Marine hydrological environment grading evaluation method and device

Abstract

The invention discloses a grading evaluation method and device for a marine hydrological environment. The method comprises the following steps: acquiring a marine hydrological acquisition data set; preprocessing the marine hydrological acquisition data set to obtain a preprocessed data set; and carrying out classification processing on the preprocessed data set to obtain marine hydrological category information. According to the method, the hydrological marine environment of the preset sea area is graded and evaluated based on the multi-source heterogeneous marine hydrological data information of the finite nodes, and the marine hydrological evaluation result is obtained, so that a marine environment basis is provided for subsequent marine operation and equipment efficiency exertion.

Description

Technical Field

[0001] The invention belongs to the field of marine environment, and in particular relates to a hierarchical evaluation method and device for marine hydrological environment. Background Art

[0002] Offshore operations and equipment performance are largely affected and restricted by marine environmental factors. Different marine environments directly affect the performance of equipment. The complex and changeable marine hydrological environment has a significant impact on equipment performance, marine operations and decision-making, and is an indispensable and important part of the marine environment.

[0003] How to conduct a graded assessment of the hydrological and marine environment of a preset sea area based on multi-source heterogeneous ocean hydrological data information of limited nodes, and obtain the ocean hydrological assessment results, thereby providing a marine environment basis for subsequent offshore operations and equipment performance, is an issue that needs to be addressed urgently. Summary of the invention

[0004] In order to perform hierarchical assessment of the hydrological and marine environment of a preset sea area based on multi-source heterogeneous marine hydrological data information of limited nodes and obtain a marine hydrological assessment result, a first aspect of an embodiment of the present invention discloses a hierarchical assessment method for the marine hydrological environment, comprising:

[0005] S1, obtain the ocean hydrological data set;

[0006] S2, preprocessing the ocean hydrological data set to obtain a preprocessed data set;

[0007] S3, classifying the preprocessed data set to obtain ocean hydrological category information.

[0008] The preprocessing of the ocean hydrological data set to obtain a preprocessed data set includes:

[0009] S21, performing data cleaning processing on the ocean hydrological collection data set to obtain a first data set;

[0010] S22, performing unified processing on the collected information of the first data set to obtain a second data set;

[0011] S23, checking and processing the second data set to obtain a preprocessed data set.

[0012] The checking and processing the second data set to obtain a pre-processed data set includes:

[0013] S231, performing boundary check processing on the second data set to obtain a third data set;

[0014] S232, performing pattern checking processing on the third data set to obtain a preprocessed data set.

[0015] The performing boundary check processing on the second data set to obtain a third data set includes:

[0016] S2311, for each data attribute of the second data set, preset a corresponding value range; the value range includes an upper limit value of the value range and a lower limit value of the value range;

[0017] S2312, for each observation data of the second data set, judging whether the value of the observation data is within the value range according to the value range of its data attribute; if the observation data is within the value range, not processing the observation data; if the observation data is not within the value range, setting the value of the observation data to the upper limit value or the lower limit value of the value range that is closest to the observation data;

[0018] S2313, performing fusion processing on all observation data after S2312 is completed to obtain a third data set;

[0019] The performing pattern checking processing on the third data set to obtain a preprocessed data set further includes:

[0020] S2321, for the observed data of each type of data attribute of the third data set, the data collection information of the observed data is used as a known independent variable, and the data value of the observed data is used as a known dependent variable. The known independent variable and the known dependent variable are used to construct a curve to be approximated, and the function approximation method is used to perform curve fitting on the curve to be approximated to obtain the best consistent approximation polynomial f(Ix) of the type of data attribute; the best consistent approximation polynomial f(Ix) is used to calculate and process the known independent variables to obtain an approximate dependent variable; it is determined whether the absolute value of the difference between the approximate dependent variable and the corresponding known dependent variable is greater than a set second regression discrimination threshold; if it is greater than the second regression discrimination threshold, the observed data is deleted from the third data set; if it is less than the second regression discrimination threshold, the observed data is not processed;

[0021] S2322, performing fusion processing on all observation data of the third data set after executing S2321 to obtain a preprocessed data set;

[0022] The classifying process of the pre-processed data set to obtain ocean hydrological category information includes:

[0023] S31, constructing a preprocessed data matrix using the preprocessed data set; the row vector of the preprocessed data matrix is ​​a vector of each type of observation data;

[0024] S32, performing weight calculation processing on the preprocessed data matrix to obtain a weight vector;

[0025] S33, obtaining a standard ocean hydrological category matrix; the row vector of the standard ocean hydrological category matrix is ​​a row vector of standard values ​​for each type of observation data;

[0026] S34, performing difference calculation processing on the pre-processed data matrix and the standard ocean hydrological category matrix to obtain a difference vector; each element of the difference vector represents a difference value of each type of observation data;

[0027] S35, using the weight vector, performing weighted summation processing on the difference vector to obtain an ocean hydrological category value;

[0028] S36, determining ocean hydrological category information according to the category interval to which the ocean hydrological category value belongs;

[0029] The step of performing weight calculation processing on the preprocessed data matrix to obtain a weight vector includes:

[0030] S321, performing feature transformation processing on the preprocessed data matrix to obtain a feature matrix;

[0031] S322, performing distribution calculation on the characteristic matrix E to obtain the value distribution value of each row vector of the characteristic matrix; the expression of the value distribution calculation process is:

[0032]

[0033] Among them, r k,i represents the i-th value distribution value of the k-th row of the feature matrix, E k,i represents the element of the kth row and the ith column of the feature matrix E, where the dimension of the matrix E is N rows and M columns;

[0034] S323, performing logarithmic weighting processing on the value distribution of each row vector to obtain a weight vector;

[0035] The calculation expression of the logarithmic weighting process is:

[0036]

[0037] Among them, H k is the kth element of the weight vector, is the mean of the value distribution of the kth row vector;

[0038] The expression for the difference calculation process is:

[0039]

[0040] Among them, B ij is the element in the i-th row and j-th column of the preprocessed data matrix, A1 ij is the element of the i-th row and j-th column of the standard ocean hydrological category matrix, ω1 and ω2 are the preset weighting values, ξ is the preset deviation value, d i is the difference value of each i-type observation data.

[0041] In a second aspect of an embodiment of the present invention, a method and device for hierarchical assessment of marine hydrological environment is disclosed, the device comprising:

[0042] A memory storing executable program code;

[0043] a processor coupled to the memory;

[0044] The processor calls the executable program code stored in the memory to execute the hierarchical assessment method of the marine hydrological environment.

[0045] According to a third aspect of an embodiment of the present invention, a computer storable medium is disclosed, wherein the computer storage medium stores computer instructions, and when the computer instructions are called, they are used to execute the hierarchical assessment method of the marine hydrological environment.

[0046] According to a fourth aspect of an embodiment of the present invention, an information data processing terminal is disclosed, wherein the information data processing terminal is used to implement the hierarchical assessment method of the marine hydrological environment.

[0047] The beneficial effects of the present invention are:

[0048] The present invention is based on multi-source heterogeneous ocean hydrological data information of finite nodes, and conducts a graded assessment of the hydrological and oceanic environment of a preset sea area to obtain an ocean hydrological assessment result, thereby providing an ocean environmental basis for subsequent offshore operations and equipment performance. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1 It is a flow chart for implementing the method of the present invention. DETAILED DESCRIPTION

[0050] Figure 1 It is a flow chart for implementing the method of the present invention.

[0051] In order to better understand the content of the present invention, an embodiment is given here.

[0052] Embodiment 1:

[0053] In a first aspect of an embodiment of the present invention, a method and device for hierarchical assessment of an ocean hydrological environment are disclosed, comprising:

[0054] S1, obtain the ocean hydrological data set;

[0055] S2, preprocessing the ocean hydrological data set to obtain a preprocessed data set;

[0056] S3, classifying the preprocessed data set to obtain ocean hydrological category information.

[0057] The S2 comprises:

[0058] S21, performing data cleaning processing on the ocean hydrological collection data set to obtain a first data set;

[0059] S22, performing unified processing on the collected information of the first data set to obtain a second data set;

[0060] S23, checking and processing the second data set to obtain a preprocessed data set;

[0061] The S23 includes:

[0062] S231, performing boundary check processing on the second data set to obtain a third data set;

[0063] S232, performing pattern checking processing on the third data set to obtain a preprocessed data set;

[0064] The performing boundary check processing on the second data set to obtain a third data set includes:

[0065] S2311, for each data attribute of the second data set, preset a corresponding value range; the value range includes an upper limit value of the value range and a lower limit value of the value range;

[0066] S2312, for each observation data of the second data set, judging whether the value of the observation data is within the value range according to the value range of its data attribute; if the observation data is within the value range, not processing the observation data; if the observation data is not within the value range, setting the value of the observation data to the upper limit value or the lower limit value of the value range that is closest to the observation data;

[0067] S2313, performing fusion processing on all observation data after S2312 is completed to obtain a third data set;

[0068] The performing pattern checking processing on the third data set to obtain a preprocessed data set further includes:

[0069] S2321, for the observed data of each type of data attribute of the third data set, the data collection information of the observed data is used as a known independent variable, and the data value of the observed data is used as a known dependent variable. The known independent variable and the known dependent variable are used to construct a curve to be approximated, and the function approximation method is used to perform curve fitting on the curve to be approximated to obtain the best consistent approximation polynomial f(Ix) of the type of data attribute; the best consistent approximation polynomial f(Ix) is used to calculate and process the known independent variables to obtain an approximate dependent variable; it is determined whether the absolute value of the difference between the approximate dependent variable and the corresponding known dependent variable is greater than a set second regression discrimination threshold; if it is greater than the second regression discrimination threshold, the observed data is deleted from the third data set; if it is less than the second regression discrimination threshold, the observed data is not processed;

[0070] S2322, performing fusion processing on all observation data of the third data set after executing S2321 to obtain a preprocessed data set;

[0071] The pre-processed data set is classified to obtain ocean hydrological category information, including:

[0072] S31, constructing a preprocessed data matrix using the preprocessed data set; the row vector of the preprocessed data matrix is ​​a vector of each type of observation data;

[0073] S32, performing weight calculation processing on the preprocessed data matrix to obtain a weight vector;

[0074] S33, obtaining a standard ocean hydrological category matrix; the row vector of the standard ocean hydrological category matrix is ​​a row vector of standard values ​​for each type of observation data;

[0075] S34, performing difference calculation processing on the pre-processed data matrix and the standard ocean hydrological category matrix to obtain a difference vector; each element of the difference vector represents a difference value of each type of observation data;

[0076] S35, using the weight vector, performing weighted summation processing on the difference vector to obtain an ocean hydrological category value;

[0077] S36, determining ocean hydrological category information according to the category interval to which the ocean hydrological category value belongs;

[0078] The step of performing weight calculation processing on the preprocessed data matrix to obtain a weight vector includes:

[0079] S321, performing feature transformation processing on the preprocessed data matrix to obtain a feature matrix;

[0080] S322, performing distribution calculation on the characteristic matrix E to obtain the value distribution value of each row vector of the characteristic matrix; the expression of the value distribution calculation process is:

[0081]

[0082] Among them, r k,i represents the i-th value distribution value of the k-th row of the feature matrix, E k,i represents the element of the kth row and the ith column of the feature matrix E, where the dimension of the matrix E is N rows and M columns;

[0083] S323, performing logarithmic weighting processing on the value distribution of each row vector to obtain a weight vector;

[0084] The calculation expression of the logarithmic weighting process is:

[0085]

[0086] Among them, H k is the kth element of the weight vector, is the mean of the value distribution of the kth row vector;

[0087] The expression for the difference calculation process is:

[0088]

[0089] Among them, B ij is the element in the i-th row and j-th column of the preprocessed data matrix, A1 ij is the element of the i-th row and j-th column of the standard ocean hydrological category matrix, ω1 and ω2 are the preset weighting values, ξ is the preset deviation value, d i is the difference value of each i-type observation data.

[0090] The feature transformation may adopt wavelet transformation or time-frequency transformation; the row vector of the feature matrix is ​​the row vector of the preprocessed data matrix;

[0091] The category interval is pre-set and includes the value interval of each ocean hydrological category information, which can be: the first category information corresponds to a value interval of [0.1, 0.3], the second category information corresponds to a value interval of [0.3, 0.5], the third category information corresponds to a value interval of [0.5, 0.7], and the fourth category information corresponds to a value interval of [0.7, 1].

[0092] The performing pattern checking processing on the third data set to obtain a preprocessed data set includes:

[0093] S2321, for the observation data of each type of data attribute of the third data set, using the data collection information of the observation data as the independent variable and the data value of the observation data as the dependent variable, perform autoregression-sliding average modeling to obtain the regression model of the type of data attribute respectively; using the regression model, perform calculation processing on the independent variable to obtain the regression data value; determine whether the absolute value of the difference between the regression data value and the corresponding dependent variable value is greater than a set first regression discrimination threshold; if it is greater than the first regression discrimination threshold, delete the observation data from the third data set; if it is less than the first regression discrimination threshold, do not process the observation data;

[0094] S2322, executing S2321 for all observation data of the third data set to obtain a preprocessed data set;

[0095] The data cleaning process includes filling missing values, smoothing noise data, and smoothing or deleting outliers; the smoothed noise data is obtained by first determining the noise data, and then smoothing the noise data according to the data before and after the noise data; the noise data is a value whose value is less than the detection sensitivity of the sensor of the observed data, or greater than the measurement upper limit of the sensor of the observed data. The outlier point can be determined by the Kalman filter method. The filling value of the missing value can be determined by averaging the measured values ​​within a certain sampling interval before and after the missing value.

[0096] The step of uniformly processing the collected information of the first data set to obtain a second data set includes:

[0097] S231, setting a unified data collection time and a unified data collection location for observation data with the same data attribute included in the same data subset of the first data set;

[0098] S232, for the observation data of the same data attribute contained in each data subset, aligning the collection time and collection location of the observation data according to the unified data collection time and the unified data collection location to obtain standardized observation data;

[0099] The aligning of the collection time and the collection location of the observation data comprises:

[0100] When the time interval for collecting the observation data is greater than the time interval for the unified data collection time, interpolation processing is performed on the adjacent observation data to obtain the observation data value at the unified data collection time, and the value is used as the standard observation data;

[0101] When the time interval for collecting the observation data is less than the time interval for collecting the unified data, sampling the observation data to obtain observation data consistent with the unified data collection time, and use the observation data as the standard observation data;

[0102] When the spatial interval of the observation data collection is greater than the spatial interval of the unified data collection location, interpolation processing is performed on the adjacent observation data to obtain the observation data value at the unified data collection location, which is used as the standard observation data;

[0103] When the spatial interval of the observation data collection is smaller than the spatial interval of the unified data collection location, the observation data is sampled to obtain observation data consistent with the unified data collection location, which is used as the standard observation data.

[0104] S233, executing S232 on the observed data of each data attribute of each data subset to obtain a standard data subset corresponding to the data subset;

[0105] S234, combining all the standard data subsets to obtain a second data set;

[0106] In a second aspect of an embodiment of the present invention, a method and device for hierarchical assessment of marine hydrological environment is disclosed, the device comprising:

[0107] A memory storing executable program code; a processor coupled to the memory; the processor calling the executable program code stored in the memory to execute the hierarchical assessment method of the marine hydrological environment.

[0108] According to a third aspect of an embodiment of the present invention, a computer storable medium is disclosed, wherein the computer storage medium stores computer instructions, and when the computer instructions are called, they are used to execute the hierarchical assessment method of the marine hydrological environment.

[0109] According to a fourth aspect of an embodiment of the present invention, an information data processing terminal is disclosed, wherein the information data processing terminal is used to implement the hierarchical assessment method of the marine hydrological environment.

[0110] The above is only an embodiment of the present application and is not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.

Claims

1. A hierarchical assessment method for marine hydrological environment, characterized in that: include: S1, obtain the ocean hydrological data set; S2, preprocessing the ocean hydrological data set to obtain a preprocessed data set; S3, classifying the preprocessed data set to obtain ocean hydrological category information.

2. The method for hierarchical assessment of the marine hydrological environment according to claim 1, characterized in that: The preprocessing of the ocean hydrological data set to obtain a preprocessed data set includes: S21, performing data cleaning processing on the ocean hydrological collection data set to obtain a first data set; S22, performing unified processing on the collected information of the first data set to obtain a second data set; S23, checking and processing the second data set to obtain a preprocessed data set.

3. The method for hierarchical assessment of the marine hydrological environment according to claim 2, characterized in that: The checking and processing the second data set to obtain a pre-processed data set includes: S231, performing boundary check processing on the second data set to obtain a third data set; S232, performing pattern checking processing on the third data set to obtain a preprocessed data set.

4. The method for hierarchical assessment of the marine hydrological environment according to claim 3, characterized in that: The performing boundary check processing on the second data set to obtain a third data set includes: S2311, for each data attribute of the second data set, preset a corresponding value range; the value range includes an upper limit value of the value range and a lower limit value of the value range; S2312, for each observation data of the second data set, judging whether the value of the observation data is within the value range according to the value range of its data attribute; if the observation data is within the value range, not processing the observation data; if the observation data is not within the value range, setting the value of the observation data to the upper limit value or the lower limit value of the value range that is closest to the observation data; S2313, performing fusion processing on all the observation data after completing S2312 to obtain a third data set.

5. The method for grading and evaluating the marine hydrological environment according to claim 4, characterized in that: The performing pattern checking processing on the third data set to obtain a preprocessed data set further includes: S2321, for the observed data of each type of data attribute of the third data set, the data collection information of the observed data is used as a known independent variable, and the data value of the observed data is used as a known dependent variable. The known independent variable and the known dependent variable are used to construct a curve to be approximated, and the function approximation method is used to perform curve fitting on the curve to be approximated to obtain the best consistent approximation polynomial f(Ix) of the type of data attribute; the best consistent approximation polynomial f(Ix) is used to calculate and process the known independent variables to obtain an approximate dependent variable; it is determined whether the absolute value of the difference between the approximate dependent variable and the corresponding known dependent variable is greater than a set second regression discrimination threshold; if it is greater than the second regression discrimination threshold, the observed data is deleted from the third data set; if it is less than the second regression discrimination threshold, the observed data is not processed; S2322, performing fusion processing on all observation data of the third data set after executing S2321 to obtain a preprocessed data set.

6. The method for grading and evaluating the marine hydrological environment according to claim 2, characterized in that: The classifying process of the pre-processed data set to obtain ocean hydrological category information includes: S31, constructing a preprocessed data matrix using the preprocessed data set; the row vector of the preprocessed data matrix is ​​a vector of each type of observation data; S32, performing weight calculation processing on the preprocessed data matrix to obtain a weight vector; S33, obtaining a standard ocean hydrological category matrix; the row vector of the standard ocean hydrological category matrix is ​​a row vector of standard values ​​for each type of observation data; S34, performing difference calculation processing on the pre-processed data matrix and the standard ocean hydrological category matrix to obtain a difference vector; each element of the difference vector represents a difference value of each type of observation data; S35, using the weight vector, performing weighted summation processing on the difference vector to obtain an ocean hydrological category value; S36, determining ocean hydrological category information according to the category interval to which the ocean hydrological category value belongs.

7. The method for grading and evaluating the marine hydrological environment according to claim 6, characterized in that: The step of performing weight calculation processing on the preprocessed data matrix to obtain a weight vector includes: S321, performing feature transformation processing on the preprocessed data matrix to obtain a feature matrix; S322, performing distribution calculation on the characteristic matrix E to obtain the value distribution value of each row vector of the characteristic matrix; the expression of the value distribution calculation process is: Among them, r k,i represents the i-th value distribution value of the k-th row of the feature matrix, E k,i represents the element of the kth row and the ith column of the feature matrix E, where the dimension of the matrix E is N rows and M columns; S323, performing logarithmic weighting processing on the value distribution of each row vector to obtain a weight vector; The calculation expression of the logarithmic weighting process is: Among them, H k is the kth element of the weight vector, is the mean of the distribution of the values ​​of the kth row vector.

8. The method for grading and evaluating the marine hydrological environment according to claim 7, characterized in that: The expression for the difference calculation process is: Among them, B ij is the element in the i-th row and j-th column of the preprocessed data matrix, A1 ij is the element of the i-th row and j-th column of the standard ocean hydrological category matrix, ω1 and ω2 are the preset weighting values, ξ is the preset deviation value, d i is the difference value of each i-type observation data.

9. A method and device for hierarchical assessment of marine hydrological environment, characterized in that: The device comprises: A memory storing executable program code; a processor coupled to the memory; The processor calls the executable program code stored in the memory to execute the hierarchical assessment method of the marine hydrological environment as described in any one of claims 1-8.

10. A computer storable medium, characterized in that: The computer storage medium stores computer instructions, and when the computer instructions are called by a computer, they are used to execute the hierarchical assessment method of the marine hydrological environment as described in any one of claims 1-8.

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