Spatial interpolation method and device

Abstract

This specification provides a spatial interpolation method and apparatus. The method includes: obtaining measured values ​​of an indicator object measured at a plurality of sample points located in a target area; using an inverse distance weighted interpolation method to interpolate a predicted position within the target area based on the measured values, thereby obtaining an inverse distance weighted interpolation result for the predicted position; using the inverse distance weighted interpolation result as a covariate of a cokriging interpolation method, and using the cokriging interpolation method to perform a secondary interpolation of the target area based on the measured values ​​and the covariate, thereby obtaining a predicted value for the indicator object.

Description

Technical Field

[0001] The embodiments of this specification relate to a regional scale interpolation method, and more particularly to a spatial interpolation method and apparatus. Background Art

[0002] In related technologies, commonly used spatial interpolation methods may include Inverse Distance Weight (IDW), Kriging, and Co-Kriging.

[0003] The interpolation result of the inverse distance weighted interpolation method is between the measured maximum and minimum values. Although it can retain the details to the greatest extent, it is prone to the "bull's eye" phenomenon.

[0004] The Kriging interpolation method uses spatial statistics as its theoretical basis, which can overcome the problem of difficult error analysis in interpolation. It has high interpolation accuracy, strong uniqueness and extrapolation capabilities, but the interpolation results are prone to smoothing.

[0005] Cokriging is an improved kriging interpolation method that can obtain more accurate spatial interpolation results by considering the correlation between two or more random processes. However, the covariates required by cokriging are difficult to obtain, or it is difficult to obtain suitable covariates, resulting in poor applicability of cokriging interpolation. Summary of the Invention

[0006] The embodiments of this specification provide a spatial interpolation method and device.

[0007] According to a first aspect of an embodiment of this specification, a spatial interpolation method is provided, the method comprising:

[0008] Obtaining measured values ​​of the indicator object measured at several sample points located in the target area;

[0009] Using the inverse distance weighted interpolation method, interpolating the to-be-interpolated points in the target area according to the measured values, to obtain inverse distance weighted interpolation results of the to-be-interpolated points;

[0010] The inverse distance weighted interpolation result is used as a covariate of the cokriging interpolation method, and the cokriging interpolation method is used to perform secondary interpolation on the to-be-interpolated points in the target area according to the measured value and the covariate to obtain the final predicted value of the to-be-interpolated point.

[0011] Optionally, the method further includes:

[0012] The measured values ​​and predicted values ​​are input into a random forest model, and the random forest model is subjected to multiple model parameter tuning until the relationship between the measured values ​​and the predicted values ​​is fitted, and the random forest model after the model parameter tuning is further used as a prediction model for predicting the indicator object; the prediction model is used to predict the spatial distribution of the indicator object in the target area.

[0013] Optionally, obtaining the measured value of the indicator object measured at each sample point in the target area includes:

[0014] Perform gridding processing on the target area, and evenly select several grids from all the grids obtained by gridding processing as sample points;

[0015] The indicator object is measured at the sample points to obtain the measured value of the indicator object at each sample point.

[0016] Optionally, after performing gridding processing on the target area and uniformly selecting a number of grids from all grids obtained by the gridding processing as sample points, the method further includes:

[0017] All grids except the sample points are used as points to be interpolated.

[0018] Optionally, the using of the inverse distance weighted interpolation method to interpolate the to-be-interpolated points in the target area according to the measured values ​​to obtain the inverse distance weighted interpolation results of the to-be-interpolated points includes:

[0019] For any point to be interpolated within the target area, inverse distance weighted interpolation is performed in the following way:

[0020] Taking the point to be interpolated as the center, search for sample points within a preset radius, and determine a preset number of target sample points closest to the point to be interpolated from the searched sample points;

[0021] Based on the measured values ​​of the preset number of target sample points, an inverse distance weighted interpolation result of the to-be-interpolated point is obtained by calculating a weighted average value; wherein the weight of each target sample point in the calculated weighted average value is inversely proportional to the distance between the target sample point and the to-be-interpolated point.

[0022] Optionally, the preset number is 12.

[0023] Optionally, the inverse distance weighted interpolation result is used as a covariate of a cokriging interpolation method, and the cokriging interpolation method is used to perform secondary interpolation on the to-be-interpolated points in the target area according to the measured value and the covariate to obtain a final predicted value of the to-be-interpolated point, including:

[0024] The inverse distance weighted interpolation result is used as the covariate of the cokriging interpolation method, the final predicted value of the point to be interpolated is used as the main variable of the cokriging interpolation method, and the final predicted value of the point to be interpolated is calculated by the following formula:

[0025]

[0026] Among them, Z(x0) represents the final predicted value of the interpolation point at position x0, Z(x 1i ) means at position x 1i p observations, T(x 2j ) means at position x 2j The q inverse distance weighted interpolation results, λ 1i and λ 2j is the weight coefficient.

[0027] Optionally, the measured values ​​and the predicted values ​​are input into a random forest model, and the random forest model is subjected to multiple model parameter tuning until the relationship between the measured values ​​and the predicted values ​​is fitted, including:

[0028] Extracting m bootstrap sample sets from the n original sample pairs consisting of the measured values ​​and the predicted values ​​in a playback manner, thereby constructing m regression trees of the random forest model; wherein the original sample pairs not selected during the sampling process constitute m out-of-bag data sets;

[0029] For each node on each regression tree, k split variables are randomly selected from all p explanatory variables, and the next branch is selected from them based on the branching goodness criterion;

[0030] Each regression tree recursively branches from top to bottom until the preset segmentation stop condition is reached.

[0031] Optionally, the indicator objects include at least one of the air quality of the target area, surface plant coverage, surface water and salt content, and underground mineral content.

[0032] Optionally, the water-salt content includes water content and total salt content.

[0033] According to a second aspect of the embodiments of this specification, a spatial interpolation device is provided, the device comprising:

[0034] A measurement unit, which obtains the measured values ​​of the indicator object measured at a number of sample points located in the target area;

[0035] an inverse distance weighted interpolation unit, which uses an inverse distance weighted interpolation method to interpolate the to-be-interpolated points in the target area according to the measured values, and obtains an inverse distance weighted interpolation result of the to-be-interpolated points;

[0036] The co-kriging interpolation unit uses the inverse distance weighted interpolation result as a co-variable of the co-kriging interpolation method, and uses the co-kriging interpolation method to perform secondary interpolation on the to-be-interpolated points in the target area according to the measured value and the co-variable, so as to obtain a final predicted value of the to-be-interpolated point.

[0037] Optionally, the device further includes:

[0038] The model prediction unit inputs the measured value and the predicted value into the random forest model, and the random forest model is subjected to multiple model parameter tuning until the relationship between the measured value and the predicted value is fitted, and the random forest model after the model parameter tuning is further used as a prediction model for predicting the indicator object; the prediction model is used to predict the spatial distribution of the indicator object in the target area.

[0039] Optionally, the measuring unit includes:

[0040] The gridding subunit performs gridding processing on the target area and uniformly selects several grids from all the grids obtained by the gridding processing as sample points;

[0041] The measurement subunit performs measurement of the indicator object on the sample points to obtain the measured value of the indicator object measured at each sample point.

[0042] Optionally, the gridding subunit is further configured to use all grids except the sample points as points to be interpolated.

[0043] Optionally, the inverse distance weighted interpolation unit includes:

[0044] For any point to be interpolated within the target area, inverse distance weighted interpolation is performed through the following search subunits and calculation subunits:

[0045] The search subunit searches for sample points within a preset radius with the point to be interpolated as the center, and determines a preset number of target sample points closest to the point to be interpolated from the searched sample points;

[0046] The calculation subunit obtains an inverse distance weighted interpolation result of the to-be-interpolated point by calculating a weighted average value based on the measured values ​​of the preset number of target sample points; wherein the weight of each target sample point in the calculated weighted average value is inversely proportional to the distance between the target sample point and the to-be-interpolated point.

[0047] Optionally, the preset number is 12.

[0048] Optionally, the cokriging interpolation unit is further configured to use the inverse distance weighted interpolation result as a covariable of the cokriging interpolation method, use the final predicted value of the point to be interpolated as the main variable of the cokriging interpolation method, and calculate the final predicted value of the point to be interpolated by the following formula:

[0049]

[0050] Among them, Z(x0) represents the final predicted value of the interpolation point at position x0, Z(x 1i ) means at position x 1i p observations, T(x2j) represents the p observations at position x 2j The q inverse distance weighted interpolation results, λ 1i and λ 2j is the weight coefficient.

[0051] Optionally, the model prediction unit includes:

[0052] Sampling subunits, extracting m bootstrap sample sets from the n original sample pairs consisting of the measured values ​​and the predicted values ​​in a playback manner, thereby constructing m regression trees of the random forest model; wherein the original sample pairs not selected during the sampling process constitute m out-of-bag data sets;

[0053] The branching subunit randomly selects k splitting variables from all p explanatory variables for each node on each regression tree, and selects the next branch based on the branching goodness criterion; each regression tree recursively performs branching operations in a top-down manner until the preset splitting stop condition is reached.

[0054] Optionally, the indicator objects include at least one of the air quality of the target area, surface plant coverage, surface water and salt content, and underground mineral content.

[0055] Optionally, the water-salt content includes water content and total salt content.

[0056] This specification provides a spatial interpolation scheme. By using the inverse distance weighted interpolation results as covariates in the cokriging interpolation method, local information can be introduced to help the cokriging interpolation better adapt to the spatial variation of actual data, thereby overcoming the shortcomings of the limited number of measured sample points for the primary variable and the instability of the variogram.

[0057] On the other hand, the inverse distance weighted interpolation method can provide local and accurate interpolation results, and largely retains extreme values ​​during the interpolation process, while cokriging interpolation can take spatial correlation into account and provide more reliable estimates. Combining the two, the interpolation results largely weaken the "bull's eye" phenomenon of the inverse distance weighted interpolation method.

[0058] On the other hand, the inverse distance weighted interpolation result used as the collaborative variable is easier to obtain.

[0059] On the other hand, the use of random forest optimization can significantly improve the accuracy of the interpolation results. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] Figure 1 is a flowchart of a spatial interpolation method provided in one embodiment of this specification;

[0061] Figure 2 A schematic diagram of the measured water-salt distribution provided in an embodiment of this specification;

[0062] Figure 3 A schematic diagram of water and salt distribution based on the inverse distance weighted interpolation method provided in one embodiment of this specification;

[0063] Figure 4 A schematic diagram of water and salt distribution based on the co-kriging interpolation method provided in an embodiment of this specification;

[0064] Figure 5 A schematic flow chart of another spatial interpolation method provided in one embodiment of this specification;

[0065] Figure 6 A schematic diagram of water and salt distribution based on random forest optimization provided in an embodiment of this specification;

[0066] Figure 7 is a hardware structure diagram of a spatial interpolation device provided in one embodiment of this specification;

[0067] Figure 8 It is a module of the spatial interpolation device provided in an embodiment of this specification. DETAILED DESCRIPTION

[0068] Exemplary embodiments will be described in detail herein, with examples illustrated in the accompanying drawings. In the following description, when referring to the drawings, identical numerals in different figures represent identical or similar elements unless otherwise indicated. The embodiments described in the following exemplary embodiments are not intended to represent all embodiments consistent with this specification. Rather, they are merely examples of apparatus and methods consistent with certain aspects of this specification, as detailed in the appended claims.

[0069] The terms used in this specification are for the purpose of describing specific embodiments only and are not intended to limit this specification. As used in this specification and the appended claims, the singular forms "a," "an," "the," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It should also be understood that the term "and / or" as used herein refers to and encompasses any and all possible combinations of one or more of the associated listed items.

[0070] It should be understood that although the terms first, second, third, etc. may be used in this specification to describe various information, such information should not be limited to these terms. These terms are merely used to distinguish information of the same type from one another. For example, first information may also be referred to as second information, and similarly, second information may also be referred to as first information without departing from the scope of this specification. Depending on the context, the term "if" as used herein may be interpreted as "when," "when," or "in response to determining."

[0071] In related technologies, commonly used spatial interpolation methods may include inverse distance weighted interpolation, kriging interpolation, and cokriging interpolation.

[0072] The interpolation result of the inverse distance weighted interpolation method is between the measured maximum and minimum values. Although it can retain the details to the greatest extent, it is prone to the "bull's eye" phenomenon.

[0073] The Kriging interpolation method uses spatial statistics as its theoretical basis, which can overcome the problem of difficult error analysis in interpolation. It has high interpolation accuracy, strong uniqueness and extrapolation capabilities, but the interpolation results are prone to smoothing.

[0074] Cokriging is an improved kriging interpolation method that can obtain more accurate spatial interpolation results by considering the correlation between two or more random processes. However, the covariates required by cokriging are difficult to obtain, or it is difficult to obtain suitable covariates, resulting in poor applicability of cokriging interpolation.

[0075] In view of this, the present invention proposes a new spatial interpolation scheme, which uses the interpolation results of the inverse distance weighted method as the co-variable of the co-kriging interpolation method; on the one hand, local information can be introduced to help the co-kriging interpolation better adapt to the spatial variation of the actual data, and can make up for the shortcomings of the small number of measured sample points of the main variable and the instability of the variance function. On the other hand, the inverse distance weighted interpolation method can provide local and accurate interpolation results, and the extreme values ​​are largely retained during the interpolation process, while the co-kriging interpolation can take into account spatial correlation and provide more reliable estimates. Combining the two, the interpolation result greatly weakens the "bull's eye" phenomenon of the inverse distance weighted method interpolation. On the other hand, the inverse distance weighted interpolation result used as the co-variable is easier to obtain.

[0076] The spatial interpolation solution provided by the present invention can be applied to various business scenarios that require spatial interpolation, such as analyzing the air quality distribution, soil salinization distribution, surface plant coverage, underground mineral distribution, etc. in a geographical area.

[0077] Taking the distribution of soil salinization as an example, soil salinization is a serious problem worldwide. Due to irrigation water, fertilization, and lime use, approximately one billion hectares of soil are affected by secondary salinization. It is estimated that due to climate change and human activities, more than 50% of global farmland will be affected by salinization by the end of the 21st century. In arid and semi-arid regions, unique climatic conditions, intensive irrigation methods, and management practices lead to a significant accumulation of salt in the soil from lower layers to upper layers, resulting in secondary salinization. Water shortages and secondary salinization have become two major factors restricting agricultural development in arid and semi-arid regions. Therefore, clarifying the distribution of soil water and salt is crucial for the scientific and rational development and utilization of saline soils.

[0078] When acquiring soil water and salt distribution data, it's difficult to measure soil water and salt data at all locations within a region due to time, space, and cost constraints. In practical applications, soil water and salt are typically measured at several sampling points evenly distributed across the region. Spatial interpolation methods are then used to predict the soil water and salt distribution within the region based on the measured values.

[0079] Since existing spatial interpolation methods (such as inverse distance weighted interpolation, kriging interpolation, and cokriging interpolation) have certain defects, the spatial interpolation method provided in this manual can predict accurate water and salt data, thereby obtaining a more accurate distribution of soil water and salt in the region.

[0080] Please refer to the following Figure 1 The spatial interpolation method shown in the figure may include the following steps:

[0081] Step 110: Obtain measured values ​​of the indicator object measured at a plurality of sample points located in the target area.

[0082] As mentioned above, the indicator objects in the present invention may include but are not limited to at least one of the air quality of the target area, surface plant coverage, surface water and salt content, and underground mineral content.

[0083] In practical applications, in order to make the sampling distribution more universal, the target area can be gridded in advance, and then a number of grids can be evenly selected from all the grids obtained by the gridding process as sample points; finally, the indicator objects are measured at the sample points to obtain the measured values ​​of the indicator objects measured at each sample point.

[0084] In this embodiment, the water-salt content of the ground surface is taken as an example for explanation. The water-salt content may include the surface water content and total salt content of the target area.

[0085] Please refer to the following Figure 2 The schematic diagram of the measured water-salt distribution is shown in Figure 2 The left side shows the measured values ​​of total salt content at each sample point. Figure 2 The right side shows the measured water content of each sample point.

[0086] Step 120 : Using the inverse distance weighted interpolation method, interpolate the points to be interpolated in the target area according to the measured values ​​to obtain inverse distance weighted interpolation results of the points to be interpolated.

[0087] After obtaining the measured values ​​of the indicator objects measured at each sample point, the inverse distance weighted interpolation method can be used to perform inverse distance weighted interpolation on the points to be interpolated in the target area according to the measured values, thereby obtaining inverse distance weighted interpolation results of the points to be interpolated.

[0088] The points to be interpolated may refer to the grids other than the sample points in all grids after the aforementioned grid processing, that is, all grids other than the grids selected as sample points may be points to be interpolated.

[0089] In practical applications, for any point to be interpolated, the sample point closest to its geographical location has the greatest contribution, that is, the contribution is inversely proportional to the distance. This is also the origin of the name of the inverse distance weighted interpolation method. In inverse distance weighted interpolation, the sample weight is inversely proportional to the distance between the sample and the point to be interpolated, so this interpolation method is called inverse distance weighted interpolation.

[0090] The following specifically introduces the use of the inverse distance weighted interpolation method to interpolate the points to be interpolated in the target area according to the measured values ​​to obtain the inverse distance weighted interpolation results of the points to be interpolated:

[0091] For any point to be interpolated within the target area, inverse distance weighted interpolation can be performed in the following way:

[0092] Taking the point to be interpolated as the center, search for sample points within a preset radius, and determine a preset number of target sample points closest to the point to be interpolated from the searched sample points;

[0093] Based on the measured values ​​of the preset number of target sample points, an inverse distance weighted interpolation result of the to-be-interpolated point is obtained by calculating a weighted average value; wherein the weight of each target sample point in the calculated weighted average value is inversely proportional to the distance between the target sample point and the to-be-interpolated point.

[0094] In this embodiment, the preset number can be an empirical value, which can be configured according to the density. Figure 2 In the example of measured water-salt distribution shown in the figure, it has been experimentally verified that when the preset number is set to 12, the interpolation result is more ideal.

[0095] The following explanation assumes that the preset number is 12. After searching for the 12 closest sample points with the interpolation point as the center, the weighted average value can be calculated based on the measured values ​​of the 12 sample points using the following formula 1 to obtain the inverse distance weighted interpolation result of the interpolation point.

[0096]

[0097] Among them, Z is the inverse distance weighted interpolation result of the interpolation point, Z i is the measured value of the i-th (i=1,…,12) sample point, d i is the distance between the i-th sample point and the point to be interpolated, and p is the distance d i The power of .

[0098] It should be noted that Formula 1 is based on a preset number of 12 sample points. If the preset number n is another value, then the value 12 in Formula 1 will also be adjusted to the value n. Furthermore, the value of p can be an empirical value. Experimental verification shows that the interpolation results when p is greater than 2 are not much different from the interpolation results when p is 2. Therefore, to reduce the computational complexity, p can be set to 2.

[0099] In the aforementioned Figure 2 Based on the measured value of the water salt content shown in the figure, the above formula 1 can be used to calculate the following: Figure 3 Schematic diagram of water and salt distribution based on inverse distance weighted interpolation method. Figure 3 The left side shows the total salt content of the inverse distance weighted interpolation result. Figure 3 The water content of the inverse distance weighted interpolation result is shown on the right side.

[0100] Step 130: Using the IDW interpolation result as a covariate of a cokriging interpolation method, and using the cokriging interpolation method, performing secondary interpolation on the to-be-interpolated points in the target area according to the measured value and the covariate, to obtain a final predicted value of the to-be-interpolated point.

[0101] Co-Kriging interpolation is an improved Kriging interpolation method that is suitable for processing multiple related variables. Its core idea is to use the spatial information of co-variables (also called secondary variables) to improve the estimation of the primary variable (also called target variable) when constructing the interpolation model of the primary variable (also called target variable). The primary variable can represent the target variable to be predicted, and the co-variables can represent one or more variables that are spatially related to the primary variable. The co-variables usually have relatively complete observation data.

[0102] As mentioned above, the problem with the cokriging interpolation method is that the required covariates are difficult to obtain, or it is difficult to obtain suitable covariates, which leads to the poor applicability of the cokriging interpolation method.

[0103] Therefore, in this embodiment, the interpolation result of the inverse distance weighted method is used as the covariate of the cokriging interpolation method to help the cokriging interpolation better adapt to the spatial variation of the actual data, which can make up for the shortcomings of few measured sample points of the main variable and the instability of the variation function.

[0104] The following is a detailed description of the improved cokriging interpolation method provided by this embodiment: the inverse distance weighted interpolation result is used as a covariate of the cokriging interpolation method, and the cokriging interpolation method is used to perform secondary interpolation on the to-be-interpolated points in the target area based on the measured values ​​and the covariate, to obtain the final predicted value of the to-be-interpolated point:

[0105] Using the inverse distance weighted interpolation result as a covariate of the cokriging interpolation method, and using the final predicted value of the to-be-interpolated point as a main variable of the cokriging interpolation method;

[0106] In this embodiment, according to the basic principle of co-kriging interpolation, the spatial correlation between the measured value and other variables is taken into consideration. Assuming that the variable Z(x) is randomly distributed in the target area, the variable Z(x) represents the value at the spatial position x. 1i (i=1,2,3…,p) there are p observation values ​​Z(x 1i ), then the variable Z(x) can be regarded as the main variable of interpolation; at the same time, there is another variable T(x), which represents the interpolation at different spatial positions x 2j (j=1,2,3…,q) has corresponding observation value T(x 2j ), where T(x 2j) is the inverse distance weighted interpolation result obtained in the previous step 120.

[0107] Since T(x) covers the locations of all known sample points of the primary variable Z(x), it can meet the prerequisites for use as a collaborative variable. Ultimately, the prediction of the primary variable Z(x) at the spatial position x0 can be used to obtain the final predicted value of the interpolation point x0.

[0108] For example, the final predicted value of the point to be interpolated can be obtained by calculating the following formula 2:

[0109]

[0110] Among them, Z(x0) represents the final predicted value of any point to be interpolated,

[0111] Z(x 1i ) represents the spatial position x 1i p observations on T(x 2j ) represents the spatial position x 2j q observations on λ 1i The weight coefficient of the main variable, λ 2j is the weight coefficient of the co-variable, p is the number of observation points of the main variable, and q is the number of observation points of the co-variable.

[0112] Furthermore, the weight coefficient λ here 1i and λ 2j It can be obtained by solving the following equation:

[0113]

[0114] Among them, C ZZ is the covariance matrix between the main variables;

[0115] C ZY is the covariance matrix between the primary and secondary variables;

[0116] C YZ is the covariance matrix between the covariates and the main variables (with C ZY same);

[0117] C YY is the covariance matrix between the covariates;

[0118] C Z0 is the covariance vector between the main variable and the point to be interpolated;

[0119] C Y0 is the covariance vector between the covariate and the point to be interpolated;

[0120] By solving this equation, the weight coefficient λ can be obtained1i and λ 2j Once the weight coefficient is determined, the co-kriging interpolation method can be used to predict the predicted value of the main variable at any interpolation point. Specifically, the weight coefficient λ can be determined. 1i and λ 2j Substitute the above formula 2 to calculate the final predicted value of the main variable at the interpolation point x0.

[0121] In the aforementioned Figure 3 Based on the water-salt content obtained by the inverse distance weighted interpolation method shown in FIG, the above formula 2 can be used to calculate the following: Figure 4 Schematic diagram of water and salt distribution based on cokriging interpolation method. Figure 4 The left side shows the predicted value of total salt content predicted by the co-kriging interpolation method. Figure 4 The right side of the figure shows the predicted water content using the co-kriging interpolation method.

[0122] Through the above embodiment, the interpolation result of the inverse distance weighted method is used as a co-variable of the co-kriging interpolation method; on the one hand, local information can be introduced to help the co-kriging interpolation better adapt to the spatial variation of the actual data, which can make up for the shortcomings of the small number of measured sample points of the main variable and the instability of the variance function. On the other hand, the inverse distance weighted interpolation method can provide local and accurate interpolation results, and the extreme values ​​are largely retained during the interpolation process, while the co-kriging interpolation can take into account spatial correlation and provide more reliable estimates. Combining the two, the interpolation result greatly weakens the "bull's eye" phenomenon of the inverse distance weighted method interpolation. On the other hand, the inverse distance weighted interpolation result used as a co-variable is easier to obtain.

[0123] With the continuous development of machine learning technology, the efficiency and accuracy of spatial interpolation results can be effectively improved by training the prediction model of prediction indicator objects through machine learning technology.

[0124] In view of this, the present invention Figure 1 Based on the spatial interpolation method shown in Figure 5 A flowchart of another spatial interpolation method is shown in FIG.

[0125] like Figure 5 As shown, in Figure 1 Based on the steps 110 to 130 shown, step 140 may be further included after step 130:

[0126] The measured values ​​and predicted values ​​are input into a random forest model, and the random forest model is subjected to multiple model parameter tuning until the relationship between the measured values ​​and the predicted values ​​is fitted, and the random forest model after the model parameter tuning is further used as a prediction model for predicting the indicator object; the prediction model is used to predict the spatial distribution of the indicator object in the target area.

[0127] In this step, we select Random Forest as the machine learning model. Random Forest is a supervised learning algorithm based on an ensemble of decision trees. The basic principle of the Random Forest model is to construct multiple decision trees and then perform voting (for classification problems) or averaging (for regression problems) to make a final prediction. The core idea of ​​Random Forest is to increase model diversity, reduce overfitting, and improve model generalization by introducing randomness.

[0128] In this embodiment, according to the basic principles of the random forest model, m bootstrap sample sets can be extracted from the n original sample pairs consisting of the measured values ​​and the predicted values ​​in a playback manner, thereby constructing m regression trees; wherein, the original sample pairs that are not selected during the sampling process constitute m out-of-bag data sets.

[0129] For each node in each regression tree, k splitting variables are randomly selected from all p explanatory variables, and the optimal branch is selected based on the branching goodness criterion. Each regression tree is recursively branched in a top-down manner until the preset splitting stop condition is reached.

[0130] In the present disclosure, m bootstrap sample sets are extracted from the n original sample pairs consisting of the measured values ​​and the predicted values ​​in a replay manner. A statistical method (such as bootstrap) can be used for sampling with replacement. Sampling with replacement allows each regression tree to have different training data, thereby enhancing model diversity.

[0131] In the random forest model, "explanatory variables" and "split variables" are closely related to the model training and decision tree construction process.

[0132] Explanatory variables are input features used to predict the target variable (usually the response or dependent variable). Simply put, they are the independent variables that the model uses to make predictions. In each regression tree, the explanatory variables can be the underlying data used to split the node. Each tree selects a subset of all explanatory variables to split the node on. These explanatory variables are typically individual features in the dataset. Assuming there are p explanatory variables in the dataset, each tree in the random forest model will use these explanatory variables to split the node.

[0133] Splitting variables are randomly selected from a subset of the explanatory variables and are used to determine how each node is split. Specifically, when constructing a regression or classification tree, each node is split by selecting several (usually a small random subset) of the explanatory variables for the current node as candidate splitting variables. The optimal splitting variable is then selected to split the node, generating the next branch. When constructing each node in the tree, the splitting variables are randomly selected from a subset of all explanatory variables. This is done to increase model diversity and reduce correlations between trees, thereby improving the accuracy and robustness of the entire forest model. By randomly selecting splitting variables, random forests can avoid overfitting and improve prediction accuracy. Typically, it is assumed that k splitting variables (where k is a hyperparameter) are randomly selected from p explanatory variables for each node split. The optimal splitting variable for node splitting is then selected based on a criterion (such as information gain or the Gini index).

[0134] In the process of building each regression tree, for the splitting of each node, the random forest does not use all the features, but randomly selects some features for splitting, which can further improve the diversity of the model.

[0135] For regression tasks, the random forest algorithm averages the predictions from all m regression trees to produce a prediction. It then fine-tunes the model parameters by measuring the error between the predictions and the measured results, and repeats the process using the fine-tuned model parameters. This process continues through multiple iterations, gradually optimizing the model parameters and minimizing the error between the predictions and the measured values ​​until the model converges or reaches convergence conditions. Finally, the random forest model with the fine-tuned model parameters can be used as a prediction model to further predict the spatial distribution of indicator objects within the target area.

[0136] In the aforementioned Figure 2 The measured values ​​of the measured water salt content are shown as well as Figure 4 Based on the predicted value of the water-salinity content predicted by the co-kriging interpolation method, the prediction model optimized by the random forest in the embodiment shown in step 140 can predict the following: Figure 6 Schematic diagram of water-salt distribution shown. Figure 6 The left side shows the total salt content output by the prediction model. Figure 6 Shown on the right is the water content output by the prediction model.

[0137] In summary, the present invention proposes a new spatial interpolation scheme, which uses the interpolation results of the inverse distance weighted method as a covariate of the cokriging interpolation method. On the one hand, local information can be introduced to help the cokriging interpolation better adapt to the spatial variation of the actual data, which can make up for the shortcomings of the small number of measured sample points of the main variable and the instability of the variogram. On the other hand, the inverse distance weighted interpolation method can provide local and accurate interpolation results, and the extreme values ​​are largely retained during the interpolation process, while the cokriging interpolation can take into account spatial correlation and provide more reliable estimates. Combining the two, the interpolation result greatly weakens the "bull's eye" phenomenon of the inverse distance weighted method interpolation. On the other hand, the inverse distance weighted interpolation result used as a covariate is easier to obtain. On the other hand, the use of random forest optimization can improve the accuracy of the interpolation result.

[0138] Corresponding to the above-mentioned spatial interpolation method embodiment, this specification also provides an embodiment of a spatial interpolation device. The device embodiment can be implemented by software, or by hardware or a combination of software and hardware. Taking software implementation as an example, as a device in a logical sense, it is formed by the processor of the device in which it is located reading the corresponding computer program in the non-volatile memory into the memory and running it. From the hardware level, if Figure 7 The figure shows a hardware structure diagram of the device where the spatial interpolation device is located in this manual, except Figure 7 In addition to the processor, network interface, memory and non-volatile memory shown, the device in which the apparatus in the embodiment is located may also include other hardware according to the actual communication function, which will not be described in detail.

[0139] See Figure 8 , is a module diagram of a spatial interpolation device provided in an embodiment of this specification, and the device corresponds to Figure 1 In the embodiment shown, the device comprises:

[0140] The measuring unit 810 obtains the measured values ​​of the indicator object measured at a plurality of sample points located in the target area;

[0141] An inverse distance weighted interpolation unit 820 uses an inverse distance weighted interpolation method to interpolate the points to be interpolated in the target area according to the measured values ​​to obtain inverse distance weighted interpolation results of the points to be interpolated;

[0142] The co-kriging interpolation unit 830 uses the inverse distance weighted interpolation result as a co-variable of the co-kriging interpolation method, and uses the co-kriging interpolation method to perform secondary interpolation on the to-be-interpolated points in the target area according to the measured value and the co-variable to obtain a final predicted value of the to-be-interpolated point.

[0143] Optionally, the device further includes:

[0144] The model prediction unit 840 inputs the measured value and the predicted value into the random forest model, and the random forest model is subjected to multiple model parameter tuning until the relationship between the measured value and the predicted value is fitted, and the random forest model after the model parameter tuning is further used as a prediction model for predicting the indicator object; the prediction model is used to predict the spatial distribution of the indicator object in the target area.

[0145] Optionally, the measuring unit 810 includes:

[0146] The gridding subunit 811 performs gridding processing on the target area and uniformly selects a number of grids from all grids obtained by the gridding processing as sample points;

[0147] The measurement subunit 813 performs measurement of the indicator object on the sample points to obtain the measured value of the indicator object at each sample point.

[0148] Optionally, the gridding subunit 811 is further configured to use all grids except the sample points as points to be interpolated.

[0149] Optionally, the inverse distance weighted interpolation unit 820 includes:

[0150] For any point to be interpolated within the target area, inverse distance weighted interpolation is performed by the following search subunit 821 and calculation subunit 823:

[0151] The search subunit 821 searches for sample points within a preset radius with the point to be interpolated as the center, and determines a preset number of target sample points closest to the point to be interpolated from the searched sample points;

[0152] The calculation subunit 823 obtains an inverse distance weighted interpolation result of the to-be-interpolated point by calculating a weighted average value based on the measured values ​​of the preset number of target sample points; wherein the weight of each target sample point in the calculated weighted average value is inversely proportional to the distance between the target sample point and the to-be-interpolated point.

[0153] Optionally, the preset number is 12.

[0154] Optionally, the cokriging interpolation unit 830 is further configured to use the IDW interpolation result as a covariate of the cokriging interpolation method, use the final predicted value of the point to be interpolated as a main variable of the cokriging interpolation method, and calculate the final predicted value of the point to be interpolated using the following formula:

[0155]

[0156] Among them, Z(x0) represents the final predicted value of the interpolation point at position x0, Z(x 1i ) means at position x 1i p observations, T(x2j) represents the p observations at position x 2j The q inverse distance weighted interpolation results, λ 1i and λ 2j is the weight coefficient.

[0157] Optionally, the model prediction unit 840 includes:

[0158] The sampling subunit 841 extracts m bootstrap sample sets from the n original sample pairs consisting of the measured values ​​and the predicted values ​​in a playback manner, thereby constructing m regression trees of the random forest model; wherein the original sample pairs not selected during the sampling process constitute m out-of-bag data sets;

[0159] The branching subunit 843 randomly selects k splitting variables from all p explanatory variables for each node on each regression tree, and selects the next branch from them based on the branching goodness criterion; each regression tree recursively performs branching operations in a top-down manner until the preset splitting stop condition is reached.

[0160] Optionally, the indicator objects include at least one of the air quality of the target area, surface plant coverage, surface water and salt content, and underground mineral content.

[0161] Optionally, the water-salt content includes water content and total salt content.

[0162] The systems, devices, modules, or units described in the above embodiments may be implemented by computer chips or entities, or by products having certain functions. A typical implementation device is a computer, which may be in the form of a personal computer, laptop computer, cellular phone, camera phone, smartphone, personal digital assistant, media player, navigation device, email transceiver, game console, tablet computer, wearable device, or any combination of these devices.

[0163] The implementation process of the functions and effects of each unit in the above-mentioned device is specifically described in the implementation process of the corresponding steps in the above-mentioned method, and will not be repeated here.

[0164] For the device embodiments, since they basically correspond to the method embodiments, the relevant parts can be referred to the partial description of the method embodiments. The device embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this specification. A person of ordinary skill in the art can understand and implement it without paying any creative work.

[0165] Corresponding to the aforementioned embodiment of the spatial interpolation method, this specification also provides an autonomous underwater vehicle, on which an autonomous unmanned system is deployed, and the autonomous unmanned system implements the aforementioned spatial interpolation method.

[0166] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on the differences from other embodiments.

[0167] Other embodiments of the present invention will readily occur to those skilled in the art after considering the specification and practicing the invention disclosed herein. This specification is intended to cover any variations, uses, or adaptations of the present invention that follow the general principles of this specification and include common knowledge or customary techniques in the art not disclosed herein. The description and examples are to be considered as exemplary only, with the true scope and spirit of the present invention being indicated by the following claims.

[0168] It should be understood that the present description is not limited to the exact structure that has been described above and shown in the drawings, and that various modifications and changes may be made without departing from the scope thereof. The scope of the present description is limited only by the appended claims.

Claims

1. A spatial interpolation method, characterized in that: The method comprises: Obtaining measured values ​​of indicator objects measured at a plurality of sample points in a target area; wherein the indicator objects include at least one of air quality, surface plant coverage, surface water salt content, and underground mineral content in the target area; Using the inverse distance weighted interpolation method, interpolating the to-be-interpolated points in the target area according to the measured values, to obtain inverse distance weighted interpolation results of the to-be-interpolated points; The inverse distance weighted interpolation result is used as a covariate of the cokriging interpolation method, and the cokriging interpolation method is used to perform secondary interpolation on the to-be-interpolated points in the target area according to the measured value and the covariate to obtain the final predicted value of the to-be-interpolated point.

2. The method according to claim 1, further comprising: The measured values ​​and predicted values ​​are input into a random forest model, and the random forest model is subjected to multiple model parameter tuning until the relationship between the measured values ​​and the predicted values ​​is fitted, and the random forest model after the model parameter tuning is further used as a prediction model for predicting the indicator object; the prediction model is used to predict the spatial distribution of the indicator object in the target area.

3. The method according to claim 1, characterized in that The obtaining of the measured values ​​of the indicator objects measured at each sample point in the target area includes: Perform gridding processing on the target area, and evenly select several grids from all the grids obtained by gridding processing as sample points; The indicator object is measured at the sample points to obtain the measured value of the indicator object at each sample point.

4. The method according to claim 3, characterized in that After performing gridding processing on the target area and uniformly selecting a number of grids from all grids obtained by the gridding processing as sample points, the method further includes: All grids except the sample points are used as points to be interpolated.

5. The method according to claim 1, wherein The method of using the inverse distance weighted interpolation method to interpolate the points to be interpolated in the target area according to the measured values ​​to obtain inverse distance weighted interpolation results of the points to be interpolated includes: For any point to be interpolated within the target area, inverse distance weighted interpolation is performed in the following way: Taking the point to be interpolated as the center, search for sample points within a preset radius, and determine a preset number of target sample points closest to the point to be interpolated from the searched sample points; Based on the measured values ​​of the preset number of target sample points, an inverse distance weighted interpolation result of the to-be-interpolated point is obtained by calculating a weighted average value; wherein the weight of each target sample point in the calculated weighted average value is inversely proportional to the distance between the target sample point and the to-be-interpolated point.

6. The method according to claim 5, characterized in that The preset number is 12.

7. The method according to claim 1, characterized in that The method uses the inverse distance weighted interpolation result as a covariate of the cokriging interpolation method, and uses the cokriging interpolation method to perform secondary interpolation on the to-be-interpolated points in the target area according to the measured value and the covariate to obtain the final predicted value of the to-be-interpolated point, including: The inverse distance weighted interpolation result is used as the covariate of the cokriging interpolation method, the final predicted value of the point to be interpolated is used as the main variable of the cokriging interpolation method, and the final predicted value of the point to be interpolated is calculated by the following formula: Among them, Z(x0) represents the final predicted value of the interpolation point at position x0, Z(x 1i ) means at position x 1i p observations, T(x 2j ) means at position x 2j The q inverse distance weighted interpolation results, λ 1i and λ 2j is the weight coefficient.

8. The method according to claim 2, characterized in that The inputting of the measured value and the predicted value into the random forest model, and the random forest model undergoing multiple model parameter tuning until the relationship between the measured value and the predicted value is fitted, includes: Extracting m bootstrap sample sets from the n original sample pairs consisting of the measured values ​​and the predicted values ​​in a playback manner, thereby constructing m regression trees of the random forest model; wherein the original sample pairs not selected during the sampling process constitute m out-of-bag data sets; For each node on each regression tree, k split variables are randomly selected from all p explanatory variables, and the next branch is selected from them based on the branching goodness criterion; Each regression tree recursively branches from top to bottom until the preset segmentation stop condition is reached.

9. The method according to claim 8, characterized in that The water-salt content includes water content and total salt content.

10. A spatial interpolation device, characterized in that: A measurement unit is configured to obtain measured values ​​of an indicator object measured at a plurality of sample points in a target area; wherein the indicator object includes at least one of the following: air quality, surface plant coverage, surface water salt content, and underground mineral content in the target area; an inverse distance weighted interpolation unit, which uses an inverse distance weighted interpolation method to interpolate the to-be-interpolated points in the target area according to the measured values, and obtains an inverse distance weighted interpolation result of the to-be-interpolated points; The co-kriging interpolation unit uses the inverse distance weighted interpolation result as a co-variable of the co-kriging interpolation method, and uses the co-kriging interpolation method to perform secondary interpolation on the to-be-interpolated points in the target area according to the measured value and the co-variable, so as to obtain a final predicted value of the to-be-interpolated point.

11. An electronic device, characterized in that: include: processor; a memory for storing processor-executable instructions; The processor is configured to execute the method according to any one of claims 1 to 9.

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