Ecological system total production value dynamic monitoring and early warning system combined with Internet of Things

Abstract

The invention relates to the technical field of ecological system monitoring and early warning, and discloses an ecological system total production value dynamic monitoring and early warning system combined with the Internet of Things, which comprises a data acquisition module used for acquiring ecological environment data of a forest ecological system, including remote sensing data, meteorological data and ground data; the data analysis module is used for determining basic data according to the remote sensing data, determining solar radiation according to the meteorological data, and determining net primary productivity according to the solar radiation and the basic data; the total production value determination module is used for determining a carbon sequestration oxygen release value according to the net primary productivity; determining a climate regulation value according to the meteorological data, and determining a total production value of the forest ecosystem according to the ground data, the carbon sequestration oxygen release value and the climate regulation value; and the monitoring and early warning module is used for carrying out early warning on the total production value of the forest ecosystem. According to the method, the timeliness and accuracy of forest resource management can be effectively improved, and the method has important practical application value for promoting protection and sustainable utilization of forest resources.

Description

Technical Field

[0001] The present invention relates to the technical field of ecosystem monitoring and early warning, and in particular to a dynamic monitoring and early warning system for ecosystem gross domestic product combined with the Internet of Things. Background Art

[0002] Gross Ecosystem Product (GEP) is an important indicator for measuring the service functions and ecological benefits of forest ecosystems. However, traditional GEP monitoring methods often rely on manual sampling and ground surveys, which are not only time-consuming and labor-intensive, but also difficult to achieve real-time monitoring of large-scale, multi-dimensional ecological data. With the development of Internet of Things technology, the use of sensors, remote sensing technology, big data analysis and other means can achieve real-time dynamic monitoring of the gross ecosystem product of forests, thereby providing a scientific basis for ecological early warning and decision-making. Therefore, this application provides a dynamic monitoring and early warning system for the gross ecosystem product combined with the Internet of Things. Summary of the invention

[0003] The purpose of the present invention is to provide a dynamic monitoring and early warning system for the gross ecosystem product combined with the Internet of Things. Traditional GEP monitoring methods often rely on manual sampling and ground surveys, which are not only time-consuming and labor-intensive, but also difficult to achieve real-time monitoring of large-scale, multi-dimensional ecological data. This system overcomes the shortcomings of traditional methods and improves accuracy.

[0004] The present invention provides a dynamic monitoring and early warning system for gross ecosystem product combined with the Internet of Things, comprising:

[0005] A data acquisition module, used to acquire ecological and environmental data of the forest ecosystem, wherein the ecological and environmental data includes remote sensing data, meteorological data and ground data;

[0006] A data analysis module, used to determine basic data of the forest ecosystem based on the remote sensing data, wherein the basic data includes forest cover type, leaf area index and normalized difference vegetation index; determine solar radiation of the forest ecosystem based on the meteorological data, and determine net primary productivity of the forest ecosystem based on the solar radiation and the basic data;

[0007] A gross production value determination module is used to determine the carbon fixation and oxygen release value of the forest ecosystem according to the net primary productivity; and determine the climate adjustment value of the forest ecosystem according to the meteorological data, and determine the gross production value of the forest ecosystem based on the ground data and the carbon fixation and oxygen release value and the climate adjustment value;

[0008] The monitoring and early warning module is used to compare the gross production value of the forest ecosystem with the preset gross production value, and to issue corresponding early warnings to the forest ecosystem according to the comparison results.

[0009] Preferably, when the data analysis module is used to determine the basic data of the forest ecosystem based on the remote sensing data, it includes:

[0010] Screening the remote sensing data to select remote sensing data having a higher resolution and a higher time frequency than a set resolution;

[0011] Perform radiation calibration and atmospheric correction on the selected remote sensing data, remove clouds and noise, and obtain pre-processed remote sensing data;

[0012] Analyze the reflectance spectral characteristics of the preprocessed remote sensing data and determine the forest cover type of the forest ecosystem based on the reflectance spectral characteristics;

[0013] The leaf area index and normalized difference vegetation index of forest ecosystem were determined based on the preprocessed remote sensing data.

[0014] Preferably, when the data analysis module is used to determine the solar radiation of the forest ecosystem based on the meteorological data, the meteorological data includes: the meteorological data includes sunshine duration, atmospheric transparency and atmospheric quality;

[0015] Determine the solar radiation of the forest ecosystem based on the sunshine duration, atmospheric transparency and atmospheric quality;

[0016] The solar radiation of the forest ecosystem is calculated according to the following formula:

[0017]

[0018] Among them, Sp represents the solar radiation of the forest ecosystem, I0 represents the solar constant, h represents the sunshine duration, P represents the atmospheric transparency, and M represents the atmospheric quality.

[0019] Preferably, when the data analysis module is used to determine the net primary productivity of the forest ecosystem according to the solar radiation and the basic data, it includes:

[0020] Determine the light energy utilization rate of the forest ecosystem according to the forest cover type;

[0021] Determine the total primary productivity of the forest ecosystem based on the solar radiation and light energy utilization rate;

[0022] Determining the autotrophic respiration of the forest ecosystem based on the leaf area index and the normalized difference vegetation index;

[0023] Determine the net primary productivity of the forest ecosystem based on the gross primary productivity and autotrophic respiration;

[0024] The net primary productivity of the forest ecosystem is calculated according to the following formula:

[0025] NPP = GPP-Ra;

[0026] NPP stands for net primary productivity, GPP stands for gross primary productivity, and Ra stands for autotrophic respiration.

[0027] Preferably, when the data analysis module is used to determine the total primary productivity of the forest ecosystem according to the solar radiation and light energy utilization rate, it includes:

[0028] Determining the effective proportion of solar radiation absorbed by vegetation based on the remote sensing data;

[0029] Determine the total primary productivity of the forest ecosystem based on the solar radiation, the effective proportion of solar radiation absorbed by vegetation and the light energy utilization rate;

[0030] The total primary productivity of the forest ecosystem is calculated according to the following formula:

[0031] GPP = Sp × FSp × LUE;

[0032] Among them, FSp represents the effective proportion of solar radiation absorbed by vegetation, and LUE represents the light energy utilization efficiency.

[0033] Preferably, the gross production value determination module is used to determine the carbon fixation and oxygen release value of the forest ecosystem according to the net primary productivity, including:

[0034] Setting productivity impact factors according to forest cover types of forest ecosystems;

[0035] Determine the carbon fixation and oxygen release value of the forest ecosystem according to the net primary productivity and the productivity influencing factor;

[0036] The carbon fixation and oxygen release value is proportional to the net primary productivity.

[0037] Preferably, the gross domestic product determination module is used to determine the climate adjustment value of the forest ecosystem according to the meteorological data, including:

[0038] The meteorological data include: temperature value, humidity value, rainfall, cloud cover, wind speed and wind direction;

[0039] Determine the year-on-year change rate of the meteorological data, and determine the climate adjustment value of the forest ecosystem based on the year-on-year change rate.

[0040] Preferably, the gross production value determination module is used to determine the gross production value of the forest ecosystem based on the ground data, according to the carbon fixation and oxygen release value and the climate regulation value, including:

[0041] The ground data include soil moisture value, soil loss, water flow and water loss;

[0042] Determine soil conservation value and water conservation value based on the ground data;

[0043] The gross product of the forest ecosystem is determined based on the carbon fixation and oxygen release value, climate regulation value, soil conservation value and water conservation value.

[0044] Preferably, the monitoring and early warning module is used to compare the gross production value of the forest ecosystem with a preset gross production value, and to issue a corresponding early warning to the forest ecosystem according to the comparison result, including:

[0045] Comparing the gross production value of the forest ecosystem with the preset gross production value, if the gross production value of the forest ecosystem is greater than or equal to the preset gross production value, then determining that the forest ecosystem is operating normally;

[0046] If the gross production value of the forest ecosystem is less than the preset gross production value, it is determined that the forest ecosystem is operating abnormally, and the difference between the gross production value of the forest ecosystem and the preset gross production value is determined;

[0047] Presetting a first preset difference and a second preset difference, wherein the first preset difference is smaller than the second preset difference;

[0048] determining an abnormality level of the forest ecosystem according to a relationship between the difference and the first preset difference and the second preset difference;

[0049] If the difference is less than the first preset difference, the abnormality level of the forest ecosystem is determined to be the first preset abnormality level L1;

[0050] If the difference is greater than or equal to the first preset difference, and the difference is less than the second preset difference, then the abnormality level of the forest ecosystem is determined to be the second preset abnormality level L2;

[0051] If the difference is greater than or equal to the second preset difference, the abnormality level of the forest ecosystem is determined to be a third preset abnormality level L3; wherein L1<L2<L3.

[0052] Preferably, when the monitoring and early warning module is used to determine the abnormal level of the forest ecosystem according to the difference, it includes:

[0053] The warning level is determined according to the abnormal level of the forest ecosystem, and a corresponding warning is issued according to the determined warning level.

[0054] Compared with the prior art, the beneficial effect of the present invention is that the present invention provides a comprehensive and automated ecosystem gross production monitoring and early warning method, which can effectively improve the timeliness and accuracy of forest resource management, and has important practical application value for promoting the protection and sustainable utilization of forest resources. The present invention collects various types of ecological environment data through a data acquisition module, provides a rich information basis for subsequent analysis, and ensures a comprehensive understanding of the forest ecosystem; uses remote sensing data to determine the basic data of the forest ecosystem, which helps to accurately grasp the status of the forest, such as coverage type, leaf area index and vegetation index; combines meteorological data to determine solar radiation, and further determines net primary productivity, which embodies scientific calculation methods and logic, and helps to accurately evaluate the production capacity of the forest; comprehensively considers factors such as net primary productivity and climate adjustment value to determine the gross production of the forest ecosystem, so that the evaluation result is more comprehensive and accurate; by comparing the gross production of the forest ecosystem with the preset gross production, and making corresponding early warnings according to the comparison results, it is possible to timely discover changes and potential risks in the forest ecosystem, so as to facilitate the adoption of corresponding measures for timely adjustment and protection. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings in the following description are only embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying any creative work.

[0056] Figure 1 It is a schematic diagram of a dynamic monitoring and early warning system for gross ecosystem product combined with the Internet of Things according to the present invention. DETAILED DESCRIPTION

[0057] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0058] like Figure 1 As shown, the present invention provides a dynamic monitoring and early warning system for gross ecosystem product combined with the Internet of Things, comprising:

[0059] The data acquisition module is used to acquire the ecological environment data of the forest ecosystem, and the ecological environment data includes remote sensing data, meteorological data and ground data.

[0060] A data analysis module is used to determine basic data of the forest ecosystem based on the remote sensing data, wherein the basic data include forest cover type, leaf area index and normalized vegetation index; and to determine the solar radiation of the forest ecosystem based on the meteorological data, and to determine the net primary productivity of the forest ecosystem based on the solar radiation and the basic data.

[0061] The gross production value determination module is used to determine the carbon fixation and oxygen release value of the forest ecosystem based on the net primary productivity; and determine the climate adjustment value of the forest ecosystem based on the meteorological data; and based on the ground data, determine the gross production value of the forest ecosystem according to the carbon fixation and oxygen release value and the climate adjustment value.

[0062] The monitoring and early warning module is used to compare the gross production value of the forest ecosystem with the preset gross production value, and to issue corresponding early warnings to the forest ecosystem according to the comparison results.

[0063] In this embodiment, the data acquisition module can obtain remote sensing data through the geospatial data cloud, the digital earth open platform or other remote sensing data platforms. Remote sensing detection is carried out through sensors carried by manned spacecraft, space stations or artificial satellites. Common sensors include cameras, multi-spectral scanners and synthetic aperture radars, etc.; or sensors are installed on platforms such as ships, cars, towers, etc. for remote sensing detection, such as laser radar (LiDAR) systems. The collected data is transmitted back to the ground station or data processing center through wireless signals to obtain remote sensing data. Meteorological data can be obtained by viewing websites, using weather APIs, visiting national meteorological data centers, etc. or other methods, and these sources provide rich historical and real-time meteorological information, such as temperature, air pressure, wind speed and precipitation. These meteorological data include temperature values, humidity values, rainfall, cloud cover, wind speed and wind direction, which are exactly what this application needs. Ground data can be obtained in various relevant data centers or measured on the spot by measuring instruments. The data acquisition module integrates these remote sensing data, meteorological data and ground data and stores them, and sends them to the data analysis module for subsequent analysis and processing.

[0064] It is understandable that the monitoring and early warning module can be connected to the main control center or to the user terminal. When the monitoring and early warning module determines that an early warning is needed, the early warning signal is sent to the main control center or the user terminal to remind the staff to handle it in time.

[0065] In some embodiments of the present application, when the data analysis module is used to determine the basic data of a forest ecosystem based on remote sensing data, it includes: screening the remote sensing data to select remote sensing data with a higher resolution and a set time frequency; performing radiation calibration and atmospheric correction on the screened remote sensing data, and removing clouds and noise to obtain preprocessed remote sensing data; analyzing the reflectance spectral characteristics of the preprocessed remote sensing data, and determining the forest coverage type of the forest ecosystem based on the reflectance spectral characteristics; and determining the leaf area index and normalized vegetation index of the forest ecosystem based on the preprocessed remote sensing data.

[0066] In the present embodiment, in the process of extracting the basic data of forest ecosystem by using the data analysis module, it is first necessary to perform a series of screening and pre-processing operations on the provided remote sensing data. Specifically, this process includes screening the remote sensing data to ensure that the selected data meets the established standards in terms of resolution and time frequency. This means that only those remote sensing data that are higher than the preset resolution threshold and time frequency threshold will be included in the analysis scope. Next, a series of pre-processing is performed on the screened remote sensing data, including radiation calibration and atmospheric correction, to ensure the accuracy of the data. In addition, it is also necessary to remove clouds and noise from the data to eliminate interference factors. Through these pre-processing steps, more accurate and reliable remote sensing data can be obtained, and then the reflectance spectral characteristics of these data can be analyzed to determine the forest cover type of the forest ecosystem. There are differences in the reflectance spectral characteristics of different forest types. By analyzing the reflectance of different bands, the forest cover types such as coniferous forests, broad-leaved forests and mixed forests can be distinguished. Finally, according to the pre-processed remote sensing data, the leaf area index and normalized vegetation index of the forest ecosystem can be further determined. The leaf area index reflects the degree of leaf coverage in the forest. Especially in mountainous areas with complex terrain, it is necessary to consider the impact of terrain on radiation transmission and use terrain correction methods or mountain radiation transmission models to improve the accuracy of leaf area index estimation. The normalized vegetation index data provides an important indicator of vegetation growth conditions, which is calculated by dividing the reflectivity difference between the infrared and red light bands by the sum. This indicator can effectively reflect the growth status and biomass of vegetation. By extracting these data, we can have a more comprehensive understanding of the health risks and biomass distribution of forest ecosystems.

[0067] In some embodiments of the present application, when the data analysis module is used to determine the solar radiation of a forest ecosystem based on the meteorological data, it includes: the meteorological data includes sunshine duration, atmospheric transparency and atmospheric quality; and the solar radiation of the forest ecosystem is determined based on the sunshine duration, atmospheric transparency and atmospheric quality.

[0068] The solar radiation of the forest ecosystem is calculated according to the following formula:

[0069]

[0070] Among them, Sp represents the solar radiation of the forest ecosystem, I0 represents the solar constant, h represents the sunshine duration, P represents the atmospheric transparency, and M represents the atmospheric quality.

[0071] In this embodiment, the data analysis module is designed to determine the solar radiation of the forest ecosystem based on meteorological data. Specifically, it includes processing and analyzing meteorological data to accurately estimate the amount of solar radiation received by the forest ecosystem. Meteorological data mainly includes three key parameters: sunshine duration, atmospheric transparency and atmospheric quality. First, sunshine duration is the total time that sunlight directly shines on the ground within a certain period of time. Secondly, atmospheric transparency is the ability of the atmosphere to transmit sunlight, which is affected by suspended particles, water vapor and other pollutants in the atmosphere. The higher the atmospheric transparency, the easier it is for sunlight to penetrate the atmosphere, thereby increasing the amount of solar radiation of the forest ecosystem. Finally, atmospheric quality refers to the concentration of pollutants in the atmosphere, which also affects the ability of sunlight to transmit. The lower the atmospheric quality, the easier it is for sunlight to reach the ground, and vice versa. The present application determines the solar radiation of the forest ecosystem based on the Bujer formula. In this way, the data analysis module can provide important solar radiation data for the management and research of the forest ecosystem.

[0072] In some embodiments of the present application, when the data analysis module is used to determine the net primary productivity of a forest ecosystem based on the solar radiation and the basic data, it includes: determining the light energy utilization rate of the forest ecosystem based on the forest cover type; determining the total primary productivity of the forest ecosystem based on the solar radiation and the light energy utilization rate; determining the autotrophic respiration of the forest ecosystem based on the leaf area index and the normalized vegetation index; and determining the net primary productivity of the forest ecosystem based on the total primary productivity and autotrophic respiration.

[0073] The net primary productivity of the forest ecosystem is calculated according to the following formula:

[0074] NPP = GPP-Ra;

[0075] NPP stands for net primary productivity, GPP stands for gross primary productivity, and Ra stands for autotrophic respiration.

[0076] In this embodiment, the data analysis module is used to determine the net primary productivity of the forest ecosystem based on solar radiation and basic data when performing its function. Specifically, the module first uses the forest cover type to determine the light energy utilization rate of the forest ecosystem. This process involves estimating the light energy absorption and conversion efficiency of different types of forests, thereby providing necessary parameters for subsequent calculations. Next, the data analysis module combines the solar radiation data and the previously determined light energy utilization rate to calculate the total primary productivity of the forest ecosystem. The total primary productivity refers to the total energy fixed by photosynthesis in a forest ecosystem within a certain period of time, which is an important indicator for evaluating the productivity of the ecosystem. In addition, the data analysis module also determines the autotrophic respiration of the forest ecosystem based on data such as the leaf area index and the normalized vegetation index. Autotrophic respiration refers to the fact that while plants are performing photosynthesis, they also consume a part of the fixed energy to carry out their own life activities. By analyzing the leaf area index and the normalized vegetation index, the autotrophic respiration of the forest ecosystem can be more accurately evaluated, thereby providing an important basis for calculating the net primary productivity. Finally, the data analysis module comprehensively considers the total primary productivity and autotrophic respiration to determine the net primary productivity of the forest ecosystem. Net primary productivity refers to the energy that a forest ecosystem can actually use for growth and reproduction after deducting the consumption of autotrophic respiration. This indicator is of great significance for assessing the health and productivity of forest ecosystems. Through the above steps, the data analysis module can accurately determine the net primary productivity of forest ecosystems based on solar radiation and basic data, providing a scientific basis for ecological research and resource management.

[0077] In some embodiments of the present application, when the data analysis module is used to determine the total primary productivity of the forest ecosystem according to the solar radiation and light energy utilization rate, it includes: determining the effective proportion of solar radiation absorbed by vegetation according to the remote sensing data; determining the total primary productivity of the forest ecosystem according to the solar radiation, the effective proportion of solar radiation absorbed by vegetation and the light energy utilization rate;

[0078] The total primary productivity of the forest ecosystem is calculated according to the following formula:

[0079] GPP = Sp × FSp × LUE;

[0080] Among them, FSp represents the effective proportion of solar radiation absorbed by vegetation, and LUE represents the light energy utilization efficiency.

[0081] In this embodiment, when the data analysis module is used to estimate the total primary productivity of the forest ecosystem, the data analysis module will first use remote sensing data to determine the effective proportion of vegetation absorbing solar radiation. This process involves detailed analysis and processing of remote sensing data to ensure that the absorption of solar radiation by vegetation can be accurately reflected. Next, the data analysis module will comprehensively consider the three key factors of the intensity of solar radiation, the effective proportion of vegetation absorbing solar radiation, and the utilization rate of light energy to determine the total primary productivity of the forest ecosystem. The intensity of solar radiation is an important external condition that affects photosynthesis, while the effective proportion of vegetation absorbing solar radiation reflects the actual utilization of these energies by vegetation. The utilization rate of light energy is an indicator that measures the efficiency of vegetation converting the absorbed light energy into biomass. By combining these three factors, the total primary productivity of the forest ecosystem can be accurately estimated, thereby providing important data support for ecological research and resource management.

[0082] In some embodiments of the present application, when the gross domestic product determination module is used to determine the carbon fixation and oxygen release values ​​of a forest ecosystem based on the net primary productivity, it includes: setting a productivity influencing factor based on the forest coverage type of the forest ecosystem; determining the carbon fixation and oxygen release values ​​of the forest ecosystem based on the net primary productivity and the productivity influencing factor; the carbon fixation and oxygen release value is proportional to the net primary productivity.

[0083] In the present embodiment, the gross domestic product determination module is designed to determine the carbon fixation and oxygen release value of the forest ecosystem based on the net primary productivity. The module will first set a productivity influencing factor based on the forest cover type of the forest ecosystem. This productivity influencing factor is determined based on different forest types and their specific ecological characteristics in order to more accurately reflect the impact of different forest types on the carbon fixation and oxygen release value. Next, this productivity influencing factor is combined with the net primary productivity to determine the carbon fixation and oxygen release value of the forest ecosystem. Ultimately, the carbon fixation and oxygen release value obtained is proportional to the net primary productivity. This means that when the net primary productivity is higher, the carbon fixation and oxygen release value of the forest ecosystem will also increase accordingly. This proportional relationship helps us better understand and evaluate the important role of forest ecosystems in carbon cycle and carbon fixation and oxygen release.

[0084] In some embodiments of the present application, the gross domestic product determination module is used to determine the climate adjustment value of the forest ecosystem based on the meteorological data, including: the meteorological data includes: temperature value, humidity value, rainfall, cloud cover, wind speed and wind direction; determining the year-on-year change rate of the meteorological data, and determining the climate adjustment value of the forest ecosystem based on the year-on-year change rate.

[0085] In this embodiment, the gross production value determination module is designed to determine the climate regulation value of the forest ecosystem based on a series of meteorological data. Specifically, these meteorological data include temperature value, humidity value, rainfall, cloud cover, wind speed and wind direction. By analyzing these data, we can further understand the role of the forest ecosystem in climate regulation. First, the year-on-year change rate of the meteorological data needs to be calculated. This step involves comparing the meteorological data in the current time period with the data in the previous same time period to obtain the change trend of each meteorological parameter. For example, the year-on-year change rate of the temperature value can reflect the rising or falling trend of the temperature in the area over the past period of time; the year-on-year change rate of the humidity value can reveal the change of air humidity; the year-on-year change rate of rainfall helps to understand the increase or decrease of precipitation; the year-on-year change rate of cloud cover can reflect the cloud coverage of the sky; the year-on-year change rate of wind speed and wind direction helps to analyze the stability of wind force and wind direction and its change trend. After calculating the year-on-year change rate of these meteorological data, the next step is to determine the climate regulation value of the forest ecosystem based on these change rates. This process involves a comprehensive analysis and evaluation of the change rates of various meteorological parameters to quantify the contribution of the forest ecosystem to climate regulation. For example, if the year-on-year change rate of temperature values ​​shows a clear downward trend, this means that the forest ecosystem has played a positive role in reducing the temperature; if the year-on-year change rate of precipitation shows an increasing trend, this may indicate that the forest ecosystem has played a certain regulatory role in increasing precipitation. Through this comprehensive analysis, the role of forest ecosystems in climate regulation can be more accurately assessed, thus providing a scientific basis for environmental protection and ecological restoration.

[0086] In some embodiments of the present application, the gross domestic product determination module is used to determine the gross domestic product of the forest ecosystem based on the ground data according to the carbon fixation and oxygen release value and the climate regulation value, including: the ground data includes soil moisture value, soil loss, water flow and water loss; soil conservation value and water conservation value are determined according to the ground data; the gross domestic product of the forest ecosystem is determined according to the carbon fixation and oxygen release value, climate regulation value, soil conservation value and water conservation value.

[0087] In this embodiment, when the gross domestic product determination module determines the gross domestic product of the forest ecosystem based on the ground data and the carbon fixation and oxygen release value and the climate regulation value, the following steps are specifically included: First, the ground data includes soil moisture value, soil loss, water flow and water loss. These data are important indicators for assessing the health and function of forest ecosystems. Next, based on the ground data, the soil conservation value and water conservation value can be further determined. The soil conservation value reflects the role of the forest ecosystem in preventing soil erosion and maintaining soil fertility, while the water conservation value reflects the importance of forests in regulating water cycles and maintaining a stable supply of water resources. Finally, the comprehensive value of the forest ecosystem is comprehensively evaluated in combination with the carbon fixation and oxygen release value, the climate regulation value, the soil conservation value and the water conservation value, and the gross domestic product of the forest ecosystem are determined accordingly.

[0088] In some embodiments of the present application, the monitoring and early warning module is used to compare the gross domestic product of the forest ecosystem with the preset gross domestic product, and to issue a corresponding early warning to the forest ecosystem based on the comparison result, including: comparing the gross domestic product of the forest ecosystem with the preset gross domestic product, if the gross domestic product of the forest ecosystem is greater than or equal to the preset gross domestic product, determining that the forest ecosystem is operating normally; if the gross domestic product of the forest ecosystem is less than the preset gross domestic product, determining that the forest ecosystem is operating abnormally, and determining the difference between the gross domestic product of the forest ecosystem and the preset gross domestic product; pre-setting a first preset difference and a second preset difference , the first preset difference is less than the second preset difference; the abnormality level of the forest ecosystem is determined according to the relationship between the difference and the first preset difference and the second preset difference; if the difference is less than the first preset difference, the abnormality level of the forest ecosystem is determined to be the first preset abnormality level L1; if the difference is greater than or equal to the first preset difference, and the difference is less than the second preset difference, the abnormality level of the forest ecosystem is determined to be the second preset abnormality level L2; if the difference is greater than or equal to the second preset difference, the abnormality level of the forest ecosystem is determined to be the third preset abnormality level L3; wherein, L1<L2<L3.

[0089] In some embodiments of the present application, when the monitoring and early warning module is used to determine the abnormality level of the forest ecosystem based on the difference, it includes: determining the early warning level according to the abnormality level of the forest ecosystem, and issuing a corresponding early warning according to the determined early warning level.

[0090] In this embodiment, the monitoring and early warning module determines the abnormal level of the forest ecosystem based on the difference, which specifically includes the following steps: First, the early warning level is determined according to the abnormal level of the forest ecosystem. This process involves a detailed analysis and evaluation of the abnormal level to ensure the accuracy of the early warning level. Then, an early warning is issued according to the determined early warning level. This stage includes formulating corresponding early warning measures and notifying relevant management departments and personnel to ensure that timely actions are taken to deal with potential ecological risks. Through this series of steps, the monitoring and early warning module effectively monitors and warns the abnormal situation of the forest ecosystem in real time, thereby ensuring the health and stability of the ecosystem.

[0091] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that they can still modify or replace the technical solution of the present invention with equivalents, and these modifications or equivalent replacements cannot cause the modified technical solution to deviate from the spirit and scope of the technical solution of the present invention.

[0092] The system provided in the above embodiment is only illustrated by the division of the above functional modules. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the modules or steps in the embodiments of the present invention can be decomposed or combined. For example, the modules in the above embodiment can be combined into one module, or further divided into multiple sub-modules to complete all or part of the functions described above. The names of the modules and steps involved in the embodiments of the present invention are only for distinguishing the modules or steps and are not regarded as improper limitations of the present invention.

[0093] Those skilled in the art should be able to appreciate that the modules and method steps of each example described in conjunction with the embodiments disclosed herein can be implemented with electronic hardware, computer software, or a combination of the two, and the programs corresponding to the software modules and method steps can be placed in random access memory, memory, read-only memory, electrically programmable ROM, electrically erasable programmable ROM, registers, hard disks, removable disks, CD-ROMs, or any other form of storage medium known in the technical field. In order to clearly illustrate the interchangeability of electronic hardware and software, the composition and steps of each example have been generally described in the above description according to the function. Whether these functions are performed in electronic hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the present invention.

Claims

1. A dynamic monitoring and early warning system for gross ecosystem product combined with the Internet of Things, characterized in that: include: A data acquisition module, used to acquire ecological and environmental data of the forest ecosystem, wherein the ecological and environmental data includes remote sensing data, meteorological data and ground data; A data analysis module, used to determine basic data of the forest ecosystem based on the remote sensing data, wherein the basic data includes forest cover type, leaf area index and normalized difference vegetation index; determine the solar radiation of the forest ecosystem based on the meteorological data, and determine the net primary productivity of the forest ecosystem based on the solar radiation and the basic data; A gross production value determination module is used to determine the carbon fixation and oxygen release value of the forest ecosystem according to the net primary productivity; determine the climate adjustment value of the forest ecosystem according to the meteorological data, and determine the gross production value of the forest ecosystem based on the ground data and the carbon fixation and oxygen release value and the climate adjustment value; The monitoring and early warning module is used to compare the gross production value of the forest ecosystem with the preset gross production value, and to issue corresponding early warnings to the forest ecosystem according to the comparison results.

2. The dynamic monitoring and early warning system for gross ecosystem product combined with the Internet of Things according to claim 1 is characterized in that: When the data analysis module is used to determine the basic data of the forest ecosystem according to the remote sensing data, it includes: Screening the remote sensing data to select remote sensing data having a higher resolution and a higher time frequency than a set resolution; Perform radiation calibration and atmospheric correction on the selected remote sensing data, remove clouds and noise, and obtain pre-processed remote sensing data; Analyze the reflectance spectral characteristics of preprocessed remote sensing data and determine the forest cover type of the forest ecosystem based on the reflectance spectral characteristics; The leaf area index and normalized difference vegetation index of forest ecosystem were determined based on the preprocessed remote sensing data.

3. The dynamic monitoring and early warning system for gross ecosystem product combined with the Internet of Things according to claim 1 is characterized in that: The data analysis module, when used to determine the solar radiation of the forest ecosystem based on the meteorological data, includes: the meteorological data includes sunshine duration, atmospheric transparency and atmospheric quality; Determine the solar radiation of the forest ecosystem based on the sunshine duration, atmospheric transparency and atmospheric quality; The solar radiation of the forest ecosystem is calculated according to the following formula: Among them, Sp represents the solar radiation of the forest ecosystem, I0 represents the solar constant, h represents the sunshine duration, P represents the atmospheric transparency, and M represents the atmospheric quality.

4. The dynamic monitoring and early warning system for gross ecosystem product combined with the Internet of Things according to claim 3 is characterized in that: When the data analysis module is used to determine the net primary productivity of the forest ecosystem according to the solar radiation and the basic data, it includes: Determine the light energy utilization rate of the forest ecosystem according to the forest cover type; Determine the total primary productivity of the forest ecosystem based on the solar radiation and light energy utilization rate; Determining the autotrophic respiration of the forest ecosystem based on the leaf area index and the normalized difference vegetation index; Determine the net primary productivity of the forest ecosystem based on the gross primary productivity and autotrophic respiration; The net primary productivity of the forest ecosystem is calculated according to the following formula: NPP = GPP-Ra; NPP stands for net primary productivity, GPP stands for gross primary productivity, and Ra stands for autotrophic respiration.

5. The dynamic monitoring and early warning system for gross ecosystem product combined with the Internet of Things according to claim 4 is characterized in that: When the data analysis module is used to determine the total primary productivity of the forest ecosystem according to the solar radiation and light energy utilization rate, it includes: Determining the effective proportion of solar radiation absorbed by vegetation based on the remote sensing data; Determine the total primary productivity of the forest ecosystem based on the solar radiation, the effective proportion of solar radiation absorbed by vegetation and the light energy utilization rate; The total primary productivity of the forest ecosystem is calculated according to the following formula: GPP = Sp × FSp × LUE; Among them, FSp represents the effective proportion of solar radiation absorbed by vegetation, and LUE represents the light energy utilization efficiency.

6. The dynamic monitoring and early warning system of gross ecosystem product combined with the Internet of Things according to claim 1 is characterized in that: The gross production value determination module is used to determine the carbon fixation and oxygen release value of the forest ecosystem according to the net primary productivity, including: Setting productivity impact factors according to forest cover types of forest ecosystems; Determine the carbon fixation and oxygen release value of the forest ecosystem according to the net primary productivity and the productivity influencing factor; The carbon fixation and oxygen release value is proportional to the net primary productivity.

7. The dynamic monitoring and early warning system of gross ecosystem product combined with the Internet of Things according to claim 1 is characterized in that: The gross production value determination module, when used to determine the climate adjustment value of the forest ecosystem according to the meteorological data, includes: The meteorological data include: temperature value, humidity value, rainfall, cloud cover, wind speed and wind direction; Determine the year-on-year change rate of the meteorological data, and determine the climate adjustment value of the forest ecosystem based on the year-on-year change rate.

8. The dynamic monitoring and early warning system of gross ecosystem product combined with the Internet of Things according to claim 1 is characterized in that: The gross production value determination module is used to determine the gross production value of the forest ecosystem based on the ground data, according to the carbon fixation and oxygen release value and the climate adjustment value, including: The ground data include soil moisture value, soil loss, water flow and water loss; Determine soil conservation value and water conservation value based on the ground data; The gross product of the forest ecosystem is determined based on the carbon fixation and oxygen release value, climate regulation value, soil conservation value and water conservation value.

9. The dynamic monitoring and early warning system for gross ecosystem product combined with the Internet of Things according to claim 1 is characterized in that: The monitoring and early warning module is used to compare the gross production value of the forest ecosystem with the preset gross production value, and to issue a corresponding early warning to the forest ecosystem according to the comparison result, including: Comparing the gross production value of the forest ecosystem with the preset gross production value, if the gross production value of the forest ecosystem is greater than or equal to the preset gross production value, then determining that the forest ecosystem is operating normally; If the gross production value of the forest ecosystem is less than the preset gross production value, it is determined that the forest ecosystem is operating abnormally, and the difference between the gross production value of the forest ecosystem and the preset gross production value is determined; Presetting a first preset difference and a second preset difference, wherein the first preset difference is smaller than the second preset difference; determining an abnormality level of the forest ecosystem according to a relationship between the difference and the first preset difference and the second preset difference; If the difference is less than the first preset difference, the abnormality level of the forest ecosystem is determined to be the first preset abnormality level L1; If the difference is greater than or equal to the first preset difference, and the difference is less than the second preset difference, then the abnormality level of the forest ecosystem is determined to be the second preset abnormality level L2; If the difference is greater than or equal to the second preset difference, the abnormality level of the forest ecosystem is determined to be a third preset abnormality level L3; wherein L1<L2<L3.

10. The dynamic monitoring and early warning system for gross ecosystem product combined with the Internet of Things according to claim 9 is characterized in that: When the monitoring and early warning module is used to determine the abnormal level of the forest ecosystem according to the difference, it includes: The warning level is determined according to the abnormal level of the forest ecosystem, and a corresponding warning is issued according to the determined warning level.

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