Equipment printing sorting method and system based on 3D printing task monitoring

Abstract

The invention discloses an equipment printing sorting method and system based on 3D printing task monitoring. The method comprises the steps that printing tasks and real-time sensing data corresponding to multiple pieces of 3D printing equipment are obtained; according to the real-time sensing data, the printing progress corresponding to each printing task is predicted; according to the printing progress and the real-time sensing data, the busy degree of each 3D printing device is determined; and according to the busy degree, the multiple 3D printing devices are sequenced to obtain a printing device sequence selected by the to-be-printed user. It can be seen that accurate equipment sorting based on the real-time progress and the equipment state can be achieved, the 3D printing task distribution efficiency and user selection convenience are improved, and the equipment overload risk is reduced.

Description

Technical Field

[0001] The present invention relates to the field of data processing technology, and in particular to a device printing sorting method and system based on 3D printing task monitoring. Background Art

[0002] With the widespread application of 3D printing technology in industrial and personalized manufacturing, users and businesses are increasingly interested in optimizing device utilization and printing experience through efficient task allocation. Existing technologies typically collect task logs and basic status data from 3D printing devices, using simple queue management or manual scheduling methods to determine the device allocation order to meet printing needs. Existing solutions lack dynamic predictive analysis of real-time printing progress and device sensor data, making it difficult to accurately assess device busyness and operating status. Commonly used fixed sorting rules are unable to adapt to complex multi-device scenarios, resulting in inefficient task allocation, inconvenient user selection, and the risk of device overload or printing delays, limiting the overall performance and user experience of the 3D printing system. Clearly, existing technologies have flaws that need to be addressed urgently. Summary of the Invention

[0003] The technical problem to be solved by the present invention is to provide a device printing sorting method and system based on 3D printing task monitoring, which can realize accurate device sorting based on real-time progress and device status, improve the efficiency of 3D printing task allocation and user selection convenience, and reduce the risk of equipment overload.

[0004] In order to solve the above technical problems, the first aspect of the present invention discloses a device printing sorting method based on 3D printing task monitoring, the method comprising: Obtain printing tasks and real-time sensor data corresponding to multiple 3D printing devices; Predicting the printing progress corresponding to each of the printing tasks based on the real-time sensing data; Determining the busyness of each of the 3D printing devices based on the printing progress and the real-time sensor data; The plurality of 3D printing devices are sorted according to the busyness to obtain a printing device sequence for selection by a user to be printed.

[0005] As an optional embodiment, in the first aspect of the present invention, the real-time sensing data includes at least one of light reflection data, temperature data, humidity data, image data, sound data and light intensity data.

[0006] As an optional embodiment, in the first aspect of the present invention, predicting the printing progress corresponding to each printing task based on the real-time sensor data includes: For each of the printing tasks, determining a task arrival time when the printing task is sent to the corresponding 3D printing device; Acquiring the real-time sensor data of the 3D printing device within a time period of the task arrival time to obtain task sensor data corresponding to the printing task; the time period is a time period formed by a preset duration starting from the task arrival time; Based on the progress prediction algorithm, the printing progress corresponding to the printing task is predicted according to the task sensing data.

[0007] As an optional embodiment, in the first aspect of the present invention, determining the task arrival time of sending the printing task to the corresponding 3D printing device includes: Get the task generation time corresponding to the print task; Obtaining historical task printing records corresponding to the 3D printing device corresponding to the printing task; Calculate the average of the time difference between the print task generation time and the print task execution start time in the historical task printing record to obtain the average arrival time; The sum of the task generation time and the average arrival time is calculated to obtain the task arrival time when the printing task is sent to the corresponding 3D printing device.

[0008] As an optional embodiment, in the first aspect of the present invention, the predicting of the printing progress corresponding to the printing task based on the task sensor data based on the progress prediction algorithm includes: Determine the task type corresponding to the print task; Determining a progress prediction neural network corresponding to the task type from a plurality of preset candidate progress prediction neural networks; the progress prediction neural network is trained using a training data set including training sensor data corresponding to the plurality of task types and corresponding work progress annotations; The task sensing data is input into the progress prediction neural network to obtain the printing progress corresponding to the printing task.

[0009] As an optional embodiment, in the first aspect of the present invention, determining the busyness of each of the 3D printing devices based on the printing progress and the real-time sensor data includes: For each of the 3D printing devices, obtaining the printing progress corresponding to all the printing tasks corresponding to the 3D printing device to obtain a printing progress set; Determining a first busy parameter corresponding to the 3D printing device according to the printing progress set; Determining a second busy parameter corresponding to the 3D printing device based on the real-time sensor data and a busy prediction algorithm model corresponding to the 3D printing device; The product of the first busy parameter and the second busy parameter is calculated to obtain the busyness of the 3D printing device.

[0010] As an optional embodiment, in the first aspect of the present invention, determining the first busy parameter corresponding to the 3D printing device according to the printing progress set includes: Calculate the weighted sum average corresponding to all the printing progress in the printing progress set to obtain a first busy parameter corresponding to the 3D printing device; wherein the calculation weight corresponding to each printing progress is the product of a first weight and a second weight; the first weight is proportional to the amount of task data corresponding to the printing task corresponding to the printing progress; and the second weight is proportional to the time difference between the task generation time corresponding to the printing task corresponding to the printing progress and the current time point.

[0011] As an optional embodiment, in the first aspect of the present invention, sorting the plurality of 3D printing devices according to the busyness to obtain a printing device sequence for selection by a user to be printed includes: Obtain the task information of the printing task sent by the user to be printed; Calculating information similarity between the task information and the printing tasks corresponding to each of the 3D printing devices to obtain a similarity parameter corresponding to each of the 3D printing devices; Calculating the product of the similarity parameter corresponding to each of the 3D printing devices and the busyness to obtain a priority parameter corresponding to each of the 3D printing devices; The plurality of 3D printing devices are sorted from largest to smallest according to the priority parameter to obtain a printing device sequence for selection by the user to be printed.

[0012] A second aspect of an embodiment of the present invention discloses a device printing sorting system based on 3D printing task monitoring, the system comprising: An acquisition module is used to obtain printing tasks and real-time sensor data corresponding to multiple 3D printing devices; A prediction module, configured to predict the printing progress corresponding to each of the printing tasks based on the real-time sensing data; a determination module, configured to determine a busyness of each of the 3D printing devices based on the printing progress and the real-time sensor data; The sorting module is configured to sort the plurality of 3D printing devices according to the busyness to obtain a printing device sequence for selection by a user to be printed.

[0013] As an optional embodiment, in the second aspect of the present invention, the real-time sensing data includes at least one of light reflection data, temperature data, humidity data, image data, sound data and light intensity data.

[0014] As an optional embodiment, in the second aspect of the present invention, the specific manner in which the prediction module predicts the printing progress corresponding to each of the printing tasks based on the real-time sensor data includes: For each of the printing tasks, determining a task arrival time when the printing task is sent to the corresponding 3D printing device; Acquiring the real-time sensor data of the 3D printing device within a time period of the task arrival time to obtain task sensor data corresponding to the printing task; the time period is a time period formed by a preset duration starting from the task arrival time; Based on the progress prediction algorithm, the printing progress corresponding to the printing task is predicted according to the task sensing data.

[0015] As an optional embodiment, in the second aspect of the present invention, the specific manner in which the prediction module determines the arrival time of the printing task sent to the corresponding 3D printing device includes: Get the task generation time corresponding to the print task; Obtaining historical task printing records corresponding to the 3D printing device corresponding to the printing task; Calculate the average of the time difference between the print task generation time and the print task execution start time in the historical task printing record to obtain the average arrival time; The sum of the task generation time and the average arrival time is calculated to obtain the task arrival time when the printing task is sent to the corresponding 3D printing device.

[0016] As an optional embodiment, in the second aspect of the present invention, the prediction module predicts the printing progress corresponding to the printing task based on the task sensor data based on the progress prediction algorithm, including: Determine the task type corresponding to the print task; Determining a progress prediction neural network corresponding to the task type from a plurality of preset candidate progress prediction neural networks; the progress prediction neural network is trained using a training data set including training sensor data corresponding to the plurality of task types and corresponding work progress annotations; The task sensing data is input into the progress prediction neural network to obtain the printing progress corresponding to the printing task.

[0017] As an optional embodiment, in the second aspect of the present invention, the specific manner in which the determination module determines the busyness of each of the 3D printing devices based on the printing progress and the real-time sensor data includes: For each of the 3D printing devices, obtaining the printing progress corresponding to all the printing tasks corresponding to the 3D printing device to obtain a printing progress set; Determining a first busy parameter corresponding to the 3D printing device according to the printing progress set; Determining a second busy parameter corresponding to the 3D printing device based on the real-time sensor data and a busy prediction algorithm model corresponding to the 3D printing device; The product of the first busy parameter and the second busy parameter is calculated to obtain the busyness of the 3D printing device.

[0018] As an optional embodiment, in the second aspect of the present invention, the specific manner in which the determining module determines the first busy parameter corresponding to the 3D printing device according to the printing progress set includes: Calculate the weighted sum average corresponding to all the printing progress in the printing progress set to obtain a first busy parameter corresponding to the 3D printing device; wherein the calculation weight corresponding to each printing progress is the product of a first weight and a second weight; the first weight is proportional to the amount of task data corresponding to the printing task corresponding to the printing progress; and the second weight is proportional to the time difference between the task generation time corresponding to the printing task corresponding to the printing progress and the current time point.

[0019] As an optional embodiment, in the second aspect of the present invention, the specific manner in which the sorting module sorts the plurality of 3D printing devices according to the busyness to obtain a printing device sequence for selection by a user to be printed includes: Obtain the task information of the printing task sent by the user to be printed; Calculating information similarity between the task information and the printing tasks corresponding to each of the 3D printing devices to obtain a similarity parameter corresponding to each of the 3D printing devices; Calculating the product of the similarity parameter corresponding to each of the 3D printing devices and the busyness to obtain a priority parameter corresponding to each of the 3D printing devices; The plurality of 3D printing devices are sorted from largest to smallest according to the priority parameter to obtain a printing device sequence for selection by the user to be printed.

[0020] A third aspect of the present invention discloses another device printing sorting system based on 3D printing task monitoring, the system comprising: 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 part or all of the steps in the device printing sorting method based on 3D printing task monitoring disclosed in the first aspect of the present invention.

[0021] The fourth aspect of the present invention discloses a computer storage medium, which stores computer instructions. When the computer instructions are called, they are used to execute some or all of the steps in the device printing sorting method based on 3D printing task monitoring 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: The present invention obtains the printing tasks and real-time sensor data of multiple 3D printing devices to predict the printing progress of each task, combines the progress and sensor data to determine the busyness of each device, and sorts the devices accordingly to generate a printing device sequence for users to select. In this way, accurate device sorting based on real-time progress and device status can be achieved, improving the efficiency of 3D printing task allocation and user selection convenience, and reducing the risk of device overload. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] 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.

[0024] Figure 1 This is a flow chart of a device printing sorting method based on 3D printing task monitoring disclosed in an embodiment of the present invention.

[0025] Figure 2 This is a structural diagram of a device printing sorting system based on 3D printing task monitoring disclosed in an embodiment of the present invention.

[0026] Figure 3 This is a structural diagram of another device printing sorting system based on 3D printing task monitoring disclosed in an embodiment of the present invention. DETAILED DESCRIPTION

[0027] In order to enable those skilled in the art to better understand the solutions of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0028] The terms "first," "second," and so on, in the description and claims of the present invention and the accompanying drawings are used to distinguish between different objects, not to describe a specific order. Furthermore, the terms "including," "having," and any variations thereof, are intended to cover non-exclusive inclusions. For example, a process, method, apparatus, product, or device comprising a series of steps or elements is not limited to the listed steps or elements but may optionally include steps or elements not listed, or may optionally include other steps or elements inherent to the process, method, product, or device.

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

[0030] The present invention discloses a device printing sorting method and system based on 3D printing task monitoring. By acquiring the printing tasks and real-time sensor data of multiple 3D printing devices, the system predicts the printing progress of each task, combines the progress and sensor data to determine the busyness of each device, and uses this information to sort the devices and generate a printing device sequence for users to select. This enables precise device sorting based on real-time progress and device status, improves the efficiency of 3D printing task allocation and user selection convenience, and reduces the risk of device overload. These are described in detail below.

[0031] Example 1 See also Figure 1 , Figure 1 This is a flow chart of a device printing sorting method based on 3D printing task monitoring disclosed in an embodiment of the present invention. Figure 1 The device printing sorting method based on 3D printing task monitoring 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 device printing sorting method based on 3D printing task monitoring may include the following operations: 101. Obtain printing tasks and real-time sensor data corresponding to multiple 3D printing devices.

[0032] 102. Predict the printing progress corresponding to each printing task based on real-time sensor data. 103. Determine the busyness of each 3D printing device based on printing progress and real-time sensor data. 104. Sort the plurality of 3D printing devices according to their busyness to obtain a printing device sequence for selection by a user to be printed.

[0033] It can be seen that the above-mentioned embodiment of the invention predicts the printing progress of each task by obtaining the printing tasks and real-time sensor data of multiple 3D printing devices, determines the busyness of each device in combination with the progress and sensor data, and sorts the devices accordingly to generate a printing device sequence for users to choose from. This can achieve accurate device sorting based on real-time progress and device status, improve the efficiency of 3D printing task allocation and user selection convenience, and reduce the risk of device overload.

[0034] As an optional embodiment, in the above steps, the real-time sensing data includes at least one of light reflection data, temperature data, humidity data, image data, sound data and light intensity data.

[0035] It can be seen that through the above optional embodiments, the content of real-time sensing data is limited to comprehensively characterize the relevant characteristics of 3D printing equipment sensing, so as to facilitate the subsequent accurate prediction of printing task progress and equipment busyness, assist in realizing accurate equipment sorting based on real-time progress and equipment status, improve the efficiency of 3D printing task allocation and user selection convenience, and reduce the risk of equipment overload.

[0036] As an optional embodiment, in the above step, predicting the printing progress corresponding to each printing task based on real-time sensor data includes: For each printing task, determining the task arrival time when the printing task is sent to the corresponding 3D printing device; Acquire real-time sensor data of the 3D printing device within a time period corresponding to the task arrival time, and obtain task sensor data corresponding to the printing task; optionally, the time period is a time period formed by a preset duration starting from the task arrival time; Based on the progress prediction algorithm and task sensor data, the printing progress corresponding to the printing task is predicted.

[0037] It can be seen that through the above optional embodiments, by determining the task arrival time of each printing task and obtaining the real-time sensor data of the corresponding 3D printing device within a period starting from this time and after a preset time as task sensor data, the printing progress is predicted based on the progress prediction algorithm, thereby realizing accurate printing progress prediction based on the task period sensor data, improving the 3D printing task management efficiency and equipment allocation accuracy, and reducing the risk of progress misjudgment.

[0038] As an optional embodiment, in the above step, determining the task arrival time of sending the printing task to the corresponding 3D printing device includes: Get the task generation time corresponding to the print task; Obtain the historical task printing records corresponding to the 3D printing device corresponding to the printing task; Calculate the average time difference between the print task generation time and the print task start execution time in the historical task printing records to obtain the average arrival time; The sum of the task generation time and the average arrival time is calculated to obtain the task arrival time when the printing task is sent to the corresponding 3D printing device.

[0039] It can be seen that through the above optional embodiments, by obtaining the task generation time of the printing task and the historical task printing records of the corresponding 3D printing device, the average value of the difference between the historical task generation time and the start execution time is calculated as the average arrival time, and the task arrival time is determined by combining the sum of the task generation time and the average arrival time, thereby realizing accurate task arrival time prediction based on historical data analysis, improving the 3D printing task scheduling efficiency and equipment allocation accuracy, and reducing the risk of time planning errors.

[0040] As an optional embodiment, in the above steps, predicting the printing progress corresponding to the printing task based on the progress prediction algorithm and the task sensor data includes: Determine the task type corresponding to the print task; Determining a progress prediction neural network corresponding to the task type from a plurality of preset candidate progress prediction neural networks; optionally, the progress prediction neural network is trained using a training data set including training sensor data corresponding to the plurality of task types and corresponding work progress annotations; The task sensor data is input into the progress prediction neural network to obtain the printing progress corresponding to the printing task.

[0041] It can be seen that through the above optional embodiments, by determining the task type of the printing task and selecting the corresponding network from the preset candidate progress prediction neural network, the task sensor data is input into the network to predict the printing progress, thereby realizing accurate printing progress prediction based on the task type customized neural network, improving the 3D printing task management efficiency and equipment allocation accuracy, and reducing the risk of progress prediction error.

[0042] As an optional embodiment, in the above step, determining the busyness of each 3D printing device based on the printing progress and real-time sensor data includes: For each 3D printing device, obtain the printing progress of all printing tasks corresponding to the 3D printing device to obtain a printing progress set; Determining a first busy parameter corresponding to the 3D printing device according to the printing progress set; Determining a second busy parameter corresponding to the 3D printing device based on the real-time sensor data and a busy prediction algorithm model corresponding to the 3D printing device; The product of the first busy parameter and the second busy parameter is calculated to obtain the busyness of the 3D printing device.

[0043] It can be seen that through the above optional embodiments, the printing progress of all printing tasks of each 3D printing device is obtained to form a printing progress set and determine the first busy parameter, and the second busy parameter is determined by combining real-time sensor data and a busy prediction algorithm model. The product of the two is calculated to obtain the device busyness, thereby realizing accurate busyness assessment based on task progress and sensor data, improving the accuracy of 3D printing device sorting and task allocation efficiency, and reducing the risk of equipment overload.

[0044] As an optional embodiment, in the above step, determining the first busy parameter corresponding to the 3D printing device according to the printing progress set includes: Calculate the weighted sum average corresponding to all printing progress in the printing progress set to obtain the first busy parameter corresponding to the 3D printing device; optionally, the calculation weight corresponding to each printing progress is the product of a first weight and a second weight; the first weight is proportional to the amount of task data corresponding to the printing task corresponding to the printing progress; the second weight is proportional to the time difference between the task generation time corresponding to the printing task corresponding to the printing progress and the current time point.

[0045] It can be seen that through the above optional embodiment, the first busy parameter is obtained by calculating the weighted sum average of each printing progress in the printing progress set of the 3D printing device, wherein the weight is the product of the first weight proportional to the task data volume and the second weight proportional to the time difference between the task generation time and the current time, thereby realizing accurate busy parameter evaluation based on the task data volume and time factors, improving the accuracy of the 3D printing device busyness analysis and the task allocation efficiency, and reducing the risk of equipment scheduling errors.

[0046] As an optional embodiment, in the above step, sorting the plurality of 3D printing devices according to their busyness to obtain a printing device sequence for selection by the user to be printed includes: Obtain the task information of the printing task sent by the user to be printed; Calculating the information similarity between the task information and the printing tasks corresponding to each 3D printing device to obtain the similarity parameter corresponding to each 3D printing device; Calculate the product of the similarity parameter and the busyness corresponding to each 3D printing device to obtain the priority parameter corresponding to each 3D printing device; The plurality of 3D printing devices are sorted from largest to smallest according to the priority parameter to obtain a printing device sequence for selection by a user to be printed.

[0047] It can be seen that through the above optional embodiments, the similarity parameter is obtained by obtaining the printing task information of the user to be printed and calculating the similarity between the information and the printing task of each 3D printing device. The priority parameter is calculated by combining the product of the similarity parameter and the busyness of the device, and the printing device sequence is generated by sorting from large to small according to the priority parameter, thereby realizing accurate device sorting based on task similarity and device status, improving the efficiency of 3D printing task allocation and user selection adaptability, and reducing the risk of device overload.

[0048] Example 2 See also Figure 2 , Figure 2 This is a schematic diagram of the structure of a device printing sorting system based on 3D printing task monitoring disclosed in an embodiment of the present invention. Figure 2 The device printing sorting system based on 3D printing task monitoring 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 equipment printing sorting system based on 3D printing task monitoring may include: The acquisition module 201 is used to acquire printing tasks and real-time sensor data corresponding to multiple 3D printing devices.

[0049] The prediction module 202 is used to predict the printing progress corresponding to each printing task based on the real-time sensor data. The determination module 203 is used to determine the busyness of each 3D printing device according to the printing progress and real-time sensor data. The sorting module 204 is configured to sort the plurality of 3D printing devices according to their busyness to obtain a sequence of printing devices for selection by a user who wants to print.

[0050] It can be seen that the above-mentioned embodiment of the invention predicts the printing progress of each task by obtaining the printing tasks and real-time sensor data of multiple 3D printing devices, determines the busyness of each device in combination with the progress and sensor data, and sorts the devices accordingly to generate a printing device sequence for users to choose from. This can achieve accurate device sorting based on real-time progress and device status, improve the efficiency of 3D printing task allocation and user selection convenience, and reduce the risk of device overload.

[0051] As an optional embodiment, the real-time sensing data includes at least one of light reflection data, temperature data, humidity data, image data, sound data and light intensity data.

[0052] It can be seen that through the above optional embodiments, the content of real-time sensing data is limited to comprehensively characterize the relevant characteristics of 3D printing equipment sensing, so as to facilitate the subsequent accurate prediction of printing task progress and equipment busyness, assist in realizing accurate equipment sorting based on real-time progress and equipment status, improve the efficiency of 3D printing task allocation and user selection convenience, and reduce the risk of equipment overload.

[0053] As an optional embodiment, the prediction module predicts the specific manner of the printing progress corresponding to each printing task based on the real-time sensor data, including: For each printing task, determining the task arrival time when the printing task is sent to the corresponding 3D printing device; Acquire real-time sensor data of the 3D printing device within a time period corresponding to the task arrival time, and obtain task sensor data corresponding to the printing task; optionally, the time period is a time period formed by a preset duration starting from the task arrival time; Based on the progress prediction algorithm and task sensor data, the printing progress corresponding to the printing task is predicted.

[0054] It can be seen that through the above optional embodiments, by determining the task arrival time of each printing task and obtaining the real-time sensor data of the corresponding 3D printing device within a period starting from this time and after a preset time as task sensor data, the printing progress is predicted based on the progress prediction algorithm, thereby realizing accurate printing progress prediction based on the task period sensor data, improving the 3D printing task management efficiency and equipment allocation accuracy, and reducing the risk of progress misjudgment.

[0055] As an optional embodiment, the specific method of the prediction module determining the task arrival time of the printing task sent to the corresponding 3D printing device includes: Get the task generation time corresponding to the print task; Obtain the historical task printing records corresponding to the 3D printing device corresponding to the printing task; Calculate the average time difference between the print task generation time and the print task start execution time in the historical task printing records to obtain the average arrival time; The sum of the task generation time and the average arrival time is calculated to obtain the task arrival time when the printing task is sent to the corresponding 3D printing device.

[0056] It can be seen that through the above optional embodiments, by obtaining the task generation time of the printing task and the historical task printing records of the corresponding 3D printing device, the average value of the difference between the historical task generation time and the start execution time is calculated as the average arrival time, and the task arrival time is determined by combining the sum of the task generation time and the average arrival time, thereby realizing accurate task arrival time prediction based on historical data analysis, improving the 3D printing task scheduling efficiency and equipment allocation accuracy, and reducing the risk of time planning errors.

[0057] As an optional embodiment, the prediction module predicts the printing progress corresponding to the printing task based on the progress prediction algorithm and the task sensor data, including: Determine the task type corresponding to the print task; Determining a progress prediction neural network corresponding to the task type from a plurality of preset candidate progress prediction neural networks; optionally, the progress prediction neural network is trained using a training data set including training sensor data corresponding to the plurality of task types and corresponding work progress annotations; The task sensor data is input into the progress prediction neural network to obtain the printing progress corresponding to the printing task.

[0058] It can be seen that through the above optional embodiments, by determining the task type of the printing task and selecting the corresponding network from the preset candidate progress prediction neural network, the task sensor data is input into the network to predict the printing progress, thereby realizing accurate printing progress prediction based on the task type customized neural network, improving the 3D printing task management efficiency and equipment allocation accuracy, and reducing the risk of progress prediction error.

[0059] As an optional embodiment, the specific manner in which the determination module determines the busyness of each 3D printing device based on the printing progress and real-time sensor data includes: For each 3D printing device, obtain the printing progress of all printing tasks corresponding to the 3D printing device to obtain a printing progress set; Determining a first busy parameter corresponding to the 3D printing device according to the printing progress set; Determining a second busy parameter corresponding to the 3D printing device based on the real-time sensor data and a busy prediction algorithm model corresponding to the 3D printing device; The product of the first busy parameter and the second busy parameter is calculated to obtain the busyness of the 3D printing device.

[0060] It can be seen that through the above optional embodiments, the printing progress of all printing tasks of each 3D printing device is obtained to form a printing progress set and determine the first busy parameter, and the second busy parameter is determined by combining real-time sensor data and a busy prediction algorithm model. The product of the two is calculated to obtain the device busyness, thereby realizing accurate busyness assessment based on task progress and sensor data, improving the accuracy of 3D printing device sorting and task allocation efficiency, and reducing the risk of equipment overload.

[0061] As an optional embodiment, the specific manner in which the determination module determines the first busy parameter corresponding to the 3D printing device according to the printing progress set includes: Calculate the weighted sum average corresponding to all printing progress in the printing progress set to obtain the first busy parameter corresponding to the 3D printing device; optionally, the calculation weight corresponding to each printing progress is the product of a first weight and a second weight; the first weight is proportional to the amount of task data corresponding to the printing task corresponding to the printing progress; the second weight is proportional to the time difference between the task generation time corresponding to the printing task corresponding to the printing progress and the current time point.

[0062] It can be seen that through the above optional embodiment, the first busy parameter is obtained by calculating the weighted sum average of each printing progress in the printing progress set of the 3D printing device, wherein the weight is the product of the first weight proportional to the task data volume and the second weight proportional to the time difference between the task generation time and the current time, thereby realizing accurate busy parameter evaluation based on the task data volume and time factors, improving the accuracy of the 3D printing device busyness analysis and the task allocation efficiency, and reducing the risk of equipment scheduling errors.

[0063] As an optional embodiment, the specific manner in which the sorting module sorts the plurality of 3D printing devices according to their busyness to obtain a printing device sequence for selection by a user to be printed includes: Obtain the task information of the printing task sent by the user to be printed; Calculating the information similarity between the task information and the printing tasks corresponding to each 3D printing device to obtain the similarity parameter corresponding to each 3D printing device; Calculate the product of the similarity parameter and the busyness corresponding to each 3D printing device to obtain the priority parameter corresponding to each 3D printing device; The plurality of 3D printing devices are sorted from largest to smallest according to the priority parameter to obtain a printing device sequence for selection by a user to be printed.

[0064] It can be seen that through the above optional embodiments, the similarity parameter is obtained by obtaining the printing task information of the user to be printed and calculating the similarity between the information and the printing task of each 3D printing device. The priority parameter is calculated by combining the product of the similarity parameter and the busyness of the device, and the printing device sequence is generated by sorting from large to small according to the priority parameter, thereby realizing accurate device sorting based on task similarity and device status, improving the efficiency of 3D printing task allocation and user selection adaptability, and reducing the risk of device overload.

[0065] Example 3 See also Figure 3 , Figure 3 This is another device printing sorting system based on 3D printing task monitoring disclosed in an embodiment of the present invention. Figure 3 The device printing sorting system based on 3D printing task monitoring 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 equipment printing sorting system based on 3D printing task monitoring may include: A memory 301 storing executable program code; a processor 302 coupled to the memory 301; The processor 302 calls the executable program code stored in the memory 301 to execute the steps of the device printing sorting method based on 3D printing task monitoring described in the first embodiment.

[0066] Example 4 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 device printing sorting method based on 3D printing task monitoring described in the first embodiment.

[0067] Example 5 An embodiment of the present invention discloses a computer program product, which includes a non-transitory computer-readable storage medium storing a computer program, and the computer program is operable to cause a computer to execute the steps of the device printing sorting method based on 3D printing task monitoring described in Example 1.

[0068] The foregoing description of specific embodiments of the present disclosure is intended to illustrate a method for performing a multi-tasking process. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims may be performed in an order different from that described in the embodiments and still achieve the desired results. Furthermore, the processes depicted in the accompanying drawings do not necessarily require the specific order shown or the sequential order shown to achieve the desired results. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

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

[0070] For the convenience of description, the above devices are described as being divided into various units according to their functions. Of course, when implementing this specification, the functions of each unit can be implemented in the same or multiple software and / or hardware.

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

[0072] This specification is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of this specification. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0073] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0074] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

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

[0076] Memory may include non-permanent storage in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. Memory is an example of a computer-readable medium.

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

[0078] It should also be noted that the terms "comprises," "includes," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, method, commodity, or apparatus that includes a series of elements includes not only those elements but also other elements not explicitly listed, or includes elements inherent to such process, method, commodity, or apparatus. In the absence of further limitations, an element defined by the phrase "comprises a ..." does not exclude the presence of other identical elements in the process, method, commodity, or apparatus that includes the element.

[0079] This specification may be described in the general context of computer-executable instructions, such as program modules, executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, and the like that perform specific tasks or implement specific abstract data types. This specification may also be practiced in distributed computing environments where tasks are performed by remote processing devices connected through a communications network. In a distributed computing environment, program modules may be located in both local and remote computer storage media, including storage devices.

[0080] The various embodiments in this specification are described in a progressive manner. Similar parts between the various embodiments can be referred to in conjunction with each other. Each embodiment focuses on the differences between the other embodiments. In particular, the system embodiments are generally similar to the method embodiments, so the description is relatively simple. For relevant parts, refer to the description of the method embodiments.

[0081] Finally, it should be noted that the device printing sorting method and system based on 3D printing task monitoring disclosed in the embodiments of the present invention are only preferred embodiments of the present invention, and are only used to illustrate the technical solutions of the present invention, rather than to limit them. Although the present invention has been described in detail with reference to the aforementioned embodiments, 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 therein 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 device printing sorting method based on 3D printing task monitoring, characterized in that: The method comprises: Obtain printing tasks and real-time sensor data corresponding to multiple 3D printing devices; Predicting the printing progress corresponding to each of the printing tasks based on the real-time sensing data; Determining the busyness of each of the 3D printing devices based on the printing progress and the real-time sensor data; The plurality of 3D printing devices are sorted according to the busyness to obtain a printing device sequence for selection by a user to be printed.

2. The device printing sorting method based on 3D printing task monitoring according to claim 1 is characterized in that: The real-time sensing data includes at least one of light reflection data, temperature data, humidity data, image data, sound data and light intensity data.

3. The device printing sorting method based on 3D printing task monitoring according to claim 1 is characterized in that: The predicting of the printing progress corresponding to each printing task according to the real-time sensing data includes: For each of the printing tasks, determining a task arrival time when the printing task is sent to the corresponding 3D printing device; Acquiring the real-time sensor data of the 3D printing device within a time period of the task arrival time to obtain task sensor data corresponding to the printing task; the time period is a time period formed by a preset duration starting from the task arrival time; Based on the progress prediction algorithm, the printing progress corresponding to the printing task is predicted according to the task sensing data.

4. The device printing sorting method based on 3D printing task monitoring according to claim 3 is characterized in that: The determining of the task arrival time of sending the printing task to the corresponding 3D printing device includes: Get the task generation time corresponding to the print task; Obtaining historical task printing records corresponding to the 3D printing device corresponding to the printing task; Calculate the average of the time difference between the print task generation time and the print task execution start time in the historical task printing record to obtain the average arrival time; The sum of the task generation time and the average arrival time is calculated to obtain the task arrival time when the printing task is sent to the corresponding 3D printing device.

5. The device printing sorting method based on 3D printing task monitoring according to claim 3 is characterized in that: The process of predicting the printing progress of the printing task based on the task sensor data based on the progress prediction algorithm includes: Determine the task type corresponding to the print task; Determining a progress prediction neural network corresponding to the task type from a plurality of preset candidate progress prediction neural networks; the progress prediction neural network is trained using a training data set including training sensor data corresponding to the plurality of task types and corresponding work progress annotations; The task sensing data is input into the progress prediction neural network to obtain the printing progress corresponding to the printing task.

6. The device printing sorting method based on 3D printing task monitoring according to claim 1 is characterized in that: Determining the busyness of each of the 3D printing devices according to the printing progress and the real-time sensor data includes: For each of the 3D printing devices, obtaining the printing progress corresponding to all the printing tasks corresponding to the 3D printing device to obtain a printing progress set; Determining a first busy parameter corresponding to the 3D printing device according to the printing progress set; Determining a second busy parameter corresponding to the 3D printing device based on the real-time sensor data and a busy prediction algorithm model corresponding to the 3D printing device; The product of the first busy parameter and the second busy parameter is calculated to obtain the busyness of the 3D printing device.

7. The device printing sorting method based on 3D printing task monitoring according to claim 6 is characterized in that: Determining a first busy parameter corresponding to the 3D printing device according to the printing progress set includes: Calculate the weighted sum average corresponding to all the printing progress in the printing progress set to obtain a first busy parameter corresponding to the 3D printing device; wherein the calculation weight corresponding to each printing progress is the product of a first weight and a second weight; the first weight is proportional to the amount of task data corresponding to the printing task corresponding to the printing progress; and the second weight is proportional to the time difference between the task generation time corresponding to the printing task corresponding to the printing progress and the current time point.

8. The device printing sorting method based on 3D printing task monitoring according to claim 1 is characterized in that: The step of sorting the plurality of 3D printing devices according to the busyness to obtain a printing device sequence for selection by a user to be printed includes: Obtain the task information of the printing task sent by the user to be printed; Calculating information similarity between the task information and the printing tasks corresponding to each of the 3D printing devices to obtain a similarity parameter corresponding to each of the 3D printing devices; Calculating the product of the similarity parameter corresponding to each of the 3D printing devices and the busyness to obtain a priority parameter corresponding to each of the 3D printing devices; The plurality of 3D printing devices are sorted from largest to smallest according to the priority parameter to obtain a printing device sequence for selection by the user to be printed.

9. A device printing sorting system based on 3D printing task monitoring, characterized in that: The system comprises: An acquisition module is used to obtain printing tasks and real-time sensor data corresponding to multiple 3D printing devices; A prediction module, configured to predict the printing progress corresponding to each of the printing tasks based on the real-time sensing data; a determination module, configured to determine a busyness of each of the 3D printing devices based on the printing progress and the real-time sensor data; The sorting module is configured to sort the plurality of 3D printing devices according to the busyness to obtain a printing device sequence for selection by a user to be printed.

10. A device printing sorting system based on 3D printing task monitoring, 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 device printing sorting method based on 3D printing task monitoring according to any one of claims 1 to 8.

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