Method, system and storage medium for early warning of forest disaster risks in property parks

Abstract

The present invention discloses a method, system and storage medium for early warning of forest disaster risks in property parks. The method includes: collecting forest information in key areas within the property park to obtain basic forest data; conducting risk analysis based on the basic forest data to predict risk events of forests in disasters; conducting simulation analysis of disaster prevention measures based on the basic forest data and risk events to reduce or avoid the occurrence of risk events; collecting forest disaster data under actual meteorological conditions, and correcting the risk analysis and simulation analysis of disaster prevention measures. The method realizes digital management of forests to prevent the risks faced by forests in bad weather, adopts information processing technology and intelligent analysis methods, and is a specific embodiment of digital application in forest management in property parks, which is conducive to accurate prevention and accurate policy implementation, and improves the benefits of park management by large property management units.

Description

Technical Field

[0001] The present invention relates to the field of forest management in property parks, and in particular to a method, system and storage medium for early warning of forest disaster risks in property parks. Background Art

[0002] With the emergence of the El Niño phenomenon, severe weather such as strong winds accompanied by heavy rains has caused trees in property parks to be blown down and branches to be broken, which in turn have fallen to vehicles, buildings and other facilities on the ground, causing varying degrees of property damage and even casualties.

[0003] To this end, it is necessary to conduct a census and identification of the trees in the property park, predict risks and formulate targeted protective measures. Summary of the invention

[0004] The main technical problem solved by the present invention is to provide a method, system and storage medium for early warning of forest disaster risks in a property park when the tree planting situation in the property park faces natural disasters such as wind and rain, so as to facilitate early prevention measures.

[0005] In order to solve the above technical problems, a technical solution adopted by the present invention is to provide a method for early warning of forest disaster risks in property parks, including the steps of: collecting forest information in key areas within the property park to obtain basic forest data; performing risk analysis based on the basic forest data to predict risk events of forests in disasters; performing disaster prevention measures simulation analysis based on the basic forest data and risk events to reduce or avoid the occurrence of risk events; collecting forest disaster data under actual meteorological conditions, and correcting the risk analysis and disaster prevention measures simulation analysis.

[0006] The method for collecting tree information in key areas of a property park includes: obtaining a spatial layout map of the property park; dividing the key areas in the spatial layout map into lines; converting the lines into a line vector map; and marking key trees on both sides of the lines on the line vector map.

[0007] The line vector diagram is: , indicating that in the line set Included Lines , ; Each line includes multiple location points, and the line include Location points ,Front Location points The parameters of each position point in are its angle and distance relative to the next position point. Expressed in geographical coordinates, or in grid coordinates in the route vector diagram; , Respectively indicate lines have Location points ,line have Location points .

[0008] The method for marking the key trees on both sides of the line on the line vector diagram includes: inserting the position data of the key trees between two adjacent position points of the line, expressed as:

[0009] ,

[0010] in, Indicates the location of a key tree, indicating that the key tree is on the route Two adjacent points on between, , Indicates the sequence number of the location point.

[0011] The risk analysis based on the basic data of the forest trees includes a risk analysis of broken branches of the forest trees: based on the basic data of the forest trees, the branch parameters of the forest trees are extracted to obtain the topological structure and parameter values ​​of the main trunk, primary branches, secondary branches and forks; based on the basic data of the forest trees, the leaf parameters are extracted to obtain the single leaf area, crown area and dense distribution topology; the branch parameters and leaf parameters are correlated and analyzed to determine the spatial position distribution of dead branches, long branches and / or heavy branches.

[0012] The risk analysis based on the basic data of the forest includes an analysis of the risk of tree tipping: based on the basic data of the forest, obtaining the parameter values ​​of the forest, including the seedling height, fixed trunk height, crown diameter, breast diameter, meter diameter, and ground diameter; establishing a calculation formula for assessing the risk of tree tipping, inputting the parameter values ​​of the forest, and calculating the risk assessment value of tree tipping; comparing the risk assessment value of tree tipping with a preset threshold, and if it is greater than or equal to the preset threshold value, marking the tree as having a risk of tipping.

[0013] The forest dumping risk assessment calculation formula is:

[0014] ,

[0015] Among them, the height of seedlings , is the crown diameter The corresponding risk weight coefficient is is the breast diameter The corresponding risk weight coefficient is Is meter diameter The corresponding risk weight coefficient is It is the ground diameter The corresponding risk weight coefficient is Is the fixed stem height The corresponding risk weight factor.

[0016] The simulation analysis of disaster prevention measures includes: for the risk event of tree toppling, disaster prevention measures are carried out by simulating the setting of reinforcement supports for the trees, wherein the reinforcement supports include support height, burial depth and erection angle.

[0017] Based on the same concept, the present application also provides a system for early warning of forest disaster risks in property parks, including: a collection unit, used to collect forest information in key areas within the property park and obtain basic forest data; a risk prediction unit, used to perform risk analysis based on the basic forest data and predict risk events of forests in disasters; a disaster prevention analysis unit, used to perform simulation analysis of disaster prevention measures based on the basic forest data and risk events, to reduce or avoid the occurrence of risk events; a correction unit, used to collect forest disaster data under actual meteorological conditions and correct the risk analysis and simulation analysis of disaster prevention measures.

[0018] In addition, the present invention also provides a storage medium, in which computer executable instructions are stored, and the computer executable instructions are configured to execute and implement the aforementioned method for early warning of forest disaster risks in property parks.

[0019] The beneficial effects of the present invention are as follows: the present invention discloses a method, system and storage medium for early warning of forest disaster risks in property parks. The method includes: collecting forest information in key areas within the property park to obtain basic forest data; conducting risk analysis based on the basic forest data to predict risk events of forests in disasters; conducting simulation analysis of disaster prevention measures based on the basic forest data and risk events to reduce or avoid the occurrence of risk events; collecting forest disaster data under actual meteorological conditions, and correcting the risk analysis and simulation analysis of disaster prevention measures. The method realizes digital management of forests to prevent the risks faced by forests in bad weather, adopts information processing technology and intelligent analysis methods, and is a specific embodiment of digital application in forest management in property parks, which is conducive to precise prevention and precise policy implementation, and improves the benefits of park management by large-scale property management units. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 is a flow chart of an embodiment of a method for early warning of forest disaster risks in a property park according to the present invention;

[0021] Figure 2is a flow chart of an embodiment of a method for early warning of forest disaster risks in a property park according to the present invention;

[0022] Figure 3 is with Figure 2 The corresponding schematic diagram;

[0023] Figure 4 is a flow chart of an embodiment of a method for early warning of forest disaster risks in a property park according to the present invention;

[0024] Figure 5 Schematic diagram of tree trunk extraction according to an embodiment of a method for early warning of tree disaster risks in a property park according to the present invention;

[0025] Figure 6 It is a schematic diagram of tree leaf division according to an embodiment of a method for early warning of tree disaster risks in a property park according to the present invention;

[0026] Figure 7 and Figure 8 It is a schematic diagram of a simulation modeling of trees according to an embodiment of a method for early warning of tree disaster risks in a property park according to the present invention;

[0027] Fig. 9 is a flow chart of an embodiment of a method for early warning of forest disaster risks in a property park according to the present invention;

[0028] Fig.10 It is a schematic diagram of forest parameter analysis according to an embodiment of a method for early warning of forest disaster risks in a property park according to the present invention;

[0029] Fig.11 It is a schematic diagram of forest reinforcement support analysis according to an embodiment of a method for early warning of forest disaster risks in a property park according to the present invention;

[0030] Fig.12 It is a schematic diagram of the composition of an embodiment of a system for early warning of forest disaster risks in property parks according to the present invention. DETAILED DESCRIPTION

[0031] In order to facilitate the understanding of the present invention, the present invention is described in more detail below in conjunction with the accompanying drawings and specific embodiments. Preferred embodiments of the present invention are provided in the accompanying drawings. However, the present invention can be implemented in many different forms and is not limited to the embodiments described in this specification. On the contrary, the purpose of providing these embodiments is to make the understanding of the disclosure of the present invention more thorough and comprehensive.

[0032] It should be noted that, unless otherwise defined, all technical and scientific terms used in this specification have the same meaning as those commonly understood by those skilled in the art of the present invention. The terms used in the specification of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention. The term "and / or" used in this specification includes any and all combinations of one or more related listed items.

[0033] Figure 1 A flowchart showing an embodiment of a method for early warning of forest disaster risks in a property park, including the steps of:

[0034] S1: Collect forest information in key areas within the property park to obtain basic forest data;

[0035] S2: Conduct risk analysis based on the basic forest data to predict risk events of forests in disasters;

[0036] S3: Conduct disaster prevention measures simulation analysis based on the forest basic data and risk events to reduce or avoid the occurrence of risk events;

[0037] S4: Collect forest disaster data under actual meteorological conditions and revise the risk analysis and disaster prevention measures simulation analysis.

[0038] It can be seen that the above method has realized the digital management of forest disaster risk warning in property parks. This management method is applicable to various types of property parks, which is conducive to large-scale property management units to carry out unified digital management of property parks distributed in multiple locations, and meet the needs of managers at all levels to directly participate in inquiries and supervision, greatly improving the accuracy, directness and efficiency of risk prevention management.

[0039] For step S1, combine Figure 2 and Figure 3 As shown, the method for collecting forest information in key areas within the property park includes:

[0040] S101: Obtaining a spatial layout diagram of a property park. The spatial layout diagram may be based on satellite remote sensing images or a bird's-eye view taken by a low-altitude aircraft.

[0041] S102: dividing the key areas in the spatial layout diagram into lines, wherein the key areas include main roads, garage entrances and exits, walls, outdoor parking lots, and the like.

[0042] S103: Converting the line correspondence into a line vector diagram, that is, converting the line in the image into a line vector diagram that can be displayed and stored by a computer, including vector data such as multiple position points on each line and distances between adjacent position points, which is convenient for processing by a computer.

[0043] S104: Mark the key trees on both sides of the line on the line vector diagram. These key trees are those that are in danger of swaying, tilting, or falling down under extreme weather conditions, and are close to roads, parked vehicles, electromechanical equipment, important entrances and exits, and equipment room facilities.

[0044] For step S103, the line vector diagram is: , in this line collection Included Lines , Each line includes multiple locations, such as include Location points , where the parameters of each previous position point except the last position point are its angle and distance relative to the next position point, that is, the previous Location points The parameters of each position point in are its angle and distance relative to the next position point, such as It is the angle and distance between the first position point and the second position point, so it is represented by a vector. It can be represented by actual geographic coordinates, or by grid coordinates defined in the route vector diagram. The grid coordinates are a simplified processing method that can represent different distance intervals according to the size of the grid, thereby relatively determining the position relationship.

[0045] Likewise, there are , Respectively indicate lines have Location points ,line have Location points , I will not go into details here.

[0046] In step S104, the key trees on both sides of the line are marked on the line vector diagram, including marking the location of the key trees on the line, that is, inserting the location data of the key trees between two adjacent location points on the line, which is expressed as:

[0047] ,

[0048] in, Indicates the location of a key tree, indicating that the key tree is on the route Two adjacent points on between, , Indicates the sequence number of the location point.

[0049] Furthermore, the tree information includes image information, historical information, parameter information, and information about nearby buildings and facilities. The methods for collecting this information include: further obtaining image information of these key trees, as well as the corresponding tree types and numbers through manual photography, drone photography, or robot photography; obtaining historical information by checking the planting or transplanting time, pruning conditions over the years, and pest and disease conditions of these key trees; obtaining parameter information by measuring the seedling height, fixed trunk height, crown diameter, breast diameter, meter diameter, ground diameter, and other parameter values ​​of these key trees; obtaining building facility information near these key trees by querying and obtaining ground buildings, underground engineering pipelines, overhead lines, and underground lines near these key trees.

[0050] These forest information are usually stored in a prescribed unified data format to form basic forest data, which is convenient for data query through computers.

[0051] For step S2, risk analysis is performed based on the basic data of the trees to predict risk events of the trees in disasters. Since key trees may experience natural phenomena such as large-scale swaying, broken branches, tilting, and lodging in disasters such as wind, rain, snow, and hail, the risk events caused are mainly due to the damage of these natural phenomena to surrounding facilities, such as the risk of damage to surrounding cables, building exterior walls, doors and windows caused by large-scale swaying, the risk of falling broken branches to pedestrians and vehicles, and the risk of damage to various surrounding facilities caused by tilting and lodging.

[0052] Specifically, Figure 4 As shown, risk analysis includes tree breaking risk analysis, including the following steps:

[0053] S201: Based on the basic data of the forest trees, extract the trunk parameters of the forest trees, and obtain the topological structure and parameter values ​​of the trunk, primary trunk, secondary trunk and twig;

[0054] S202: Based on the basic data of trees, extract the leaf parameters of trees to obtain single leaf area, crown area, and dense distribution topology;

[0055] S203: performing correlation analysis on the branch parameters and the leaf parameters to determine the spatial position distribution of dead branches, long branches and / or heavy branches.

[0056] Specifically, in step S201, combined with Figure 5, obtain the image data L1 of a tree from the basic data of the tree, remove the leaves from the image data L1 to leave the branches, obtain the branch data L2 of the tree, and then extract the parameters of the branch data L2 to obtain the topological structure of the trunk A1, the first-level branch B1, the second-level branch B2 and the branch C1. And further, according to the parameter data of the tree and combined with the image analysis technology, the parameter values ​​of each branch in the branch data L2 can be obtained, including the diameter value, length branch, and bifurcation angle value of each branch.

[0057] In step S202, combining Figure 6 , extract leaf parameters from the image data L1, including obtaining the single leaf area (which can be the average of multiple single leaf areas) and the area of ​​the entire crown. It should be noted that the crown area is related to the imaging angle of the image. Images of the trees taken at different angles (for example, three images taken at intervals of 120 degrees) can be obtained to obtain the corresponding crown areas at different angles, and then calculate the average value. The crown area is used to calculate the windward side of the wind force and the pressure of snow cover on the crown.

[0058] exist Figure 6 There is also a dense distribution topology of leaves in the figure. It can be seen that the leaf cover topology structure D1 is further divided into a dense distribution area D11 and a sparse distribution area D12. Through this topological structure, the density distribution of leaves in the crown is obtained, which is conducive to accurately determining the mechanical spatial distribution of the windward resistance calculation of the crown and the snowfall gravity calculation.

[0059] For step S203, based on the branch topological structure of the trees and the leaf density distribution zones of the trees obtained in the first two steps, the branch parameters and leaf parameters can be correlated and analyzed in the same spatial structure of the trees to determine the leaf density distribution corresponding to different branch positions, and the dead branches (sparse leaves distribution), heavy branches (dense leaves), long branches or short branches (leaf distribution length in the extension direction) can be accurately determined.

[0060] Specifically, the above-mentioned branch extraction and leaf extraction analysis of the forest image can be implemented through a trained neural network model, for example, through a DNN network (Deep Neural Networks) to extract branches and leaves.

[0061] Furthermore, the trees can be modeled based on the basic tree data to construct a three-dimensional space model corresponding to the trees, thereby performing simulation analysis on the bearing capacity of branches and leaves of the three-dimensional model of the trees. Figure 7 and Figure 8Examples of displaying the same 3D simulated forest at different angles are shown. Based on this, we can further construct wind direction and speed, rainfall, snow, hail and other meteorological conditions, and simulate the dead branches being blown off by wind and rain, the long branches being blown by wind and rain, and the heavy branches being broken by wind, rain and snow under different wind conditions.

[0062] Specifically, Fig. 9 As shown, risk analysis includes tree fall risk analysis, including the following steps:

[0063] S211: Based on the basic data of forest trees, obtain the forest tree parameter values, including seedling height, fixed trunk height, crown diameter, breast diameter, meter diameter, and ground diameter;

[0064] S212: Establishing a tree dumping risk assessment calculation formula, inputting the tree parameter value, and calculating the tree dumping risk assessment value;

[0065] S213: Compare the tree toppling risk assessment value with a preset threshold, and if it is greater than or equal to the preset threshold, mark the tree as having a toppling risk.

[0066] Specifically, for step S211, combined with Fig.10 As shown, including the height of the seedlings , used to indicate the vertical distance from the root of a tree to the highest point of its crown. Taking into account the annual growth of trees, the height of seedlings should be measured regularly; It is the vertical height from the ground to the branching point of the tree. The trunk height of the trees on both sides of the road is usually set at 2.8 to 3.2 meters to ensure sufficient passage space and open sight. The trunk height is crucial to the safety of vehicles and pedestrians, and also determines the branching structure and future crown development of the tree.

[0067] Crown diameter It is the maximum horizontal width of the tree crown, a key parameter for planning garden space and evaluating shading effects; DBH It is the diameter of the trunk of a tree at the first predetermined distance from the ground, such as 1.3 meters. The breast diameter is usually used as an indicator to measure the maturity of trees. The breast diameter of an adult tree is generally more than 15 cm. The breast diameter is an important indicator for evaluating the structural stability and wind resistance of seedlings, and has guiding significance for the support and protection measures of trees. It is the diameter of the trunk of a tree at the second predetermined distance from the ground (less than the first predetermined distance), such as 1 meter. The annual growth rate of the meter diameter should be controlled between 0.8 and 1.2 centimeters to ensure the healthy growth of the trees. The meter diameter is a direct reflection of the growth rate and health of the trees, and has a practical guiding role in formulating reasonable maintenance plans and adjusting the frequency of fertilization and watering. It is the diameter of the tree trunk at the third predetermined distance above the ground (less than the second predetermined distance), such as 5 cm. The ground diameter is usually positively correlated with the transplanting survival rate of the tree. It is generally recommended that the ground diameter be above 4 cm. The ground diameter reflects the development of the tree root system and is of great significance for the transplanting, transportation and post-planting management of the tree.

[0068] In addition, there is the ball diameter below the ground, which is the diameter of the soil ball when the tree is transplanted. The ball diameter is generally 8 to 10 times the tree's breast diameter to ensure that the root system is fully protected. The size of the ball diameter directly affects the survival rate and recovery speed of the tree after transplantation, and is one of the key factors for the success of tree transplantation.

[0069] Specifically, for step S212, the forest dumping risk assessment calculation formula is:

[0070]

[0071] in, is the crown diameter The corresponding risk weight factor is used to indicate the crown diameter Contribution to tree dumping. is the breast diameter The corresponding risk weight coefficient is Is meter diameter The corresponding risk weight coefficient is It is the ground diameter The corresponding risk weight coefficient is Is the fixed stem height The corresponding risk weight coefficients are related to the type and age of trees.

[0072] For the same type of trees, the risk assessment value is calculated by using the calculation formula and the corresponding parameter values. The larger the calculated assessment value, the greater the corresponding dumping risk. Therefore, by setting a preset threshold to compare with the calculated risk value, it is determined whether risk marking and focus are needed.

[0073] For step S3, a simulation analysis of disaster prevention measures is performed based on the basic forest data and risk events to reduce or avoid the occurrence of risk events.

[0074] Specifically, for tree branch breakage risk events, Figure 7 and Figure 8The three-dimensional simulation environment of the forest shown in the figure first establishes a three-dimensional simulation forest that is consistent with the actual forest, and then cuts the dead branches and heavy branches to obtain the trimmed three-dimensional simulation forest. Then, the computer simulation method is used to test the corresponding risk resistance of the trimmed three-dimensional simulation forest under different meteorological conditions. The above simulation method can still be implemented by deep learning of neural networks.

[0075] Specifically, for the risk of tree dumping, based on Fig.11 As shown, the disaster prevention treatment is carried out. Specifically, the reinforcement support is composed of a support height , burial depth , erection angle It is determined by three main parameters.

[0076] Height of support Refers to the distance between the support and the ground. Generally speaking, the higher the support height, the higher the bearing capacity of the support and the stability of the tree. However, the higher the support height, the more difficult it is to set up and the higher the cost. The support height is generally 1 / 2-1 / 3 of the fixed trunk height. However, the support height value is not fixed and should be determined by the size of the tree crown. The wind resistance that the crown of evergreen trees can form is greater than that of deciduous trees of the same specification. The support height of evergreen trees should be greater than that of deciduous trees to increase stability.

[0077] Angle of erection It refers to the angle between the inclined support (i.e. the diagonal brace) and the ground. The angle should be set moderately and should not be too large or too small. If the support angle is too small, the vertical distance between the diagonal brace and the tree trunk will increase, making the space occupied by the support too large and the support too abrupt. If the support angle is too large, the vertical distance between the diagonal brace and the tree trunk will decrease, and the diagonal brace cannot form a good angle with the ground, and cannot effectively disperse the force transmitted from the tree crown.

[0078] Buried depth Refers to the depth of the diagonal brace buried in the soil. The greater the burial depth, the more stable the support is, but the longer the length of the supporting material required is. It should be adjusted according to the soil conditions. If it is sandy soil, the burial depth should be increased. At the same time, bricks can be added to the base of the diagonal brace to allow the support to bear force on the bricks and enhance stability.

[0079] You can also add cross braces and determine the height at which they should be set. , usually set at 1 / 2 of the support height. Of course, for trees with smaller crown diameters, larger pruning before planting, and smaller breast diameters (such as less than 15 cm), no cross bracing is required.

[0080] The above-mentioned reinforcement support treatment method can also be carried out in a simulation environment, thereby enhancing the anti-lodging ability of the trees.

[0081] For step S4, forest disaster data under actual meteorological conditions are collected to revise the risk analysis and disaster prevention measures simulation analysis.

[0082] Specifically, it mainly collects data on forest disasters that occurred in the property park, records the meteorological conditions at the time, and collects data on the actual broken branches and fallen trees, so as to verify the effectiveness of the established risk assessment method and revise the aforementioned risk analysis and disaster prevention measures simulation analysis. For example, the correction amount is added to the forest fall risk assessment calculation formula. ,Right now:

[0083] ,

[0084] Among them, the correction It can be positive or negative, mainly to make the risk value more accurate, because the risk value after correction is related to historical data that has occurred in the past.

[0085] Based on the same inventive concept, Fig.12 As shown, the present application also provides a system for early warning of forest disaster risks in property parks, including:

[0086] The collection unit 11 is used to collect forest information in key areas within the property park to obtain basic forest data.

[0087] The risk prediction unit 12 is used to perform risk analysis based on the basic forest data and predict risk events of forests in disasters.

[0088] The disaster prevention analysis unit 13 is used to perform simulation analysis of disaster prevention measures based on the forest basic data and risk events to reduce or avoid the occurrence of risk events.

[0089] The correction unit 14 is used to collect forest disaster data under actual meteorological conditions and to correct the risk analysis and disaster prevention measures simulation analysis.

[0090] The implementation method of each unit in the system has been described in the previous embodiments of the method for early warning of forest disaster risks in property parks, and will not be repeated here.

[0091] It can be seen that the present invention discloses a method and system for early warning of forest disaster risks in property parks. The method includes: collecting forest information in key areas within the property park to obtain basic forest data; conducting risk analysis based on the basic forest data to predict risk events of forests in disasters; conducting simulation analysis of disaster prevention measures based on the basic forest data and risk events to reduce or avoid the occurrence of risk events; collecting forest disaster data under actual meteorological conditions, and correcting the risk analysis and simulation analysis of disaster prevention measures. The method realizes digital management of forests to prevent the risks faced by forests in bad weather, adopts information processing technology and intelligent analysis methods, and is a specific embodiment of digital application in forest management in property parks, which is conducive to precise prevention and precise policy implementation, and improves the efficiency of park management by large-scale property management units.

[0092] In addition, the present invention also provides a computer-readable storage medium, which stores computer-executable instructions, and the computer-executable instructions are configured to execute and implement the steps of the method for property park forest disaster risk warning in the above-mentioned embodiments.

[0093] In the embodiment of the storage medium of the present invention, all technical features of the above-mentioned embodiments of the method for warning of forest disaster risks in property parks are included, and the descriptions and explanations are basically the same as those of the above-mentioned embodiments of the method for warning of forest disaster risks in property parks, and will not be repeated here.

[0094] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus a necessary general hardware platform, and of course by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product is stored in a computer-readable storage medium (such as ROM / RAM, magnetic disk, optical disk) as described above to execute the methods described in each embodiment of the present invention.

[0095] The above descriptions are merely embodiments of the present invention and are not intended to limit the protection scope of the present invention. Any equivalent structural transformations made using the contents of the present invention description and drawings, or directly or indirectly applied in other related technical fields, are also included in the protection scope of the present invention.

Claims

1. A method for early warning of forest disaster risks in a property park, characterized in that: Includes steps: Collect forest information in key areas within the property park to obtain basic forest data; Conduct risk analysis based on the basic forest data to predict risk events of forests in disasters; Conduct disaster prevention measures simulation analysis based on the basic forest data and risk events to reduce or avoid the occurrence of risk events; Collect forest disaster data under actual meteorological conditions and revise the risk analysis and disaster prevention measures simulation analysis; The risk analysis based on the basic forest data includes the risk analysis of forest dumping: Based on the basic data of forest trees, the forest tree parameter values ​​are obtained, including seedling height, fixed trunk height, crown diameter, breast diameter, meter diameter, and ground diameter; Establishing a forest dumping risk assessment calculation formula, inputting the forest parameter values, and calculating the forest dumping risk assessment value; Compare the tree dumping risk assessment value with a preset threshold, and if it is greater than or equal to the preset threshold, mark the tree as having a dumping risk; The forest dumping risk assessment calculation formula is: ， in, is the height of the seedlings, is the crown diameter The corresponding risk weight coefficient is is the breast diameter The corresponding risk weight coefficient is Is meter diameter The corresponding risk weight coefficient is It is the ground diameter The corresponding risk weight coefficient is Is the fixed stem height The corresponding risk weight factor.

2. The method for early warning of forest disaster risks in a property park according to claim 1, characterized in that: Methods for collecting forest information in key areas within a property park include: Obtain the spatial layout of the property park; Dividing the key areas in the spatial layout diagram into lines; Converting the line correspondence into a line vector diagram; The key trees on both sides of the route are marked on the route vector diagram.

3. The method for early warning of forest disaster risks in a property park according to claim 2, characterized in that: The line vector diagram is: , indicating that in the line set Included Lines , ; Each of the routes includes multiple location points. include Location points ,Front Location points The parameters of each position point in are its angle and distance relative to the next position point. Expressed in geographical coordinates, or in grid coordinates in the route vector diagram; , Respectively indicate lines have Location points ,line have Location points .

4. The method for early warning of forest disaster risks in a property park according to claim 3 is characterized in that: The method for marking the key trees on both sides of the line on the line vector diagram includes: Insert the location data of the key trees between two adjacent location points on the line, expressed as: ， in, Indicates the location of a key tree, indicating that the key tree is on the route Two adjacent points on between, , Indicates the sequence number of the location point.

5. The method for early warning of forest disaster risks in property parks according to claim 1, characterized in that: Conduct risk analysis based on the basic tree data, including risk analysis of tree breakage: Based on the basic data of the forest trees, the branch parameters of the forest trees are extracted to obtain the topological structure and parameter values ​​of the trunk, primary branches, secondary branches and branches; Based on the basic data of trees, leaf parameters of the trees are extracted to obtain single leaf area, crown area, and dense distribution topology; Correlation analysis was performed on branch parameters and leaf parameters to determine the spatial distribution of dead branches, long branches and / or heavy branches.

6. The method for early warning of forest disaster risks in a property park according to claim 1, characterized in that: The simulation analysis of disaster prevention measures includes: for the risk event of tree toppling, disaster prevention measures are carried out by simulating the setting of reinforcement supports for the trees, wherein the reinforcement supports include support height, burial depth and erection angle.

7. A system for early warning of forest disaster risks in property parks, characterized in that: include: The collection unit is used to collect forest information in key areas within the property park and obtain basic forest data; A risk prediction unit, used to perform risk analysis based on the basic data of the forest and predict risk events of the forest in disasters; A disaster prevention analysis unit, used to perform simulation analysis of disaster prevention measures based on the forest basic data and risk events, so as to reduce or avoid the occurrence of risk events; A correction unit, used to collect forest disaster data under actual meteorological conditions and to correct the risk analysis and disaster prevention measures simulation analysis; The risk prediction unit performs risk analysis based on the forest basic data, including risk analysis of tree dumping: Based on the basic data of forest trees, the forest tree parameter values ​​are obtained, including seedling height, fixed trunk height, crown diameter, breast diameter, meter diameter, and ground diameter; Establishing a forest dumping risk assessment calculation formula, inputting the forest parameter values, and calculating the forest dumping risk assessment value; Compare the tree dumping risk assessment value with a preset threshold, and if it is greater than or equal to the preset threshold, mark the tree as having a dumping risk; The forest dumping risk assessment calculation formula is: ， in, is the height of the seedlings, is the crown diameter The corresponding risk weight coefficient is is the breast diameter The corresponding risk weight coefficient is Is meter diameter The corresponding risk weight coefficient is It is the ground diameter The corresponding risk weight coefficient is Is the fixed stem height The corresponding risk weight factor.

8. A storage medium, characterized in that: The storage medium stores computer executable instructions, and the computer executable instructions are configured to execute and implement the method for early warning of forest disaster risks in property parks as described in any one of claims 1-6.

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