Health monitoring system for looseness of standard knot nut of tower crane

Abstract

The invention discloses a tower crane standard knot nut looseness health monitoring system, which relates to the technical field of engineering machinery health monitoring and comprises a nut looseness monitoring sensor, a data acquisition module, a wireless communication module, a remote monitoring module and an alarm module. According to the tower crane standard knot nut loosening health monitoring system, the loosening state of the tower crane standard knot nut can be monitored in real time, the data acquisition module can rapidly process and convert the signals output by the nut loosening monitoring sensor to obtain real-time data of the nut, safety accidents caused by nut loosening are effectively prevented, and the safety of the tower crane standard knot nut loosening health monitoring system is improved. The safety of tower crane operation is improved, the wireless communication module adopted by the system can transmit monitoring data to remote monitoring equipment in real time, so that a worker can remotely monitor the loosening condition of a nut of the tower crane, the working efficiency is greatly improved, the labor cost is reduced, and historical data and real-time data are analyzed through the data analysis system.

Description

Technical Field

[0001] The present invention relates to the technical field of engineering machinery health monitoring, and in particular to a tower crane standard section nut loosening health monitoring system. Background Art

[0002] In the field of construction, tower cranes are key lifting equipment, and the stability of their standard section structures is directly related to construction safety and efficiency. Standard sections are connected by high-strength bolts, and the tightening state of the nuts is an important link in ensuring the reliability of the overall structure of the tower crane. With the development of scale and complexity in construction projects, tower cranes have high operating frequencies and large load variations. The nuts are easily loosened due to factors such as vibration and alternating loads. If not discovered in time, the standard section connection may fail, causing major safety accidents. Therefore, real-time and accurate health monitoring of the looseness of the nuts of the tower crane standard sections has become an important research direction in the field of health monitoring technology for engineering machinery. At present, the industry's monitoring methods for nut loosening mainly include manual regular inspections, visual inspections, and simple mechanical limit devices. Although these methods can detect obvious loosening problems to a certain extent, it is difficult to achieve dynamic tracking of nut loosening trends and risk prediction.

[0003] In existing technologies, some automated monitoring systems attempt to obtain data through strain sensors or vibration sensors, but they still face many challenges in practical applications. For example, traditional monitoring systems mostly use a single-point monitoring method, lacking a comprehensive analysis of the coordinated loosening effects of multiple nuts, making it difficult to accurately assess the overall structural safety status of the standard section. At the same time, the data collection and transmission process is susceptible to interference from the complex electromagnetic environment of the construction site, resulting in signal distortion or transmission delays, affecting the reliability of the monitoring results. In addition, existing systems generally lack the ability to deeply mine historical data and predict trends, making it impossible to identify the potential risks of nut loosening in advance. The real-time and intelligent level of monitoring needs to be improved. To this end, we propose a health monitoring system for nut loosening in tower crane standard sections. Summary of the Invention

[0004] In order to solve the above technical problems, a tower crane standard section nut loosening health monitoring system is provided. This technical solution solves the above problems.

[0005] In order to achieve the above objects, the technical solution adopted by the present invention is:

[0006] A tower crane standard section nut loosening health monitoring system includes: a nut loosening monitoring sensor, a data acquisition module, a wireless communication module, a remote monitoring module and an alarm module;

[0007] The nut loosening monitoring sensor is installed at the nut of the tower crane standard section, and is used to detect whether the nut is loose in real time and output a nut loosening status signal;

[0008] The data acquisition module is electrically connected to the nut loosening monitoring sensor, and is used to receive the signal of the nut loosening monitoring sensor, process and convert the signal to obtain real-time data of the standard section nut loosening;

[0009] The wireless communication module is electrically connected to the data acquisition module and is used to transmit the data processed by the data acquisition module to the remote monitoring device via wireless mode;

[0010] The remote monitoring module includes a display and a data analysis system. The display is used to display the loosening status of the tower crane standard section nuts in real time. The data analysis system is used to analyze the loosening status of the nuts and determine whether there is a loosening risk.

[0011] The alarm module is electrically connected to the remote monitoring module and is used to send an alarm signal when it is detected that the nut is loose or there is a risk of loosening.

[0012] Preferably, the nut loosening monitoring sensor is a strain sensor, comprising a strain gauge attached to the surface of the nut connection structure and a conditioning circuit for providing an excitation voltage to the strain gauge. The strain gauge undergoes a resistance change as the mechanical deformation caused by the loosening of the nut, and the conditioning circuit converts the resistance change into a voltage signal output;

[0013] The resistance change of the strain gauge and the strain caused by the loosening of the nut satisfy:

[0014]

[0015] Where ΔR is the resistance change of the strain gauge, R is the initial resistance of the strain gauge, K is the sensitivity coefficient of the strain gauge, and ε is the strain caused by the loosening of the nut. ΔL is the displacement change caused by the loosening of the nut, L0 is the initial length of the monitoring point, and the data acquisition module collects the voltage signal and calculates the strain value according to the above formula to determine whether the nut is loose.

[0016] Preferably, the data acquisition module includes a signal amplification circuit, an anti-aliasing filter, an analog-to-digital converter and a microprocessor;

[0017] The signal amplification circuit amplifies the voltage signal output by the nut loosening monitoring sensor. The amplification factor A is determined by the circuit structure composed of operational amplifiers. The anti-aliasing filter adopts a Butterworth low-pass filter. The cutoff frequency fc is determined according to the maximum frequency of the nut loosening signal, satisfying fc ≥ 2fmax, where fmax is the highest frequency component in the signal.

[0018] The analog-to-digital converter converts the filtered analog signal into a digital signal. The microprocessor processes the digital signal in real time, including digital filtering and eigenvalue extraction. The digital filtering uses a mean filter algorithm to average the data of N consecutive sampling points. The calculation formula is:

[0019]

[0020] Where Y n is the mean filter output value of the nth sampling point, Y i is the data of the i-th original sampling point, and N is the number of continuous sampling points involved in the mean calculation;

[0021] The displacement and strain parameters of nut loosening are calculated using the processed signals.

[0022] Preferably, the wireless communication module adopts narrowband Internet of Things communication technology, operates in an authorized frequency band, and uses CRC coding for error detection during data transmission;

[0023] The data acquisition module encapsulates the processed real-time data into a data frame. The frame structure includes a frame header, a data area, and a CRC check area. The wireless communication module sends the data frame to the remote monitoring device through the antenna. The remote monitoring device performs a CRC check on the received data frame. If the check passes, the data is parsed. If the check fails, a retransmission is requested.

[0024] Preferably, the data analysis system includes a database and a loosening risk assessment model;

[0025] The database is used to store historical monitoring data, tower crane standard section model parameters, and nut specification parameter information;

[0026] The loosening risk assessment model is based on real-time data and historical data analysis. By setting a strain threshold and a displacement threshold, when the strain value calculated in real time is greater than or equal to the strain threshold, and when the displacement value is greater than or equal to the displacement threshold, it is determined that there is a loosening risk.

[0027] The time series analysis method is used to process historical data and establish a trend prediction model for the loosening state of nuts. The model expression is:

[0028] y(t)=a1y(t-1)+…+a n y(tn)+ε(t)+a1ε(t-1)+…+b m ε((tm)

[0029] Where y(t) is the strain or displacement signal value at time t, a i is the coefficient of the autoregressive (AR) part (i=1,2,…,n, n is the autoregressive order), b j is the coefficient of the moving average (MA) part (j = 1, 2, …, m, where m is the moving average order), and ε(t) is the white noise (random error term) at the moment;

[0030] By predicting strain and displacement values ​​at future moments, loosening risks can be determined in advance.

[0031] Preferably, the alarm module includes a sound alarm unit and a light alarm unit;

[0032] The sound alarm unit uses a buzzer, which emits sound signals of different frequencies to indicate different alarm levels. The light alarm unit uses LED lights, and different colors of light indicate different risk levels.

[0033] When the data analysis system determines that the nut is loose, the alarm module emits a high-frequency beep and a red light alarm. When it determines that there is a risk of loosening, it emits a low-frequency beep and a yellow light alarm.

[0034] At the same time, the alarm module also sends text messages or push notifications to the staff's mobile terminals through the wireless communication module. The notification content includes the tower crane location, nut looseness status and risk level.

[0035] Preferably, a plurality of nut loosening monitoring sensors are provided, which are respectively installed at different nuts of the tower crane standard section. Each sensor has a unique identification code. When receiving the signal, the data acquisition module distinguishes the monitoring data of different nuts according to the identification code.

[0036] The data acquisition module synchronously collects data from multiple sensors, and the acquisition frequency f satisfies the Nyquist sampling theorem, that is, f≥2fmax, where fmax is the highest frequency component of the nut loosening signal.

[0037] Preferably, the display of the remote monitoring module adopts a human-computer interaction interface for displaying the position, number, current strain value, displacement value, loosening state and historical data curve of each nut in real time, and the staff sets the monitoring parameters through the interface;

[0038] The data analysis system conducts a comprehensive analysis of the monitoring data of multiple nuts and calculates the overall loosening risk index of the standard section. The risk index is calculated using the weighted average method, and the formula is:

[0039] R=∑(w i ×r i )

[0040] Where R is the overall loosening risk index of the tower crane standard section, w i is the weight of the i-th nut, which is determined according to the importance of the nut's position in the standard section, r i is the risk level value of the i-th nut.

[0041] Preferably, the data acquisition module further includes a power management unit;

[0042] The power management unit is powered by a combination of solar panels and batteries. The solar panels are installed on the top of the tower crane to convert solar energy into electrical energy, while the batteries are used to store electrical energy and power the data acquisition module, wireless communication module, etc.

[0043] The power management unit has charging control and discharge protection functions. When the battery power is lower than the set threshold, it will issue a low-battery alarm signal, which will be transmitted to the remote monitoring module through the wireless communication module to remind the staff to replace or recharge the battery in time.

[0044] Preferably, the wireless communication module also has a positioning function. When processing data, the data acquisition module binds the geographical location information with the nut loosening monitoring data. When displaying and analyzing data, the remote monitoring module marks the tower crane position on the electronic map and displays the loosening status of the standard section nut at that position.

[0045] At the same time, the data analysis system analyzes the impact of environmental factors on nut loosening based on tower crane data in different geographical locations, establishes a correlation model between environmental factors and nut loosening, and improves the accuracy of loosening risk judgment.

[0046] Compared with the prior art, the present invention has the following beneficial effects:

[0047] The tower crane standard section nut loosening health monitoring system proposed in the present invention can monitor the loosening status of the tower crane standard section nuts in real time. Through the signal output by the nut loosening monitoring sensor, the data acquisition module can quickly process and convert the real-time data of the nuts, effectively prevent safety accidents caused by nut loosening, and improve the safety of tower crane operation. The wireless communication module adopted by the system can transmit the monitoring data to the remote monitoring equipment in real time, so that the staff can remotely monitor the loosening of the tower crane nuts, greatly improving work efficiency and reducing labor costs. Through the analysis of historical data and real-time data by the data analysis system, it can be judged in advance whether there is a risk of loosening of the nuts, providing sufficient time for staff to carry out maintenance and inspection, and avoiding equipment damage and construction delays caused by loose nuts. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 It is a system module framework diagram of the present invention;

[0049] Figure 2 It is a system workflow diagram of the present invention. DETAILED DESCRIPTION

[0050] The following description is intended to disclose the present invention so that those skilled in the art can implement the present invention. The preferred embodiments described below are merely examples, and those skilled in the art may conceive of other obvious variations.

[0051] Reference Figure 1 As shown, a tower crane standard section nut loosening health monitoring system includes: a nut loosening monitoring sensor, a data acquisition module, a wireless communication module, a remote monitoring module and an alarm module;

[0052] The nut loosening monitoring sensor is installed at the standard section nut of the tower crane, and is used to detect whether the nut is loose in real time and output a nut loosening status signal; the sensor housing adopts a waterproof and dustproof design, and is coated with an anti-corrosion coating on the surface to adapt to harsh outdoor environments; during installation, the nut surface needs to be cleaned, and the strain gauge is fixed with high-strength epoxy resin glue to ensure that it is completely in contact with the nut surface. After installation, the zero drift is adjusted through a calibration program, and the calibration data is stored in the data acquisition module; the sensor has an integrated temperature compensation circuit to eliminate the influence of ambient temperature changes on resistance measurement and improve detection accuracy;

[0053] The data acquisition module is electrically connected to the nut loosening monitoring sensor, and is used to receive the signal of the nut loosening monitoring sensor, process and convert the signal to obtain real-time data of the loosening of the standard section nut; the signal amplification circuit uses a low-noise precision operational amplifier AD620, the amplification factor A is set to 100 times, and the power supply voltage is ±5V; the cutoff frequency fc of the anti-aliasing filter is set to 50Hz, and a fourth-order Butterworth low-pass filter is used, and the stopband attenuation rate reaches -80dB / dec; the analog-to-digital converter uses a 24-bit high-precision ADC chip ADS1256, with a sampling frequency of 1kHz and a resolution of 0.1μV; the microprocessor uses an STM32F407 with a built-in digital filtering algorithm, and the mean filter window length N is set to 10, and the strain value is calculated in real time and transmitted to the wireless communication module via the SPI interface; the data acquisition module has a built-in self-test function, which automatically performs sensor path detection every hour, and triggers the upload of a fault code if an abnormal signal is found;

[0054] The wireless communication module is electrically connected to the data acquisition module and is used to wirelessly transmit the data processed by the data acquisition module to the remote monitoring device; the NB-IoT communication protocol is adopted, the operating frequency band is B5 (850MHz), and the data transmission interval can be configured to be 10 seconds to 10 minutes; the data frame format is defined as follows: the frame header is a 4-byte synchronization code 0xAA55AA55, the data area contains the sensor ID (2 bytes), the timestamp (4 bytes), the strain value (4-byte floating point), the displacement value (4-byte floating point), and the CRC checksum area is a 2-byte CCITT standard checksum; the communication module has a built-in retransmission mechanism. If the remote end does not return an ACK confirmation signal, the data will be automatically retransmitted after 3 seconds, and the maximum number of retries is 5;

[0055] The remote monitoring module includes a display and a data analysis system. The display is used to display the loosening status of the standard section nuts of the tower crane in real time, and the data analysis system is used to analyze the loosening status of the nuts and determine whether there is a loosening risk. The display interface is divided into a main view and a detailed view. The main view uses a three-dimensional tower crane model to display the position and color-coded status of each nut (green for normal, yellow for warning, and red for alarm). The detailed view can retrieve the historical data curve and current parameters of any nut. The data analysis system is deployed on a cloud server, uses a MySQL database to store data, and implements a loosening risk assessment model through a Python script. A daily report is automatically generated at dawn and sent to a designated email address.

[0056] The alarm module is electrically connected to the remote monitoring module and is used to issue an alarm signal when a loose nut or a risk of loosening is detected. The sound alarm unit uses an SMD chip buzzer, with a high-frequency alarm tone of 3kHz continuous pulses and a low-frequency alarm tone of 1kHz intermittent pulses. The light alarm unit uses an RGB LED, with red indicating full brightness and yellow indicating flashing mode (1Hz). When the alarm is triggered, the system automatically locks the abnormal nut number and pops up a handling guide on the display interface, including options such as "immediately stop for inspection" or "increase monitoring frequency."

[0057] The nut loosening monitoring sensor is a strain gauge sensor, comprising a strain gauge adhered to the surface of the nut connection structure and a conditioning circuit for providing an excitation voltage to the strain gauge. The strain gauge undergoes a change in resistance as the mechanical deformation caused by the loosening of the nut occurs, and the conditioning circuit converts the resistance change into a voltage signal for output. The strain gauge is made of constant copper foil, has a nominal resistance of 120Ω, and a sensitivity factor of K = 2.0. The excitation voltage is supplied by a constant voltage source with a voltage value of 2.5V. The resistance change is converted into a differential voltage output via a Wheatstone bridge configuration. The conditioning circuit integrates an INA128 instrumentation amplifier with a common-mode rejection ratio of ≥100dB. The output signal is low-pass filtered by RC and then fed into a data acquisition module.

[0058] The resistance change of the strain gauge and the strain caused by the loosening of the nut satisfy:

[0059]

[0060] Where ΔR is the resistance change of the strain gauge, R is the initial resistance of the strain gauge, K is the sensitivity coefficient of the strain gauge, and ε is the strain caused by the loosening of the nut. ΔL is the displacement change caused by the loosening of the nut, L0 is the initial length of the monitoring point, and the data acquisition module collects the voltage signal and calculates the strain value according to the above formula to determine whether the nut is loose. The temperature compensation coefficient α is introduced in the calculation, and the correction formula is to correct the measurement ε 修正 =ε 测量 -α(T-T0), where T is the current temperature and T0 is the calibration temperature;

[0061] The data acquisition module includes a signal amplification circuit, an anti-aliasing filter, an analog-to-digital converter, and a microprocessor. The signal amplification circuit amplifies the voltage signal output by the nut loosening monitoring sensor. The amplification factor A is determined by the circuit structure composed of an operational amplifier. The anti-aliasing filter adopts a Butterworth low-pass filter. The cutoff frequency fc is determined according to the highest frequency of the nut loosening signal and satisfies fc≥2fmax, where fmax is the highest frequency component in the signal. The analog-to-digital converter converts the filtered analog signal into a digital signal. The microprocessor processes the digital signal in real time, including digital filtering and eigenvalue extraction. The digital filtering adopts a mean filtering algorithm to average the data of N consecutive sampling points. The calculation formula is:

[0062]

[0063] Where Y n is the mean filter output value of the nth sampling point, Y i is the data of the i-th original sampling point, and N is the number of continuous sampling points involved in the mean calculation. The displacement and strain parameters of the nut loosening are calculated using the processed signal. The microprocessor also calculates the standard deviation σ. If σ exceeds the threshold, it is determined to be a transient impact interference and the abnormal data is eliminated.

[0064] The wireless communication module adopts narrowband Internet of Things communication technology, operates in the authorized frequency band, and adopts CRC coding for error detection during data transmission. The data acquisition module encapsulates the processed real-time data into data frames. The frame structure includes a frame header, a data area and a CRC check area. The wireless communication module sends the data frames to the remote monitoring device via the antenna. The remote monitoring device performs a CRC check on the received data frames. If the check passes, the data is parsed. If the check fails, a retransmission is requested. The communication module supports APN private network access to ensure data transmission security and supports base station positioning function with a positioning accuracy of ≤50 meters.

[0065] The data analysis system includes a database and a loosening risk assessment model; the database is used to store historical monitoring data, tower crane standard section model parameters, and nut specification parameter information; the loosening risk assessment model performs analysis based on real-time data and historical data, and by setting a strain threshold and a displacement threshold, determines that there is a loosening risk when the strain value calculated in real time is greater than or equal to the strain threshold, and when the displacement value is greater than or equal to the displacement threshold; the historical data is processed using a time series analysis method to establish a trend prediction model for the nut loosening state, and the model expression is:

[0066] y(t)=a1y(t-1)+…+a n y(tn)+ε(t)+a1ε(t-1)+…+b m ε((tm)

[0067] Where y(t) is the strain or displacement signal value at time t, a i is the coefficient of the autoregressive (AR) part (i=1,2,…,n, n is the autoregressive order), b j is the coefficient of the moving average (MA) part (j = 1, 2, …, m, where m is the moving average order), and ε(t) is the white noise (random error term) at the time. By predicting the strain and displacement values ​​at future times, the loosening risk can be determined in advance. The model parameters are updated online using the least squares method, and the coefficients are optimized every 6 hours.

[0068] The alarm module includes a sound alarm unit and a light alarm unit; the sound alarm unit uses a buzzer to emit sound signals of different frequencies to indicate different alarm levels, and the light alarm unit uses an LED light, and different colors of light indicate different risk levels; when the data analysis system determines that the nut is loose, the alarm module emits a high-frequency buzzer and a red light alarm; when it determines that there is a loose risk, it emits a low-frequency buzzer and a yellow light alarm; at the same time, the alarm module also sends a text message or push notification to the staff's mobile terminal through the wireless communication module. The notification content includes the tower crane location, nut loosening status and risk level; the text message content template is: "Alarm: Tower crane number [XXX] nut [YYY] looseness level [ZZZ], please deal with it immediately";

[0069] The nut loosening monitoring sensors are provided in plurality and are respectively installed at different nuts of the tower crane standard section. Each sensor has a unique identification code. When receiving the signal, the data acquisition module distinguishes the monitoring data of different nuts according to the identification code. The data acquisition module synchronously acquires data from the multiple sensors. The acquisition frequency f satisfies the Nyquist sampling theorem, i.e., f≥2fmax, where fmax is the highest frequency component of the nut loosening signal. A hardware-triggered synchronization mode is adopted. All sensors start sampling under the same clock signal, and the time deviation is less than 1 μs.

[0070] The display of the remote monitoring module adopts a human-computer interaction interface to display the position, number, current strain value, displacement value, loosening status and historical data curve of each nut in real time. The staff sets the monitoring parameters through the interface; the data analysis system comprehensively analyzes the monitoring data of multiple nuts and calculates the overall loosening risk index of the standard section. The risk index is calculated using the weighted average method, and the formula is:

[0071] R=∑(w i ×r i )

[0072] Where R is the overall loosening risk index of the tower crane standard section, w iis the weight of the i-th nut, which is determined according to the importance of the nut's position in the standard section, r i is the risk level value of the i-th nut; the weight distribution rule is: the top nut weight is 0.3, the middle nut weight is 0.5, and the bottom nut weight is 0.2;

[0073] The data acquisition module also includes a power management unit; the power management unit adopts a combination of solar panels and batteries for power supply. The solar panels are installed on the top of the tower crane and are used to convert solar energy into electrical energy. The batteries are used to store electrical energy and power the data acquisition module, wireless communication module, etc.; the solar panel has a power of 20W and an open circuit voltage of 18V. The battery is a lithium iron battery pack with a capacity of 12Ah and supports an operating temperature of -20°C to 60°C; the power management unit has charging control and discharge protection functions. When the battery power falls below the set threshold, a low-battery alarm signal is issued and transmitted to the remote monitoring module through the wireless communication module, reminding the staff to replace or charge the battery in time; after the low-battery alarm is triggered, the system automatically switches to power saving mode, reducing the data acquisition frequency to 1 / 5 of the original value;

[0074] The wireless communication module also has a positioning function. When processing data, the data acquisition module binds the geographical location information with the nut loosening monitoring data. When displaying and analyzing data, the remote monitoring module marks the tower crane location on the electronic map and displays the loosening status of the standard section nut at that location. At the same time, the data analysis system analyzes the impact of environmental factors on nut loosening based on tower crane data at different geographical locations, establishes a correlation model between environmental factors and nut loosening, and improves the accuracy of loosening risk judgment. Environmental factors include wind speed, humidity, and temperature. The multivariate regression analysis fitting formula is:

[0075] ε=β0+β1V+β2H+β3T

[0076] Where V is wind speed, H is humidity, T is temperature, and β is the fitting coefficient;

[0077] Example 1: This system was deployed at a high-rise construction site. A total of 12 monitoring sensors were installed on the standard sections of a tower crane. The data acquisition frequency was set to 10 Hz, the strain threshold was 200 με, and the displacement threshold was 0.5 mm. After three months of operation, sensor N7 detected that the strain value continuously exceeded the threshold, triggering a red alarm. An on-site inspection revealed that the preload of the nut had decreased due to vibration. Prompt tightening eliminated the risk. During the same period, the prediction model accurately issued three warnings of potential loosening risks, with an average lead time of 48 hours.

[0078] Example 2: In a tower crane application in a coastal area with strong winds, the system upgraded the sensor protection level to IP68 for high salt fog environments and added a signal enhancement antenna to the communication module. Monitoring data showed that when wind speeds exceeded 15m / s, the strain value fluctuations of the bottom nut increased. The system automatically adjusted the displacement threshold to 0.8mm to reduce the false alarm rate. During a typhoon, the system successfully identified abnormal displacement trends of two nuts, triggering a yellow warning. Workers reinforced the nuts in advance, avoiding structural hazards.

[0079] Reference Figure 2 As shown, the use process of the present invention is as follows: a nut loosening monitoring sensor is installed at the nut of the standard section of the tower crane, and the sensor detects the loosening status of the nut in real time and outputs a signal. The data acquisition module receives the sensor signal, performs amplification, filtering, analog-to-digital conversion and other processing, and then transmits the digital signal to the microprocessor for further analysis. The microprocessor calculates the displacement and strain parameters of the nut, and determines whether there is a loosening risk. Subsequently, the wireless communication module transmits the processed data to the remote monitoring device through narrowband Internet of Things communication technology. The display in the remote monitoring module shows the loosening status of the nut in real time, and the data analysis system evaluates the loosening risk based on real-time and historical data. Once the nut is found to be loose or there is a risk, the alarm module immediately issues a sound and light alarm, and notifies the staff through text messages or push notifications. The staff can take timely measures based on the alarm information to ensure the safe operation of the tower crane.

[0080] The above shows and describes the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The above embodiments and descriptions merely illustrate the principles of the present invention. Various changes and modifications may be made to the present invention without departing from the spirit and scope of the present invention. Such changes and modifications are intended to fall within the scope of the present invention. The scope of protection claimed by the present invention is defined by the appended claims and their equivalents.

Claims

1. A tower crane standard section nut loosening health monitoring system, characterized in that: include: Nut loosening monitoring sensor, data acquisition module, wireless communication module, remote monitoring module and alarm module; The nut loosening monitoring sensor is installed at the nut of the tower crane standard section, and is used to detect whether the nut is loose in real time and output a nut loosening status signal; The data acquisition module is electrically connected to the nut loosening monitoring sensor, and is used to receive the signal of the nut loosening monitoring sensor, process and convert the signal to obtain real-time data of the standard section nut loosening; The wireless communication module is electrically connected to the data acquisition module and is used to transmit the data processed by the data acquisition module to the remote monitoring device via wireless mode; The remote monitoring module includes a display and a data analysis system. The display is used to display the loosening status of the tower crane standard section nuts in real time. The data analysis system is used to analyze the loosening status of the nuts and determine whether there is a loosening risk. The alarm module is electrically connected to the remote monitoring module and is used to send an alarm signal when it is detected that the nut is loose or there is a risk of loosening.

2. A tower crane standard section nut loosening health monitoring system according to claim 1, characterized in that: The nut loosening monitoring sensor is a strain gauge sensor, which includes a strain gauge attached to the surface of the nut connection structure and a conditioning circuit for providing an excitation voltage to the strain gauge. The strain gauge changes resistance as the mechanical deformation caused by the loosening of the nut occurs, and the conditioning circuit converts the resistance change into a voltage signal output; The resistance change of the strain gauge and the strain caused by the loosening of the nut satisfy: Where ΔR is the resistance change of the strain gauge, R is the initial resistance of the strain gauge, K is the sensitivity coefficient of the strain gauge, and ε is the strain caused by the loosening of the nut. ΔL is the displacement change caused by the loosening of the nut, L0 is the initial length of the monitoring point, and the data acquisition module collects the voltage signal and calculates the strain value according to the above formula to determine whether the nut is loose.

3. A tower crane standard section nut loosening health monitoring system according to claim 1, characterized in that: The data acquisition module includes a signal amplification circuit, an anti-aliasing filter, an analog-to-digital converter and a microprocessor; The signal amplification circuit amplifies the voltage signal output by the nut loosening monitoring sensor. The amplification factor A is determined by the circuit structure composed of operational amplifiers. The anti-aliasing filter adopts a Butterworth low-pass filter. The cutoff frequency fc is determined according to the maximum frequency of the nut loosening signal, satisfying fc ≥ 2fmax, where fmax is the highest frequency component in the signal. The analog-to-digital converter converts the filtered analog signal into a digital signal. The microprocessor processes the digital signal in real time, including digital filtering and eigenvalue extraction. The digital filtering uses a mean filter algorithm to average the data of N consecutive sampling points. The calculation formula is: Where Y n is the mean filter output value of the nth sampling point, Y i is the data of the i-th original sampling point, and N is the number of continuous sampling points involved in the mean calculation; The displacement and strain parameters of nut loosening are calculated using the processed signals.

4. A tower crane standard section nut loosening health monitoring system according to claim 1, characterized in that: The wireless communication module adopts narrowband Internet of Things communication technology, operates in the authorized frequency band, and uses CRC coding for error detection during data transmission; The data acquisition module encapsulates the processed real-time data into a data frame. The frame structure includes a frame header, a data area, and a CRC check area. The wireless communication module sends the data frame to the remote monitoring device through the antenna. The remote monitoring device performs a CRC check on the received data frame. If the check passes, the data is parsed. If the check fails, a retransmission is requested.

5. The tower crane standard section nut loosening health monitoring system according to claim 1 is characterized in that: The data analysis system includes a database and a loose risk assessment model; The database is used to store historical monitoring data, tower crane standard section model parameters, and nut specification parameter information; The loosening risk assessment model is based on real-time data and historical data analysis. By setting a strain threshold and a displacement threshold, when the strain value calculated in real time is greater than or equal to the strain threshold, and when the displacement value is greater than or equal to the displacement threshold, it is determined that there is a loosening risk. The time series analysis method is used to process historical data and establish a trend prediction model for the loosening state of nuts. The model expression is: y(t)=a1y(t-1)+…+a n y(t-n)+ε(t)+a1ε(t-1)+…+b m ε((t-m) Where y(t) is the strain or displacement signal value at time t, a i is the coefficient of the autoregressive (AR) part (i=1,2,…,n, n is the autoregressive order), b j is the coefficient of the moving average (MA) part (j = 1, 2, …, m, where m is the moving average order), and ε(t) is the white noise (random error term) at the moment; By predicting strain and displacement values ​​at future moments, loosening risks can be determined in advance.

6. The tower crane standard section nut loosening health monitoring system according to claim 1 is characterized in that: The alarm module includes a sound alarm unit and a light alarm unit; The sound alarm unit uses a buzzer, which emits sound signals of different frequencies to indicate different alarm levels. The light alarm unit uses LED lights, and different colors of light indicate different risk levels. When the data analysis system determines that the nut is loose, the alarm module emits a high-frequency beep and a red light alarm. When it determines that there is a risk of loosening, it emits a low-frequency beep and a yellow light alarm. At the same time, the alarm module also sends text messages or push notifications to the staff's mobile terminals through the wireless communication module. The notification content includes the tower crane location, nut looseness status and risk level.

7. The tower crane standard section nut loosening health monitoring system according to claim 1 is characterized in that: The nut loosening monitoring sensors are provided in plurality and are respectively installed at different nuts of the tower crane standard section. Each sensor has a unique identification code. When receiving the signal, the data acquisition module distinguishes the monitoring data of different nuts according to the identification code. The data acquisition module synchronously collects data from multiple sensors, and the acquisition frequency f satisfies the Nyquist sampling theorem, that is, f≥2fmax, where fmax is the highest frequency component of the nut loosening signal.

8. The tower crane standard section nut loosening health monitoring system according to claim 1 is characterized in that: The display of the remote monitoring module adopts a human-computer interaction interface to display the position, number, current strain value, displacement value, looseness status and historical data curve of each nut in real time. The staff sets the monitoring parameters through the interface; The data analysis system conducts a comprehensive analysis of the monitoring data of multiple nuts and calculates the overall loosening risk index of the standard section. The risk index is calculated using the weighted average method, and the formula is: R=∑(w i ×r i ) Where R is the overall loosening risk index of the tower crane standard section, w i is the weight of the i-th nut, which is determined according to the importance of the nut's position in the standard section, r i is the risk level value of the i-th nut.

9. The tower crane standard section nut loosening health monitoring system according to claim 1 is characterized in that: The data acquisition module also includes a power management unit; The power management unit is powered by a combination of solar panels and batteries. The solar panels are installed on the top of the tower crane to convert solar energy into electrical energy, while the batteries are used to store electrical energy and power the data acquisition module, wireless communication module, etc. The power management unit has charging control and discharge protection functions. When the battery power is lower than the set threshold, it will issue a low-battery alarm signal, which will be transmitted to the remote monitoring module through the wireless communication module to remind the staff to replace or recharge the battery in time.

10. The tower crane standard section nut loosening health monitoring system according to claim 1 is characterized in that: The wireless communication module also has a positioning function. When processing data, the data acquisition module binds the geographical location information with the nut loosening monitoring data. When displaying and analyzing data, the remote monitoring module marks the tower crane position on the electronic map and displays the loosening status of the standard section nut at that location. At the same time, the data analysis system analyzes the impact of environmental factors on nut loosening based on tower crane data in different geographical locations, establishes a correlation model between environmental factors and nut loosening, and improves the accuracy of loosening risk judgment.

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