A forest ecological monitoring and evaluation method and system based on big data

Abstract

The application relates to the field of forestry monitoring, in particular to a forestry ecological monitoring and evaluation method and system based on big data; the method comprises the following steps: acquiring ecological observation data, single-tree monitoring data and regional monitoring data of a forestry ecological region, setting a big data platform, and constructing a single-tree ecological model; according to the big data platform and the single-tree ecological model, acquiring single-tree change data and regional change data, constructing ecological nodes and a forestry ecological network according to the single-tree change data and the regional change data; according to the forestry ecological network, obtaining a point cloud quantity change curve, an active area value change curve and a change region, and according to the change region and a forestry ecological subregion, obtaining an ecological region value; according to the ecological region value, the single-tree monitoring data and the forestry ecological subregion, obtaining an ecological health value, according to the ecological health value, obtaining an ecological comprehensive value, and evaluating the forestry ecological region through the ecological comprehensive value. The application can improve the forestry evaluation precision.

Description

Technical Field

[0001] This invention relates to the field of forestry monitoring, and specifically to a forestry ecological monitoring and assessment method and system based on big data. Background Technology

[0002] With the increasing severity of global climate change, forest degradation, and biodiversity loss, the health of forest ecosystems has become a key guarantee for ecological security and sustainable development. Forest ecosystems are an important part of the Earth's ecosystem, with important ecological functions such as soil and water conservation, water source conservation, climate regulation, air purification, and biodiversity maintenance. They are crucial to maintaining the Earth's ecological balance and the sustainable development of human society.

[0003] In existing technologies, human activities are often present within forestry ecological areas. The impact of human activities on forestry ecology may vary, and it is often impossible to monitor and accurately assess forestry ecology in real time. The inability to accurately assess the health and risk status of forestry ecology is a problem that we need to solve. Summary of the Invention

[0004] The purpose of this invention is to address the problems existing in the background technology by proposing a forestry ecological monitoring and assessment method based on big data.

[0005] The technical solution of this invention: a forestry ecological monitoring and assessment method based on big data, comprising the following steps:

[0006] S1. Obtain ecological observation data, individual tree monitoring data, and regional monitoring data of forestry ecological areas; set up a big data platform; analyze the ecological observation data and individual tree monitoring data of forestry ecological areas through the big data platform; and construct individual tree ecological models.

[0007] S2. Through a big data platform, monitor the single-tree ecological model in real time, obtain single-tree change data and regional change data, and construct ecological nodes and forestry ecological networks based on the single-tree change data and regional change data.

[0008] S3. Analyze the forestry ecological network to obtain the point cloud quantity change curve and the activity area value change curve. Analyze the point cloud quantity change curve and the activity area value change curve to obtain the changed area. Based on the changed area and the forestry ecological sub-region, obtain the ecological region value.

[0009] S4. Based on the ecological area value, individual tree monitoring data, and forestry ecological sub-regions, obtain the ecological health value. Based on the ecological health value, obtain the ecological comprehensive value. Use the ecological comprehensive value to assess the forestry ecological area and obtain the assessment results.

[0010] Preferably, the process of acquiring ecological observation data, individual tree monitoring data, and regional monitoring data of forestry ecological areas, setting up a big data platform, and analyzing the ecological observation data and individual tree monitoring data of forestry ecological areas through the big data platform to construct individual tree ecological models includes:

[0011] The forestry ecological region is divided into several sub-regions, which are denoted as forestry ecological sub-regions;

[0012] Ecological observation data is acquired using lidar technology. This data includes single-tree point cloud data and observation time. The ecological observation data, single-tree monitoring data, and regional monitoring data are stored in a big data platform, and a three-dimensional coordinate system is established. The single-tree point cloud data of the ecological observation data is input into the corresponding position in the three-dimensional coordinate system to construct a single-tree structure model of the forestry ecological sub-region.

[0013] Forestry monitoring stations were set up; individual tree monitoring data included chlorophyll content and leaf area index; regional monitoring data included the extent and duration of human activities.

[0014] The chlorophyll content and leaf area index of individual tree monitoring data are input into the corresponding positions of the individual tree structure model to obtain the individual tree ecological model, and then the individual tree ecological model is uploaded to the big data platform.

[0015] Preferably, the process of using a big data platform to monitor the individual tree ecological model in real time, acquiring individual tree change data and regional change data, and constructing ecological nodes and forestry ecological networks based on the individual tree change data and regional change data includes:

[0016] Real-time monitoring of single-tree point cloud data and regional monitoring data within the single-tree ecological model is performed to obtain single-tree change data and regional change data. The single-tree change data and regional change data are then uploaded to the big data platform. The single-tree change data includes the change in single-tree point cloud data and the time of the change; the regional change data includes the scope of changes in human activities and the time of the changes.

[0017] The forestry ecological sub-regions within the big data platform are analyzed. When individual tree change data is obtained within a forestry ecological sub-region, ecological node A is constructed; when regional change data is obtained within a forestry ecological sub-region, ecological node B is constructed. Regional links are constructed between all ecological nodes A and B within the forestry ecological sub-region. For ecological nodes A and B within the same forestry ecological sub-region, when the individual tree change time corresponding to ecological node A coincides with the change activity time corresponding to ecological node B, a temporal link is constructed. Through regional and temporal link relationships, ecological nodes A and B are linked to obtain the forestry ecological network.

[0018] Preferably, the process of analyzing the forestry ecological network to obtain the point cloud quantity change curve and the activity area value change curve includes:

[0019] The forestry ecological network within the forestry ecological sub-region was analyzed to obtain ecological node C and its change time.

[0020] The forestry ecological network is analyzed to obtain the number of three-dimensional coordinate points in the point cloud data of changed individual trees and the time of change of individual trees. The number of three-dimensional coordinate points is recorded as the point cloud quantity. A two-dimensional coordinate system is established for the point cloud quantity of the changed individual trees with respect to the forestry ecological sub-region. A point cloud quantity change curve is generated based on the obtained point cloud quantity. The generated point cloud quantity change curve is mapped into the two-dimensional coordinate system.

[0021] The area and time of human activity changes are obtained and uploaded to a big data platform. The area of ​​human activity changes is recorded as the activity area value. A two-dimensional coordinate system of the activity area value of the change activity time with respect to the forestry ecological sub-region is established. An activity area value change curve is generated based on the obtained activity area value. The generated activity area value change curve is mapped into the two-dimensional coordinate system.

[0022] Preferably, the process of analyzing the point cloud quantity change curve and the activity area value change curve to obtain the changed area, and obtaining the ecological area value based on the changed area and the forestry ecological sub-region, is as follows:

[0023] The curves of point cloud quantity change and activity area value change are analyzed. By analyzing the change time of individual trees in the curve of point cloud quantity change or the change time of activity in the curve of activity area value change, the change time of individual trees in ecological node A, the change time of activity in ecological node B and the change time of ecological node C are analyzed respectively. Regions A, B and C are obtained and recorded as the change areas.

[0024] By changing the region, the forestry ecological sub-regions are analyzed. Based on the point cloud quantity change curve corresponding to region A, the individual tree fluctuation anomaly is obtained; based on the activity area value change curve corresponding to region B, the behavior fluctuation anomaly is obtained; based on the point cloud quantity change curve and activity area value change curve corresponding to region C, the individual tree fluctuation value and behavior fluctuation value are obtained. The individual tree fluctuation anomaly, behavior fluctuation anomaly, individual tree fluctuation value and behavior fluctuation value are recorded as the ecological region value.

[0025] Preferably, the process of obtaining ecological health values ​​based on ecological area values, individual tree monitoring data, and forestry ecological sub-regions includes:

[0026] The ecological region values ​​are analyzed. When there are outliers in the fluctuation of individual trees within a forestry ecological sub-region, the monitoring data of individual trees within the time period corresponding to the outlier are obtained. Based on the individual tree monitoring data, the ecological health value R is obtained. a When outlier fluctuations exist in individual trees within a forestry ecological sub-region, they are related to human activities. Individual tree monitoring data for the time period corresponding to the fluctuation values ​​are obtained. Based on the individual tree monitoring data, individual tree fluctuation values, and behavioral fluctuation values, the ecological health value R is obtained. c .

[0027] Preferably, based on the ecological health value, an ecological comprehensive value is obtained, and the forestry ecological area is assessed using the ecological comprehensive value. The process of obtaining the assessment results includes:

[0028] Based on the ecological health value R of the forestry ecological sub-region a and ecological health value R c To obtain the comprehensive ecological value of the forestry ecological sub-region;

[0029] Set an ecological comprehensive assessment value;

[0030] When the comprehensive ecological value is greater than or equal to the comprehensive ecological assessment value, the ecological condition of the forestry ecological sub-region is good, and assessment result one is generated.

[0031] When the comprehensive ecological value is less than the comprehensive ecological assessment value, the ecological status of the forestry ecological sub-region needs to be observed. It is necessary to regulate human activities in the forestry ecological sub-region. Risk phenomena may occur in the forestry ecological region, generating assessment result two. Assessment result one and assessment result two generated by the big data platform are sent to forestry-related personnel.

[0032] This invention also discloses a forestry ecological monitoring and assessment system based on big data, including a management center, which is communicatively connected to a data monitoring module, a data analysis module, a data processing module, and a data assessment module.

[0033] The data monitoring module is used to acquire ecological observation data, individual tree monitoring data and regional monitoring data of forestry ecological areas, set up a big data platform, and analyze the ecological observation data and individual tree monitoring data of forestry ecological areas through the big data platform to construct individual tree ecological models.

[0034] The data analysis module is used to monitor the single-tree ecological model in real time through the big data platform, obtain single-tree change data and regional change data, and construct ecological nodes and forestry ecological networks based on the single-tree change data and regional change data.

[0035] The data processing module is used to analyze the forestry ecological network, obtain the point cloud quantity change curve and the activity area value change curve, analyze the point cloud quantity change curve and the activity area value change curve to obtain the changed area, and obtain the ecological area value based on the changed area and the forestry ecological sub-region.

[0036] The data evaluation module is used to obtain ecological health values ​​based on ecological area values, individual tree monitoring data, and forestry ecological sub-regions. Based on the ecological health values, an ecological comprehensive value is obtained, and the forestry ecological area is evaluated using the ecological comprehensive value to obtain the evaluation results.

[0037] Compared with existing technologies, the above-mentioned technical solution of the present invention has the following beneficial technical effects: Monitoring forestry ecological areas through single-tree ecological models allows for timely understanding of single-tree ecological changes, comprehensive monitoring of forestry ecology, and improved accuracy of forestry ecological monitoring; analysis combined with forestry ecological networks enables the acquisition of single-tree ecological changes, facilitating real-time acquisition of forestry ecological changes and improving the accuracy of ecological assessment; comprehensive analysis combining point cloud quantity change curves and activity area value change curves, based on which change areas are identified, and analysis of the impact of human activities on single-tree ecology is conducted, enhancing the scientific rigor and timeliness of forestry ecological analysis; combining ecological area values ​​helps in analyzing the impact of human activities on single-tree ecology, improving the accuracy of forestry ecological assessment and contributing to the efficiency of forestry ecological management; through comprehensive ecological values, forestry managers can assess, analyze, and manage forestry ecological areas, improving the accuracy of forestry ecological area status assessment; and through the comprehensive ecological values ​​and assessment results of forestry ecological areas, forestry resource management can be optimized, promoting ecological protection and sustainable development. Attached Figure Description

[0038] Figure 1 This is a flowchart of one embodiment of the present invention. Detailed Implementation

[0039] Example 1, as Figure 1 As shown, the present invention proposes a forestry ecological monitoring and assessment method based on big data, which includes the following steps:

[0040] S1. Obtain ecological observation data, individual tree monitoring data, and regional monitoring data of forestry ecological areas; set up a big data platform; analyze the ecological observation data and individual tree monitoring data of forestry ecological areas through the big data platform; and construct individual tree ecological models.

[0041] S2. Through a big data platform, monitor the single-tree ecological model in real time, obtain single-tree change data and regional change data, and construct ecological nodes and forestry ecological networks based on the single-tree change data and regional change data.

[0042] S3. Analyze the forestry ecological network to obtain the point cloud quantity change curve and the activity area value change curve. Analyze the point cloud quantity change curve and the activity area value change curve to obtain the changed area. Based on the changed area and the forestry ecological sub-region, obtain the ecological region value.

[0043] S4. Based on the ecological area value, individual tree monitoring data, and forestry ecological sub-regions, obtain the ecological health value. Based on the ecological health value, obtain the ecological comprehensive value. Use the ecological comprehensive value to assess the forestry ecological area and obtain the assessment results.

[0044] It should be further explained that, in the specific implementation process, the process of obtaining ecological observation data, individual tree monitoring data, and regional monitoring data of forestry ecological areas, and constructing individual tree ecological models based on the ecological observation data of forestry ecological areas and individual tree monitoring data, is as follows:

[0045] The forestry ecological region is the region corresponding to forestry ecology. The forestry ecological region is divided into several sub-regions, which are referred to as forestry ecological sub-regions. The forestry ecological sub-regions include single tree ecology, which refers to a single tree and the tree ecological information related to a single tree.

[0046] The big data platform is used to store and analyze data related to forestry ecological areas;

[0047] Ecological observation data is acquired using lidar technology; the ecological observation data includes single-tree point cloud data and observation time; the single-tree point cloud data refers to the three-dimensional spatial data of a single tree, including x, y, and z coordinates; the observation time refers to the time when the single-tree point cloud data was acquired.

[0048] Ecological observation data, individual tree monitoring data, and regional monitoring data are stored in a big data platform, and a three-dimensional coordinate system is established. Individual tree point cloud data of ecological observation data are input into the corresponding positions of the three-dimensional coordinate system to construct individual tree structure models of forestry ecological sub-regions.

[0049] Forestry monitoring stations are set up; each forestry monitoring station is equipped with multiple Internet of Things (IoT) monitoring devices; the IoT monitoring devices include sensors and spectrometers, used to acquire individual tree monitoring data and regional monitoring data; the individual tree monitoring data includes chlorophyll content and leaf area index; the regional monitoring data refers to monitoring data within a forestry ecological sub-region, including the range and duration of human activities;

[0050] The chlorophyll content and leaf area index of individual tree monitoring data are input into the corresponding positions of the individual tree structure model to obtain the individual tree ecological model, and then the individual tree ecological model is uploaded to the big data platform.

[0051] It should be further explained that, in the specific implementation process, the individual tree ecological model is monitored in real time through a big data platform to obtain individual tree change data and regional change data. Based on the individual tree change data and regional change data, the process of constructing ecological nodes and forestry ecological networks is as follows:

[0052] Real-time monitoring is performed on the single-tree point cloud data and regional monitoring data within the single-tree ecological model. If there are discrepancies between the real-time monitored single-tree point cloud data and regional monitoring data and the single-tree point cloud data and regional monitoring data within the single-tree ecological model, then single-tree change data and regional change data are acquired and uploaded to the big data platform. The single-tree change data includes the changed single-tree point cloud data and the time of the change; the regional change data includes the scope of changes in human activities and the time of the changes.

[0053] The forestry ecological sub-regions within the big data platform are analyzed. When individual tree change data is obtained within a forestry ecological sub-region, ecological node A is constructed; when regional change data is obtained within a forestry ecological sub-region, ecological node B is constructed. Regional links are constructed between all ecological nodes A and B within the forestry ecological sub-region. For ecological nodes A and B within the same forestry ecological sub-region, when the individual tree change time corresponding to ecological node A coincides with the change activity time corresponding to ecological node B, a temporal link is constructed. Through regional and temporal link relationships, ecological nodes A and B are linked to obtain the forestry ecological network, and the individual tree change point cloud data and the scope of human activity changes are marked within the corresponding ecological nodes.

[0054] It should be further explained that, in the specific implementation process, the forestry ecological network is analyzed to obtain the point cloud quantity change curve and the activity area value change curve. The changed areas are then analyzed to identify the changed areas. Based on the changed areas and the forestry ecological sub-regions, the ecological region value is obtained as follows:

[0055] The forestry ecological network within the forestry ecological sub-region is analyzed. When ecological node A and ecological node B within the forestry ecological sub-region have a temporal link relationship, ecological node A and ecological node B are merged into one ecological node, denoted as ecological node C. The time of change of individual tree corresponding to ecological node A and the time of change activity corresponding to ecological node B are denoted as the same change time. The same change time, the point cloud data of the changed individual tree, and the range of human activity change are marked in ecological node C.

[0056] The forestry ecological network is analyzed to obtain the number of three-dimensional coordinate points in the point cloud data of changed individual trees and the time of change of individual trees. The number of three-dimensional coordinate points is recorded as the point cloud quantity. A two-dimensional coordinate system is established for the point cloud quantity of the changed individual trees with respect to the forestry ecological sub-region. A point cloud quantity change curve is generated based on the obtained point cloud quantity. The generated point cloud quantity change curve is mapped into the two-dimensional coordinate system.

[0057] The process involves obtaining the area and time of changes in human activity, uploading these data to a big data platform, recording the area of ​​changes as the activity area value, establishing a two-dimensional coordinate system for the activity area value of the forestry ecological sub-region based on the changed activity time, generating an activity area value change curve based on the obtained activity area value, and mapping the generated activity area value change curve onto the two-dimensional coordinate system.

[0058] Analyze the point cloud quantity change curve and the activity area value change curve. When the change time of a single tree in the point cloud quantity change curve or the change time of the activity area value change curve is consistent with the change time in ecological node C, the time region corresponding to the point cloud quantity change curve segment or the activity area value change curve segment is recorded as region C. When the change time of a single tree in the point cloud quantity change curve or the change time of the activity area value change curve is consistent with the change time of a single tree in ecological node A, the time region corresponding to the point cloud quantity change curve segment or the activity area value change curve segment is recorded as region A. When the change time of a single tree in the point cloud quantity change curve or the change time of the activity area value change curve is consistent with the change time in ecological node B, the time region corresponding to the point cloud quantity change curve segment or the activity area value change curve segment is recorded as region B. Regions A, B, and C are recorded as the change regions.

[0059] By changing the region, the forestry ecological sub-regions are analyzed. Based on the point cloud quantity change curve corresponding to region A, the rate of change of the point cloud quantity change curve corresponding to region A is obtained and recorded as the individual tree fluctuation anomaly. Based on the activity area value change curve corresponding to region B, the rate of change of the activity area value change curve corresponding to region B is obtained and recorded as the behavior fluctuation anomaly. Based on the point cloud quantity change curve and the activity area value change curve corresponding to region C, the rates of change of the point cloud quantity change curve and the activity area value change curve corresponding to region C are obtained and recorded as the individual tree fluctuation value and the behavior fluctuation value, respectively. The individual tree fluctuation anomaly, the behavior fluctuation anomaly, the individual tree fluctuation value, and the behavior fluctuation value are recorded as ecological region values ​​and marked within the forestry ecological sub-regions.

[0060] It should be further explained that, in the specific implementation process, an ecological health value is obtained based on the ecological area value, individual tree monitoring data, and forestry ecological sub-regions. Based on the ecological health value, an ecological comprehensive value is obtained. The forestry ecological area is then assessed using this ecological comprehensive value, and the process of obtaining the assessment results is as follows:

[0061] Analyzing the ecological region values, when outliers exist in individual trees within a forestry ecological sub-region, it indicates a change in the tree condition within that sub-region, unrelated to human activities. Individual tree monitoring data is obtained for the time periods corresponding to these outliers, and the ecological health value R is derived based on this data. a ;

[0062]

[0063] Among them, R a The ecological health value of a single tree in region A, which is a sub-region of forestry ecology, is represented by α, which is the ecological health coefficient and is related to chlorophyll content and leaf area index. The average chlorophyll content in region A; The mean leaf area index in region A;

[0064] When there are outliers in behavioral fluctuations within a forestry ecological sub-region, it indicates that human activities within the forestry ecological sub-region have not affected changes in the condition of trees.

[0065] When outlier fluctuations exist in individual trees within a forestry ecological sub-region, it indicates a change in the condition of trees within that sub-region, related to human activities. Individual tree monitoring data for the time period corresponding to the individual tree fluctuation value is obtained. Based on the individual tree monitoring data, individual tree fluctuation values, and behavioral fluctuation values, the ecological health value R is calculated. c ;

[0066]

[0067] Among them, R c b represents the ecological health value of a single tree in region C, a sub-region of forestry ecology; and x represents the fluctuation value of a single tree. The mean chlorophyll content in region C; The mean leaf area index in region C;

[0068] Based on the ecological health value R of the forestry ecological sub-region a and ecological health value R c The ecological comprehensive value Z of the forestry ecological sub-region was obtained. e ;

[0069]

[0070] Among them, Z eHere, 'e' represents the ecological comprehensive value of the forestry ecological sub-region, 'a' represents the sub-region number, and 'ti' represents the region number. a f is the time span value corresponding to region A; f is the total number of regions A; c is the number of region C; ti c is the time span value corresponding to region C; g is the total number of regions C; TI is the total time span value of the forestry ecological sub-regions;

[0071] Set an ecological comprehensive assessment value;

[0072] When the comprehensive ecological value is greater than or equal to the comprehensive ecological assessment value, the ecological condition of the forestry ecological sub-region is good, and assessment result one is generated.

[0073] When the comprehensive ecological value is less than the comprehensive ecological assessment value, the ecological status of the forestry ecological sub-region needs to be observed. It is necessary to regulate human activities in the forestry ecological sub-region. Risk phenomena may occur in the forestry ecological region, generating assessment result two. Assessment result one and assessment result two generated by the big data platform are sent to forestry-related personnel.

[0074] Example 2: The forestry ecological monitoring and assessment system based on big data proposed in this invention is applied to the forestry ecological monitoring and assessment method based on big data described in Example 1. Specifically, it includes a management center, which is communicatively connected to a data monitoring module, a data analysis module, a data processing module, and a data assessment module.

[0075] The data monitoring module is used to acquire ecological observation data, individual tree monitoring data and regional monitoring data of forestry ecological areas, set up a big data platform, and analyze the ecological observation data and individual tree monitoring data of forestry ecological areas through the big data platform to construct individual tree ecological models.

[0076] The data analysis module is used to monitor the single-tree ecological model in real time through the big data platform, obtain single-tree change data and regional change data, and construct ecological nodes and forestry ecological networks based on the single-tree change data and regional change data.

[0077] The data processing module is used to analyze the forestry ecological network, obtain the point cloud quantity change curve and the activity area value change curve, analyze the point cloud quantity change curve and the activity area value change curve to obtain the changed area, and obtain the ecological area value based on the changed area and the forestry ecological sub-region.

[0078] The data evaluation module is used to obtain ecological health values ​​based on ecological area values, individual tree monitoring data, and forestry ecological sub-regions. Based on the ecological health values, an ecological comprehensive value is obtained, and the forestry ecological area is evaluated using the ecological comprehensive value to obtain the evaluation results.

[0079] The embodiments of the present invention have been described in detail above with reference to the accompanying drawings. However, the present invention is not limited thereto. Various changes can be made within the scope of knowledge possessed by those skilled in the art without departing from the spirit of the present invention.

Claims

1. A forestry ecological monitoring and assessment method based on big data, characterized in that, Includes the following steps: S1. Obtain ecological observation data, individual tree monitoring data, and regional monitoring data of forestry ecological areas; set up a big data platform; analyze the ecological observation data and individual tree monitoring data of forestry ecological areas through the big data platform; and construct individual tree ecological models. S2. Through a big data platform, monitor the single-tree ecological model in real time, obtain single-tree change data and regional change data, and construct ecological nodes and forestry ecological networks based on the single-tree change data and regional change data. S3. Analyze the forestry ecological network to obtain the point cloud quantity change curve and the activity area value change curve. Analyze the point cloud quantity change curve and the activity area value change curve to obtain the changed area. Based on the changed area and the forestry ecological sub-region, obtain the ecological region value. S4. Based on the ecological area value, single tree monitoring data, and forestry ecological sub-regions, obtain the ecological health value; based on the ecological health value, obtain the ecological comprehensive value; evaluate the forestry ecological area using the ecological comprehensive value, and obtain the evaluation result. The process of acquiring ecological observation data, individual tree monitoring data, and regional monitoring data for forestry ecological areas, setting up a big data platform, and analyzing the ecological observation data and individual tree monitoring data of forestry ecological areas through the big data platform to construct individual tree ecological models includes: The forestry ecological region is divided into several sub-regions, which are denoted as forestry ecological sub-regions; Ecological observation data is acquired using lidar technology. This data includes single-tree point cloud data and observation time. The ecological observation data, single-tree monitoring data, and regional monitoring data are stored in a big data platform, and a three-dimensional coordinate system is established. The single-tree point cloud data of the ecological observation data is input into the corresponding position in the three-dimensional coordinate system to construct a single-tree structure model of the forestry ecological sub-region. Forestry monitoring stations were set up; individual tree monitoring data included chlorophyll content and leaf area index; regional monitoring data included the extent and duration of human activities. The chlorophyll content and leaf area index of individual tree monitoring data are input into the corresponding positions of the individual tree structure model to obtain the individual tree ecological model, and then the individual tree ecological model is uploaded to the big data platform.

2. The forestry ecological monitoring and assessment method based on big data according to claim 1, characterized in that, The process of using a big data platform to monitor individual tree ecological models in real time, acquiring data on changes in individual trees and regions, and constructing ecological nodes and forestry ecological networks based on this data includes: Real-time monitoring of single-tree point cloud data and regional monitoring data within the single-tree ecological model is performed to obtain single-tree change data and regional change data. The single-tree change data and regional change data are then uploaded to the big data platform. The single-tree change data includes the change in single-tree point cloud data and the time of the change; the regional change data includes the scope of changes in human activities and the time of the changes. The forestry ecological sub-regions within the big data platform are analyzed. When individual tree change data is obtained within a forestry ecological sub-region, ecological node A is constructed; when regional change data is obtained within a forestry ecological sub-region, ecological node B is constructed. Regional links are constructed between all ecological nodes A and B within the forestry ecological sub-region. For ecological nodes A and B within the same forestry ecological sub-region, when the individual tree change time corresponding to ecological node A coincides with the change activity time corresponding to ecological node B, a temporal link is constructed. Through regional and temporal link relationships, ecological nodes A and B are linked to obtain the forestry ecological network.

3. The forestry ecological monitoring and assessment method based on big data according to claim 2, characterized in that, The process of analyzing forestry ecological networks to obtain curves showing changes in point cloud quantity and activity area includes: The forestry ecological network within the forestry ecological sub-region was analyzed to obtain ecological node C and its change time. The forestry ecological network is analyzed to obtain the number of three-dimensional coordinate points in the point cloud data of changed individual trees and the time of change of individual trees. The number of three-dimensional coordinate points is recorded as the point cloud quantity. A two-dimensional coordinate system is established for the point cloud quantity of the changed individual trees with respect to the forestry ecological sub-region. A point cloud quantity change curve is generated based on the obtained point cloud quantity. The generated point cloud quantity change curve is mapped into the two-dimensional coordinate system. The area and time of human activity changes are obtained and uploaded to a big data platform. The area of ​​human activity changes is recorded as the activity area value. A two-dimensional coordinate system of the activity area value of the change activity time with respect to the forestry ecological sub-region is established. An activity area value change curve is generated based on the obtained activity area value. The generated activity area value change curve is mapped into the two-dimensional coordinate system.

4. The forestry ecological monitoring and assessment method based on big data according to claim 3, characterized in that, The process of analyzing the point cloud quantity change curve and the activity area value change curve to obtain the changed area, and then obtaining the ecological area value based on the changed area and the forestry ecological sub-region, is as follows: The curves of point cloud quantity change and activity area value change are analyzed. By analyzing the change time of individual trees in the curve of point cloud quantity change or the change time of activity in the curve of activity area value change, the change time of individual trees in ecological node A, the change time of activity in ecological node B and the change time of ecological node C are analyzed respectively to obtain region A, region B and region C, and region A, region B and region C are recorded as the change region. By changing the region, the forestry ecological sub-regions are analyzed. Based on the point cloud quantity change curve corresponding to region A, the individual tree fluctuation anomaly is obtained; based on the activity area value change curve corresponding to region B, the behavior fluctuation anomaly is obtained; based on the point cloud quantity change curve and activity area value change curve corresponding to region C, the individual tree fluctuation value and behavior fluctuation value are obtained. The individual tree fluctuation anomaly, behavior fluctuation anomaly, individual tree fluctuation value and behavior fluctuation value are recorded as the ecological region value.

5. The forestry ecological monitoring and assessment method based on big data according to claim 4, characterized in that, The process of obtaining ecological health values ​​based on ecological area values, individual tree monitoring data, and forestry ecological sub-regions includes: The ecological region values ​​are analyzed. When there are outliers in the fluctuation of individual trees within a forestry ecological sub-region, the monitoring data of individual trees within the time period corresponding to the outlier are obtained. Based on the individual tree monitoring data, the ecological health value R is obtained. a When individual tree fluctuation values ​​exist within a forestry ecological sub-region and are related to human activities, individual tree monitoring data for the time period corresponding to the individual tree fluctuation values ​​are obtained. Based on the individual tree monitoring data, individual tree fluctuation values, and behavioral fluctuation values, the ecological health value R is obtained. c .

6. The forestry ecological monitoring and assessment method based on big data according to claim 5, characterized in that, Based on the ecological health value, an ecological comprehensive value is obtained. This ecological comprehensive value is then used to assess the forestry ecological region. The process of obtaining the assessment results includes: Based on the ecological health value R of the forestry ecological sub-region a and ecological health value R c To obtain the comprehensive ecological value of the forestry ecological sub-region; Set an ecological comprehensive assessment value; When the comprehensive ecological value is greater than or equal to the comprehensive ecological assessment value, the ecological condition of the forestry ecological sub-region is good, and assessment result one is generated. When the comprehensive ecological value is less than the comprehensive ecological assessment value, the ecological status of the forestry ecological sub-region needs to be observed. It is necessary to regulate human activities in the forestry ecological sub-region. Risk phenomena may occur in the forestry ecological region, generating assessment result two. Assessment result one and assessment result two generated by the big data platform are sent to forestry-related personnel.

7. A forestry ecological monitoring and assessment system based on big data, specifically applied to the forestry ecological monitoring and assessment method based on big data as described in any one of claims 1 to 6, comprising a management center, characterized in that, The management center's communication connections include a data monitoring module, a data analysis module, a data processing module, and a data evaluation module. The data monitoring module is used to acquire ecological observation data, individual tree monitoring data and regional monitoring data of forestry ecological areas, set up a big data platform, and analyze the ecological observation data and individual tree monitoring data of forestry ecological areas through the big data platform to construct individual tree ecological models. The data analysis module is used to monitor the single-tree ecological model in real time through the big data platform, obtain single-tree change data and regional change data, and construct ecological nodes and forestry ecological networks based on the single-tree change data and regional change data. The data processing module is used to analyze the forestry ecological network, obtain the point cloud quantity change curve and the activity area value change curve, analyze the point cloud quantity change curve and the activity area value change curve to obtain the changed area, and obtain the ecological area value based on the changed area and the forestry ecological sub-region. The data evaluation module is used to obtain ecological health values ​​based on ecological area values, individual tree monitoring data, and forestry ecological sub-regions. Based on the ecological health values, an ecological comprehensive value is obtained, and the forestry ecological area is evaluated using the ecological comprehensive value to obtain the evaluation results.

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