Real-time evaluation method for forest ecological carbon sink based on multi-weather parameter fusion
By using a multi-meteorological parameter fusion method, combining the time-series patterns of forest carbon sequestration to screen key data and dynamically adjust weights, the problem of redundant data interference and time period adaptation in existing carbon sequestration assessments has been solved, enabling real-time and accurate assessment of forest ecological carbon sequestrations.
Patent Information
- Application Number
- CN202610548323.7
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2026-04-23
- Publication Date
- 2026-06-09
AI Technical Summary
Existing forest ecological carbon sink assessment technologies fail to incorporate the temporal variation characteristics of forest carbon sink accumulation, cannot distinguish between carbon sink sensitive and non-sensitive periods, are susceptible to interference from redundant data, and data fusion does not adapt to the temporal fluctuation characteristics of carbon sinks. As a result, the assessment results are detached from the actual growth status of forests, and lack accuracy and applicability.
By using a multi-meteorological parameter fusion method and combining the temporal patterns of forest carbon sequestration, sensitive and non-sensitive periods for carbon sequestration are divided, key data are screened and redundant data are removed, adaptive fusion units are used to dynamically adjust data weights, and carbon sequestration is calculated in conjunction with the basic attributes of forest ecology to generate real-time assessment results.
It achieves precise data locking and high matching between forest carbon sink changes during carbon sink assessment, eliminates redundant data interference, adapts to carbon sink change characteristics in different time periods and forest scenarios, fully restores the real-time change status of carbon sinks, and improves the accuracy and applicability of assessment.
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Figure CN122175414A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of forest ecological technology, specifically to a real-time assessment method for forest ecological carbon sequestration based on the fusion of multiple meteorological parameters. Background Technology
[0002] Forest ecosystems are the most important carbon sink carriers in terrestrial ecosystems, occupying a core position in the implementation of the dual-carbon strategy, ecological environmental protection, and sustainable management of forest resources. Accurate acquisition of real-time dynamic data on forest ecological carbon sinks is a key basis for carbon sink value accounting, ecological management and control decisions, and forest protection and restoration. As ecological monitoring develops towards digitalization and refinement, the industry has an urgent need for real-time assessment technologies that are aligned with the forest carbon sink generation mechanism.
[0003] However, existing forest ecological carbon sink assessment technologies have some shortcomings. Most methods do not combine data analysis with the temporal changes in forest carbon sink accumulation, cannot distinguish the data differences between carbon sink sensitive periods and non-sensitive periods, and are easily affected by redundant data. Data fusion adopts a fixed weight model, which cannot adapt to the temporal fluctuations of carbon sinks, and the fusion logic is disconnected from the carbon sink driving mechanism. Carbon sink accounting does not combine the basic attributes of the forest itself, such as forest stand structure and vegetation composition, to make scenario corrections. The assessment results are detached from the actual growth state of the forest and cannot truly reflect the real-time changes in carbon sinks, resulting in insufficient accuracy and applicability. Summary of the Invention
[0004] The purpose of this invention is to overcome the shortcomings of existing technologies and provide a real-time assessment method for forest ecological carbon sinks by integrating multiple meteorological parameters. This invention uses a time-series analysis unit to combine the time-series patterns of forest carbon sink accumulation, divides the carbon sink sensitive period into a non-sensitive period, and completes the key data screening and eliminates redundant data based on the correlation between meteorological data and carbon sink changes. This scheme takes the time-series change mechanism of forest carbon sinks itself as the core basis, abandons the logic of indiscriminate data processing, accurately locks meteorological data that are highly correlated with carbon sink changes, and fits the inherent characteristics of carbon sink formation and fluctuation from the data screening stage, eliminating the interference of redundant data on the assessment process, and providing a data source support that is highly matched with carbon sink changes for carbon sink accounting.
[0005] To solve the above-mentioned technical problems, this invention provides the following technical solution: a real-time assessment method for forest ecological carbon sequestration based on the fusion of multiple meteorological parameters, the specific steps of which are as follows:
[0006] Data Acquisition: Obtain multi-source meteorological data of the target forest ecological area through ground meteorological monitoring stations, meteorological data push interfaces, and historical meteorological databases, and transmit the acquired multi-source meteorological data;
[0007] Data preprocessing: The data processing unit receives the transmitted multi-source meteorological data and preprocesses the multi-source meteorological data to obtain preprocessed multi-source meteorological data;
[0008] Data screening: Based on the preprocessed multi-source meteorological data, the correlation between multi-source meteorological data and carbon sink changes is analyzed by combining the time series analysis unit with the carbon sink accumulation time series pattern. The contribution of multi-source meteorological data to carbon sink assessment is calculated, key data is screened and redundant data is removed, and the screened key data is transmitted.
[0009] Data fusion: The adaptive fusion unit receives the key data transmitted, adjusts the weight coefficients of the key data according to the characteristics of carbon sink changes over time, forms a time-series correlated dynamic adaptation processing system, and obtains the fused data;
[0010] Data accounting: Based on the fused data, the carbon sequestration unit retrieves the basic attribute parameters of the forest ecology, combines the fused data with the basic attribute parameters, calculates the forest ecological carbon sequestration value for the corresponding time period, and generates the real-time assessment result of the forest ecological carbon sequestration for the corresponding time period based on the forest ecological carbon sequestration value.
[0011] Furthermore, in the data acquisition process, ground meteorological monitoring stations are deployed at different forest stand locations within the target forest ecological area to collect real-time meteorological elements in the forest. The meteorological data push interface is used to connect to the short-term meteorological data release terminal. The historical meteorological database stores historical meteorological records of the target forest ecological area. The multi-source meteorological data includes real-time monitoring data, short-term forecast data, and historical data for the same period, covering meteorological parameters such as temperature, precipitation, sunshine, humidity, and wind speed. The time span of the short-term forecast data is the next 72 hours, and the acquisition sequence of the multi-source meteorological data is uniformly set to the hourly level.
[0012] Furthermore, the specific steps for preprocessing multi-source meteorological data in the data preprocessing are as follows: outlier values in the multi-source meteorological data are removed using the three sigma criterion; for data positions with gaps after outlier removal, linear interpolation is used to fill in the missing meteorological parameter values; time alignment is performed on the completed multi-source meteorological data to map multi-source meteorological data from different sources to the same timestamp, unifying the time base of the multi-source meteorological data; and normalization is then performed to eliminate the dimensional differences of different meteorological parameters, thereby obtaining the preprocessed multi-source meteorological data.
[0013] Furthermore, in the data screening process, the time-series analysis unit divides the preprocessed multi-source meteorological data into carbon sequestration-sensitive and non-sensitive periods according to the time-series pattern of forest carbon sequestration accumulation. Then, it performs time-series analysis on the multi-source meteorological data and carbon sequestration accumulation changes in each period to obtain the time-series correlation coefficient, that is, the degree of correlation between multi-source meteorological data and carbon sequestration changes. The carbon sequestration accumulation time-series pattern shows that forest carbon sequestration exhibits stage-based accumulation and fluctuation characteristics with the vegetation growth cycle and daily cycle. Then, the carbon sequestration contribution of various meteorological data is calculated by combining the carbon sequestration contribution calculation formula with the correlation coefficient. Then, a contribution standard is set, and the carbon sequestration contribution is compared with the contribution standard. Key data such as temperature, precipitation, sunshine, humidity, and wind speed that meet the contribution standard are screened, while redundant data that do not meet the contribution standard are eliminated.
[0014] Furthermore, in the data screening process, the formula for calculating the carbon sink contribution is: ,in, For the first Carbon sequestration contribution of meteorological data, when Time is key data. At that time, the data was considered redundant; 0.8 was the contribution standard, and it could be dynamically adjusted according to forest type and seasonal characteristics. For the first The time-series correlation coefficient between meteorological data and carbon sink changes. For the first The time-series cumulative factor for meteorological data is determined based on the time-series classification of carbon sink accumulation patterns, with values higher for carbon sink-sensitive periods than for non-sensitive periods. For the preprocessed first Normalized values of meteorological data For the first The basic correction coefficient for meteorological data is determined based on the vegetation type of the target forest and the current seasonal characteristics, and is used to adapt to the differences in the basic impact of meteorological parameters on carbon sequestration under different scenarios.
[0015] Furthermore, in the data fusion process, the adaptive fusion unit receives the transmitted key data, identifies the current time period as a sensitive or non-sensitive period for carbon sink changes based on the time-series characteristics of carbon sink accumulation, determines the basic weight of each key data, allocates the basic weight to non-sensitive periods in a proportional manner, determines the time period adjustment coefficient based on the carbon sink change characteristics of the current time period, calculates the dynamic weight of each key data through the dynamic weight allocation calculation formula, and performs weighted fusion of the key data based on the dynamic weight to form a time-series-related dynamic adaptation processing system, thereby obtaining the fused data.
[0016] Furthermore, in the data fusion, the dynamic weight allocation calculation formula is as follows: ,in, For the first Dynamic weights of key data types For the first The basic weights of key data categories are determined, and non-sensitive periods are allocated proportionally. This is a time-period adjustment coefficient; the value is 1.5-3 during periods sensitive to carbon sink changes, and 1 during periods not sensitive to changes. For the first Carbon sequestration contribution of meteorological data The weighted smoothing transition factor is determined based on the continuous temporal characteristics.
[0017] Furthermore, in the data accounting process, the carbon sequestration accounting unit retrieves the basic attribute parameters of the target forest ecology, including stand density, vegetation coverage, and tree species composition. Combining the fused data with the basic attribute parameters, the unit calculates the forest ecological carbon sequestration value for the corresponding time period using the real-time forest ecological carbon sequestration accounting formula. Based on the forest ecological carbon sequestration value, the unit generates the real-time assessment result of the forest ecological carbon sequestration for the corresponding time period. The real-time assessment result of the forest ecological carbon sequestration is generated in hourly time series units, including the forest ecological carbon sequestration value for the corresponding time period and the time series change curve composed of the forest ecological carbon sequestration value for consecutive time periods.
[0018] Furthermore, in the data accounting, the real-time accounting formula for forest ecological carbon sequestration is as follows: ,in, This represents the forest ecological carbon sink value for the corresponding period. For the first Dynamic weights of key data types For the first in the merged data The numerical values of key data categories This is a correction coefficient for the comprehensive basic attributes of forest ecology, determined based on the stand density, vegetation cover, and tree species composition of the target forest. This represents the total number of categories for key data.
[0019] Compared with existing technologies, this real-time assessment method for forest ecological carbon sequestration based on the fusion of multiple meteorological parameters has the following advantages:
[0020] I. This invention, through a time-series analysis unit combined with the temporal patterns of forest carbon sink accumulation, divides carbon sink sensitive periods into non-sensitive periods. Based on the correlation between meteorological data and carbon sink changes, it completes key data screening and eliminates redundant data. This scheme takes the temporal change mechanism of forest carbon sink itself as its core basis, abandons the logic of indiscriminate data processing, accurately locks meteorological data that are highly correlated with carbon sink changes, and aligns with the inherent characteristics of carbon sink formation and fluctuations from the data screening stage, eliminating the interference of redundant data on the assessment process, and providing data source support that is highly matched with carbon sink changes for carbon sink accounting.
[0021] Second, this invention uses an adaptive fusion unit to dynamically adjust data weights based on the characteristics of carbon sink changes over time periods and complete weighted fusion. The carbon sink accounting unit combines the basic attributes of the forest ecosystem to calculate the carbon sink value and generate the assessment result. This scheme breaks through the technical limitations of fixed-weight fusion, achieves dynamic adaptation of weights according to the differences in carbon sink time periods, and performs accounting in combination with the basic attributes of the forest itself. This allows the carbon sink assessment process to adapt to the carbon sink change characteristics of different time periods and different forest scenarios, fully restores the real-time change status of forest ecological carbon sinks, and realizes the temporal adaptation and scenario-based quantification of carbon sink assessment.
[0022] Other advantages, objectives and features of the invention will be set forth in part in the description which follows, and in part will be apparent to those skilled in the art from the following examination or study, or may be learned from the practice of the invention. Attached Figure Description
[0023] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are merely some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without any creative effort.
[0024] Figure 1 A flowchart for a real-time assessment method of forest ecological carbon sequestration based on the fusion of multiple meteorological parameters;
[0025] Figure 2 A framework diagram for a real-time assessment method of forest ecological carbon sinks based on the fusion of multiple meteorological parameters;
[0026] Figure 3 A framework diagram for data screening in a real-time assessment method for forest ecological carbon sequestration based on the fusion of multiple meteorological parameters. Detailed Implementation
[0027] To further illustrate the technical means and effects of the present invention in achieving its intended purpose, the following detailed description of the specific implementation methods, structures, features, and effects of the present invention, in conjunction with the accompanying drawings and preferred embodiments, is provided below.
[0028] Example:
[0029] In the subtropical evergreen broad-leaved forest ecosystem, this region is located in a low-latitude mountainous area and includes various forest stand types such as pure Masson pine forests, mixed broad-leaved and Chinese fir forests, and secondary evergreen broad-leaved forests. It has high vegetation coverage and rich tree species composition. The forest carbon sink exhibits phased accumulation and fluctuation characteristics with the alternation of day and night and seasonal changes. Moreover, the ecological monitoring needs in the region are mainly real-time, refined, and scenario-based, requiring long-term and stable acquisition of dynamic changes in carbon sink data.
[0030] Data Collection: Standardized deployment of ground meteorological monitoring stations was completed at different forest stand locations within the target forest ecological area. Stations were deployed in pure forest areas, mixed forest areas, open forest edges, and closed forest canopy areas to ensure comprehensive collection of real-time meteorological elements under different microenvironments within the forest, avoiding data bias caused by single-location collection. A meteorological data push interface was directly connected to a short-term meteorological data dissemination terminal to stably obtain future weather forecasts for the target area. Complete historical meteorological records for several consecutive years were retrieved from the regional ecological monitoring historical meteorological database to ensure the continuity and completeness of historical data. Real-time monitoring data collected from the ground meteorological monitoring stations was then transmitted to the target forest ecological area. The system integrates short-term forecast data obtained from data and meteorological data push interfaces with historical data from historical meteorological databases to form comprehensive multi-source meteorological data. This multi-source meteorological data fully includes five major categories of meteorological parameters: temperature, precipitation, sunshine, humidity, and wind speed, meeting the meteorological data requirements for forest carbon sink assessment. The time span of the short-term forecast data is set to the next 72 hours, consistent with the regional short-term ecological monitoring cycle. To ensure the temporal consistency of all meteorological data, the acquisition time sequence of all multi-source meteorological data is uniformly set to the hourly level, ensuring that meteorological data from different sources and of different types remain synchronized in the time dimension. The acquired multi-source meteorological data is stably transmitted to the data processing unit, such as... Figure 1 As shown, this provides complete raw data support for subsequent data preprocessing, ensuring the comprehensiveness, timeliness, and standardization of the data from the data source level.
[0031] Data Preprocessing: After receiving all transmitted multi-source meteorological data, the data processing unit performs integrated preprocessing operations on the multi-source meteorological data, ensuring no data omissions and no processing deviations throughout the process. First, the three-sigma criterion is used to accurately remove outliers from the multi-source meteorological data. Invalid data such as abnormal temperature values, sudden wind speed values, and erroneous humidity values caused by sensor malfunctions or extreme weather interference during the acquisition process are comprehensively cleaned up to ensure that the retained data are all valid data that conform to the actual meteorological conditions of the forest. After the outlier removal is completed, linear interpolation is used to standardize and complete the missing data positions caused by brief equipment offline or signal transmission interruptions in the data sequence, filling in the missing meteorological parameter values to ensure the temporal continuity of each type of meteorological data. To avoid biases in subsequent analysis due to missing data, a time-series alignment operation is performed on the multi-source meteorological data after anomaly removal and gap filling. This maps all meteorological data from three different sources—ground monitoring stations, meteorological push interfaces, and historical databases—to the same timestamp, completely eliminating time base differences between different data sources and unifying the time standard of all meteorological data. Finally, the time-series aligned meteorological data undergoes normalization processing to eliminate dimensional differences between different meteorological parameters such as temperature, precipitation, sunshine, humidity, and wind speed, ensuring that all types of meteorological data fall within a unified numerical range. This prevents dimensional differences from interfering with subsequent data screening, fusion, and calculation. Through this preprocessing process, standardized, normalized, and time-series unified preprocessed multi-source meteorological data is obtained, such as… Figure 2 As shown, this provides high-quality processed data for the data screening process, ensuring the accuracy of the entire evaluation process from a data quality perspective.
[0032] Data Screening: The preprocessed multi-source meteorological data is input into the time-series analysis unit. The time-series analysis unit then performs data screening. First, it divides the data into time periods based on the forest carbon sequestration accumulation time-series pattern. This pattern shows that forest carbon sequestration exhibits phased accumulation and fluctuation characteristics with the vegetation growth cycle and daily cycle. Combining this with the growth characteristics of the target subtropical evergreen broad-leaved forest, the preprocessed multi-source meteorological data is precisely divided into carbon sequestration sensitive periods and non-sensitive periods according to time series. The daytime period, when vegetation photosynthesis is vigorous, is the carbon sequestration sensitive period. The nighttime period, which is the dominant period, is considered a non-sensitive period, allowing for the time-series classification of meteorological data. After time-series segmentation, the time-series analysis unit performs time-series analysis on the multi-source meteorological data and carbon sink accumulation changes for each period. This yields the time-series correlation coefficient, which accurately characterizes the degree of correlation between multi-source meteorological data and carbon sink changes. This coefficient is a quantified measure of the correlation between meteorological data and carbon sink changes, directly reflecting the close relationship between each type of meteorological data and forest carbon sink changes. Subsequently, the carbon sink contribution is calculated using the carbon sink contribution calculation formula, combined with the time-series correlation coefficient. The carbon sink contribution calculation formula is as follows: ,in, For the first Carbon sequestration contribution of meteorological data, when Time is key data. At that time, the data was considered redundant; 0.8 was the contribution standard, and it could be dynamically adjusted according to forest type and seasonal characteristics. For the first The time-series correlation coefficient between meteorological data and carbon sink changes. For the first The time-series cumulative factor for meteorological data is determined based on the time-series classification of carbon sink accumulation patterns, with values higher for carbon sink-sensitive periods than for non-sensitive periods. For the preprocessed first Normalized values of meteorological data For the first The basic correction coefficient for meteorological data is determined based on the vegetation type and current seasonal characteristics of the target forest, used to adapt to the differences in the basic impact of meteorological parameters on carbon sequestration under different scenarios. Based on the forest type of subtropical evergreen broad-leaved forest and the characteristics of the spring growing season, the contribution standard is dynamically set to 0.8. The carbon sequestration contribution of various meteorological data is compared one by one with this contribution standard, and key data such as temperature, precipitation, sunshine, humidity, and wind speed with a carbon sequestration contribution of 0.8 are selected. These key data are highly correlated with changes in forest carbon sequestration. Redundant data with a carbon sequestration contribution below 0.8 are directly removed to avoid interference from redundant data in subsequent fusion and calculation stages. After the key data selection is completed, the selected key data is transmitted to the adaptive fusion unit, such as... Figure 3 As shown, it provides core and effective data for the data fusion process, enabling accurate data filtering and optimized configuration.
[0033] Data Fusion: After receiving the transmitted key data, the adaptive fusion unit initiates the dynamic weight fusion processing flow. First, based on the temporal characteristics of carbon sink accumulation, the current processing period is accurately identified to determine whether it belongs to a carbon sink change-sensitive or non-sensitive period. After clarifying the period attribute, a corresponding basic weight is assigned to each type of key data. For non-sensitive periods, the basic weight is evenly distributed proportionally to ensure that the basic impact of various key data types remains consistent within the non-sensitive period. Next, based on the carbon sink change characteristics of the currently identified period, a corresponding period adjustment coefficient is determined. The period adjustment coefficient for carbon sink change-sensitive periods is between 1.5 and 3, while the period adjustment coefficient for non-sensitive periods is fixed at 1, adapting to the intensity differences in carbon sink change across different periods. Subsequently, using the dynamic weight allocation calculation formula, combined with the basic weight of the key data, the period adjustment coefficient, the carbon sink contribution, and the weight smoothing transition factor, the dynamic weight of each type of key data is calculated. The weight smoothing transition factor is assigned based on the temporal continuity characteristics of the meteorological data, ensuring a smooth and abrupt adjustment process for the dynamic weight. The dynamic weight allocation calculation formula is as follows: ,in, For the first Dynamic weights of key data types For the first The basic weights of key data categories are determined, and non-sensitive periods are allocated proportionally. This is a time-period adjustment coefficient; the value is 1.5-3 during periods sensitive to carbon sink changes, and 1 during periods not sensitive to changes. For the first Carbon sequestration contribution of meteorological data As a weighted smoothing transition factor, based on the calculated dynamic weights, all key data are weighted and fused, allowing various key data to be optimized and integrated according to their correlation with carbon sink changes and time period characteristics, forming a time-series correlation dynamic adaptation processing system that is fully adapted to the time-series characteristics of carbon sink changes. It can automatically adjust the data fusion rules according to the time-series differences of carbon sink changes, get rid of the limitations of the fixed weight fusion mode, and after weighted fusion, finally obtain the fused data that meets the requirements of carbon sink accounting. The fused data integrates key meteorological data and conforms to the time-series change pattern of forest carbon sink.
[0034] Data Calculation: The fused data is input into the carbon sequestration unit for real-time forest ecological carbon sequestration calculation. The carbon sequestration unit first directly retrieves the basic attribute parameters of the target subtropical evergreen broad-leaved forest ecological region. These basic attribute parameters comprehensively include three core indicators: stand density, vegetation cover, and tree species composition, reflecting the target forest's intrinsic growth characteristics and structural state, and serving as an important basis for carbon sequestration calculation. Subsequently, the real-time forest ecological carbon sequestration calculation formula is used, combined with the fused data and the retrieved basic attribute parameters, to calculate the forest ecological carbon sequestration value corresponding to the current processing period. This value reflects the target forest's actual carbon sequestration capacity during the current period. The real-time forest ecological carbon sequestration calculation formula is as follows: ,in, This represents the forest ecological carbon sink value for the corresponding period. For the first Dynamic weights of key data types For the first in the merged data The numerical values of key data categories This is a correction coefficient for the comprehensive basic attributes of forest ecology, determined based on the stand density, vegetation cover, and tree species composition of the target forest. This represents the total number of key data categories. Based on forest ecological carbon sink values, real-time assessment results of forest ecological carbon sinks for corresponding time periods are generated through carbon sink accounting units. The assessment results are generated in hourly time series units, synchronized with the hourly time series of data collection, ensuring the real-time nature of the assessment results. The generated assessment results include the forest ecological carbon sink value corresponding to the current single time period, as well as the time series change curve composed of forest ecological carbon sink values from multiple consecutive time periods, fully presenting the dynamic fluctuation trend of carbon sinks and clearly reflecting the accumulation and fluctuation characteristics of forest carbon sinks over time. Through a complete accounting process, real-time assessment of forest ecological carbon sinks in subtropical evergreen broad-leaved forest ecological regions by integrating multiple meteorological parameters is realized. The final output assessment results can accurately, in real time, and comprehensively reflect the dynamic change status of carbon sinks in the target forest ecological region, meeting the practical application needs of real-time monitoring and refined assessment of forest ecological carbon sinks.
[0035] In summary, by standardizing the processing of multi-source meteorological data obtained from multiple channels, filtering effective data by combining the time-series patterns of forest carbon sequestration accumulation, and completing data fusion processing through dynamic adaptation, carbon sequestration numerical accounting is completed and evaluation results are generated by combining the basic attributes of forest ecology. The overall approach closely matches the time-series changes of forest carbon sequestration with the growth status of the forest itself, accurately reproducing the real-time changes of forest ecological carbon sequestration, and providing feasible technical support for the refined monitoring and scientific management of forest ecological carbon sequestration.
[0036] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some modifications or alterations to the above-disclosed technical content to create equivalent embodiments without departing from the scope of the present invention. Any simple modifications, equivalent changes and alterations made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the scope of the present invention.
Claims
1. A real-time assessment method for forest ecological carbon sequestration based on the fusion of multiple meteorological parameters, characterized in that, The specific steps of this method are as follows: Data Acquisition: Obtain multi-source meteorological data of the target forest ecological area through ground meteorological monitoring stations, meteorological data push interfaces, and historical meteorological databases, and transmit the acquired multi-source meteorological data; Data preprocessing: The data processing unit receives the transmitted multi-source meteorological data and preprocesses the multi-source meteorological data to obtain preprocessed multi-source meteorological data; Data screening: Based on the preprocessed multi-source meteorological data, the correlation between multi-source meteorological data and carbon sink changes is analyzed by combining the time series analysis unit with the carbon sink accumulation time series pattern. The contribution of multi-source meteorological data to carbon sink assessment is calculated, key data is screened and redundant data is removed, and the screened key data is transmitted. Data fusion: The adaptive fusion unit receives the key data transmitted, adjusts the weight coefficients of the key data according to the characteristics of carbon sink changes over time, forms a time-series correlated dynamic adaptation processing system, and obtains the fused data; Data accounting: Based on the fused data, the carbon sequestration unit retrieves the basic attribute parameters of the forest ecology, combines the fused data with the basic attribute parameters, calculates the forest ecological carbon sequestration value for the corresponding time period, and generates the real-time assessment result of the forest ecological carbon sequestration for the corresponding time period based on the forest ecological carbon sequestration value.
2. The method for real-time assessment of forest ecological carbon sequestration based on the fusion of multiple meteorological parameters according to claim 1, characterized in that, In the data acquisition process, ground meteorological monitoring stations are deployed at different forest stand locations within the target forest ecological area to collect real-time meteorological elements in the forest. The meteorological data push interface is used to connect to the short-term meteorological data release terminal. The historical meteorological database stores historical meteorological records of the target forest ecological area. The multi-source meteorological data includes real-time monitoring data, short-term forecast data, and historical data for the same period, covering meteorological parameters such as temperature, precipitation, light intensity, humidity, and wind speed. The time span of the short-term forecast data is the next 72 hours. The acquisition sequence of the multi-source meteorological data is uniformly set to the hourly level.
3. The method for real-time assessment of forest ecological carbon sequestration based on the fusion of multiple meteorological parameters according to claim 1, characterized in that, The specific steps for preprocessing multi-source meteorological data in the data preprocessing process are as follows: Abnormal values in the multi-source meteorological data are removed; for data positions with gaps after removing abnormal values, linear interpolation is used to fill in the missing meteorological parameter values; time-series alignment is performed on the completed multi-source meteorological data to map multi-source meteorological data from different sources to the same timestamp, unifying the time base of the multi-source meteorological data; and normalization is then performed to eliminate the dimensional differences of different meteorological parameters, thereby obtaining the preprocessed multi-source meteorological data.
4. The method for real-time assessment of forest ecological carbon sequestration based on the fusion of multiple meteorological parameters according to claim 1, characterized in that, In the data screening process, the time series analysis unit divides the preprocessed multi-source meteorological data into carbon sequestration sensitive periods and non-sensitive periods according to the time series pattern of forest carbon sequestration accumulation. Then, it performs time series analysis on the multi-source meteorological data and carbon sequestration accumulation changes in each period to obtain the time series correlation coefficient, that is, the correlation between multi-source meteorological data and carbon sequestration changes. The carbon sequestration accumulation time series pattern shows that forest carbon sequestration exhibits stage-based accumulation and fluctuation characteristics with the vegetation growth cycle and daily cycle. Then, the carbon sink contribution of various meteorological data is calculated by combining the carbon sink contribution calculation formula with the correlation degree. Then, the contribution standard is set, and the carbon sink contribution is compared with the contribution standard. Key data such as temperature, precipitation, sunshine, humidity and wind speed that meet the contribution standard are selected, and redundant data that do not meet the contribution standard are eliminated.
5. The method for real-time assessment of forest ecological carbon sequestration based on the fusion of multiple meteorological parameters according to claim 4, characterized in that, In the data screening process, the formula for calculating the carbon sink contribution is as follows: ,in, For the first Carbon sequestration contribution of meteorological data, when Time is key data. At that time, the data was considered redundant; 0.8 was the contribution standard, and it could be dynamically adjusted according to forest type and seasonal characteristics. For the first The time-series correlation coefficient between meteorological data and carbon sink changes. For the first The time-series cumulative factor for meteorological data is determined based on the time-series classification of carbon sink accumulation patterns, with values higher for carbon sink-sensitive periods than for non-sensitive periods. For the preprocessed first Normalized values of meteorological data For the first The basic correction factor for meteorological data is determined based on the vegetation type of the target forest and the current seasonal characteristics.
6. The method for real-time assessment of forest ecological carbon sequestration based on the fusion of multiple meteorological parameters according to claim 1, characterized in that, In the data fusion process, the adaptive fusion unit receives the transmitted key data, identifies the current period as a sensitive or non-sensitive period for carbon sink changes based on the time-series characteristics of carbon sink accumulation, determines the basic weight of each key data, allocates the basic weight to non-sensitive periods in a proportional manner, determines the period adjustment coefficient based on the carbon sink change characteristics of the current period, and then calculates the dynamic weight of each key data through the dynamic weight allocation calculation formula. Based on the dynamic weight, the key data is weighted and fused to form a time-series-related dynamic adaptation processing system, and the fused data is obtained.
7. The method for real-time assessment of forest ecological carbon sequestration based on the fusion of multiple meteorological parameters according to claim 6, characterized in that, In the data fusion process, the dynamic weight allocation calculation formula is as follows: ,in, For the first Dynamic weights of key data types For the first The basic weights of key data categories are determined, and non-sensitive periods are allocated proportionally. This is a time-period adjustment coefficient; the value is 1.5-3 during periods sensitive to carbon sequestration changes, and 1 during periods not sensitive to changes. For the first Carbon sequestration contribution of meteorological data The weighted smoothing transition factor is determined based on the continuous temporal characteristics.
8. The method for real-time assessment of forest ecological carbon sequestration based on the fusion of multiple meteorological parameters according to claim 1, characterized in that, In the data accounting process, the carbon sequestration accounting unit retrieves the basic attribute parameters of the target forest ecology, including stand density, vegetation coverage, and tree species composition. Combining the fused data with the basic attribute parameters, the unit calculates the forest ecological carbon sequestration value for the corresponding time period using the real-time forest ecological carbon sequestration accounting formula. Based on the forest ecological carbon sequestration value, the unit generates the real-time assessment result of the forest ecological carbon sequestration for the corresponding time period. The real-time assessment result of the forest ecological carbon sequestration is generated in hourly time series units, including the forest ecological carbon sequestration value for the corresponding time period and the time series change curve composed of the forest ecological carbon sequestration values for consecutive time periods.
9. The method for real-time assessment of forest ecological carbon sequestration based on the fusion of multiple meteorological parameters according to claim 8, characterized in that, In the data accounting, the real-time accounting formula for forest ecological carbon sequestration is as follows: ,in, This represents the forest ecological carbon sink value for the corresponding period. For the first Dynamic weights of key data types For the first in the merged data The numerical values of key data categories, This is a correction coefficient for the comprehensive basic attributes of forest ecology, determined based on the stand density, vegetation cover, and tree species composition of the target forest. This represents the total number of categories for key data.