A remote monitoring system for large-tonnage cranes based on the Internet of Things

Abstract

The present invention discloses a large-tonnage crane remote monitoring system based on the Internet of Things, which belongs to the field of crane control technology and specifically comprises the following steps: collecting the rotational speed of a crane motor during operation and recording the weight of an object carried by the crane each time; collecting noise during operation of the crane and filtering environmental noise to obtain crane noise; dividing the weight of objects to be carried by the crane into several levels, performing linear fitting on the relationship between the change in crane noise with the rotational speed and the change in crane noise with the load at different levels, and calculating the fitting degree of the noise with the rotational speed and the fitting degree of the noise with the load after the linear fitting; when the fitting degree of the noise with the load is high, reducing the noise by adjusting the load carrying time distribution; and when the fitting degree of the noise with the rotational speed is high, reducing the noise by reducing the speed of the crane motor. The present invention realizes effective control of the working noise of the crane.

Description

Technical Field

[0001] The present invention relates to the technical field of crane control, and in particular to a remote monitoring system for large-tonnage cranes based on the Internet of Things. Background Art

[0002] With the rapid development of industrial automation and information technology, the Internet of Things (IoT) has been widely adopted in various engineering equipment, particularly in the monitoring and management of large-tonnage cranes. Traditional crane monitoring systems are typically limited to monitoring basic parameters such as weight, speed, and position, but lack effective means of monitoring the noise pollution generated during crane operation.

[0003] Noise pollution not only affects the working environment but can also have long-term consequences for operators’ health. Therefore, real-time monitoring and analysis of crane noise is crucial for improving operational efficiency, ensuring operational safety, and maintaining environmental protection.

[0004] The noise characteristics of a crane operating under different loads and speeds may vary. The greater the load on the motor, the louder the noise. Similarly, the motor's speed directly affects the noise level. When noise is excessive, simply adjusting the load or speed may not necessarily reduce it. Analyzing the relationship between noise and load or speed is necessary to adjust the crane's operating state accordingly and avoid excessive noise. Summary of the Invention

[0005] The purpose of this invention is to provide a large-tonnage crane remote monitoring system based on the Internet of Things to solve the following technical problems:

[0006] When the noise is too loud, simply adjusting the load or speed may not necessarily reduce the noise. It is necessary to analyze the relationship between noise and load or speed, so as to adjust the working state of the crane in a targeted manner.

[0007] The purpose of the present invention can be achieved through the following technical solutions:

[0008] A remote monitoring system for large-tonnage cranes based on the Internet of Things, including:

[0009] The data acquisition module is used to collect the speed of the crane's motor and record the weight of the object carried by the crane each time, which is the load;

[0010] The noise monitoring module is used to collect the noise when the crane is working and filter the ambient noise to obtain the crane noise;

[0011] The noise fitting module is used to divide the weight of the objects to be transported by the crane into several levels, and perform linear fitting on the relationship between the crane noise and the speed, and the relationship between the crane noise and the load at different levels, and calculate the fitting degree Ra of the noise and speed after linear fitting. 2 , and the noise and load fit Rb 2 ;

[0012] Crane control module, used when in noise and load fit Rb 2 Greater than the fitting degree Ra of noise and speed 2 When the weight level is greater than the night construction standard, the load objects with noise levels greater than the night construction standard are selected and transmitted to the crane control interface; when the noise and speed fit Ra 2 Greater than the noise and load fit Rb 2 When the weight level is different, the load objects with noise greater than the construction standard of the current period are screened out and transmitted to the crane control interface, and a prompt is given to reduce the crane motor speed when carrying the load object.

[0013] As a further solution of the present invention: the calculation process of the degree of fit is:

[0014] Establish a rectangular coordinate system, use the logarithm of the speed or the logarithm of the load as the independent variable, and the noise decibel as the dependent variable for linear fitting. After linear fitting, obtain the coordinate values of each point of the linear fitting function. Select several coordinate points, and the fitting degree R 2 The calculation formula is: ; where j is a or b, y i is the vertical coordinate value of the actual collected noise corresponding to any coordinate point in the linear fitting function, is the value of the vertical coordinate of the function corresponding to any coordinate point in the linear fitting function, It is the average value of the actual noise vertical coordinate.

[0015] As a further solution of the present invention: when there is a degree of fit Ra in any weight level 2 and the fitness Rb 2 When the difference is greater than the preset threshold m and less than the preset threshold n, the weight level is divided into several sub-weight levels again, and the relationship between the crane noise and the speed change and the crane noise and the load change under different sub-weight levels are linearly fitted.

[0016] As a further embodiment of the present invention: when there is a degree of fit Ra in any weight class or sub-weight class 2 and the fitness Rb 2When the difference is less than the preset threshold m, the load objects with noise greater than the construction standard of the current period are screened out and transmitted to the crane control interface, and a prompt is given to reduce the crane motor speed when carrying the load object.

[0017] As a further solution of the present invention: the process of filtering the environmental noise is:

[0018] Based on a deep learning model, the model is trained to identify and separate motor operation noise from environmental noise, and the noise is captured separately through several microphones in different directions, eliminating the noise audio in the opposite direction of the motor position.

[0019] As a further solution of the present invention: when the noise and speed fit Ra is detected 2 , and the noise and load fit Rb 2 When both are less than the preset fitting degree, the crane is inspected.

[0020] As a further solution of the present invention: when the crane noise decibel continues to be greater than 70 decibels for more than a preset time, an alarm is issued on the crane control interface.

[0021] Beneficial effects of the present invention:

[0022] The present invention collects and analyzes the speed, load and noise of the crane motor in real time during operation, filters the environmental noise through advanced signal processing technology, divides the weight levels of the transported objects, analyzes the fit between the crane noise and the motor speed or load at different weight levels, and thus obtains the correlation between the speed or load and noise under different conditions, and then classifies and processes them. When the speed fit is large, it can be processed by reducing the speed and increasing the torque. When the load fit is large and the noise is also large, since the load weight cannot be changed, it is recommended that the load object that generates large noise be transported during daytime working hours, thereby reducing the degree of nuisance to the public and achieving high-efficiency control of the crane. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] The present invention will be further described below with reference to the accompanying drawings.

[0024] Figure 1 It is a schematic flow diagram of the present invention. DETAILED DESCRIPTION

[0025] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making any creative efforts shall fall within the scope of protection of the present invention.

[0026] See also Figure 1 As shown, the present invention is a remote monitoring system for large-tonnage cranes based on the Internet of Things, comprising:

[0027] The data acquisition module is used to collect the speed of the crane's motor and record the weight of the object carried by the crane each time, which is the load;

[0028] The noise monitoring module is used to collect the noise when the crane is working and filter the ambient noise to obtain the crane noise;

[0029] The noise fitting module is used to divide the weight of the objects to be transported by the crane into several levels, and perform linear fitting on the relationship between the crane noise and the speed, and the relationship between the crane noise and the load at different levels, and calculate the fitting degree Ra of the noise and speed after linear fitting. 2 , and the noise and load fit Rb 2 ;

[0030] Crane control module, used when in noise and load fit Rb 2 Greater than the fitting degree Ra of noise and speed 2 When the weight level is greater than the night construction standard, the load objects with noise levels greater than the night construction standard are selected and transmitted to the crane control interface; when the noise and speed fit Ra 2 Greater than the noise and load fit Rb 2 When the weight level is different, the load objects with noise greater than the construction standard of the current period are screened out and transmitted to the crane control interface, and a prompt is given to reduce the crane motor speed when carrying the load object.

[0031] This invention uses a noise fitting module to linearly fit noise characteristics under different loads and speeds, enabling dynamic assessment of crane noise as it changes with operating conditions, resulting in more precise noise control. Based on the noise-load and noise-speed fits, the crane control module intelligently screens loads that may generate excessive noise and prompts the operator to adjust operating strategies, such as reducing motor speed, to reduce noise pollution. Based on IoT technology, remote monitoring is enabled, allowing managers to keep track of the crane's operating status and noise levels, enhancing the intelligent management of the equipment.

[0032] In another preferred embodiment of the present invention, the calculation process of the degree of fit is:

[0033] Establish a rectangular coordinate system, use the logarithm of the speed or the logarithm of the load as the independent variable, and the noise decibel as the dependent variable for linear fitting. After linear fitting, obtain the coordinate values of each point of the linear fitting function. Select several coordinate points, and the fitting degree R 2 The calculation formula is: ; where j is a or b, y i is the vertical coordinate value of the actual collected noise corresponding to any coordinate point in the linear fitting function, is the value of the vertical coordinate of the function corresponding to any coordinate point in the linear fitting function, It is the average value of the actual noise vertical coordinate.

[0034] Linear fitting, a crucial tool in data analysis and statistical modeling, relies on describing the relationship between two variables using a straight line. This relationship typically manifests as the influence of one variable on another. Calculating the goodness of fit of a linear fit, or the coefficient of determination (R²), is crucial because it directly reflects how well the function fits the data.

[0035] Calculating the goodness of fit of linear fitting is of great significance for evaluating the validity of the model, guiding the selection and optimization of the model, and ensuring the accuracy and reliability of the research results.

[0036] In another preferred embodiment of the present invention, when there is a degree of fit Ra in any weight class 2 and the fitness Rb 2 When the difference is greater than the preset threshold m and less than the preset threshold n, the weight level is divided into several sub-weight levels again, and the relationship between the crane noise and the speed change and the crane noise and the load change under different sub-weight levels are linearly fitted.

[0037] The weight levels are further subdivided according to the differences in fitting degree, and the noise change relationship under different sub-weight levels is refitted, thereby improving the precision of noise control.

[0038] In another preferred embodiment of the present invention, when there is a fitness degree Ra in any weight class or sub-weight class 2 and the fitness Rb 2 When the difference is less than the preset threshold m, the load objects with noise greater than the construction standard of the current period are screened out and transmitted to the crane control interface, and a prompt is given to reduce the crane motor speed when carrying the load object.

[0039] In another preferred embodiment of the present invention, the process of filtering the environmental noise is:

[0040] Based on a deep learning model, the model is trained to identify and separate motor operation noise from environmental noise, and the noise is captured separately through several microphones in different directions, eliminating the noise audio in the opposite direction of the motor position.

[0041] By combining a noise monitoring module with a deep learning model, the present invention enables the system to effectively identify and separate crane operation noise from environmental noise, and improves the accuracy of noise data collection by identifying noise from different directions.

[0042] In another preferred embodiment of the present invention, when the noise and speed fit Ra is detected 2 , and the noise and load fit Rb 2 When both are less than the preset fitting degree, the crane is inspected.

[0043] In another preferred embodiment of the present invention, when the crane noise decibel continues to be greater than 70 decibels for longer than a preset time, an alarm is issued on the crane control interface.

[0044] The above is a detailed description of an embodiment of the present invention. However, the content described is only a preferred embodiment of the present invention and should not be considered to limit the scope of the present invention. All equivalent changes and improvements made within the scope of the present invention should still fall within the scope of the patent coverage of the present invention.

Claims

1. A remote monitoring system for large-tonnage cranes based on the Internet of Things, characterized in that: include: The data acquisition module is used to collect the speed of the crane's motor and record the weight of the object carried by the crane each time, which is the load; The noise monitoring module is used to collect the noise when the crane is working and filter the ambient noise to obtain the crane noise; The noise fitting module is used to divide the weight of the objects to be transported by the crane into several levels, and perform linear fitting on the relationship between the crane noise and the speed, and the relationship between the crane noise and the load at different levels, and calculate the fitting degree Ra of the noise and speed after linear fitting. 2 , and the noise and load fit Rb 2 ; Crane control module, used when in noise and load fit Rb 2 Greater than the fitting degree Ra of noise and speed 2 When the weight level is greater than the night construction standard, the load objects with noise levels greater than the night construction standard are selected and transmitted to the crane control interface; when the noise and speed fit Ra 2 Greater than the noise and load fit Rb 2 When the weight level is different, the load objects with noise levels higher than the construction standard for the current period are selected and transmitted to the crane control interface, and a prompt is given to reduce the crane motor speed when carrying the load objects; The calculation process of the goodness of fit is: Establish a rectangular coordinate system, use the logarithm of the speed or the logarithm of the load as the independent variable, and the noise decibel as the dependent variable for linear fitting. After linear fitting, obtain the coordinate values of each point of the linear fitting function. Select several coordinate points, and the fitting degree R 2 The calculation formula is: ; Where j is a or b, is the value of the vertical coordinate of the function corresponding to any coordinate point in the linear fitting function, is the average value of the actual noise vertical coordinate.

2. The large-tonnage crane remote monitoring system based on the Internet of Things according to claim 1 is characterized in that: When there is a goodness of fit Ra in any weight class 2 and the fitness Rb 2 When the difference is greater than the preset threshold m and less than the preset threshold n, the weight level is divided into several sub-weight levels again, and the relationship between the crane noise and the speed change and the crane noise and the load change under different sub-weight levels are linearly fitted.

3. The large-tonnage crane remote monitoring system based on the Internet of Things according to claim 2 is characterized in that: When there is a goodness of fit Ra in any weight class or sub-weight class 2 and the fitness Rb 2 When the difference is less than the preset threshold m, the load objects with noise greater than the construction standard of the current period are screened out and transmitted to the crane control interface, and a prompt is given to reduce the crane motor speed when carrying the load object.

4. The remote monitoring system for large-tonnage cranes based on the Internet of Things according to claim 1 is characterized in that: The process of environmental noise filtering is as follows: Based on a deep learning model, the model is trained to identify and separate motor operation noise from environmental noise, and the noise is captured separately through several microphones in different directions, eliminating the noise audio in the opposite direction of the motor position.

5. The large-tonnage crane remote monitoring system based on the Internet of Things according to claim 1 is characterized in that: When the noise and speed fit Ra is detected 2 , and the noise and load fit Rb 2 When both are less than the preset fitting degree, the crane is inspected.

6. The large-tonnage crane remote monitoring system based on the Internet of Things according to claim 1 is characterized in that: When the crane noise decibel continues to be greater than 70 decibels for longer than the preset time, an alarm will be issued on the crane control interface.

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