A capacitor element stacking method and system

Abstract

The present invention discloses a capacitor element stacking method and system, which relates to the field of gold spraying process technology. The method obtains the element image of the capacitor to be stacked, and inputs the element image into a pre-trained capacitor detection model, wherein the capacitor detection model includes a feature extraction network and a detection network. The feature extraction network extracts features from the element image to obtain a capacitor feature map. The detection network is used to identify and locate the capacitor feature map to obtain the component model and component coordinates corresponding to the capacitor image to be stacked. The loading equipment corresponding to the component model is controlled to move to the component coordinates, and the capacitor to be stacked is stacked. The existing capacitor element stacking method mainly uses a loading robot to clamp the outer diameter of the component and then put it into the component frame. However, due to the variety of capacitor component models, the error probability of the loading robot clamping the component stacking method is gradually increased, which reduces the technical problem of the loading robot's adaptability in stacking capacitor elements.

Description

Technical Field

[0001] The present invention relates to the technical field of gold spraying technology, and in particular to a capacitor element stacking method and system. Background Art

[0002] With the development of flexible power transmission projects, civilian new energy devices, and high-energy, high-pulse-power military equipment, the application and demand for dry-type capacitors, particularly high-end, high-voltage, and high-capacity capacitors, has grown exponentially. Component gold spraying is a key process in dry-type capacitor production. To improve processing efficiency, after the components are rolled, they are typically manually stacked onto a component frame, and then the entire frame is gold sprayed centrally. With rising labor costs, there is an urgent need for a method to improve the efficiency of capacitor component stacking.

[0003] At present, the existing capacitor element stacking method mainly relies on a loading robot to clamp the outer diameter of the element and then place it into the element frame. However, due to the diverse models of capacitor elements and frequent production changes, the error probability of the loading robot clamping element stacking method has gradually increased, reducing the adaptability of the loading robot to stack capacitor elements. Summary of the Invention

[0004] The present invention provides a capacitor element stacking method and system, which solves the technical problem that the existing capacitor element stacking method mainly relies on a loading robot to clamp the outer diameter of the element and then place it into the element frame. However, due to the diverse models of capacitor elements and frequent production changes, the error probability of the loading robot clamping the element stacking method is gradually increased, which reduces the adaptability of the loading robot to stack capacitor elements.

[0005] A first aspect of the present invention provides a capacitor element stacking method, comprising:

[0006] Obtaining a component image of a capacitor to be stacked, and inputting the component image into a pre-trained capacitor detection model, wherein the capacitor detection model includes a feature extraction network and a detection network;

[0007] Extracting features from the component image using the feature extraction network to obtain a capacitor feature map;

[0008] Using the detection network to perform identification and positioning detection on the capacitor characteristic image to obtain the component model and component coordinates corresponding to the capacitor image to be stacked;

[0009] The loading equipment corresponding to the component model is controlled to move to the component coordinate, and the capacitors to be stacked are stacked.

[0010] Optionally, the feature extraction network includes a 3×3 convolutional layer, a 1×1 standard convolutional layer, an extraction branch, and a feature fusion layer. The step of extracting features from the component image through the feature extraction network to obtain a capacitor feature map includes:

[0011] Performing feature extraction on the component image through a 3×3 convolutional layer to obtain a first component feature map;

[0012] Performing feature extraction on the first component feature map through a 1×1 standard convolutional layer to obtain a second component feature map;

[0013] Performing feature extraction on the first component feature map by the extraction branch to obtain a third component feature map, wherein the extraction branch includes a first recognition module, a 2×2 maximum pooling layer, a first recognition module, a cascaded attention module, a second recognition module, and a 1×1 convolutional layer connected in sequence;

[0014] A feature fusion layer is used to perform feature fusion on the second element feature map and the third element feature map to obtain a capacitor feature map.

[0015] Optionally, the step of extracting features from the first component feature map by the extraction branch to obtain a third component feature map includes:

[0016] Extracting features from the first component feature map through a first recognition module, a 2×2 maximum pooling layer, and a first recognition module in sequence to obtain a first recognition feature map, wherein the first recognition module includes a 3×3 convolutional layer, a batch normalization layer, and a Leaky-ReLU activation layer connected in sequence;

[0017] Performing feature extraction on the first recognition feature map using the cascaded attention module to obtain a second recognition feature map, wherein the cascaded attention module includes a 1×1 convolution layer, a feature fusion layer, a feature fusion layer, a 3×3 convolution layer, and a feature fusion layer;

[0018] Performing feature extraction on the second recognition feature map through a second recognition module to obtain a third recognition feature map, wherein the second recognition module includes a 3×3 convolutional layer, a batch normalization layer, and a DY-ReLU activation layer connected in sequence;

[0019] Feature extraction is performed on the third recognition feature map through a 1×1 convolutional layer to obtain a third component feature map.

[0020] Optionally, the step of using the cascaded attention module to extract features from the first recognition feature map to obtain a second recognition feature map includes:

[0021] Performing feature extraction on the first recognition feature map through a 1×1 convolutional layer to obtain a first feature map;

[0022] Performing feature extraction on the first recognition feature map through a 3×3 convolutional layer to obtain a second feature map;

[0023] Using a feature fusion layer to perform feature fusion on the first feature map and the first recognition feature map to obtain a first fused feature map;

[0024] Using a feature fusion layer to perform feature fusion on the first fused feature map and the first recognition feature map to obtain a second fused feature map;

[0025] A feature fusion layer is used to perform feature fusion on the second feature map and the second fused feature map to obtain a second recognition feature map.

[0026] Optionally, the step of extracting features from the second recognition feature map by a second recognition module to obtain a third recognition feature map includes:

[0027] Performing feature extraction on the second recognition feature map through a 3×3 convolutional layer to obtain a third feature map;

[0028] Performing a batch normalization operation on the third feature map through a batch normalization layer to obtain a fourth feature map;

[0029] A DY-ReLU activation layer is used to perform a nonlinear operation on the fourth feature map to obtain a third recognition feature map.

[0030] Optionally, the detection network includes a fully connected layer and a Softmax layer, and the step of using the detection network to identify and locate the capacitor characteristic map to obtain the component model and component coordinates corresponding to the capacitor image to be stacked includes:

[0031] Performing feature mapping on the capacitor feature map through a fully connected layer to obtain a component mapping feature map;

[0032] A Softmax layer is used to perform an identification and positioning operation on the component mapping feature map to obtain the component model and component coordinates corresponding to the capacitor image to be stacked.

[0033] Optionally, the step of controlling the loading device corresponding to the component model to move to the component coordinate and performing a stacking operation on the capacitors to be stacked includes:

[0034] The loading device corresponding to the component model is used as the target device, and the target device is controlled to move from the stacking position to the component coordinates;

[0035] The target device is controlled to place the capacitor to be stacked at the stacking position.

[0036] A second aspect of the present invention provides a capacitor element stacking system, comprising:

[0037] an acquisition module, configured to acquire component images of capacitors to be stacked, and input the component images into a pre-trained capacitor detection model, wherein the capacitor detection model includes a feature extraction network and a detection network;

[0038] an extraction module, configured to extract features from the component image using the feature extraction network to obtain a capacitor feature map;

[0039] an identification module, configured to use the detection network to perform identification and positioning detection on the capacitor characteristic image, and obtain component models and component coordinates corresponding to the capacitor image to be stacked;

[0040] The stacking module is used to control the loading equipment corresponding to the component model to move to the component coordinate and perform a stacking operation on the capacitors to be stacked.

[0041] A third aspect of the present invention provides an electronic device comprising a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor executes the steps of any one of the above-described capacitor element stacking methods.

[0042] A fourth aspect of the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed, implements the capacitor element stacking method as described in any one of the above items.

[0043] It can be seen from the above technical solutions that the present invention has the following advantages:

[0044] The present invention obtains component images of capacitors to be stacked, inputs the component images into a pre-trained capacitor detection model, obtains the component models and component coordinates of the capacitors to be stacked, and then controls the loading equipment corresponding to the component models to move to the component coordinates to perform the stacking operation on the capacitors to be stacked. This overcomes the technical problem of existing capacitor component stacking methods, which mainly rely on loading robots to clamp the components by their outer diameters and then place them into component frames. However, due to the variety of capacitor component models and frequent production changes, the error probability of the loading robot clamping component stacking method also gradually increases. Compared with traditional component stacking methods, the present invention uses a pre-trained capacitor detection model to accurately identify the component models of the capacitors to be stacked, and then calls the loading equipment corresponding to the component models to perform the stacking operation on the capacitors to be stacked, thereby improving the efficiency and adaptability of the loading robot in stacking capacitor components. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0046] Figure 1 A flowchart of a capacitor element stacking method provided in Example 1 of the present invention;

[0047] Figure 2 A flowchart of a capacitor element stacking method provided in Embodiment 2 of the present invention;

[0048] Figure 3 A schematic diagram of the structure of a capacitor detection model provided in the second embodiment of the present invention;

[0049] Figure 4 A schematic diagram of the pin clamping of the feeding device provided in the second embodiment of the present invention;

[0050] Figure 5 A structural block diagram of a capacitor element stacking system provided in Embodiment 3 of the present invention;

[0051] Figure 6 This is a structural block diagram of an electronic device provided in Embodiment 4 of the present invention;

[0052] exist Figure 4 middle:

[0053] 1. Capacitors to be stacked; 2. Mandrel hole; 3. Insertion pins; 4. Robot arm. DETAILED DESCRIPTION

[0054] An embodiment of the present invention provides a capacitor element stacking method and system, which is used to solve the technical problem that the existing capacitor element stacking method mainly relies on a loading robot to clamp the outer diameter of the element and then place it into the element frame. However, due to the diverse models of capacitor elements and frequent production changes, the error probability of the loading robot clamping element stacking method is gradually increased, which reduces the adaptability of the loading robot to stack capacitor elements.

[0055] In order to make the purpose, features, and advantages of the present invention more obvious and easy to understand, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the embodiments described below are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0056] See also Figure 1 , Figure 1 This is a flowchart of the steps of a capacitor element stacking method provided in Embodiment 1 of the present invention.

[0057] The present invention provides a capacitor element stacking method, comprising:

[0058] Step 101: Acquire component images of capacitors to be stacked, and input the component images into a pre-trained capacitor detection model, wherein the capacitor detection model includes a feature extraction network and a detection network;

[0059] In an embodiment of the present invention, when the capacitors to be stacked on the conveyor belt are limited by a blocking member, a component image of the capacitors to be stacked is collected by a preset visual inspection system, and the component image is input into a pre-trained capacitor detection model, wherein the capacitor detection model includes a feature extraction network and a detection network.

[0060] It should be noted that the training process of the capacitor detection model is specifically as follows: 1. Obtain multiple training component images, perform image preprocessing on the multiple training component images, and obtain a component feature set. 2. Use the component feature set to train the capacitor detection model to obtain training detection data. 3. Calculate the training loss function value of the component feature set based on the training detection data (that is, select standard data associated with the training detection data from the component feature set, calculate the deviation value between the training detection data and the standard data, and use the deviation value as the training loss function value). 4. When the training loss function value is greater than or equal to the preset standard loss function value, use the grid search method or the random search method to adjust the network parameters of the capacitor detection model until the training loss function value is less than the preset standard loss function value. 5. When the training loss function value is less than the preset standard loss function value, a trained capacitor detection model is generated.

[0061] Step 102: extract features from the component image using a feature extraction network to obtain a capacitor feature map;

[0062] In an embodiment of the present invention, feature extraction is performed on a component image through a feature extraction network to obtain a capacitor feature map, wherein the feature extraction network includes a 3×3 convolution layer, a 1×1 standard convolution layer, an extraction branch and a feature fusion layer.

[0063] Step 103: Using a detection network to perform identification and positioning detection on the capacitor characteristic image to obtain the component model and component coordinates corresponding to the capacitor image to be stacked;

[0064] In an embodiment of the present invention, a detection network is used to identify and locate the capacitor feature map to obtain the component model and component coordinates corresponding to the capacitor image to be stacked, wherein the detection network includes a fully connected layer and a Softmax layer.

[0065] Step 104 : Control the loading device corresponding to the component model to move to the component coordinates, and perform a stacking operation on the capacitors to be stacked.

[0066] In an embodiment of the present invention, after determining the component model and component coordinates corresponding to the image of the capacitor to be stacked, the corresponding loading robot is called according to the component model and moved to the component coordinates to perform a stacking operation on the capacitor to be stacked.

[0067] It should be noted that each loading robot is associated with a unique component frame or stacking reference template. After the loading robot moves to the component coordinates, the robot's manipulator inserts and clamps the capacitors to be stacked. After completing the pin insertion and clamping, the capacitors to be stacked are placed on the loading robot's unique component frame or stacking reference template.

[0068] In an embodiment of the present invention, by obtaining component images of capacitors to be stacked and inputting the component images into a pre-trained capacitor detection model, the component model and component coordinates of the capacitors to be stacked are obtained, and then the loading equipment corresponding to the component model is controlled to move to the component coordinates to perform the stacking operation on the capacitors to be stacked. This overcomes the technical problem that the existing capacitor component stacking method mainly relies on a loading robot to clamp the outer diameter of the component and then place it into the component frame, but due to the variety of capacitor component models and frequent production changes, the error probability of the loading robot clamping the component stacking method is gradually increasing. Compared with the traditional component stacking method, the present invention uses a pre-trained capacitor detection model to accurately identify the component model of the capacitor to be stacked, and then calls the loading equipment corresponding to the component model to perform the stacking operation on the capacitor to be stacked, thereby improving the efficiency and adaptability of the loading robot in stacking capacitor components.

[0069] See also Figure 2 , Figure 2 This is a flowchart of a capacitor element stacking method provided in the second embodiment of the present invention.

[0070] The present invention provides a capacitor element stacking method, comprising:

[0071] Step 201: Acquire component images of capacitors to be stacked, and input the component images into a pre-trained capacitor detection model, wherein the capacitor detection model includes a feature extraction network and a detection network;

[0072] It should be noted that the capacitor element stacking method provided by the present invention can be applied to a preset capacitor element stacking system, which includes a blocking member, a visual inspection system, a control system, and a loading robot. The blocking member is set at the end of the conveyor belt of the capacitor element. When the capacitor element is blocked and limited by the blocking member, the visual inspection system is triggered to photograph the capacitor element, and the model, size and position data of the capacitor element are obtained based on the captured image data. The control system determines the corresponding loading robot based on the model data of the capacitor element detected by the visual inspection system, and sends the model, size and position data to the loading robot. The manipulator of the loading robot uses pins to clamp the capacitor element and perform stacking operations.

[0073] It is worth mentioning that the device used to detect the presence of a capacitor element at the blocking member can be a pressure sensor set at the bottom of the conveyor belt end. When the pressure sensor detects pressure, it means that there is a capacitor element at the end of the conveyor belt; it can also be an infrared detection member set on the blocking member. When the emission signal of the infrared detection member is blocked, it means that there is a capacitor element at the end of the conveyor belt.

[0074] In an embodiment of the present invention, when a component image of a capacitor to be stacked is acquired, the component image is input into a pre-trained capacitor detection model, wherein the capacitor detection model includes a feature extraction network and a detection network.

[0075] Step 202: extract features from the component image using a feature extraction network to obtain a capacitor feature map;

[0076] Further, see Figure 3 As shown, the feature extraction network includes a 3×3 convolution layer, a 1×1 standard convolution layer, an extraction branch, and a feature fusion layer. Step 202 includes the following sub-steps:

[0077] S11. Extract features from the component image through a 3×3 convolutional layer to obtain a first component feature map;

[0078] In the embodiment of the present invention, a 3×3 convolutional layer is used to extract features from the component image to obtain a first component feature map.

[0079] S12, performing feature extraction on the first component feature map through a 1×1 standard convolutional layer to obtain a second component feature map;

[0080] In the embodiment of the present invention, a 1×1 standard convolutional layer is used to perform feature extraction on the first component feature map to obtain the second component feature map.

[0081] S13. Perform feature extraction on the first component feature map through an extraction branch to obtain a third component feature map, wherein the extraction branch includes a first recognition module, a 2×2 maximum pooling layer, a first recognition module, a cascaded attention module, a second recognition module, and a 1×1 convolutional layer connected in sequence;

[0082] Further, see Figure 3 As shown, S13 includes the following sub-steps:

[0083] S131, extracting features from the first component feature map through a first recognition module, a 2×2 maximum pooling layer, and a first recognition module in sequence to obtain a first recognition feature map, wherein the first recognition module includes a 3×3 convolutional layer, a batch normalization layer, and a Leaky-ReLU activation layer connected in sequence;

[0084] In this embodiment of the present invention, a first recognition model is used to extract features from a first component feature map to obtain a first intermediate component feature map. This first intermediate component feature map is then downsampled using a 2×2 max pooling layer to obtain a second intermediate component feature map. The first recognition model is then used to extract features from the second intermediate component feature map to obtain a first recognition feature map. The first recognition module includes a 3×3 convolutional layer, a batch normalization layer, and a Leaky-ReLU activation layer connected in sequence.

[0085] S132. Use a cascaded attention module to extract features from the first recognition feature map to obtain a second recognition feature map, wherein the cascaded attention module includes a 1×1 convolution layer, a feature fusion layer, a feature fusion layer, a 3×3 convolution layer, and a feature fusion layer;

[0086] Furthermore, S132 includes the following sub-steps:

[0087] S1321, extracting features from the first recognition feature map through a 1×1 convolutional layer to obtain a first feature map;

[0088] In an embodiment of the present invention, a convolution operation is performed on the first recognition feature map through a 1×1 convolution layer to obtain the first feature map.

[0089] It should be noted that the convolution operation extracts features from the data by sliding the convolution kernel (also called filter) on the input data (such as an image or feature map) and calculating the dot product of the convolution kernel and the local data at each position.

[0090] It is worth mentioning that the 1×1 convolution layer is a special convolution layer in which the convolution kernel size is 1 × 1. This means that the convolution kernel only considers one pixel at each position on the input feature map, but operates on the channel dimension.

[0091] S1322. Perform feature extraction on the first recognition feature map through a 3×3 convolutional layer to obtain a second feature map;

[0092] In the embodiment of the present invention, a convolution operation is performed on the first recognition feature map through a 3×3 convolution layer to obtain a second feature map.

[0093] S1323: Using a feature fusion layer to fuse the first feature map and the first recognition feature map to obtain a first fused feature map;

[0094] In the embodiment of the present invention, the first feature map and the first recognition feature map are fused by a feature fusion layer to obtain a first fused feature map.

[0095] It's important to note that different feature maps may contain different types of information, which may have limitations when used alone. However, feature fusion can complement their strengths and provide a more comprehensive and rich feature representation. Furthermore, fusing multiple feature maps can provide the model with more supervisory information, helping it converge to the optimal solution more quickly.

[0096] S1324: Using a feature fusion layer to perform feature fusion on the first fused feature map and the first recognition feature map to obtain a second fused feature map;

[0097] In the embodiment of the present invention, the first fused feature map and the first recognition feature map are input into the feature fusion layer for feature fusion to obtain a second fused feature map.

[0098] S1325. Use a feature fusion layer to perform feature fusion on the second feature map and the second fused feature map to obtain a second recognition feature map.

[0099] In the embodiment of the present invention, the second feature map and the second fused feature map are input into the feature fusion layer for feature fusion to obtain a second recognition feature map.

[0100] S133. Perform feature extraction on the second recognition feature map using a second recognition module to obtain a third recognition feature map, wherein the second recognition module includes a 3×3 convolutional layer, a batch normalization layer, and a DY-ReLU activation layer connected in sequence;

[0101] Furthermore, S133 includes the following sub-steps:

[0102] S1331, extracting features from the second recognition feature map through a 3×3 convolutional layer to obtain a third feature map;

[0103] In the embodiment of the present invention, a convolution operation is performed on the second recognition feature map through a 3×3 convolution layer to obtain a third feature map.

[0104] S1332. Perform a batch normalization operation on the third feature map through a batch normalization layer to obtain a fourth feature map;

[0105] In this embodiment of the present invention, the third feature map is input into a batch normalization layer for batch normalization operation to obtain a fourth feature map.

[0106] S1333. Use a DY-ReLU activation layer to perform a nonlinear operation on the fourth feature map to obtain a third recognition feature map.

[0107] In an embodiment of the present invention, the fourth feature map is input into the DY-ReLU activation layer for nonlinear operation to obtain a third recognition feature map.

[0108] It should be noted that the parameters of the Leaky-ReLU activation layer (such as the slope of the negative half-axis) are fixed and do not automatically adjust during training. This means that it may not adapt well to changes in data. For certain complex data distributions, fixed parameters may not achieve optimal activation effects. Compared to the Leaky-ReLU activation layer, the DY-ReLU activation layer has the ability to adaptively learn and can dynamically adjust the parameters of the activation function based on the characteristics of the input data. This allows it to better adapt to different data distributions and task requirements, and exhibits greater flexibility and adaptability when processing complex data. For example, in different datasets or at different training stages, DY-ReLU can automatically adjust its parameters to optimize model performance.

[0109] It's worth noting that replacing the Leaky-ReLU activation layer with a DY-ReLU activation layer helps the model converge to the optimal solution more quickly. During training, the DY-ReLU activation layer can promptly adjust the activation function parameters based on data feedback, allowing the model to adapt more quickly to the distribution of training data, thereby accelerating convergence. This adaptive adjustment also helps improve training stability, reducing the likelihood of the model oscillating or falling into a local optimum during training.

[0110] S134. Perform feature extraction on the third recognition feature map through a 1×1 convolutional layer to obtain a third component feature map.

[0111] In an embodiment of the present invention, a convolution operation is performed on the third recognition feature map through a 1×1 convolution layer to obtain a third component feature map.

[0112] S14. Use a feature fusion layer to perform feature fusion on the second component feature map and the third component feature map to obtain a capacitor feature map.

[0113] In an embodiment of the present invention, the second element feature map and the third element feature map are input into a feature fusion layer for feature fusion to obtain a capacitor feature map.

[0114] Step 203: Using a detection network to perform identification and positioning detection on the capacitor characteristic image to obtain the component model and component coordinates corresponding to the capacitor image to be stacked;

[0115] Furthermore, the detection network includes a fully connected layer and a Softmax layer, and step 203 includes the following sub-steps:

[0116] S21, feature mapping the capacitor feature map through a fully connected layer to obtain a component mapping feature map;

[0117] In an embodiment of the present invention, a fully connected layer is used to perform nonlinear mapping on the capacitor characteristic map to obtain a component mapping characteristic map.

[0118] S22. Use the Softmax layer to perform identification and positioning operations on the component mapping feature map to obtain the component model and component coordinates corresponding to the capacitor image to be stacked.

[0119] In the embodiment of the present invention, the component mapping feature map is identified and located through the Softmax layer to obtain the component model and component coordinates corresponding to the capacitor image to be stacked.

[0120] Step 204: Use the loading device corresponding to the component model as the target device, and control the target device to move from the stacking position to the component coordinates;

[0121] In an embodiment of the present invention, a corresponding loading robot is determined according to the identified component model, and the loading robot is used as a target device to control the target device to move from the stacking position to the component coordinate.

[0122] It should be noted that when the target device is already loading the component of this model, wait until the target device is idle before controlling the target device to move to the component coordinates.

[0123] Step 205: Control the target device to place the capacitors to be stacked at a stacking position.

[0124] In an embodiment of the present invention, a robotic arm of the target device is controlled to clamp the capacitors to be stacked, and move the robotic arm to a corresponding stacking position of the target device, and then place the capacitors to be stacked at the stacking position.

[0125] It should be noted that, see Figure 4 As shown, a core shaft hole 2 is opened on the capacitor to be stacked 1, and the target device (i.e., a loading robot, one end of the loading robot pin 3 is connected to the manipulator 4 to transmit power, and the other end can be inserted into the core shaft hole 2 of the component) inserts the pin 3 on the manipulator 4 into the core shaft hole 2 of the capacitor to be stacked 1, thereby picking up the capacitor to be stacked for component clamping.

[0126] In an embodiment of the present invention, by obtaining component images of capacitors to be stacked and inputting the component images into a pre-trained capacitor detection model, the component model and component coordinates of the capacitors to be stacked are obtained, and then the loading equipment corresponding to the component model is controlled to move to the component coordinates to perform the stacking operation on the capacitors to be stacked. This overcomes the technical problem that the existing capacitor component stacking method mainly relies on a loading robot to clamp the outer diameter of the component and then place it into the component frame, but due to the variety of capacitor component models and frequent production changes, the error probability of the loading robot clamping the component stacking method is gradually increasing. Compared with the traditional component stacking method, the present invention uses a pre-trained capacitor detection model to accurately identify the component model of the capacitor to be stacked, and then calls the loading equipment corresponding to the component model to perform the stacking operation on the capacitor to be stacked, thereby improving the efficiency and adaptability of the loading robot in stacking capacitor components.

[0127] See also Figure 5 , Figure 5 This is a structural block diagram of a capacitor element stacking system provided in Example 3 of the present invention.

[0128] The present invention provides a capacitor element stacking system, comprising:

[0129] An acquisition module 301 is configured to acquire component images of capacitors to be stacked and input the component images into a pre-trained capacitor detection model, wherein the capacitor detection model includes a feature extraction network and a detection network;

[0130] An extraction module 302 is configured to extract features from the component image using a feature extraction network to obtain a capacitor feature map;

[0131] Identification module 303, used to identify and locate the capacitor characteristic image using a detection network to obtain the component model and component coordinates corresponding to the capacitor image to be stacked;

[0132] The stacking module 304 is used to control the loading equipment corresponding to the component model to move to the component coordinate and perform a stacking operation on the capacitors to be stacked.

[0133] Furthermore, the feature extraction network includes a 3×3 convolutional layer, a 1×1 standard convolutional layer, an extraction branch, and a feature fusion layer. The extraction module 302 includes:

[0134] A first extraction submodule is used to extract features from the component image through a 3×3 convolutional layer to obtain a first component feature map;

[0135] A second extraction submodule is used to extract features from the first component feature map through a 1×1 standard convolutional layer to obtain a second component feature map;

[0136] a third extraction submodule, configured to extract features from the first component feature map through an extraction branch to obtain a third component feature map, wherein the extraction branch includes a first recognition module, a 2×2 maximum pooling layer, a first recognition module, a cascaded attention module, a second recognition module, and a 1×1 convolutional layer connected in sequence;

[0137] The first fusion submodule is used to use a feature fusion layer to perform feature fusion on the second element feature map and the third element feature map to obtain a capacitor feature map.

[0138] Furthermore, the third extraction submodule includes:

[0139] a first extraction unit, configured to extract features from the first component feature map through a first recognition module, a 2×2 maximum pooling layer, and a first recognition module in sequence to obtain a first recognition feature map, wherein the first recognition module includes a 3×3 convolutional layer, a batch normalization layer, and a Leaky-ReLU activation layer connected in sequence;

[0140] a second extraction unit, configured to perform feature extraction on the first recognition feature map using a cascaded attention module to obtain a second recognition feature map, wherein the cascaded attention module includes a 1×1 convolution layer, a feature fusion layer, a feature fusion layer, a 3×3 convolution layer, and a feature fusion layer;

[0141] a third extraction unit, configured to perform feature extraction on the second recognition feature map through a second recognition module to obtain a third recognition feature map, wherein the second recognition module includes a 3×3 convolutional layer, a batch normalization layer, and a DY-ReLU activation layer connected in sequence;

[0142] The fourth extraction unit is used to perform feature extraction on the third recognition feature map through a 1×1 convolution layer to obtain a third component feature map.

[0143] Furthermore, the second extraction unit includes:

[0144] A first extraction subunit is configured to extract features from the first recognition feature map through a 1×1 convolutional layer to obtain a first feature map;

[0145] A second extraction subunit is configured to extract features from the first recognition feature map through a 3×3 convolutional layer to obtain a second feature map;

[0146] A first fusion subunit is configured to perform feature fusion on the first feature map and the first recognition feature map using a feature fusion layer to obtain a first fused feature map;

[0147] The second fusion subunit is configured to perform feature fusion on the first fused feature map and the first recognition feature map using a feature fusion layer to obtain a second fused feature map;

[0148] The third fusion subunit is used to use the feature fusion layer to perform feature fusion on the second feature map and the second fused feature map to obtain a second recognition feature map.

[0149] Furthermore, the third extraction unit includes:

[0150] A third extraction subunit is configured to perform feature extraction on the second recognition feature map through a 3×3 convolutional layer to obtain a third feature map;

[0151] a batch normalization subunit, configured to perform a batch normalization operation on the third feature map through a batch normalization layer to obtain a fourth feature map;

[0152] The nonlinear operation subunit is used to perform a nonlinear operation on the fourth feature map using a DY-ReLU activation layer to obtain a third recognition feature map.

[0153] Furthermore, the detection network includes a fully connected layer and a Softmax layer, and the recognition module 303 includes:

[0154] A mapping submodule, configured to perform feature mapping on the capacitor feature map through a fully connected layer to obtain a component mapping feature map;

[0155] The identification and positioning submodule is used to perform identification and positioning operations on the component mapping feature map using the Softmax layer to obtain the component model and component coordinates corresponding to the capacitor image to be stacked.

[0156] Furthermore, the stacking module 304 includes:

[0157] The matching submodule is used to set the loading device corresponding to the component model as the target device and control the target device to move from the stacking position to the component coordinates;

[0158] The stacking submodule is used to control the target device to place the capacitors to be stacked at the stacking position.

[0159] See also Figure 6 , Figure 6 This is a structural block diagram of an electronic device provided in Example 4 of the present invention.

[0160] An electronic device according to an embodiment of the present invention includes a memory 401 and a processor 402 , wherein the memory 402 stores a computer program. When the computer program is executed by the processor 402 , the processor 402 executes the capacitor element stacking method according to any of the above embodiments.

[0161] Memory 401 may be an electronic memory such as flash memory, EEPROM (Electrically Erasable Programmable Read-Only Memory), EPROM, hard disk, or ROM. Memory 401 has storage space 403 for program code 413 for executing any of the method steps described above. For example, storage space 403 for program code may include individual program codes 413 for implementing various steps in the method described above. These program codes may be read from or written to one or more computer program products. These computer program products include program code carriers such as hard disks, compact disks (CDs), memory cards, or floppy disks. The program codes may be compressed, for example, in a suitable format. When executed by a processing device, these codes cause the processing device to execute the various steps in the method described above.

[0162] The fifth embodiment of the present invention further provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the capacitor element stacking method according to any of the above embodiments is implemented.

[0163] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0164] In the several embodiments provided in this application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are merely schematic. For example, the division of units is only a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interface, device or unit, which can be electrical, mechanical or other forms.

[0165] Units described as separate components may or may not be physically separate, and components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0166] In addition, the functional units in the various embodiments of the present invention may be integrated into a single processing unit, each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0167] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the portion that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the various embodiments of the method of the present invention. The aforementioned storage medium includes various media that can store program code, such as USB flash drives, mobile hard drives, read-only memories (ROMs), random access memories (RAMs), magnetic disks, or optical disks.

[0168] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. A capacitor element stacking method, characterized in that: include: Obtaining a component image of a capacitor to be stacked, and inputting the component image into a pre-trained capacitor detection model, wherein the capacitor detection model includes a feature extraction network and a detection network; Extracting features from the component image using the feature extraction network to obtain a capacitor feature map; Using the detection network to perform identification and positioning detection on the capacitor characteristic image to obtain the component model and component coordinates corresponding to the component image of the capacitor to be stacked; Controlling the loading equipment corresponding to the component model to move to the component coordinates and stacking the capacitors to be stacked; The feature extraction network includes a 3×3 convolution layer, a 1×1 standard convolution layer, an extraction branch, and a feature fusion layer. The step of extracting features from the component image through the feature extraction network to obtain a capacitor feature map includes: Performing feature extraction on the component image through a 3×3 convolutional layer to obtain a first component feature map; Performing feature extraction on the first component feature map through a 1×1 standard convolutional layer to obtain a second component feature map; Performing feature extraction on the first component feature map by the extraction branch to obtain a third component feature map, wherein the extraction branch includes a first recognition module, a 2×2 maximum pooling layer, a first recognition module, a cascaded attention module, a second recognition module, and a 1×1 convolutional layer connected in sequence; Using a feature fusion layer to perform feature fusion on the second element feature map and the third element feature map to obtain a capacitor feature map; The step of extracting features from the first component characteristic graph by the extraction branch to obtain a third component characteristic graph includes: Extracting features from the first component feature map through a first recognition module, a 2×2 maximum pooling layer, and a first recognition module in sequence to obtain a first recognition feature map, wherein the first recognition module includes a 3×3 convolutional layer, a batch normalization layer, and a Leaky-ReLU activation layer connected in sequence; Performing feature extraction on the first recognition feature map using the cascaded attention module to obtain a second recognition feature map, wherein the cascaded attention module includes a 1×1 convolution layer, a feature fusion layer, a feature fusion layer, a 3×3 convolution layer, and a feature fusion layer; Performing feature extraction on the second recognition feature map through a second recognition module to obtain a third recognition feature map, wherein the second recognition module includes a 3×3 convolutional layer, a batch normalization layer, and a DY-ReLU activation layer connected in sequence; Performing feature extraction on the third recognition feature map through a 1×1 convolutional layer to obtain a third component feature map; The step of using the cascaded attention module to extract features from the first recognition feature map to obtain a second recognition feature map includes: Performing feature extraction on the first recognition feature map through a 1×1 convolutional layer to obtain a first feature map; Performing feature extraction on the first recognition feature map through a 3×3 convolutional layer to obtain a second feature map; Using a feature fusion layer to perform feature fusion on the first feature map and the first recognition feature map to obtain a first fused feature map; Using a feature fusion layer to perform feature fusion on the first fused feature map and the first recognition feature map to obtain a second fused feature map; A feature fusion layer is used to perform feature fusion on the second feature map and the second fused feature map to obtain a second recognition feature map.

2. The capacitor element stacking method according to claim 1, wherein: The step of extracting features from the second recognition feature map by the second recognition module to obtain a third recognition feature map includes: Performing feature extraction on the second recognition feature map through a 3×3 convolutional layer to obtain a third feature map; Performing a batch normalization operation on the third feature map through a batch normalization layer to obtain a fourth feature map; A DY-ReLU activation layer is used to perform a nonlinear operation on the fourth feature map to obtain a third recognition feature map.

3. The capacitor element stacking method according to claim 1, wherein: The detection network includes a fully connected layer and a Softmax layer. The step of using the detection network to identify and locate the capacitor characteristic map to obtain the component model and component coordinates corresponding to the component image of the capacitor to be stacked includes: Performing feature mapping on the capacitor feature map through a fully connected layer to obtain a component mapping feature map; A Softmax layer is used to perform an identification and positioning operation on the component mapping feature map to obtain a component model and component coordinates corresponding to the component image of the capacitor to be stacked.

4. The capacitor element stacking method according to claim 1, wherein: The step of controlling the loading device corresponding to the component model to move to the component coordinate and stacking the capacitors to be stacked includes: The loading device corresponding to the component model is used as the target device, and the target device is controlled to move from the stacking position to the component coordinates; The target device is controlled to place the capacitor to be stacked at the stacking position.

5. A capacitor element stacking system, based on the capacitor element stacking method according to any one of claims 1 to 4, characterized in that: include: an acquisition module, configured to acquire component images of capacitors to be stacked, and input the component images into a pre-trained capacitor detection model, wherein the capacitor detection model includes a feature extraction network and a detection network; an extraction module, configured to extract features from the component image using the feature extraction network to obtain a capacitor feature map; an identification module, configured to use the detection network to perform identification and positioning detection on the capacitor characteristic image, and obtain a component model and component coordinates corresponding to the component image of the capacitor to be stacked; The stacking module is used to control the loading equipment corresponding to the component model to move to the component coordinate and perform a stacking operation on the capacitors to be stacked.

6. An electronic device, characterized in that: The method comprises a memory and a processor, wherein a computer program is stored in the memory, and when the computer program is executed by the processor, the processor executes the steps of the capacitor element stacking method according to any one of claims 1 to 4.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed, the capacitor element stacking method according to any one of claims 1 to 4 is implemented.

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