An optimal strategy planning and analysis method for matching survey lines for engineering applications

Abstract

The invention relates to an optimal strategy planning and analysis method for matching survey lines for engineering applications. The method adopts a method of simplifying a route curve into a combination of finite line segments to determine the range near a route curve; a route library is established according to a set of survey lines accumulated from historical data; finally, based on a point-to-line segment distance algorithm, relevant screening condition parameters are set to screen all survey lines in an adaptation area, thereby realizing the screening of the optimal navigation survey line. The invention studies the selection of survey lines in a matching adaptation area by combining current matching means such as topography, geomagnetism and gravity, and searches based on a point-to-line segment distance method. On the basis of the establishment of a route library, the process of dividing regions for regional selection is abandoned in the final survey line selection stage, and route planning and analysis of survey lines is performed to obtain the optimal navigation survey line.

Description

Technical Field

[0001] The invention belongs to the field of inertial and gravity combined navigation systems, and in particular to an optimal strategy planning and analysis method for matching survey lines for engineering applications. Background Art

[0002] Underwater gravity, magnetism, and terrain distribution have certain solid properties and are a kind of physical reference quantity suitable for underwater navigation. When using this geophysical reference quantity for navigation, it is necessary to select a survey line with good characteristics. For a certain navigation area, the applicable survey line may not be unique. In this case, it is necessary to study a survey line route planning analysis method for engineering applications to select a suitable navigation survey line. Summary of the invention

[0003] The purpose of the present invention is to overcome the shortcomings of the prior art and propose a matching survey line optimal strategy planning and analysis method for engineering applications. It can combine the current matching means such as terrain, geomagnetism and gravity, conduct research on the selection of survey lines in matching adaptation areas, and search based on the distance method from point to line segment. On the basis of the establishment of a survey line library, the process of dividing areas for regional selection is abandoned in the final survey line selection stage, and survey line route planning analysis is carried out.

[0004] The present invention solves the technical problem by adopting the following technical solutions:

[0005] A matching survey line optimal strategy planning and analysis method for engineering applications includes the following steps:

[0006] Step 1, determining the vicinity of the route curve by simplifying the route curve as a combination of finite line segments;

[0007] Step 2: Establish a survey line database based on the survey line collection accumulated from historical data;

[0008] Step 3: Based on the point-to-line distance algorithm, set relevant screening condition parameters, screen all survey lines in the adaptation area, and select the optimal navigation survey line.

[0009] Moreover, the specific implementation method of step 1 is: adopt the judgment method of curve curvature greater than 150° for judgment, and take the curve curvature greater than 150° as a characteristic track point, and the line segment connecting the two characteristic track points as the regular curve fitting of the route curve.

[0010] Moreover, the specific implementation method of step 2 is:

[0011] Step 2.1, calculate the eigenvalue;

[0012] Step 2.2, selecting the preferred area according to the fusion feature value calculated in step 2.1;

[0013] Step 2.3: Select the preferred area according to step 2.2 and build a collection of survey line libraries.

[0014] Moreover, the characteristic values ​​of step 2.1 include: entropy, anomaly gradient, standard deviation and plane correlation;

[0015] The calculation method of entropy is:

[0016]

[0017] Among them, p i is the probability of an outlier appearing, the field intensity set of a feature region is V: V = {f(i, j)}, f(i, j) is the field intensity at the coordinate (i, j), the outliers in the feature region are quantized to M levels, let n i is the number of outliers in the i-level interval, and p is obtained i :

[0018]

[0019] The longitude entropy is:

[0020]

[0021] The latitudinal entropy is:

[0022]

[0023] Among them, H i is the entropy of each row, H i is the entropy of each column, M is the number of discrete points in the longitude direction, and N is the number of discrete points in the latitude direction. The larger the entropy, the more uniform the intensity change, and the richer the navigation information provided;

[0024] The calculation method of anomaly gradient is: the gradient of anomaly in longitude direction is:

[0025]

[0026] The gradient of the anomaly in the latitudinal direction is:

[0027]

[0028] The abnormal gradient is:

[0029]

[0030] Ms is the mean operator, Diff(:,i) is the gradient difference in longitude, Diff(:,j) is the gradient difference in latitude, Dx is the step length in longitude, and Dy is the step length in latitude. The larger the anomaly gradient, the richer the information about the spatial variation of the feature.

[0031] The standard deviation is calculated as:

[0032] δ 2 =Var(f(i,j))

[0033] The standard deviation of longitude δx is:

[0034]

[0035] The latitude standard deviation δy is:

[0036]

[0037] The calculation method of plane correlation is: The outlier plane longitude correlation is:

[0038]

[0039] The outlier plane latitude correlation is:

[0040]

[0041] The outlier plane correlation is:

[0042]

[0043] in, Among them, K is the number of sampling points, λ is the position longitude, is the location latitude.

[0044] Moreover, the specific implementation method of step 2.2 is: the eigenvalue parameters calculated in step 2.1 are normalized using a regularization method to obtain an eigenvalue matrix:

[0045]

[0046] Build an orthogonal factor model:

[0047] X=μ+AF+ε

[0048] Calculate the sample mean

[0049]

[0050] Calculate the sample covariance matrix S:

[0051]

[0052] in,

[0053] Compute the sample correlation matrix:

[0054] R=(r ij )

[0055] in, λ1≥λ2≥…≥λ p ≥0 is the eigenvalue of the sample correlation matrix R, and its corresponding unit orthogonal vector is l1,l2,…,l p ; Select the sum of squares of loads factors, and determine the number of common factors m; let Then A=(a1,…a m ) is the factor loading matrix,

[0056] Extract variables X with factor loading values ​​greater than 0.5 i , calculate the corresponding f k =∑a i +X i , calculate the corresponding variance percentage, and use the factors with cumulative variance percentage greater than 90% as the evaluation criteria. Use the variance percentage corresponding to each factor as the weight ratio to calculate the weight of each factor; count the factor scores of each area according to the factor weight, and select the area with higher scores as the adaptation area.

[0057] Moreover, the specific implementation method of step 2.3 is: perform simulation calculations in the adaptation area, and include the survey lines within the target accuracy range into the adaptation area survey line library file; at the same time, the historical preferred survey line routes are also included in the survey line library file.

[0058] Moreover, the specific implementation method of step 3 is: taking the starting point of the survey line in the survey line library as the representative of the survey line, calculating the distance from the point to the line segment fitting track curve, screening the points within the set distance range and the survey lines in the survey line library, and realizing the survey line route selection; the selected survey line route driving planning is carried out according to the principle of estimated accuracy from low to high or from front to back, and the survey line route planning analysis is carried out to screen out the optimal navigation survey line.

[0059] The advantages and positive effects of the present invention are:

[0060] The present invention adopts a method of simplifying the route curve into a combination of finite line segments to determine the range near the route curve; a route library is established based on a set of survey lines accumulated from historical data; finally, based on a point-to-line segment distance algorithm, relevant screening condition parameters are set to screen all survey lines in the adaptation area, thereby realizing the screening of the optimal navigation survey line. The present invention conducts research on the selection of survey lines in the matching adaptation area by combining current matching means such as terrain, geomagnetism and gravity, and searches based on the point-to-line segment distance method. On the basis of the establishment of the route library, the process of dividing the area for regional selection is abandoned in the final survey line selection stage, and a survey line route planning analysis is performed to obtain the optimal navigation survey line. DETAILED DESCRIPTION

[0061] The present invention is further described below.

[0062] A matching survey line optimal strategy planning and analysis method for engineering applications includes the following steps:

[0063] Step 1: Determine the range near the route curve by simplifying the route curve into a combination of finite line segments.

[0064] The judgment method of curve curvature greater than 150° is adopted for judgment, and the curve curvature greater than 150° is taken as a characteristic track point, and the line segment connecting the two characteristic track points is used as the regular curve fitting of the route curve.

[0065] Step 2: Establish a survey line library based on the survey line collection accumulated from historical data.

[0066] Step 2.1: Calculate the eigenvalue.

[0067] Eigenvalues ​​include: entropy, anomaly gradient, standard deviation, and plane correlation;

[0068] The calculation method of entropy is:

[0069]

[0070] Among them, p i is the probability of an outlier appearing, the field intensity set of a feature region is V: V = {f(i, j)}, f(i, j) is the field intensity at the coordinate (i, j), the outliers in the feature region are quantized to M levels, let n i is the number of outliers in the i-level interval, and p is obtained i :

[0071]

[0072] The longitude entropy is:

[0073]

[0074] The latitudinal entropy is:

[0075]

[0076] Among them, H i is the entropy of each row, H i is the entropy of each column, M is the number of discrete points in the longitude direction, and N is the number of discrete points in the latitude direction. The larger the entropy, the more uniform the intensity change, and the richer the navigation information provided;

[0077] The calculation method of anomaly gradient is: the gradient of anomaly in longitude direction is:

[0078]

[0079] The gradient of the anomaly in the latitudinal direction is:

[0080]

[0081] The abnormal gradient is:

[0082]

[0083] Ms is the mean operator, Diff(:,i) is the gradient difference in longitude, Diff(:,j) is the gradient difference in latitude, Dx is the step length in longitude, and Dy is the step length in latitude. The larger the anomaly gradient, the richer the information about the spatial variation of the feature.

[0084] The standard deviation is calculated as:

[0085] δ 2 =Var(f(i,j))

[0086] The standard deviation of longitude δx is:

[0087]

[0088] The latitude standard deviation δy is:

[0089]

[0090] The calculation method of plane correlation is: The outlier plane longitude correlation is:

[0091]

[0092] The outlier plane latitude correlation is:

[0093]

[0094] The outlier plane correlation is:

[0095]

[0096] in, Among them, K is the number of sampling points, λ is the position longitude, is the location latitude.

[0097] Step 2.2: Select the preferred area based on the fusion feature value calculated in step 2.1.

[0098] The eigenvalue parameters calculated in step 2.1 are normalized using the regularization method to obtain the eigenvalue matrix:

[0099]

[0100] Build an orthogonal factor model:

[0101] X=μ+AF+ε

[0102] Calculate the sample mean

[0103]

[0104] Calculate the sample covariance matrix S:

[0105]

[0106] in,

[0107] Compute the sample correlation matrix:

[0108] R=(r ij )

[0109] in,

[0110] λ1≥λ2≥…≥λ p ≥0 is the eigenvalue of the sample correlation matrix R, and its corresponding unit orthogonal vector is l1,l2,…,l p ;

[0111] Select Sum of Squares of Loadings The factors of , and determine the number of common factors m;

[0112] make Then A=(a1,...a m ) is the factor loading matrix.

[0113] Extract variables X with factor loading values ​​greater than 0.5 i , calculate the corresponding f k =∑a i +X i , calculate the corresponding variance percentage, and use the factors with cumulative variance percentage greater than 90% as the evaluation criteria. Use the variance percentage corresponding to each factor as the weight ratio to calculate the weight of each factor; count the factor scores of each area according to the factor weight, and select the area with higher scores as the adaptation area.

[0114] Step 2.3: Select the preferred area according to step 2.2 and build a collection of survey line libraries.

[0115] Simulation calculations are performed in the adaptation area, and the survey lines within the target accuracy range are included in the survey line library file of the adaptation area; at the same time, the routes of the historically preferred survey lines are also included in the survey line library file.

[0116] Step 3: Based on the point-to-line distance algorithm, set relevant screening condition parameters, screen all survey lines in the adaptation area, and select the optimal navigation survey line.

[0117] The starting point of the survey line in the survey line library is taken as the representative of the survey line, the distance from the point to the line segment fitting track curve is calculated, and the points within the set distance range and the survey lines in the survey line library are screened to realize the survey line route selection; for the selected survey line route driving planning, the survey line route planning analysis is carried out according to the principle of estimated accuracy from low to high or from front to back, and the optimal navigation survey line is screened out.

[0118] It should be emphasized that the embodiments described in the present invention are illustrative rather than restrictive. Therefore, the present invention includes but is not limited to the embodiments described in the specific implementation manner. Any other implementation manners derived by those skilled in the art based on the technical solution of the present invention also fall within the scope of protection of the present invention.

Claims

1. A matching survey line optimal strategy planning and analysis method for engineering applications, characterized by: The following steps are involved: Step 1, determining the vicinity of the route curve by simplifying the route curve as a combination of finite line segments; The specific implementation method of step 1 is: use the judgment method of the curve curvature greater than 150° for judgment, and take the curve curvature greater than 150° as a characteristic track point, and the line segment connecting the two characteristic track points as the regular curve fitting of the route curve; Step 2: Establish a survey line database based on the survey line collection accumulated from historical data; The specific implementation method of step 2 is: Step 2.1, calculate the eigenvalue; The eigenvalues ​​of step 2.1 include: entropy, anomaly gradient, standard deviation, and plane correlation; Step 2.2, selecting the preferred area according to the fusion feature value calculated in step 2.1; Step 2.3, according to step 2.2, select the preferred area and build a collection of survey line libraries; Step 3: Based on the point-to-line distance algorithm, set relevant screening condition parameters, screen all survey lines in the adaptation area, and select the optimal navigation survey line; The specific implementation method of step 3 is: take the starting point of the survey line in the survey line library as the representative of the survey line, calculate the distance from the point to the line segment fitting track curve, select the points within the set distance range and the survey lines in the survey line library, and realize the survey line route selection; The selected survey line route driving plan is analyzed according to the principle of estimated accuracy from low to high or from early to late in time to select the optimal navigation survey line.

2. The method for optimal strategy planning and analysis of matching measurement lines for engineering applications according to claim 1 is characterized by: The calculation method of entropy in step 2.1 is: Among them, p i is the probability of an outlier appearing, the field intensity set of a feature region is V: V = {f(i, j)}, f(i, j) is the field intensity at the coordinate (i, j), the outliers in the feature region are quantized to M levels, let n i is the number of outliers in the i-level interval, and p is obtained i : The longitude entropy is: The latitudinal entropy is: Among them, H i is the entropy of each row, H i is the entropy of each column, M is the number of discrete points in the longitude direction, and N is the number of discrete points in the latitude direction. The larger the entropy, the more uniform the intensity change, and the richer the navigation information provided; The calculation method of anomaly gradient is: the gradient of anomaly in longitude direction is: The gradient of the anomaly in the latitudinal direction is: The abnormal gradient is: Among them, Ms is the mean operator, Diff(:,i) is the gradient difference in the longitude direction, Diff(:,j) is the gradient difference in the latitude direction, Dx is the step length in the longitude direction, and Dy is the step length in the latitude direction. The larger the anomaly gradient, the richer the information about the spatial variation of the feature. The standard deviation is calculated as: δ 2 =Var(f(i,j)) The standard deviation of longitude δx is: The latitude standard deviation δy is: The calculation method of plane correlation is: The outlier plane longitude correlation is: The outlier plane latitude correlation is: The outlier plane correlation is: in, Among them, K is the number of sampling points, λ is the position longitude, is the location latitude.

3. The method for optimal strategy planning and analysis of matching measurement lines for engineering applications according to claim 1 is characterized in that: The specific implementation method of step 2.2 is: the eigenvalue parameters calculated in step 2.1 are normalized using a regularization method to obtain an eigenvalue matrix: Build an orthogonal factor model: X=μ+AF+ε Calculate the sample mean X: Calculate the sample covariance matrix S: in, Compute the sample correlation matrix: R＝(r ij ) in, i, j=1, 2,...,p, λ1≥λ2≥...≥λ p ≥0 is the eigenvalue of the sample correlation matrix R, and its corresponding unit orthogonal vector is l1,l2,…,l p ; Select the sum of squares of loads factors, and determine the number of common factors m; let Where i = 1, 2, ..., m, then A = a1, ...a m is the factor loading matrix; Extract variables X with factor loading values ​​greater than 0.5 i , calculate the corresponding f k =∑a i +X i , calculate the corresponding variance percentage, and use the factors with cumulative variance percentage greater than 90% as the evaluation criteria. Use the variance percentage corresponding to each factor as the weight ratio to calculate the weight of each factor; count the factor scores of each area according to the factor weight, and select the area with higher scores as the adaptation area.

4. The method for optimal strategy planning and analysis of matching measurement lines for engineering applications according to claim 1, characterized in that: The specific implementation method of step 2.3 is: perform simulation calculations in the adaptation area, and include the survey lines within the target accuracy range into the adaptation area survey line library file; at the same time, the historical preferred survey line routes are also included in the survey line library file.

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