A method and system for monitoring safety during the production process of cylinder equipment

Abstract

The present invention discloses a method and system for monitoring the safety of the production process of cylindrical equipment, which relates to the field of industrial safety monitoring technology, including: real-time acquisition of video streams of the production area of ​​cylindrical equipment and uploading; automatic identification of which process segment of the production process the current operation content belongs to based on the video stream information; if no identification result is found, returning to step one and continuously looping monitoring; if there is an identification result, calling the corresponding detection algorithm based on the identified process segment to identify the safety risks existing in the production of the process segment; outputting the result of the safety monitoring based on the obtained detection result according to the set risk judgment conditions; if the monitoring result is that there is no safety risk, returning to step one; if the monitoring result is that there is a safety risk, triggering a safety alarm. The present invention can effectively prevent the occurrence of safety accidents and reduce the false recognition rate by classifying and identifying different scenarios in the entire production process according to their risk characteristics, thereby improving the overall production safety level and production efficiency.

Description

Technical Field

[0001] The present invention relates to the technical field of industrial safety monitoring, and in particular to a method and system for monitoring the safety of a production process of a drum-type equipment. Background Art

[0002] Cylinder equipment is widely used in the industrial field. For example, the hot air moistening cylinder can be used to increase the temperature and humidity of tobacco leaves in the tobacco processing industry; the drying cylinder of the tobacco drying machine can be used to evenly dry tobacco.

[0003] The production process for cylindrical equipment is also relatively complex. Specifically, the steel plate must first be rolled into a roll using a plate rolling machine. Several rolls are then welded together to form a large cylinder. Finally, the required components are welded inside the cylinder. This process presents numerous potential safety risks, necessitating certain protective measures. For example, during hoisting operations, the hoisted object may slip, so no one should stand under it. When operating a plate rolling machine, operators must wear safety goggles to avoid injury from splashes. When welding externally, welding overalls, protective gloves, and a protective mask must be worn. Furthermore, debris must not be placed on the surface of the roll to prevent slippage and injury to those below. Safety belts must be worn when welding at height. When welding internally, fans are used to assist ventilation in the enclosed workspace, and welding overalls, protective gloves, and a protective mask must be worn.

[0004] Current manual monitoring methods struggle to accurately monitor the entire manufacturing process in real time and are prone to missed and false detections. There are no dedicated safety monitoring methods or systems for barrel-type equipment production. Furthermore, most current visual inspection methods are unable to adapt to diverse and changing scenarios, focusing solely on a single scenario. These methods are complex and inefficient. For example, patent CN202310818645.5 proposes a safe welding system and method based on Yolov5, including: a robotic arm and a control module, wherein the robotic arm is equipped with a welding gun, and the control module is connected to the control end of the robotic arm to control the robotic arm to weld the workpiece to be welded; a camera input module for acquiring video source data of the work site; a safety helmet detection module, which is equipped with a first target detection model based on Yolov5, for identifying the workers in the video source data, and identifying whether the workers are wearing safety helmets, and outputting safety helmet detection results; a workpiece quality inspection module, which is equipped with a second target detection model based on Yolov5, for performing quality inspection on the workpieces in the video source data, and outputting welding quality inspection results; a display module for visually displaying the safety helmet detection results and the welding quality inspection results.

[0005] Therefore, there is an urgent need for a method that can automatically identify safety risks in the entire production process of drum equipment to improve production safety and efficiency. Summary of the Invention

[0006] The purpose of the present invention is to provide a safety monitoring method and system specifically for the production process of cylindrical equipment in response to the above-mentioned problems. By monitoring the safety behavior of the entire cylindrical equipment production process in real time throughout the entire process, it can effectively warn and prevent the occurrence of safety accidents, thereby improving the overall production safety level and production efficiency; and in monitoring and identification, segmented identification is performed, first judging the process segment, and then further identifying and monitoring the different safety risks of different process segments, effectively reducing the complexity of identification, and more quickly and accurately identifying safety risks in the production process.

[0007] The technical solutions of the present invention are as follows:

[0008] The present invention discloses a method for safety monitoring of a production process of a drum type equipment, comprising the following steps:

[0009] Step 1: Collect and upload the video stream of the drum equipment production area in real time;

[0010] Step 2: Automatically identify which process segment of the production process the current operation belongs to based on the video stream information. The process segments include the hoisting segment, the roll forming segment, the external welding segment, and the internal welding segment. If no identification result is found, return to step 1 and continue the loop monitoring. If an identification result is found, the corresponding detection algorithm is called based on the identified process segment to identify safety risks in the production of the process segment.

[0011] Step 3: Output the security monitoring result based on the identification result obtained in step 2 according to the set risk judgment conditions; if the monitoring result is no security risk, return to step 1 and continue the loop monitoring; if the detection monitoring shows that there is a security risk, trigger the security alarm, and then return to step 1 and continue the loop monitoring.

[0012] Furthermore, the identification characteristics of the hoisting section in step 2 are: the crane is performing hoisting operations and there are objects hoisted; the identification characteristics of the drum forming section are: the plate rolling machine is in operation and the steel plate is being rolled; the identification characteristics of the external welding section are: the staff are performing welding operations on the outside of the drum; and the identification characteristics of the internal welding section are: the staff are performing welding operations on the inside of the drum.

[0013] Furthermore, the specific implementation method of step 2 includes:

[0014] S21: Extract the videos collected in step 1, including the videos of lifting operations, plate rolling, external welding of the drum, and internal welding of the drum; divide the videos into segments of every 10 frames, label each segment with a category, and construct an action recognition dataset;

[0015] S22: Extract target images containing a hook, a reel, a human body, goggles, a protective mask, protective gloves, welding overalls, a safety belt, debris, and a fan from the video collected in step 1; annotate the targets in the target images, generate an annotation file containing target location and category information, and construct a target detection dataset;

[0016] S23: Use action recognition algorithm and target detection algorithm to train action recognition dataset and target detection dataset respectively;

[0017] S24: Use the trained action recognition model and target detection model to identify which process segment the current job content belongs to and the security risks of the process segment.

[0018] Furthermore, in step 2, if the current process section is the hoisting section, detecting whether there is a person standing under the hoisted object specifically includes:

[0019] First, identify the hook and the person, and obtain the coordinate information of the human body detection frame as [ X min-p , Y min-p , X max-p , Y max-p ], the coordinate information of the hook detection frame is [ X min-h , Y min-h , X max-h , Y maxh ];

[0020] Secondly, calculate the horizontal Euclidean distance between the center points of the hook detection frame and the human body detection frame.

[0021]

[0022] Finally, if the horizontal Euclidean distance d Less than the specified threshold d th , it means that there is a person standing under the hanging object; if the horizontal Euclidean distance d Greater than the specified threshold d th , it means that there is no one standing under the suspended object.

[0023] Furthermore, in step 2, if the current process segment is the roll forming segment, check whether the plate rolling machine operator is wearing goggles; if the current process segment is the external welding segment, check whether there are debris on the roll, whether the welder is wearing welding work clothes, protective gloves and protective masks, and whether a safety belt is worn for high-altitude work; if it is detected that there are debris on the roll or the welder is not wearing protective equipment, directly output that there is a safety risk; if there is no debris on the roll and the welder is wearing protective equipment, further determine whether there is high-altitude work, and if there is no high-altitude work, output that there is no safety risk; if there is high-altitude work, further determine whether a safety belt is worn, and if not, output that there is a safety risk.

[0024] Further, the judgment and identification method of high-altitude operation:

[0025] S31: Obtain the coordinate information of the human body detection frame as [ X min-p , Y min-p , X max-p , Y max-p ], the coordinate information of the roll detection frame is [ X min-r , Y min-r , X max-r , Y max-r ];

[0026] S32: Calculate the height difference between the human body and the center point of the reel: ;

[0027] S33: Calculate the intersection-over-union ratio of the human body and the roll: ,in, ; S P and S R are the areas of the human body detection frame and the roll detection frame respectively;

[0028] S32: Based on the intersection and union of the human body detection frame and the roll detection frame IOU and the height difference of the center point h Determine whether a person is working at height; the conditions for determining whether a person is working at height on the reel are: h>h th and 0 <IOU<IOU th , h th is the height difference threshold between the human body and the center point of the reel. IOU th The threshold of intersection ratio between the human body and the reel is set.

[0029] Furthermore, in step 2, if the current process section is the internal welding section, it is detected whether the welder is wearing welding overalls, protective gloves and protective masks, and whether a fan is placed outside the reel to assist ventilation; the wearing of welding overalls, protective gloves and protective masks, and the placement of the fan are detected in parallel. As long as any of them is not detected, a safety alarm is triggered.

[0030] Furthermore, in step three, the risk assessment conditions include: people standing under the suspended objects; the plate rolling machine operator not wearing goggles; the welder not wearing welding overalls, protective gloves and protective masks; the welder not wearing a safety belt when working at height; the welder working inside the cylinder without fan-assisted ventilation.

[0031] The present invention also discloses a safety monitoring system for the production process of drum equipment, comprising a video acquisition module, a safety monitoring module and an alarm module;

[0032] The video acquisition module is used to collect the video stream of the production area in real time;

[0033] The safety monitoring module is used to first identify which process segment of the production process the current operation content belongs to, and then use corresponding methods according to different process segments to detect safety risks in the production of the process segment;

[0034] The alarm module generates an alarm based on the type of event triggered by the security risk.

[0035] Furthermore, the safety monitoring module includes a classification submodule and a detection submodule;

[0036] The classification submodule uses a video understanding algorithm to automatically identify the process segment of the current operation, such as the hoisting segment, the roll forming segment, the external welding segment, or the internal welding segment;

[0037] The detection submodule calls corresponding detection algorithms for each process segment to identify security risks of different process segments.

[0038] In summary, due to the adoption of the above technical solution, the beneficial effects of the present invention are:

[0039] 1. The present invention realizes real-time and accurate monitoring of the entire production process of cylindrical equipment, and is dedicated to the production of cylindrical equipment, solving the problem that there is no safety monitoring system dedicated to the production process of cylindrical equipment.

[0040] 2. The present invention can automatically monitor and identify safety risks in the production process, effectively warn and prevent the occurrence of safety accidents, and solve the problem that manual monitoring methods are difficult to achieve real-time and accurate monitoring of the entire processing process, and are prone to missed detections and false detections.

[0041] 3. The present invention improves the efficiency of video surveillance recognition. In video recognition processing, different from the existing method of judging and identifying all behaviors and features in the production process, based on the characteristics of the production process of cylinder equipment, the process segment is first judged, and then the specific process segment is judged. The entire process is carried out in segments, and then the analysis and judgment can be focused, which simplifies the judgment and recognition methods, is more targeted, and has higher processing efficiency.

[0042] 4. The present invention designs a detection algorithm specifically for the production process of cylinder equipment, which significantly improves the detection efficiency.

[0043] 5. The present invention can effectively prevent the occurrence of safety accidents and reduce the misidentification rate by classifying and identifying different scenarios in the entire production process according to their risk characteristics, thereby improving the overall production safety level and production efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] The present invention will now be described by way of example with reference to the accompanying drawings, in which:

[0045] Figure 1 It is a flow chart of a method for safety monitoring of a production process of a cylinder type equipment according to the present invention.

[0046] Figure 2 Schematic diagram of the steps for identifying safety risks of external welding sections in an embodiment of the present invention.

[0047] Figure 3 It is a flow chart of a safety monitoring system for a production process of a cylinder type equipment according to the present invention. DETAILED DESCRIPTION

[0048] All features disclosed in this specification, or all steps in the disclosed methods or processes, except mutually exclusive features and / or steps, can be combined in any manner.

[0049] Any feature disclosed in this specification (including any appended claims and abstract), unless otherwise stated, may be replaced by other equivalent or similar features. In other words, unless otherwise stated, each feature is only an example of a series of equivalent or similar features.

[0050] The features and performance of the present invention are further described in detail below with reference to the embodiments.

[0051] The present invention provides a method for safety monitoring of a production process of a drum type equipment, comprising the following steps:

[0052] Step 1: Collect and upload the video stream of the drum equipment production area in real time;

[0053] Step 2: Automatically identify which process segment of the production process the current operation belongs to based on the video stream information. The process segments include the hoisting segment, the roll forming segment, the external welding segment, and the internal welding segment. If no identification result is found, return to step 1 and continue the loop monitoring. If an identification result is found, the corresponding detection algorithm is called based on the identified process segment to identify safety risks in the production of the process segment.

[0054] Step 3: Output the security monitoring result based on the identification result obtained in step 2 according to the set risk judgment conditions; if the monitoring result is no security risk, return to step 1 and continue the loop monitoring; if the monitoring result is a security risk, trigger a security alarm, and then return to step 1 and continue the loop monitoring.

[0055] like Figure 1 As shown, in one embodiment, a method for safety monitoring of a production process of a drum type equipment is disclosed, and the specific steps include:

[0056] Step 1: The video acquisition module collects the video stream of the drum equipment production area in real time and inputs it into the safety monitoring module.

[0057] Step 2: The classification submodule in the safety monitoring module automatically identifies the process segments of the current operation content based on the video stream information, including the lifting segment, roll forming segment, external welding segment, and internal welding segment; if there is no classification result, it means that there is no operation in the production area, and the system returns to step 1 to continue the loop monitoring.

[0058] Step 3: The detection submodule calls the corresponding detection algorithm for the current process segment to identify security risks.

[0059] If the current process section is the hoisting section, check whether there is anyone standing under the hoisted object;

[0060] If it is the roll forming section, check whether the plate rolling machine operator is wearing goggles;

[0061] If it is an external welding section, check whether there are any debris on the reel; whether the welders are wearing welding overalls, protective gloves and protective masks; and whether they are wearing safety belts when working at heights.

[0062] If it is an internal welding section, check whether the welders are wearing welding overalls, protective gloves and protective masks, and whether there is a fan outside the reel to assist ventilation.

[0063] Step 4: Analyze the identification results output in step 3 and output the safety monitoring results according to the set risk judgment conditions; if the result is no safety risk, return to step 1 and continue the loop monitoring; if there is a safety risk, trigger the safety alarm, and then return to step 1 and continue the loop monitoring.

[0064] The risk assessment conditions include: people standing under the suspended objects; the plate rolling machine operator not wearing goggles; the welder not wearing welding overalls, protective gloves and protective masks; the welder not wearing a safety belt when working at height; the welder not having fans to assist ventilation when working inside the cylinder.

[0065] Step 5: The alarm module broadcasts the event in the production area in the form of a voice broadcast according to the type of event triggered; at the same time, it sends a notification to the workshop management personnel so that they can take corresponding measures in time.

[0066] The specific implementation methods of step 2 include:

[0067] 1. Data collection: Collect operation videos of lifting operations, plate rolling by the plate rolling machine, external welding of the roll, and internal welding of the roll.

[0068] 2. Data Processing: The video is divided into segments of 10 frames each, and each segment is labeled with a category to construct an action recognition dataset. The video segments are labeled as hoisting, roll forming, external welding, and internal welding. The hoisting segment is characterized by the crane performing a hoisting operation with an object. The roll forming segment is characterized by the plate rolling machine being in operation and the steel plate being rolled. The external welding segment is characterized by workers welding the outside of the roll. The internal welding segment is characterized by workers welding the inside of the roll.

[0069] 3. Model training: The TSM-ResNet18 action recognition algorithm is used to train the action recognition dataset, and the weight model trained on the public dataset Kinetics is used as the pre-training model.

[0070] 4. Process segment identification: The trained action recognition model is used to analyze the video stream information collected in step 1 in real time to identify the process segment of the current operation content.

[0071] The specific implementation methods of step three include:

[0072] 1. Data collection: Extract images of objects such as hooks, reels, human bodies, goggles, protective masks, protective gloves, welding overalls, safety belts, debris, and fans from the video.

[0073] 2. Data processing: Use image annotation tools to annotate the objects in the image, generate annotation files containing the object location and category information, and build an object detection dataset.

[0074] 3. Model Training: The yolov8n object detection algorithm was trained on the object detection dataset. A weighted model trained on the public coco128 dataset was used as a pre-training model. Four algorithm models were obtained, including the hoisting segment algorithm, the roll forming segment algorithm, the external welding segment algorithm, and the internal welding segment algorithm. The hoisting segment algorithm was trained on objects such as the human body, the roll, and the hook; the roll forming segment algorithm was trained on objects such as goggles and the human body; the external welding segment algorithm was trained on objects such as the human body, protective mask, protective gloves, welding overalls, safety belts, and debris; and the internal welding segment algorithm was trained on objects such as the human body, protective mask, protective gloves, welding overalls, and fans.

[0075] 4. Safety risk identification: Based on the process segment identified in step 2, enable the corresponding detection algorithm to identify the safety risks in the production of the process segment.

[0076] like Figure 2 As shown, in the external welding section of step 3: if it is detected that the protective equipment (welding overalls, protective gloves and protective mask) is not worn or there is debris on the reel, a safety risk is directly output; if there is no debris on the reel and the welder is wearing protective equipment, it is further determined whether there is any high-altitude work. If there is no high-altitude work, it is output that there is no safety risk; if there is any high-altitude work, it is further determined whether a safety belt is worn. If not, a safety risk is output.

[0077] In step 3, the internal welding section: welding overalls, protective gloves and protective masks, and fans are tested in parallel. If any of them is not detected, a safety alarm is triggered.

[0078] The detection method of the person standing under the hanging object in step 3 is: first identify the hook and the person, and obtain the coordinate information of the human body detection frame as [ X min-p , Y min-p , X max-p , Y max-p ], the coordinate information of the hook detection frame is [ X min-h , Y min-h , X max-h , Y maxh ]; Secondly, calculate the horizontal Euclidean distance between the center points of the hook detection frame and the human body detection frame , if the horizontal Euclidean distance d Less than the specified threshold d th , it means that there is a person standing under the hanging object; if the horizontal Euclidean distance d Greater than the specified threshold d th, it means that there is no one standing under the suspended object.

[0079] The method for identifying high-altitude work in step 3 is: according to the intersection and union of the human body detection frame and the roll detection frame, IOU The height difference between the center point and the human body is used to determine whether the person is working at height; the coordinate information of the human body detection frame is [ X min-p , Y min-p , X max-p , Y max-p ], the coordinate information of the roll detection frame is [ X min-r , Y min-r , X max-r , Y max-r ];

[0080] Height difference between human body and center point of reel:

[0081] ;

[0082] The intersection ratio of the human body and the roll:

[0083] ;

[0084] in:

[0085]

[0086] and are the areas of the human detection frame and the roll detection frame respectively.

[0087] The conditions for judging whether a person is working at height on the drum are: h > h th and 0 <IOU<IOU th This means that the person is above the reel and there is a certain overlap between the reels, ensuring that the person is indeed near the reel and avoiding misjudging people in the distance as climbing.

[0088] In the embodiment of the present invention, d th =200Px, h th =160Px, IOU th =0.3, the threshold can be adjusted according to the actual engineering environment d th 、 h th and IOUth .

[0089] like Figure 3 As shown, the present invention also discloses a safety monitoring system for the production process of drum-type equipment, including a video acquisition module, a safety monitoring module and an alarm module; the safety monitoring module includes a classification submodule and a detection submodule. The video acquisition module collects the video stream of the production area in real time. The classification submodule in the safety monitoring module uses a video understanding algorithm to automatically identify the process segments described in the current operation (hoisting segment, roll forming segment, external welding segment, internal welding segment). The detection submodule calls the corresponding detection algorithm for each process segment to identify the safety risks of different process segments; the alarm module broadcasts the corresponding voice according to the type of event triggered by the safety risk, and sends a notification to the workshop management personnel at the same time. By classifying and identifying different scenarios in the entire production process according to their existing risk characteristics, it is possible to effectively warn and prevent the occurrence of safety accidents, reduce the misidentification rate, and thus improve the overall production safety level and production efficiency.

[0090] The above-described embodiments merely represent specific implementation methods of the present application. While the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of protection of the present application. It should be noted that a person skilled in the art would be able to make numerous variations and improvements without departing from the technical concept of the present application, and all such variations and improvements fall within the scope of protection of the present application.

Claims

1. A method for safety monitoring of the production process of cylinder equipment, characterized in that: The following steps are involved: Step 1: Collect and upload the video stream of the drum equipment production area in real time; Step 2: Automatically identify which process segment of the production process the current operation belongs to based on the video stream information. The process segments include the hoisting segment, the roll forming segment, the external welding segment, and the internal welding segment. If no identification result is found, return to step 1 and continue the loop monitoring. If an identification result is found, the corresponding detection algorithm is called based on the identified process segment to identify safety risks in the production of the process segment. Step 3: Output the security monitoring results based on the identification results obtained in step 2 according to the set risk judgment conditions; If the monitoring result shows that there is no safety risk, the system returns to step 1 and continues the monitoring cycle; if the monitoring result shows that there is a safety risk, a safety alarm is triggered, and the system returns to step 1 and continues the monitoring cycle; In step 2, if the current process segment is the roll forming segment, check whether the plate rolling machine operator is wearing goggles; if the current process segment is the external welding segment, check whether there is any debris on the roll, whether the welder is wearing welding overalls, protective gloves and protective masks, and whether a safety belt is worn during high-altitude operations; if it is detected that there is debris on the roll or the welder is not wearing protective equipment, directly output that there is a safety risk; if there is no debris on the roll and the welder is wearing protective equipment, further determine whether there is high-altitude operation. If there is no high-altitude operation, output that there is no safety risk; if there is high-altitude operation, further determine whether a safety belt is worn. If not, output that there is a safety risk; Identification method for height work: S31: Obtain the coordinate information of the human body detection frame as [ X min-p , Y min-p , X max-p , Y max-p ], the coordinate information of the roll detection frame is [ X min-r , Y min-r , X max-r , Y max-r ]; S32: Calculate the height difference between the human body and the center point of the reel ; S33: Calculate the intersection-over-union ratio of the human body and the roll: ， in, ; S P and S R are the areas of the human body detection frame and the roll detection frame respectively; S34: Based on the intersection and union of the human body detection frame and the roll detection frame IOU and the height difference of the center point h Determine whether a person is working at height; The conditions for judging whether a person is working at height on the drum are: h > h th and 0 <IOU<IOU th , h th is the height difference threshold between the human body and the center point of the roll detection frame. IOU th It is the intersection-over-union ratio threshold between the human body and the roll detection frame.

2. The method for safety monitoring of the production process of cylindrical equipment according to claim 1, characterized in that: The identification characteristics of the hoisting section in step 2 are: the crane is performing hoisting operations and there are objects hoisted; the identification characteristics of the drum forming section are: the plate rolling machine is in operation and the steel plate is being rolled; the identification characteristics of the external welding section are: the staff are performing welding operations on the outside of the drum; the identification characteristics of the internal welding section are: the staff are performing welding operations on the inside of the drum.

3. The method for safety monitoring of the production process of cylindrical equipment according to claim 1 or 2, wherein: The specific implementation method of step 2 includes: S21: Extract the videos collected in step 1, including the videos of lifting operations, plate rolling, external welding of the drum, and internal welding of the drum; divide the videos into segments of every 10 frames, label each segment with a category, and construct an action recognition dataset; S22: Extract target images containing a hook, a reel, a human body, goggles, a protective mask, protective gloves, welding overalls, a safety belt, debris, and a fan from the video collected in step 1; annotate the targets in the target images, generate an annotation file containing target location and category information, and construct a target detection dataset; S23: Use action recognition algorithm and target detection algorithm to train action recognition dataset and target detection dataset respectively; S24: Use the trained action recognition model and target detection model to identify which process segment the current job content belongs to and the security risks of the process segment.

4. The method for safety monitoring of the production process of cylindrical equipment according to claim 1 is characterized in that: In step 2, if the current process section is the hoisting section, check whether there is a person standing under the hoisted object, specifically including: First, identify the hook and the person, and obtain the coordinate information of the human body detection frame as [ X min-p , Y min-p , X max-p , Y max-p ], the coordinate information of the hook detection frame is [ X min-h , Y min-h , X max-h , Y maxh ]; Secondly, calculate the horizontal Euclidean distance between the center points of the hook detection frame and the human body detection frame. Finally, if the horizontal Euclidean distance d Less than the specified threshold d th , it means that there is a person standing under the hanging object; if the horizontal Euclidean distance d Greater than the specified threshold d th , it means that there is no one standing under the suspended object.

5. The method for safety monitoring of the production process of cylindrical equipment according to claim 1 is characterized in that: In step 2, if the current process section is the internal welding section, check whether the welder is wearing welding overalls, protective gloves and protective masks, and whether there is a fan for auxiliary ventilation outside the reel; the wearing of welding overalls, protective gloves and protective masks, as well as the placement of the fan are tested in parallel. As long as any of them is not detected, a safety alarm is triggered.

6. The method for safety monitoring of the production process of cylindrical equipment according to claim 1 is characterized in that: In step three, the risk assessment conditions include: people standing under the suspended objects; the plate rolling machine operator not wearing goggles; there are debris on the reel; the welder not wearing welding overalls, protective gloves and protective masks; the welder not wearing a safety belt when working at height; the welder working inside the drum without fan-assisted ventilation.

7. A safety monitoring system for the production process of cylinder equipment, characterized in that: The method for safety monitoring of the production process of cylindrical equipment according to claim 1 comprises a video acquisition module, a safety monitoring module and an alarm module; The video acquisition module is used to collect the video stream of the production area in real time; The safety monitoring module is used to first identify which process segment of the production process the current operation content belongs to, and then use corresponding algorithms according to different process segments to detect safety risks in the production of the process segment; The alarm module generates an alarm based on the type of event triggered by the security risk.

8. The safety monitoring system for the production process of cylindrical equipment according to claim 7 is characterized in that: The safety monitoring module includes a classification submodule and a detection submodule; The classification submodule uses a video understanding algorithm to automatically identify the process segment of the current operation, such as the hoisting segment, the roll forming segment, the external welding segment, or the internal welding segment; The detection submodule calls corresponding detection algorithms for each process segment to identify security risks of different process segments.

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