A method and system for rural real estate surveying based on a UAV

Abstract

The application discloses a kind of rural real estate surveying and mapping method and system based on unmanned plane, it is related to unmanned plane surveying and mapping technical field, the method is called GIS database by unmanned plane task management system to obtain boundary coordinate and space range, and automatically generates flight route to collect image data and soil data, initial boundary line is generated after image processing and is converted into vector data and right unit is formed by right line matching;Soil nutrient state evaluation is carried out by constructing soil nutrient response index Inu;When soil nutrient level reaches standard, collect soil state data, and carry out soil potential evaluation by fitting and constructing resource evolution utilization index Isu, and the evaluation result is marked as different grade right unit by GIS system, realize the visual display of land attribute and cloud sharing.This method fuses remote sensing image processing, soil spectrum analysis and ecological stability evaluation, improves the intelligent level of rural land surveying and mapping and data utilization depth.

Description

Technical Field

[0001] This invention relates to the field of unmanned aerial vehicle (UAV) surveying technology, specifically to a method and system for rural real estate surveying based on UAVs. Background Technology

[0002] With the continuous maturation of remote sensing technology, unmanned aerial vehicle (UAV) platforms, and Geographic Information System (GIS) technology, the application of UAVs in rural real estate surveying is becoming increasingly widespread. Compared with traditional manual methods, UAVs have advantages such as rapid deployment, low cost, and high data acquisition efficiency, enabling rapid collection and updating of real estate boundaries, spatial forms, and land use conditions in complex rural terrain environments. However, in the vast areas of forest and arable land in rural areas, it is difficult to accurately identify land attributes and ownership units using only two-dimensional images or aerial photographs. In terms of agricultural land resource assessment, core parameters such as soil quality, fertility levels, and utilization potential lack integration and evaluation, making it difficult to support government land planning and precise policy implementation for farmers. Therefore, how to integrate soil parameter acquisition capabilities with UAV surveying tasks without changing the existing hardware structure, and enhance land use decision support capabilities through data processing, has become a key issue that current rural real estate surveying systems urgently need to address.

[0003] Currently, most rural real estate surveying methods still rely primarily on aerial imagery and manual surveying, resulting in problems such as unclear boundary identification, inaccurate feature classification, and a lack of attribute information. For example, the boundary between homesteads and cultivated land often changes due to natural evolution or human intervention, making it difficult for traditional image processing algorithms to automatically identify the actual ownership units. Furthermore, surveying systems typically only statically record location and area, neglecting the collection and analysis of dynamic data such as soil properties, nutrient status, and land use potential. In addition, the lack of systematic assessment indicators and feedback mechanisms for the sustainable use of land leads to the failure to promptly identify some degraded land, while high-quality plots lack effective identification and management optimization, thus hindering the scientific management and efficient utilization of rural land resources overall. Summary of the Invention

[0004] To address the shortcomings of existing technologies, this invention provides a method and system for rural real estate surveying based on unmanned aerial vehicles (UAVs), which solves the problems mentioned in the background.

[0005] To achieve the above objectives, the present invention provides the following technical solution: a method for rural real estate surveying based on unmanned aerial vehicles (UAVs), comprising the following steps:

[0006] S1. By reading the boundary coordinates and spatial range information of the real estate to be measured, and generating a flight path after calibration, the drone collects image data and soil data in real time based on the industrial camera and sensor group on board.

[0007] S2. After image processing, the image data is vectorized and topologically corrected to form closed boundaries. After matching with the cadastral database ownership lines to generate ownership units, the soil data is dimensionless to obtain soil optical data set and soil environmental data set.

[0008] S3. After combining the soil optical data set with the soil surface temperature, perform chemical nutrient response analysis on the soil and generate a soil nutrient status assessment.

[0009] S4. When the soil nutrient status assessment indicates that the soil nutrient level meets the standard, the soil status data collected by the sensor group is processed into a dimensionless data set to obtain the soil metabolism data set. After performing cycle stability analysis based on the soil metabolism data set, the data set is then fitted with the chemical nutrient response analysis results and the soil environment data set to perform soil potential analysis.

[0010] S5. Transmit the assessment results to the geographic information system in real time, mark the ownership units and form layers, and distribute them to management departments and farmers through the cloud sharing interface.

[0011] Preferably, S1 includes S11 and S12;

[0012] S11. The UAV's mission management system calls the Geographic Information System (GIS) database to read the boundary coordinates and spatial range information of the rural real estate to be measured, and automatically generates a flight path covering the survey area through joint calibration of the Inertial Measurement Unit (IMU) and the Global Positioning System (GPS).

[0013] S12. The drone takes off along the flight path and performs surveying tasks, collecting image data and soil data in real time based on the industrial camera and sensor group carried by the drone.

[0014] The industrial camera has a resolution of no less than 20 million pixels, a frame rate and exposure of no less than 30fps, and a spectral response range of 400-1000nm.

[0015] The sensor group includes a spectral sensor, an infrared thermal imager, and a microwave radiometer;

[0016] The spectral sensor is used to collect reflectance spectral data;

[0017] The infrared thermal imager is used to collect soil surface temperature data;

[0018] The microwave radiometer is used to collect soil surface moisture data.

[0019] Preferably, step S2 transmits the image data and soil data acquired by the UAV in real time to the edge node for image processing and data processing via a wireless network, specifically including steps S21, S22 and S23.

[0020] S21. Image processing is used to perform distortion correction, image stitching and brightness equalization on image data. Then, image segmentation algorithm is used to divide different land cover types into regions. Finally, edge detection algorithm is used to identify the gray-scale change positions of the divided regions and generate initial boundary lines.

[0021] The distortion correction is based on the three-axis angles of the UAV's inertial measurement unit (IMU) and GPS positioning coordinates, and uses a projection geometric inversion algorithm to eliminate geometric distortion errors in the image data; image stitching uses a feature point matching algorithm to identify common feature points in adjacent images, and uses affine transformation to spatially register the image data into an orthophoto covering the entire area; brightness equalization processing uses a histogram matching algorithm to adjust the grayscale histogram of the image data to a uniform distribution, eliminating local over-brightness and under-brightness;

[0022] S22. After image processing, the boundary pixel lines are converted into geographic vector data through a vectorization algorithm. The initial boundary lines are continuously corrected by eliminating hanging lines and breakpoints using topology correction rules to form complete closed boundary lines. Then, the closed boundary lines are automatically matched and corrected with the ownership lines in the cadastral database through a coordinate registration algorithm to generate ownership units. The ownership units are classified into homestead units, road units, forest land units, and cultivated land units according to their spatial range.

[0023] S23. Data processing is used to extract soil data of forest land units and cultivated land units in real time based on edge nodes, and to perform dimensionless processing to obtain soil optical data sets and soil environmental data sets.

[0024] The dimensionless processing is performed by removing the dimensional influence of power distribution characteristic data using the Max-Min method.

[0025] The soil optical data set includes soil reflectance tf;

[0026] The soil environmental data set includes soil surface temperature tc and soil surface humidity hs.

[0027] Preferably, S3 includes S31;

[0028] S31. Based on the sensitive wavelength ranges of soil nutrients in the near-infrared and visible light ranges, the target wavelength interval [λ1, λ2] is selected. Then, based on the soil optical data set, a chemical nutrient response analysis of the soil is performed. Combined with soil surface temperature tc to correct for spectral differences caused by temperature, a soil nutrient response index Inu is constructed to analyze the soil's chemical nutrient level, reflecting the soil's nutrient absorption capacity and fertility status. Specifically: In the formula, ln represents the logarithmic function, λ1 represents the lower limit wavelength of integration (set to 620 nm), λ2 represents the upper limit wavelength of integration (set to 2200 nm), and tf(λ) represents the soil reflectance at wavelength λ. This represents the overall spectral energy reflection intensity of the soil within the wavelength range [λ1,λ2].

[0029] Preferably, S3 further includes S32;

[0030] S32. Calculate the average soil nutrient response index Inu for historical soil nutrient levels meeting the standards using statistical methods, and preset the soil nutrient standard threshold Yb based on the average value. Then, compare it with the real-time acquired soil nutrient response index Inu to assess the soil nutrient status. The specific assessment scheme is as follows.

[0031] When the soil nutrient response index Inu < soil nutrient standard threshold Yb, it indicates that the soil nutrients are insufficient. At this time, the current ownership unit is marked as the third level, relevant personnel are notified to carry out soil remediation, and the frequency of drone mapping of the current plot is increased by 20%.

[0032] When the soil nutrient response index Inu ≥ soil nutrient standard threshold Yb, it indicates that the soil nutrient level meets the standard. At this time, maintain the normal UAV mapping frequency and perform microbial stability analysis.

[0033] Preferably, S4 includes S41 and S42;

[0034] S41. When the soil nutrient status assessment indicates that the soil nutrient level meets the standards, soil status data shall be collected in real time based on the sensor group deployed within the current ownership unit.

[0035] The sensor group includes a soil gas sensor and an integrated acoustic porosity meter.

[0036] The soil gas sensor is used to collect the carbon dioxide release rate ΔCO2 of the microbial community in the soil.

[0037] The integrated acoustic porosity meter is used to collect soil porosity kx.

[0038] Soil state data is transmitted to edge nodes via wireless network for dimensionless processing to obtain soil metabolic data sets, including carbon dioxide release rate ΔCO2 and soil porosity kx.

[0039] S42. Based on the soil metabolism data set, a cycle stability analysis was conducted on the ownership units where soil nutrient levels met the standards, and a cycle stability index Ibi was constructed to analyze the long-term stability of the soil ecosystem, reflecting the intensity of biological cycles under the combined action of soil microorganisms and pore structure. Specifically: In the formula, n represents the number of microbial communities, and Mi The proportion of the i-th soil microbial community is obtained through on-site detection using a portable DNA barcode sequencing chip, ΔCO 2，i This represents the carbon dioxide release rate of the i-th community per unit time.

[0040] Preferably, S4 further includes S43;

[0041] S43. Fit the chemical nutrient response analysis results and cycle stability analysis results to generate soil potential analysis and conduct soil potential assessment, specifically including S431 and S432.

[0042] S431. The soil nutrient response index Inu and the cycle stability index Ibi were fitted, and soil potential analysis was performed in conjunction with the soil environmental data set. A resource evolution and utilization index Isu was constructed to analyze the ultimate sustainable land use potential. Specifically: In the formula, ln represents the logarithmic function, and hs ref This indicates the surface moisture of the soil under standard conditions.

[0043] Preferably, in step S432, the average value of the resource evolution and utilization index Isu for historical soil nutrient levels meeting the standards is calculated using statistical methods, and a comprehensive utilization potential threshold Lq is preset based on the average value. Then, the soil potential is assessed by comparing the average value with the real-time acquired resource evolution and utilization index Isu. The specific assessment scheme is as follows.

[0044] When the resource evolution utilization index Isu < the comprehensive utilization potential threshold Lq, it indicates that the land is gradually degrading. At this time, the current ownership unit is marked as the second level, and the drone is adjusted to conduct an additional low-altitude patrol in the current ownership unit. If the iterative analysis still shows gradual degradation, relevant personnel are notified to carry out soil remediation.

[0045] When the resource evolution and utilization index Isu is greater than or equal to the comprehensive utilization potential threshold Lq, it indicates that the land use potential meets the requirements. At this time, the current ownership unit is marked as the first level, and the normal UAV mapping frequency is maintained.

[0046] Preferably, S5 includes S51 and S52;

[0047] S51. Establish a communication connection between the edge node and the geographic information system (GIS) through a wireless network, and transmit the results of soil nutrient status assessment and soil potential assessment to the geographic information system (GIS) in real time. Mark the ownership units according to the assessment results, and mark the ownership units with insufficient soil nutrients, land degradation and land use potential that meet the requirements as red, yellow and green respectively. After clicking on the ownership unit, display the various attributes of the currently clicked ownership unit.

[0048] The attributes include land use status, area of ​​ownership units, soil nutrient response status, cyclic stability status, and land potential.

[0049] S52. Distribute the labeled GIS layers to management departments and farmers through the cloud sharing interface.

[0050] A rural real estate surveying system based on unmanned aerial vehicles (UAVs) includes a data acquisition module, a data processing module, a nutrient analysis module, a potential assessment module, and a result sharing module.

[0051] The data acquisition module is used to read the boundary coordinates and spatial range information of the real estate to be measured, generate a flight path after calibration, and then collect image data and soil data in real time based on the industrial camera and sensor group carried by the UAV.

[0052] The data processing module is used to process the image data, form closed boundaries through vectorization and topological correction, generate ownership units by matching with the ownership lines of the cadastral database, and then perform dimensionless processing on the soil data to obtain soil optical data sets and soil environmental data sets.

[0053] The nutrient analysis module is used to combine soil optical data sets with soil surface temperature to perform chemical nutrient response analysis on the soil and generate a soil nutrient status assessment.

[0054] The potential assessment module is used to collect soil state data based on the sensor group when the soil nutrient status assessment shows that the soil nutrient level meets the standard. After dimensionless processing, the soil metabolic data group is obtained. Then, the cycle stability analysis is performed based on the soil metabolic data group, and the results are fitted with the chemical nutrient response analysis results and the soil environment data group to perform soil potential analysis.

[0055] The result sharing module is used to transmit the evaluation results to the geographic information system in real time, mark the ownership units and form layers, and distribute them to management departments and farmers through the cloud sharing interface.

[0056] This invention provides a method and system for rural real estate surveying based on unmanned aerial vehicles (UAVs). It has the following beneficial effects:

[0057] (1) This method automatically generates a flight path covering the target plot by calling the boundary coordinates and spatial range information of the real estate to be measured in the GIS database and under the joint calibration of the inertial measurement unit (IMU) and the global positioning system (GPS), thereby improving the autonomy and path accuracy of the surveying and mapping task. The industrial camera and multimodal sensor group carried by the UAV can collect high-resolution image data and soil data in real time during flight, avoiding the problems of heavy reliance on manpower, difficulty in entering and exiting the site, and slow data acquisition in traditional surveying and mapping. By uploading the data to the edge node in real time and performing distortion correction, image stitching, brightness equalization, image segmentation and boundary vectorization processing, the ownership unit division is completed and matched with the ownership line of the cadastral database to form classified plots with spatial attributes, thereby improving the automation, standardization and refinement of rural plot boundary identification.

[0058] (2) This method constructs a soil nutrient response index (Inu) to assess soil nutrient status by performing radiation correction and temperature correction on soil reflectance in the near-infrared and visible light sensitive bands. This reflects the comprehensive fertility level of soil organic matter, iron oxide, water content, and clay minerals. If the soil nutrient level meets the standard, soil state data is further collected, and after dimensionless processing, a soil metabolism data set is obtained to construct a cycle stability index (Ibi), which is used to quantify the ecological stability level under the coupled influence of soil structure looseness and biological activity. By fitting the soil nutrient response index (Inu) and the cycle stability index (Ibi), and combining them with the soil environmental data set to form a resource evolution and utilization index (Isu), soil potential is assessed. This allows the system to not only identify short-term soil quality but also predict land degradation trends, providing visualized decision support for farmland protection, land restoration, and agricultural planning.

[0059] (3) This method associates all assessment results with ownership units and dynamically labels them in red, yellow, and green in the GIS layer, corresponding to three states: insufficient soil nutrients, land degradation, and good land potential, respectively, demonstrating the readability, operability, and real-time nature of the surveying results. Managers or farmers can click on any unit to view its attribute data, and distribute it to agricultural management systems, policy decision-making platforms, and related application terminals through the cloud sharing interface, thus establishing a closed-loop chain of data collection, assessment and analysis, layer display, and information sharing. This method improves the data intelligence level and multi-dimensional depth of rural real estate surveying and mapping, expands the ecological dimension of land use evaluation, and has application and promotion value in protecting the red line of arable land, promoting the improvement of arable land quality, and building a dynamic monitoring system for agricultural resources. Attached Figure Description

[0060] Figure 1 This is a schematic diagram illustrating the steps of a rural real estate surveying method based on unmanned aerial vehicles (UAVs) according to the present invention.

[0061] Figure 2This is a schematic diagram of the process of a rural real estate surveying system based on unmanned aerial vehicles (UAVs) according to the present invention.

[0062] Figure 3 This is a flowchart illustrating the steps and principles of a rural real estate surveying method based on unmanned aerial vehicles (UAVs) according to the present invention. Detailed Implementation

[0063] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0064] Example 1: Please refer to Figure 1 This invention provides a method for rural real estate surveying based on unmanned aerial vehicles (UAVs). To achieve the above objectives, this invention employs the following technical solution, comprising the following steps:

[0065] S1. By reading the boundary coordinates and spatial range information of the real estate to be measured, and generating a flight path after calibration, the drone collects image data and soil data in real time based on the industrial camera and sensor group on board.

[0066] S2. After image processing, the image data is vectorized and topologically corrected to form closed boundaries. After matching with the cadastral database ownership lines to generate ownership units, the soil data is dimensionless to obtain soil optical data set and soil environmental data set.

[0067] S3. After combining the soil optical data set with the soil surface temperature, perform chemical nutrient response analysis on the soil and generate a soil nutrient status assessment.

[0068] S4. When the soil nutrient status assessment indicates that the soil nutrient level meets the standard, the soil status data collected by the sensor group is processed into a dimensionless data set to obtain the soil metabolism data set. After performing cycle stability analysis based on the soil metabolism data set, the data set is then fitted with the chemical nutrient response analysis results and the soil environment data set to perform soil potential analysis.

[0069] S5. Transmit the assessment results to the geographic information system in real time, mark the ownership units and form layers, and distribute them to management departments and farmers through the cloud sharing interface.

[0070] In this embodiment, a drone is used as the main operating platform. Combined with GIS database access and joint calibration technology using an inertial measurement unit (IMU) and a global positioning system (GPS), the boundary coordinates and spatial extent information of rural real estate are rapidly retrieved and path generated, forming a flight path oriented towards the measured plots. During the drone's flight, image and soil data are simultaneously collected using the industrial camera and sensor array onboard the drone. The operation relies on automated flight control and data acquisition, overcoming the inefficient process of traditionally relying on ground surveyors to record data point by point. This completes the unified collection of spatial geometry and surface environmental factors, providing continuous basic data support for subsequent boundary extraction and soil condition analysis. After acquiring image and soil data, image distortion correction, stitching, and vectorization are performed using edge nodes. Combined with cadastral database ownership lines, closed boundaries are constructed to establish ownership units. Simultaneously, the Max-Min method is used to perform dimensionless processing on the soil data, removing interference factors from different sensor dimensions, and obtaining soil optical data sets and soil environmental data sets. This process combines soil optical data sets with soil surface temperature to analyze the chemical nutrient response of soil, generating the soil nutrient response index Inu for soil nutrient status assessment and identification of the current fertility status of the plot. When the soil nutrient level meets the standards, soil state data is further incorporated to calculate the cycle stability index Ibi, which is then fitted with the chemical nutrient response analysis results for soil potential analysis, generating the resource evolution and utilization index Isu for soil potential analysis. Unlike existing surveying methods that are limited to geometric boundary descriptions, this process introduces a quantitative dimension of soil ecological attributes beyond ownership structures, providing multiple bases for plot hierarchical management. Based on the obtained soil nutrient status, cycle stability, and resource evolution and utilization levels, a GIS layer is constructed to form a plot information map covering multiple elements such as attributes, boundaries, nutrients, and potential. This map is synchronized to management departments and users via a cloud interface. This establishes an update channel for dynamic data, addressing the previous limitation of surveying results being used only for paper archiving or manual review. By visually classifying and labeling each ownership unit, and combining this with a click-based attribute viewing method, the system supports the identification, comparison, and response control of homesteads, farmland, and forest land under conditions such as nutrient depletion and soil structure deterioration. Compared with traditional land surveying methods, this method introduces soil state analysis, stability modeling, and information distribution mechanisms, constructing a more comprehensive decision support path for farmland supervision, land classification, and agricultural planning.

[0071] Example 2: Please refer to Figure 1 and Figure 3 Specifically: S1 includes S11 and S12;

[0072] S11. The UAV's mission management system calls the Geographic Information System (GIS) database to read the boundary coordinates and spatial range information of the rural real estate to be measured, and automatically generates a flight path covering the survey area through joint calibration of the Inertial Measurement Unit (IMU) and the Global Positioning System (GPS).

[0073] S12. The drone takes off along the flight path and performs surveying tasks, collecting image data and soil data in real time based on the industrial camera and sensor group carried by the drone.

[0074] The industrial camera has a resolution of no less than 20 million pixels, a frame rate and exposure of no less than 30fps, and a spectral response range of 400-1000nm.

[0075] The sensor group includes a spectral sensor, an infrared thermal imager, and a microwave radiometer;

[0076] The spectral sensor is used to collect reflectance spectral data;

[0077] The infrared thermal imager is used to collect soil surface temperature data;

[0078] The microwave radiometer is used to collect soil surface moisture data.

[0079] In this embodiment, the UAV mission management system accesses a GIS database to obtain the boundary coordinates and spatial extent information of the rural real estate to be surveyed. A flight path covering the survey area is generated through joint calibration of the inertial measurement unit (IMU) and the global positioning system (GPS), enabling the UAV to fly stably along the predetermined path and carry out the surveying task. During flight, an industrial camera acquires high-resolution, multi-frame-rate image data, a spectral sensor acquires reflectance spectra, an infrared thermal imager acquires soil surface temperature, and a microwave radiometer acquires soil surface moisture, forming a synchronous acquisition mechanism for spatial and soil environmental data. This method achieves continuous regional image acquisition and accurate extraction of surface environmental parameters while avoiding the limitations of traditional ground surveying that relies on manual point-by-point recording. It ensures consistency between boundary data and soil data in both space and time, providing a data foundation for subsequent boundary generation and soil analysis. This approach improves spatial positioning accuracy, operational efficiency, and multi-dimensional data fusion.

[0080] Example 3: Please refer to Figure 1 and Figure 3 Specifically: S2 transmits the image data and soil data acquired by the UAV in real time to the edge node for image processing and data processing via a wireless network, specifically including S21, S22 and S23;

[0081] S21. Image processing is used to perform distortion correction, image stitching and brightness equalization on image data. Then, image segmentation algorithm is used to divide different land cover types into regions. Finally, edge detection algorithm is used to identify the gray-scale change positions of the divided regions and generate initial boundary lines.

[0082] The distortion correction is based on the three-axis angles of the UAV's inertial measurement unit (IMU) and GPS positioning coordinates, and uses a projection geometric inversion algorithm to eliminate geometric distortion errors in the image data; image stitching uses a feature point matching algorithm to identify common feature points in adjacent images, and uses affine transformation to spatially register the image data into an orthophoto covering the entire area; brightness equalization processing uses a histogram matching algorithm to adjust the grayscale histogram of the image data to a uniform distribution, eliminating local over-brightness and under-brightness;

[0083] S22. After image processing, the boundary pixel lines are converted into geographic vector data through a vectorization algorithm. The initial boundary lines are continuously corrected by eliminating hanging lines and breakpoints using topology correction rules to form complete closed boundary lines. Then, the closed boundary lines are automatically matched and corrected with the ownership lines in the cadastral database through a coordinate registration algorithm to generate ownership units. The ownership units are classified into homestead units, road units, forest land units, and cultivated land units according to their spatial range.

[0084] S23. Data processing is used to extract soil data of forest land units and cultivated land units in real time based on edge nodes, and to perform dimensionless processing to obtain soil optical data sets and soil environmental data sets.

[0085] The dimensionless processing is performed by removing the dimensional influence of power distribution characteristic data using the Max-Min method.

[0086] The soil optical data set includes soil reflectance tf;

[0087] The soil environmental data set includes soil surface temperature tc and soil surface humidity hs.

[0088] In this embodiment, image data and soil data acquired by the UAV are transmitted to edge nodes in real time via a wireless network. These data undergo distortion correction, image stitching, and brightness equalization processing. Initial boundary lines are generated using image segmentation and edge detection algorithms, and closed boundaries are formed after vectorization and topological correction. These boundaries are then matched with ownership lines in the cadastral database to generate ownership units, classifying residential land, roads, forest land, and cultivated land. Simultaneously, soil data for forest land and cultivated land is extracted and dimensionlessly processed using the Max-Min method, constructing soil optical data sets and environmental data sets. This process eliminates image errors, ensures boundary coherence, and provides clearer spatial ranges for ownership units. The soil data exhibits comparability and uniformity. Compared to traditional methods relying on manual drawing and single-indicator comparison, this method achieves automated processing and multi-dimensional integrated analysis of surveying data, improving boundary accuracy, attribute completeness, and quantitative expression of soil conditions.

[0089] Example 4

[0090] Please refer to Figure 1 and Figure 3 Specifically: S3 includes S31;

[0091] S31. Based on the sensitive wavelength ranges of soil nutrients in the near-infrared and visible light ranges, the target wavelength interval [λ1, λ2] is selected. Then, based on the soil optical data set, a chemical nutrient response analysis of the soil is performed. Combined with soil surface temperature tc to correct for spectral differences caused by temperature, a soil nutrient response index Inu is constructed to analyze the soil's chemical nutrient level, reflecting the soil's nutrient absorption capacity and fertility status. Specifically: In the formula, ln represents the logarithmic function, λ1 represents the lower limit wavelength of integration, set to 620 nm, which is the red light absorption band used to detect soil organic matter and iron oxides, λ2 represents the upper limit wavelength of integration, set to 2200 nm, which is the short-wave infrared soil absorption band used to detect clay minerals, soil moisture content, and carbonates, and tf(λ) represents the soil reflectance at wavelength λ. This represents the overall spectral energy reflection intensity of the soil within the wavelength range [λ1,λ2].

[0092] S3 further includes S32;

[0093] S32. Calculate the average soil nutrient response index Inu for historical soil nutrient levels meeting the standards using statistical methods, and preset the soil nutrient standard threshold Yb based on the average value. Then, compare it with the real-time acquired soil nutrient response index Inu to assess the soil nutrient status. The specific assessment scheme is as follows.

[0094] When the soil nutrient response index Inu < soil nutrient standard threshold Yb, it indicates that the soil nutrients are insufficient. At this time, the current ownership unit is marked as the third level, relevant personnel are notified to carry out soil remediation, and the frequency of drone mapping of the current plot is increased by 20%.

[0095] When the soil nutrient response index Inu ≥ soil nutrient standard threshold Yb, it indicates that the soil nutrient level meets the standard. At this time, maintain the normal UAV mapping frequency and perform microbial stability analysis.

[0096] In this embodiment, based on the spectral characteristics of soil in the near-infrared and visible light sensitive bands, the target wavelength range [λ1, λ2] is selected. Combined with soil reflectance and surface temperature data, a soil nutrient response index Inu is constructed to quantitatively analyze the levels of soil chemical nutrients such as organic matter, iron oxide, water content, and clay minerals. Through a combination of spectral integration, temperature correction, and logarithmic compression, the reflectance characteristics of soil in the near-infrared and visible light sensitive bands are transformed into a comprehensive index characterizing the state of soil chemical nutrients. In spectroscopy and radiometry, the total effect of a physical quantity within a specific wavelength range is usually expressed in integral form, i.e. Where f(λ) represents the physical signal at wavelength λ, it is a fundamental expression in spectroscopy used to obtain the total energy and total reflection characteristics of a wavelength band. In this formula... This represents the cumulative amount of soil reflectance within the wavelength range [λ1, λ2]. Within a specific sensitive wavelength band, it represents the overall reflectance intensity of the soil to incident light, directly reflecting the presence and concentration of nutrient components. The average reflectance is represented by tc, which converts the cumulative value into band-average reflectance to make the integral result independent of the band width. The denominator tc represents the soil surface temperature, used to correct for spectral drift caused by thermal effects. Increased temperature alters the distribution of molecular vibrational energy levels, affecting the intensity of spectral absorption peaks. The logarithmic function ln is used to compress the ratio of the cumulative spectral intensity to the temperature-corrected value into a relatively convergent interval. In the formula, the soil reflectance tf(λ) is a dimensionless energy ratio, although the integral... The result has dimensions, but after dividing by the interval length (λ2−λ1), it becomes a dimensionless average again. The denominator, soil surface temperature tc, is a correction factor added after dimensionless processing. Therefore, the soil nutrient response index Inu is a dimensionless index. Subsequently, the average soil nutrient response index Inu of historically compliant soil nutrient levels was used to set the soil nutrient standard threshold Yb, and compared with the real-time soil nutrient response index Inu to classify and label the nutrient status of the current plot. When the soil nutrient response index Inu is lower than the soil nutrient standard threshold Yb, it is judged as nutrient deficiency, triggering a third-level marker and issuing a soil remediation prompt. At the same time, the frequency of UAV mapping for the current plot is increased to ensure dynamic tracking. When the soil nutrient response index Inu is greater than or equal to the soil nutrient standard threshold Yb, it is judged as nutrient compliance, maintaining normal mapping frequency and conducting microbial stability analysis. This process not only incorporates nutrient monitoring into the surveying process, but also dynamically integrates soil conditions into management through a grading and frequency control mechanism. This achieves the goal of quantitatively assessing land fertility while surveying boundaries, enabling early detection of soil degradation trends and targeted remediation and monitoring measures. It brings effectiveness to refined land management and sustainable agricultural use.

[0097] Example 5: Please refer to Figure 1 and Figure 3 Specifically: S4 includes S41 and S42;

[0098] S41. When the soil nutrient status assessment indicates that the soil nutrient level meets the standards, soil status data shall be collected in real time based on the sensor group deployed within the current ownership unit.

[0099] The sensor group includes a soil gas sensor and an integrated acoustic porosity meter.

[0100] The soil gas sensor is used to collect the carbon dioxide release rate ΔCO2 of the microbial community in the soil, which represents the rate at which the microbial community releases carbon dioxide per unit time.

[0101] The integrated acoustic porosity meter is used to collect soil porosity kx, which indicates the degree of looseness of soil structure;

[0102] Soil state data is transmitted to edge nodes via wireless network for dimensionless processing to obtain soil metabolic data sets, including carbon dioxide release rate ΔCO2 and soil porosity kx.

[0103] S42. Based on the soil metabolism data set, a cycle stability analysis was conducted on the ownership units where soil nutrient levels met the standards, and a cycle stability index Ibi was constructed to analyze the long-term stability of the soil ecosystem, reflecting the intensity of biological cycles under the combined action of soil microorganisms and pore structure. Specifically: In the formula, n represents the number of microbial communities, and M i The proportion of the i-th soil microbial community is obtained through on-site detection using a portable DNA barcode sequencing chip, ΔCO 2，i This represents the carbon dioxide release rate of the i-th community per unit time.

[0104] S4 also includes S43;

[0105] S43. Fit the chemical nutrient response analysis results and cycle stability analysis results to generate soil potential analysis and conduct soil potential assessment, specifically including S431 and S432.

[0106] S431. The soil nutrient response index Inu and the cycle stability index Ibi were fitted, and soil potential analysis was performed in conjunction with the soil environmental data set. A resource evolution and utilization index Isu was constructed to analyze the ultimate sustainable land use potential. Specifically: In the formula, ln represents the logarithmic function, and hs ref This indicates the surface moisture of the soil under standard conditions.

[0107] S432. Calculate the average value of the resource evolution and utilization index Isu for historical soil nutrient levels that meet the standards using statistical methods, and preset the comprehensive utilization potential threshold Lq based on the average value. Then, conduct a soil potential assessment with the real-time acquired resource evolution and utilization index Isu. The specific assessment scheme is as follows.

[0108] When the resource evolution utilization index Isu < the comprehensive utilization potential threshold Lq, it indicates that the land is gradually degrading. At this time, the current ownership unit is marked as the second level, and the drone is adjusted to conduct an additional low-altitude patrol in the current ownership unit. If the iterative analysis still shows gradual degradation, relevant personnel are notified to carry out soil remediation.

[0109] When the resource evolution and utilization index Isu is greater than or equal to the comprehensive utilization potential threshold Lq, it indicates that the land use potential meets the requirements. At this time, the current ownership unit is marked as the first level, and the normal UAV mapping frequency is maintained.

[0110] In this embodiment, when the soil nutrient status is assessed as meeting the standard, the carbon dioxide release rate ΔCO2 and soil porosity kx of the microbial community are collected by soil gas sensors and an integrated acoustic porosity meter deployed within the ownership unit. These data are then processed dimensionlessly through edge nodes to form a soil metabolic dataset. Combined with the soil microbial community proportion M... iA cycle stability index, Ibi, is constructed by combining the carbon dioxide release rate ΔCO2 with the microbial activity index (Ibi) to characterize the combined effects of microbial activity and pore structure on ecological cycles. The formula reflects the intensity of soil biological cycles maintained by the overall metabolic activity of the microbial community under certain pore structure constraints. In community ecology, the evaluation of multi-community effects often employs a weighted summation method, assigning the proportion M of each community to the summation. i Its metabolic rate ΔCO 2，i Multiply the results, then sum them over all communities to obtain the overall metabolic intensity of the community, and then apply the community-weighted average method. The derivation of this formula, based on the community-weighted average method, divides the total metabolic intensity by the porosity kx, introducing the constraint of physical structure. This allows the index to reflect not only the metabolic level but also the limitation of soil looseness on cycling. After normalization, the square root is taken to form the cycling stability index Ibi. In the formula, the proportion M of the soil microbial community... i The carbon dioxide release rate ΔCO2 and soil porosity kx are dimensionless values ​​after dimensionless processing, so the cycle stability index Ibi is also dimensionless. The nutrient response index Inu is then fitted to the cycle stability index Ibi, and corrected using soil environmental data to generate the resource evolution and utilization index Isu, which quantitatively assesses the sustainable use potential of land. Soil use potential essentially depends on chemical nutrient levels and ecological cycle stability. In the ecological indicator system, indicators from different dimensions are coupled additively to reflect the overall level; the formula originates from the ratio method in physics. This reflects the ratio between output and limitation, combining the soil nutrient response index Inu and the cycle stability index Ibi to represent the soil's potential supply and cycling capacity. The denominator represents the humidity correction, reflecting the constraint of soil moisture deviation on the cyclic process; ln(1+tc) represents the temperature correction, reflecting the nonlinear effect of temperature correction; the overall denominator is... Composed of humidity constraints and temperature corrections, it represents the comprehensive inhibition of soil environmental conditions on nutrient and cycling processes. In the formula, the soil nutrient response index Inu and the cycling stability index Ibi are both dimensionless indices. The humidity ratio is also dimensionless. Soil surface temperature (tc) has already been dimensionless, therefore the ln term of the logarithmic function remains dimensionless, making the resource evolution utilization index (Isu) a dimensionless index. Furthermore, using the comprehensive utilization potential threshold (Lq) as a criterion, land parcels are classified and dynamically labeled, completing a joint analysis of soil fertility and structure, identifying land degradation trends and potential states, ensuring the scientific rigor and continuity of soil resource evaluation, and providing direct evidence for UAV mapping frequency scheduling, land parcel classification management, and soil remediation early warning. This results in a comprehensive mapping outcome combining spatial boundaries and ecological attributes in rural real estate mapping.

[0111] Example 6: Please refer to Figure 1 Specifically: S5 includes S51 and S52;

[0112] S51. Establish a communication connection between the edge node and the geographic information system (GIS) through a wireless network, and transmit the results of soil nutrient status assessment and soil potential assessment to the geographic information system (GIS) in real time. Mark the ownership units according to the assessment results, and mark the ownership units with insufficient soil nutrients, land degradation and land use potential that meet the requirements as red, yellow and green respectively. After clicking on the ownership unit, display the various attributes of the currently clicked ownership unit.

[0113] The attributes include land use status, area of ​​ownership units, soil nutrient response status, cyclic stability status, and land potential.

[0114] S52. Distribute the labeled GIS layers to management departments and farmers through the cloud sharing interface.

[0115] In this embodiment, a wireless network establishes a communication connection between edge nodes and a Geographic Information System (GIS), synchronously transmitting soil nutrient status assessment and soil potential assessment results to the GIS. Ownership units are then categorized and labeled with red, yellow, and green colors based on their different statuses. Clicking on a unit visually displays its land use, area, nutrient response, cycle stability, and utilization potential. The labeled GIS layer is distributed to management departments and farmers via a cloud-based sharing interface, enabling unified access and synchronous updates of the same data to multiple parties. Through this implementation, the surveying results not only possess visualization and hierarchical characteristics but also shareability and dynamism, allowing rural real estate surveying data to serve land supervision, agricultural planning, and farmer decision-making. Improvements are achieved in ownership unit identification accuracy, soil status assessment depth, and information transmission efficiency, leading to an overall enhancement of the scientific nature of land management and the sustainability of agricultural production.

[0116] Example 7: Please refer to Figure 2 A rural real estate surveying system based on unmanned aerial vehicles (UAVs) includes a data acquisition module, a data processing module, a nutrient analysis module, a potential assessment module, and a result sharing module.

[0117] The data acquisition module is used to read the boundary coordinates and spatial range information of the real estate to be measured, generate a flight path after calibration, and then collect image data and soil data in real time based on the industrial camera and sensor group carried by the UAV.

[0118] The data processing module is used to process the image data, form closed boundaries through vectorization and topological correction, generate ownership units by matching with the ownership lines of the cadastral database, and then perform dimensionless processing on the soil data to obtain soil optical data sets and soil environmental data sets.

[0119] The nutrient analysis module is used to combine soil optical data sets with soil surface temperature to perform chemical nutrient response analysis on the soil and generate a soil nutrient status assessment.

[0120] The potential assessment module is used to collect soil state data based on the sensor group when the soil nutrient status assessment shows that the soil nutrient level meets the standard. After dimensionless processing, the soil metabolic data group is obtained. Then, the cycle stability analysis is performed based on the soil metabolic data group, and the results are fitted with the chemical nutrient response analysis results and the soil environment data group to perform soil potential analysis.

[0121] The result sharing module is used to transmit the evaluation results to the geographic information system in real time, mark the ownership units and form layers, and distribute them to management departments and farmers through the cloud sharing interface.

[0122] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A method for rural real estate surveying based on unmanned aerial vehicles (UAVs), characterized in that: Includes the following steps: S1. By reading the boundary coordinates and spatial range information of the real estate to be measured, and generating a flight path after calibration, the drone collects image data and soil data in real time based on the industrial camera and sensor group on board. S2. After image processing, the image data is vectorized and topologically corrected to form closed boundaries. After matching with the cadastral database ownership lines to generate ownership units, the soil data is dimensionless to obtain soil optical data set and soil environmental data set. S3. After combining the soil optical data set with the soil surface temperature, a chemical nutrient response analysis of the soil was performed to construct the soil nutrient response index Inu and generate a soil nutrient status assessment, specifically: In the formula, ln represents the logarithmic function, λ1 represents the lower limit wavelength of integration (set to 620 nm), λ2 represents the upper limit wavelength of integration (set to 2200 nm), and tf(λ) represents the soil reflectance at wavelength λ. tc represents the overall spectral energy reflectance of the soil within the wavelength range [λ1,λ2], and tc represents the soil surface temperature. S4. When the soil nutrient status assessment indicates that the soil nutrient level meets the standards, soil status data collected by the sensor set is processed dimensionlessly to obtain a soil metabolic data set. Cyclic stability analysis is then performed based on this data set, and a cyclic stability index Ibi is constructed, specifically as follows: In the formula, n represents the number of microbial communities, and M i The proportion of the i-th soil microbial community is obtained through on-site detection using a portable DNA barcode sequencing chip, ΔCO 2，i This represents the carbon dioxide release rate of the i-th community per unit time. The data is then fitted with chemical nutrient response analysis results and soil environmental data to perform soil potential analysis, and a resource evolution and utilization index, Isu, is constructed as follows: In the formula, ln represents the logarithmic function, and hs ref The value represents the surface moisture of the soil under standard conditions; hs represents the surface moisture of the soil. S5. Transmit the assessment results to the geographic information system in real time, mark the ownership units and form layers, and distribute them to management departments and farmers through the cloud sharing interface.

2. The method for rural real estate surveying based on unmanned aerial vehicles (UAVs) according to claim 1, characterized in that: S1 includes S11 and S12; S11. The UAV's mission management system calls the Geographic Information System (GIS) database to read the boundary coordinates and spatial range information of the rural real estate to be measured, and automatically generates a flight path covering the survey area under the joint calibration of the Inertial Measurement Unit (IMU) and the Global Positioning System (GPS). S12. The drone takes off along the flight path and performs surveying tasks, collecting image data and soil data in real time based on the industrial camera and sensor group carried by the drone. The industrial camera has a resolution of no less than 20 million pixels, a frame rate and exposure of no less than 30fps, and a spectral response range of 400-1000nm. The sensor group includes a spectral sensor, an infrared thermal imager, and a microwave radiometer; The spectral sensor is used to collect reflectance spectral data; The infrared thermal imager is used to collect soil surface temperature data; The microwave radiometer is used to collect soil surface moisture data.

3. The method for rural real estate surveying based on unmanned aerial vehicles (UAVs) according to claim 2, characterized in that: The S2 method transmits the real-time image and soil data acquired by the UAV to the edge node via a wireless network for image and data processing, specifically including S21, S22 and S23. S21. Image processing is used to perform distortion correction, image stitching and brightness equalization on image data. Then, image segmentation algorithm is used to divide different land cover types into regions. Finally, edge detection algorithm is used to identify the gray-scale change positions of the divided regions and generate initial boundary lines. The distortion correction is based on the three-axis angles of the UAV's inertial measurement unit (IMU) and GPS positioning coordinates, and uses a projection geometric inversion algorithm to eliminate geometric distortion errors in the image data; image stitching uses a feature point matching algorithm to identify common feature points in adjacent images, and uses affine transformation to spatially register the image data into an orthophoto covering the entire area; brightness equalization processing uses a histogram matching algorithm to adjust the grayscale histogram of the image data to a uniform distribution, eliminating local over-brightness and under-brightness; S22. After image processing, the boundary pixel lines are converted into geographic vector data through a vectorization algorithm. The initial boundary lines are continuously corrected by eliminating hanging lines and breakpoints using topology correction rules to form complete closed boundary lines. Then, the closed boundary lines are automatically matched and corrected with the ownership lines in the cadastral database through a coordinate registration algorithm to generate ownership units. The ownership units are classified into homestead units, road units, forest land units, and cultivated land units according to their spatial range. S23. Data processing is used to extract soil data of forest land units and cultivated land units in real time based on edge nodes, and to perform dimensionless processing to obtain soil optical data sets and soil environmental data sets. The soil optical data set includes soil reflectance tf; The soil environmental data set includes soil surface temperature tc and soil surface humidity hs.

4. The method for rural real estate surveying based on unmanned aerial vehicles (UAVs) according to claim 3, characterized in that: S3 includes S31; S31. Based on the sensitive wavelength ranges of soil nutrients in the near-infrared and visible light ranges, the target wavelength range [λ1,λ2] is intercepted. Based on the soil optical data set, the chemical nutrient response of the soil is analyzed. Combined with the soil surface temperature tc to correct the spectral differences caused by temperature, the soil nutrient response index Inu is constructed to analyze the chemical nutrient level of the soil and reflect the soil nutrient absorption capacity and fertility status.

5. A method for rural real estate surveying based on unmanned aerial vehicles (UAVs) according to claim 4, characterized in that: S3 further includes S32; S32. Calculate the average soil nutrient response index Inu for historical soil nutrient levels meeting the standards using statistical methods, and preset the soil nutrient standard threshold Yb based on the average value. Then, compare it with the real-time acquired soil nutrient response index Inu to assess the soil nutrient status. The specific assessment scheme is as follows. When the soil nutrient response index Inu < soil nutrient standard threshold Yb, it indicates that the soil nutrients are insufficient. At this time, the current ownership unit is marked as the third level, relevant personnel are notified to carry out soil remediation, and the frequency of drone mapping of the current plot is increased by 20%. When the soil nutrient response index Inu ≥ soil nutrient standard threshold Yb, it indicates that the soil nutrient level meets the standard. At this time, maintain the normal UAV mapping frequency and perform microbial stability analysis.

6. A method for rural real estate surveying based on unmanned aerial vehicles (UAVs) according to claim 5, characterized in that: S4 includes S41 and S42; S41. When the soil nutrient status assessment indicates that the soil nutrient level meets the standards, soil status data shall be collected in real time based on the sensor group deployed within the current ownership unit. The sensor group includes a soil gas sensor and an integrated acoustic porosity meter. The soil gas sensor is used to collect the carbon dioxide release rate ΔCO2 of the microbial community in the soil. The integrated acoustic porosity meter is used to collect soil porosity kx. Soil state data is transmitted to edge nodes via wireless network for dimensionless processing to obtain soil metabolic data sets, including carbon dioxide release rate ΔCO2 and soil porosity kx. S42. Based on the soil metabolism data set, conduct cycle stability analysis on the ownership units where soil nutrient levels meet the standards. This analysis is used to analyze the long-term stability of the soil ecosystem and reflect the intensity of biological cycles under the combined action of soil microorganisms and pore structure.

7. A method for rural real estate surveying based on unmanned aerial vehicles (UAVs) according to claim 6, characterized in that: S4 also includes S43; S43. Fit the chemical nutrient response analysis results and cycle stability analysis results to generate soil potential analysis and conduct soil potential assessment, specifically including S431 and S432. S431. The soil nutrient response index Inu and the cycle stability index Ibi were fitted, and soil potential analysis was performed in combination with the soil environmental data set to analyze the ultimate sustainable use potential of the land.

8. A method for rural real estate surveying based on unmanned aerial vehicles (UAVs) according to claim 7, characterized in that: S432. Calculate the average value of the resource evolution and utilization index Isu for historical soil nutrient levels that meet the standards using statistical methods, and preset the comprehensive utilization potential threshold Lq based on the average value. Then, conduct a soil potential assessment with the real-time acquired resource evolution and utilization index Isu. The specific assessment scheme is as follows. When the resource evolution utilization index Isu < the comprehensive utilization potential threshold Lq, it indicates that the land is gradually degrading. At this time, the current ownership unit is marked as the second level, and the drone is adjusted to conduct an additional low-altitude patrol in the current ownership unit. If the iterative analysis still shows gradual degradation, relevant personnel are notified to carry out soil remediation. When the resource evolution and utilization index Isu is greater than or equal to the comprehensive utilization potential threshold Lq, it indicates that the land use potential meets the requirements. At this time, the current ownership unit is marked as the first level, and the normal UAV mapping frequency is maintained.

9. A method for rural real estate surveying based on unmanned aerial vehicles (UAVs) according to claim 1, characterized in that: S5 includes S51 and S52; S51. Establish a communication connection between the edge node and the geographic information system (GIS) through a wireless network, and transmit the results of soil nutrient status assessment and soil potential assessment to the geographic information system (GIS) in real time. Mark the ownership units according to the assessment results, and mark the ownership units with insufficient soil nutrients, land degradation and land use potential that meet the requirements as red, yellow and green respectively. After clicking on the ownership unit, display the various attributes of the currently clicked ownership unit. The attributes include land use status, area of ​​ownership units, soil nutrient response status, cyclic stability status, and land potential. S52. Distribute the labeled GIS layers to management departments and farmers through the cloud sharing interface.

10. A rural real estate surveying system based on unmanned aerial vehicles (UAVs), comprising the rural real estate surveying method based on UAVs as described in any one of claims 1-9, characterized in that: It includes a data acquisition module, a data processing module, a nutrient analysis module, a potential assessment module, and a results sharing module; The data acquisition module is used to read the boundary coordinates and spatial range information of the real estate to be measured, generate a flight path after calibration, and then collect image data and soil data in real time based on the industrial camera and sensor group carried by the UAV. The data processing module is used to process the image data, form closed boundaries through vectorization and topological correction, generate ownership units by matching with the ownership lines of the cadastral database, and then perform dimensionless processing on the soil data to obtain soil optical data sets and soil environmental data sets. The nutrient analysis module is used to combine soil optical data sets with soil surface temperature to perform chemical nutrient response analysis on the soil and generate a soil nutrient status assessment. The potential assessment module is used to collect soil state data based on the sensor group when the soil nutrient status assessment shows that the soil nutrient level meets the standard. After dimensionless processing, the soil metabolic data group is obtained. Then, the cycle stability analysis is performed based on the soil metabolic data group, and the results are fitted with the chemical nutrient response analysis results and the soil environment data group to perform soil potential analysis. The result sharing module is used to transmit the evaluation results to the geographic information system in real time, mark the ownership units and form layers, and distribute them to management departments and farmers through the cloud sharing interface.

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