A method and system for remotely sharing 3D printers based on 5G communication

Abstract

This invention discloses a method and system for remotely sharing a 3D printer based on 5G communication, relating to the field of shared 3D printing technology. The method establishes communication between a sharing platform and the 3D printer via an API interface, allowing users to upload printing task models through the platform. It collects real-time status and operational data sets from the 3D printer, calculates a matching availability score coefficient (kyx), and performs a preliminary evaluation against a device availability score threshold (A). When the 3D printer meets the allocation criteria, it analyzes the device material data and task requirements, uses a multi-objective optimization algorithm for task matching, outputs a comprehensive task matching index (ZHP), and performs a deeper evaluation against a preset task printing matching threshold (B) to allocate suitable printing tasks to the 3D printer. This method effectively improves the utilization rate of 3D printer resources, optimizes the task allocation process, and ensures high efficiency in task execution and high printing quality.

Description

Technical Field

[0001] This invention relates to the field of shared 3D printing technology, specifically to a method and system for remote shared 3D printers based on 5G communication. Background Technology

[0002] With the rapid development of modern manufacturing technologies, 3D printing has gradually expanded from industrial manufacturing to multiple fields such as medical, educational, and consumer goods, becoming an important force driving social innovation. At the same time, the concept of resource-sharing economy is gaining increasing popularity, giving rise to various emerging service models centered on sharing. However, the high cost of traditional 3D printing equipment generally limits its adoption by small and medium-sized enterprises and individual users. Introducing 5G communication technology to achieve remote shared 3D printing has become an important means of solving these problems. The high bandwidth and low latency of 5G networks provide technical support for real-time data transmission and equipment monitoring, enabling sharing platforms to quickly respond to user needs, efficiently allocate tasks, and manage equipment. This promotes the popularization of shared manufacturing models, improves production efficiency and resource utilization, and facilitates the development of the manufacturing industry towards greater intelligence and intensification.

[0003] Currently, detailed task matching for 3D printers in 3D printing sharing platforms still faces several challenges and limitations. Existing technologies typically rely on basic status data and coarse load analysis when matching printing tasks with equipment, making it difficult to dynamically and accurately adjust task allocation. This simplistic matching method leads to resource waste, uneven equipment load, and delays or even equipment damage under high loads. Traditional methods struggle to fully utilize real-time data for efficient task allocation, impacting 3D printer utilization and output quality. Furthermore, the lack of data integration on equipment material status and stability also limits the accurate assessment of equipment availability. Summary of the Invention

[0004] To address the shortcomings of existing technologies, this invention provides a method and system for remotely sharing a 3D printer based on 5G communication, which solves the problems mentioned in the background.

[0005] To achieve the above objectives, the present invention provides a method for remotely sharing a 3D printer based on 5G communication, comprising the following steps:

[0006] S1. Establish a communication connection between the 3D printer and the sharing platform through the API communication interface and register relevant information. Users register personal accounts through the sharing platform and upload the 3D model files to be printed through the 5G communication network.

[0007] S2. The shared platform collects equipment status data and operation data in real time through the printer task monitoring system and the sensor group installed on the 3D printer. After preprocessing, it obtains stable operation data and transmits it to a relational database for storage via the 5G communication network.

[0008] S3. Based on the stable operating data set, perform summary calculations to obtain the printer load coefficient dfz and printer health coefficient sjk;

[0009] S4. Based on the printer load coefficient dfz and printer health coefficient sjk, combined with the network latency yc, a summary calculation is performed to obtain the matching availability score coefficient kyx, and then the availability is evaluated by comparing it with the preset device availability score threshold A.

[0010] S5. When the initial assessment indicates that the 3D printer can be assigned tasks, the printing data set is collected in real time, and the equipment material data set is obtained through preprocessing. The equipment material data set is summarized and calculated to obtain the material availability index cky and the printer task requirement matching degree rwp. Combined with the matching availability score coefficient kyx, a comprehensive calculation is performed to obtain the comprehensive task matching index ZHP. Then, a deep evaluation is performed with the preset task printing matching threshold B.

[0011] Preferably, S1 includes S11 and S12;

[0012] S11. Establish a communication connection between the 3D printer and the sharing platform through the API communication interface, and register detailed information, including the device model, supported material types, current status and geographical location. The sharing platform reviews and verifies all connected devices.

[0013] S12. Users register through the sharing platform, create personal accounts, and set personal information and preferences. After registration, users upload the 3D model files to be printed to the sharing platform via the 5G communication network through the user-end APP, and provide a detailed description of the project, including the required materials, printing size and expected completion time.

[0014] Preferably, S2 includes S21, S22 and S23;

[0015] S21. Collect the equipment status data of the 3D printer in real time through the 3D printer task monitoring system and transmit it to the sharing platform through the 5G communication network.

[0016] S22. Real-time acquisition of 3D printer operation data via a sensor group installed on the 3D printer, the sensor group including a temperature sensor and a current sensor.

[0017] The sensor array communicates with the sharing platform via a 5G communication network and transmits the operational data to the sharing platform in real time.

[0018] S23. The shared platform receives the running data group in real time, and then performs noise reduction, missing value filling, deduplication and dimensionless processing on the running data group and the equipment status data group to obtain the stable running data group.

[0019] The shared platform organizes stable operation data groups based on factors affecting 3D printer load and equipment health, and divides the stable operation data groups into load data groups and health data groups.

[0020] The load data set includes task occupancy ratio zy, queue waiting task quantity dl, operating temperature yw, and material replacement frequency pl;

[0021] The status data group includes runtime ys, printing speed sd, temperature fluctuation range wd, and current fluctuation range db;

[0022] The shared platform will transmit stable data sets to a relational database for storage via a 5G communication network, and label each data set with a timestamp, data identifier, data type, and data source.

[0023] Preferably, S3 includes S31, S32 and S33;

[0024] S31. The shared platform analyzes the health status of the 3D printer and the load status of the processed files based on the acquired stable operating data set of the 3D printer.

[0025] S32. Analyze the load status of the 3D printer based on the obtained load data set, summarize and calculate, and obtain the printer load coefficient dfz.

[0026] The printer load factor dfz is calculated using the following formula;

[0027]

[0028] In the formula, dfz i Let zy represent the load factor of the i-th 3D printer. i dl represents the current task occupancy rate of the i-th 3D printer. i Let yx represent the number of tasks waiting in the queue for the i-th 3D printer. i yw represents the priority of the printing task of the i-th 3D printer. i pl represents the average operating temperature of the i-th 3D printer. i Let represent the material replacement frequency of the i-th 3D printer, k1 and k2 represent the adjustment coefficients of the queue waiting task quantity dl and the material replacement frequency pl, respectively, and ∈ represents the minimum value to prevent the denominator from being zero;

[0029] S33. Analyze the operating status and health of the 3D printer based on the acquired status data set, summarize and calculate, and obtain the printer health coefficient sjk;

[0030] The printer health coefficient (sjk) is calculated using the following formula;

[0031]

[0032] In the formula, ln represents the logarithmic function, and sjk i ys represents the health coefficient of the i-th 3D printer. i sd represents the runtime of the i-th 3D printer. i wd represents the printing speed of the i-th 3D printer. i This represents the temperature fluctuation range of the i-th 3D printer, in dB. i denoted by , where represents the current fluctuation amplitude of the i-th 3D printer, and k3 represents the weighting coefficient between temperature fluctuation wd and current fluctuation db.

[0033] Preferably, S4 includes S41 and S42;

[0034] S41. The shared platform conducts a preliminary analysis of the 3D printer's status and task load. Combining the impact of 5G communication network latency on the 3D printer status analysis, a task execution availability model is constructed through an intelligent matching algorithm. The obtained printer load coefficient dfz and printer health coefficient sjk are input into the task execution availability model to calculate and obtain the matching availability score coefficient kyx.

[0035] The availability score coefficient kyx is calculated using the following formula;

[0036]

[0037] In the formula, kyx i yc represents the matching availability score coefficient for the i-th 3D printer. i Let ln represent the network latency of the i-th 3D printer, and ln represent the logarithmic function.

[0038] Preferably, S42 includes S421 and S422;

[0039] S421. Extract the historical stable data set of 3D printer operation from the relational database and input it into the task execution availability model. Use statistical analysis to calculate the mean of the historical matching availability score coefficient and set the obtained mean as the equipment availability score threshold A.

[0040] S422. Compare the obtained matching availability score coefficient kyx with the equipment availability score threshold A to preliminarily assess the task allocation of the 3D printer. The specific assessment scheme is as follows.

[0041] When the matching availability score coefficient kyx is greater than or equal to the device availability score threshold A, the device can be assigned tasks and subjected to in-depth evaluation;

[0042] When the matching availability score coefficient kyx is less than the device availability score threshold A, the device cannot be assigned tasks. This indicates that the 3D printer has overload, aging, or malfunction issues. The sharing platform will mark this 3D printer with a registration mark and generate an early warning message, which will be transmitted to relevant personnel for inspection via the 5G communication network.

[0043] Preferably, S5 includes S51, S52 and S53;

[0044] S51. When the initial assessment indicates that the 3D printer can be assigned tasks, the printing material data and performance data of the 3D printer are analyzed based on the printing tasks uploaded to the sharing platform by the user's APP. The printing data sets are collected in real time through the installed encoder and the 3D printer task monitoring system.

[0045] The encoder communicates with the sharing platform via a 5G communication network and transmits the print data sets to the sharing platform in real time;

[0046] The sharing platform receives the printing data set in real time, and then performs noise reduction, missing value filling, deduplication and dimensionless processing on the printing data set to obtain the equipment material data set.

[0047] The sharing platform organizes equipment material data groups based on factors such as 3D printer material surplus and task matching, dividing them into material availability data groups and task matching data groups.

[0048] The material availability data set includes the remaining material inventory kc, the material consumption rate xh, and the replenishment time gy;

[0049] The task matching data set includes the peak stable printing speed in milliseconds and the supported resolution in seconds.

[0050] The shared platform transmits equipment and material data sets to a relational database via a 5G communication network for storage, and marks each data set with a timestamp, data identifier, data type, and data source.

[0051] S52. Based on the obtained material availability data set, perform summary calculations to obtain the material availability index cky;

[0052] The material availability index cky is calculated using the following formula;

[0053]

[0054] In the formula, cky i kc represents the material availability index for the i-th 3D printer. i xh represents the remaining material inventory of the i-th 3D printer. i gy represents the material consumption rate of the i-th 3D printer. i Let represent the time required to restock the i-th 3D printer, log represents the logarithmic function, and ∈ denotes the minimum value to prevent the denominator from being zero;

[0055] Based on the acquired task matching data set and combined with the printing requirements of the user's printing task, a summary calculation is performed to obtain the printer task requirement matching degree rwp;

[0056] The printer task requirement matching degree (rwp) is calculated using the following formula;

[0057]

[0058] In the formula, z represents the compatibility coefficient of the 3D printer's ability to support basic materials, and ms i sf represents the peak stable printing speed of the i-th 3D printer. i Let represent the supported resolution of the i-th 3D printer, cg represent the resolution requirement of the task, ts represent the required printing speed of the task, sl represent the required number of prints of the task, and tt represent the required printing time of the task.

[0059] Preferably, S53 includes S531 and S532;

[0060] S531. By analyzing the performance indicators and available materials of 3D printers, a comprehensive analysis of real-time and historical data of 3D printers is conducted, and a multi-objective optimization algorithm is used to deeply evaluate the adaptability of each 3D printer. A task matching model is constructed, and the obtained matching availability score coefficient kyx, material availability index cky, and printer task requirement matching degree rwp are input into the task matching model. The task matching model is used to summarize and calculate, and the comprehensive task matching index ZHP is output to assign a suitable printing task to each 3D printer.

[0061] The Comprehensive Task Matching Index (ZHP) is calculated using the following formula;

[0062]

[0063] In the formula, ln represents the logarithmic function.

[0064] Preferably, in step S532, by collecting a large amount of equipment and material data of existing 3D printers, including 3D printers that can be assigned tasks and those that cannot, and combining the matching availability score coefficient kyx of each 3D printer with the printing requirements of the users of the sharing platform, the data is input into the task matching model for comprehensive analysis, the comprehensive task matching index ZHP of each 3D printer is calculated, and the average value is calculated by statistical analysis, and a preset task printing matching threshold B is set.

[0065] The task printing matching threshold B and the comprehensive task matching index ZHP are used to evaluate the task matching degree of the 3D printer in depth. The specific evaluation scheme is as follows:

[0066] When the comprehensive task matching index ZHP is greater than or equal to the task printing matching threshold B, it means that the 3D printer meets the printing requirements and the printing task will be assigned.

[0067] When the comprehensive task matching index ZHP is less than the task printing matching threshold B, it indicates that the 3D printer does not meet the printing requirements. At this time, an overload signal is generated and transmitted to S4 through the 5G communication network to redistribute the printing task.

[0068] A remote shared 3D printer system based on 5G communication includes a device access module, a device status acquisition module, an intelligent analysis module, an availability assessment module, and a task matching module;

[0069] The device access module establishes a communication connection between the 3D printer and the sharing platform through the API communication interface and registers relevant information. Users register personal accounts through the sharing platform and upload the 3D model files to be printed through the 5G communication network.

[0070] The equipment status acquisition module utilizes a shared platform to collect equipment status data and operation data in real time through the printer task monitoring system and the sensor group installed on the 3D printer. After preprocessing, it obtains stable operation data and transmits it to a relational database for storage via a 5G communication network.

[0071] The intelligent analysis module is used to perform summary calculations based on the stable operating data group and the network latency yc to obtain the printer load coefficient dfz and the printer health coefficient sjk.

[0072] The availability assessment module is used to perform a summary calculation based on the printer load coefficient dfz and the printer health coefficient sjk to obtain the matching availability score coefficient kyx, and then perform availability assessment with the preset device availability score threshold A.

[0073] The task matching module is used to collect printing data sets in real time when the 3D printer is initially assessed as capable of being assigned tasks. It then preprocesses the data to obtain equipment and material data sets, summarizes and calculates the material availability index cky and printer task requirement matching degree rwp based on the equipment and material data sets, and combines them with the matching availability score coefficient kyx to perform a comprehensive calculation to obtain the comprehensive task matching index ZHP. Finally, it performs a deep evaluation with the preset task printing matching threshold B.

[0074] This invention provides a method and system for remotely sharing a 3D printer based on 5G communication. It has the following beneficial effects:

[0075] (1) This method enables real-time communication between the sharing platform and the 3D printer through high-speed transmission via the 5G communication network. Users can register accounts on the sharing platform and upload the 3D model files they wish to print. Simultaneously, the sharing platform registers detailed equipment information for each connected 3D printer, including model, material type, and geographical location. After the equipment is connected, the sharing platform monitors the 3D printer using API communication interfaces and sensor arrays, collecting real-time equipment status and operational data. This data is pre-processed and stored in a relational database, providing fundamental data support for subsequent equipment health assessments and load analysis.

[0076] (2) This method uses the shared platform to assess the real-time load status and health of devices by calculating the printer load coefficient dfz and the printer health coefficient sjk. The shared platform, considering 5G network latency, designed a task execution availability model. It assesses device availability by calculating the matching availability score coefficient kyx, ensuring that tasks can be assigned to devices with suitable performance and healthy status. After initially screening available devices, the shared platform analyzes material data and task matching requirements. It determines the degree of matching between devices and tasks by calculating the material availability index cky and the printer task requirement matching degree rwp, laying the foundation for refined task allocation.

[0077] (3) In terms of task matching and resource allocation, this method utilizes a multi-objective optimization algorithm, combining the matching availability score coefficient kyx, the material availability index cky, and the printer task requirement matching degree rwp to construct a comprehensive task matching index ZHP, achieving intelligent allocation. In this model, the material surplus, consumption rate, and supported resolution of the equipment are all included as influencing factors in the calculation, ensuring that the allocated tasks match the equipment performance and reducing printing failures caused by insufficient materials and substandard performance. When the comprehensive task matching index ZHP of the 3D printer reaches the preset task printing matching threshold B, the sharing platform automatically allocates printing tasks; otherwise, it marks the equipment as unavailable and generates an overload warning message. Ultimately, this method effectively improves equipment utilization, optimizes resource allocation, and avoids problems such as equipment overload, insufficient materials, and improper allocation. Through precise data collection and intelligent matching, this method achieves efficient allocation of 3D printing tasks, improves the operating efficiency and user experience of the sharing platform, and promotes the intelligent and automated development of 3D printing sharing services. Attached Figure Description

[0078] Figure 1 This is a schematic diagram illustrating the steps of a remote shared 3D printer method based on 5G communication according to the present invention.

[0079] Figure 2 This is a schematic diagram of a remote shared 3D printer system based on 5G communication according to the present invention. Detailed Implementation

[0080] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0081] Example 1

[0082] Please see Figure 1 This invention provides a method for remotely sharing a 3D printer based on 5G communication. To achieve the above objectives, this invention employs the following technical solution, comprising the following steps:

[0083] S1. Establish a communication connection between the 3D printer and the sharing platform through the API communication interface and register relevant information. Users register personal accounts through the sharing platform and upload the 3D model files to be printed through the 5G communication network.

[0084] S2. The shared platform collects equipment status data and operation data in real time through the printer task monitoring system and the sensor group installed on the 3D printer. After preprocessing, it obtains stable operation data and transmits it to a relational database for storage via the 5G communication network.

[0085] S3. Based on the stable operating data set, perform summary calculations to obtain the printer load coefficient dfz and printer health coefficient sjk;

[0086] S4. Based on the printer load coefficient dfz and printer health coefficient sjk, combined with the network latency yc, a summary calculation is performed to obtain the matching availability score coefficient kyx, and then the availability is evaluated by comparing it with the preset device availability score threshold A.

[0087] S5. When the initial assessment indicates that the 3D printer can be assigned tasks, the printing data set is collected in real time, and the equipment material data set is obtained through preprocessing. The equipment material data set is summarized and calculated to obtain the material availability index cky and the printer task requirement matching degree rwp. Combined with the matching availability score coefficient kyx, a comprehensive calculation is performed to obtain the comprehensive task matching index ZHP. Then, a deep evaluation is performed with the preset task printing matching threshold B.

[0088] In this embodiment, the remote shared 3D printer method based on 5G communication realizes a complete process from task submission to intelligent allocation through five steps, significantly optimizing the use and management of shared printing resources. In module S1, the 3D printer connects to the sharing platform via an API interface. Users register and upload 3D model files on the sharing platform, forming a fast and efficient device-user interface process. Module S2 utilizes sensor arrays and the 5G network to collect and transmit device status and operating data in real time, and stores the pre-processed data in a relational database, providing complete basic data for subsequent intelligent analysis and improving the accuracy and real-time performance of data processing. In modules S3 and S4, the sharing platform uses data analysis to calculate the printer load coefficient dfz and printer health coefficient sjk, and combines the network latency yc to calculate the matching availability score coefficient kyx, thereby providing a basis for the preliminary availability assessment of 3D printer tasks. This process ensures that the load and health status of the device are considered before task allocation, avoiding the misjudgment problems caused by over-reliance on manual checks and simple scheduling algorithms compared to traditional technologies. By comparing the matching availability score coefficient kyx with the preset equipment availability score threshold A, automatic equipment screening and initial task allocation are achieved, optimizing resource scheduling and equipment utilization efficiency. The S5 module deepens task evaluation by collecting printing data in real time and calculating the material availability index cky and printer task requirement matching degree rwp. Combined with the matching availability score coefficient kyx, a comprehensive task matching index ZHP is calculated, which is then compared with the preset task printing matching threshold B for in-depth evaluation, ensuring that equipment materials and performance meet printing task requirements. Compared to traditional 3D printing sharing technologies, this method, combining multi-dimensional data and a multi-parameter evaluation model, significantly improves the accuracy of task allocation and equipment utilization, reducing printing failures and resource waste. Overall, this method achieves the goals of optimized resource allocation, improved printing task completion rate, and enhanced system stability, promoting technological advancements in intelligent printing scheduling.

[0089] Example 2

[0090] This embodiment is an explanation based on Embodiment 1. Please refer to it. Figure 1 Specifically: S1 includes S11 and S12;

[0091] S11. Establish a communication connection between the 3D printer and the sharing platform through the API communication interface, and register detailed information, including the device model, supported material types, current status and geographical location. The sharing platform reviews and verifies all connected devices to ensure that the devices can be used for sharing and meet quality and safety standards.

[0092] S12. Users register through the sharing platform, create personal accounts, and set personal information and preferences. After registration, users upload the 3D model files to be printed to the sharing platform via the 5G communication network through the user-end APP, and provide a detailed description of the project, including the required materials, printing size and expected completion time.

[0093] In this embodiment, through the detailed steps of module S1, the method ensures smooth access between the 3D printer and the sharing platform and convenient user registration. In step S11, detailed registration and information verification of the 3D printer are achieved through the API interface, ensuring that the accessed devices meet quality and safety standards and improving the reliability of device sharing and the overall credibility of the sharing platform. In step S12, users upload 3D model files and provide detailed project requirements through a simple registration process and a user-side APP, achieving seamless connection between users and the sharing platform. This design significantly improves the user experience, reduces the complexity of registration and project submission, allows users to quickly start printing tasks, and ensures the availability of devices and the matching degree of tasks. This efficient device verification and user upload process, combined with the high-speed transmission of the 5G network, optimizes resource allocation and improves the service quality and intelligent management level of the sharing platform.

[0094] Example 3

[0095] This embodiment is an explanation based on Embodiment 2. Please refer to it. Figure 1 Specifically: S2 includes S21, S22 and S23;

[0096] S21. Collect the equipment status data group of the 3D printer in real time through the 3D printer task monitoring system, and transmit it to the sharing platform through the 5G communication network. The specific equipment status data collection steps are as follows.

[0097] The shared platform extracts the current task status and queue length of pending tasks recorded by the 3D printer task monitoring system in real time through the API communication interface, records the start and end times of tasks, and calculates the task occupancy ratio zy, queue waiting task quantity dl, runtime ys and printing speed sd within the current time.

[0098] The material replacement frequency (pl) is obtained by recording historical material replacement records through the 3D printer task monitoring system.

[0099] S22. Real-time acquisition of 3D printer operation data via a sensor group installed on the 3D printer, the sensor group including a temperature sensor and a current sensor.

[0100] The sensor array communicates with the sharing platform via a 5G communication network and transmits the operational data to the sharing platform in real time.

[0101] The operating temperature yw of the 3D printer is collected in real time by a temperature sensor. The shared platform analyzes the temperature data fluctuations of each component, calculates the maximum and minimum temperature difference, and obtains the temperature fluctuation amplitude wd.

[0102] The current fluctuation amplitude (dB) is calculated by monitoring current changes using a current sensor.

[0103] S23. The shared platform receives the running data group in real time, and then performs noise reduction, missing value filling, deduplication and dimensionless processing on the running data group and the equipment status data group to obtain the stable running data group.

[0104] The shared platform organizes stable operation data groups based on factors affecting 3D printer load and equipment health, and divides the stable operation data groups into load data groups and health data groups.

[0105] The load data set includes task occupancy ratio zy, queue waiting task quantity dl, operating temperature yw, and material replacement frequency pl;

[0106] The status data group includes runtime ys, printing speed sd, temperature fluctuation range wd, and current fluctuation range db;

[0107] The shared platform will transmit stable data sets to a relational database for storage via a 5G communication network, and label each data set with a timestamp, data identifier, data type, and data source.

[0108] In this embodiment, within module S2, the shared platform achieves precise monitoring and data analysis of printing tasks by real-time acquisition of the 3D printer's device status data set and operational data set. Utilizing a 5G communication network and API interface, the shared platform establishes a stable data transmission link with the 3D printer task monitoring system and sensor group, transmitting the real-time collected device operational data to the shared platform. After denoising, filling missing values, deduplication, and dimensionless processing of the operational and device status data sets, high-quality, stable operational data sets are provided. These data sets are then divided into load data sets and health data sets and stored in a relational database, ensuring data integrity, timeliness, and structure. This process significantly improves the ability to accurately assess device health and load status, enabling the shared platform to respond promptly to changes in device status and optimize task allocation and resource utilization. This real-time, automated data processing and management method reduces manual monitoring and human error, improves device stability and printing task success rate, and fully demonstrates the effective integration and application of 5G communication and data processing technologies.

[0109] Example 4

[0110] This embodiment is an explanation based on Embodiment 3. Please refer to it. Figure 1 Specifically: S3 includes S31, S32 and S33;

[0111] S31. The shared platform analyzes the health status of the 3D printer and the load status of the processed files based on the acquired stable operating data set of the 3D printer.

[0112] S32. Analyze the load status of the 3D printer based on the obtained load data set, summarize and calculate, and obtain the printer load coefficient dfz.

[0113] The printer load factor dfz is calculated using the following formula;

[0114]

[0115] In the formula, dfz i Let zy represent the load factor of the i-th 3D printer. i dl represents the current task occupancy rate of the i-th 3D printer. i Let yx represent the number of tasks waiting in the queue for the i-th 3D printer. i This indicates the priority of the printing task for the i-th 3D printer. pl represents the average operating temperature of the i-th 3D printer. i Let represent the material replacement frequency of the i-th 3D printer, k1 and k2 represent the adjustment coefficients of the queue waiting task quantity dl and the material replacement frequency pl, respectively, and ∈ represents the minimum value to prevent the denominator from being zero;

[0116] S33. Analyze the operating status and health of the 3D printer based on the acquired status data set, summarize and calculate, and obtain the printer health coefficient sjk;

[0117] The printer health coefficient (sjk) is calculated using the following formula;

[0118]

[0119] In the formula, ln represents the logarithmic function, and sjk i ys represents the health coefficient of the i-th 3D printer. i sd represents the runtime of the i-th 3D printer. i wd represents the printing speed of the i-th 3D printer. i This represents the temperature fluctuation range of the i-th 3D printer, in dB. i denoted by , where represents the current fluctuation amplitude of the i-th 3D printer, and k3 represents the weighting coefficient between temperature fluctuation wd and current fluctuation db.

[0120] In this embodiment, S31 provides the ability to monitor and diagnose equipment health in real time through the analysis of stable data sets, ensuring the stability and reliability of the equipment during long-term use. The calculation methods for the printer load factor dfz and printer health factor sjk in S32 and S33 comprehensively consider multiple parameters such as task occupancy, queue length, runtime, temperature, and current fluctuations, forming a refined assessment of the equipment's operating status. Compared to traditional methods that rely solely on simple indicators, this multi-dimensional and data-driven analysis approach can more accurately identify the equipment's load pressure and health status, preventing potential overload and malfunctions, thereby extending equipment lifespan and improving the accuracy of task allocation and overall system efficiency.

[0121] Example 5

[0122] This embodiment is an explanation based on Embodiment 4. Please refer to it. Figure 1 Specifically: S4 includes S41 and S42;

[0123] S41. The shared platform conducts a preliminary analysis of the 3D printer's status and task load. Combining the impact of 5G communication network latency on the 3D printer status analysis, a task execution availability model is constructed through an intelligent matching algorithm. The obtained printer load coefficient dfz and printer health coefficient sjk are input into the task execution availability model to calculate and obtain the matching availability score coefficient kyx.

[0124] The availability score coefficient kyx is calculated using the following formula;

[0125]

[0126] In the formula, kyx i yc represents the matching availability score coefficient for the i-th 3D printer. i Let ln represent the network latency of the i-th 3D printer, and ln represent the logarithmic function.

[0127] S42 includes S421 and S422;

[0128] S421. Extract the historical stable data set of 3D printer operation from the relational database and input it into the task execution availability model. Use statistical analysis to calculate the mean of the historical matching availability score coefficient and set the obtained mean as the equipment availability score threshold A.

[0129] S422. Compare the obtained matching availability score coefficient kyx with the equipment availability score threshold A to preliminarily assess the task allocation of the 3D printer. The specific assessment scheme is as follows.

[0130] When the matching availability score coefficient kyx is greater than or equal to the device availability score threshold A, the device can be assigned tasks and subjected to in-depth evaluation;

[0131] When the matching availability score coefficient kyx is less than the device availability score threshold A, the device cannot be assigned tasks. This indicates that the 3D printer has overload, aging, or malfunction issues. The sharing platform will mark this 3D printer with a registration mark and generate an early warning message, which will be transmitted to relevant personnel for inspection via the 5G communication network.

[0132] In this embodiment, the sharing platform performs preliminary intelligent analysis of the 3D printer's status and task load, and constructs a task execution availability model by incorporating 5G network latency factors. The calculation of the availability score coefficient kyx and statistical analysis of historical data enable the sharing platform to set a device availability score threshold A, thereby more accurately assessing the 3D printer's task allocation capabilities. This method ensures that task allocation not only depends on the current device status but also comprehensively considers network latency and historical device operating data, making the assessment more comprehensive and dynamic. By combining real-time and historical data for evaluation, the sharing platform can identify potential device overload, aging, and malfunction issues before task allocation, generating early warning information and notifying relevant personnel for maintenance. This refined and intelligent evaluation mechanism improves system stability and task execution reliability, avoiding task failures or device overload caused by insufficient status judgment in traditional methods, thus significantly improving the accuracy of overall resource management and equipment maintenance.

[0133] Example 6

[0134] This embodiment is an explanation based on Embodiment 5. Please refer to it. Figure 1 Specifically: S5 includes S51, S52 and S53;

[0135] S51. When the initial assessment indicates that the 3D printer can be assigned tasks, the printing material data and performance data of the 3D printer are analyzed based on the printing tasks uploaded to the sharing platform by the user's APP. The printing data sets are collected in real time through the installed encoder and the 3D printer task monitoring system. The specific steps for collecting the printing data sets are as follows.

[0136] By installing encoders on the print head and transmission mechanism of the 3D printer, the number of prints and the amount of material consumed by the 3D printer are monitored in real time to obtain the remaining material inventory kc. Combined with the number of prints and the amount of material consumed per unit time during the printing process recorded by the 3D printer task monitoring system, the material consumption rate xh and the printing speed are obtained. After extracting the peak value of the printing speed during stable operation, the peak value of the stable printing speed ms is obtained.

[0137] The time required for replenishment can be obtained by using historical replenishment records from the 3D printer task monitoring system.

[0138] Find the supported resolution sf of the 3D printer by its model and factory settings;

[0139] The encoder communicates with the sharing platform via a 5G communication network and transmits the print data sets to the sharing platform in real time;

[0140] The sharing platform receives the printing data set in real time, and then performs noise reduction, missing value filling, deduplication and dimensionless processing on the printing data set to obtain the equipment material data set.

[0141] The sharing platform organizes equipment material data groups based on factors such as 3D printer material surplus and task matching, dividing them into material availability data groups and task matching data groups.

[0142] The material availability data set includes the remaining material inventory kc, the material consumption rate xh, and the replenishment time gy;

[0143] The task matching data set includes the peak stable printing speed in milliseconds and the supported resolution in seconds.

[0144] The shared platform transmits equipment and material data sets to a relational database via a 5G communication network for storage, and marks each data set with a timestamp, data identifier, data type, and data source.

[0145] S52. Based on the obtained material availability data set, perform summary calculations to obtain the material availability index cky;

[0146] The material availability index cky is calculated using the following formula;

[0147]

[0148] In the formula, cky i kc represents the material availability index for the i-th 3D printer. i xh represents the remaining material inventory of the i-th 3D printer. i gy represents the material consumption rate of the i-th 3D printer. i Let represent the time required to restock the i-th 3D printer, log represents the logarithmic function, and ∈ denotes the minimum value to prevent the denominator from being zero;

[0149] Based on the acquired task matching data set and combined with the printing requirements of the user's printing task, a summary calculation is performed to obtain the printer task requirement matching degree rwp;

[0150] The printer task requirement matching degree (rwp) is calculated using the following formula;

[0151]

[0152] In the formula, z represents the compatibility coefficient of the 3D printer with the base material support capability, which includes the material information supported in the 3D printer's technical manual and specification sheet, and ms. i sf represents the peak stable printing speed of the i-th 3D printer. i Let represent the supported resolution of the i-th 3D printer, cg represent the resolution requirement of the task, ts represent the required printing speed of the task, sl represent the required number of prints of the task, and tt represent the required printing time of the task.

[0153] S53 includes S531 and S532;

[0154] S531. By analyzing the performance indicators and available materials of 3D printers, and comprehensively analyzing the real-time and historical data of 3D printers, a multi-objective optimization algorithm is used to deeply evaluate the adaptability of each 3D printer, a task matching model is constructed, and the obtained matching availability score coefficient kyx, material availability index cky, and printer task requirement matching degree rwp are input into the task matching model. The task matching model is used to summarize and calculate, and output the comprehensive task matching index ZHP, so as to assign appropriate printing tasks to each 3D printer and avoid problems such as 3D printer overload and unqualified printing.

[0155] The Comprehensive Task Matching Index (ZHP) is calculated using the following formula;

[0156]

[0157] In the formula, ln represents the logarithmic function.

[0158] S532. By collecting a large amount of equipment and material data of existing 3D printers, including 3D printers that can be assigned tasks and those that cannot, and combining the matching availability score coefficient kyx of each 3D printer with the printing requirements of the users of the sharing platform, inputting it into the task matching model for comprehensive analysis, calculating the comprehensive task matching index ZHP of each 3D printer, and calculating the average value through statistical analysis, and setting the preset task printing matching threshold B.

[0159] The task printing matching threshold B and the comprehensive task matching index ZHP are used to evaluate the task matching degree of the 3D printer in depth. The specific evaluation scheme is as follows:

[0160] When the comprehensive task matching index ZHP is greater than or equal to the task printing matching threshold B, it means that the 3D printer meets the printing requirements and the printing task will be assigned.

[0161] When the comprehensive task matching index ZHP is less than the task printing matching threshold B, it indicates that the 3D printer does not meet the printing requirements. At this time, an overload signal is generated and transmitted to S4 through the 5G communication network to redistribute the printing task.

[0162] In this embodiment, by utilizing encoders and a task monitoring system to collect printing data in real time, the method achieves precise monitoring of the equipment's remaining material, consumption rate, and printing performance data, ensuring task-equipment matching. By calculating the material availability index (KY) and the printer task requirement matching degree (RWP), the sharing platform can accurately identify whether the equipment is suitable for a specific task, thereby reducing material waste and the risk of task failure. The comprehensive task matching model constructed using a multi-objective optimization algorithm effectively integrates equipment status, material availability, and task requirements, maximizing equipment utilization while ensuring print quality. Through in-depth evaluation using the comprehensive task matching index (ZHP), the sharing platform can identify and prioritize the allocation of the most suitable equipment, while promptly issuing warnings and adjusting unsuitable equipment to avoid overload and performance degradation, thus enhancing the sharing platform's intelligence and sustainable operation capabilities. This method significantly improves the accuracy of task allocation, the success rate of task completion, and the long-term stability of equipment, achieving a dual improvement in performance and efficiency based on current technology.

[0163] Example 7

[0164] Please refer to Figure 1 and Figure 2 A remote shared 3D printer system based on 5G communication includes a device access module, a device status acquisition module, an intelligent analysis module, an availability assessment module, and a task matching module.

[0165] The device access module establishes a communication connection between the 3D printer and the sharing platform through the API communication interface and registers relevant information. Users register personal accounts through the sharing platform and upload the 3D model files to be printed through the 5G communication network.

[0166] The equipment status acquisition module utilizes a shared platform to collect equipment status data and operation data in real time through the printer task monitoring system and the sensor group installed on the 3D printer. After preprocessing, it obtains stable operation data and transmits it to a relational database for storage via a 5G communication network.

[0167] The intelligent analysis module is used to perform summary calculations based on the stable operating data group and the network latency yc to obtain the printer load coefficient dfz and the printer health coefficient sjk.

[0168] The availability assessment module is used to perform a summary calculation based on the printer load coefficient dfz and the printer health coefficient sjk to obtain the matching availability score coefficient kyx, and then perform availability assessment with the preset device availability score threshold A.

[0169] The task matching module is used to collect printing data sets in real time when the 3D printer is initially assessed as capable of being assigned tasks. It then preprocesses the data to obtain equipment and material data sets, summarizes and calculates the material availability index cky and printer task requirement matching degree rwp based on the equipment and material data sets, and combines them with the matching availability score coefficient kyx to perform a comprehensive calculation to obtain the comprehensive task matching index ZHP. Finally, it performs a deep evaluation with the preset task printing matching threshold B.

[0170] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A method for remotely sharing a 3D printer based on 5G communication, characterized in that: Includes the following steps: S1. Establish a communication connection between the 3D printer and the sharing platform through the API communication interface and register relevant information. Users register personal accounts through the sharing platform and upload the 3D model files to be printed through the 5G communication network. S2. The shared platform collects equipment status data and operation data in real time through the printer task monitoring system and the sensor group installed on the 3D printer. After preprocessing, it obtains stable operation data and transmits it to a relational database for storage via the 5G communication network. S3. Based on the stable operating data set, perform summary calculations to obtain the printer load coefficient dfz and printer health coefficient sjk; S4. Based on the printer load coefficient dfz and printer health coefficient sjk, combined with the network latency yc, a summary calculation is performed to obtain the matching availability score coefficient kyx, and then the availability is evaluated by comparing it with the preset device availability score threshold A. S5. When the initial assessment indicates that the 3D printer can be assigned tasks, the printing data set is collected in real time, the equipment material data set is preprocessed, and the equipment material data set is summarized and calculated to obtain the material availability index cky and the printer task requirement matching degree rwp. Combined with the matching availability score coefficient kyx, a comprehensive calculation is performed to obtain the comprehensive task matching index ZHP. Then, a deep evaluation is performed with the preset task printing matching threshold B. S3 includes S31, S32 and S33; S31. The shared platform analyzes the health status of the 3D printer and the load status of the processed files based on the acquired stable operating data set of the 3D printer. S32. Analyze the load status of the 3D printer based on the obtained load data set, summarize and calculate, and obtain the printer load coefficient dfz. The printer load factor dfz is calculated using the following formula; In the formula, dfz i Let zy represent the load factor of the i-th 3D printer. i dl represents the current task occupancy rate of the i-th 3D printer. i Let yx represent the number of tasks waiting in the queue for the i-th 3D printer. i This indicates the priority of the printing task for the i-th 3D printer. pl represents the average operating temperature of the i-th 3D printer. i Let represent the material replacement frequency of the i-th 3D printer, k1 and k2 represent the adjustment coefficients of the queue waiting task quantity dl and the material replacement frequency pl, respectively, and ∈ represents the minimum value to prevent the denominator from being zero; S33. Analyze the operating status and health of the 3D printer based on the acquired status data set, summarize and calculate, and obtain the printer health coefficient sjk; The printer health coefficient (sjk) is calculated using the following formula; In the formula, ln represents the logarithmic function, and sjk i ys represents the health coefficient of the i-th 3D printer. i sd represents the runtime of the i-th 3D printer. i wd represents the printing speed of the i-th 3D printer. i This represents the temperature fluctuation range of the i-th 3D printer, in dB. i represents the current fluctuation amplitude of the i-th 3D printer, and k3 represents the weighting coefficient of temperature fluctuation wd and current fluctuation db. S4 includes S41 and S42; S41. The shared platform conducts a preliminary analysis of the 3D printer's status and task load. Combining the impact of 5G communication network latency on the 3D printer status analysis, a task execution availability model is constructed through an intelligent matching algorithm. The obtained printer load coefficient dfz and printer health coefficient sjk are input into the task execution availability model to calculate and obtain the matching availability score coefficient kyx. The availability score coefficient kyx is calculated using the following formula; In the formula, kyx i yc represents the matching availability score coefficient for the i-th 3D printer. i Let ln represent the network latency of the i-th 3D printer, and ln represent the logarithmic function.

2. The method for remotely sharing a 3D printer based on 5G communication according to claim 1, characterized in that: S1 includes S11 and S12; S11. Establish a communication connection between the 3D printer and the sharing platform through the API communication interface, and register detailed information, including the device model, supported material types, current status and geographical location. The sharing platform reviews and verifies all connected devices. S12. Users register through the sharing platform, create personal accounts, and set personal information and preferences. After registration, users upload the 3D model files to be printed to the sharing platform via the 5G communication network through the user-end APP, and provide a detailed description of the project, including the required materials, printing size, and expected completion time.

3. The method for remotely sharing a 3D printer based on 5G communication according to claim 2, characterized in that: S2 includes S21, S22 and S23; S21. Collect the equipment status data of the 3D printer in real time through the 3D printer task monitoring system and transmit it to the sharing platform through the 5G communication network. S22. Real-time acquisition of 3D printer operation data via a sensor group installed on the 3D printer, the sensor group including a temperature sensor and a current sensor. The sensor array communicates with the sharing platform via a 5G communication network and transmits the operational data to the sharing platform in real time. S23. The shared platform receives the running data group in real time, and then performs noise reduction, missing value filling, deduplication and dimensionless processing on the running data group and the equipment status data group to obtain the stable running data group. The shared platform organizes stable operation data groups based on factors affecting 3D printer load and equipment health, and divides the stable operation data groups into load data groups and health data groups. The load data set includes task occupancy ratio zy, queue waiting task quantity dl, operating temperature yw, and material replacement frequency pl; The status data group includes runtime ys, printing speed sd, temperature fluctuation range wd, and current fluctuation range db; The shared platform will transmit stable data sets to a relational database for storage via a 5G communication network, and label each data set with a timestamp, data identifier, data type, and data source.

4. A method for remotely sharing a 3D printer based on 5G communication according to claim 1, characterized in that: S42 includes S421 and S422; S421. Extract the historical stable data set of 3D printer operation from the relational database and input it into the task execution availability model. Use statistical analysis to calculate the mean of the historical matching availability score coefficient and set the obtained mean as the equipment availability score threshold A. S422. Compare the obtained matching availability score coefficient kyx with the equipment availability score threshold A to preliminarily assess the task allocation of the 3D printer. The specific assessment scheme is as follows. When the matching availability score coefficient kyx is greater than or equal to the device availability score threshold A, the device can be assigned tasks and subjected to in-depth evaluation; When the matching availability score coefficient kyx is less than the device availability score threshold A, the device cannot be assigned tasks. This indicates that the 3D printer has overload, aging, or malfunction issues. The sharing platform will mark this 3D printer with a registration mark and generate an early warning message, which will be transmitted to relevant personnel for inspection via the 5G communication network.

5. A method for remotely sharing a 3D printer based on 5G communication according to claim 1, characterized in that: S5 includes S51, S52 and S53; S51. When the initial assessment indicates that the 3D printer can be assigned tasks, the printing material data and performance data of the 3D printer are analyzed based on the printing tasks uploaded to the sharing platform by the user's APP. The printing data sets are collected in real time through the installed encoder and the 3D printer task monitoring system. The encoder communicates with the sharing platform via a 5G communication network and transmits the print data sets to the sharing platform in real time; The sharing platform receives the printing data set in real time, and then performs noise reduction, missing value filling, deduplication and dimensionless processing on the printing data set to obtain the equipment material data set. The sharing platform organizes equipment material data groups based on factors such as 3D printer material surplus and task matching, dividing them into material availability data groups and task matching data groups. The material availability data set includes the remaining material inventory kc, the material consumption rate xh, and the replenishment time gy; The task matching data set includes the peak stable printing speed in milliseconds and the supported resolution in seconds. The shared platform transmits equipment and material data sets to a relational database via a 5G communication network for storage, and marks each data set with a timestamp, data identifier, data type, and data source. S52. Based on the obtained material availability data set, perform summary calculations to obtain the material availability index cky; The material availability index cky is calculated using the following formula; In the formula, cky i kc represents the material availability index for the i-th 3D printer. i xh represents the remaining material inventory of the i-th 3D printer. i gy represents the material consumption rate of the i-th 3D printer. i Let represent the time required to restock the i-th 3D printer, log represents the logarithmic function, and ∈ denotes the minimum value to prevent the denominator from being zero; Based on the acquired task matching data set and combined with the printing requirements of the user's printing task, a summary calculation is performed to obtain the printer task requirement matching degree rwp; The printer task requirement matching degree (rwp) is calculated using the following formula; In the formula, z represents the compatibility coefficient of the 3D printer's ability to support basic materials, and ms i sf represents the peak stable printing speed of the i-th 3D printer. i Let represent the supported resolution of the i-th 3D printer, cg represent the resolution requirement of the task, ts represent the required printing speed of the task, sl represent the required number of prints of the task, and tt represent the required printing time of the task.

6. A method for remotely sharing a 3D printer based on 5G communication according to claim 5, characterized in that: S53 includes S531 and S532; S531. By analyzing the performance indicators and available materials of 3D printers, a comprehensive analysis of real-time and historical data of 3D printers is conducted, and a multi-objective optimization algorithm is used to deeply evaluate the adaptability of each 3D printer. A task matching model is constructed, and the obtained matching availability score coefficient kyx, material availability index cky, and printer task requirement matching degree rwp are input into the task matching model. The task matching model is used to summarize and calculate, and the comprehensive task matching index ZHP is output to assign a suitable printing task to each 3D printer. The Comprehensive Task Matching Index (ZHP) is calculated using the following formula; In the formula, ln represents the logarithmic function.

7. A method for remotely sharing a 3D printer based on 5G communication according to claim 6, characterized in that: S532. By collecting a large amount of equipment and material data of existing 3D printers, including 3D printers that can be assigned tasks and those that cannot, and combining the matching availability score coefficient kyx of each 3D printer with the printing requirements of the users of the sharing platform, inputting it into the task matching model for comprehensive analysis, calculating the comprehensive task matching index ZHP of each 3D printer, and calculating the average value through statistical analysis, and setting the preset task printing matching threshold B. The task printing matching threshold B and the comprehensive task matching index ZHP are used to evaluate the task matching degree of the 3D printer in depth. The specific evaluation scheme is as follows: When the comprehensive task matching index ZHP is greater than or equal to the task printing matching threshold B, it means that the 3D printer meets the printing requirements and the printing task will be assigned. When the comprehensive task matching index ZHP is less than the task printing matching threshold B, it indicates that the 3D printer does not meet the printing requirements. At this time, an overload signal is generated and transmitted to S4 through the 5G communication network to redistribute the printing task.

8. A remote shared 3D printer system based on 5G communication, comprising the remote shared 3D printer method based on 5G communication as described in any one of claims 1-7, characterized in that: It includes a device access module, a device status acquisition module, an intelligent analysis module, an availability assessment module, and a task matching module; The device access module establishes a communication connection between the 3D printer and the sharing platform through the API communication interface and registers relevant information. Users register personal accounts through the sharing platform and upload the 3D model files to be printed through the 5G communication network. The equipment status acquisition module utilizes a shared platform to collect equipment status data and operation data in real time through the printer task monitoring system and the sensor group installed on the 3D printer. After preprocessing, it obtains stable operation data and transmits it to a relational database for storage via a 5G communication network. The intelligent analysis module is used to perform summary calculations based on the stable operating data group and the network latency yc to obtain the printer load coefficient dfz and the printer health coefficient sjk. The availability assessment module is used to perform a summary calculation based on the printer load coefficient dfz and the printer health coefficient sjk to obtain the matching availability score coefficient kyx, and then perform availability assessment with the preset device availability score threshold A. The task matching module is used to collect printing data sets in real time when the 3D printer is initially assessed as capable of being assigned tasks. It then preprocesses the data to obtain equipment and material data sets, summarizes and calculates the material availability index cky and printer task requirement matching degree rwp based on the equipment and material data sets, and combines them with the matching availability score coefficient kyx to perform a comprehensive calculation to obtain the comprehensive task matching index ZHP. Finally, it performs a deep evaluation with the preset task printing matching threshold B.

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