Method and system for determining biodiversity key area based on population number in area range

Abstract

The invention belongs to the technical field of ecological environment protection, and particularly relates to a method and system for determining a biodiversity key area based on the population number in an area range, and the method comprises the steps: collecting and analyzing key data, such as an environment influence coefficient, a species diversity index and a population density value; and combining the established threat factor quantification standard and the weight distribution system, and adopting a scientific calculation model to obtain the threat coefficient of each sample region. Then, through setting a reasonable threat coefficient threshold value, screening out a key region of biodiversity protection; spatial analysis is carried out by utilizing a GIS technology, distribution and mutual relations of the key areas in geographic space are determined, and priority ranking is further carried out according to threat degrees and protection values. The invention aims to provide accurate, rapid and resource-focused decision support for biodiversity protection.

Description

Technical Field

[0001] The present invention belongs to the technical field of ecological environment protection, and specifically relates to a method and system for determining key areas of biodiversity based on the number of populations within a region. Background Art

[0002] In the current field of biodiversity conservation, the widely used technical means mainly rely on manual surveys and statistics. This method usually involves professional ecologists or biologists going deep into the natural environment to observe and record species, numbers and distribution ranges on the spot, while collecting environmental factor data such as climate and soil conditions. These data are then used to assess the current status of biodiversity and serve as the basis for formulating conservation strategies. In addition, geographic information systems (GIS) are also used to integrate and analyze spatial data to help understand the relationship between species distribution and environmental factors. However, these traditional methods and technical means have certain limitations in data collection, processing and analysis.

[0003] Although traditional manual survey and statistical methods have played an important role in biodiversity conservation, their shortcomings are becoming increasingly prominent. First, the data collection cycle is long, requiring a lot of manpower, material resources and time, which is costly and inefficient. Secondly, due to the influence of human factors and environmental changes, data accuracy is often difficult to guarantee, and there are large errors and uncertainties. Furthermore, traditional methods lack real-time performance and are difficult to reflect the dynamic changes of biodiversity in a timely manner. In addition, the delineation of biodiversity conservation areas often relies on empirical judgment and lacks a scientific and systematic evaluation system, resulting in insufficient pertinence and effectiveness of conservation measures. Finally, with the complexity of biodiversity and the increase in protection needs, traditional methods are unable to cope with large-scale data and complex environmental relationships, and are unable to meet the needs of modern biodiversity conservation. Summary of the invention

[0004] The purpose of the present invention is to provide a method and system for determining key areas of biodiversity based on the number of populations within a region, so as to solve the problems raised in the background technology.

[0005] The present invention achieves the above-mentioned purpose through the following technical solutions:

[0006] In a first aspect, the present invention proposes a method for determining a key biodiversity area based on the population size within a region, the method comprising:

[0007] Obtain environmental information and species information in multiple pre-divided sample areas in real time, and determine the environmental impact coefficient and species diversity index based on the species information and environmental information;

[0008] Based on the GIS platform, the environmental information and species information are spatialized to construct an ecological niche model, and the species distribution area of ​​the set area is predicted based on the ecological niche model, and the population density value of each species in each sample area is output;

[0009] The threat coefficient of each sample area is determined based on the environmental impact coefficient, the species diversity index and the population density value, and the key area for biodiversity protection is determined according to the relationship between the threat coefficient and the set threshold.

[0010] Furthermore, the environmental information includes collected soil environmental factors, climate environmental factors and water quality environmental factors, and the species information includes species richness.

[0011] Furthermore, the environmental impact coefficient E and the species diversity index D are determined based on the species information and environmental information based on the following formula:

[0012] E=(V1*i1+V2*i2+V3*i3) / 3

[0013] Among them, V1 is the soil environmental factor, i1 is the weight of the soil environmental factor, V2 is the climate environmental factor, i2 is the weight of the climate environmental factor, V3 is the water quality environmental factor, i3 is the weight of the water quality environmental factor;

[0014]

[0015] D2=1-∑Pi 2

[0016]

[0017]

[0018] Among them, D1 is the Shannon-Weiner index, D2 is the Simpson index, D3 is the Margalef index, s is the species richness, P is the i is the proportion of the ith species to the total number of species, x1 is the weight of the Shannon-Weiner index, x2 is the weight of the Simpson index, and x3 is the weight of the Margalef index.

[0019] Furthermore, the environmental information and species information are spatialized based on the GIS platform to construct an ecological niche model, and the species distribution area is predicted for the set area based on the ecological niche model, and the population density value of each species in each sample area is output, including:

[0020] (1) In the GIS platform, obtain and integrate real-time environmental information and species information within the set area, and use the spatial analysis function of the GIS platform to convert environmental information and species information into spatial data;

[0021] (2) constructing an ecological niche model based on the spatial data, wherein the ecological niche model is used to reflect the interaction between species and environmental factors, and performing fitting optimization on the ecological niche model based on a machine learning algorithm;

[0022] (3) The optimized niche model is used to predict the species distribution area within the set area, and the population density value C of each species in each sample area is determined based on the following formula: C = X / Y, where X is the number of species and Y is the area of ​​each sample area.

[0023] Furthermore, the threat coefficient is determined based on the following formula:

[0024] T=(E*w1)+(D*w2)+(C*w3)

[0025] Among them, T is the threat coefficient, E is the environmental impact coefficient, w1 is the weight of the environmental impact coefficient, D is the species diversity index, w2 is the weight of the species diversity index, C is the population density value, and w3 is the weight of the population density value.

[0026] In the second aspect, the present invention proposes a system for determining key biodiversity areas based on the number of populations within a region, which is applied to any of the above methods. The system includes a dynamic monitoring station, a GIS platform wirelessly connected to the station, and a cloud assessment platform wirelessly connected to the dynamic monitoring station and the GIS platform; wherein,

[0027] The dynamic monitoring station is used to obtain environmental information and species information in a plurality of pre-divided sample areas in real time, and determine the environmental impact coefficient and species diversity index based on the species information and environmental information; wherein the environmental information includes soil environmental factors, climate environmental factors and water quality environmental factors, and the species information includes species richness;

[0028] The GIS platform is used to spatialize environmental information and species information to construct an ecological niche model, predict species distribution areas in the set area based on the ecological niche model, and output population density values ​​of each species in each sample area;

[0029] The cloud assessment platform is used to determine the threat coefficient of each sample area based on the environmental impact coefficient, the species diversity index and the population density value, and to determine the key areas for biodiversity protection according to the relationship between the threat coefficient and the set threshold.

[0030] Furthermore, the dynamic monitoring station includes at least one ground monitoring station and at least one unmanned aerial vehicle wirelessly connected thereto, wherein:

[0031] The ground monitoring station is equipped with several infrared cameras for automatic identification and counting of species. The drone is equipped with built-in environmental sensors for real-time monitoring of climate and soil environmental factors within a set area. The environmental sensors include temperature sensors, humidity sensors, light intensity sensors, air pressure sensors, soil pH sensors and soil moisture sensors.

[0032] The beneficial effects of the present invention are:

[0033] 1. Through comprehensive analysis of multi-source data and spatial processing on the GIS platform, the present invention can accurately identify key areas with the richest biodiversity and the greatest threat, providing reliable basic data support for the formulation of effective protection measures.

[0034] 2. By constructing an optimized ecological niche model and introducing a multidimensional threat coefficient assessment method, the present invention can provide a more comprehensive and scientific basis for ecological protection decision-making, and help to formulate more reasonable and effective protection strategies and measures.

[0035] 3. The present invention determines the threat coefficient through the environmental impact coefficient, species diversity index and population density value, which are indicators that can reflect the survival status of species, and assigns them corresponding weights to calculate the threat coefficient, thereby comprehensively evaluating the protection needs and urgency of biodiversity in different regions.

[0036] 4. The present invention can quickly obtain a large amount of accurate environmental and species information through real-time monitoring by dynamic monitoring stations and drones, greatly shortening the data collection cycle and improving evaluation efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Figure 1 It is a flow chart of a method for determining key areas of biodiversity based on population numbers within a region provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0038] The present application is further described in detail below in conjunction with the accompanying drawings. It is necessary to point out here that the following specific implementation methods are only used to further illustrate the present application and cannot be understood as limiting the scope of protection of the present application. Technical personnel in this field can make some non-essential improvements and adjustments to the present application based on the above application content.

[0039] Example 1

[0040] like Figure 1As shown, this embodiment proposes a method for determining a key biodiversity area based on the population size within a region, and the method includes the following steps:

[0041] S1. Obtain environmental information and species information in multiple pre-divided sample areas in real time, and determine the environmental impact coefficient and species diversity index based on the species information and environmental information; environmental information includes soil environmental factors, climate environmental factors and water quality environmental factors, and species information includes species richness.

[0042] S2. Based on the GIS platform, the environmental information and species information are spatialized to construct an ecological niche model. Based on the ecological niche model, the species distribution area is predicted for the set area, and the population density value of each species in each sample area is output.

[0043] S3. Determine the threat coefficient of each sample area based on the environmental impact coefficient, species diversity index and population density value, and determine the key areas for biodiversity conservation based on the relationship between the threat coefficient and the set threshold.

[0044] Further preferably, in step S1, the environmental impact coefficient E and the species diversity index D are determined according to the species information and the environmental information, based on the following formula:

[0045] E=(V1*i1+V2*i2+V3*i3) / 3

[0046] Among them, V1 is the soil environmental factor, i1 is the weight of the soil environmental factor, V2 is the climate environmental factor, i2 is the weight of the climate environmental factor, V3 is the water quality environmental factor, i3 is the weight of the water quality environmental factor;

[0047]

[0048]

[0049] Among them, D1 is the Shannon-Weiner index, D2 is the Simpson index, D3 is the Margalef index, S is the species richness, P is the i is the proportion of the ith species to the total number of species, x1 is the weight of the Shannon-Weiner index, x2 is the weight of the Simpson index, and x3 is the weight of the Margalef index.

[0050] This embodiment first determines which environmental factors have a significant impact on biodiversity, such as temperature (V1), humidity (V2), soil pH (V3), etc. Then, based on expert evaluation or historical data analysis, a corresponding weight (i1, i2, i3) is assigned to each environmental factor. These weights reflect the degree of impact of each factor on biodiversity. Secondly, because the dimensions and value ranges of different environmental factors may be different, it is necessary to standardize the collected environmental data so that they can be compared and calculated on a unified scale. Commonly used standardization methods include Z-score standardization and minimum-maximum standardization. The environmental impact coefficient E comprehensively reflects the potential impact of the environment at this point on biodiversity. Determination of species richness S: Through pictures and video materials taken by ground monitoring stations and drones, the species types in each monitoring point are identified and recorded to obtain the species richness S. Species proportion P i Calculation: For each monitoring point, count the number of individuals or relative frequency of each species, and calculate the proportion P of each species at that point. i . Proportion P i It is usually expressed as the percentage of the number of individuals of the species to the total number of individuals. Calculation of species diversity index D: Use the Shannon-Wiener diversity index, Simpson diversity index, and Margalef diversity index formulas to calculate the species diversity index D, thereby comprehensively reflecting the biodiversity status of the monitoring point.

[0051] It can be understood that step S1 can accurately quantify the degree of impact of the environment at different monitoring points on biodiversity and the actual status of species diversity through the calculation of the environmental impact coefficient E and the species diversity index D, providing a reliable basis for subsequent evaluation decisions. In addition, the use of methods such as weighted summation and species diversity index reduces the interference of human factors in data interpretation, making the evaluation results more objective and scientific. The calculation results of the environmental impact coefficient and the species diversity index can serve as an important basis for formulating biodiversity conservation policies and measures, helping decision makers to more accurately identify areas and species that need priority protection.

[0052] Further preferably, in step S2, the environmental information and species information are spatialized based on the GIS platform to construct an ecological niche model, and the species distribution area is predicted for the set area based on the ecological niche model, and the population density value of each species in each sample area is output, including:

[0053] (1) In the GIS platform, obtain and integrate real-time environmental information and species information within the set area, and use the spatial analysis function of the GIS platform to convert environmental information and species information into spatial data;

[0054] (2) Constructing an ecological niche model based on spatial data. The ecological niche model is used to reflect the interaction between species and environmental factors, and the ecological niche model is fitted and optimized based on machine learning algorithms;

[0055] (3) The optimized niche model is used to predict the species distribution area within the set area, and the population density value C of each species in each sample area is determined based on the following formula: C = X / Y, where X is the number of species and Y is the area of ​​each sample area.

[0056] Accordingly, step S2 is the construction of the ecological niche model and the prediction of species distribution, which includes the following subdivision steps:

[0057] S2.1 Spatial digitization

[0058] Data preparation: Organize the collected environmental data and species data into a format suitable for processing by the GIS platform, such as Shapefile, GeoJSON, etc.

[0059] Establishment of spatial database: In the GIS platform, a spatial database containing environmental data and species data is established to realize spatial storage and management of data.

[0060] S2.2 Construction of ecological niche model

[0061] Algorithm selection: According to the characteristics of the data and analysis requirements, select the appropriate machine learning algorithm to build the niche model. Commonly used algorithms include random forest, support vector machine, gradient boosting tree, etc. These algorithms can handle complex data relationships and have good prediction performance.

[0062] Model training: Use historical data and current data to train the selected algorithm, and improve the model's prediction accuracy and generalization ability by adjusting parameters and optimizing model structure.

[0063] Model validation: Use an independent validation dataset to validate the trained model and evaluate the model’s predictive performance and stability. If the model performance does not meet the requirements, you need to readjust the parameters or select another algorithm for training.

[0064] S2.3 Species distribution prediction

[0065] Prediction area setting: Set the area range that needs to be predicted in the GIS platform to ensure that the environmental data and species data in the area have been integrated into the spatial database.

[0066] Model application: Apply the trained niche model to the set prediction area and predict the distribution of each species based on the environmental data in the area.

[0067] Result visualization: The prediction results are displayed in the form of a map, including the potential distribution area, population density and other information of each species. At the same time, detailed reports and analysis charts can be generated as needed.

[0068] Correspondingly, step S2 can efficiently obtain species distribution information over a large area by constructing an ecological niche model and using the GIS platform to predict species distribution, saving a lot of manpower and material resources; the species distribution prediction results can provide strong support for biodiversity conservation and ecological environment management, and help decision makers formulate more scientific and reasonable conservation strategies and management measures.

[0069] Further preferably, in step S3, the threat coefficient is determined based on the following formula:

[0070] T=(E*w1)+(D*w2)+(C*w3)

[0071] Among them, T is the threat coefficient, E is the environmental impact coefficient, w1 is the weight of the environmental impact coefficient, D is the species diversity index, w2 is the weight of the species diversity index, C is the population density value, and w3 is the weight of the population density value.

[0072] Step S3 also includes threshold setting: one or more thresholds of threat coefficients are set according to the goals and actual conditions of biodiversity conservation. These thresholds are used to distinguish areas with different levels of biodiversity conservation, which is usually a process based on expert judgment, historical data analysis and comprehensive consideration of conservation goals. The thresholds may vary due to different biodiversity conservation needs, regional characteristics and species characteristics. Generally speaking, the threshold can be set as a critical value that can trigger conservation actions. When the threat coefficient exceeds this threshold, it means that the biodiversity in the area may face serious threats and corresponding conservation measures need to be taken. In actual operations, this threshold may be dynamically adjusted based on the protection effect and implementation feedback.

[0073] More specifically, with respect to the determination of key areas, this embodiment compares the threat coefficient of each sample area with a set threshold. Areas exceeding the threshold are considered key areas for biodiversity conservation, which may face a higher risk of biodiversity loss. In addition, GIS technology is used to perform spatial analysis on the screened key areas to understand their distribution characteristics and interrelationships in geographic space. This helps to further assess the importance and conservation value of these areas in the ecosystem. It also includes priority sorting, which prioritizes key areas according to their threat level and conservation value. High-priority areas should be given priority in formulating and implementing conservation measures.

[0074] Based on the same inventive concept, this embodiment proposes a system for determining key biodiversity areas based on the number of populations within a region, which is applied to execute the above method. The system includes a dynamic monitoring station, a GIS platform wirelessly connected to the station, and a cloud assessment platform wirelessly connected to the dynamic monitoring station and the GIS platform; wherein,

[0075] The dynamic monitoring station is used to obtain environmental information and species information in multiple pre-divided sample areas in real time, and determine the environmental impact coefficient and species diversity index based on the species information and environmental information; the environmental information includes the collection of soil environmental factors, climate environmental factors and water quality environmental factors, and the species information includes species richness;

[0076] The GIS platform is used to spatialize environmental information and species information to construct an ecological niche model, predict species distribution areas within a set area based on the ecological niche model, and output population density values ​​of each species in each sample area;

[0077] The cloud assessment platform is used to determine the threat coefficient of each sample area based on the environmental impact coefficient, species diversity index and population density value, and to determine the key areas for biodiversity conservation based on the relationship between the threat coefficient and the set threshold.

[0078] It can be understood that the overall architecture of the system in this embodiment is as follows: Dynamic monitoring station: as the data collection front end, including ground monitoring stations and drone monitoring systems. GIS platform: processes spatial data, builds ecological niche models, and predicts species distribution. Cloud evaluation platform: integrates data processing, analysis, evaluation and decision support functions.

[0079] Further preferably, the dynamic monitoring station includes at least one ground monitoring station and at least one unmanned aerial vehicle wirelessly connected thereto, wherein the ground monitoring station is equipped with a number of infrared cameras for automatic identification and counting of species, and the unmanned aerial vehicle is equipped with built-in environmental sensors for real-time monitoring of climate environmental factors and soil environmental factors within a set area, and the environmental sensors include temperature sensors, humidity sensors, light intensity sensors, air pressure sensors, soil pH sensors and soil moisture sensors (soil data can be obtained by obtaining soil samples through a sampling structure carried by the unmanned aerial vehicle).

[0080] As an example, the key biodiversity areas are determined based on the above system, including the following steps:

[0081] Implementation step 1: Data collection and processing

[0082] Deploy dynamic monitoring stations: Several ground monitoring stations are deployed in the predetermined area, each equipped with an infrared camera to automatically identify and record species and quantities. At the same time, drones are deployed to regularly fly over the monitoring area, carrying environmental sensors (such as temperature sensors, humidity sensors, light intensity sensors, air pressure sensors, soil pH sensors, and soil moisture sensors) to collect data on climate and soil environmental factors.

[0083] Data integration: Integrate the data collected by ground monitoring stations and drones into the cloud evaluation platform, conduct preliminary data cleaning and verification, and ensure the accuracy and completeness of the data.

[0084] Implementation step 2: Calculation of environmental impact coefficient and species diversity index

[0085] Calculation of environmental impact coefficient: Based on the integrated environmental data (soil environmental factors, climate environmental factors, water quality environmental factors), combined with the preset weights (i1, i2, i3), the environmental impact coefficient E is calculated using the formula E=(V1*i1+V2*i2+V3*i3) / 3.

[0086] Calculation of species diversity index: According to the species richness S and the proportion of each species in the total number of species Pi, the Shannon-Weiner index (D1), Simpson index (D2), and Margalef index (D3) are calculated respectively, and the formula is used Calculate the species diversity index D.

[0087] Implementation step 3: Niche model construction and species distribution prediction

[0088] Spatial data: In the GIS platform, the integrated environmental information and species information are converted into spatial data to build a spatial database.

[0089] Ecological niche model construction: Based on the spatial database, the spatial analysis function of the GIS platform is used to construct an ecological niche model to reflect the interaction between species and environmental factors. The ecological niche model is fitted and optimized through machine learning algorithms (such as random forests, neural networks, etc.) to improve the model prediction accuracy.

[0090] Species distribution prediction: Use the optimized niche model to predict the species distribution within the set area, and output the population density value C of each species in each sample area (C = X / Y, where X is the number of species and Y is the area of ​​the sample area).

[0091] Implementation Step 4: Calculation of Threat Factor and Determination of Key Areas

[0092] Calculation of threat coefficient: Based on the environmental impact coefficient E, species diversity index D and population density value C, combined with their respective weights (w1, w2, w3), the threat coefficient T of each sample area is calculated using the formula T = (E*w1) + (D*w2) + (C*w3).

[0093] Determination of key biodiversity areas: Compare the calculated threat coefficient T with the set threshold to determine the key areas for biodiversity conservation. Areas with a threat coefficient higher than the threshold will be considered key areas for biodiversity conservation and corresponding protection measures will be taken.

[0094] It is obvious to those skilled in the art that the embodiments of the present invention are not limited to the details of the above exemplary embodiments, and that the embodiments of the present invention can be implemented in other specific forms without departing from the spirit or basic features of the embodiments of the present invention. Therefore, from any point of view, the embodiments should be regarded as exemplary and non-restrictive, and the scope of the embodiments of the present invention is limited by the attached claims rather than the above description, so it is intended to include all changes that fall within the meaning and scope of the equivalent elements of the claims in the embodiments of the present invention. Any figure mark in the claims should not be regarded as limiting the claims involved. In addition, it is obvious that the word "comprising" does not exclude other units or steps, and the singular does not exclude the plural. Multiple units, modules or devices stated in the system, device or terminal claims can also be implemented by the same unit, module or device through software or hardware. The words first, second, etc. are used to indicate names, and do not indicate any particular order.

[0095] The above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit it. Although the present application has been described in detail with reference to the aforementioned embodiments, a person skilled in the art should understand that the technical solutions described in the aforementioned embodiments can still be modified, or some of the technical features can be replaced by equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A method for determining key biodiversity areas based on the number of species within a region, characterized in that: Methods include: Obtain environmental information and species information in multiple pre-divided sample areas in real time, and determine the environmental impact coefficient and species diversity index based on the species information and environmental information; Based on the GIS platform, the environmental information and species information are spatialized to construct an ecological niche model, and the species distribution area of ​​the set area is predicted based on the ecological niche model, and the population density value of each species in each sample area is output; The threat coefficient of each sample area is determined based on the environmental impact coefficient, the species diversity index and the population density value, and the key area for biodiversity protection is determined according to the relationship between the threat coefficient and the set threshold.

2. The method of claim 1 for determining key biodiversity areas based on the number of species within a region, characterized in that: The environmental information includes collected soil environmental factors, climate environmental factors and water quality environmental factors, and the species information includes species richness.

3. The method of determining key biodiversity areas based on the number of species within a region according to claim 2, characterized in that: The environmental impact coefficient E and the species diversity index D are determined based on the species information and environmental information based on the following formula: E=(V1*i1+V2*i2+V3*i3) / 3 Among them, V1 is the soil environmental factor, i1 is the weight of the soil environmental factor, V2 is the climate environmental factor, i2 is the weight of the climate environmental factor, V3 is the water quality environmental factor, i3 is the weight of the water quality environmental factor; <h2 style=";text-align:left;direction:ltr">D2 = 1 - ∑Pi<h2 style=";text-align:left;direction:ltr"> 2 Among them, D1 is the Shannon-Weiner index, D2 is the Simpson index, D3 is the Margalef index, S is the species richness, P is the i is the proportion of the ith species to the total number of species, x1 is the weight of the Shannon-Weiner index, x2 is the weight of the Simpson index, and x3 is the weight of the Margalef index.

4. The method of claim 3 for determining key biodiversity areas based on the number of species within a region, characterized in that: The method of spatializing environmental information and species information based on the GIS platform to construct an ecological niche model, predicting the species distribution area of ​​the set area based on the ecological niche model, and outputting the population density value of each species in each sample area includes: (1) In the GIS platform, obtain and integrate real-time environmental information and species information within the set area, and use the spatial analysis function of the GIS platform to convert environmental information and species information into spatial data; (2) constructing an ecological niche model based on the spatial data, wherein the ecological niche model is used to reflect the interaction between species and environmental factors, and performing fitting optimization on the ecological niche model based on a machine learning algorithm; (3) The optimized niche model is used to predict the species distribution area within the set area, and the population density value C of each species in each sample area is determined based on the following formula: C = X / Y, where X is the number of species and Y is the area of ​​each sample area.

5. The method of claim 4 for determining key biodiversity areas based on the number of species within a region, characterized in that: The threat factor is determined based on the following formula: T=(E*w1)+(D*w2)+(C*w3) Among them, T is the threat coefficient, E is the environmental impact coefficient, w1 is the weight of the environmental impact coefficient, D is the species diversity index, w2 is the weight of the species diversity index, C is the population density value, and w3 is the weight of the population density value.

6. A system for determining key biodiversity areas based on the number of species within a region, characterized in that: Applied to perform the method described in any one of claims 1 to 5, the system includes a dynamic monitoring station, a GIS platform wirelessly connected to the dynamic monitoring station, and a cloud evaluation platform wirelessly connected to the dynamic monitoring station and the GIS platform; wherein, The dynamic monitoring station is used to obtain environmental information and species information in a plurality of pre-divided sample areas in real time, and determine the environmental impact coefficient and species diversity index based on the species information and environmental information; wherein the environmental information includes soil environmental factors, climate environmental factors and water quality environmental factors, and the species information includes species richness; The GIS platform is used to spatialize environmental information and species information to construct an ecological niche model, predict species distribution areas in the set area based on the ecological niche model, and output population density values ​​of each species in each sample area; The cloud assessment platform is used to determine the threat coefficient of each sample area based on the environmental impact coefficient, the species diversity index and the population density value, and to determine the key areas for biodiversity protection according to the relationship between the threat coefficient and the set threshold.

7. A system for determining key biodiversity areas based on population numbers within a region according to claim 6, characterized in that: The dynamic monitoring station includes at least one ground monitoring station and at least one unmanned aerial vehicle wirelessly connected thereto, wherein: The ground monitoring station is equipped with several infrared cameras for automatic identification and counting of species. The drone is equipped with built-in environmental sensors for real-time monitoring of climate and soil environmental factors within a set area. The environmental sensors include temperature sensors, humidity sensors, light intensity sensors, air pressure sensors, soil pH sensors and soil moisture sensors.

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