A mobile hoisting equipment safety monitoring device and method

Abstract

The application discloses a kind of mobile hoisting equipment safety monitoring device and method, device includes data monitoring system, anti-overturning monitoring system, anti-collision monitoring system, hierarchical early warning system, the data monitoring system is used to monitor acquisition data, and based on the data obtained by anti-overturning monitoring system and anti-collision monitoring system carries out anti-overturning coefficient calculation and predicts the path and motion in hoisting rotation process and analysis, the hierarchical early warning system is carried out hierarchical early warning by anti-overturning coefficient and the path and motion analysis situation in hoisting rotation process.The safety state of equipment is quantitatively analyzed and early warned in the present application, reduces the experience dependence of emergency time processing to operator, improves the safety of hoisting operation.

Description

Technical Field

[0001] This invention relates to the field of hoisting construction monitoring technology. More specifically, this invention relates to a safety monitoring device and method for mobile hoisting equipment. Background Technology

[0002] Currently, mobile lifting equipment such as truck cranes, crawler cranes, and all-terrain cranes on the market mainly monitor the operating data of the equipment, including wind speed, lifting capacity, torque, height, and tilt angle. The corresponding alarm thresholds are specified according to the standards or equipment manuals. The safety status of the equipment itself needs to be judged by the driver or the person in charge based on experience.

[0003] Actual operating conditions are quite complex. For example, the ground bearing capacity may not meet the requirements, and during the operation, some outriggers may sink, or sudden winds may cause the suspended load to vibrate, resulting in changes in the lateral overturning resistance coefficient. The corresponding thresholds need to be downgraded according to the construction conditions. Currently, there is no monitoring system that can make such judgments.

[0004] To address the actual operating conditions of mobile hoisting equipment, a safety monitoring device and method for mobile hoisting equipment are developed. This device provides quantitative analysis and early warning of the equipment's safety status, reducing reliance on operators' experience in handling emergencies and improving the safety of hoisting operations. Summary of the Invention

[0005] One objective of this invention is to provide a safety monitoring device and method for mobile hoisting equipment, which quantitatively analyzes and provides early warnings of the equipment's safety status, reduces reliance on operators' experience in handling emergency situations, and improves the safety of hoisting operations.

[0006] To address the aforementioned technical problems, this invention provides a safety monitoring device for mobile hoisting equipment, comprising a data monitoring system, an anti-overturning monitoring system, an anti-collision monitoring system, and a graded early warning system. The data monitoring system is used to monitor and acquire data, and based on the acquired data, the anti-overturning monitoring system and the anti-collision monitoring system calculate the anti-overturning coefficient and predict and analyze the path and movement during the hoisting rotation process. The graded early warning system provides graded early warnings based on the anti-overturning coefficient and the analysis of the path and movement during the hoisting rotation process.

[0007] Preferably, the data monitoring system prioritizes collecting data from the hoisting equipment's own control system. If the hoisting equipment is not equipped with a communication interface or the hoisting equipment's own control system does not collect data, an external sensor is used. The data acquired by the data monitoring system includes amplitude, lifting weight, boom angle, slewing angle, crane tilt angle, wind force, wind direction, and load sway parameters.

[0008] Preferably, the anti-overturning monitoring system acquires the maximum lifting weight critical value, boom length (amplitude), boom angle, wind force, wind direction, load sway parameters, lifting weight, and crane tilt angle, calculates the stabilizing moment and compares it with the timely lateral overturning moment, thereby calculating the timely anti-overturning coefficient.

[0009] Preferably, the anti-collision monitoring system includes a solar camera, an image processing module, an alarm module, and a communication module installed at the top of the boom. The solar camera is used to acquire images of the hook's working area and transmit them to the image processing module. The image processing module preprocesses and analyzes the received images. The alarm module acquires the analysis results, generates corresponding alarm signals, and transmits them to the graded early warning system for warning and display via the communication module.

[0010] Preferably, the graded early warning system has an anti-overturning coefficient threshold of 1.2 for level 1, 1.1 for level 2, and 1 for level 3; an inclination angle threshold of 1% for level 1, 2% for level 2, and 3% for level 3; and direct voice prompts for personnel detection and collision warnings for suspended objects.

[0011] The present invention also provides a method for safety monitoring using a mobile hoisting equipment safety monitoring device, comprising the following steps: Step 1: Obtain various data through the data monitoring system and make judgments; Step 2: Calculate the overturning resistance coefficient and determine whether it meets the requirements. If it does, calculate the overturning resistance coefficient in real time. If not, analyze the reasons and issue a warning through voice prompts to determine whether rectification is required. If so, rectify; if not, continue to determine the reasons. Step 3: Determine if there is a load. If not, check the load. If there is, continue to determine if there is rotation. If there is no rotation, return. If there is rotation, continue to determine if the rotation path will collide. If there will be a collision, a voice prompt will be given to stop construction. If there will be no collision, the rotation path will be determined in real time to determine if there will be a collision. Step 4: Check the crane's various working status data to determine if they exceed the set limits. If not, monitor in real time; if so, provide a voice prompt and determine if rectification is needed.

[0012] Preferably, in step two, the method for calculating the overturning resistance coefficient is as follows: First, calculate the stabilizing moment Mstabilizing_min and the timely lateral overturning moment Mtime; second, the overturning resistance coefficient K = Mstabilizing_min / Mtime.

[0013] Preferably, the maximum lifting capacity critical value, boom length (i.e., amplitude), and boom angle are obtained, and the stabilizing torque Mstabilizing_min is calculated using the formula: Mstabilizing_min = 1.4W × L × cosα; where W is the maximum lifting capacity critical value, kN; L is the boom length, m; and α is the boom angle. The timely lateral overturning moment Mtime includes the wind load moment Mwind, the lateral moment of the suspended object Msuspended object, and the crane overturning moment Mcrane. The wind load moment Mwind is calculated by acquiring wind force, wind direction, suspended object angle, amplitude, and crane body dimensions. The lateral moment of the suspended object Msuspended object is calculated by acquiring the suspended object sway parameters and suspended weight. The crane overturning moment Mcrane is calculated by acquiring the suspended object angle, amplitude, crane tilt angle, and suspended weight. The three moments are projected onto the same direction and summed to obtain the timely lateral overturning moment Mtime.

[0014] Preferably, in step three, the collision warning process includes: First, acquire image data and perform preprocessing; Secondly, the preprocessed image data is sent to the target detection algorithm module, which uses a deep learning framework to train a detection model for personnel, suspended objects, and structures, and is configured with safety helmet detection, thereby identifying personnel under the hook and whether they are wearing safety helmets. Furthermore, based on behavior analysis algorithms, combined with trajectory prediction and speed calculation algorithms, the dynamics of personnel and suspended objects are analyzed and predicted in real time to identify whether personnel have entered dangerous areas and whether suspended objects will collide. Finally, through the anomaly detection algorithm module, a safe distance threshold is set to determine in real time whether personnel enter or approach the dangerous working area below the hook and whether a collision is about to occur, and to trigger the alarm mechanism in real time.

[0015] Preferably, it also includes monitoring the stress state of the outriggers, installing a pressure sensor at the oil outlet of the outrigger extension hydraulic cylinder to detect the pressure in real time, and issuing an early warning if the pressure change decreases to more than a set proportion.

[0016] The present invention has at least the following beneficial effects: Based on the original equipment data, this invention enables timely overturning and collision warning functions during construction operations through simple deployment and low cost. By analyzing the original data in the anti-overturning calculation, it identifies the key factors causing safety risks, provides operators with handling suggestions, and replaces operators in making quantitative judgments on the safety status of the equipment. This reduces reliance on the experience of operators, provides operational references for on-site operators, and can effectively ensure the safety of hoisting operations.

[0017] Other advantages, objectives and features of the present invention will become apparent in part from the following description, and in part from those skilled in the art through study and practice of the invention. Attached Figure Description

[0018] Figure 1 This is an overall flowchart of the present invention; Figure 2 This is a flowchart of the overturning resistance coefficient calculation method of the present invention. Detailed Implementation

[0019] To better understand the purpose, structure, and function of this invention, the invention will be further described in detail below with reference to the accompanying drawings, so that those skilled in the art can implement it based on the description.

[0020] It should be noted that, unless otherwise specified, the experimental methods described in the following embodiments are all conventional methods, and the reagents and materials described are all commercially available unless otherwise specified. In the description of this invention, the terms "lateral", "longitudinal", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", and "outer" indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing this invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this invention.

[0021] This invention provides a safety monitoring device for mobile hoisting equipment, characterized in that it includes a data monitoring system, an anti-overturning monitoring system, an anti-collision monitoring system, and a graded early warning system. The data monitoring system is used to monitor and acquire data, and based on the acquired data, the anti-overturning monitoring system and the anti-collision monitoring system calculate the anti-overturning coefficient and predict and analyze the path and movement during the hoisting rotation process. The graded early warning system provides graded early warnings based on the anti-overturning coefficient and the analysis of the path and movement during the hoisting rotation process.

[0022] The safety monitoring device is installed on the hoisting equipment, and its power supply is taken from the hoisting equipment's power supply. Its on / off state is synchronized with the hoisting equipment's operation. The safety monitoring device prioritizes collecting data from the crane's control system. For cranes without communication interfaces or with incomplete sensor data, external sensors are used. The data required by this monitoring system includes: amplitude, load, main boom angle, auxiliary boom angle, slewing angle, tilt angle, wind force, wind direction, and load sway parameters.

[0023] A solar-powered camera is installed at the top of the boom and communicates with a host computer via a wireless network. The programmable controller processes the received data and displays it on the monitoring screen. When a warning is triggered, the screen flashes as a reminder and a voice prompt is activated to announce the current warning. The voice prompt has multiple built-in languages, and the driver can select the appropriate language.

[0024] The anti-collision monitoring system includes a solar-powered camera, an image processing module, an alarm module, and a communication module installed at the top of the boom. The solar-powered camera is used to acquire images of the hook's working area and transmit them to the image processing module. The image processing module preprocesses and analyzes the received images. The alarm module acquires the analysis results, generates corresponding alarm signals, and transmits them to the graded early warning system via the communication module for early warning and display.

[0025] like Figure 1 As shown, the present invention provides a method for safety monitoring using a mobile hoisting equipment safety monitoring device, comprising the following steps: Step 1: Obtain various data through the data monitoring system and make judgments; Step 2: Calculate the overturning resistance coefficient and determine whether it meets the requirements. If it does, calculate the overturning resistance coefficient in real time. If not, analyze the reasons and issue a warning through voice prompts to determine whether rectification is required. If so, rectify; if not, continue to determine the reasons. Step 3: Determine if there is a load. If not, check the load. If there is, continue to determine if there is rotation. If there is no rotation, return. If there is rotation, continue to determine if the rotation path will collide. If there will be a collision, a voice prompt will be given to stop construction. If there will be no collision, the rotation path will be determined in real time to determine if there will be a collision. Step 4: Monitor the crane's various operating status data to determine if they exceed the set limits. If not, monitor in real time; if so, provide a voice prompt and determine if rectification is needed. Check if lifting weight, radius, height, slewing, wind speed, and operating commands exceed the set range. Issue voice prompts to operators in emergency situations to assist them in handling emergencies.

[0026] like Figure 2 As shown, the overturning resistance coefficient is calculated as follows.

[0027] Import the equipment's own load table, such as the dimensions and design parameters of the hoisting equipment itself, and calculate the equipment's rated parameters required for the overturning resistance coefficient; read the mobile hoisting equipment's own working status data and the data from its equipped sensors, such as the data obtained by the hoisting equipment's own control system, to obtain data such as wind speed, outrigger reaction force, lifting weight, lifting height, and slewing angle, and combine this with the equipment's rated parameters to achieve dynamic calculation of the overturning resistance coefficient and analyze the risk factors.

[0028] The overturning stability is calculated using the moment method: by inputting the crane's boom length (amplitude), boom angle, and the critical condition of maximum lifting weight, the stabilizing moment Mstabilizing_min, i.e., the minimum lateral overturning moment, is calculated. By comparing it with the real-time lateral overturning moment Mreal-time, the real-time overturning coefficient is calculated.

[0029] M_stable_min = 1.4W × L × cosα; where W is the maximum critical lifting weight (kN); L is the boom length (m); and α is the boom angle.

[0030] During the operation, it is necessary to consider that the lateral moment consists of three parts: wind load moment M_wind, lateral moment of the suspended object M_suspended object, and crane overturning moment M_crane.

[0031] Wind load is divided into crane main unit wind load and boom wind load: the lateral wind load moment Mwind is calculated as the projected area of ​​the crane main unit and boom perpendicular to the wind direction. The wind force, wind direction, angle and amplitude of the suspended object, and crane main body dimensions are obtained to calculate the wind load moment Mwind. Wind load moment Mwind = wind load × lever arm, wind load = standard wind pressure value × projected area.

[0032] Lateral moment of the suspended load: During the lifting process, the suspended load will deviate and sway. A camera installed at the end of the boom monitors the sway angle of the suspended load and assigns a force load M_load. The sway parameters and weight of the suspended load are obtained to calculate the lateral moment M_load. Lateral moment M_load = Horizontal inertial force generated by the sway of the suspended load × (Current working amplitude + Lateral offset distance of the suspended load). Horizontal inertial force generated by the sway of the suspended load = Total weight of the suspended load and lifting equipment × tanθ, where θ is the instantaneous sway angle of the suspended load measured by the camera.

[0033] Crane overturning moment: During operation, factors such as ground bearing capacity, support, and boom tilt can cause the crane to tilt. The tilt of the crane boom and other parts results in a lateral overturning moment Mcrane. To calculate the overturning moment Mcrane, obtain the angle and amplitude of the lifted object, the crane tilt angle, and the lifted weight. Overturning moment Mcrane = Additional lateral moment generated by the lifted weight + Additional lateral moment generated by the boom's own weight; where, the additional lateral moment generated by the lifted weight = lifted weight W × sinβ (overall crane tilt angle) × vertical height H of the hook.

[0034] Finally, the three moments are calculated in the same direction: the three moments M_wind, M_lifted object, and M_crane are projected onto one of the same directions and accumulated to obtain the maximum moment, which is the timely lateral overturning moment M_time. The overturning resistance coefficient K is then calculated as M_stability_min / M_time.

[0035] The anti-collision monitoring system includes a solar-powered camera installed at the top of the boom, which communicates with a host computer via a wireless network. Based on visual recognition technology, it identifies suspended objects, personnel, and structures to achieve anti-collision function during the slewing process, avoiding collisions with people and structures.

[0036] The collision warning detection system hardware configuration includes: a solar-powered high-definition camera, an image processing host computer, an alarm device, and a network communication module. The solar-powered high-definition camera is installed above the hook's operating area and features high resolution, a wide viewing angle, and low-light adaptability, ensuring clear imaging in all weather conditions. The processed image data is transmitted to the image processing host computer via wired or wireless means.

[0037] The image processing host computer is the core edge computing processor, employing edge computing technology to receive, preprocess, and perform preliminary analysis of image data. After receiving image data from the camera, the host computer first performs preprocessing steps, including image denoising, contrast enhancement, and color correction, to improve the detection accuracy of subsequent algorithms. The system specifically includes three algorithms: object detection algorithm, behavior analysis algorithm, and anomaly detection algorithm.

[0038] The preprocessed image data is fed into the object detection algorithm module. This module, based on a deep learning framework, uses a pre-trained personnel detection model to identify people in the images. Simultaneously, by configuring a safety helmet detection module, the system can identify whether anyone is not wearing a safety helmet, thereby further improving safety. Specifically, the object detection algorithm uses a deep learning framework to train detection models for personnel, suspended objects, and structures, and is configured with safety helmet detection, enabling efficient identification of personnel below the hook.

[0039] Building upon target detection, the behavior analysis algorithm module combines trajectory prediction and velocity calculation algorithms to perform real-time analysis of personnel dynamics. By calculating parameters such as the personnel's movement trajectory and velocity, the system can determine whether a person is approaching or has entered the danger zone below the hook. The behavior analysis algorithm, by combining trajectory prediction and velocity calculation algorithms, analyzes and predicts the dynamics of personnel and suspended loads in real time, identifying whether personnel have entered danger zones and whether a collision is likely.

[0040] The anomaly detection algorithm module monitors the relative position between personnel and the hook in real time based on a set safety distance threshold. Once personnel are detected entering or approaching the danger zone, the system immediately triggers an alarm mechanism. The anomaly detection algorithm uses a set safety distance threshold to determine in real time whether personnel have entered or approached the hazardous work area below the hook and whether a collision is imminent, and triggers the alarm mechanism accordingly.

[0041] The alarm device ensures a rapid alarm signal and sends logic to the main control platform upon detecting a dangerous situation. When the system detects an anomaly, the alarm device immediately emits an audible and visual alarm to attract the attention of on-site personnel. Simultaneously, the alarm device transmits the alarm information to the main control platform via a network communication module. Upon receiving the alarm information, the main control platform can further analyze the cause and location of the alarm and display the relevant information to management personnel through the monitoring system interface. Management personnel can take appropriate measures based on the actual situation, such as notifying on-site personnel to evacuate or suspending hook operations. Furthermore, the main control platform can record alarm information in a historical database for subsequent data analysis and accident tracing.

[0042] The network communication module employs advanced communication technologies such as 4G / 5G and Wi-Fi to achieve real-time transmission of monitoring data to the main control platform, facilitating remote monitoring and data analysis. This allows managers to monitor the safety status of the crane's operating area in real time, even when far from the site. The network communication module also supports remote configuration and upgrades. Managers can remotely set and adjust the system through the main control platform to adapt to different application scenarios and needs. Simultaneously, the network communication module supports uploading monitoring data to a cloud server for storage and analysis. Utilizing big data and artificial intelligence technologies, the system can perform in-depth mining and analysis of historical data to uncover potential safety hazards and patterns.

[0043] The graded early warning system uses a programmable logic controller (PLC) to read raw data from the equipment and sensor data, processes the data, and displays it on a monitor deployed in the driver's cab. When the equipment's safety status approaches or exceeds a threshold, the PLC triggers a voice prompt to remind the operator to take timely action.

[0044] The early warning system includes a recording function, which records all working status data of the crane at the time and video data for 30 seconds before and after the early warning at a frequency of 1Hz.

[0045] The voice prompts include Chinese, English, and other languages, which users can choose according to their needs.

[0046] The early warning items are divided into routine early warnings and special alerts. Routine early warnings include load alarms, amplitude alarms, height alarms, slewing alarms, wind speed alarms, operating instructions, and wind speed alarms.

[0047] Special alerts include outrigger status, overturning resistance coefficient, tilt angle, personnel detection, and load collision alarm. The overturning resistance coefficient is tiered at 1.2, 1.1, and 1, and the tilt angle is tiered at 1%, 2%, and 3%. Personnel detection and load collision alarms provide direct alerts.

[0048] Outrigger stress status is a unique alarm feature for mobile lifting equipment such as truck cranes with outrigger supports. A pressure sensor is installed at the outlet of the outrigger extension hydraulic cylinder to detect real-time pressure. If, during operation, the pressure suddenly decreases by a certain percentage, it indicates a potential safety hazard with the outrigger, and an alert is issued by the warning system. The outrigger stress status primarily refers to mobile lifting equipment using hydraulic support, such as truck cranes and crawler cranes. A pressure sensor is installed at the return oil pipe of the outrigger's hydraulic cylinder to detect changes in oil pressure and determine the outrigger's stress status. The detection principle is as follows: after the outrigger hydraulic cylinder has completed its support, the outrigger reaction force is directly proportional to the oil pressure and related to the rotation position of the load. When the outrigger reaction force suddenly decreases, the pressure in the corresponding outrigger hydraulic cylinder also suddenly decreases, indicating a change in the outrigger's support status.

[0049] It is understood that the present invention has been described through some embodiments, and those skilled in the art will recognize that various changes or equivalent substitutions can be made to these features and embodiments without departing from the spirit and scope of the invention. Although embodiments of the present invention have been disclosed above, they are not limited to the applications listed in the specification and embodiments. It can be applied to various fields suitable for the present invention, and other modifications can be easily implemented by those skilled in the art. Therefore, without departing from the general concept defined by the claims and their equivalents, the present invention is not limited to the specific details and examples shown and described herein.

Claims

1. A safety monitoring device for mobile hoisting equipment, characterized in that, It includes a data monitoring system, an anti-overturning monitoring system, an anti-collision monitoring system, and a graded early warning system. The data monitoring system is used to monitor and acquire data, and based on the acquired data, the anti-overturning monitoring system and the anti-collision monitoring system calculate the anti-overturning coefficient and predict and analyze the path and movement during the hoisting rotation process. The graded early warning system provides graded early warnings based on the anti-overturning coefficient and the analysis of the path and movement during the hoisting rotation process.

2. The mobile hoisting equipment safety monitoring device as described in claim 1, characterized in that, The data monitoring system prioritizes collecting data from the hoisting equipment's own control system. If the hoisting equipment is not equipped with a communication interface or the hoisting equipment's own control system does not collect data, external sensors are used. The data acquired by the data monitoring system includes amplitude, lifting weight, boom angle, slewing angle, crane tilt angle, wind force, wind direction, and load sway parameters.

3. The mobile hoisting equipment safety monitoring device as described in claim 1, characterized in that, The anti-overturning monitoring system acquires the maximum lifting weight critical value, boom length (amplitude), boom angle, wind force, wind direction, load sway parameters, lifting weight, and crane tilt angle. It calculates the stabilizing moment and compares it with the timely lateral overturning moment to calculate the timely anti-overturning coefficient.

4. The mobile hoisting equipment safety monitoring device as described in claim 1, characterized in that, The anti-collision monitoring system includes a solar-powered camera, an image processing module, an alarm module, and a communication module installed at the top of the boom. The solar-powered camera is used to acquire images of the hook's working area and transmit them to the image processing module. The image processing module preprocesses and analyzes the received images. The alarm module acquires the analysis results, generates corresponding alarm signals, and transmits them to the graded early warning system via the communication module for early warning and display.

5. The mobile hoisting equipment safety monitoring device as described in claim 1, characterized in that, The graded early warning system has an anti-overturning coefficient threshold of 1.2 for Level 1, 1.1 for Level 2, and 1 for Level 3; an inclination angle threshold of 1% for Level 1, 2% for Level 2, and 3% for Level 3; and direct voice prompts for personnel detection and collision warnings for suspended objects.

6. A method for safety monitoring using a mobile hoisting equipment safety monitoring device, characterized in that, Includes the following steps: Step 1: Obtain various data through the data monitoring system and make judgments; Step 2: Calculate the overturning resistance coefficient and determine whether it meets the requirements. If it does, calculate the overturning resistance coefficient in real time. If not, analyze the reasons and issue a warning through voice prompts to determine whether rectification is required. If so, rectify; if not, continue to determine the reasons. Step 3: Determine if there is a load. If not, check the load. If there is, continue to determine if there is rotation. If there is no rotation, return. If there is rotation, continue to determine if the rotation path will collide. If there will be a collision, a voice prompt will be given to stop construction. If there will be no collision, the rotation path will be determined in real time to determine if there will be a collision. Step 4: Check the crane's various working status data to determine if they exceed the set limits. If not, monitor in real time; if so, provide a voice prompt and determine if rectification is needed.

7. The method for safety monitoring using a mobile hoisting equipment safety monitoring device as described in claim 6, characterized in that, In step two, the overturning resistance coefficient is calculated as follows: First, calculate the stabilizing moment Mstabilizing_min and the timely lateral overturning moment Mtime; second, the overturning resistance coefficient K = Mstabilizing_min / Mtime.

8. The method for safety monitoring using a mobile hoisting equipment safety monitoring device as described in claim 7, characterized in that, To obtain the maximum lifting capacity critical value, boom length (amplitude), and boom angle, calculate the stabilizing torque Mstabilizing_min using the formula: Mstabilizing_min = 1.4W × L × cosα; where W is the maximum lifting capacity critical value (kN); L is the boom length (m); and α is the boom angle. The real-time lateral overturning moment M includes wind load moment M_wind, lateral moment of the suspended object M_suspended object, and overturning moment of the crane M_crane; obtain wind force, wind direction, angle and amplitude of the suspended object, and main body dimensions of the crane to calculate the wind load moment M_wind; obtain the sway parameters and weight of the suspended object to calculate the lateral moment of the suspended object M_suspended object; Obtain the angle and amplitude of the suspended object, the crane tilt angle, and the suspended weight, and calculate the crane overturning moment M_crane; project the three moments onto the same direction and sum them up to obtain the appropriate lateral overturning moment M_appropriate.

9. The method for safety monitoring using a mobile hoisting equipment safety monitoring device as described in claim 6, characterized in that, In step three, the collision warning process includes: First, acquire image data and perform preprocessing; Secondly, the preprocessed image data is sent to the target detection algorithm module, which uses a deep learning framework to train a detection model for personnel, suspended objects, and structures, and is configured with safety helmet detection, thereby identifying personnel under the hook and whether they are wearing safety helmets. Furthermore, based on behavior analysis algorithms, combined with trajectory prediction and speed calculation algorithms, the dynamics of personnel and suspended objects are analyzed and predicted in real time to identify whether personnel have entered dangerous areas and whether suspended objects will collide. Finally, through the anomaly detection algorithm module, a safe distance threshold is set to determine in real time whether personnel enter or approach the dangerous working area below the hook and whether a collision is about to occur, and to trigger the alarm mechanism in real time.

10. The method for safety monitoring using a mobile hoisting equipment safety monitoring device as described in claim 6, characterized in that, It also includes monitoring the stress state of the outriggers. Pressure sensors are installed at the oil outlet of the outrigger extension hydraulic cylinder to detect the pressure in real time. If the pressure change decreases to a set ratio, an early warning will be issued.

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