Large-tonnage crane remote monitoring system based on Internet of Things

Abstract

The invention discloses a large-tonnage crane remote monitoring system based on the Internet of Things, and belongs to the technical field of crane control, and the large-tonnage crane remote monitoring system specifically comprises the following steps: collecting the rotating speed of a motor of a crane during operation, and recording the weight of an object carried by the crane every time; noise generated when the crane works is collected, environment noise is filtered, and crane noise is obtained; the method comprises the following steps: dividing the weight of an object needing to be carried by a crane into a plurality of grades, respectively carrying out linear fitting on the change relation of the crane noise along with the rotating speed and the change relation of the crane noise along with the load under different grades, and calculating the fitting degree of the noise and the rotating speed and the fitting degree of the noise and the load after linear fitting; when the fitting degree of the noise and the load is high, the noise is reduced by adjusting the carrying time distribution of the load, and when the fitting degree of the noise and the rotating speed is high, the noise is reduced by reducing the rotating speed of a crane motor; according to the invention, the working noise of the crane is effectively controlled.

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, Internet of Things (IoT) technology has been widely used in various engineering equipment, especially in the monitoring and management of large-tonnage cranes. Traditional crane monitoring systems can usually only monitor basic parameters such as weight, speed and position, but lack effective monitoring methods for noise pollution generated during crane operation.

[0003] Noise pollution not only affects the working environment, but may also have a long-term impact on the health of operators. Therefore, real-time monitoring and analysis of the noise generated by cranes is of great significance for improving work efficiency, ensuring operational safety, and maintaining environmental protection.

[0004] The noise characteristics of the crane may be different when working under different loads and speeds. The greater the load of the motor, the greater the noise. Similarly, the running speed of the motor also directly affects the size of the noise. 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 to avoid excessive noise from the crane. Summary of the invention

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

[0006] The purpose of the present invention can be achieved through the following technical solutions:

[0007] A remote monitoring system for large-tonnage cranes based on the Internet of Things, comprising:

[0008] 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 environmental noise to obtain the crane noise; The noise fitting module is used to divide the weight of the objects that the crane needs to carry into several levels, and to 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 to calculate the fitting degree Ra of the noise and the speed after linear fitting. 2 , and the noise and load fit Rb 2 ; Crane control module, used when the noise and load fit Rb 2 Greater than the fit between noise and speed Ra 2 When the weight level is greater than the night construction standard, the load objects with noise 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.

[0009] As a further solution of the present invention: the calculation process of the degree of fit is: A rectangular coordinate system is established, and the logarithm of the speed or the logarithm of the load is used as the independent variable, and the noise decibel is used as the dependent variable for linear fitting. After linear fitting, the coordinate values ​​of each point of the linear fitting function are obtained. A number of coordinate points are selected, and the fitting degree R 2 The calculation formula is: ; where j is a or b, y i is the ordinate value of the actual collected noise corresponding to any coordinate point in the linear fitting function, is the value of the function ordinate corresponding to any coordinate point in the linear fitting function, It is the average value of the actual noise collected.

[0010] As a further solution of the present invention: when there is a fit degree 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 relationship between the crane noise and the load change under different sub-weight levels are linearly fitted again.

[0011] 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 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.

[0012] As a further solution of the present invention: the process of environmental noise filtering is: Based on the deep learning model, the model is trained to identify and separate the motor operation noise and environmental noise, and the noise is captured separately through several microphones in different directions, and the noise audio in the opposite direction of the motor position is eliminated.

[0013] As a further solution of the present invention: when the fitting degree Ra of the noise and the speed is detected 2 , and the noise and load fit Rb 2 When both are less than the preset degree of fit, the crane is inspected.

[0014] 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.

[0015] Beneficial effects of the present invention: 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 under different weight levels, thereby obtaining the correlation between the speed or load and the 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 efficient control of the crane. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] The present invention will be further described below in conjunction with the accompanying drawings.

[0017] Figure 1 It is a schematic diagram of the process of the present invention. DETAILED DESCRIPTION

[0018] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. 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 creative work are within the scope of protection of the present invention.

[0019] 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: 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 environmental noise to obtain the crane noise; The noise fitting module is used to divide the weight of the objects that the crane needs to carry into several levels, and to 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 to calculate the fitting degree Ra of the noise and the speed after linear fitting. 2 , and the noise and load fit Rb 2 ; Crane control module, used when the noise and load fit Rb 2 Greater than the fit between noise and speed Ra 2 When the weight level is greater than the night construction standard, the load objects with noise 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.

[0020] The present invention uses a noise fitting module to perform linear fitting on noise characteristics under different loads and speeds, thus achieving a dynamic evaluation of the crane noise as the working conditions change, making noise control more accurate. The crane control module intelligently screens out load objects that may generate excessive noise based on the fit between noise and load and noise and speed, and prompts the operator to adjust the operating strategy, such as reducing the motor speed, to reduce noise pollution. Based on the Internet of Things technology, remote monitoring can be achieved, which facilitates managers to understand the working status and noise level of the crane at any time, thereby improving the intelligent management level of the equipment.

[0021] In another preferred embodiment of the present invention, the calculation process of the degree of fit is: A rectangular coordinate system is established, and the logarithm of the speed or the logarithm of the load is used as the independent variable, and the noise decibel is used as the dependent variable for linear fitting. After linear fitting, the coordinate values ​​of each point of the linear fitting function are obtained. A number of coordinate points are selected, and the fitting degree R 2 The calculation formula is: ; where j is a or b, y i is the ordinate value of the actual collected noise corresponding to any coordinate point in the linear fitting function, is the value of the function ordinate corresponding to any coordinate point in the linear fitting function, It is the average value of the actual noise collected.

[0022] As an important tool in data analysis and statistical modeling, linear fitting is based on describing the relationship between two variables through a straight line. This relationship is usually expressed as the influence of one variable on another. Calculating the goodness of fit of the linear fit, that is, the coefficient of determination R², plays a vital role because it directly reflects how well the function fits the data.

[0023] 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.

[0024] In another preferred embodiment of the present invention, when there is a fit degree 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 relationship between the crane noise and the load change under different sub-weight levels are linearly fitted again.

[0025] The weight levels are further subdivided according to the differences in fit, and the noise variation relationships under different sub-weight levels are refitted, thereby improving the refinement of noise control.

[0026] In another preferred embodiment of the present invention, when there is a fit 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.

[0027] In another preferred embodiment of the present invention, the process of filtering the environmental noise is: Based on the deep learning model, the model is trained to identify and separate the motor operation noise and environmental noise, and the noise is captured separately through several microphones in different directions, and the noise audio in the opposite direction of the motor position is eliminated.

[0028] The present invention combines a noise monitoring module with a deep learning model, and the system can effectively identify and separate crane operation noise and environmental noise, and improve the accuracy of noise data collection by identifying noise in different directions.

[0029] In another preferred embodiment of the present invention, when the noise and the speed fit Ra are detected 2 , and the noise and load fit Rb 2 When both are less than the preset degree of fit, the crane is inspected.

[0030] In another preferred embodiment 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.

[0031] The above is a detailed description of an embodiment of the present invention, but the content is only a preferred embodiment of the present invention and cannot be considered to limit the scope of implementation 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 environmental noise to obtain the crane noise; The noise fitting module is used to divide the weight of the objects that the crane needs to carry into several levels, and to 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 to calculate the fitting degree Ra of the noise and the speed after linear fitting. 2 , and the fit of noise and load Rb 2 ; Crane control module, used when the noise and load fit Rb 2 Greater than the fit between noise and speed Ra 2 When the weight level is greater than the night construction standard, the load objects with noise 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.

2. The large-tonnage crane remote monitoring system based on the Internet of Things according to claim 1 is characterized in that: The calculation process of the goodness of fit is: A rectangular coordinate system is established, and the logarithm of the speed or the logarithm of the load is used as the independent variable, and the noise decibel is used as the dependent variable for linear fitting. After linear fitting, the coordinate values ​​of each point of the linear fitting function are obtained. A number of coordinate points are selected, and the fitting degree R 2 The calculation formula is: ; where j is a or b, y i is the ordinate value of the actual collected noise corresponding to any coordinate point in the linear fitting function, is the value of the function ordinate corresponding to any coordinate point in the linear fitting function, It is the average value of the actual noise collected.

3. 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 relationship between the crane noise and the load change under different sub-weight levels are linearly fitted again.

4. The large-tonnage crane remote monitoring system based on the Internet of Things according to claim 3 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.

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

6. 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 fit of noise and load Rb 2 When both are less than the preset degree of fit, the crane is inspected.

7. 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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