3D Printer Control Method Using Cloud Data Processing

Abstract

The present invention relates to the technical field of 3D printers, and discloses a 3D printer control method applying cloud data processing, including the following steps: First, configure the light source lamp beads of the printer locally as lamp beads with independently controllable light intensity; connect and interact the printer with the cloud server; determine the optimized printing parameters corresponding to the optimal printing effect parameters of the target specific product through the correlation model between the printing parameters and the printing effect parameters; the cloud server optimizes the correlation model between the printing parameters and the printing effect parameters based on the updated data and provides the optimized printing parameters of the specific product for a new user when printing the same specific product through the correlation model between the optimized printing parameters and the printing effect parameters.

Description

Technical Field

[0001] The present invention belongs to the technical field of 3D printers, and in particular, relates to a 3D printer control method using cloud data processing. Background Art

[0002] In a 3D printer using photocuring, the control of the light source is very important. For example, it is necessary to control the light source to adjust the uniformity of the light on the printing projection plane, and adjust the light intensity in different areas as much as possible to make the light uniform enough, so as to obtain a printing effect with good precision and high efficiency. In the prior art, the control of the lamp beads of the light source is usually all integrated control; the lamp beads cannot be adjusted separately, and when the operator prints a specific product, the specific product will also have different degrees of impact on the projection surface during the printing process. Because the printed product is personalized, the light impact caused by the personalized rules during the printing process is also uncertain; then this impact is also personalized, so it is generally difficult to achieve a relatively perfect printing effect in the prior art; that is, when printing a specific product; whether the prior art simply adjusts the light source or compensates for the light intensity, the effect is not very good. The problem of light intensity itself or the personalized characteristics of the specific product may cause the model to break, the model to have obvious layer lines, the surface to soften, etc., or cause the model to expand greatly, which also increases the printing time. Summary of the invention

[0003] In order to solve the above technical problems, the basic concept of the technical solution adopted by the present invention is:

[0004] A 3D printer control method using cloud data processing includes the following steps:

[0005] S1 first configures the local light source lamp beads of the printer to be lamp beads with independently controllable light intensity;

[0006] S2 connects and interacts all printers with the cloud server;

[0007] After the local user in S3 sets the printing parameters and determines the specific product to be printed, the local user in S3 exchanges the information of “the local user sets the printing parameters and determines the specific product to be printed” to the cloud server;

[0008] S4 After each printing is completed, the local user exchanges the printing effect parameter information of the specific target product with the cloud server;

[0009] The S5 cloud server establishes a correlation model between the printing parameters and the printing effect parameters based on the printing data of the same specific product. The printing data of the same specific product refers to the printing parameters and printing effect parameters of the same specific product uploaded by different users. Through the correlation model between the printing parameters and the printing effect parameters, the optimized printing parameters corresponding to the optimal printing effect parameters of the target specific product can be determined.

[0010] The S6 cloud server optimizes the correlation model between the printing parameters and the printing effect parameters based on the updated data, and provides the optimized printing parameters of the specific product for new users to print the same specific product through the optimized correlation model between the printing parameters and the printing effect parameters.

[0011] Furthermore, configure the light source lamp beads of the 3D printer locally as lamp beads with independently controllable light intensity. Specifically, use a single-chip microcomputer or the local controller of the 3D printer to control multiple lamp beads and independently adjust the light intensity respectively. The specific steps are as follows: Select a single-chip microcomputer with a large number of I / O ports to ensure that the number of I / O ports of the single-chip microcomputer or the local controller of the 3D printer can connect all the lamps to be controlled; Install an LED driver or an LED controller for each lamp; These drivers or controllers can convert the signals output by the single-chip microcomputer or the local controller of the 3D printer into the current and voltage required by the LED; Use the I / O ports of the single-chip microcomputer or the local controller of the 3D printer to control each LED driver or LED controller; Through programming, connect each I / O port to the corresponding driver or controller, and use the signals output by the single-chip microcomputer or the local controller of the 3D printer to control the on / off state of the LED; To adjust the light intensity, a PWM dimmer can be installed for each LED; The PWM dimmer can control the light intensity by adjusting the duty cycle of the LED current; Connect the control signals of each PWM dimmer to different I / O ports of the single-chip microcomputer or the local controller of the 3D printer; Through programming, the duty cycle of each PWM dimmer can be independently adjusted to control the light intensity of each LED; A timer or a counter can be used to generate the PWM waveform; Through programming, connect the output of the timer or counter to the corresponding PWM dimmer, and use the signals output by the single-chip microcomputer or the local controller of the 3D printer to control the duty cycle of the PWM waveform; Write a control program; Write a program using the programming language of the single-chip microcomputer or the local controller of the 3D printer to control the on / off state and light intensity of each LED.

[0012] Furthermore, the steps for connecting and interacting the 3D printer with the cloud server are as follows:

[0013] Determine the connection method: First, it is necessary to determine the connection method between the 3D printer and the cloud server; Some 3D printers can be connected to the cloud server through Wi-Fi or a wired network, while other printers may require the use of dedicated hardware or adapters.

[0014] Configure a 3D printer: Configure and set up, set up the network connection, configure the printer driver, and set the communication protocol with the cloud server; Create a cloud account: Create an account on the cloud service provider's website and ensure that the cloud server instance has been enabled; Connect the 3D printer: Use the options provided by the cloud server software or SDK to connect the 3D printer to the cloud server; The IP address and port number of the printer are required.

[0015] Furthermore, the printing parameters include the light intensity set for each LED bead when curing each layer by light curing, and the printing effect parameters include whether there is model fracture, whether there are obvious layer lines in the model, whether the surface is soft, whether the model expands, and the printing time.

[0016] Furthermore, establishing the correlation model between the printing parameters and the printing effect parameters specifically includes: Quantifying the printing parameters and the printing effect parameters into vectors respectively by using the printing data, and establishing the correlation model between the printing parameters and the printing effect parameters through the quantified vectors; It also includes establishing the correlation model between the printing parameters and the printing effect parameters through the decision tree model or the random forest model.

[0017] Furthermore, establishing the correlation model between the printing parameters and the printing effect parameters through the quantified vectors includes: Calculating the Euclidean distance between the quantified vector of the printing parameters and the quantified vector of the printing effect parameters to establish the correlation: This model is used to measure the straight-line distance between two vectors; It assumes that the closer the distance between two vectors, the higher their similarity; Calculating the cosine similarity between the quantified vector of the printing parameters and the quantified vector of the printing effect parameters to establish the correlation: This model is used to measure the angle between two vectors; It assumes that the smaller the angle between two vectors, the higher their similarity; Calculating the Pearson correlation coefficient between the quantified vector of the printing parameters and the quantified vector of the printing effect parameters to establish the correlation: This model is used to measure the linear correlation degree between two vectors; Its value ranges from -1 to 1, and the larger the value, the higher the correlation; Calculating the Spearman rank correlation coefficient between the quantified vector of the printing parameters and the quantified vector of the printing effect parameters to establish the correlation: This model is used to measure the monotonic correlation degree between two vectors; Its value ranges from -1 to 1, and the larger the value, the higher the correlation; Calculating the Manhattan distance between the quantified vector of the printing parameters and the quantified vector of the printing effect parameters to establish the correlation: This model is used to measure the absolute distance between two vectors; It assumes that the closer the distance between two vectors, the higher their similarity. The present invention has the following beneficial effects compared with the prior art:

[0018] This application enables each printing user to control each light bead during the printing process layer by layer, along with the corresponding printing efficiency and printing effect after printing, and upload them to the cloud server. After the cloud server learns and optimizes this data, during subsequent 3D printing processes on other user terminals, the server provides this data to the users. The users directly load this data onto the local printer to control the changes of different light beads, and then complete the entire printing process. After printing, the data is still transmitted to the cloud server. Through this sharing method, the exchange and optimization of printing parameters between different printing users are indirectly achieved, and the cloud server can continuously learn and optimize the corresponding parameters, and then help other subsequent printing users based on this. In this way, the printing effect and efficiency are improved, and this technology perfectly solves the problems of the personalization of printed products and unpredictable printing. Because in the existing control of light sources, there are many unpredictable problems due to personalization. And this application can perfectly solve this problem even without understanding the problem mechanism, just through the data interaction method of the cloud server, and then achieve the improvement of printing quality and printing efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 It is a flowchart of the method of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0020] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. The following embodiments are used to illustrate the present invention.

[0021] To solve the problem of poor printing effect in the prior art, this application applies a 3D printer control method using cloud data processing, as Figure 1 shown:

[0022] It includes the steps: S1 First, configure the light source light beads of the printer locally as light beads whose light intensity can be independently controlled;

[0023] In S1, for example, specifically using a single-chip microcomputer or a local printer controller to control multiple light beads and independently adjust the light intensity respectively, the following steps can be adopted:

[0024] 1. Select a single-chip microcomputer with a large number of IO ports to ensure that the number of IO ports of the single-chip microcomputer or the local printer controller can connect all the lights to be controlled;

[0025] 2. Install an LED driver or an LED controller for each light; these drivers or controllers can convert the signals output by the single-chip microcomputer or the local printer controller into the current and voltage required by the LED;

[0026] 3. Control each LED driver or LED controller using the I / O ports of a microcontroller or the local controller of a printer; through programming, connect each I / O port to the corresponding driver or controller, and use the signals output by the microcontroller or the local controller of the printer to control the on / off state of the LEDs.

[0027] 4. To adjust the light intensity, a PWM (pulse width modulation) dimmer can be installed for each LED; the PWM dimmer can control the light intensity by adjusting the duty cycle of the LED current.

[0028] 5. Connect the control signals of each PWM dimmer to different I / O ports of the microcontroller or the local controller of the printer; through programming, the duty cycle of each PWM dimmer can be adjusted independently, thereby controlling the light intensity of each LED.

[0029] 6. A timer or counter can be used to generate the PWM waveform; through programming, connect the output of the timer or counter to the corresponding PWM dimmer, and use the signals output by the microcontroller or the local controller of the printer to control the duty cycle of the PWM waveform.

[0030] 7. Write a control program; write a program using the programming language of the microcontroller or the local controller of the printer to control the on / off state and light intensity of each LED.

[0031] The control of a large number of LEDs can use a serial communication interface (such as I2C or SPI) to simplify the control of multiple LEDs.

[0032] S2 Connect each printer to the cloud server and interact. Steps to connect a 3D printer to the cloud server:

[0033] Determine the connection method: First, it is necessary to determine the connection method between the 3D printer and the cloud server; some 3D printers can be connected to the cloud server via Wi-Fi or a wired network, while other printers may require the use of dedicated hardware or adapters.

[0034] Configure the 3D printer: Configure and set up, set the network connection, configure the printer driver, and set the communication protocol with the cloud server.

[0035] Create a cloud account: Create an account on the cloud service provider's website and ensure that the cloud server instance has been enabled.

[0036] Connect the 3D printer: Use the options provided by the cloud server software or SDK to connect the 3D printer to the cloud server; the IP address and port number of the printer are required.

[0037] The local user in S3 sets the printing parameters. The printing parameters include the light intensity set for each LED bead during each layer of photocuring. After determining the specific product to be printed, the information of "the printing parameters set by the local user and the specific product to be printed" is interacted with the cloud server;

[0038] In S4, after each printing is completed, the local user interacts the printing effect parameter information of the target specific product with the cloud server. The printing effect parameters include whether there is model fracture, whether there are obvious layer lines in the model, whether the surface is soft, whether the model expands, and the printing time;

[0039] In S5, the cloud server establishes a correlation model between the printing parameters and the printing effect parameters based on the printing data of the same specific product. Here, the printing data of the same specific product refers to the printing parameters (including the light intensity set for each LED bead during each layer of photocuring) and the printing effect parameters (including whether there is model fracture, whether there are obvious layer lines in the model, whether the surface is soft, whether the model expands, and the printing time) uploaded by different users for the same specific product. Through the correlation model between the printing parameters and the printing effect parameters, the optimized printing parameters corresponding to the optimal printing effect parameters of the target specific product can be determined, that is, including the optimized light intensity set for each LED bead;

[0040] Specifically, establishing the correlation model between the printing parameters and the printing effect parameters specifically includes: quantifying the printing parameters and the printing effect parameters in the printing data into vectors respectively, and establishing the correlation model between the printing parameters and the printing effect parameters through the quantified vectors; it also includes establishing the correlation model between the printing parameters and the printing effect parameters through a decision tree model or a random forest model;

[0041] To process the printing parameters and the printing effect parameters into vectors, put them into a list or an array, and then use an appropriate function or method to convert them into vectors are all prior arts, which depends on the programming language and library used.

[0042] The following are some examples showing the methods of converting parameters into vectors in different programming languages:

[0043] **Python (using the NumPy library)**:

[0044] ```python

[0045] import numpy as np

[0046] # Assume you have three parameters

[0047] a = 10

[0048] b = 20

[0049] c = 30

[0050] # Put them into a list

[0051] parameters = [a, b, c]

[0052] # Use the array function of numpy to convert the list to a vector

[0053] vector = np.array(parameters)

[0054] ```

[0055] **R**:

[0056] ```R

[0057] # Assume you have three parameters

[0058] a <- -10

[0059] b <- -20

[0060] c <- -30

[0061] # Put them into a vector

[0062] parameters <- c(a, b, c)

[0063] ```

[0064] **MATLAB**:

[0065] ```matlab

[0066] % Assume you have three parameters

[0067] a = 10;

[0068] b = 20;

[0069] c = 30;

[0070] % Put them into a vector

[0071] parameters = [a, b, c];

[0072] ```

[0073] These examples show how to convert parameters to vectors.

[0074] Establishing a correlation model between the printed parameters and the printing effect parameters through the quantified vectors includes:

[0075] 1. Establish a correlation by calculating the Euclidean distance between the printed parameter quantization vector and the printing effect parameter quantization vector: Generally, this model is used to measure the straight-line distance between two vectors; it assumes that the closer the distance between two vectors, the higher their similarity;

[0076] 2. Establish a correlation by calculating the cosine similarity between the printed parameter quantization vector and the printing effect parameter quantization vector: Generally, this model is used to measure the angle between two vectors; it assumes that the smaller the angle between two vectors, the higher their similarity;

[0077] 3. Establish a correlation by calculating the Pearson correlation coefficient between the printed parameter quantization vector and the printing effect parameter quantization vector: Generally, this model is used to measure the degree of linear correlation between two vectors; its value ranges from -1 to 1, and the larger the value, the higher the correlation;

[0078] 4. Establish a correlation by calculating the Spearman rank correlation coefficient between the printed parameter quantization vector and the printing effect parameter quantization vector: Generally, this model is used to measure the degree of monotonic correlation between two vectors; its value ranges from -1 to 1, and the larger the value, the higher the correlation;

[0079] 5. Establish a correlation by calculating the Manhattan distance between the printed parameter quantization vector and the printing effect parameter quantization vector: This model is used to measure the absolute distance between two vectors; it assumes that the closer the distance between two vectors, the higher their similarity;

[0080] S6 The cloud server optimizes the correlation model of the printed parameters and the printing effect parameters based on the updated data, and provides the optimized printed parameters of the specific product for new users to print the same specific product through the optimized correlation model of the printed parameters and the printing effect parameters. The optimized printed parameters of the specific product include the optimized light intensity set for each LED bead.

[0081] It can be seen that this application supports each printing user to control each light bead during the printing process of each layer of the light beads during their printing, as well as the corresponding printing efficiency and printing effect after printing, and upload them to the cloud server; then after the cloud server learns and optimizes this data; during the subsequent 3D printing process, for other subsequent user terminals during 3D printing, the server provides this data to the user, and the user directly loads this data on the local printer to control the changes of different light beads, and then completes the entire printing process, and still transmits the data to the cloud server after printing; through this sharing method, the exchange and optimization of printing parameters between different printing users are indirectly realized, and the cloud server can continuously learn and optimize the corresponding parameters, and then help other subsequent printing users on this basis; in this way, the printing effect and efficiency are improved, and this technology perfectly solves the problems of personalization of printed products and unpredictable printing, because in the existing control of light sources, there will be many unpredictable problems in personalization; and this application can even solve this problem perfectly only through this cloud server data interaction method without understanding the problem mechanism, and then achieve the improvement of printing quality and printing efficiency.

[0082] The decision tree model involved in this application is a supervised learning algorithm, which represents the decision-making process in the form of a tree structure similar to a flowchart. Each internal node (branch node / tree node) represents a feature or attribute, and each leaf node represents a classification. The process of the decision tree making a decision starts from the root node, tests the corresponding feature attributes in the item to be classified, and selects the output branch according to its value until reaching the leaf node, and takes the category stored in the leaf node as the decision result.

[0083] The decision tree model has the following characteristics:

[0084] 1. It can process various data types, including discrete and continuous values;

[0085] 2. It is not sensitive to the missing of intermediate values;

[0086] 3. It can clearly show which fields are more important;

[0087] 4. There may be problems of overfitting.

[0088] When constructing a decision tree, it is necessary to select appropriate features for partitioning, which can be achieved through algorithms such as information gain and information gain ratio.

[0089] The random forest model involved in this application. Random forest is an ensemble learning method. It is based on the decision tree model. By randomly selecting features and data, multiple decision trees are generated, and then the results are aggregated by voting for classification or regression prediction. The basic principle of the random forest is that when building each tree, two basic principles of "data randomness" and "feature randomness" are followed. Data randomness means randomly sampling data with replacement from all data as the training data for one of the decision tree models. Feature randomness means assuming that the dimension of each sample is M, specifying a constant k < M, and randomly selecting k features from the M features.

[0090] The embodiments to be protected in this application include:

[0091] A 3D printer control method applying cloud data processing, such as Figure 1 shown as:

[0092] It includes the following steps:

[0093] S1 First, configure the light source lamp beads locally on the printer as lamp beads with independently controllable light intensity;

[0094] S2 Connect and interact the printer with the cloud server;

[0095] S3 After the local user sets the printing parameters and determines the specific product to be printed, interact the information of "the local user sets the printing parameters and determines the specific product to be printed" to the cloud server;

[0096] S4 After each printing is completed, the local user interacts the printing effect parameter information of the target specific product to the cloud server;

[0097] S5 The cloud server establishes a correlation model between the printing parameters and the printing effect parameters based on the printing data of the same specific product. The printing data of the same specific product refers to the printing parameters and printing effect parameters of the same specific product uploaded by different users; through the correlation model between the printing parameters and the printing effect parameters, the optimized printing parameters corresponding to the optimal printing effect parameters of the target specific product can be determined;

[0098] S6 The cloud server optimizes the correlation model between the printing parameters and the printing effect parameters based on the updated data and provides the optimized printing parameters of the specific product for new users to print the same specific product through the optimized correlation model between the printing parameters and the printing effect parameters.

[0099] Preferably, configure the light source lamp beads locally on the printer as lamp beads with independently controllable light intensity. Specifically, use a single-chip microcomputer or the local controller of the printer to control multiple lamp beads and independently adjust the light intensity respectively. The specific steps are:

[0100] 1. Select a microcontroller with a large number of I / O ports to ensure that the I / O ports of the microcontroller or the local controller of the printer can be connected to all the lights that need to be controlled;

[0101] 2. Install an LED driver or an LED controller for each light; these drivers or controllers can convert the signals output by the microcontroller or the local controller of the printer into the current and voltage required by the LED;

[0102] 3. Use the I / O ports of the microcontroller or the local controller of the printer to control each LED driver or LED controller; through programming, connect each I / O port to the corresponding driver or controller, and use the signals output by the microcontroller or the local controller of the printer to control the on / off state of the LED;

[0103] 4. To adjust the light intensity, a PWM dimmer can be installed for each LED; the PWM dimmer can control the light intensity by adjusting the duty cycle of the LED current;

[0104] 5. Connect the control signal of each PWM dimmer to a different I / O port of the microcontroller or the local controller of the printer; through programming, the duty cycle of each PWM dimmer can be adjusted independently, thereby controlling the light intensity of each LED;

[0105] 6. A timer or a counter can be used to generate a PWM waveform; through programming, connect the output of the timer or the counter to the corresponding PWM dimmer, and use the signals output by the microcontroller or the local controller of the printer to control the duty cycle of the PWM waveform;

[0106] 7. Write a control program; use the programming language of the microcontroller or the local controller of the printer to write a program to control the on / off state and light intensity of each LED.

[0107] Preferably, the steps for connecting and interacting the 3D printer with the cloud server are as follows:

[0108] Determine the connection method: First, it is necessary to determine the connection method between the 3D printer and the cloud server; some 3D printers can be connected to the cloud server through Wi-Fi or a wired network, while other printers may require the use of dedicated hardware or adapters;

[0109] Configure the 3D printer: Configure and set up the network connection, configure the printer driver, and set the communication protocol with the cloud server; Create a cloud account: Create an account on the cloud service provider's website and ensure that the cloud server instance has been enabled; Connect the 3D printer: Use the options provided by the cloud server software or SDK to connect the 3D printer to the cloud server; the IP address and port number of the printer are required.

[0110] Preferably, the printing parameters include the light intensity set for each light bead during the photocuring of each layer, and the printing effect parameters include whether the model breaks, whether obvious layer lines appear in the model, whether the surface is soft, whether the model expands, and the printing time.

[0111] Preferably, establishing a correlation model between the printing parameters and the printing effect parameters specifically includes: quantifying the printing parameters and the printing effect parameters into vectors respectively by using the printing data, and establishing a correlation model between the printing parameters and the printing effect parameters through the quantified vectors; it also includes establishing a correlation model between the printing parameters and the printing effect parameters through a decision tree model or a random forest model.

[0112] Preferably, establishing a correlation model between the printing parameters and the printing effect parameters through the quantified vectors includes:

[0113] 1. Calculating the Euclidean distance between the quantified vector of the printing parameters and the quantified vector of the printing effect parameters to establish the correlation: This model is used to measure the straight-line distance between two vectors; it assumes that the closer the distance between two vectors, the higher their similarity;

[0114] 2. Calculating the cosine similarity between the quantified vector of the printing parameters and the quantified vector of the printing effect parameters: This model is used to measure the angle between two vectors; it assumes that the smaller the angle between two vectors, the higher their similarity;

[0115] 3. Calculating the Pearson correlation coefficient between the quantified vector of the printing parameters and the quantified vector of the printing effect parameters: This model is used to measure the linear correlation degree between two vectors; its value ranges from -1 to 1, and the larger the value, the higher the correlation;

[0116] 4. Calculating the Spearman rank correlation coefficient between the quantified vector of the printing parameters and the quantified vector of the printing effect parameters: This model is used to measure the monotonic correlation degree between two vectors; its value ranges from -1 to 1, and the larger the value, the higher the correlation;

[0117] 5. Calculating the Manhattan distance between the quantified vector of the printing parameters and the quantified vector of the printing effect parameters: This model is used to measure the absolute distance between two vectors; it assumes that the closer the distance between two vectors, the higher their similarity.

[0118] The embodiment of the present application also provides a computer device, which may include a terminal device or a server, and the data calculation program of the 3D printer control method for applying cloud data processing described above may be configured in this computer device. The following introduces this computer device.

[0119] If the computer device is a terminal device, the embodiment of the present application provides a terminal device. Taking the terminal device as a mobile phone as an example:

[0120] The mobile phone includes components such as a Radio Frequency (RF) circuit, a memory, an input unit, a display unit, sensors, an audio circuit, a Wireless Fidelity (WiFi) module, a processor, and a power supply.

[0121] The RF circuit can be used for receiving and transmitting information or signals during a call. Specifically, after receiving the downlink information from the base station, it is sent to the processor for processing; in addition, the uplink data designed is sent to the base station. Generally, the RF circuit includes but is not limited to an antenna, at least one amplifier, a transceiver, a coupler, a Low Noise Amplifier (LNA), a duplexer, etc. In addition, the RF circuit can also communicate with the network and other devices through wireless communication. The above wireless communication can use any communication standard or protocol, including but not limited to the Global System of Mobile communication (GSM), General Packet Radio Service (GPRS), Code Division Multiple Access (CDMA), Wideband Code Division Multiple Access (WCDMA), Long Term Evolution (LTE), email, Short Messaging Service (SMS), etc.

[0122] The memory can be used to store software programs and modules. The processor executes various functional applications and data processing of the mobile phone by running the software programs and modules stored in the memory. The memory mainly includes a program storage area and a data storage area. Among them, the program storage area can store an operating system, application programs required for at least one function (such as a sound playback function, an image playback function, etc.); the data storage area can store data created according to the use of the mobile phone (such as audio data, phone book, etc.). In addition, the memory can include high-speed random access memory and can also include non-volatile memory, such as at least one magnetic disk storage device, a flash memory device, or other non-volatile solid-state storage devices.

[0123] The input unit can be used to receive input digital or character information, and generate key signal inputs related to the user settings and function controls of the mobile phone. Specifically, the input unit may include a touch panel and other input devices. The touch panel, also known as a touch screen, can collect touch operations of the user thereon or nearby (such as operations of the user using a finger, a stylus or any suitable object or accessory on or near the touch panel), and drive corresponding connection devices according to a pre-set program. Optionally, the touch panel may include two parts: a touch detection device and a touch controller. Among them, the touch detection device detects the touch position of the user, detects the signal brought by the touch operation, and transmits the signal to the touch controller; the touch controller receives the touch information from the touch detection device, converts it into contact coordinates, and then sends it to the processor, and can receive and execute the commands sent by the processor. In addition, various types such as resistive, capacitive, infrared, and surface acoustic wave can be used to implement the touch panel. In addition to the touch panel, the input unit may further include other input devices. Specifically, the other input devices may include, but are not limited to, one or more of a physical keyboard, function keys (such as volume control keys, power on / off keys, etc.), a trackball, a mouse, a joystick, etc.

[0124] The display unit can be used to display information input by the user or information provided to the user and various menus of the mobile phone. The display unit may include a display panel. Optionally, the display panel can be configured in the form of a liquid crystal display (LCD for short), an organic light-emitting diode (OLED for short), etc. Further, the touch panel can cover the display panel. When the touch panel detects a touch operation thereon or nearby, it transmits it to the processor to determine the type of touch event. Subsequently, the processor provides corresponding visual output on the display panel according to the type of touch event. Although in the figure, the touch panel and the display panel are implemented as two independent components to realize the input and output functions of the mobile phone, in some embodiments, the touch panel and the display panel can be integrated to realize the input and output functions of the mobile phone.

[0125] The mobile phone may also include at least one sensor, such as a light sensor, a motion sensor, and other sensors. Specifically, the light sensor may include an ambient light sensor and a proximity sensor. Among them, the ambient light sensor can configure the brightness of the display panel according to the brightness of the ambient light, and the proximity sensor can turn off the display panel and / or the backlight when the mobile phone is moved to the ear. As a kind of motion sensor, the accelerometer sensor can detect the magnitude of acceleration in various directions (generally three axes), and can detect the magnitude and direction of gravity when stationary, and can be used in applications for identifying the posture of the mobile phone (such as horizontal and vertical screen switching, related games, magnetometer posture calibration), vibration recognition related functions (such as pedometer, tapping), etc.; as for other sensors such as gyroscope, barometer, hygrometer, thermometer, infrared sensor that the mobile phone can also be configured with, they will not be elaborated here.

[0126] The audio circuit, speaker, and microphone can provide an audio interface between the user and the mobile phone. The audio circuit can transmit the electrical signal converted from the received audio data to the speaker, and the speaker converts it into a sound signal for output; on the other hand, the microphone converts the collected sound signal into an electrical signal, which is received by the audio circuit and then converted into audio data. After the audio data is output to the processor for processing, it is sent via the RF circuit to, for example, another mobile phone, or the audio data is output to the memory for further processing.

[0127] WiFi belongs to short - range wireless transmission technology. The mobile phone can help users send and receive emails, browse the web, and access streaming media through the WiFi module, which provides users with wireless broadband Internet access. Although the WiFi module is shown in the figure, it can be understood that it does not belong to an essential component of the mobile phone and can be omitted entirely within the scope of not changing the essence of the invention as needed.

[0128] The processor is the control center of the mobile phone. It connects all parts of the entire mobile phone using various interfaces and circuits. By running or executing software programs and / or modules stored in the memory, and by calling the data stored in the memory, it executes various functions of the mobile phone and processes data, thereby monitoring the mobile phone as a whole. Optionally, the processor may include one or more processing units; preferably, the processor may integrate an application processor and a modem processor. Among them, the application processor mainly processes the operating system, user interface, and application programs, etc., and the modem processor mainly processes wireless communication. It can be understood that the above - mentioned modem processor may not be integrated into the processor.

[0129] The mobile phone also includes a power source (such as a battery) for powering each component. Preferably, the power source can be logically connected to the processor through a power management system, so as to realize functions such as management of charging, discharging, and power consumption management through the power management system.

[0130] Although not shown, the mobile phone may further include a camera, a Bluetooth module, etc., which will not be elaborated here.

[0131] If the computer device is a server, the embodiments of the present application further provide a server. Servers may vary greatly due to configuration or performance differences and may include one or more central processing units (CPUs) (for example, one or more processors) and a memory, and one or more storage media for storing application programs or data (for example, one or more mass storage devices). Among them, the memory and the storage media may be transient storage or persistent storage. The programs stored in the storage media may include one or more modules, and each module may include a series of instruction operations on the server. Further, the central processing unit may be configured to communicate with the storage media and execute a series of instruction operations in the storage media on the server.

[0132] The server may further include one or more power supplies, one or more wired or wireless network interfaces, one or more input / output interfaces, and / or one or more operating systems, such as Windows ServerTM, Mac OS XTM, UnixTM, LinuxTM, FreeBSDTM, etc.

[0133] In addition, the embodiments of the present application further provide a storage medium for storing a computer program, and the computer program is used to execute the method provided in the above embodiments.

[0134] The embodiments of the present application further provide a computer program product including instructions. When it runs on a computer, it causes the computer to execute the method provided in the above embodiments.

[0135] Those of ordinary skill in the art can understand that all or part of the steps of implementing the above method embodiments can be completed by hardware related to program instructions. The foregoing program can be stored in a computer-readable storage medium. When the program is executed, it executes the steps including the above method embodiments; and the foregoing storage medium can be at least one of the following media: read-only memory (English: Read-only Memory, abbreviation: ROM), RAM, magnetic disk, or optical disk, etc., which can store program codes.

[0136] Therefore, the present application also discloses a system for controlling a 3D printer applying cloud data processing, a computer program product including instructions. When it runs on a computer, it causes the computer to execute the method for configuring a local area network access point address as described above.

[0137] It should be noted that the various embodiments in this specification are described in a progressive manner. For the same or similar parts among the various embodiments, reference can be made to each other, and the key points of each embodiment are the differences from other embodiments. In particular, for the device and system embodiments, since they are basically similar to the method embodiments, the description is relatively simple, and reference can be made to the corresponding parts of the method embodiments for the relevant content. The device and system embodiments described above are merely illustrative. The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed to multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment. Those of ordinary skill in the art can understand and implement it without creative efforts.

[0138] As described above, it is only a specific implementation manner of this application, but the protection scope of this application is not limited thereto. Any changes or substitutions that can be easily thought of by those skilled in the art within the technical scope disclosed in this application should be covered by the protection scope of this application.

Claims

1. A 3D printer control method using cloud data processing, characterized in that: The steps include: S1 first configures the local light source lamp beads of the 3D printer to be lamp beads with independently controllable light intensity; S2 connects and interacts all 3D printers with the cloud server; After the local user in S3 sets the printing parameters and determines the specific product to be printed, the information "the local user sets the printing parameters and determines the specific product to be printed" is exchanged to the cloud server; S4 After each printing is completed, the local user exchanges the printing effect parameter information of the specific target product with the cloud server; The S5 cloud server establishes a correlation model between printing parameters and printing effect parameters based on the printing data of the same specific product. The printing data of the same specific product refers to the printing parameters and printing effect parameters for the same specific product uploaded by different users. The optimized printing parameters corresponding to the optimal printing effect parameters of the target specific product are determined through the correlation model between printing parameters and printing effect parameters. S6 The cloud server optimizes the correlation model between the printing parameters and the printing effect parameters based on the updated data and provides the optimized printing parameters of the specific product to a new user when printing the same specific product through the correlation model between the printing parameters and the printing effect parameters after the optimization; Configure the local light source lamp beads of the 3D printer as lamp beads with independently controllable light intensity. Specifically, use a single-chip microcomputer or a local controller of the 3D printer to control multiple lamp beads and adjust the light intensity independently. The specific steps are: select a single-chip microcomputer with multiple IO ports, and ensure that the number of IO ports of the single-chip microcomputer or the local controller of the 3D printer can connect all the lamp beads that need to be controlled; install an LED driver or LED controller for each lamp bead; these drivers or controllers convert the signals output by the single-chip microcomputer or the local controller of the 3D printer into the current and voltage required by the lamp bead; use the IO port of the single-chip microcomputer or the local controller of the 3D printer to control each LED driver or LED controller; connect each IO port to the corresponding driver or controller through programming, and use the signal output by the single-chip microcomputer or the local controller of the 3D printer to control the switch state of the lamp bead; To adjust the light intensity, install a PWM dimmer for each lamp bead; the PWM dimmer controls the light intensity by adjusting the duty cycle of the LED current; connect the control signal of each PWM dimmer to different IO ports of the microcontroller or the local controller of the 3D printer; independently adjust the duty cycle of each PWM dimmer through programming to control the light intensity of each lamp bead; use a timer or counter to generate a PWM waveform; connect the output of the timer or counter to the corresponding PWM dimmer through programming, and use the signal output by the microcontroller or the local controller of the 3D printer to control the duty cycle of the PWM waveform; write a control program; write a program using the programming language of the microcontroller or the local controller of the 3D printer to control the switching state and light intensity of each lamp bead.

2. The 3D printer control method using cloud data processing according to claim 1, characterized in that: Steps to connect and interact with the 3D printer and the cloud server: Determine the connection method: First, you need to determine how the 3D printer and the cloud server are connected; some 3D printers connect to the cloud server via Wi-Fi or a wired network, while other 3D printers require the use of dedicated hardware or adapters; Configure the 3D printer: configuration and setup, set up the network connection, configure the 3D printer driver, and set up the communication protocol with the cloud server; create a cloud account: create an account on the cloud service provider website and make sure the cloud server instance is enabled; connect the 3D printer: use the options provided by the cloud server software or SDK to connect the 3D printer to the cloud server; the IP address and port number of the 3D printer are required.

3. The 3D printer control method using cloud data processing according to claim 1, characterized in that: The printing parameters include the light intensity set for each lamp bead when light-curing each layer. The printing effect parameters include whether the model is broken, whether the model has obvious layer lines, whether the surface is soft, whether the model expands, and the printing time.

4. The 3D printer control method using cloud data processing according to claim 1, characterized in that: Establishing a correlation model between printing parameters and printing effect parameters specifically includes: quantizing the printing parameters and printing effect parameters in the printing data into vectors respectively, and establishing a correlation model between the printing parameters and printing effect parameters through the quantized vectors; and also includes establishing a correlation model between the printing parameters and printing effect parameters through a decision tree model or a random forest model.

5. The 3D printer control method using cloud data processing according to claim 4, characterized in that: Establishing a correlation model between printing parameters and printing effect parameters through quantized vectors includes: calculating the Euclidean distance between the printing parameter quantization vector and the printing effect parameter quantization vector to establish correlation, this model is used to measure the straight-line distance between two vectors, and it is assumed that the closer the distance between the two vectors, the higher their similarity; calculating the cosine similarity between the printing parameter quantization vector and the printing effect parameter quantization vector to establish correlation, this model is used to measure the angle between the two vectors, and it is assumed that the smaller the angle between the two vectors, the higher their similarity; calculating the Pearson correlation coefficient between the printing parameter quantization vector and the printing effect parameter quantization vector to establish correlation, this model is used to measure the linear correlation between the two vectors, and its value range is between -1 and 1, and the larger the value, the higher the correlation; The Spearman rank correlation coefficient between the printed parameter quantization vector and the printed effect parameter quantization vector is calculated to establish the correlation. This model is used to measure the degree of monotonic correlation between two vectors. Its value range is between -1 and 1. The larger the value, the higher the correlation. The Manhattan distance between the printed parameter quantization vector and the printed effect parameter quantization vector is calculated to establish the correlation. This model is used to measure the absolute distance between two vectors. It assumes that the closer the distance between two vectors, the higher their similarity.

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