Navigation error trend-considered terrain adaptation area division method in prior nautical chart, program, equipment and storage medium

Abstract

The invention belongs to the technical field of underwater navigation and positioning, and particularly relates to a method, a program and equipment for dividing a terrain adaptation area in a prior nautical chart considering a navigation error trend, and a storage medium. According to the submarine topography matching navigation method, the influence of various topography characteristic parameters on the submarine topography matching navigation method is researched, and seven topography characteristic parameters in a grid map and an isobath map are selected as factors influencing the submarine topography matching navigation performance; taking the topographic feature parameter, the submarine topography matching navigation positioning error and the topography matching navigation result change condition as input data and output data respectively to be substituted into a twin support vector machine to obtain a relationship between the topographic feature parameter and the submarine topography matching navigation positioning error as well as the relationship between the topographic feature parameter and the submarine topography matching navigation positioning error and the relationship between the topographic feature parameter and the error change condition; and further completing calculation of submarine topography matching navigation positioning errors and error change conditions of the priori nautical chart, and further realizing adaptation area division of the priori nautical chart.

Description

Technical Field

[0001] The present invention belongs to the field of underwater navigation and positioning technology, and specifically relates to a method, program, device and storage medium for dividing terrain adaptation areas in a priori nautical chart taking into account navigation error trends. Background Art

[0002] With the continuous improvement of AUV endurance, the current underwater navigation technology cannot provide a feasible navigation method for long-range AUV underwater navigation. The terrain matching navigation method using single-beam sonar and particle filtering can provide bounded navigation errors in long-term underwater navigation, and has the potential to achieve long-term underwater navigation. At the same time, compared with multi-beam sonar, single-beam sonar can effectively reduce the energy consumption of the terrain matching navigation system and extend the underwater endurance of AUV to a limited extent. However, due to the limited measurement information of single-beam sonar, the robustness of its navigation results is poor. Therefore, it is necessary to improve the accuracy and robustness of terrain matching navigation results by finding terrain adaptation areas.

[0003] The current methods for dividing the terrain matching navigation adaptation zone focus on evaluating terrain adaptability by using a variety of terrain feature parameters and terrain matching navigation results through methods such as neural networks or support vector machines. However, due to the existence of a Markov process in the terrain matching navigation results based on particle filtering, only using navigation positioning results for dividing the adaptation zone will result in long-term error accumulation. Although the terrain adaptation zone has been reached, the navigation results still have large errors at this time. This causes the existing terrain adaptation zone division method to be unable to identify the adaptation zone at this time, resulting in failure of the adaptation zone division.

[0004] The patent application with the publication date of August 4, 2020, publication number CN109000656A, and invention name "Underwater Terrain Matching Navigation Adaptation Zone Selection Method Based on Spatial Clustering" selects six terrain points in the terrain area, divides the seabed terrain into six classification areas through spatial clustering, and then selects the terrain adaptation zone. However, this method does not take into account a variety of terrain feature parameters in the adaptation zone division, resulting in insufficient robustness of the adaptation zone division results obtained when using single-beam sonar for terrain matching navigation.

[0005] The patent application with publication date of November 8, 2019, publication number of CN106767834A, and invention name of "A method for dividing underwater terrain matching adaptation zones for AUV based on fuzzy entropy value" uses membership function and fuzzy rules to solve the adaptation ability of sub-maps in the grid, and then realizes the division of adaptation zones. However, this method does not take into account the accumulation of navigation errors in terrain matching navigation methods with Markov processes, resulting in inaccurate division of adaptation zones when applied to terrain matching navigation adaptation zones based on particle filtering. Summary of the invention

[0006] The purpose of the present invention is to solve the problem of poor navigation robustness in the single-beam sonar submarine terrain matching navigation method, and to provide a method, program, device and storage medium for dividing terrain adaptation areas in a priori nautical charts that considers the trend of navigation errors. The present invention uses a variety of terrain feature parameters as the input of a twin support vector machine, and brings the terrain matching navigation results and the navigation error divergence trend as the output of the twin support vector machine into the terrain adaptation area division, which can effectively improve the accuracy of the terrain adaptation area division.

[0007] A method for dividing terrain adaptation areas in a priori nautical chart considering navigation error trends comprises the following steps:

[0008] Step 1: Generate a bathymetric map based on the prior nautical chart data, and divide the bathymetric map into bathymetric sub-maps; generate a raster map based on the prior nautical chart, and use the same sub-map division operation as the bathymetric map to divide the raster map into raster sub-maps that are the same as the bathymetric sub-maps;

[0009] Step 2: For each location point in the prior nautical chart, the characteristic parameters of the depth contour sub-map and the characteristic parameters of the grid sub-map are used together as the terrain characteristic parameters of the location point;

[0010] Step 3: The AUV performs terrain matching navigation in the real sea area corresponding to the prior nautical chart, obtains water depth measurement values ​​based on single-beam sonar at each path point, obtains position measurement values ​​based on the inertial navigation system, and obtains water depth estimation values ​​through the single-beam sonar seabed terrain matching navigation algorithm;

[0011] Step 4: Calculate the horizontal Euclidean distance between the estimated water depth and the actual position at each path point, and calculate the average positioning error of the water depth data at each path point through Monte Carlo experiments, as well as the change in positioning error between this path point and the previous path point, to construct a training data set;

[0012] Step 5: Use the training data set to train the twin support vector machine. The terrain feature parameters obtained from the prior nautical chart according to the actual position of the path point are used as the input of the twin support vector machine, and the average positioning error and positioning error change value of the water depth data of the path point are used as the output of the twin support vector machine.

[0013] Step 6: Input the terrain feature parameters corresponding to each position point in the prior nautical chart into the trained twin support vector machine to obtain the average positioning error and positioning error change value of the water depth data corresponding to each position point in the prior nautical chart;

[0014] The mean of the average positioning error of the water depth data and the mean of the positioning error change value are obtained, and the smaller value of the two means is used as the adaptation area division standard; the position points where the average positioning error of the water depth data is less than the adaptation area division standard are divided into the adaptation area set, and the remaining position points are divided into the non-adaptation area set;

[0015] Step 7: Cluster the positioning error change value data set corresponding to all position points in the prior nautical chart, and divide the data into three clustering areas, where the boundary value of the first area is negative and the boundary value of the second area is positive;

[0016] For a position point in the adaptation area set, if its positioning error change value is greater than the second area boundary value, it is moved to the non-adaptation area set;

[0017] For a position point in the non-adaptation area set, if its positioning error change value is less than the first area boundary value, it is moved to the adaptation area set;

[0018] Finally, all the position points in the set of adaptation areas constitute the terrain adaptation area in the priori nautical chart, and all the position points in the set of non-adaptation areas constitute the terrain non-adaptation area in the priori nautical chart.

[0019] Furthermore, the terrain characteristic parameters of the location point in step 2 are specifically:

[0020] The bathymetric density δ and bathymetric smoothness ξ of the bathymetric submap where the location point in the prior nautical chart is located, as well as the terrain elevation standard deviation σ, terrain elevation entropy H, terrain correlation coefficient R, terrain slope S and terrain roughness θ of the grid submap where the location point is located are taken as the terrain characteristic parameters [δ, ξ, σ, H, R, S, θ] of the location point; the seven terrain characteristic parameters are normalized respectively.

[0021] Furthermore, the isobath density δ of the isobath submap is calculated by: taking the number of isobaths in the isobath submap as the isobath density δ;

[0022] The calculation method of the depth-contour smoothness ξ of the depth-contour submap is as follows: discretize each depth-contour in the depth-contour submap to obtain depth-contour points; smooth the depth-contours, discretize the smoothed depth-contours to obtain smoothed depth-contour points, calculate the minimum value of each depth-contour point from the smoothed depth-contour point, and take the average value as the depth-contour smoothness ξ of the depth-contour submap;

[0023]

[0024] Among them, (x c ,y c ) represents the isobath points obtained by discretizing the isobaths, and O is the number of isobath points; (x o ,y o) represents the smoothed isobath points obtained by discretizing the smoothed isobaths;

[0025] The calculation method of the terrain elevation standard deviation σ of the grid sub-map is:

[0026]

[0027] Wherein, h(i,j) represents the water depth data at the position point (i,j) in the grid sub-map; M and N represent the length and width of the grid sub-map respectively;

[0028] The calculation method of the terrain elevation entropy H of the grid sub-map is:

[0029]

[0030] The calculation method of the terrain correlation coefficient R of the grid sub-map is:

[0031]

[0032] R=(R x +R y ) / 2

[0033] The calculation method of the terrain slope S of the grid sub-map is:

[0034]

[0035] The calculation method of the terrain roughness θ of the grid sub-map is:

[0036]

[0037] Furthermore, the average positioning error set of the water depth data of the W path points obtained in step 4 is {E w} and the positioning error change value set {Error w}, for the set {E w} and {Error w}Cluster them separately, remove the path points corresponding to the outlier data, and the data of the remaining path points constitute the training data set.

[0038] Furthermore, the step 6 is specifically as follows: inputting the terrain feature parameters corresponding to each position point in the prior nautical chart into the trained twin support vector machine to obtain the average positioning error set of water depth data {map error} and the set of positioning error change values ​​{map change};

[0039] Get the mean of the average positioning error of the water depth data error The median of the change in positioning errorerror , the smaller of the two means is used as the division standard for the adaptation area error ; The average positioning error of the water depth data is less than the division standard of the adaptation area error The position points are divided into the adaptation area set, and the remaining position points are divided into the non-adaptation area set;

[0040]

[0041] Among them, suitable i =1 means that the position point i is divided into the adaptation area set, suitable i =0 means that the position point i is divided into the non-adaptation area set; Represents the average positioning error value of the water depth data corresponding to position point i.

[0042] Furthermore, the step 7 is specifically as follows: for the positioning error change value set {map change}, the error change value has two cases, positive and negative, and the K-Means clustering method is used to map {map change} is divided into three clustering areas, the boundary value of the first area is a negative value, the second area boundary value Is a positive value; according to the change in positioning error corresponding to position point i Divide the adaptation area:

[0043]

[0044] For a point i in the adaptation zone set, if suitable i = -1, then move it to the non-adaptive zone set;

[0045] For the location points in the non-adaptive zone set, if suitable i =1, it is moved to the adaptation zone set.

[0046] An application of a method for dividing terrain adaptation areas in a priori nautical chart that takes into account the trend of navigation error, brings the terrain adaptation areas in the priori nautical chart into path planning as a reward function. Through path planning, the planned path to the target point passes through the terrain adaptation areas as much as possible, thereby improving the accuracy and robustness of navigation results during path tracking.

[0047] A computer device / equipment / system comprises a memory, a processor and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of the method for dividing terrain adaptation areas in a priori nautical charts taking into account navigation error trends.

[0048] A computer-readable storage medium stores a computer program / instruction thereon, which, when executed by a processor, implements the steps of the method for dividing terrain adaptation areas in a priori nautical charts taking into account navigation error trends.

[0049] A computer program product includes a computer program / instruction, which, when executed by a processor, implements the steps of the method for dividing a terrain adaptation zone in a priori nautical chart taking into account a navigation error trend.

[0050] The beneficial effects of the present invention are:

[0051] The present invention studies the influence of various terrain feature parameters on the submarine terrain matching navigation method, selects seven terrain feature parameters in raster maps and isobath maps as factors affecting the submarine terrain matching navigation performance, and performs terrain matching navigation. The terrain feature parameters, the submarine terrain matching navigation positioning error, and the change of the terrain matching navigation results are respectively brought into the twin support vector machine as input data and output data, and the relationship between the terrain feature parameters and the submarine terrain matching navigation positioning error and the error change is obtained, thereby completing the calculation of the submarine terrain matching navigation positioning error and the error change of the prior nautical chart, and then realizing the adaptation area division of the prior nautical chart. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Figure 1 It is a flow chart of the present invention.

[0053] Figure 2 It is a flow chart of the terrain adaptation area division in the present invention.

[0054] Figure 3 Flowchart of a density-based spatial clustering method for noisy data.

[0055] Figure 4 It is a flow chart of the rough division and fine division of the terrain adaptation area in the present invention. DETAILED DESCRIPTION

[0056] The present invention is further described below in conjunction with the accompanying drawings.

[0057] The present invention relates to a method for dividing a terrain adaptation zone in a priori nautical chart considering the trend of navigation error, including calculation of terrain characteristic parameters of a priori nautical chart, calculation of terrain characteristic parameters of path points, calculation of matching positioning errors of path points, determination of the relationship between terrain characteristic parameters and terrain matching positioning errors, calculation of terrain matching positioning errors of a priori nautical chart, and division of terrain adaptation zones, etc. According to the grid division standard of the priori nautical chart submap, it is divided into a bathymetric map submap and a grid map submap; the bathymetric density, bathymetric smoothness, terrain elevation standard deviation, terrain elevation entropy, terrain roughness, terrain slope, and terrain correlation coefficient of each area are calculated as terrain characteristic parameters, and terrain matching navigation is performed using single-beam sonar terrain data, and the relationship between terrain characteristic parameters and terrain matching positioning results is established, thereby obtaining the terrain matching positioning results of the entire priori nautical chart, and then completing the division of the adaptation zone.

[0058] A method for dividing terrain adaptation areas in a priori nautical chart considering navigation error trends comprises the following steps:

[0059] Step 1: Generate a bathymetric map based on prior chart data, divide the bathymetric map into bathymetric submaps, and calculate the bathymetric density and bathymetric smoothness of each bathymetric submap;

[0060] Step 2: Generate a raster map based on the prior nautical chart, and use the same sub-map division operation as the bathymetric map to divide the raster map into raster sub-maps that are the same as the bathymetric sub-maps, and calculate the terrain elevation standard deviation, terrain elevation entropy, terrain correlation coefficient, terrain slope, and terrain roughness of each raster sub-map;

[0061] Step 3: For each location point in the prior nautical chart, the bathymetric density δ and bathymetric smoothness ξ of the bathymetric submap, as well as the terrain elevation standard deviation σ, terrain elevation entropy H, terrain correlation coefficient R, terrain slope S and terrain roughness θ of the grid submap are used as the terrain characteristic parameters [δ, ξ, σ, H, R, S, θ] of the location point; the seven terrain characteristic parameters are normalized respectively;

[0062] Step 4: The AUV performs terrain matching navigation in the real sea area corresponding to the prior nautical chart, obtains water depth measurement values ​​based on single-beam sonar at each path point, obtains position measurement values ​​based on the inertial navigation system, and obtains water depth estimation values ​​through the single-beam sonar seabed terrain matching navigation algorithm;

[0063] Step 5: Calculate the horizontal Euclidean distance between the estimated water depth and the actual position at each path point, and calculate the average positioning error of the water depth data at each path point through Monte Carlo experiments, as well as the change in positioning error between this path point and the previous path point, to construct a training data set;

[0064] Step 6: There are mismatches in terrain matching navigation, which may cause some navigation results to fail to truly reflect the navigability of the terrain. Therefore, the navigation positioning errors are clustered.

[0065] For the average positioning error set {E w} and the positioning error change value set {Error w}, for the set {E w} and {Error w}Clustering is performed separately, and the path points corresponding to the outlier data are eliminated. The data of the remaining path points constitute the training data set;

[0066] Step 7: Use the training data set to train the twin support vector machine. The terrain feature parameters obtained from the prior nautical chart according to the actual position of the path point are used as the input of the twin support vector machine. The average positioning error and positioning error change value of the water depth data of the path point are used as the output of the twin support vector machine.

[0067] Step 8: Input the terrain feature parameters corresponding to each location point in the prior nautical chart into the trained twin support vector machine to obtain the average positioning error set of water depth data {map error} and the set of positioning error change values ​​{map change};

[0068] Get the mean of the average positioning error of the water depth data error The median of the change in positioning error error , the smaller of the two means is used as the division standard for the adaptation area error ; The average positioning error of the water depth data is less than the division standard of the adaptation area error The position points are divided into the adaptation area set, and the remaining position points are divided into the non-adaptation area set;

[0069]

[0070] Among them, suitable i =1 means that the position point i is divided into the adaptation area set, suitable i =0 means that the position point i is divided into the non-adaptation area set; Indicates the average positioning error value of the water depth data corresponding to the position point i;

[0071] Step 9: For the positioning error change value set {map change}, the error change value has two cases, positive and negative, and the K-Means clustering method is used to map {map change} is divided into three clustering areas, the boundary value of the first area is a negative value, the second area boundary value Is a positive value; according to the change in positioning error corresponding to position point i Divide the adaptation area:

[0072]

[0073] For a point i in the adaptation zone set, if suitable i = -1, then move it to the non-adaptive zone set;

[0074] For the location points in the non-adaptive zone set, if suitable i =1, it is moved to the adaptation zone set.

[0075] Finally, all the position points in the set of adaptation areas constitute the terrain adaptation area in the priori nautical chart, and all the position points in the set of non-adaptation areas constitute the terrain non-adaptation area in the priori nautical chart.

[0076] The terrain adaptation area in the prior nautical chart is brought into the path planning as a reward function. Through path planning, the planned path to the target point passes through the terrain adaptation area as much as possible, thereby improving the accuracy and robustness of the navigation results during path tracking.

[0077] The calculation method of the isobath density δ of the isobath submap is as follows: the number of isobaths in the isobath submap is taken as the isobath density δ;

[0078] The calculation method of the depth-contour smoothness ξ of the depth-contour submap is as follows: discretize each depth-contour in the depth-contour submap to obtain depth-contour points; smooth the depth-contours, discretize the smoothed depth-contours to obtain smoothed depth-contour points, calculate the minimum value of each depth-contour point from the smoothed depth-contour point, and take the average value as the depth-contour smoothness ξ of the depth-contour submap;

[0079]

[0080] Among them, (x c ,y c ) represents the isobath points obtained by discretizing the isobaths, and O is the number of isobath points; (x o ,y o ) represents the smoothed isobath points obtained by discretizing the smoothed isobaths;

[0081] The calculation method of the terrain elevation standard deviation σ of the grid sub-map is:

[0082]

[0083] Wherein, h(i,j) represents the water depth data at the position point (i,j) in the grid sub-map; M and N represent the length and width of the grid sub-map respectively;

[0084] The calculation method of the terrain elevation entropy H of the grid sub-map is:

[0085]

[0086] The calculation method of the terrain correlation coefficient R of the grid sub-map is:

[0087]

[0088] R=(R x +R y ) / 2

[0089] The calculation method of the terrain slope S of the grid sub-map is:

[0090]

[0091] The calculation method of the terrain roughness θ of the grid sub-map is:

[0092]

[0093] The clustering method may adopt a density-based noisy data space clustering method, which is applicable to noisy data sets and can find clusters of any shape and can effectively handle outliers;

[0094] For the navigation positioning error data set containing W path points, Error = {Error1, ..., Error n}, first input data to calculate the Euclidean distance According to the calculated Euclidean distance result, the distance used to calculate the cutoff distance d is obtained. c Parameter t, calculate the distance d ij , and let d ij =d ji , i,j∈Error, and then determine the truncation distance d c . Calculate the density based on the cutoff distance And generate descending subscript sequence The density The calculation formula is

[0095] Calculating distance And the number of categories Determine the final cluster center and output the clustering result. It is defined by calculating the minimum distance between point i and other high-density points, calculated as:

[0096] Among them, the indicator set The representation is as follows:

[0097]

[0098] In the current single-beam sonar seabed terrain matching navigation method, due to the poor information measurement of single-beam sonar, the obtained positioning results have poor robustness. There are few studies on the division of adaptation zones for single-beam sonar seabed terrain matching. At the same time, the Markov process of terrain matching navigation error is not taken into account in the division of terrain adaptation zones, which makes it difficult to complete the division of adaptation zones through multiple terrain feature parameters.

[0099] The present invention studies the influence of various terrain feature parameters on the submarine terrain matching navigation method, selects seven terrain feature parameters in raster maps and isobath maps as factors affecting the submarine terrain matching navigation performance, and performs terrain matching navigation. The terrain feature parameters, the submarine terrain matching navigation positioning error, and the change of the terrain matching navigation results are respectively brought into the twin support vector machine as input data and output data, and the relationship between the terrain feature parameters and the submarine terrain matching navigation positioning error and the error change is obtained, thereby completing the calculation of the submarine terrain matching navigation positioning error and the error change of the prior nautical chart, and then realizing the adaptation area division of the prior nautical chart.

[0100] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A method for dividing terrain adaptation areas in a priori nautical chart considering navigation error trends, characterized in that: The following steps are involved: Step 1: Generate a bathymetric map based on the prior nautical chart data, and divide the bathymetric map into bathymetric sub-maps; generate a raster map based on the prior nautical chart, and use the same sub-map division operation as the bathymetric map to divide the raster map into raster sub-maps that are the same as the bathymetric sub-maps; Step 2: For each location point in the prior nautical chart, the characteristic parameters of the depth contour sub-map and the characteristic parameters of the grid sub-map are used together as the terrain characteristic parameters of the location point; Step 3: The AUV performs terrain matching navigation in the real sea area corresponding to the prior nautical chart, obtains water depth measurement values ​​based on single-beam sonar at each path point, obtains position measurement values ​​based on the inertial navigation system, and obtains water depth estimation values ​​through the single-beam sonar seabed terrain matching navigation algorithm; Step 4: Calculate the horizontal Euclidean distance between the estimated water depth and the actual position at each path point, and calculate the average positioning error of the water depth data at each path point through Monte Carlo experiments, as well as the change in positioning error between this path point and the previous path point, to construct a training data set; Step 5: Use the training data set to train the twin support vector machine. The terrain feature parameters obtained from the prior nautical chart according to the actual position of the path point are used as the input of the twin support vector machine, and the average positioning error and positioning error change value of the water depth data of the path point are used as the output of the twin support vector machine. Step 6: Input the terrain feature parameters corresponding to each position point in the prior nautical chart into the trained twin support vector machine to obtain the average positioning error and positioning error change value of the water depth data corresponding to each position point in the prior nautical chart; The mean of the average positioning error of the water depth data and the mean of the positioning error change value are obtained, and the smaller value of the two means is used as the adaptation area division standard; the position points where the average positioning error of the water depth data is less than the adaptation area division standard are divided into the adaptation area set, and the remaining position points are divided into the non-adaptation area set; Step 7: Cluster the positioning error change value data set corresponding to all position points in the prior nautical chart, and divide the data into three clustering areas, where the boundary value of the first area is negative and the boundary value of the second area is positive; For a position point in the adaptation area set, if its positioning error change value is greater than the second area boundary value, it is moved to the non-adaptation area set; For a position point in the non-adaptation area set, if its positioning error change value is less than the first area boundary value, it is moved to the adaptation area set; Finally, all the position points in the set of adaptation areas constitute the terrain adaptation area in the priori nautical chart, and all the position points in the set of non-adaptation areas constitute the terrain non-adaptation area in the priori nautical chart.

2. The method for dividing terrain adaptation areas in a priori nautical chart considering navigation error trends according to claim 1 is characterized in that: The terrain characteristic parameters of the location point in step 2 are specifically: The bathymetric density δ and bathymetric smoothness ξ of the bathymetric submap where the location point in the prior nautical chart is located, as well as the terrain elevation standard deviation σ, terrain elevation entropy H, terrain correlation coefficient R, terrain slope S and terrain roughness θ of the grid submap where the location point is located are taken as the terrain characteristic parameters [δ, ξ, σ, H, R, S, θ] of the location point; the seven terrain characteristic parameters are normalized respectively.

3. The method for dividing terrain adaptation areas in a priori nautical chart considering navigation error trends according to claim 2 is characterized by: The calculation method of the isobath density δ of the isobath submap is as follows: the number of isobaths in the isobath submap is taken as the isobath density δ; The calculation method of the depth-contour smoothness ξ of the depth-contour submap is as follows: discretize each depth-contour in the depth-contour submap to obtain depth-contour points; smooth the depth-contours, discretize the smoothed depth-contours to obtain smoothed depth-contour points, calculate the minimum value of each depth-contour point from the smoothed depth-contour point, and take the average value as the depth-contour smoothness ξ of the depth-contour submap; Among them, (x c ,y c ) represents the isobath points obtained by discretizing the isobaths, and O is the number of isobath points; (x o ,y o ) represents the smoothed isobath points obtained by discretizing the smoothed isobaths; The calculation method of the terrain elevation standard deviation σ of the grid sub-map is: Wherein, h(i,j) represents the water depth data at the position point (i,j) in the grid sub-map; M and N represent the length and width of the grid sub-map respectively; The calculation method of the terrain elevation entropy H of the grid sub-map is: The calculation method of the terrain correlation coefficient R of the grid sub-map is: The calculation method of the terrain slope S of the grid sub-map is: The calculation method of the terrain roughness θ of the grid sub-map is:

4. The method for dividing terrain adaptation areas in a priori nautical chart considering navigation error trends according to claim 1 is characterized in that: The average positioning error set of the water depth data of the W path points obtained in step 4 is {E w } and the positioning error change value set {Error w }, for the set {E w } and {Error w }Cluster them separately, remove the path points corresponding to the outlier data, and the data of the remaining path points constitute the training data set.

5. The method for dividing terrain adaptation areas in a priori nautical chart considering navigation error trends according to claim 1, characterized in that: The step 6 is specifically as follows: inputting the terrain feature parameters corresponding to each position point in the prior nautical chart into the trained twin support vector machine to obtain the average positioning error set of water depth data {map error } and the set of positioning error change values ​​{map change }; Get the mean of the average positioning error of the water depth data error The median of the change in positioning error error , the smaller of the two means is used as the division standard for the adaptation area error ; The average positioning error of the water depth data is less than the division standard of the adaptation area error The position points are divided into the adaptation area set, and the remaining position points are divided into the non-adaptation area set; Among them, suitable i =1 means that the position point i is divided into the adaptation area set, suitable i =0 means that the position point i is divided into the non-adaptation area set; Represents the average positioning error value of the water depth data corresponding to position point i.

6. The method for dividing terrain adaptation areas in a priori nautical chart considering navigation error trends according to claim 5 is characterized by: The step 7 is specifically as follows: for the positioning error change value set {map change }, the error change value has two cases, positive and negative, and the K-Means clustering method is used to map {map change } is divided into three clustering areas, the boundary value of the first area is a negative value, the second area boundary value Is a positive value; according to the change in positioning error corresponding to position point i Divide the adaptation area: For a point i in the adaptation zone set, if suitable i = -1, then move it to the non-adaptive zone set; For the location points in the non-adaptive zone set, if suitable i =1, it is moved to the adaptation zone set.

7. An application of the method for dividing terrain adaptation areas in a priori nautical chart taking into account navigation error trends as described in claim 1, characterized in that: The terrain adaptation area in the prior nautical chart is brought into the path planning as a reward function. Through path planning, the planned path to the target point passes through the terrain adaptation area as much as possible, thereby improving the accuracy and robustness of the navigation results during path tracking.

8. A computer device / equipment / system comprising a memory, a processor and a computer program stored in the memory, characterized in that: The processor executes the computer program to implement the steps of the method according to any one of claims 1 to 7.

9. A computer-readable storage medium having a computer program / instruction stored thereon, characterized in that: When the computer program / instructions are executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer program product comprising a computer program / instructions, characterized in that: When the computer program / instructions are executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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