A method and system for regional mapping of real estate surveying and mapping projects based on drones

Abstract

The present invention discloses a method and system for sub-region mapping of real estate mapping projects based on unmanned aerial vehicles. The method includes the following steps: S1. According to the geographical information of the regional scope of the real estate mapping project, divide the entire regional scope into multiple unmanned aerial vehicle mapping regions and set the first hue layer; S2. Obtain the GIS geographical locations and regional scopes corresponding to each specific real estate unit within each mapping region and set them as the second hue layer; S3. Select an AI to control the unmanned aerial vehicle mapping device and plan the optimal flight trajectory; S4. Conduct aerial photography on each mapping region in sequence to generate preprocessed pictures; S5. The ground station optimizes the flight trajectory and speed of the AI-controlled unmanned aerial vehicle; S6. Obtain the accurate geographical locations and regional scopes corresponding to each specific real estate unit; S7. Perform stitching to generate the real estate mapping geographical base map results. The present invention can greatly improve work efficiency and the accuracy of mapping results.

Description

Technical Field

[0001] The present invention relates to the technical field of real estate surveying and mapping, and particularly to a method and system for sub-region surveying and mapping of real estate surveying and mapping projects based on unmanned aerial vehicles (UAVs). Background Art

[0002] According to the provisions of existing laws and regulations, the content of real estate registration includes multiple aspects, such as the ownership and other right status of immovables such as land, sea areas, and the buildings, structures, forests, and forest trees thereon. The natural conditions are one of the main contents of real estate registration. The data such as location, boundary, spatial boundary, and area in the natural conditions are all realized through surveying and mapping means. Therefore, surveying and mapping play a crucial role in real estate investigation and registration. Real estate refers to immovable property according to natural properties or legal provisions, such as land, houses, exploration rights, mining rights, and other land attachments, land products not yet separated from the land, and other things added to the land by nature or by human effort and unable to be separated. The characteristics of real estate as a natural object are: immovability, also known as location fixity, that is, the geographical location is fixed; individuality, also known as uniqueness, including location differences, utilization degree differences, and right differences; durability, also known as long lifespan, such as land is not damaged or destroyed due to use or placement, and it appreciates; limited quantity, also known as limited supply, the total amount of land is fixed and limited, and the economic supply is elastic.

[0003] In the real estate surveying and mapping work during the on-site investigation, right confirmation, and registration of real estate, the real estate surveying and mapping geographical base map is an essential basic map. It is the positioning basis for real estate surveying and mapping and also the carrier platform for real estate special topics. In order to smoothly carry out the data registration of real estate projects, first, an unmanned aerial vehicle conducts aerial orthophoto photography over the corresponding survey area, and then the captured color photos are transmitted to the background. The background staff divides the boundaries according to the different colors under the current environmental characteristics, so as to determine the administrative regions and corresponding data of the real estate project. However, using the existing surveying and mapping methods, due to the relatively high flight altitude during UAV control, and the lack of effective verification and calibration means for various data during the on-site data collection and subsequent manual analysis and processing, the work efficiency is low, and the accuracy of the surveying and mapping results cannot meet the requirements. Therefore, improvement is needed. Summary of the Invention

[0004] Aiming at the existing deficiencies, the present invention provides a method and system for sub-region surveying and mapping of real estate surveying and mapping projects based on unmanned aerial vehicles, adopting a new technical solution to improve work efficiency and the accuracy of surveying and mapping results, and solve the problems of the above-mentioned existing technologies.

[0005] To achieve the above object, the present invention provides the following technical solutions:

[0006] A method for regional mapping of real estate surveying and mapping projects based on drones, characterized by the following steps:

[0007] S1. According to the geographical information of the regional scope of the real estate surveying and mapping project, divide the entire regional scope into multiple drone surveying and mapping areas, and each drone surveying and mapping area can be completed with surveying and mapping coverage in one drone flight; according to the highest elevation corresponding to each surveying and mapping area, set it as the color of the first layer, that is, the first hue layer, and the colors set for each adjacent drone surveying and mapping area are different and staggered.

[0008] Among them, the geographical information of the regional scope of the real estate surveying and mapping project and the highest elevation information corresponding to each surveying and mapping area are obtained in advance through satellite remote sensing data.

[0009] S2. Obtain the GIS geographical locations and regional scopes corresponding to each specific real estate unit within each surveying and mapping area, and then set the approximate geographical locations and regional scopes corresponding to each specific real estate unit as the color of the second layer, that is, the second hue layer, and the colors of each adjacent specific real estate unit are different and staggered.

[0010] Among them, each specific real estate unit includes land, rivers, waters, sea areas, and buildings, structures, forests, and forest trees fixed on them.

[0011] S3. Based on the setting of the colors of the first hue layer and the second hue layer of each surveying and mapping area that are staggered and stacked with each other, select an AI-controlled drone surveying and mapping device, combine with the aerial surveying and mapping method to generate the border of the surveying and mapping area, and then set the border of the surveying and mapping area for the final single flight surveying by measuring the maximum horizontal distance a and the maximum vertical distance b spanned by the surveying target area, and plan the optimal flight trajectory.

[0012] S4. Use an AI to control the drone and, according to the planned optimal flight trajectory, conduct aerial photography on each surveying and mapping area in turn. The ground station processes the pictures taken by the drone in real time and transmitted back to generate preprocessed pictures.

[0013] Among them, the control process of the drone flight speed includes the following steps:

[0014] S41. Select a preset flight altitude and airworthiness speed range as needed.

[0015] S42. During the actual flight process, the AI program judges the influence of the surrounding environment on the flight speed and data transmission and copying.

[0016] S43. The AI program adjusts the flight speed in real time to make the aerial surveying data obtained by the drone during the flight process meet the required accuracy and real-time transmission requirements.

[0017] S5. The ground station optimizes the flight trajectory and speed of the AI-controlled drone by analyzing the pre-processed images, and uses a GNSS positioning unit, an infrared signal receiver, and an infrared signal transmitter to perform real-time positioning on the position of the AI-controlled drone. On the premise of ensuring the mapping efficiency, the same area is repeatedly mapped at a set ratio, and the information of the repeatedly mapped same area is used as the benchmark for positioning correction and splicing verification;

[0018] Among them, the set ratio for repeatedly mapping the same area is an area with a unilateral overlapping coverage of 20-30%, and the information of the repeatedly mapped same area is used as the benchmark for positioning correction during navigation and later splicing verification;

[0019] S6. The ground station divides the obtained pre-processed images into multiple pieces of images, and then performs double-layer hue display analysis on the images within each piece of image. According to the results of the double-layer hue display analysis, the data of each specific real estate unit within each mapping area is identified and processed to obtain the accurate geographical location and regional scope corresponding to each specific real estate unit;

[0020] S7. After each of the multiple drone mapping areas is processed separately, they are spliced together to finally cover the entire area of the real estate mapping project, and positioning correction and splicing verification are performed according to the information of the repeatedly mapped same area to obtain the accurate data of all specific real estate units within the real estate mapping project area. According to the set scale, a real estate mapping geographical base map result is finally generated, which details the location, boundary, spatial boundary, and area data of each specific real estate unit.

[0021] A regional mapping system for real estate mapping projects based on drones, characterized by including the following interconnected and communicating units:

[0022] An AI-controlled drone (1) unit capable of aerial photography;

[0023] A ground control station (2) for controlling the AI-controlled drone (1) and receiving aerial photography data;

[0024] An in-house processing and quality control unit for processing the regional scope of the real estate mapping project, processing aerial photography data, and outputting the real estate mapping geographical base map result;

[0025] Among them, the ground control station (2) includes:

[0026] A picture receiving module (3) for receiving pictures taken by the AI-controlled drone (1) for the mapping area to form pre-processed pictures;

[0027] A user area setting module (4) for pre-dividing the geographical location into regions;

[0028] A hue display module (5) for performing hue display on the same area;

[0029] A hue matching module (6) for color-matching immovables at the same geographical location;

[0030] And a controller (7) for statistically processing each data, wherein the controller (7) is electrically connected to the picture receiving module (3), the user area setting module (4), and the hue matching module (6), and a GNSS positioning unit (16), an infrared signal receiver (17), and an infrared signal transmitter (18) are connected to the AI-controlled unmanned aerial vehicle (1).

[0031] The beneficial effects of the present invention are as follows:

[0032] The immovable property surveying and mapping project sub-region surveying method and system provided by the present invention perform pre-processing on the immovable property surveying and mapping project sub-region surveying, obtain high-quality measurement information based on the AI-controlled unmanned aerial vehicle, and perform efficient post-processing, and can quickly and accurately process a large number of immovable property data. Through various technical improvements, the problems of low accuracy of current measurement data, insufficient processing capacity, and low efficiency of processing methods are finally solved, and the work efficiency and the accuracy of the surveying and mapping results can be greatly improved. After actual testing, the technology of the present invention can improve the efficiency by more than 30% and the accuracy by more than 10% compared with the existing conventional unmanned aerial vehicle surveying and mapping. Description of the Drawings

[0033] Figure 1 It is a schematic composition diagram of the immovable property surveying and mapping project sub-region surveying system based on the unmanned aerial vehicle in Embodiment 1 of the present invention;

[0034] Figure 2 It is a schematic composition structure diagram of the immovable property surveying and mapping project sub-region surveying system based on the unmanned aerial vehicle in Embodiment 2 of the present invention;

[0035] Figure 3 It is a schematic composition structure diagram of the immovable property surveying and mapping project sub-region surveying system based on the unmanned aerial vehicle in Embodiment 3 of the present invention;

[0036] Figure 4 It is a schematic module structure diagram of the environmental detector in Embodiment 3 of the present invention;

[0037] Figure 5 It is a schematic module structure diagram of the immovable property surveying and mapping project sub-region surveying system based on the unmanned aerial vehicle in Embodiment 4 of the present invention;

[0038] Figure 6 It is a schematic module composition structure diagram of the immovable property surveying and mapping project sub-region surveying system based on the unmanned aerial vehicle in Embodiment 5 of the present invention.

[0039] Figure 7 Schematic diagram of the 3D model of a partial survey object in a sub-region of a real estate surveying and mapping project in Embodiment 1 of the present invention;

[0040] Figure 8 Result of a single-region local geographic base map made from a sub-region survey of a real estate surveying and mapping project based on a 3D model in Embodiment 1 of the present invention;

[0041] Figure 9 Schematic diagram of an orthophoto of a partial survey object in a sub-region of a real estate surveying and mapping project in Embodiment 1 of the present invention;

[0042] Figure 10 Result of a single-region local geographic base map made from a sub-region survey of a real estate surveying and mapping project based on an orthophoto in Embodiment 1 of the present invention.

[0043] Figure 11 Result of a geographic base map obtained by splicing and synthesizing sub-region surveys of a real estate surveying and mapping project in Embodiment 1 of the present invention.

[0044] In the reference numerals: 1, AI-controlled unmanned aerial vehicle; 2, ground control station; 3, picture receiving module; 4, user area setting module; 5, hue display module; 6, hue matching module; 7, controller; 8, environment detector; 9, humidity detector; 10, temperature detector; 11, wind speed detector; 12, user information detector; 13, automatic obstacle avoidance sensor; 14, low battery alarm device 15; GNSS position sensor; 16, GNSS positioning unit; 17, infrared signal receiver; 18, infrared signal transmitter. Detailed implementation manners

[0045] The invention will be further described below in conjunction with embodiments.

[0046] Embodiment 1:

[0047] Refer to the appendix Figures 1-11 , the method for sub-region survey of a real estate surveying and mapping project based on an unmanned aerial vehicle provided in this embodiment is to draw a real estate surveying and mapping geographic base map of the land use, building distribution, etc. of each small area (local area) through sub-region survey for multiple small areas within a set large area, and then process and splice to finally obtain a geographic base map result with a scale of 1:100,000, which specifically includes the following steps:

[0048] S1. According to the geographic information of the regional scope of the real estate surveying and mapping project, divide the entire regional scope into multiple unmanned aerial vehicle surveying areas, and each unmanned aerial vehicle surveying area can complete survey coverage during one flight of the unmanned aerial vehicle; set the color of the highest elevation corresponding to each surveying area as the color of the first layer, that is, the first hue layer, and the colors set for each adjacent unmanned aerial vehicle surveying area are different (can be set as light blue, light green, green, blue, yellow, red, etc.) and are staggered;

[0049] S2. Obtain the GIS geographical locations and regional ranges corresponding to each specific real estate unit within each survey area, and then set the approximate geographical locations and regional ranges corresponding to each specific real estate unit as the second-layer color, that is, the second hue layer. The colors of adjacent specific real estate units are different and are set alternately;

[0050] S3. Based on the setting of the interlaced and stacked colors of the first hue layer and the second hue layer of each survey area, select an AI-controlled drone surveying and mapping device, combine it with the aerial surveying and mapping method to generate the survey area border, and then set the final survey area border for a single flight survey by measuring the maximum horizontal distance a and the maximum vertical distance b across the survey target area, and plan the optimal flight trajectory;

[0051] S4. Use the AI to control the drone and conduct aerial photography imaging on each survey area in turn according to the planned optimal flight trajectory. The ground station processes the pictures taken by the drone in real time and transmitted back to generate preprocessed pictures;

[0052] S5. The ground station optimizes the flight trajectory and flight speed of the AI-controlled drone by analyzing the preprocessed pictures, and uses a GNSS positioning unit, an infrared signal receiver, and an infrared signal transmitter to perform real-time positioning on the position of the AI-controlled drone. On the premise of ensuring the surveying and mapping efficiency, repeat the surveying and mapping of the same area in a set proportion. The information of the same area in this repeated surveying and mapping is used as the benchmark for positioning correction and splicing verification;

[0053] S6. The ground station divides the obtained preprocessed pictures into multiple pieces of pictures, and then performs double-layer hue display analysis on the images within each piece of picture. According to the results of the double-layer hue display analysis, identify and process the data of each specific real estate unit within each survey area, and obtain the accurate geographical locations and regional ranges corresponding to each specific real estate unit;

[0054] S7. After processing each of the multiple drone survey areas separately, perform splicing, finally covering the entire regional range of the real estate surveying and mapping project, and perform positioning correction and splicing verification according to the information of the same area in the repeated surveying and mapping, obtain the accurate data of all specific real estate units within the real estate surveying and mapping project area, and finally generate a real estate surveying and mapping geographical base map result that details the location, boundary, spatial boundary, and area data of each specific real estate unit according to the set scale.

[0055] A sub-regional surveying and mapping system for a real estate surveying and mapping project based on drones, used to implement the aforementioned regional surveying and mapping method for a real estate surveying and mapping project based on drones, including the following units that are connected and communicate with each other:

[0056] An AI-controlled drone 1 unit capable of aerial photography;

[0057] A ground control station 2 for controlling the AI - controlled drone 1 and receiving aerial survey data;

[0058] An in - house processing and quality control unit for processing the area range of real - estate surveying and mapping projects, aerial survey data processing, and outputting the geographical base map results of real - estate surveying and mapping;

[0059] Among them, the ground control station 2 includes:

[0060] A picture receiving module 3 for receiving pictures taken by the AI - controlled drone 1 for the surveyed area and forming pre - processed pictures;

[0061] A user area setting module 4 for pre - dividing the geographical location into areas;

[0062] A hue display module 5 for displaying the hue of the same area;

[0063] A hue matching module 6 for color - matching real - estate properties at the same geographical location;

[0064] And a controller 7 for statistically processing each data, where the controller 7 is electrically connected to the picture receiving module 3, the user area setting module 4, and the hue matching module 6. A GNSS positioning unit 16, an infrared signal receiver 17, and an infrared signal transmitter 18 are connected to the AI - controlled drone 1.

[0065] The working steps of this embodiment are mainly as follows: First, generate the border of the surveyed area by combining parameters such as the single - flight distance and effective surveyed area of the AI - controlled drone surveying device and the surveying method, and set the final border of the surveyed area by measuring the maximum horizontal distance a and the maximum vertical distance b spanned by the surveyed target area; then use the AI - controlled drone to take aerial images and process the real - time taken pictures into pre - processed pictures; and optimize the flight trajectory and flight speed of the AI - controlled drone, and use the GNSS positioning unit, infrared signal receiver, and infrared signal transmitter to real - time locate the position of the AI - controlled drone to avoid the repeated surveying process of the same area while ensuring the surveying effect; divide the obtained pre - processed pictures into multiple pieces of pictures, then display the hue of the images within each piece of picture, pre - set the images corresponding to the land to the same hue, and set other real - estate properties in different colors to support the flight control of the AI - controlled program for automatic drone surveying.

[0066] Appendix Figure 7 is a partial three - dimensional model schematic diagram of the sub - area survey of the real - estate surveying and mapping project of this embodiment; Appendix Figure 8The local geographic base map result made by dividing the 3D model real estate surveying and mapping project into regional surveys in this embodiment; Figure 9 It is a schematic diagram of the local orthophoto image of the real estate surveying and mapping project divided into regions in the embodiment of the present invention; Figure 10 The local geographic base map result made by dividing the orthophoto image real estate surveying and mapping project into regional surveys in this embodiment; Figure 11 It is the overall geographic base map result of this surveying and mapping project obtained after splicing, and its scale is 1:100,000.

[0067] The method and system for dividing the real estate surveying and mapping project into regional surveys provided by the embodiment of the present invention are for a specific large-scale regional surveying and mapping project. First, it is divided into regions according to the performance characteristics of the unmanned aerial vehicle (UAV), and each region is surveyed and mapped by the UAV in one flight; the pre-processing of the real estate surveying and mapping project divided into regional surveys is carried out first, then high-quality measurement information is obtained based on the AI-controlled UAV, and efficient post-processing is carried out. It can quickly obtain high-precision data for a large number of real estate projects and quickly and accurately process this data; through technical improvements in many aspects, it finally solves the problems of low accuracy of current measurement data, insufficient processing capacity, and low efficiency of processing methods, and can greatly improve work efficiency and the accuracy of surveying and mapping results. After the actual test of the surveying and mapping project of the embodiment of the present invention, the technology of the present invention can improve the efficiency by more than 30% and the accuracy by more than 10% compared with the existing conventional UAV surveying and mapping.

[0068] Embodiment 2:

[0069] As Figure 2 shown, the method and system for dividing the real estate surveying and mapping project into regional surveys based on the UAV provided in this embodiment are basically the same as those in Embodiment 1, and the difference is that: a user information detector 12 for adjusting the data of the controller 7 is also connected to the controller 7. By setting the user information detector 12, multiple registered users are supported in the later stage, each acting as an administrator, to synchronously receive or adjust data.

[0070] Embodiment 3:

[0071] Referring to Figure 3 、 Figure 4 , the method and system for dividing the real estate surveying and mapping project into regional surveys based on the UAV provided in this embodiment are basically the same as those in Embodiments 1 and 2, and the difference is that:

[0072] An environment detector 8 is connected to the front end of the AI-controlled UAV 1; the environment detector 8 includes a wind speed detector 11, a temperature detector 10, and a humidity detector 9. By setting the environment detector 8, the influence of the environment on the data shooting of the AI-controlled UAV 1 can be detected in real time, and the obtained data is sent to the AI control program and the controller 7 to adjust the flight state of the UAV.

[0073] Example 4:

[0074] Refer to Figure 5 , the method and system for regional mapping of real estate surveying and mapping projects based on unmanned aerial vehicles provided in this embodiment are basically the same as those in Embodiments 1-3, and the differences are as follows:

[0075] An automatic obstacle avoidance sensor 13 for preventing the unmanned aerial vehicle from colliding is also connected to the AI-controlled unmanned aerial vehicle 1. By setting the automatic obstacle avoidance sensor 13, it is possible to avoid the AI-controlled unmanned aerial vehicle 1 from colliding during flight, which affects the detection efficiency.

[0076] Example 5:

[0077] Refer to Figure 6 , the method and system for regional mapping of real estate surveying and mapping projects based on unmanned aerial vehicles provided in this embodiment are basically the same as those in Embodiments 1 and 2, and the differences are as follows:

[0078] A low battery alarm device 14 and a GNSS position sensor 15 capable of detecting the position of the unmanned aerial vehicle are also connected to the AI-controlled unmanned aerial vehicle 1. By setting the low battery alarm device 14, a quick reminder is given when the AI-controlled unmanned aerial vehicle 1 has a low battery, avoiding the problem of poor data detection efficiency caused by too low battery power (affecting the flight speed when it is lower than 30%). At the same time, by setting the GNSS position sensor 15, the accurate spatial coordinate position data of the AI-controlled unmanned aerial vehicle 1 can be monitored in real time and associated with the taken pictures.

[0079] In the above embodiments of the present invention, according to various working characteristics of unmanned aerial vehicle surveying and mapping, geographical locations are pre-set to divide regions and corresponding colors are assigned; then the geographical locations corresponding to the real estate are obtained, and then they are unified with the colors of the corresponding geographical locations; finally, according to the division of geographical locations, the real estate data information corresponding to the same geographical location is obtained, and finally the result of the regional division of the real estate surveying and mapping project is obtained. Therefore, through the above method and system, the real estate data can be surveyed quickly and accurately, and the problems of inaccurate data measurement, poor measurement effect and low measurement efficiency caused by manual data measurement at present are solved.

[0080] The foregoing has shown and described the basic principles, main features and advantages of the present invention. For those skilled in the art, it is obvious that the present invention is not limited to the details of the above-described exemplary embodiments, and without departing from the spirit or basic characteristics of the present invention, the present invention can be implemented in other specific forms. Therefore, from any point of view, the embodiments should be regarded as exemplary and non-limiting. The scope of the present invention is defined by the appended claims rather than the above description. Therefore, it is intended to embrace all changes within the meaning and scope of the equivalent elements of the claims in the present invention. Although the embodiments of the present invention have been shown and described, for those of ordinary skill in the art, it can be understood that various changes, modifications, substitutions and variations can be made to these embodiments without departing from the principles and spirit of the present invention. The scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for regional surveying and mapping of real estate surveying and mapping projects based on drones, characterized in that, It includes the following steps: S1. According to the geographical information of the real estate surveying and mapping project area, divide the entire area into multiple UAV surveying and mapping areas, and each UAV surveying and mapping area can complete surveying coverage during a single UAV flight; according to the elevation of the highest point corresponding to each surveying area, set it as the color of the first layer, that is, the first hue layer, and the colors set for adjacent UAV surveying and mapping areas are different and staggered; S2. Obtain the GIS geographical locations and area ranges corresponding to each specific real estate unit within each surveying area, and then set the approximate geographical locations and area ranges corresponding to each specific real estate unit as the color of the second layer, that is, the second hue layer, and the colors of adjacent specific real estate units are different and staggered; S3. Based on the settings of the interlaced and stacked colors of the first hue layer and the second hue layer of each surveying area, select an AI-controlled UAV surveying and mapping device, combine with the aerial surveying and mapping method to generate the border of the surveying area, and then set the border of the surveying area for the final single-flight survey by measuring the maximum horizontal distance a and the maximum vertical distance b across the surveying target area, and plan the optimal flight trajectory; S4. Use an AI to control the UAV and perform aerial photography on each surveying area in sequence according to the planned optimal flight trajectory. The ground station processes the pictures taken by the UAV in real time and transmitted back to generate preprocessed pictures; S5. The ground station optimizes the flight trajectory and flight speed of the AI-controlled UAV by analyzing the preprocessed pictures, and uses a GNSS positioning unit, an infrared signal receiver, and an infrared signal transmitter to perform real-time positioning on the position of the AI-controlled UAV. On the premise of ensuring the surveying efficiency, repeat the survey of the same area at a set ratio, and the information of the repeated survey of the same area is used as the benchmark for positioning correction and stitching verification; S6. The ground station divides the obtained preprocessed pictures into multiple pieces of pictures, and then performs double-layer hue display analysis on the images within each piece of picture. According to the results of the double-layer hue display analysis, identify and process the data of each specific real estate unit within each surveying area to obtain the accurate geographical locations and area ranges corresponding to each specific real estate unit; S7. After processing each of the multiple UAV surveying and mapping areas respectively, perform stitching, finally covering the entire area of the real estate surveying and mapping project, and perform positioning correction and stitching verification according to the information of the repeated survey of the same area to obtain the accurate data of all specific real estate units within the real estate surveying and mapping project area. According to the set scale, finally generate a real estate surveying and mapping geographical base map result that details the location, boundary, spatial boundary, and area data of each specific real estate unit.

2. The method for regional mapping of real estate surveying and mapping projects based on drones according to claim 1, characterized in that, The geographical information of the real estate surveying and mapping project area and the elevation information of the highest point corresponding to each surveying area in step S1 are obtained in advance through satellite remote sensing data.

3. The method for regional mapping of real estate surveying and mapping projects based on drones according to claim 1, wherein, Each specific real estate unit in step S2 includes land, rivers, waters, sea areas, and the buildings, structures, forests, and forest trees fixed on them.

4. The method for regional surveying and mapping of real estate surveying and mapping projects based on unmanned aerial vehicles according to claim 1, characterized in that, In step S4, the control process of the UAV flight speed includes the following steps: S41. Select a preset flight altitude and airworthiness speed range as needed; S42. During actual flight, the AI program judges the influence of the surrounding environment on the flight speed and data transmission and copying; S43. The AI program adjusts the flight speed in real time so that the aerial survey data obtained by the UAV during flight meets the required accuracy and real-time transmission requirements.

5. The method for regional surveying and mapping of real estate surveying and mapping projects based on drones according to claim 1, characterized in that: In step S5, the set ratio for repeated surveying of the same area is an area of unilateral overlapping coverage of 20 - 30%. The information of the same area for repeated surveying is used as a benchmark for positioning correction and later splicing and proofreading during the navigation process.

6. A sub-region mapping system for an unmanned aerial vehicle-based real estate mapping project, which is used to implement the sub-region mapping method for an unmanned aerial vehicle-based real estate mapping project according to any one of claims 1 to 5, characterized in that, It includes the following interconnected and communicating units: An AI-controlled UAV (1) unit capable of aerial survey; A ground control station (2) for controlling the AI-controlled UAV (1) and receiving aerial survey data; An in-house processing and quality control unit for processing the area scope of the real estate surveying project, processing aerial survey data, and outputting the geographical base map results of real estate surveying; Among them, the ground control station (2) includes: A picture receiving module (3) for receiving pictures taken by the AI-controlled UAV (1) for the surveyed area and forming preprocessed pictures; A user area setting module (4) for pre-dividing geographical locations into regions; A hue display module (5) for displaying the hue of the same area; A hue matching module (6) for color-matching real estate at the same geographical location; And a controller (7) for statistically processing each data. The controller (7) is electrically connected to the picture receiving module (3), the user area setting module (4), and the hue matching module (6). A GNSS positioning unit (16), an infrared signal receiver (17), and an infrared signal transmitter (18) are connected to the AI-controlled UAV (1).

7. The regional mapping system for real estate surveying projects based on drones according to claim 6, characterized in that, An environment detector (8) is connected to the front end of the AI-controlled UAV (1); the environment detector (8) includes a wind speed detector (11), a temperature detector (10), and a humidity detector (9); a user information detector (12) for adjusting the data of the controller (7) is also connected to the controller (7).

8. The sub-region mapping system for real estate mapping projects based on unmanned aerial vehicles according to claim 6, wherein An automatic obstacle avoidance sensor (13) for preventing the UAV from colliding is also provided on the AI-controlled UAV (1).

9. The sub-region mapping system for real estate mapping projects based on unmanned aerial vehicles according to claim 6, characterized in that, A low battery alarm device (14) and a GNSS position sensor (15) capable of detecting the position of the UAV are also connected to the AI-controlled UAV (1).

Images