阅读说明：本技术 一种塔吊吊臂形变检测装置及方法 (Tower crane boom deformation detection device and method ) 是由 舒远 蔡江 于 2019-11-05 设计创作，主要内容包括：本申请实施例提供一种塔吊吊臂形变检测装置及方法,涉及塔吊技术领域。该装置包括：反射片组,排列安装在吊臂长度方向的检测节点上,用于反射电磁波；发射器,用于向对应的反射片发射电磁波；接收器,用于接收对应的反射片反射的电磁波,并记录所述发射器发射电磁波和所述接收器接收电磁波的时间；分析系统,用于计算发射器发射电磁到对应的接收器接收电磁波的时间数据,并对所述时间数据进行分析,得到吊臂的形变量。通过在吊臂上设置反射片,利用电磁波发射和接收的时间间隔的变化检测吊臂的形变量,解决了现有的方法繁琐的供电线路及数据传输线路给日常维护及故障排查带来许多不便的问题。(The embodiment of the application provides a device and a method for detecting deformation of a suspension arm of a tower crane, and relates to the technical field of tower cranes. The device includes: the reflector group is arranged on the detection node in the length direction of the suspension arm and used for reflecting electromagnetic waves; the emitter is used for emitting electromagnetic waves to the corresponding reflector plate; the receiver is used for receiving the electromagnetic waves reflected by the corresponding reflector plate and recording the time for the transmitter to transmit the electromagnetic waves and the time for the receiver to receive the electromagnetic waves; and the analysis system is used for calculating the time data of the electromagnetic waves transmitted by the transmitter to the corresponding receiver and received by the receiver, and analyzing the time data to obtain the deformation quantity of the suspension arm. The reflector plate is arranged on the suspension arm, and the deformation quantity of the suspension arm is detected by using the change of the time interval between the transmission and the reception of the electromagnetic wave, so that the problem that the conventional method is inconvenient for routine maintenance and troubleshooting due to a complicated power supply line and a data transmission line is solved.)

1. The utility model provides a tower crane davit deformation detection device which characterized in that, the device includes:

the reflector group is arranged on the detection node in the length direction of the suspension arm and used for reflecting electromagnetic waves;

the emitter is used for emitting electromagnetic waves to the corresponding reflector plate;

the receiver is used for receiving the electromagnetic waves reflected by the corresponding reflector plate and recording the time for the transmitter to transmit the electromagnetic waves and the time for the receiver to receive the electromagnetic waves;

and the analysis system is used for calculating the time data of the electromagnetic waves transmitted by the transmitter to the corresponding receiver and received by the receiver, and analyzing the time data to obtain the deformation quantity of the suspension arm.

2. The tower crane boom deformation detection device of claim 1, wherein the device further comprises:

and the alarm device is used for giving an alarm when the deformation of the suspension arm reaches a preset elastic range threshold value.

3. The tower crane boom deformation detection device of claim 1, wherein:

and the analysis system is used for inputting the time data of the electromagnetic waves transmitted by the transmitter and the electromagnetic waves received by the receiver into the deep learning neural network model so as to calculate the deformation quantity of the suspension arm.

4. A deformation detection method for a tower crane suspension arm is characterized by comprising the following steps:

receiving the transmitting time and the receiving time of the electromagnetic waves fed back by the transmitter and the receiver;

acquiring time data from the transmission to the reception of the electromagnetic waves corresponding to each detection point according to the transmission time and the reception time;

and acquiring the deformation quantity of each detection node of the suspension arm according to the time data.

5. The tower crane boom deformation detection method according to claim 4, further comprising:

fitting the deformation quantity of each detection node to obtain a deformation curve distributed along the length direction of the suspension arm;

acquiring the deformation quantity of the suspension arm according to the deformation curve;

and when the deformation reaches a preset elastic range threshold value, alarming.

6. The tower crane boom deformation detection method according to claim 4, wherein the obtaining deformation quantities of the detection nodes of the boom according to the time data comprises:

and acquiring deformation quantities of all detection nodes of the tower crane boom by using the time data and a preset deep learning neural network model.

7. The training method of the neural network model applied to tower crane boom deformation detection is characterized by comprising the following steps of:

acquiring round-trip time data of electromagnetic waves corresponding to the detection node and corresponding deformation data;

training a training model of a preset deep learning neural network algorithm by using the round trip time data and the deformation data of the electromagnetic waves as a training data set to obtain a training result;

and acquiring the deep learning neural network model according to the training result.

8. The network model training method applied to tower crane boom deformation detection according to claim 7, wherein the training of the training model of the preset deep learning neural network algorithm is performed by using the round trip time data and the deformation data of the electromagnetic waves as a training data set to obtain a training result, and the method comprises the following steps:

taking the round trip time data of the electromagnetic waves as an input layer and the deformation data as an output layer to determine a training model based on a deep learning neural network algorithm;

acquiring output parameters and corresponding target parameters of a hidden layer and an output layer of the training model;

acquiring errors of the output parameters and the target parameters of the hidden layer and the output layer;

and when the error is smaller than a preset threshold value, acquiring a training result.

9. An electronic device, characterized in that the electronic device comprises a memory and a processor, wherein the memory is used for storing a computer program, and the processor runs the computer program to make the computer device execute the tower crane boom deformation detection method according to any one of claims 4 to 6.

10. A readable storage medium, wherein computer program instructions are stored in the readable storage medium, and when the computer program instructions are read and executed by a processor, the method for detecting deformation of a tower crane boom according to any one of claims 4 to 6 is executed.

Technical Field

The application relates to the technical field of tower cranes, in particular to a device and a method for detecting deformation of a suspension arm of a tower crane.

Background

The tower crane is influenced by the swing of a hanging object and wind power in the working process, and the parts of a tower body, a hanging arm and the like are often subjected to elastic deformation in a reciprocating mode. If the tower crane works under the working condition of over-torque or the balance arm is not weighted according to the designed weight-balancing specification, the tower body and the suspension arm are irreversibly plastically deformed, and serious accidents such as the breakage, the overturn and the like of the suspension arm of the tower crane are directly caused. Therefore, monitoring the deformation condition of the key structural component of the tower crane is significant for preventing accidents.

In the prior art, the deformation condition of a tower crane boom is directly detected by additionally arranging a pressure sensor, a displacement sensor, a deformation sensor and the like; or the indirect detection scheme of calculating the real-time moment by reading the weight of the suspended object and the amplitude value of the amplitude-variable trolley and judging whether the real-time moment exceeds the designed value. The two detection modes relate to the problems of power supply and data transmission of various sensors, the suspension arm of the tower crane is generally as long as about 50 meters, if the deformation condition of each key node on the suspension arm needs to be accurately measured, a plurality of sensors need to be additionally arranged, and complicated power supply lines and data transmission lines bring inconvenience and many problems to daily maintenance and troubleshooting.

Disclosure of Invention

An object of the embodiment of the application is to provide a tower crane boom deformation detection device, method and system, through set up the reflector plate on the boom, utilize the electromagnetic wave transmission and the deformation volume of receiving time interval's change detection boom, solved the loaded down with trivial details power supply line of current method and data transmission line and brought many inconvenient problems for routine maintenance and troubleshooting.

The embodiment of the application provides a tower crane davit deformation detection device, the device includes:

the reflector group is arranged on the detection node in the length direction of the suspension arm and used for reflecting electromagnetic waves;

the emitter is used for emitting electromagnetic waves to the corresponding reflector plate;

the receiver is used for receiving the electromagnetic waves reflected by the corresponding reflector plate and recording the time for the transmitter to transmit the electromagnetic waves and the time for the receiver to receive the electromagnetic waves;

and the analysis system is used for calculating the time data of the electromagnetic waves transmitted by the transmitter to the corresponding receiver and received by the receiver, and analyzing the time data to obtain the deformation quantity of the suspension arm.

In the implementation process, the reflection sheet is arranged on each detection node on the suspension arm of the tower crane, after the transmitter transmits electromagnetic waves to the corresponding reflection sheet, the electromagnetic waves are received by the receiver through reflection of the reflection sheet, and after the suspension arm deforms, the time for the transmitter to transmit the electromagnetic waves to the receiver to receive the electromagnetic waves changes, so that the deformation quantity of the corresponding detection point of the suspension arm can be obtained according to the time data for the transmitter to transmit the electromagnetic waves to the corresponding receiver to receive the electromagnetic waves, the deformation quantity of the suspension arm can be effectively detected, a passive deformation data acquisition mode is adopted, the method is simpler and more reliable, various sensors are avoided being arranged on the suspension arm, and the problem that the conventional complex power supply line and data transmission line bring inconvenience to daily maintenance and troubleshooting is solved. According to the method, the reflector plate is arranged on the suspension arm, the deformation quantity of the suspension arm is detected by using the change of the time interval between the transmission and the reception of the electromagnetic wave, the deformation quantity is different, and the propagation time of the electromagnetic wave can be directly influenced, so that the method not only improves the accuracy of deformation quantity measurement, but also cannot influence the normal work and use of the suspension arm, the device is simple in structure, does not need to consider the problem of power supply, is convenient to maintain and troubleshoot, and is low in cost.

Further, the apparatus further comprises:

and the alarm device is used for giving an alarm when the deformation of the suspension arm reaches a preset elastic range threshold value.

In the implementation process, when the deformation reaches the preset elastic range threshold value, an alarm is given, a driver is reminded of checking the reason of overlarge deformation in time, and the condition that the deformation is overlarge and irreversible damage is caused to the suspension arm is avoided.

Further, the analysis system is used for inputting time data of the electromagnetic waves transmitted by the transmitter and received by the receiver into the deep learning neural network model so as to calculate the deformation quantity of the suspension arm.

In the implementation process, the deformation quantity of each detection point is obtained by calculating by using the deep learning neural network model, so that the accuracy of a deformation quantity calculation result can be improved, and the detection effect on the deformation quantity of the suspension arm is achieved.

The embodiment of the application further provides a method for detecting deformation of the suspension arm of the tower crane, which comprises the following steps:

receiving the transmitting time and the receiving time of the electromagnetic waves fed back by the transmitter and the receiver;

acquiring time data from the transmission to the reception of the electromagnetic waves corresponding to each detection point according to the transmission time and the reception time;

and acquiring deformation quantity of each detection node of the tower crane boom according to the time data.

In the implementation process, the transmitting time and the receiving time of the electromagnetic waves recorded by the transmitter and the receiver are received, so that the time data of transmitting the electromagnetic waves to the receiver corresponding to each detection point is obtained, and the deformation amount of the detection nodes can be obtained according to the propagation time of the electromagnetic waves of each detection node by utilizing the rule because the change of the deformation amount of the suspension arm can directly influence the propagation time of the electromagnetic waves. In the implementation process of the method, the reflector plates are arranged on all detection nodes of the suspension arm, various sensors are not needed, inconvenience caused by the arrangement of the sensors is avoided, and a plurality of inconveniences and problems caused by routine maintenance and fault troubleshooting due to complicated power supply lines and data transmission lines in the conventional method are solved.

Further, the method further comprises:

fitting the deformation quantity of each detection node to obtain a deformation curve distributed along the length direction of the suspension arm;

judging the deformation quantity of the suspension arm according to the deformation curve;

and when the deformation reaches a preset elastic range threshold value, alarming.

In the implementation process, the deformation quantity of each detection node is fitted to obtain the deformation quantity of the whole suspension arm, the deformation quantity of the suspension arm is evaluated, and when the deformation quantity reaches a preset elastic range threshold value, an alarm is given to avoid the condition that the deformation quantity of the suspension arm exceeds the elastic deformation range and causes irreversible damage to the suspension arm.

Further, the acquiring deformation quantities of the detection nodes of the suspension arm according to the time data includes:

and acquiring deformation quantities of all detection nodes of the tower crane boom by using the time data and a preset deep learning neural network model.

In the implementation process, the input time data is resolved through a preset deep learning neural network model to obtain the deformation quantity of each detection node, so that the deformation quantity of each detection node of the suspension arm can be accurately obtained, and the distribution condition of the deformation quantity of the suspension arm is obtained.

The embodiment of the application further provides a training method of the neural network model applied to tower crane boom deformation detection, and the method comprises the following steps:

acquiring round-trip time data of electromagnetic waves corresponding to the detection node and corresponding deformation data;

training a training model of a preset deep learning neural network algorithm by using the round trip time data and the deformation data of the electromagnetic waves as a training data set to obtain a training result;

and acquiring the deep learning neural network model according to the training result.

In the implementation process, a plurality of groups of electromagnetic wave round-trip time data and deformation data are collected to be used as a training set to train the training model, so that the accuracy of the training result is improved.

Further, the training a training model of a preset deep learning neural network algorithm by using the round trip time data and the deformation data of the electromagnetic waves as a training data set to obtain a training result includes:

taking the round trip time data of the electromagnetic waves as an input layer and the deformation data as an output layer to determine a training model based on a deep learning neural network algorithm;

acquiring output parameters and corresponding target parameters of a hidden layer and an output layer of the training model;

acquiring errors of the output parameters and the target parameters of the hidden layer and the output layer;

and when the error is smaller than a preset threshold value, acquiring a training result.

In the implementation process, the training model is repeatedly trained to obtain the deep learning neural network model meeting the error range, so that the accuracy of the deformation quantity detection result is improved.

The embodiment of the application further provides electronic equipment, the electronic equipment comprises a memory and a processor, the memory is used for storing a computer program, and the processor runs the computer program to enable the computer equipment to execute any one of the above-mentioned tower crane boom deformation detection methods.

The embodiment of the application further provides a readable storage medium, wherein computer program instructions are stored in the readable storage medium, and when the computer program instructions are read and run by a processor, the method for detecting the deformation of the suspension arm of the tower crane is implemented.

Drawings

In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are required to be used in the embodiments of the present application will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present application and therefore should not be considered as limiting the scope, and that those skilled in the art can also obtain other related drawings based on the drawings without inventive efforts.

FIG. 1 is a block diagram of a device for detecting deformation of a suspension arm of a tower crane according to an embodiment of the present disclosure;

FIG. 2 is a schematic structural diagram of a tower crane boom deformation detection device provided in an embodiment of the present application;

FIG. 3 is a schematic diagram of a transmitting and receiving array board structure provided in an embodiment of the present application;

FIG. 4 is a schematic diagram of a deformation of a suspension arm provided in an embodiment of the present application;

FIG. 5 is a schematic diagram illustrating deformation of a suspension arm according to an embodiment of the present disclosure;

FIG. 6 is a flow chart of a method for detecting deformation of a suspension arm of a tower crane provided in the embodiment of the present application;

FIG. 7 is a schematic diagram of a process for performing an alarm according to an embodiment of the present application;

FIG. 8 is a flowchart of a training method of a neural network model applied to tower crane boom deformation detection provided in the embodiment of the present application;

FIG. 9 is a flow chart of a training process of a neural network provided in an embodiment of the present application;

fig. 10 is a schematic diagram of a neural network training model provided in an embodiment of the present application.

Icon:

100-a set of reflectors; 101-a reflector plate; 201-a transmitter; 202-a receiver; 300-an analysis system; 400-an alarm device; 500-variable amplitude trolley; 600-a driver's cab; 700-transmit and receive array board.

Detailed Description

The technical solutions in the embodiments of the present application will be described below with reference to the drawings in the embodiments of the present application.

It should be noted that: like reference numbers and letters refer to like items in the following figures, and thus, once an item is defined in one figure, it need not be further defined and explained in subsequent figures. Meanwhile, in the description of the present application, the terms "first", "second", and the like are used only for distinguishing the description, and are not to be construed as indicating or implying relative importance.