CN110645925A - Tower crane boom deformation detection device and method - Google Patents
Tower crane boom deformation detection device and method Download PDFInfo
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- CN110645925A CN110645925A CN201911074859.6A CN201911074859A CN110645925A CN 110645925 A CN110645925 A CN 110645925A CN 201911074859 A CN201911074859 A CN 201911074859A CN 110645925 A CN110645925 A CN 110645925A
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- G01—MEASURING; TESTING
- G01B—MEASURING LENGTH, THICKNESS OR SIMILAR LINEAR DIMENSIONS; MEASURING ANGLES; MEASURING AREAS; MEASURING IRREGULARITIES OF SURFACES OR CONTOURS
- G01B15/00—Measuring arrangements characterised by the use of electromagnetic waves or particle radiation, e.g. by the use of microwaves, X-rays, gamma rays or electrons
- G01B15/06—Measuring arrangements characterised by the use of electromagnetic waves or particle radiation, e.g. by the use of microwaves, X-rays, gamma rays or electrons for measuring the deformation in a solid
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Abstract
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.
Description
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.
Example 1
Referring to fig. 1, 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. The device includes:
the reflector group 100 is arranged on a detection node in the length direction of the tower crane boom and used for reflecting electromagnetic waves;
according to the length and the detection requirement of the suspension arm, a plurality of detection nodes are uniformly distributed along the length direction of the suspension arm, a reflection sheet 101 is arranged on each detection node, and the height of the reflection sheet 101 can be increased in sequence from the initial end to the tail end of the suspension arm, so that the reflection sheet 101 in front can be prevented from shielding the reflection sheet 101 behind to receive electromagnetic waves.
A transmitter 201 for transmitting electromagnetic waves to the corresponding reflective sheet 101;
a receiver 202 for receiving the electromagnetic wave reflected by the corresponding reflective sheet 101 and recording the time when the transmitter 201 transmits the electromagnetic wave and the receiver 202 receives the electromagnetic wave;
illustratively, each reflector 101 is correspondingly provided with a transmitter 201 and a receiver 202, so as to record the propagation time of the electromagnetic wave on each detection node; the transmitter 201 and receiver 202 corresponding to each transmitting patch can modulate to transmit and receive electromagnetic waves of different frequencies.
And the analysis system 300 is configured to calculate time data of the electromagnetic waves transmitted from the transmitter 201 to the corresponding receiver 202 and receive the electromagnetic waves, and analyze the time data to obtain a deformation amount of the boom.
For example, when the suspension arm deforms, the reflector plate 101 on each detection node of the suspension arm also deviates in position and angle along with the deformation of the suspension arm, the deformation degree of the different positions of the suspension arm corresponding to each detection node is different, and the deviation amount of the position and angle of the corresponding reflector plate 101 is also different, so that the propagation time of the electromagnetic wave also changes. On the same detection node, when the deformation amount changes, the propagation time of the electromagnetic wave also changes, so the analysis system 300 can obtain the deformation amount of the boom through the propagation time of the electromagnetic wave on each detection node.
Illustratively, the apparatus further comprises:
and the alarm device 400 is used for giving an alarm when the deformation amount of the suspension arm reaches a preset elastic range threshold value.
For example, an elastic range threshold value can be preset according to the elastic deformation range which can be borne by the suspension arm, when the elastic range threshold value is reached, an alarm is given, a driver is timely reminded to check the reason of overlarge deformation, and irreversible damage to the suspension arm due to overlarge deformation is avoided. As an example, the alarm device 400 may be an audible and visual alarm device.
Illustratively, the analysis system 300 is configured to input time data of the electromagnetic wave transmitted by the transmitter 201 and the electromagnetic wave received by the receiver 202 into the deep learning neural network model to calculate the deformation amount of the boom.
The deformation quantity of each detection point is obtained by calculation through the deep learning neural network model, the accuracy of the deformation quantity calculation result can be improved, and the effect of detecting the deformation quantity of the suspension arm is achieved.
In the implementation process, the reflector plate 101 is installed on each detection node on the suspension arm of the tower crane, when the transmitter 201 transmits electromagnetic waves to the corresponding reflector plate 101, the electromagnetic waves are received by the receiver 202 through reflection of the reflector plate 101, and when the suspension arm deforms, the time for the transmitter 201 to transmit the electromagnetic waves to the receiver 202 to receive the electromagnetic waves changes, so that the deformation amount of the corresponding detection point of the suspension arm can be obtained according to the time data for the transmitter 201 to transmit the electromagnetic waves to the corresponding receiver 202 to receive the electromagnetic waves, the deformation amount of the suspension arm can be effectively detected, and a passive deformation data acquisition mode is adopted, so that the method is simpler and more reliable, various sensors are prevented from being installed on the suspension arm, and the problem that the conventional method for providing complicated power supply lines and data transmission lines brings inconvenience for daily maintenance and troubleshooting. According to the method, the reflector plate 101 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 power supply problem, is convenient to maintain and troubleshoot, and is low in cost.
Specifically, as shown in fig. 2, the structural schematic diagram of the device for detecting the deformation of the suspension arm of the tower crane is shown. The device specifically includes:
the reflector group 100 comprises 6 reflectors 101 which are uniformly arranged on a detection node of the suspension arm along the length direction of the suspension arm, and the height of each reflector 101 is sequentially increased along the length direction of the suspension arm (from left to right). .
The transmitter 201 and the receiver 202 are respectively arranged on each reflector plate 101, namely 6 groups of transmitters 201 and receivers 202 are arranged, and the transmitters 201 and the receivers 202 are arranged at the left end (starting end) of the suspension arm.
In the present embodiment, as shown in fig. 3, a structure of a transmitting and receiving array board 700 is schematically illustrated. The transmitters 201 and the receivers 202 are arranged on the transmitting and receiving array plate 700, each set of the transmitters 201 and the receivers 202 is arranged on the transmitting and receiving array plate 700 from bottom to top and corresponds to the reflection sheet 101 which reflects the electromagnetic waves thereof, so that the transmitters 201 transmit the electromagnetic waves, and the corresponding reflection sheet 101 reflects the electromagnetic waves to the receivers 202 in the same set, so as to be received by the receivers 202. Each set of transmitter 201 and receiver 202 may be modulated to transmit and receive electromagnetic waves of different frequencies.
The transmitting and receiving array board 700 is disposed at the left end of the boom near the cab 600.
The analysis system 300 is arranged in the cab 600 and is used for receiving the transmitting time and the receiving time returned by the transmitter 201 and the receiver 202, calculating 6 groups of time data corresponding to the propagation time of the electromagnetic waves of 6 detection nodes according to the transmitting time and the receiving time returned by the transmitter 201 and the receiver 202, calculating deformation quantities of the 6 detection points by using a deep learning neural network model, and determining the deformation quantity of the boom according to the deformation quantities of the 6 detection points.
As shown in fig. 4-5, which are schematic diagrams of the deformation of the boom. As shown in fig. 5, the right arrow in the figure indicates the deformation tendency; when the suspension arm is deformed, for example, the suspension arm bends downwards, the reflector 101 at the deformed position also shifts correspondingly, for example, inclines rightwards, and the propagation time of the electromagnetic wave at the position will be prolonged. Therefore, when 6 sets of electromagnetic waves with different frequencies are emitted from the corresponding emitters 201 and reflected by the reflective sheet 101 to the corresponding receivers 202 for reception, the analysis system 300 obtains the propagation time of each set of electromagnetic waves from emission to reception, the deformation shapes of the boom are different, and the propagation times of the electromagnetic waves detected corresponding to the 6 sets of detection points are different.
The alarm device 400 is installed in the cab 600 and used for giving an alarm when the deformation of the suspension arm reaches a preset elastic range threshold value, for example, an audible and visual alarm device can be adopted to give out audible and visual alarm so as to remind a driver to check the reason that the deformation is too large, and the situation that the deformation of the suspension arm exceeds the elastic deformation range and causes irreversible damage to the suspension arm is avoided.
Example 2
The embodiment of the application also provides a tower crane boom deformation detection method, which is a flow chart of the tower crane boom deformation detection method as shown in fig. 6. The method can be applied to the analysis system 300, and specifically can include the following steps:
step S100: receiving the transmitting time and the receiving time of the electromagnetic wave fed back by the transmitter 201 and the receiver 202;
for example, after 6 groups of transmitters 201 modulate different electromagnetic waves, the emitted electromagnetic waves are reflected back to the receivers 202 corresponding to the respective frequencies through the reflective sheet 101.
Step S200: 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;
illustratively, the analysis system 300 collects the travel time of each set of electromagnetic waves from transmission to reception.
Step S300: and acquiring the deformation quantity of each detection node of the suspension arm according to the time data.
Specifically, deformation quantities of all detection nodes of the tower crane boom are obtained by utilizing the time data and a preset deep learning neural network model.
In an example, 6 groups of propagation time are used as an input data set of the deep learning neural network model, and the input data are solved to obtain deformation quantities corresponding to 6 detection points on the suspension arm.
In the implementation process, the transmitting time and the receiving time of the electromagnetic wave recorded by the transmitter 201 and the receiver 202 are received, so that time data of transmitting the electromagnetic wave to the receiver corresponding to each detection point is obtained, and since the change of the deformation amount of the suspension arm directly affects the propagation time of the electromagnetic wave, the deformation amount of the detection node can be obtained according to the propagation time of the electromagnetic wave of each detection node by using the rule. In the implementation process of the method, the reflector plate 101 is arranged on each detection node of the suspension arm, various sensors are not needed, the inconvenience caused by the arrangement of the sensors is avoided, and the problems that the daily maintenance and the troubleshooting are inconvenient and a lot of problems are caused by the complicated power supply line and data transmission line of the existing method are solved.
For example, as shown in fig. 7, a schematic flow chart for performing an alarm is shown. The method further comprises the following steps:
step S401: fitting the deformation quantity of each detection node to obtain a deformation curve distributed along the length direction of the suspension arm;
step S402: acquiring the deformation quantity of the suspension arm according to the deformation curve;
step S403: and when the deformation reaches a preset elastic range threshold value, alarming.
In an example, the deformation quantity of the 6 detection points is fitted by adopting a fitting function method to obtain a deformation curve distributed along the length direction of the suspension arm, the deformation curve is analyzed to judge whether the deformation of the suspension arm is within an elastic range, if the deformation reaches a set elastic range threshold value (an irreversible plastic deformation stage), an alarm is triggered, and an audible and visual alarm gives an audible and visual alarm to a driver.
And fitting the deformation quantity of each detection node to obtain the deformation quantity of the whole suspension arm, evaluating the deformation quantity of the suspension arm, and alarming when the deformation quantity of the suspension arm reaches a preset elastic range threshold value to avoid irreversible damage to the suspension arm due to the fact that the deformation quantity of the suspension arm exceeds the elastic deformation range.
Example 3
The embodiment of the application further provides a training method of the neural network model applied to tower crane boom deformation detection, and as shown in fig. 8, the training method is a flowchart of the training method of the neural network model applied to tower crane boom deformation detection. The method may specifically comprise the steps of:
step S500: acquiring round-trip time data of electromagnetic waves corresponding to the detection node and corresponding deformation data;
step S600: 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;
step S700: and acquiring the deep learning neural network model according to the training result.
And 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.
For example, as shown in fig. 9, which is a flow chart of training a neural network, step S600 may specifically include:
step S601: 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;
fig. 10 is a schematic diagram of a neural network training model. The leftmost end is an input layer, the right end is an output layer, the arrow below indicates information forward propagation, and the arrow above indicates error backward propagation; and (3) taking the propagation time data of the electromagnetic wave obtained by each detection node as an input layer, taking the deformation quantity of each detection node as a corresponding output layer, and taking the middle layer as a hidden layer to train the model.
Step S602: acquiring output parameters and corresponding target parameters of a hidden layer and an output layer of the training model;
step S603: acquiring errors of the output parameters and the target parameters of the hidden layer and the output layer;
step S604: and when the error is smaller than a preset threshold value, acquiring a training result.
In an example, the actual outputs of the hidden layer and the output layer are obtained according to the given input vector of the input layer and the target output of the output layer;
obtaining an error between the target output and the actual output;
judging whether the error is within an allowable range, namely whether the error is smaller than a preset threshold value;
if so, finishing training, acquiring a fixed weight and a threshold, and obtaining a trained deep learning neural network model according to the fixed weight and the threshold;
if not, calculating the error of the neuron in the network layer, obtaining the error gradient, updating the weight and the threshold value, and repeating the training process.
By the training method, the deep learning neural network model meeting the error range is obtained, so that the accuracy of calculation of the deformation quantity of each detection node is improved.
Example 4
An embodiment of the present application further provides an electronic device, where the electronic device includes 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 device to execute any one of the tower crane boom deformation detection methods of embodiment 3.
The embodiment of the application further provides a readable storage medium, wherein a computer program instruction is stored in the readable storage medium, and when the computer program instruction is read and executed by a processor, the method for detecting the deformation of the suspension arm of the tower crane in any one of the embodiments 3 is executed.
In the embodiments provided in the present application, it should be understood that the disclosed apparatus and method can be implemented in other ways. The apparatus embodiments described above are merely illustrative, and for example, the flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of apparatus, methods and computer program products according to various embodiments of the present application. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
In addition, functional modules in the embodiments of the present application may be integrated together to form an independent part, or each module may exist separately, or two or more modules may be integrated to form an independent part.
The functions, if implemented in the form of software functional modules and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present application or portions thereof that substantially contribute to the prior art may be embodied in the form of a software product stored in a storage medium and including instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
The above description is only an example of the present application and is not intended to limit the scope of the present application, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, improvement and the like made within the spirit and principle of the present application shall be included in the protection scope 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.
The above description is only for the specific embodiments of the present application, but the scope of the present application is not limited thereto, and any person skilled in the art can easily conceive of the changes or substitutions within the technical scope of the present application, and shall be covered by the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
It is noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.
Claims (10)
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.
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