Flow data prediction method and system based on multi-nozzle data

Abstract

The application discloses a flow data prediction method and system based on multi-nozzle data, and the method comprises the following steps: acquiring sensing data of multiple nozzles with the same caliber in the same printing area of a target 3D printing device; predicting flow data corresponding to a target nozzle based on a neural network algorithm according to the sensing data of all the nozzles; determining a reference flow interval corresponding to the target nozzle according to a current printing time and a printing plan corresponding to the target 3D printing device; and judging whether the target 3D printing device has a printing abnormality according to the flow data and the reference flow interval. It can be seen that the application can more accurately and efficiently predict the flow of the nozzle, improve the monitoring effect and efficiency of the 3D printing flow, and improve the stability of 3D printing.

Description

Technical Field

[0001] This invention relates to the field of data processing technology, and in particular to a method and system for predicting flow data based on multi-nozzle data. Background Technology

[0002] The realization of FDM 3D printing technology and photopolymerization 3D printing technology is closely related to the precise control of nozzle flow. How to effectively control and monitor nozzle flow is a crucial technical issue. Current technologies mostly monitor nozzle flow using only sensor data from a single nozzle, without considering the integration of sensor data from multiple nozzles to predict flow and identify anomalies. Therefore, their monitoring accuracy is low and their effectiveness is poor. Clearly, existing technologies have shortcomings that urgently need to be addressed. Summary of the Invention

[0003] The technical problem to be solved by the present invention is to provide a flow data prediction method and system based on multi-nozzle data, which can more accurately and efficiently predict the flow of the nozzle, improve the monitoring effect and efficiency of 3D printing flow, and improve the stability of 3D printing.

[0004] To address the aforementioned technical problems, the first aspect of this invention discloses a flow data prediction method based on multi-nozzle data, the method comprising: Acquire sensor data from multiple nozzles of the same diameter in the same printing area of ​​the target 3D printing equipment; Based on the sensor data of all the nozzles, and using a neural network algorithm, the flow rate data corresponding to the target nozzle is predicted. Based on the current printing time and the printing plan corresponding to the target 3D printing equipment, determine the reference flow range corresponding to the target nozzle; Based on the flow data and the reference flow range, determine whether the target 3D printing equipment has printing abnormalities.

[0005] As an optional implementation, in the first aspect of the invention, the sensing data includes at least one of image sensing data, sound sensing data, flow meter sensing data, and temperature sensing data.

[0006] As an optional implementation, in the first aspect of the invention, predicting the flow rate data corresponding to the target nozzle based on the sensing data of all the nozzles and using a neural network algorithm includes: The sensing data of each nozzle is input into the flow prediction neural network corresponding to each nozzle to obtain the predicted flow rate of each nozzle. Based on the preset data correlation between nozzles and the predicted flow rate corresponding to each nozzle, the flow rate data corresponding to the target nozzle is determined.

[0007] As an optional implementation, in the first aspect of the present invention, the flow prediction neural network is a CNN network, which is trained using a training dataset including training sensor data of multiple corresponding nozzles and corresponding flow labels.

[0008] As an optional implementation, in the first aspect of the present invention, determining the flow rate data corresponding to the target nozzle based on a preset data association relationship between nozzles and the predicted flow rate corresponding to each nozzle includes: For each nozzle, a mathematical correspondence model between the nozzle and the target nozzle is determined; the mathematical correspondence model is obtained by fitting and analyzing the historical flow data of the nozzle and the target nozzle. The predicted flow rate corresponding to the nozzle is input into the mathematical correspondence model to obtain the candidate flow rate corresponding to the nozzle; Calculate the weighted summation average of the candidate flow rates corresponding to all the nozzles to obtain the flow rate data corresponding to the target nozzle; wherein, the weighted calculation weight corresponding to the candidate flow rate of each nozzle is inversely proportional to the position distance corresponding to the nozzle; the position distance is the position distance between the nozzle and the target nozzle.

[0009] As an optional implementation, in the first aspect of the invention, determining the reference flow range corresponding to the target nozzle based on the current printing time and the printing plan corresponding to the target 3D printing equipment includes: Obtain the printing plan corresponding to the target 3D printing equipment; Based on the current printing time and the aforementioned printing plan, determine the current printing stage; Based on the printing stage flow model corresponding to the target nozzle and the current printing stage, a reference flow range corresponding to the target nozzle is determined; the printing stage flow model is obtained by analyzing the flow data of the target nozzle at different printing stages in a historical time period.

[0010] As an optional implementation, in the first aspect of the invention, the target nozzle is a nozzle in the printing area that is not the nozzle in question, and its orifice diameter is the same as that of the nozzle.

[0011] As an optional implementation, in the first aspect of the present invention, determining whether the target 3D printing device has a printing abnormality based on the flow data and the reference flow range includes: Determine whether the traffic data is within the reference traffic range, and obtain the determination result; When the judgment result is yes, it is determined that the target 3D printing device does not have a printing abnormality; If the judgment result is negative, it is determined that the target 3D printing device has a printing abnormality.

[0012] A second aspect of this invention discloses a flow rate data prediction system based on multi-nozzle data, the system comprising: The acquisition module is used to acquire sensor data of multiple nozzles of the same diameter in the same printing area of ​​the target 3D printing equipment; The prediction module is used to predict the flow rate data corresponding to the target nozzle based on the sensing data of all the nozzles and a neural network algorithm. The determination module is used to determine the reference flow range corresponding to the target nozzle based on the current printing time and the printing plan corresponding to the target 3D printing equipment; The judgment module is used to determine whether the target 3D printing device has a printing abnormality based on the flow data and the reference flow range.

[0013] As an optional implementation, in a second aspect of the invention, the sensing data includes at least one of image sensing data, sound sensing data, flow meter sensing data, and temperature sensing data.

[0014] As an optional implementation, in a second aspect of the invention, the specific method by which the prediction module predicts the flow rate data corresponding to the target nozzle based on the sensing data of all the nozzles and using a neural network algorithm includes: The sensing data of each nozzle is input into the flow prediction neural network corresponding to each nozzle to obtain the predicted flow rate of each nozzle. Based on the preset data correlation between nozzles and the predicted flow rate corresponding to each nozzle, the flow rate data corresponding to the target nozzle is determined.

[0015] As an optional implementation, in a second aspect of the invention, the flow prediction neural network is a CNN network, trained using a training dataset that includes training sensor data of multiple corresponding nozzles and corresponding flow labels.

[0016] As an optional implementation, in a second aspect of the invention, the prediction module determines the specific method by which it determines the flow rate data corresponding to the target nozzle based on a preset data association relationship between nozzles and the predicted flow rate corresponding to each nozzle, including: For each nozzle, a mathematical correspondence model between the nozzle and the target nozzle is determined; the mathematical correspondence model is obtained by fitting and analyzing the historical flow data of the nozzle and the target nozzle. The predicted flow rate corresponding to the nozzle is input into the mathematical correspondence model to obtain the candidate flow rate corresponding to the nozzle; Calculate the weighted summation average of the candidate flow rates corresponding to all the nozzles to obtain the flow rate data corresponding to the target nozzle; wherein, the weighted calculation weight corresponding to the candidate flow rate of each nozzle is inversely proportional to the position distance corresponding to the nozzle; the position distance is the position distance between the nozzle and the target nozzle.

[0017] As an optional implementation, in a second aspect of the invention, the specific method by which the determining module determines the reference flow range corresponding to the target nozzle based on the current printing time and the printing plan corresponding to the target 3D printing equipment includes: Obtain the printing plan corresponding to the target 3D printing equipment; Based on the current printing time and the aforementioned printing plan, determine the current printing stage; Based on the printing stage flow model corresponding to the target nozzle and the current printing stage, a reference flow range corresponding to the target nozzle is determined; the printing stage flow model is obtained by analyzing the flow data of the target nozzle at different printing stages in a historical time period.

[0018] As an optional implementation, in a second aspect of the invention, the target nozzle is a nozzle in the printing area that is not the nozzle in question, and its orifice diameter is the same as that of the nozzle in question.

[0019] As an optional implementation, in a second aspect of the invention, the specific method by which the determining module determines whether the target 3D printing device has a printing abnormality based on the flow data and the reference flow range includes: Determine whether the traffic data is within the reference traffic range, and obtain the determination result; When the judgment result is yes, it is determined that the target 3D printing device does not have a printing abnormality; If the judgment result is negative, it is determined that the target 3D printing device has a printing abnormality.

[0020] A third aspect of the present invention discloses another flow rate data prediction system based on multi-nozzle data, the system comprising: Memory containing executable program code; A processor coupled to the memory; The processor calls the executable program code stored in the memory to execute some or all of the steps in the flow data prediction method based on multi-nozzle data disclosed in the first aspect of the present invention.

[0021] The fourth aspect of the present invention discloses a computer storage medium storing computer instructions, which, when invoked, are used to execute some or all of the steps in the flow data prediction method based on multi-nozzle data disclosed in the first aspect of the present invention.

[0022] Compared with the prior art, the embodiments of the present invention have the following beneficial effects: This invention can predict the flow rate of a target nozzle based on sensor data from multiple nozzles of the same diameter in the same printing area of ​​a target 3D printing device. Then, based on the current printing time and printing plan, it determines the reference flow rate range corresponding to the target nozzle. This allows for a comprehensive judgment on whether there are printing abnormalities in the target 3D printing device, thereby enabling more accurate and efficient prediction of nozzle flow rate, improving the monitoring effect and efficiency of 3D printing flow rate, and enhancing the stability of 3D printing. Attached Figure Description

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

[0024] Figure 1 This is a flowchart illustrating a flow data prediction method based on multi-nozzle data disclosed in an embodiment of the present invention.

[0025] Figure 2 This is a schematic diagram of the structure of a flow data prediction system based on multi-nozzle data disclosed in an embodiment of the present invention.

[0026] Figure 3 This is a schematic diagram of another flow data prediction system based on multi-nozzle data disclosed in an embodiment of the present invention. Detailed Implementation

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

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

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

[0030] This invention discloses a flow rate prediction method and system based on multi-nozzle data. It can predict the flow rate data of a target nozzle based on sensor data from multiple nozzles of the same diameter in the same printing area of ​​a target 3D printing equipment. Then, based on the current printing time and printing plan, it determines the reference flow rate range corresponding to the target nozzle, thereby comprehensively judging whether there are printing anomalies in the target 3D printing equipment. This allows for more accurate and efficient prediction of nozzle flow rate, improving the monitoring effect and efficiency of 3D printing flow rate and enhancing the stability of 3D printing. Detailed descriptions follow.

[0031] Example 1 Please see Figure 1 , Figure 1 This is a flowchart illustrating a flow data prediction method based on multi-nozzle data disclosed in an embodiment of the present invention. Figure 1 The described flow data prediction method based on multi-nozzle data can be applied to data processing systems / data processing equipment / data processing servers (wherein, the server includes local processing servers or cloud processing servers). For example... Figure 1 As shown, the flow rate data prediction method based on multi-nozzle data may include the following operations: 101. Acquire sensor data of multiple nozzles of the same diameter in the same printing area of ​​the target 3D printing equipment.

[0032] 102. Based on the sensor data of all nozzles, predict the flow rate data corresponding to the target nozzle using a neural network algorithm. 103. Based on the current printing time and the printing plan corresponding to the target 3D printing equipment, determine the reference flow range corresponding to the target nozzle. 104. Based on the flow data and reference flow range, determine whether the target 3D printing equipment has printing abnormalities.

[0033] As can be seen, the above-described embodiments of the invention can predict the flow rate data of the target nozzle based on the sensing data of multiple nozzles of the same diameter in the same printing area of ​​the target 3D printing equipment, and then determine the reference flow rate range corresponding to the target nozzle based on the current printing time and printing plan. In this way, a comprehensive judgment can be made on whether there is a printing abnormality in the target 3D printing equipment, thereby more accurately and efficiently predicting the flow rate of the nozzle, improving the monitoring effect and efficiency of 3D printing flow rate, and improving the stability of 3D printing.

[0034] As an optional embodiment, the sensing data in the above steps includes at least one of image sensing data, sound sensing data, flow meter sensing data, and temperature sensing data.

[0035] As can be seen, the content of the sensing data is clarified through the above optional embodiments, which can more accurately characterize the working characteristics of the nozzle, so as to facilitate subsequent flow prediction, help to achieve more accurate and efficient prediction of the nozzle flow, improve the monitoring effect and efficiency of 3D printing flow, and improve the stability of 3D printing.

[0036] As an optional embodiment, the step above, predicting the flow rate data corresponding to the target nozzle based on the sensor data of all nozzles and using a neural network algorithm, includes: The sensor data of each nozzle is input into the flow prediction neural network corresponding to each nozzle to obtain the predicted flow rate for each nozzle. Based on the preset data correlation between nozzles and the predicted flow rate for each nozzle, the flow rate data corresponding to the target nozzle is determined.

[0037] As can be seen, through the above optional embodiments, the predicted flow rate of each nozzle can be predicted based on the flow prediction neural network corresponding to each nozzle, and the flow rate data corresponding to the target nozzle can be accurately calculated based on the data correlation between nozzles, so as to facilitate subsequent anomaly judgment, achieve more accurate and efficient prediction of nozzle flow rate, improve the monitoring effect and efficiency of 3D printing flow rate, and improve the stability of 3D printing.

[0038] As an optional embodiment, in the above steps, the flow prediction neural network is a CNN network, which is trained using training sensor data including multiple corresponding nozzles and corresponding flow labels.

[0039] As can be seen, the network details and training dataset details of the flow prediction neural network are clarified through the above optional embodiments, which can accurately predict the flow of the nozzle, so as to facilitate subsequent anomaly judgment, achieve more accurate and efficient prediction of the nozzle flow, improve the monitoring effect and efficiency of 3D printing flow, and improve the stability of 3D printing.

[0040] As an optional embodiment, the step described above, determining the flow rate data corresponding to the target nozzle based on the preset data association relationship between nozzles and the predicted flow rate corresponding to each nozzle, includes: For each nozzle, a mathematical correspondence model between the nozzle and the target nozzle is determined; optionally, the mathematical correspondence model is obtained by fitting and analyzing the historical flow data of the nozzle and the target nozzle. The predicted flow rate corresponding to the nozzle is input into the mathematical correspondence model to obtain the candidate flow rate corresponding to the nozzle; Calculate the weighted average of the candidate flow rates corresponding to all nozzles to obtain the flow rate data corresponding to the target nozzle; optionally, the weight of the candidate flow rate corresponding to each nozzle is inversely proportional to the position distance of the nozzle; the position distance is the position distance between the nozzle and the target nozzle.

[0041] As can be seen, through the above optional embodiments, the accurate flow rate of the target nozzle can be determined by weighted calculation based on the mathematical correspondence model between the nozzle and the target nozzle and the predicted flow rate of the nozzle. The position distance weight is considered to improve the prediction accuracy, so as to facilitate subsequent anomaly judgment, achieve more accurate and efficient prediction of the nozzle flow rate, improve the monitoring effect and efficiency of 3D printing flow rate, and improve the stability of 3D printing.

[0042] As an optional embodiment, the step above, determining the reference flow range corresponding to the target nozzle based on the current printing time and the printing plan corresponding to the target 3D printing equipment, includes: Obtain the printing plan corresponding to the target 3D printing equipment; Determine the current printing stage based on the current printing time and printing schedule; Based on the printing stage flow model corresponding to the target nozzle and the current printing stage, the reference flow range corresponding to the target nozzle is determined; the printing stage flow model is obtained by analyzing the flow data of the target nozzle at different printing stages in historical time periods.

[0043] As can be seen, through the above optional embodiments, the current printing stage can be determined according to the printing plan corresponding to the target 3D printing equipment and the current time, and then the reference flow range can be determined according to the printing stage flow model corresponding to the target nozzle, so as to facilitate subsequent anomaly judgment, achieve more accurate and efficient prediction of nozzle flow, improve the monitoring effect and efficiency of 3D printing flow, and improve the stability of 3D printing.

[0044] As an optional embodiment, in the above steps, the target nozzle is a nozzle in the printing area that is not the nozzle corresponding to the above-mentioned sensing data, and its orifice diameter is the same as that of the above-mentioned nozzle.

[0045] As can be seen, the above optional embodiments limit the target nozzle to have the same orifice diameter as other nozzles used to collect sensor data for flow prediction, but not to one of them. This allows for accurate prediction of the target nozzle's flow rate based on multiple other nozzles. In practical applications, flow rate model fitting analysis can be performed on only one nozzle during the printing stage, reducing data analysis costs and achieving more accurate and efficient prediction of nozzle flow rate. This improves the monitoring effect and efficiency of 3D printing flow rate and enhances the stability of 3D printing.

[0046] As an optional embodiment, the step above, determining whether the target 3D printing device has printing abnormalities based on the flow data and reference flow range, includes: Determine whether the traffic data is within the reference traffic range and obtain the determination result; If the judgment result is yes, it is determined that the target 3D printing equipment does not have printing abnormalities; If the result is negative, it is determined that the target 3D printing equipment has a printing abnormality.

[0047] As can be seen, through the above optional embodiments, it is possible to determine whether the flow rate data is within the reference flow rate range to achieve anomaly detection, thereby enabling more accurate and efficient prediction of nozzle flow rate, improving the monitoring effect and efficiency of 3D printing flow rate, and enhancing the stability of 3D printing.

[0048] Example 2 Please see Figure 2 , Figure 2 This is a schematic diagram of a flow data prediction system based on multi-nozzle data disclosed in an embodiment of the present invention. Figure 2 The described flow data prediction system based on multi-nozzle data can be applied to data processing systems / data processing equipment / data processing servers (wherein, the server includes local processing servers or cloud processing servers). For example... Figure 2 As shown, the flow data prediction system based on multi-nozzle data may include: The acquisition module 201 is used to acquire sensing data of multiple nozzles of the same diameter in the same printing area of ​​the target 3D printing equipment.

[0049] The prediction module 202 is used to predict the flow data corresponding to the target nozzle based on the sensor data of all nozzles and a neural network algorithm. The determination module 203 is used to determine the reference flow range corresponding to the target nozzle based on the current printing time and the printing plan corresponding to the target 3D printing equipment. The judgment module 204 is used to determine whether there is a printing abnormality in the target 3D printing equipment based on the flow data and the reference flow range.

[0050] As can be seen, the above-described embodiments of the invention can predict the flow rate data of the target nozzle based on the sensing data of multiple nozzles of the same diameter in the same printing area of ​​the target 3D printing equipment, and then determine the reference flow rate range corresponding to the target nozzle based on the current printing time and printing plan. In this way, a comprehensive judgment can be made on whether there is a printing abnormality in the target 3D printing equipment, thereby more accurately and efficiently predicting the flow rate of the nozzle, improving the monitoring effect and efficiency of 3D printing flow rate, and improving the stability of 3D printing.

[0051] As an optional embodiment, the sensing data includes at least one of image sensing data, sound sensing data, flow meter sensing data, and temperature sensing data.

[0052] As can be seen, the content of the sensing data is clarified through the above optional embodiments, which can more accurately characterize the working characteristics of the nozzle, so as to facilitate subsequent flow prediction, help to achieve more accurate and efficient prediction of the nozzle flow, improve the monitoring effect and efficiency of 3D printing flow, and improve the stability of 3D printing.

[0053] As an optional embodiment, the prediction module predicts the flow rate data corresponding to the target nozzle based on the sensor data of all nozzles and a neural network algorithm, including the following specific methods: The sensor data of each nozzle is input into the flow prediction neural network corresponding to each nozzle to obtain the predicted flow rate for each nozzle. Based on the preset data correlation between nozzles and the predicted flow rate for each nozzle, the flow rate data corresponding to the target nozzle is determined.

[0054] As can be seen, through the above optional embodiments, the predicted flow rate of each nozzle can be predicted based on the flow prediction neural network corresponding to each nozzle, and the flow rate data corresponding to the target nozzle can be accurately calculated based on the data correlation between nozzles, so as to facilitate subsequent anomaly judgment, achieve more accurate and efficient prediction of nozzle flow rate, improve the monitoring effect and efficiency of 3D printing flow rate, and improve the stability of 3D printing.

[0055] As an optional embodiment, the flow prediction neural network is a CNN network, trained using a training dataset that includes training sensor data with multiple corresponding nozzles and corresponding flow labels.

[0056] As can be seen, the network details and training dataset details of the flow prediction neural network are clarified through the above optional embodiments, which can accurately predict the flow of the nozzle, so as to facilitate subsequent anomaly judgment, achieve more accurate and efficient prediction of the nozzle flow, improve the monitoring effect and efficiency of 3D printing flow, and improve the stability of 3D printing.

[0057] As an optional embodiment, the prediction module determines the specific method of the flow rate data corresponding to the target nozzle based on the preset data correlation between nozzles and the predicted flow rate corresponding to each nozzle, including: For each nozzle, a mathematical correspondence model between the nozzle and the target nozzle is determined; optionally, the mathematical correspondence model is obtained by fitting and analyzing the historical flow data of the nozzle and the target nozzle. The predicted flow rate corresponding to the nozzle is input into the mathematical correspondence model to obtain the candidate flow rate corresponding to the nozzle; Calculate the weighted average of the candidate flow rates corresponding to all nozzles to obtain the flow rate data corresponding to the target nozzle; optionally, the weight of the candidate flow rate corresponding to each nozzle is inversely proportional to the position distance of the nozzle; the position distance is the position distance between the nozzle and the target nozzle.

[0058] As can be seen, through the above optional embodiments, the accurate flow rate of the target nozzle can be determined by weighted calculation based on the mathematical correspondence model between the nozzle and the target nozzle and the predicted flow rate of the nozzle. The position distance weight is considered to improve the prediction accuracy, so as to facilitate subsequent anomaly judgment, achieve more accurate and efficient prediction of the nozzle flow rate, improve the monitoring effect and efficiency of 3D printing flow rate, and improve the stability of 3D printing.

[0059] As an optional embodiment, the determining module determines the specific method for determining the reference flow range corresponding to the target nozzle based on the current printing time and the printing plan corresponding to the target 3D printing equipment, including: Obtain the printing plan corresponding to the target 3D printing equipment; Determine the current printing stage based on the current printing time and printing schedule; Based on the printing stage flow model corresponding to the target nozzle and the current printing stage, the reference flow range corresponding to the target nozzle is determined; the printing stage flow model is obtained by analyzing the flow data of the target nozzle at different printing stages in historical time periods.

[0060] As can be seen, through the above optional embodiments, the current printing stage can be determined according to the printing plan corresponding to the target 3D printing equipment and the current time, and then the reference flow range can be determined according to the printing stage flow model corresponding to the target nozzle, so as to facilitate subsequent anomaly judgment, achieve more accurate and efficient prediction of nozzle flow, improve the monitoring effect and efficiency of 3D printing flow, and improve the stability of 3D printing.

[0061] As an optional embodiment, the target nozzle is a nozzle in the printing area that is not the nozzle corresponding to the above-mentioned sensor data, and its diameter is the same as that of the above-mentioned nozzle.

[0062] As can be seen, the above optional embodiments limit the target nozzle to have the same orifice diameter as other nozzles used to collect sensor data for flow prediction, but not to one of them. This allows for accurate prediction of the target nozzle's flow rate based on multiple other nozzles. In practical applications, flow rate model fitting analysis can be performed on only one nozzle during the printing stage, reducing data analysis costs and achieving more accurate and efficient prediction of nozzle flow rate. This improves the monitoring effect and efficiency of 3D printing flow rate and enhances the stability of 3D printing.

[0063] As an optional embodiment, the determination module determines whether the target 3D printing device has printing abnormalities based on traffic data and a reference traffic range, including the following specific methods: Determine whether the traffic data is within the reference traffic range and obtain the determination result; If the judgment result is yes, it is determined that the target 3D printing equipment does not have printing abnormalities; If the result is negative, it is determined that the target 3D printing equipment has a printing abnormality.

[0064] As can be seen, through the above optional embodiments, it is possible to determine whether the flow rate data is within the reference flow rate range to achieve anomaly detection, thereby enabling more accurate and efficient prediction of nozzle flow rate, improving the monitoring effect and efficiency of 3D printing flow rate, and enhancing the stability of 3D printing.

[0065] Example 3 Please see Figure 3 , Figure 3 This is another flow data prediction system based on multi-nozzle data disclosed in the embodiments of the present invention. Figure 3 The described flow data prediction system based on multi-nozzle data is applied in a data processing system / data processing equipment / data processing server (wherein, the server includes a local processing server or a cloud processing server). For example... Figure 3 As shown, the flow data prediction system based on multi-nozzle data may include: Memory 301 storing executable program code; Processor 302 coupled to memory 301; The processor 302 calls the executable program code stored in the memory 301 to execute the steps of the flow data prediction method based on multi-nozzle data described in Embodiment 1.

[0066] Example 4 This invention discloses a computer read storage medium that stores a computer program for electronic data interchange, wherein the computer program causes a computer to execute the steps of the flow data prediction method based on multi-nozzle data described in Embodiment 1.

[0067] Example 5 This invention discloses a computer program product, which includes a non-transitory computer-readable storage medium storing a computer program, and the computer program is operable to cause a computer to perform the steps of the flow data prediction method based on multi-nozzle data described in Embodiment 1.

[0068] The foregoing has described specific embodiments of this specification; other embodiments are within the scope of the appended claims. In some cases, the actions or steps described in the claims may be performed in a different order than those shown in the embodiments and may still achieve the desired result. Furthermore, the processes depicted in the drawings do not necessarily have to follow the specific or sequential order shown to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0069] The systems, devices, modules, or units described in the above embodiments can be implemented by computer chips or entities, or by products with certain functions. A typical implementation device is a computer. Specifically, a computer can be, for example, a personal computer, laptop computer, cellular phone, camera phone, smartphone, personal digital assistant, media player, navigation device, email device, game console, tablet computer, wearable device, or any combination of these devices.

[0070] For ease of description, the above devices are described in terms of function, divided into various units. Of course, in implementing this specification, the functions of each unit can be implemented in one or more software and / or hardware components.

[0071] Those skilled in the art will understand that the embodiments of this specification can be provided as methods, systems, or computer program products. Therefore, the embodiments of this specification can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the embodiments of this specification can take the form of a computer program product implemented on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0072] This specification is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this specification. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create a machine for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0073] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0074] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0075] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0076] Memory may include non-persistent storage in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0077] Computer-readable media includes both permanent and non-permanent, removable and non-removable media that can store information using any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0078] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0079] This specification can be described in the general context of computer-executable instructions that are executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc., that perform a specific task or implement a specific abstract data type. This specification can also be practiced in distributed computing environments, where tasks are performed by remote processing devices connected via a communication network. In distributed computing environments, program modules can reside in local and remote computer storage media, including storage devices.

[0080] The various embodiments in this specification are described in a progressive manner. Similar or identical parts between embodiments can be referred to interchangeably. Each embodiment focuses on describing the differences from other embodiments. In particular, the system embodiments are basically similar to the method embodiments, so the description is relatively simple; relevant parts can be referred to the descriptions in the method embodiments.

[0081] Finally, it should be noted that the flow data prediction method and system based on multi-nozzle data disclosed in the embodiments of the present invention are merely preferred embodiments of the present invention and are only used to illustrate the technical solutions of the present invention, not to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A flow rate data prediction method based on multi-nozzle data, characterized in that, The method includes: Acquire sensor data from multiple nozzles of the same diameter in the same printing area of ​​the target 3D printing equipment; Based on the sensor data of all the nozzles, and using a neural network algorithm, the flow rate data corresponding to the target nozzle is predicted, including: The sensing data of each nozzle is input into the flow prediction neural network corresponding to each nozzle to obtain the predicted flow rate of each nozzle. For each nozzle, a mathematical correspondence model between the nozzle and the target nozzle is determined; the mathematical correspondence model is obtained by fitting and analyzing the historical flow data of the nozzle and the target nozzle. The predicted flow rate corresponding to the nozzle is input into the mathematical correspondence model to obtain the candidate flow rate corresponding to the nozzle; Calculate the weighted summation average of the candidate flow rates corresponding to all the nozzles to obtain the flow rate data corresponding to the target nozzle; wherein, the weighted calculation weight of the candidate flow rate corresponding to each nozzle is inversely proportional to the position distance corresponding to the nozzle; the position distance is the position distance between the nozzle and the target nozzle; the target nozzle is a nozzle in the printing area that is not the nozzle corresponding to the sensor data, and its orifice diameter is the same as that of the nozzle corresponding to the sensor data. Based on the current printing time and the printing plan corresponding to the target 3D printing equipment, determine the reference flow range corresponding to the target nozzle; Based on the flow data and the reference flow range, determine whether the target 3D printing equipment has printing abnormalities.

2. The flow rate data prediction method based on multi-nozzle data according to claim 1, characterized in that, The sensing data includes at least one of image sensing data, sound sensing data, flow meter sensing data, and temperature sensing data.

3. The flow rate data prediction method based on multi-nozzle data according to claim 1, characterized in that, The flow prediction neural network is a CNN network, which is trained using a training dataset that includes training sensor data corresponding to multiple nozzles and corresponding flow labels.

4. The flow rate data prediction method based on multi-nozzle data according to claim 1, characterized in that, The step of determining the reference flow range corresponding to the target nozzle based on the current printing time and the printing plan corresponding to the target 3D printing equipment includes: Obtain the printing plan corresponding to the target 3D printing equipment; Based on the current printing time and the aforementioned printing plan, determine the current printing stage; Based on the printing stage flow model corresponding to the target nozzle and the current printing stage, a reference flow range corresponding to the target nozzle is determined; the printing stage flow model is obtained by analyzing the flow data of the target nozzle at different printing stages in a historical time period.

5. The flow rate data prediction method based on multi-nozzle data according to claim 1, characterized in that, The step of determining whether the target 3D printing device has a printing abnormality based on the flow data and the reference flow range includes: Determine whether the traffic data is within the reference traffic range, and obtain the determination result; When the judgment result is yes, it is determined that the target 3D printing device does not have a printing abnormality; If the judgment result is negative, it is determined that the target 3D printing device has a printing abnormality.

6. A flow rate data prediction system based on multi-nozzle data, characterized in that, The system executes the flow data prediction method based on multi-nozzle data as described in any one of claims 1-5, the system comprising: The acquisition module is used to acquire sensor data of multiple nozzles of the same diameter in the same printing area of ​​the target 3D printing equipment; The prediction module is used to predict the flow rate data corresponding to the target nozzle based on the sensing data of all the nozzles and a neural network algorithm. The determination module is used to determine the reference flow range corresponding to the target nozzle based on the current printing time and the printing plan corresponding to the target 3D printing equipment; The judgment module is used to determine whether the target 3D printing device has a printing abnormality based on the flow data and the reference flow range.

7. A flow rate data prediction system based on multi-nozzle data, characterized in that, The system includes: Memory containing executable program code; A processor coupled to the memory; The processor calls the executable program code stored in the memory to execute the flow data prediction method based on multi-nozzle data as described in any one of claims 1-5.

Images