Scaffold body inclination monitoring equipment based on Internet of Things
By using IoT sensor arrays and data processing equipment to monitor the tilt of scaffolding in real time, calculate the collapse risk index and send early warnings, the problem of response delay and wiring costs in tilt monitoring at construction sites is solved, thereby improving construction safety and efficiency.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-11-27
- Publication Date
- 2026-03-31
AI Technical Summary
In existing technologies, the tilt monitoring of construction scaffolding structures suffers from problems such as strong subjectivity of manual detection, serious response delays, high deployment costs of wired systems, and fire hazards.
By employing IoT-based sensor arrays and data processing equipment, the tilt angle, time, and latitude and longitude information of the scaffolding are collected in real time. The data is transmitted to the data processing equipment via a wireless communication module to calculate the tilt risk index and send early warning signals, thus avoiding wiring costs and fire hazards.
It enables real-time monitoring and early warning of scaffold tilt, improving safety and efficiency, reducing wiring costs and fire risks, and ensuring the safety of the construction site.
Smart Images
Figure CN121761839A_ABST
Abstract
Description
Technical Field
[0001] This application relates to the technical field of engineering measurement and monitoring, and in particular to an Internet of Things-based scaffolding tilt monitoring device. Background Technology
[0002] The construction industry is developing rapidly, and construction scaffolding, as an important temporary support facility in the construction process, plays a crucial role in ensuring structural stability, directly affecting the safety and efficiency of operations. Currently, the industry commonly uses steel-structured composite construction scaffolding, which possesses strong load-bearing capacity. However, under long-term loads or external impacts, construction scaffolding is prone to localized deformation or overall tilting. Traditional manual inspection methods are insufficient to meet the needs of real-time monitoring, and although new monitoring solutions have been introduced in recent years, they have not been widely adopted. According to relevant reports, a large number of construction scaffolding collapses are related to the failure to detect tilting in a timely manner.
[0003] In related technologies, the following methods are commonly used to address the issue of monitoring the tilt of construction scaffolding: First, manual visual inspection, where workers visually inspect the verticality of the scaffolding; this method requires no additional hardware costs. Second, periodic professional inspections using equipment such as laser rangefinders, which offer a certain level of measurement accuracy. Third, wired tilt monitoring systems, employing tilt sensors paired with a bus, and transmitting data via dedicated cables and power lines.
[0004] However, the relevant technologies have significant drawbacks. Manual inspection is highly subjective, suffers from severe response delays, and cannot promptly detect the tilting of construction scaffolding. Wired systems require modifications to the construction site infrastructure, incur high cabling costs, and the continuous power supply increases safety hazards. Summary of the Invention
[0005] To overcome the above-mentioned technical problems, this application provides a scaffolding tilt monitoring device based on the Internet of Things.
[0006] The scaffolding tilt monitoring device based on the Internet of Things provided in this application adopts the following technical solution: An IoT-based scaffolding tilt monitoring device includes multiple sensor groups, a communication module group, and a data processing device. The sensor groups and the data processing device are communicatively connected via the communication module group. The sensor groups collect the tilt angle θx along the horizontal X-axis, the tilt angle θy along the horizontal Y-axis, the time t during data collection, and the sensor's own latitude and longitude. The sensor groups then upload the θx, θy, t, and latitude / longitude data to the data processing device via the communication module group. The data processing device receives the θx, θy, t, and latitude / longitude data and processes it. The processed data is input into a built-in risk index R calculation formula to obtain the tilt risk index R of the target scaffolding. Based on R, the data processing device determines whether to send a warning signal to an alarm terminal via the communication module group.
[0007] By adopting the above technical solution, the sensor group can collect the tilt angle, collection time, and latitude and longitude information of the target scaffold structure and upload it to the data processing equipment. After processing the data, the data processing equipment calculates the tilt risk index and can also determine whether to send an early warning signal to the alarm terminal based on the index. This enables real-time monitoring and early warning of scaffold tilt, avoiding scaffold collapse accidents caused by failure to detect tilt in time, improving the safety and efficiency of the operation. Moreover, there is no need to lay dedicated cables and power supply lines, reducing wiring costs and fire hazards. Since the sensor group only measures the tilt angle and tilt angle change, and the complex data processing is performed by the data processing equipment, the sensor group on site does not have the problem of insufficient battery life and frequent battery replacement.
[0008] Optionally, the data processing method of the data processing device includes: when θx changes, calculating the angular velocity ωx along the horizontal X-axis and the rate of change of angle αx based on the data of θx and t in the dataset, until θx no longer changes within a preset time window, then ceasing calculation, and the values of ωx and αx are returned to zero; when θy changes, calculating the angular velocity ωy along the horizontal Y-axis and the rate of change of angle αy based on the data of θy and t in the dataset, until θy no longer changes within a preset time window, then ceasing calculation, and the values of ωy and αy are returned to zero; inputting θx, θy, ωx, ωy, αx, and αy into the tilting risk prediction model to obtain the tilting risk prediction result of the target scaffolding structure.
[0009] By adopting the above technical solution, the angular velocity and rate of change of the horizontal X and Y axes can be calculated based on the tilt angle of the scaffold structure along the horizontal X and Y axes and the corresponding time data. Combined with the tilt angle, angular velocity and rate of change of the angle, the tilt risk prediction model is obtained to obtain the tilt risk prediction result of the target scaffold structure. This provides a more accurate risk assessment for scaffold structure tilt monitoring, helps to detect potential tilt hazards of scaffold structure in a timely manner, and ensures the safety and efficiency of construction operations.
[0010] Optionally, the data processing device is also used to align the acquisition frequencies of the multiple sensor groups to the same time axis.
[0011] By adopting the above technical solution, data from multiple sensor groups can be processed in the same time dimension, avoiding data confusion caused by different collection frequencies, improving the accuracy and reliability of the collapse risk prediction results, and enabling more precise assessment and early warning of the collapse risk of the target scaffolding structure.
[0012] Optionally, the formula for calculating the risk index R is as follows: R = Weighting coefficient 1 × (X-axis offset parameter) + Weighting coefficient 2 × (Y-axis offset parameter) + Weighting coefficient 3 × (X-axis angular velocity parameter) + Weighting coefficient 4 × (Y-axis angular velocity parameter) + Weighting coefficient 5 × (X-axis angular acceleration parameter) + Weighting coefficient 6 × (Y-axis angular acceleration parameter) in The X-axis offset parameter is sin|θx|. The Y-axis offset parameter is sin|θy|. The X-axis angular velocity parameter is ωx, with units of rad / s. The Y-axis angular velocity parameter is ωy, with units of rad / s. The X-axis angular acceleration parameter is αx, with units of rad / s². The Y-axis angular acceleration parameter is αy, and its unit is rad / s².
[0013] By adopting the above technical solution, multiple sensor groups are used to collect the tilt angle, collection time, and latitude and longitude information of the target scaffold structure along the horizontal X and Y axes and upload them to the data processing equipment. The data processing equipment calculates the angular velocity and rate of change of angle along the horizontal X and Y axes. Combined with a specific risk index R calculation formula, and taking into account the offset, angular velocity, and angular acceleration parameters of the X and Y axes, the tilt risk index R of the target scaffold structure can be obtained more accurately. When the R value exceeds the preset warning threshold, an early warning signal is triggered, realizing real-time and accurate monitoring of the tilt of the scaffold structure.
[0014] Optionally, the calculation formula for the risk index R is updated by adjusting it through a weight coefficient change model. This weight coefficient change model is a pre-trained deep learning model, which is pre-trained as follows: A load-bearing simulation is performed on the target scaffold structure, including collision and tipping simulations. The values θx, θy, and t of the target scaffold structure during the simulation are collected, and ωx, ωy, αx, and αy are calculated based on θx, θy, and t. Simultaneously, sample data of the scaffold structure in its normal state and before tipping are recorded. Weight coefficients 1, 2, 3, 4, and 5 are periodically changed. The system assesses whether sample data is at risk of tipping over due to collision by analyzing the values of θx and θy within a preset time window before tipping occurs, as well as the values of ωx, ωy, αx, and αy after changes in θx and θy. Training samples containing features of θx, θy, t, ωx, ωy, αx, and αy, along with corresponding risk labels, are generated. The labeled sample data is then divided into training and testing sets. The training set is used to train the basic prediction model, while the testing set data is used to evaluate the model's performance and generate an ROC curve. When the model's accuracy and recall reach preset thresholds, the tipping risk prediction model training is complete, and the trained model is finally distributed to the data processing equipment.
[0015] By adopting the above technical solution, the calculation formula of the risk index R is adjusted using a pre-trained deep learning model, namely the weight coefficient change model. Through simulations of load-bearing, collision, and tipping over of the target scaffolding, relevant data is collected during the simulation, and angular velocity and angle change rate are calculated. Sample data under normal conditions and before tipping are recorded. The weight coefficients are periodically changed to determine the risk of tipping over the sample data, generating training samples. Training and testing sets are then divided for model training and evaluation. When the accuracy and recall reach preset thresholds, training is completed and the model is deployed. This allows the calculation formula of the risk index R to more accurately reflect the tipping risk of the target scaffolding, improving the accuracy and reliability of scaffolding tilt monitoring equipment in predicting scaffolding tipping risk.
[0016] Optionally, it also includes a server, which is used to collect historical θx, θy, t, ωx, ωy, αx, αy of the target scaffolding from the data processing device, and use the collected historical θx, θy, t, ωx, ωy, αx, αy to iterate the calculation formula of the risk index R, and send the iterated equipment failure prediction model to the data processing device.
[0017] By adopting the above technical solution, and using the server to collect historical tilt angles, times, angular velocities, and angular accelerations of the target scaffold structure, the calculation formula of the risk index R can be iterated, making the calculation of the risk index R more accurate. The iterated equipment failure prediction model can be sent to the data processing equipment, which can improve the accuracy of predicting the risk of scaffold structure collapse and promptly detect potential collapse risks of scaffold structure.
[0018] Optionally, the sensor group includes a dual-axis tilt wireless sensor mounted on the scaffold frame and a position sensor mounted on the ground at the location of the scaffold frame; the communication module group includes a first communication module integrated into the dual-axis tilt wireless sensor and a second communication module integrated into the position sensor.
[0019] By adopting the above technical solution, the tilt angle of the scaffold frame along the horizontal X and Y axes can be collected using a dual-axis tilt wireless sensor, and the position sensor can collect the latitude and longitude information of the scaffold frame's location. The combination of wireless sensors and communication modules avoids the wiring costs and fire hazards associated with the modification of construction site infrastructure when deploying wired systems, and realizes the wireless transmission of scaffold frame tilt data and position information, which facilitates the data processing equipment to receive and process the data for scaffold frame tilt monitoring.
[0020] Optionally, the alarm terminal may include one or more of the following: a buzzer installed on the scaffold frame, a smartphone, and a central control room.
[0021] By adopting the above technical solution, the sensor group collects the tilt angle, time, and latitude and longitude information of the target scaffold and uploads it to the data processing equipment. The data processing equipment receives and processes the data and calculates the tilt risk index. Based on the index, it determines whether to send an early warning signal. The alarm terminal uses one or more of the following: a buzzer installed on the scaffold, a smartphone, and a central control room. This allows personnel in different scenarios to be informed of the scaffold tilt situation in a timely manner, realizing real-time early warning through multiple channels and in multiple scenarios, and improving the timeliness of response and the comprehensiveness of information transmission in scaffold tilt monitoring.
[0022] In summary, this application includes at least one of the following beneficial technical effects: 1. The sensor group collects the tilt angle, collection time and latitude and longitude information of the target scaffold and uploads it to the data processing equipment. It can monitor the tilt of the scaffold in real time, which solves the problem of serious response delay and inability to detect the tilt of the scaffold in time by manual inspection. 2. The data processing equipment calculates the tipping risk index based on the collected data and determines whether to send an early warning signal, which can promptly warn of the risk of scaffolding tipping and ensure work safety and efficiency; 3. Data transmission is achieved by using wireless sensor groups and communication module groups, avoiding the high costs and fire hazards associated with the deployment of wired systems that require modifications to the infrastructure at the construction site. Attached Figure Description
[0023] Figure 1 This is a flowchart of an IoT-based scaffolding tilt monitoring device provided in an embodiment of this application. Detailed Implementation
[0024] This application discloses an IoT-based scaffolding tilt monitoring device, comprising multiple sensor groups, a communication module group, and a data processing device. The sensor groups and the data processing device are connected via the communication module group, enabling real-time collection of scaffolding tilt-related data and transmission to the data processing device for processing and analysis, thus promptly detecting scaffolding tilt risks. This is because the sensor groups collect key data about the target scaffolding structure, the communication module group ensures effective data transmission, and the data processing device analyzes and judges the data, thereby achieving the monitoring and early warning of scaffolding tilt.
[0025] Specifically, the sensor array includes a dual-axis tilt wireless sensor mounted on the scaffold frame and a position sensor mounted on the ground at the location of the scaffold frame. The dual-axis tilt wireless sensor collects the tilt angle θx along the horizontal X-axis and the tilt angle θy along the horizontal Y-axis of the target scaffold frame. This sensor typically uses a high-precision tilt measurement element with excellent stability and anti-interference capabilities to ensure measurement accuracy. The position sensor collects the time t during data acquisition and its own latitude and longitude information. It generally uses satellite positioning technology or other high-precision positioning technologies to accurately obtain position and time information. The dual-axis tilt wireless sensor and the position sensor can be fixedly mounted on the scaffold frame and the ground respectively to ensure stable positioning and accurate data collection.
[0026] The communication module group includes a first communication module integrated into a dual-axis tilt wireless sensor and a second communication module integrated into a position sensor. The first and second communication modules are used to transmit data collected by the sensors to a data processing device. Both modules can be NB-IoT modules, supporting an operating temperature range of -40℃ to 85℃, adapting to various environmental conditions. The function of the communication module group is to transmit data collected by the sensor group to the data processing device. In addition to the aforementioned NB-IoT modules, the modules in the communication module group can also be replaced with LoRaWAN modules, which feature low power consumption and long-range communication, suitable for construction site environments with high power consumption requirements and long communication distances. Different communication modules can be selected based on the actual layout and signal coverage of the construction site.
[0027] The data processing device receives θx, θy, t, and the latitude and longitude information of the position sensors in the sensor group, and processes the data. The data processing device can be a server or a computer with powerful data processing capabilities. It has a large-capacity storage unit and a high-performance processor to ensure rapid processing of large amounts of data. An alternative data processing device can be an industrial-grade embedded processing device, which is smaller in size and more suitable for applications with limited space. The data processing device receives data from the sensor group through a communication module group and performs preliminary processing and analysis of the data.
[0028] The data processing method of the data processing equipment includes calculating the angular velocity ωx along the horizontal X-axis and the rate of change of angle αx based on the θx and t data in the dataset when θx changes, until θx remains unchanged for a preset time window, at which point the calculation stops, and the values of ωx and αx are returned to zero. Similarly, calculating the angular velocity ωy along the horizontal Y-axis and the rate of change of angle αy based on the θy and t data in the dataset when θy changes, until θy remains unchanged for a preset time window, at which point the calculation stops, and the values of ωy and αy are returned to zero. The data processing equipment is also used to align the acquisition frequencies of multiple sensor groups to the same time axis, which ensures data consistency and accuracy, facilitating subsequent analysis and processing.
[0029] The data processing equipment inputs the processed data into the built-in risk index R calculation formula to obtain the collapse risk index R of the target scaffold structure. The calculation formula for the risk index R is: R = weighting coefficient 1 × (X-axis offset parameter) + weighting coefficient 2 × (Y-axis offset parameter) + weighting coefficient 3 × (X-axis angular velocity parameter) + weighting coefficient 4 × (Y-axis angular velocity parameter) + weighting coefficient 5 × (X-axis angular acceleration parameter) + weighting coefficient 6 × (Y-axis angular acceleration parameter), where the X-axis offset parameter is sin|θx|, the Y-axis offset parameter is sin|θy|, the X-axis angular velocity parameter is ωx (in rad / s), the Y-axis angular velocity parameter is ωy (in rad / s), the X-axis angular acceleration parameter is αx (in rad / s²), and the Y-axis angular acceleration parameter is αy (in rad / s²).
[0030] The risk index R is updated using a weighted coefficient change model, which is a pre-trained deep learning model. The training process involves: simulating a load-bearing manned structure on the target scaffold, including collision and tipping simulations. The model collects θx, θy, and t values of the target scaffold during the simulation and calculates ωx, ωy, αx, and αy based on θx, θy, and t. Simultaneously, it records sample data of the scaffold in its normal state and before tipping. Weighted coefficients 1, 2, 3, 4, and 5 are periodically changed, adjusting θx and θy values within a preset time window before tipping and ωx and θy values after changes in θx and θy. The values of x, ωy, αx, and αy are used to determine whether the sample data is at risk of tipping over due to collision. Training samples containing features θx, θy, t, ωx, ωy, αx, and αy and corresponding risk labels are generated. The labeled sample data is then divided into training and test sets. The basic prediction model is trained using the training set, and the model performance is evaluated using the test set data to generate ROC curves. When the model's accuracy and recall reach preset thresholds, the tipping risk prediction model training is complete, and the trained model is finally sent to the data processing equipment.
[0031] The data processing equipment determines whether to send a warning signal to the alarm terminal via the communication module group based on the risk index R. The alarm terminal includes one or more of the following: a buzzer installed on the scaffold structure, a smartphone, and a central control room. When the risk index R exceeds a preset threshold, the data processing equipment immediately sends a warning signal to the alarm terminal via the communication module group. This preset threshold changes according to the type of scaffold structure, the number of workers on it, and the construction equipment. For example, the buzzer will emit a loud alarm to alert nearby workers; the smartphone will receive a push notification to keep workers informed; and the central control room can comprehensively monitor the status of multiple scaffold structures and make corresponding decisions.
[0032] This embodiment also includes a server, which collects historical θx, θy, t, ωx, ωy, αx, and αy data of the target scaffolding structure from the data processing device. The server then uses this collected historical data to iterate the calculation formula for the risk index R, and sends the iterated equipment failure prediction model back to the data processing device. The server typically has powerful storage and computing capabilities, enabling it to analyze and process large amounts of historical data, thereby continuously optimizing the calculation formula for the risk index R and improving the accuracy and reliability of monitoring.
[0033] The implementation principle of the scaffolding tilt monitoring device based on the Internet of Things (IoT) in this embodiment is as follows: This embodiment uses multiple sensor groups to collect key data such as the tilt angle, position, and time of the scaffolding in real time, and transmits the data to a data processing device via a communication module group. The data processing device processes and analyzes the data, calculates a risk index R, and determines whether to issue a warning signal based on R. Simultaneously, the server iteratively processes historical data to optimize the risk index calculation formula. This comprehensive monitoring and early warning system can accurately detect the tilt risk of scaffolding in real time, avoiding collapse accidents caused by scaffolding tilt, greatly improving the safety and efficiency of construction operations, and overcoming the problems of subjective manual detection, high wiring costs of wired systems, and insufficient battery life of wireless solutions in existing technologies.
[0034] The above are all preferred embodiments of this application, and are not intended to limit the scope of protection of this application. Therefore, all equivalent changes made in accordance with the structure, shape and principle of this application should be covered within the scope of protection of this application.
Claims
1. An Internet of Things based scaffold frame tilting monitoring device, characterized in that, The application relates to a risk index calculation method for a target scaffold, which comprises a plurality of sensor groups, a communication module group and a data processing device; the sensor groups are in communication connection with the data processing device through the communication module group; the sensor groups are used for collecting the inclination angle theta x of a target scaffold along a horizontal X axis, the inclination angle theta y along a horizontal Y axis, the time t when data is collected and the longitude and latitude information where the sensor groups are located, and uploading the inclination angle theta x, the inclination angle theta y, the time t and the longitude and latitude information where the sensor groups are located to the data processing device through the communication module group; the data processing device is used for receiving the inclination angle theta x, the inclination angle theta y, the time t and the longitude and latitude information where the sensor groups are located, and processing the data; the data processing device inputs the processed data into a built-in risk index R calculation formula to obtain the toppling risk index R of the target scaffold; and the data processing device judges whether to send an early warning signal to an alarm terminal through the communication module group according to R.
2. The Internet of Things based scaffold tower body tilt monitoring apparatus according to claim 1, wherein, The data processing method of the data processing device comprises the following steps: when the inclination angle theta x changes, the angular velocity omega x along the horizontal X axis and the angle change rate alpha x are calculated according to the data of the inclination angle theta x and the time t in the data set, and the values of the angular velocity omega x and the angle change rate alpha x are reset to zero until the inclination angle theta x does not change continuously within a preset time window; when the inclination angle theta y changes, the angular velocity omega y along the horizontal Y axis and the angle change rate alpha y are calculated according to the data of the inclination angle theta y and the time t in the data set, and the values of the angular velocity omega y and the angle change rate alpha y are reset to zero until the inclination angle theta y does not change continuously within a preset time window; the inclination angle theta x, the inclination angle theta y, the angular velocity omega x, the angular velocity omega y, the angle change rate alpha x and the angle change rate alpha y are input into the toppling risk prediction model to obtain the toppling risk prediction result of the target scaffold.
3. The Internet of Things based scaffold tower body tilt monitoring apparatus according to claim 2, wherein, The data processing device is further used for aligning the collection frequencies of the plurality of sensor groups to the same time axis.
4. The Internet of Things based scaffold tower body tilt monitoring apparatus according to claim 2, wherein, The calculation formula of the risk index R is R=weight coefficient 1*(X axis offset parameter)+weight coefficient 2*(Y axis offset parameter)+weight coefficient 3*(X axis angular velocity parameter)+weight coefficient 4*(Y axis angular velocity parameter)+weight coefficient 5*(X axis angular acceleration parameter)+weight coefficient 6*(Y axis angular acceleration parameter) wherein the X axis offset parameter is sin|theta x|, the Y axis offset parameter is sin|theta y|, the X axis angular velocity parameter is omega x, and the unit is rad / s, the Y axis angular velocity parameter is omega y, and the unit is rad / s, the X axis angular acceleration parameter is alpha x, and the unit is rad / s2, and the Y axis angular acceleration parameter is alpha y, and the unit is rad / s2.
5. The Internet of Things based scaffold tower body tilt monitoring apparatus according to claim 4, wherein, The risk index R is calculated by a weight coefficient change model, the weight coefficient change model is a deep learning model trained in advance, the weight coefficient change model is trained in the following way: a target scaffold body is simulated under a load, collision simulation and collision and dumping simulation are performed during the simulation, θx, θy, t of the target scaffold body during the simulation are collected, ωx, ωy, αx, αy are calculated according to θx, θy, t, sample data of the scaffold body in a normal state and before dumping are recorded, weight coefficient 1, weight coefficient 2, weight coefficient 3, weight coefficient 4 and weight coefficient 5 are periodically changed, whether the sample data is at risk of collision and dumping is judged according to the values of θx, θy within a preset time window before dumping and the values of ωx, ωy, αx, αy after θx, θy change, training samples containing θx, θy, t, ωx, ωy, αx, αy characteristics and corresponding risk labels are generated, then the labeled sample data is divided into a training set and a test set, the training set is used to train the constructed basic prediction model, the model performance is evaluated by the test set data and an ROC curve is generated, when the model performance accuracy and recall rate reach a preset threshold, the training of the dumping risk prediction model is completed, and finally the trained model is sent to a data processing device.
6. The Internet of Things based scaffold tower body tilt monitoring apparatus according to claim 5, wherein, The server is also used to collect historical θx, θy, t, ωx, ωy, αx, αy of the target scaffold body from the data processing device, and to iterate the calculation formula of the risk index R using the collected historical θx, θy, t, ωx, ωy, αx, αy, and to send the iterated device failure prediction model to the data processing device.
7. The Internet of Things based scaffold tower body tilt monitoring apparatus according to claim 1, wherein, The sensor group includes a dual-axis inclination wireless sensor arranged on the scaffold body and a position sensor arranged on the ground where the scaffold body is located; the communication module group includes a first communication module integrated in the dual-axis inclination wireless sensor and a second communication module integrated in the position sensor.
8. The Internet of things based scaffold tower body tilt monitoring apparatus as claimed in claim 1 wherein, The alarm terminal includes one or more of a buzzer, a smartphone and a central control room installed on the scaffold body.