Image recognition-based 3D printing device monitoring angle control method and system

Abstract

The application discloses a kind of based on image recognition 3D printing equipment monitoring angle control method and system, the method includes: the historical working data and sensing data of the 3D printing equipment to be monitored are obtained;Based on prediction algorithm, according to the sensing data, the historical working fault corresponding to the 3D printing equipment is predicted;According to the historical working fault and the historical working data, the sensing accurate parameter corresponding to the sensing data is determined;According to the sensing accurate parameter and the current monitoring posture of the image monitoring equipment corresponding to the 3D printing equipment, the image monitoring angle corresponding to the 3D printing equipment is determined;The image monitoring angle is used to indicate the view angle of at least one image monitoring device to the 3D printing equipment.It can be seen that the application can realize accurate image monitoring angle optimization based on fault prediction and sensing accuracy, improve the accuracy and efficiency of 3D printing equipment monitoring, reduce the risk of fault missed detection.

Description

Technical Field

[0001] This invention relates to the field of data processing technology, and in particular to a method and system for controlling the monitoring angle of 3D printing equipment based on image recognition. Background Technology

[0002] With the widespread application of 3D printing equipment in high-precision manufacturing, enterprises and users are increasingly emphasizing the need to reduce equipment failure risks through precise monitoring. Existing technologies typically collect historical operational and sensor data from the 3D printing equipment, employing fixed monitoring strategies or simple image analysis methods to adjust monitoring angles to ensure controllable equipment operation. However, existing solutions lack dynamic prediction of historical failures and in-depth analysis of sensor data accuracy, making it difficult to effectively assess equipment status and optimize image monitoring angles. Commonly used static monitoring settings are unsuitable for complex printing scenarios, resulting in insufficient monitoring accuracy, frequent missed fault detections, or low monitoring efficiency, thus limiting the reliability and production efficiency of 3D printing equipment. Therefore, existing technologies have shortcomings that urgently need to be addressed. Summary of the Invention

[0003] The technical problem to be solved by the present invention is to provide a method and system for controlling the monitoring angle of 3D printing equipment based on image recognition, which can realize precise image monitoring angle optimization based on fault prediction and sensing accuracy, improve the accuracy and efficiency of 3D printing equipment monitoring, and reduce the risk of missed fault detection.

[0004] To address the aforementioned technical problems, the first aspect of this invention discloses a method for monitoring the angle control of a 3D printing device based on image recognition, the method comprising:

[0005] Acquire historical operating data and sensor data of the 3D printing equipment to be monitored;

[0006] Based on the prediction algorithm, the historical operational faults of the 3D printing equipment are predicted according to the sensor data.

[0007] Based on the historical operational faults and the historical operational data, determine the accurate sensing parameters corresponding to the sensing data;

[0008] Based on the accurate sensing parameters and the current monitoring posture of the image monitoring device corresponding to the 3D printing device, the image monitoring angle corresponding to the 3D printing device is determined; the image monitoring angle is used to indicate the framing angle of the 3D printing device by at least one image monitoring device.

[0009] As an optional implementation, in a first aspect of the invention, the sensing data includes at least one of light reflection data, temperature data, humidity data, image data, sound data, and light intensity data.

[0010] As an optional implementation, in the first aspect of the present invention, the step of predicting historical operational faults of the 3D printing equipment based on the sensor data using a prediction algorithm includes:

[0011] The historical sensor data corresponding to each historical time point in the sensor data is input into the trained fault prediction neural network to obtain the fault probability and fault type corresponding to the historical sensor data at each historical time point; the fault prediction neural network is an RNN network structure, which is trained on a training dataset that includes multiple training sensor data and corresponding fault type labels.

[0012] Based on the historical time points, the fault probabilities corresponding to all the historical sensor data are sorted from morning to night to obtain a fault probability sequence;

[0013] Based on the historical time points, the fault types corresponding to all the historical sensor data are sorted from morning to evening to obtain a fault type sequence;

[0014] Based on the fault probability sequence and the fault type sequence, the historical working faults corresponding to the 3D printing equipment are determined.

[0015] As an optional implementation, in the first aspect of the present invention, determining the historical operating faults corresponding to the 3D printing equipment based on the fault probability sequence and the fault type sequence includes:

[0016] Select consecutive failure time point combinations from the failure probability sequence;

[0017] Select associated fault time point combinations from the fault type sequence; the associated fault time point combinations include multiple consecutive historical time points corresponding to the fault types that satisfy preset association type rules;

[0018] Calculate the intersection between the consecutive failure time point combinations and the associated failure time point combinations to obtain at least one failure time point;

[0019] All the aforementioned fault time points and their corresponding fault types are identified as historical operational faults of the 3D printing equipment.

[0020] As an optional implementation, in the first aspect of the present invention, the combination of consecutive failure time points includes a plurality of consecutive historical time points whose failure probabilities satisfy a preset data change rule; the data change rule is used to limit the failure probability to be greater than a preset probability threshold and the corresponding continuous change rate to be greater than a preset change rate threshold.

[0021] As an optional implementation, in the first aspect of the present invention, determining the accurate sensing parameters corresponding to the sensing data based on the historical operational faults and the historical operational data includes:

[0022] Based on the data identifier matching algorithm, multiple fault error data in the historical working data were identified;

[0023] Calculate the time similarity between the data time points corresponding to all the fault error data and the fault time points in the historical working faults;

[0024] Calculate the type similarity between the error types corresponding to all the aforementioned fault error data and the fault types in the historical operational faults;

[0025] Based on the time similarity and the type similarity, the accurate sensing parameters corresponding to the sensing data are calculated.

[0026] As an optional implementation, in the first aspect of the present invention, calculating the sensing accuracy parameters corresponding to the sensing data based on the time similarity and the type similarity includes:

[0027] The weighted summation of the time similarity and the type similarity is calculated to obtain the accurate sensing parameters corresponding to the sensing data; wherein, the calculation weight corresponding to the time similarity is inversely proportional to the data concentration corresponding to all the data time points, and the calculation weight corresponding to the type similarity is inversely proportional to the type discrimination corresponding to all the error types; the type discrimination is the average number of data belonging to the same type in all the error types.

[0028] As an optional implementation, in the first aspect of the present invention, determining the image monitoring angle corresponding to the 3D printing device based on the sensing accuracy parameters and the current monitoring posture of the image monitoring device corresponding to the 3D printing device includes:

[0029] Obtain the current monitoring posture of the image monitoring device corresponding to the 3D printing equipment;

[0030] Based on the preset correspondence between posture and image angle, the current monitoring angle of the image monitoring device is determined according to the current monitoring posture;

[0031] Calculate the angle correction value that is inversely proportional to the accuracy parameter of the sensing;

[0032] The current monitoring angle is adjusted towards the preset optimal monitoring angle by adjusting the angle correction value to obtain the image monitoring angle corresponding to the 3D printing equipment.

[0033] A second aspect of this invention discloses a monitoring angle control system for 3D printing equipment based on image recognition, the system comprising:

[0034] The acquisition module is used to acquire historical working data and sensor data of the 3D printing equipment to be monitored;

[0035] The prediction module is used to predict the historical operational faults of the 3D printing equipment based on the sensor data and a prediction algorithm.

[0036] The first determining module is used to determine the accurate sensing parameters corresponding to the sensing data based on the historical working faults and the historical working data.

[0037] The second determining module is used to determine the image monitoring angle corresponding to the 3D printing device based on the sensing accuracy parameters and the current monitoring posture of the image monitoring device corresponding to the 3D printing device; the image monitoring angle is used to indicate the viewing angle of the 3D printing device by at least one image monitoring device.

[0038] As an optional implementation, in a second aspect of the invention, the sensing data includes at least one of light reflection data, temperature data, humidity data, image data, sound data, and light intensity data.

[0039] As an optional implementation, in a second aspect of the invention, the prediction module, based on a prediction algorithm and the sensing data, predicts the specific method by which it predicts the historical operational faults corresponding to the 3D printing equipment, including:

[0040] The historical sensor data corresponding to each historical time point in the sensor data is input into the trained fault prediction neural network to obtain the fault probability and fault type corresponding to the historical sensor data at each historical time point; the fault prediction neural network is an RNN network structure, which is trained on a training dataset that includes multiple training sensor data and corresponding fault type labels.

[0041] Based on the historical time points, the fault probabilities corresponding to all the historical sensor data are sorted from morning to night to obtain a fault probability sequence;

[0042] Based on the historical time points, the fault types corresponding to all the historical sensor data are sorted from morning to evening to obtain a fault type sequence;

[0043] Based on the fault probability sequence and the fault type sequence, the historical working faults corresponding to the 3D printing equipment are determined.

[0044] As an optional implementation, in a second aspect of the invention, the prediction module determines the specific method by which it determines the historical working faults corresponding to the 3D printing equipment based on the fault probability sequence and the fault type sequence, including:

[0045] Select consecutive failure time point combinations from the failure probability sequence;

[0046] Select associated fault time point combinations from the fault type sequence; the associated fault time point combinations include multiple consecutive historical time points corresponding to the fault types that satisfy preset association type rules;

[0047] Calculate the intersection between the consecutive failure time point combinations and the associated failure time point combinations to obtain at least one failure time point;

[0048] All the aforementioned fault time points and their corresponding fault types are identified as historical operational faults of the 3D printing equipment.

[0049] As an optional implementation, in a second aspect of the present invention, the combination of consecutive failure time points includes multiple consecutive historical time points whose failure probabilities satisfy a preset data change rule; the data change rule is used to limit the failure probability to be greater than a preset probability threshold and the corresponding continuous change rate to be greater than a preset change rate threshold.

[0050] As an optional implementation, in a second aspect of the invention, the specific method by which the first determining module determines the accurate sensing parameters corresponding to the sensing data based on the historical operational faults and the historical operational data includes:

[0051] Based on the data identifier matching algorithm, multiple fault error data in the historical working data were identified;

[0052] Calculate the time similarity between the data time points corresponding to all the fault error data and the fault time points in the historical working faults;

[0053] Calculate the type similarity between the error types corresponding to all the aforementioned fault error data and the fault types in the historical operational faults;

[0054] Based on the time similarity and the type similarity, the accurate sensing parameters corresponding to the sensing data are calculated.

[0055] As an optional implementation, in a second aspect of the invention, the specific method by which the first determining module calculates the sensing accuracy parameters corresponding to the sensing data based on the time similarity and the type similarity includes:

[0056] The weighted summation of the time similarity and the type similarity is calculated to obtain the accurate sensing parameters corresponding to the sensing data; wherein, the calculation weight corresponding to the time similarity is inversely proportional to the data concentration corresponding to all the data time points, and the calculation weight corresponding to the type similarity is inversely proportional to the type discrimination corresponding to all the error types; the type discrimination is the average number of data belonging to the same type in all the error types.

[0057] As an optional implementation, in a second aspect of the invention, the second determining module determines the specific method of the image monitoring angle corresponding to the 3D printing device based on the sensing accuracy parameters and the current monitoring posture of the image monitoring device corresponding to the 3D printing device, including:

[0058] Obtain the current monitoring posture of the image monitoring device corresponding to the 3D printing equipment;

[0059] Based on the preset correspondence between posture and image angle, the current monitoring angle of the image monitoring device is determined according to the current monitoring posture;

[0060] Calculate the angle correction value that is inversely proportional to the accuracy parameter of the sensing;

[0061] The current monitoring angle is adjusted towards the preset optimal monitoring angle by adjusting the angle correction value to obtain the image monitoring angle corresponding to the 3D printing equipment.

[0062] A third aspect of this invention discloses another image recognition-based 3D printing equipment monitoring angle control system, the system comprising:

[0063] Memory containing executable program code;

[0064] A processor coupled to the memory;

[0065] The processor calls the executable program code stored in the memory to execute some or all of the steps in the image recognition-based 3D printing equipment monitoring angle control method disclosed in the first aspect of the present invention.

[0066] The fourth aspect of the present invention discloses a computer storage medium storing computer instructions, which, when invoked, are used to execute some or all of the steps in the image recognition-based 3D printing equipment monitoring angle control method disclosed in the first aspect of the present invention.

[0067] Compared with the prior art, the embodiments of the present invention have the following beneficial effects:

[0068] This invention acquires historical working data and sensor data of the 3D printing equipment to be monitored, predicts historical working faults based on a prediction algorithm, determines accurate sensor parameters by combining historical working faults and historical working data, and determines the image monitoring angle based on the accurate sensor parameters and the current monitoring posture of the image monitoring equipment. This enables precise image monitoring angle optimization based on fault prediction and sensor accuracy, improving the accuracy and efficiency of 3D printing equipment monitoring and reducing the risk of missed fault detection. Attached Figure Description

[0069] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0070] Figure 1 This is a flowchart illustrating a method for controlling the monitoring angle of a 3D printing device based on image recognition, as disclosed in an embodiment of the present invention.

[0071] Figure 2 This is a schematic diagram of the structure of a monitoring angle control system for a 3D printing equipment based on image recognition, as disclosed in an embodiment of the present invention.

[0072] Figure 3 This is a schematic diagram of another image recognition-based 3D printing equipment monitoring angle control system disclosed in an embodiment of the present invention. Detailed Implementation

[0073] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0074] The terms "first," "second," etc., used in the specification, claims, and accompanying drawings of this invention are used to distinguish different objects, not to describe a specific order. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, apparatus, product, or device that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to these processes, methods, products, or devices.

[0075] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of the invention. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0076] This invention discloses a method and system for controlling the monitoring angle of 3D printing equipment based on image recognition. By acquiring historical operating data and sensor data of the 3D printing equipment to be monitored, a prediction algorithm is used to predict historical operating faults. The system then combines historical operating faults and historical operating data to determine accurate sensor parameters. Finally, based on these accurate sensor parameters and the current monitoring posture of the image monitoring equipment, the image monitoring angle is determined. This enables precise optimization of the image monitoring angle based on fault prediction and sensor accuracy, improving the accuracy and efficiency of 3D printing equipment monitoring and reducing the risk of missed fault detection. Detailed explanations follow.

[0077] Example 1

[0078] Please see Figure 1 , Figure 1 This is a flowchart illustrating a method for monitoring the angle control of a 3D printing device based on image recognition, as disclosed in an embodiment of the present invention. Figure 1 The described image recognition-based 3D printing equipment monitoring angle control method can be applied to data processing systems / data processing equipment / data processing servers (wherein, the server includes a local processing server or a cloud processing server). For example... Figure 1 As shown, the image recognition-based 3D printing equipment monitoring angle control method may include the following operations:

[0079] 101. Obtain historical working data and sensor data of the 3D printing equipment to be monitored.

[0080] 102. Based on the prediction algorithm, predict the historical working failures of the 3D printing equipment according to the sensor data.

[0081] 103. Based on historical faults and historical data, determine the accurate sensing parameters corresponding to the sensing data.

[0082] 104. Based on the accurate sensor parameters and the current monitoring posture of the image monitoring device corresponding to the 3D printing equipment, determine the image monitoring angle corresponding to the 3D printing equipment.

[0083] Optionally, the image monitoring angle is used to indicate the viewing angle of at least one image monitoring device toward the 3D printing device.

[0084] As can be seen, the above-described embodiments of the invention acquire historical working data and sensor data of the 3D printing equipment to be monitored, predict historical working faults based on prediction algorithms, determine accurate sensing parameters by combining historical working faults and historical working data, and determine the image monitoring angle based on the accurate sensing parameters and the current monitoring posture of the image monitoring equipment. This enables precise image monitoring angle optimization based on fault prediction and sensing accuracy, improving the accuracy and efficiency of 3D printing equipment monitoring and reducing the risk of missed fault detection.

[0085] As an optional embodiment, the sensing data in the above steps includes at least one of light reflection data, temperature data, humidity data, image data, sound data, and light intensity data.

[0086] As can be seen, the content of the sensing data is defined through the above optional embodiments to comprehensively characterize the relevant features of the printing equipment's sensing, so as to facilitate accurate fault prediction and accurate sensing judgment in the future. This helps to achieve precise image monitoring angle optimization based on fault prediction and sensing accuracy, improve the accuracy and efficiency of 3D printing equipment monitoring, and reduce the risk of missed fault detection.

[0087] As an optional embodiment, the step above, predicting historical operational faults of the 3D printing equipment based on the prediction algorithm and sensor data, includes:

[0088] The historical sensor data corresponding to each historical time point in the sensor data is input into the trained fault prediction neural network to obtain the fault probability and fault type corresponding to the historical sensor data at each historical time point; optionally, the fault prediction neural network is an RNN network structure, which is trained using a training dataset that includes multiple training sensor data and corresponding fault type labels.

[0089] Based on historical time points from morning to night, the failure probabilities corresponding to all historical sensor data are sorted to obtain a failure probability sequence.

[0090] Based on historical time points from morning to evening, the fault types corresponding to all historical sensor data are sorted to obtain a fault type sequence;

[0091] Based on the failure probability sequence and failure type sequence, the historical working failures corresponding to the 3D printing equipment are determined.

[0092] As can be seen, through the above optional embodiments, by inputting the historical sensor data of the 3D printing equipment into the trained RNN fault prediction neural network according to the historical time points, the fault probability and fault type corresponding to each time point are obtained and sorted from early to late to form a fault probability sequence and a fault type sequence. Combining the two, historical working faults are determined, thereby realizing accurate fault analysis based on time-series fault prediction, improving the accuracy and reliability of 3D printing equipment monitoring, and reducing the risk of fault misjudgment.

[0093] As an optional embodiment, the step above, determining the historical operating faults corresponding to the 3D printing equipment based on the fault probability sequence and the fault type sequence, includes:

[0094] Select consecutive failure time point combinations from the failure probability sequence;

[0095] Select associated fault time point combinations from the fault type sequence; optionally, the associated fault time point combinations include multiple consecutive historical time points with corresponding fault types that satisfy preset association type rules.

[0096] Calculate the intersection between consecutive failure time point combinations and associated failure time point combinations to obtain at least one failure time point;

[0097] All failure times and corresponding failure types are identified as historical operational failures of the 3D printing equipment.

[0098] As can be seen, through the above optional embodiments, by selecting consecutive failure time point combinations from the failure probability sequence and selecting consecutive historical time points that meet the preset association type rules from the failure type sequence to form associated failure time point combinations, the intersection of the two is calculated to obtain the failure time point and its corresponding failure type, which is determined as the historical working failure of the 3D printing equipment, thereby realizing accurate fault identification based on time sequence and type association, improving the accuracy of equipment monitoring and the efficiency of fault diagnosis, and reducing the risk of missed detection.

[0099] As an optional embodiment, the combination of consecutive fault time points in the above steps includes multiple consecutive historical time points whose corresponding fault probabilities satisfy preset data change rules; the data change rules are used to limit the fault probability to be greater than a preset probability threshold and the corresponding continuous change rate to be greater than a preset change rate threshold.

[0100] As can be seen, the above optional embodiments limit the selection of continuous historical time points from the fault probability sequence that meet the fault probability exceeding the probability threshold and the continuous rate of change exceeding the rate of change threshold to form a continuous fault time point combination, so as to facilitate subsequent accurate fault identification based on probability change and type association, improve the accuracy of equipment monitoring and fault diagnosis efficiency, and reduce the risk of missed fault detection.

[0101] As an optional embodiment, the step above, determining the accurate sensing parameters corresponding to the sensing data based on historical operational faults and historical operational data, includes:

[0102] Based on the data identifier matching algorithm, multiple fault error data in historical working data were identified;

[0103] Calculate the time similarity between the data time points corresponding to all fault error data and the fault time points in historical working faults;

[0104] Calculate the type similarity between the error types corresponding to all fault error data and the fault types in historical operational faults;

[0105] Based on time similarity and type similarity, calculate the accurate sensing parameters corresponding to the sensing data.

[0106] As can be seen, through the above optional embodiments, by identifying fault error data in historical working data based on data identifier matching algorithm, calculating the time similarity between the data time point and the historical working fault time point, and the type similarity between the error type and the fault type, and combining the two to determine the accurate sensing parameters of the sensing data, the accurate sensing accuracy assessment based on time and type matching can be achieved, thereby improving the reliability of 3D printing equipment monitoring and reducing the risk of fault misjudgment.

[0107] As an optional embodiment, the step above, calculating the accurate sensing parameters corresponding to the sensing data based on time similarity and type similarity, includes:

[0108] Calculate the weighted summation of time similarity and type similarity to obtain the accurate sensing parameters corresponding to the sensing data; optionally, the calculation weight corresponding to time similarity is inversely proportional to the data centrality corresponding to all data time points, and the calculation weight corresponding to type similarity is inversely proportional to the type discrimination corresponding to all error types; the type discrimination is the average number of data belonging to the same type in all error types.

[0109] As can be seen, through the above optional embodiments, by calculating the time similarity between the fault error data time point and the historical working fault time point, and the type similarity between the error type and the fault type, and by calculating the weighted summation average with weights related to data centrality and type distinguishability, the accurate sensing parameters of the sensing data are determined. This enables precise sensing accuracy assessment based on time and type feature weighted analysis, improves the reliability of 3D printing equipment monitoring and fault diagnosis accuracy, and reduces the risk of misjudgment.

[0110] As an optional embodiment, the step above, determining the image monitoring angle corresponding to the 3D printing equipment based on the accurate sensing parameters and the current monitoring posture of the image monitoring equipment corresponding to the 3D printing equipment, includes:

[0111] Obtain the current monitoring posture of the image monitoring device corresponding to the 3D printing equipment;

[0112] Based on the preset correspondence between posture and image angle, the current monitoring angle of the image monitoring device is determined according to the current monitoring posture.

[0113] Calculate the angle correction value, which is inversely proportional to the accurate sensing parameters;

[0114] Adjust the current monitoring angle towards the preset optimal monitoring angle to obtain the image monitoring angle corresponding to the 3D printing equipment.

[0115] As can be seen, through the above optional embodiments, by acquiring the current monitoring posture of the 3D printing equipment image monitoring device and determining the current monitoring angle based on the preset posture and image angle correspondence, calculating the angle correction value that is inversely proportional to the sensing accuracy parameter, and adjusting the current monitoring angle towards the optimal monitoring angle direction to obtain the final image monitoring angle, the precise monitoring angle optimization based on sensing accuracy and posture analysis is achieved, thereby improving the accuracy and efficiency of 3D printing equipment monitoring and reducing the risk of missed fault detection.

[0116] Example 2

[0117] Please see Figure 2 , Figure 2 This is a schematic diagram of a monitoring angle control system for a 3D printing equipment based on image recognition, as disclosed in an embodiment of the present invention. Figure 2 The described image recognition-based 3D printing equipment monitoring angle control system can be applied to data processing systems / data processing equipment / data processing servers (wherein, the server includes a local processing server or a cloud processing server). For example... Figure 2 As shown, the image recognition-based 3D printing equipment monitoring angle control system may include:

[0118] The acquisition module 201 is used to acquire historical working data and sensor data of the 3D printing equipment to be monitored.

[0119] The prediction module 202 is used to predict the historical working faults of the 3D printing equipment based on the prediction algorithm and sensor data.

[0120] The first determining module 203 is used to determine the accurate sensing parameters corresponding to the sensing data based on historical working faults and historical working data.

[0121] The second determining module 204 is used to determine the image monitoring angle corresponding to the 3D printing equipment based on the accurate sensing parameters and the current monitoring posture of the image monitoring equipment corresponding to the 3D printing equipment.

[0122] Optionally, the image monitoring angle is used to indicate the viewing angle of at least one image monitoring device toward the 3D printing device.

[0123] As can be seen, the above-described embodiments of the invention acquire historical working data and sensor data of the 3D printing equipment to be monitored, predict historical working faults based on prediction algorithms, determine accurate sensing parameters by combining historical working faults and historical working data, and determine the image monitoring angle based on the accurate sensing parameters and the current monitoring posture of the image monitoring equipment. This enables precise image monitoring angle optimization based on fault prediction and sensing accuracy, improving the accuracy and efficiency of 3D printing equipment monitoring and reducing the risk of missed fault detection.

[0124] As an optional embodiment, the sensing data includes at least one of light reflection data, temperature data, humidity data, image data, sound data, and light intensity data.

[0125] As can be seen, the content of the sensing data is defined through the above optional embodiments to comprehensively characterize the relevant features of the printing equipment's sensing, so as to facilitate accurate fault prediction and accurate sensing judgment in the future. This helps to achieve precise image monitoring angle optimization based on fault prediction and sensing accuracy, improve the accuracy and efficiency of 3D printing equipment monitoring, and reduce the risk of missed fault detection.

[0126] As an optional embodiment, the prediction module, based on a prediction algorithm and sensor data, predicts the specific methods by which the prediction module predicts the historical operational faults of the 3D printing equipment, including:

[0127] The historical sensor data corresponding to each historical time point in the sensor data is input into the trained fault prediction neural network to obtain the fault probability and fault type corresponding to the historical sensor data at each historical time point; optionally, the fault prediction neural network is an RNN network structure, which is trained using a training dataset that includes multiple training sensor data and corresponding fault type labels.

[0128] Based on historical time points from morning to night, the failure probabilities corresponding to all historical sensor data are sorted to obtain a failure probability sequence.

[0129] Based on historical time points from morning to evening, the fault types corresponding to all historical sensor data are sorted to obtain a fault type sequence;

[0130] Based on the failure probability sequence and failure type sequence, the historical working failures corresponding to the 3D printing equipment are determined.

[0131] As can be seen, through the above optional embodiments, by inputting the historical sensor data of the 3D printing equipment into the trained RNN fault prediction neural network according to the historical time points, the fault probability and fault type corresponding to each time point are obtained and sorted from early to late to form a fault probability sequence and a fault type sequence. Combining the two, historical working faults are determined, thereby realizing accurate fault analysis based on time-series fault prediction, improving the accuracy and reliability of 3D printing equipment monitoring, and reducing the risk of fault misjudgment.

[0132] As an optional embodiment, the prediction module determines the specific method of historical working failures corresponding to the 3D printing equipment based on the failure probability sequence and the failure type sequence, including:

[0133] Select consecutive failure time point combinations from the failure probability sequence;

[0134] Select associated fault time point combinations from the fault type sequence; optionally, the associated fault time point combinations include multiple consecutive historical time points with corresponding fault types that satisfy preset association type rules.

[0135] Calculate the intersection between consecutive failure time point combinations and associated failure time point combinations to obtain at least one failure time point;

[0136] All failure times and corresponding failure types are identified as historical operational failures of the 3D printing equipment.

[0137] As can be seen, through the above optional embodiments, by selecting consecutive failure time point combinations from the failure probability sequence and selecting consecutive historical time points that meet the preset association type rules from the failure type sequence to form associated failure time point combinations, the intersection of the two is calculated to obtain the failure time point and its corresponding failure type, which is determined as the historical working failure of the 3D printing equipment, thereby realizing accurate fault identification based on time sequence and type association, improving the accuracy of equipment monitoring and the efficiency of fault diagnosis, and reducing the risk of missed detection.

[0138] As an optional embodiment, the combination of consecutive failure time points includes multiple consecutive historical time points whose corresponding failure probabilities satisfy preset data change rules; the data change rules are used to limit the failure probability to be greater than a preset probability threshold and the corresponding continuous change rate to be greater than a preset change rate threshold.

[0139] As can be seen, the above optional embodiments limit the selection of continuous historical time points from the fault probability sequence that meet the fault probability exceeding the probability threshold and the continuous rate of change exceeding the rate of change threshold to form a continuous fault time point combination, so as to facilitate subsequent accurate fault identification based on probability change and type association, improve the accuracy of equipment monitoring and fault diagnosis efficiency, and reduce the risk of missed fault detection.

[0140] As an optional embodiment, the first determining module determines the specific method by which it determines the accurate sensing parameters corresponding to the sensing data based on historical operational faults and historical operational data, including:

[0141] Based on the data identifier matching algorithm, multiple fault error data in historical working data were identified;

[0142] Calculate the time similarity between the data time points corresponding to all fault error data and the fault time points in historical working faults;

[0143] Calculate the type similarity between the error types corresponding to all fault error data and the fault types in historical operational faults;

[0144] Based on time similarity and type similarity, calculate the accurate sensing parameters corresponding to the sensing data.

[0145] As can be seen, through the above optional embodiments, by identifying fault error data in historical working data based on data identifier matching algorithm, calculating the time similarity between the data time point and the historical working fault time point, and the type similarity between the error type and the fault type, and combining the two to determine the accurate sensing parameters of the sensing data, the accurate sensing accuracy assessment based on time and type matching can be achieved, thereby improving the reliability of 3D printing equipment monitoring and reducing the risk of fault misjudgment.

[0146] As an optional embodiment, the specific method by which the first determining module calculates the accurate sensing parameters corresponding to the sensing data based on time similarity and type similarity includes:

[0147] Calculate the weighted summation of time similarity and type similarity to obtain the accurate sensing parameters corresponding to the sensing data; optionally, the calculation weight corresponding to time similarity is inversely proportional to the data centrality corresponding to all data time points, and the calculation weight corresponding to type similarity is inversely proportional to the type discrimination corresponding to all error types; the type discrimination is the average number of data belonging to the same type in all error types.

[0148] As can be seen, through the above optional embodiments, by calculating the time similarity between the fault error data time point and the historical working fault time point, and the type similarity between the error type and the fault type, and by calculating the weighted summation average with weights related to data centrality and type distinguishability, the accurate sensing parameters of the sensing data are determined. This enables precise sensing accuracy assessment based on time and type feature weighted analysis, improves the reliability of 3D printing equipment monitoring and fault diagnosis accuracy, and reduces the risk of misjudgment.

[0149] As an optional embodiment, the second determining module determines the specific method of the image monitoring angle corresponding to the 3D printing equipment based on the accurate sensing parameters and the current monitoring posture of the image monitoring equipment corresponding to the 3D printing equipment, including:

[0150] Obtain the current monitoring posture of the image monitoring device corresponding to the 3D printing equipment;

[0151] Based on the preset correspondence between posture and image angle, the current monitoring angle of the image monitoring device is determined according to the current monitoring posture.

[0152] Calculate the angle correction value, which is inversely proportional to the accurate sensing parameters;

[0153] Adjust the current monitoring angle towards the preset optimal monitoring angle to obtain the image monitoring angle corresponding to the 3D printing equipment.

[0154] As can be seen, through the above optional embodiments, by acquiring the current monitoring posture of the 3D printing equipment image monitoring device and determining the current monitoring angle based on the preset posture and image angle correspondence, calculating the angle correction value that is inversely proportional to the sensing accuracy parameter, and adjusting the current monitoring angle towards the optimal monitoring angle direction to obtain the final image monitoring angle, the precise monitoring angle optimization based on sensing accuracy and posture analysis is achieved, thereby improving the accuracy and efficiency of 3D printing equipment monitoring and reducing the risk of missed fault detection.

[0155] Example 3

[0156] Please see Figure 3 , Figure 3 This is another image recognition-based 3D printing equipment monitoring angle control system disclosed in the embodiments of the present invention. Figure 3 The described image recognition-based 3D printing equipment monitoring angle control system is applied in a data processing system / data processing equipment / data processing server (wherein, the server includes a local processing server or a cloud processing server). For example... Figure 3 As shown, the image recognition-based 3D printing equipment monitoring angle control system may include:

[0157] Memory 301 storing executable program code;

[0158] Processor 302 coupled to memory 301;

[0159] The processor 302 calls the executable program code stored in the memory 301 to execute the steps of the image recognition-based 3D printing equipment monitoring angle control method described in Embodiment 1.

[0160] Example 4

[0161] This invention discloses a computer read storage medium that stores a computer program for electronic data interchange, wherein the computer program causes a computer to execute the steps of the image recognition-based 3D printing equipment monitoring angle control method described in Embodiment 1.

[0162] Example 5

[0163] This invention discloses a computer program product, which includes a non-transitory computer-readable storage medium storing a computer program, and the computer program is operable to cause a computer to perform the steps of the image recognition-based 3D printing equipment monitoring angle control method described in Embodiment 1.

[0164] The foregoing has described specific embodiments of this specification; other embodiments are within the scope of the appended claims. In some cases, the actions or steps described in the claims may be performed in a different order than those shown in the embodiments and may still achieve the desired result. Furthermore, the processes depicted in the drawings do not necessarily have to follow the specific or sequential order shown to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0165] The systems, devices, modules, or units described in the above embodiments can be implemented by computer chips or entities, or by products with certain functions. A typical implementation device is a computer. Specifically, a computer can be, for example, a personal computer, laptop computer, cellular phone, camera phone, smartphone, personal digital assistant, media player, navigation device, email device, game console, tablet computer, wearable device, or any combination of these devices.

[0166] For ease of description, the above devices are described in terms of function, divided into various units. Of course, in implementing this specification, the functions of each unit can be implemented in one or more software and / or hardware components.

[0167] Those skilled in the art will understand that the embodiments of this specification can be provided as methods, systems, or computer program products. Therefore, the embodiments of this specification can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the embodiments of this specification can take the form of a computer program product implemented on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0168] This specification is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this specification. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create a machine for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0169] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0170] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0171] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0172] Memory may include non-persistent storage in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0173] Computer-readable media includes both permanent and non-permanent, removable and non-removable media that can store information using any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0174] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0175] This specification can be described in the general context of computer-executable instructions that are executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc., that perform a specific task or implement a specific abstract data type. This specification can also be practiced in distributed computing environments, where tasks are performed by remote processing devices connected via a communication network. In distributed computing environments, program modules can reside in local and remote computer storage media, including storage devices.

[0176] The various embodiments in this specification are described in a progressive manner. Similar or identical parts between embodiments can be referred to interchangeably. Each embodiment focuses on describing the differences from other embodiments. In particular, the system embodiments are basically similar to the method embodiments, so the description is relatively simple; relevant parts can be referred to the descriptions in the method embodiments.

[0177] Finally, it should be noted that the image recognition-based 3D printing equipment monitoring angle control method and system disclosed in the embodiments of the present invention are merely preferred embodiments of the present invention and are only used to illustrate the technical solutions of the present invention, not to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for monitoring the angle control of a 3D printing device based on image recognition, characterized in that, The method includes: Acquire historical operating data and sensor data of the 3D printing equipment to be monitored; Based on the prediction algorithm and the sensor data, the historical operational faults of the 3D printing equipment are predicted, including: The historical sensor data corresponding to each historical time point in the sensor data is input into the trained fault prediction neural network to obtain the fault probability and fault type corresponding to the historical sensor data at each historical time point; the fault prediction neural network is an RNN network structure, which is trained on a training dataset that includes multiple training sensor data and corresponding fault type labels. Based on the historical time points, the fault probabilities corresponding to all the historical sensor data are sorted from morning to night to obtain a fault probability sequence; Based on the historical time points, the fault types corresponding to all the historical sensor data are sorted from morning to evening to obtain a fault type sequence; Select consecutive failure time point combinations from the failure probability sequence; Select associated fault time point combinations from the fault type sequence; the associated fault time point combinations include multiple consecutive historical time points corresponding to the fault types that satisfy preset association type rules; Calculate the intersection between the consecutive failure time point combinations and the associated failure time point combinations to obtain at least one failure time point; All the aforementioned fault time points and corresponding fault types are identified as historical working faults of the 3D printing equipment. Based on the historical operational faults and the historical operational data, determine the accurate sensing parameters corresponding to the sensing data, including: Based on the data identifier matching algorithm, multiple fault error data in the historical working data were identified; Calculate the time similarity between the data time points corresponding to all the fault error data and the fault time points in the historical working faults; Calculate the type similarity between the error types corresponding to all the aforementioned fault error data and the fault types in the historical operational faults; The weighted summation average of the time similarity and the type similarity is calculated to obtain the sensing accuracy parameters corresponding to the sensing data; wherein, the calculation weight corresponding to the time similarity is inversely proportional to the data concentration corresponding to all the data time points, and the calculation weight corresponding to the type similarity is inversely proportional to the type discrimination corresponding to all the error types; the type discrimination is the average number of data belonging to the same type in all the error types; Based on the accurate sensing parameters and the current monitoring posture of the image monitoring device corresponding to the 3D printing device, the image monitoring angle corresponding to the 3D printing device is determined; the image monitoring angle is used to indicate the framing angle of the 3D printing device by at least one image monitoring device.

2. The image recognition-based 3D printing equipment monitoring angle control method according to claim 1, characterized in that, The sensing data includes at least one of light reflection data, temperature data, humidity data, image data, sound data, and light intensity data.

3. The image recognition-based 3D printing equipment monitoring angle control method according to claim 1, characterized in that, The combination of consecutive fault time points includes multiple corresponding consecutive historical time points whose fault probabilities satisfy preset data change rules; the data change rules are used to limit the fault probability to be greater than a preset probability threshold and the corresponding continuous change rate to be greater than a preset change rate threshold.

4. The image recognition-based 3D printing equipment monitoring angle control method according to claim 1, characterized in that, The step of determining the image monitoring angle corresponding to the 3D printing equipment based on the accurate sensing parameters and the current monitoring posture of the image monitoring equipment corresponding to the 3D printing equipment includes: Obtain the current monitoring posture of the image monitoring device corresponding to the 3D printing equipment; Based on the preset correspondence between posture and image angle, the current monitoring angle of the image monitoring device is determined according to the current monitoring posture; Calculate the angle correction value that is inversely proportional to the accuracy parameter of the sensing; The current monitoring angle is adjusted towards the preset optimal monitoring angle by adjusting the angle correction value to obtain the image monitoring angle corresponding to the 3D printing equipment.

5. A monitoring angle control system for 3D printing equipment based on image recognition, characterized in that, The system is used to execute the image recognition-based 3D printing equipment monitoring angle control method as described in any one of claims 1-4, the system comprising: The acquisition module is used to acquire historical working data and sensor data of the 3D printing equipment to be monitored; The prediction module is used to predict the historical operational faults of the 3D printing equipment based on the sensor data and a prediction algorithm. The first determining module is used to determine the accurate sensing parameters corresponding to the sensing data based on the historical working faults and the historical working data. The second determining module is used to determine the image monitoring angle corresponding to the 3D printing device based on the sensing accuracy parameters and the current monitoring posture of the image monitoring device corresponding to the 3D printing device; the image monitoring angle is used to indicate the viewing angle of the 3D printing device by at least one image monitoring device.

6. A monitoring angle control system for 3D printing equipment based on image recognition, characterized in that, The system includes: Memory containing executable program code; A processor coupled to the memory; The processor calls the executable program code stored in the memory to execute the image recognition-based 3D printing equipment monitoring angle control method as described in any one of claims 1-4.

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