Tidal flat shallow stratum deformation prediction device and method

Abstract

This application discloses a device and method for predicting deformation of shallow strata on tidal flats, relating to the field of stratum deformation prediction. The device comprises: a plurality of node components connected in sequence and vertically inserted into the shallow stratum at a target tidal flat monitoring point; a rainfall sensor and a tide sensor are respectively arranged within a set radius of the target tidal flat monitoring point; a controller is respectively connected to the node components, rainfall sensor, and tide sensor at the tail of the sensor array; a triaxial acceleration sensor is used to collect triaxial accelerations of the corresponding node components at different times; the controller is used to: obtain a raw data set at the target tidal flat monitoring point; determine N groups of state sequences based on the feature sequences, rainfall sequences, and tide height sequences of N data clusters; and input each group of state sequences into different trained prediction models to obtain N deformation sequences; and determine the future deformation of the shallow stratum at the target tidal flat monitoring point based on the N deformation sequences. This application effectively predicts the future deformation of the shallow stratum on the tidal flat monitoring point.

Description

Technical Field

[0001] The present application relates to the technical field of stratum deformation prediction, and in particular to a device and method for predicting tidal flat shallow stratum deformation. Background Art

[0002] Tidal flats, one of the most common landforms in coastal areas, have important economic, ecological, and research value. In recent years, a large number of researchers have conducted in-depth research on the formation, protection, and evolution of tidal flats.

[0003] As a natural landform affected by multi-scale factors, the tidal flat's stratum deformation can reflect its complex evolution law. In particular, the deformation of the shallow strata of the tidal flat is often affected by the superposition of multiple environmental factors. The deformation prediction of the shallow strata of the tidal flat is conducive to understanding the generation mechanism and evolution mechanism of the tidal flat environment, and can also provide a scientific basis for predicting natural geological disasters.

[0004] Because shallow tidal flats contain high water content, are susceptible to periodic tides, and experience irregular changes, conventional technologies are unable to predict their deformation. Currently, commonly used technologies include LiDAR, GNSS, and InSAR. However, these technologies are only suitable for monitoring specific deformation within shallow tidal flats and cannot predict their future deformation. Therefore, predicting deformation within shallow tidal flats remains a significant challenge. Summary of the Invention

[0005] The purpose of this application is to provide a device and method for predicting deformation of shallow tidal flats, which can effectively predict the future deformation of shallow tidal flats.

[0006] To achieve the above objectives, this application provides the following solutions:

[0007] In a first aspect, the present application provides a device for predicting deformation of shallow strata on tidal flats, the device comprising: a sensor array, a rainfall sensor, a tide sensor, and a controller; the sensor array comprising a plurality of node components; each node component being equipped with a triaxial acceleration sensor;

[0008] A plurality of node components are connected in sequence and vertically penetrate into the shallow layer of the target tidal flat monitoring point; the rainfall sensor and the tide sensor are respectively arranged within a set radius range of the target tidal flat monitoring point; the controller is respectively connected to the node component at the tail of the sensor array, the rainfall sensor, and the tide sensor;

[0009] The three-axis acceleration sensor is used to collect the three-axis acceleration of the corresponding node component at different times; the rainfall sensor is used to collect the rainfall at different times of the target tidal flat monitoring point; the tide sensor is used to collect the tide height at different times of the target tidal flat monitoring point; the controller is used to: obtain the original data set of the target tidal flat monitoring point within a set time period; the original data set includes: the original three-axis acceleration data set, the original rainfall data set and the original tide height data set; the original three-axis acceleration data set includes the node three-axis acceleration at different times; the node three-axis acceleration includes the three-axis acceleration of different node components; the original rainfall data set includes the rainfall at different times; the original tide height data set includes the tide height at different times; based on the original three-axis acceleration data set, the node displacement deformation at different times is calculated to obtain the displacement deformation data set; the node displacement deformation includes the displacement deformation of different node components; the displacement deformation data set is hierarchically clustered to obtain N data clusters, perform principal component analysis on each data cluster, select principal component components with contributions greater than a set threshold and calculate the average value of the principal component components to obtain feature sequences of the N data clusters; N is an integer greater than 0; perform cumulative calculation on the original rainfall data set, and correct the sequence length after the cumulative calculation to obtain a rainfall sequence; perform sliding average calculation on the original tide height data set, and correct the sequence length after the sliding average calculation to obtain a tide height sequence; determine N groups of state sequences based on the feature sequences of the N data clusters, the rainfall sequence and the tide height sequence, and input each group of state sequences into different trained prediction models to obtain N deformation sequences; a group of state sequences includes: a feature sequence of a data cluster, the rainfall sequence and the tide height sequence; a group of state sequences corresponds to a trained prediction model; based on the N deformation sequences, determine the future deformation of the shallow stratum at the target tidal flat monitoring point; a deformation sequence corresponds to the deformation value of a stratum.

[0010] In a second aspect, the present application further provides a method for predicting deformation of a tidal flat shallow stratum, which is applied to the tidal flat shallow stratum deformation prediction device described in the first aspect, and the method for predicting deformation of a tidal flat shallow stratum comprises:

[0011] Acquire a raw data set of a target tidal flat monitoring point within a set time period; the raw data set includes: a raw triaxial acceleration data set, a raw rainfall data set, and a raw tide height data set; the raw triaxial acceleration data set includes the triaxial accelerations of nodes at different times; the node triaxial accelerations include the triaxial accelerations of different node components; the raw rainfall data set includes rainfall at different times; the raw tide height data set includes tide heights at different times;

[0012] Based on the original triaxial acceleration data set, calculating the node displacement deformation at different times to obtain a displacement deformation data set; the node displacement deformation includes the displacement deformation of different node components;

[0013] Hierarchical clustering of the displacement deformation dataset is performed to obtain N data clusters, and principal component analysis is performed on each data cluster. The principal component components whose contribution is greater than a set threshold are selected and the average value of the principal component components is calculated to obtain the characteristic sequence of the N data clusters; N is an integer greater than 0;

[0014] Performing cumulative calculation on the original rainfall data set and correcting the length of the sequence after the cumulative calculation to obtain a rainfall sequence;

[0015] Performing a sliding average calculation on the original tide height data set and correcting the sequence length after the sliding average calculation to obtain a tide height sequence;

[0016] Determining N groups of state sequences based on the feature sequences of the N data clusters, the rainfall sequence, and the tide height sequence, and inputting each group of state sequences into different trained prediction models to obtain N deformation sequences; a group of state sequences includes: a feature sequence of a data cluster, the rainfall sequence, and the tide height sequence; a group of state sequences corresponds to one trained prediction model;

[0017] Based on N deformation sequences, the future deformation of the shallow strata at the target tidal flat monitoring point is determined; one deformation sequence corresponds to the deformation value of one stratum.

[0018] According to the specific embodiments provided in this application, this application discloses the following technical effects:

[0019] The present application effectively collects the triaxial acceleration data of different node components by inserting a sensor array into the shallow stratum of the target tidal flat monitoring point, and further calculates the displacement deformation of the different node components, thereby obtaining a displacement deformation data set that characterizes the deformation of different strata. In order to realize the division of deformation data of different strata, the present application also divides the displacement deformation data set into N data clusters by hierarchical clustering, and performs principal component analysis on each data cluster, and finally obtains the characteristic sequence of N data clusters, i.e., the characteristic sequence corresponding to each stratum. In order to improve the effectiveness of the deformation prediction of the shallow stratum of the tidal flat, the present application also collects the environmental data of the target tidal flat monitoring point by laying out rainfall sensors and tide sensors. After further calculation and correction, a rainfall sequence and a tide height sequence are obtained. Finally, it is only necessary to combine the rainfall sequence and the tide height sequence with the characteristic sequence of each data cluster and input them into the corresponding trained prediction model respectively to obtain a deformation sequence that characterizes the future deformation of each stratum. All deformation sequences jointly characterize the future deformation of the shallow stratum of the target tidal flat monitoring point. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0021] Figure 1 A diagram showing the layout of a device for predicting deformation of shallow tidal flats provided in an embodiment of the present application;

[0022] Figure 2 A structural diagram of a node component provided in an embodiment of the present application;

[0023] Figure 3 Flowchart of a method for predicting tidal flat shallow stratum deformation provided in an embodiment of the present application.

[0024] Explanation of symbols:

[0025] Sensor array-1, node component-11, cabin-111, hatch-112, controller-2. DETAILED DESCRIPTION

[0026] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0027] The purpose of this application is to provide a device and method for predicting deformation of shallow tidal flats, which can effectively predict the future deformation of shallow tidal flats.

[0028] In order to make the above-mentioned purposes, features and advantages of the present application more obvious and easy to understand, the present application is further described in detail below with reference to the accompanying drawings and specific implementation methods.

[0029] Example 1

[0030] This embodiment provides a device for predicting deformation of shallow tidal flats. Figure 1 As shown, the tidal flat shallow layer deformation prediction device includes: a sensor array 1, a rainfall sensor (not shown in the figure), a tide sensor (not shown in the figure) and a controller 2; the sensor array 1 includes a plurality of node components 11; each node component 11 is equipped with a triaxial acceleration sensor, specifically as follows:

[0031] Multiple node components 11 are connected in sequence and vertically penetrate into the shallow layer of the target tidal flat monitoring point. Figure 2As shown, the node component 11 further includes: a cabin 111 and a cabin cover 112; the cabin cover 112 is sealed and fixed to the cabin 111, and is used to protect the triaxial acceleration sensor inside the cabin.

[0032] As a preferred embodiment, a plurality of node components 11 are connected in series in sequence, and every two node components 11 are connected by a watertight cable. The length of the sensor array 1 is greater than 1m, and the number of node components 11 is greater than 3. In order to facilitate the vertical penetration of the sensor array 1 into the shallow stratum of the target tidal flat monitoring point, it is necessary to use a penetrometer and achieve it through manual assistance. Among them, the penetrometer is a hollow barrel-shaped structure, and a conical component is arranged at the head end of the structure. In actual operation, the sensor array 1 is first vertically inserted from the end of the structure, and then the entire structure is inserted into the shallow stratum of the target tidal flat monitoring point. After the sensor array 1 is stabilized inside the stratum, the structure can be directly pulled out. In addition, the cabin 111 and the hatch 112 should be made of 316 material to prevent the entire node component from rusting after the watertightness fails.

[0033] The rainfall sensor and the tide sensor are respectively arranged within the set radius of the target tidal flat monitoring point; the controller 2 is respectively connected to the node component 11 at the tail of the sensor array, the rainfall sensor and the tide sensor.

[0034] As a preferred embodiment, the rainfall sensor and tide sensor are required to be installed within a 1km radius of the target tidal flat monitoring point. The rainfall sensor is installed at a dock within a 1km radius of the target tidal flat monitoring point, and the tide sensor is installed on the tidal flat within a 1km radius of the target tidal flat monitoring point. The controller 2, rainfall sensor, and tide sensor are all connected by a watertight cable. In addition, the tide sensor has the function of monitoring the in-situ tide height and can store the in-situ monitoring data locally in various ways. The operating frequency of the rainfall sensor, tide sensor, and triaxial acceleration sensor in each node component 11 is consistent and all higher than 1Hz, and their single operating time is greater than or equal to one week.

[0035] Furthermore, the three-axis acceleration sensor is used to collect the three-axis acceleration of the corresponding node component at different times; the rainfall sensor is used to collect the rainfall at different times at the target tidal flat monitoring point (around); and the tide sensor is used to collect the tide height at different times at the target tidal flat monitoring point (around).

[0036] Controller 2 is used to: obtain the original data set of the target tidal flat monitoring point within a set time period; the original data set includes: an original triaxial acceleration data set, an original rainfall data set and an original tide height data set; the original triaxial acceleration data set includes the triaxial acceleration of the node at different times; the triaxial acceleration of the node includes the triaxial acceleration of different node components; the original rainfall data set includes the rainfall at different times; the original tide height data set includes the tide height at different times; based on the original triaxial acceleration data set, calculate the node displacement deformation at different times to obtain a displacement deformation data set; the node displacement deformation includes the displacement deformation of different node components; hierarchical clustering of the displacement deformation data set to obtain N data clusters, perform principal component analysis on each data cluster, select the principal component component whose contribution is greater than the set threshold and calculate the principal component component The average value is calculated to obtain the characteristic sequences of N data clusters; N is an integer greater than 0; the original rainfall data set is accumulated and the sequence length after the accumulation calculation is corrected to obtain the rainfall sequence; the original tide height data set is sliding averaged and the sequence length after the sliding average calculation is corrected to obtain the tide height sequence; N groups of state sequences are determined based on the characteristic sequences, rainfall sequences and tide height sequences of the N data clusters, and each group of state sequences is input into different trained prediction models to obtain N deformation sequences; a group of state sequences includes: a characteristic sequence of a data cluster, a rainfall sequence and a tide height sequence; a group of state sequences corresponds to a trained prediction model; based on the N deformation sequences, the future deformation of the shallow stratum at the target tidal flat monitoring point is determined; a deformation sequence corresponds to the deformation value of a stratum.

[0037] As a preferred embodiment, the controller 2 is further configured to control the operating frequency and storage frequency of the sensor array 1 , the rainfall sensor, and the tide sensor, and store the original data set locally.

[0038] Example 2

[0039] This embodiment provides a method for predicting deformation of shallow tidal flats, which is applied to the above-mentioned device for predicting deformation of shallow tidal flats. Figure 3 As shown in FIG, the method for predicting deformation of shallow tidal flats includes:

[0040] Step S1: Obtain the original data set of the target tidal flat monitoring point within a set time period.

[0041] In this embodiment, the original data set includes: an original triaxial acceleration data set, an original rainfall data set, and an original tide height data set; the original triaxial acceleration data set A0=[a 0 ,a 1 ,...,a t ]Including the node triaxial acceleration a at different times t; The triaxial acceleration of the node includes the triaxial acceleration of different node components The original rainfall data set P0=[p0,p1,...,p t ] including the rainfall at different times p t ; Original tide height data set H0=[h0,h1,...,h t ]Including the tide height h at different times t .

[0042] In one example, in order to predict the deformation of the shallow ground at a target tidal flat monitoring point in the next week, it is necessary to obtain the original dataset of the target tidal flat monitoring point this week and perform a prediction based on the dataset.

[0043] Step S2: Based on the original triaxial acceleration data set, calculate the node displacement deformation at different times to obtain a displacement deformation data set; the node displacement deformation includes the displacement deformation of different node components.

[0044] In this embodiment, step S2 specifically includes:

[0045] The first step is to calculate the node yaw angle at different times based on the original three-axis acceleration data set. The node yaw angle includes the yaw angles corresponding to different node components. The calculation formula for the yaw angle is:

[0046]

[0047] Where, is the yaw angle of the j-th node component at time t, is the acceleration of the j-th node component in the X-axis direction at time t, and g is the acceleration due to gravity.

[0048] The second step is to calculate the node displacement deformation at different times based on the node yaw angle at different times using the linear model, specifically:

[0049]

[0050] Where, is the displacement deformation of the i-th node component at time t, is the X-axis deformation of the i-th node component at time t, is the Y-axis deformation of the i-th node component at time t, is the Z-axis deformation of the i-th node component at time t, l is the distance between node components, and n is the number of node components.

[0051] In addition, because the sensor array in this embodiment is vertically penetrated into the shallow layer of the target tidal flat monitoring point, only the X-axis and Y-axis deformations of each node component are considered, and the Z-axis deformation is ignored.

[0052] The third step is to obtain the displacement deformation data set based on the node displacement deformation at different times.

[0053] Step S3: Perform hierarchical clustering on the displacement deformation data set to obtain N data clusters, perform principal component analysis on each data cluster, select the principal component components whose contribution is greater than the set threshold, and calculate the average value of the principal component components to obtain the characteristic sequence of the N data clusters; N is an integer greater than 0.

[0054] As a preferred implementation, the number of data clusters obtained by hierarchical clustering is set to 3, that is, the number of data clusters corresponds to the number of layers in conventional shallow strata, the clustering distance is set to Euclidean distance, and the number of leaf nodes of the hierarchical tree is set according to the size of the data set.

[0055] In this embodiment, step S3 specifically includes:

[0056] The first step is to divide the node displacement deformation at different times into the X-axis and Y-axis directions based on the node displacement dataset, obtaining the X-axis displacement dataset and the Y-axis displacement dataset. Since the displacement deformation in the Z-axis direction is not considered, only the X-axis and Y-axis directions are required.

[0057] In the second step, hierarchical clustering of the X-axis displacement dataset is performed to obtain N data clusters. Principal component analysis is performed on each data cluster. Principal component components with a contribution greater than 90% are selected and their average values ​​are calculated to obtain the X-axis direction sequences of the N data clusters. Although the node components in the sensor matrix are interconnected, the displacement deformations of adjacent node components may be clustered into different data clusters due to different deformation patterns. In addition, the contribution of each data cluster is calculated as follows:

[0058]

[0059] In the formula, CCR is the contribution value, λ k is the kth principal component, and n is the number of principal components (the same as the number of node components in the sensing matrix).

[0060] In the third step, hierarchical clustering of the Y-axis displacement dataset was performed to obtain N data clusters. Principal component analysis was performed on each cluster. Principal components with a contribution greater than 90% were selected and their averages were calculated to obtain the Y-axis direction sequences for the N data clusters. The basic principle behind this analysis is the same as that used for the X-axis displacement dataset in the second step, so this will not be repeated here.

[0061] The fourth step is to obtain the feature sequences of the N data clusters based on their X-axis sequences and their Y-axis sequences. The feature sequences of the N data clusters obtained ultimately correspond to the deformation characteristics of each stratum in the shallow subsurface, and this feature sequence plays a crucial role in shallow subsurface deformation prediction and subsequent model training.

[0062] Step S4: performing cumulative calculation on the original rainfall data set and correcting the sequence length after the cumulative calculation to obtain a rainfall sequence.

[0063] In this embodiment, the cumulative sequence obtained after cumulative calculation is recorded as:

[0064]

[0065] It should be noted that the length of the accumulated sequence is inconsistent with the original sequence, so it needs to be corrected to In order to ensure consistency with the original sequence length, the correction method is to expand the cumulative sequence by continuously copying the last value until it reaches the same length as the original sequence.

[0066] As a preferred embodiment, when performing accumulation operation on the data in the original rainfall data set, the accumulated data points are set to be between 24 hours and 36 hours.

[0067] Step S5: Perform sliding average calculation on the original tide height data set and correct the sequence length after the sliding average calculation to obtain a tide height sequence.

[0068] As a preferred embodiment, the sliding window size for the sliding average calculation of the original tidal height dataset is generally set to half the local tidal period (6 hours for diurnal tides, 3 hours for semidiurnal tides, and 3 hours for irregular tides). The correction method is also to expand the accumulated sequence by continuously copying the last value until it reaches the same length as the original sequence.

[0069] Step S6: Determine N groups of state sequences based on the feature sequences, rainfall sequences, and tide height sequences of the N data clusters, and input each group of state sequences into different trained prediction models to obtain N deformation sequences; a group of state sequences includes: a feature sequence, rainfall sequence, and tide height sequence of a data cluster; a group of state sequences corresponds to a trained prediction model.

[0070] As a preferred embodiment, before executing step S6, it is necessary to obtain N trained prediction models. The number of trained prediction models, the number of data clusters obtained by hierarchical clustering, and the number of strata in the shallow strata of the target tidal flat monitoring point are the same, and the training process of each prediction model is also the same, as follows:

[0071] The first step is to obtain the original datasets of the target tidal flat monitoring points in different historical time periods. Generally, multiple sets of original datasets are obtained for adjacent historical time periods, for example, the original datasets for T-1 week, T-2 week, and T-3 week are obtained separately.

[0072] In the second step, the same process as steps S2 to S5 above is performed on all original data sets, and the processed data is divided into a training set and a test set in a ratio of 8:2. The prediction model is trained on the training set, and the accuracy of the prediction model is verified on the test set.

[0073] The third step is to use the random forest regression model as the prediction model and set the parameters of the random forest regression model. The number of random forest regression model trees should be determined by the amount of training data. The default value is 100. The training is stopped when the number of training times reaches 1000 or the Gini coefficient is less than 1e -4 .

[0074] The fourth step is to train the random forest regression model. For example, the rainfall sequence, tide height sequence and feature sequence of the first data cluster obtained from the original data set of week T-2 are used as input, and the original sequence of the first data cluster obtained from the original data set of week T-1 is used as output to train the prediction model corresponding to the first data cluster (i.e., the first type layer in the shallow layer). Similarly, the rainfall sequence, tide height sequence and feature sequence of the first data cluster obtained from the original data set of week T-3 are used as input, and the original sequence of the first data cluster obtained from the original data set of week T-2 is used as output to train the prediction model corresponding to the first data cluster (i.e., the first type layer in the shallow layer). The above basic training principles are all based on the rainfall sequence, tide height sequence and feature sequence of a single data cluster in the historical time period as input, and the original sequence of a single data cluster in the time period after the historical time period as output, and the prediction model corresponding to each data cluster (i.e., each type layer in the shallow layer) is trained in this way.

[0075] The fifth step is to verify the training accuracy of the random forest regression model. The main accuracy verification calculation indicators include: root mean square error (RMSE), absolute error (MAE), and coefficient of determination (R2). With the goal of RMSE and MAE close to 0 and R2 close to 1, the trained random forest regression model is tuned for accuracy.

[0076] Step S7: Based on the N deformation sequences, determine the future deformation of the shallow strata at the target tidal flat monitoring point.

[0077] In this embodiment, a deformation sequence corresponds to a deformation value of a stratum. By observing the deformation values ​​corresponding to different deformation sequences, the deformation state of each type of stratum in the shallow stratum at the target tidal flat monitoring point can be determined. This deformation state is the future deformation of the shallow stratum on the tidal flat.

[0078] In summary, this application effectively predicts the future deformation of shallow tidal flat layers, which helps researchers understand the generation and evolution mechanisms of tidal flat environments and provides a strong scientific basis for the prediction of natural geological disasters.

[0079] The various embodiments in this specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the various embodiments can be referenced to each other.

[0080] This document uses specific examples to illustrate the principles and implementation methods of this application. The description of the above examples is only intended to help understand the method and core concept of this application. At the same time, for those skilled in the art, based on the concept of this application, there may be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting this application.

Claims

1. A device for predicting deformation of shallow tidal flats, characterized in that: The tidal flat shallow layer deformation prediction device comprises: a sensor array, a rainfall sensor, a tide sensor and a controller; the sensor array comprises a plurality of node components; each of the node components is equipped with a triaxial acceleration sensor; A plurality of node components are connected in sequence and vertically penetrate into the shallow layer of the target tidal flat monitoring point; the rainfall sensor and the tide sensor are respectively arranged within a set radius range of the target tidal flat monitoring point; the controller is respectively connected to the node component at the tail of the sensor array, the rainfall sensor, and the tide sensor; The triaxial acceleration sensor is used to collect triaxial accelerations of corresponding node components at different times; the rainfall sensor is used to collect rainfall at different times at the target tidal flat monitoring point; the tide sensor is used to collect tide heights at different times at the target tidal flat monitoring point; and the controller is used to: Acquire a raw data set of a target tidal flat monitoring point within a set time period; the raw data set includes: a raw triaxial acceleration data set, a raw rainfall data set, and a raw tide height data set; the raw triaxial acceleration data set includes the triaxial accelerations of nodes at different times; the node triaxial accelerations include the triaxial accelerations of different node components; the raw rainfall data set includes rainfall at different times; the raw tide height data set includes tide heights at different times; Based on the original triaxial acceleration data set, calculating the node displacement deformation at different times to obtain a displacement deformation data set; the node displacement deformation includes the displacement deformation of different node components; Hierarchical clustering of the displacement deformation dataset is performed to obtain N data clusters, and principal component analysis is performed on each data cluster. The principal component components whose contribution is greater than a set threshold are selected and the average value of the principal component components is calculated to obtain the characteristic sequence of the N data clusters; N is an integer greater than 0; Performing cumulative calculation on the original rainfall data set and correcting the length of the sequence after the cumulative calculation to obtain a rainfall sequence; Performing a sliding average calculation on the original tide height data set and correcting the sequence length after the sliding average calculation to obtain a tide height sequence; Determining N groups of state sequences based on the feature sequences of the N data clusters, the rainfall sequence, and the tide height sequence, and inputting each group of state sequences into different trained prediction models to obtain N deformation sequences; a group of state sequences includes: a feature sequence of a data cluster, the rainfall sequence, and the tide height sequence; a group of state sequences corresponds to one trained prediction model; Based on N deformation sequences, the future deformation of the shallow strata at the target tidal flat monitoring point is determined; one deformation sequence corresponds to the deformation value of one stratum.

2. The device for predicting deformation of shallow tidal flats according to claim 1, characterized in that: The controller is further used to control the operating frequency and storage frequency of the sensor array, the rainfall sensor and the tide sensor.

3. The device for predicting deformation of shallow tidal flats according to claim 1, characterized in that: The node component further includes: a cabin and a cabin cover; the cabin cover is sealed and fixed to the cabin and is used to protect the triaxial acceleration sensor inside the cabin.

4. The device for predicting deformation of shallow tidal flats according to claim 1, characterized in that: The length of the sensor array is greater than 1m; the number of the node components is greater than 3.

5. The device for predicting deformation of shallow tidal flats according to claim 1, characterized in that: The node components are connected by watertight cables.

6. A method for predicting deformation of shallow tidal flats, characterized in that: The method for predicting deformation of a tidal flat shallow stratum is applied to the device for predicting deformation of a tidal flat shallow stratum according to any one of claims 1 to 5, and the method for predicting deformation of a tidal flat shallow stratum comprises: Acquire a raw data set of a target tidal flat monitoring point within a set time period; the raw data set includes: a raw triaxial acceleration data set, a raw rainfall data set, and a raw tide height data set; the raw triaxial acceleration data set includes the triaxial accelerations of nodes at different times; the node triaxial accelerations include the triaxial accelerations of different node components; the raw rainfall data set includes rainfall at different times; the raw tide height data set includes tide heights at different times; Based on the original triaxial acceleration data set, calculating the node displacement deformation at different times to obtain a displacement deformation data set; the node displacement deformation includes the displacement deformation of different node components; Hierarchical clustering of the displacement deformation dataset is performed to obtain N data clusters, and principal component analysis is performed on each data cluster. The principal component components whose contribution is greater than a set threshold are selected and the average value of the principal component components is calculated to obtain the characteristic sequence of the N data clusters; N is an integer greater than 0; Performing cumulative calculation on the original rainfall data set and correcting the length of the sequence after the cumulative calculation to obtain a rainfall sequence; Performing a sliding average calculation on the original tide height data set and correcting the sequence length after the sliding average calculation to obtain a tide height sequence; Determining N groups of state sequences based on the feature sequences of the N data clusters, the rainfall sequence, and the tide height sequence, and inputting each group of state sequences into different trained prediction models to obtain N deformation sequences; a group of state sequences includes: a feature sequence of a data cluster, the rainfall sequence, and the tide height sequence; a group of state sequences corresponds to one trained prediction model; Based on N deformation sequences, the future deformation of the shallow strata at the target tidal flat monitoring point is determined; one deformation sequence corresponds to the deformation value of one stratum.

7. The method for predicting deformation of shallow tidal flats according to claim 6, characterized in that: Based on the original triaxial acceleration data set, the node displacement deformation at different times is calculated to obtain a displacement deformation data set, which specifically includes: Calculating the node yaw angle at different times based on the original three-axis acceleration data set; the node yaw angle includes the yaw angles corresponding to different node components; Based on the node yaw angle at different times, the node displacement deformation at different times is calculated using the linear model; Based on the node displacement and deformation at different times, a displacement deformation data set is obtained.

8. The method for predicting deformation of shallow tidal flats according to claim 7, characterized in that: The straight line model is: Where, is the displacement deformation of the i-th node component at time t, is the X-axis deformation of the i-th node component at time t, is the Y-axis deformation of the i-th node component at time t, is the Z-axis deformation of the i-th node component at time t, is the yaw angle of the jth node component at time t, l is the distance between node components, and n is the number of node components.

9. The method for predicting deformation of shallow tidal flats according to claim 6, wherein: Hierarchical clustering of the displacement deformation dataset is performed to obtain N data clusters. Principal component analysis is performed on each data cluster. The principal component components with a contribution greater than a set threshold are selected and the average value of the principal component components is calculated to obtain the characteristic sequence of the N data clusters, which specifically includes: Based on the displacement deformation data set, the node displacement deformation at different times is divided into the X-axis and Y-axis directions to obtain an X-axis displacement data set and a Y-axis displacement data set; Hierarchical clustering of the X-axis displacement data set is performed to obtain N data clusters, and principal component analysis is performed on each data cluster. The principal component components with a contribution greater than 90% are selected and the average value of the principal component components is calculated to obtain the X-axis direction sequence of the N data clusters; Hierarchical clustering of the Y-axis displacement data set is performed to obtain N data clusters, and principal component analysis is performed on each data cluster. The principal component components with a contribution greater than 90% are selected and the average value of the principal component components is calculated to obtain the Y-axis direction sequence of the N data clusters; Based on the X-axis direction sequences of the N data clusters and the Y-axis direction sequences of the N data clusters, the feature sequences of the N data clusters are obtained.

10. The method for predicting deformation of shallow tidal flats according to claim 6, characterized in that: The prediction model is a random forest regression model.

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