A 3D printing remote monitoring method and system based on the Internet of Things

Abstract

The application provides a 3D printing remote monitoring method and system based on the Internet of Things, which realizes the collection of data in the 3D printing remote monitoring by collecting the sensing data in the 3D printing remote monitoring based on sensors in real time, and obtaining target sensing data by preprocessing the sensing data, provides a data basis for the 3D printing remote monitoring, transmits the target sensing data to a monitoring center based on the Internet of Things, realizes the transmission of the sensing data, provides a basis for the remote monitoring, evaluates the 3D printing based on the target sensing data based on a printing task, obtains an evaluation result, realizes the real-time analysis of the 3D printing process, determines the adjustment data of the 3D printing based on the evaluation result, and transmits the adjustment data to the 3D printing equipment based on the Internet of Things, realizes the real-time adjustment of the 3D printing, finally monitors the printing state anytime and anywhere, discovers problems in time and makes remote adjustment, effectively improves the reliability and efficiency of the printing process, and reduces the production risk.

Description

Technical Field

[0001] This invention relates to the field of remote monitoring technology for printing, and in particular to a method and system for remote monitoring of 3D printing based on the Internet of Things. Background Technology

[0002] In recent years, with the widespread application of 3D printing technology in various fields such as manufacturing, medical care, and education in China, significant progress has been made in the design and optimization research of related equipment. As an information manufacturing technology, the design of 3D printing equipment meets the requirements of high precision and high efficiency.

[0003] 3D printing equipment is usually located inside the factory, making it impossible for staff to monitor the 3D printing process in real time. When 3D printing equipment malfunctions, it cannot be detected in time, leading to interruptions in 3D printing. It is also impossible to check and adjust the 3D printing efficiency in a timely manner, resulting in low 3D printing efficiency. Therefore, how to achieve remote monitoring of 3D printing is of great significance to the development of 3D printing technology. Summary of the Invention

[0004] This invention provides a remote monitoring method and system for 3D printing based on the Internet of Things (IoT) to solve the problems mentioned in the background art.

[0005] A remote monitoring method for 3D printing based on the Internet of Things includes:

[0006] S1: Real-time acquisition of sensor data in remote 3D printing based on sensors, and preprocessing of the sensor data to obtain target sensor data;

[0007] S2: Transmit the target sensor data to the monitoring center based on the Internet of Things;

[0008] S3: Evaluate 3D printing based on the printing task and the target sensing data to obtain the evaluation results;

[0009] S4: Based on the evaluation results, determine the adjustment data for 3D printing and transmit it to the 3D printing equipment via the Internet of Things.

[0010] Preferably, in step S1, the sensing data is preprocessed to obtain the target sensing data, including:

[0011] Abnormal data is deleted from the sensor data, and data is supplemented based on interpolation to obtain abnormal processing data;

[0012] The anomaly processing data is filtered and denoised to obtain denoised data;

[0013] Based on the standard data format, the denoised data is standardized to obtain the target sensing data.

[0014] Preferably, in step S2, before transmitting the target sensor data to the monitoring center based on the Internet of Things, the method further includes:

[0015] Based on the Internet of Things, a shared key is distributed between the 3D printing equipment and the monitoring center. The shared key is used to perform mutual authentication between the 3D printing equipment and the monitoring center. Data transmission can be carried out after successful authentication.

[0016] The target sensor data is encrypted and then transmitted to the monitoring center via the Internet of Things.

[0017] Preferably, in step S2, transmitting the target sensor data to the monitoring center based on the Internet of Things includes:

[0018] Based on the pre-defined data security level, the target sensing data is classified into low-level data to be encrypted and high-level data to be encrypted.

[0019] Low-level data to be encrypted is encrypted using a lightweight encryption algorithm to obtain encrypted data, while high-level data to be encrypted is encrypted using a high-strength encryption algorithm to obtain encrypted data.

[0020] Based on the node status of transmission nodes in the Internet of Things, the amount of encrypted data is solved, transmission performance information and transmission energy consumption information are predicted, and it is determined whether the transmission performance information and transmission energy consumption information meet the preset transmission standards.

[0021] If so, the encrypted data shall be used as the target encrypted data;

[0022] Otherwise, use a lighter-weight encryption algorithm than the current encryption algorithm to encrypt both the low-level and high-level data to be encrypted, and obtain the target encrypted data;

[0023] Generate a timestamp for the target encrypted data, and transmit the encrypted data in an orderly manner based on the timestamp to the Internet of Things-based transmission node;

[0024] The edge nodes in the transmission nodes are obtained, and the encrypted data passing through the edge nodes is encrypted again before being transmitted to the monitoring center.

[0025] After obtaining the secondary encrypted data, the monitoring center verifies the data integrity based on the verification identifier value in the secondary encrypted data;

[0026] After successful verification, the double-encrypted data is received and decrypted.

[0027] Otherwise, issue a warning.

[0028] Preferably, in step S3, the 3D printing is evaluated based on the printing task and the target sensing data to obtain an evaluation result, including:

[0029] The detection value of the target sensing data is compared with the preset standard range of the corresponding data type to determine whether the detection value is within the preset standard range.

[0030] If so, it confirms that 3D printing is running normally and no adjustment of operating parameters is required;

[0031] Otherwise, if the 3D printing process is found to be malfunctioning, the operating parameters need to be adjusted.

[0032] Preferably, in step S3, evaluating 3D printing based on the printing task and the target sensing data to obtain an evaluation result further includes:

[0033] Based on the printing task, determine the printing efficiency requirements and printing quality standards;

[0034] Obtain first sensor data affecting printing efficiency and second sensor data affecting printing quality from the target sensor data, and obtain first abnormal data from the first sensor data and second abnormal data from the second sensor data.

[0035] Based on printing efficiency, a first association rule is determined among the first abnormal data. Based on the first association rule and the size of the outlier of the first abnormal data, a printing efficiency evaluation value is determined.

[0036] Based on print quality, a second association rule is determined between the second abnormal data. Based on the second association rule and the size of the outlier value of the second abnormal data, a print quality evaluation value is determined.

[0037] Obtain the overlap ratio between the first abnormal data and the second abnormal data, and determine the interaction influence weight based on the overlap ratio;

[0038] Based on the printing efficiency evaluation value, printing quality evaluation value, and interaction influence weight, a comprehensive evaluation value for 3D printing is determined.

[0039] The difference between abnormal data in the target sensing data and the data within the preset standard range is used as the direct evaluation data. The printing efficiency evaluation value, printing quality evaluation value, and comprehensive evaluation value are used as the target evaluation data. The evaluation result is obtained based on the direct evaluation data and the target evaluation data.

[0040] Preferably, based on the printing efficiency evaluation value, the printing quality evaluation value, and the interaction influence weight, a comprehensive evaluation value for 3D printing is determined, including:

[0041] Obtain the average of the print efficiency assessment value and the print quality assessment value;

[0042] Obtain the difference between 1 and the interaction influence weight, and calculate the product of the difference and the mean as the comprehensive evaluation value of 3D printing.

[0043] Preferably, in step S4, determining adjustment data for 3D printing based on the evaluation results includes:

[0044] The anomaly types and anomaly differences, as well as the comprehensive evaluation value, are determined from the evaluation results.

[0045] Based on the aforementioned anomaly type and anomaly differences, an initial adjustment value is determined;

[0046] Based on the comprehensive evaluation value and the anomaly type, the adjustment range for the initial adjustment value is determined;

[0047] The adjustment data for 3D printing is determined based on the stated adjustment range and initial adjustment value.

[0048] Preferably, in step S4, after obtaining the adjustment data, it is transmitted to the 3D printing device via the Internet of Things, including:

[0049] The adjustment data is transmitted to the 3D printing equipment using IoT encryption.

[0050] The 3D printing equipment adjusts the 3D printing operation data based on the adjustment data.

[0051] A remote monitoring system for 3D printing based on the Internet of Things (IoT) includes:

[0052] The data acquisition module is used to collect sensor data in real time during 3D printing remotely based on sensors, and to preprocess the sensor data to obtain target sensor data.

[0053] The data transmission module is used to transmit the target sensor data to the monitoring center based on the Internet of Things;

[0054] The evaluation module is used to evaluate 3D printing based on the printing task and the target sensing data, and obtain the evaluation results;

[0055] The adjustment transmission module is used to determine adjustment data for 3D printing based on the evaluation results and transmit it to the 3D printing equipment via the Internet of Things.

[0056] Compared with the prior art, the present invention has achieved the following beneficial effects:

[0057] By collecting sensor data in real time during remote 3D printing and preprocessing the data to obtain target sensor data, remote data acquisition for 3D printing is achieved, providing a data foundation for remote monitoring of 3D printing. Based on the Internet of Things (IoT), the target sensor data is transmitted to the monitoring center, providing a basis for remote monitoring. Based on the printing task and the target sensor data, the 3D printing process is evaluated, and the evaluation results are obtained, enabling real-time analysis of the 3D printing process. Based on the evaluation results, adjustment data for 3D printing is determined and transmitted to the 3D printing equipment via IoT, enabling real-time adjustments to the 3D printing process. Ultimately, the printing status can be monitored anytime, anywhere, problems can be detected promptly, and remote adjustments can be made, effectively improving the reliability and efficiency of the printing process and reducing production risks.

[0058] Other features and advantages of the invention will be set forth in the description which follows, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention may be realized and obtained by means of the structures particularly pointed out in this application.

[0059] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0060] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used in conjunction with embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings:

[0061] Figure 1 This is a flowchart of a remote monitoring method for 3D printing based on the Internet of Things in an embodiment of the present invention;

[0062] Figure 2 This is a flowchart illustrating the process of obtaining target sensing data in an embodiment of the present invention;

[0063] Figure 3 This is a structural diagram of a 3D printing remote monitoring system based on the Internet of Things in an embodiment of the present invention;

[0064] Figure 4 This is a structural diagram of the 3D printing equipment in an embodiment of the present invention. Detailed Implementation

[0065] The preferred embodiments of the present invention will be described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are for illustration and explanation only and are not intended to limit the present invention.

[0066] Example 1: This embodiment of the invention provides a remote monitoring method for 3D printing based on the Internet of Things, such as... Figure 1 As shown, it includes:

[0067] S1: Real-time acquisition of sensor data in remote 3D printing based on sensors, and preprocessing of the sensor data to obtain target sensor data;

[0068] S2: Transmit the target sensor data to the monitoring center based on the Internet of Things;

[0069] S3: Evaluate 3D printing based on the printing task and the target sensing data to obtain the evaluation results;

[0070] S4: Based on the evaluation results, determine the adjustment data for 3D printing and transmit it to the 3D printing equipment via the Internet of Things.

[0071] In this embodiment, the sensing data includes sensing data such as temperature, pressure, speed, and position.

[0072] In this embodiment, the preprocessing of the sensor data includes data anomaly handling, data denoising, and data standardization.

[0073] In this embodiment, the target sensor data is transmitted to the monitoring center via the Internet of Things (IoT) in encrypted form to ensure transmission security.

[0074] In this embodiment, the metrics for evaluating 3D printing include printing efficiency, printing accuracy, and printing process safety.

[0075] In this embodiment, the adjustment data for 3D printing includes speed, pressure, position, etc.

[0076] In this embodiment, such as Figure 4 As shown, the main body of the 3D printing equipment is made of 6061 aluminum alloy profile, and the crossbeam is made of carbon fiber composite material with a bending stiffness ≥500N / mm². With the base plane as the reference plane, the perpendicularity error of the X / Y / Z axes is ensured to be ≤0.02mm / m through laser calibration.

[0077] Musculoskeletal system:

[0078] X / Y axis: linear guide rail, equipped with GT2 timing belt, timing belt pulley reduction ratio 3:1;

[0079] Z-axis: T-type lead screw driven on both sides, with double nut preload to eliminate backlash.

[0080] Printing platform:

[0081] Heated bed: silicone heating film, dual-zone PID temperature control, surface covered with magnetic steel plate;

[0082] Core module design;

[0083] Quick-change nozzle system with interface design: magnetic coupling + 4-pin electrical interface, supports hot-swapping, and switching time <3 minutes.

[0084] Fiber feeding path: remote extruder, tube guide, reducing nozzle mass to below 80g;

[0085] Packing system:

[0086] Structure: Aluminum alloy frame + 5mm acrylic panel, with a filter replacement port at the top.

[0087] Environmental control: Dual fans combined with PID temperature control ensure that the internal temperature fluctuation is ≤±2℃.

[0088] The beneficial effects of the above design scheme are as follows: By collecting sensor data in real time during remote 3D printing and preprocessing the sensor data to obtain target sensor data, the system can collect data in remote 3D printing, providing a data foundation for remote monitoring of 3D printing. Based on the Internet of Things (IoT), the target sensor data is transmitted to the monitoring center, providing a basis for remote monitoring. Based on the printing task and the target sensor data, the 3D printing process is evaluated to obtain evaluation results, enabling real-time analysis of the 3D printing process. Based on the evaluation results, adjustment data for 3D printing is determined and transmitted to the 3D printing equipment via the IoT, enabling real-time adjustment of the 3D printing process. Ultimately, the printing status can be monitored anytime, anywhere, problems can be detected promptly, and remote adjustments can be made, effectively improving the reliability and efficiency of the printing process and reducing production risks.

[0089] Example 2: Based on Example 1, this embodiment of the invention provides a remote monitoring method for 3D printing based on the Internet of Things, such as... Figure 2 As shown, in step S1, the sensing data is preprocessed to obtain the target sensing data, including:

[0090] Abnormal data is deleted from the sensor data, and data is supplemented based on interpolation to obtain abnormal processing data;

[0091] The anomaly processing data is filtered and denoised to obtain denoised data;

[0092] Based on the standard data format, the denoised data is standardized to obtain the target sensing data.

[0093] The beneficial effects of the above design scheme are: by collecting sensor data in real time during 3D printing remotely based on sensors and preprocessing the sensor data to obtain target sensor data, the data collection during 3D printing remotely is realized, providing a data foundation for remote monitoring of 3D printing.

[0094] Example 3: Based on Example 1, this embodiment of the invention provides a remote monitoring method for 3D printing based on the Internet of Things (IoT). In step S2, before transmitting the target sensor data to the monitoring center based on the IoT, the method further includes:

[0095] Based on the Internet of Things, a shared key is distributed between the 3D printing equipment and the monitoring center. The shared key is used to perform mutual authentication between the 3D printing equipment and the monitoring center. Data transmission can be carried out after successful authentication.

[0096] The target sensor data is encrypted and then transmitted to the monitoring center via the Internet of Things.

[0097] In this embodiment, the monitoring center can decrypt the target sensor data according to the pre-distributed key.

[0098] In this embodiment, the shared key can be dynamically updated periodically to ensure security.

[0099] The beneficial effects of the above design scheme are as follows: by distributing a shared key to the 3D printing equipment and the monitoring center based on the Internet of Things, the shared key is used to authenticate both parties, and data can be transmitted after successful authentication. The target sensing data is encrypted and then transmitted to the monitoring center via the Internet of Things, thus providing a foundation for the secure transmission of target sensing data.

[0100] Example 4: Based on Example 1, this embodiment of the invention provides a remote monitoring method for 3D printing based on the Internet of Things (IoT). In step S2, transmitting the target sensor data to the monitoring center based on the IoT includes:

[0101] Based on the pre-defined data security level, the target sensing data is classified into low-level data to be encrypted and high-level data to be encrypted.

[0102] Low-level data to be encrypted is encrypted using a lightweight encryption algorithm to obtain encrypted data, while high-level data to be encrypted is encrypted using a high-strength encryption algorithm to obtain encrypted data.

[0103] Based on the node status of transmission nodes in the Internet of Things, the amount of encrypted data is solved, transmission performance information and transmission energy consumption information are predicted, and it is determined whether the transmission performance information and transmission energy consumption information meet the preset transmission standards.

[0104] If so, the encrypted data shall be used as the target encrypted data;

[0105] Otherwise, use a lighter-weight encryption algorithm than the current encryption algorithm to encrypt both the low-level and high-level data to be encrypted, and obtain the target encrypted data;

[0106] Generate a timestamp for the target encrypted data, and transmit the encrypted data in an orderly manner based on the timestamp to the Internet of Things-based transmission node;

[0107] The edge nodes in the transmission nodes are obtained, and the encrypted data passing through the edge nodes is encrypted again before being transmitted to the monitoring center.

[0108] After obtaining the secondary encrypted data, the monitoring center verifies the data integrity based on the verification identifier value in the secondary encrypted data;

[0109] After successful verification, the double-encrypted data is received and decrypted.

[0110] Otherwise, issue a warning.

[0111] In this embodiment, a timestamp is generated for the target encrypted data. The encrypted data is transmitted in an orderly manner based on the timestamp to ensure that all data is transmitted to the monitoring center and to avoid duplicate or missed transmissions.

[0112] In this embodiment, the preset transmission standard is set to the lowest performance threshold message and the highest energy consumption threshold message.

[0113] In this embodiment, the edge nodes have low security, and the encrypted data of the edge nodes is encrypted a second time to ensure the security of transmission.

[0114] In this embodiment, verifying data integrity based on the verification identifier value in the secondary encrypted data involves first verifying the verification identifier value in the secondary encrypted data using a pre-configured key, and then decrypting the data after successful verification.

[0115] In this embodiment, the verification identifier value is marked on the 3D printing device before transmission.

[0116] The beneficial effects of the above design scheme are as follows: Encrypted data is obtained by encrypting low-level data using a lightweight encryption algorithm, and high-level data is obtained by encrypting high-level data using a high-strength encryption algorithm; based on the node status of IoT transmission nodes, the data volume of encrypted data is addressed, transmission performance information and transmission energy consumption information are predicted, and it is determined whether the transmission performance information and transmission energy consumption information meet the preset transmission standards; if so, the encrypted data is used as the target encrypted data; otherwise, a lighter encryption algorithm than the current encryption algorithm is used to encrypt both the low-level and high-level data to be encrypted, obtaining the target encrypted data, thus ensuring secure data transmission. The system employs tiered encryption of data progress and verification of energy consumption and performance to ensure smooth and timely data transmission, enabling rapid data transfer and guaranteeing transmission speed. This ensures the monitoring center can promptly acquire sensor data. After acquiring the secondary encrypted data, the monitoring center verifies the data integrity based on the verification identifier value within the secondary encrypted data. If the verification is successful, the secondary encrypted data is received and decrypted; otherwise, an early warning is issued. This ensures the monitoring center receives data securely and completely, and provides timely warnings to staff during spring and summer anomalies. The system achieves secure, fast, and complete transmission of sensor data, with secondary encryption at edge nodes during transmission to guarantee security.

[0117] Example 5: Based on Example 1, this embodiment of the invention provides a remote monitoring method for 3D printing based on the Internet of Things. In step S3, the 3D printing is evaluated based on the printing task and the target sensor data to obtain the evaluation result, including:

[0118] The detection value of the target sensing data is compared with the preset standard range of the corresponding data type to determine whether the detection value is within the preset standard range.

[0119] If so, it confirms that 3D printing is running normally and no adjustment of operating parameters is required;

[0120] Otherwise, if the 3D printing process is found to be malfunctioning, the operating parameters need to be adjusted.

[0121] The beneficial effects of the above design scheme are: by comparing the detection value based on the target sensing data with the preset standard range of the corresponding data type, it can be determined whether the detection value is within the preset standard range, thereby realizing the preliminary analysis and evaluation of the 3D printing process and providing a basis for further evaluation and adjustment.

[0122] Example 6: Based on Example 1, this embodiment of the invention provides a remote monitoring method for 3D printing based on the Internet of Things. In step S3, the 3D printing is evaluated based on the printing task and the target sensor data to obtain the evaluation result. The method further includes:

[0123] Based on the printing task, determine the printing efficiency requirements and printing quality standards;

[0124] Obtain first sensor data affecting printing efficiency and second sensor data affecting printing quality from the target sensor data, and obtain first abnormal data from the first sensor data and second abnormal data from the second sensor data.

[0125] Based on printing efficiency, a first association rule is determined among the first abnormal data. Based on the first association rule and the size of the outlier of the first abnormal data, a printing efficiency evaluation value is determined.

[0126] Based on print quality, a second association rule is determined between the second abnormal data. Based on the second association rule and the size of the outlier value of the second abnormal data, a print quality evaluation value is determined.

[0127] Obtain the overlap ratio between the first abnormal data and the second abnormal data, and determine the interaction influence weight based on the overlap ratio;

[0128] Based on the printing efficiency evaluation value, printing quality evaluation value, and interaction influence weight, a comprehensive evaluation value for 3D printing is determined.

[0129] The difference between abnormal data in the target sensing data and the data within the preset standard range is used as the direct evaluation data. The printing efficiency evaluation value, printing quality evaluation value, and comprehensive evaluation value are used as the target evaluation data. The evaluation result is obtained based on the direct evaluation data and the target evaluation data.

[0130] In this embodiment, the first sensing data and the second sensing data may overlap, and the first abnormal data and the second abnormal data may overlap.

[0131] In this embodiment, the first association rule is, for example, when the temperature is within a certain range, the speed is too slow, indicating that the printing efficiency is lower, and when the speed is moderate, the printing efficiency is higher.

[0132] In this embodiment, the second association rule is, for example, that when the speed is within a certain range, the higher the temperature, the lower the print quality, and the lower the temperature, the higher the print quality.

[0133] In this embodiment, the higher the overlap ratio, the greater the corresponding interaction influence weight. A greater interaction influence weight indicates that the efficiency and quality are reduced due to abnormal data in the same related part. When making adjustments, we can focus on abnormal data in the same related part to improve adjustment efficiency.

[0134] The beneficial effects of the above design scheme are: by evaluating abnormal data from two aspects, namely printing quality and printing efficiency, and learning the correlation between abnormal data and the correlation between printing quality and printing efficiency, a comprehensive evaluation of the 3D printing process can be carried out, ensuring the accuracy and comprehensiveness of the final evaluation results, and providing an accurate basis for further adjustment of the 3D printing process parameters.

[0135] Example 7: Based on Example 6, this embodiment of the invention provides a remote monitoring method for 3D printing based on the Internet of Things (IoT). Based on the printing efficiency evaluation value, printing quality evaluation value, and interaction influence weights, a comprehensive evaluation value for 3D printing is determined, including:

[0136] Obtain the average of the print efficiency assessment value and the print quality assessment value;

[0137] Obtain the difference between 1 and the interaction influence weight, and calculate the product of the difference and the mean as the comprehensive evaluation value of 3D printing.

[0138] In this embodiment, the interaction influence weight is less than 1.

[0139] The beneficial effect of the above design scheme is that it provides a method for determining the comprehensive evaluation value of 3D printing, providing an accurate basis for further adjustment of the 3D printing process parameters.

[0140] Example 8: Based on Example 6, this embodiment of the invention provides a remote monitoring method for 3D printing based on the Internet of Things. In step S4, based on the evaluation results, adjustment data for 3D printing is determined, including:

[0141] The anomaly types and anomaly differences, as well as the comprehensive evaluation value, are determined from the evaluation results.

[0142] Based on the aforementioned anomaly type and anomaly differences, an initial adjustment value is determined;

[0143] Based on the comprehensive evaluation value and the anomaly type, the adjustment range for the initial adjustment value is determined;

[0144] The adjustment data for 3D printing is determined based on the stated adjustment range and initial adjustment value.

[0145] In this embodiment, when an anomaly occurs during 3D printing, in order to avoid excessive adjustment and instability in equipment operation, it is necessary to further determine the adjustment range based on the comprehensive evaluation value and the anomaly type, and gradually reach the initial adjustment value according to the adjustment range.

[0146] In this embodiment, the smaller the comprehensive evaluation value, the higher the degree of abnormality, and the smaller the corresponding adjustment range.

[0147] The beneficial effects of the above design scheme are as follows: by determining the anomaly type and anomaly difference, as well as the comprehensive evaluation value, from the evaluation results, an initial adjustment value is determined based on the anomaly type and anomaly difference; based on the comprehensive evaluation value and the anomaly type, the adjustment range of the initial adjustment value is determined; and the adjustment data for 3D printing is determined according to the adjustment range and the initial adjustment value, ensuring the accuracy and rationality of the obtained adjustment data, thereby effectively improving the reliability and efficiency of the printing process and reducing production risks.

[0148] Example 9: Based on Example 8, this embodiment of the invention provides a remote monitoring method for 3D printing based on the Internet of Things (IoT). In step S4, after obtaining the adjustment data, it is transmitted to the 3D printing equipment via the IoT, including:

[0149] The adjustment data is transmitted to the 3D printing equipment using IoT encryption.

[0150] The 3D printing equipment adjusts the 3D printing operation data based on the adjustment data.

[0151] The beneficial effects of the above design scheme are: by transmitting the adjustment data to the 3D printing equipment via IoT encryption, the 3D printing equipment adjusts the 3D printing operation data based on the adjustment data, ensuring the accuracy and rationality of the obtained adjustment data, thereby effectively improving the reliability and efficiency of the printing process and reducing production risks.

[0152] Example 10: This embodiment of the invention provides a remote monitoring system for 3D printing based on the Internet of Things, such as... Figure 3 As shown, it includes:

[0153] The data acquisition module is used to collect sensor data in real time during 3D printing remotely based on sensors, and to preprocess the sensor data to obtain target sensor data.

[0154] The data transmission module is used to transmit the target sensor data to the monitoring center based on the Internet of Things;

[0155] The evaluation module is used to evaluate 3D printing based on the printing task and the target sensing data, and obtain the evaluation results;

[0156] The adjustment transmission module is used to determine adjustment data for 3D printing based on the evaluation results and transmit it to the 3D printing equipment via the Internet of Things.

[0157] In this embodiment, the sensing data includes sensing data such as temperature, pressure, speed, and position.

[0158] In this embodiment, the preprocessing of the sensor data includes data anomaly handling, data denoising, and data standardization.

[0159] In this embodiment, the target sensor data is transmitted to the monitoring center via the Internet of Things (IoT) in encrypted form to ensure transmission security.

[0160] In this embodiment, the metrics for evaluating 3D printing include printing efficiency, printing accuracy, and printing process safety.

[0161] In this embodiment, the adjustment data for 3D printing includes speed, pressure, position, etc.

[0162] The beneficial effects of the above design scheme are as follows: By collecting sensor data in real time during remote 3D printing and preprocessing the sensor data to obtain target sensor data, the system can collect data in remote 3D printing, providing a data foundation for remote monitoring of 3D printing. Based on the Internet of Things (IoT), the target sensor data is transmitted to the monitoring center, providing a basis for remote monitoring. Based on the printing task and the target sensor data, the 3D printing process is evaluated to obtain evaluation results, enabling real-time analysis of the 3D printing process. Based on the evaluation results, adjustment data for 3D printing is determined and transmitted to the 3D printing equipment via the IoT, enabling real-time adjustment of the 3D printing process. Ultimately, the printing status can be monitored anytime, anywhere, problems can be detected promptly, and remote adjustments can be made, effectively improving the reliability and efficiency of the printing process and reducing production risks.

[0163] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of this application and its equivalents, this invention also intends to include these modifications and variations.

Claims

1. A remote monitoring method for 3D printing based on the Internet of Things, characterized in that, include: S1: Real-time acquisition of sensor data in remote 3D printing based on sensors, and preprocessing of the sensor data to obtain target sensor data; S2: Transmit the target sensor data to the monitoring center based on the Internet of Things; S3: Evaluate the 3D printing based on the printing task and the target sensing data to obtain evaluation results, including: Based on the printing task, determine the printing efficiency requirements and printing quality standards; Obtain first sensor data affecting printing efficiency and second sensor data affecting printing quality from the target sensor data, and obtain first abnormal data from the first sensor data and second abnormal data from the second sensor data. Based on printing efficiency, a first association rule is determined among the first abnormal data. Based on the first association rule and the size of the outlier of the first abnormal data, a printing efficiency evaluation value is determined. Based on print quality, a second association rule is determined between the second abnormal data. Based on the second association rule and the size of the outlier value of the second abnormal data, a print quality evaluation value is determined. Obtain the overlap ratio between the first abnormal data and the second abnormal data, and determine the interaction influence weight based on the overlap ratio; Based on the printing efficiency evaluation value, printing quality evaluation value, and interaction influence weight, a comprehensive evaluation value for 3D printing is determined. The difference between abnormal data in the target sensing data and the data within the preset standard range is used as the direct evaluation data, and the printing efficiency evaluation value, printing quality evaluation value and comprehensive evaluation value are used as the target evaluation data. The evaluation result is obtained based on the direct evaluation data and the target evaluation data. S4: Based on the evaluation results, determine the adjustment data for 3D printing and transmit it to the 3D printing equipment via the Internet of Things.

2. The method for remote monitoring of 3D printing based on the Internet of Things according to claim 1, characterized in that, In step S1, the sensing data is preprocessed to obtain the target sensing data, including: Abnormal data is deleted from the sensor data, and data is supplemented based on interpolation to obtain abnormal processing data; The anomaly processing data is filtered and denoised to obtain denoised data; Based on the standard data format, the denoised data is standardized to obtain the target sensing data.

3. The method for remote monitoring of 3D printing based on the Internet of Things according to claim 1, characterized in that, In step S2, before transmitting the target sensor data to the monitoring center based on the Internet of Things, the following steps are also included: Based on the Internet of Things, a shared key is distributed between the 3D printing equipment and the monitoring center. The shared key is used to perform mutual authentication between the 3D printing equipment and the monitoring center. After successful authentication, the target sensor data can be transmitted. The target sensor data is encrypted and then transmitted to the monitoring center via the Internet of Things.

4. The method for remote monitoring of 3D printing based on the Internet of Things according to claim 1, characterized in that, In step S2, transmitting the target sensor data to the monitoring center based on the Internet of Things includes: Based on the pre-defined data security level, the target sensing data is classified into low-level data to be encrypted and high-level data to be encrypted. Low-level data to be encrypted is encrypted using a lightweight encryption algorithm to obtain encrypted data, while high-level data to be encrypted is encrypted using a high-strength encryption algorithm to obtain encrypted data. Based on the node status of transmission nodes in the Internet of Things, the amount of encrypted data is solved, transmission performance information and transmission energy consumption information are predicted, and it is determined whether the transmission performance information and transmission energy consumption information meet the preset transmission standards. If so, the encrypted data shall be used as the target encrypted data; Otherwise, use a lighter-weight encryption algorithm than the current encryption algorithm to encrypt both the low-level and high-level data to be encrypted, and obtain the target encrypted data; Generate a timestamp for the target encrypted data, and transmit the encrypted data in an orderly manner based on the timestamp to the Internet of Things-based transmission node; The edge nodes in the transmission nodes are obtained, and the encrypted data passing through the edge nodes is encrypted again before being transmitted to the monitoring center. After obtaining the secondary encrypted data, the monitoring center verifies the data integrity based on the verification identifier value in the secondary encrypted data; After successful verification, the double-encrypted data is received and decrypted. Otherwise, issue a warning.

5. The method for remote monitoring of 3D printing based on the Internet of Things according to claim 1, characterized in that, In step S3, the 3D printing is evaluated based on the printing task and the target sensing data to obtain evaluation results, including: The detection value of the target sensing data is compared with the preset standard range of the corresponding data type to determine whether the detection value is within the preset standard range. If so, it confirms that 3D printing is running normally and no adjustment of operating parameters is required; Otherwise, if the 3D printing process is found to be malfunctioning, the operating parameters need to be adjusted.

6. The method for remote monitoring of 3D printing based on the Internet of Things according to claim 1, characterized in that, Based on the aforementioned printing efficiency evaluation value, printing quality evaluation value, and interaction weights, a comprehensive evaluation value for 3D printing is determined, including: Obtain the average of the print efficiency assessment value and the print quality assessment value; Obtain the difference between 1 and the interaction influence weight, and calculate the product of the difference and the mean as the comprehensive evaluation value of 3D printing.

7. The method for remote monitoring of 3D printing based on the Internet of Things according to claim 1, characterized in that, In step S4, based on the evaluation results, adjustment data for 3D printing is determined, including: The anomaly types and anomaly differences, as well as the comprehensive evaluation value, are determined from the evaluation results. Based on the aforementioned anomaly type and anomaly differences, an initial adjustment value is determined; Based on the comprehensive evaluation value and the anomaly type, the adjustment range for the initial adjustment value is determined; The adjustment data for 3D printing is determined based on the stated adjustment range and initial adjustment value.

8. The method for remote monitoring of 3D printing based on the Internet of Things according to claim 7, characterized in that, In step S4, after obtaining the adjustment data, it is transmitted to the 3D printing equipment based on the Internet of Things, including: The adjustment data is transmitted to the 3D printing equipment using IoT encryption. The 3D printing equipment adjusts the 3D printing operation data based on the adjustment data.

9. A remote monitoring system for 3D printing based on the Internet of Things, used to implement the remote monitoring method for 3D printing as described in claim 1, characterized in that, include: The data acquisition module is used to collect sensor data in real time during 3D printing remotely based on sensors, and to preprocess the sensor data to obtain target sensor data. The data transmission module is used to transmit the target sensor data to the monitoring center based on the Internet of Things; The evaluation module is used to evaluate 3D printing based on the printing task and the target sensing data, and obtain the evaluation results; The adjustment transmission module is used to determine adjustment data for 3D printing based on the evaluation results and transmit it to the 3D printing equipment via the Internet of Things.

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