodiments, by inputting each frame of image data into the trained working area recognition model to extract the working area feature points, calculating the feature similarity between the feature points of any two frames of images and taking the average value, screening the preferred images whose average value exceeds the threshold, calculating the coordinate average value of the working area feature points in the preferred image to obtain the working area representation position and the geometric center point coordinate average value to obtain the discharge head position, and then calculating the relative position relationship between the two to determine the relative position of the discharge head in the working area, thereby realizing precise discharge head positioning based on feature points and image screening, improving the accuracy of the tilt parameter analysis of the 3D printing platform and the leveling control efficiency, and reducing the risk of printing failure.

[0199] As an optional embodiment, the specific manner in which the analysis module analyzes the tilt parameters corresponding to the 3D printing platform according to the relative position and movement speed includes:

[0200] Get the printing operations of the 3D printing task in the current time period;

[0201] Determine the reference position and reference movement speed of the discharge head corresponding to the printing operation in a preset database;

[0202] According to the reference position and reference movement speed, as well as the relative position and movement speed, the tilt parameters corresponding to the 3D printing platform are analyzed.

[0203] It can be seen that through the above optional embodiments, by obtaining the printing operation of the 3D printing task in the current time period and determining the reference position and reference movement speed of the corresponding discharge head from the preset database, the tilt parameters of the 3D printing platform are analyzed in combination with the real-time acquired relative position and movement speed of the discharge head, thereby realizing accurate tilt parameter evaluation based on reference data and actual data, improving the accuracy of platform leveling control and printing stability, and reducing the risk of printing failure caused by tilt.

[0204] As an optional embodiment, the analysis module analyzes the tilt parameters corresponding to the 3D printing platform according to the reference position and reference motion speed, as well as the relative position and motion speed, in a specific manner including:

[0205] Calculating the position difference between the relative position and the reference position;

[0206] Calculating the speed difference between the movement speed and the reference movement speed;

[0207] The difference between the weighted sum of the position difference and the speed difference and a preset reference threshold is calculated to obtain the tilt parameter corresponding to the 3D printing platform.

[0208] It can be seen that through the above optional embodiments, by calculating the position difference between the relative position of the discharge head and the reference position and the speed difference between the movement speed and the reference movement speed, the difference between the weighted sum of the two and the preset reference threshold is calculated as the tilt parameter of the 3D printing platform, thereby realizing accurate tilt parameter evaluation based on position and speed differences, improving the accuracy of platform leveling control and printing stability, and reducing the risk of printing failure caused by tilt.

[0209] As an optional embodiment, the control module determines the specific manner of the leveling control instruction corresponding to the leveling mechanism of the 3D printing platform according to the tilt parameter and the preset leveling control rule, including:

[0210] Determine whether the tilt parameter is greater than a preset parameter threshold and obtain a determination result;

[0211] When the judgment result is no, no further operation is performed;

[0212] When the judgment result is yes, a leveling control instruction corresponding to the leveling mechanism of the 3D printing platform is determined based on the position difference and the speed difference.

[0213] It can be seen that through the above optional embodiments, by judging whether the tilt parameter of the 3D printing platform exceeds the preset parameter threshold, if it does not exceed the threshold, no operation is performed; if it exceeds the threshold, a leveling control instruction of the leveling mechanism is generated based on the position difference and the speed difference, thereby realizing precise leveling control based on the degree of tilt, improving the stability and printing accuracy of the 3D printing platform, and reducing the risk of printing failure caused by tilt.

[0214] As an optional embodiment, the control module determines a specific method of the leveling control instruction corresponding to the leveling mechanism of the 3D printing platform based on the position difference and the speed difference, including:

[0215] According to a preset correspondence between the position difference and the tilt state, a corresponding first tilt state parameter is determined based on the position difference degree;

[0216] According to a preset correspondence between the speed difference and the tilt state, a corresponding second tilt state parameter is determined based on the speed difference;

[0217] Calculating a weighted sum of the first tilt state parameter and the second tilt state parameter to obtain a tilt state of the 3D printing platform;

[0218] According to the tilt state, based on a preset mathematical correspondence between the tilt state and the leveling instruction, a leveling control instruction corresponding to the leveling mechanism of the 3D printing platform is determined.

[0219] It can be seen that through the above optional embodiments, the first tilt state parameter corresponding to the position difference is determined based on the preset correspondence between the position difference and the tilt state, and the second tilt state parameter corresponding to the speed difference is determined based on the correspondence between the speed difference and the tilt state. The weighted sum of the two is calculated to obtain the tilt state of the 3D printing platform, and the leveling control instruction of the leveling mechanism is generated according to the preset mathematical correspondence between the tilt state and the leveling instruction, thereby realizing accurate tilt state evaluation and leveling control based on multi-dimensional difference analysis, improving the stability and printing accuracy of the 3D printing platform, and reducing the risk of printing failure caused by tilt.

[0220] Example 3

[0221] See also Figure 3 , Figure 3 This is another 3D printing platform leveling system based on dynamic analysis of the discharge head disclosed in an embodiment of the present invention. Figure 3 The 3D printing platform leveling system based on dynamic analysis of the discharge head is applied to a data processing system / data processing device / data processing server (wherein the server includes a local processing server or a cloud processing server). Figure 3 As shown, the 3D printing platform leveling system based on the dynamic analysis of the discharge head may include:

[0222] A memory 301 storing executable program code;

[0223] a processor 302 coupled to the memory 301;

[0224] 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 the dynamic analysis of the discharge head described in the first embodiment.

[0225] Example 4

[0226] An embodiment of the present invention discloses a computer-readable storage medium storing a computer program for electronic data exchange, wherein the computer program enables a computer to execute the steps of the 3D printing platform leveling method based on discharge head dynamic analysis described in the first embodiment.

[0227] Example 5

[0228] An embodiment of the present 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 enable a computer to execute the steps of the 3D printing platform leveling method based on discharge head dynamic analysis described in Example 1.

[0229] The foregoing description of specific embodiments of the present disclosure is intended to illustrate a method for performing a multi-tasking process. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims may be performed in an order different from that described in the embodiments and still achieve the desired results. Furthermore, the processes depicted in the accompanying drawings do not necessarily require the specific order shown or the sequential order shown to achieve the desired results. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0230] The systems, devices, modules, or units described in the above embodiments may be implemented by computer chips or entities, or by products having certain functions. A typical implementation device is a computer. Specifically, the computer may be, for example, a personal computer, a laptop computer, a cellular phone, a camera phone, a smartphone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or a combination of any of these devices.

[0231] For the convenience of description, the above devices are described as being divided into various units according to their functions. Of course, when implementing this specification, the functions of each unit can be implemented in the same or multiple software and / or hardware.

[0232] Those skilled in the art will appreciate that the embodiments of this specification may be provided as methods, systems, or computer program products. Therefore, the embodiments of this specification may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Furthermore, the embodiments of this specification may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0233] This specification is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of this specification. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0234] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0235] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0236] In a typical configuration, a computing device includes one or more processors (CPUs), input / output interfaces, network interfaces, and memory.

[0237] Memory may include non-permanent storage in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. Memory is an example of a computer-readable medium.

[0238] Computer-readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. The information can be computer-readable instructions, data structures, program modules 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 technology, compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassettes, magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer-readable media does not include transitory media such as modulated data signals and carrier waves.

[0239] It should also be noted that the terms "comprises," "includes," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, method, commodity, or apparatus that includes a series of elements includes not only those elements but also other elements not explicitly listed, or includes elements inherent to such process, method, commodity, or apparatus. In the absence of further limitations, an element defined by the phrase "comprises a ..." does not exclude the presence of other identical elements in the process, method, commodity, or apparatus that includes the element.

[0240] This specification may be described in the general context of computer-executable instructions, such as program modules, executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, and the like that perform specific tasks or implement specific abstract data types. This specification may also be practiced in distributed computing environments where tasks are performed by remote processing devices connected through a communications network. In a distributed computing environment, program modules may be located in both local and remote computer storage media, including storage devices.

[0241] The various embodiments in this specification are described in a progressive manner. Similar parts between the various embodiments can be referred to in conjunction with each other. Each embodiment focuses on the differences between the other embodiments. In particular, the system embodiments are generally similar to the method embodiments, so the description is relatively simple. For relevant parts, refer to the description of the method embodiments.

[0242] Finally, it should be noted that the 3D printing platform leveling method and system based on the dynamic analysis of the discharge head disclosed in the embodiment of the present invention only discloses a preferred embodiment of the present invention, which is only used to illustrate the technical solution of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, ordinary technicians in this field should understand that it is still possible to modify the technical solutions described in the aforementioned embodiments, or to replace some of the technical features therein with equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. A 3D printing platform leveling method based on dynamic analysis of the discharge head, characterized in that: The method comprises: When executing 3D printing tasks, real-time image data of the discharge head of the 3D printing platform is obtained; Determine the relative position and movement speed of the discharge head in the working area based on the image data and an image recognition algorithm; Analyzing the tilt parameters corresponding to the 3D printing platform according to the relative position and the movement speed includes: Obtaining the printing operation of the 3D printing task in the current time period; Determining a reference position and a reference movement speed of a discharge head corresponding to the printing operation in a preset database; Calculating a position difference between the relative position and the reference position; Calculating a speed difference between the movement speed and the reference movement speed; Calculating a difference between a weighted sum of the position difference and the speed difference and a preset reference threshold value to obtain a tilt parameter corresponding to the 3D printing platform; According to the tilt parameter and based on a preset leveling control rule, determining a leveling control instruction corresponding to the leveling mechanism of the 3D printing platform includes: Determining whether the tilt parameter is greater than a preset parameter threshold and obtaining a determination result; When the judgment result is no, no further operation is performed; When the judgment result is yes, determining a corresponding first tilt state parameter based on the position difference according to a preset correspondence between the position difference and the tilt state; According to a preset correspondence between the speed difference and the tilt state, determining a corresponding second tilt state parameter based on the speed difference; Calculating a weighted sum of the first tilt state parameter and the second tilt state parameter to obtain a tilt state of the 3D printing platform; According to the tilt state, based on a preset mathematical correspondence between the tilt state and the leveling instruction, a leveling control instruction corresponding to the leveling mechanism of the 3D printing platform is determined; the leveling control instruction is used to control the leveling mechanism to level the 3D printing platform to reduce the tilt parameter.

2. The 3D printing platform leveling method based on discharge head dynamic analysis according to claim 1, characterized in that: Determining the relative position and movement speed of the discharge head in the working area based on the image data and an image recognition algorithm includes: Inputting each of the image data into a trained image segmentation model to obtain a discharge head region in each of the image data; the image segmentation model is trained using a training data set comprising a plurality of training images and corresponding discharge head region annotations; Determining an acquisition time point corresponding to each of the image data; The relative position and movement speed of the discharging head in the working area are determined according to the acquisition time points and the discharging head areas corresponding to all the image data.

3. The 3D printing platform leveling method based on discharge head dynamic analysis according to claim 2, characterized in that: The determining the relative position and movement speed of the discharge head in the working area according to the acquisition time points and the discharge head area corresponding to all the image data includes: Calculating the geometric center point corresponding to the discharge head area in each of the image data; Connecting the geometric center points of all the image data from early to late based on the acquisition time points to obtain a movement trajectory of the discharge head; Calculating the ratio of the length of the moving track of the discharging head to the longest time interval corresponding to all the acquisition time points to obtain the corresponding movement speed of the discharging head; The relative position of the discharge head in the working area is calculated based on all the geometric center points.

4. The 3D printing platform leveling method based on discharge head dynamic analysis according to claim 3 is characterized in that: Calculating the relative position of the discharge head in the working area based on all the geometric center points includes: Inputting each of the image data into a trained work area recognition model to obtain work area feature points in each of the image data; the work area recognition model is trained by a training data set including a plurality of training work images and corresponding work area annotations; Calculating feature similarity between any two feature points in the working area of ​​the image data; Calculating an average value of all the feature similarities corresponding to each of the image data, and screening out image data having the average value greater than a preset threshold, to obtain a plurality of preferred images; Calculating the average coordinates of the feature points of the working area in all the preferred images to obtain a representative position of the working area; Calculate the average coordinates of all the geometric center points to obtain the position of the discharge head; The relative position relationship between the position of the discharge head and the representative position of the working area is calculated to obtain the relative position of the discharge head in the working area.

5. A 3D printing platform leveling system based on dynamic analysis of the discharge head, characterized in that: The system comprises: An acquisition module is used to acquire image data of the discharge head of the 3D printing platform in real time when executing a 3D printing task; a determination module, configured to determine the relative position and movement speed of the discharge head in the working area based on the image data and an image recognition algorithm; An analysis module, configured to analyze the tilt parameters corresponding to the 3D printing platform according to the relative position and the movement speed, comprising: Obtaining the printing operation of the 3D printing task in the current time period; Determining a reference position and a reference movement speed of a discharge head corresponding to the printing operation in a preset database; Calculating a position difference between the relative position and the reference position; Calculating a speed difference between the movement speed and the reference movement speed; Calculating a difference between a weighted sum of the position difference and the speed difference and a preset reference threshold value to obtain a tilt parameter corresponding to the 3D printing platform; A control module is configured to determine, according to the tilt parameter and based on a preset leveling control rule, a leveling control instruction corresponding to the leveling mechanism of the 3D printing platform, including: Determining whether the tilt parameter is greater than a preset parameter threshold and obtaining a determination result; When the judgment result is no, no further operation is performed; When the judgment result is yes, determining a corresponding first tilt state parameter based on the position difference according to a preset correspondence between the position difference and the tilt state; According to a preset correspondence between the speed difference and the tilt state, determining a corresponding second tilt state parameter based on the speed difference; Calculating a weighted sum of the first tilt state parameter and the second tilt state parameter to obtain a tilt state of the 3D printing platform; According to the tilt state, based on a preset mathematical correspondence between the tilt state and the leveling instruction, a leveling control instruction corresponding to the leveling mechanism of the 3D printing platform is determined; the leveling control instruction is used to control the leveling mechanism to level the 3D printing platform to reduce the tilt parameter.

6. A 3D printing platform leveling system based on dynamic analysis of the discharge head, characterized in that: The system comprises: a memory storing 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 discharge head dynamic analysis according to any one of claims 1 to 4.

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