3D printing control method and system based on multi-nozzle flow data

Abstract

The present invention discloses a 3D printing control method and system based on multi-nozzle flow data. The method comprises: obtaining historical flow data and historical printing plans for multiple nozzles of the same caliber for a target 3D printing device; the historical flow data includes flow data for multiple different printing materials; fitting and analyzing the historical flow data and historical printing plans based on a dynamic programming algorithm to obtain a mathematical relationship model between the flow rate and time of the target 3D printing device; obtaining real-time flow data and real-time printing material type of the target nozzle; and determining whether the target 3D printing device has an abnormal printing speed based on the mathematical relationship model, the real-time flow data, and the real-time printing material type. This method can efficiently and accurately monitor the printing speed of a 3D printing device and determine abnormalities, thereby improving monitoring accuracy and printing stability.

Description

Technical Field

[0001] The present invention relates to the field of data processing technology, and in particular to a 3D printing control method and system based on multi-nozzle flow data. Background Art

[0002] With the development of FDM 3D printing and light-curing 3D printing technology, users' requirements for printing accuracy are also increasing. This places higher demands on the printing control accuracy of printing equipment. Among them, how to effectively monitor and control printing speed is a key technical issue. When monitoring and controlling printing speed, most existing technologies only use single sensor data to determine the current printing progress to achieve speed judgment. They do not consider combining flow data from multiple nozzles and printing plans to comprehensively determine whether the printing speed is reasonable. As a result, their monitoring accuracy is low and the effect is poor. It can be seen that the existing technology has defects 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 control method and system based on multi-nozzle flow data, which can efficiently and accurately monitor the printing speed and judge abnormalities of the 3D printing equipment, thereby improving monitoring accuracy and printing stability.

[0004] In order to solve the above technical problems, the first aspect of the present invention discloses a 3D printing control method based on multi-nozzle flow data, the method comprising:

[0005] Obtaining historical flow data and historical printing plans for multiple nozzles of the same caliber of a target 3D printing device; the historical flow data includes flow data for multiple different printing materials;

[0006] According to the dynamic programming algorithm, the historical flow data and the historical printing plan, a mathematical relationship model between the flow rate and time of the target 3D printing device is obtained by fitting analysis;

[0007] Obtain real-time flow data and real-time printing material type of the target nozzle;

[0008] It is determined whether the target 3D printing device has an abnormal printing speed according to the mathematical relationship model, the real-time flow data, and the real-time printing material type.

[0009] As an optional embodiment, in the first aspect of the present invention, the historical flow data includes flow data of a plurality of different printing materials of each nozzle at different historical time points.

[0010] As an optional implementation, in the first aspect of the present invention, the historical printing plan includes the printing phases and printing task types corresponding to the target 3D printing device at different historical time points.

[0011] As an optional embodiment, in the first aspect of the present invention, the mathematical relationship model between the flow rate and time of the target 3D printing device is obtained by fitting and analyzing the dynamic programming algorithm, the historical flow rate data, and the historical printing plan, including:

[0012] Classifying multiple historical time points according to the historical printing plan to obtain multiple historical time periods;

[0013] According to the data content of the historical flow data in each historical time period, based on a dynamic programming algorithm, a mathematical relationship model between the flow rate and the time period of the target 3D printing device is obtained through fitting analysis.

[0014] As an optional embodiment, in the first aspect of the present invention, the multiple historical time points are classified according to the historical printing plan to obtain multiple historical time periods, including:

[0015] Determining the printing phase and printing task type of the historical printing plan at each historical time point to obtain printing job information corresponding to each historical time point;

[0016] Calculating the information similarity of the printing job information between any two adjacent historical time points;

[0017] The two adjacent historical time points whose information similarity is greater than a preset similarity threshold are grouped into one historical time period to obtain multiple historical time periods.

[0018] As an optional embodiment, in the first aspect of the present invention, the mathematical relationship model between the flow rate and time period of the target 3D printing device is obtained by fitting and analyzing the data content of the historical flow data in each historical time period based on a dynamic programming algorithm, including:

[0019] Determine a plurality of flow data and printing material types corresponding to different nozzles in each of the historical time periods of the historical flow data;

[0020] For each of the nozzles, based on a dynamic programming algorithm and a preset mathematical polynomial relationship model, a linear fit is performed on the flow data of the nozzle in multiple historical time periods and the corresponding printing material type to obtain a mathematical relationship model of the flow, material and time period corresponding to the nozzle.

[0021] As an optional embodiment, in the first aspect of the present invention, determining whether the target 3D printing device has an abnormal printing speed based on the mathematical relationship model, the real-time flow data, and the real-time printing material type includes:

[0022] Determining the mathematical relationship model corresponding to the target nozzle;

[0023] Inputting the real-time printing material type and the current time point into the mathematical relationship model to obtain the real-time reference flow rate corresponding to the target nozzle;

[0024] It is determined whether the target 3D printing device has an abnormal printing speed according to the real-time flow data and the real-time reference flow.

[0025] As an optional embodiment, in the first aspect of the present invention, determining whether the target 3D printing device has an abnormal printing speed based on the real-time flow data and the real-time reference flow includes:

[0026] Calculating a flow rate difference between the real-time flow rate data and the real-time reference flow rate;

[0027] Determine whether the flow difference is greater than a preset difference threshold, and obtain a determination result;

[0028] When the judgment result is no, determining that the target 3D printing device does not have an abnormal printing speed;

[0029] When the judgment result is yes, determining the positive and negative signs of the flow difference;

[0030] According to the positive and negative signs, it is determined that the printing speed problem of the target 3D printing device is printing too fast or printing too slow.

[0031] A second aspect of an embodiment of the present invention discloses a 3D printing control system based on multi-nozzle flow data, the system comprising:

[0032] A first acquisition module is configured to acquire historical flow data and historical printing plans of multiple nozzles of the same caliber of a target 3D printing device; the historical flow data includes flow data of multiple different printing materials;

[0033] An analysis module, configured to obtain a mathematical relationship model between flow rate and time of the target 3D printing device by fitting and analyzing the dynamic programming algorithm, the historical flow rate data, and the historical printing plan;

[0034] A second acquisition module is used to obtain real-time flow data and real-time printing material type of the target nozzle;

[0035] The judgment module is used to judge whether the target 3D printing device has an abnormal printing speed according to the mathematical relationship model, the real-time flow data and the real-time printing material type.

[0036] As an optional embodiment, in the second aspect of the present invention, the historical flow data includes flow data of a plurality of different printing materials of each nozzle at different historical time points.

[0037] As an optional implementation, in the second aspect of the present invention, the historical printing plan includes the printing phases and printing task types corresponding to the target 3D printing device at different historical time points.

[0038] As an optional embodiment, in the second aspect of the present invention, the specific manner in which the analysis module obtains a mathematical relationship model between the flow rate and time of the target 3D printing device by fitting and analyzing the historical flow rate data and the historical printing plan based on a dynamic programming algorithm includes:

[0039] Classifying multiple historical time points according to the historical printing plan to obtain multiple historical time periods;

[0040] According to the data content of the historical flow data in each historical time period, based on a dynamic programming algorithm, a mathematical relationship model between the flow rate and the time period of the target 3D printing device is obtained through fitting analysis.

[0041] As an optional embodiment, in the second aspect of the present invention, the analysis module classifies the multiple historical time points according to the historical printing plan to obtain a specific manner of multiple historical time periods, including:

[0042] Determining the printing phase and printing task type of the historical printing plan at each historical time point to obtain printing job information corresponding to each historical time point;

[0043] Calculating the information similarity of the printing job information between any two adjacent historical time points;

[0044] The two adjacent historical time points whose information similarity is greater than a preset similarity threshold are grouped into one historical time period to obtain multiple historical time periods.

[0045] As an optional embodiment, in the second aspect of the present invention, the specific manner in which the analysis module obtains a mathematical relationship model between the flow rate and time period of the target 3D printing device through fitting analysis based on the data content of the historical flow data in each historical time period using a dynamic programming algorithm includes:

[0046] Determine a plurality of flow data and printing material types corresponding to different nozzles in each of the historical time periods of the historical flow data;

[0047] For each of the nozzles, based on a dynamic programming algorithm and a preset mathematical polynomial relationship model, a linear fit is performed on the flow data of the nozzle in multiple historical time periods and the corresponding printing material type to obtain a mathematical relationship model of the flow, material and time period corresponding to the nozzle.

[0048] As an optional embodiment, in the second aspect of the present invention, the specific manner in which the judgment module judges whether the target 3D printing device has an abnormal printing speed based on the mathematical relationship model, the real-time flow data, and the real-time printing material type includes:

[0049] Determining the mathematical relationship model corresponding to the target nozzle;

[0050] Inputting the real-time printing material type and the current time point into the mathematical relationship model to obtain the real-time reference flow rate corresponding to the target nozzle;

[0051] It is determined whether the target 3D printing device has an abnormal printing speed according to the real-time flow data and the real-time reference flow.

[0052] As an optional embodiment, in the second aspect of the present invention, the specific manner in which the judgment module judges whether the target 3D printing device has an abnormal printing speed based on the real-time flow data and the real-time reference flow includes:

[0053] Calculating a flow rate difference between the real-time flow rate data and the real-time reference flow rate;

[0054] Determine whether the flow difference is greater than a preset difference threshold, and obtain a determination result;

[0055] When the judgment result is no, determining that the target 3D printing device does not have an abnormal printing speed;

[0056] When the judgment result is yes, determining the positive and negative signs of the flow difference;

[0057] According to the positive and negative signs, it is determined that the printing speed problem of the target 3D printing device is printing too fast or printing too slow.

[0058] A third aspect of the present invention discloses another 3D printing control system based on multi-nozzle flow data, the system comprising:

[0059] a memory storing executable program code;

[0060] a processor coupled to the memory;

[0061] The processor calls the executable program code stored in the memory to execute part or all of the steps in the 3D printing control method based on multi-nozzle flow data disclosed in the first aspect of the present invention.

[0062] 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 control method based on multi-nozzle flow data disclosed in the first aspect of the present invention.

[0063] Compared with the prior art, the embodiments of the present invention have the following beneficial effects:

[0064] The present invention can obtain a mathematical relationship model between the flow rate and time of a target 3D printing device based on historical flow data and historical printing plan fitting analysis of multiple nozzles of the same caliber of the target 3D printing device. This model can be used to accurately determine whether the target 3D printing device has an abnormal printing speed based on real-time flow data and printing type, thereby enabling efficient and accurate monitoring and abnormality judgment of the printing speed of the 3D printing device, thereby improving monitoring accuracy and printing stability. BRIEF DESCRIPTION OF THE DRAWINGS

[0065] 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.

[0066] Figure 1 This is a flow chart of a 3D printing control method based on multi-nozzle flow data disclosed in an embodiment of the present invention.

[0067] Figure 2 This is a structural diagram of a 3D printing control system based on multi-nozzle flow data disclosed in an embodiment of the present invention.

[0068] Figure 3 This is a schematic structural diagram of another 3D printing control system based on multi-nozzle flow data disclosed in an embodiment of the present invention. DETAILED DESCRIPTION

[0069] 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.

[0070] 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.

[0071] 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.

[0072] The present invention discloses a 3D printing control method and system based on multi-nozzle flow data. This method can derive a mathematical relationship model between the flow rate and time of a target 3D printing device based on historical flow data and historical printing plans for multiple nozzles of the same caliber. This model can then be used to accurately determine whether the target 3D printing device is experiencing abnormal printing speeds based on real-time flow data and print type. This method enables efficient and accurate monitoring of the 3D printing device's printing speed and abnormality detection, improving monitoring accuracy and printing stability. These are described in detail below.

[0073] Example 1

[0074] See also Figure 1 , Figure 1 This is a flow chart of a 3D printing control method based on multi-nozzle flow data disclosed in an embodiment of the present invention. Figure 1 The described 3D printing control method based on multi-nozzle flow data 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 control method based on multi-nozzle flow data may include the following operations:

[0075] 101. Obtain historical flow data and historical printing plans of multiple nozzles of the same caliber for a target 3D printing device.

[0076] Optionally, the historical flow data includes flow data of multiple different printing materials.

[0077] 102. Based on the dynamic programming algorithm, historical flow data and historical printing plans, a mathematical relationship model between the flow and time of the target 3D printing equipment is obtained through fitting analysis.

[0078] 103. Obtain real-time flow data and real-time printing material type of the target nozzle.

[0079] 104. Determine whether the target 3D printing device has an abnormal printing speed based on the mathematical relationship model, real-time flow data, and real-time printing material type.

[0080] It can be seen that the above-mentioned embodiment of the invention can obtain a mathematical relationship model between the flow rate and time of the target 3D printing device based on the historical flow rate data and historical printing plan fitting analysis of multiple nozzles of the same caliber of the target 3D printing device, so as to accurately determine whether the target 3D printing device has an abnormal printing speed based on real-time flow rate data and printing type, thereby being able to efficiently and accurately monitor and judge abnormalities of the printing speed of the 3D printing device, thereby improving monitoring accuracy and printing stability.

[0081] As an optional embodiment, in the above steps, the historical flow data includes flow data of multiple different printing materials of each nozzle at different historical time points.

[0082] It can be seen that through the above optional embodiments, the content of the historical flow data is clarified, which can more accurately characterize the changes in the historical flow of the nozzle over time and material, so as to facilitate subsequent fitting analysis to obtain an accurate mathematical relationship model, and assist in realizing efficient and accurate monitoring and abnormal judgment of the printing speed of the 3D printing equipment, thereby improving monitoring accuracy and printing stability.

[0083] As an optional embodiment, in the above steps, the historical printing plan includes the printing phases and printing task types corresponding to the target 3D printing device at different historical time points.

[0084] It can be seen that through the above optional embodiments, the content of the historical printing plan is clarified, which can more accurately characterize the printing working conditions of the 3D printing device at different historical time points, so as to facilitate the subsequent fitting analysis to obtain an accurate mathematical relationship model, and assist in realizing efficient and accurate monitoring and abnormal judgment of the printing speed of the 3D printing device, thereby improving monitoring accuracy and printing stability.

[0085] As an optional embodiment, in the above steps, the mathematical relationship model between the flow rate and time of the target 3D printing device is obtained by fitting and analyzing the dynamic programming algorithm, historical flow data, and historical printing plans, including:

[0086] According to the historical printing plan, multiple historical time points are classified to obtain multiple historical time periods;

[0087] According to the data content of historical traffic data in each historical time period, based on the dynamic programming algorithm, a mathematical relationship model between the traffic flow and time period of the target 3D printing equipment is obtained through fitting analysis.

[0088] It can be seen that through the above optional embodiments, multiple historical time points can be classified to obtain multiple historical time periods, and then based on the data content of the historical traffic data in each of the historical time periods, a mathematical relationship model can be obtained based on the dynamic programming algorithm fitting analysis to achieve accurate anomaly judgment in the subsequent process, assist in achieving efficient and accurate monitoring and anomaly judgment of the printing speed of the 3D printing equipment, and improve monitoring accuracy and printing stability.

[0089] As an optional embodiment, in the above step, multiple historical time points are classified according to the historical printing plan to obtain multiple historical time periods, including:

[0090] Determine the printing phase and printing task type of the historical printing plan at each historical time point to obtain the printing job information corresponding to each historical time point;

[0091] Calculate the information similarity of the printing job information between any two adjacent historical time points;

[0092] Two adjacent historical time points whose information similarity is greater than a preset similarity threshold are grouped into one historical time period to obtain multiple historical time periods.

[0093] It can be seen that through the above optional embodiments, printing work information can be obtained based on the printing stage and printing task type of the historical printing plan at each historical time point, and then based on the calculation and screening grouping of information similarity, multiple historical time periods can be obtained to facilitate subsequent fitting analysis to obtain an accurate mathematical relationship model, which assists in realizing efficient and accurate monitoring and abnormal judgment of the printing speed of the 3D printing equipment, thereby improving monitoring accuracy and printing stability.

[0094] As an optional embodiment, in the above steps, based on the data content of the historical traffic data in each historical time period, a mathematical relationship model between the traffic flow of the target 3D printing device and the time period is obtained by fitting and analyzing based on a dynamic programming algorithm, including:

[0095] Determine a plurality of flow data and printing material types corresponding to different nozzles in each historical time period of historical flow data;

[0096] For each nozzle, based on the dynamic programming algorithm and the preset mathematical polynomial relationship model, linear fitting is performed on the flow data of the nozzle in multiple historical time periods and the corresponding printing material type to obtain the mathematical relationship model of the flow, material and time period corresponding to the nozzle.

[0097] It can be seen that through the above optional embodiments, a fitting analysis can be performed based on the nozzle flow data and the corresponding printing material type in multiple historical time periods to obtain an accurate mathematical relationship model corresponding to each nozzle, so as to facilitate subsequent abnormal monitoring and judgment of printing speed, and assist in realizing efficient and accurate monitoring and abnormal judgment of the printing speed of 3D printing equipment, thereby improving monitoring accuracy and printing stability.

[0098] As an optional embodiment, in the above step, determining whether the target 3D printing device has an abnormal printing speed based on the mathematical relationship model, the real-time flow data, and the real-time printing material type includes:

[0099] Determine the mathematical relationship model corresponding to the target nozzle;

[0100] Input the real-time printing material type and the current time point into the mathematical relationship model to obtain the real-time reference flow rate corresponding to the target nozzle;

[0101] Based on the real-time traffic data and the real-time reference traffic, it is determined whether the target 3D printing device has abnormal printing speed.

[0102] It can be seen that through the above-mentioned optional embodiments, the reference flow rate can be calculated based on the real-time printing material type and the current time point according to the mathematical relationship model corresponding to the target nozzle, and then abnormality judgment can be realized based on the reference flow rate and the real-time flow rate, so that the printing speed of the 3D printing device can be efficiently and accurately monitored and abnormality judgment can be realized, thereby improving the monitoring accuracy and printing stability.

[0103] As an optional embodiment, in the above step, judging whether the target 3D printing device has an abnormal printing speed based on the real-time traffic data and the real-time reference traffic includes:

[0104] Calculate the flow difference between the real-time flow data and the real-time reference flow;

[0105] Determine whether the flow difference is greater than a preset difference threshold and obtain a determination result;

[0106] When the judgment result is no, it is determined that the target 3D printing device does not have an abnormal printing speed;

[0107] When the judgment result is yes, determine the positive and negative signs of the flow difference;

[0108] According to the positive and negative signs, it is determined that the printing speed problem of the target 3D printing device is printing too fast or too slow.

[0109] It can be seen that through the above optional embodiments, it is possible to determine whether the printing speed of the 3D printing device is abnormal and whether the further abnormality is too fast or too slow based on the judgment between the flow difference between the real-time flow data and the real-time reference flow and the difference threshold. This can achieve efficient and accurate monitoring and abnormal judgment of the printing speed of the 3D printing device, thereby improving monitoring accuracy and printing stability.

[0110] Example 2

[0111] See also Figure 2 , Figure 2 This is a schematic diagram of the structure of a 3D printing control system based on multi-nozzle flow data disclosed in an embodiment of the present invention. Figure 2 The 3D printing control system based on multi-nozzle flow data 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 control system based on multi-nozzle flow data may include:

[0112] The first acquisition module 201 is configured to acquire historical flow rate data and historical printing plans of multiple nozzles of the same caliber of a target 3D printing device.

[0113] Optionally, the historical flow data includes flow data of multiple different printing materials.

[0114] The analysis module 202 is used to obtain a mathematical relationship model between the flow rate and time of the target 3D printing device through fitting analysis based on a dynamic programming algorithm, historical flow rate data, and historical printing plans.

[0115] The second acquisition module 203 is used to acquire the real-time flow rate data and real-time printing material type of the target nozzle.

[0116] The judgment module 204 is used to judge whether the target 3D printing device has an abnormal printing speed according to the mathematical relationship model, the real-time flow data and the real-time printing material type.

[0117] It can be seen that the above-mentioned embodiment of the invention can obtain a mathematical relationship model between the flow rate and time of the target 3D printing device based on the historical flow rate data and historical printing plan fitting analysis of multiple nozzles of the same caliber of the target 3D printing device, so as to accurately determine whether the target 3D printing device has an abnormal printing speed based on real-time flow rate data and printing type, thereby being able to efficiently and accurately monitor and judge abnormalities of the printing speed of the 3D printing device, thereby improving monitoring accuracy and printing stability.

[0118] As an optional embodiment, the historical flow rate data includes flow rate data of a plurality of different printing materials of each nozzle at different historical time points.

[0119] It can be seen that through the above optional embodiments, the content of the historical flow data is clarified, which can more accurately characterize the changes in the historical flow of the nozzle over time and material, so as to facilitate subsequent fitting analysis to obtain an accurate mathematical relationship model, and assist in realizing efficient and accurate monitoring and abnormal judgment of the printing speed of the 3D printing equipment, thereby improving monitoring accuracy and printing stability.

[0120] As an optional embodiment, the historical printing plan includes the printing stages and printing task types corresponding to the target 3D printing device at different historical time points.

[0121] It can be seen that through the above optional embodiments, the content of the historical printing plan is clarified, which can more accurately characterize the printing working conditions of the 3D printing device at different historical time points, so as to facilitate the subsequent fitting analysis to obtain an accurate mathematical relationship model, and assist in realizing efficient and accurate monitoring and abnormal judgment of the printing speed of the 3D printing device, thereby improving monitoring accuracy and printing stability.

[0122] As an optional embodiment, the analysis module obtains a mathematical relationship model of the flow rate and time of the target 3D printing device by fitting and analyzing the dynamic programming algorithm, historical flow rate data, and historical printing plans, including:

[0123] According to the historical printing plan, multiple historical time points are classified to obtain multiple historical time periods;

[0124] According to the data content of historical traffic data in each historical time period, based on the dynamic programming algorithm, a mathematical relationship model between the traffic flow and time period of the target 3D printing equipment is obtained through fitting analysis.

[0125] It can be seen that through the above optional embodiments, multiple historical time points can be classified to obtain multiple historical time periods, and then based on the data content of the historical traffic data in each of the historical time periods, a mathematical relationship model can be obtained based on the dynamic programming algorithm fitting analysis to achieve accurate anomaly judgment in the subsequent process, assist in achieving efficient and accurate monitoring and anomaly judgment of the printing speed of the 3D printing equipment, and improve monitoring accuracy and printing stability.

[0126] As an optional embodiment, the analysis module classifies multiple historical time points according to the historical printing plan to obtain specific methods of multiple historical time periods, including:

[0127] Determine the printing phase and printing task type of the historical printing plan at each historical time point to obtain the printing job information corresponding to each historical time point;

[0128] Calculate the information similarity of the printing job information between any two adjacent historical time points;

[0129] Two adjacent historical time points whose information similarity is greater than a preset similarity threshold are grouped into one historical time period to obtain multiple historical time periods.

[0130] It can be seen that through the above optional embodiments, printing work information can be obtained based on the printing stage and printing task type of the historical printing plan at each historical time point, and then based on the calculation and screening grouping of information similarity, multiple historical time periods can be obtained to facilitate subsequent fitting analysis to obtain an accurate mathematical relationship model, which assists in realizing efficient and accurate monitoring and abnormal judgment of the printing speed of the 3D printing equipment, thereby improving monitoring accuracy and printing stability.

[0131] As an optional embodiment, the analysis module obtains a mathematical relationship model between the flow rate and the time period of the target 3D printing device by fitting and analyzing the data content of the historical flow data in each historical time period based on a dynamic programming algorithm, including:

[0132] Determine a plurality of flow data and printing material types corresponding to different nozzles in each historical time period of historical flow data;

[0133] For each nozzle, based on the dynamic programming algorithm and the preset mathematical polynomial relationship model, linear fitting is performed on the flow data of the nozzle in multiple historical time periods and the corresponding printing material type to obtain the mathematical relationship model of the flow, material and time period corresponding to the nozzle.

[0134] It can be seen that through the above optional embodiments, a fitting analysis can be performed based on the nozzle flow data and the corresponding printing material type in multiple historical time periods to obtain an accurate mathematical relationship model corresponding to each nozzle, so as to facilitate subsequent abnormal monitoring and judgment of printing speed, and assist in realizing efficient and accurate monitoring and abnormal judgment of the printing speed of 3D printing equipment, thereby improving monitoring accuracy and printing stability.

[0135] As an optional embodiment, the specific manner in which the judgment module judges whether the target 3D printing device has an abnormal printing speed based on the mathematical relationship model, the real-time flow data, and the real-time printing material type includes:

[0136] Determine the mathematical relationship model corresponding to the target nozzle;

[0137] Input the real-time printing material type and the current time point into the mathematical relationship model to obtain the real-time reference flow rate corresponding to the target nozzle;

[0138] Based on the real-time traffic data and the real-time reference traffic, it is determined whether the target 3D printing device has abnormal printing speed.

[0139] It can be seen that through the above-mentioned optional embodiments, the reference flow rate can be calculated based on the real-time printing material type and the current time point according to the mathematical relationship model corresponding to the target nozzle, and then abnormality judgment can be realized based on the reference flow rate and the real-time flow rate, so that the printing speed of the 3D printing device can be efficiently and accurately monitored and abnormality judgment can be realized, thereby improving the monitoring accuracy and printing stability.

[0140] As an optional embodiment, the specific manner in which the judgment module judges whether the target 3D printing device has an abnormal printing speed based on the real-time traffic data and the real-time reference traffic includes:

[0141] Calculate the flow difference between the real-time flow data and the real-time reference flow;

[0142] Determine whether the flow difference is greater than a preset difference threshold and obtain a determination result;

[0143] When the judgment result is no, it is determined that the target 3D printing device does not have an abnormal printing speed;

[0144] When the judgment result is yes, determine the positive and negative signs of the flow difference;

[0145] According to the positive and negative signs, it is determined that the printing speed problem of the target 3D printing device is printing too fast or too slow.

[0146] It can be seen that through the above optional embodiments, it is possible to determine whether the printing speed of the 3D printing device is abnormal and whether the further abnormality is too fast or too slow based on the judgment between the flow difference between the real-time flow data and the real-time reference flow and the difference threshold. This can achieve efficient and accurate monitoring and abnormal judgment of the printing speed of the 3D printing device, thereby improving monitoring accuracy and printing stability.

[0147] Example 3

[0148] See also Figure 3 , Figure 3 This is another 3D printing control system based on multi-nozzle flow data disclosed in an embodiment of the present invention. Figure 3 The described 3D printing control system based on multi-nozzle flow data 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 control system based on multi-nozzle flow data may include:

[0149] A memory 301 storing executable program code;

[0150] a processor 302 coupled to the memory 301;

[0151] The processor 302 calls the executable program code stored in the memory 301 to execute the steps of the 3D printing control method based on multi-nozzle flow data described in the first embodiment.

[0152] Example 4

[0153] 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 control method based on multi-nozzle flow data described in the first embodiment.

[0154] Example 5

[0155] 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 control method based on multi-nozzle flow data described in Example 1.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] In a typical configuration, a computing device includes one or more processors (CPUs), input / output interfaces, network interfaces, and memory.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] Finally, it should be noted that the 3D printing control method and system based on multi-nozzle flow data 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 control method based on multi-nozzle flow data, characterized in that: The method comprises: Obtaining historical flow data and historical printing plans for multiple nozzles of the same caliber of a target 3D printing device; the historical flow data including flow data for multiple different printing materials for each nozzle at different historical time points; the historical printing plans including the printing stages and printing task types corresponding to the target 3D printing device at different historical time points; According to the dynamic programming algorithm, the historical flow data and the historical printing plan, a mathematical relationship model between the flow rate and time of the target 3D printing device is obtained through fitting analysis, including: Classifying multiple historical time points according to the historical printing plan to obtain multiple historical time periods; According to the data content of the historical flow data in each historical time period, based on a dynamic programming algorithm, a mathematical relationship model between the flow rate and the time period of the target 3D printing device is obtained by fitting analysis; Obtain real-time flow data and real-time printing material type of the target nozzle; It is determined whether the target 3D printing device has an abnormal printing speed according to the mathematical relationship model, the real-time flow data, and the real-time printing material type.

2. The 3D printing control method based on multi-nozzle flow data according to claim 1, characterized in that: The method of classifying the plurality of historical time points according to the historical printing plan to obtain a plurality of historical time periods includes: Determining the printing phase and printing task type of the historical printing plan at each historical time point to obtain printing job information corresponding to each historical time point; Calculating the information similarity of the printing job information between any two adjacent historical time points; The two adjacent historical time points whose information similarity is greater than a preset similarity threshold are grouped into one historical time period to obtain multiple historical time periods.

3. The 3D printing control method based on multi-nozzle flow data according to claim 1, characterized in that: The mathematical relationship model between the flow rate and the time period of the target 3D printing device is obtained by fitting and analyzing the data content of the historical flow data in each historical time period based on a dynamic programming algorithm, including: Determine a plurality of flow data and printing material types corresponding to different nozzles in each of the historical time periods of the historical flow data; For each of the nozzles, based on a dynamic programming algorithm and a preset mathematical polynomial relationship model, a linear fit is performed on the flow data of the nozzle in multiple historical time periods and the corresponding printing material type to obtain a mathematical relationship model of the flow, material and time period corresponding to the nozzle.

4. The 3D printing control method based on multi-nozzle flow data according to claim 3, characterized in that: The determining, based on the mathematical relationship model, the real-time flow data, and the real-time printing material type, whether the target 3D printing device has an abnormal printing speed includes: Determining the mathematical relationship model corresponding to the target nozzle; Inputting the real-time printing material type and the current time point into the mathematical relationship model to obtain the real-time reference flow rate corresponding to the target nozzle; It is determined whether the target 3D printing device has an abnormal printing speed according to the real-time flow data and the real-time reference flow.

5. The 3D printing control method based on multi-nozzle flow data according to claim 4, characterized in that: The determining, based on the real-time traffic data and the real-time reference traffic, whether the target 3D printing device has an abnormal printing speed includes: Calculating a flow rate difference between the real-time flow rate data and the real-time reference flow rate; Determine whether the flow difference is greater than a preset difference threshold, and obtain a determination result; When the judgment result is no, determining that the target 3D printing device does not have an abnormal printing speed; When the judgment result is yes, determining the positive and negative signs of the flow difference; According to the positive and negative signs, it is determined that the printing speed problem of the target 3D printing device is printing too fast or printing too slow.

6. A 3D printing control system based on multi-nozzle flow data, characterized in that: The system comprises: A first acquisition module is configured to acquire historical flow data and historical printing plans of multiple nozzles of the same caliber for a target 3D printing device; the historical flow data includes flow data of multiple different printing materials for each nozzle at different historical time points; the historical printing plans include printing stages and printing task types corresponding to the target 3D printing device at different historical time points; An analysis module is configured to obtain a mathematical relationship model between the flow rate and time of the target 3D printing device by fitting and analyzing the dynamic programming algorithm, the historical flow rate data, and the historical printing plan, including: Classifying multiple historical time points according to the historical printing plan to obtain multiple historical time periods; According to the data content of the historical flow data in each historical time period, based on a dynamic programming algorithm, a mathematical relationship model between the flow rate and the time period of the target 3D printing device is obtained by fitting analysis; A second acquisition module is used to obtain real-time flow data and real-time printing material type of the target nozzle; The judgment module is used to judge whether the target 3D printing device has an abnormal printing speed according to the mathematical relationship model, the real-time flow data and the real-time printing material type.

7. A 3D printing control system based on multi-nozzle flow data, 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 control method based on multi-nozzle flow data according to any one of claims 1 to 5.

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