Intelligent river system grading method and device based on feature local standardization

Abstract

The invention belongs to the technical field of computer map making, and particularly relates to an intelligent river system grading method and device based on feature local standardization, and the method comprises the steps: carrying out the preprocessing and river reach feature extraction according to an existing river system grading result; secondly, carrying out local standardization on river reach features; then inputting into a random forest classification model for supervised learning, and applying the trained model to main and branch classification of an ungraded river system; and finally, establishing a hierarchical tree of the river system according to a main and branch flow classification result, and assigning hierarchical codes to the river reach in the river system. According to the method, the automatic river system grading effect and the manual grading result have high consistency, high-quality automatic river system grading can be achieved, and further applications such as river system selection and drawing expression are supported.

Description

Technical Field

[0001] The present invention belongs to the technical field of computer map making, and in particular relates to a method and device for intelligently classifying river systems based on local standardization of features. Background Art

[0002] River system classification is the key point of river system selection (Tan Xiao, Wu Fang, Huang Qi, et al. Multi-criteria decision model for mainstream identification and its application in river system structuring [J]. Acta Geodaetica et Cartographica Sinica, 2005, 34(2):154-160.DOI:10.3321 / j.issn:1001-1595.2005.02.012.). Common classification methods include Horton classification (HORTON RE. Erosional development of streams and their drainage basins; hydrophysical approach to quantitative morphology [J]. Geological Society of America bulletin, 1945, 56(3):275-370.DOI:10.1130 / 0016-7606(1945)56[275:EDOSAT]2.0.CO;2.), Strahler classification (STRAHLER A N. Quantitative analysis of Watershed geomorphology[J]. Eos, Transactions American Geophysical Union, 1957, 38(6): 913-920. DOI: 10.1029 / TR038i006p00913.), Shreve classification (SHREVE R L. Statistical law of stream numbers[J]. The Journal of Geology, 1966, 74(1): 17-37.), etc. Compared with the simple increase in the hierarchy starting from the terminal tributary, the Horton classification defines the smallest unbranched tributary as the first level and the mainstream as the highest level to establish the hierarchy of the river system, which has become the main principle of the current river system classification. The difficulty lies in how to accurately identify the mainstream.Therefore, relevant scholars have carried out a lot of research. Tan Xiao et al. (2005) determined the mainstream of river systems at all levels by the maximum membership weighted average programming method in the multi-criteria decision-making model; Zhang Yuanyu et al. (Zhang Yuanyu, Li Lin, Jin Yuping, et al. Research on the structured drawing model of tree-like river system based on graph theory [J]. Journal of Wuhan University (Information Science Edition), 2004, 29(6): 537-539, 543.), Zhai Renjian et al. (Zhai Renjian, Xue Benxin. Research on the structured model of river system for automatic integration [J]. Journal of Surveying and Mapping Science and Technology, 2007, 24(4): 294-298, 302.) and Guo Qingsheng et al. (Guo Qingsheng, Huang Yuanlin. Automatic reasoning of the mainstream of tree-like river system [J]. Journal of Wuhan University (Information Science Edition), 2008, 33(9): 978-981.) based on the 180° hypothesis (PAIVA J, EGENHOFER MJ, FRANK AU. Spatial reasoning about flow directions: toward an ontology for river networks [C] / / Proceedings of the XV International Congress for Photogrammetry and Remote Sensing. [S.1.]: ISPRS, 1992: 224-318.), respectively based on different judgment rules, identify the mainstream of the river system through river section search. Li Chengming et al. (Li Chengming, Yin Yong, Wu Wei, et al. Construction and simplification of tree-like river system hierarchical relationship based on stroke feature constraints [J]. Acta Geodaetica et Cartographica Sinica, 2018, 47(4): 537-546. DOI: 10.11947 / i.AGCS.2018.20170141.) based on the "good continuity" principle of stroke (THOMSON RC, RICHARDSON DE. The'good continuation'principle of perceptual organization applied to the generalization of road networks [C] / / Proceedings of the ICA 19th international cartographic conference. Ottawa, Canada, 1999: 1215-1223.), judged the hierarchical relationship of river systems through stroke connections. The above method mainly starts from the local structure of the river system, and it is difficult to fit all situations through the defined rules.To this end, Duan Peixiang et al. (Duan Peixiang, Qian Haizhong, He Haiwei, et al. Naive Bayesian tree-based river system automatic classification method based on case studies [J]. Acta Geodaetica et Cartographica Sinica, 2019, 48(8): 975-984. DOI: 10.11947 / j.AGCS.2019.20180370.) used the naive Bayesian algorithm to avoid the problem of subjective setting of indicator weights through case studies of existing river system classification results. However, the influence of river basin characteristics was not considered in feature selection. Summary of the invention

[0003] The present invention proposes a method and device for intelligent classification of river systems based on local feature standardization. The method of the present invention focuses on the automation and intelligence of river system classification to better meet the requirements of applications such as river system selection and cartographic expression. First, six river section feature indicators are designed based on the geometric structure of the river section and the local and overall structure of the river system. After the river section features are locally standardized, they are input into the random forest algorithm for supervised training. The unclassified river system is input into the trained model, and a hierarchical tree of the river system is established according to the classification results of the main tributaries, and hierarchical codes are assigned to the river sections in the river system.

[0004] In order to achieve the above object, the present invention provides the following technical solutions:

[0005] The intelligent river system classification method based on local standardization of features includes the following steps:

[0006] S1: Based on the existing river system classification results, topological preprocessing and river section feature extraction are performed;

[0007] The river section characteristics are the characteristics of the main tributaries of the river section, which are selected based on the geometry of the river section and the local and overall structure of the river system, including 6 river section characteristic indicators:

[0008] (1) Length of the river section (RLen). It indicates the length of the current river section. The larger the RLen value, the greater the probability that it is the mainstream river section.

[0009] (2) Upstream reach length (URL). This indicates the total length of the current river section and its upstream reaches. The larger the URL value, the greater the probability that it is a mainstream river section.

[0010] (3) River section drainage area (WArea). It refers to the area of ​​the area that the current river section flows through. WArea reflects the influence range of the river section. The larger the area, the greater the probability that it is a mainstream river section.

[0011] (4) Upstream Rise Area (URA). It refers to the total area of ​​the area that the current river section and its upstream river sections flow through. URA reflects the influence range of the river section and its upstream river sections. The larger the area, the greater the probability that it is a mainstream river section.

[0012] (5) Confluence angle (BAngle). It indicates the confluence angle between the upstream and downstream river sections. The closer the BAngle is to 180°, the greater the probability that it is a mainstream river section.

[0013] (6) Number of tributaries of a river section (TNum). This indicates the number of upstream river sections of the current river section. The larger the TNum value, the greater the probability that it is a mainstream river section.

[0014] S2: Locally standardize the river section characteristics; local standardization of river section characteristics is to use the maximum value of the characteristics of the adjacent upstream river section to standardize the characteristics, specifically, the characteristic value of the current river section is divided by the maximum value of the characteristics of the adjacent upstream river section.

[0015] Among them, the three types of features, URL, URA and TNum, are locally standardized, while the three types of features, RLen, WArea and Bangle, are standardized using Min-Max.

[0016] S3: Input the river section characteristics obtained after local standardization into the random forest classification model for supervised learning, and apply the trained model to the classification of main tributaries in unclassified river systems; the application means that after the training is completed, the trained model is used to identify the main tributaries of each river section in the river system to be classified, and derive the mainstream probability of each river section.

[0017] S4: Identification and hierarchical structuring of river system mainstream: handle abnormal situations according to the mainstream probability, and then use the main tributary classification results to establish a river system hierarchy tree and assign hierarchical codes to the river sections in the river system.

[0018] The specific operation of handling anomalies is: compare the mainstream probability of each river section, select the river section with the optimal value as the mainstream, and set the remaining river sections as tributaries.

[0019] The specific operations of assigning hierarchical codes are as follows:

[0020] S4.1: Create a new hierarchical tree root node to store the river section ID of the river system estuary, and start from the river section and the tree node where it is located to execute S4.2; after all the main tributaries of the river section are judged and the hierarchical tree is constructed, execute S4.4.

[0021] S4.2: Determine the type of the upstream river section of the river section. If the upstream river section is a mainstream river section, store its ID in the tree node and continue to execute S4.2; otherwise, execute S4.3.

[0022] S4.3: Create a new hierarchical tree child node, which stores the river section ID and continues to determine its upstream river section. If the upstream river section is the mainstream river section, continue to execute S4.2; otherwise, execute S4.3.

[0023] S4.4: According to the hierarchical classification of the hierarchical tree, the river sections contained in all leaf nodes are assigned a value of 1, the river sections contained in the upper-level tree nodes are assigned a value of 2, and so on; the river sections contained in the root node are assigned the highest level, and the hierarchical code of the river system is established.

[0024] The present invention also provides a river system intelligent classification device based on local standardization of features, comprising:

[0025] The river section feature extraction module is used to extract the characteristics of six river sections and perform local standardization of the river section features based on the existing river system classification results.

[0026] The training prediction module is used to input the standardized river section characteristics and main tributary labels into the random forest classification model for supervised training. After the training is completed, the trained model is used to identify the main tributaries of each river section in the river system to be classified, and the mainstream probability of each river section is derived.

[0027] The result output module is used to process abnormal situations according to the mainstream probability, and then use the main tributary classification results to establish a river system hierarchy tree and assign hierarchical codes to the river sections in the river system.

[0028] The present invention also provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the method for intelligent river system classification based on local standardization of features is implemented.

[0029] The present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the method for intelligent classification of river systems based on local standardization of features is implemented.

[0030] The present invention has the following beneficial effects:

[0031] The river system intelligent classification results of the present invention have a high consistency with the manual classification results, and can achieve high-quality automatic classification of river systems. Compared with other machine learning algorithms, the automatic classification of river systems supported by the random forest algorithm has higher accuracy and better classification effect, and the local standardization of features can significantly improve the classification accuracy of the machine learning algorithm; compared with other river system classification methods, the river system classification results of the present invention can improve the accuracy of river system selection. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] Figure 1 Flowchart for the implementation of an intelligent classification method for river systems based on local normalization of features.

[0033] Figure 2 This is a schematic diagram of the river section characteristics.

[0034] Figure 3This is a schematic diagram of the confluence angle calculation.

[0035] Figure 4 Construct a river system hierarchy tree and assign hierarchical codes.

[0036] Figure 5 This is the test data for automatic classification of river systems.

[0037] Figure 6 Mainstream classification results of different standardization methods.

[0038] Figure 7 The mainstream identification results of river system 9 using different standardization methods.

[0039] Figure 8 The feature importance results of different normalization methods.

[0040] Fig. 9 Mainstream classification results of different machine learning algorithms. DETAILED DESCRIPTION

[0041] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution in the embodiment of the present invention will be clearly described below in conjunction with the drawings in the embodiment of the present invention. Obviously, the described embodiment is a part of the embodiment of the present invention, not all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0042] Example 1

[0043] The present invention firstly designs 6 river section characteristic indicators as the characteristics of main tributary classification based on the geometric structure of the river section and the local and overall structure of the river system; secondly, in view of the differences in the numerical range and dimension of different characteristics of the river section, a local standardization processing strategy of the river section characteristics is introduced; then, 6 characteristics of the samples in the existing river system classification results are extracted, and after local standardization processing, they are input into the random forest algorithm for supervised learning, and the trained model is applied to the main tributary classification of the unclassified river system; finally, abnormal situations are processed according to the main tributary classification probability, and the river system hierarchy tree is established using the main tributary classification results, and the hierarchical codes are assigned to the river sections in the river system.

[0044] The implementation process of intelligent river system classification based on local standardization of characteristics is as follows: Figure 1 As shown, the specific implementation process includes:

[0045] S1: Based on the existing river system classification results, perform topological preprocessing and river segment feature extraction

[0046] The topology preprocessing is to use GIS software to perform topology checking, and then manually edit and process to ensure that the river systems are connected (that is, there is no suspension or disconnection between the river systems).

[0047] Feature selection is an important step in applying machine learning methods. Selecting effective features can improve the accuracy of the model, help to mine implicit main and tributary classification knowledge, and obtain a reliable main and tributary classification model. According to the three laws of river system composition proposed by Horton: river system length law, river system area law and river system number law, the length, number, basin area, etc. of the river system change geometrically with the level of the river system, that is, the mainstream should meet the requirements of length priority, basin area priority and tributary number priority relative to the tributaries. In addition, according to the 180° hypothesis (PAIVA J, EGENHOFER MJ, FRANK AU. Spatial reasoning about flow directions: toward an ontology for river networks [C] / / Proceedings of the XV International Congress for Photogrammetry and Remote Sensing. [S.1.]: ISPRS, 1992: 224-318.), the flow direction of the upstream and downstream sections of the mainstream confluence point is almost in a straight line, so the upstream and downstream sections of the mainstream should meet the direction consistency. In summary, based on the geometric structure of the river section and the local and overall structure of the river system, six indicators are selected as the characteristics of the main tributary classification, including:

[0048] (1) Length of the river section (RLen). It indicates the length of the current river section. The larger the RLen value, the greater the probability that it is the mainstream river section.

[0049] (2) Upstream reach length (URL). This indicates the total length of the current river section and its upstream reaches. The larger the URL value, the greater the probability that it is a mainstream river section.

[0050] (3) River section drainage area (WArea). It refers to the area of ​​the area that the current river section flows through. Figure 2 As shown, river section R i The drainage area is WArea i , River R j The drainage area is WArea j , River R k The drainage area is WArea kWArea reflects the influence range of a river section. The larger the area, the greater the probability that it is a mainstream river section. Considering that the spatial density differences between river sections lead to changes in the spatial morphology of the river network, the river section watershed unit is constructed using the constrained Delaunay triangulation and skeleton line connection method (Ai Tinghua, Liu Yaolin, Huang Yafeng. Hierarchical triangulation and map synthesis of watershed areas [J]. Acta Geodaetica et Cartographica Sinica, 2007, 36(2): 231-236, 243.).

[0051] (4) Upstream drainage area (URA). It refers to the total area of ​​the area flowed by the current river section and its upstream river sections. Figure 2 As shown, river section R j The total area of ​​the area through which the upstream river flows (i.e. the upstream basin area) is URA. j , River R k The total area of ​​the area through which the upstream river flows (i.e. the upstream basin area) is URA. k URA reflects the influence range of a river section and its upstream sections. The larger the area, the greater the probability that it is a mainstream river section.

[0052] (5) Confluence angle (BAngle). It indicates the confluence angle between the upstream and downstream river sections. Figure 3 As shown, river section R i The confluence angle is calculated as the angle with the downstream river section R k BAngle i The closer the BAngle is to 180°, the greater the probability that it is the mainstream river section. Figure 3 The arrows in the middle indicate the flow direction.

[0053] (6) Number of tributaries of a river section (TNum). This indicates the number of upstream river sections of the current river section. The larger the TNum value, the greater the probability that it is a mainstream river section.

[0054] S2: Local standardization of river section characteristics

[0055] There are differences in the numerical range and dimension of different characteristics of river sections. In order to facilitate data comparison, ensure better convergence of the model and prevent overfitting, it is usually necessary to use standardization methods to eliminate the differences between characteristics. Commonly used data standardization methods include Z-Score standardization, Min-Max standardization, etc. They mainly perform standardization from the perspective of the overall data. In order to avoid the overall standardization weakening the correlation between the characteristics of the upstream river section, the local standardization of river section characteristics is introduced for optimization.

[0056] The local standardization of river section characteristics is to use the maximum value of the characteristics of the adjacent upstream river section to standardize the characteristics. Figure 2 As shown, taking the characteristic URA as an example, the river section R iThe adjacent upstream river sections are river sections R j and R k , River R j and R k The upstream basin areas are URA j and URAk, then URA j The local standardization is completed by dividing URAj and URAk by max(URAj, URAk). The local and overall structural features of the three types of river systems, URL, URA and TNum, are locally standardized, while the three types of features, RLen, WArea and Bangle, are standardized using Min-Max to obtain the final standardized results of river system features for training and learning.

[0057] S3: Training and application of river system main tributary classification model

[0058] The main tributary relationship of a river system is included in the main tributary relationship of a river section. The determination of the main tributary relationship of a river section is the basis for realizing the automatic classification of a river system. Therefore, the problem of automatic classification of a river system can be transformed into a binary classification problem of identifying the main tributaries of a river section. In order to avoid the shortcomings of the traditional rule-based main tributary identification method in the subjective setting of the indicator weights, the present invention introduces a machine learning algorithm to acquire the ability to classify the main tributaries by learning the existing river system hierarchical classification results, and applies it to the automatic classification of the river system to be classified. Random forest is an integrated learning model based on the Bagging (Bootstrap Aggregating) strategy. Based on the decision tree, the prediction values ​​of multiple decision trees are integrated to obtain the final prediction result. Random forest has the characteristics of easy implementation, low computational overhead, excellent classification performance, and good at processing a large number of samples and features. Therefore, the random forest algorithm is used as the basis of the classification and identification model of the main tributaries of the river system.

[0059] By extracting the characteristics of six river sections from the existing river system classification results and applying local standardization of the river section characteristics, the standardized river section characteristics and main tributary labels are input into the random forest classification model for supervised training. After the training is completed, the trained classification model is used to identify the main tributaries of each river section in the river system to be classified, and the mainstream probability of each river section is derived.

[0060] S4: Identification and hierarchical structuring of river system mainstreams

[0061] After the classification of the main and tributary streams of a river section is completed, there may be a situation where there is no mainstream stream upstream of the river section or there are multiple mainstream streams at the same time, which is inconsistent with the objective reality. In this case, the mainstream probability of each river section is compared, and the river section with the optimal value is selected as the mainstream, and the remaining river sections are set as tributaries (such as Figure 4 (as shown in (a)).

[0062] After the mainstream of the river system is identified, it is necessary to establish a hierarchical tree (HTree) of the river system based on the relationship between the main and tributary rivers. Then, the HTree generates a hierarchical code (RFCoding, random forest coding) for each river section in the river system. The specific steps are as follows:

[0063] S4.1: Create a new HTree root node to store the river section ID of the river system estuary, and start from the river section and the tree node where it is located to execute S4.2; after all the main tributaries of the river section are judged, the HTree tree is constructed (such as Figure 4 (b)), execute S4.4.

[0064] S4.2: Determine the type of the upstream river section of the river section. If the upstream river section is a mainstream river section, store its ID in the tree node and continue to execute S4.2; otherwise, execute S4.3.

[0065] S4.3: Create a new HTree tree child node, which stores the river section ID and continues to determine its upstream river section. If the upstream river section is the mainstream river section, continue to execute S4.2; otherwise, execute S4.3.

[0066] S4.4: According to the hierarchical classification of the HTree tree, the river sections contained in all leaf nodes are assigned a value of 1, the river sections contained in the upper level tree nodes are assigned a value of 2, and so on; the river sections contained in the root node are assigned the highest level, and the RFCoding code of the river system is established (such as Figure 4 (c)).

[0067] Example 2

[0068] In order to verify the effectiveness and reliability of the present invention, experiments were conducted on the classification effects of main and tributary rivers using different standardization methods and different machine learning methods. The experimental data were selected from the river systems of 10 watershed units (such as Figure 5 As shown in the figure, main tributary samples were manually marked, and 5 of them were selected as the existing river system classification results to input the classification model for training. Another 5 were selected as the river systems to be classified to input the trained classification model for testing. The training data contained 2882 samples and the test data contained 4037 samples.

[0069] Experiment 1 selection Figure 5 The river section characteristics are calculated using the data from the random forest classification model. The main tributary samples are then generated using the Min-Max standardization, Z-Score standardization, and the local standardization method of the present invention. The samples are then input into the random forest classification model for training and testing (the classification results are shown in Figure 2). Figure 6As shown). It can be seen from the analysis that Min-Max standardization and Z-Score standardization perform overall standardization on the data. From the classification results, the difference between the two is small; compared with Min-Max standardization and Z-Score standardization, two commonly used feature standardization methods, the local standardization of the present invention has the best test set classification accuracy and mainstream recognition accuracy.

[0070] like Figure 7 As shown, river system 9 in the test data is selected, and its mainstream identification and hierarchical structured results are visually analyzed: the manual classification of river system 9 only includes 5 levels, while the classification results through Min-Max standardization and Z-Score standardization include 9 levels. The river system levels have increased significantly, which is obviously inconsistent with the manual classification results; secondly, from the perspective of the highest-level mainstream, the mainstream they identify does not consider the length priority, basin area priority and tributary number priority enough. The highest-level mainstream is shorter, and the controlled basin area and tributaries are smaller. In comparison, the number of levels divided by local standardization and the highest-level mainstream identification results of the present invention are consistent with the manual classification results, and the mainstream identification results of the other levels are approximately consistent, achieving a better mainstream identification result.

[0071] like Figure 8 As shown in the figure, the influence of different standardization methods on the importance of each feature in the random forest model is compared. Min-Max standardization and Z-Score standardization rely more on the feature Bangle, and insufficiently consider the features URL, URA, and TNum, which is consistent with the conclusion of visual analysis. The local standardization of the present invention pays more attention to the features URL and URA, while taking into account the features Bangle and TNum, and takes into account the length priority, watershed area priority, tributary number priority, and direction priority more comprehensively.

[0072] Experiment 2 compares the effects of different machine learning algorithms on the identification results of the main tributaries of the river system. Gaussian distribution naive Bayes and decision tree are selected as comparison algorithms. The training data are all processed by the local standardization of the features of the present invention. The experimental results are as follows: Fig. 9 As shown in the figure, both the Gaussian distribution naive Bayes and decision tree algorithms can obtain high test set classification accuracy, but after the main tributary identification process, their accuracy has decreased to a certain extent, indicating that there is a deviation in the probability of mainstream classification between the two; in comparison, the random forest method has the highest test set classification accuracy, and after the main tributary identification process, the main tributary identification accuracy is further improved.

[0073] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A river system intelligent classification method based on local standardization of features, characterized by: The following steps are involved: S1: Based on the existing river system classification results, topological preprocessing and river section feature extraction are performed; S2: Local standardization of river section characteristics; local standardization of river section characteristics is to divide the characteristic value of the current river section by the maximum characteristic value of the adjacent upstream river section; S3: Input the river segment characteristics obtained after local normalization into the random forest classification model for supervised learning, and apply the trained model to the classification of main tributaries of unclassified river systems; S4: Identification and hierarchical structuring of river system mainstream: handle abnormal situations according to the mainstream probability, and then use the main tributary classification results to establish a river system hierarchy tree and assign hierarchical codes to the river sections in the river system.

2. The method for intelligent river system classification based on local standardization of features according to claim 1 is characterized in that: In step S1, the river section characteristics include: (1) Length of river section RLen , indicating the length of the current river section; (2) Length of upstream river section URL , represents the total length of the current river section and its upstream river section; (3) River section drainage area WArea , indicating the area through which the current river section flows; (4) Upstream watershed area URA , represents the total area of ​​the area flowed by the current river section and its upstream river sections; (5) Confluence angle BAngle , represents the confluence angle between the upstream and downstream reaches; (6) Number of tributaries in a river section TNum , indicating the number of upstream river sections of the current river section.

3. The method for intelligent river system classification based on local standardization of features according to claim 1 is characterized in that: In step S2, URL , URA and TNum The three types of features are locally normalized. RLen , WArea and Bangle The three types of features are processed using Min-Max normalization.

4. The method for intelligent river system classification based on local standardization of features according to claim 1 is characterized in that: In step S3, the application refers to using the trained model to identify the main tributaries of each river section in the river system to be classified after the training is completed, and deriving the mainstream probability of each river section.

5. The method for intelligent river system classification based on local standardization of features according to claim 1 is characterized in that: In step S4, the operation of handling the abnormality is: comparing the mainstream probability of each river section, selecting the river section with the optimal value as the mainstream, and setting the remaining river sections as tributaries.

6. The method for intelligent river system classification based on local standardization of features according to claim 1 is characterized in that: In step S4, the operation of assigning the level code is: S4.1: Create a new hierarchical tree root node to store the river section ID of the river system estuary, and start from the river section and the tree node where it is located, and execute S4.2; after all the main tributaries of the river section are judged and the hierarchical tree is constructed, execute S4.4; S4.2: Determine the type of the upstream river section of the river section. If the upstream river section is a mainstream river section, store its ID in the tree node and continue to execute S4.2; Otherwise, execute S4.3; S4.3: Create a new hierarchical tree child node, which stores the river section ID and continues to determine its upstream river section. If the upstream river section is the mainstream river section, continue to execute S4.2; Otherwise, execute S4.3; S4.4: According to the hierarchical classification of the hierarchical tree, the river sections contained in all leaf nodes are assigned a value of 1, the river sections contained in the upper-level tree nodes are assigned a value of 2, and so on; the river sections contained in the root node are assigned the highest level, and the hierarchical code of the river system is established.

7. A river system intelligent classification device based on local standardization of features, characterized in that: include: River section feature extraction module, which is used to extract river section features and perform local standardization of river section features based on existing river system classification results; The training prediction module is used to input the standardized river section characteristics and main tributary labels into the random forest classification model for supervised training; the trained model is used to identify the main tributaries of each river section in the river system to be classified, and derive the mainstream probability of each river section; The result output module is used to process abnormal situations according to the mainstream probability, and then use the main tributary classification results to establish a river system hierarchy tree and assign hierarchical codes to the river sections in the river system.

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the program, the method according to any one of claims 1 to 6 is implemented.

9. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 6 is implemented.

Images