A method, device, equipment, medium and product for determining water mass membership

Abstract

The present application discloses a method, device, equipment, medium and product for determining the membership of a water mass, which relates to the marine field. The method includes: according to a set density range, the density of the first water mass in the first set area is stratified with a set step length, and the weighted center coordinates of the first water mass in each density layer are determined; according to the set density range, the density of the second water mass in the second set area is stratified with the set step length, and the weighted center coordinates of the second water mass in each density layer are determined; the first water mass and the second water mass are both known water masses; the distance between the weighted center coordinates of the first water mass and the weighted center coordinates of the second water mass in each density layer is calculated; the density layer where the minimum value of the above coordinate distances is located is used as the boundary density layer of the temperature-salinity curve of the first water mass and the second water mass. The present application improves the accuracy of determining the boundary density layer of the temperature-salinity curve between water masses in different regions.

Description

Technical Field

[0001] The present application relates to the field of marine technology, and in particular to a method, device, equipment, medium and product for determining water mass membership. Background Art

[0002] In oceanography, a water mass refers to a large body of water that has relatively uniform physical, chemical, and biological characteristics and roughly consistent change trends due to similar sources and formation mechanisms, but is significantly different from the surrounding seawater. Most of the coastal waters of a certain country are located in the mid-latitude temperate monsoon zone, with distinct alternations of four seasons and significant seasonal changes; the shallow sea is less than 200 meters deep, with a wide area, scattered islands, and complex coastlines; the eastern waters have a strong Kuroshio and its branches, and the western waters have many rivers flowing into the sea, further complicating the research on water masses and changes in their properties in the shallow sea areas of a certain country. In a complex water mass environment, for the calculation of the membership of an unknown water mass to two known water masses, the existing technology generally has the problem of low data processing accuracy, which is difficult to meet actual needs. Summary of the invention

[0003] The purpose of the present application is to provide a method, device, equipment, medium and product for determining water mass membership, thereby improving the accuracy of determining the boundary density layer of the temperature-salinity curve.

[0004] To achieve the above objectives, this application provides the following solutions:

[0005] In a first aspect, the present application provides a method for determining water mass membership, comprising:

[0006] According to the set density range, the density of the first water mass in the first set area is layered with a set step length, and the weighted center coordinates of the first water mass in each density layer are determined;

[0007] According to the set density range, for the second water group in the second set area, the density is layered with the set step length, and the weighted center coordinates of the second water group in each density layer are determined; the first water group and the second water group are both known water groups;

[0008] Calculating the distance between the weighted center coordinates of the first water group and the weighted center coordinates of the second water group in each density layer;

[0009] The density layer where the minimum value of each of the distances is located is used as the boundary density layer of the temperature-salinity curve of the first water mass and the second water mass;

[0010] Based on the boundary density layer, the similarity between the third water cluster to be analyzed and the first water cluster, as well as the similarity between the third water cluster and the second water cluster are calculated respectively; wherein the similarity between the third water cluster and the first water cluster represents the degree of subordination of the third water cluster to the first water cluster, and the similarity between the third water cluster and the second water cluster represents the degree of subordination of the third water cluster to the second water cluster.

[0011] Optionally, according to the set density range, the density of the first water mass in the first set area is layered with a set step length, specifically including:

[0012] Acquire data of a plurality of sampling points in the first water mass, wherein the data of each sampling point includes temperature, salinity and pressure;

[0013] Reorganizing the data of each sampling point in the first water mass to obtain reorganized sampling point data;

[0014] Based on each of the reorganized sampling point data and the physical relationship, a seawater density is calculated;

[0015] Determining the set density range according to the maximum and minimum values ​​of each of the seawater densities, wherein the lower limit of the set density range is less than the minimum value, and the upper limit of the set density range is less than the maximum value;

[0016] According to the set density range, the density of the first water mass in the first set area is layered with a set step length.

[0017] Optionally, based on each of the reorganized sampling point data and the physical relationship, a seawater density is calculated, specifically including:

[0018] For each of the reorganized sampling point data, the seawater density is calculated from the reorganized sampling point data based on the physical relationship between absolute salinity, latent heat temperature and pressure; the absolute salinity is determined by the salinity in the reorganized sampling point data, and the latent heat temperature is determined by the temperature in the reorganized sampling point data.

[0019] Optionally, determining the weighted center coordinates of the first water mass in each density layer specifically includes:

[0020] For any density layer where the first water mass is located, determine a 95% confidence ellipse of water particles in the density layer, and use the water particles within the 95% confidence ellipse as target data points;

[0021] Calculate the weight of each target data point according to the distance between each target data point and the data mean within the 95% confidence ellipse;

[0022] The weighted center coordinates of the first water group in the density layer are calculated according to the weights of the target data points.

[0023] Optionally, the weighted center coordinates of the first water mass in each density layer are expressed as:

[0024] , ;

[0025] in, is the abscissa of the first water mass in a density layer, is the ordinate of the first water mass in a density layer, n is the number of target data points, is the weight of the i-th target data point, is the horizontal coordinate of the i-th target data point, is the ordinate of the i-th target data point;

[0026] ;

[0027] in, is the distance between the ith target data point and the data mean within the 95% confidence ellipse, Is a positive number.

[0028] Optionally, respectively calculating the similarity between the third water group to be analyzed and the first water group and the similarity between the third water group and the second water group specifically includes:

[0029] According to the set density range, the density of the third water mass in the second set area is stratified with the set step length, and the extreme salinity and temperature of the third water mass in each density layer are screened out;

[0030] Based on the boundary density layer, determine the water group to which the third water group belongs at the salinity extreme value and the temperature extreme value of each density layer, calculate the salinity data variance of the water group to which the salinity extreme value belongs and the temperature data variance of the water group to which the temperature extreme value belongs, and obtain the salinity data variance and the temperature data variance of each density layer; the water group to which the third water group belongs is the first water group, the second water group, or the third water group;

[0031] Initializing a weight matrix, taking the maximum variance value of the salinity data variance and the temperature data variance of the third water mass in each density layer as an element in the weight matrix;

[0032] Based on the weight matrix, the similarity between the third water group and the first water group, and the similarity between the third water group and the second water group are calculated using the least square method.

[0033] In a second aspect, the present application provides a water mass membership determination device, the water mass membership determination device comprising:

[0034] A first water mass density stratification module, for stratifying the density of the first water mass in the first set area with a set step length according to a set density range, and determining the weighted center coordinates of the first water mass in each density layer;

[0035] A second water mass density stratification module, for stratifying the density of the second water mass in the second set area with the set step length according to the set density range, and determining the weighted center coordinates of the second water mass in each density layer; the first water mass and the second water mass are both known water masses;

[0036] A distance calculation module, used for calculating the distance between the weighted center coordinates of the first water group and the weighted center coordinates of the second water group in each density layer;

[0037] A boundary density layer determination module, used for taking the density layer where the minimum value of each distance is located as the boundary density layer of the temperature-salinity curve of the first water mass and the second water mass;

[0038] A similarity calculation module is used to calculate the similarity between the third water cluster to be analyzed and the first water cluster, and the similarity between the third water cluster and the second water cluster based on the boundary density layer; wherein the similarity between the third water cluster and the first water cluster represents the membership of the third water cluster to the first water cluster, and the similarity between the third water cluster and the second water cluster represents the membership of the third water cluster to the second water cluster.

[0039] In a third aspect, the present application provides a computer device comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of any of the above-described methods for determining water mass membership.

[0040] In a fourth aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of any of the above-mentioned methods for determining water mass membership.

[0041] In a fifth aspect, the present application provides a computer program product, including a computer program, which, when executed by a processor, implements the steps of any of the above-mentioned methods for determining water mass membership.

[0042] According to the specific embodiments provided by the present application, the present application discloses the following technical effects: the present application provides a method, device, equipment, medium and product for determining the degree of membership of a water mass, which stratifies two known water masses according to density, calculates the weighted center coordinates of each density layer, and then calculates the distance between each density layer of the two known water masses, thereby determining the boundary density layer, and based on in-depth computational analysis of the data of each density layer, effectively reduces the impact of external interference and improves the resolution accuracy of water mass boundary identification. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. 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 creative work.

[0044] Figure 1 A schematic flow chart of a method for determining water group membership provided in one embodiment of the present application.

[0045] Figure 2 A schematic diagram of the structure of a computer device provided in one embodiment of the present application. DETAILED DESCRIPTION

[0046] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0047] In order to make the above-mentioned objects, 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.

[0048] This application provides a method for determining water mass membership, such as Figure 1 The water group membership determination method shown includes steps 101 to 105.

[0049] Step 101: according to a set density range, a step size is set for a first water mass in a first set area to divide the density into layers, and the weighted center coordinates of the first water mass in each density layer are determined.

[0050] Step 102: According to the set density range, the density of the second water group in the second set area is layered with the set step size, and the weighted center coordinates of the second water group in each density layer are determined; the first water group and the second water group are both known water groups.

[0051] Step 103: Calculate the distance between the weighted center coordinates of the first water group and the weighted center coordinates of the second water group in each density layer.

[0052] Step 104: taking the density layer where the minimum value of each distance is located as the boundary density layer of the temperature-salinity curve of the first water mass and the second water mass.

[0053] In step 104, each distance refers to the distance between two weighted center coordinates in all density layers.

[0054] Step 105: Based on the boundary density layer, respectively calculate the similarity between the third water cluster to be analyzed and the first water cluster, and the similarity between the third water cluster and the second water cluster; wherein the similarity between the third water cluster and the first water cluster represents the membership of the third water cluster to the first water cluster, and the similarity between the third water cluster and the second water cluster represents the membership of the third water cluster to the second water cluster.

[0055] In an exemplary embodiment, step 101 specifically includes steps 1011 to 1015 .

[0056] Step 1011: Acquire data of multiple sampling points in the first water mass, wherein the data of each sampling point includes temperature, salinity and pressure.

[0057] Step 1012: reorganize the data of each sampling point in the first water mass to obtain reorganized sampling point data.

[0058] Step 1013: Calculate the seawater density based on the physical relationship according to the reorganized sampling point data.

[0059] Step 1014: Determine the set corresponding density range according to the maximum and minimum values ​​of each seawater density. The lower limit of the set density range is lower than the minimum value, and the upper limit of the set density range is higher than the maximum value. For example: the data density range is 21-29 Kg / m 3 , then the set density range is 20~30 Kg / m 3 The set density range is then divided into m layers, and the density interval of each layer is usually set to 0.05Kg / m 3 . For example: 20~20.05Kg / m 3 On this basis, the water mass in each density layer is regarded as a collection of multiple small water masses, and the temperature and salinity data corresponding to the water particles in each density layer are extracted and screened.

[0060] Step 1015: According to the set density range, the density of the first water mass in the first set area is layered with a set step length.

[0061] The principle of stratifying the density of the second water mass in step 102 is the same as the principle of stratifying the density of the first water mass in step 101 .

[0062] In an exemplary embodiment, the temperature (T), salinity (S) and pressure (p) data in a water mass data are reorganized into columns, respectively, and the three columns of data are in the order k When each temperature data point All salinity data points at the same location and time and pressure data points Correspondingly. For example, there are three sampling point data, as follows:

[0063] Sampling point 1: , , dbar;

[0064] Sampling point 2: , , dbar;

[0065] Sampling point 3: , , dbar.

[0066] After reorganizing the above sampling point data, the temperature, salinity and pressure data are arranged into columns respectively, and the position order remains consistent, as shown below:

[0067] Reorganized data T: , , ;

[0068] Reorganized data S: , 35, ;

[0069] Reorganized data p: , dbar, dbar.

[0070] In an exemplary embodiment, the seawater density is calculated based on the reorganized sampling point data, specifically including: based on the physical relationship between absolute salinity, latent heat temperature and pressure, the seawater density is calculated in combination with the reorganized sampling point data; wherein the absolute salinity is determined according to the salinity in the reorganized sampling point data, and the latent heat temperature is determined according to the temperature in the reorganized sampling point data. The formula for calculating the seawater density refers to the physical model defined by the internationally accepted TEOS-10 standard formula, and the specific seawater thermodynamic equation is: .

[0071] in, is the specific volume of seawater (m³ / kg), is the absolute salinity, is the latent heat temperature, For pressure, is density, the absolute salinity is determined according to the salinity in the sampling point data after the reorganization, and the latent heat temperature is determined according to the temperature in the sampling point data after the reorganization. Specifically, the absolute salinity is the salinity S plus a regional correction As a result, the latent heat temperature It is the temperature after removing the compression effect from the measured temperature T.

[0072] In an exemplary embodiment, determining the weighted center coordinates of the first water mass in each density layer specifically includes: determining a 95% confidence ellipse of water particles in any density layer where the first water mass is located, and taking the water particles within the 95% confidence ellipse as target data points.

[0073] The weight of each target data point is calculated based on the distance between each target data point and the data mean within the 95% confidence ellipse.

[0074] The 95% confidence ellipse is expressed as: .

[0075] Where z is any two-dimensional point in the 95% confidence ellipse , μ is the data mean of each two-dimensional point ; is the quantile of the chi-square distribution, and the two-dimensional point is the target data point. is the horizontal coordinate of the two-dimensional point, is the horizontal coordinate of the two-dimensional point, is the mean of the horizontal coordinates of each two-dimensional point, is the mean of the ordinates of each two-dimensional point.

[0076] The weighted center coordinates of the first water group in the density layer are calculated according to the weights of the target data points.

[0077] The coordinates of the points inside the confidence ellipse are .

[0078] The weighted center coordinates of the first water mass in each density layer are expressed as: , .

[0079] in, is the abscissa of the first water mass in a density layer, is the ordinate of the first water mass in a density layer, n is the number of target data points, is the weight of the i-th target data point, is the horizontal coordinate of the i-th target data point, is the ordinate of the i-th target data point.

[0080] .

[0081] in, is the distance between the ith target data point and the data mean within the 95% confidence ellipse, A positive number to avoid the denominator being zero.

[0082] The weighted center coordinates of each water mass and each density layer are obtained by the above method. and , is the abscissa of the second water mass in a density layer, is the ordinate of the second water mass in a density layer.

[0083] The density layers include the 1st density layer to the mth density layer.

[0084] Wherein, step 103 specifically includes: the coordinate distance is specifically the Euclidean distance, and the calculation formula of the coordinate distance is: , is the coordinate distance.

[0085] Compare the coordinate distances d of all corresponding density layers, and let d be the jth density layer where the minimum value is located. for , No. The density layer is the boundary density layer of the temperature-salinity curve (thermosalinity curve) of two water masses. The density of the density layer increases with the increase of the number of layers.

[0086] In an exemplary embodiment, step 105 specifically includes the following steps 1051 to 1054 .

[0087] Step 1051: According to the set density range, the density of the third water mass in the second set area is stratified with the set step length, and the extreme salinity and temperature of the third water mass in each density layer are screened out.

[0088] Step 1052: Based on the boundary density layer, determine the water group to which the third water group belongs at the salinity extreme value and the temperature extreme value of each density layer, calculate the salinity data variance of the water group to which the salinity extreme value belongs and the temperature data variance of the water group to which the temperature extreme value belongs, and obtain the salinity data variance and the temperature data variance of each density layer; the water group to which the third water group belongs is the first water group, the second water group, or the third water group.

[0089] Step 1053: Initialize the weight matrix, and use the maximum variance value of the salinity data variance and the temperature data variance of the third water mass in each density layer as an element in the weight matrix.

[0090] Step 1054: Based on the weight matrix, a least square method is used to calculate the similarity between the third water group and the first water group, and the similarity between the third water group and the second water group.

[0091] Above the junction, if the maximum salinity belongs to the first water mass, the characteristics of the water mass below the junction, i.e. the junction density layer, are more inclined to the second water mass. The accuracy of the junction is crucial to determine the membership of the third water mass. Since seawater usually flows along isopycnal surfaces, it can be approximately considered that the characteristics of the third water mass in a certain density layer are formed by mixing the first and second water masses of the same density in some way.

[0092] In an exemplary embodiment, step 105 specifically includes: based on the density range and layer number settings set above, performing multi-layer classification processing on the third water mass data, and determining the corresponding salinity and temperature extremes in each density layer.

[0093] The temperature-salinity curve of the third water group is usually between the first and second water groups. <j min When the salinity of the first water mass is greater than that of the second water mass, the maximum salinity of the first and third water masses usually approaches the characteristics of the first water mass. min When j is , the salinity of the second water group is greater than that of the first water group, and the minimum salinity of the first and third water groups is usually close to the characteristics of the first water group. Similarly, when judging and evaluating the similarity of the third water group to the second water group, when j is <j_ min When the salinity of the first water group is greater than that of the second water group, the minimum salinity of the second and third water groups usually approaches the characteristics of the second water group. min When , the salinity of the second water mass is greater than that of the first water mass, and the maximum salinity of the second and third water masses usually approaches the characteristics of the second water mass.

[0094] Based on the boundary density layer, the water group to which the third water group belongs in the extreme salinity and temperature of each density layer is judged, specifically including: when determining the similarity between the third water group and the first water group, when the number of density layers is less than the number of boundary density layers, if the property of the first water group is greater than that of the second water group, then the similarity relationship between the third water group and the first water group in the density layer should take the maximum value of the first water group and the third water group for subsequent calculation; when the number of density layers is greater than the number of boundary density layers, if the property of the second water group is greater than that of the first water group, then the similarity relationship between the third water group and the first water group in the density layer should take the maximum value of the first water group and the third water group for subsequent calculation. The minimum value of the third water group is used for subsequent calculation; similarly, when determining the similarity between the third water group and the second water group, when the number of density layers is less than the number of boundary density layers, if the attribute of the first water group is greater than that of the second water group, then the similarity relationship between the third water group and the second water group in this density layer should take the minimum value of the second water group and the third water group for subsequent calculation; when the number of density layers is greater than the number of boundary density layers, if the attribute of the second water group is greater than that of the first water group, then the phase velocity relationship between the third water group and the second water group in this density layer should take the maximum value of the second water group and the third water group for subsequent calculation; the attributes include salinity and temperature.

[0095] Based on the above method, the extreme values ​​of salinity and temperature corresponding to the jth density layer are screened out respectively, and it is determined whether the water group to which the extreme value belongs belongs to the third water group or the first water group (or the second water group), and the variance of the temperature and salinity data of the water group to which the temperature and salinity extreme values ​​belong is calculated. The temperature and salinity data of the third water group are standardized and converted into dimensionless indicators so that different indicators can be compared and weighted.

[0096] The maximum variance is calculated in each density layer and used as the elements of the weight matrix W, which reflects the importance of different variables. It is then used as the weight matrix in the lsqnonneg optimization process. lsqnonneg is a function used in MATLAB to solve non-negative least squares problems. The lsqnonneg function is used to solve the non-negative least squares problem to calculate the similarity of the temperature-salinity characteristics of the third water mass with those of the first and second water masses.

[0097] After the water mass temperature and salinity of each density layer of the known water mass are processed by anomaly and standardization, matrices G and V are formed respectively. G represents the characteristics of the known water mass, that is, the standardized temperature-salinity characteristics of the first water mass or the second water mass, and V represents the standardized data of the third water mass, that is, the standardized temperature-salinity characteristics of the third water mass. The weight matrix W is composed of the variance normalization results of temperature and salinity.

[0098] The above method provides a reasonable solution by minimizing the weighted errors of temperature and salinity anomalies, and provides a reasonable solution for the temperature-salinity characteristics of the third water mass to be linearly expressed as a combination of the temperature-salinity characteristics of the first and second water masses.

[0099] The expression of the temperature-salinity characteristic C of the third water mass is: C=k A ·A+k B ·B.

[0100] A and B represent the temperature-salinity characteristic vectors of the first and second water masses, respectively. A and k B is a non-negative weight, indicating the contribution of the first and second water groups to the similarity of the characteristics of the third water group, and usually satisfies k A and k B Normalization constraint that the sum is 1.

[0101] In an exemplary embodiment, the first water mass is South China Sea water, and the second water mass is Kuroshio water. The weight k calculated by non-negative least squares method A and k B， The mixing ratio of the South China Sea water and the Kuroshio water in the third water mass can be reflected to judge the influence of the South China Sea water and the Kuroshio water on the third set area. The weight k obtained by the non-negative least squares method A and k B This reflects the mixing ratio of the South China Sea water and the Kuroshio water of the third water mass.

[0102] Based on the temperature, salinity and density data of water masses, this application can more accurately identify the boundary layer position of different water masses and effectively determine the affiliation of specific water masses with other water masses. This application method is widely used in the field of ocean water mass analysis. By significantly improving the data processing accuracy and noise resistance in complex water mass environments, it effectively overcomes the limitations of traditional methods in identifying water mass boundaries and mixing characteristics analysis.

[0103] This application fully considers the physical characteristics that the flow of seawater is affected by the distribution of density gradients and usually flows along isopycnic surfaces, and proposes an efficient density layer stratification analysis strategy, which can greatly simplify the processing flow of complex data, especially for the scenario of processing large-scale data, and can significantly improve the efficiency and accuracy of water mass identification. Therefore, based on the above stratification strategy, when judging the intersection layer of the temperature-salinity curve of two water masses, based on high-precision water mass characteristics and efficient data noise reduction technology, the accuracy of the water mass characteristics in each density layer is effectively improved, and the interference of data points deviating from the characteristic center on the analysis results is reduced, ultimately ensuring the accuracy and reliability of the analysis results.

[0104] Based on the same inventive concept, the present application also provides a water group membership determination device for implementing the water group membership determination method involved above. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the above method, so the specific limitations of one or more water group membership determination device embodiments provided below can refer to the limitations of the water group membership determination method above, and will not be repeated here.

[0105] In an exemplary embodiment, the present application provides a water mass membership determination device including the following modules.

[0106] The first water mass density stratification module is used to stratify the density of the first water mass in the first set area with a set step length according to a set density range, and determine the weighted center coordinates of the first water mass in each density layer.

[0107] The second water mass density stratification module stratifies the density of the second water mass in the second set area with the set step size according to the set density range, and determines the weighted center coordinates of the second water mass in each density layer; the first water mass and the second water mass are both known water masses.

[0108] The distance calculation module is used to calculate the distance between the weighted center coordinates of the first water group and the weighted center coordinates of the second water group in each density layer.

[0109] The boundary density layer determination module is used to use the density layer where the minimum value of each distance is located as the boundary density layer of the temperature-salinity curve of the first water mass and the second water mass.

[0110] A similarity calculation module is used to calculate the similarity between the third water cluster to be analyzed and the first water cluster, and the similarity between the third water cluster and the second water cluster based on the boundary density layer; wherein the similarity between the third water cluster and the first water cluster represents the membership of the third water cluster to the first water cluster, and the similarity between the third water cluster and the second water cluster represents the membership of the third water cluster to the second water cluster.

[0111] In an exemplary embodiment, the present application provides a computer device, which may be a server or a terminal, and its internal structure diagram may be as follows: Figure 2As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, abbreviated as I / O), and a communication interface. Among them, the processor, the memory, and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store water mass membership determination data. The input / output interface of the computer device is used to exchange information between the processor and external devices. The communication interface of the computer device is used to communicate with external terminals through a network connection. When the computer program is executed by the processor, it implements a method for determining water mass membership.

[0112] Those skilled in the art can understand that Figure 2 The structure shown in is only a block diagram of some structures related to the solution of this application, and does not constitute a limitation on the computer device to which the solution of this application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine some components, or have different component arrangements. In an exemplary embodiment, a computer device is provided, including a memory and a processor. A computer program is stored in the memory, and when the processor executes the computer program, the steps in the above method embodiments are implemented.

[0113] In an exemplary embodiment, a computer-readable storage medium is provided, storing a computer program, and when the computer program is executed by the processor, the steps in the above method embodiments are implemented.

[0114] In an exemplary embodiment, a computer program product is provided, including a computer program, and when the computer program is executed by the processor, the steps in the above method embodiments are implemented.

[0115] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use, and processing of relevant data need to comply with relevant regulations.

[0116] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above methods. Among them, any reference to a memory, database, or other medium used in the embodiments provided in the present application can include at least one of non-volatile and volatile memories. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc.

[0117] The databases involved in the embodiments provided in the present application can include at least one of relational databases and non-relational databases. Non-relational databases can include distributed databases based on blockchain, etc., without limitation. The processors involved in the embodiments provided in the present application can be general-purpose processors, central processors, graphics processors, digital signal processors, data processing logics of programmable logics, etc., without limitation.

[0118] The technical features of the above embodiments can be combined arbitrarily. For the sake of brevity of description, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, it should be considered as the scope described in this specification.

[0119] Specific examples are used in this article to elaborate on the principles and implementation manners of the present application. The description of the above embodiments is only used to help understand the method and its core idea of the present application; at the same time, for those of ordinary skill in the art, according to the idea of the present application, there will be changes in the specific implementation manners and application scopes. In summary, the content of this specification should not be construed as a limitation to the present application.

Claims

1. A method for determining water mass membership, characterized in that: The water mass membership determination method comprises: According to the set density range, the density of the first water mass in the first set area is layered with a set step length, and the weighted center coordinates of the first water mass in each density layer are determined; According to the set density range, the density of the second water group in the second set area is layered with the set step length, and the weighted center coordinates of the second water group in each density layer are determined; the first water group and the second water group are both known water groups; Calculating the distance between the weighted center coordinates of the first water group and the weighted center coordinates of the second water group in each density layer; The density layer where the minimum value of each distance is located is used as the boundary density layer of the temperature-salinity curve of the first water mass and the second water mass; Based on the boundary density layer, respectively calculating the similarity between the third water cluster to be analyzed and the first water cluster, and the similarity between the third water cluster and the second water cluster; wherein the similarity between the third water cluster and the first water cluster represents the membership of the third water cluster to the first water cluster, and the similarity between the third water cluster and the second water cluster represents the membership of the third water cluster to the second water cluster; According to the set density range, the density of the first water mass in the first set area is layered with a set step length, specifically including: Acquire data of a plurality of sampling points in the first water mass, wherein the data of each sampling point includes temperature, salinity and pressure; Reorganizing the data of each sampling point in the first water mass to obtain reorganized sampling point data; Based on each of the reorganized sampling point data and the physical relationship, a seawater density is calculated; Determining the set density range according to the maximum and minimum values ​​of each of the seawater densities, wherein the lower limit of the set density range is lower than the minimum value, and the upper limit of the set density range is lower than the maximum value; According to the set density range, the density of the first water mass in the first set area is layered with a set step length.

2. The water group membership determination method according to claim 1, characterized in that: Based on each of the reorganized sampling point data and physical relationships, a seawater density is calculated, specifically including: For each of the reorganized sampling point data, the seawater density is calculated from the reorganized sampling point data based on the physical relationship between absolute salinity, latent heat temperature and pressure; the absolute salinity is determined by the salinity in the reorganized sampling point data, and the latent heat temperature is determined by the temperature in the reorganized sampling point data.

3. The method for determining water group membership according to claim 1, characterized in that: Determining the weighted center coordinates of the first water mass in each density layer specifically includes: For any density layer where the first water mass is located, determine a 95% confidence ellipse of water particles in the density layer, and use the water particles within the 95% confidence ellipse as target data points; Calculate the weight of each target data point according to the distance between each target data point and the data mean within the 95% confidence ellipse; Based on the weight of each of the target data points, the weighted center coordinates of the first water group in the density layer are calculated.

4. The method for determining water group membership according to claim 3, characterized in that: The weighted center coordinates of the first water mass in each density layer are expressed as: ， ； in, is the abscissa of the first water mass in a density layer, is the ordinate of the first water mass in a density layer, n is the number of target data points, is the weight of the i-th target data point, is the horizontal coordinate of the i-th target data point, is the ordinate of the i-th target data point; ； in, is the distance between the ith target data point and the data mean within the 95% confidence ellipse, Is a positive number.

5. The method for determining water group membership according to claim 1, characterized in that: Respectively calculating the similarity between the third water group to be analyzed and the first water group and the similarity between the third water group and the second water group, specifically includes: According to the set density range, for the third water mass in the second set area, the density is stratified with the set step length, and the extreme salinity and temperature of the third water mass in each density layer are screened out; Based on the boundary density layer, determine the water group to which the third water group belongs at the salinity extreme value and the temperature extreme value of each density layer, calculate the salinity data variance of the water group to which the salinity extreme value belongs and the temperature data variance of the water group to which the temperature extreme value belongs, and obtain the salinity data variance and the temperature data variance of each density layer; the water group to which the third water group belongs is the first water group, the second water group, or the third water group; Initializing a weight matrix, taking the maximum variance value of the salinity data variance and the temperature data variance of the third water mass in each density layer as an element in the weight matrix; Based on the weight matrix, the similarity between the third water group and the first water group, and the similarity between the third water group and the second water group are calculated using the least square method.

6. A water mass membership determination device, characterized in that: The water mass membership determination device comprises: A first water mass density stratification module, for stratifying the density of the first water mass in the first set area with a set step length according to a set density range, and determining the weighted center coordinates of the first water mass in each density layer; A second water mass density stratification module, for stratifying the density of the second water mass in the second set area with the set step length according to the set density range, and determining the weighted center coordinates of the second water mass in each density layer; the first water mass and the second water mass are both known water masses; A distance calculation module, used to calculate the distance between the weighted center coordinates of the first water group and the weighted center coordinates of the second water group in each density layer; A boundary density layer determination module, used for taking the density layer where the minimum value of each distance is located as the boundary density layer of the temperature-salinity curve of the first water mass and the second water mass; A similarity calculation module, used to calculate the similarity between the third water cluster to be analyzed and the first water cluster, and the similarity between the third water cluster and the second water cluster, respectively, based on the boundary density layer; wherein the similarity between the third water cluster and the first water cluster represents the membership of the third water cluster to the first water cluster, and the similarity between the third water cluster and the second water cluster represents the membership of the third water cluster to the second water cluster; According to the set density range, the density of the first water mass in the first set area is layered with a set step length, specifically including: Acquire data of a plurality of sampling points in the first water mass, wherein the data of each sampling point includes temperature, salinity and pressure; Reorganizing the data of each sampling point in the first water mass to obtain reorganized sampling point data; Based on each of the reorganized sampling point data and the physical relationship, a seawater density is calculated; Determining the set density range according to the maximum and minimum values ​​of each of the seawater densities, wherein the lower limit of the set density range is lower than the minimum value, and the upper limit of the set density range is lower than the maximum value; According to the set density range, the density of the first water mass in the first set area is layered with a set step length.

7. A computer device comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the water group membership determination method according to any one of claims 1 to 5.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the water group membership determination method described in any one of claims 1 to 5 is implemented.

9. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the water group membership determination method described in any one of claims 1 to 5 is implemented.

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