A forestation configuration method and system based on geographic data

Abstract

The present invention relates to the field of afforestation configuration technology, and is a afforestation configuration method and system based on geographic data, and the specific method includes: collecting initial geographic data of historical afforestation areas; performing feature selection processing and data dimension reduction processing on the initial geographic data; classifying afforestation design patterns of different afforestation areas to be selected, and evaluating the applicability of afforestation design patterns; updating the alternative afforestation design patterns of afforestation areas under different geographical conditions to the afforestation personnel design platform, and automatically providing suggestions to afforestation personnel. The present invention solves the problem in the prior art that the requirements of afforestation engineering management involve many aspects such as plot segmentation, garden landscaping, basal fertilizer application, shading, support, horizontal belt landscaping, working roads, etc., which leads to a huge workload when the design content is complicated.

Description

Technical Field

[0001] The invention relates to the technical field of afforestation configuration, and is an afforestation configuration method and system based on geographic data. Background Art

[0002] As an important measure to restore ecology and increase carbon sinks, afforestation has extensive social and economic benefits. However, traditional afforestation methods often rely on experience, resulting in high costs and unsatisfactory ecological effects. In existing technologies, afforestation engineering management faces many challenges. First, there are many factors involved in the afforestation process, including plot division, garden landscaping, basal fertilizer application, shading, support, horizontal belt landscaping and operation roads, etc. The design and implementation of these factors require professional knowledge and rich experience. When the design content is complex, the workload is huge and prone to errors, which affects the effect and efficiency of the entire afforestation project. Secondly, the lack of afforestation design models for different geographical conditions and climate characteristics leads to poor results when the same afforestation model is applied in different regions. Existing afforestation schemes often fail to fully consider the geographical, climatic, soil and other factors in the region, which greatly reduces the afforestation effect and resource utilization efficiency; in addition, most of the current afforestation configuration methods rely on manual experience and simple indicator evaluation, lacking systematic analysis and comprehensive application of historical data. This limitation makes it impossible to accurately evaluate the adaptability of different afforestation design models under specific environmental conditions, and thus cannot provide accurate decision-making support for afforestation personnel. Therefore, there is an urgent need for a scientific afforestation configuration system that can comprehensively analyze the geographical data of historical afforestation areas, reasonably configure different afforestation design modes, reduce the workload in the afforestation process, and improve the afforestation effect. Summary of the invention

[0003] The technical problem to be solved by the present invention is that in the prior art, the requirements of afforestation engineering management involve many aspects such as plot division, garden landscaping, basal fertilizer application, shading, support, horizontal belt preparation, work roads, etc., which leads to a huge workload when the design content is complex. A afforestation configuration method and system based on geographic data are proposed.

[0004] In order to achieve the above object, the technical solution of a afforestation configuration method based on geographic data of the present invention comprises the following steps:

[0005] S1: Collect initial geographic data of historical afforestation areas;

[0006] S2: Construct a geographic data feature integration model, and perform feature selection and data dimension reduction on the initial geographic data;

[0007] S3: Classify the afforestation design patterns of different candidate afforestation areas according to the geographic data integrated by the geographic data feature integration model;

[0008] S4: Analyze the dynamic usage data of various afforestation design models under different geographical conditions and evaluate the applicability of afforestation design models;

[0009] S5: Based on S3-S4, update the alternative afforestation design models for the areas to be afforested under different geographical conditions to the afforestation personnel design platform.

[0010] Specifically, in S1, the initial geographic data of the historical afforestation area includes: soil type data, land slope data, altitude data, temperature data and rainfall data.

[0011] Specifically, S2 includes the following specific steps:

[0012] S21: Constructing a geographic data feature integration model;

[0013] S22: extracting initial geographic data, and performing data preprocessing on the initial geographic data, wherein the data preprocessing includes mean value completion processing and data standardization processing, wherein the data standardization processing is specifically as follows:

[0014] ;

[0015] Among them, i is a subscript, indicating the data number of the initial geographic data. The data group numbers corresponding to soil type data, land slope data, altitude data, temperature data and rainfall data are 1, 2, 3, 4, and 5 respectively;

[0016] Indicates the data volume of the jth data in the initial geographic data set numbered i;

[0017] It represents the amount of data after data standardization processing of the j-th data in the initial geographic data group numbered i;

[0018] are the mean and standard deviation of the data features in the initial geographic data group numbered i, respectively;

[0019] S23: extracting the soil type data and land slope data after data preprocessing, and converting the soil type data and land slope data into one-hot encoding.

[0020] Specifically, S2 also includes the following specific steps:

[0021] S24: extracting the unique hot code of soil type data and the unique hot code of land slope data to form a geographic feature matrix R, and constructing Y vegetation data feature labels, wherein the vegetation data feature labels include: single hole scattered planting type, furrow dense planting type, container seedling type, soil ball seedling type, bamboo seedling type, and economic forest seedling type;

[0022] S25: Calculate the mean vector of each vegetation data feature label. The specific calculation strategy is:

[0023] ;

[0024] in, is the mean vector of the y-th vegetation data feature label; Represents the number of row vector samples in the geographic feature matrix R in the y-th vegetation data feature label; middle is a subscript, indicating the u-th sample in the y-th vegetation data feature label. is the feature vector of the u-th row vector sample in the y-th vegetation data feature label;

[0025] S26: Calculate the intra-class scatter matrix , the specific calculation strategy is:

[0026] ;

[0027] S27: Calculate the mean vector of all row vector samples in the geographic feature matrix R , the specific calculation strategy is: ; Represents the eigenvector of the vth row vector sample; N is the total number of row vector samples;

[0028] S28: Calculate the inter-class scatter matrix for each class , the specific calculation strategy is:

[0029] ;

[0030] S29: According to S21-S28, perform data dimensionality reduction constraints, and the data dimensionality reduction constraints are: ,in, is the feature vector, is the corresponding eigenvalue;

[0031] S210: Select the first k eigenvectors corresponding to the maximum eigenvalue as discriminant vectors, obtain a discriminant matrix W composed of k discriminant vectors, and project the geographic feature matrix R onto the discriminant vectors to obtain a reduced-dimensional geographic feature matrix Z. .

[0032] Specifically, S3 includes the following specific steps:

[0033] S31, extracting the row vectors of the geographic feature matrix Z after dimension reduction, obtaining a set of eigenvectors of the geographic feature matrix, and normalizing the set of eigenvectors of the geographic feature matrix;

[0034] S32. Divide the feature vector set of the geographic feature matrix into a training set and a test set in a ratio of 9:1, input the training set into the afforestation design pattern classification model for training, the afforestation design pattern classification model takes the correlation between the afforestation area and the area to be afforested as output, and takes the actual correlation as the classification target to train the afforestation design pattern classification model.

[0035] Specifically, the specific formula for normalizing the feature vector set of the geographic feature matrix in S31 is:

[0036] ;

[0037] in, is the normalized data, x is the specific value of the data, is the maximum value of the feature, is the minimum value of the characteristic;

[0038] In S32, the calculation formula of the classification accuracy of the afforestation design pattern classification model is:

[0039] ;

[0040] Among them, zq is the classification accuracy, r is the number of the afforestation area, n is the total number of afforestation areas, a is the similarity correlation, and A is the actual correlation.

[0041] Specifically, S4 includes the following specific steps:

[0042] S41: extracting the afforestation design pattern with the highest classification accuracy in S3, and analyzing the actual dynamic usage data of each afforestation design pattern under different geographical conditions. The analysis of the dynamic usage data includes: calculating and obtaining the geographical condition applicability coefficient And calculate the configuration value applicable coefficient ;

[0043] S42: Performing an evaluation of the suitability SY of the afforestation design mode, wherein the calculation strategy for evaluating the suitability of the afforestation design mode is: ,in, are the evaluation proportionality coefficients, respectively.

[0044] Specifically, in S41, the geographical condition applicable coefficient The calculation strategy is:

[0045] ;

[0046] Among them, cs is a subscript, indicating that the afforestation design pattern with the highest classification accuracy is actually used for the csth time, and CS indicates the total number of times the afforestation design pattern with the highest classification accuracy is actually used; H is the total number of tree name categories, and h is a subscript, indicating the tree name category;

[0047] Indicates the number of surviving trees with tree name category h; Indicates the evaluation ratio coefficient of trees with tree name category h;

[0048] They represent the maximum and minimum number of surviving trees respectively;

[0049] In terms of value, The closest amount of data;

[0050] In S41, the configuration value applicable coefficient The calculation strategy is:

[0051] ;

[0052] in, are the tree maintenance proportional coefficient and the tree growth proportional coefficient respectively;

[0053] g is a subscript, which refers to the tree species with the gth economic characteristic in the afforestation design model with the highest classification accuracy; They respectively represent the area of ​​the tree species growth region with the g-th economic characteristic after actual afforestation at the current moment, and the designed area of ​​the tree species growth region with the g-th economic characteristic in the afforestation design stage; The maintenance cycle duration is It refers to the actual growth time of seedlings after afforestation.

[0054] Specifically, S5 includes: arranging the applicability of alternative afforestation design patterns for areas to be afforested under different geographical conditions in descending order, giving priority to updating the top five alternative afforestation design patterns for areas to be afforested under different geographical conditions with the best applicability of afforestation design patterns to the afforestation personnel design platform, and automatically providing suggestions to afforestation personnel.

[0055] In addition, the afforestation configuration system based on geographic data of the present invention includes the following modules:

[0056] Initial geographic data collection module, data feature integration module, afforestation design pattern classification module, suitability assessment module, configuration pattern update module;

[0057] The initial geographic data collection module is used to collect initial geographic data of historical afforestation areas;

[0058] The data feature integration module is used to construct a geographic data feature integration model and perform feature selection and data dimension reduction processing on the initial geographic data;

[0059] The afforestation design pattern classification module classifies the afforestation design patterns of different afforestation areas to be selected according to the geographic data integrated by the geographic data feature integration model;

[0060] The suitability evaluation module is used to analyze the dynamic usage data of various afforestation design patterns under different geographical conditions and to evaluate the suitability of the afforestation design patterns;

[0061] The configuration mode update module is used to update the alternative afforestation design modes for the areas to be afforested under different geographical conditions to the afforestation personnel design platform, and automatically provide suggestions to the afforestation personnel.

[0062] A computer-readable storage medium stores instructions, and when a computer reads the instructions, the computer is enabled to execute the afforestation configuration method based on geographic data.

[0063] An electronic device includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the above-mentioned afforestation configuration method based on geographic data is implemented.

[0064] Compared with the prior art, the technical effects of the present invention are as follows:

[0065] 1. The present invention constructs a geographic data feature integration model and performs feature selection and dimensionality reduction processing on the data. This method can accurately integrate and analyze the geographic data of historical afforestation areas. This scientific data processing and analysis method enables the afforestation design model to be accurately classified and matched according to actual geographical conditions, thereby improving the scientificity and accuracy of afforestation design and helping to select the most suitable afforestation plan.

[0066] 2. The present invention can effectively optimize resource allocation by dynamically evaluating the applicability of different afforestation design modes and adjusting them according to actual dynamic usage data. By giving priority to recommending applicable afforestation design modes and updating them to the afforestation personnel design platform, the design efficiency can be greatly improved, the workload of manual selection and adjustment can be reduced, and the optimal allocation of resources can be ensured.

[0067] 3. The present invention dynamically updates alternative afforestation design patterns according to different geographical conditions, taking into account various geographical features such as soil type, slope aspect, climate, etc. By updating and providing suggestions in real time, afforestation design can better adapt to different environmental conditions, thereby improving the adaptability and sustainability of afforestation projects and ensuring long-term ecological benefits and project success. BRIEF DESCRIPTION OF THE DRAWINGS

[0068] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative labor. Among them:

[0069] Figure 1 A schematic diagram of a flow chart of a afforestation configuration method based on geographic data of the present invention;

[0070] Figure 2 It is a structural schematic diagram of a afforestation configuration system based on geographic data of the present invention. DETAILED DESCRIPTION

[0071] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the accompanying drawings.

[0072] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0073] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.

[0074] Embodiment 1:

[0075] like Figure 1 As shown, a afforestation configuration method based on geographic data in an embodiment of the present invention is as follows: Figure 1 As shown, the specific steps are as follows:

[0076] S1: Collect initial geographic data of historical afforestation areas;

[0077] In S1, the initial geographic data of the historical afforestation area includes: soil type data, land slope data, altitude data, temperature data and rainfall data.

[0078] S2: Construct a geographic data feature integration model, and perform feature selection and data dimension reduction on the initial geographic data;

[0079] S2 includes the following specific steps:

[0080] S21: Constructing a geographic data feature integration model;

[0081] S22: extracting initial geographic data and performing data preprocessing on the initial geographic data, wherein the data preprocessing includes mean value completion processing and data standardization processing, wherein the data standardization processing is specifically as follows:

[0082] ;

[0083] Among them, i is a subscript, which indicates the data group number of the initial geographic data. The data group numbers corresponding to soil type data, land slope data, altitude data, temperature data and rainfall data are 1, 2, 3, 4, and 5 respectively;

[0084] Indicates the data volume of the jth data in the initial geographic data set numbered i;

[0085] It represents the amount of data after data standardization processing of the j-th data in the initial geographic data group numbered i;

[0086] are the mean and standard deviation of the data features in the initial geographic data group numbered i, respectively;

[0087] S23: extracting soil type data and land slope data after data preprocessing, and converting the soil type data and land slope data into a one-hot encoding;

[0088] Exemplarily, in this embodiment, a one-hot encoding method of soil type data is provided, specifically including: the one-hot encoding of soil type data is: ,in, It means that the main soil type with the largest proportion in the mixed soil is clay; It means that the main soil type with the largest proportion in the mixed soil is sandy soil; It means that the main soil type with the largest proportion in the mixed soil is silt soil; It means that the major soil type that accounts for the largest proportion of the mixed soil is other soil types than clay, sand and silt;

[0089] Exemplarily, in this embodiment, a one-hot encoding method of land aspect data is also provided, specifically including: the one-hot encoding of land aspect data is: ,in, Indicates that the main direction of the land slope is north; Indicates that the main direction of the land slope is east; Indicates that the main direction of the land slope is south; Indicates that the main direction of the land slope is west.

[0090] S2 also includes the following specific steps:

[0091] S24: extracting the unique hot code of soil type data and the unique hot code of land slope data to form a geographic feature matrix R, and constructing Y vegetation data feature labels, wherein the vegetation data feature labels include: single-hole scattered planting type, furrow dense planting type, container seedling type, soil ball seedling type, bamboo seedling type, and economic forest seedling type;

[0092] S25: Calculate the mean vector of each vegetation data feature label. The specific calculation strategy is:

[0093] ;

[0094] in, is the mean vector of the y-th vegetation data feature label; Represents the number of row vector samples in the geographic feature matrix R in the y-th vegetation data feature label; middle is a subscript, indicating the u-th sample in the y-th vegetation data feature label. is the feature vector of the u-th row vector sample in the y-th vegetation data feature label;

[0095] S26: Calculate the intra-class scatter matrix , the specific calculation strategy is:

[0096] ;

[0097] S27: Calculate the mean vector of all row vector samples in the geographic feature matrix R , the specific calculation strategy is: ; Represents the eigenvector of the vth row vector sample; N is the total number of row vector samples;

[0098] S28: Calculate the inter-class scatter matrix for each class , the specific calculation strategy is:

[0099] ;

[0100] S29: According to S21-S28, perform data dimensionality reduction constraints. Specifically, the data dimensionality reduction constraints are: ,in, is the feature vector, is the corresponding eigenvalue;

[0101] S210: Select the first k eigenvectors corresponding to the maximum eigenvalue as discriminant vectors, obtain a discriminant matrix W composed of k discriminant vectors, and project the geographic feature matrix R onto the discriminant vectors to obtain a reduced-dimensional geographic feature matrix Z. .

[0102] S3: Classify the afforestation design patterns of different candidate afforestation areas according to the geographic data integrated by the geographic data feature integration model;

[0103] S3 includes the following specific steps:

[0104] S31, extracting the row vectors of the geographic feature matrix Z after dimension reduction, obtaining a set of eigenvectors of the geographic feature matrix, and normalizing the set of eigenvectors of the geographic feature matrix;

[0105] S32. Divide the feature vector set of the geographic feature matrix into a training set and a test set in a ratio of 9:1, input the training set into the afforestation design pattern classification model for training, the afforestation design pattern classification model takes the correlation between the afforestation area and the area to be afforested as output, and takes the actual correlation as the classification target to train the afforestation design pattern classification model.

[0106] The specific formula for normalizing the feature vector set of the geographic feature matrix in S31 is:

[0107] ;

[0108] in, is the normalized data, x is the specific value of the data, is the maximum value of the feature, is the minimum value of the characteristic;

[0109] In S32, in S32, the calculation formula of the classification accuracy of the afforestation design pattern classification model is:

[0110] ;

[0111] Among them, zq is the classification accuracy, r is the number of the afforestation area, n is the total number of afforestation areas, a is the similarity correlation, and A is the actual correlation.

[0112] S4: Analyze the dynamic usage data of various afforestation design models under different geographical conditions and evaluate the applicability of afforestation design models;

[0113] S4 includes the following specific steps:

[0114] S41: extracting the afforestation design pattern with the highest classification accuracy in S3, and analyzing the actual dynamic usage data of each afforestation design pattern under different geographical conditions. The analysis of the dynamic usage data includes: calculating and obtaining the geographical condition applicability coefficient And calculate the applicable coefficient of configuration value ;

[0115] S42: Performing an evaluation of the suitability SY of the afforestation design mode, wherein the calculation strategy for evaluating the suitability of the afforestation design mode is: ,in, are the evaluation proportionality coefficients, respectively.

[0116] In S41, the geographical condition applicable coefficient The calculation strategy is:

[0117] ;

[0118] Among them, cs is a subscript, indicating that the afforestation design pattern with the highest classification accuracy is actually used for the csth time, and CS indicates the total number of times the afforestation design pattern with the highest classification accuracy is actually used; H is the total number of tree name categories, and h is a subscript, indicating the tree name category;

[0119] Indicates the number of surviving trees with tree name category h; Indicates the evaluation ratio coefficient of trees with tree name category h;

[0120] They represent the maximum and minimum number of surviving trees respectively;

[0121] In terms of value, The closest amount of data.

[0122] In S41, the configuration value applicable coefficient The calculation strategy is:

[0123] ;

[0124] in, are the tree maintenance proportional coefficient and the tree growth proportional coefficient respectively;

[0125] g is a subscript, which refers to the tree species with the gth economic characteristic in the afforestation design model with the highest classification accuracy; They respectively represent the area of ​​the tree species growth region with the g-th economic characteristic after actual afforestation at the current moment, and the designed area of ​​the tree species growth region with the g-th economic characteristic in the afforestation design stage; The maintenance cycle duration is It refers to the actual growth time of seedlings after afforestation.

[0126] S5: Based on S3-S4, the alternative afforestation design modes for the areas to be afforested under different geographical conditions are updated to the afforestation personnel design platform, and suggestions are automatically provided to the afforestation personnel.

[0127] S5 includes: arranging the applicability of the alternative afforestation design patterns for the areas to be afforested under different geographical conditions in descending order, giving priority to updating the top five alternative afforestation design patterns for the areas to be afforested under different geographical conditions to the afforestation personnel design platform, and automatically providing suggestions to the afforestation personnel.

[0128] Embodiment 2:

[0129] like Figure 2 As shown, a afforestation configuration system based on geographic data according to an embodiment of the present invention is as follows: Figure 2 As shown, it includes the following modules:

[0130] Initial geographic data collection module, data feature integration module, afforestation design pattern classification module, suitability assessment module, configuration pattern update module;

[0131] The initial geographic data collection module is used to collect initial geographic data of historical afforestation areas;

[0132] The data feature integration module is used to construct a geographic data feature integration model and perform feature selection and data dimension reduction processing on the initial geographic data;

[0133] The afforestation design pattern classification module classifies the afforestation design patterns of different afforestation areas to be selected according to the geographic data integrated by the geographic data feature integration model;

[0134] The suitability evaluation module is used to analyze the dynamic usage data of various afforestation design patterns under different geographical conditions and to evaluate the suitability of the afforestation design patterns;

[0135] The configuration mode update module is used to update the alternative afforestation design modes for the areas to be afforested under different geographical conditions to the afforestation personnel design platform, and automatically provide suggestions to the afforestation personnel.

[0136] Embodiment three:

[0137] This embodiment provides an electronic device, including: a processor and a memory, wherein the memory stores a computer program that can be called by the processor;

[0138] The processor executes the above-mentioned afforestation configuration method based on geographic data by calling the computer program stored in the memory.

[0139] The electronic device may have relatively large differences due to different configurations or performances, and may include one or more processors (Central Processing Units, CPU) and one or more memories, wherein the memory stores at least one computer program, and the computer program is loaded and executed by the processor to implement a afforestation configuration method based on geographic data provided in the above method embodiment. The electronic device may also include other components for realizing the functions of the device. For example, the electronic device may also have components such as a wired or wireless network interface and an input and output interface to input and output data. This embodiment will not be described in detail here.

[0140] Embodiment 4:

[0141] This embodiment provides a computer-readable storage medium having a rewritable computer program stored thereon;

[0142] When the computer program runs on a computer device, the computer device is enabled to execute the above-mentioned afforestation configuration method based on geographic data.

[0143] For example, the computer readable storage medium can be a read-only memory (ROM), a random access memory (RAM), a compact disc (CD-ROM), a magnetic tape, a floppy disk, an optical data storage device, etc.

[0144] It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0145] It should be understood that determining B based on A does not mean determining B only based on A. B can also be determined based on A and / or other information.

[0146] The above embodiments can be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented by software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When a computer instruction or computer program is loaded or executed on a computer, a process or function according to an embodiment of the present invention is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. Computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, computer instructions can be transmitted from one website site, computer, server or data center to another website site, computer, server or data center through a wired network or / and a wireless network. The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or data center that contains one or more available media sets. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state hard disk.

[0147] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed in the present invention can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0148] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0149] In the several embodiments provided by the present invention, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic, for example, the division of units is only one, and there may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0150] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0151] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0152] In the description of this specification, the description with reference to the terms "one embodiment", "example", "specific example", etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

[0153] In summary, compared with the prior art, the technical effects of the present invention are as follows:

[0154] 1. The present invention constructs a geographic data feature integration model and performs feature selection and dimensionality reduction processing on the data. This method can accurately integrate and analyze the geographic data of historical afforestation areas. This scientific data processing and analysis method enables the afforestation design model to be accurately classified and matched according to actual geographical conditions, thereby improving the scientificity and accuracy of afforestation design and helping to select the most suitable afforestation plan.

[0155] 2. The present invention can effectively optimize resource allocation by dynamically evaluating the applicability of different afforestation design modes and adjusting them according to actual dynamic usage data. By giving priority to recommending applicable afforestation design modes and updating them to the afforestation personnel design platform, the design efficiency can be greatly improved, the workload of manual selection and adjustment can be reduced, and the optimal allocation of resources can be ensured.

[0156] 3. The present invention dynamically updates alternative afforestation design patterns according to different geographical conditions, taking into account various geographical features such as soil type, slope aspect, climate, etc. By updating and providing suggestions in real time, afforestation design can better adapt to different environmental conditions, thereby improving the adaptability and sustainability of afforestation projects and ensuring long-term ecological benefits and project success.

[0157] The above shows and describes the basic principles and main features of the present invention and the advantages of the present invention. It should be understood by those skilled in the art that the present invention is not limited to the above embodiments. The above embodiments and descriptions are only for explaining the principles of the present invention. Without departing from the spirit and scope of the present invention, the present invention may have various changes and improvements, which fall within the scope of the present invention to be protected. The scope of protection of the present invention is defined by the attached claims and their equivalents.

Claims

1. A afforestation configuration method based on geographic data, characterized in that: The method comprises the following specific steps: S1: Collect initial geographic data of historical afforestation areas; S2: Construct a geographic data feature integration model, and perform feature selection and data dimension reduction on the initial geographic data; S3: Classify the afforestation design patterns of different candidate afforestation areas according to the geographic data integrated by the geographic data feature integration model; S4: Analyze the dynamic usage data of various afforestation design models under different geographical conditions and evaluate the applicability of afforestation design models; S5: Based on S3-S4, update the alternative afforestation design modes for the afforestation areas under different geographical conditions to the afforestation personnel design platform; S4 includes the following specific steps: S41: extracting the afforestation design pattern with the highest classification accuracy in S3, and analyzing the actual dynamic usage data of each afforestation design pattern under different geographical conditions. The analysis of the dynamic usage data includes: calculating and obtaining the geographical condition applicability coefficient And calculate the applicable coefficient of configuration value ; S42: Performing an evaluation of the suitability SY of the afforestation design mode, wherein the calculation strategy for evaluating the suitability of the afforestation design mode is: ,in, are the evaluation proportionality coefficients, respectively; In S41, the geographical condition applicable coefficient The calculation strategy is: ； Among them, cs is a subscript, indicating that the afforestation design pattern with the highest classification accuracy is actually used for the csth time, and CS indicates the total number of times the afforestation design pattern with the highest classification accuracy is actually used; H is the total number of tree name categories, and h is a subscript, indicating the tree name category; Indicates the number of surviving trees with tree name category h; Indicates the evaluation ratio coefficient of trees with tree name category h; They represent the maximum and minimum number of surviving trees respectively; In terms of value, The closest amount of data; In S41, the configuration value applicable coefficient The calculation strategy is: ； in, are the tree maintenance proportional coefficient and the tree growth proportional coefficient respectively; g is a subscript, which refers to the tree species with the gth economic characteristic in the afforestation design model with the highest classification accuracy; They respectively represent the area of ​​the tree species growth region with the g-th economic characteristic after actual afforestation at the current moment, and the designed area of ​​the tree species growth region with the g-th economic characteristic in the afforestation design stage; The maintenance cycle duration is It is the actual growth time of seedlings after afforestation; S5 includes: arranging the applicability of the alternative afforestation design patterns for the areas to be afforested under different geographical conditions in descending order, giving priority to updating the top five alternative afforestation design patterns for the areas to be afforested under different geographical conditions to the afforestation personnel design platform, and automatically providing suggestions to the afforestation personnel.

2. The afforestation configuration method based on geographic data according to claim 1, characterized in that: In S1, the initial geographic data of the historical afforestation area includes: soil type data, land slope data, altitude data, temperature data and rainfall data.

3. A afforestation configuration method based on geographic data according to claim 2, characterized in that: S2 includes the following specific steps: S21: Constructing a geographic data feature integration model; S22: extracting initial geographic data, and performing data preprocessing on the initial geographic data, wherein the data preprocessing includes mean value completion processing and data standardization processing, wherein the data standardization processing is specifically as follows: ； Among them, i is a subscript, which indicates the data group number of the initial geographic data. The data group numbers corresponding to soil type data, land slope data, altitude data, temperature data and rainfall data are 1, 2, 3, 4, and 5 respectively; Indicates the data volume of the jth data in the initial geographic data set numbered i; It represents the amount of data after data standardization processing of the j-th data in the initial geographic data group numbered i; are the mean and standard deviation of the data features in the initial geographic data group numbered i, respectively; S23: extracting the soil type data and land slope data after data preprocessing, and converting the soil type data and land slope data into one-hot encoding.

4. A afforestation configuration method based on geographic data according to claim 3, characterized in that: S2 also includes the following specific steps: S24: extracting the unique hot code of soil type data and the unique hot code of land slope data to form a geographic feature matrix R, and constructing Y vegetation data feature labels, wherein the vegetation data feature labels include: single hole scattered planting type, furrow dense planting type, container seedling type, soil ball seedling type, bamboo seedling type, and economic forest seedling type; S25: Calculate the mean vector of each vegetation data feature label. The specific calculation strategy is: ； in, is the mean vector of the y-th vegetation data feature label; Represents the number of row vector samples in the geographic feature matrix R in the y-th vegetation data feature label; middle is a subscript, indicating the u-th sample in the y-th vegetation data feature label. is the feature vector of the u-th row vector sample in the y-th vegetation data feature label; S26: Calculate the intra-class scatter matrix , the specific calculation strategy is: ； S27: Calculate the mean vector of all row vector samples in the geographic feature matrix R , the specific calculation strategy is: ; Represents the eigenvector of the vth row vector sample; N is the total number of row vector samples; S28: Calculate the inter-class scatter matrix for each class , the specific calculation strategy is: ； S29: According to S21-S28, perform data dimensionality reduction constraints, and the data dimensionality reduction constraints are: ,in, is the feature vector, is the corresponding eigenvalue; S210: Select the first k eigenvectors corresponding to the maximum eigenvalue as discriminant vectors, obtain a discriminant matrix W composed of k discriminant vectors, and project the geographic feature matrix R onto the discriminant vectors to obtain a reduced-dimensional geographic feature matrix Z. .

5. The afforestation configuration method based on geographic data according to claim 4, characterized in that: S3 includes the following specific steps: S31, extracting the row vectors of the geographic feature matrix Z after dimension reduction, obtaining a set of eigenvectors of the geographic feature matrix, and normalizing the set of eigenvectors of the geographic feature matrix; S32. Divide the feature vector set of the geographic feature matrix into a training set and a test set in a ratio of 9:1, input the training set into the afforestation design pattern classification model for training, the afforestation design pattern classification model takes the correlation between the afforestation area and the area to be afforested as output, and takes the actual correlation as the classification target to train the afforestation design pattern classification model.

6. A afforestation configuration method based on geographic data according to claim 5, characterized in that: The specific formula for normalizing the feature vector set of the geographic feature matrix in S31 is: ； in, is the normalized data, x is the specific value of the data, is the maximum value of the feature, is the minimum value of the characteristic; In S32, the calculation formula of the classification accuracy of the afforestation design pattern classification model is: ； Among them, zq is the classification accuracy, r is the number of the afforestation area, n is the total number of afforestation areas, a is the similarity correlation, and A is the actual correlation.

7. A afforestation configuration system based on geographic data, which is used to implement the afforestation configuration method based on geographic data as described in any one of claims 1 to 6, characterized in that: The system includes the following modules: Initial geographic data collection module, data feature integration module, afforestation design pattern classification module, suitability assessment module, configuration pattern update module; The initial geographic data collection module is used to collect initial geographic data of historical afforestation areas; The data feature integration module is used to construct a geographic data feature integration model and perform feature selection and data dimension reduction processing on the initial geographic data; The afforestation design pattern classification module classifies the afforestation design patterns of different afforestation areas to be selected according to the geographic data integrated by the geographic data feature integration model; The suitability evaluation module is used to analyze the dynamic usage data of various afforestation design patterns under different geographical conditions and to evaluate the suitability of the afforestation design patterns; The configuration mode update module is used to update the alternative afforestation design modes for the areas to be afforested under different geographical conditions to the afforestation personnel design platform.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, a afforestation configuration method based on geographic data as described in any one of claims 1 to 6 is implemented.

9. An electronic device, characterized in that: include: A memory for storing instructions; A processor is used to execute the instructions so that the device performs operations to implement a afforestation configuration method based on geographic data as described in any one of claims 1-6.

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