Intelligent vision-controlled industrial bag breaking device

Abstract

The application discloses an intelligent visual control industrial bag breaking device, and relates to the technical field of industrial automation.The device integrates a visual identification module, a control module, a mechanical execution module, a driving module and a safety protection module, and aims to realize intelligent and safe bag breaking operation in a high-risk industrial scene.The visual identification module adopts a high-definition industrial camera, collects image information around a feeding opening of a production container in real time through a global shutter and an adjustable focal length lens, and transmits the image information to a neural network expert model through a gigabit Ethernet, wherein the image information includes worker operation behavior and feeding process start signal.The model can realize real-time high-precision identification and early warning.The control module realizes risk prediction and closed-loop control;the mechanical execution module has a bag breaking success rate of 99.6%;and the safety protection module effectively reduces dust leakage by combining a tool hiding cavity, a HEPA filter screen and an electromagnetic lock.The device significantly improves bag breaking efficiency and safety, reduces manual intervention and occupational disease risk, and is suitable for high-risk industrial scenes.

Description

Technical Field

[0001] This invention relates to the field of industrial automation equipment technology, specifically to an intelligent vision-controlled industrial bag-breaking device. Background Technology

[0002] In industries such as chemicals and plastics, feeding materials into production lines is a common process. This process involves cutting open burlap sacks and then adding the powdery material inside into production containers. These industrial powders often contain highly toxic substances, posing a significant threat to worker health. Currently, there are two methods to achieve this:

[0003] 1. Manual bag breaking and placement;

[0004] 2. Fix the bag breaking device directly above the opening of the production container, and then use a robotic arm to grab the powder bag and slide it through the bag breaking blade.

[0005] The first method mentioned above requires workers to wear protective clothing, resulting in a harsh working environment and ongoing risks to their lives and health. It is gradually being phased out in the industry.

[0006] The second method above requires the bag-breaking device to be fixedly installed at the feeding port, which is not compatible with the industrial production needs of adding multiple materials (powder, liquid, etc.) at the same feeding port. After the feeding of one material is completed, it hinders the feeding of subsequent materials, which greatly affects the factory's production efficiency.

[0007] The existing technology, which is fixedly installed above the feeding port, can complete the bag-breaking task, but it has the following defects and shortcomings:

[0008] First, existing production equipment needs to be modified, but only a few pieces of production equipment can be modified, so this solution has a narrow scope of application.

[0009] Second, it cannot adapt to the needs of feeding various materials, and it obstructs the subsequent feeding process of different types of powders, thus slowing down production efficiency.

[0010] Third, the toxic residues in powder or liquid materials pose a significant threat to workers' health.

[0011] Therefore, an intelligent vision-controlled bag-breaking device is proposed for use in chemical, plastic and other industrial fields to realize automatic bag breaking and feeding of powder bags, and can adapt to the feeding needs of various materials. Summary of the Invention

[0012] The purpose of this invention is to provide an intelligent vision-controlled industrial bag-breaking device to solve the problems of narrow applicability, inability to adapt to various material feeding requirements, and threats to worker health of existing industrial bag-breaking devices. The invention provides an intelligent vision-controlled industrial bag-breaking device that can adapt to various material feeding requirements without modifying existing equipment and protect worker health.

[0013] To achieve the above objectives, the present invention provides the following technical solution: an intelligent vision-controlled industrial bag-breaking device, comprising a vision recognition module, a control module, a mechanical execution module, a drive module, and a safety protection module;

[0014] The visual recognition module includes a high-definition industrial camera and a neural network expert model. The high-definition industrial camera is fixedly installed on the side of the production container to clearly capture image information around the feeding port. The image information is transmitted to the neural network expert model in real time via the network. The neural network expert model can perform real-time high-precision recognition and early warning of personnel operation behavior, material and equipment status in industrial scenarios.

[0015] The control module includes an abnormal behavior risk prediction unit and a real-time behavior compliance detection unit, which are respectively connected to the visual recognition module and the drive module.

[0016] The mechanical execution module includes a bag-breaking knife, a folding mechanism, a weight sensor, and a moving slide rail. The bag-breaking knife is fixedly installed on the knife seat at the front end of the folding mechanism. The folding mechanism includes multiple hinges and multiple connecting rods. The folding angle of the folding mechanism is adjustable. The moving slide rail is driven by the drive module to move along a fixed bracket next to the production container. The weight sensor is fixedly installed below the production container.

[0017] The drive module includes a servo motor and a planetary reducer. The servo motor and the planetary reducer are respectively connected to the control module and the mechanical execution module, and the motor has a built-in absolute encoder.

[0018] The safety protection module includes a knife concealment cavity and a sealing device. When the bag-breaking knife is retracted, the knife can be bent and retracted into the knife concealment cavity. The sealing device includes a sealing ring installed around the knife seat and a negative pressure air extraction port for sucking up dust generated during the bag-breaking process. The dust sucked in by the negative pressure air extraction port is filtered through a HEPA filter before being discharged.

[0019] Preferably, the abnormal behavior risk prediction unit and the real-time behavior compliance detection unit are specifically connected to the visual recognition module and the drive module via a CAN bus.

[0020] Preferably, the bag-breaking knife is made of Cr12MoV high-speed steel, vacuum quenched to HRC60-62, with a blade angle of 30° and a length of 150mm.

[0021] Preferably, the hinge is a high-strength aluminum alloy hinge, and there are four hinges; the connecting rod is a carbon fiber connecting rod, and there are three connecting rods.

[0022] Preferably, the movable slide rail is a ball screw slide table.

[0023] Preferably, the movable slide rail is a magnetic levitation slide rail.

[0024] Preferably, the servo motor is an AC servo motor with a power of 500W.

[0025] Preferably, the output shaft torque of the planetary reducer is ≥50 N·m.

[0026] Preferably, an electromagnetic lock is fixedly installed at the opening of the tool concealment cavity.

[0027] Preferably, the safety protection module further includes a gas detection sensor, which can be a VOC sensor.

[0028] Beneficial effects

[0029] This invention provides an intelligent vision-controlled industrial bag-breaking device, which has the following beneficial effects:

[0030] 1. Wider adaptability: No need to modify existing production equipment, it can be installed next to the container via a movable slide rail and can be adapted to more than 90% of the feeding ports of existing production lines, solving the problem of the narrow adaptability of traditional fixed devices.

[0031] 2. Higher production efficiency: Supports seamless switching between various material feeding methods, with a total time of ≤2s from bag breaking to collection, saving more than 80% of switching time compared to traditional devices, and improving the overall efficiency of the production line by 30%.

[0032] 3. Enhanced Safety: Behavioral compliance detection can identify violations 0.5 seconds in advance, with an emergency stop response time of ≤20ms, reducing the accident rate by 90% compared to traditional sensor protection; the sealing device ensures dust leakage is ≤0.1mg / m³. 3 It meets the occupational exposure limits for hazardous factors in industrial workplaces as required by GBZ2.1-2019, reducing worker health risks by 95%.

[0033] 4. Higher level of intelligence: Closed-loop control of bag breaking effect ensures a bag breaking success rate of ≥99.5% (95% for traditional devices) and a residue of ≤3%, reducing material waste and subsequent cleaning costs.

[0034] 5. No manual intervention is required throughout the process. Visual recognition automatically triggers equipment actions, and workers only need to operate the material loading process normally, reducing training costs by 50%. Attached Figure Description

[0035] Figure 1 This is a structural block diagram of an intelligent vision-controlled industrial bag-breaking device proposed in this invention.

[0036] Figure 2 This is a schematic diagram of the folded state of the mechanical execution module of an intelligent vision-controlled industrial bag-breaking device proposed in this invention;

[0037] Figure 3 This is a three-dimensional structural diagram of the bag-breaking blade and folding mechanism of an intelligent vision-controlled industrial bag-breaking device proposed in this invention;

[0038] Figure 4 This is a flowchart of the neural network expert model for an intelligent vision-controlled industrial bag-breaking device proposed in this invention.

[0039] Figure 5 This is a schematic diagram of the closed-loop control of the bag-breaking effect feedback of an intelligent vision-controlled industrial bag-breaking device proposed in this invention.

[0040] Figure 3 In the middle: 1. Bag-breaking knife; 2. Folding mechanism; 3. Knife holder; 4. Feeding port. Detailed Implementation

[0041] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0042] Example 1, please refer to Figure 1-5 The present invention provides a technical solution: an intelligent vision-controlled industrial bag-breaking device, comprising a vision recognition module, a control module, a mechanical execution module, a drive module, and a safety protection module;

[0043] The visual recognition module includes a high-definition industrial camera and a neural network expert model. The high-definition industrial camera is fixedly installed on the side of the production container to clearly capture image information around the feeding port 4. The high-definition industrial camera has a resolution of 1920×1080, a frame rate of 30fps, and uses a global shutter to avoid motion blur. The lens focal length is adjustable from 8mm to 25mm. It is installed on the side of the production container at a distance of 1.5 meters from the feeding port 4 and can clearly capture image information within a 3m×2m range around the feeding port 4. The image information includes worker operation behavior, feeding process start signals, etc. The image information is transmitted in real time to the neural network expert model through a gigabit Ethernet network. The neural network expert model can perform real-time high-precision recognition and early warning of personnel operation behavior, material and equipment status in industrial scenarios.

[0044] The training steps for the neural network expert model are as follows: Based on the improved YOLOv8 network architecture, at least 50,000 labeled images of industrial scenes (including worker operations, material status, equipment status, etc.) are used for training. The dataset contains at least 20 typical worker actions (such as raising a hand to start equipment, wearing protective equipment, reaching into danger zones, etc.) and at least 10 types of material loading scenarios (powder, liquid, etc.), and is expanded to at least 100,000 images through data augmentation techniques such as random cropping, brightness perturbation (±30%), and Gaussian noise (σ=0.02). The training uses the CSPDarknet53 backbone network to extract features, introduces a focus mechanism (CBAM) to enhance the feature weights of key regions, and uses the CIoU loss function to optimize bounding box regression. It is trained for 200 rounds on an NVIDIA A100 graphics card, achieving a behavior recognition accuracy of 98.5%.

[0045] The neural network expert model's recognition steps are as follows: After receiving high-definition industrial camera images, preprocessing (denoising and distortion correction) is performed first. Then, multi-scale feature maps (80×80, 40×40, 20×20) are extracted through the backbone network. After feature fusion, the images are input into the detection head, and the target category (e.g., "worker raises hand" or "not wearing a safety helmet"), confidence score, and bounding box coordinates are output. Temporal analysis of worker operation behavior is performed, and false detections are removed through a 3-frame sliding window filter. The final recognition result (including action category and confidence score ≥0.85) is output every 100ms.

[0046] The control module uses an STM32H743 microprocessor as its core, with a main frequency of 480MHz and built-in 1MB SRAM and 2MB Flash. The control module includes an abnormal behavior risk prediction unit and a real-time behavior compliance detection unit, which are respectively connected to the visual recognition module and the drive module.

[0047] The abnormal behavior risk prediction unit and the real-time behavior compliance detection unit are specifically connected to the visual recognition module and the drive module via a CAN bus (1Mbps baud rate).

[0048] The abnormal behavior risk prediction unit includes an abnormal behavior risk prediction model: a time-series prediction model built on an LSTM network, which takes the worker action sequence features of the past 500ms as input and outputs the action probability distribution in the next 1s (e.g., "switching to liquid feeding" probability 85%), with a prediction accuracy of ≥90% and triggers equipment response 300ms-500ms in advance.

[0049] The real-time behavior compliance detection unit includes a real-time behavior compliance detection algorithm: it has 10 built-in industrial safety rules (such as "not wearing a safety helmet → violation" and "hands entering a dangerous area → high risk"). The algorithm identifies the results through a neural network expert model and matches them with the rule base. When a violation is detected, a three-level response is triggered within 20ms: ① audible and visual alarm (buzzer + red warning light); ② emergency stop of the drive module; ③ sending alarm information to the factory MES system.

[0050] See details Figure 5 The closed-loop control logic of the control module is as follows: it receives the recognition results transmitted by the vision recognition module and the status feedback (position, speed, tool status) of the mechanical execution module. When it is recognized that the worker is about to start the next loading process, it sends a control command to the drive module and dynamically adjusts the drive parameters through the PID algorithm to ensure that the action error is ≤0.5mm.

[0051] The mechanical execution module includes a bag-breaking knife 1, a folding mechanism 2, a weight sensor, and a moving slide rail. The bag-breaking knife 1 is fixedly installed on the knife holder 3 at the front end of the folding mechanism 2. The bag-breaking knife 1 is made of Cr12MoV high-speed steel, which is vacuum quenched to HRC60-62. The blade angle is 30° and the length is 150mm.

[0052] Closed-loop feedback for bag breaking effect: The vision module detects the amount of residual powder after bag breaking in real time (identifying the percentage of residual area) and the damaged area of ​​the bag (pixel percentage). Simultaneously, a weight sensor (accuracy ±5g, sampling rate 10Hz) is installed below the production container to calculate the deviation between the actual material discharge and the theoretical value (theoretical value = bag weight × filling rate). When the deviation > 5% or the residual area > 10%, the control module automatically adjusts the cutter movement speed (±10%), cutting depth (±0.5mm), and the positioning accuracy of the moving slide rail (±0.1mm) until the deviation ≤ 3%.

[0053] The folding mechanism 2 includes multiple hinges and multiple connecting rods. The folding angle of the folding mechanism 2 is adjustable from 0 to 90°, the folding time is ≤1s, the extended length of the bag-breaking knife 1 after unfolding is 300mm, and the folded length is 120mm. The moving slide rail is driven by the drive module to move along the fixed bracket next to the production container. The moving slide rail is specifically a ball screw slide table with an effective stroke of 1 meter, a positioning accuracy of ±0.05mm, a repeatability of ±0.02mm, and a maximum moving speed of 500mm / s.

[0054] The hinges are high-strength aluminum alloy hinges, there are four hinges, and they can bear a load of 50kg. The connecting rods are carbon fiber connecting rods, there are three connecting rods, and the diameter is 10mm.

[0055] The drive module includes a servo motor and a planetary reducer. The servo motor is specifically an AC servo motor with a power of 500W, preferably 500W, and a rated speed of 3000rpm. The servo motor and the planetary reducer (reduction ratio 10:1) are connected to the control module and the mechanical execution module respectively via an EtherCAT bus. The motor has a built-in absolute encoder with a resolution of 23 bits, which can provide real-time feedback of position information. The output shaft torque of the planetary reducer is ≥50N・m, ensuring that the folding mechanism 2 and the moving slide rail can still operate smoothly under a load of 20kg.

[0056] The safety protection module includes a knife concealment chamber and a sealing device. The knife concealment chamber is made of 304 stainless steel, 2mm thick, with internal dimensions of 160mm × 50mm × 50mm. When the bag-breaking knife 1 is retracted, it can bend and retract into the knife concealment chamber. The sealing device includes a fluororubber sealing ring installed around the knife holder 3 and a negative pressure exhaust port for sucking up dust generated during the bag-breaking process. The fluororubber sealing ring is temperature resistant from -20℃ to 200℃, and the flow rate of the negative pressure exhaust port is 5L / min. The dust sucked in by the negative pressure exhaust port is filtered through a HEPA filter before being discharged. The HEPA filter has a dust filtration efficiency of up to 99.97%, thus ensuring that the dust leakage is ≤0.1mg / m³. 3 .

[0057] The opening of the tool concealment chamber is fixedly equipped with an electromagnetic lock, powered by DC24V and with a suction force of 100N. By setting the electromagnetic lock, it can be ensured that the tool concealment chamber cannot be opened when not in operation.

[0058] Application in powder feeding in chemical production: This intelligent vision-controlled industrial bag-breaking device is applied to the feeding process of a highly toxic powder (such as acrylonitrile powder) in chemical production.

[0059] Visual recognition module: A high-definition industrial camera is installed 1.5 meters to the side of the production container. After specialized training, the neural network expert model has an accuracy rate of 99.2% in recognizing the action of "workers raising their hands to start the liquid feeding pump" and an accuracy rate of 98.7% in recognizing "not wearing a gas mask". The single frame processing time is 15ms.

[0060] Control module: The STM32H743 microprocessor receives vision signals via the CAN bus. When it detects that the worker is about to start liquid feeding, it issues a retract command within 45ms, with a motion control error of 0.3mm.

[0061] Mechanical execution module: The bag-breaking knife 1 achieves a 99.6% success rate in breaking 25kg kraft paper bags. After feedback closed-loop adjustment, the residual amount is controlled within 2%, and the weight deviation is 2.8%. The folding mechanism 2 has an unfolding time of 0.8s and a folding time of 0.9s, and the positioning accuracy of the moving slide rail is 0.04mm.

[0062] Drive module: When the servo motor drives the mechanical actuator module to move, the speed is smooth and without jerking, with a maximum acceleration of 0.5g.

[0063] Safety protection module: The tool concealment chamber is completely sealed after being retracted, and the sealing device keeps the dust concentration in the operating area stable at 0.08 mg / m³. 3 It is far below the occupational exposure limit (0.3 mg / m³). 3 ).

[0064] Experimental data:

[0065] It can run continuously for 8 hours, break 1200 bags, and stop without failure, reducing the number of failures by 100% compared to traditional equipment;

[0066] The switching time from bag breaking to bag folding is 1.8 seconds, which is 82% faster than the traditional device (10 seconds).

[0067] The accuracy rate of worker violation identification was 98.5%, the emergency stop response time was 18ms, and no safety accidents occurred.

[0068] The material waste rate has been reduced from 8% in traditional equipment to 2.5%, saving approximately 50,000 yuan in material costs annually.

[0069] Experiments show that the device can fully meet the feeding requirements of highly toxic chemical powders, balancing efficiency and safety.

[0070] The intelligent vision-controlled industrial bag-breaking device of the present invention can be widely used in industrial fields such as chemical, plastic, metallurgy, and pharmaceutical industries that require the breaking and feeding of powder bags.

[0071] In addition, federated learning can be introduced into neural network expert models to optimize the model in conjunction with multiple factories while protecting data privacy, thereby improving the recognition rate under complex working conditions.

[0072] The control module can be connected to an industrial internet platform, supporting remote monitoring, fault diagnosis, and parameter optimization, thereby improving the level of intelligent management.

[0073] Example 2, please refer to Figure 1-5 Including Embodiment 1, and based on Embodiment 1, the present invention provides a technical solution: the movable slide rail is specifically a magnetic levitation slide rail, and the mechanical execution module uses a magnetic levitation slide rail to replace the ball screw, further reducing noise (≤60dB) and maintenance requirements.

[0074] Example 3, please refer to Figure 1-5 Including Embodiment 2, and based on Embodiment 2, the present invention provides a technical solution: the safety protection module further includes a gas detection sensor, which can be a VOC sensor. By adding a gas detection sensor, the safety protection module can achieve coordinated monitoring of dust and toxic gases.

[0075] Workflow:

[0076] 1. During the bag breaking operation, the control module drives the mechanical execution module to move along the sliding rail to the bag breaking position (positioning time ≤ 0.5s), the folding mechanism 2 unfolds (time 0.8s), the bag breaking cutter 1 extends, and breaks the powder bag grasped by the robot arm (cutter movement speed 300mm / s), and puts the powder into the production container.

[0077] 2. The visual recognition module acquires images in real time, and the neural network expert model outputs the recognition result every 100ms.

[0078] 3. When the system detects that a worker is about to start the next material feeding process (such as raising his hand to walk towards the liquid feeding valve), or detects a violation (such as not wearing gloves), the vision recognition module transmits the result to the control module.

[0079] 4. Within 50ms, the control module issues a command, and the drive module drives the mechanical execution module to perform the following actions: the bag-breaking knife 1 is put into the knife hiding cavity (electromagnetic lock is locked), the folding mechanism 2 folds (takes 0.9s), and the whole thing moves along the moving slide rail to the standby position away from the feeding port 4 (distance from the feeding port ≥ 1.5 meters).

[0080] 5. When the bag needs to be broken again, the control module receives a start signal (such as visual recognition that the powder bag is in place), drives the mechanical execution module back to the working position, and repeats step 1.

[0081] The accompanying drawings of the embodiments disclosed in this invention only involve the structures involved in the embodiments of this disclosure. Other structures can refer to the general design. In the absence of conflict, the same embodiment and different embodiments of this invention can be combined with each other.

[0082] In conclusion, the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. An intelligent vision-controlled industrial bag-breaking device, characterized in that: It includes a vision recognition module, a control module, a mechanical execution module, a drive module, and a safety protection module; The visual recognition module includes a high-definition industrial camera and a neural network expert model. The high-definition industrial camera is fixedly installed on the side of the production container to clearly capture image information around the feeding port. The image information is transmitted to the neural network expert model in real time via the network. The neural network expert model can perform real-time high-precision recognition and early warning of personnel operation behavior, material and equipment status in industrial scenarios. The control module includes an abnormal behavior risk prediction unit and a real-time behavior compliance detection unit, which are respectively connected to the visual recognition module and the drive module. The mechanical execution module includes a bag-breaking knife, a folding mechanism, a weight sensor, and a moving slide rail. The bag-breaking knife is fixedly installed on the knife seat at the front end of the folding mechanism. The folding mechanism includes multiple hinges and multiple connecting rods. The folding angle of the folding mechanism is adjustable. The moving slide rail is driven by the drive module to move along a fixed bracket next to the production container. The weight sensor is fixedly installed below the production container. The drive module includes a servo motor and a planetary reducer. The servo motor and the planetary reducer are respectively connected to the control module and the mechanical execution module, and the motor has a built-in absolute encoder. The safety protection module includes a knife concealment cavity and a sealing device. When the bag-breaking knife is retracted, the bag-breaking knife can be bent and retracted into the knife concealment cavity. The sealing device includes a sealing ring installed around the knife seat and a negative pressure air extraction port for sucking up dust generated during the bag-breaking process. The dust sucked in by the negative pressure air extraction port is filtered through a HEPA filter before being discharged. The workflow of the intelligent vision-controlled industrial bag-breaking device includes: Step 1. During the bag breaking operation, the control module drives the mechanical execution module to move along the sliding rail to the bag breaking position through the drive module. The folding mechanism unfolds, the bag breaking cutter extends, and the bag breaking is performed on the powder bag grasped by the robot arm, and the powder is put into the production container. Step 2. The visual recognition module acquires images in real time, and the neural network expert model outputs the recognition result every 100ms; Step 3. When the vision recognition module detects that a worker is about to start the next material loading process, or detects a violation, the vision recognition module transmits the result to the control module; Step 4. Within 50ms, the control module issues a command, and the drive module drives the mechanical execution module to perform the following actions: the bag-breaking knife is retracted into the knife concealment cavity, the folding mechanism is folded, and the whole unit moves along the moving slide rail to the standby position away from the feeding port. Step 5. When the bag needs to be broken again, the control module receives the start signal and drives the mechanical actuator to return to the working position, repeating step 1.

2. The intelligent vision-controlled industrial bag-breaking device according to claim 1, characterized in that: The abnormal behavior risk prediction unit and the real-time behavior compliance detection unit are specifically connected to the visual recognition module and the drive module via a CAN bus.

3. The intelligent vision-controlled industrial bag-breaking device according to claim 1, characterized in that: The bag-breaking knife is made of Cr12MoV high-speed steel, which is vacuum quenched to HRC60-62, with a blade angle of 30° and a length of 150mm.

4. The intelligent vision-controlled industrial bag-breaking device according to claim 1, characterized in that: The hinges are specifically high-strength aluminum alloy hinges, and there are four hinges in total. The connecting rods are specifically carbon fiber connecting rods, and there are three connecting rods in total.

5. The intelligent vision-controlled industrial bag-breaking device according to claim 1, characterized in that: The movable slide rail is specifically a ball screw slide table.

6. The intelligent vision-controlled industrial bag-breaking device according to claim 1, characterized in that: The movable slide rail is specifically a magnetic levitation slide rail.

7. The intelligent vision-controlled industrial bag-breaking device according to claim 1, characterized in that: The servo motor is specifically an AC servo motor with a power of 500W.

8. The intelligent vision-controlled industrial bag-breaking device according to claim 1, characterized in that: The output shaft torque of the planetary reducer is ≥50N. . m.

9. The intelligent vision-controlled industrial bag-breaking device according to claim 1, characterized in that: An electromagnetic lock is fixedly installed at the opening of the tool concealment cavity.

10. An intelligent vision-controlled industrial bag-breaking device according to any one of claims 1-9, characterized in that: The safety protection module also includes a gas detection sensor, which is a VOC sensor.

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