3D printing platform leveling method and system based on multi-point sound analysis

Abstract

The present invention discloses a 3D printing platform leveling method and system based on multi-point sound analysis, the method comprising: obtaining sound data corresponding to multiple platform positions of a 3D printing platform; determining the overall tilt risk of the 3D printing platform based on the sound data; when the overall tilt risk is greater than a preset risk threshold, screening multiple dangerous platform positions based on the sound data; determining the leveling control instructions corresponding to the leveling mechanism of the 3D printing platform based on the sound data of the dangerous platform positions and a prediction algorithm; the leveling control instructions are used to control the leveling mechanism to level the 3D printing platform. It can be seen that the present invention can achieve accurate tilt risk assessment and leveling control based on sound analysis, 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 multi-point sound analysis. Background Art

[0002] With the widespread application of 3D printing technology in high-precision manufacturing, companies and users are increasingly focusing on improving printing quality and success rates by enhancing platform stability. Existing technologies typically collect mechanical sensor data from the 3D printing platform and use fixed threshold monitoring or manual calibration methods to adjust the platform position to ensure printing accuracy. Existing solutions lack dynamic analysis of platform position sound data and prediction of tilt risks, making it difficult to accurately identify dangerous platform positions and optimize leveling control. Commonly used static leveling strategies are unable to adapt to complex printing environments, resulting in insufficiently accurate tilt risk assessments, which can easily lead to platform instability or printing failures, limiting the stability and production efficiency of the 3D printing system. Clearly, existing technologies have flaws that need to be addressed 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 multi-point sound analysis, which can realize accurate tilt risk assessment and leveling control based on sound analysis, 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 multi-point sound analysis, the method comprising:

[0005] Acquire sound data corresponding to multiple platform positions of the 3D printing platform;

[0006] determining an overall tilt risk of the 3D printing platform based on the sound data;

[0007] When the overall tilt risk is greater than a preset risk threshold, screening out multiple dangerous platform positions based on the sound data;

[0008] According to the sound data at the dangerous platform position, based on a prediction algorithm, 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.

[0009] As an optional embodiment, in the first aspect of the present invention, the platform position is the base corner position, base center position, printing area position, discharge head position or shell outside position of the 3D printing platform.

[0010] As an optional embodiment, in the first aspect of the present invention, determining the overall tilt risk of the 3D printing platform based on the sound data includes:

[0011] Based on an abnormal data screening algorithm, screening out a plurality of abnormal sound data from all the sound data;

[0012] Inputting each abnormal sound data and the corresponding platform location into a trained risk identification model to obtain a predicted risk corresponding to each abnormal sound data; the risk identification model is trained using a training data set including training sound data of multiple platform locations and corresponding risk annotations;

[0013] The overall tilt risk of the 3D printing platform is determined based on the predicted risks corresponding to all the abnormal sound data.

[0014] As an optional embodiment, in the first aspect of the present invention, the step of screening out a plurality of abnormal sound data from all the sound data based on the abnormal data screening algorithm includes:

[0015] Calculating frequency component data corresponding to each of the sound data based on a wavelet transform algorithm;

[0016] For the sound data corresponding to any two platform positions belonging to the same platform area, calculating the spectral similarity between the frequency component data of the two sound data; the platform area is the base area, printing area, working part area or shell area of ​​the 3D printing platform;

[0017] For each piece of sound data, calculating the average value of all the spectrum similarities corresponding to the sound data to obtain the abnormality parameter corresponding to the sound data;

[0018] All the sound data whose abnormal parameters are greater than a first parameter threshold are screened out to obtain a plurality of abnormal sound data.

[0019] As an optional embodiment, in the first aspect of the present invention, determining the overall tilt risk of the 3D printing platform based on the predicted risks corresponding to all the abnormal sound data includes:

[0020] Calculate the weighted sum average of the predicted risks corresponding to all the abnormal sound data to obtain the overall tilt risk of the 3D printing platform; wherein the calculated weight corresponding to each predicted risk is the product of a first weight and a second weight; the first weight is proportional to the difference between the predicted risk and the average risk value; the average risk value is the average of all the predicted risks; and the second weight is proportional to the abnormal parameter corresponding to the abnormal sound data corresponding to the predicted risk.

[0021] As an optional embodiment, in the first aspect of the present invention, screening out multiple dangerous platform locations based on the sound data includes:

[0022] determining a platform position corresponding to each abnormal sound data as a candidate platform position;

[0023] Determining the candidate platform position with the highest predicted risk as a reference position;

[0024] For each candidate platform position except the reference position, calculating a position distance between the candidate platform position and the reference position;

[0025] calculating a correction weight inversely proportional to the distance from the position;

[0026] Calculating the product of the correction weight and the predicted risk corresponding to the candidate platform position to obtain the corrected risk corresponding to the candidate platform position;

[0027] All the candidate platform positions whose corrected risks are greater than a preset first risk threshold and the reference position are determined as a plurality of dangerous platform positions.

[0028] As an optional embodiment, in the first aspect of the present invention, determining, based on the sound data of the dangerous platform position and a prediction algorithm, a leveling control instruction corresponding to the leveling mechanism of the 3D printing platform includes:

[0029] Clustering all of the dangerous platform locations based on the predicted risk to obtain a location set;

[0030] For each height control component of the leveling mechanism of the 3D printing platform, calculating an average value of the distance between the position of the height control component and each of the dangerous platform positions in the position set to obtain a control necessity parameter of the height control component;

[0031] Determine whether the control necessity parameter is greater than a preset second parameter threshold. If not, ignore the control requirement of the height control component. If so, generate a leveling control instruction corresponding to the height control component including a height change parameter; the height change parameter is proportional to the control necessity parameter.

[0032] As an optional embodiment, in the first aspect of the present invention, the predicted risk corresponding to each of the dangerous platform positions in the position set is greater than a preset second risk threshold; the position distance between any two of the dangerous platform positions in the position set is less than a preset distance threshold.

[0033] A second aspect of an embodiment of the present invention discloses a 3D printing platform leveling system based on multi-point sound analysis, the system comprising:

[0034] An acquisition module, used to acquire sound data corresponding to multiple platform positions of the 3D printing platform;

[0035] a determination module, configured to determine an overall tilt risk of the 3D printing platform based on the sound data;

[0036] A screening module, configured to screen out a plurality of dangerous platform positions according to the sound data when the overall tilt risk is greater than a preset risk threshold;

[0037] A control module is configured to determine, based on a prediction algorithm and according to the sound data at the dangerous platform position, a leveling control instruction corresponding to a leveling mechanism of the 3D printing platform; the leveling control instruction is configured to control the leveling mechanism to level the 3D printing platform.

[0038] As an optional embodiment, in the second aspect of the present invention, the platform position is the base corner position, base center position, printing area position, discharge head position or shell outside position of the 3D printing platform.

[0039] As an optional embodiment, in the second aspect of the present invention, the specific manner in which the determination module determines the overall tilt risk of the 3D printing platform based on the sound data includes:

[0040] Based on an abnormal data screening algorithm, screening out a plurality of abnormal sound data from all the sound data;

[0041] Inputting each abnormal sound data and the corresponding platform location into a trained risk identification model to obtain a predicted risk corresponding to each abnormal sound data; the risk identification model is trained using a training data set including training sound data of multiple platform locations and corresponding risk annotations;

[0042] The overall tilt risk of the 3D printing platform is determined based on the predicted risks corresponding to all the abnormal sound data.

[0043] As an optional embodiment, in the second aspect of the present invention, the specific manner in which the determination module screens out a plurality of abnormal sound data from all the sound data based on the abnormal data screening algorithm includes:

[0044] Calculating frequency component data corresponding to each of the sound data based on a wavelet transform algorithm;

[0045] For the sound data corresponding to any two platform positions belonging to the same platform area, calculating the spectral similarity between the frequency component data of the two sound data; the platform area is the base area, printing area, working part area or shell area of ​​the 3D printing platform;

[0046] For each piece of sound data, calculating the average value of all the spectrum similarities corresponding to the sound data to obtain the abnormality parameter corresponding to the sound data;

[0047] All the sound data whose abnormal parameters are greater than a first parameter threshold are screened out to obtain a plurality of abnormal sound data.

[0048] As an optional embodiment, in the second aspect of the present invention, the specific manner in which the determination module determines the overall tilt risk of the 3D printing platform based on the predicted risks corresponding to all the abnormal sound data includes:

[0049] Calculate the weighted sum average of the predicted risks corresponding to all the abnormal sound data to obtain the overall tilt risk of the 3D printing platform; wherein the calculated weight corresponding to each predicted risk is the product of a first weight and a second weight; the first weight is proportional to the difference between the predicted risk and the average risk value; the average risk value is the average of all the predicted risks; and the second weight is proportional to the abnormal parameter corresponding to the abnormal sound data corresponding to the predicted risk.

[0050] As an optional embodiment, in the second aspect of the present invention, the specific manner in which the screening module screens out multiple dangerous platform locations based on the sound data includes:

[0051] determining a platform position corresponding to each abnormal sound data as a candidate platform position;

[0052] Determining the candidate platform position with the highest predicted risk as a reference position;

[0053] For each candidate platform position except the reference position, calculating a position distance between the candidate platform position and the reference position;

[0054] calculating a correction weight inversely proportional to the distance from the position;

[0055] Calculating the product of the correction weight and the predicted risk corresponding to the candidate platform position to obtain the corrected risk corresponding to the candidate platform position;

[0056] All the candidate platform positions whose corrected risks are greater than a preset first risk threshold and the reference position are determined as a plurality of dangerous platform positions.

[0057] As an optional embodiment, in the second aspect of the present invention, the control module determines, based on the sound data at the dangerous platform position and a prediction algorithm, a specific manner of determining the leveling control instruction corresponding to the leveling mechanism of the 3D printing platform, including:

[0058] Clustering all of the dangerous platform locations based on the predicted risk to obtain a location set;

[0059] For each height control component of the leveling mechanism of the 3D printing platform, calculating an average value of the distance between the position of the height control component and each of the dangerous platform positions in the position set to obtain a control necessity parameter of the height control component;

[0060] Determine whether the control necessity parameter is greater than a preset second parameter threshold. If not, ignore the control requirement of the height control component. If so, generate a leveling control instruction corresponding to the height control component including a height change parameter; the height change parameter is proportional to the control necessity parameter.

[0061] As an optional embodiment, in the second aspect of the present invention, the predicted risk corresponding to each of the dangerous platform positions in the position set is greater than a preset second risk threshold; the position distance between any two of the dangerous platform positions in the position set is less than a preset distance threshold.

[0062] A third aspect of the present invention discloses another 3D printing platform leveling system based on multi-point sound analysis, the system comprising:

[0063] a memory storing executable program code;

[0064] a processor coupled to the memory;

[0065] 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 multi-point sound analysis disclosed in the first aspect of the present invention.

[0066] 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 multi-point sound analysis disclosed in the first aspect of the present invention.

[0067] Compared with the prior art, the embodiments of the present invention have the following beneficial effects:

[0068] The present invention obtains sound data from multiple platform positions of a 3D printing platform and determines the overall tilt risk. When the risk exceeds a preset threshold, dangerous platform positions are screened out, and leveling mechanism control instructions are generated through a prediction algorithm based on their sound data. This enables accurate tilt risk assessment and leveling control based on sound analysis, improves the stability and printing accuracy of the 3D printing platform, and reduces the risk of printing failure caused by tilt. BRIEF DESCRIPTION OF THE DRAWINGS

[0069] 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.

[0070] Figure 1 The present invention discloses a flow chart of a 3D printing platform leveling method based on multi-point sound analysis.

[0071] Figure 2 This is a structural schematic diagram of a 3D printing platform leveling system based on multi-point sound analysis disclosed in an embodiment of the present invention.

[0072] Figure 3 This is a structural diagram of another 3D printing platform leveling system based on multi-point sound analysis disclosed in an embodiment of the present invention. DETAILED DESCRIPTION

[0073] 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.

[0074] 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.

[0075] 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.

[0076] The present invention discloses a 3D printing platform leveling method and system based on multi-point sound analysis. This method acquires sound data from multiple platform locations on a 3D printing platform and determines the overall tilt risk. When the risk exceeds a preset threshold, dangerous platform locations are screened out. Based on the sound data, a predictive algorithm is used to generate control instructions for the leveling mechanism. This method enables precise tilt risk assessment and leveling control based on sound analysis, improving the stability and printing accuracy of the 3D printing platform and reducing the risk of printing failure caused by tilt. These are described in detail below.

[0077] Example 1

[0078] See also Figure 1 , Figure 1 This is a flow chart of a 3D printing platform leveling method based on multi-point sound analysis disclosed in an embodiment of the present invention. Figure 1 The described 3D printing platform leveling method based on multi-point sound analysis 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 multi-point sound analysis may include the following operations:

[0079] 101. Acquire sound data corresponding to multiple platform positions of a 3D printing platform.

[0080] 102. Determine the overall tilt risk of the 3D printing platform based on sound data.

[0081] 103. When the overall tilt risk is greater than a preset risk threshold, multiple dangerous platform locations are screened out based on the sound data.

[0082] 104. Determine the leveling control instructions corresponding to the leveling mechanism of the 3D printing platform based on the sound data of the dangerous platform position and the prediction algorithm.

[0083] Specifically, the sound data is sound data acquired during the operation of the 3D printing platform.

[0084] Optionally, the leveling control instruction is used to control the leveling mechanism to level the 3D printing platform.

[0085] Optionally, the sound data may include vibration sound wave data, mechanical operation sound data and / or environmental noise data, which is not limited in the present invention.

[0086] Optionally, the platform position may be a coordinate point of a base area, a printing area, a working component area, or a shell area, which is not limited in the present invention.

[0087] Optionally, the sound data may be acquired based on a microphone array, an acoustic sensor, or an ultrasonic sensor, which is not limited in the present invention.

[0088] It can be seen that the above-mentioned embodiment of the invention obtains sound data of multiple platform positions of the 3D printing platform and determines the overall tilt risk. When the risk exceeds a preset threshold, the dangerous platform position is screened out, and the leveling mechanism control instructions are generated through a prediction algorithm based on its sound data, thereby realizing accurate tilt risk assessment and leveling control based on sound analysis, improving the stability and printing accuracy of the 3D printing platform, and reducing the risk of printing failure caused by tilt.

[0089] As an optional embodiment, in the above steps, the platform position is the corner position of the base of the 3D printing platform, the center position of the base, the position in the printing area, the discharge head position or the position outside the shell.

[0090] It can be seen that through the above optional embodiments, the position type of the platform position is defined to comprehensively characterize the position characteristics of the printing platform when working, assist in realizing accurate tilt risk assessment and leveling control based on sound analysis, improve the stability and printing accuracy of the 3D printing platform, and reduce the risk of printing failure caused by tilt.

[0091] As an optional embodiment, in the above step, determining the overall tilt risk of the 3D printing platform based on the sound data includes:

[0092] Based on the abnormal data screening algorithm, multiple abnormal sound data are screened out from all sound data;

[0093] Input each abnormal sound data and the corresponding platform location into a trained risk identification model to obtain the predicted risk corresponding to each abnormal sound data; optionally, the risk identification model is trained using a training dataset including training sound data of multiple platform locations and corresponding risk annotations;

[0094] The overall tilt risk of the 3D printing platform is determined based on the predicted risks corresponding to all abnormal sound data.

[0095] Optionally, the abnormal data screening algorithm may be a statistical anomaly detection algorithm, a machine learning classification algorithm, or a time series analysis algorithm, which is not limited in the present invention.

[0096] Optionally, the abnormal sound data may be frequency abnormality data, amplitude abnormality data or waveform abnormality data, which is not limited in the present invention.

[0097] Optionally, the screening process may be optimized in combination with the ambient noise level or the equipment operating status, which is not limited in the present invention.

[0098] Optionally, the risk identification model may be a convolutional neural network, a recurrent neural network, or a support vector machine model, which is not limited in the present invention.

[0099] Optionally, the predicted risk may be a risk probability, a risk level, or a risk score, which is not limited in the present invention.

[0100] Optionally, the training data set may include historical sound data, simulation data, or real-time collected data, which is not limited in the present invention.

[0101] It can be seen that through the above optional embodiments, abnormal sound data is screened out from the sound data based on the abnormal data screening algorithm, each abnormal sound data and the corresponding platform position are input into the trained risk identification model to predict the risk, and the overall tilt risk of the 3D printing platform is determined based on all predicted risks, thereby realizing accurate tilt risk assessment based on abnormal sound and position, improving the accuracy of platform stability monitoring and printing reliability, and reducing the risk of tilt failure.

[0102] As an optional embodiment, in the above step, based on the abnormal data screening algorithm, a plurality of abnormal sound data are screened out from all the sound data, including:

[0103] Based on the wavelet transform algorithm, the frequency component data corresponding to each sound data is calculated;

[0104] For the sound data corresponding to any two platform positions belonging to the same platform area, calculating the spectral similarity between the frequency component data of the two sound data; optionally, the platform area is the base area, printing area, working part area or shell area of ​​the 3D printing platform;

[0105] For each sound data, calculate the average value of all spectrum similarities corresponding to the sound data to obtain the abnormality parameter corresponding to the sound data;

[0106] All sound data having an abnormal parameter greater than a first parameter threshold are screened out to obtain a plurality of abnormal sound data.

[0107] Optionally, the wavelet transform algorithm may be a discrete wavelet transform, a continuous wavelet transform or a wavelet packet decomposition algorithm, which is not limited in the present invention.

[0108] Optionally, the frequency component data may be a spectrum distribution, a frequency peak, or a frequency energy characteristic, which is not limited in the present invention.

[0109] Optionally, the calculation process may be optimized in combination with the sampling rate or the signal length, which is not limited in the present invention.

[0110] Optionally, the spectrum similarity may be calculated based on cosine similarity, Euclidean distance, or correlation coefficient, which is not limited in the present invention.

[0111] Optionally, the platform area may be divided according to function, geometry or material properties, which is not limited in the present invention.

[0112] Optionally, the calculation of the spectrum similarity may be combined with frequency range or signal strength for weighted processing, which is not limited in the present invention.

[0113] Optionally, the first parameter threshold may be a fixed threshold, a dynamic threshold, or a threshold adjusted based on a scenario, which is not limited in the present invention.

[0114] It can be seen that through the above optional embodiments, by calculating the frequency component data of each sound data based on the wavelet transform algorithm, the spectral similarity is calculated for the frequency component data of the sound data at any two platform positions in the same platform area, the average value of all spectral similarities of each sound data is calculated as the abnormal parameter, and the sound data with abnormal parameters exceeding the threshold value is screened out as abnormal sound data, thereby realizing accurate abnormal sound recognition based on spectral analysis and regional division, improving the accuracy and stability of the 3D printing platform tilt risk assessment, and reducing the risk of printing failure.

[0115] As an optional embodiment, in the above step, determining the overall tilt risk of the 3D printing platform based on the predicted risks corresponding to all abnormal sound data includes:

[0116] Calculate the weighted sum average of the predicted risks corresponding to all abnormal sound data to obtain the overall tilt risk of the 3D printing platform; optionally, the calculated weight corresponding to each predicted risk is the product of a first weight and a second weight; the first weight is proportional to the difference between the predicted risk and the average risk value; the average risk value is the average of all predicted risks; the second weight is proportional to the abnormal parameter corresponding to the abnormal sound data corresponding to the predicted risk.

[0117] Optionally, the calculation of the first weight and the second weight may be based on normalization processing or nonlinear mapping, which is not limited in the present invention.

[0118] It can be seen that through the above optional embodiments, the weighted sum average of the predicted risks corresponding to all abnormal sound data is calculated as the overall tilt risk of the 3D printing platform, where the weight is limited to be proportional to the difference between the predicted risk and the average risk value and proportional to the abnormal parameters of the abnormal sound data, thereby achieving accurate tilt risk assessment based on risk differences and abnormality levels, improving the accuracy of 3D printing platform stability monitoring and printing reliability, and reducing the risk of tilt failure.

[0119] As an optional embodiment, in the above step, screening out multiple dangerous platform locations based on the sound data includes:

[0120] Determine the platform position corresponding to each abnormal sound data as a candidate platform position;

[0121] The candidate platform location with the highest predicted risk is determined as the reference location;

[0122] For each candidate platform position other than the reference position, calculating the position distance between the candidate platform position and the reference position;

[0123] Calculate the correction weight that is inversely proportional to the position distance;

[0124] Calculate the product of the correction weight and the predicted risk corresponding to the candidate platform position to obtain the corrected risk corresponding to the candidate platform position;

[0125] All candidate platform positions with corrected risks greater than a preset first risk threshold and the reference position are determined as a plurality of dangerous platform positions.

[0126] It can be seen that through the above optional embodiments, by determining the platform position corresponding to each abnormal sound data as a candidate platform position and selecting the one with the highest predicted risk as the reference position, calculating the distance between other candidate platform positions and the reference position and determining the correction weight inversely proportional to the distance accordingly, calculating the product of the correction weight and the predicted risk to obtain the corrected risk, screening the candidate platform positions and reference positions whose corrected risks exceed the threshold as dangerous platform positions, thereby realizing accurate dangerous position screening based on risk and position distance, improving the accuracy of 3D printing platform tilt risk assessment and leveling control efficiency, and reducing the risk of printing failure.

[0127] 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 sound data of the dangerous platform position and the prediction algorithm includes:

[0128] Cluster all dangerous platform locations based on predicted risk to obtain a location set;

[0129] For each height control component of the leveling mechanism of the 3D printing platform, calculating an average value of the distance between the position of the height control component and each dangerous platform position in the position set to obtain a control necessity parameter of the height control component;

[0130] Determine whether the control necessity parameter is greater than a preset second parameter threshold. If not, ignore the control requirement of the height control component. If so, generate a leveling control instruction corresponding to the height control component including a height change parameter; the height change parameter is proportional to the control necessity parameter.

[0131] Optionally, the clustering may be based on K-means clustering, hierarchical clustering or density clustering algorithms, which is not limited in the present invention.

[0132] Optionally, the location set may include one or more spatially related location subsets, which is not limited in the present invention.

[0133] Optionally, the clustering process may be optimized in combination with risk distribution or location distance, which is not limited in the present invention.

[0134] Optionally, the height control component may be a servo motor, a hydraulic regulator or a mechanical screw, which is not limited in the present invention.

[0135] It can be seen that through the above optional embodiments, by clustering the dangerous platform positions based on the predicted risks to form a position set, the average value of the distance between each height control component of the 3D printing platform leveling mechanism and each dangerous platform position in the position set is calculated as the control necessity parameter, and it is determined whether it exceeds the second threshold. If it exceeds the threshold, a leveling control instruction containing a height change parameter proportional to the control necessity parameter is generated, otherwise the control demand is ignored, thereby realizing precise leveling control optimization based on risk clustering and distance analysis, improving the stability and printing accuracy of the 3D printing platform, and reducing the risk of printing failure caused by tilt.

[0136] As an optional embodiment, in the above steps, the predicted risk corresponding to each dangerous platform position in the position set is greater than a preset second risk threshold; the position distance between any two dangerous platform positions in the position set is less than the preset distance threshold.

[0137] Optionally, the second risk threshold may be a fixed threshold, a dynamic threshold, or a threshold adjusted based on a scenario, which is not limited in the present invention.

[0138] Optionally, the distance threshold may be a value set based on platform size, sensor accuracy, or mission requirements, which is not limited in the present invention.

[0139] It can be seen that through the above optional embodiments, the position risk limit and distance limit in the position set are defined, so that closer and riskier positions are included in the height control range, assisting in realizing accurate tilt risk assessment and leveling control based on sound analysis, improving the stability and printing accuracy of the 3D printing platform, and reducing the risk of printing failure caused by tilt.

[0140] Example 2

[0141] See also Figure 2 , Figure 2 This is a schematic diagram of the structure of a 3D printing platform leveling system based on multi-point sound analysis disclosed in an embodiment of the present invention. Figure 2 The 3D printing platform leveling system based on multi-point sound analysis described above 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 multi-point sound analysis may include:

[0142] The acquisition module 201 is used to acquire sound data corresponding to multiple platform positions of the 3D printing platform.

[0143] The determination module 202 is configured to determine the overall tilt risk of the 3D printing platform based on the sound data.

[0144] The screening module 203 is configured to screen out multiple dangerous platform positions based on the sound data when the overall tilt risk is greater than a preset risk threshold.

[0145] The control module 204 is configured to determine, based on the sound data of the dangerous platform position and a prediction algorithm, a leveling control instruction corresponding to the leveling mechanism of the 3D printing platform.

[0146] Optionally, the leveling control instruction is used to control the leveling mechanism to level the 3D printing platform.

[0147] It can be seen that the above-mentioned embodiment of the invention obtains sound data of multiple platform positions of the 3D printing platform and determines the overall tilt risk. When the risk exceeds a preset threshold, the dangerous platform position is screened out, and the leveling mechanism control instructions are generated through a prediction algorithm based on its sound data, thereby realizing accurate tilt risk assessment and leveling control based on sound analysis, improving the stability and printing accuracy of the 3D printing platform, and reducing the risk of printing failure caused by tilt.

[0148] As an optional embodiment, the platform position is a corner position of the base of the 3D printing platform, a center position of the base, a position within the printing area, a discharge head position, or a position outside the shell.

[0149] It can be seen that through the above optional embodiments, the position type of the platform position is defined to comprehensively characterize the position characteristics of the printing platform when working, assist in realizing accurate tilt risk assessment and leveling control based on sound analysis, improve the stability and printing accuracy of the 3D printing platform, and reduce the risk of printing failure caused by tilt.

[0150] As an optional embodiment, the specific manner in which the determination module determines the overall tilt risk of the 3D printing platform based on the sound data includes:

[0151] Based on the abnormal data screening algorithm, multiple abnormal sound data are screened out from all sound data;

[0152] Input each abnormal sound data and the corresponding platform location into a trained risk identification model to obtain the predicted risk corresponding to each abnormal sound data; optionally, the risk identification model is trained using a training dataset including training sound data of multiple platform locations and corresponding risk annotations;

[0153] The overall tilt risk of the 3D printing platform is determined based on the predicted risks corresponding to all abnormal sound data.

[0154] It can be seen that through the above optional embodiments, abnormal sound data is screened out from the sound data based on the abnormal data screening algorithm, each abnormal sound data and the corresponding platform position are input into the trained risk identification model to predict the risk, and the overall tilt risk of the 3D printing platform is determined based on all predicted risks, thereby realizing accurate tilt risk assessment based on abnormal sound and position, improving the accuracy of platform stability monitoring and printing reliability, and reducing the risk of tilt failure.

[0155] As an optional embodiment, the specific manner in which the determination module selects a plurality of abnormal sound data from all sound data based on the abnormal data screening algorithm includes:

[0156] Based on the wavelet transform algorithm, the frequency component data corresponding to each sound data is calculated;

[0157] For the sound data corresponding to any two platform positions belonging to the same platform area, calculating the spectral similarity between the frequency component data of the two sound data; optionally, the platform area is the base area, printing area, working part area or shell area of ​​the 3D printing platform;

[0158] For each sound data, calculate the average value of all spectrum similarities corresponding to the sound data to obtain the abnormality parameter corresponding to the sound data;

[0159] All sound data having an abnormal parameter greater than a first parameter threshold are screened out to obtain a plurality of abnormal sound data.

[0160] It can be seen that through the above optional embodiments, by calculating the frequency component data of each sound data based on the wavelet transform algorithm, the spectral similarity is calculated for the frequency component data of the sound data at any two platform positions in the same platform area, the average value of all spectral similarities of each sound data is calculated as the abnormal parameter, and the sound data with abnormal parameters exceeding the threshold value is screened out as abnormal sound data, thereby realizing accurate abnormal sound recognition based on spectral analysis and regional division, improving the accuracy and stability of the 3D printing platform tilt risk assessment, and reducing the risk of printing failure.

[0161] As an optional embodiment, the specific method in which the determination module determines the overall tilt risk of the 3D printing platform based on the predicted risks corresponding to all abnormal sound data includes:

[0162] Calculate the weighted sum average of the predicted risks corresponding to all abnormal sound data to obtain the overall tilt risk of the 3D printing platform; optionally, the calculated weight corresponding to each predicted risk is the product of a first weight and a second weight; the first weight is proportional to the difference between the predicted risk and the average risk value; the average risk value is the average of all predicted risks; the second weight is proportional to the abnormal parameter corresponding to the abnormal sound data corresponding to the predicted risk.

[0163] It can be seen that through the above optional embodiments, the weighted sum average of the predicted risks corresponding to all abnormal sound data is calculated as the overall tilt risk of the 3D printing platform, where the weight is limited to be proportional to the difference between the predicted risk and the average risk value and proportional to the abnormal parameters of the abnormal sound data, thereby achieving accurate tilt risk assessment based on risk differences and abnormality levels, improving the accuracy of 3D printing platform stability monitoring and printing reliability, and reducing the risk of tilt failure.

[0164] As an optional embodiment, the specific method of the screening module screening out multiple dangerous platform locations based on the sound data includes:

[0165] Determine the platform position corresponding to each abnormal sound data as a candidate platform position;

[0166] The candidate platform location with the highest predicted risk is determined as the reference location;

[0167] For each candidate platform position other than the reference position, calculating the position distance between the candidate platform position and the reference position;

[0168] Calculate the correction weight that is inversely proportional to the position distance;

[0169] Calculate the product of the correction weight and the predicted risk corresponding to the candidate platform position to obtain the corrected risk corresponding to the candidate platform position;

[0170] All candidate platform positions with corrected risks greater than a preset first risk threshold and the reference position are determined as a plurality of dangerous platform positions.

[0171] It can be seen that through the above optional embodiments, by determining the platform position corresponding to each abnormal sound data as a candidate platform position and selecting the one with the highest predicted risk as the reference position, calculating the distance between other candidate platform positions and the reference position and determining the correction weight inversely proportional to the distance accordingly, calculating the product of the correction weight and the predicted risk to obtain the corrected risk, screening the candidate platform positions and reference positions whose corrected risks exceed the threshold as dangerous platform positions, thereby realizing accurate dangerous position screening based on risk and position distance, improving the accuracy of 3D printing platform tilt risk assessment and leveling control efficiency, and reducing the risk of printing failure.

[0172] 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 based on the sound data of the dangerous platform position and the prediction algorithm, including:

[0173] Cluster all dangerous platform locations based on predicted risk to obtain a location set;

[0174] For each height control component of the leveling mechanism of the 3D printing platform, calculating an average value of the distance between the position of the height control component and each dangerous platform position in the position set to obtain a control necessity parameter of the height control component;

[0175] Determine whether the control necessity parameter is greater than a preset second parameter threshold. If not, ignore the control requirement of the height control component. If so, generate a leveling control instruction corresponding to the height control component including a height change parameter; the height change parameter is proportional to the control necessity parameter.

[0176] It can be seen that through the above optional embodiments, by clustering the dangerous platform positions based on the predicted risks to form a position set, the average value of the distance between each height control component of the 3D printing platform leveling mechanism and each dangerous platform position in the position set is calculated as the control necessity parameter, and it is determined whether it exceeds the second threshold. If it exceeds the threshold, a leveling control instruction containing a height change parameter proportional to the control necessity parameter is generated, otherwise the control demand is ignored, thereby realizing precise leveling control optimization based on risk clustering and distance analysis, improving the stability and printing accuracy of the 3D printing platform, and reducing the risk of printing failure caused by tilt.

[0177] As an optional embodiment, the predicted risk corresponding to each dangerous platform position in the position set is greater than a preset second risk threshold; and the position distance between any two dangerous platform positions in the position set is less than a preset distance threshold.

[0178] It can be seen that through the above optional embodiments, the position risk limit and distance limit in the position set are defined, so that closer and riskier positions are included in the height control range, assisting in realizing accurate tilt risk assessment and leveling control based on sound analysis, improving the stability and printing accuracy of the 3D printing platform, and reducing the risk of printing failure caused by tilt.

[0179] Example 3

[0180] See also Figure 3 , Figure 3 This is another 3D printing platform leveling system based on multi-point sound analysis disclosed in an embodiment of the present invention. Figure 3 The described 3D printing platform leveling system based on multi-point sound analysis 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 multi-point sound analysis may include:

[0181] A memory 301 storing executable program code;

[0182] a processor 302 coupled to the memory 301;

[0183] 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 multi-point sound analysis described in the first embodiment.

[0184] Example 4

[0185] 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 multi-point sound analysis described in the first embodiment.

[0186] Example 5

[0187] 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 cause a computer to execute the steps of the 3D printing platform leveling method based on multi-point sound analysis described in Example 1.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] In a typical configuration, a computing device includes one or more processors (CPUs), input / output interfaces, network interfaces, and memory.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] 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.

[0201] Finally, it should be noted that the 3D printing platform leveling method and system based on multi-point sound analysis disclosed in the embodiments of the present invention are only preferred embodiments of the present invention, and are only used to illustrate the technical solutions of the present invention, rather than to limit them. Although the present invention has been described in detail with reference to the aforementioned embodiments, it should be understood by those skilled in the art that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features thereof may be replaced by 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 multi-point sound analysis, characterized in that: The method comprises: Acquire sound data corresponding to multiple platform positions of the 3D printing platform; Determining an overall tilt risk of the 3D printing platform based on the sound data includes: Based on an abnormal data screening algorithm, screening out a plurality of abnormal sound data from all the sound data; Inputting each abnormal sound data and the corresponding platform location into a trained risk identification model to obtain a predicted risk corresponding to each abnormal sound data; the risk identification model is trained using a training data set including training sound data of multiple platform locations and corresponding risk annotations; Determining an overall tilt risk of the 3D printing platform based on the predicted risks corresponding to all of the abnormal sound data; When the overall tilt risk is greater than a preset risk threshold, screening out multiple dangerous platform positions based on the sound data; According to the sound data at the dangerous platform position, based on a prediction algorithm, 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.

2. The 3D printing platform leveling method based on multi-point sound analysis according to claim 1, characterized in that: The platform position is a corner position of the base of the 3D printing platform, a center position of the base, a position within the printing area, a discharge head position or a position outside the shell.

3. The 3D printing platform leveling method based on multi-point sound analysis according to claim 1, characterized in that: The method of screening out a plurality of abnormal sound data from all the sound data based on the abnormal data screening algorithm includes: Calculating frequency component data corresponding to each of the sound data based on a wavelet transform algorithm; For the sound data corresponding to any two platform positions belonging to the same platform area, calculating the spectral similarity between the frequency component data of the two sound data; the platform area is the base area, printing area, working part area or shell area of ​​the 3D printing platform; For each piece of sound data, calculating the average value of all the spectrum similarities corresponding to the sound data to obtain the abnormality parameter corresponding to the sound data; All the sound data whose abnormal parameters are greater than a first parameter threshold are screened out to obtain a plurality of abnormal sound data.

4. The 3D printing platform leveling method based on multi-point sound analysis according to claim 3, characterized in that: Determining the overall tilt risk of the 3D printing platform based on the predicted risks corresponding to all the abnormal sound data includes: Calculate the weighted sum average of the predicted risks corresponding to all the abnormal sound data to obtain the overall tilt risk of the 3D printing platform; wherein the calculated weight corresponding to each predicted risk is the product of a first weight and a second weight; the first weight is proportional to the difference between the predicted risk and the average risk value; the average risk value is the average of all the predicted risks; and the second weight is proportional to the abnormal parameter corresponding to the abnormal sound data corresponding to the predicted risk.

5. The 3D printing platform leveling method based on multi-point sound analysis according to claim 1, characterized in that: The step of screening out multiple dangerous platform locations based on the sound data includes: determining a platform position corresponding to each abnormal sound data as a candidate platform position; Determining the candidate platform position with the highest predicted risk as a reference position; For each candidate platform position except the reference position, calculating a position distance between the candidate platform position and the reference position; calculating a correction weight inversely proportional to the distance from the position; Calculating the product of the correction weight and the predicted risk corresponding to the candidate platform position to obtain the corrected risk corresponding to the candidate platform position; All the candidate platform positions whose corrected risks are greater than a preset first risk threshold and the reference position are determined as a plurality of dangerous platform positions.

6. The 3D printing platform leveling method based on multi-point sound analysis according to claim 1, characterized in that: The step of determining, based on a prediction algorithm and according to the sound data of the dangerous platform position, a leveling control instruction corresponding to the leveling mechanism of the 3D printing platform comprises: Clustering all of the dangerous platform locations based on the predicted risk to obtain a location set; For each height control component of the leveling mechanism of the 3D printing platform, calculating an average value of the distance between the position of the height control component and each of the dangerous platform positions in the position set to obtain a control necessity parameter of the height control component; Determine whether the control necessity parameter is greater than a preset second parameter threshold. If not, ignore the control requirement of the height control component. If so, generate a leveling control instruction corresponding to the height control component including a height change parameter; the height change parameter is proportional to the control necessity parameter.

7. The 3D printing platform leveling method based on multi-point sound analysis according to claim 6, characterized in that: The predicted risk corresponding to each of the dangerous platform positions in the position set is greater than a preset second risk threshold; and the position distance between any two of the dangerous platform positions in the position set is less than a preset distance threshold.

8. A 3D printing platform leveling system based on multi-point sound analysis, characterized in that: The system comprises: An acquisition module, used to acquire sound data corresponding to multiple platform positions of the 3D printing platform; A determination module, configured to determine an overall tilt risk of the 3D printing platform based on the sound data, comprising: Based on an abnormal data screening algorithm, screening out a plurality of abnormal sound data from all the sound data; Inputting each abnormal sound data and the corresponding platform location into a trained risk identification model to obtain a predicted risk corresponding to each abnormal sound data; the risk identification model is trained using a training data set including training sound data of multiple platform locations and corresponding risk annotations; Determining an overall tilt risk of the 3D printing platform based on the predicted risks corresponding to all of the abnormal sound data; A screening module, configured to screen out a plurality of dangerous platform positions according to the sound data when the overall tilt risk is greater than a preset risk threshold; A control module is configured to determine, based on a prediction algorithm and according to the sound data at the dangerous platform position, a leveling control instruction corresponding to a leveling mechanism of the 3D printing platform; the leveling control instruction is configured to control the leveling mechanism to level the 3D printing platform.

9. A 3D printing platform leveling system based on multi-point sound analysis, 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 multi-point sound analysis according to any one of claims 1 to 7.

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