A metal coil handling and stacking method and system based on AGV

Abstract

The present invention belongs to the technical field of smart factories. A method and system for transporting and stacking metal coils based on AGV is provided. The method comprises: obtaining a high-definition image of the target metal coil, extracting security feature data based on the high-definition image, processing the security feature data using a security risk assessment model, and obtaining a transport risk value; when the transport risk value is higher than the risk threshold, determining the placement posture of the target metal coil, and screening the target AGV based on the placement posture and the transport risk value; placing the target metal coil on the bearing component of the target AGV according to the placement posture, and controlling the target AGV to automatically transport the target metal coil to the stacking point in the stacking area. The solution of the present invention determines the appropriate placement posture and appropriate AGV of the target metal coil on the AGV based on the risk factors of the target metal coil, thereby reducing the safety risk of the AGV during transportation.

Description

Technical Field

[0001] The present invention relates to the technical field of smart factories, and in particular to an AGV-based metal coil handling and stacking method and system. Background Art

[0002] Metal coils are metal materials with certain size, shape and performance produced through rolling process. Metal parts are widely used in automobile manufacturing, home appliance manufacturing, aerospace and other fields.

[0003] Metal coils are stacked in production workshops and then moved to production equipment when needed. Currently, many production workshops are using automated guided vehicles (AGVs) to automatically handle metal coils, significantly improving their handling efficiency. However, existing AGV-based methods for handling and stacking metal coils primarily focus on planning AGV routes and ensuring precise stacking at specific locations, while neglecting the safety of the AGVs during handling, making handling accidents more likely. This technical issue has yet to be effectively addressed. Summary of the Invention

[0004] In this regard, the present invention provides an AGV-based metal coil handling and stacking method, system, electronic equipment, computer storage medium and computer program product to solve the above technical problems.

[0005] The present invention discloses a method for transporting and stacking metal coils based on AGV, which comprises the following steps: acquiring a high-definition image of a target metal coil, extracting safety feature data based on the high-definition image, processing the safety feature data using a safety risk assessment model, and obtaining a transport risk value; when the transport risk value is higher than a risk threshold, determining the placement posture of the target metal coil, and screening a target AGV based on the placement posture and the transport risk value; placing the target metal coil on a carrying component of a target AGV according to the placement posture, and controlling the target AGV to automatically transport the target metal coil to a stacking point in a stacking area.

[0006] In some embodiments, obtaining a high-definition image of the target metal coil and extracting security feature data based on the high-definition image includes: capturing a panoramic image of the initial stacking area of ​​the metal coils using a high-definition camera, identifying the target metal coil from the panoramic image based on the current production task information, and capturing a high-definition image of the target metal coil; using image processing technology to identify and extract the security feature data of the target metal coil in the high-definition image, the security feature data including at least the number of binding straps, the uniformity of distribution of the binding straps, the tightness of the binding straps, and whether the metal coil is covered with protective material; wherein the binding straps are steel straps or plastic straps.

[0007] In some embodiments, the use of a safety risk assessment model to process the safety feature data to obtain a handling risk value includes: using a convolutional network to perform feature extraction on the number of the binding straps, the distribution uniformity of the binding straps, the tightness of the binding straps, and whether the metal roll is covered with protective material, respectively, to obtain quantity features, distribution uniformity features, tightness features, and protective material features, and integrating the quantity features, the distribution uniformity features, the tightness features, and the protective material features into risk features; inputting the risk features into the safety risk assessment model to obtain the handling risk value predicted and output by the safety risk assessment model.

[0008] In some embodiments, inputting the risk feature into the safety risk assessment model to obtain the handling risk value predicted and output by the safety risk assessment model includes: inputting the risk feature into the safety risk assessment model to obtain a first handling risk value predicted and output by the safety risk assessment model; identifying a target binding strap adjacent to the end of the target metal coil based on the high-definition image, and calculating the interval distance between the target binding strap and the end of the target metal coil; wherein the target binding strap refers to a binding strap adjacent to the end of the target metal coil and located on the outermost metal plate of the target metal coil; determining a risk adjustment coefficient based on the interval distance, and multiplying the risk adjustment coefficient by the first handling risk value to obtain a second handling risk value, and the second handling risk value is the final handling risk value.

[0009] In some embodiments, determining the risk adjustment coefficient based on the spacing distance includes: obtaining the metal type and the number of winding layers of the target metal coil, and predicting the standard spacing distance based on the metal type and the number of winding layers; calculating the difference between the spacing distance and the standard spacing distance, and deriving the risk adjustment coefficient based on matching the difference with a preset first positive correlation.

[0010] In some embodiments, when the handling risk value is higher than a risk threshold, the placement posture of the target metal coil is determined, and a target AGV is obtained by screening based on the placement posture and the handling risk value, including: when the handling risk value is higher than the risk threshold, determining the placement posture of the target metal coil so that the end of the target metal coil is located at the bottom; determining a length interval of the load-bearing clamping device based on the handling risk value and a preset second positive correlation, and obtaining the target AGV based on the length interval.

[0011] The present invention also discloses a metal coil handling and stacking system based on AGV, which includes a risk analysis module, a screening module, and a scheduling module; the risk analysis module is used to obtain a high-definition image of the target metal coil, extract safety feature data based on the high-definition image, and process the safety feature data using a safety risk assessment model to obtain a handling risk value; the screening module is used to determine the placement posture of the target metal coil when the handling risk value is higher than a risk threshold, and obtain a target AGV based on the placement posture and the handling risk value; the scheduling module is used to place the target metal coil on the carrying component of the target AGV according to the placement posture, and control the target AGV to automatically transport the target metal coil to the stacking point in the stacking area.

[0012] The present invention also discloses an electronic device, comprising: at least one processor, a memory, and a computer program stored in the memory and executable on the at least one processor, wherein the processor executes the computer program to implement any of the methods described above.

[0013] The present invention further discloses a computer storage medium, wherein the computer-readable storage medium stores a computer program, and the computer program is executed by a processor to implement any of the above methods.

[0014] The present invention also discloses a computer program product. When the computer program product is run on a terminal, the terminal implements any of the above methods when executing the computer program product.

[0015] Compared with the existing technology that only focuses on the planning of the AGV's transportation route and the precise stacking at the stacking points, the solution of the present invention focuses on the safety of the AGV during the transportation process, and specifically determines the appropriate placement posture of the target metal roll on the AGV and the appropriate AGV based on the extracted risk factors of the target metal roll, thereby reducing the probability of safety risks occurring during the AGV's transportation. Even if an accident occurs during the transportation, the risk of injury to people and property can be minimized. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the embodiments. It should be understood that the following drawings only illustrate certain embodiments of the present invention and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other relevant drawings can be obtained based on these drawings without paying any creative work.

[0017] Figure 1 The present invention is a flowchart of an AGV-based metal coil handling and stacking method disclosed in an embodiment of the present invention.

[0018] Figure 2 3 is a corresponding schematic diagram of the target metal coil and the carrier clamping device disclosed in an embodiment of the present invention.

[0019] Figure 3 It is a structural schematic diagram of an AGV-based metal coil handling and stacking system disclosed in an embodiment of the present invention. DETAILED DESCRIPTION

[0020] The following specific embodiments illustrate the implementation of this application. People familiar with this technology can easily understand the other advantages and functions of this application from the content disclosed in this specification. Obviously, the described embodiments are part of the embodiments of this application, but not all of them. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0021] In addition, the technical features involved in the different embodiments of the present application described below can be combined with each other as long as they do not conflict with each other.

[0022] Metal coils are stacked in the production workshop and then transported to production equipment when needed. Currently, many production workshops are using AGVs (Automated Guided Vehicles) to automatically transport metal coils, significantly improving coil handling efficiency. However, existing AGV-based metal coil handling and stacking methods primarily focus on planning AGV transport routes and precisely stacking coils at stacking points, while neglecting the safety of the AGVs during the handling process, making handling accidents more likely. This technical issue has yet to be effectively addressed.

[0023] In view of the above technical problems existing in the prior art, Figure 1 As shown, an embodiment of the present invention discloses a method for transporting and stacking metal coils based on an AGV (Automated Guided Vehicle), the method comprising the following steps: acquiring a high-definition image of a target metal coil, extracting safety feature data based on the high-definition image, processing the safety feature data using a safety risk assessment model, and obtaining a transport risk value; when the transport risk value is higher than a risk threshold, determining the placement posture of the target metal coil, and screening a target AGV based on the placement posture and the transport risk value; placing the target metal coil on a carrying component of the target AGV according to the placement posture, and controlling the target AGV to automatically transport the target metal coil to a stacking point in a stacking area.

[0024] In the aforementioned solution of the present invention, a high-definition camera is deployed in the production workshop. When a metal coil needs to be moved, the camera is controlled to capture a high-definition image of the target metal coil. The security feature data of the target metal coil is then extracted from this image. The extracted security feature data is then processed using a pre-built security risk assessment model to obtain a handling risk value. The handling risk value represents the potential risk posed by the target metal coil during handling due to inherent factors. When the handling risk value is above a threshold, special handling methods are required. When the handling risk value is below the threshold, no special handling methods are required.

[0025] Next, for target metal coils with handling risk values ​​above the risk threshold, a safe placement posture is determined. This posture minimizes the risk of injury or damage to personnel and property, even in the event of a safety incident. Next, target AGVs are screened based on the placement posture and handling risk value, identifying the most suitable target AGV. It should be noted that a production workshop may employ a variety of AGV types, varying in load rating, load-bearing clamp length, and other factors.

[0026] Finally, the target metal coil is placed on the target AGV's load-bearing component in the safest placement position determined, and the target AGV is controlled to automatically transport the target metal coil to the stacking point in the stacking area. Thus, the automatic stacking of the target metal coil is completed.

[0027] It can be seen that compared with the existing technology that only focuses on the planning of the AGV's transportation route and the precise stacking at the stacking points, the solution of the present invention focuses on the safety of the AGV during the transportation process, and specifically determines the appropriate placement posture of the target metal roll on the AGV and the appropriate AGV based on the extracted risk factors of the target metal roll, thereby reducing the probability of safety risks occurring during the transportation of the AGV, and even if an accident occurs during the transportation, the risk of injury to people and property can be minimized.

[0028] In some embodiments, obtaining a high-definition image of the target metal coil and extracting security feature data based on the high-definition image includes: capturing a panoramic image of the initial stacking area of ​​the metal coils using a high-definition camera, identifying the target metal coil from the panoramic image based on the current production task information, and capturing a high-definition image of the target metal coil; using image processing technology to identify and extract the security feature data of the target metal coil in the high-definition image, the security feature data including at least the number of binding straps, the uniformity of distribution of the binding straps, the tightness of the binding straps, and whether the metal coil is covered with protective material; wherein the binding straps are steel straps or plastic straps.

[0029] In this embodiment of the present invention, after receiving new production task information, the information is parsed to determine the type of metal coil required for production, namely the target metal coil. Multiple types of metal coils are stacked in the initial stacking area. First, a panoramic image of the initial stacking area is captured. Based on the stacking zones of the metal coils and the special markings attached to the coils, the type of metal coil required for this production, namely the target metal coil, is determined. The target metal coil may be the topmost coil of the corresponding type. Then, a high-definition camera is controlled to capture a panoramic image of the target metal coil.

[0030] Next, image processing techniques are used to identify and extract at least the number of binding straps, their distribution uniformity, their tightness, and whether the metal roll is covered with protective material. This information constitutes the security signature data of the target metal roll. Binding straps refer to the multiple steel or plastic straps used to bind and restrain the metal roll. Binding strap uniformity refers to, for example, the variance of the spacing between two adjacent binding straps in each group. A larger variance indicates a lower uniformity, and vice versa. Binding strap tightness refers to the degree of contact between the binding strap and the metal roll. A completely tight binding strap indicates a higher tightness, while a looser one indicates a lower tightness. Furthermore, the number of binding straps and whether the metal roll is covered with protective material also impact the safety of the target metal roll during handling, primarily the probability of it breaking apart. Clearly, the fewer binding straps, the less uniform their distribution, the lower their tightness, and the absence of protective material on the metal roll, the higher the probability of it breaking apart. Subsequently, the handling risk value of the target metal coil can be obtained based on the above safety feature data analysis.

[0031] It should be noted that after some metal rolls are rolled up, they will be covered with a layer of protective material and then bound and reinforced with multiple straps, so not all metal rolls are covered with protective material.

[0032] In some embodiments, the use of a safety risk assessment model to process the safety feature data to obtain a handling risk value includes: using a convolutional network to perform feature extraction on the number of the binding straps, the distribution uniformity of the binding straps, the tightness of the binding straps, and whether the metal roll is covered with protective material, respectively, to obtain quantity features, distribution uniformity features, tightness features, and protective material features, and integrating the quantity features, the distribution uniformity features, the tightness features, and the protective material features into risk features; inputting the risk features into the safety risk assessment model to obtain the handling risk value predicted and output by the safety risk assessment model.

[0033] In the embodiments of the present invention, the AGV inevitably shakes during transport, and the amplitude of this shaking can be quite significant in some unsafe sections. Furthermore, the target metal roll acts like a spring, and the accumulated stress can cause the end of the roll to lose contact with the main body. If the binding straps or protective materials restraining this tension break or loosen, the roll can break apart, potentially causing personal injury or property damage. Furthermore, as previously mentioned, the aforementioned safety features affect the target metal roll's stability, or the probability of its breaking apart.

[0034] To this end, the present invention is designed to use a convolutional network to first extract features from the security feature data, such as the number of binding straps, the uniformity of the distribution of the binding straps, the tightness of the binding straps, and whether the metal roll is covered with protective materials. The extracted features are then integrated to obtain risk features that can be used to analyze the probability of the target metal roll breaking. The pre-built safety risk assessment model conducts an in-depth analysis of the features to obtain the handling risk value.

[0035] It should be noted that the above security risk assessment model is based on a deep learning algorithm and mainly includes an input layer, a convolutional layer, an activation function layer, a pooling layer, a fully connected layer, and an output layer. The specific functions of each structure are described below: Input layer: Used to receive the above risk characteristics.

[0036] Convolutional layer: This layer convolves the input risk features with multiple convolution kernels (also called filters) to extract local features from the risk features. Each convolution kernel generates a feature map that contains the feature information of the risk features at different scales and directions.

[0037] Activation function layer: Activation functions such as ReLU, Sigmoid, or Tanh can perform nonlinear transformations on the output of the convolutional layer, enabling the model to learn more complex feature representations.

[0038] Pooling layer: Downsamples the feature maps output by the convolutional layer to reduce the data dimension and computational complexity. Common pooling methods include max pooling and average pooling. Pooling layers preserve key information in feature maps while reducing redundancy and noise.

[0039] Fully connected layers: One or two fully connected layers can integrate feature information and perform classification or regression tasks. Each neuron in a fully connected layer is connected to all neurons in the previous layer, and linear transformations and nonlinear activations are performed through weights and bias parameters.

[0040] Output layer: The output layer uses a regression layer. The regression layer maps the output of the fully connected layer to the range of risk values ​​through linear transformation and activation function (such as sigmoid or tanh), thereby obtaining the corresponding transportation risk value.

[0041] In some embodiments, inputting the risk feature into the safety risk assessment model to obtain the handling risk value predicted and output by the safety risk assessment model includes: inputting the risk feature into the safety risk assessment model to obtain a first handling risk value predicted and output by the safety risk assessment model; identifying a target binding strap adjacent to the end of the target metal coil based on the high-definition image, and calculating the interval distance between the target binding strap and the end of the target metal coil; wherein the target binding strap refers to a binding strap adjacent to the end of the target metal coil and located on the outermost metal plate of the target metal coil; determining a risk adjustment coefficient based on the interval distance, and multiplying the risk adjustment coefficient by the first handling risk value to obtain a second handling risk value, and the second handling risk value is the final handling risk value.

[0042] In an embodiment of the present invention, the present invention first uses a safety risk assessment model to analyze the risk characteristics derived above to obtain an initial first handling risk value. Then, based on the high-definition image, the target binding strap immediately adjacent to the end of the target metal roll is identified. This target binding strap is located on the outermost metal sheet of the target roll and immediately adjacent to the end of the target metal roll. For example, if the target metal roll is wound in 10 layers (the innermost layer is the 1st layer and the outermost layer is the 10th layer), the binding straps immediately adjacent to the end of the target metal roll include the binding strap located on the 10th layer and the binding strap located on the 9th layer. The binding strap located on the 10th layer is the target binding strap. Next, the distance between the target binding strap and the end of the target metal roll is calculated. This distance affects the target binding strap's ability to restrain the end of the target metal roll. Generally speaking, a larger distance indicates a weaker binding strap's ability to restrain the end of the target metal roll, meaning it is more likely to break apart. Conversely, a smaller distance indicates a stronger binding strap, meaning it is less likely to break apart. On this basis, the present invention determines a risk adjustment coefficient according to the interval distance, and then multiplies the risk adjustment coefficient by the first transportation risk value to obtain a second transportation risk value, and the second transportation risk value is the final transportation risk value.

[0043] In some embodiments, determining the risk adjustment coefficient based on the spacing distance includes: obtaining the metal type and the number of winding layers of the target metal coil, and predicting the standard spacing distance based on the metal type and the number of winding layers; calculating the difference between the spacing distance and the standard spacing distance, and deriving the risk adjustment coefficient based on matching the difference with a preset first positive correlation.

[0044] In an embodiment of the present invention, after determining the target metal coil, the metal type (material, thickness, and other parameters related to elastic force) and the number of winding layers of the target metal coil can be retrieved from the backend system. Different types of metals, after being wound with different numbers of layers, experience different stress levels at the end of the metal coil. Therefore, the present invention provides a method for predicting a standard spacing distance based on the obtained metal type and number of winding layers. This standard spacing distance refers to the distance between the target binding strap's attachment location and the end of the target metal coil. Specifically, attaching the target binding strap at this location minimizes the probability of the target metal coil's end snapping open (this attachment location is generally not the very end of the target metal coil, as it is prone to slippage, increasing the probability of the target metal coil's end snapping open). However, attaching the target binding strap further away from this attachment location (i.e., at a greater spacing distance) increases the probability of the target metal coil's end snapping open as the spacing distance increases. The prediction of the standard spacing distance in this embodiment can be implemented based on a preset model, for example, it can also be constructed based on the aforementioned deep learning algorithm, which will not be described in detail here.

[0045] Then, the difference between the interval distance and the standard interval distance is calculated, and the risk adjustment coefficient is obtained by matching the difference with the preset first positive correlation relationship. The preset first positive correlation relationship can be expressed in the form of a comparison table.

[0046] In some embodiments, when the handling risk value is higher than a risk threshold, the placement posture of the target metal coil is determined, and a target AGV is obtained by screening based on the placement posture and the handling risk value, including: when the handling risk value is higher than the risk threshold, determining the placement posture of the target metal coil so that the end of the target metal coil is located at the bottom; determining a length interval of the load-bearing clamping device based on the handling risk value and a preset second positive correlation, and obtaining the target AGV based on the length interval.

[0047] In an embodiment of the present invention, when the handling risk value is higher than the risk threshold, it indicates that there is a probability that the end of the target metal roll will break apart during the handling process. At this time, the placement posture of the target metal roll can be determined as the end of the target metal roll is at the bottom. Since the end of the target metal roll is located between the two clamping devices of the load-bearing part, it will not bounce outward even if it breaks apart, and will not cause damage to the surrounding people and objects. At the same time, it is also set to determine the length interval of the load-bearing part clamping device according to the size of the handling risk value, and then the target AGV is screened according to this length interval. Figure 2As shown in the figure, for example, if the load-carrying clamping device length range is determined to be 20-30 cm based on the handling risk value, then Type A AGVs with a load-carrying clamping device length of 25 cm (i.e., the slope length of the gray area in the figure) are selected as target AGVs. Obviously, the higher the handling risk value, the more AGVs with longer load-carrying clamping devices (i.e., the larger the load-carrying clamping device length range) are selected.

[0048] like Figure 3 As shown, an embodiment of the present invention further discloses a metal coil handling and stacking system based on an AGV (Automated Guided Vehicle), the system comprising a risk analysis module, a screening module, and a scheduling module; the risk analysis module is used to obtain a high-definition image of a target metal coil, extract safety feature data based on the high-definition image, and process the safety feature data using a safety risk assessment model to obtain a handling risk value; the screening module is used to determine the placement posture of the target metal coil when the handling risk value is higher than a risk threshold, and obtain a target AGV based on the placement posture and the handling risk value; the scheduling module is used to place the target metal coil on the carrying component of the target AGV according to the placement posture, and control the target AGV to automatically transport the target metal coil to the stacking point in the stacking area.

[0049] An embodiment of the present invention further discloses an electronic device, comprising: at least one processor, a memory, and a computer program stored in the memory and executable on the at least one processor, wherein the processor executes the computer program to implement the method described in the aforementioned embodiment.

[0050] An embodiment of the present invention further discloses a computer storage medium, wherein the computer storage medium stores a computer program, and the computer program is executed by a processor to implement the method described in the above embodiment.

[0051] The embodiment of the present invention further discloses a computer program product. When the computer program product is run on a terminal, the terminal implements the method described in the above embodiment.

[0052] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, as well as combinations of processes and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowcharts and / or block diagrams. Figure 1a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0053] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0054] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0055] The above description is merely a preferred embodiment of the present invention and is not intended to limit the scope of protection of the present invention.

Claims

1. A metal coil handling and stacking method based on AGV, characterized in that: The method comprises the following steps: obtaining a high-definition image of a target metal coil, extracting safety feature data based on the high-definition image, and processing the safety feature data using a safety risk assessment model to obtain a handling risk value; When the transport risk value is higher than the risk threshold, determining the placement posture of the target metal coil, and selecting a target AGV according to the placement posture and the transport risk value; Placing the target metal roll on the carrying component of the target AGV according to the placement posture, and controlling the target AGV to automatically transport the target metal roll to the stacking point in the stacking area; Obtaining a high-definition image of a target metal coil and extracting safety feature data based on the high-definition image, including: capturing a panoramic image of an initial stacking area of ​​the metal coils using a high-definition camera, identifying the target metal coil from the panoramic image based on current production task information, and capturing a high-definition image of the target metal coil; using image processing technology to identify and extract the safety feature data of the target metal coil from the high-definition image, the safety feature data including at least the number of binding straps, the uniformity of distribution of the binding straps, the tightness of the binding straps, and whether the metal coil is covered with protective material; wherein the binding straps are steel straps or plastic straps; Processing the security feature data using a security risk assessment model to obtain a handling risk value, including: using a convolutional network to extract features of the number of the binding straps, the distribution uniformity of the binding straps, the tightness of the binding straps, and whether the metal roll is covered with protective material, respectively, to obtain quantity features, distribution uniformity features, tightness features, and protective material features; integrating the quantity features, distribution uniformity features, tightness features, and protective material features into risk features; inputting the risk features into the security risk assessment model to obtain a handling risk value predicted and output by the security risk assessment model; Inputting the risk feature into the safety risk assessment model to obtain a handling risk value predicted and output by the safety risk assessment model, including: inputting the risk feature into the safety risk assessment model to obtain a first handling risk value predicted and output by the safety risk assessment model; identifying a target binding strap adjacent to the end of a target metal coil based on the high-definition image, and calculating a distance between the target binding strap and the end of the target metal coil; wherein the target binding strap refers to a binding strap adjacent to the end of the target metal coil and located on the outermost metal plate of the target metal coil; determining a risk adjustment coefficient based on the distance, and multiplying the first handling risk value by the risk adjustment coefficient to obtain a second handling risk value, where the second handling risk value is the final handling risk value; Determining a risk adjustment coefficient based on the spacing distance includes: obtaining the metal type and the number of winding layers of the target metal coil, and predicting a standard spacing distance based on the metal type and the number of winding layers; calculating the difference between the spacing distance and the standard spacing distance, and deriving the risk adjustment coefficient based on matching the difference with a preset first positive correlation relationship.

2. The AGV-based metal coil handling and stacking method according to claim 1, characterized in that: When the handling risk value is higher than the risk threshold, the placement posture of the target metal coil is determined, and the target AGV is obtained by screening according to the placement posture and the handling risk value, including: when the handling risk value is higher than the risk threshold, determining the placement posture of the target metal coil so that the end of the target metal coil is located at the bottom; determining the length interval of the load-bearing clamping device according to the handling risk value and a preset second positive correlation relationship, and obtaining the target AGV by screening according to the length interval.

3. An AGV-based metal coil handling and stacking system, the system being based on the method of claim 1 or 2, the system comprising a risk analysis module, a screening module, and a scheduling module; characterized in that: The risk analysis module is used to obtain a high-definition image of the target metal coil, extract safety feature data based on the high-definition image, and process the safety feature data using a safety risk assessment model to obtain a handling risk value; The screening module is configured to determine the placement posture of the target metal coil when the transport risk value is higher than a risk threshold, and to screen and obtain a target AGV based on the placement posture and the transport risk value; The scheduling module is used to place the target metal roll on the carrying component of the target AGV according to the placement posture, and control the target AGV to automatically transport the target metal roll to the stacking point in the stacking area.

4. An electronic device comprising: At least one processor, a memory, and a computer program stored in the memory and executable on the at least one processor, wherein the processor executes the computer program to implement the method according to claim 1 or 2.

5. A computer storage medium storing a computer program, characterized in that: The computer program is executed by a processor to implement the method according to claim 1 or 2.

6. A computer program product comprising a computer program stored on a non-transitory computer-readable medium, characterized in that: When the computer program is executed by a processor, the method according to claim 1 or 2 is implemented.

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