CN113511592B - Shore bridge monitoring method and system and shore bridge - Google Patents

Abstract

The application relates to the technical field of monitoring of a quay bridge, in particular to a quay bridge monitoring method and system and the quay bridge. The method comprises the following steps: acquiring real-time vibration information and attitude information of a quay crane in the running process; acquiring wind speed information of a shore bridge in the running process; inputting the real-time vibration information, the attitude information and the wind speed information into a neural network model to obtain prediction state information, wherein the prediction state information comprises the prediction vibration information; generating alarm information when the predicted vibration information reaches a first preset condition; and predicting the operation state of the quay bridge by combining the attitude information, the real-time vibration information, the wind speed information and other environmental factors. Because the actual running environment of the shore bridge is considered, the running state generated by the shore bridge can be predicted more accurately, and operators can timely perform operation of adjusting the state of the shore bridge according to the predicted running state, so that the attitude information of the shore bridge is changed, the integral vibration of the shore bridge is adjusted, and smooth loading and unloading of objects are facilitated.

Description

Shore bridge monitoring method and system and shore bridge

Technical Field

The application relates to the technical field of monitoring of a quay bridge, in particular to a quay bridge monitoring method and system and the quay bridge.

Background

The shore bridge is an object lifting device built in a port or a wharf, and is mainly used for lifting, loading and unloading objects such as containers and the like on ships.

In the related art, the integral structure of the shore bridge is huge, and during the operation, the shore bridge can vibrate due to the influence of inertia or air flow. At present, the vibration intensity of the shore bridge mainly depends on the manual judgment of operators, so that larger deviation is easy to occur in the judgment result, and when the vibration amplitude of the shore bridge is larger, the object suspended in control is caused to deviate, so that the handling and loading and unloading of the object can be influenced.

Content of the application

In view of the above, the application provides a method and a system for monitoring a quay crane and the quay crane, which solve or improve the problem that the handling and loading of objects are affected by the fact that the vibration intensity of the quay crane is artificially judged to have larger deviation in the running process of the quay crane.

In a first aspect, the present application provides a method for monitoring a quay crane, the method comprising: acquiring real-time vibration information and attitude information of the shore bridge in the running process; acquiring wind speed information of the shore bridge in the running process; inputting the real-time vibration information, the attitude information and the wind speed information into a neural network model to obtain prediction state information, wherein the prediction state information comprises prediction vibration information; and generating alarm information when the predicted vibration information reaches a first preset condition.

According to the shore bridge monitoring method provided by the application, in the operation process of the shore bridge, the real-time vibration information, the attitude information and the wind speed information of the shore bridge are acquired, and then the real-time vibration information, the attitude information and the wind speed information are input into the neural network model to acquire the prediction state information of the shore bridge, so that the prediction vibration information of the shore bridge in the operation process is mastered, and when the prediction vibration information is lower than a preset condition, alarm information is generated; in the operation process of the shore bridge, the vibration of the shore bridge is predicted through a computer program, so that the operation state of the shore bridge is accurately judged, and when the predicted vibration information reaches a first preset condition, alarm information is sent out to prompt operators. And predicting the operation state of the quay bridge by combining the attitude information, the real-time vibration information, the wind speed information and other environmental factors. Because the actual running environment of the shore bridge is considered, the running state generated by the shore bridge can be predicted more accurately, and operators can timely perform operation of adjusting the state of the shore bridge according to the predicted running state, so that the attitude information of the shore bridge is changed, the integral vibration of the shore bridge is adjusted, and smooth loading and unloading of objects are facilitated.

With reference to the first aspect, in a possible implementation manner, the predicted state information further includes predicted wheel pressure information of each of a plurality of angle wheels, where the shore bridge monitoring method further includes: comparing the plurality of predicted wheel pressure information to generate a comparison information result; when the comparison information result reaches a second preset condition, generating alarm information; wherein the comparison information result varies with the variation of the predicted vibration information.

With reference to the first aspect, in one possible implementation manner, the training process of the neural network includes: inputting a multi-information sample consisting of a vibration information sample, a posture information sample and a wind speed information sample into the neural network model to obtain predicted vibration training information and predicted wheel pressure training information output by the neural network model; calculating loss based on the predicted vibration training information and standard vibration information, and calculating loss based on the predicted wheel pressure training information and standard wheel pressure information, wherein the standard vibration information and the standard wheel pressure information correspond to the multi-information sample; and adjusting parameters of the neural network model based on the loss calculation result.

With reference to the first aspect, in a possible implementation manner, the acquiring the gesture information includes one or more of the following combinations: and acquiring the trolley information, the crane weight information, the girder position information and the trolley acceleration information of the shore bridge.

With reference to the first aspect, in a possible implementation manner, the acquiring the trolley information includes: and acquiring the trolley position information and the trolley acceleration information.

With reference to the first aspect, in a possible implementation manner, acquiring the real-time vibration information includes: and acquiring the front large Liang Shishi vibration information, the upper cross beam real-time vibration information and the rear large Liang Shishi vibration information of the shore bridge.

With reference to the first aspect, in a possible implementation manner, the acquiring the real-time vibration information specifically includes: acquiring initial vibration information of a quay crane in the running process; and filtering the initial vibration information to obtain the real-time vibration information.

With reference to the first aspect, in one possible implementation manner, the shore bridge monitoring method further includes: and acquiring the predicted vibration information and displaying the predicted vibration information in real time.

In a second aspect, the present application further provides a quay crane monitoring system, configured to implement the quay crane monitoring method described above, where the quay crane monitoring system includes: the vibration monitoring module is used for monitoring the real-time vibration information of the shore bridge in the running process; the attitude monitoring module is used for monitoring the attitude information of the shore bridge in the running process; the wind speed monitoring module is used for monitoring the wind speed information of the shore bridge in the running process; the computing module is respectively in communication connection with the vibration monitoring module, the gesture monitoring module and the wind speed monitoring module to acquire the vibration information, the gesture information and the wind speed information, and is used for acquiring prediction state information according to a neural network model after inputting the vibration information, the gesture information and the wind speed information, wherein the prediction state information comprises prediction vibration information; and the early warning module is in communication connection with the calculation module to acquire the predicted vibration information, and the early warning module is configured to send out alarm information when the predicted vibration information reaches a first preset condition.

In combination with the second aspect, the shore bridge monitoring system is characterized in that the vibration monitoring module, the gesture monitoring module and the wind speed monitoring module are arranged to respectively transmit real-time vibration information, gesture information and wind speed information of the shore bridge in the operation process, the real-time vibration information, the gesture information and the wind speed information are transmitted to the calculation module, the calculation module obtains prediction state information according to the neural network model, accordingly, the prediction vibration information in the operation process of the shore bridge is obtained smoothly, and accordingly, an alarm is sent out by an early warning module to an operator according to the prediction vibration information, so that the operator is prompted, monitoring of vibration of the shore bridge is achieved through a computer program, accuracy of vibration monitoring of the shore bridge is improved, and smooth loading and unloading of the shore bridge to an object is facilitated.

In a third aspect, the present application also provides a quay crane comprising: the shore bridge monitoring system; and a main body for installing the quay crane monitoring system.

In combination with the third aspect, the shore bridge monitoring system is arranged to monitor the operation state of the shore bridge in the operation process of the shore bridge, so that the shore bridge can conveniently and smoothly load and unload objects.

Drawings

Fig. 1 is a schematic diagram of a shore bridge monitoring method according to some embodiments of the present application.

FIG. 2 is a schematic diagram of predicted wheel pressure information according to some embodiments of the application.

Fig. 3 is a schematic diagram illustrating a training process of a neural network according to some embodiments of the present application.

Fig. 4 is a schematic diagram showing the construction of real-time vibration information processing according to some embodiments of the present application.

Fig. 5 is a schematic diagram of a quay monitoring system according to some embodiments of the present application.

Fig. 6 is a schematic diagram illustrating a vibration detection module according to some embodiments of the application.

Fig. 7 is a schematic diagram illustrating the configuration of a gesture monitoring module in some embodiments of the application.

Fig. 8 is a schematic diagram showing the constitution of a calculation module in some embodiments of the application.

Fig. 9 is a schematic diagram illustrating a configuration of an early warning module according to some embodiments of the application.

Fig. 10 is a schematic structural view of a quay bridge according to some embodiments of the present application.

Fig. 11 is a schematic structural diagram of an electronic device according to some embodiments of the present application.

Detailed Description

The following description of the embodiments of the present application will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present application, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.

Summary of the application

In the operation process of the shore bridge, the shore bridge can vibrate due to the influence of inertia or air flow in the environment, but the vibration amplitude of the shore bridge is difficult to judge by naked eyes due to the huge overall structure of the shore bridge, and the judgment result is easy to have larger deviation by mainly relying on the experience of operators, so that the smooth loading and unloading of the shore bridge to objects are affected. In the prior art, in order to solve the problem, the vibration sensor is installed on the quay to monitor the vibration amplitude of the quay, so that an operator can know the operation state of the quay conveniently, but the vibration sensor can only monitor the vibration of the quay, and the operator still needs to predict the operation state of the quay by relying on experience, so that a large deviation still exists in judging the operation state of the quay, and the loading and unloading of objects are affected.

In order to solve the problems, the application provides a method, a system and a quay bridge for monitoring the quay bridge, which can acquire the environmental factors (such as wind speed) of the quay bridge and the running states (such as crane weight information and trolley information) of the quay bridge, combine the environmental factors with the running states of the quay bridge, form a multi-signal composed of the quay bridge environmental factors and the running states of the quay bridge, and acquire the simulation relation between the multi-signal and the quay bridge vibration through simulation training, so that when the environmental factors or the running states of the quay bridge change, the running states of the quay bridge can be monitored in real time, and the vibration of the quay bridge can be predicted automatically through simulation calculation, thereby improving the judgment precision of the running states of the multi-quay bridge and facilitating the smooth loading and unloading of objects.

It should be noted that the method for monitoring the quay crane provided by the application can be used for the quay crane in any scene. Specifically, the shore bridge monitoring system is designed to collect environmental factors and the operation state of the shore bridge, so that the operation state of the shore bridge is smoothly predicted. The application does not limit the concrete constitution of the quay bridge monitoring system. For example, the embodiment of the present application may be a quay for loading and unloading a large transport vehicle such as a ship in a large port, or may be a quay for lifting an object on a small dock.

Having described the basic principles of the present application, various non-limiting embodiments of the present application will now be described in detail with reference to the accompanying drawings.

Exemplary quay bridge monitoring method

Fig. 1 is a schematic diagram of a shore bridge monitoring method according to some embodiments of the present application. Referring to fig. 1, the quay monitoring method includes:

and step S100, acquiring real-time vibration information and attitude information of the quay crane in the running process.

The real-time vibration information is used for representing the vibration amplitude and frequency of the quay bridge during operation. Because the integral structure of the quay crane is bigger, the mass of objects lifted by the quay crane is also bigger, and meanwhile, the path of the quay crane in the process of lifting the objects is longer. Therefore, in the process of hoisting objects by the shore bridge, vibration is inevitably generated under the comprehensive actions of self-power reasons, air flow in the environment and other factors.

The attitude information is used for representing the running state of the quay crane, such as the weight of an object lifted by the quay crane, the running speed of the quay crane, the running acceleration of the quay crane and the like, and is directly related to real-time vibration of the quay crane in the running process.

In an ideal situation without considering environmental factors, the heavier the weight of the object lifted by the quay crane, the slower the running speed of the quay crane and the lower the running acceleration of the quay crane, the real-time vibration generated by the quay crane should be minimal.

Step S200, obtaining wind speed information of the shore bridge in the running process. The wind speed information characterizes the information of the air flow in the environment during operation of the quay crane. In the flowing process of air, objects lifted by the shore bridge and the shore bridge can shake.

Especially when the dimensions of the quay are large and the whole is high, (the height of the quay may reach tens of meters) and the wind speed at high places may be larger than the wind speed at low places, whereas the stability of the side of the quay far from the ground is poor with respect to the side of the quay near the ground, especially when the object is suspended in the air, so that the wind speed information is extremely important for the influence of the quay vibration.

And step S300, inputting the real-time vibration information, the attitude information and the wind speed information into a neural network model to obtain the prediction state information, wherein the prediction state information comprises the prediction vibration information. The prediction state information characterizes the state of the quay bridge which appears next in the running process, is the prediction of the motion state of the quay bridge, and is related to real-time vibration information, attitude information and wind speed information.

The neural network model is a pre-trained artificial intelligent model capable of obtaining prediction state information based on implementation vibration information, attitude information and wind speed information. However, the specific network architecture and training mode of the neural network model are not strictly limited in the application.

The change rule of the real-time vibration information, the gesture information and the wind speed information generally belongs to gradual change, so that the obtained prediction state information generally also gradually changes.

In some scenarios, real-time vibration information, attitude information, and wind speed information may be abrupt. Specifically, for example, when the quay crane just lifts an object or just drops the object, the weight of the lifting appliance may be suddenly changed, so that both the real-time vibration information and the gesture information may be suddenly changed. And the flow rate of the air may also be suddenly changed, so that the wind speed information is suddenly changed.

Any one of the real-time vibration information, the attitude information and the wind speed information is suddenly changed or any of the real-time vibration information, the attitude information and the wind speed information is suddenly changed, and the prediction state information is changed accordingly, so that the operation state of the shore bridge is accurately predicted, particularly the vibration information of the shore bridge can be predicted, an operator can conveniently follow the prediction vibration information in time to adjust the operation of the shore bridge, and smooth lifting of objects is facilitated.

Step S400, when the predicted vibration information reaches a first preset condition, an alarm signal is generated.

The first preset condition characterizes a standard of safe operating state of the quay bridge. In an embodiment of the present application, the predicted vibration information may be measured by a value of an electrical signal, and the first preset condition may also include a value represented by the electrical signal. When the predicted vibration information is equal to or greater than the numerical value, it is determined that the predicted vibration information reaches a first preset condition. At this time, an alarm should be sent to an operator, so that the operator can quickly adjust the operation of the quay crane or stop the operation of the quay crane, thereby changing the attitude information and the real-time vibration information of the quay crane to reduce the predicted vibration information below the first preset condition.

According to the method, in the operation process of the shore bridge, the vibration generated by the shore bridge in the operation process can be predicted automatically by acquiring the operation state of the shore bridge and the information in the environment, the prediction result can be changed according to the change of the operation state of the shore bridge and the change of the information in the environment, so that the accuracy of the vibration prediction of the shore bridge is improved, when the vibration of the shore bridge reaches the first preset condition, an alarm is sent out to remind an operator, and the operator can correspondingly adjust the shore bridge, so that smooth loading and unloading of the object by the shore bridge are facilitated.

FIG. 2 is a schematic diagram of predicted wheel pressure information according to some embodiments of the application. Referring to fig. 2, the predicted state information further includes predicted wheel pressure information of each of the plurality of corner wheels, wherein the quay crane monitoring method further includes:

s500, comparing the plurality of predicted wheel pressure information to generate a comparison information result. The wheel pressure of the horn wheel represents the pressure born by the horn wheel in the operation process of the shore bridge. In particular, when the quay bridge does not vibrate in an ideal state, the plurality of horn wheels of the quay bridge should equally divide the weight of the whole quay bridge, and the wheel pressure values of the plurality of horn wheels should be infinitely close to zero in phase difference amplitude.

However, in practical situations, the vibration of the land bridge is unavoidable, so that the wheel pressure of the horn wheel changes with the vibration of the land bridge. When the quay bridge vibrates, the whole quay bridge can deviate by a certain amplitude, so that the wheel pressure of any one or any one side of the plurality of horn wheels of the quay bridge is increased, and the wheel pressures of other horn wheels are reduced. As in the ideal case, the smaller the amplitude of vibration of the quay bridge as a whole, the smaller the range of wheel pressure variation of the horn wheel. The wheel pressure of the castor wheel has a direct connection with the vibration of the quay bridge.

Meanwhile, as the movement of the shore bridge is realized through the horn wheel, when the wheel pressure of the horn wheel is large, the horn wheel can be damaged, so that the shore bridge cannot continue to operate, and therefore, the wheel pressure of the horn wheel is predicted, the prediction state information of the shore bridge is more accurately obtained, and the possibility that the shore bridge cannot normally operate can be reduced.

The comparison information results show that the variation range of the wheel pressure is represented, because the sum of the pressures born by the plurality of corner wheels in the process of lifting the object is constant, when the wheel pressure of one or one side of the plurality of corner wheels is increased, the rest wheel pressures are correspondingly reduced, and the variation range of the wheel pressure is obtained through the comparison of the two wheel pressures, so that the running state of the quay bridge is accurately judged.

And S510, generating alarm information when the comparison information result reaches a second preset condition, wherein the comparison information result changes along with the change of the predicted vibration information. The second preset condition is a standard for judging the comparison information result, the standard can be an electric signal value, and when the comparison information result is larger than or equal to the electric signal value, the comparison information result can be considered to reach the second preset condition, so that early warning information is sent out.

Through the steps, in the operation process of the shore bridge, the wheel pressure of the angle wheel of the shore bridge is predicted to generate a predicted wheel pressure signal, and the vibration amplitude of the shore bridge is further obtained through the comparison of the predicted wheel pressure signal, so that the prediction accuracy of the vibration of the shore bridge is improved.

Fig. 3 is a schematic diagram illustrating a training process of a neural network according to some embodiments of the present application. Referring to fig. 3, the training process of the neural network includes:

S310, inputting a vibration information sample, a posture information sample and a multi-information sample formed by a wind speed information sample into the neural network model, and obtaining predicted vibration training information and predicted wheel pressure training information output by the neural network model.

The multi-information samples represent the operational status of the quay bridge. The multi-information sample can be generated by measuring according to the actual operation condition of the shore bridge on site, and the multi-information sample can also be generated by performing simulation operation in a laboratory.

Since the neural network model training requires a large number of multi-information samples, in order to facilitate the acquisition of the multi-information samples, the sources of the multi-information samples can be divided into an experimental part and a measurement part, the experimental part is collected by simulating operation in a laboratory, the measurement part is used for measuring and collecting actual quay crane operation conditions in the field, and a large number of multi-information samples are formed by combining the experimental part and the measurement part.

The ratio of the experimental part to the measuring part can be determined according to the actual situation. In particular, the experimental part and the measurement part may each account for half. When the laboratory equipment is complete and the accuracy of the experimental result is high, the multi-information samples in the experimental part can occupy most, such as 70% of all the multi-information samples.

The predicted vibration training information and the predicted wheel pressure training information represent corresponding generated results when a multi-information sample is input, namely, the predicted results of the operation state of the quay bridge in the model training stage.

S320, calculating loss based on the predicted vibration training information and the standard vibration information, and calculating loss based on the predicted wheel pressure training information and the standard wheel pressure training information, wherein the standard vibration information and the standard wheel pressure information correspond to the multi-information sample.

The standard vibration information characterizes the actual vibration information of the landing bridge at the corresponding multi-information sample. The predicted vibration training information is the predicted vibration information automatically calculated by the neural network model under the corresponding multi-information sample.

And (3) calculating through the predicted vibration training information and the characterization vibration information, and knowing whether a difference exists between the predicted vibration training information and the standard vibration information. When the plurality of predicted vibration training information is compared with the plurality of standard vibration information, it can be judged whether the predicted vibration information is in an actual vibration information which approaches the quay at infinity. When there is a large difference, the parameter correspondence of the neural network needs to be adjusted so that the predicted vibration training information approaches the standard vibration information.

The predicted wheel pressure training information represents the predicted wheel pressure information of the lower corner wheel of the corresponding multi-information sample, and the standard wheel pressure information represents the actual wheel pressure of the corresponding multi-information sample. The calculation of the predicted wheel pressure training information and the standard wheel pressure training information is consistent with the principles of the standard vibration information and the predicted vibration training information, and will not be described in detail herein.

And S330, adjusting the neural network model parameters based on the loss calculation result. The neural network model parameters are combined with the multi-information samples to calculate the predicted vibration training information and the predicted wheel pressure training information. The predicted vibration training information and the predicted wheel pressure training information are changed by adjusting the neural network model parameters. Therefore, the predicted vibration training information approaches the standard vibration information infinitely, and the predicted wheel pressure training information approaches the standard wheel pressure information.

Through the steps, each time a multi-information sample is input, a piece of predicted vibration training information and predicted wheel pressure training information are correspondingly generated, when a large number of different multi-information samples are input, parameters of the neural network model are adjusted through loss calculation results, when the regression accuracy of the neural network training is achieved, the neural network model is trained, and when one multi-information sample is arbitrarily input, the neural network model correspondingly outputs a specific predicted vibration signal and a predicted wheel pressure signal according to a simulation relation. The simulated relationship between the multi-information sample and the predicted vibration information and the predicted wheel pressure information can be represented by the trained neural network model.

In some embodiments of the present application, acquiring pose information may include one or more combinations of: trolley information, sling information, girder position information and trolley acceleration information.

The trolley information characterizes the running state of the trolley of the quay crane, such as the running speed and the running acceleration of the trolley.

The weight born by the lifting appliance represented by the lifting weight information in the process of lifting the object, and the lifting weight information may change due to inconsistent object states. If the object is lifted, the force of the lifting tool needs to be larger than the self weight of the object because the object needs to be lifted. When the object is put down, the force of the lifting tool needs to be smaller than the self weight of the object in order for the object to descend smoothly.

The position of the girder represented by girder position information can be referred to when the trolley moves, the position information of the girder and the girder can be compared, and the position information of the trolley relative to the shore bridge can be acquired more accurately. Due to the change of the position of the trolley, the positions of the lifting appliance and the object are changed along with the movement of the trolley, so that the overall stress condition of the trolley is changed.

The speed of the overall movement of the shore bridge represented by the acceleration information of the large vehicle directly influences the vibration of the shore bridge. In particular, when the moving speed of the quay is high, the impact strength of the quay and air can be increased, so that the quay is easier to vibrate, and particularly, the side of the quay away from the ground is more prone to vibration.

Through the steps, in the operation process of the shore bridge, the attitude change of the shore bridge is more accurately obtained through monitoring the information of each part of the shore bridge, so that the accuracy of the vibration information of the shore bridge is improved.

In some embodiments of the application, obtaining the dolly information comprises: trolley position information and trolley acceleration information. The trolley position information is position information characterizing the trolley relative to the quay crane.

Because the lifting appliance drives the object to synchronously move along with the movement of the trolley, the whole stress condition of the shore bridge is changed at any moment in the process of moving the object.

The speed of the trolley represented by the trolley acceleration information changes quickly and slowly. When the acceleration of the trolley is large, the whole stress of the shore bridge is possibly uneven due to the fact that the speed change is rapid, and vibration is easier to generate. Therefore, the position information and the acceleration information of the trolley have important reference values for the attitude information of the quay crane.

Through the steps, when the shore bridge is used for lifting objects, the information of the trolley is acquired at any time to master the position of the trolley relative to the shore bridge and the acceleration of the trolley, so that the attitude information of the shore bridge can be acquired more accurately.

In some embodiments of the application, acquiring real-time vibration information includes: and acquiring front large Liang Shishi vibration information, upper beam real-time vibration information and rear large Liang Shishi vibration information of the shore bridge. Because the integral structure of the shore bridge is large, the vibration of each part of the shore bridge may be different in the operation process of the shore bridge, and thus, the real-time vibration information of each part of the shore bridge is obtained by obtaining the vibration information of the front girder, the upper girder and the rear girder of the shore bridge.

Through the steps, when the real-time vibration information of the shore bridge is obtained, the real-time vibration information of each component of the shore bridge is obtained at the same time, so that the real-time vibration information of the whole structure of the shore bridge is obtained, and further, the more accurate posture information of the shore bridge is obtained.

Fig. 4 is a schematic diagram illustrating real-time vibration information processing according to some embodiments of the present application. Referring to fig. 4, acquiring real-time vibration information includes:

s1120, obtaining initial vibration information of the quay crane in the running process.

The initial vibration information represents vibration information directly measured by each part of the quay, and a plurality of initial vibration information needs to be fused to form the integral initial vibration information of the quay because the vibration of the quay part may have a difference. The initial vibration information can be an electric signal representing the vibration amplitude, an electric signal representing the vibration frequency, or other signals representing the integral vibration of the quay bridge.

S1121, filtering the initial vibration information to obtain real-time vibration information. The initial vibration information is processed in a filtering processing mode such as wavelet decomposition, so that the initial vibration information of all parts of the shore bridge is fused smoothly to form the integral vibration information of the shore bridge, and the integral real-time vibration information of the shore bridge is acquired smoothly.

Through the steps, when the real-time vibration information of the shore bridge is acquired, the real-time vibration information of each part of the shore bridge is integrated by processing the initial vibration information to form the integral real-time vibration information of the shore bridge, so that the real-time vibration information of the shore bridge is acquired more accurately.

In some embodiments of the present application, the quay bridge monitoring method further includes: and obtaining and displaying the predicted vibration information and the predicted wheel pressure information in real time. After the predicted vibration information and the predicted wheel pressure information are obtained, the predicted vibration information and the predicted wheel pressure information can be converted into the real-time display which can be displayed on a display screen to operators, and the operators can know the running state of the shore bridge conveniently.

Through the steps, an operator can change the operation state of the shore bridge according to the displayed predicted vibration information and the predicted wheel pressure information, so that the possibility that the vibration amplitude or the wheel pressure information of the shore bridge is large in the operation process is reduced.

Exemplary quay bridge monitoring System

Fig. 5 is a schematic diagram of a quay monitoring system according to some embodiments of the present application. The quay monitoring system is used for realizing the quay monitoring method described in any embodiment. Referring to fig. 5, the safety monitoring system includes: vibration monitoring module 910, gesture monitoring module 920, wind speed monitoring module 930, computing module 940, and pre-warning module 950. The vibration monitoring module 910 is configured to monitor real-time vibration information of the quay crane during operation. The attitude monitoring module 920 is configured to monitor attitude information of the quay crane during operation. The wind speed monitoring module 930 is configured to monitor wind speed information of the quay crane during operation.

The calculation module 940 is communicatively connected to the vibration monitoring module 910, the gesture monitoring module 920, and the wind speed monitoring module 930, respectively, to obtain real-time vibration information, gesture information, and wind speed information. The calculation module 940 is configured to input vibration information, attitude information, and wind speed information into the neural network model to obtain predicted state information, the predicted state information including predicted vibration information.

The pre-warning module 950 is communicatively coupled to the computing module 940 to obtain the predicted vibration information, the pre-warning module 950 being configured to issue an alarm message when the predicted vibration information reaches a first predetermined condition.

When the shore bridge is in operation, the real-time vibration information of the shore bridge is monitored by the vibration monitoring module 910, the gesture monitoring module 920 monitors the gesture information of the shore bridge, the wind speed monitoring module 930 monitors the wind speed in the environment, and the real-time vibration information, the gesture information and the wind speed information are input into the neural network model through the calculating module 940 to obtain the predicted state information, so as to obtain the predicted vibration information of the shore bridge, and when the predicted vibration information reaches the first preset condition, the early warning module 950 sends out alarm information to prompt an operator. Therefore, the prediction of the operation state of the quay crane is automatically and accurately completed, and the operation personnel can conveniently operate the quay crane to smoothly complete the loading and unloading of objects.

In some embodiments of the present application, the first preset condition may be a set vibration safety value, and the early warning module 950 sends an alarm signal when the predicted vibration information is equal to or greater than the vibration safety value.

In some embodiments of the present application, the predicted state signal further includes a predicted wheel pressure signal, and the predicted wheel pressure signals of the plurality of corner wheels are compared to generate a comparison information result, the comparison information result is changed along with the change of the predicted vibration signal, and when the comparison information result meets a second preset condition, an alarm signal is sent, so as to further improve the judgment of the vibration precision of the quay bridge.

In some embodiments of the present application, the second preset condition is that the comparison information result is determined to satisfy the second preset condition when the comparison information result is equal to or greater than the wheel pressure preset value. And when the comparison information result is smaller than the wheel pressure preset value, determining that the comparison information result does not meet the second preset condition.

Referring to fig. 5, in some embodiments of the present application, the shore bridge monitoring system further includes a wheel pressure monitoring module 960, wherein the wheel pressure monitoring module 960 monitors respective wheel pressure information of a plurality of angle wheels of the shore bridge to obtain real-time wheel pressure signals of the angle wheels, and compares the real-time wheel pressure signals of the plurality of angle wheels to determine real-time vibration amplitude of the shore bridge.

In some embodiments of the application, the wheel pressure monitoring module 960 includes a pressure sensor that may be disposed on the caster to obtain the wheel pressure of the caster in real time.

Fig. 6 is a schematic diagram illustrating a vibration detection module according to some embodiments of the application. Referring to fig. 6, the vibration monitoring module 910 includes a vibration sensor 911 and a processor 912. The vibration sensor 911 is disposed on the quay to acquire initial vibration information of the quay, and the vibration sensor 911 is communicatively connected to the processor 912, and the processor 912 performs filtering processing, such as wavelet decomposition, after acquiring the initial vibration information to acquire vibration characteristics, such as vibration frequency, vibration mean value, vibration peak value, etc., and generates safe real-time vibration information according to the vibration characteristics.

In some embodiments of the present application, the vibration sensor 911 may be an acceleration sensor B21, or other sensors that can monitor the vibration of the quay bridge. The present application is not limited in the kind of the vibration sensor 911. The vibration sensor 911 may be provided in plural, and one or more vibration sensors 911 may be provided on the girder head, the upper cross beam on the sea side or the road side of the shore bridge and the tail of the rear girder, or the vibration sensor 911 may be provided on other structures of the shore bridge to monitor the vibration of each part of the shore bridge in real time.

Fig. 7 is a schematic diagram illustrating the configuration of a gesture monitoring module in some embodiments of the application. Referring to fig. 7, the attitude monitoring module 920 includes a trolley monitoring section 921, a spreader monitoring section 922, a girder monitoring section 923, and a cart monitoring section 924. The dolly monitoring part 921 is for monitoring dolly information. The hanger monitoring part 922 is used for monitoring hanger weight information of the hanger. The girder monitoring unit 923 monitors girder position information. The cart monitoring unit 924 monitors cart acceleration information of a cart.

In the operation process of the shore bridge, the lifting appliance is used for lifting objects to generate lifting weight information, the trolley drives the lifting appliance to move to generate trolley information, and the trolley drives the shore bridge to move to generate trolley acceleration information, so that the attitude information of the shore bridge is obtained in real time through monitoring the trolley information, the lifting weight information, the girder position information and the trolley acceleration information.

In some embodiments of the present application, the cart monitoring section 921 may include a position sensor and an acceleration sensor. The position sensor monitors the position information of the trolley in real time, and the acceleration sensor acquires the acceleration information of the trolley in real time.

Fig. 8 is a schematic diagram showing the constitution of a calculation module in some embodiments of the application. Referring to fig. 8, the calculation module 940 includes a data transmission portion 941 and a PLC data collection and calculator 942. One end of the data transmission portion 941 is respectively in communication connection with the vibration monitoring module 910, the gesture monitoring module 920 and the wind speed monitoring module 930, and the other end of the data transmission portion 941 is in communication connection with the PLC data acquisition and calculator 942. When the predicted vibration information is acquired, the real-time vibration information, the posture information and the wind speed information are transmitted to the PLC data acquisition and calculator 942 through the data transmission portion 941, and the PLC data acquisition and calculator 942 performs neural network calculation on the real-time vibration information, the posture information and the wind speed information to successfully acquire the corresponding simulation relationship.

Fig. 9 is a schematic diagram illustrating a configuration of an early warning module according to some embodiments of the application. Referring to fig. 9, the warning module 950 includes an audible warning portion 951 and a light warning portion 952. When the predicted vibration signal satisfies the first preset condition or the predicted wheel pressure signal does not satisfy the second preset condition, both the audible alarm portion 951 and the light alarm portion 952 issue an alarm to prompt the relevant operator.

In some embodiments of the present application, the audible alarm 951 may employ a buzzer, and the light alarm 952 may employ an alarm lamp.

In some embodiments of the present application, the quay monitoring system further includes a display module communicatively coupled to the computing module 940, the display module configured to display the predicted vibration information so that an operator can directly observe the predicted vibration information of the quay. The display module can also display the predicted wheel pressure information at the same time, so that operators can know the wheel pressure information of the angle wheels. The display module may also be in communication connection with the vibration monitoring module 910, the gesture monitoring module 920, and the wind speed monitoring module 930, so as to synchronously display real-time vibration information, gesture information, and wind speed information, so that an operator can know the operation state of the shore bridge.

Exemplary quay bridge

Fig. 10 is a schematic structural view of a quay bridge according to some embodiments of the present application. Referring to fig. 10, the quay comprises the quay monitoring system and body 700 described in any of the embodiments above. The quay monitoring system is mounted on the main body 700.

In the operation process of the quay, the quay monitoring system predicts the operation state of the main body 700, so that an operator can adjust the operation of the quay in time, and the loading and unloading of objects are facilitated.

The shore bridge is provided with the shore bridge monitoring system, so that the shore bridge has all the technical effects of the shore bridge monitoring system, and the details are not repeated herein.

Exemplary electronic device

Fig. 11 is a schematic structural diagram of an electronic device according to some embodiments of the present application. As shown in fig. 11, the electronic device 110 includes: one or more processors 1101 and memory 1102; and computer program instructions stored in the memory 1102, which when executed by the processor 1101, cause the processor 1101 to perform the quay bridge monitoring method as in any of the embodiments described above.

The processor 1101 may be a Central Processing Unit (CPU) or other form of processing unit having data processing capabilities and/or instruction execution capabilities, and may control other components in the electronic device to perform desired functions.

Memory 1102 may include one or more computer program products that may include various forms of computer-readable storage media, such as volatile memory and/or non-volatile memory. The volatile memory may include, for example, random Access Memory (RAM) and/or cache memory (cache), and the like. The non-volatile memory may include, for example, read Only Memory (ROM), hard disk, flash memory, and the like. On which one or more computer program instructions may be stored, which may be executed by the processor 1101 to implement the steps in the quay bridge monitoring method and/or other desired functions of the various embodiments of the present application described above. Information such as ambient air humidity, temperature altitude, and light intensity may also be stored in the computer readable storage medium.

In one example, the electronic device 110 may further include: an input device 1103 and an output device 1104, which are interconnected by a bus system and/or other form of connection mechanism (not shown in FIG. 11).

For example, where the electronic device is a stand-alone device, the input device 1103 may be a communication network connector for receiving the acquired input signal from an external, removable device. In addition, the input device 1103 may also include, for example, a keyboard, mouse, microphone, and the like.

The output device 1104 may output various information to the outside, and may include, for example, a display, a buzzer, a dolly, a cart, a remote output apparatus connected thereto, and the like.

Of course, only some of the components of the electronic device 110 that are relevant to the present application are shown in fig. 11 for simplicity, components such as buses, input/output interfaces, etc. are omitted. In addition, the electronic device 110 may include any other suitable components depending on the particular application.

Exemplary computer program product and computer readable storage Medium

In addition to the above-described methods and apparatus, embodiments of the application may also be a computer program product comprising computer program instructions which, when executed by a processor, cause the processor to perform the steps of the quay monitoring method of any of the embodiments described above.

The computer program product may include program code for performing operations of embodiments of the present application in any combination of one or more programming languages, including an object oriented programming language such as Java, C++ or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computing device, partly on the user's device, as a stand-alone software package, partly on the user's computing device, partly on a remote computing device, or entirely on the remote computing device or server.

Furthermore, embodiments of the application may also be a computer-readable storage medium, on which computer program instructions are stored which, when being executed by a processor, cause the processor to perform the steps in a quay monitoring method according to various embodiments of the application described in the above section "exemplary quay monitoring method" of the specification.

The computer readable storage medium may employ any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. The readable storage medium may include, for example, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any of the foregoing. More specific examples (a non-exhaustive list) of the readable storage medium would include the following: an electrical connection having one or more wires, a portable disk, a hard disk, random access memory ((RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

The basic principles of the present application have been described above in connection with specific embodiments, however, it should be noted that the advantages, benefits, effects, etc. mentioned in the present application are merely examples and not intended to be limiting, and these advantages, benefits, effects, etc. are not to be considered as essential to the various embodiments of the present application. Furthermore, the specific details disclosed herein are for purposes of illustration and understanding only, and are not intended to be limiting, as the application is not necessarily limited to practice with the above described specific details.

The block diagrams of the modules, quads, methods and systems according to the application are only illustrative examples and are not intended to require or imply that the connections, arrangements, configurations must be made in the manner shown in the block diagrams. As will be appreciated by one of skill in the art, the modules, quads, methods, and systems may be connected, arranged, configured in any manner. Words such as "including," "comprising," "having," and the like are words of openness and mean "including but not limited to," and are used interchangeably therewith. The terms "or" and "as used herein refer to and are used interchangeably with the term" and/or "unless the context clearly indicates otherwise. The term "such as" as used herein refers to, and is used interchangeably with, the phrase "such as, but not limited to.

It is also pointed out that in the method, system and quay crane according to the application the components or steps may be disassembled and/or reassembled. Such decomposition and/or recombination should be considered as equivalent aspects of the present application.

The previous description of the disclosed aspects is provided to enable any person skilled in the art to make or use the present application. Various modifications to these aspects will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other aspects without departing from the scope of the application. Thus, the present application is not intended to be limited to the aspects shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

The foregoing description has been presented for purposes of illustration and description. Furthermore, this description is not intended to limit embodiments of the application to the form disclosed herein. Although a number of example aspects and embodiments have been discussed above, a person of ordinary skill in the art will recognize certain variations, modifications, alterations, additions, and subcombinations thereof.

The foregoing description of the preferred embodiments of the application is not intended to be limiting, but rather is to be construed as including any modifications, equivalents, and alternatives falling within the spirit and principles of the application.

Claims (8)

1. A method of monitoring a quay bridge, the method comprising:

acquiring real-time vibration information and attitude information of the shore bridge in the running process, wherein the real-time vibration information comprises the following components: front large Liang Shishi vibration information and upper beam real-time vibration information of the shore bridge and rear large Liang Shishi vibration information of the shore bridge;

acquiring wind speed information of the shore bridge in the running process; inputting the real-time vibration information, the attitude information and the wind speed information into a neural network model to obtain prediction state information, and adjusting the state of the shore bridge according to the prediction state information so as to change the attitude of the shore bridge and adjust the integral vibration of the shore bridge, thereby facilitating the smooth loading and unloading of objects;

The prediction state information comprises prediction vibration information and prediction wheel pressure information of each of a plurality of horn wheels of the quay crane;

when the predicted vibration information reaches a first preset condition, generating alarm information;

comparing the plurality of predicted wheel pressure information to generate a comparison information result, and generating alarm information when the comparison information result reaches a second preset condition;

wherein the comparison information result varies with the variation of the predicted vibration information.

2. The quay bridge monitoring method according to claim 1, wherein the training process of the neural network comprises:

inputting a multi-information sample consisting of a vibration information sample, a posture information sample and a wind speed information sample into the neural network model to obtain predicted vibration training information and predicted wheel pressure training information output by the neural network model;

calculating loss based on the predicted vibration training information and the standard vibration information, and calculating loss based on the predicted wheel pressure training information and the standard wheel pressure training information, wherein the standard vibration information and the standard wheel pressure information correspond to the multi-information sample; and

and adjusting parameters of the neural network model based on the loss calculation result.

3. The quay bridge monitoring method according to any one of claims 1 to 2, wherein the acquiring the attitude information comprises one or more combinations of:

and acquiring the trolley information, the crane weight information, the girder position information and the trolley acceleration information of the shore bridge.

4. A quay bridge monitoring method according to claim 3, wherein the obtaining the trolley information comprises:

and acquiring the trolley position information and the trolley acceleration information.

5. The quay bridge monitoring method according to claim 1, wherein the acquiring the real-time vibration information specifically includes:

acquiring initial vibration information of a quay crane in the running process;

and filtering the initial vibration information to obtain the real-time vibration information.

6. The quay monitoring method according to claim 1, further comprising:

and acquiring the predicted vibration information and the predicted wheel pressure information and displaying the predicted vibration information and the predicted wheel pressure information in real time.

7. A quay monitoring system for implementing a quay monitoring method according to any one of the preceding claims 1 to 6, characterized in that the quay monitoring system comprises:

the vibration monitoring module is used for monitoring real-time vibration information of the shore bridge in the operation process, and the real-time vibration information comprises: front large Liang Shishi vibration information and upper beam real-time vibration information of the shore bridge and rear large Liang Shishi vibration information of the shore bridge;

The attitude monitoring module is used for monitoring the attitude information of the shore bridge in the running process;

the wind speed monitoring module is used for monitoring the wind speed information of the shore bridge in the running process;

the computing module is respectively in communication connection with the vibration monitoring module, the attitude monitoring module and the wind speed monitoring module to acquire the vibration information, the attitude information and the wind speed information, and is used for acquiring prediction state information according to a neural network model after inputting the vibration information, the attitude information and the wind speed information, and adjusting the state of the shore bridge according to the prediction state information so as to change the attitude of the shore bridge and adjust the integral vibration of the shore bridge, thereby facilitating smooth loading and unloading of objects;

the prediction state information comprises prediction vibration information and prediction wheel pressure information of each of a plurality of horn wheels of the quay crane;

the early warning module is in communication connection with the calculation module to acquire the predicted vibration information, and the early warning module is configured to send out alarm information when the predicted vibration information reaches a first preset condition;

comparing the plurality of predicted wheel pressure information to generate a comparison information result, and generating alarm information when the comparison information result reaches a second preset condition;

Wherein the comparison information result varies with the variation of the predicted vibration information.

8. A quay bridge, comprising:

a quay crane monitoring system according to claim 7; and

and the main body is used for installing the quay crane monitoring system.