A method, medium and system for constructing a deep-sea hydrodynamic three-dimensional environmental field

Abstract

The application provides a deep-sea hydrodynamic three-dimensional environment field reconstruction method, medium and system, and belongs to the technical field of deep-sea hydrodynamics. The deep-sea hydrodynamic three-dimensional environment field reconstruction method comprises the following steps: dividing a deep-sea water area to be measured into multiple cubic blocks; taking each cubic block as a node, collecting seawater dynamic time domain data of each node every fixed time, and preprocessing the seawater dynamic time domain data; taking the preprocessed seawater dynamic time domain data corresponding to the node as node attributes, taking the relationship of the node attributes as edges, and establishing a complex network model; performing steady-state calculation on the complex network model to obtain a steady-state complex network model; and taking the edges in the obtained steady-state complex network model as a steady-state relationship data set, and describing a deep-sea hydrodynamic three-dimensional environment field by using the steady-state relationship data set.

Description

Technical Field

[0001] This invention belongs to the field of deep-sea dynamics technology, specifically, it relates to a method, medium and system for reconstructing a three-dimensional environmental field of deep-sea dynamics. Background Technology

[0002] Complex networks are a special type of network structure that abstracts elements in a complex system as nodes and relationships between elements as edges. Not all networks are complex networks; they must satisfy the following three characteristics: 1) Small-world property: The characteristic path lengths between points in the network are small, approaching random networks, but the aggregation coefficient is high, approaching regular networks. 2) Scale-free property: A few nodes in the network have large degree values, while most nodes have small degree values, and the degree distribution of nodes follows a power-law distribution. 3) Community structure property: Nodes in complex networks often exhibit clustering characteristics, meaning that the connections between nodes within a community are very strong, while the connections between nodes within and outside the community are significantly weakened.

[0003] Seawater is a fluid, constantly in motion, which enables a high rate of material and energy circulation within the ocean. Seawater and its various components constitute the marine environment, vital for human survival and development. Seawater movement primarily includes waves, tides, and ocean currents, among which currents include wind-driven currents, density currents, and compensation currents. Wind-driven currents are large-scale currents formed when prevailing winds blow across the sea surface, causing water to drift and pull from the upper layers to the lower layers. Density currents are caused by differences in temperature and salinity between sea areas, leading to variations in seawater density. Compensation currents, formed by wind and density differences, reduce the amount of seawater flowing out of the area. To compensate for this loss, seawater from adjacent areas flows in, creating currents that are further divided into vertical and horizontal compensation currents.

[0004] When monitoring deep-sea motion, it is necessary to construct a three-dimensional dynamic environment field of deep-sea water. Currently, the construction of the three-dimensional dynamic environment field of deep-sea water mainly relies on data collected by ocean dynamic satellites and ocean environment profiles. This method can only obtain a macroscopic three-dimensional dynamic environment field of deep-sea water, which makes the detailed description of the three-dimensional dynamic environment field of deep-sea water and the prediction of seawater motion inaccurate. Summary of the Invention

[0005] In view of this, the present invention provides a method, medium and system for constructing a deep-sea dynamic three-dimensional environment field, which can solve the technical problem of only being able to obtain a macroscopic deep-sea dynamic three-dimensional environment field.

[0006] This invention is implemented as follows:

[0007] The first aspect of the present invention provides a method for constructing a three-dimensional dynamic environment field in deep-sea water, which specifically includes the following steps:

[0008] S10: Divide the deep-sea area to be measured into multiple cubic blocks;

[0009] S20: Using each cube block as a node, collect the time-domain data of the seawater dynamics of each node at fixed intervals, and preprocess the seawater dynamic time-domain data; wherein, the seawater dynamic time-domain data includes: depth, seawater temperature, seawater salinity, sea surface wind force, and sea surface wind speed.

[0010] S30: Using the preprocessed seawater dynamic time-domain data corresponding to the node as node attributes, and using the relationships of the node attributes to form edges, a complex network model is established.

[0011] S40: Perform steady-state calculations on the complex network model to obtain a steady-state complex network model;

[0012] S50: Use the edges in the obtained steady-state complex network model as the steady-state relation dataset, and use the steady-state relation dataset to describe the deep-sea dynamic three-dimensional environment field.

[0013] Based on the above technical solution, the method for constructing a three-dimensional dynamic environment field in deep-sea waters according to the present invention can be further improved as follows:

[0014] The method for preprocessing the time-domain data of seawater dynamics is the cosine normalization method.

[0015] Step S30 further includes deleting the edges between non-adjacent cube blocks in the complex network model corresponding to the acquisition time.

[0016] Specifically, step S40 includes:

[0017] S41: Calculate the temporal clustering coefficient for each node;

[0018] S42: For nodes whose time-domain clustering coefficient is greater than the time-domain clustering coefficient threshold, perform steady-state calculations to obtain the time-domain steady-state data of the nodes;

[0019] S43: The complex network model is updated by adding the time-domain steady-state data of each node to the node attributes to obtain a steady-state complex network model.

[0020] Furthermore, the specific steps of step S42 include:

[0021] Step 1: Establish a power transfer matrix with a specified node as the active node and multiple nodes adjacent to the specified node as passive nodes, and extract multiple power transfer vectors from the power transfer matrix.

[0022] Step 2: Use the nearest neighbor propagation clustering algorithm to cluster the multiple dynamic transmission vectors into one class, which is used as the dynamic steady-state transmission vector of the specified node, and use the endpoint coordinates of the dynamic steady-state vector as the time-domain steady-state data of the node.

[0023] Furthermore, the threshold value for the time-domain clustering coefficient is 0.

[0024] Furthermore, all node temporal clustering coefficients are arranged in ascending order to form a temporal clustering coefficient sequence, and the temporal clustering coefficient corresponding to the sequence number of the total number of 5% of the temporal clustering coefficients is used as the temporal clustering coefficient threshold.

[0025] A second aspect of the present invention provides a computer-readable storage medium storing program instructions for implementing the above-described method for constructing a three-dimensional dynamic environment field in deep-sea water.

[0026] A third aspect of the present invention provides a deep-sea dynamic three-dimensional environment field construction system, including the aforementioned computer-readable storage medium.

[0027] Compared with existing technologies, the beneficial effects of the deep-sea hydrodynamic three-dimensional environment field reconstruction method, medium, and system provided by this invention are as follows: the deep-sea area to be measured is divided into multiple cubic blocks, and the depth, seawater temperature, seawater salinity, wave field, sea surface wind force, and sea surface wind speed of each cubic block are collected at fixed time intervals. This effectively realizes the collection of detailed parameters of the hydrodynamic three-dimensional environment field of the sea area to be measured. These detailed parameters can be used to build a complex network model and perform steady-state calculations. The edges in the obtained steady-state complex network model are used as steady-state relation datasets, and the deep-sea hydrodynamic three-dimensional environment field is described by the steady-state relation datasets. This description can realize the description of the deep-sea hydrodynamic three-dimensional environment field in detail. Attached Figure Description

[0028] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the description of the embodiments of the present invention will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0029] in, Figure 1 This is a flowchart of a method for constructing a three-dimensional dynamic environment field in deep-sea water, as provided in the first aspect of the present invention.

[0030] Figure 2 This is a flowchart of the step "Perform steady-state calculations on the complex network model to obtain a steady-state complex network model";

[0031] Figure 3 This is a flowchart of the nearest neighbor propagation clustering (AP) algorithm. Detailed Implementation

[0032] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0033] Therefore, the following detailed description of the embodiments of the invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the invention without inventive effort are within the scope of protection of the invention.

[0034] It should be noted that similar labels and letters in the following figures indicate similar items. Therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures.

[0035] In the description of this invention, it should be understood that the terms "center," "longitudinal," "lateral," "length," "width," "thickness," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," "outer," "clockwise," and "counterclockwise," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing this invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this invention.

[0036] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified.

[0037] like Figure 1-2The diagram shown is a flowchart of a method for constructing a three-dimensional dynamic environment field in deep-sea waters, provided by the first aspect of this invention. The method specifically includes the following steps:

[0038] S10: The deep-sea area to be measured is divided into multiple cubic blocks; there are M rows, L columns, and P layers. Each cubic block is represented as follows:

[0039] V α,β,γ

[0040] Where α ranges from 1 to M, representing the row where the cube block is located;

[0041] The value of β ranges from 1 to L, representing the column in which the cube block is located;

[0042] The value of γ ranges from 1 to P, representing the layer in which the cube block is located;

[0043] S20: Using each cube block as a node, collect the time-domain data of the marine dynamics of each node at fixed time intervals, and preprocess the marine dynamic time-domain data; wherein, the marine dynamic time-domain dataset includes: depth, seawater temperature, seawater salinity, and sea surface wind force and sea surface wind speed; the fixed time interval is the time interval for collection, generally 0.25 to 2 hours, preferably 0.5 hours; the marine dynamic time-domain data of each node is represented as follows:

[0044] X α，β，γ，t =[C height C temp，t C sult，t C wf，t C wv，t ]

[0045] The time-domain representation of the hydrodynamic data for each node is as follows:

[0046] X α，β，γ =[X α，β，γ，0 , ......, X α，β，γ，t-1 ]

[0047] In the formula, X a，β，γ，t Represents node V α，β，γ Time-domain data of hydrodynamics at time t; C height，t C represents the depth of the node. temp，t C represents the temperature of the node at time t. sult，t C represents the salinity of the node at time t; wf，t Let C represent the sea surface wind force at time t. If the node is not a sea surface node, then C... wf，t =0; C wv，t Let C represent the sea surface wind speed at time t. If the node is not a sea surface node, then C... wv，t =0;Xα，β，γ Represents node V α，β，γ Time-domain data of marine dynamics.

[0048] S30: Using the preprocessed time-domain data of marine dynamics corresponding to the nodes as node attributes, and using the relationships between node attributes to form edges, a complex network model is established.

[0049] In complex network models, a graph G(V, E) represents the complex network, where V are the vertices of the graph, i.e., the individuals in the network, and the set of edges E represents the relationships between the individuals in the network, whether they are related or interact. Using the adjacency matrix in graph theory, the topological structure is represented in matrix form, and the adjacency matrix element is defined as: A ij if(v i v j )∈Ethen A ij =1, meaning that the matrix element corresponding to the two vertices associated according to the defined relationship is 1;

[0050] Degree: The number of other nodes connected to node i. For undirected graphs, in-degree and out-degree can also be defined, denoted as...

[0051] S40: Perform steady-state calculations on the complex network model to obtain the steady-state complex network model;

[0052] Specifically, step S40 includes:

[0053] S41: Calculate the temporal clustering coefficient for each node;

[0054] Among them, the clustering coefficient

[0055] Node i has k i An edge connects to other nodes, and the number of edges between these nodes is E. i Then the clustering coefficient C i =2E i k i (k i -1);

[0056] Here, a triple containing i can be i associated with j and k, but j and k are not associated, and therefore do not form a triangle; that is, this relationship is not transitive at these three vertices. N is the total number of nodes, and the average clustering coefficient of the entire network is denoted as . C=0 for all isolated nodes and C=1 for fully connected networks; the clustering coefficient C of a completely random graph is ~O(N-1). Therefore, if the clustering coefficient is greater than O(N-1), it indicates that the network has a significant clustering effect and will form more clustered substructures, or even a hierarchical structure.

[0057] S42: For nodes whose time-domain clustering coefficient is greater than the time-domain clustering coefficient threshold, perform steady-state calculations to obtain the node's time-domain steady-state data;

[0058] S43: Add the time-domain steady-state data of each node to the node attributes to update the complex network model and obtain a steady-state complex network model.

[0059] In the dynamic propagation problem of deep-sea water, where S is a static node, I is a dynamic node, β is the dynamic propagation rate, and μ is the recovery rate, the problem is expressed as:

[0060]

[0061] When we disregard the topology of complex networks, the density ρ of dynamic nodes I It changes according to the following differential equation:

[0062]

[0063] Now, let's introduce the topology of complex networks. Let A represent the probability that the i-th node is a dynamic node. ij Representing the adjacency matrix, the differential equation becomes

[0064]

[0065] To avoid excessive microscopic details that would lead to very high computational complexity, a degree distribution is introduced, denoted as P. kk′ Given a node of degree k, the probability that it is connected to a node of degree k' is given by the differential equation:

[0066]

[0067] Through the above transformation, the micro-topology of the network is mapped to a low-dimensional problem through degree.

[0068] The steady-state solution can be considered below.

[0069] remember The steady-state solution is:

[0070]

[0071] in, The effective propagation rate of the dynamics;

[0072] Substituting the steady-state solution into the expression for Θ, we obtain the fixed-point equation Θ(λ,k)=∑k′Pkk′λk′Θ(λ,k′)1+λk′Θ(λ,k′),

[0073]

[0074] When the effective dynamic propagation rate reaches a critical value, a dynamic phase transition will occur, i.e.

[0075] λ>λc

[0076] At that time, the steady-state distribution of the entire system will exhibit dynamic node ρ. I >0, when λ<λc, the dynamics are completely controlled, and the steady-state distribution of ρ I =0;

[0077] S50: The edges in the obtained steady-state complex network model are used as the steady-state relation dataset, and the steady-state relation dataset is used to describe the dynamic three-dimensional environment field of deep seawater.

[0078] In the above technical solution, the method for preprocessing seawater dynamic time-domain data is cosine normalization.

[0079] In the above technical solution, step S30 further includes: deleting the edges between non-adjacent cube blocks in the complex network model corresponding to the acquisition time.

[0080] Furthermore, in the above technical solution, step S42 specifically includes the following steps:

[0081] Step 1: Establish a power transfer matrix with the specified node as the active node and multiple nodes adjacent to the specified node as passive nodes, and extract multiple power transfer vectors from the power transfer matrix.

[0082] Step 2: Use the nearest neighbor propagation clustering algorithm to cluster multiple dynamic transfer vectors into one class, which is used as the dynamic steady-state transfer vector of the specified node. The endpoint coordinates of the dynamic steady-state vector are used as the time-domain steady-state data of the node.

[0083] Among them, the nearest neighbor propagation clustering algorithm (AP) is a graph-based clustering algorithm. Its basic idea is to treat all samples to be clustered as nodes in a network, and each as a potential cluster center. The samples are connected by similarity lines to form a network (similarity matrix S). Then, through the transmission of messages (attractiveness and availability) along the edges of the network, the cluster centers of the sample set are calculated. A flowchart of the nearest neighbor propagation clustering algorithm is attached. Figure 3 As shown.

[0084] The similarity matrix S(j,h) is calculated based on the standardized and preprocessed sample set to be clustered. S(j,h) represents the ability of data point h to serve as the cluster center of data point j, and negative Euclidean distance is generally used.

[0085] S(j,h)=-||x j -xh || 2

[0086] For all samples to be clustered in the network, using the concept of adjacency matrices in graph theory, we can calculate the attraction matrix R(j,h) and the membership matrix A(j,h). Here, R(j,h) represents the attractiveness of each candidate cluster center h relative to other candidate cluster centers h′ to the sample j to be clustered, and A(j,h) represents the membership degree of each sample j to the candidate cluster center h.

[0087]

[0088]

[0089] A t+1 (j,h)=∑ j′≠h max{0,R t+1 (j′,h)}

[0090] To avoid oscillations in R(j,h) and A(j,h) during iterative calculations, a decay factor ε is introduced:

[0091] R t+1 (j,h)=(1-ε)R t+1 (j,h)+εR t (j,h)

[0092] A t+1 (j,h)=(1-ε)A t+1 (j,h)+εA t (j,h)

[0093] The attenuation factor ε ranges from (0, 1).

[0094] The AP algorithm achieves clustering by iteratively updating the values ​​of the attraction matrix R(j,h) and the membership matrix A(j,h). When R(j,h) and A(j,h) reach stability or the maximum number of iterations is reached, the algorithm terminates and selects the sample to be clustered with the largest R(j,h) + A(j,h) as the cluster center c, which is then used as the dynamic steady-state transfer vector of the specified node.

[0095] Furthermore, in the above technical solution, the threshold for the temporal clustering coefficient is 0.

[0096] Furthermore, in the above technical solution, all node temporal clustering coefficients are arranged in ascending order to form a temporal clustering coefficient sequence, and the temporal clustering coefficient corresponding to the sequence number of 5% of the total number of temporal clustering coefficients is used as the temporal clustering coefficient threshold.

[0097] A second aspect of the present invention provides a computer-readable storage medium storing program instructions for implementing the above-described method for constructing a three-dimensional dynamic environment field in deep-sea water.

[0098] A third aspect of the present invention provides a deep-sea dynamic three-dimensional environment field construction system, including the aforementioned computer-readable storage medium.

[0099] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for constructing a three-dimensional dynamic environment field in deep-sea water, characterized in that, Specifically, the following steps are included: S10: Divide the deep-sea area to be measured into multiple cubic blocks; S20: Using each cube block as a node, collect the time-domain data of the marine dynamics of each node at fixed intervals, and preprocess the time-domain data of the marine dynamics. S30: Using the preprocessed seawater dynamic time-domain data corresponding to the node as node attributes, and using the relationships of the node attributes to form edges, a complex network model is established. S40: Perform steady-state calculations on the complex network model to obtain a steady-state complex network model; S50: Use the edges in the obtained steady-state complex network model as the steady-state relation dataset, and use the steady-state relation dataset to describe the deep-sea dynamic three-dimensional environment field; Step S40 specifically includes: S41: Calculate the temporal clustering coefficient for each node; S42: For nodes whose time-domain clustering coefficient is greater than the time-domain clustering coefficient threshold, perform steady-state calculations to obtain the time-domain steady-state data of the nodes; S43: Update the complex network model by adding the time-domain steady-state data of each node to the node attributes to obtain a steady-state complex network model; The marine dynamic time-domain data includes: depth, seawater temperature, seawater salinity, sea surface wind force, and sea surface wind speed.

2. The method for constructing a three-dimensional dynamic environment field in deep-sea waters according to claim 1, characterized in that, The method for preprocessing the time-domain data of marine dynamics is the cosine normalization method.

3. The method for constructing a three-dimensional dynamic environment field in deep-sea waters according to claim 1, characterized in that, Step S30 further includes: deleting the edges between non-adjacent cube blocks in the complex network model corresponding to the time of acquisition.

4. The method for constructing a three-dimensional dynamic environment field in deep-sea waters according to claim 3, characterized in that, The specific steps of step S42 include: Step 1: Establish a power transfer matrix with a specified node as the active node and multiple nodes adjacent to the specified node as passive nodes, and extract multiple power transfer vectors from the power transfer matrix. Step 2: Use the nearest neighbor propagation clustering algorithm to cluster the multiple dynamic transfer vectors into one class, which is used as the dynamic steady-state transfer vector of the specified node. The endpoint coordinates of the dynamic steady-state transfer vector are used as the time-domain steady-state data of the node.

5. The method for constructing a three-dimensional dynamic environment field in deep-sea waters according to claim 4, characterized in that, The threshold for the time-domain clustering coefficient is 0.

6. The method for constructing a three-dimensional dynamic environment field in deep-sea water according to claim 5, characterized in that, Arrange all node temporal clustering coefficients in ascending order to form a temporal clustering coefficient sequence, and use the temporal clustering coefficient corresponding to the sequence number of the 5% total number of temporal clustering coefficients as the temporal clustering coefficient threshold.

7. A computer-readable storage medium, characterized in that, The system stores program instructions for implementing the method for constructing a three-dimensional dynamic environment field in deep-sea water as described in any one of claims 1 to 6.

8. A deep-sea dynamic three-dimensional environment field construction system, characterized in that, Includes the computer-readable storage medium as described in claim 7.

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