A method and system for monitoring deformation of a port structure

Abstract

The present application provides a kind of port structure deformation monitoring method and system, belong to port safety technical field.Utilize GNSS sensor to the position information of port terminal is monitored to obtain the spatial coordinate information of port terminal;According to wharf horizontal coordinate information and wharf vertical coordinate information in spatial coordinate information, based on the first prediction method and the second prediction method, the lateral displacement and settlement displacement prediction of port terminal is carried out;Finally, based on lateral displacement prediction curve and settlement displacement prediction curve, port structure deformation early warning is carried out.The monitoring method of the present application only needs to utilize the spatial coordinate information of port terminal, and the required input data amount is small;Different prediction methods are used to predict the lateral displacement and settlement displacement of port terminal, which can take into account the relationship between algorithm complexity and prediction accuracy;When setting the prediction coefficient of the prediction method, the fluctuation of the data observation value is considered, and the stability and prediction sensitivity of the prediction method are considered.

Description

Technical Field

[0001] The present invention belongs to the technical field of port safety, and in particular relates to a method and system for monitoring port structure deformation. Background Art

[0002] Maritime transport is an important supporting link in my country's trade connection with the world, and the port terminal structure is the foundation of maritime transport. If the port structure or terminal structure is damaged or there is a hidden danger of deformation, it is easy to lead to serious consequences such as instability of the main structure or slope of the port or terminal, damage to ancillary facilities or operating equipment, ship berthing accidents, reduced loading and unloading efficiency and berthing capacity, pollution leakage, and even port suspension. Therefore, it is necessary to monitor the deformation of port structures. Ordinary port structure deformation is easy to observe with the naked eye. However, structural changes that are not easily perceived by the naked eye, such as port terminal settlement, displacement, and slope deformation, may lead to a decline in the stability and service performance of the port structure. In the prior art with patent number CN202411579762, displacement, stress, and load parameters of the pile foundation of the high-pile terminal are monitored to generate real-time monitoring parameter data. The working condition of the pile foundation structure is identified through the real-time monitoring parameter data to generate pile foundation structure working condition data. Multi-source load feature analysis is performed on the pile foundation structure working condition data, and load coupling effect calculation is performed to generate load action data. Soil-pile interaction analysis is performed based on the load action data to generate soil-pile response data. Material performance degradation evaluation is performed based on the soil-pile response data to generate material status data. Patent number CN201710689 The prior art of Patent No. 325 first calculates the additional stress in each soil layer of the super-long pile group. The additional stress is then compared with the initial consolidation pressure. Based on the comparison result, the corresponding soil layer compression calculation formula is selected to calculate the compression. Finally, the compression is calculated using the layered summation method, thereby predicting the settlement of the super-long pile group. The prior art of Patent No. CN202211586898 first obtains a deep learning sample set of lateral displacement of high-piled wharf pile foundations, establishes a deep learning prediction model for lateral displacement of high-piled wharf pile foundations, and then uses the deep learning sample set to train the prediction model to obtain an optimized deep learning prediction model for lateral displacement of high-piled wharf pile foundations. Finally, the real-time measured data of the high-piled wharf piles is input into the optimized deep learning prediction model for lateral displacement of high-piled wharf pile foundations to obtain the model output prediction results. Some prior art methods rely on a large amount of port structure operating condition data and load data for structural deformation monitoring, which requires a large amount of data processing and storage space. Others require the construction of complex calculation methods or AI models for structural deformation monitoring, which are easily affected by data bias and some models have a serious black box effect, making it difficult to trace the source. Summary of the Invention

[0003] In order to solve the above problems existing in the prior art, the present invention proposes a port structure deformation monitoring method and system to solve the problem that the existing port structure deformation monitoring method needs to rely on a large amount of port operating condition data and has complex algorithms.

[0004] In order to achieve the above-mentioned purpose, the present invention adopts the following technical scheme: A port structure deformation monitoring method includes the following steps: Step S1: Using a GNSS sensor (Global Navigation Satellite System, i.e., a global navigation satellite system sensor) to monitor the position information of a port terminal to obtain spatial coordinate information of the port terminal; Step S2: Based on the horizontal coordinate information of the terminal in the spatial coordinate information, the lateral displacement of the port terminal is predicted based on a first prediction method to obtain a lateral displacement prediction curve; Step S3: Based on the vertical coordinate information of the terminal in the spatial coordinate information, the settlement displacement of the port terminal is predicted based on a second prediction method to obtain a settlement displacement prediction curve; Step S4: Based on the lateral displacement prediction curve and the settlement displacement prediction curve, an early warning of port structure deformation is issued.

[0005] Furthermore, the first prediction method in step S2 specifically includes the following sub-steps: Step S21: Obtain the predicted value of the horizontal coordinate information of the dock at time t+1 according to the following formula:

[0006] ;

[0007] Among them, Y t+1 represents the predicted value of the horizontal coordinate information of the dock at time t+1, y t+1 Indicates the observed value of the horizontal coordinate information of the dock at time t+1, Y t The predicted value of the horizontal coordinate information of the wharf at time t is represented by Y0, the predicted value of the horizontal coordinate information of the wharf at time 0 is the average value of all the observed values ​​of the horizontal coordinate information of the wharf, and ρ represents the first prediction coefficient. Step S22: The predicted value of the horizontal coordinate information of the wharf at time t+1 is Y t+1 As the first predicted value of the horizontal coordinate of the wharf, then return to step S21 to obtain the second predicted value of the horizontal coordinate of the wharf, and so on; step S23: draw a lateral displacement prediction curve based on all the obtained predicted values ​​of the horizontal coordinates of the wharf.

[0008] Furthermore, the first prediction coefficient ρ in step S21 is obtained specifically according to the following formula:

[0009] ;

[0010] Where i represents the time sequence number, y i represents the observed value of the horizontal coordinate information of the dock at time i, N represents the total number of observed values ​​of the horizontal coordinate information of the dock, represents the average value of the observed values ​​of the horizontal coordinate information of the dock, and TH represents the first data deviation threshold.

[0011] Furthermore, the second prediction method in step S3 specifically includes the following sub-steps: Step S31: Obtain the predicted value of the vertical coordinate information of the terminal at time t+1 according to the following formula:

[0012] ;

[0013] Among them, X' t+1 represents the first predicted value of the vertical coordinate information of the terminal at time t+1, x t+1 Indicates the observed value of the vertical coordinate information of the dock at time t+1, X' t The first predicted value of the vertical coordinate information of the terminal at time t is X'0, which is the first value of all the observed values ​​of the vertical coordinate information of the terminal at time 0. t+1 Indicates the second predicted value of the vertical coordinate information of the terminal at time t+1, X'' t The second predicted value of the dock vertical coordinate information at time t is represented by X'0. The second predicted value of the dock vertical coordinate information at time 0 is also the first value of all the observed values ​​of the dock vertical coordinate information. α represents the second prediction coefficient. Step S32: Based on the first predicted value of the dock vertical coordinate information at time t+1 obtained in step S31 and the second predicted value of the dock vertical coordinate information at time t+1, calculate the first predicted value of the dock vertical coordinate. Then return to step S31 to obtain the second predicted value of the dock horizontal coordinate, and so on. Step S33: Based on all the obtained predicted values ​​of the dock vertical coordinates, draw a settlement displacement prediction curve.

[0014] Furthermore, the second prediction coefficient α in step S31 is obtained specifically according to the following formula:

[0015] ;

[0016] Where i represents the time sequence number, x i represents the vertical coordinate information observation value of the terminal at time i, N represents the total number of horizontal coordinate information observation values ​​of the terminal, represents the average value of the observed values ​​of the vertical coordinate information of the terminal, and PTH represents the second data deviation threshold.

[0017] Furthermore, in step S32, the predicted vertical coordinate of the first wharf is calculated according to the following formula:

[0018] ;

[0019] where x' t+1It represents the first predicted value of the vertical coordinate information of the terminal at time t+1, that is, the first predicted value of the vertical coordinate of the terminal.

[0020] Furthermore, in step S4, based on the lateral displacement prediction curve and the settlement displacement prediction curve, the port structure deformation monitoring result analysis is performed, specifically, the value of the lateral displacement prediction curve at the kth moment in the future is obtained and recorded as the first value, the value of the settlement displacement prediction curve at the kth moment in the future is obtained and recorded as the second value, and when it is determined that the first value is greater than the first preset warning value and the second value is greater than the second preset warning value, a warning signal is output to issue a port structure deformation warning.

[0021] The present invention also provides a port structure deformation monitoring system for executing the above-mentioned port structure deformation monitoring method, comprising a data acquisition unit, a prediction curve generation unit and an early warning unit, wherein the data acquisition unit is connected to the prediction curve generation unit, and the prediction curve generation unit is connected to the early warning unit, and is characterized in that the data acquisition unit is used to obtain spatial coordinate information of the port terminal, the prediction curve generation unit is used to generate a lateral displacement prediction curve and a settlement displacement prediction curve, and the early warning unit is used to provide a port structure deformation early warning.

[0022] The beneficial technical effects of the present invention compared with the prior art are:

[0023] (1) Only the spatial coordinate information of the port terminal is needed to monitor the port's lateral displacement and settlement displacement as analytical indicators for determining the deformation of the port structure. The monitoring method requires a small amount of input data; (2) Different prediction methods are used to predict the lateral displacement and settlement displacement of the port terminal. For the variable of lateral displacement, which changes relatively irregularly, the focus is on the changing trend of recent data, and the relationship between the complexity of the algorithm and the prediction accuracy is balanced through short-term prediction. For the variable of settlement displacement, which changes linearly, the focus is on the changing trend of recent data and the linear trend capture of the data, which can more accurately predict future data conditions; (3) When setting the prediction coefficient of the prediction method of the present invention, the fluctuation of the data observation value is taken into account, and the stability and prediction sensitivity of the prediction method are taken into account. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] To more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for the embodiments or the description of the prior art. Obviously, the drawings described below are merely exemplary, and those skilled in the art can derive other implementation drawings based on the provided drawings without inventive effort.

[0025] Figure 1A flow chart of a port structure deformation monitoring method of the present application;

[0026] Figure 2 A flow chart of a process for obtaining a transverse displacement prediction curve in the present application;

[0027] Figure 3 A flow chart of a process for obtaining a settlement displacement prediction curve in the present application. DETAILED DESCRIPTION

[0028] 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. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of protection of the present application.

[0029] The concepts involved in the present application will be described below in combination with the drawings. It should be pointed out here that the descriptions of the various concepts below are only for the purpose of making the content of the present application easier to understand, and do not represent a limitation on the scope of protection of the present application; meanwhile, the embodiments in the present application and the features in the embodiments can be combined with each other without conflict. The present application will be described in detail below with reference to the drawings and in combination with the embodiments.

[0030] In combination with the drawings in the specification Figure 1 A port structure deformation monitoring method, comprising the following steps: step S1: using a GNSS sensor installed at a concrete pile foundation, a bearing beam or a support structure of a port wharf to monitor position information of the port wharf, to obtain spatial coordinate information of the port wharf, wherein the spatial coordinate information is three-dimensional coordinate information of the port wharf.

[0031] Step S2: based on a first prediction method, performing transverse displacement prediction of the port wharf according to wharf horizontal coordinate information in the spatial coordinate information (i.e. three-dimensional coordinate information), to obtain a transverse displacement prediction curve; in combination with the drawings in the specification Figure 2 The first prediction method in step S2 specifically comprises the following sub-steps: step S21: obtaining a predicted value of wharf horizontal coordinate information at time t+1 according to the following formula:

[0032] ;

[0033] wherein Y t+1 represents the predicted value of wharf horizontal coordinate information at time t+1, y t+1 represents an observed value of wharf horizontal coordinate information at time t+1, Y tY0 represents the predicted value of the wharf horizontal coordinate information at time t, the predicted value of the wharf horizontal coordinate information at time 0, that is, the initial prediction of the first prediction method, and is the average of all observed values of the wharf horizontal coordinate information (for example, if the observed values of the wharf horizontal coordinate information are time series with a quantity of 300, Y0 is equal to the sum of all observed values of the wharf horizontal coordinate information divided by 300). In the above formula, p represents the first prediction coefficient, and the specific value is as follows:

[0034] ;

[0035] wherein i represents the time sequence number, y i represents the observed value of the wharf horizontal coordinate information at time i, N represents the total number of observed values of the wharf horizontal coordinate information, represents the average of the observed values of the wharf horizontal coordinate information, and TH represents the first data deviation threshold. It can be understood that, reflects the fluctuation of the observed values of the wharf horizontal coordinate information, and when the fluctuation is greater than the threshold TH, that is, when the fluctuation is large, the first prediction coefficient is greater than 0.3 and less than 0.5, which can ensure the stability of the first prediction method, and when the fluctuation is small, the first prediction coefficient is greater than 0.5 and less than 0.7, which can improve the sensitivity performance of the first prediction method.

[0036] Step S22: obtaining the predicted value Y t+1 as the first wharf horizontal coordinate prediction, and then returning to step S21 to obtain the second wharf horizontal coordinate prediction based on the first wharf horizontal coordinate prediction, and so on, so as to obtain the wharf horizontal coordinate prediction in a future period of time, and the number of times of repeating the above step S21 can be completely set by manual setting; step S23: drawing a transverse displacement prediction curve according to all the obtained wharf horizontal coordinate predictions. It should be noted that the above transverse displacement prediction curve is a data sequence, and through the transverse displacement prediction curve, the transverse displacement prediction data of the port wharf at a certain time point in a future period of time can be obtained.

[0037] Step S3: according to the wharf vertical coordinate information in the spatial coordinate information, performing settlement displacement prediction of the port wharf based on a second prediction method to obtain a settlement displacement prediction curve; in combination with the drawings Figure 3 in the description, the second prediction method in the step S3 specifically includes the following sub-steps: step S31: obtaining the predicted value Y

[0038] ;

[0039] wherein X' t+1 represents the first predicted value of the wharf vertical coordinate information at time t+1, x t+1Indicates the observed value of the vertical coordinate information of the dock at time t+1, X' t The first predicted value of the vertical coordinate information of the terminal at time t is X'0, which is the first value of all the observed values ​​of the vertical coordinate information of the terminal at time 0. t+1 Indicates the second predicted value of the vertical coordinate information of the terminal at time t+1, X'' t represents the second predicted value of the dock vertical coordinate information at time t. The second predicted value X'0 of the dock vertical coordinate information at time 0 also takes the first value of all dock vertical coordinate information observations. It can be seen that the second prediction method of the present invention is based on the first prediction method, which helps to further reduce the complexity of the algorithm.

[0040] α represents the second prediction coefficient, which is as follows:

[0041] ;

[0042] Where i represents the time sequence number, x i represents the vertical coordinate information observation value of the terminal at time i, N represents the total number of horizontal coordinate information observation values ​​of the terminal, represents the average value of the dock vertical coordinate information observations, and PTH represents the second data deviation threshold. Similar to the first prediction algorithm, the setting of the second prediction coefficient is associated with the fluctuation of the dock vertical coordinate information observations, which can take into account both the stability and prediction sensitivity of the prediction method.

[0043] Step S32: Based on the first predicted value of the dock vertical coordinate information at time t+1 obtained in step S31 and the second predicted value of the dock vertical coordinate information at time t+1, calculate the first predicted value of the dock vertical coordinate:

[0044] ;

[0045] where x' t+1 It represents the first predicted value of the vertical coordinate information of the terminal at time t+1, that is, the first predicted value of the vertical coordinate of the terminal.

[0046] Then return to step S31, obtain the second wharf horizontal coordinate prediction value based on the first wharf vertical coordinate prediction value, and so on; step S33: draw a settlement displacement prediction curve with a time series based on all the obtained wharf vertical coordinate prediction values.

[0047] Step S4: Based on the lateral displacement prediction curve and the settlement displacement prediction curve, a port structure deformation warning is performed. Specifically, based on the lateral displacement prediction curve and the settlement displacement prediction curve, the port structure deformation monitoring results are analyzed. Specifically, the value of the lateral displacement prediction curve at the kth moment in the future is obtained and recorded as a first value, and the value of the settlement displacement prediction curve at the kth moment in the future is obtained and recorded as a second value. The first value and the second value are compared with the corresponding preset warning values. When the first value is greater than the first preset warning value and the second value is greater than the second preset warning value, it is determined that the lateral displacement and settlement displacement of the port terminal at the kth moment in the future are too large, and the port structure will be seriously deformed. Therefore, a warning signal is output to warn of the port structure deformation, so as to remind maintenance and repair personnel to conduct a comprehensive inspection of the current port structure and lay a good foundation for subsequent maintenance work.

[0048] The present invention also provides a port structure deformation monitoring system for executing the above-mentioned port structure deformation monitoring method, comprising a data acquisition unit, a prediction curve generation unit and an early warning unit, wherein the data acquisition unit is connected to the prediction curve generation unit, and the prediction curve generation unit is connected to the early warning unit, and is characterized in that the data acquisition unit is used to obtain spatial coordinate information of the port terminal, the prediction curve generation unit is used to generate a lateral displacement prediction curve and a settlement displacement prediction curve, and the early warning unit is used to provide a port structure deformation early warning.

[0049] The embodiments and / or implementation methods described above are only used to illustrate the preferred embodiments and / or implementation methods for realizing the technology of the present invention, and do not impose any form of limitation on the implementation methods of the technology of the present invention. Any person skilled in the art may make slight changes or modifications to other equivalent embodiments without departing from the scope of the technical means disclosed in the content of the present invention, but they should still be regarded as technologies or embodiments that are essentially the same as the present invention.

[0050] This article uses specific examples to illustrate the principles and implementation methods of this application. The description of the above embodiments is only used to help understand the method and core ideas of this application. The above is only the preferred implementation method of this application. It should be pointed out that due to the limitations of textual expression, there are objectively infinite specific structures. For ordinary technicians in this technical field, without departing from the principles of this application, they can also make several improvements, modifications or changes, and can also combine the above technical features in an appropriate manner; these improvements, modifications, changes or combinations, or the direct application of the inventive concept and technical solution to other occasions without improvement, should be regarded as the scope of protection of this application.

Claims

1. A method for monitoring port structure deformation, characterized in that: The steps include: Step S1: Using a GNSS sensor to monitor the location information of the port terminal to obtain the spatial coordinate information of the port terminal; Step S2: performing a lateral displacement prediction of the port terminal based on the first prediction method according to the horizontal coordinate information of the terminal in the spatial coordinate information to obtain a lateral displacement prediction curve; Step S3: performing settlement and displacement prediction of the port terminal based on the second prediction method according to the vertical coordinate information of the terminal in the spatial coordinate information to obtain a settlement and displacement prediction curve; Step S4: performing port structure deformation early warning based on the lateral displacement prediction curve and the settlement displacement prediction curve; The first prediction method in step S2 specifically includes the following sub-steps: Step S21: Obtain the predicted value of the horizontal coordinate information of the dock at time t+1 according to the following formula: ； Among them, Y t+1 Represents the predicted value of the horizontal coordinate information of the dock at time t+1, y t+1 Indicates the horizontal coordinate information observation value of the dock at time t+1, Y t represents the predicted value of the horizontal coordinate information of the wharf at time t, the predicted value of the horizontal coordinate information of the wharf at time 0 Y0 is the average value of all the observed values ​​of the horizontal coordinate information of the wharf, and ρ represents the first prediction coefficient; Step S22: The predicted value Y of the horizontal coordinate information of the wharf at time t+1 t+1 As the first predicted horizontal coordinate of the wharf, then return to step S21 to obtain the second predicted horizontal coordinate of the wharf, and so on; Step S23: drawing a lateral displacement prediction curve based on the obtained predicted values ​​of all the wharf horizontal coordinates; The second prediction method in step S3 specifically includes the following sub-steps: Step S31: Obtain the predicted value of the vertical coordinate information of the terminal at time t+1 according to the following formula: ； Among them, X' t+1 represents the first predicted value of the vertical coordinate information of the terminal at time t+1, x t+1 Indicates the observed value of the vertical coordinate information of the dock at time t+1, X' t The first predicted value of the vertical coordinate information of the terminal at time t is X'0, which is the first value of all the observed values ​​of the vertical coordinate information of the terminal at time 0. t+1 Indicates the second predicted value of the vertical coordinate information of the terminal at time t+1, X'' t represents the second predicted value of the dock vertical coordinate information at time t. The second predicted value X"0 of the dock vertical coordinate information at time 0 also takes the first value of all dock vertical coordinate information observations. α represents the second prediction coefficient. Step S32: Based on the first predicted value of the dock vertical coordinate information at time t+1 obtained in step S31 and the second predicted value of the dock vertical coordinate information at time t+1, calculate the first predicted value of the dock vertical coordinate, then return to step S31 to obtain the second predicted value of the dock horizontal coordinate, and so on; Step S33: Draw a settlement displacement prediction curve based on the obtained predicted values ​​of all the wharf vertical coordinates.

2. The port structure deformation monitoring method according to claim 1, characterized in that: The first prediction coefficient ρ in step S21 is specifically obtained according to the following formula: ； Where i represents the time sequence number, y i represents the observed value of the horizontal coordinate information of the dock at time i, N represents the total number of observed values ​​of the horizontal coordinate information of the dock, represents the average value of the observed values ​​of the horizontal coordinate information of the dock, and TH represents the first data deviation threshold.

3. The port structure deformation monitoring method according to claim 1, characterized in that: The second prediction coefficient α in step S31 is specifically obtained according to the following formula: ； Where i represents the time sequence number, x i represents the vertical coordinate information observation value of the terminal at time i, N represents the total number of horizontal coordinate information observation values ​​of the terminal, represents the average value of the observed values ​​of the vertical coordinate information of the terminal, and PTH represents the second data deviation threshold.

4. The port structure deformation monitoring method according to claim 1, characterized in that: In step S32, the predicted vertical coordinate of the first wharf is calculated according to the following formula: ； where x' t+1 It represents the first predicted value of the vertical coordinate information of the terminal at time t+1, that is, the first predicted value of the vertical coordinate of the terminal.

5. The port structure deformation monitoring method according to claim 1, characterized in that: In step S4, the port structure deformation monitoring result analysis is performed based on the lateral displacement prediction curve and the settlement displacement prediction curve. Specifically, the value of the lateral displacement prediction curve at the kth moment in the future is obtained and recorded as the first value, and the value of the settlement displacement prediction curve at the kth moment in the future is obtained and recorded as the second value. When it is determined that the first value is greater than a first preset warning value and the second value is greater than a second preset warning value, a warning signal is output to issue a port structure deformation warning.

6. A port structure deformation monitoring system for executing a port structure deformation monitoring method according to any one of claims 1 to 5, comprising a data acquisition unit, a prediction curve generation unit, and an early warning unit, wherein the data acquisition unit is connected to the prediction curve generation unit, and the prediction curve generation unit is connected to the early warning unit, characterized in that: The data acquisition unit is used to obtain the spatial coordinate information of the port terminal, the prediction curve production unit is used to generate the lateral displacement prediction curve and the settlement displacement prediction curve, and the early warning unit is used to provide early warning of port structure deformation.

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