Ground surface matrix space heterogeneity vegetation environment response evaluation method and system

Abstract

The invention relates to the technical field of ecological geological survey and evaluation, in particular to a surface matrix spatial heterogeneity vegetation environment response evaluation method and system. Firstly, an exploration high-angle image, actual hydrological data and meteorological data of a to-be-evaluated area are acquired through acquisition equipment and sent to a cloud server, and the cloud server performs image recognition on the exploration high-angle image to obtain actual surface matrix data and actual vegetation growth data of the to-be-evaluated area; and then constructing a neural network model-based surface matrix spatial heterogeneity vegetation environment response evaluation model, and estimating vegetation growth data of the to-be-evaluated region after a future preset duration based on the surface matrix data through the evaluation model. And then the pre-estimated vegetation growth data is compared with the collected actual vegetation growth data, so that the influence condition of the surface matrix on the ecological environment can be accurately and clearly known.

Description

Technical Field

[0001] The present invention relates to the technical field of ecological geological survey and evaluation, and particularly relates to a method and system for evaluating the response of vegetation environment to the spatial heterogeneity of surface substrate. Background Art

[0002] Surface substrate refers to the basic substances that are currently exposed in the shallow part of the earth's land surface or at the bottom of water bodies, mainly formed by natural substances through natural processes, and are currently or can incubate and support various natural resources such as forests, grasslands, and water. The spatial heterogeneity of surface substrate refers to the unevenness of the composition, structure, process, and functional characteristics of surface substrate in spatial distribution; as an important part of the earth's surface layer, the spatial heterogeneity of surface substrate has a significant impact on vegetation distribution and growth. Accurately evaluating the spatial heterogeneity of surface substrate and its response to vegetation environment changes is crucial for fields such as ecological protection, vegetation restoration, and land resource management. However, currently, there is a lack of a solution that can evaluate the impact on the natural environment based on surface substrate data, making it difficult to meet the current environmental management requirements. Summary of the Invention

[0003] The main object of the present invention is to provide a method and system for evaluating the response of vegetation environment to the spatial heterogeneity of surface substrate, aiming to solve the problem that there is currently a lack of a solution for evaluating the impact on the natural environment based on surface substrate data.

[0004] The technical solution proposed by the present invention is as follows:

[0005] A method for evaluating the response of vegetation environment to the spatial heterogeneity of surface substrate, which is applied to a system for evaluating the response of vegetation environment to the spatial heterogeneity of surface substrate; the system includes a collection device and a cloud server that are communicatively connected to each other; the method includes:

[0006] The cloud server obtains the exploration aerial images, actual hydrological data, and actual meteorological data of the area to be evaluated collected by the collection device;

[0007] The cloud server performs image recognition on the exploration aerial images to obtain the actual surface substrate data and actual vegetation growth data of the area to be evaluated;

[0008] The cloud server constructs an evaluation model for the response of vegetation environment to the spatial heterogeneity of surface substrate based on a neural network model and trains it using historical data, where the evaluation model can obtain the vegetation growth data after a preset time based on the surface substrate data, vegetation growth data, hydrological data, and meteorological data at the current moment;

[0009] The cloud server inputs the actual surface substrate data, actual vegetation growth data, actual hydrological data, and actual meteorological data into the evaluation model, and uses the vegetation growth data output by the evaluation model as the predicted vegetation growth data after a preset time period;

[0010] The cloud server generates the environmental impact result of the surface substrate in the area to be evaluated on ecological vegetation based on the actual vegetation growth data and the predicted vegetation growth data after a preset time period.

[0011] Preferably, the acquisition device includes a sensor group and a drone; the drone is provided with a controller and a camera module; the controller and the sensor group are both wirelessly communicatively connected to the cloud server; multiple monitoring position points are set in the area to be evaluated; the cloud server obtains the exploration aerial images, actual hydrological data, and actual meteorological data of the area to be evaluated collected by the acquisition device, including:

[0012] The controller controls the drone to fly directly above each monitoring position point in sequence, and takes pictures through the camera module directly above each monitoring position point to obtain the exploration aerial images of each monitoring position point;

[0013] The controller sends the exploration aerial images of each monitoring position point to the cloud server.

[0014] Preferably, the sensor group includes a water level sensor, a water temperature sensor, a water flow rate sensor, a water quality monitoring sensor, and a meteorological monitoring sensor; one sensor group is correspondingly set at each monitoring position point; the cloud server obtains the exploration aerial images, actual hydrological data, and actual meteorological data of the area to be evaluated collected by the acquisition device, and also includes:

[0015] The cloud server obtains the real-time water level of the monitoring position point collected by the water level sensor, the real-time water temperature of the monitoring position point collected by the water temperature sensor, the real-time water flow rate of the monitoring position point collected by the water flow rate sensor, the real-time pH value of the monitoring position point collected by the water quality monitoring sensor, the real-time rainfall, real-time temperature, and real-time light intensity of the monitoring position point collected by the meteorological monitoring sensor.

[0016] Preferably, after the cloud server obtains the real-time water level of the monitoring position point collected by the water level sensor, the real-time water temperature of the monitoring position point collected by the water temperature sensor, the real-time water flow rate of the monitoring position point collected by the water flow rate sensor, the real-time pH value of the monitoring position point collected by the water quality monitoring sensor, the real-time rainfall, real-time temperature, and real-time light intensity of the monitoring position point collected by the meteorological monitoring sensor, it further includes:

[0017] The cloud server takes the average of the real-time water levels at each monitoring location point within a preset past time period to obtain an average water level value, takes the average of the real-time water temperatures at each monitoring location point within a preset past time period to obtain an average water temperature value, takes the average of the real-time water flow velocities at each monitoring location point within a preset past time period to obtain an average water flow velocity, takes the average of the real-time pH values at each monitoring location point within a preset past time period to obtain an average pH value, takes the average of the real-time rainfall amounts at each monitoring location point within a preset past time period to obtain an average rainfall amount, takes the average of the real-time air temperatures at each monitoring location point within a preset past time period to obtain an average air temperature, and takes the average of the real-time light intensities at each monitoring location point within a preset past time period to obtain an average light intensity;

[0018] The cloud server uses the average water level value, the average water temperature value, the average water flow velocity, and the average pH value as the hydrological data of the area to be evaluated;

[0019] The cloud server uses the average rainfall amount, the average air temperature, and the average light intensity as the meteorological data of the area to be evaluated.

[0020] Preferably, the cloud server performs image recognition on the exploration downward-looking images to obtain the actual surface substrate data and actual vegetation growth data of the area to be evaluated, including:

[0021] The cloud server performs image recognition on the exploration downward-looking images of each monitoring location point to obtain the surface substrate type, landform type, vegetation type, and vegetation coverage of each monitoring location point;

[0022] The cloud server uses the set of surface substrate types of each monitoring location point and the set of landform types of each monitoring location point as the actual surface substrate data of the area to be evaluated;

[0023] The cloud server uses the set of vegetation types of each monitoring location point and the average of the vegetation coverages of each monitoring location point as the actual vegetation growth data of the area to be evaluated.

[0024] Preferably, the cloud server generates the environmental impact result of the surface substrate on ecological vegetation in the area to be evaluated based on the actual vegetation growth data and the predicted vegetation growth data after a preset time period, including:

[0025] The cloud server determines whether the first condition and the second condition are both satisfied. Among them, the first condition is that the number of vegetation types in the predicted vegetation growth data is greater than the number of vegetation types in the actual vegetation growth data, and the second condition is that the vegetation coverage in the predicted vegetation growth data is greater than the vegetation coverage in the actual vegetation growth data;

[0026] If so, the cloud server generates the environmental impact result of the surface substrate of the area to be evaluated on the ecological vegetation, and the environmental impact result is a positive impact;

[0027] If not, the cloud server generates the environmental impact result of the surface substrate of the area to be evaluated on the ecological vegetation, and the environmental impact result is a negative impact.

[0028] Preferably, an ultrasonic speaker for emitting ultrasonic waves is correspondingly arranged at each monitoring position point, and the drone is further provided with an ultrasonic sensor for receiving ultrasonic signals; both the ultrasonic speaker and the ultrasonic sensor are communicatively connected to the controller; the controller controls the drone to fly directly above each monitoring position point in sequence, and takes pictures through the camera module directly above each monitoring position point to obtain the exploration overhead images of each monitoring position point, including:

[0029] The controller controls the drone to fly to a preset starting position point, where the starting position point is close to the area to be evaluated;

[0030] The cloud server arranges the ultrasonic speakers in ascending order of the distance from the starting position point based on the position coordinate data of each monitoring position point, and sends the sorting to the controller;

[0031] The controller controls the ultrasonic speaker closest to the starting position point to start, and marks the ultrasonic speaker in the starting state as the active speaker;

[0032] The controller acquires the ultrasonic signal collected by the ultrasonic sensor, and controls the drone to fly directly above the active speaker based on the ultrasonic signal;

[0033] When the drone flies directly above the active speaker, the controller starts the camera module to take pictures to obtain the exploration overhead image of the monitoring position point corresponding to the active speaker;

[0034] The controller controls the active speaker to stop, removes the mark of the active speaker, and controls the next ultrasonic speaker to start according to the sorting, and marks the started ultrasonic speaker as the active speaker;

[0035] The controller acquires the ultrasonic signal collected by the ultrasonic sensor again, and controls the drone to fly directly above the active speaker based on the ultrasonic signal until all the ultrasonic speakers have been started.

[0036] Preferably, the controller acquires the ultrasonic signal collected by the ultrasonic sensor, and controls the drone to fly directly above the active speaker based on the ultrasonic signal, including:

[0037] The controller controls the drone to fly in a straight line from the starting position point in any direction for a preset distance and then stop, and marks the position where the drone is at the current moment as the first position point;

[0038] The controller controls the drone to fly along a circular trajectory with the first position point as the starting point, the starting position point as the center, and the preset distance as the radius, and marks the intensity value of the ultrasonic signal collected in real time by the ultrasonic sensor during the flight as the intensity value to be analyzed;

[0039] The controller determines the preferred flight path to the active speaker based on the intensity value to be analyzed;

[0040] The controller controls the drone to fly to directly above the active speaker along the preferred flight path. Among them, when the intensity value of the ultrasonic signal collected in real time by the ultrasonic sensor reaches the maximum, it means that the drone flies to directly above the active speaker.

[0041] Preferably, the controller determines the preferred flight path to the active speaker based on the intensity value to be analyzed, including:

[0042] The controller marks the position point where the drone is when the intensity value to be analyzed is the maximum as the second position point, and controls the drone to fly to the second position point;

[0043] The controller determines the preferred flight path. Among them, the starting point of the preferred flight path is the second position point, the direction of the preferred flight path is the straight line direction where the starting position point and the second position point are located, and it is away from the starting position point.

[0044] The present invention also proposes a system for evaluating the response of vegetation environment to spatial heterogeneity of surface substrate, which applies the method for evaluating the response of vegetation environment to spatial heterogeneity of surface substrate; the system includes a collection device and a cloud server that are communicatively connected to each other.

[0045] Through the above technical solutions, the following beneficial effects can be achieved:

[0046] The method for evaluating the response of vegetation environment to the spatial heterogeneity of surface substrate proposed by the present invention can solve the problem that there is currently a lack of a solution for evaluating the impact on the natural environment based on surface substrate data. First, the exploration aerial images, actual hydrological data, and actual meteorological data of the area to be evaluated collected by the collection device are sent to the cloud server, and the cloud server performs image recognition on the exploration aerial images to obtain the actual surface substrate data and actual vegetation growth data of the area to be evaluated. Then, a model for evaluating the response of vegetation environment to the spatial heterogeneity of surface substrate based on a neural network model is constructed. Through the evaluation model, the vegetation growth data of the area to be evaluated after a preset time in the future can be predicted based on the surface substrate data, and then the predicted vegetation growth data is compared with the collected actual vegetation growth data, so as to accurately and clearly understand the impact of the surface substrate on the ecological environment, filling the gap in the technical solution. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on the structures shown in these drawings.

[0048] Figure 1 It is a flowchart of the steps of the first embodiment of a method for evaluating the response of vegetation environment to the spatial heterogeneity of surface substrate proposed by the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0049] It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0050] The present invention proposes a method and system for evaluating the response of vegetation environment to the spatial heterogeneity of surface substrate.

[0051] As shown in the Figure 1 figure, in the first embodiment of a method for evaluating the response of vegetation environment to the spatial heterogeneity of surface substrate proposed by the present invention, this method for evaluating the response of vegetation environment to the spatial heterogeneity of surface substrate is applied to a system for evaluating the response of vegetation environment to the spatial heterogeneity of surface substrate; the system includes a collection device and a cloud server that are communicatively connected to each other; this embodiment includes the following steps:

[0052] Step S110: The cloud server obtains the exploration aerial images, actual hydrological data, and actual meteorological data of the area to be evaluated collected by the collection device.

[0053] Step S120: The cloud server performs image recognition on the exploration aerial images to obtain the actual surface substrate data and actual vegetation growth data of the area to be evaluated.

[0054] Specifically, the actual surface substrate data and the actual vegetation growth data are spatial distribution data.

[0055] Step S130: The cloud server constructs a surface substrate spatial heterogeneity vegetation environment response evaluation model based on a neural network model and trains it using historical data. Among them, the evaluation model can obtain the vegetation growth data after a preset time period (for example, one week) based on the surface substrate data, vegetation growth data, hydrological data, and meteorological data at the current moment.

[0056] Specifically, the evaluation model here can also be constructed based on random forest and support vector machine; the above historical data includes the historical surface substrate data, historical vegetation growth data, historical hydrological data, and historical meteorological data at a certain moment as the input parameters of the evaluation model, and the historical vegetation growth data corresponding to the moment after a preset time period as the output parameters of the evaluation model.

[0057] Step S140: The cloud server inputs the actual surface substrate data, actual vegetation growth data, actual hydrological data, and actual meteorological data into the evaluation model, and uses the vegetation growth data output by the evaluation model as the predicted vegetation growth data after a preset time period.

[0058] Step S150: The cloud server generates the environmental impact result of the surface substrate of the area to be evaluated on ecological vegetation based on the actual vegetation growth data and the predicted vegetation growth data after a preset time period.

[0059] Specifically, the environmental impact result here is either a positive impact or a negative impact.

[0060] The surface substrate spatial heterogeneity vegetation environment response evaluation method proposed by the present invention can solve the problem that there is currently a lack of a solution for evaluating the impact on the natural environment based on surface substrate data; first, the exploration aerial images, actual hydrological data, and actual meteorological data of the area to be evaluated collected by the acquisition device are sent to the cloud server, and the cloud server performs image recognition on the exploration aerial images to obtain the actual surface substrate data and actual vegetation growth data of the area to be evaluated; then, a surface substrate spatial heterogeneity vegetation environment response evaluation model based on a neural network model is constructed. Through the evaluation model, the vegetation growth data of the area to be evaluated after a preset time period in the future can be predicted based on the surface substrate data, and then the predicted vegetation growth data is compared with the collected actual vegetation growth data, so as to accurately and clearly know the impact of the surface substrate on the ecological environment, filling the gap in the technical solution.

[0061] In addition, the present technical solution also has the following advantages:

[0062] It can quantitatively evaluate the response of the surface substrate spatial heterogeneity vegetation environment, providing a scientific basis for vegetation restoration, ecological protection, and environmental management; higher evaluation accuracy: through multi-source data fusion and an evaluation model based on neural network, comprehensively considering the spatial distribution characteristics, attribute characteristics of the surface substrate, and vegetation growth conditions, it realizes the quantitative evaluation of the response of the surface substrate spatial heterogeneity vegetation environment, improving the evaluation accuracy. Through this technical solution, it can provide support for decision-making: the evaluation results can also be visually displayed in the form of maps, charts, etc., thus providing intuitive decision-making support for vegetation restoration, ecological protection, and environmental management, and helping to formulate scientific and reasonable environmental management policies. This solution can also promote interdisciplinary research: this solution integrates ecology, geographic information system, environmental science, and data analysis technologies, can promote interdisciplinary research, and provides new ideas and methods for the evaluation of the response of the surface substrate spatial heterogeneity vegetation environment.

[0063] In the second embodiment of a method for evaluating the response of the surface substrate spatial heterogeneity vegetation environment proposed by the present invention, based on the first embodiment, the acquisition device includes a sensor group and a drone; the drone is provided with a controller and a camera module; the controller and the sensor group are both wirelessly communicatively connected to the cloud server; multiple monitoring position points are set in the area to be evaluated (by setting multiple monitoring position points, the monitoring data of the area to be evaluated can be enriched and improved); step S110 includes the following steps:

[0064] Step S210: The controller controls the drone to fly directly above each monitoring position point in turn, and takes pictures through the camera module directly above each monitoring position point to obtain exploration aerial photographs of each monitoring position point.

[0065] Specifically, the monitoring position points are specifically determined by the operator based on the on-site environment of the area to be evaluated.

[0066] Step S220: The controller sends the exploration aerial photographs of each monitoring position point to the cloud server.

[0067] Specifically, each monitoring position point corresponds to an exploration aerial photograph.

[0068] In the third embodiment of a method for evaluating the response of the surface substrate spatial heterogeneity vegetation environment proposed by the present invention, based on the second embodiment, the sensor group includes a water level sensor, a water temperature sensor, a water flow velocity sensor, a water quality monitoring sensor, and a meteorological monitoring sensor; one sensor group is correspondingly set at each monitoring position point; step S110 further includes the following steps:

[0069] Step S310: The cloud server obtains the real-time water level at the monitoring location points collected by the water level sensor, the real-time water temperature at the monitoring location points collected by the water temperature sensor, the real-time water flow velocity at the monitoring location points collected by the water flow velocity sensor, the real-time pH value at the monitoring location points collected by the water quality monitoring sensor, the real-time rainfall, real-time air temperature, and real-time light intensity at the monitoring location points collected by the meteorological monitoring sensor.

[0070] Specifically, this embodiment presents a solution for collecting relevant data based on the sensor group, facilitating subsequent obtaining of hydrological data and meteorological data based on the relevant data.

[0071] In the fourth embodiment of a method for evaluating the response of surface substrate spatial heterogeneity to the vegetation environment proposed by the present invention, based on the third embodiment, after step S310, the following steps are further included:

[0072] Step S410: The cloud server takes the average of the real-time water levels at each monitoring location point within a preset past duration to obtain an average water level value, takes the average of the real-time water temperatures at each monitoring location point within a preset past duration to obtain an average water temperature value, takes the average of the real-time water flow velocities at each monitoring location point within a preset past duration to obtain an average water flow velocity, takes the average of the real-time pH values at each monitoring location point within a preset past duration to obtain an average pH value, takes the average of the real-time rainfall amounts at each monitoring location point within a preset past duration to obtain an average rainfall amount, takes the average of the real-time air temperatures at each monitoring location point within a preset past duration to obtain an average air temperature, and takes the average of the real-time light intensities at each monitoring location point within a preset past duration to obtain an average light intensity.

[0073] Step S420: The cloud server uses the average water level value, the average water temperature value, the average water flow velocity, and the average pH value as the hydrological data of the area to be evaluated.

[0074] Specifically, in this embodiment, the average water level value, the average water temperature value, the average water flow velocity, and the average pH value are used as the hydrological data of the area to be evaluated; the hydrological data can reflect the growth conditions of vegetation.

[0075] Step S430: The cloud server uses the average rainfall amount, the average air temperature, and the average light intensity as the meteorological data of the area to be evaluated.

[0076] Specifically, in this embodiment, the average rainfall amount, the average air temperature, and the average light intensity are used as the meteorological data of the area to be evaluated; the hydrological data can also reflect the growth conditions of vegetation.

[0077] In the fifth embodiment of a method for evaluating the response of surface substrate spatial heterogeneity to the vegetation environment proposed by the present invention, based on the second embodiment, step S120 includes the following steps:

[0078] Step S510: The cloud server performs image recognition on the aerial exploration images of each monitoring location point to obtain the surface matrix type, landform type, vegetation type and vegetation coverage of each monitoring location point.

[0079] Specifically, the surface matrix type is one or more of igneous rock, sedimentary rock, metamorphic rock, boulder, coarse gravel, medium gravel, fine gravel, coarse bone soil, sand, loam, clay, silt, mud, and deep-sea clay; the landform type is one or more of mountain, plateau, hill, plain, and basin; the vegetation type is one or more of grassland, wetland, and tundra; the vegetation coverage is a percentage value, and the larger the percentage, the higher the corresponding vegetation coverage. The above-mentioned different surface matrix types, different landform types, different vegetation types, and different vegetation coverage will all present different shape and color characteristics on the exploration overhead image, so the surface matrix type, landform type, vegetation type, and vegetation coverage of each monitoring location can be obtained through image recognition.

[0080] Step S520: The cloud server uses the collection of surface matrix types of each monitoring location point and the collection of landform types of each monitoring location point as actual surface matrix data of the area to be evaluated.

[0081] Specifically, this embodiment provides a specific solution for obtaining actual surface matrix data of the area to be evaluated based on the exploration overhead image.

[0082] Step S530: The cloud server uses the collection of vegetation types at each monitoring location and the average value of vegetation coverage at each monitoring location as actual vegetation growth data of the area to be evaluated.

[0083] Specifically, this embodiment provides a specific solution for obtaining actual vegetation growth data of the area to be evaluated based on the exploration overhead image.

[0084] In a sixth embodiment of a method for evaluating vegetation environmental response to spatially heterogeneous surface substrates proposed by the present invention, based on the fifth embodiment, step S150 includes the following steps:

[0085] Step S610: The cloud server determines whether the first condition and the second condition are met at the same time, wherein the first condition is: the number of vegetation types in the estimated vegetation growth data is greater than the number of vegetation types in the actual vegetation growth data, and the second condition is: the vegetation coverage in the estimated vegetation growth data is greater than the vegetation coverage in the actual vegetation growth data.

[0086] If so, step S620 is executed: the cloud server generates an environmental impact result of the surface matrix of the area to be assessed on the ecological vegetation, and the environmental impact result is a positive impact.

[0087] Specifically, if both the first condition and the second condition are satisfied, it indicates that both the vegetation type and the vegetation coverage are expected to increase after a preset future duration, proving that the environmental impact of the surface substrate on ecological vegetation is a positive impact.

[0088] If not, step S630 is executed: The cloud server generates the environmental impact result of the surface substrate of the area to be evaluated on ecological vegetation, and the environmental impact result is a negative impact.

[0089] Specifically, on the contrary, if the first condition and the second condition cannot be satisfied simultaneously, it proves that the environmental impact of the surface substrate on ecological vegetation is a negative impact.

[0090] In the seventh embodiment of a method for evaluating the response of vegetation environment to the spatial heterogeneity of surface substrate proposed by the present invention, based on the second embodiment, an ultrasonic speaker for emitting ultrasonic waves is correspondingly arranged at each monitoring position point, and the drone is further provided with an ultrasonic sensor for receiving ultrasonic signals; both the ultrasonic speaker and the ultrasonic sensor are communicatively connected to the controller; step S210 includes the following steps:

[0091] Step S710: The controller controls the drone to fly to a preset starting position point, where the starting position point is close to the area to be evaluated.

[0092] Step S720: The cloud server arranges the ultrasonic speakers in ascending order of the distance from the starting position point based on the position coordinate data of each monitoring position point, and sends the sorting to the controller.

[0093] Specifically, since the ultrasonic speakers are correspondingly arranged at the monitoring position points, the ultrasonic speakers are arranged in ascending order of the distance from the starting position point, so that the drone can fly to each monitoring position point in ascending order of the distance subsequently, so as to capture the exploration aerial images of each monitoring position point.

[0094] Step S730: The controller controls the ultrasonic speaker closest to the starting position point to start, and marks the ultrasonic speaker in the starting state as the active speaker.

[0095] Step S740: The controller acquires the ultrasonic signals collected by the ultrasonic sensor, and controls the drone to fly directly above the active speaker based on the ultrasonic signals.

[0096] Specifically, the ultrasonic sensor can collect the ultrasonic signals emitted by the active speaker in real time. Based on the change in the intensity of the ultrasonic signals, the distance relationship between the drone and the active speaker can be obtained, which is convenient for the controller to control the drone to fly directly above the active speaker.

[0097] Step S750: When the drone flies directly above the active speaker, the controller activates the camera module to take pictures, so as to obtain an exploration aerial view image of the monitoring position point corresponding to the active speaker.

[0098] Step S760: The controller controls the active speaker to stop, removes the mark of the active speaker, and controls the next ultrasonic speaker to start according to the sequence, and marks the started ultrasonic speaker as the active speaker.

[0099] Step S770: Execute step S740 again until all ultrasonic speakers have been started.

[0100] Specifically, repeat step S740 and subsequent steps until all ultrasonic speakers have been started, which means that exploration aerial view images have been taken for all monitoring position points.

[0101] In the eighth embodiment of a method for evaluating the response of surface matrix spatial heterogeneity to vegetation environment proposed by the present invention, based on the seventh embodiment, step S740 includes the following steps:

[0102] Step S810: The controller controls the drone to fly in a straight line a preset distance (for example, 5 meters) in any direction (for example, due south) from the starting position point and then stop, and marks the position where the drone is located at the current moment as the first position point.

[0103] Specifically, after the drone flies to the first position point, the intensity of the ultrasonic signal collected may increase (the first position point is closer to the active speaker than the starting position point), or may decrease (the first position point is farther from the active speaker than the starting position point).

[0104] Step S820: The controller controls the drone to fly along a circular trajectory with the first position point as the starting point, the starting position point as the center, and the preset distance as the radius, and marks the intensity value of the ultrasonic signal collected in real time by the ultrasonic sensor during the flight as the intensity value to be analyzed.

[0105] Specifically, the drone flies along a circular trajectory with the first position point as the starting point, the starting position point as the center, and the preset distance as the radius; there must be a maximum value in the intensity value of the ultrasonic signal (intensity value to be analyzed) collected during the flight, and the optimal flight path to the active speaker can be determined based on this maximum value.

[0106] Step S830: The controller determines the optimal flight path to the active speaker based on the intensity value to be analyzed.

[0107] Step S840: The controller controls the drone to fly directly above the active speaker along the preferred flight path. Among them, when the intensity value of the ultrasonic signal collected by the ultrasonic sensor in real time reaches the maximum, it means that the drone has flown directly above the active speaker.

[0108] In the ninth embodiment of a method for evaluating the response of surface substrate spatial heterogeneity to vegetation environment proposed by the present invention, based on the eighth embodiment, step S830 includes the following steps:

[0109] Step S910: The controller marks the position point where the drone is located when the intensity value to be analyzed is the maximum as the second position point, and controls the drone to fly to the second position point.

[0110] Step S920: The controller determines the preferred flight path. The starting point of the preferred flight path is the second position point, and the direction of the preferred flight path is the straight line direction where the starting position point and the second position point are located together, and away from the starting position point.

[0111] Specifically, it can be known that the second position point is the position point on the circular trajectory of the drone's flight that is closest to the active speaker, and the active speaker must be on the straight line where the starting position point and the second position point are located together; therefore, after the drone flies to the second position point, moving away from the starting position point and along the straight line direction where the starting position point and the second position point are located together to the position of the active speaker is the optimal flight path.

[0112] The present invention also proposes a system for evaluating the response of surface substrate spatial heterogeneity to vegetation environment, which applies the method for evaluating the response of surface substrate spatial heterogeneity to vegetation environment; the system includes an acquisition device and a cloud server that are communicatively connected to each other.

[0113] The serial numbers of the above embodiments of the present invention are only for description and do not represent the advantages and disadvantages of the embodiments.

[0114] The embodiments of the present invention have been described above in conjunction with the accompanying drawings. However, the present invention is not limited to the above specific embodiments. The above specific embodiments are only illustrative and not restrictive. Under the inspiration of the present invention, those of ordinary skill in the art can also make many forms without departing from the spirit and scope protected by the claims of the present invention. These all belong to the protection scope of the present invention.

Claims

1. A method for evaluating the response of vegetation environment to spatial heterogeneity of surface substrate, characterized in that, Applied to the surface substrate spatial heterogeneity vegetation environment response evaluation system; the system includes a collection device and a cloud server that are communicatively connected to each other; the method includes: The cloud server obtains the exploration aerial images, actual hydrological data, and actual meteorological data of the area to be evaluated collected by the collection device; The cloud server performs image recognition on the exploration aerial images to obtain the actual surface substrate data and actual vegetation growth data of the area to be evaluated; The cloud server constructs a surface substrate spatial heterogeneity vegetation environment response evaluation model based on a neural network model and trains it using historical data, where the evaluation model can obtain the vegetation growth data after a preset time based on the surface substrate data, vegetation growth data, hydrological data, and meteorological data at the current moment; The cloud server inputs the actual surface substrate data, actual vegetation growth data, actual hydrological data, and actual meteorological data into the evaluation model, and uses the vegetation growth data output by the evaluation model as the estimated vegetation growth data after a preset time; The cloud server generates the environmental impact result of the surface substrate on ecological vegetation in the area to be evaluated based on the actual vegetation growth data and the estimated vegetation growth data after a preset time.

2. The method for evaluating the response of vegetation environment to the spatial heterogeneity of surface substrate according to claim 1, wherein, The collection device includes a sensor group and a drone; the drone is provided with a controller and a camera module; the controller and the sensor group are both wirelessly communicatively connected to the cloud server; multiple monitoring position points are set in the area to be evaluated; The cloud server obtains the exploration aerial images, actual hydrological data, and actual meteorological data of the area to be evaluated collected by the collection device, including: The controller controls the drone to fly directly above each monitoring position point in turn, and takes pictures through the camera module directly above each monitoring position point to obtain the exploration aerial images of each monitoring position point; The controller sends the exploration aerial images of each monitoring position point to the cloud server.

3. The method for evaluating the response of a surface substrate spatial heterogeneity vegetation environment according to claim 2, characterized in that, The sensor group includes a water level sensor, a water temperature sensor, a water flow velocity sensor, a water quality monitoring sensor, and a meteorological monitoring sensor; one sensor group is correspondingly set at each monitoring position point; the cloud server obtains the exploration aerial images, actual hydrological data, and actual meteorological data of the area to be evaluated collected by the collection device, and further includes: The cloud server obtains the real-time water level of the monitoring position point collected by the water level sensor, the real-time water temperature of the monitoring position point collected by the water temperature sensor, the real-time water flow velocity of the monitoring position point collected by the water flow velocity sensor, the real-time pH value of the monitoring position point collected by the water quality monitoring sensor, the real-time rainfall, real-time temperature, and real-time light intensity of the monitoring position point collected by the meteorological monitoring sensor.

4. The method for evaluating the response of vegetation environment to spatial heterogeneity of surface substrate according to claim 3, characterized in that After the cloud server obtains the real-time water level of the monitoring position point collected by the water level sensor, the real-time water temperature of the monitoring position point collected by the water temperature sensor, the real-time water flow velocity of the monitoring position point collected by the water flow velocity sensor, the real-time pH value of the monitoring position point collected by the water quality monitoring sensor, the real-time rainfall, real-time temperature, and real-time light intensity of the monitoring position point collected by the meteorological monitoring sensor, it further includes: The cloud server takes the average of the real-time water levels at each monitoring location point within a preset past time period to obtain an average water level value, takes the average of the real-time water temperatures at each monitoring location point within a preset past time period to obtain an average water temperature value, takes the average of the real-time water flow velocities at each monitoring location point within a preset past time period to obtain an average water flow velocity, takes the average of the real-time pH values at each monitoring location point within a preset past time period to obtain an average pH value, takes the average of the real-time rainfall amounts at each monitoring location point within a preset past time period to obtain an average rainfall amount, takes the average of the real-time air temperatures at each monitoring location point within a preset past time period to obtain an average air temperature, and takes the average of the real-time light intensities at each monitoring location point within a preset past time period to obtain an average light intensity; The cloud server uses the average water level value, the average water temperature value, the average water flow velocity, and the average pH value as the hydrological data of the area to be evaluated; The cloud server uses the average rainfall amount, the average air temperature, and the average light intensity as the meteorological data of the area to be evaluated.

5. The method for evaluating the response of vegetation environment to spatial heterogeneity of surface substrate according to claim 2, characterized in that The cloud server performs image recognition on the exploration aerial images to obtain the actual surface substrate data and actual vegetation growth data of the area to be evaluated, including: The cloud server performs image recognition on the exploration aerial images of each monitoring location point to obtain the surface substrate type, landform type, vegetation type, and vegetation coverage of each monitoring location point; The cloud server uses the set of surface substrate types of each monitoring location point and the set of landform types of each monitoring location point as the actual surface substrate data of the area to be evaluated; The cloud server uses the set of vegetation types of each monitoring location point and the average of the vegetation coverages of each monitoring location point as the actual vegetation growth data of the area to be evaluated.

6. The method for evaluating the response of vegetation environment to spatial heterogeneity of surface substrate according to claim 5, characterized in that The cloud server generates the environmental impact result of the surface substrate of the area to be evaluated on ecological vegetation based on the actual vegetation growth data and the estimated vegetation growth data after a preset time period, including: The cloud server determines whether the first condition and the second condition are both satisfied. Among them, the first condition is that the number of vegetation types in the estimated vegetation growth data is greater than the number of vegetation types in the actual vegetation growth data, and the second condition is that the vegetation coverage in the estimated vegetation growth data is greater than the vegetation coverage in the actual vegetation growth data; If so, the cloud server generates the environmental impact result of the surface substrate of the area to be evaluated on ecological vegetation, and the environmental impact result is a positive impact; If not, the cloud server generates the environmental impact result of the surface substrate of the area to be evaluated on ecological vegetation, and the environmental impact result is a negative impact.

7. The method for evaluating the response of vegetation environment to spatial heterogeneity of surface substrate according to claim 2, wherein, An ultrasonic speaker for emitting ultrasonic waves is correspondingly arranged at each monitoring location point, and the drone is also provided with an ultrasonic sensor for receiving ultrasonic signals; the ultrasonic speaker and the ultrasonic sensor are both communicatively connected to the controller; the controller controls the drone to fly directly above each monitoring location point in sequence, and takes pictures through the imaging module directly above each monitoring location point to obtain the exploration aerial images of each monitoring location point, including: The controller controls the UAV to fly to a preset starting position point, where the starting position point is close to the area to be evaluated; The cloud server arranges the ultrasonic speakers in ascending order of the distance from the starting position point based on the position coordinate data of each monitoring position point, and sends the sorting to the controller; The controller controls the ultrasonic speaker closest to the starting position point to start, and marks the ultrasonic speaker in the starting state as the active speaker; The controller acquires the ultrasonic signal collected by the ultrasonic sensor, and controls the UAV to fly directly above the active speaker based on the ultrasonic signal; When the UAV flies directly above the active speaker, the controller starts the camera module to take pictures to obtain an exploration overhead image of the monitoring position point corresponding to the active speaker; The controller controls the active speaker to stop, removes the mark of the active speaker, and controls the next ultrasonic speaker to start according to the sorting, and marks the started ultrasonic speaker as the active speaker; The controller acquires the ultrasonic signal collected by the ultrasonic sensor again, and controls the UAV to fly directly above the active speaker based on the ultrasonic signal until all ultrasonic speakers have been started.

8. The method for evaluating the response of vegetation environment to the spatial heterogeneity of surface substrate according to claim 7, wherein, The controller acquires the ultrasonic signal collected by the ultrasonic sensor, and controls the UAV to fly directly above the active speaker based on the ultrasonic signal, including: The controller controls the UAV to fly in a straight line in any direction from the starting position point for a preset distance and then stop, and marks the position where the UAV is located at the current moment as the first position point; The controller controls the UAV to fly along a circular trajectory with the first position point as the starting point, the starting position point as the center, and the preset distance as the radius, and marks the intensity value of the ultrasonic signal collected in real time by the ultrasonic sensor during the flight as the intensity value to be analyzed; The controller determines the preferred flight path to fly to the active speaker based on the intensity value to be analyzed; The controller controls the UAV to fly directly above the active speaker according to the preferred flight path, where when the intensity value of the ultrasonic signal collected in real time by the ultrasonic sensor reaches the maximum, it means that the UAV flies directly above the active speaker.

9. The method for evaluating the response of vegetation environment to the spatial heterogeneity of surface matrix according to claim 8, characterized in that The controller determines the preferred flight path to fly to the active speaker based on the intensity value to be analyzed, including: The controller marks the position point where the UAV is located when the intensity value to be analyzed is the maximum as the second position point, and controls the UAV to fly to the second position point; The controller determines the preferred flight path, where the starting point of the preferred flight path is the second position point, the direction of the preferred flight path is the straight line direction where the starting position point and the second position point are located together, and is away from the starting position point.

10. An evaluation system for the response of vegetation environment to the spatial heterogeneity of surface substrate, characterized in that, Apply the method for evaluating the response of the surface substrate spatial heterogeneity vegetation environment as described in any one of claims 1-9; the system includes a collection device and a cloud server that are communicatively connected to each other.

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