CN113511592A - Shore bridge monitoring method and system and shore bridge - Google Patents
Shore bridge monitoring method and system and shore bridge Download PDFInfo
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- CN113511592A CN113511592A CN202110744946.9A CN202110744946A CN113511592A CN 113511592 A CN113511592 A CN 113511592A CN 202110744946 A CN202110744946 A CN 202110744946A CN 113511592 A CN113511592 A CN 113511592A
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B66—HOISTING; LIFTING; HAULING
- B66C—CRANES; LOAD-ENGAGING ELEMENTS OR DEVICES FOR CRANES, CAPSTANS, WINCHES, OR TACKLES
- B66C15/00—Safety gear
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- B66C15/065—Arrangements or use of warning devices electrical
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Abstract
The application relates to the technical field of shore bridge monitoring, in particular to a shore bridge monitoring method and system and a shore bridge. The method comprises the following steps: acquiring real-time vibration information and attitude information of a shore bridge in the operation process; acquiring wind speed information of a shore bridge in the operation process; inputting the real-time vibration information, the attitude information and the wind speed information into a neural network model to obtain predicted state information, wherein the predicted state information comprises predicted vibration information; when the predicted vibration information reaches a first preset condition, generating alarm information; the operation state of the shore bridge is predicted by combining the attitude information, the real-time vibration information, the wind speed information and other environmental factors. The actual operation environment of the shore bridge is considered, so that the operation state of the shore bridge can be more accurately predicted, the operation personnel can timely adjust the state of the shore bridge according to the predicted operation state, the attitude information of the shore bridge is changed, the vibration of the whole shore bridge is adjusted, and the smooth loading and unloading of objects are facilitated.
Description
Technical Field
The application relates to the technical field of shore bridge monitoring, in particular to a shore bridge monitoring method and system and a shore bridge.
Background
The shore bridge is an object hoisting device built at a port or a wharf, and is mainly used for hoisting, loading and unloading objects such as containers on ships.
In the related art, the integral structure of the shore bridge is huge, and the shore bridge can vibrate due to inertia or air flow influence in the operation process. At present, the vibration intensity of the shore bridge is mainly judged by operators, so that the judgment result is easy to have large deviation, and when the vibration amplitude of the shore bridge is large, the shore bridge is suspended on an object to be controlled to deviate, so that the shore bridge can influence the hoisting and loading of the object.
Content of application
In view of the above, the application provides a shore bridge monitoring method and system, and a shore bridge, which solve or improve the problem that the hoisting, loading and unloading of objects are influenced by manually judging the large deviation of the vibration intensity of the shore bridge in the operation process of the shore bridge.
In a first aspect, the present application provides a shore bridge monitoring method, including: acquiring real-time vibration information and attitude information of the shore bridge in the operation process; acquiring wind speed information of the shore bridge in the operation process; inputting the real-time vibration information, the attitude information and the wind speed information into a neural network model to obtain predicted state information, wherein the predicted state information comprises predicted vibration information; and generating alarm information when the predicted vibration information reaches a first preset condition.
According to the shore bridge monitoring method, 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 obtained, and then the real-time vibration information, the attitude information and the wind speed information are input into a neural network model to obtain the predicted state information of the shore bridge, so that the predicted vibration information of the shore bridge in the operation process is mastered, and when the predicted vibration information is lower than a preset condition, alarm information is generated; in the process of operating the shore bridge, the vibration of the shore bridge is predicted through a computer program, so that the operating state of the shore bridge is accurately judged, and when the predicted vibration information reaches a first preset condition, alarm information is sent to prompt operating personnel. The operation state of the shore bridge is predicted by combining the attitude information, the real-time vibration information, the wind speed information and other environmental factors. The actual operation environment of the shore bridge is considered, so that the operation state of the shore bridge can be more accurately predicted, the operation personnel can timely adjust the state of the shore bridge according to the predicted operation state, the attitude information of the shore bridge is changed, the vibration of the whole shore bridge is adjusted, and the 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 corner 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 variation of the predicted vibration information.
With reference to the first aspect, in a 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 obtaining the posture information includes one or more of the following: and acquiring trolley information, hoisting weight information, girder position information and crane acceleration information of the shore bridge.
With reference to the first aspect, in a possible implementation manner, the acquiring the cart information includes: and acquiring trolley position information and trolley acceleration information.
With reference to the first aspect, in a possible implementation manner, the acquiring the real-time vibration information includes: and acquiring real-time vibration information of a front girder, real-time vibration information of an upper girder and real-time vibration information of a rear girder 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 shore bridge in the operation process; and filtering the initial vibration information to obtain the real-time vibration information.
With reference to the first aspect, in a 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 shore bridge monitoring system, for implementing the shore bridge monitoring method described above, where the shore bridge monitoring system includes: the vibration monitoring module is used for monitoring the real-time vibration information of the shore bridge in the operation process; the attitude monitoring module is used for monitoring the attitude information of the shore bridge in the operation process; the wind speed monitoring module is used for monitoring the wind speed information of the shore bridge in the operation process; the calculation module is in communication connection with the vibration monitoring module, the attitude monitoring module and the wind speed monitoring module respectively to acquire the vibration information, the attitude information and the wind speed information, and is used for acquiring predicted state information according to a neural network model after inputting the vibration information, the attitude information and the wind speed information, wherein the predicted state information comprises predicted vibration information; and the early warning module is in communication connection with the computing module to acquire the predicted vibration information, and is configured to send out warning information when the predicted vibration information reaches a first preset condition.
In combination with the second aspect, a shore bridge monitoring system is provided with a vibration monitoring module, an attitude monitoring module and a wind speed monitoring module, the vibration information, the attitude information and the wind speed information of a shore bridge in the operation process are transmitted to a calculation module, the calculation module obtains predicted state information according to a neural network model, the predicted vibration information in the operation process of the shore bridge is obtained smoothly, an early warning module is used for sending an alarm to an operator according to the predicted vibration information, the operator is prompted, the vibration of the shore bridge is monitored through a computer program, the accuracy of vibration monitoring of the shore bridge is improved, and the shore bridge can load and unload objects smoothly.
In a third aspect, the present application further provides a shore bridge, comprising: the shore bridge monitoring system; and a main body for installing the shore bridge monitoring system.
In combination with the third aspect, a shore bridge is provided, and through the arrangement of the shore bridge monitoring system, the shore bridge monitoring system monitors 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 illustrating a shore bridge monitoring method according to some embodiments of the present disclosure.
Fig. 2 is a schematic diagram illustrating the components of the predicted wheel pressure information according to some embodiments of the present disclosure.
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 illustrating the real-time vibration information processing according to some embodiments of the present disclosure.
Fig. 5 is a schematic diagram illustrating a construction of a shore bridge monitoring system according to some embodiments of the present disclosure.
Fig. 6 is a schematic diagram illustrating a configuration of a vibration detection module according to some embodiments of the present disclosure.
FIG. 7 is a schematic diagram of an attitude monitoring module according to some embodiments of the present application.
FIG. 8 is a block diagram of a computing module in some embodiments of the present application.
Fig. 9 is a schematic diagram illustrating the configuration of the early warning module in some embodiments of the present application.
Fig. 10 is a schematic structural diagram of a shore bridge according to some embodiments of the present disclosure.
Fig. 11 is a schematic structural diagram of an electronic device according to some embodiments of the present application.
Detailed Description
The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
Summary of the application
The bank bridge is at the operation in-process, because the influence that air flows in inertia or the environment, the bank bridge can produce the vibration, but because the overall structure of bank bridge self is bigger, its vibration amplitude naked eye is difficult to judge, mainly relies on operation personnel's experience to judge, and the judgement result has great deviation easily, influences the loading and unloading smoothly of bank bridge to the object. In prior art, in order to solve this problem, usually, the vibration amplitude of the shore bridge is monitored by installing a vibration sensor on the shore bridge, so that an operator can conveniently know the operation state of the shore bridge, but the vibration sensor can only monitor the vibration of the shore bridge, and the operator still needs to predict the operation state of the shore bridge by depending on experience, so that the judgment on the operation state of the shore bridge still has great deviation, and the loading and unloading of objects are influenced.
In order to solve the above problems, the idea of the present application is to provide a shore bridge monitoring method, system and shore bridge, which can obtain a shore bridge environmental factor (such as wind speed) and a shore bridge self-operating state (such as hoisting information, trolley information, etc.), and integrate the environmental factor with the shore bridge self-operating state, so as to form a multi-signal composed of the shore bridge environmental factor and the shore bridge self-operating state, and then obtain a simulation relationship between the multi-signal and the shore bridge vibration through simulation training, so that when the environmental factor or the shore bridge self-operating state changes, not only the shore bridge operating state can be monitored in real time, but also the shore bridge vibration can be predicted automatically through simulation calculation, thereby improving the accuracy of determining the multi-shore bridge operating state, and facilitating smooth loading and unloading of objects.
It should be noted that the shore bridge monitoring method provided by the present application may be used for a shore bridge in any scenario. Specifically, the design of the shore bridge monitoring system is to collect environmental factors and the running state of the shore bridge, so that the running state of the shore bridge can be predicted smoothly. The specific structure of the land bridge monitoring system is not limited in the application. For example, the embodiment of the present invention may be a quay crane for loading and unloading a large transport vehicle such as a ship in a large port, or may be a quay crane for hoisting an object on a small dock.
Having described the general principles of the present application, various non-limiting embodiments of the present application will now be described with reference to the accompanying drawings.
Exemplary shore bridge monitoring method
Fig. 1 is a schematic diagram illustrating a shore bridge monitoring method according to some embodiments of the present disclosure. Referring to fig. 1, the shore bridge monitoring method includes:
and S100, acquiring real-time vibration information and attitude information of the shore bridge in the operation process.
The real-time vibration information is used for representing the amplitude and frequency of vibration generated by the shore bridge in the operation process. Because the integral structure of the shore bridge is larger, the mass of the objects lifted by the shore bridge is also larger, and meanwhile, the path which is traveled by the shore bridge in the process of lifting the objects is also longer. Therefore, in the process of lifting and transporting objects on the shore bridge, vibration is inevitably generated due to the combined action of factors such as self power, air flow in the environment and the like.
The attitude information represents the operation state of the shore bridge, such as the weight of an object lifted by the shore bridge, the self operation speed, the self operation acceleration and the like, and is directly related to the real-time vibration of the shore bridge in the operation process.
Under the ideal condition of not considering environmental factors, the real-time vibration generated by the shore bridge is minimum under the conditions that the heavier the weight of an object lifted by the shore bridge is, the slower the running speed of the shore bridge is and the lower the running acceleration of the shore bridge is.
And S200, acquiring wind speed information of the shore bridge in the operation process. The wind speed information represents the information of the air flow in the environment during the operation of the shore bridge. In the flowing process, the air can cause the quay crane and objects lifted by the quay crane to shake.
Especially when the shore bridge is large in size and high in overall height (the height of the shore bridge may reach tens of meters), the wind speed at the high position may be larger than that at the low position, and the stability of the side of the shore bridge far away from the ground is poor compared with that of the side of the shore bridge near the ground, especially when an object is suspended in the air, so that the influence of the wind speed information on the vibration of the shore bridge is very important.
And step S300, inputting the real-time vibration information, the attitude information and the wind speed information into a neural network model to obtain predicted state information, wherein the predicted state information comprises predicted vibration information. The prediction state information represents the state of the shore bridge which appears next in the operation process, and is the prediction of the motion state of the shore bridge, and the state is related to the real-time vibration information, the attitude information and the wind speed information.
The neural network model is a pre-trained artificial intelligence model which can obtain prediction state information based on the implementation vibration information, the attitude information and the wind speed information. However, the specific network architecture and training mode of the neural network model are not strictly limited in the present application.
Since the change rules of the real-time vibration information, the attitude information and the wind speed information are generally gradually changed, the acquired predicted state information is also gradually changed in general.
In some scenarios, the real-time vibration information, attitude information, and wind speed information may be subject to sudden changes. Specifically, when the shore bridge just lifts or drops an object, the weight of the lifting appliance may change suddenly, so that the real-time vibration information and the attitude information both change suddenly. The air flow rate may also be abruptly changed, and the wind speed information is accordingly abruptly changed.
The prediction state information can be changed when any one of the real-time vibration information, the attitude information and the wind speed information changes suddenly or any several of the real-time vibration information, so that the operation state of the shore bridge is accurately predicted, particularly the vibration information of the shore bridge can be predicted, the operation of operators on the shore bridge can be adjusted conveniently by timely following the predicted vibration information, and the objects can be conveniently and smoothly lifted.
And step S400, when the predicted vibration information reaches a first preset condition, generating an alarm signal.
The first predetermined condition is indicative of a criterion for a safe operating state of the shore 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 predetermined condition may also include a value represented by the electrical signal. When the predicted vibration information is equal to or greater than the value, it can be determined that the predicted vibration information reaches the first preset condition. At this time, an alarm should be given to the operator, and the operator can quickly adjust the operation of the shore bridge or stop the operation of the shore bridge, so as to change the attitude information and the real-time vibration information of the shore bridge, and to reduce the predicted vibration information to be under the first preset condition.
Through the method steps, in the operation process of the shore bridge, the vibration generated in the operation process of the shore bridge is automatically predicted 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 a first preset condition, an alarm is given to remind an operator, the operator accordingly adjusts the shore bridge, and the smooth loading and unloading of objects by the shore bridge are facilitated.
Fig. 2 is a schematic diagram illustrating the components of the predicted wheel pressure information according to some embodiments of the present disclosure. Referring to fig. 2, the predicted state information further includes predicted wheel pressure information of each of the plurality of corner wheels, wherein the shore bridge monitoring method further includes:
and S500, comparing the plurality of pieces of predicted wheel pressure information to generate a comparison information result. The wheel pressure of the corner wheel is characterized by the pressure born by the corner wheel during the operation of the shore bridge. Specifically, when the quay crane does not vibrate in an ideal state, the plurality of corner wheels of the quay crane should equally divide the weight of the whole quay crane, and the difference of the wheel pressure values of the plurality of corner wheels should approach zero without limitation.
However, in practice, the vibration of the shore bridge cannot be avoided, and thus the wheel pressure of the corner wheel changes along with the vibration of the shore bridge. When the bank bridge vibrates, the bank bridge can deviate to a certain extent on the whole, so that the wheel pressure of any one of a plurality of corner wheels of the bank bridge or the corner wheels on any side is increased, and the wheel pressure of other corner wheels is reduced. In an ideal case, the smaller the vibration amplitude of the entire shore bridge, the smaller the range of variation in wheel pressure of the caster. The wheel pressure of the caster has a direct link with the vibration of the shore bridge.
Meanwhile, the bank bridge is moved through the corner wheels, when the wheel pressure of the corner wheels is large, the corner wheels can be damaged, the bank bridge cannot continue to operate, the wheel pressure of the corner wheels is predicted, the predicted state information of the bank bridge is accurately acquired, and the possibility that the bank bridge cannot normally operate can be reduced.
And what contrast information result represented is the variation range of wheel pressure, because the pressure sum that a plurality of horn rings were undertaken under the handling object in-process is certain, when the wheel pressure of one or one side in a plurality of horn rings increases, other wheel pressures must correspondingly reduce, obtain the variation range of wheel pressure through the contrast of both to the running state of accurate judgement bank bridge.
And S510, when the comparison information result reaches a second preset condition, generating alarm information, 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 greater than or equal to the electric signal value, the comparison information result can be considered to reach the second preset condition, so that the early warning information is sent out.
Through the steps, in the operation process of the shore bridge, the wheel pressure of the corner 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 comparison of the predicted wheel pressure signal, so that the prediction precision 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 multi-information sample consisting of a vibration information sample, a posture information sample and a wind speed information sample into the neural network model, and obtaining the predicted vibration training information and the predicted wheel pressure training information output by the neural network model.
The multiple information samples represent the operating state of the shore bridge. The multi-information sample can be generated by measuring according to the actual operation condition of the onsite shore bridge, and can also be generated by performing simulation operation in a laboratory.
Because a large number of multi-information samples are needed for training the neural network model, in order to obtain the multi-information samples, the sources of the multi-information samples can be divided into an experimental part and a measuring part, the experimental part is collected by simulating operation in a laboratory, the measuring part is measured and collected from the actual operation condition of the onsite shore bridge, and the experimental part and the measuring part are combined to form the large number of multi-information samples.
The proportion of the experimental part and the measurement part can be determined according to the needs of actual conditions. Specifically, the experimental part and the measurement part may each account for half. When the equipment in the laboratory is complete and the precision of the experimental result is high, the multiple information samples of the experimental part can account for most of the multiple information samples, for example, 70% of all the multiple information samples.
The predicted vibration training information and the predicted wheel pressure training information represent the corresponding generated result when a multi-information sample is input, namely the prediction result of the operation state of the shore bridge in the model training stage.
And 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 multiple information samples.
The standard vibration information characterizes the actual vibration information of the shore bridge under the corresponding multi-information sample. And the predicted vibration training information is the predicted vibration information automatically calculated by the neural network model under the corresponding multi-information sample.
And calculating through the predicted vibration training information and the characterization vibration information to know whether the difference exists between the predicted vibration training information and the standard vibration information. When the plurality of predicted vibration training information are compared with the plurality of standard vibration information, whether the predicted vibration information is infinitely close to the actual vibration information of the shore bridge can be judged. When there is a large difference, the parameters of the neural network need to be correspondingly 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 corner wheel under the corresponding multi-information sample, and the standard wheel pressure information represents the actual wheel pressure under 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 is not repeated herein.
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 and obtain 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 parameters of the neural network model. Therefore, the predicted vibration training information approaches to the standard vibration information infinitely, and the predicted wheel pressure training information approaches to the standard wheel pressure information.
Through the steps, 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 completes training, and when any multi-information sample is input, the neural network model correspondingly outputs a specific predicted vibration signal and a specific predicted wheel pressure signal according to a simulation relation. The simulation relation between the multi-information samples and the predicted vibration information and the predicted wheel pressure information can be embodied through the trained neural network model.
In some embodiments of the present application, obtaining the pose information may include one or more of the following: trolley information, hoisting weight information, girder position information and cart acceleration information.
The trolley information represents the running state of the trolley of the shore bridge, such as the running speed and the running acceleration of the trolley.
The weight borne by the lifting appliance represented by the lifting weight information in the process of lifting the object can be changed due to inconsistent object states. When an object is lifted, the force of the lifting appliance needs to be larger than the self weight of the object due to the need of lifting the object. When the object is put down, the force of the lifting appliance needs to be smaller than the self weight of the object in order for the object to descend smoothly.
The girder position information represents the position of the girder, and the trolley can refer to the position of the girder when moving, and the position information of the girder and the trolley can be compared, so that the position information of the trolley relative to the shore bridge can be more accurately obtained. Due to the fact that the position of the trolley is changed, the positions of the lifting appliance and the object are changed along with the movement of the trolley, and therefore the overall stress condition of the trolley is changed.
The speed of the whole bank bridge moving represented by the acceleration information of the cart directly influences the vibration of the bank bridge. Specifically, when the speed of movement of the quay crane is high, the intensity of impact between the quay crane and the air increases, which results in that the quay crane vibrates more easily, particularly on the side of the quay crane away from the ground.
Through the steps, the attitude change of the shore bridge is more accurately acquired through monitoring the information of each part of the shore bridge in the operation process of the shore bridge, so that the accuracy of predicting the vibration information of the shore bridge is improved.
In some embodiments of the present application, obtaining cart information comprises: trolley position information and trolley acceleration information. The trolley position information is information representing the position of the trolley relative to the shore bridge.
Because along with the removal of dolly, the hoist drives object synchronous motion, and the object removes the in-process, and the holistic atress condition of bank bridge changes constantly.
And the change speed of the trolley represented by the acceleration information of the trolley is high or low. 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 fast, and vibration is easy to generate. Therefore, the position information and the acceleration information of the trolley have important reference value for the attitude information of the shore bridge.
Through the steps, when the shore bridge hoists the object, the information of the trolley is acquired constantly to grasp the position of the trolley relative to the shore bridge and the acceleration of the trolley per se, so that the attitude information of the shore bridge is acquired more accurately.
In some embodiments of the present application, obtaining real-time vibration information includes: and acquiring real-time vibration information of a front girder, real-time vibration information of an upper girder and real-time vibration information of a rear girder of the shore bridge. Because the integral structure of the shore bridge is large, the vibration of each part of the shore bridge may have difference in the operation process of the shore bridge, and therefore 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 acquired, the real-time vibration information of each part of the shore bridge is acquired simultaneously, so that the real-time vibration information of the whole structure of the shore bridge is acquired, and more accurate attitude information of the shore bridge is acquired.
Fig. 4 is a schematic diagram of the real-time vibration information processing in some embodiments of the present application. Referring to fig. 4, acquiring real-time vibration information includes:
and S1120, acquiring initial vibration information of the shore bridge in the operation process.
The initial vibration information represents vibration information directly measured by each part of the shore bridge, and because the vibration of the shore bridge part may have differences, a plurality of pieces of initial vibration information need to be fused to form the integral initial vibration information of the shore bridge. The initial vibration information may be an electrical signal representing the vibration amplitude, may also be an electrical signal representing the vibration frequency, or may be another signal representing the vibration of the whole shore bridge.
And S1121, filtering the initial vibration information to acquire real-time vibration information. And processing the initial vibration information in a filtering processing mode such as wavelet decomposition, so that the initial vibration information of each part of the shore bridge is smoothly fused to form the integral vibration information of the shore bridge, and the integral real-time vibration information of the shore bridge is smoothly obtained.
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 to form the whole real-time vibration information of the shore bridge through processing the initial vibration information, so that the real-time vibration information of the shore bridge is acquired more accurately.
In some embodiments of the present application, the shore bridge monitoring method further includes: and acquiring 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 information which can be displayed on a display screen to be displayed to an operator in real time, and the operator can conveniently know the running state of the shore bridge.
Through the steps, the operator can change the operation state of the shore bridge according to the displayed predicted vibration information and 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 shore bridge monitoring system
Fig. 5 is a schematic diagram illustrating a construction of a shore bridge monitoring system according to some embodiments of the present disclosure. The shore bridge monitoring system is used for realizing the shore bridge monitoring method described in any one of the above embodiments. Referring to fig. 5, the security monitoring system includes: a vibration monitoring module 910, an attitude monitoring module 920, a wind speed monitoring module 930, a calculation module 940, and an early warning module 950. The vibration monitoring module 910 is used for monitoring real-time vibration information of the shore bridge during operation. The attitude monitoring module 920 is used for monitoring attitude information of the shore bridge in the operation process. The wind speed monitoring module 930 is used for monitoring wind speed information of the shore bridge in the operation process.
The calculation module 940 is respectively connected to the vibration monitoring module 910, the attitude monitoring module 920 and the wind speed monitoring module 930 in a communication manner to obtain real-time vibration information, attitude information and wind speed information. The calculation module 940 is configured to input the vibration information, the attitude information, and the wind speed information into the neural network model to obtain predicted state information, which includes predicted vibration information.
The pre-warning module 950 is communicatively connected to the calculation module 940 to obtain the predicted vibration information, and the pre-warning module 950 is configured to send out warning information when the predicted vibration information reaches a first preset condition.
When the shore bridge is in operation, the vibration monitoring module 910 monitors real-time vibration information of the shore bridge, the attitude monitoring module 920 monitors attitude information of the shore bridge, the wind speed monitoring module 930 monitors wind speed in an environment, and then the real-time vibration information, the attitude information and the wind speed information are input into the neural network model through the calculation module 940 to obtain predicted state information, so that predicted vibration information of the shore bridge is obtained, and when the predicted vibration information reaches a first preset condition, the early warning module 950 sends alarm information to prompt an operator. Therefore, the prediction of the operation state of the opposite bank bridge is automatically and accurately finished, and the operation personnel can conveniently operate the bank bridge to smoothly finish 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 status signal further includes a predicted wheel pressure signal, the predicted wheel pressure signals of the plurality of corner wheels are compared to generate a comparison information result, the comparison information result changes with a change of the predicted vibration signal, and when the comparison information result satisfies a second preset condition, an alarm signal is sent out, so as to further improve the judgment of the vibration precision of the opposite-bank bridge.
In some embodiments of the present application, when the second preset condition is that the comparison information result is greater than or equal to the wheel pressure preset value, it is determined that the comparison information result satisfies the second preset condition. And when the comparison information result is smaller than the wheel pressure preset value, determining that the comparison information result does not meet a second preset condition.
Referring to fig. 5, in some embodiments of the present application, the quayside crane monitoring system further includes a wheel pressure monitoring module 960, where the wheel pressure monitoring module 960 monitors wheel pressure information of each of a plurality of corner wheels of the quayside crane to obtain a real-time wheel pressure signal of the corner wheel, and compares the real-time wheel pressure signals of the plurality of corner wheels to determine a real-time vibration amplitude of the quayside crane.
In some embodiments of the present application, the wheel pressure monitoring module 960 includes a pressure sensor, which 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 configuration of a vibration detection module according to some embodiments of the present disclosure. Referring to fig. 6, the vibration monitoring module 910 includes a vibration sensor 911 and a processor 912. The vibration sensor 911 is arranged on the shore bridge to acquire initial vibration information of the shore bridge, the vibration sensor 911 is in communication connection with 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 and the like, so as to generate 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 shore bridge. The present application does not limit the kind of the vibration sensor 911. The vibration sensors 911 can be arranged in a plurality of numbers, one or more vibration sensors 911 can be arranged at the head of a girder of the shore bridge, an upper beam close to the sea side or the road side and at the tail of a rear girder, and the vibration sensors 911 can also be arranged on other structures of the shore bridge so as to monitor the vibration of each part of the shore bridge in real time.
FIG. 7 is a schematic diagram of an attitude monitoring module according to some embodiments of the present application. Referring to fig. 7, the posture monitoring module 920 includes a trolley monitoring part 921, a spreader monitoring part 922, a girder monitoring part 923, and a cart monitoring part 924. The cart monitoring unit 921 is used to monitor cart information. The spreader monitoring unit 922 monitors the information on the lifting weight of the spreader. The girder monitoring portion 923 is used for monitoring position information of the girder. The cart monitoring unit 924 is configured to monitor cart acceleration information of the cart.
In the operation process of the shore bridge, lifting weight information is generated by lifting objects through a lifting appliance, the trolley drives the lifting appliance to move to generate trolley information, the cart drives the shore bridge to move to generate cart acceleration information, and therefore attitude information of the shore bridge is obtained in real time through monitoring of the trolley information, the lifting weight information, the girder position information and the cart 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 block diagram of a computing module in some embodiments of the present application. Referring to fig. 8, the calculation module 940 includes a data transmission part 941 and a PLC data acquisition and calculator 942. One end of the data transmission portion 941 is in communication connection with the vibration monitoring module 910, the attitude monitoring module 920 and the wind speed monitoring module 930, respectively, and the other end of the data transmission portion 941 is in communication connection with the PLC data acquisition and calculation unit 942. When the predicted vibration information is obtained, the real-time vibration information, the attitude information, and the wind speed information are transmitted to the PLC data collecting and calculating unit 942 through the data transmitting unit 941, and the PLC data collecting and calculating unit 942 performs neural network calculation on the real-time vibration information, the attitude information, and the wind speed information to smoothly obtain a corresponding simulation relationship.
Fig. 9 is a schematic diagram illustrating the configuration of the early warning module in some embodiments of the present application. Referring to fig. 9, the early warning module 950 includes an audio alarm part 951 and a light alarm part 952. When the predicted vibration signal meets the first preset condition or the predicted wheel pressure signal does not meet the second preset condition, the sound alarm portion 951 and the light alarm portion 952 both give an alarm to prompt relevant operators.
In some embodiments of the present application, the sound alarm portion 951 may employ a buzzer, and the light alarm portion 952 may employ an alarm lamp.
In some embodiments of the present application, the shore bridge monitoring system further includes a display module, the display module is communicatively connected to the computing module 940, and the display module is configured to display the predicted vibration information, so that the operator can directly observe the predicted vibration information of the shore bridge. The display module can also display the predicted wheel pressure information at the same time so that the operator can know the wheel pressure information of the corner wheel conveniently. The display module can also be in communication connection with the vibration monitoring module 910, the attitude monitoring module 920 and the wind speed monitoring module 930, so as to synchronously display the real-time vibration information, the attitude information and the wind speed information, so that the operating personnel can know the operating state of the shore bridge.
Exemplary shore bridge
Fig. 10 is a schematic structural diagram of a shore bridge according to some embodiments of the present disclosure. Referring to fig. 10, the shore bridge comprises a shore bridge monitoring system and a main body 700 as described in any of the embodiments described above. The shore bridge monitoring system is installed on the main body 700.
In the operation process of the shore bridge, the operation state of the main body 700 is predicted by the shore bridge monitoring system, so that the operation of the shore bridge can be adjusted by operators in time, and the objects can be loaded and unloaded conveniently.
Since the shore bridge is provided with the shore bridge monitoring system, the shore bridge has all the technical effects of the shore bridge monitoring system, which are not described herein again.
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 a shore bridge monitoring method according to any one 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), cache memory (cache), and/or the like. The non-volatile memory may include, for example, Read Only Memory (ROM), hard disk, flash memory, etc. One or more computer program instructions may be stored on the computer-readable storage medium and executed by the processor 1101 to implement the steps in the shore bridge monitoring method of the various embodiments of the present application described above and/or other desired functions. Information such as air humidity, temperature altitude, and illumination intensity in the environment 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, when the electronic device is a stand-alone device, the input device 1103 may be a communication network connector for receiving the collected input signal from an external mobile device. The input devices 1103 may also include, for example, a keyboard, a mouse, a microphone, and so forth.
The output device 1104 may output various information to the outside, and may include, for example, a display, a buzzer, a cart connected thereto, a remote output device, and the like.
Of course, for simplicity, only some of the components of the electronic device 110 relevant to the present application are shown in fig. 11, and components such as a bus, an input device/output interface, and the like are omitted. In addition, 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 method and apparatus, embodiments of the present 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 shore bridge monitoring method according to any of the above-described embodiments.
The computer program product may write program code for carrying out operations for 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 and partly on a remote computing device, or entirely on the remote computing device or server.
Furthermore, embodiments of the present application may also be a computer-readable storage medium having stored thereon computer program instructions which, when executed by a processor, cause the processor to perform the steps in the shore bridge monitoring method according to various embodiments of the present application described in the "exemplary shore bridge monitoring method" section above in this description.
The computer-readable storage medium may take any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may include, for example, but 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 include: an electrical connection having one or more wires, a portable disk, a hard disk, a random access memory ((RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
The foregoing describes the general principles of the present application in conjunction with specific embodiments, however, it is noted that the advantages, effects, etc. mentioned in the present application are merely examples and are not limiting, and they should not be considered essential to the various embodiments of the present application. Furthermore, the foregoing disclosure of specific details is for the purpose of illustration and description and is not intended to be limiting, since the foregoing disclosure is not intended to be exhaustive or to limit the disclosure to the precise details disclosed.
The block diagrams of the modules, shore bridges, methods and systems referred to in this application are only given as illustrative examples and are not intended to require or imply that they must be connected, arranged or configured in the manner shown in the block diagrams. These modules, shore bridges, methods, and systems may be connected, arranged, configured in any manner, as will be appreciated by those skilled in the art. Words such as "including," "comprising," "having," and the like are open-ended words that mean "including, but not limited to," and are used interchangeably therewith. The words "or" and "as used herein mean, and are used interchangeably with, the word" and/or, "unless the context clearly dictates otherwise. The word "such as" is used herein to mean, and is used interchangeably with, the phrase "such as but not limited to".
It should also be noted that in the method, system and shore bridge of the present application, the components or steps may be decomposed and/or recombined. These decompositions and/or recombinations are to be considered as equivalents 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, the description is not intended to limit embodiments of the application to the form disclosed herein. While a number of example aspects and embodiments have been discussed above, those of skill in the art will recognize certain variations, modifications, alterations, additions and sub-combinations thereof.
The above description is only exemplary of the present application and should not be taken as limiting the present application, as any modifications, equivalents and the like that are within the spirit and principle of the present application should be included in the scope of the present application.
Claims (10)
1. A shore bridge monitoring method, comprising:
acquiring real-time vibration information and attitude information of the shore bridge in the operation process;
acquiring wind speed information of the shore bridge in the operation process; inputting the real-time vibration information, the attitude information and the wind speed information into a neural network model to obtain predicted state information, wherein the predicted state information comprises predicted vibration information; and
and when the predicted vibration information reaches a first preset condition, generating alarm information.
2. The shore bridge monitoring method according to claim 1, wherein said predicted state information further includes predicted wheel pressure information for each of a plurality of corner wheels, wherein said shore bridge monitoring method further comprises:
comparing the plurality of predicted wheel pressure information to generate a comparison information result; and
when the comparison information result reaches a second preset condition, generating alarm information;
wherein the comparison information result varies with variation of the predicted vibration information.
3. The shore bridge monitoring method according to claim 2, 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 standard vibration information, and calculating loss based on the predicted wheel pressure training information and standard wheel pressure training 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.
4. The shore bridge monitoring method according to any one of claims 1 to 3, wherein said obtaining said attitude information comprises one or more of the following in combination:
and acquiring trolley information, hoisting weight information, girder position information and crane acceleration information of the shore bridge.
5. The shore bridge monitoring method of claim 4, wherein said obtaining said trolley information comprises:
and acquiring trolley position information and trolley acceleration information.
6. The shore bridge monitoring method according to any one of claims 1 to 5, wherein obtaining the real-time vibration information comprises:
and acquiring real-time vibration information of a front girder, real-time vibration information of an upper girder and real-time vibration information of a rear girder of the shore bridge.
7. The shore bridge monitoring method according to claim 6, wherein the obtaining of the real-time vibration information specifically comprises:
acquiring initial vibration information of a shore bridge in the operation process;
and filtering the initial vibration information to obtain the real-time vibration information.
8. The shore bridge monitoring method according to claim 2, 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.
9. A shore bridge monitoring system for implementing the shore bridge monitoring method of any one of claims 1 to 8, characterized in that it comprises:
the vibration monitoring module is used for monitoring the real-time vibration information of the shore bridge in the operation process;
the attitude monitoring module is used for monitoring the attitude information of the shore bridge in the operation process;
the wind speed monitoring module is used for monitoring the wind speed information of the shore bridge in the operation process;
the calculation module is in communication connection with the vibration monitoring module, the attitude monitoring module and the wind speed monitoring module respectively to acquire the vibration information, the attitude information and the wind speed information, and is used for acquiring predicted state information according to a neural network model after inputting the vibration information, the attitude information and the wind speed information, wherein the predicted state information comprises predicted vibration information; and
the early warning module is in communication connection with the computing module to acquire the predicted vibration information, and is configured to send out warning information when the predicted vibration information reaches a first preset condition.
10. A shore bridge, comprising:
a shore bridge monitoring system as claimed in claim 9; and
the main part is used for installing the shore bridge monitoring system.
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| CN115096378A (en) * | 2022-08-29 | 2022-09-23 | 宇一精工科技(南通)有限公司 | Intelligent recognition method for dislocation fault of rubber pad |
| CN116957366A (en) * | 2023-09-20 | 2023-10-27 | 中交第四航务工程勘察设计院有限公司 | Port data acquisition and evaluation method and system based on Internet of things |
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| CN116957366A (en) * | 2023-09-20 | 2023-10-27 | 中交第四航务工程勘察设计院有限公司 | Port data acquisition and evaluation method and system based on Internet of things |
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