Detection method, detection device, electronic equipment and storage medium of galvanometer

Abstract

The application discloses a detection method and device of a galvanometer, an electronic device and a storage medium. Through application of the technical solutions of the embodiments of the application, an image detection model that has been pre-trained can be used to identify image features in each galvanometer image, and then it can be determined whether the number of pollutants and the area of pollutants on the lens of the galvanometer exceed the standard. Thus, the effect of automatically identifying whether the lens pollutants exceed the standard according to the image detection method is achieved.

Description

Technical Field

[0001] This application relates to equipment testing technology, and in particular to a method for testing a galvanometer, a testing device, an electronic device, and a storage medium. Background Technology

[0002] In related technologies, during the welding process of galvanometers with other equipment, the problem of contaminants adhering to the galvanometer lenses to varying degrees often occurs.

[0003] For example, if the contaminant is welding slag, the welding slag spatter that occurs during the welding process can cause the spatter to adhere to the protective lens of the galvanometer field lens. As a result, during the light emission process of the galvanometer, the welding slag adsorbed on the field lens blocks part of the light, which in turn causes the laser energy of the galvanometer to be attenuated to varying degrees. In severe cases, it can even cause the welded product to be scrapped.

[0004] Therefore, how to detect the pass rate of galvanometers has become a problem that needs to be solved.

[0005] The above statements are for the purpose of providing background information in relation to this application only and do not necessarily constitute prior art. Summary of the Invention

[0006] This application provides a method, apparatus, electronic device, and storage medium for detecting galvanometers. This alleviates the problem in related technologies where the pass rate of galvanometers cannot be detected, leading to poor equipment performance.

[0007] According to one aspect of the embodiments of this application, a method for detecting a galvanometer is provided, comprising: In response to the detection initiation conditions for the galvanometer to be tested, an image of the lens of the galvanometer to be tested is acquired; feature recognition is performed on the lens image to determine the contaminant features corresponding to the lens image. The contaminant features are used to reflect the degree of contaminant adhesion on the lens of the galvanometer to be tested; based on the contaminant features, the detection result of the galvanometer to be tested is determined. By applying the technical solution of the embodiments of this application, a pre-trained image detection model can be used to identify the image features in each galvanometer image, and thereby determine whether there are excessive contaminants on the lens of the galvanometer. This avoids the problem of poor equipment performance caused by the inability to automatically detect the pass rate of the galvanometer in related technologies. On the other hand, since it is machine detection, it can achieve the effect of real-time contaminant detection during the equipment processing of the galvanometer.

[0008] Optionally, in another embodiment based on the method described above in this application, feature recognition is performed on the lens image to determine the contaminant features corresponding to the lens image. This includes: using a preset image detection model to identify the number of contaminants present in the lens image and the contamination area corresponding to each contaminant; and using the number of contaminants and the corresponding contamination area as contaminant features. By applying the technical solution of the embodiments of this application, a pre-trained image detection model can be used to identify image features in each galvanometer image, thereby determining whether there are excessive amounts of contaminants and excessive contaminant areas on the lens of the galvanometer. This achieves the effect of automatically identifying whether lens contaminants exceed the standard based on an image detection method.

[0009] Optionally, in another embodiment based on the method described above in this application, a preset image detection model is used to identify the number of contaminants present in the lens image and the contamination area corresponding to each contaminant. This includes: using the preset image detection model to identify the lens image and obtain at least one attachment feature present in the lens image, the attachment feature reflecting the attachments present in the lens; using at least one of the attachment features including attachment size features, attachment color features, and attachment outline features, to filter out contaminant features from the at least one attachment feature; and based on the contaminant features, identifying the number of contaminants present in the lens image and the contamination area corresponding to each contaminant. By applying the technical solution of the embodiments of this application, an image detection model can first be used to identify the image features in each galvanometer image, and then it can be determined whether there are attachments, including contaminants, on the lens of the galvanometer. Furthermore, based on the object features of the attachments, the contaminants can be further filtered out. This determines whether there is an excessive number of contaminants and the excessive area of ​​contaminants in the lens. This achieves the effect of automatically identifying whether the contaminants in the lens exceed the standard based on the image detection method.

[0010] Optionally, in another embodiment based on the method described above in this application, determining the detection result of the galvanometer to be tested based on contaminant characteristics includes: obtaining the weighing weight of the galvanometer to be tested; determining that the weighing weight of the galvanometer to be tested is greater than a preset weight value; and determining the detection result of the galvanometer to be tested based on contaminant characteristics. By applying the technical solution of the embodiments of this application, it is possible to first detect whether the galvanometer exceeds the standard, and only if it is determined that its weight exceeds the standard will the subsequent image detection steps be initiated. This avoids the problem of wasting system detection resources caused by performing image detection on each galvanometer unnecessarily.

[0011] Optionally, in another embodiment based on the method described above in this application, determining the detection result of the galvanometer to be tested based on contaminant characteristics includes: determining, based on the contaminant characteristics, that the quantity of contaminants and the corresponding contamination area exceed a first contaminant standard value, and determining that the detection result of the galvanometer to be tested is unqualified. By applying the technical solution of the embodiments of this application, an image detection model can be used to identify image features in each galvanometer image, and thereby determine whether there is an excessive quantity of contaminants and an excessively large contaminant area on the lens of the galvanometer. This achieves the effect of automatically identifying whether the contaminants on the lens exceed the standard based on an image detection method.

[0012] Optionally, in another embodiment based on the method described above, in response to the detection start condition for the galvanometer to be tested, acquiring lens images of the galvanometer to be tested includes: receiving a welding start command for welding processing of the galvanometer to be tested; determining that the time between the current moment and the time of receiving the welding start command reaches a preset time, and periodically acquiring lens images of the galvanometer to be tested. By applying the technical solution of the embodiments of this application, contaminant detection can be initiated in real time or periodically during the welding process of the galvanometer, thereby avoiding the drawback of related technologies where contaminant detection can only be performed manually when the galvanometer is outside the equipment processing flow, which consumes a significant amount of detection resources.

[0013] Optionally, in another embodiment based on the method described above in this application, determining the detection result of the galvanometer to be tested based on contaminant characteristics includes: determining the image acquisition time of the lens image; and determining the detection result of the galvanometer to be tested based on the interval between the image acquisition time and the command reception time, and the contaminant characteristics corresponding to the lens image. By applying the technical solution of the embodiments of this application, different contaminant reference standards can be selected specifically according to the interval between the image acquisition time of the galvanometer and the starting point of the welding process to determine the detection result of the galvanometer to be tested.

[0014] Optionally, in another embodiment based on the method described above, the detection result of the galvanometer to be tested is determined based on the interval between the image acquisition time and the instruction reception time, and the contaminant characteristics corresponding to the lens image. This includes: acquiring a preset contamination time correlation table, and selecting a second contaminant standard value that matches the interval based on the contamination time correlation table; and determining the detection result of the galvanometer to be tested based on the comparison result between the contaminant characteristics and the second contaminant standard value. By applying the technical solution of the embodiments of this application, different contaminant reference standards can be selected specifically according to the interval between the image acquisition time of the galvanometer and the starting point of the welding process to determine the detection result of the galvanometer to be tested. This achieves the effect of quickly determining whether the galvanometer is contaminated by excessive contaminants.

[0015] Optionally, in another embodiment based on the method described above in this application, after determining the detection result of the galvanometer to be tested based on the pollutant characteristics, the method further includes: establishing a mapping relationship between the galvanometer to be tested, the corresponding detection result, and the corresponding lens image; and storing the mapping relationship in a preset storage area; receiving a collection instruction sent by the equipment detection system, extracting the mapping relationship from the preset storage area, and sending it to the equipment detection system. By applying the technical solution of the embodiments of this application, the relationship between the detection result of each galvanometer to be tested and the corresponding galvanometer image can be automatically saved, so that business personnel can easily retrieve it at any time when a query is needed.

[0016] According to another aspect of the embodiments of this application, a detection device for a galvanometer is provided, comprising: an acquisition module configured to acquire a lens image of the galvanometer to be tested in response to a detection initiation condition for the galvanometer to be tested; an identification module configured to perform feature identification on the lens image to determine contaminant features corresponding to the lens image, the contaminant features being used to reflect the degree of contaminant adhesion on the lens of the galvanometer to be tested; and a determination module configured to determine the detection result of the galvanometer to be tested based on the contaminant features. By applying the technical solution of the embodiments of this application, a pre-trained image detection model can be used to identify image features in each galvanometer image, thereby determining whether there is an excessive amount of contaminants and the area of ​​excessive contaminants on the lens of the galvanometer. This achieves the effect of automatically identifying whether the contaminants on the lens exceed the standard based on an image detection method.

[0017] According to another aspect of the embodiments of this application, an electronic device is provided, comprising: Memory, used to store executable instructions; and A processor, configured to execute the executable instructions with the memory to perform the operation of any of the above-described galvanometer detection methods.

[0018] According to another aspect of the embodiments of this application, a computer-readable storage medium is provided for storing computer-readable instructions, which, when executed, perform the operation of any of the above-described galvanometer detection methods.

[0019] The above description is only an overview of the technical solution of this application. In order to better understand the technical means of this application and to implement it in accordance with the contents of the specification, and to make the above and other effects, features and advantages of this application more obvious and understandable, the following are specific embodiments of this application. Attached Figure Description

[0020] The accompanying drawings, which form part of this specification, illustrate embodiments of this application and, together with the description, serve to explain the principles of this application.

[0021] This application can be more clearly understood with reference to the accompanying drawings and the following detailed description, wherein: Figure 1 A schematic diagram of a galvanometer to be tested, including a lens, provided in an embodiment of this application is shown; Figure 2 A schematic diagram of a galvanometer detection method provided in an embodiment of this application is shown; Figure 3 A schematic flowchart of a galvanometer detection method according to an embodiment of this application is shown; Figure 4 This invention provides a schematic diagram of the structure of an electronic device according to an embodiment of the present application. Figure 5 This illustration shows a schematic diagram of the structure of an electronic device according to an embodiment of this application; Figure 6 A schematic diagram of a storage medium provided in one embodiment of this application is shown; Among them, 1011 is the lens and 1012 is the galvanometer to be tested. Detailed Implementation

[0022] Various exemplary embodiments of the present application will now be described in detail with reference to the accompanying drawings. It should be noted that, unless otherwise specifically stated, the relative arrangement, numerical expressions, and values ​​of the components and steps set forth in these embodiments do not limit the scope of the present application.

[0023] At the same time, it should be understood that, for ease of description, the dimensions of the various parts shown in the accompanying drawings are not drawn according to actual scale.

[0024] The following description of at least one exemplary embodiment is merely illustrative and is not intended to limit the scope of this application or its application or use.

[0025] Techniques, methods, and equipment known to those skilled in the art may not be discussed in detail, but where appropriate, such techniques, methods, and equipment should be considered part of the specification.

[0026] It should be noted that similar labels and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be discussed further in subsequent figures.

[0027] Furthermore, the technical solutions of the various embodiments of this application can be combined with each other, but only if they are based on the ability of those skilled in the art to implement them. When the combination of technical solutions is contradictory or cannot be implemented, it should be considered that such combination of technical solutions does not exist and is not within the scope of protection claimed by this application.

[0028] It should be noted that all directional indications (such as up, down, left, right, front, back, etc.) in the embodiments of this application are only used to explain the relative occlusion position relationship and movement of each component in a certain specific posture (as shown in the figure). If the specific posture changes, the directional indication will also change accordingly.

[0029] The following is combined Figures 1-3 This application describes a detection method for a galvanometer according to exemplary embodiments thereof. It should be noted that the following application scenarios are shown only to facilitate understanding of the spirit and principles of this application, and the embodiments of this application are not limited in any way. Rather, the embodiments of this application can be applied to any applicable scenario.

[0030] In related technologies, with the rapid development of lithium battery technology, the application of galvanometers in new energy vehicles has gradually become more widespread.

[0031] Among them, galvanometer welding is an emerging welding method, also known as vibration friction welding or vibration friction stir welding. Its basic principle is to use a vibrating galvanometer to apply pressure at the joint of two metal parts, causing friction at the contact surface to generate heat, and then stirring the thermoplasticized metal to achieve welding.

[0032] Furthermore, the most important piece of equipment in galvanometer welding is the galvanometer, a tool that produces high-frequency vibrations. It consists of three parts: a vibration source, an oscillator, and a reflector. When voltage is applied to the vibration source, the vibration source generates high-frequency vibrations, which are transmitted to the oscillator. The oscillator then transmits the vibrations to the reflector, and finally, the vibrations are emitted through the reflector.

[0033] Furthermore, in the welding process using a galvanometer, after two metal parts have undergone appropriate treatment, they are precisely aligned and pressed together. Vibration is then applied through the galvanometer's vibration source, causing friction, heating, and softening of the contact surfaces. At this point, the galvanometer applies downward pressure to appropriately pressurize the joint, improving weld quality. Subsequently, the galvanometer gradually moves upward, continuing to apply vibration to stir the contact surfaces, thus achieving welding.

[0034] Understandably, compared to traditional welding methods, galvanometer welding has the following advantages: (1) No external welding materials are required, which reduces production costs and improves connection strength.

[0035] (2) Fast welding speed, short time, and high production efficiency. Because the welding process only involves friction heating, the welding can be completed in one go, unlike traditional welding which requires multiple processes.

[0036] However, in the process of welding galvanometers with other equipment, varying degrees of contaminants often adhere to the galvanometer lenses. For example, taking welding slag as a contaminant, the slag spatter generated during welding can cause the spatter to adhere to the protective lens of the galvanometer's field lens. As a result, during the light emission process, the weld slag adsorbed on the field lens blocks part of the light, leading to varying degrees of attenuation of the galvanometer's laser energy. In severe cases, it can even render the welded galvanometer product unusable.

[0037] Therefore, in order to address the aforementioned issue of the inability to detect the pass rate of galvanometers, which could lead to potential safety hazards, [further measures are needed].

[0038] This application provides a method for detecting contaminants on a galvanometer. The method involves using a pre-trained image detection model to identify image features in each galvanometer image, and then determining whether there are excessive amounts and areas of contaminants on the lens. This achieves the effect of automatically identifying whether contaminants on the lens exceed the standard based on an image detection method.

[0039] In one embodiment, this application also proposes a method for detecting a galvanometer, a detection device, an electronic device, and a storage medium.

[0040] Figure 2 A schematic flowchart illustrating a method for detecting a galvanometer according to an embodiment of this application is shown. Figure 2 As shown, the method includes: S201, in response to the detection start condition for the galvanometer to be tested, acquire the lens image of the galvanometer to be tested.

[0041] In this embodiment, the detection initiation condition for the galvanometer to be tested is not specifically limited. For example, the detection initiation condition can be determined at a certain time point before the welding process is performed on the galvanometer to be tested. Alternatively, the detection initiation condition can be determined at a certain time point during the welding process on the galvanometer to be tested. The detection initiation condition can also be determined at a certain time point after the welding process on the galvanometer to be tested.

[0042] It is understandable that when the detection is initiated before the galvanometer to be tested undergoes welding, it means that the galvanometer may have been contaminated by welding of other galvanometers during placement (e.g., by welding slag from adjacent galvanometers splashing onto its own lens).

[0043] In one approach, the number of galvanometers to be tested can be one or more. Understandably, one galvanometer to be tested can correspond to one or more lens images. When one galvanometer to be tested corresponds to multiple lens images, these multiple lens images can be acquired periodically.

[0044] In another approach, if a galvanometer to be tested includes multiple galvanometers, its corresponding lens image can be the lens image of any one or more of these lenses.

[0045] In one approach, the lens image of the galvanometer to be tested can be obtained by an image acquisition device located in a preset area of ​​the galvanometer.

[0046] S202, perform feature recognition on the lens image to determine the contaminant features corresponding to the lens image. The contaminant features are used to reflect the degree of contaminant adhesion in the lens of the galvanometer to be tested.

[0047] First, it should be noted that the embodiments of this application do not specifically limit the device for feature recognition of lens images. For example, it can be a smart device or a server. Among them, the smart device can be a PC (Personal Computer), or a mobile terminal device with the function of storing image detection models, such as a smartphone, tablet computer, or portable computer.

[0048] In one embodiment, the contaminants can be welding slag, waste, etc. It is understood that such contaminants can adhere to the lenses of the galvanometer field lens, causing partial light blocking during the light emission process.

[0049] Furthermore, after acquiring an image of the lens containing the galvanometer lens, this embodiment of the application, in order to avoid the drawback of related technologies that cannot detect contaminants, can utilize a preset image detection model to identify the features of contaminants, thereby determining the degree of contaminant adhesion on the lens.

[0050] This application does not specifically limit the image detection model. For example, it can be a Convolutional Neural Network (CNN). As an example, a CNN is a type of feedforward neural network that includes convolutional computation and has a deep structure; it is one of the representative algorithms of deep learning. CNNs possess representation learning capabilities, enabling them to perform translation-invariant classification of input information according to their hierarchical structure. Thanks to the powerful feature representation capabilities of CNNs for images, they have achieved remarkable results in image classification, object detection, semantic segmentation, and other fields.

[0051] Furthermore, this application can use an image detection model to detect the feature information of contaminants in the lens image, and then determine whether there are contaminants exceeding the standard in the lens.

[0052] S203, based on the characteristics of pollutants, determine the detection results of the galvanometer to be tested.

[0053] Understandably, when this application embodiment determines, based on the characteristics of the contaminants, that the lens of the testing galvanometer contains a large number of contaminants and / or one or more contaminants with a large area, the test result of the galvanometer is determined to be unqualified. Conversely, it is considered qualified. This achieves a way to avoid the problem of reduced performance caused by excessive contaminants in the lens.

[0054] In one embodiment, the test result can be "qualified", "unqualified", "unable to determine", etc. In another embodiment, the test result can also be "qualification level 10%", "qualification level 50%", "qualification level 80%", etc.

[0055] In summary, the technical solution of this application embodiment is as follows: in response to the detection start condition for the galvanometer to be tested, a lens image of the galvanometer to be tested is acquired; feature recognition is performed on the lens image to determine the contaminant features corresponding to the lens image, the contaminant features are used to reflect the degree of contaminant adhesion in the lens of the galvanometer to be tested; based on the contaminant features, the detection result of the galvanometer to be tested is determined.

[0056] The description of the various embodiments above tends to emphasize the differences between the various embodiments. The similarities or similarities between them can be referred to, and for the sake of brevity, they will not be repeated here.

[0057] By applying the technical solution of this application embodiment, a pre-trained image detection model can be used to identify image features in each galvanometer image, thereby determining whether there are excessive amounts of contaminants and the area of ​​excessive contaminants on the lens of the galvanometer. This achieves the effect of automatically identifying whether lens contaminants exceed standards based on an image detection method.

[0058] Optionally, in another embodiment based on the method described above in this application, feature recognition is performed on the lens image to determine the contaminant features corresponding to the lens image, including: using a preset image detection model to identify the number of contaminants present in the lens image and the contamination area corresponding to each contaminant; and using the number of contaminants and the corresponding contamination area as contaminant features.

[0059] In one approach, embodiments of this application may use an image detection model to detect the feature information of contaminants in a lens image, and thereby determine whether there are contaminants exceeding the standard in the lens.

[0060] For example, in this embodiment of the application, the lens image can first be input into a preset image detection model, and the output of the last fully connected layer (FC) in the image detection model can be used as the recognition result of the feature data corresponding to the lens image, that is, the pollutant feature.

[0061] Understandably, if the contaminant characteristics indicate an excessive amount of contaminants (for example, excessive amounts of slag, waste, or other contaminants generated by the galvanometer itself or adjacent locations during the welding process splash onto the galvanometer lens), then the galvanometer is considered to be unqualified for testing.

[0062] Similarly, if the contaminant characteristics indicate that the area of ​​one or more contaminants is too large (for example, during the welding process of the galvanometer, the area of ​​waste residue, waste materials, and other contaminants generated by the galvanometer itself or adjacent positions splashed onto the galvanometer lens is too large), then it also means that the galvanometer is unqualified for testing.

[0063] By applying the technical solution of this application embodiment, a pre-trained image detection model can be used to identify image features in each galvanometer image, thereby determining whether there are excessive amounts of contaminants and the area of ​​excessive contaminants on the lens of the galvanometer. This achieves the effect of automatically identifying whether lens contaminants exceed standards based on an image detection method.

[0064] Optionally, in another embodiment based on the method described above in this application, a preset image detection model is used to identify the number of contaminants present in the lens image and the contamination area corresponding to each contaminant, including: using the preset image detection model to identify the lens image to obtain at least one attachment feature present in the lens image, the attachment feature being used to reflect the attachments present in the lens; using at least one of the attachment features including attachment size features, attachment color features, and attachment outline features to filter out contaminant features from the at least one attachment feature; and based on the contaminant features, identifying the number of contaminants present in the lens image and the contamination area corresponding to each contaminant.

[0065] In one approach, embodiments of this application may first use an image detection model to detect feature information of deposits present in the lens image, and then determine whether one or more deposits exist in the lens. For example, the deposits may be objects such as water droplets, dust, or lint, including contaminants.

[0066] Understandably, although these deposits are also unnecessary, they are easily removed. Therefore, this application does not classify them as contaminants. That is, the real factor affecting the galvanometer's pass rate is the presence of contaminants such as fly ash and waste in the lens.

[0067] In one embodiment, this application can detect whether each attachment is a contaminant based on its size, color, and outline characteristics.

[0068] By applying the technical solution of this application embodiment, an image detection model can first be used to identify image features in each galvanometer image, and then determine whether there are any adhering substances, including contaminants, on the lens of the galvanometer. Furthermore, based on the object characteristics of the adhering substances, the contaminants can be further screened out. This determines whether there are excessive amounts of contaminants in the lens and the area of ​​excessive contaminants. Thus, an effect of automatically identifying whether contaminants in a lens exceed the standard based on an image detection method is achieved.

[0069] Optionally, in another embodiment based on the method described above in this application, the detection result of the galvanometer to be tested can be determined based on the characteristics of the contaminants, including: obtaining the weighing weight of the galvanometer to be tested; determining that the weighing weight of the galvanometer to be tested is greater than a preset weight value, and determining the detection result of the galvanometer to be tested based on the characteristics of the contaminants.

[0070] In one approach, the galvanometer may first be weighed. Understandably, it is only assumed to be potentially contaminated if its weight exceeds a certain threshold. Only then will the image detection model be activated.

[0071] By applying the technical solution of this application embodiment, it is possible to first detect whether the galvanometer exceeds the limit, and only if it is determined that its weight exceeds the limit will the subsequent image detection steps be initiated. This avoids the problem of wasting system detection resources caused by performing image detection on each galvanometer unnecessarily.

[0072] Optionally, in another embodiment based on the method described above in this application, determining the detection result of the galvanometer to be tested based on the characteristics of the pollutants includes: determining, based on the characteristics of the pollutants, that the quantity of the pollutants and the corresponding contamination area exceed the first pollutant standard value, and determining that the detection result of the galvanometer to be tested is unqualified.

[0073] By applying the technical solution of this application embodiment, an image detection model can be used to identify image features in each galvanometer image, thereby determining whether there is an excessive amount of contaminants or an excessively large area of ​​contaminants on the lens of the galvanometer. This achieves the effect of automatically identifying whether lens contaminants exceed the standard based on an image detection method.

[0074] Optionally, in another embodiment based on the method described above in this application, in response to the detection start condition for the galvanometer to be tested, acquiring lens images of the galvanometer to be tested includes: receiving a welding start command for welding processing of the galvanometer to be tested; determining that the time between the current time and the command receiving time of the welding start command reaches a preset time, and periodically acquiring lens images of the galvanometer to be tested.

[0075] In one embodiment of this application, contaminant detection is performed on the galvanometer during the welding process. For example, an image of the galvanometer lens can be acquired and input into an image detection model 5 or 10 minutes after the welding process begins.

[0076] Furthermore, this is because the relevant technology relies solely on manual inspection of the galvanometer's pass rate. This also makes it impossible to detect problems with a particular galvanometer in a timely manner. Understandably, starting the inspection process during the welding of the galvanometer would allow for immediate detection of contamination of the galvanometer lenses, thus avoiding potentially unnecessary subsequent welding processes (because significant contamination at the weld joint can lead to problems such as weak welds).

[0077] In another embodiment, the present application outlines the selection of detecting contaminants on the galvanometer before welding is performed. For example, a lens image of the galvanometer can be acquired 5 or 10 minutes before welding is detected and input into an image detection model.

[0078] Furthermore, if a galvanometer is contaminated by spatter from other galvanometers before welding, its welding performance may be affected (e.g., the presence of contaminants at the weld joint can lead to an unreliable weld). Therefore, this application allows for inspection before the galvanometer enters the manufacturing process, ensuring that all galvanometers entering the welding process are qualified products.

[0079] By applying the technical solution of the embodiments of this application, the detection of contaminants can be initiated in real time or periodically during the welding process of the galvanometer, thereby avoiding the drawback of the related technology that only allows manual detection of contaminants when the galvanometer is outside the equipment processing flow, which consumes a lot of detection resources.

[0080] Optionally, in another embodiment based on the method described above in this application, determining the detection result of the galvanometer to be tested based on the contaminant characteristics includes: determining the image acquisition time of the lens image; and determining the detection result of the galvanometer to be tested based on the interval between the image acquisition time and the instruction reception time, and the contaminant characteristics corresponding to the lens image.

[0081] By applying the technical solution of the embodiments of this application, different contaminant reference standards can be selected in a targeted manner based on the time interval between the image acquisition time of the galvanometer and the starting point of the welding process, thereby determining the detection result of the galvanometer to be tested.

[0082] Optionally, in another embodiment based on the method described above in this application, the detection result of the galvanometer to be tested is determined based on the interval between the image acquisition time and the instruction reception time, and the contaminant characteristics corresponding to the lens image. This includes: acquiring a preset contaminant time association table, and selecting a second contaminant standard value that matches the interval based on the contaminant time association table; and determining the detection result of the galvanometer to be tested based on the comparison result between the contaminant characteristics and the second contaminant standard value.

[0083] In one embodiment of this application, contaminant detection can be performed on the galvanometer during the welding process. For example, an image of the galvanometer lens can be acquired and input into an image detection model 5 or 10 minutes after the welding process begins.

[0084] Understandably, the corresponding contaminant reference standards will differ for lenses that have undergone welding processes of varying durations. Therefore, this application can select a matching contamination time correlation table based on the different interval durations (i.e., the interval between the start of the welding process of the galvanometer and the current time of acquiring its lens image). The detection results of the galvanometer under test are then determined accordingly.

[0085] For example, for a galvanometer lens that has been welded for 5 minutes (i.e., with an interval of 5 minutes), this embodiment of the application can determine that its matching second contaminant standard value is a contaminant based on a contamination time correlation table. In other words, this embodiment of the application allows the presence of one contaminant for a galvanometer lens that has been welded for 5 minutes. Therefore, if a contaminant is subsequently detected in the image of this lens, the detection result is considered acceptable.

[0086] For example, for a galvanometer lens that has been welded for 15 minutes (i.e., with a 15-minute interval), this embodiment of the application can determine, based on the contamination time correlation table, that the matching second contaminant standard value is a contamination area of ​​2 square meters. In other words, this embodiment of the application allows a contaminant area of ​​less than 2 square meters for a galvanometer lens that has been welded for 15 minutes. Therefore, if a contaminant area of ​​more than 2 square meters is subsequently detected in the image of this lens, the detection result is considered unqualified.

[0087] By applying the technical solution of this application embodiment, different contaminant reference standards can be selected specifically based on the interval between the image acquisition time of the galvanometer and the start point of the welding process, thereby determining the detection result of the galvanometer to be tested. This achieves the effect of quickly determining whether the galvanometer is contaminated by excessive contaminants.

[0088] Optionally, in another embodiment based on the method described above in this application, after determining the detection result of the galvanometer to be tested based on the pollutant characteristics, the method further includes: establishing a mapping relationship between the galvanometer to be tested, the corresponding detection result, and the corresponding lens image; and storing the mapping relationship in a preset storage area; receiving a collection instruction sent by the device detection system, extracting the mapping relationship from the preset storage area, and sending it to the device detection system.

[0089] By applying the technical solution of the embodiments of this application, the detection results of each galvanometer to be tested and the relationship between the corresponding galvanometer images can be automatically saved, so that business personnel can easily retrieve them at any time when there is a need for subsequent queries.

[0090] As an example, combining Figure 3 The detection method for the galvanometer proposed in the embodiments of this application will be described in detail below: Step 1: Receive the welding start command for welding the galvanometer to be inspected.

[0091] Step 2: Determine that the time interval between the current moment and the moment the welding start command is received reaches the preset time interval, and then acquire the lens image of the galvanometer to be tested.

[0092] In one embodiment, the preset duration is not specifically limited. As an example, the preset duration can be determined based on the number of galvanometers to be tested.

[0093] Step 3: Determine the image acquisition time of the lens image and obtain the preset contamination time association table.

[0094] In one approach, the contamination time correlation table can be pre-obtained by business personnel based on data on contaminants adhering to the galvanometer lens during historical periods.

[0095] Step 4: Based on the pollution time correlation table, select the standard value of the second pollutant that matches the interval length.

[0096] The interval duration is the time interval between the image acquisition time and the instruction reception time.

[0097] Step 5: Use a preset image detection model to identify the lens image and obtain at least one attachment feature present in the lens image.

[0098] Among them, the attachment characteristics are used to reflect the attachments present in the lens.

[0099] Understandably, the deposits may contain contaminants, as well as water droplets, dust, and other unwanted objects.

[0100] Step 6: Using at least one of the following features of the attached material, namely, the size feature of the attached material, the color feature of the attached material, and the outline feature of the attached material, filter out the pollutant features from at least one feature of the attached material.

[0101] Step 7: Based on the characteristics of the contaminants, identify the number of contaminants present in the lens image and the contamination area corresponding to each contaminant.

[0102] Step 8: If the quantity of pollutants and the corresponding contaminated area exceed the standard value of the second pollutant, the test result of the galvanometer to be tested is determined to be unqualified.

[0103] In this embodiment, a method can be implemented to prevent weld slag from adsorbing onto the protective lens of a galvanometer during the welding process. Specifically, this embodiment can trigger an alarm on the quality monitoring equipment through signal interaction between the galvanometer under test and the camera. This effectively eliminates batch defects in galvanometer products.

[0104] In one embodiment of this application, the main device can trigger a photo-taking signal, and after the camera takes a picture, the image detection model calculates the contaminant characteristics (e.g., weld slag area, weld slag quantity, etc.) in the image. After obtaining the results, the quality monitoring software automatically outputs a judgment result, allowing the quality monitoring software to decide whether to issue an alarm.

[0105] By applying the technical solution of this application embodiment, a pre-trained image detection model can be used to identify image features in each galvanometer image, thereby determining whether there are excessive amounts of contaminants and the area of ​​excessive contaminants on the lens of the galvanometer. This achieves the effect of automatically identifying whether lens contaminants exceed standards based on an image detection method.

[0106] The description of the various embodiments above tends to emphasize the differences between the various embodiments. The similarities or similarities between them can be referred to, and for the sake of brevity, they will not be repeated here.

[0107] Optionally, in another embodiment of this application, such as Figure 4 As shown, this application also provides a detection device for a galvanometer. This includes: The acquisition module 301 is configured to acquire lens images of the galvanometer under test in response to a detection start condition for the galvanometer under test; The identification module 302 is configured to perform feature recognition on the lens image to determine the contaminant features corresponding to the lens image. The contaminant features are used to reflect the degree of contaminant adhesion in the lens of the galvanometer to be tested. The determination module 303 is configured to determine the detection result of the galvanometer to be detected based on the characteristics of the pollutant.

[0108] In another embodiment of this application, the identification module 302 is configured as follows: Using a preset image detection model, the number of contaminants present in the lens image and the contamination area corresponding to each contaminant are identified. The quantity of pollutants and the corresponding contaminated area are used as the characteristics of the pollutants.

[0109] By applying the technical solution of this application embodiment, a pre-trained image detection model can be used to identify image features in each galvanometer image, thereby determining whether there are excessive amounts of contaminants and the area of ​​excessive contaminants on the lens of the galvanometer. This achieves the effect of automatically identifying whether lens contaminants exceed standards based on an image detection method.

[0110] In another embodiment of this application, the identification module 302 is configured as follows: The lens image is identified using a preset image detection model to obtain at least one attachment feature present in the lens image. The attachment feature is used to reflect the attachment present in the lens. By utilizing at least one of the attachment characteristics, including attachment size characteristics, attachment color characteristics, and attachment outline characteristics, pollutant characteristics are screened from the at least one attachment characteristic; Based on the pollutant characteristics, the number of pollutants present in the lens image and the pollution area corresponding to each pollutant are identified.

[0111] In another embodiment of this application, the identification module 302 is configured as follows: Obtain the weighing weight of the galvanometer to be tested; The weighing weight of the galvanometer to be tested is determined to be greater than a preset weight value. Based on the characteristics of the contaminant, the test result of the galvanometer to be tested is determined.

[0112] In another embodiment of this application, the determining module 303 is configured to: Based on the characteristics of the pollutants, if the quantity of the pollutants and the corresponding contaminated area exceed the standard value of the first pollutant, the test result of the galvanometer to be tested is determined to be unqualified.

[0113] In another embodiment of this application, the acquisition module 301 is configured as follows: Receive a welding start command for welding the galvanometer to be tested; The time interval between the current moment and the moment the welding start command is received is determined to be a preset time interval, and the lens images of the galvanometer to be tested are periodically acquired.

[0114] In another embodiment of this application, the identification module 302 is configured as follows: Determine the image acquisition time for acquiring the lens image; Based on the time interval between the image acquisition time and the instruction reception time, and the contaminant characteristics corresponding to the lens image, the detection result of the galvanometer to be detected is determined.

[0115] In another embodiment of this application, the identification module 302 is configured as follows: Obtain a preset pollution time correlation table, and based on the pollution time correlation table, select a second pollutant standard value that matches the interval duration; Based on the comparison between the pollutant characteristics and the second pollutant standard value, the detection result of the galvanometer to be tested is determined.

[0116] In another embodiment of this application, the identification module 302 is configured as follows: Establish a mapping relationship between the galvanometer to be tested, the corresponding test results, and the corresponding lens image; and store the mapping relationship in a preset storage area; The system receives a collection command from the device detection system, extracts the mapping relationship from the preset storage area, and sends it to the device detection system.

[0117] The description of the various embodiments above tends to emphasize the differences between the various embodiments. The similarities or similarities between them can be referred to, and for the sake of brevity, they will not be repeated here.

[0118] This application also provides an electronic device for performing the above-described galvanometer detection method. Please refer to... Figure 5 This illustrates a schematic diagram of an electronic device provided by some embodiments of this application. For example... Figure 5 As shown, the electronic device 3 includes: a processor 400, a memory 401, a bus 402, and a communication interface 403. The processor 400, the communication interface 403, and the memory 401 are connected via the bus 402. The memory 401 stores a computer program that can run on the processor 400. When the processor 400 runs the computer program, it executes the galvanometer detection method provided in any of the foregoing embodiments of this application.

[0119] The memory 401 may include high-speed random access memory (RAM) or non-volatile memory, such as at least one disk storage device. Communication between this device network element and at least one other network element is achieved through at least one communication interface 403 (which can be wired or wireless), such as the Internet, wide area network, local area network, metropolitan area network, etc.

[0120] Bus 402 can be an ISA bus, PCI bus, or EISA bus, etc. The bus can be divided into an address bus, a data bus, a control bus, etc. The memory 401 is used to store programs. After receiving an execution instruction, the processor 400 executes the program. The data identification method disclosed in any of the foregoing embodiments of this application can be applied to the processor 400, or implemented by the processor 400.

[0121] The processor 400 may be an integrated circuit chip with signal processing capabilities. In implementation, each step of the above method can be completed through integrated logic circuits in the processor 400 or through software instructions. The processor 400 may be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it may also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), an off-the-shelf programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. It can implement or execute the methods, steps, and logic block diagrams disclosed in the embodiments of this application.

[0122] The general-purpose processor can be a microprocessor or any conventional processor. The steps of the method disclosed in the embodiments of this application can be directly implemented by a hardware decoding processor, or implemented by a combination of hardware and software modules in the decoding processor. The software modules can reside in random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, registers, or other mature storage media in the art. This storage medium is located in memory 401, and processor 400 reads information from memory 401 and, in conjunction with its hardware, completes the steps of the above method.

[0123] The electronic device provided in this application embodiment and the galvanometer detection method provided in this application embodiment are based on the same inventive concept and have the same beneficial effects as the methods they employ, operate or implement.

[0124] The description of the various embodiments above tends to emphasize the differences between the various embodiments. The similarities or similarities between them can be referred to, and for the sake of brevity, they will not be repeated here.

[0125] This application also provides a computer-readable storage medium corresponding to the galvanometer detection method provided in the foregoing embodiments. Please refer to... Figure 6 The computer-readable storage medium shown is an optical disc 50, on which a computer program (i.e., a program product) is stored. When the computer program is run by a processor, it executes the galvanometer detection method provided in any of the foregoing embodiments.

[0126] It should be noted that examples of the computer-readable storage medium may also 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 optical and magnetic storage media, which will not be elaborated here.

[0127] The computer-readable storage medium provided in the above embodiments of this application and the data identification method provided in the embodiments of this application are based on the same inventive concept and have the same beneficial effects as the methods adopted, run or implemented by the applications stored therein.

[0128] It should be noted that: Numerous specific details are set forth in the specification provided herein. However, it will be understood that embodiments of this application may be practiced without these specific details. In some instances, well-known structures and techniques have not been shown in detail so as not to obscure the understanding of this specification.

[0129] Similarly, it should be understood that, for the sake of brevity and to aid in understanding one or more of the various inventive aspects, in the above description of exemplary embodiments of this application, various features of this application are sometimes grouped together in a single embodiment, figure, or description thereof. However, this disclosure should not be construed as reflecting a schematic diagram in which the claimed application requires more features than expressly recited in each claim. Rather, as reflected in the following claims, inventive aspects lie in fewer than all features of a single foregoing disclosed embodiment. Therefore, the claims following the detailed description are hereby expressly incorporated into that detailed description, wherein each claim itself is a separate embodiment of this application.

[0130] Furthermore, those skilled in the art will understand that although some embodiments described herein include certain features but not others included in other embodiments, combinations of features from different embodiments are intended to be within the scope of this application and form different embodiments. For example, in the following claims, any of the claimed embodiments can be used in any combination.

[0131] The above description is merely a preferred embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method for detecting galvanometers, characterized in that, include: Receive welding start command for welding the galvanometer to be inspected. Before welding, the weight of the galvanometer to be tested is obtained; It is determined that the weighing weight of the galvanometer to be tested is greater than a preset weight value; During the welding process of the galvanometer, in response to the detection start condition for the galvanometer to be tested, the acquisition module set at the preset area of ​​the galvanometer to be tested acquires the lens image of the galvanometer to be tested; The lens image is subjected to feature recognition to determine the contaminant features corresponding to the lens image. The contaminant features are used to reflect the degree of contaminant adhesion in the lens of the galvanometer to be tested. The contaminant features include the number of contaminants and the contamination area corresponding to each contaminant. Based on the characteristics of the pollutants, the detection result of the galvanometer to be tested is determined. The determination of the detection result of the galvanometer to be tested includes: Determine the timing of receiving the welding start command; Determine the image acquisition time for acquiring the lens image; Determine that the interval between the image acquisition time and the instruction reception time reaches a preset duration, obtain a preset pollution time association table, and select a second pollutant standard value that matches the interval duration based on the pollution time association table; Based on the comparison between the pollutant characteristics and the second pollutant standard value, the detection result of the galvanometer to be tested is determined.

2. The detection method for the galvanometer as described in claim 1, characterized in that, The step of performing feature recognition on the lens image to determine the contaminant features corresponding to the lens image includes: Using a preset image detection model, the number of contaminants present in the lens image and the contamination area corresponding to each contaminant are identified. The quantity of pollutants and the corresponding contaminated area are used as the characteristics of the pollutants.

3. The detection method for the galvanometer as described in claim 2, characterized in that, The step of using a preset image detection model to identify the number of contaminants present in the lens image and the contamination area corresponding to each contaminant includes: The lens image is identified using a preset image detection model to obtain at least one attachment feature present in the lens image. The attachment feature is used to reflect the attachment present in the lens. By utilizing at least one of the attachment characteristics, including attachment size characteristics, attachment color characteristics, and attachment outline characteristics, pollutant characteristics are screened from the at least one attachment characteristic; Based on the pollutant characteristics, the number of pollutants present in the lens image and the pollution area corresponding to each pollutant are identified.

4. The detection method for the galvanometer as described in claim 1, characterized in that, The step of determining the detection result of the galvanometer to be detected based on the characteristics of the pollutants includes: Based on the characteristics of the pollutants, if the quantity of the pollutants and the corresponding contaminated area exceed the standard value of the first pollutant, the test result of the galvanometer to be tested is determined to be unqualified.

5. The detection method for the galvanometer as described in claim 1, characterized in that, The step of acquiring lens images of the galvanometer under test in response to a detection initiation condition includes: Once the time interval between the current moment and the moment the instruction is received reaches a preset duration, the lens images of the galvanometer to be tested are periodically acquired.

6. The detection method for the galvanometer as described in claim 1, characterized in that, After determining the detection result of the galvanometer to be detected based on the pollutant characteristics, the method further includes: Establish a mapping relationship between the galvanometer to be tested, the corresponding test results, and the corresponding lens image; and store the mapping relationship in a preset storage area; The system receives a collection command from the device detection system, extracts the mapping relationship from the preset storage area, and sends it to the device detection system.

7. A detection device for a galvanometer, characterized in that, include: The acquisition module is set at a preset area of ​​the galvanometer to be tested. The acquisition module is configured to acquire the weight of the galvanometer to be tested before welding, and determine that the weight of the galvanometer to be tested is greater than a preset weight value. During the welding process of the galvanometer, in response to the detection start condition for the galvanometer to be tested, the acquisition module acquires the lens image of the galvanometer to be tested. The identification module is configured to perform feature identification on the lens image to determine the contaminant features corresponding to the lens image. The contaminant features are used to reflect the degree of contaminant adhesion in the lens of the galvanometer to be tested. The contaminant features include the number of contaminants and the contamination area corresponding to each contaminant. The determination module is configured to determine the detection result of the galvanometer to be detected based on the characteristics of the contaminant. The determination of the detection result of the galvanometer to be tested includes: Based on the welding start command for welding the galvanometer to be tested, the command reception time of the welding start command is determined; Determine the image acquisition time for acquiring the lens image; Determine that the interval between the image acquisition time and the instruction reception time reaches a preset duration, obtain a preset pollution time association table, and select a second pollutant standard value that matches the interval duration based on the pollution time association table; Based on the comparison between the pollutant characteristics and the second pollutant standard value, the detection result of the galvanometer to be tested is determined.

8. An electronic device, characterized in that, include: Memory, used to store executable instructions; as well as, Processor, configured to execute the executable instructions to perform claim 1 The operation of the detection method for any of the galvanometers described in 6.

9. A computer-readable storage medium for storing computer-readable instructions, characterized in that, When the instruction is executed, claim 1 is performed. The operation of the detection method for any of the galvanometers described in 6.

Images