A method for constructing a core database for grassland ecological restoration monitoring

Abstract

The present invention discloses a method for constructing a core database for grassland ecological restoration monitoring, which relates to the technical field of database construction. The method includes: determining the ecological restoration scope, calibrating collection areas within the ecological restoration scope, wherein the collection areas are located within a certain range; obtaining grassland ecological restoration data within the collection scope, wherein the grassland ecological restoration data includes planting data, meteorological data, and land data, and the land types contained in the connecting area between adjacent collection areas are included in the collection areas on both sides; constructing an ecological data restoration model based on the grassland ecological restoration data within the collection area, obtaining meteorological data in the connecting area, predicting grassland ecological restoration data in the connecting area based on the meteorological data and land data, and establishing a database with statistics of grassland ecological restoration data in the collection area and the connecting area. According to the grassland ecological restoration scope, the grassland ecological restoration data within the collection area is determined, and the ecological data restoration model is established.

Description

Technical Field

[0001] The present invention relates to the technical field of database construction, and in particular to a method for constructing a core database for grassland ecological restoration monitoring. Background Art

[0002] Grassland ecological restoration monitoring is a systematic process that involves data collection and analysis from multiple aspects. Grassland survey: Through field surveys and remote sensing technology, data on grassland types, areas, distribution, quality, and utilization status are collected. Establishment of a monitoring and evaluation system: Build an integrated air-space-ground grassland monitoring network to strengthen dynamic grassland monitoring, and establish a mechanism for the submission, regular release, and information sharing of grassland monitoring and evaluation data. Grassland protection and restoration planning: Based on the national land space planning, formulate grassland functional zoning, protection objectives, and management measures. Grassland protection: Implement more stringent protection and management to ensure that the grassland area does not decrease, the quality does not decline, and the use does not change. Grassland ecological restoration: Implement vegetation and soil restoration on degraded grasslands to enhance the ecological and production functions of grasslands.

[0003] Announcement No. CN116383222A discloses a method for constructing a core database for grassland ecological restoration monitoring, which is divided into four steps: data collection, data processing and storage, data integration and transformation, and database construction. This method combines historical data and real-time data on grassland ecological restoration to establish a dynamic database. Data with short change cycles and high frequencies, such as meteorological and vegetation changes, in the database are kept frequently used and updated, making it convenient for staff to provide the latest grassland ecological data for subsequent proposals and on-site restoration. It combines DEM data, drone detailed photography, dynamic online maps and other methods to present grassland ecological restoration details in terms of spatial and temporal changes, contributing to the grid management system of grassland ecological resources.

[0004] The core data of grassland ecological restoration monitoring usually include the following aspects:

[0005] Vegetation coverage: reflected by vegetation indices (such as NDVI), it is an important indicator for assessing the ecological status of grasslands. Soil quality: including soil moisture, organic matter content, pH value, etc., reflecting the health of the soil. Hydrological data: including precipitation, groundwater level, etc., are crucial for the water cycle of grassland ecosystems. Biodiversity: record the distribution and number of different species to monitor the diversity of ecosystems. Degradation degree: assess the degree of grassland degradation by monitoring the growth of vegetation and soil erosion. Restoration effect: evaluate the effectiveness of restoration measures by comparing data changes before and after restoration.

[0006] For the construction of the database, sufficient data is needed to ensure the accuracy of the data stored in the database. However, grassland ecology is changing. It takes a long time to collect the entire grassland ecological data to complete the database construction. When the time span is large, the grassland ecological data will change, affecting the accuracy of the data stored in the database. Summary of the Invention

[0007] One of the purposes of the present invention is to provide a method for constructing a core database for grassland ecological restoration monitoring, which collects grassland ecological restoration data from a certain area and predicts grassland ecological restoration data outside the collection area based on the grassland ecological restoration data within the collection area, thereby obtaining complete grassland ecological restoration data, constructing a complete database, and reducing the time used for database construction.

[0008] To achieve the above objectives, the present invention is implemented through the following technical solutions: A method for constructing a core database for grassland ecological restoration monitoring, comprising:

[0009] Determine the scope of ecological restoration, demarcate the collection areas within the ecological restoration scope, and ensure that the collection areas are within a certain range;

[0010] Acquire grassland ecological restoration data within the collection range. Grassland ecological restoration data includes: planting data, meteorological data, and land data. The land types contained in the connecting area between adjacent collection areas are included in the collection areas on both sides.

[0011] Build an ecological data restoration model based on the grassland ecological restoration data in the collection area, obtain meteorological data in the connected area, predict grassland ecological restoration data in the connected area through meteorological data and land data, and establish a database based on the grassland ecological restoration data in the collection area and the connected area;

[0012] The drone collects ecological restoration images in the connected area, obtains planting data in the ecological restoration images, corrects the data of the connected area in the database using the planting data in the ecological restoration images, obtains correction values ​​using the planting data, and adjusts the ecological restoration data of the connected area based on the correction values;

[0013] Synchronize the corrected ecological restoration data to the database and update the connected area data in the database.

[0014] In one or more embodiments of the present invention, the land type within the scope of ecological restoration is obtained, the range of the land type is determined, the water retention and fertility status are determined based on the land type, the interval range between the collection areas is limited according to the water retention and fertility status, the collection areas are marked within the interval range, and the land type corresponding to the collection area is marked.

[0015] In one or more embodiments of the present invention, the planting data is the growth status and data of the ecology at that location in the grassland, including the type of planting and the growth status;

[0016] Meteorological data refers to the ecological environmental data of the location in the grassland, including sunlight duration, temperature, humidity, and precipitation;

[0017] Land data refers to the ecological planting soil data of the grassland, including water content, fertility, water retention, permeability, and pH value.

[0018] In one or more embodiments of the present invention, the land data q of the connected area is calculated by using the land data in the adjacent acquisition areas. The land data q of the connected area is the land data used to predict the planting data:

[0019]

[0020] Where G is the standard value of land data, Q1 is the difference between the first adjacent acquisition area and the standard value of land data, Q2 is the difference between the standard value of land data in the second adjacent acquisition area, L is the distance between the first adjacent acquisition area and the second adjacent acquisition area, in meters, and x is a variable.

[0021] In one or more embodiments of the present invention, the method for constructing an ecological data restoration model is as follows:

[0022] Obtain ecological restoration data in multiple collection areas, including planting data, land data, and meteorological data;

[0023] Collect soil samples to determine the nutrient content in the soil and assess soil fertility and plant growth requirements;

[0024] Statistical analysis, using statistical methods to obtain the relationship between soil nutrients, meteorological data and planting data;

[0025] An ecological data restoration model is constructed based on soil nutrient elements, meteorological data and planting data.

[0026] In one or more embodiments of the present invention, there are two types of predictions for the land data q of the connected area. One is the prediction of the planting data of the current connected area, which obtains the meteorological data from the time when the plants in the connected area are planted to the current time and the land data q of the connected area, and calculates the planting data based on the meteorological data and the land data;

[0027] The second is the prediction of planting data for the connected area after a period of time, which is based on the meteorological data monitoring during the period of time according to the weather forecast and the calculation of planting data for the connected area land data.

[0028] In one or more embodiments of the present invention, the ecological data restoration model can make predictions within the scope of ecological restoration after a period of time, obtain changes in the ecology after a period of maintenance, and can provide early warning for ecological restoration based on the degree of ecological changes.

[0029] In one or more embodiments of the present invention, after obtaining complete ecological restoration data within the scope of ecological restoration, tables and fields are designed, the relationship between tables is determined, a database system is selected to create a database, tables, fields and data relationships are defined, the complete ecological restoration data is imported into the database, and the ecological restoration data of the collection area and the connection area are distinguished and marked.

[0030] In one or more embodiments of the present invention, the drone collects an overall image of the connected area, analyzes the collection position in the overall image, obtains an ecological restoration image of the collection position, segments the plant image in the ecological restoration image through an image algorithm, analyzes the growth status of the plant image, and determines the plant planting data based on the growth status.

[0031] In one or more embodiments of the present invention, the actual planting data in the connected area is obtained through the ecological restoration image to calculate the correction value of the land data. When the meteorological data does not change, the planting data in the ecological data restoration model is changed to obtain the difference ratio K between the actual planting data in the connected area at multiple locations and the planting data predicted by the ecological data restoration model:

[0032] K=j i / j o ;

[0033] Among them, j i is the actual planting data in the connection area, j o Predicting planting data for ecological data restoration models;

[0034] Determine the correction value f based on the difference ratio:

[0035] f = K*(g*i);

[0036] Where g is the soil data under the difference ratio K, g = q*K, and i is the impact value;

[0037] Adjust the land data for that location based on the correction value.

[0038] Through the above technical solution, the present invention has the following beneficial effects:

[0039] 1. According to the scope of grassland ecological restoration, determine the grassland ecological restoration data within the collection area, establish an ecological data restoration model, predict grassland ecological restoration data outside the collection area, and obtain grassland ecological restoration status outside the collection area through drones to verify whether the prediction of grassland ecological restoration data is accurate and reduce the time to build a core database for grassland ecological restoration monitoring.

[0040] 2. The grassland ecological restoration data are divided into planting data, meteorological data and land data. The collection areas within the grassland ecological restoration range are determined based on the land data. The collection areas are separated by more than a certain distance, and adjacent collection areas contain land data between the collection areas. The land data outside the collection area is established based on the land data in the adjacent collection areas.

[0041] 3. After determining the land data, combine it with meteorological data to predict the planting data to determine the grassland ecological restoration data. After obtaining the grassland ecological restoration data in the collection area, establish an ecological data restoration model to predict complete grassland ecological restoration data and build the core database more quickly.

[0042] 4. After completing the prediction of grassland ecological restoration data, use drones to obtain grassland ecological restoration images outside the collection area, obtain grassland ecological restoration data in the grassland ecological restoration images, verify the accuracy of the predicted grassland ecological restoration data, use drones to collect grassland ecological restoration data outside multiple collection areas to obtain correction values, and correct the grassland ecological restoration data outside the collection area. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 This is a schematic diagram of the database construction method of the present invention;

[0044] Figure 2 Schematic diagram of the ecological data restoration model construction method of the present invention. DETAILED DESCRIPTION

[0045] The following drawings illustrate various embodiments of the present invention. For clarity, many practical details are included in the following description. However, it should be understood that these practical details are not intended to limit the present invention. In other words, in some embodiments of the present invention, these practical details are optional. Furthermore, features from different embodiments may be interchangeably applicable, where practically possible.

[0046] Unless otherwise defined, all words used herein (including technical and scientific terms) have their ordinary meanings as understood by those skilled in the art. Furthermore, the definitions of the above-mentioned words in commonly used dictionaries should be interpreted in the context of this specification as having the same meanings as those in the relevant field of the present invention. Unless otherwise explicitly defined, these words should not be interpreted as having idealized or overly formal meanings.

[0047] See also Figure 1-Figure 2 The present invention provides a method for constructing a core database for grassland ecological restoration monitoring, which collects grassland ecological restoration data, establishes a monitoring core database, and reduces the database construction time.

[0048] In one embodiment, the database construction method includes:

[0049] Determine the scope of ecological restoration, demarcate the collection areas within the ecological restoration scope, and ensure that the collection areas are within a certain range;

[0050] Acquire grassland ecological restoration data within the collection range. Grassland ecological restoration data includes: planting data, meteorological data, and land data. The land types contained in the connecting area between adjacent collection areas are included in the collection areas on both sides.

[0051] Build an ecological data restoration model based on the grassland ecological restoration data in the collection area, obtain meteorological data in the connected area, predict grassland ecological restoration data in the connected area through meteorological data and land data, and establish a database based on the grassland ecological restoration data in the collection area and the connected area;

[0052] The drone collects ecological restoration images in the connected area, obtains planting data in the ecological restoration images, corrects the data of the connected area in the database using the planting data in the ecological restoration images, obtains correction values ​​using the planting data, and adjusts the ecological restoration data of the connected area based on the correction values;

[0053] Synchronize the corrected ecological restoration data to the database and update the connected area data in the database.

[0054] In this embodiment, obtaining ecological restoration data in the connected area by prediction can reduce the time spent on building a database. It is only necessary to determine the location and scope of the collection area, obtain the ecological restoration data in the collection area, and predict the ecological restoration data in the connected area based on the ecological restoration data in the collection area to build a complete database, thereby greatly reducing the collection time of grassland ecology.

[0055] Among them, the land types in adjacent collection areas are limited to include the land types in the connected areas, which can obtain the changes in planting data grown on the land type, thereby avoiding the planting data prediction deviation caused by differences in land types, and thus ensuring the accuracy of the data inside the database.

[0056] Since the ecological restoration data of the connected areas is predicted through the ecological restoration model, when collecting ecological restoration data in the area, the ecological restoration images of the connected areas can be obtained synchronously through drones. After the prediction of the ecological restoration model is constructed, corrections can be made quickly, further ensuring the speed of database construction.

[0057] In one embodiment, the land type within the ecological restoration scope is obtained, the range of the land type is determined, the water retention and fertility status are determined based on the land type, the interval range between the collection areas is limited according to the water retention and fertility status, the collection areas are marked within the interval range, and the land type corresponding to the collection area is marked.

[0058] In this embodiment, different land types have different water retention and fertility conditions. The interval range of the collection area is determined according to the water retention and fertility conditions, which can avoid the large gap between the ecological restoration data in the collection area and the ecological restoration data in the connection area due to land type reasons, and inaccurate prediction of the ecological restoration data in the connection area.

[0059] Among them, in order to reduce the number of collection areas, the same collection area may include multiple land types, and the scope of the collection area is not fixed. The present invention does not limit the collection scope of the collection area, and it is sufficient to obtain the planting data and land data of the land type.

[0060] In one embodiment, the planting data is the growth status and data of the ecology at that location in the grassland, including the type of planting and the growth status;

[0061] Meteorological data refers to the ecological environmental data of the location in the grassland, including sunlight duration, temperature, humidity, and precipitation;

[0062] Land data refers to the ecological planting soil data of the grassland, including water content, fertility, water retention, permeability, and pH value.

[0063] In this embodiment, planting data, meteorological data and land data complement each other, and the three types of data can be used to predict the ecological restoration of connected areas. Among them, the land types in the connecting areas between adjacent collection areas are all included in the collection areas. Therefore, the gap in land data will not be large, and meteorological data can obtain more accurate data through surveying. Therefore, by constructing an ecological data restoration model through the ecological restoration data obtained in the collection area, planting data can be predicted when the meteorological data and soil data are certain.

[0064] Among them, the predicted planting data is the planting data of the connected area. Since the land data of the connected area is calculated through the soil data of the adjacent collection areas, it is not the actual land data of the connected area. Therefore, after the planting data is predicted, the land data is corrected in combination with the ecological restoration images collected by the drone.

[0065] In one embodiment, land data q of the connected region is calculated using land data in adjacent acquisition regions. The land data q of the connected region is the land data used to predict the planting data:

[0066]

[0067] Where G is the standard value of land data, Q1 is the difference between the first adjacent acquisition area and the standard value of land data, Q2 is the difference between the standard value of land data in the second adjacent acquisition area, L is the distance between the first adjacent acquisition area and the second adjacent acquisition area, in meters, and x is a variable.

[0068] In this embodiment, there is a difference in the land data between two adjacent collection areas, and the land data between the connected areas will change with the distance. Therefore, the variable x is the distance from the location to a certain collection area. The land data changes at different locations, thereby obtaining the changes in land data at multiple locations.

[0069] The changes in land data at different locations are calculated by taking the difference between the land data in two adjacent collection areas, which is used to calculate the planting data at different locations.

[0070] In one embodiment, the method for constructing an ecological data restoration model is as follows:

[0071] Obtain ecological restoration data in multiple collection areas, including planting data, land data, and meteorological data;

[0072] Collect soil samples to determine the nutrient content in the soil and assess soil fertility and plant growth requirements;

[0073] Statistical analysis, using statistical methods to obtain the relationship between soil nutrients, meteorological data and planting data;

[0074] An ecological data restoration model is constructed based on soil nutrient elements, meteorological data and planting data.

[0075] In this embodiment, the establishment of an ecological data restoration model can calculate the planting data based on the meteorological data and soil data of the connected area, thereby predicting the ecological restoration data of the connected area, completing the complete ecological prediction data of the grassland, and constructing a database, thereby ensuring the speed of database construction.

[0076] The statistical method is regression analysis. For example, the data collected are as follows:

[0077] Meteorological data: average temperature (℃), precipitation (mm), sunshine hours (hours).

[0078] Planting data: plant species, sown area (hectares), yield (tonnes).

[0079] Land data: soil type, pH value, organic matter content (%).

[0080] First, a linear regression model was established in which growth potential was the dependent variable (Y) and weather and land data were the independent variables (X).

[0081] The model can be expressed as:

[0082] Y=β0+β1X1+β2X2+…+β n X n +∈

[0083] Among them, Y is the growth potential, β0 is the intercept, (β0, β1, β2, ..., β n ) are the coefficients of the respective variables, (X0, X, X2, …, X n ) is the independent variable (e.g., average temperature, precipitation, etc.), and ε is the error term.

[0084] The model is fitted by the least squares method to obtain the coefficient of each independent variable, which indicates the impact of the corresponding variable on output.

[0085] For example, the model results show:

[0086] The coefficient of average temperature is positive, and rising temperature will accelerate growth.

[0087] The coefficient for precipitation is negative; excessive precipitation will slow growth.

[0088] The coefficient of soil pH is positive, and a soil pH close to neutral is conducive to faster growth.

[0089] In one embodiment, there are two types of predictions for the land data q of the connected area. One is the prediction of the planting data of the current connected area, which obtains the meteorological data from the time when the plants in the connected area are planted to the current time and the land data q of the connected area, and calculates the planting data based on the meteorological data and the land data;

[0090] The second is the prediction of planting data for the connected area after a period of time, which is based on the meteorological data monitoring during the period of time according to the weather forecast and the calculation of planting data for the connected area land data.

[0091] In this embodiment, by calculating the planting data through meteorological data and planting, the changes in planting data under different influencing factors can be obtained, which can facilitate the calibration of land data in the connected area through ecological restoration images, and the prediction of future planting data can obtain the changes in plants after a period of time.

[0092] In one embodiment, the ecological data restoration model can make predictions within the scope of ecological restoration over a period of time, obtain changes in the ecology after a period of maintenance, and provide early warnings for ecological restoration based on the degree of ecological changes.

[0093] In this embodiment, by providing early warning of the ecology after a period of time, the correctness of ecological restoration is guaranteed, the problem of negative restoration is avoided, and corresponding strategy adjustments can be made according to changes in meteorological data and the like.

[0094] In one embodiment, after obtaining complete ecological restoration data within the scope of ecological restoration, tables and fields are designed, the relationship between tables is determined, a database system is selected to create a database, tables, fields and data relationships are defined, the complete ecological restoration data is imported into the database, and the ecological restoration data of the collection area and the connection area are distinguished and marked.

[0095] In this embodiment, the database system is PostgreSQL plus PostGIS extension, which distinguishes the ecological restoration data of the collection area and the connection area. Among them, the ecological restoration data mark of the connection area can also mark the number of corrections, so that the corresponding data can be searched more quickly.

[0096] In one embodiment, a drone collects an overall image of a connected area, analyzes the collection location in the overall image, obtains an ecological restoration image of the collection location, segments the plant image in the ecological restoration image through an image algorithm, analyzes the growth status of the plant image, and determines the plant planting data based on the growth status.

[0097] In this embodiment, since plants present different states at different stages, the ecological restoration images collected by drones can obtain the growth of plants, thereby determining whether the plants are consistent with the predicted planting data.

[0098] The collection of the overall image can obtain the transition positions of different growth states when planting the same plants. The transition positions are part of the collection positions, and the ecological restoration image collection is carried out by random collection position method at the positions with the same growth state.

[0099] In one embodiment, the actual planting data in the connected area is obtained through the ecological restoration image to calculate the correction value of the land data. When the meteorological data does not change, the planting data in the ecological data restoration model is changed to obtain the difference ratio K between the actual planting data in the connected area at multiple locations and the planting data predicted by the ecological data restoration model:

[0100] K=j i / j o ;

[0101] Among them, j i is the actual planting data in the connection area, j o Predicting planting data for ecological data restoration models;

[0102] Determine the correction value f based on the difference ratio:

[0103] f = K*(g*i);

[0104] Where g is the soil data under the difference ratio K, g = q*K, and i is the impact value;

[0105] Adjust the land data for that location based on the correction value.

[0106] In this example, given the same land data, the corresponding ecological growth trends are also essentially the same. Therefore, the change in soil data is determined by calculating the difference ratio, while the impact value is the impact of historical fertilization records and plant rotation patterns on the land. For example, if we know the specific planting data and land data of the first plot of land, as well as the planting data of the second plot of land, we can use the proportional relationship to estimate the land data of the second plot of land.

[0107] The planting data of the first piece of land is P1;

[0108] The planting data of the second plot of land is P2;

[0109] The land data of the first piece of land is T1;

[0110] The land data for the second piece of land is T2;

[0111] Given (P1=1.2*P2), calculate (T2).

[0112] Assuming that land data (such as soil nutrient content) is proportional to planting data (such as plant yield), there is a linear relationship between (T1) and (P1) and between (T2) and (P2). This relationship is expressed by the following formula:

[0113] T1=k×P1;

[0114] T2=k×P2;

[0115] Where (k) is the proportional constant. Since (P1=1.2*P2), we can deduce:

[0116] T1 = k × (1.2 × P2);

[0117] T1 = 1.2 × (k × P2);

[0118] T1=1.2×T2;

[0119] Therefore, obtaining the value of (T1), we can calculate (T2) by the following formula:

[0120] T2=1.2*T1;

[0121] Since the assumption of a direct proportional relationship between land data and planting data may not always hold true in reality, changes in land data may be affected by a variety of factors, including meteorological data, historical fertilization records, and plant rotation patterns. Therefore, it is necessary to obtain the impact values ​​of different meteorological data, historical fertilization records, and ecological planting plant data on land data through experimental data to determine the final impact value.

[0122] In summary, the technical solutions disclosed in the above embodiments of the present invention have at least the following advantages:

[0123] 1. According to the scope of grassland ecological restoration, determine the grassland ecological restoration data within the collection area, establish an ecological data restoration model, predict grassland ecological restoration data outside the collection area, and obtain grassland ecological restoration status outside the collection area through drones to verify whether the prediction of grassland ecological restoration data is accurate and reduce the time to build a core database for grassland ecological restoration monitoring.

[0124] 2. The grassland ecological restoration data are divided into planting data, meteorological data and land data. The collection areas within the grassland ecological restoration range are determined based on the land data. The collection areas are separated by more than a certain distance, and adjacent collection areas contain land data between the collection areas. The land data outside the collection area is established based on the land data in the adjacent collection areas.

[0125] 3. After determining the land data, combine it with meteorological data to predict the planting data to determine the grassland ecological restoration data. After obtaining the grassland ecological restoration data in the collection area, establish an ecological data restoration model to predict complete grassland ecological restoration data and build the core database more quickly.

[0126] 4. After completing the prediction of grassland ecological restoration data, use drones to obtain grassland ecological restoration images outside the collection area, obtain grassland ecological restoration data in the grassland ecological restoration images, verify the accuracy of the predicted grassland ecological restoration data, use drones to collect grassland ecological restoration data outside multiple collection areas to obtain correction values, and correct the grassland ecological restoration data outside the collection area.

[0127] Although the present invention is disclosed in conjunction with the above embodiments, it is not intended to limit the present invention. Anyone familiar with this technology can make various changes and modifications without departing from the spirit and scope of the present invention. Therefore, the scope of protection of the present invention should be based on the definition of the attached claims.

Claims

1. A method for constructing a core database for grassland ecological restoration monitoring, characterized in that: include: Determine the scope of ecological restoration, demarcate the collection areas within the ecological restoration scope, and ensure that the collection areas are within a certain range; Acquire grassland ecological restoration data within the collection range. Grassland ecological restoration data includes: planting data, meteorological data, and land data. The land types contained in the connecting area between adjacent collection areas are included in the collection areas on both sides. The land types in the adjacent collection areas include the land types in the connected areas, and the changes in planting data grown on the land types are obtained; when collecting ecological restoration data in the area, the ecological restoration images in the connected areas are obtained through drones; Calculate land data in the connected area by using land data in adjacent collection areas q , the land data of the connection area q Land data used to predict planting data: ； in, G is the standard value of land data, Q 1 is the difference between the first adjacent acquisition area and the standard value of land data, Q 2 is the standard value difference of land data in the second adjacent acquisition area, L is the distance between the first adjacent collection area and the second adjacent collection area, in meters. x For variables, there is a difference in the land data between two adjacent collection areas. The land data between the connected areas will change with the distance. x The distance from the current location to a certain collection area, thereby obtaining the changes in land data at multiple locations; Obtain the land type within the ecological restoration scope, determine the range of land types, determine water retention and fertility status based on land types, limit the interval range between collection areas based on water retention and fertility status, mark the collection areas within the interval range, and mark the land type corresponding to the collection area; determine the interval range of collection areas based on water retention and fertility status to reduce the gap between ecological restoration data in the collection area and ecological restoration data in the connected area; Build an ecological data restoration model based on the grassland ecological restoration data in the collection area, obtain meteorological data in the connected area, predict grassland ecological restoration data in the connected area through meteorological data and land data, and establish a database based on the grassland ecological restoration data in the collection area and the connected area; The method for constructing an ecological data restoration model is as follows: Obtain ecological restoration data in multiple collection areas, including planting data, land data, and meteorological data; Collect soil samples to determine the nutrient content in the soil and assess soil fertility and plant growth requirements; Statistical analysis, using statistical methods to obtain the relationship between soil nutrients, meteorological data and planting data; Construct an ecological data restoration model based on soil nutrient elements, meteorological data, and planting data; The drone collects ecological restoration images in the connected area, obtains planting data in the ecological restoration images, corrects the data of the connected area in the database using the planting data in the ecological restoration images, obtains correction values ​​using the planting data, and adjusts the ecological restoration data of the connected area based on the correction values; The actual planting data in the connected area is obtained through the ecological restoration image to calculate the correction value of the land data. When the meteorological data does not change, the planting data in the ecological data restoration model is changed to obtain the difference ratio between the actual planting data in the connected area at multiple locations and the planting data predicted by the ecological data restoration model. K : K=j i / j o ； in, j i To connect the actual planting data in the area, j o Predicting planting data for ecological data restoration models; Determine the correction value based on the difference ratio f : f=K*(g*i) ； in, g is the difference ratio K The soil data below, g=q*K , i is the impact value; Adjust the land data at the location according to the correction value; Synchronize the corrected ecological restoration data to the database and update the connected area data in the database; Planting data refers to the ecological growth status and data of the location in the grassland, including the type of planting and growth status; Meteorological data refers to the ecological environmental data of the location in the grassland, including sunlight duration, temperature, humidity, and precipitation; Land data refers to the ecological planting soil data of the location in the grassland, including water content, fertility, water retention, permeability, and pH value; For connecting regional land data q There are two types of predictions. One is the current connected area planting data prediction, which obtains the meteorological data from the time the plants are planted in the connected area to the current time and the land data of the connected area. q , calculate planting data through meteorological data and land data; The second is to predict the planting data of the connected area after a period of time, based on the meteorological data monitoring during the period of time according to the weather forecast and the land data of the connected area to calculate the planting data; The ecological data restoration model can predict the ecological restoration range over a period of time, obtain the changes in the ecology after a period of maintenance, and provide early warning for ecological restoration based on the degree of ecological changes; The statistical method is regression analysis, and the data collected are as follows: Meteorological data: average temperature, precipitation, sunshine hours; Planting data: plant species, sown area, yield; Land data: soil type, pH value, organic matter content; First, a linear regression model was established, in which growth trend was the dependent variable Y, and meteorological and land data were the independent variables X; The model is represented as: ； in, Y It's growth. β 0 is the intercept, ( β 0 , β 1 , β 2 ,…, β n ) are the coefficients of the respective variables, ( X 1 , X 2 ,…, X n ) is the independent variable, ε is the error term; The model is fitted by the least squares method to obtain the coefficient of each independent variable, which indicates the impact of the corresponding variable on output.

2. The method for constructing a core database for grassland ecological restoration monitoring according to claim 1, characterized in that: After obtaining complete ecological restoration data within the scope of ecological restoration, design tables and fields, determine the relationship between tables, select a database system to create a database, define tables, fields and data relationships, import the complete ecological restoration data into the database, and distinguish and mark the ecological restoration data of the collection area and the connection area.

3. The method for constructing a core database for grassland ecological restoration monitoring according to claim 2, characterized in that: The drone collects the overall image of the connected area, analyzes the collection location in the overall image, obtains the ecological restoration image of the collection location, segments the plant image in the ecological restoration image through image algorithms, analyzes the growth status of the plant image, and determines the plant planting data based on the growth status.

Images