Method and system for statistical analysis and prediction of carbon emission information

Abstract

The invention relates to the technical field of carbon emission analysis and prediction, and discloses a carbon emission information statistical analysis and prediction method and system. A forest region is macroscopically divided into a plurality of independent feature region blocks through an online data acquisition module and an on-site data acquisition module; a representative forest growth condition sampling area is selected through analysis and planning, the sampling area can be optimized, the number of the sampling areas is reduced, random repeated sampling is avoided, meanwhile, the representativeness and accuracy of sampling data can be improved, and forest actual growth condition data are obtained through area sampling. The data processing optimization module analyzes, optimizes and calibrates the sampled data, the analysis and prediction module analyzes the calibrated data and constructs a prediction model, the time-lag effect and dragonfly algorithm optimization are introduced to improve the prediction precision, forestry carbon emission data are deeply mined and analyzed, and the future carbon emission trend is estimated. And accurate prediction of carbon emission in the future is realized.

Description

Technical Field

[0001] The present invention relates to the technical field of carbon emission analysis and prediction, and specifically to a method and system for statistical analysis and prediction of carbon emission information. Background Art

[0002] Forestry carbon sink refers to the absorption of carbon dioxide in the atmosphere by forests and its fixation in vegetation or soil, thereby reducing the concentration of the gas in the atmosphere. Forests are the largest carbon pool in terrestrial ecosystems and play a very important and unique role in reducing the concentration of greenhouse gases in the atmosphere and mitigating global warming. Carbon sink afforestation and forest management carbon sink projects are project activities with special requirements for increasing forest carbon sinks and implementing carbon sink measurement and monitoring throughout the afforestation and forest management activities. Forestry carbon stock monitoring is of great significance for understanding the carbon storage capacity of forest ecosystems, assessing the impact of climate change, and formulating response strategies. Accurate monitoring of forestry carbon reserves can achieve sustainable use of forest resources and ensure that forest resources can maintain their ecological functions while providing economic value.

[0003] Among them, carbon emission detection is a common means of forestry carbon sink monitoring. By detecting carbon emissions in forest areas, the status of forestry carbon sinks can be directly reflected. Due to the vastness of forest areas, it is impossible to carry out overall monitoring. For forestry carbon emission detection, regional sampling detection combined with overall prediction is often used. In order to improve the accuracy of the results, a multi-region sampling method is often used. The selection of sampling areas is highly random. Not only is the sampling workload large, but the sampling areas are also quite different, making it difficult to ensure data accuracy, which can easily lead to deviations in prediction results. Therefore, a method and system for statistical analysis and prediction of carbon emission information is proposed. Summary of the invention

[0004] The purpose of the present invention is to provide a method and system for statistical analysis and prediction of carbon emission information to solve the problems raised in the above background technology.

[0005] To achieve the above-mentioned purpose, the present invention provides the following technical solutions: 1. A system for statistical analysis and prediction of carbon emission information, comprising: a forest area data collection unit, a meteorological data collection unit, a macro data collection unit, a wireless communication unit, a data optimization unit, an analysis and prediction unit, a control terminal and a cloud database;

[0006] The forest area data collection unit is used for collecting tree data in a fixed area within the forest area, and the forest area data collection unit includes a tree data collection module and an environment data collection module;

[0007] The meteorological data collection unit is used for real-time collection of forest area meteorological data, and the meteorological data collection unit includes a field meteorological data collection module and a cloud meteorological data collection module;

[0008] The macro data collection unit is used for the overall data collection of tree growth characteristics in the forest area, and the macro data collection unit includes an online data collection module and a field data collection module;

[0009] The wireless communication unit is used for remote data transmission between the forest area and the cloud, and the wireless communication unit includes a regional communication module and a wireless communication module;

[0010] The data optimization unit is used for collecting data aggregation and data optimization processing, and the data optimization unit includes a data processing optimization module;

[0011] The analysis and prediction unit is used for analysis and prediction of carbon emissions in forest areas and dynamic prediction of carbon sinks in forest areas. The analysis and prediction unit includes an analysis and prediction module;

[0012] The control terminal is used for overall operation control of the system, and the cloud database is used for storage and call support of cloud data.

[0013] Preferably, the tree data collection module is used for collecting tree growth information, and the environmental data collection module is used for collecting environmental temperature and humidity and soil environmental data of the tree growth area.

[0014] Preferably, the field meteorological data acquisition module is installed in the forest to collect the climate information of the forest area in real time through field sampling. The cloud meteorological data acquisition module obtains the real-time and future climate information of the forest area from the cloud, integrates the cloud climate data with the field climate data, and then uploads it to the analysis and prediction module for comprehensive analysis and calculation.

[0015] Preferably, the online data acquisition module is used to obtain online satellite remote sensing measurement data of the forest area, and the field data acquisition module collects forest growth distribution, canopy height distribution, forest sparseness distribution, and forest type distribution information in the field through high-altitude survey drones.

[0016] Preferably, the regional communication module is a small regional communication base station used to aggregate sampling information within a single sampling area and to send data to the wireless communication module as a signal relay. The wireless communication module is a large communication base station that transmits the aggregated data to the cloud via wireless communication.

[0017] Preferably, the data processing optimization module is used for summary analysis and preprocessing optimization of uploaded data to remove abnormal data and improve data accuracy; the analysis and prediction module is used for comprehensive analysis and calculation of the data processed by the data processing optimization module and combines the big data support in the cloud database to perform accurate analysis and prediction of forest carbon sinks and carbon emissions.

[0018] A method for statistical analysis and prediction of carbon emission information, comprising the following steps:

[0019] S1. Macro-zoning of forest areas. Through online data collection module and field data collection module, data on tree growth distribution, canopy height distribution, tree sparseness distribution, and tree type distribution in forest areas are collected from the cloud and field, respectively. The forest area is divided into multiple independent characteristic regional blocks from a macro perspective, providing a reference for the selection of sampling areas. The later macro sampling interval is 1-3 months to facilitate the planning and adjustment of sampling areas;

[0020] S2. Sample area planning and location determination. According to the characteristic area of ​​the forest area, representative tree growth blocks are selected as sampling areas through comprehensive analysis and planning, and the sampling area is optimized to obtain more accurate data on the actual growth of the trees;

[0021] S3. Field survey and marking: Based on the planned sampling area, personnel arrive at the sampling area and select representative trees through field survey and carry out installation point marking and regional records for tree data collection module and environmental data collection module. At the same time, according to the field survey, preset points are selected for the installation of meteorological data collection unit and wireless communication unit;

[0022] S4, fixed-point sampling and installation, according to the surveyed installation fixed-point marks, the tree data acquisition module and the environmental data acquisition module are fixed-point installed in the sampling area of ​​the forest area, and field survey is carried out for the layout and installation of the meteorological data acquisition unit and the wireless communication unit;

[0023] S5. Sampling data is summarized and uploaded. The tree data collection module and the environmental data collection module are used to sample the forest data in each area in real time, and the meteorological data collection unit is used to collect the real-time meteorological data of the forest area. The collected data is summarized in the regional communication module and then uploaded to the cloud by the wireless communication module;

[0024] S6. Sampling data processing optimization: optimize the data processing and calculation through the data processing optimization module to eliminate error data and improve data accuracy;

[0025] S7, carbon emission analysis and prediction, the optimized data is uploaded to the analysis and prediction module, and the analysis and prediction module uses the data in the cloud database to conduct a comprehensive analysis and calculation of the sampled data to obtain the predicted data.

[0026] Preferably, the above S6 includes the following sub-steps:

[0027] S601, data cleaning, processing missing values, detecting and correcting outliers on the collected data to ensure data integrity and accuracy;

[0028] S602, data normalization, in order to eliminate the influence of different dimensions, the data is normalized and mapped to a specific interval to improve numerical stability.

[0029] Preferably, in S601 above, a missing value filling formula is used to process the missing values ​​of the sampled data, and the formula is as follows:

[0030]

[0031] Where: x missing : missing values ​​that need to be filled;

[0032] X i : the i-th observation in the data set;

[0033] n: the number of non-missing values ​​in the dataset;

[0034] The formula for outlier detection and correction is as follows:

[0035]

[0036] Where: Z: standardized variable value.

[0037] X: original variable value;

[0038] μ: mean of the data set;

[0039] σ: standard deviation of the data set;

[0040] In S602, data normalization is used to process the sampled data, and the formula is as follows:

[0041]

[0042] Where: x: original data.

[0043] x′: normalized data;

[0044] xmi n : The minimum value in the data set;

[0045] x x : The maximum value in the data set.

[0046] Preferably, the above S7 includes the following sub-steps:

[0047] S701. Model training: Based on the improved GM (1, N) power model, the dragonfly algorithm is used to optimize parameters for model training and verification. The improved GM (1, N) power model formula is:

[0048] C(t)=β×Cγ(t-1)+(1-β)×Cγ(tn)

[0049] Where: C(t): represents the forest carbon sink at time t;

[0050] β: Model parameter, used to control the influence weight of carbon sink at the current moment and the previous moment;

[0051] γ: model parameter, used to adjust the size of the power exponent;

[0052] n: time lag period, indicating how many time units ago the data affect the current carbon sink;

[0053] S702, error analysis, by comparing the actual value and the predicted value, calculating the root mean square error (RMSE) and other indicators to evaluate the prediction effect of the model, where the root mean square error (RMSE) calculation formula is:

[0054]

[0055] Where: RMSE: root mean square error;

[0056] y i : actual value;

[0057] Predicted value;

[0058] n: sample size.

[0059] S703. Result output: Generate forest carbon sink forecast data for the next several years and provide detailed forecast reports and chart displays.

[0060] Compared with the prior art, the present invention adopts the above technical solution and has the following technical effects:

[0061] 1. The present invention collects forest area data through online data collection modules and field data collection modules at a macro level, using sensors, remote sensing satellites, drones and other equipment, and divides the forest area into multiple independent characteristic area blocks at a macro level. It selects representative sampling areas for forest growth conditions through analysis and planning, and can optimize the sampling areas. While reducing the number of sampling areas to avoid random repeated sampling, it can also improve the representativeness and accuracy of the sampling data, reduce the sampling workload and improve the prediction accuracy.

[0062] 2. Obtain actual forest growth data through regional sampling, analyze and optimize the sampling data through the data processing optimization module, and use the analysis and prediction module to analyze the calibrated data and build a prediction model. The introduction of time lag effect and dragonfly algorithm optimization improves the prediction accuracy, conducts in-depth mining and analysis of forestry carbon emission data, estimates future carbon emission trends, and achieves accurate prediction of future carbon emissions, providing reliable technical support for later decision-making and planning of forests. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] In order to more clearly illustrate the embodiments of the present application 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 described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0064] Figure 1 It is a schematic diagram of the overall unit structure of the system of the present invention;

[0065] Figure 2 It is a schematic diagram of the overall module structure of the system of the present invention;

[0066] Figure 3 It is a schematic flow chart of the analysis and prediction working steps of the present invention. DETAILED DESCRIPTION

[0067] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0068] It should be noted that the structures, proportions, sizes, etc. illustrated in the drawings of this specification are only used to match the contents disclosed in the specification for people familiar with this technology to understand and read, and are not used to limit the conditions under which this application can be implemented. Therefore, they have no substantive technical significance. Any structural modification, change in proportional relationship or adjustment of size should still fall within the scope of the technical content disclosed in this application without affecting the effects and purposes that can be achieved by this application.

[0069] Example

[0070] See also Figure 1-3 , the present invention provides a technical solution: a system for statistical analysis and prediction of carbon emission information, comprising: a forest area data collection unit, a meteorological data collection unit, a macro data collection unit, a wireless communication unit, a data optimization unit, an analysis and prediction unit, a control terminal and a cloud database;

[0071] The forest area data collection unit is used to collect forest data in fixed areas within the forest area. The forest area data collection unit includes a tree data collection module and an environmental data collection module. Specifically, the tree data collection module is used to collect tree growth information. Tree growth data is collected and sampled by installing tree thickness detectors, tree height detectors, etc. on the upper part of the trees. The environmental data collection module includes temperature and humidity sensors, oxygen content sensors, etc., which are used to collect environmental temperature and humidity and soil environmental data in the tree growth area.

[0072] The meteorological data collection unit is used for real-time collection of meteorological data in the forest area. The meteorological data collection unit includes a field meteorological data collection module and a cloud meteorological data collection module. The field meteorological data collection module contains sensors such as wind speed and direction, temperature and humidity, and air pressure. It is installed in the forest to collect climate information of the forest area in real time through field sampling. The cloud meteorological data collection module obtains the real-time and future climate information of the forest area from the cloud, integrates the cloud climate data with the field climate data, and then uploads it to the analysis and prediction module for comprehensive analysis and calculation. Through comprehensive comparative analysis of the cloud climate data and the field climate data, more accurate real-time and future predicted meteorological data of the forest area can be obtained, providing accurate variable reference data for carbon emission analysis and prediction.

[0073] The macro data collection unit is used for the overall data collection of tree growth characteristics in the forest area. The macro data collection unit includes an online data collection module and a field data collection module. The online data collection module is used to obtain online satellite remote sensing measurement data of the forest area, and obtain information such as tree growth distribution, canopy height distribution, tree sparseness distribution, and tree type distribution from the cloud. The field data collection module collects information such as tree growth distribution, canopy height distribution, tree sparseness distribution, and tree type distribution in the field through high-altitude survey drones. Through comprehensive comparative analysis of online and offline information, the forest area is divided into multiple independent characteristic area blocks from a macro perspective, providing a reference for the selection of sampling areas. Sensors, remote sensing satellites, drones and other equipment are used to collect forest area data. From a macro perspective, the forest area is divided into multiple independent characteristic area blocks, and representative sampling areas for tree growth conditions are selected through analysis and planning. The sampling area can be optimized to reduce the number of sampling areas and avoid random repeated sampling. At the same time, the representativeness and accuracy of the sampling data can be improved, which can reduce the sampling workload and improve the prediction accuracy.

[0074] The wireless communication unit is used for remote data transmission between the forest area and the cloud. The wireless communication unit includes a regional communication module and a wireless communication module. The regional communication module is a small regional communication base station used to summarize the sampling information within a single sampling area and serve as a signal relay to send data to the wireless communication module. The wireless communication module is a large communication base station that transmits the summarized data to the cloud through wireless communication.

[0075] The data optimization unit is used for the aggregation and optimization of collected data. The data optimization unit includes a data processing optimization module. The data processing optimization module is used for the aggregation and analysis of uploaded data and preprocessing optimization to remove abnormal data and improve data accuracy. The analysis and prediction unit is used for the analysis and prediction of carbon emissions in forest areas and the dynamic prediction of carbon sinks in forest areas. The analysis and prediction unit includes an analysis and prediction module. The analysis and prediction module is used to conduct comprehensive analysis and calculation on the data processed by the data processing optimization module, and combine the big data support in the cloud database to conduct accurate analysis and prediction of carbon sinks and carbon emissions in forest areas. The sampling data is analyzed, optimized and calibrated through the data processing optimization module, and the analysis and prediction module is used to analyze and construct a prediction model for the calibrated data. The time lag effect and dragonfly algorithm optimization are introduced to improve the prediction accuracy. The forestry carbon emission data is deeply mined and analyzed, and the future carbon emission trend is estimated to achieve accurate prediction of future carbon emissions, providing reliable technical support for the later decision-making and planning of forests. The control terminal is used for the overall operation control of the system, and the cloud database is used for storage and call support of cloud data.

[0076] A method for statistical analysis and prediction of carbon emission information, comprising the following steps:

[0077] S1. Macro-zoning of forest areas. Through online data collection modules and field data collection modules, data on tree growth distribution, canopy height distribution, tree sparseness distribution, and tree type distribution in forest areas are collected from the cloud and field, respectively. The forest area is divided into multiple independent characteristic area blocks from a macro perspective to provide a reference for the selection of sampling areas. To avoid the impact of later tree growth on sampling data, the later macro sampling interval is 2 months. Every 2 months, the characteristic area blocks are redefined through drones and satellite remote sensing data to facilitate the planning and adjustment of the sampling area. The adjustment is made based on the deviation value. When the sampling area deviates from the characteristic by more than 30%, the sampling area needs to be re-adjusted and planned;

[0078] S2. Sample area planning and location. According to the characteristic area of ​​the forest area, the representative tree growth area is selected as the sampling area through comprehensive analysis and planning. The sampling area is optimized to obtain more accurate data on the actual growth of the trees. The sampling area is selected according to the macroscopic characteristics of the trees. While improving the accuracy of the tree sampling data, the number of sampling areas can be reduced, which is conducive to reducing the sampling workload;

[0079] S3. Field survey and marking: Based on the planned sampling area, personnel arrive at the sampling area and select representative trees through field survey and carry out installation point marking and regional records for tree data collection module and environmental data collection module. At the same time, according to the field survey, preset points are selected for the installation of meteorological data collection unit and wireless communication unit;

[0080] S4, fixed-point sampling and installation, according to the surveyed installation fixed-point marks, the tree data acquisition module and the environmental data acquisition module are fixed-point installed in the sampling area of ​​the forest area, and field survey is carried out for the layout and installation of the meteorological data acquisition unit and the wireless communication unit;

[0081] S5. Sampling data is summarized and uploaded. The tree data collection module and the environmental data collection module are used to sample the forest data in each area in real time, and the meteorological data collection unit is used to collect the real-time meteorological data of the forest area. The collected data is summarized in the regional communication module and then uploaded to the cloud by the wireless communication module;

[0082] S6, sampling data processing optimization, optimizing the data through the data processing optimization module, eliminating error data and improving the accuracy of the data. Specifically, S6 includes the following sub-steps:

[0083] S601, data cleaning, missing value processing, outlier detection and correction of the collected data to ensure data integrity and accuracy; missing value filling formula is used to process the missing values ​​of the sampled data, the formula is as follows:

[0084]

[0085] Where: x missing : missing values ​​that need to be filled;

[0086] X i : the i-th observation in the data set;

[0087] n: the number of non-missing values ​​in the dataset;

[0088] The formula for outlier detection and correction is as follows:

[0089]

[0090] Where: Z: standardized variable value.

[0091] X: original variable value;

[0092] μ: mean of the data set;

[0093] σ: standard deviation of the data set;

[0094] S602, data normalization, in order to eliminate the influence of different dimensions, the data is normalized and mapped to a specific interval to improve numerical stability. The specific formula is as follows:

[0095]

[0096] Where: x: original data.

[0097] x′: normalized data;

[0098] xmi n : The minimum value in the data set;

[0099] x x : The maximum value in the data set.

[0100] S7, carbon emission analysis and prediction, the optimized data is uploaded to the analysis and prediction module, and the analysis and prediction module obtains the prediction data by comprehensively analyzing and calculating the sampled data by referring to the data in the cloud database. Specifically, S7 includes the following sub-steps:

[0101] S701. Model training: Based on the improved GM (1, N) power model, the dragonfly algorithm is used to optimize parameters for model training and verification. The improved GM (1, N) power model formula is:

[0102] C(t)=β×Cγ(t-1)+(1-β)×Cγ(tn)

[0103] Where: C(t): represents the forest carbon sink at time t;

[0104] β: Model parameter, used to control the influence weight of carbon sink at the current moment and the previous moment;

[0105] γ: model parameter, used to adjust the size of the power exponent;

[0106] n: time lag period, indicating how many time units ago the data affect the current carbon sink;

[0107] S702, error analysis, by comparing the actual value and the predicted value, calculating the root mean square error (RMSE) and other indicators to evaluate the prediction effect of the model, where the root mean square error (RMSE) calculation formula is:

[0108]

[0109] Where: RMSE: root mean square error;

[0110] y i : actual value;

[0111] Predicted value;

[0112] n: sample size.

[0113] S703. Result output: Generate forest carbon sink forecast data for the next several years and provide detailed forecast reports and chart displays.

[0114] In summary, the present invention collects forest area data through online data collection modules and field data collection modules at a macro level, using sensors, remote sensing satellites, drones and other equipment, and divides the forest area into multiple independent characteristic area blocks at a macro level. The representative sampling areas for forest growth are selected through analysis and planning, and the sampling areas can be optimized to reduce the number of sampling areas and avoid random repeated sampling while improving the representativeness and accuracy of the sampling data. The sampling workload can be reduced and the prediction accuracy can be improved. The actual growth data of the forest can be obtained through regional sampling, and the sampling data is analyzed, optimized and calibrated through the data processing optimization module. The analysis and prediction module is used to analyze and construct a prediction model for the calibrated data. The time lag effect and dragonfly algorithm optimization are introduced to improve the prediction accuracy. The forestry carbon emission data is deeply mined and analyzed, and the future carbon emission trend is estimated to achieve accurate prediction of future carbon emissions, providing reliable technical support for the later decision-making and planning of forests.

[0115] It will be appreciated by those skilled in the art that the features described in the various embodiments and / or claims of the present invention may be combined and / or combined in various ways, even if such combinations and / or combinations are not explicitly described in the present invention. In particular, the features described in the various embodiments and / or claims of the present invention may be combined and / or combined in various ways without departing from the spirit and teachings of the present invention. All of these combinations and / or combinations fall within the scope of the present invention.

Claims

1. A system for statistical analysis and prediction of carbon emission information, characterized in that: include: Forest data collection unit, meteorological data collection unit, macro data collection unit, wireless communication unit, data optimization unit, analysis and prediction unit, control terminal and cloud database; The forest area data collection unit is used for collecting tree data in a fixed area within the forest area, and the forest area data collection unit includes a tree data collection module and an environment data collection module; The meteorological data collection unit is used for real-time collection of forest area meteorological data, and the meteorological data collection unit includes a field meteorological data collection module and a cloud meteorological data collection module; The macro data collection unit is used for the overall data collection of tree growth characteristics in the forest area, and the macro data collection unit includes an online data collection module and a field data collection module; The wireless communication unit is used for remote data transmission between the forest area and the cloud, and the wireless communication unit includes a regional communication module and a wireless communication module; The data optimization unit is used for collecting data aggregation and data optimization processing, and the data optimization unit includes a data processing optimization module; The analysis and prediction unit is used for analysis and prediction of carbon emissions in forest areas and dynamic prediction of carbon sinks in forest areas. The analysis and prediction unit includes an analysis and prediction module; The control terminal is used for overall operation control of the system, and the cloud database is used for storage and call support of cloud data.

2. A system for statistical analysis and prediction of carbon emission information according to claim 1, characterized in that: The tree data collection module is used for collecting tree growth information, and the environmental data collection module is used for collecting environmental temperature and humidity and soil environmental data of the tree growth area.

3. A system for statistical analysis and prediction of carbon emission information according to claim 2, characterized in that: The field meteorological data acquisition module is installed in the forest to collect the climate information of the forest area in real time through field sampling. The cloud meteorological data acquisition module obtains the real-time and future climate information of the forest area from the cloud, integrates the cloud climate data with the field climate data, and then uploads it to the analysis and prediction module for comprehensive analysis and calculation.

4. A system for statistical analysis and prediction of carbon emission information according to claim 3, characterized in that: The online data acquisition module is used to obtain online satellite remote sensing measurement data of the forest area, and the field data acquisition module collects forest growth distribution, canopy height distribution, forest sparseness distribution, and forest type distribution information in the field through high-altitude survey drones.

5. A system for statistical analysis and prediction of carbon emission information according to claim 4, characterized in that: The regional communication module is a small regional communication base station used to aggregate sampling information within a single sampling area and send data to the wireless communication module as a signal relay. The wireless communication module is a large communication base station that transmits the aggregated data to the cloud via wireless communication.

6. A system for statistical analysis and prediction of carbon emission information according to claim 5, characterized in that: The data processing optimization module is used for the summary analysis and preprocessing optimization of uploaded data to remove abnormal data and improve data accuracy. The analysis and prediction module is used to perform comprehensive analysis and calculation on the data processed by the data processing optimization module and combine the big data support in the cloud database to perform accurate analysis and prediction of forest carbon sinks and carbon emissions.

7. A method for statistical analysis and prediction of carbon emission information according to any one of claims 1 to 6, characterized in that: The following steps are involved: S1. Macro-zoning of forest areas. Through online data collection module and field data collection module, data on tree growth distribution, canopy height distribution, tree sparseness distribution, and tree type distribution in forest areas are collected from the cloud and field, respectively. The forest area is divided into multiple independent characteristic regional blocks from a macro perspective, providing a reference for the selection of sampling areas. The later macro sampling interval is 1-3 months to facilitate the planning and adjustment of sampling areas; S2. Sample area planning and location determination. According to the characteristic area of ​​the forest area, representative tree growth blocks are selected as sampling areas through comprehensive analysis and planning, and the sampling area is optimized to obtain more accurate data on the actual growth of the trees; S3. Field survey and marking: Based on the planned sampling area, personnel arrive at the sampling area and select representative trees through field survey and carry out installation point marking and regional records for tree data collection module and environmental data collection module. At the same time, according to the field survey, preset points are selected for the installation of meteorological data collection unit and wireless communication unit; S4, fixed-point sampling and installation, according to the surveyed installation fixed-point marks, the tree data acquisition module and the environmental data acquisition module are fixed-point installed in the sampling area of ​​the forest area, and field survey is carried out for the layout and installation of the meteorological data acquisition unit and the wireless communication unit; S5. Sampling data is summarized and uploaded. The tree data collection module and the environmental data collection module are used to sample the forest data in each area in real time, and the meteorological data collection unit is used to collect the real-time meteorological data of the forest area. The collected data is summarized in the regional communication module and then uploaded to the cloud by the wireless communication module; S6. Sampling data processing optimization: optimize the data processing and calculation through the data processing optimization module to eliminate error data and improve data accuracy; S7, carbon emission analysis and prediction, the optimized data is uploaded to the analysis and prediction module, and the analysis and prediction module uses the data in the cloud database to conduct a comprehensive analysis and calculation of the sampled data to obtain the predicted data.

8. A method for statistical analysis and prediction of carbon emission information according to claim 7, characterized in that: The S6 comprises the following sub-steps: S601, data cleaning, processing missing values, detecting and correcting outliers on the collected data to ensure data integrity and accuracy; S602, data normalization, in order to eliminate the influence of different dimensions, the data is normalized and mapped to a specific interval to improve numerical stability.

9. A method for statistical analysis and prediction of carbon emission information according to claim 8, characterized in that: In S601, a missing value filling formula is used to process the missing values ​​of the sampled data. The formula is as follows: Where: x missing : missing values ​​that need to be filled; X i : the i-th observation in the data set; n: the number of non-missing values ​​in the dataset; The formula for outlier detection and correction is as follows: Where: Z: standardized variable value. X: original variable value; μ: mean of the data set; σ: standard deviation of the data set; In S602, data normalization is used to process the sampled data, and the formula is as follows: Where: x: original data. x′: normalized data; x min : The minimum value in the data set; x max : The maximum value in the data set.

10. The method for statistical analysis and prediction of carbon emission information according to claim 7, characterized in that: The S7 comprises the following sub-steps: S701. Model training: Based on the improved GM (1, N) power model, the dragonfly algorithm is used to optimize parameters for model training and verification. The improved GM (1, N) power model formula is: C(t)=β×C γ (t-1)+(1-β)×C γ (t-n) Where: C(t): represents the forest carbon sink at time t; β: Model parameter, used to control the influence weight of carbon sink at the current moment and the previous moment; γ: model parameter, used to adjust the size of the power exponent; n: time lag period, indicating how many time units ago the data affect the current carbon sink; S702, error analysis, by comparing the actual value and the predicted value, calculating the root mean square error (RMSE) and other indicators to evaluate the prediction effect of the model, where the root mean square error (RMSE) calculation formula is: Where: RMSE: root mean square error; y i : actual value; : predicted value; n: sample size. S703. Result output: Generate forest carbon sink forecast data for the next several years and provide detailed forecast reports and chart displays.

Images