Nozzle flow data processing method and system based on guide rail motion comparison

Abstract

The present invention discloses a nozzle flow data processing method and system based on guide rail motion comparison, the method comprising: obtaining the real-time flow information of the target printing nozzle and the sensor information of the corresponding nozzle guide rail component; identifying the real-time speed information of the target printing nozzle according to the speed recognition algorithm and the sensor information of the nozzle guide rail component; determining the reference flow information of the target printing nozzle according to the mathematical relationship between the speed and flow corresponding to the target printing nozzle and the real-time speed information; judging whether the target printing nozzle has flow anomaly according to the real-time flow information and the reference flow information, and obtaining an abnormal judgment result. It can be seen that the present invention can monitor the flow anomaly of the 3D printing nozzle more accurately and efficiently, and timely correct the flow anomaly of 3D printing, thereby improving the stability and accuracy of 3D printing.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and in particular to a method and system for processing nozzle flow data based on guide rail motion comparison. Background Art

[0002] With the development of 3D printing technology and the increasing demand for 3D printing, more and more 3D printing equipment is being used in various production tasks to improve production efficiency, and the precision requirements for 3D printing are also increasing. Among them, how to effectively and accurately control the flow of 3D printing nozzles to improve printing accuracy has become an important technical issue. However, when the existing technology monitors the flow of 3D printing nozzles, most of them are only realized through the flow monitoring equipment of the nozzle itself, and the mathematical relationship between the change of nozzle potential energy caused by the guide rail movement of the nozzle and the nozzle flow is not fully considered. Therefore, it is impossible to effectively combine the guide rail movement to improve the effect of flow monitoring. The monitoring effect is poor and printing errors cannot be effectively avoided. It can be seen that the existing technology has defects that need to be solved urgently. Summary of the invention

[0003] The technical problem to be solved by the present invention is to provide a nozzle flow data processing method and system based on guide rail motion comparison, which can more accurately and efficiently monitor the flow abnormality of 3D printing nozzles, correct the flow abnormality of 3D printing in time, and improve the stability and accuracy of 3D printing.

[0004] In order to solve the above technical problems, the first aspect of the present invention discloses a method for processing nozzle flow data based on guide rail motion comparison, the method comprising:

[0005] Obtaining real-time flow information of the target print head and sensor information of the corresponding print head guide components;

[0006] According to the speed recognition algorithm and the sensor information of the nozzle guide component, the real-time speed information of the target printing nozzle is identified;

[0007] Determining reference flow information of the target print head according to a mathematical relationship between a speed and a flow rate corresponding to the target print head and the real-time speed information;

[0008] According to the real-time flow information and the reference flow information, it is determined whether the target print head has flow anomaly, and an anomaly determination result is obtained.

[0009] As an optional embodiment, in the first aspect of the present invention, the sensing information includes image information, sound information, infrared sensing information, infrared ranging information and guide rail axis speed sensing information.

[0010] As an optional embodiment, in the first aspect of the present invention, the nozzle guide rail component includes guide rail axes corresponding to multiple degrees of freedom of movement.

[0011] As an optional embodiment, in the first aspect of the present invention, the real-time speed information of the target print head is identified according to the speed identification algorithm and the sensor information of the nozzle guide component, including:

[0012] For the sensor information of each guide rail component of the nozzle guide rail component, the sensor information of the guide rail component is input into the motion information prediction neural network corresponding to the corresponding component type to obtain the predicted motion information corresponding to the guide rail component; the predicted motion information includes the motion direction, motion speed and motion acceleration; the component type is the guide rail axis corresponding to different degrees of freedom of motion;

[0013] Determine the component predicted speed information of the target print head corresponding to the guide rail component according to the motion correlation relationship between the component corresponding to the guide rail component and the print head, and the predicted motion information;

[0014] The weighted average value of the predicted speed information of the components corresponding to all the guide rail components is calculated to obtain the real-time speed information of the target print head.

[0015] As an optional embodiment, in the first aspect of the present invention, the motion information prediction neural network is a CNN neural network, which is trained by a training data set including training sensor information of multiple corresponding component types and corresponding motion information annotations.

[0016] As an optional embodiment, in the first aspect of the present invention, when calculating the weighted sum average of the component predicted speed information corresponding to all the guide rail components, the weight corresponding to each component predicted speed information includes a first weight and a second weight; the first weight is proportional to the position distance between the corresponding guide rail component and the target print head; the second weight is proportional to the directional angle difference between the direction of the degree of freedom of movement corresponding to the corresponding guide rail component and a preset standard degree of freedom direction; the standard degree of freedom direction is obtained by weighted summing and averaging the degree of freedom of movement corresponding to all the guide rail components.

[0017] As an optional implementation, in the first aspect of the present invention, the determining the reference flow information of the target print head according to the mathematical relationship between the speed and flow corresponding to the target print head and the real-time speed information includes:

[0018] Based on the printing device model to which the target printing nozzle belongs, a mathematical relationship model of the speed and flow rate corresponding to the printing device model is determined in a preset mathematical relationship database; the mathematical relationship model is obtained by fitting calculation of multiple historical speed information and historical flow rate information of the printing nozzle corresponding to the printing device model;

[0019] The real-time speed information is input into the mathematical relationship model to obtain reference flow information of the target print head.

[0020] As an optional implementation, in the first aspect of the present invention, judging whether there is flow abnormality in the target print head according to the real-time flow information and the reference flow information, and obtaining the abnormality judgment result, includes:

[0021] Calculating a flow rate difference between the real-time flow rate information and the reference flow rate information;

[0022] Determine whether the flow difference is greater than a preset first difference threshold, and obtain a first determination result;

[0023] If the first judgment result is yes, determining that the target print head has a flow abnormality;

[0024] If the first judgment result is no, determine whether the flow difference is less than a second difference threshold, and obtain a second judgment result; the second difference threshold is less than the first difference threshold;

[0025] If the second judgment result is yes, determining that there is no flow abnormality in the target print head;

[0026] If the second judgment result is no, calculating the average value of all the flow rate differences of the target print head in the most recent historical time period to obtain a historical flow rate difference parameter;

[0027] It is determined whether the historical flow difference parameter is greater than the first difference threshold value. If so, it is determined that there is a flow abnormality in the target print head. If not, it is determined that there is no flow abnormality in the target print head.

[0028] A second aspect of an embodiment of the present invention discloses a nozzle flow data processing system based on guide rail motion comparison, the system comprising:

[0029] An acquisition module, used to acquire real-time flow information of a target printing nozzle and sensor information of a corresponding nozzle guide component;

[0030] An identification module, used for identifying the real-time speed information of the target printing nozzle according to the speed identification algorithm and the sensor information of the nozzle guide component;

[0031] A determination module, configured to determine reference flow information of the target print head according to a mathematical relationship between a speed and a flow rate corresponding to the target print head and the real-time speed information;

[0032] The judgment module is used to judge whether there is flow abnormality in the target printing nozzle according to the real-time flow information and the reference flow information, and obtain an abnormality judgment result.

[0033] As an optional embodiment, in the second aspect of the present invention, the sensing information includes image information, sound information, infrared sensing information, infrared ranging information and guide rail axis speed sensing information.

[0034] As an optional embodiment, in the second aspect of the present invention, the nozzle guide rail component includes guide rail axes corresponding to multiple degrees of freedom of movement.

[0035] As an optional embodiment, in the second aspect of the present invention, the specific manner in which the recognition module recognizes the real-time speed information of the target print head according to the speed recognition algorithm and the sensor information of the nozzle guide component includes:

[0036] For the sensor information of each guide rail component of the nozzle guide rail component, the sensor information of the guide rail component is input into the motion information prediction neural network corresponding to the corresponding component type to obtain the predicted motion information corresponding to the guide rail component; the predicted motion information includes the motion direction, motion speed and motion acceleration; the component type is the guide rail axis corresponding to different degrees of freedom of motion;

[0037] Determine the component predicted speed information of the target print head corresponding to the guide rail component according to the motion correlation relationship between the component corresponding to the guide rail component and the print head, and the predicted motion information;

[0038] The weighted average value of the predicted speed information of the components corresponding to all the guide rail components is calculated to obtain the real-time speed information of the target print head.

[0039] As an optional embodiment, in the second aspect of the present invention, the motion information prediction neural network is a CNN neural network, which is trained by a training data set including training sensor information of multiple corresponding component types and corresponding motion information annotations.

[0040] As an optional embodiment, in the second aspect of the present invention, when calculating the weighted sum average of the component predicted speed information corresponding to all the guide rail components, the weight corresponding to each component predicted speed information includes a first weight and a second weight; the first weight is proportional to the position distance between the corresponding guide rail component and the target print head; the second weight is proportional to the directional angle difference between the direction of the degree of freedom of movement corresponding to the corresponding guide rail component and a preset standard degree of freedom direction; the standard degree of freedom direction is obtained by weighted summing and averaging the degree of freedom of movement corresponding to all the guide rail components.

[0041] As an optional implementation, in the second aspect of the present invention, the specific manner in which the determination module determines the reference flow information of the target print head according to the mathematical relationship between the speed and flow corresponding to the target print head and the real-time speed information includes:

[0042] Based on the printing device model to which the target printing nozzle belongs, a mathematical relationship model of the speed and flow rate corresponding to the printing device model is determined in a preset mathematical relationship database; the mathematical relationship model is obtained by fitting calculation of multiple historical speed information and historical flow rate information of the printing nozzle corresponding to the printing device model;

[0043] The real-time speed information is input into the mathematical relationship model to obtain reference flow information of the target print head.

[0044] As an optional implementation, in the second aspect of the present invention, the judgment module judges whether there is a flow abnormality in the target print head according to the real-time flow information and the reference flow information, and a specific method for obtaining the abnormality judgment result includes:

[0045] Calculating a flow rate difference between the real-time flow rate information and the reference flow rate information;

[0046] Determine whether the flow difference is greater than a preset first difference threshold, and obtain a first determination result;

[0047] If the first judgment result is yes, determining that the target print head has a flow abnormality;

[0048] If the first judgment result is no, determine whether the flow difference is less than a second difference threshold, and obtain a second judgment result; the second difference threshold is less than the first difference threshold;

[0049] If the second judgment result is yes, determining that there is no flow abnormality in the target print head;

[0050] If the second judgment result is no, calculating the average value of all the flow rate differences of the target print head in the most recent historical time period to obtain a historical flow rate difference parameter;

[0051] It is determined whether the historical flow difference parameter is greater than the first difference threshold value. If so, it is determined that there is a flow abnormality in the target print head. If not, it is determined that there is no flow abnormality in the target print head.

[0052] The third aspect of the present invention discloses another nozzle flow data processing system based on guide rail motion comparison, the system comprising:

[0053] A memory storing executable program code;

[0054] a processor coupled to the memory;

[0055] The processor calls the executable program code stored in the memory to execute part or all of the steps in the nozzle flow data processing method based on guide rail motion comparison disclosed in the first aspect of the present invention.

[0056] 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 part or all of the steps in the nozzle flow data processing method based on guide rail motion comparison disclosed in the first aspect of the present invention.

[0057] Compared with the prior art, the embodiments of the present invention have the following beneficial effects:

[0058] The present invention can accurately identify the real-time speed information of the printing nozzle according to the speed recognition algorithm and the sensor information of the nozzle guide component, and then determine the reference flow rate based on the mathematical relationship between the speed and flow rate corresponding to the target printing nozzle and the real-time speed, so as to accurately judge the flow rate abnormality of the target printing nozzle, thereby being able to more accurately and efficiently monitor the flow rate abnormality of the 3D printing nozzle, and timely correct the flow rate abnormality of 3D printing, thereby improving the stability and accuracy of 3D printing. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] 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.

[0060] Figure 1 It is a flow chart of a method for processing nozzle flow data based on guide rail motion comparison disclosed in an embodiment of the present invention.

[0061] Figure 2 It is a structural schematic diagram of a nozzle flow data processing system based on guide rail motion comparison disclosed in an embodiment of the present invention.

[0062] Figure 3 It is a structural schematic diagram of another nozzle flow data processing system based on guide rail motion comparison disclosed in an embodiment of the present invention. DETAILED DESCRIPTION

[0063] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in 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. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0064] The terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish different objects, rather than to describe a specific order. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, device, product or equipment that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units that are not listed, or may optionally include other steps or units that are inherent to these processes, methods, products or equipment.

[0065] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present invention. The appearance of the phrase in various places in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0066] The present invention discloses a nozzle flow data processing method and system based on guide rail motion comparison, which can accurately identify the real-time speed information of the printing nozzle according to the speed identification algorithm and the sensor information of the nozzle guide rail component, and then determine the reference flow based on the mathematical relationship between the speed and flow corresponding to the target printing nozzle and the real-time speed, so as to accurately judge the flow anomaly of the target printing nozzle, thereby being able to more accurately and efficiently monitor the flow anomaly of the 3D printing nozzle, and timely correct the flow anomaly of 3D printing, and improve the stability and accuracy of 3D printing. The following are detailed descriptions.

[0067] Embodiment 1

[0068] See also Figure 1 , Figure 1 1 is a flow chart of a method for processing nozzle flow data based on guide rail motion comparison disclosed in an embodiment of the present invention. Figure 1 The nozzle flow data processing method based on rail motion comparison 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 1 As shown, the nozzle flow data processing method based on guide rail motion comparison may include the following operations:

[0069] 101. Obtain real-time flow information of a target printing nozzle and sensor information of a corresponding nozzle guide component.

[0070] 102. According to the speed recognition algorithm and the sensor information of the nozzle guide component, the real-time speed information of the target printing nozzle is identified.

[0071] 103. Determine reference flow information of the target print nozzle according to the mathematical relationship between the speed and flow corresponding to the target print nozzle and the real-time speed information.

[0072] 104. According to the real-time flow information and the reference flow information, determine whether there is flow abnormality in the target print head, and obtain an abnormality determination result.

[0073] It can be seen that the above-mentioned embodiments of the invention can accurately identify the real-time speed information of the printing nozzle according to the speed identification algorithm and the sensor information of the nozzle guide component, and then determine the reference flow rate based on the mathematical relationship between the speed and flow rate corresponding to the target printing nozzle and the real-time speed, so as to accurately judge the flow abnormality of the target printing nozzle, thereby being able to more accurately and efficiently monitor the flow abnormality of the 3D printing nozzle, and promptly correct the flow abnormality of 3D printing, thereby improving the stability and accuracy of 3D printing.

[0074] As an optional embodiment, in the above steps, the sensing information includes image information, sound information, infrared sensing information, infrared ranging information and guide rail axis speed sensing information.

[0075] It can be seen that through the above optional embodiments, the content of the sensor information is limited, which can more accurately characterize the movement of the guide rail component, assist in more accurate and efficient monitoring of the flow abnormality of the 3D printing nozzle, and timely correct the flow abnormality of 3D printing, thereby improving the stability and accuracy of 3D printing.

[0076] As an optional embodiment, in the above steps, the nozzle guide rail component includes guide rail axes corresponding to multiple degrees of freedom of movement.

[0077] It can be seen that through the above-mentioned optional embodiments, the component type of the nozzle guide rail component is limited, and its sensor information can be more accurately used to accurately predict the speed of the nozzle, thereby assisting in more accurate and efficient monitoring of the flow abnormality of the 3D printing nozzle, so as to promptly correct the flow abnormality of 3D printing and improve the stability and accuracy of 3D printing.

[0078] As an optional embodiment, in the above steps, identifying the real-time speed information of the target print head according to the speed recognition algorithm and the sensor information of the nozzle guide component includes:

[0079] For the sensor information of each guide rail component of the nozzle guide rail component, the sensor information of the guide rail component is input into the motion information prediction neural network corresponding to the corresponding component type to obtain the predicted motion information corresponding to the guide rail component; optionally, the predicted motion information includes the motion direction, motion speed and motion acceleration; the component type is the guide rail axis corresponding to different degrees of freedom of motion;

[0080] Determine the component predicted speed information of the target printing nozzle corresponding to the guide rail component according to the motion correlation relationship between the component corresponding to the guide rail component and the nozzle, and the predicted motion information;

[0081] The weighted average value of the predicted speed information of the components corresponding to all guide rail components is calculated to obtain the real-time speed information of the target print head.

[0082] It can be seen that through the above-mentioned optional embodiments, the motion information can be predicted based on the motion information prediction neural network corresponding to the component type based on the sensor information of each guide rail component, and then the predicted nozzle speed corresponding to the component is determined based on the motion correlation relationship between the guide rail component and the nozzle, and the precise real-time speed information of the target printing nozzle is determined based on the weighted sum calculation, so as to facilitate subsequent abnormality judgment, assist in realizing more accurate and efficient monitoring of the flow abnormality of the 3D printing nozzle, and timely correct the flow abnormality of 3D printing, thereby improving the stability and accuracy of 3D printing.

[0083] As an optional embodiment, in the above steps, the motion information prediction neural network is a CNN neural network, which is trained by a training data set including training sensor information of multiple corresponding component types and corresponding motion information annotations.

[0084] It can be seen that through the above optional embodiments, the model details and training details of the motion information prediction neural network are limited to accurately predict the motion information of the components, assist in more accurate and efficient monitoring of the flow abnormalities of the 3D printing nozzle, and promptly correct the flow abnormalities of 3D printing, thereby improving the stability and accuracy of 3D printing.

[0085] As an optional embodiment, in the above steps, when calculating the weighted sum average of the component predicted speed information corresponding to all guide rail components, the weight corresponding to each component predicted speed information includes a first weight and a second weight; the first weight is proportional to the position distance between the corresponding guide rail component and the target print head; the second weight is proportional to the directional angle difference between the movement freedom direction corresponding to the corresponding guide rail component and the preset standard degree of freedom direction; the standard degree of freedom direction is obtained by weighted summing and averaging the movement freedom directions corresponding to all guide rail components.

[0086] It can be seen that through the above-mentioned optional embodiments, it is possible to set the importance weights of different guide rail components based on the differences in position distance and angle, so that the calculated real-time speed information is more reasonable and accurate, which is convenient for subsequent abnormality judgment, and helps to achieve more accurate and efficient monitoring of the flow abnormality of the 3D printing nozzle, so as to correct the flow abnormality of 3D printing in time and improve the stability and accuracy of 3D printing.

[0087] As an optional embodiment, in the above steps, determining the reference flow information of the target print head according to the mathematical relationship between the speed and flow corresponding to the target print head and the real-time speed information includes:

[0088] Based on the printing device model to which the target printing nozzle belongs, a mathematical relationship model of the speed and flow rate corresponding to the printing device model is determined in a preset mathematical relationship database; optionally, the mathematical relationship model is obtained by fitting calculation of multiple historical speed information and historical flow rate information of the printing nozzle corresponding to the printing device model;

[0089] The real-time speed information is input into the mathematical relationship model to obtain the reference flow information of the target print head.

[0090] It can be seen that through the above-mentioned optional embodiments, the corresponding mathematical relationship model of speed and flow can be screened out based on the printing device model to which the target print nozzle belongs, and the reference flow information of the target print nozzle can be accurately determined based on this, so as to facilitate subsequent abnormality judgment, and assist in more accurate and efficient monitoring of the flow abnormality of the 3D printing nozzle, so as to correct the flow abnormality of 3D printing in time and improve the stability and accuracy of 3D printing.

[0091] As an optional embodiment, in the above steps, judging whether there is flow abnormality in the target print head according to the real-time flow information and the reference flow information, and obtaining the abnormality judgment result includes:

[0092] Calculate the flow difference between the real-time flow information and the reference flow information;

[0093] Determine whether the flow difference is greater than a preset first difference threshold, and obtain a first determination result;

[0094] If the first judgment result is yes, it is determined that the target print head has a flow abnormality;

[0095] If the first judgment result is no, determine whether the flow difference is less than a second difference threshold value to obtain a second judgment result; optionally, the second difference threshold value is less than the first difference threshold value;

[0096] If the second judgment result is yes, it is determined that there is no flow abnormality in the target print head;

[0097] If the second judgment result is no, the average value of all flow rate differences of the target print head in the most recent historical time period is calculated to obtain a historical flow rate difference parameter;

[0098] It is determined whether the historical flow difference parameter is greater than a first difference threshold value. If so, it is determined that a flow abnormality exists in the target print head. If not, it is determined that no flow abnormality exists in the target print head.

[0099] It can be seen that through the above optional embodiments, accurate flow anomaly judgment can be achieved based on the flow difference between real-time flow information and reference flow information and the judgment of multiple thresholds, so as to more accurately and efficiently monitor the flow anomaly of the 3D printing nozzle, and promptly correct the flow anomaly of 3D printing, thereby improving the stability and accuracy of 3D printing.

[0100] Embodiment 2

[0101] See also Figure 2 , Figure 2 Schematic diagram of a nozzle flow data processing system based on guide rail motion comparison disclosed in an embodiment of the present invention. Figure 2 The nozzle flow data processing system based on rail motion comparison 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 nozzle flow data processing system based on guide rail motion comparison may include:

[0102] The acquisition module 201 is used to acquire the real-time flow information of the target printing nozzle and the sensor information of the corresponding nozzle guide component.

[0103] The identification module 202 is used to identify the real-time speed information of the target printing nozzle according to the speed identification algorithm and the sensor information of the nozzle guide component.

[0104] The determination module 203 is used to determine the reference flow information of the target print head according to the mathematical relationship between the speed and flow corresponding to the target print head and the real-time speed information.

[0105] The judgment module 204 is used to judge whether there is flow abnormality in the target printing nozzle according to the real-time flow information and the reference flow information, and obtain an abnormality judgment result.

[0106] It can be seen that the above-mentioned embodiments of the invention can accurately identify the real-time speed information of the printing nozzle according to the speed identification algorithm and the sensor information of the nozzle guide component, and then determine the reference flow rate based on the mathematical relationship between the speed and flow rate corresponding to the target printing nozzle and the real-time speed, so as to accurately judge the flow abnormality of the target printing nozzle, thereby being able to more accurately and efficiently monitor the flow abnormality of the 3D printing nozzle, and promptly correct the flow abnormality of 3D printing, thereby improving the stability and accuracy of 3D printing.

[0107] As an optional embodiment, the sensing information includes image information, sound information, infrared sensing information, infrared ranging information and guide rail axis speed sensing information.

[0108] It can be seen that through the above optional embodiments, the content of the sensor information is limited, which can more accurately characterize the movement of the guide rail component, assist in more accurate and efficient monitoring of the flow abnormality of the 3D printing nozzle, and timely correct the flow abnormality of 3D printing, thereby improving the stability and accuracy of 3D printing.

[0109] As an optional embodiment, the nozzle guide rail component includes guide rail axes corresponding to multiple degrees of freedom of movement.

[0110] It can be seen that through the above-mentioned optional embodiments, the component type of the nozzle guide rail component is limited, and its sensor information can be more accurately used to accurately predict the speed of the nozzle, thereby assisting in more accurate and efficient monitoring of the flow abnormality of the 3D printing nozzle, so as to promptly correct the flow abnormality of 3D printing and improve the stability and accuracy of 3D printing.

[0111] As an optional embodiment, the specific manner in which the recognition module recognizes the real-time speed information of the target print head according to the speed recognition algorithm and the sensor information of the nozzle guide component includes:

[0112] For the sensor information of each guide rail component of the nozzle guide rail component, the sensor information of the guide rail component is input into the motion information prediction neural network corresponding to the corresponding component type to obtain the predicted motion information corresponding to the guide rail component; optionally, the predicted motion information includes the motion direction, motion speed and motion acceleration; the component type is the guide rail axis corresponding to different degrees of freedom of motion;

[0113] Determine the component predicted speed information of the target printing nozzle corresponding to the guide rail component according to the motion correlation relationship between the component corresponding to the guide rail component and the nozzle, and the predicted motion information;

[0114] The weighted average value of the predicted speed information of the components corresponding to all guide rail components is calculated to obtain the real-time speed information of the target print head.

[0115] It can be seen that through the above-mentioned optional embodiments, the motion information can be predicted based on the motion information prediction neural network corresponding to the component type based on the sensor information of each guide rail component, and then the predicted nozzle speed corresponding to the component is determined based on the motion correlation relationship between the guide rail component and the nozzle, and the precise real-time speed information of the target printing nozzle is determined based on the weighted sum calculation, so as to facilitate subsequent abnormality judgment, assist in realizing more accurate and efficient monitoring of the flow abnormality of the 3D printing nozzle, and timely correct the flow abnormality of 3D printing, thereby improving the stability and accuracy of 3D printing.

[0116] As an optional embodiment, the motion information prediction neural network is a CNN neural network, which is trained by a training data set including training sensor information of multiple corresponding component types and corresponding motion information annotations.

[0117] It can be seen that through the above optional embodiments, the model details and training details of the motion information prediction neural network are limited to accurately predict the motion information of the components, assist in more accurate and efficient monitoring of the flow abnormalities of the 3D printing nozzle, and promptly correct the flow abnormalities of 3D printing, thereby improving the stability and accuracy of 3D printing.

[0118] As an optional embodiment, when calculating the weighted sum average of the component predicted speed information corresponding to all guide rail components, the weight corresponding to each component predicted speed information includes a first weight and a second weight; the first weight is proportional to the position distance between the corresponding guide rail component and the target print head; the second weight is proportional to the directional angle difference between the movement freedom direction corresponding to the corresponding guide rail component and the preset standard degree of freedom direction; the standard degree of freedom direction is obtained by weighted summing and averaging the movement freedom directions corresponding to all guide rail components.

[0119] It can be seen that through the above-mentioned optional embodiments, it is possible to set the importance weights of different guide rail components based on the differences in position distance and angle, so that the calculated real-time speed information is more reasonable and accurate, which is convenient for subsequent abnormality judgment, and helps to achieve more accurate and efficient monitoring of the flow abnormality of the 3D printing nozzle, so as to correct the flow abnormality of 3D printing in time and improve the stability and accuracy of 3D printing.

[0120] As an optional embodiment, the specific manner in which the determination module determines the reference flow information of the target print head according to the mathematical relationship between the speed and flow corresponding to the target print head and the real-time speed information includes:

[0121] Based on the printing device model to which the target printing nozzle belongs, a mathematical relationship model of the speed and flow rate corresponding to the printing device model is determined in a preset mathematical relationship database; optionally, the mathematical relationship model is obtained by fitting calculation of multiple historical speed information and historical flow rate information of the printing nozzle corresponding to the printing device model;

[0122] The real-time speed information is input into the mathematical relationship model to obtain the reference flow information of the target print head.

[0123] It can be seen that through the above-mentioned optional embodiments, the corresponding mathematical relationship model of speed and flow can be screened out based on the printing device model to which the target print nozzle belongs, and the reference flow information of the target print nozzle can be accurately determined based on this, so as to facilitate subsequent abnormality judgment, and assist in more accurate and efficient monitoring of the flow abnormality of the 3D printing nozzle, so as to correct the flow abnormality of 3D printing in time and improve the stability and accuracy of 3D printing.

[0124] As an optional embodiment, the judgment module judges whether there is flow abnormality in the target print head according to the real-time flow information and the reference flow information, and obtains the specific method of the abnormality judgment result, including:

[0125] Calculate the flow difference between the real-time flow information and the reference flow information;

[0126] Determine whether the flow difference is greater than a preset first difference threshold, and obtain a first determination result;

[0127] If the first judgment result is yes, it is determined that the target print head has a flow abnormality;

[0128] If the first judgment result is no, determine whether the flow difference is less than a second difference threshold value to obtain a second judgment result; optionally, the second difference threshold value is less than the first difference threshold value;

[0129] If the second judgment result is yes, it is determined that there is no flow abnormality in the target print head;

[0130] If the second judgment result is no, the average value of all flow rate differences of the target print head in the most recent historical time period is calculated to obtain a historical flow rate difference parameter;

[0131] It is determined whether the historical flow difference parameter is greater than a first difference threshold value. If so, it is determined that a flow abnormality exists in the target print head. If not, it is determined that no flow abnormality exists in the target print head.

[0132] It can be seen that through the above optional embodiments, accurate flow anomaly judgment can be achieved based on the flow difference between real-time flow information and reference flow information and the judgment of multiple thresholds, so as to more accurately and efficiently monitor the flow anomaly of the 3D printing nozzle, and promptly correct the flow anomaly of 3D printing, thereby improving the stability and accuracy of 3D printing.

[0133] Embodiment 3

[0134] See also Figure 3 , Figure 3 It is another nozzle flow data processing system based on guide rail motion comparison disclosed in an embodiment of the present invention. Figure 3 The nozzle flow data processing system based on rail motion comparison 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 nozzle flow data processing system based on guide rail motion comparison may include:

[0135] A memory 301 storing executable program codes;

[0136] a processor 302 coupled to the memory 301;

[0137] The processor 302 calls the executable program code stored in the memory 301 to execute the steps of the nozzle flow data processing method based on guide rail motion comparison described in the first embodiment.

[0138] Embodiment 4

[0139] 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 method for processing nozzle flow data based on guide rail motion comparison described in the first embodiment.

[0140] Embodiment 5

[0141] An embodiment of the present invention discloses a computer program product, which includes a non-transitory computer-readable storage medium storing a computer program, and the computer program is operable to enable a computer to execute the steps of the nozzle flow data processing method based on guide rail motion comparison described in Example 1.

[0142] The above describes specific embodiments of the present specification, and other embodiments are within the scope of the appended claims. In some cases, the actions or steps recorded in the claims can be performed in an order different from that in the embodiments and still achieve the desired results. In addition, the processes depicted in the accompanying drawings do not necessarily have to be performed in the specific order or sequential order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0143] The systems, devices, modules or units described in the above embodiments may be implemented by computer chips or entities, or by products with 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 smart phone, 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.

[0144] For the convenience of description, the above device is described in 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.

[0145] 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 be in the form of complete hardware embodiments, complete software embodiments, or embodiments in combination with software and hardware. Moreover, the embodiments of this specification may be in the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0146] 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 the 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.

[0147] These computer program instructions may also be stored in a computer readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture including an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0148] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process in the computer or other programmable device. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0149] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0150] The 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. The memory is an example of a computer-readable medium.

[0151] 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. 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 disk read-only memory (CD-ROM), digital versatile disk (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 temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0152] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0153] This specification may be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc. 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 communication network. In a distributed computing environment, program modules may be located in local and remote computer storage media, including storage devices.

[0154] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the system embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0155] Finally, it should be noted that the method and system for processing nozzle flow data based on guide rail motion comparison disclosed in the embodiment of the present invention only disclose the preferred embodiments of the present invention, which are only used to illustrate the technical solution of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments can still be modified, or some of the technical features can 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 method for processing nozzle flow data based on guide rail motion comparison, characterized in that: The method comprises: Acquire real-time flow information of a target print head and sensor information of a corresponding nozzle guide rail component; the nozzle guide rail component includes guide rail axes corresponding to a plurality of degrees of freedom of motion; According to the speed recognition algorithm and the sensor information of the nozzle guide component, the real-time speed information of the target printing nozzle is identified, including: For the sensor information of each guide rail component of the nozzle guide rail component, the sensor information of the guide rail component is input into the motion information prediction neural network corresponding to the corresponding component type to obtain the predicted motion information corresponding to the guide rail component; the predicted motion information includes the motion direction, motion speed and motion acceleration; the component type is the guide rail axis corresponding to different degrees of freedom of motion; Determine the component predicted speed information of the target print head corresponding to the guide rail component according to the motion correlation relationship between the component corresponding to the guide rail component and the print head, and the predicted motion information; Calculate the weighted sum average of the component predicted speed information corresponding to all the guide rail components to obtain the real-time speed information of the target print head; when calculating the weighted sum average of the component predicted speed information corresponding to all the guide rail components, the weight corresponding to each component predicted speed information includes a first weight and a second weight; the first weight is proportional to the position distance between the corresponding guide rail component and the target print head; the second weight is proportional to the direction angle difference between the motion degree of freedom direction corresponding to the corresponding guide rail component and a preset standard degree of freedom direction; the standard degree of freedom direction is obtained by weighted summing and averaging the motion degree of freedom directions corresponding to all the guide rail components; Determining reference flow information of the target print head according to a mathematical relationship between a speed and a flow rate corresponding to the target print head and the real-time speed information; According to the real-time flow information and the reference flow information, it is determined whether the target print head has flow anomaly, and an anomaly determination result is obtained.

2. The nozzle flow data processing method based on guide rail motion comparison according to claim 1 is characterized in that: The sensing information includes image information, sound information, infrared sensing information, infrared ranging information and guide rail axis speed sensing information.

3. The nozzle flow data processing method based on guide rail motion comparison according to claim 1 is characterized in that: The motion information prediction neural network is a CNN neural network, which is trained by a training data set including training sensor information of multiple corresponding component types and corresponding motion information annotations.

4. The method for processing nozzle flow data based on guide rail motion comparison according to claim 1, characterized in that: Determining the reference flow information of the target print head according to the mathematical relationship between the speed and flow corresponding to the target print head and the real-time speed information includes: Based on the printing device model to which the target printing nozzle belongs, a mathematical relationship model of the speed and flow rate corresponding to the printing device model is determined in a preset mathematical relationship database; the mathematical relationship model is obtained by fitting calculation of multiple historical speed information and historical flow rate information of the printing nozzle corresponding to the printing device model; The real-time speed information is input into the mathematical relationship model to obtain reference flow information of the target print head.

5. The method for processing nozzle flow data based on guide rail motion comparison according to claim 1, characterized in that: The determining, based on the real-time flow information and the reference flow information, whether the target print head has a flow abnormality, and obtaining an abnormality determination result, includes: Calculating a flow rate difference between the real-time flow rate information and the reference flow rate information; Determine whether the flow difference is greater than a preset first difference threshold, and obtain a first determination result; If the first judgment result is yes, determining that the target print head has a flow abnormality; If the first judgment result is no, determine whether the flow difference is less than a second difference threshold, and obtain a second judgment result; the second difference threshold is less than the first difference threshold; If the second judgment result is yes, determining that there is no flow abnormality in the target print head; If the second judgment result is no, calculating the average value of all the flow rate differences of the target print head in the most recent historical time period to obtain a historical flow rate difference parameter; It is determined whether the historical flow difference parameter is greater than the first difference threshold value. If so, it is determined that there is a flow abnormality in the target print head. If not, it is determined that there is no flow abnormality in the target print head.

6. A nozzle flow data processing system based on guide rail motion comparison, characterized in that: The system comprises: An acquisition module, used to acquire real-time flow information of a target printing nozzle and sensor information of a corresponding nozzle guide rail component; the nozzle guide rail component includes guide rail axes corresponding to a plurality of degrees of freedom of motion; The identification module is used to identify the real-time speed information of the target printing nozzle according to the speed identification algorithm and the sensor information of the nozzle guide component, including: For the sensor information of each guide rail component of the nozzle guide rail component, the sensor information of the guide rail component is input into the motion information prediction neural network corresponding to the corresponding component type to obtain the predicted motion information corresponding to the guide rail component; the predicted motion information includes the motion direction, motion speed and motion acceleration; the component type is the guide rail axis corresponding to different degrees of freedom of motion; Determine the component predicted speed information of the target print head corresponding to the guide rail component according to the motion correlation relationship between the component corresponding to the guide rail component and the print head, and the predicted motion information; Calculate the weighted sum average of the component predicted speed information corresponding to all the guide rail components to obtain the real-time speed information of the target print head; when calculating the weighted sum average of the component predicted speed information corresponding to all the guide rail components, the weight corresponding to each component predicted speed information includes a first weight and a second weight; the first weight is proportional to the position distance between the corresponding guide rail component and the target print head; the second weight is proportional to the direction angle difference between the motion degree of freedom direction corresponding to the corresponding guide rail component and a preset standard degree of freedom direction; the standard degree of freedom direction is obtained by weighted summing and averaging the motion degree of freedom directions corresponding to all the guide rail components; A determination module, configured to determine reference flow information of the target print head according to a mathematical relationship between a speed and a flow rate corresponding to the target print head and the real-time speed information; The judgment module is used to judge whether there is flow abnormality in the target printing nozzle according to the real-time flow information and the reference flow information, and obtain an abnormality judgment result.

7. A nozzle flow data processing system based on guide rail motion comparison, 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 nozzle flow data processing method based on guide rail motion comparison as described in any one of claims 1-5.

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