Geographic surveying and mapping data processing and analyzing method based on artificial intelligence

Abstract

The invention discloses an artificial intelligence-based geographic surveying and mapping data processing analysis method, which comprises the following steps of: acquiring geographic surveying and mapping data, acquiring a region label by utilizing a region feature recognition model, and establishing a three-dimensional geographic model; performing region division on the three-dimensional geographic model through boundary coordinates, obtaining region tags, obtaining a first development index according to a first mapping relation, obtaining an associated region tag and an associated index according to a second mapping relation, obtaining a distance index according to a third mapping relation, and obtaining a second development index according to the distance index; obtaining a second development index through the correlation index and the distance index, and establishing a development prediction model to obtain a development label set; determining a target development label by using a greedy algorithm, and updating the three-dimensional geographic model; obtaining a regional development ecological value, and carrying out regional ecological early warning; according to the method, accurate prediction is carried out for regional development, a more reasonable and effective development plan is made for geographic regional development, the rationality and convenience of urban layout are improved, and ecological development is guaranteed.

Description

Technical Field

[0001] The present invention relates to the technical field of surveying and mapping data processing, and in particular to a geographic surveying and mapping data processing and analysis method based on artificial intelligence. Background Art

[0002] In recent years, with the development of society and the advancement of science and technology, the demand for geographic information has become increasingly higher, especially in the fields of urban planning, land management, and environmental monitoring. Traditional surveying and mapping methods have some limitations in collecting high-precision spatial data, such as high time cost and poor data accuracy.

[0003] At present, the Chinese invention with publication number CN118674886B, an intelligent geographic surveying and mapping data processing method and system, although it establishes a three-dimensional city model by collecting urban three-dimensional geographic surveying and mapping data, and records urban events and change data, obtains a predicted three-dimensional city model by analyzing the types of change data and predicting the optimal change data, thereby improving the efficiency of processing multi-source data and greatly improving the accuracy and flexibility of urban change prediction, thereby realizing efficient urban geographic three-dimensional surveying and mapping data management, but it does not make more reasonable allocation and planning of urban regional development based on the relationship between geographical regions, and does not evaluate and warn the urban ecological situation, which is not conducive to urban ecological development. Summary of the Invention

[0004] The technical problem solved by the present invention is that the existing technology does not make more reasonable allocation and planning of urban regional development based on the relationship between geographical regions, and does not evaluate and warn of urban ecological conditions, which is not conducive to urban ecological development.

[0005] To solve the above technical problems, the present invention provides a method for processing and analyzing geographic surveying and mapping data based on artificial intelligence, which is characterized by comprising the following steps:

[0006] Step S1, obtaining geographic surveying and mapping data, obtaining regional labels based on the geographic surveying and mapping data using a regional feature recognition model, and establishing a three-dimensional geographic model;

[0007] Step S2: dividing the three-dimensional geographic model into regions using boundary coordinates, obtaining region labels, obtaining a first development index based on a first mapping relationship, obtaining associated region labels and an associated index based on a second mapping relationship, obtaining a distance index based on a third mapping relationship, obtaining a second development index using the associated index and the distance index, and establishing a development prediction model based on historical geographic surveying and mapping data to obtain a development label set;

[0008] Step S3, using a greedy algorithm to determine the target development label according to the development label set, and updating the three-dimensional geographic model;

[0009] Step S4, obtaining a regional development ecological value according to the first development index, the second development index, and the development label, and performing a regional ecological early warning according to the regional development ecological value;

[0010] As a preferred solution of the method for processing and analyzing geographic surveying and mapping data based on artificial intelligence described in the present invention, step S1 specifically includes the following steps:

[0011] Step S101, acquiring geographic surveying and mapping data, and performing data preprocessing on the geographic surveying and mapping data, wherein the data preprocessing includes data filtering, data correction, and coordinate conversion;

[0012] Step S102: Acquire historical geographic surveying and mapping data, retrieve image data from the historical geographic surveying and mapping data, annotate the image data with regional labels, and input the annotated image data into a convolutional neural network with the image data as input and the regional labels as output. Training is terminated until the output fit is greater than or equal to a first fit expectation threshold, thereby obtaining a regional feature recognition model.

[0013] Step S103: inputting the geographic surveying and mapping data into a regional feature recognition model, obtaining labels for each region, and establishing a three-dimensional geographic model;

[0014] As a preferred solution of the method for processing and analyzing geographic surveying and mapping data based on artificial intelligence described in the present invention, step S2 specifically includes the following steps:

[0015] Step S201: Divide the three-dimensional geographic model into regions according to boundary coordinates, obtain region numbers and region labels, and obtain a first development index according to a first mapping relationship, wherein the first mapping relationship includes a mapping relationship between the region label and the first development index;

[0016] Step S202: Obtain associated region labels using a second mapping relationship based on the region labels, obtain associated regions from the three-dimensional geographic model based on the associated region labels, obtain three-dimensional coordinates of the associated regions, obtain associated distances using the three-dimensional coordinates of the associated regions, obtain an association index of the associated regions based on the second mapping relationship, obtain a distance index of each associated region based on the association distance using a third mapping relationship, perform a weighted calculation on the association index and the distance index of each region to obtain a second development index, wherein the second mapping relationship includes an association mapping relationship and an association index mapping relationship between each region label, and the third mapping relationship includes a mapping relationship between an association distance and an association index;

[0017] Step S203, obtaining historical geographic surveying and mapping data, repeating step S202, obtaining the historical associated region label, historical associated index, and historical distance index of the historical geographic test data, annotating the historical geographic test data with the development label, and inputting the annotated historical geographic test data into a machine learning model, using the historical associated region label, the historical associated index, and the historical distance index as model inputs, and the development label as data output, until the output fit is greater than or equal to a second fit expectation threshold, then stopping training and obtaining a development prediction model;

[0018] Step S204: obtaining a first associated region label, a first associated index, and a first distance index for a first range of each region of the geographic surveying and mapping data, and inputting the first associated region label, the first associated index, and the first distance index into a development prediction model to obtain a first development label; obtaining a second associated region label, a second associated index, and a second distance index for a second range of each region of the geographic surveying and mapping data, and using the development prediction model to obtain a second development label for each region; repeating the above steps to obtain a third development label for each region; and combining the first development label, the second development label, and the third development label into a development label set;

[0019] As a preferred solution of the method for processing and analyzing geographic surveying and mapping data based on artificial intelligence described in the present invention, step S3 specifically includes the following steps:

[0020] Step S301, obtaining an objective function according to the correlation index and the distance index, and using the objective function to obtain a development target value of a first development label, a development target value of a second development label, and a development target value of a third development label;

[0021] Step S302, defining the development tag with the largest development target value as a target development tag, and updating the three-dimensional geographic model according to the target development tag;

[0022] As a preferred solution of the method for processing and analyzing geographic surveying and mapping data based on artificial intelligence described in the present invention, step S4 specifically includes the following steps:

[0023] Step S401: Obtaining a development ecological value for each region of the three-dimensional geographic model according to a fourth mapping relationship using the target development label of each region, obtaining an ecological value within a unit range based on the development ecological value, and obtaining an ecological development label within the unit range based on the ecological value, wherein the ecological development label includes qualified ecological development and unqualified ecological development.

[0024] Step S402: Obtain a unit range with an ecological development label of unqualified ecological development, define each area within the unit range with an ecological development value less than or equal to an expected threshold as an unqualified ecological development area, and issue an ecological warning for each unqualified ecological development area;

[0025] As a preferred solution of the artificial intelligence-based geographic surveying and mapping data processing and analysis method of the present invention, the process of establishing a regional feature recognition model includes:

[0026] Performing image enhancement and image noise reduction on input image data to obtain an enhanced image, performing a first convolution operation on the enhanced image to extract a first local feature of the enhanced image, performing a first pooling operation using a maximum pooling method to extract the first local feature with the largest feature point product in the local area as a second local feature, performing a second convolution operation and a second pooling operation on the second local feature to obtain a third local feature, performing feature fusion on the third local features of each local area of the image to obtain image features, and inputting the image features and the image region labels into output neurons for training to obtain a regional feature recognition model;

[0027] As a preferred solution of the artificial intelligence-based geographic surveying and mapping data processing and analysis method of the present invention, the calculation expression of the objective function is:

[0028] F(R i )=∑p j *w j ;

[0029] Among them, R i is the development target value, p j is the regional convenience value corresponding to each association index, w j is the distance index;

[0030] As a preferred solution of the method for processing and analyzing geographic surveying and mapping data based on artificial intelligence described in the present invention, the regional labels include urban administrative areas, cultural and educational areas, medical and health areas, commercial service areas, education and scientific research areas, social welfare areas, and housing security areas;

[0031] As a preferred solution of the artificial intelligence-based geographic surveying and mapping data processing and analysis method described in the present invention, the development labels include urban administrative areas, cultural and educational areas, medical and health areas, commercial service areas, education and scientific research, social welfare areas and housing security areas.

[0032] The beneficial effects of the present invention are as follows: the present invention obtains associated regional labels, associated indexes and distance indexes through regional labels of various regions, establishes a development prediction model based on historical geographic surveying and mapping data, and utilizes the development prediction model to obtain a development label set based on associated regional labels, associated indexes and distance indexes, so as to make accurate predictions for regional development, which is conducive to conveniently and directly understanding the development trends of various geographical regions.

[0033] The greedy algorithm is used to obtain the target development label of the first development label, the second development label and the third development label according to the development label set, which is the optimal development label, to make a more reasonable and effective development plan for the development of the geographical area and improve the rationality and convenience of the urban layout.

[0034] The ecological conditions of each area in the updated three-dimensional geographic model are evaluated and corresponding ecological early warnings are carried out to ensure the ecological development of the corresponding geographical areas and benefit the overall ecological layout. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 A schematic diagram of the basic flow of a method for processing and analyzing geographic surveying and mapping data based on artificial intelligence is provided as an embodiment of the present invention. DETAILED DESCRIPTION

[0036] 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 drawings. It is obvious that the described embodiments are only part of the embodiments of the present invention, but not all of the embodiments.

[0037] Example, see Figure 1 , as one embodiment of the present invention, provides a geographic surveying and mapping data processing and analysis method based on artificial intelligence, characterized in that it includes the following steps:

[0038] Step S1, obtaining geographic surveying and mapping data, obtaining regional labels based on the geographic surveying and mapping data using a regional feature recognition model, and establishing a three-dimensional geographic model;

[0039] Step S2: dividing the three-dimensional geographic model into regions using boundary coordinates, obtaining region labels, obtaining a first development index based on a first mapping relationship, obtaining associated region labels and an associated index based on a second mapping relationship, obtaining a distance index based on a third mapping relationship, obtaining a second development index using the associated index and the distance index, and establishing a development prediction model based on historical geographic surveying and mapping data to obtain a development label set;

[0040] Step S3, using a greedy algorithm to determine the target development label according to the development label set, and updating the three-dimensional geographic model;

[0041] Step S4: obtaining a regional development ecological value according to the first development index, the second development index and the development label, and performing a regional ecological early warning according to the regional development ecological value.

[0042] In this embodiment, the greedy algorithm refers to a computer algorithm that always makes the best choice at the moment when solving a problem.

[0043] In this embodiment, the associated regional labels, associated indexes and distance indexes are obtained through the regional labels of each region, and a development prediction model is established based on historical geographic surveying and mapping data. The development prediction model is used to obtain a development label set based on the associated regional labels, associated indexes and distance indexes, and accurate predictions are made for regional development, which is conducive to conveniently and directly understanding the development trends of various geographical regions.

[0044] The greedy algorithm is used to obtain the target development label of the first development label, the second development label and the third development label according to the development label set, which is the optimal development label, to make a more reasonable and effective development plan for the development of the geographical area and improve the rationality and convenience of the urban layout.

[0045] The ecological conditions of each area in the updated three-dimensional geographic model are evaluated and corresponding ecological early warnings are carried out to ensure the ecological development of the corresponding geographical areas and benefit the overall ecological layout.

[0046] The step S1 specifically includes the following steps:

[0047] Step S101, acquiring geographic surveying and mapping data, and performing data preprocessing on the geographic surveying and mapping data, wherein the data preprocessing includes data filtering, data correction, and coordinate conversion;

[0048] Step S102: Acquire historical geographic surveying and mapping data, retrieve image data from the historical geographic surveying and mapping data, annotate the image data with regional labels, and input the annotated image data into a convolutional neural network with the image data as input and the regional labels as output. Training is terminated until the output fit is greater than or equal to a first fit expectation threshold, thereby obtaining a regional feature recognition model.

[0049] Step S103: input the geographic surveying and mapping data into a regional feature recognition model, obtain labels for each region, and establish a three-dimensional geographic model.

[0050] In this embodiment, data filtering is a data processing used to reduce or eliminate noise and interference in data;

[0051] In this embodiment, data correction is a data processing method that aims to reduce the skewness coefficient of the data so that the data distribution is closer to the normal distribution;

[0052] In this embodiment, coordinate transformation is a data processing method for transforming the position description of a spatial entity from one coordinate system to another coordinate system;

[0053] In this embodiment, the first fitting expected threshold is 96%.

[0054] In this embodiment, a regional feature recognition model is established based on historical geographic surveying and mapping data, which is conducive to using computer technology to directly identify regional features, obtain regional labels, and establish a three-dimensional geographic model, saving a lot of manpower and material resources.

[0055] The step S2 specifically includes the following steps:

[0056] Step S201: Divide the three-dimensional geographic model into regions according to boundary coordinates, obtain region numbers and region labels, and obtain a first development index according to a first mapping relationship, wherein the first mapping relationship includes a mapping relationship between the region label and the first development index;

[0057] Step S202: Obtain associated region labels using a second mapping relationship based on the region labels, obtain associated regions from the three-dimensional geographic model based on the associated region labels, obtain three-dimensional coordinates of the associated regions, obtain associated distances using the three-dimensional coordinates of the associated regions, obtain an association index of the associated regions based on the second mapping relationship, obtain a distance index of each associated region based on the association distance using a third mapping relationship, perform a weighted calculation on the association index and the distance index of each region to obtain a second development index, wherein the second mapping relationship includes an association mapping relationship and an association index mapping relationship between each region label, and the third mapping relationship includes a mapping relationship between an association distance and an association index;

[0058] Step S203: Acquire historical geographic surveying and mapping data, repeat step S202, obtain historical associated region labels, historical associated indexes, and historical distance indexes of historical geographic test data, annotate the historical geographic test data with development labels, and input the annotated historical geographic test data into a machine learning model, using the historical associated region labels, historical associated indexes, and historical distance indexes as model inputs and the development labels as data outputs. Training is stopped until the output fit is greater than or equal to a second fit expectation threshold, thereby obtaining a development prediction model.

[0059] Step S204, obtain the first associated area label, the first associated index and the first distance index of the first range of each area of the geographic surveying and mapping data, and input the first associated area label, the first associated index and the first distance index into the development prediction model to obtain the first development label, obtain the second associated area label, the second associated index and the second distance index of the second range of each area of the geographic surveying and mapping data, and use the development prediction model to obtain the second development label of each area, and repeat the above steps to obtain the third development label of each area, and combine the first development label, the second development label and the third development label into a development label set.

[0060] In this embodiment, the second fitting expected threshold is 95%.

[0061] In this embodiment, the associated regional labels, associated indexes and distance indexes are obtained through the regional labels of each region, and a development prediction model is established based on historical geographic surveying and mapping data. The development prediction model is used to obtain a development label set based on the associated regional labels, associated indexes and distance indexes, and accurate predictions are made for regional development, which is conducive to conveniently and directly understanding the development trends of various geographical regions.

[0062] The step S3 specifically includes the following steps:

[0063] Step S301, obtaining an objective function according to the correlation index and the distance index, and using the objective function to obtain a development target value of a first development label, a development target value of a second development label, and a development target value of a third development label;

[0064] Step S302 : defining the development tag with the largest development target value as a target development tag, and updating the three-dimensional geographic model according to the target development tag.

[0065] In this embodiment, a greedy algorithm is used to obtain the target development label of the first development label, the second development label and the third development label according to the development label set, which is the optimal development label, to make a more reasonable and effective development plan for the development of the geographical area and improve the rationality and convenience of the urban layout.

[0066] The step S4 specifically includes the following steps:

[0067] Step S401: Obtaining a development ecological value for each region of the three-dimensional geographic model according to a fourth mapping relationship using the target development label of each region, obtaining an ecological value within a unit range based on the development ecological value, and obtaining an ecological development label within the unit range based on the ecological value, wherein the ecological development label includes qualified ecological development and unqualified ecological development.

[0068] Step S402: Obtain a unit range with an ecological development label of unqualified ecological development, define each area within the unit range whose development ecological value is less than or equal to the expected threshold as an unqualified ecological development area, and issue an ecological warning for each unqualified ecological development area.

[0069] In this embodiment, the ecological conditions of each area of the updated three-dimensional geographic model are evaluated and corresponding ecological early warnings are carried out, thereby ensuring the ecological development of the corresponding geographic area and being beneficial to the overall ecological layout.

[0070] The process of establishing a regional feature recognition model includes:

[0071] Image enhancement and image denoising are performed on the input image data to obtain an enhanced image, a first convolution operation is performed on the enhanced image to extract the first local feature of the enhanced image, and a first pooling operation is performed using the maximum pooling method to extract the first local feature with the largest feature point product in the local area as the second local feature, a second convolution operation and a second pooling operation are performed on the second local feature to obtain a third local feature, feature fusion is performed on the third local feature of each local area of the image to obtain image features, and the image features and the image region label are input into and output from neurons for training to obtain a regional feature recognition model.

[0072] In this embodiment, feature fusion technology and convolutional neural network are used to establish a regional feature recognition model, which improves the model performance and recognition accuracy, and saves a lot of manpower and material resources for processing geographic surveying and mapping image data.

[0073] The calculation expression of the objective function is:

[0074] F(R i )=∑p j *w j ;

[0075] Among them, R i is the development target value, p j is the regional convenience value corresponding to each association index, w j is the distance index.

[0076] In this embodiment, a greedy algorithm is used according to the objective function to obtain the target development label, that is, the optimal development label, to make a more reasonable and effective development plan for the development of the geographical area and improve the rationality and convenience of the urban layout.

[0077] The regional labels include urban administrative areas, cultural and educational areas, medical and health areas, commercial service areas, education and scientific research areas, social welfare areas and housing security areas.

[0078] In this embodiment, obtaining the regional labels provides detailed data support for obtaining the target development labels according to the regional labels, which is helpful for understanding the development status and development trends of each geographical region in detail.

[0079] The development labels include urban administrative areas, cultural and educational areas, medical and health areas, commercial service areas, education and scientific research, social welfare areas and housing security areas.

[0080] In this embodiment, by obtaining development labels, multiple selection targets are provided for the development of geographical regions, which is conducive to understanding the development trends of various geographical regions and rationally planning land resources.

[0081] In this embodiment, the associated regional labels, associated indexes and distance indexes are obtained through the regional labels of each region, and a development prediction model is established based on historical geographic surveying and mapping data. The development prediction model is used to obtain a development label set based on the associated regional labels, associated indexes and distance indexes, and accurate predictions are made for regional development, which is conducive to conveniently and directly understanding the development trends of various geographical regions.

[0082] The greedy algorithm is used to obtain the target development label of the first development label, the second development label and the third development label according to the development label set, which is the optimal development label, to make a more reasonable and effective development plan for the development of the geographical area and improve the rationality and convenience of the urban layout.

[0083] The ecological conditions of each area in the updated three-dimensional geographic model are evaluated and corresponding ecological early warnings are carried out to ensure the ecological development of the corresponding geographical areas and benefit the overall ecological layout.

[0084] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a fully hardware embodiment, a fully software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media containing computer-usable program code. The storage medium may be implemented by any type of volatile or non-volatile storage device, or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0085] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A method for processing and analyzing geographic surveying and mapping data based on artificial intelligence, characterized in that: The following steps are involved: Step S1, obtaining geographic surveying and mapping data, obtaining regional labels based on the geographic surveying and mapping data using a regional feature recognition model, and establishing a three-dimensional geographic model; Step S2: dividing the three-dimensional geographic model into regions using boundary coordinates, obtaining region labels, obtaining a first development index based on a first mapping relationship, obtaining associated region labels and an associated index based on a second mapping relationship, obtaining a distance index based on a third mapping relationship, obtaining a second development index using the associated index and the distance index, and establishing a development prediction model based on historical geographic surveying and mapping data to obtain a development label set; Step S3, using a greedy algorithm to determine the target development label according to the development label set, and updating the three-dimensional geographic model; Step S4: obtaining a regional development ecological value according to the first development index, the second development index and the development label, and performing a regional ecological early warning according to the regional development ecological value.

2. The method for processing and analyzing geographic surveying and mapping data based on artificial intelligence according to claim 1, wherein: The step S1 specifically includes the following steps: Step S101, acquiring geographic surveying and mapping data, and performing data preprocessing on the geographic surveying and mapping data, wherein the data preprocessing includes data filtering, data correction, and coordinate conversion; Step S102: Acquire historical geographic surveying and mapping data, retrieve image data from the historical geographic surveying and mapping data, annotate the image data with regional labels, and input the annotated image data into a convolutional neural network with the image data as input and the regional labels as output. Training is terminated until the output fit is greater than or equal to a first fit expectation threshold, thereby obtaining a regional feature recognition model. Step S103: input the geographic surveying and mapping data into a regional feature recognition model, obtain labels for each region, and establish a three-dimensional geographic model.

3. The method for processing and analyzing geographic surveying and mapping data based on artificial intelligence according to claim 1, wherein: The step S2 specifically includes the following steps: Step S201: Divide the three-dimensional geographic model into regions according to boundary coordinates, obtain region numbers and region labels, and obtain a first development index according to a first mapping relationship, wherein the first mapping relationship includes a mapping relationship between the region label and the first development index; Step S202: Obtain associated region labels using a second mapping relationship based on the region labels, obtain associated regions from the three-dimensional geographic model based on the associated region labels, obtain three-dimensional coordinates of the associated regions, obtain associated distances using the three-dimensional coordinates of the associated regions, obtain an association index of the associated regions based on the second mapping relationship, obtain a distance index of each associated region based on the association distance using a third mapping relationship, perform a weighted calculation on the association index and the distance index of each region to obtain a second development index, wherein the second mapping relationship includes an association mapping relationship and an association index mapping relationship between each region label, and the third mapping relationship includes a mapping relationship between an association distance and an association index; Step S203: Acquire historical geographic surveying and mapping data, repeat step S202, obtain historical associated region labels, historical associated indexes, and historical distance indexes of historical geographic test data, annotate the historical geographic test data with development labels, and input the annotated historical geographic test data into a machine learning model, using the historical associated region labels, historical associated indexes, and historical distance indexes as model inputs and the development labels as data outputs. Training is stopped until the output fit is greater than or equal to a second fit expectation threshold, thereby obtaining a development prediction model. Step S204, obtain the first associated area label, the first associated index and the first distance index of the first range of each area of the geographic surveying and mapping data, and input the first associated area label, the first associated index and the first distance index into the development prediction model to obtain the first development label, obtain the second associated area label, the second associated index and the second distance index of the second range of each area of the geographic surveying and mapping data, and use the development prediction model to obtain the second development label of each area, and repeat the above steps to obtain the third development label of each area, and combine the first development label, the second development label and the third development label into a development label set.

4. The method for processing and analyzing geographic surveying and mapping data based on artificial intelligence according to claim 1, wherein: The step S3 specifically includes the following steps: Step S301, obtaining an objective function according to the correlation index and the distance index, and using the objective function to obtain a development target value of a first development label, a development target value of a second development label, and a development target value of a third development label; Step S302 : defining the development tag with the largest development target value as a target development tag, and updating the three-dimensional geographic model according to the target development tag.

5. The method for processing and analyzing geographic surveying and mapping data based on artificial intelligence according to claim 1, wherein: The step S4 specifically includes the following steps: Step S401: Obtaining a development ecological value for each region of the three-dimensional geographic model according to a fourth mapping relationship using the target development label of each region, obtaining an ecological value within a unit range based on the development ecological value, and obtaining an ecological development label within the unit range based on the ecological value, wherein the ecological development label includes qualified ecological development and unqualified ecological development. Step S402: Obtain a unit range with an ecological development label of unqualified ecological development, define each area within the unit range whose development ecological value is less than or equal to the expected threshold as an unqualified ecological development area, and issue an ecological warning for each unqualified ecological development area.

6. The method for processing and analyzing geographic surveying and mapping data based on artificial intelligence according to claim 2, characterized in that: The process of establishing a regional feature recognition model includes: Image enhancement and image denoising are performed on the input image data to obtain an enhanced image, a first convolution operation is performed on the enhanced image to extract the first local feature of the enhanced image, and a first pooling operation is performed using the maximum pooling method to extract the first local feature with the largest feature point product in the local area as the second local feature, a second convolution operation and a second pooling operation are performed on the second local feature to obtain a third local feature, feature fusion is performed on the third local feature of each local area of the image to obtain image features, and the image features and the image region label are input into and output from neurons for training to obtain a regional feature recognition model.

7. The method for processing and analyzing geographic surveying and mapping data based on artificial intelligence according to claim 4, characterized in that: The calculation expression of the objective function is: F(R i )=∑p j *w j ; Among them, R i is the development target value, p j is the regional convenience value corresponding to each association index, w j is the distance index.

8. The method for processing and analyzing geographic surveying and mapping data based on artificial intelligence according to claim 1, wherein: The regional labels include urban administrative areas, cultural and educational areas, medical and health areas, commercial service areas, education and scientific research areas, social welfare areas and housing security areas.

9. The method for processing and analyzing geographic surveying and mapping data based on artificial intelligence according to claim 1, wherein: The development labels include urban administrative areas, cultural and educational areas, medical and health areas, commercial service areas, education and scientific research, social welfare areas and housing security areas.

10. 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 computer program, the method for processing and analyzing geographic surveying and mapping data based on artificial intelligence as described in any one of claims 1 to 9 is implemented.

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