Big data-based bamboo reed carbon sink dynamic evaluation method and system

Abstract

The invention discloses a big data-based bamboo reed carbon sink dynamic evaluation method and system, and relates to the technical field of bamboo reed carbon sink evaluation data processing. The big data-based dynamic assessment method for the carbon sink amount of the bamboo reed comprises the following steps of pre-assessment and judgment of the carbon sink amount of the bamboo reed; monitoring data acquisition optimization; and dynamically evaluating and optimizing the carbon sink amount of the bamboo reed. According to the method, bamboo reed carbon sink quantity pre-evaluation is performed on the preset evaluation area through the bamboo reed carbon sink quantity pre-evaluation parameters in the preset time period, whether rationality judgment of bamboo reed carbon sink quantity dynamic evaluation is performed or not is judged, if yes, whether monitoring data acquisition optimization is performed or not is judged, and if not, feedback is performed. Finally, according to the bamboo reed carbon sink evaluation parameters, the bamboo reed carbon sink dynamic evaluation is carried out, whether the bamboo reed carbon sink dynamic evaluation optimization is carried out or not is judged, and feedback is carried out, so that the accuracy of the bamboo reed carbon sink dynamic evaluation is improved, and the problem that the accuracy of the bamboo reed carbon sink dynamic evaluation is low due to data acquisition differences in the prior art is solved.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing for evaluating the carbon sink of Phragmites australis, and in particular to a method and system for dynamically evaluating the carbon sink of Phragmites australis based on big data. Background Art

[0002] Phragmites australis is a perennial grass plant that is not only a high-quality biomass energy raw material (can replace coal for power generation), but also has a short planting cycle and high yield compared to traditional forests, and can achieve large amounts of carbon fixation in a relatively short period of time.

[0003] As an important carbon sink, Phragmites australis has broad application prospects in the carbon market. Therefore, a big data-based dynamic assessment method and system for Phragmites australis carbon sequestration was developed. This approach leverages existing technologies and methods, including remote sensing, biomass estimation methods, and carbon cycle models. Remote sensing technology, particularly image analysis, is used to determine Phragmites australis vegetation cover. Remote sensing images are used to monitor vegetation status using various vegetation indices (such as NDVI and EVI). Carbon sequestration calculations typically rely on carbon cycle models, which simulate the carbon uptake and storage processes of Phragmites australis. The synergistic effect of these technologies can achieve real-time and accurate assessment of Phragmites australis carbon sequestration, playing a significant role in promoting the development of the Phragmites australis carbon sequestration industry and the standardized development of the carbon sequestration market.

[0004] Existing technologies, by installing observation towers and weather stations, monitor indicators such as atmospheric carbon dioxide concentration, soil carbon dioxide emission rate, water evaporation, and photosynthetic rate in forest ecosystems from the ground, thereby accurately assessing changes in carbon storage in forest systems.

[0005] For example, the invention patent announcement with announcement number: CN116070080B discloses a forest carbon stock and carbon sink value monitoring system and dynamic evaluation method, including: an Internet of Things sample plot module including a tree diameter measurement sensor and a communication relay device, the communication relay device is used to build a wireless ad hoc network in the monitoring sample plot, collect the tree diameter sensor measurement data and then transmit the data back, the sample plot mobile data survey and collection module includes a data collection application system, the data collection application system is used to obtain server-side information, connect the communication relay device and the tree diameter measurement sensor at the survey site to collect, enter and calculate data, and configure and manage the communication relay device and the data measurement sensor on site, the forest carbon stock and carbon sink value assessment module includes a forest carbon stock and carbon sink value dynamic assessment system and a support platform supporting the operation of the forest carbon stock and carbon sink value dynamic assessment system, the forest carbon stock and carbon sink value dynamic assessment system is used to summarize, count and analyze sample plot dynamic monitoring data, configure forest carbon stock calculation data, manage the operation status of the Internet of Things sample plot, and periodically obtain and evaluate the forest carbon stock and carbon sink value in the monitoring area.

[0006] For example, the invention patent announcement with announcement number: CN118410304B discloses a method and system for dynamic monitoring and accounting of carbon sinks in a mulberry garden ecosystem, including: obtaining lidar data and remote sensing optical images of the target area for preprocessing, and extracting multi-source characteristic variables in the accounting area; obtaining carbon sink baseline data of the accounting area, extracting the spatiotemporal characteristics of the carbon sink estimation sequence, using the spatiotemporal characteristics to screen high-response characteristic variables from the multi-source characteristic variables, and constructing a carbon sink dynamic monitoring model; obtaining corresponding characteristic variable parameters based on the current botanical characteristics and pruning characteristics of the target mulberry garden, and using the model output to obtain the current carbon sink estimation data of the target mulberry garden.

[0007] However, in the process of implementing the technical solutions of the invention in the embodiments of the present application, the present application found that the above technology has at least the following technical problems: In the existing technology, because other plants may coexist with reeds in reed wetlands, it is impossible to accurately distinguish between reeds and other plants when measuring biomass, thus introducing errors. In addition, environmental interference may also affect the normal operation of monitoring equipment, resulting in data interference. Due to the lack of long-term continuous dynamic monitoring data, it is impossible to accurately understand the change patterns of parameters over time, environmental factors and other factors, making it difficult to fully consider these dynamic changes in the model. During the monitoring process, more attention may be paid to some common and easy-to-measure indicators, while some key indicators that have a significant impact on carbon sequestration are ignored. There is a problem of low accuracy in the dynamic assessment of reed carbon sequestration due to differences in data collection. Summary of the Invention

[0008] The embodiments of the present application provide a method and system for dynamic assessment of carbon sinks in Phragmites australis based on big data, thereby solving the problem of low accuracy of dynamic assessment of carbon sinks in Phragmites australis caused by differences in data collection in the prior art, and achieving improved accuracy of dynamic assessment of carbon sinks in Phragmites australis.

[0009] An embodiment of the present application provides a dynamic assessment method for reed bamboo carbon sink based on big data, comprising the following steps: S1, performing a pre-assessment of reed bamboo carbon sink in a preset assessment area according to pre-assessment parameters of reed bamboo carbon sink within a preset time period, and judging whether to perform a rationality judgment of the dynamic assessment of reed bamboo carbon sink; if so, performing a rationality judgment of the dynamic assessment of reed bamboo carbon sink according to carbon sink monitoring parameters, and judging whether to perform monitoring data collection optimization; otherwise, performing pre-assessment optimization of reed bamboo carbon sink; S2, if monitoring data collection optimization is performed, performing a dynamic assessment of reed bamboo carbon sink according to the reed bamboo carbon sink assessment parameters after the monitoring data collection optimization; otherwise, performing a dynamic assessment of reed bamboo carbon sink directly according to the reed bamboo carbon sink assessment parameters; S3, judging whether to perform a dynamic assessment optimization of reed bamboo carbon sink based on the result of the dynamic assessment of reed bamboo carbon sink; if so, performing feedback after the dynamic assessment optimization of reed bamboo carbon sink; otherwise, performing feedback directly.

[0010] The embodiment of the present application provides a dynamic assessment system of reed bamboo carbon sink based on big data, including: a reed bamboo carbon sink pre-assessment determination module, a monitoring data collection optimization module and a reed bamboo carbon sink dynamic assessment optimization module; wherein the reed bamboo carbon sink pre-assessment determination module is used to perform a reed bamboo carbon sink pre-assessment on a preset assessment area according to the reed bamboo carbon sink pre-assessment parameters within a preset time period, and judge whether to perform a rationality judgment of the dynamic assessment of the reed bamboo carbon sink; if so, the rationality judgment of the dynamic assessment of the reed bamboo carbon sink is performed according to the carbon sink monitoring parameters, and judge whether to perform a monitoring Data collection optimization is performed, otherwise, preliminary optimization of the carbon sink of Phragmites australis is performed; the monitoring data collection optimization module is used to perform dynamic evaluation of the carbon sink of Phragmites australis according to the carbon sink evaluation parameters of the Phragmites australis after the monitoring data collection optimization is performed, otherwise, dynamic evaluation of the carbon sink of Phragmites australis is performed directly according to the carbon sink evaluation parameters of the Phragmites australis; the dynamic evaluation optimization module of the Phragmites australis carbon sink is used to determine whether to perform dynamic evaluation optimization of the carbon sink of Phragmites australis based on the result of the dynamic evaluation of the carbon sink of Phragmites australis, and if so, provide feedback after the dynamic evaluation optimization of the carbon sink of Phragmites australis, otherwise, provide feedback directly.

[0011] One or more technical solutions provided in the embodiments of this application have at least the following technical effects or advantages: 1. Preliminary assessment of the carbon sink of Phragmites australis is carried out through the preliminary assessment parameters of the carbon sink of Phragmites australis, and the rationality of whether to conduct dynamic assessment of the carbon sink of Phragmites australis is judged. If so, it is judged whether to conduct monitoring data collection optimization. Otherwise, feedback is given. Finally, dynamic assessment of the carbon sink of Phragmites australis is carried out according to the assessment parameters of the carbon sink of Phragmites australis, and it is judged whether to conduct dynamic assessment optimization of the carbon sink of Phragmites australis, thereby improving the efficiency of the dynamic assessment of the carbon sink of Phragmites australis, and then achieving the improvement of the accuracy of the dynamic assessment of the carbon sink of Phragmites australis, and effectively solving the problem of low accuracy of dynamic assessment of the carbon sink of Phragmites australis caused by data collection differences in the existing technology.

[0012] 2. By processing the vegetation coverage of Phragmites australis, soil microbial biomass carbon, soil respiration rate and Phragmites australis carbon sequestration rate, we obtained the Phragmites australis vegetation coverage contrast coefficient, soil microbial biomass carbon contrast coefficient, soil respiration rate inverse contrast coefficient and Phragmites australis carbon sequestration rate contrast coefficient, and then obtained the carbon sequestration pre-assessment index, thereby quantitatively assessing the carbon sequestration potential of Phragmites australis in the preset assessment area, and thus achieving the improvement of the dynamic assessment efficiency of Phragmites australis carbon sequestration.

[0013] 3. The carbon absorption-collection correlation coefficient was processed to obtain the carbon absorption-collection relative deviation coefficient, and then the representative coefficient of the monitoring data was processed to obtain the representative relative deviation coefficient of the monitoring data. Then, the root mean square error of the carbon sink of Phragmites australis was processed to obtain the root mean square error comparison coefficient of the carbon sink of Phragmites australis. Finally, the carbon absorption-collection relative deviation coefficient, the representative relative deviation coefficient of the monitoring data, and the root mean square error comparison coefficient of the carbon sink of Phragmites australis were processed to obtain the reasonable index of the dynamic assessment of the carbon sink, thereby quantitatively evaluating the rationality of the dynamic assessment results of the carbon sink of Phragmites australis, and thus achieving the accuracy of the dynamic assessment of the carbon sink of Phragmites australis. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Figure 1 A flowchart of a method for dynamic assessment of carbon sequestration in Phragmites australis based on big data provided in an embodiment of the present application; Figure 2 A flow chart of the dynamic assessment of the carbon sequestration of Phragmites australis provided in the embodiments of the present application; Figure 3 A flowchart of the optimization of the preliminary assessment of the carbon sequestration of Phragmites australis provided in the embodiment of the present application; Figure 4 A structural diagram of a big data-based dynamic assessment system for carbon sequestration in Phragmites australis provided in an embodiment of the present application. DETAILED DESCRIPTION

[0015] The embodiment of the present application solves the problem of low accuracy of dynamic assessment of reed carbon sinks due to data collection differences in the prior art by providing a method and system for dynamic assessment of reed carbon sinks based on big data. The method performs a preliminary assessment of reed carbon sinks in a preset assessment area through preliminary assessment parameters of reed carbon sinks within a preset time period, and determines the rationality of whether to perform dynamic assessment of reed carbon sinks. If so, it determines whether to perform monitoring data collection optimization. Otherwise, feedback is performed. Finally, dynamic assessment of reed carbon sinks is performed according to the reed carbon sink assessment parameters, and it is determined whether to perform dynamic assessment optimization of reed carbon sinks. Feedback is performed at the same time, thereby improving the accuracy of dynamic assessment of reed carbon sinks.

[0016] The technical solution in the embodiments of the present application is to solve the problem of low accuracy of dynamic assessment of the carbon sequestration of Phragmites australis caused by differences in data collection. The overall idea is as follows: The carbon sink of Phragmites australis is pre-assessed through the carbon sink pre-assessment parameters to determine whether to conduct a dynamic assessment of the carbon sink of Phragmites australis. If so, it is determined whether to optimize the monitoring data collection. Otherwise, feedback is given. Finally, the carbon sink of Phragmites australis is dynamically assessed based on the carbon sink assessment parameters, and it is determined whether to optimize the dynamic assessment of the carbon sink of Phragmites australis, thereby improving the accuracy of the dynamic assessment of the carbon sink of Phragmites australis.

[0017] In order to better understand the above technical solution, the above technical solution will be described in detail below with reference to the accompanying drawings and specific implementation methods.

[0018] like Figure 1 As shown, it is a flow chart of a method for dynamic assessment of reed bamboo carbon sink based on big data provided in an embodiment of the present application, the method comprising the following steps: S1, preliminary assessment of reed bamboo carbon sink: performing preliminary assessment of reed bamboo carbon sink in a preset assessment area according to preliminary assessment parameters of reed bamboo carbon sink within a preset time period, and judging whether to perform rationality assessment of dynamic assessment of reed bamboo carbon sink; if so, performing rationality assessment of dynamic assessment of reed bamboo carbon sink according to carbon sink monitoring parameters, and judging whether to perform monitoring data collection optimization; otherwise, performing preliminary assessment optimization of reed bamboo carbon sink; the preset time period is set by preset personnel; carbon sink refers to the amount of carbon dioxide absorbed and fixed from the atmosphere by reed bamboo through physiological processes such as photosynthesis, usually measured in terms of mass of carbon dioxide absorbed per unit area or overall within a preset time span (such as one year) (such as tons).

[0019] S2, monitoring data collection optimization: if monitoring data collection optimization is performed, the dynamic assessment of the Arundo donax carbon sink is performed based on the Arundo donax carbon sink assessment parameters after the monitoring data collection optimization; otherwise, the dynamic assessment of the Arundo donax carbon sink is performed directly based on the Arundo donax carbon sink assessment parameters.

[0020] S3, dynamic assessment and optimization of the carbon sink of Phragmites australis: based on the results of the dynamic assessment of the carbon sink of Phragmites australis, it is determined whether to perform the dynamic assessment and optimization of the carbon sink of Phragmites australis. If so, feedback is provided after the dynamic assessment and optimization of the carbon sink of Phragmites australis. Otherwise, feedback is provided directly.

[0021] Before designing a dynamic assessment method for carbon sequestration of Phragmites australis based on big data, a database for storing various set data is established. The database includes but is not limited to preset Phragmites australis vegetation coverage, preset soil microbial biomass carbon and carbon sequestration pre-assessment adjustment parameters. Various numerical values therein are directly set by technical personnel. Among them, the setting basis of the preset Phragmites australis vegetation coverage can be set according to the preset personnel. For example, the preset Phragmites australis vegetation coverage is represented by the average value of the Phragmites australis vegetation coverage in the historical time period in the database. In addition, various numerical values in the database can be set and fine-tuned by technical personnel according to actual debugging.

[0022] In this embodiment, if Figure 2 As shown, it is a flow chart of the dynamic evaluation of the carbon sink of reed bamboo provided in the embodiment of the present application, and the specific logic is: perform a preliminary evaluation of the carbon sink of reed bamboo, and judge whether the carbon sink preliminary evaluation index is greater than the preset reed bamboo carbon sink preliminary evaluation result threshold value. If so, then perform a rationality judgment of the dynamic evaluation of the carbon sink of reed bamboo, otherwise perform a preliminary evaluation optimization of the carbon sink of reed bamboo, and if the preliminary evaluation optimization of the carbon sink of reed bamboo is performed, then judge whether the optimized preliminary evaluation index of the carbon sink is greater than the preset reed bamboo carbon sink preliminary evaluation result threshold value. If so, then perform a rationality judgment of the dynamic evaluation of the carbon sink of reed bamboo, otherwise provide feedback; if the preliminary evaluation optimization of the carbon sink of reed bamboo is performed, then judge whether the optimized preliminary evaluation index of the carbon sink is greater than the preset reed bamboo carbon sink preliminary evaluation result threshold value. If so, then perform a rationality judgment of the dynamic evaluation of the carbon sink of reed bamboo, otherwise provide feedback; The rationality of the dynamic assessment of the carbon sink of Phragmites australis is judged to determine whether the rational index of the dynamic assessment of the carbon sink is greater than the preset rational threshold of the dynamic assessment of the carbon sink. If so, the dynamic assessment of the carbon sink of Phragmites australis is directly carried out. Otherwise, the dynamic assessment of the carbon sink of Phragmites australis is carried out after the monitoring data collection is optimized. The first carbon sink standard deviation is judged to be greater than the first preset carbon sink standard deviation. If so, feedback is given after the dynamic assessment of the carbon sink of Phragmites australis is optimized. Otherwise, the second carbon sink standard deviation is judged to be greater than the second preset carbon sink standard deviation. If so, the preset personnel are prompted to adjust the planting density of Phragmites australis. Otherwise, feedback is given.

[0023] In this application, various environmental factors may interfere with data accuracy. For example, when measuring the biomass of Phragmites australis, interference from other plants (such as weeds growing together with Phragmites australis) may result in inaccurate biomass measurements because the biomass of Phragmites australis and other plants cannot be accurately distinguished. Parameters in the Phragmites australis carbon sink dynamic assessment model may change over time, due to environmental factors, and other factors. For example, Phragmites australis has different physiological characteristics at different growth stages, and its carbon absorption and fixation capacity also changes. However, the Phragmites australis carbon sink dynamic assessment model may not reasonably adjust parameters for different growth stages, thus affecting the effectiveness of the carbon sink assessment. In the process of the Phragmites australis carbon sink dynamic assessment, only some common indicators, such as Phragmites australis biomass and soil organic matter content, may be focused on, while some key indicators with significant impacts on carbon sinks may be overlooked. For example, indicators such as the type and number of soil microorganisms and soil enzyme activity play a key role in soil carbon cycling. Even if some indicators are monitored, their dynamic changes may not be continuously monitored. For example, only measuring the soil organic matter content at a certain point in time without monitoring its changes in different seasons and years will make it impossible to accurately understand the dynamic changes of the soil carbon pool, thus affecting the effectiveness of carbon sequestration assessment.

[0024] Through the pre-assessment of the carbon sink of reed bamboo, an estimated value of the carbon sink of reed bamboo can be quickly obtained, which provides a basic reference for subsequent decision-making and evaluation, and is conducive to determining whether it is necessary to further carry out a more accurate dynamic assessment of the carbon sink of reed bamboo; by judging whether to make a rationality judgment, unnecessary complex assessment work can be avoided and the efficiency of the dynamic assessment of the carbon sink of reed bamboo can be improved. If the carbon sink pre-assessment index is greater than the preset threshold value of the pre-assessment result of the carbon sink of reed bamboo, the rationality judgment of the dynamic assessment of the carbon sink of reed bamboo can be directly carried out. There is no need to further judge whether to make a rationality judgment of the dynamic assessment of the carbon sink of reed bamboo based on the carbon sink pre-assessment index after the optimization of the pre-assessment of the carbon sink of reed bamboo, which saves resources and improves the efficiency of the dynamic assessment of the carbon sink of reed bamboo; thereby ensuring that the subsequent dynamic assessment of the carbon sink of reed bamboo can more accurately reflect the actual situation of the carbon sink of reed bamboo, thereby improving the reliability of the dynamic assessment of the carbon sink of reed bamboo.

[0025] By optimizing the collection of monitoring data, the collection quality of the assessment parameters of the carbon sink of Phragmites australis was improved, thereby making the dynamic assessment results of the carbon sink of Phragmites australis based on the optimized data of the monitoring data collection and the assessment parameters of the carbon sink of Phragmites australis more accurate and reliable, and able to better reflect the actual situation of the carbon sink of Phragmites australis; in the absence of the need for optimization of the collection of monitoring data, the dynamic assessment of the carbon sink of Phragmites australis was directly carried out, which simplified the process of the dynamic assessment of the carbon sink of Phragmites australis and improved the efficiency of the dynamic assessment of the carbon sink of Phragmites australis.

[0026] Optimizing the dynamic assessment of carbon sequestration in Phragmites australis will help improve its accuracy and reliability.

[0027] Furthermore, a preliminary assessment of the Arundo donax carbon sink is performed on the preset assessment area according to the preliminary assessment parameters of the Arundo donax carbon sink within the preset time period. The specific method is as follows: The Phragmites australis vegetation coverage is compared with the preset Phragmites australis vegetation coverage obtained from the preset database to obtain the Phragmites australis vegetation coverage comparison coefficient, that is, Where, Indicates the Phragmites australis vegetation coverage in the nth preset assessment area. The vegetation coverage is obtained through remote sensing images (such as the Normalized Difference Vegetation Index and the Enhanced Vegetation Index). The specific process is as follows: First, the remote sensing images of the preset assessment area acquired within a preset time period are preprocessed, including radiation correction, geometric correction, atmospheric correction, etc., to improve the quality and accuracy of the images. Then, supervised classification or unsupervised classification methods are used to classify the preprocessed images to distinguish the Phragmites australis areas from other plant areas. Supervised classification requires selecting a preset number of training samples and classifying the images using a classification algorithm (such as maximum likelihood method, support vector machine method, etc.); unsupervised classification automatically classifies the objects based on the spectral characteristics of the images. Finally, based on the classification results, the Phragmites australis coverage area in the preset assessment area is calculated as a ratio of the area of the preset assessment area to the area of the preset assessment area to obtain the Phragmites australis vegetation coverage rate. The Phragmites australis coverage area in the preset assessment area is calculated and statistically analyzed using Geographic Information System (GIS) software (such as ArcGIS, QGIS, etc.). It represents the preset Phragmites australis vegetation coverage, which is set by the preset personnel, for example, represented by the average value of Phragmites australis vegetation coverage in the historical time period; the comparison processing in this application represents the ratio operation.

[0028] The soil microbial biomass carbon is compared with the preset soil microbial biomass carbon obtained from the preset database to obtain the soil microbial biomass carbon comparison coefficient, that is, Where, It represents the soil microbial biomass carbon in the nth preset assessment area, which is expressed by measuring the difference in ultraviolet absorbance between fumigated and unfumigated soil extracts under preset ultraviolet light (280 nm ultraviolet light); Indicates the preset soil microbial biomass carbon, which is set by the preset personnel, for example, expressed by the average value of soil microbial biomass carbon in a historical period.

[0029] According to the soil respiration rate and the preset soil respiration rate obtained from the preset database, the inverse contrast processing is performed to obtain the soil respiration rate inverse contrast coefficient, that is, Where, Indicates the soil respiration rate of the nth preset assessment area, which is measured on-site by the soil respiration measurement system and recorded in terms of soil respiration per unit time. This data is set to non-zero; It represents the preset soil respiration rate, which is set by the preset personnel, for example, represented by the average value of the soil respiration rate in a historical time period; the inverse contrast processing in this application means performing a ratio operation followed by a reciprocal operation.

[0030] The carbon sequestration rate of Phragmites australis is compared with the preset carbon sequestration rate of Phragmites australis obtained from the preset database to obtain the carbon sequestration rate comparison coefficient of Phragmites australis, that is, Where, The carbon sequestration rate of Phragmites australis in the nth preset assessment area is calculated by calculating the ratio of the change in carbon storage per unit area to the preset time interval (set by the preset personnel based on the application scenario). The carbon storage is calculated by multiplying the sum of the aboveground biomass and the belowground biomass by the carbon content (usually 0.45-0.50). The aboveground biomass is calculated by substituting the diameter at breast height and plant height into the allometric growth equation; the belowground biomass is calculated by multiplying the aboveground biomass by the root-to-shoot ratio (set by the preset personnel, for example, the root-to-shoot ratio of Phragmites australis is usually 0.2-0.3). The specific formula of the allometric growth equation is: ,in, is the aboveground biomass; is the diameter at breast height (DBH), which is the diameter at the base of the Phragmites australis stem measured by a DBH ruler; is the plant height, which is expressed by the height of Phragmites australis measured by a height meter; It is a species-specific constant reflecting the basic biomass level and is set by the designer; The elastic coefficient of breast diameter is set by the designer, usually 1.5-2.5; The tree height elastic coefficient is set by the designer and is usually 0.5-1.0; It indicates the preset carbon sequestration rate of Phragmites australis, which is set by the preset personnel, for example, it is represented by the average carbon sequestration rate of Phragmites australis in the historical time period.

[0031] Carbon sequestration pre-assessment adjustment parameters were introduced, and the Phragmites australis vegetation coverage contrast coefficient, soil microbial biomass carbon contrast coefficient, soil respiration rate inverse contrast coefficient, and Phragmites australis carbon sequestration rate contrast coefficient were assigned and coupled to obtain the carbon sequestration pre-assessment index. The carbon sequestration pre-assessment index was used to quantitatively assess the carbon sequestration potential of the preset assessment area in the Phragmites australis carbon sequestration monitoring area.

[0032] The specific expression of the carbon sink pre-assessment index is: ; Where n represents the number of the preset assessment area in the Luzhu carbon sink monitoring area, , N represents the total number of pre-set assessment areas in the Luzhu carbon sink monitoring area, represents the preliminary assessment index of the carbon sink of Phragmites australis in the nth preset assessment area, represents the first carbon sink pre-assessment adjustment parameter, represents the second carbon sink pre-assessment adjustment parameter, represents the third carbon sink pre-assessment adjustment parameter, Represents the fourth carbon sink pre-assessment adjustment parameter.

[0033] The carbon sink pre-assessment adjustment parameters involved are obtained from a preset database, the first carbon sink pre-assessment adjustment parameter represents the degree of influence of reed bamboo vegetation coverage on the carbon sink pre-assessment index, the second carbon sink pre-assessment adjustment parameter represents the degree of influence of soil microbial biomass carbon on the carbon sink pre-assessment index, the third carbon sink pre-assessment adjustment parameter represents the degree of influence of soil respiration rate on the carbon sink pre-assessment index, and the fourth carbon sink pre-assessment adjustment parameter represents the degree of influence of reed bamboo carbon sink rate on the carbon sink pre-assessment index; the sum of the four is 1, for example, the reed bamboo vegetation coverage and the preset first carbon sink pre-assessment adjustment parameter form a reed bamboo vegetation coverage mapping set, and the real-time reed bamboo vegetation coverage is input into the reed bamboo vegetation coverage mapping set to obtain the corresponding first carbon sink pre-assessment Adjustment parameters; soil microbial biomass carbon and the preset second carbon sink pre-assessment adjustment parameter form a soil microbial biomass carbon mapping set, and the real-time soil microbial biomass carbon is input into the soil microbial biomass carbon mapping set to obtain the corresponding second carbon sink pre-assessment adjustment parameter; soil respiration rate and the preset third carbon sink pre-assessment adjustment parameter form a soil respiration rate mapping set, and the real-time soil respiration rate is input into the soil respiration rate mapping set to obtain the corresponding third carbon sink pre-assessment adjustment parameter; reed carbon sink rate and the preset fourth carbon sink pre-assessment adjustment parameter form a reed carbon sink rate mapping set, and the real-time reed carbon sink rate is input into the reed carbon sink rate mapping set to obtain the corresponding fourth carbon sink pre-assessment adjustment parameter; the mapping relationship therein can be one-to-one or many-to-one.

[0034] In this embodiment, a higher coverage of Phragmites australis vegetation generally increases the carbon sequestration rate of Phragmites australis, because a larger leaf area can carry out more photosynthesis; an increase in soil microbial biomass carbon is often accompanied by an increase in soil respiration rate, and microbial activity promotes the decomposition of organic matter and releases carbon dioxide, which may weaken the long-term storage of carbon; a higher soil respiration rate may lead to a decrease in the soil's carbon storage capacity, which in turn may lead to a decrease in the carbon sequestration rate of Phragmites australis, thereby affecting the amount of Phragmites australis carbon sink; an increase in soil microbial biomass carbon will promote the decomposition of organic matter, thereby affecting the storage and release of soil carbon, and indirectly affecting the carbon sequestration rate of Phragmites australis.

[0035] The calculated Phragmites australis vegetation coverage contrast coefficient can reflect the degree of deviation between the Phragmites australis vegetation coverage in the preset assessment area and the preset Phragmites australis vegetation coverage; the calculated soil microbial biomass carbon contrast coefficient can reflect the degree of deviation between the soil microbial biomass carbon in the preset assessment area and the preset soil microbial biomass carbon; the calculated soil respiration rate inverse contrast coefficient can reflect the degree of deviation between the soil respiration rate in the preset assessment area and the preset soil respiration rate; the calculated Phragmites australis carbon sink rate contrast coefficient can reflect the degree of deviation between the Phragmites australis carbon sink rate in the preset assessment area and the preset Phragmites australis carbon sink rate; through the above steps, the carbon sink potential of the preset assessment area in the Phragmites australis carbon sink monitoring area is quantitatively assessed, which provides a basis for the subsequent optimization of the Phragmites australis carbon sink pre-assessment, thereby realizing the effective assessment of the Phragmites australis carbon sink in the Phragmites australis carbon sink monitoring area, avoiding unnecessary dynamic assessment of the Phragmites australis carbon sink, and improving the overall efficiency of the dynamic assessment of the Phragmites australis carbon sink.

[0036] Furthermore, the rationality of determining whether to conduct a dynamic assessment of the carbon sequestration of Phragmites australis is determined by the following specific procedures: Determine whether the carbon sink pre-assessment index is greater than the preset Luzhu carbon sink pre-assessment result threshold obtained from the preset database; if the carbon sink pre-assessment index is greater than the preset Luzhu carbon sink pre-assessment result threshold obtained from the preset database, then make a rationality judgment on the dynamic assessment of Luzhu carbon sink; otherwise, perform Luzhu carbon sink pre-assessment optimization; the preset Luzhu carbon sink pre-assessment result threshold is set by a preset person, for example, represented by the average value of the carbon sink pre-assessment index in a historical time period.

[0037] The preliminary assessment and optimization of the carbon sequestration of Phragmites australis includes the optimization of Phragmites australis vegetation coverage and the optimal control of environmental interference.

[0038] Determine whether the carbon sequestration pre-assessment index after the optimization of the Phragmites australis vegetation coverage is greater than the preset Phragmites australis carbon sequestration pre-assessment result threshold obtained from the preset database. If the carbon sequestration pre-assessment index after the optimization of the Phragmites australis vegetation coverage is greater than the preset Phragmites australis carbon sequestration pre-assessment result threshold obtained from the preset database, then the rationality of the dynamic assessment of the Phragmites australis carbon sequestration is determined; otherwise, environmental interference optimization control is performed.

[0039] Determine whether the carbon sequestration pre-assessment index after optimized environmental interference control is greater than the preset threshold value of the reed bamboo carbon sequestration pre-assessment result obtained from the preset database. If the carbon sequestration pre-assessment index after optimized reed bamboo vegetation coverage is greater than the preset threshold value of the reed bamboo carbon sequestration pre-assessment result obtained from the preset database, then make a rationality judgment on the dynamic assessment of the reed bamboo carbon sequestration; otherwise, provide feedback.

[0040] Specifically, the optimization of reed bamboo vegetation coverage means that the carbon sequestration of reed bamboo is pre-assessed based on the corrected reed bamboo vegetation coverage; the corrected reed bamboo vegetation coverage is obtained by correcting the reed bamboo vegetation coverage in the preset assessment area through the reed bamboo area classification error; the reed bamboo area classification error is obtained by performing relative deviation processing on the reed bamboo classification area and the standard reed bamboo area obtained in the standard assessment area (i.e., the area where the accurate reed bamboo area is known); the reed bamboo classification area is obtained by classifying the remote sensing images of the standard assessment area obtained within the preset time period, and then calculating and counting the area through GIS software; the standard reed bamboo area is set by the preset personnel.

[0041] Specifically, the specific process of environmental interference optimization control is as follows: The environmental parameters and the preliminary assessment results of the carbon sequestration of Phragmites australis within a preset time period are input into the environmental compensation mapping set to obtain the environmental compensation factor. The environmental parameters include the average ambient temperature, the average ambient humidity, and the average light. The environmental compensation mapping set is a set obtained from a preset database that represents the mapping relationship between the environmental parameters, the preliminary assessment results of the carbon sequestration of Phragmites australis and the environmental compensation factor within a preset time period; the average ambient temperature is obtained by averaging the temperatures at preset time points within the preset time period obtained by the temperature sensor; the average ambient humidity is obtained by averaging the humidity at preset time points within the preset time period obtained by the humidity sensor; and the average light is obtained by averaging the light at preset time points within the preset time period obtained by the light sensor.

[0042] The carbon sink pre-assessment index is corrected according to the environmental compensation factor to obtain a corrected carbon sink pre-assessment index; it is determined whether the corrected carbon sink pre-assessment index is greater than a preset threshold value of the reed bamboo carbon sink pre-assessment result obtained from a preset database; if so, a rationality judgment of the dynamic assessment of the reed bamboo carbon sink is performed; otherwise, feedback is given.

[0043] In this embodiment, if Figure 3 As shown, it is a flowchart of the optimization of the pre-assessment of the carbon sink of reed provided in the embodiment of the present application, and the specific logic is: determine whether the carbon sink pre-assessment index is greater than the preset reed carbon sink pre-assessment result threshold value, if so, perform a rationality judgment of the dynamic assessment of the carbon sink of reed, otherwise perform reed vegetation coverage optimization, if the carbon sink pre-assessment index after the optimization of the reed vegetation coverage is greater than the preset reed carbon sink pre-assessment result threshold value, then perform a rationality judgment of the dynamic assessment of the carbon sink of reed, otherwise perform environmental interference optimization control; the reed vegetation coverage optimization means performing a pre-assessment of the carbon sink of reed according to the corrected reed vegetation coverage rate; the specific process of the environmental interference optimization control is as follows: correct the carbon sink pre-assessment index according to the environmental compensation factor to obtain a corrected carbon sink pre-assessment index; determine whether the corrected carbon sink pre-assessment index is greater than the preset reed carbon sink pre-assessment result threshold value, if it is, perform a rationality judgment of the dynamic assessment of the carbon sink of reed, otherwise provide feedback.

[0044] By judging the carbon sink pre-assessment index, it is helpful to quickly screen out the situations where the carbon sink pre-assessment index is better, directly enter the rationality judgment of the dynamic assessment of the carbon sink of Luzhu, and improve the efficiency of the dynamic assessment of the carbon sink of Luzhu; for the situation where the carbon sink pre-assessment index is not ideal, the pre-assessment optimization of the carbon sink of Luzhu can be carried out in time to avoid unnecessary dynamic assessment of the carbon sink of Luzhu and save resources.

[0045] By judging the calculated corrected Phragmites australis vegetation coverage, the inaccurate pre-assessment of Phragmites australis carbon sinks caused by classification errors was reduced, the accuracy of pre-assessment of Phragmites australis carbon sinks was improved, and a more reliable basis was provided for the subsequent rationality of judging whether to conduct a dynamic assessment of Phragmites australis carbon sinks.

[0046] The environmental compensation factor is obtained through the environmental compensation mapping set, and the degree of influence of environmental factors on the carbon sequestration capacity of Phragmites australis is quantified, providing a scientific basis for the subsequent revision of the carbon sequestration pre-assessment index; the carbon sequestration pre-assessment index is revised through the environmental compensation factor, eliminating the interference of environmental factors, so that the revised carbon sequestration pre-assessment index can better reflect the true carbon sequestration capacity of Phragmites australis, and further improve the accuracy of the carbon sequestration pre-assessment of Phragmites australis; by judging the revised carbon sequestration pre-assessment index, the better the carbon sequestration pre-assessment index is, the rationality of the dynamic assessment of the carbon sequestration of Phragmites australis is judged again; feedback is given to the still unsatisfactory situations, prompting the assessors to conduct in-depth inspections and improvements to ensure the reliability of the final dynamic assessment of the carbon sequestration of Phragmites australis, thereby improving the efficiency and quality of the dynamic assessment of the carbon sequestration of Phragmites australis.

[0047] Furthermore, the rationality of the dynamic assessment of the carbon sequestration of Phragmites australis was determined based on the carbon sequestration monitoring parameters. The specific method is as follows: The relative deviation coefficient of carbon absorption-collection is obtained by performing relative deviation processing based on the carbon absorption-collection correlation coefficient and the preset carbon absorption-collection correlation coefficient obtained from the preset database, that is, Where, Represents the carbon absorption-collection correlation coefficient of the nth preset assessment area. The carbon absorption-collection correlation coefficient is calculated by multiplying the ratio of the current carbon absorption rate to the monitoring data collection frequency in the corresponding time period by the carbon absorption-collection balance factor. The carbon absorption-collection balance factor is calculated by multiplying the average value of the monitoring data collection frequency in the historical time period by the average value of the carbon absorption rate in the historical time period. Indicates the preset carbon absorption-collection correlation coefficient, which is set by the preset personnel.

[0048] The relative deviation coefficient of the monitoring data representative coefficient and the preset monitoring data representative coefficient obtained from the preset database are processed to obtain the relative deviation coefficient of the monitoring data representative, that is, Where, The representative coefficient of monitoring data for the nth preset assessment area represents the average value between the proportion of monitoring data volume in each growth stage and the proportion of duration of the growth stage; Indicates the preset monitoring data representative coefficient, which is set by the preset personnel.

[0049] The root mean square error of the Arundo donax carbon sink is compared with the root mean square error of the preset Arundo donax carbon sink obtained from the preset database to obtain the root mean square error comparison coefficient of the Arundo donax carbon sink, that is, Where, The root mean square error of the Arundo donax carbon sink in the nth preset assessment area is obtained by performing a square operation on the average of the squared differences between the predicted values within the preset time period obtained by the dynamic assessment model for Arundo donax carbon sink and the actual carbon sink values at the preset time points within the preset time period; It represents the root mean square error of the preset carbon sink of Phragmites australis, which is set by the preset personnel. For example, it is represented by the average value of the root mean square error of the carbon sink of Phragmites australis in the historical time period.

[0050] The rationality judgment compensation value was introduced, and the carbon absorption-collection relative deviation coefficient, the monitoring data representative relative deviation coefficient, and the root mean square error comparison coefficient of the Phragmites australis carbon sink were assigned and coupled. Then, an inverse proportional operation was performed to obtain the rationality index of the dynamic assessment of the carbon sink. The rationality index of the dynamic assessment of the carbon sink was used to quantitatively evaluate the rationality of the dynamic assessment results of the Phragmites australis carbon sink.

[0051] The specific expression of the reasonable index of dynamic assessment of carbon sink is: ; Where n represents the number of the preset assessment area in the Luzhu carbon sink monitoring area, , N represents the total number of pre-set assessment areas in the Luzhu carbon sink monitoring area, Indicates the reasonable index of dynamic assessment of carbon sink in the nth preset assessment area, represents the first rationality judgment compensation value, represents the second rationality judgment compensation value, Indicates the third rationality judgment compensation value.

[0052] The rationality judgment compensation value involved is obtained from the preset database, the first rationality judgment compensation value represents the influence of the carbon absorption-collection correlation coefficient on the rational index of dynamic assessment of carbon sink, the second rationality judgment compensation value represents the influence of the monitoring data representative coefficient on the rational index of dynamic assessment of carbon sink, and the third rationality judgment compensation value represents the influence of the root mean square error of the reed carbon sink on the rational index of dynamic assessment of carbon sink; the sum of the three is 1, for example, the carbon absorption-collection correlation coefficient and the preset first rationality judgment compensation value form a carbon absorption-collection correlation coefficient mapping set, and the real-time carbon absorption-collection correlation coefficient is input into the carbon absorption The collection-collection correlation coefficient mapping set obtains the corresponding first rationality judgment compensation value; the monitoring data representative coefficient and the preset second rationality judgment compensation value form a monitoring data representative coefficient mapping set, and the real-time monitoring data representative coefficient is input into the monitoring data representative coefficient mapping set to obtain the corresponding second rationality judgment compensation value; the root mean square error of the reed carbon sink and the preset third rationality judgment compensation value form a reed carbon sink root mean square error mapping set, and the real-time root mean square error of the reed carbon sink is input into the root mean square error mapping set to obtain the corresponding third rationality judgment compensation value; the mapping relationship therein can be one-to-one or many-to-one.

[0053] In this embodiment, a higher carbon absorption-collection correlation coefficient may lead to a larger representative coefficient of the monitoring data, because the monitoring data collection frequency is more closely matched with the carbon absorption rate, and more growth stages can be covered; a higher carbon absorption-collection correlation coefficient may lead to a lower root mean square error of the corresponding Phragmites australis carbon sink, because the monitoring data collection frequency is more closely matched with the carbon absorption rate, which can reduce the prediction bias; a higher monitoring data representative coefficient may lead to a lower root mean square error of the Phragmites australis carbon sink, because the data representativeness is better, which can reduce the prediction bias.

[0054] The calculated carbon absorption-collection relative deviation coefficient can reflect the degree of deviation between the carbon absorption-collection correlation coefficient and the preset carbon absorption-collection correlation coefficient, and quantify the degree of matching between the monitoring data collection frequency and the carbon absorption rate; the calculated monitoring data representative relative deviation coefficient can reflect the degree of deviation between the monitoring data representative coefficient and the preset monitoring data representative coefficient, and quantify the rationality of the spatiotemporal distribution of the reed carbon sink assessment parameters; the calculated reed carbon sink root mean square error comparison coefficient can reflect the degree of deviation between the reed carbon sink root mean square error and the preset reed carbon sink root mean square error, and quantify the prediction accuracy of the reed carbon sink dynamic assessment model; through the above parameters, the rationality of the dynamic assessment results of the reed carbon sink is quantitatively evaluated, which provides a basis for the subsequent optimization of monitoring data collection, thereby achieving the accuracy of the dynamic assessment of the reed carbon sink.

[0055] Furthermore, it is determined whether to optimize the monitoring data collection. The specific process is as follows: Determine whether the carbon sink dynamic assessment rationality index is greater than the preset carbon sink dynamic assessment rationality threshold obtained from the preset database. If the carbon sink dynamic assessment rationality index is greater than the preset carbon sink dynamic assessment rationality threshold obtained from the preset database, it means that the carbon sink dynamic assessment is qualified, and the subsequent Luzhu carbon sink dynamic assessment is continued. Otherwise, the monitoring data collection is optimized. The preset carbon sink dynamic assessment rationality threshold is set by the preset personnel, for example, it is represented by the average value of the carbon sink dynamic assessment rationality index in the historical time period.

[0056] Monitoring data collection optimization includes first data collection frequency optimization, second data collection frequency optimization and monitoring data filling.

[0057] Determine whether the carbon sequestration dynamic assessment rationality index after the first data collection frequency optimization is greater than the preset carbon sequestration dynamic assessment rationality threshold obtained from the preset database; if so, continue with the subsequent dynamic assessment of the reed bamboo carbon sequestration; otherwise, perform the second data collection frequency optimization.

[0058] Determine whether the carbon sequestration dynamic assessment rationality index after the second data collection frequency optimization is greater than the preset carbon sequestration dynamic assessment rationality threshold obtained from the preset database; if so, continue with the subsequent dynamic assessment of the reed bamboo carbon sequestration; otherwise, perform monitoring data filling.

[0059] Determine whether the reasonable index of dynamic assessment of carbon sinks after the monitoring data is filled is greater than the preset reasonable threshold of dynamic assessment of carbon sinks obtained from the preset database. If the reasonable index of dynamic assessment of carbon sinks after the monitoring data is filled is greater than the preset reasonable threshold of dynamic assessment of carbon sinks obtained from the preset database, then continue with the subsequent dynamic assessment of carbon sinks in Phragmites australis; otherwise, provide feedback. Filling of monitoring data indicates that data interpolation method (such as linear interpolation) is used for filling.

[0060] Specifically, the specific process of optimizing the first data collection frequency is as follows: Data collection was performed according to the first revised monitoring data collection frequency corresponding to each growth stage, and the rationality of the dynamic assessment of the carbon sequestration of Phragmites australis was re-determined. The first revised monitoring data collection frequency corresponding to each growth stage was obtained by correcting the monitoring data collection frequency of each growth stage through the monitoring data representative coefficient.

[0061] Specifically, the specific process of optimizing the second data collection frequency is as follows: Data collection is performed according to the second revised monitoring data collection frequency. The second revised monitoring data collection frequency is obtained by correcting the current monitoring data collection frequency through the carbon sink dynamic assessment deviation coefficient. The carbon sink dynamic assessment deviation coefficient is obtained by performing relative deviation processing on the carbon sink dynamic assessment reasonable index and the preset carbon sink dynamic assessment reasonable threshold.

[0062] In this embodiment, by comparing the reasonable index of dynamic carbon sink assessment, cases with unsatisfactory reasonable index of dynamic carbon sink assessment can be quickly screened out, and targeted monitoring data collection optimization can be carried out to avoid complex optimization operations on all results of dynamic carbon sink assessment of reed bamboo, thereby improving the efficiency of dynamic carbon sink assessment of reed bamboo and ensuring the reliability of dynamic carbon sink assessment of reed bamboo.

[0063] First, the optimization of data collection frequency takes into account the characteristics of different growth stages of Phragmites australis. The collection frequency is corrected through the representative coefficient of monitoring data, so that data collection is more in line with the actual growth situation of Phragmites australis, which improves the representativeness and accuracy of monitoring data in each growth stage, thereby improving the rationality of the dynamic assessment of Phragmites australis carbon sequestration.

[0064] The second data collection frequency optimization corrects the current collection frequency based on the dynamic assessment deviation coefficient of carbon sinks. It can dynamically adjust the collection frequency according to the deviation between the assessment results and the preset threshold, so that the collection frequency is more adapted to the assessment needs, further reducing the assessment deviation and improving the accuracy of the assessment results.

[0065] The data interpolation method was used to fill in the monitoring data, which improved the data set of the carbon sink assessment parameters of Phragmites australis, avoided the inaccurate results of the dynamic assessment of the carbon sink of Phragmites australis due to differences in data collection, and improved the reliability and accuracy of the dynamic assessment of the carbon sink of Phragmites australis.

[0066] Furthermore, based on the results of the dynamic assessment of the carbon sequestration of Phragmites australis, it is determined whether to optimize the dynamic assessment of the carbon sequestration of Phragmites australis. The specific process is as follows: A1, determine whether the first carbon sink standard deviation within a preset number of preset time periods in a preset assessment area is greater than the first preset carbon sink standard deviation obtained from a preset database; if the first carbon sink standard deviation within a preset number of preset time periods in the preset assessment area is greater than the first preset carbon sink standard deviation obtained from the preset database, perform dynamic assessment optimization of the Luzhu carbon sink; otherwise, execute A2; the first carbon sink standard deviation is obtained by performing standard deviation calculation on the carbon sink within a preset time period of a preset number (set by preset personnel) in the preset assessment area; the first preset carbon sink standard deviation is represented by the average value of the first carbon sink standard deviation in the historical time period.

[0067] A2, determine whether the second carbon sink standard deviation of each preset assessment area in the reed carbon sink monitoring area is greater than the second preset carbon sink standard deviation obtained from the preset database; if the second carbon sink standard deviation of each preset assessment area in the reed carbon sink monitoring area is greater than the second preset carbon sink standard deviation obtained from the preset database, prompt the preset personnel to adjust the reed planting density; the second carbon sink standard deviation is obtained by performing standard deviation calculation on the carbon sink of each preset assessment area in the reed carbon sink monitoring area; the second preset carbon sink standard deviation is represented by the average value of the second carbon sink standard deviation in the historical time period.

[0068] The optimization of the dynamic assessment of Luzhu carbon sinks includes increasing the frequency of data collection and regional inspections; increasing the frequency of data collection means increasing the data collection frequency of corresponding monitoring points step by step, and increasing the data collection frequency according to a preset ratio, which is set by the preset personnel; regional inspections mean prompting the preset personnel to use mobile monitoring equipment (such as unmanned vehicles) to conduct regional inspections of the preset assessment area.

[0069] In this embodiment, by judging the first carbon sink standard deviation, it is possible to timely discover abnormal fluctuations in carbon sinks in a preset assessment area within a preset time period; when the first carbon sink standard deviation is larger, the dynamic assessment and optimization of the reed carbon sink is carried out in a timely manner, and increasing the data collection frequency can obtain more detailed and timely carbon sink data, which helps to more accurately understand the changing trend of the reed carbon sink; regional inspections can discover other factors that may affect the carbon sink through on-site inspections, such as pests and diseases, environmental changes, etc., thereby providing more comprehensive information for the dynamic assessment of the reed carbon sink and improving the accuracy and reliability of the dynamic assessment of the reed carbon sink.

[0070] By judging the second carbon sink standard deviation, areas with larger second carbon sink standard deviation can be identified, prompting the preset personnel to adjust the planting density of reed bamboo. This is a targeted solution that can optimize the growth environment of reed bamboo and improve its carbon sink capacity, thereby stabilizing the carbon sink and making the carbon sink assessment results in the reed bamboo carbon sink monitoring area more stable and accurate.

[0071] Furthermore, the specific method for dynamic assessment of the carbon sequestration of Phragmites australis based on the optimized carbon sequestration assessment parameters collected from monitoring data is as follows: The obtained carbon sink assessment parameters of Phragmites australis were input into the dynamic carbon sink assessment model of Phragmites australis, and the model was initialized to obtain model output data. The initialization settings included but were not limited to defining the model structure, assigning parameter values, and setting the time step, which were set by preset personnel. The dynamic carbon sink assessment model of Phragmites australis was the Lund-Potsdam-Jena General Ecosystem Simulator (LPJ-GUESS), which was used to quantitatively assess the carbon sink capacity of Phragmites australis. The model output data included carbon fixation by photosynthesis, carbon consumption by maintenance respiration, and carbon consumption by growth respiration. For Phragmites australis carbon sink assessments with larger areas of Phragmites australis carbon sink monitoring, the model required to have better spatial scalability and the ability to simulate regional environmental heterogeneity. The LPJ-GUESS model can integrate meteorological and soil data from the Phragmites australis carbon sink monitoring area to simulate the growth and carbon cycle of Phragmites australis within the Phragmites australis carbon sink monitoring area, making it suitable for regional-scale Phragmites australis carbon sink assessment.

[0072] It should be added that the parameters for the assessment of the carbon sequestration of Phragmites australis include meteorological data, soil data, Phragmites australis growth data and model parameters. The meteorological data refer to the long-term meteorological data of the preset assessment area obtained from the meteorological station, including but not limited to the daily average temperature, precipitation, and relative humidity; the soil data include but are not limited to the soil type, texture, pH, organic matter content, and nutrient content of the assessment area, which are obtained through field sampling and laboratory analysis; the Phragmites australis growth data refer to the data obtained from field measurements of Phragmites australis at different growth stages, including but not limited to plant height, stem diameter, leaf area, and biomass; the model parameters refer to the various parameters required for the dynamic assessment model of the Phragmites australis carbon sequestration, including but not limited to photosynthetic parameters, respiratory parameters, and carbon distribution coefficient, which are obtained through experimental measurements, literature review, or model default values.

[0073] The model output data was biased and processed to obtain the carbon sink of Arundo donax. The carbon sink of Arundo donax is numerically equal to the net primary productivity because the net primary productivity has deducted the carbon consumed by Arundo donax's own respiration, representing the actual amount of carbon accumulated and fixed in the body of Arundo donax. The net primary productivity is obtained by taking the difference between the carbon fixed by photosynthesis, the carbon consumed by maintenance respiration, and the carbon consumed by growth respiration. The net primary productivity refers to the total amount of organic carbon fixed by Arundo donax through photosynthesis per unit time and per unit area, minus the carbon consumed by its own respiration, representing the actual amount of carbon accumulated by Arundo donax.

[0074] In this embodiment, accurate meteorological data can provide the dynamic assessment model of the arundinaceae carbon sink with meteorological background information of the arundinaceae growth environment, thereby affecting the photosynthesis efficiency, thereby improving the accuracy of the carbon sink assessment; soil data affects the growth rate and health status of arundinaceae, thereby affecting the carbon sink capacity of arundinaceae; arundinaceae growth data directly reflects the actual growth conditions of arundinaceae, and more accurately reflects the carbon fixation and consumption process of arundinaceae at different growth stages, making the arundinaceae carbon sink more in line with the actual situation; model parameters can more accurately describe the physiological characteristics and carbon cycle process of arundinaceae, thereby affecting the overall growth and carbon sink capacity of arundinaceae.

[0075] The LPJ-GUESS model is a comprehensive ecosystem simulation model that can comprehensively consider the impact of multiple factors such as meteorology, soil, and plant physiology on the carbon cycle of the Phragmites australis ecosystem, providing an important quantitative basis for accurately assessing the carbon sequestration of Phragmites australis.

[0076] like Figure 4 As shown, it is a structural diagram of a dynamic assessment system of reed bamboo carbon sink based on big data provided in an embodiment of the present application. The dynamic assessment system of reed bamboo carbon sink based on big data provided in an embodiment of the present application includes: a reed bamboo carbon sink pre-assessment and judgment module, a monitoring data acquisition optimization module and a reed bamboo carbon sink dynamic assessment optimization module.

[0077] Among them, the reed bamboo carbon sink pre-assessment judgment module is used to conduct a pre-assessment of the reed bamboo carbon sink in the preset assessment area according to the reed bamboo carbon sink pre-assessment parameters within the preset time period, and judge whether to conduct a rationality judgment of the dynamic assessment of the reed bamboo carbon sink. If so, the rationality of the dynamic assessment of the reed bamboo carbon sink is judged according to the carbon sink monitoring parameters, and whether to conduct monitoring data collection optimization. Otherwise, the reed bamboo carbon sink pre-assessment optimization is performed.

[0078] The monitoring data collection optimization module is used to perform a dynamic assessment of the carbon sink of Phragmites australis according to the carbon sink assessment parameters of the Phragmites australis after the monitoring data collection optimization is performed, otherwise the dynamic assessment of the carbon sink of Phragmites australis is performed directly according to the carbon sink assessment parameters of the Phragmites australis.

[0079] The dynamic assessment and optimization module of the reed bamboo carbon sink is used to determine whether to perform dynamic assessment and optimization of the reed bamboo carbon sink based on the results of the dynamic assessment of the reed bamboo carbon sink. If so, feedback is given after the dynamic assessment and optimization of the reed bamboo carbon sink; otherwise, feedback is given directly.

[0080] In this embodiment, multi-source data such as satellite remote sensing, Internet of Things sensors, and historical meteorological databases are processed and stored through big data technology; data can also be collected from different devices (such as sensors, remote sensing satellites, drones, etc.), and can be quickly summarized and processed to provide accurate real-time carbon sink data; big data technology can help track and analyze changes in carbon sinks in real time, and automatically adjust the dynamic assessment model of reed bamboo carbon sinks according to the changing trend of carbon sinks, thereby providing a more accurate dynamic assessment of reed bamboo carbon sinks; through big data technology, adaptive optimization is performed to ensure the accuracy and real-time nature of the dynamic assessment of reed bamboo carbon sinks; the big data involved in this application uses distributed storage (such as Hadoop) and computing architecture to ensure the efficiency and scalability of data processing.

[0081] Through the Luzhu carbon sink pre-assessment judgment module, the rationality of the Luzhu carbon sink pre-assessment results and the Luzhu carbon sink dynamic assessment can be judged and executed, which can effectively screen out areas that need further assessment and optimization, and improve the accuracy of subsequent carbon sink assessments.

[0082] By optimizing the monitoring data collection module, the accuracy and reliability of carbon sequestration assessment can be improved, especially when there are discrepancies in data collection, ensuring the accuracy of subsequent dynamic assessment of carbon sequestration in Phragmites australis.

[0083] Through the dynamic assessment optimization module of reed bamboo carbon sink, the continuous optimization of the dynamic assessment of reed bamboo carbon sink can continuously improve the accuracy of the dynamic assessment of reed bamboo carbon sink, enhance the adaptability and scientific nature of the dynamic assessment system of reed bamboo carbon sink, especially when dealing with the differences in carbon sequestration capacity of reed bamboo at different growth stages, the dynamic assessment can improve the comprehensive assessment effect of the dynamic assessment system of reed bamboo carbon sink.

[0084] To sum up, the embodiment of the present application performs a pre-assessment of the carbon sink of reed bamboo through the pre-assessment parameters of the carbon sink of reed bamboo, determines the rationality of whether to perform a dynamic assessment of the carbon sink of reed bamboo, and if so, determines whether to perform monitoring data collection optimization, otherwise provides feedback, and finally performs a dynamic assessment of the carbon sink of reed bamboo according to the carbon sink assessment parameters, and determines whether to perform a dynamic assessment optimization of the carbon sink of reed bamboo, thereby improving the efficiency of the dynamic assessment of the carbon sink of reed bamboo, and further achieving an improvement in the accuracy of the dynamic assessment of the carbon sink of reed bamboo, effectively solving the problem of low accuracy of the dynamic assessment of the carbon sink of reed bamboo caused by data collection differences in the prior art.

[0085] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Thus, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0086] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, as well as combinations of processes and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowcharts and / or block diagrams. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0087] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0088] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0089] Although the preferred embodiments of the present invention have been described, those skilled in the art may make additional changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0090] Obviously, those skilled in the art may make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if such changes and modifications fall within the scope of the claims and their equivalents, the present invention is intended to include such changes and modifications.

Claims

1. A dynamic assessment method for the carbon sink of Phragmites australis based on big data, characterized in that: The following steps are involved: S1, performing a pre-assessment of the carbon sink of Phragmites australis in a preset assessment area according to the pre-assessment parameters of the carbon sink of Phragmites australis within a preset time period, and determining whether to perform a rationality determination of the dynamic assessment of the carbon sink of Phragmites australis; if so, performing a rationality determination of the dynamic assessment of the carbon sink of Phragmites australis according to the carbon sink monitoring parameters, and determining whether to perform monitoring data collection optimization; otherwise, performing a pre-assessment optimization of the carbon sink of Phragmites australis; S2, if the monitoring data collection is optimized, then the dynamic assessment of the Arundo donax carbon sink is performed according to the Arundo donax carbon sink assessment parameters after the monitoring data collection optimization; otherwise, the dynamic assessment of the Arundo donax carbon sink is performed directly according to the Arundo donax carbon sink assessment parameters; S3, judging whether to perform dynamic assessment optimization of the Arundo donax carbon sink based on the result of the dynamic assessment of the Arundo donax carbon sink; if so, providing feedback after the dynamic assessment optimization of the Arundo donax carbon sink; otherwise, providing feedback directly.

2. The method for dynamic assessment of carbon sequestration in Phragmites australis based on big data as claimed in claim 1, characterized in that: The specific method of performing a preliminary assessment of the amount of carbon sink of Phragmites australis in a preset assessment area according to the preliminary assessment parameters of the amount of carbon sink of Phragmites australis within a preset time period is as follows: Comparing the Phragmites australis vegetation coverage with the preset Phragmites australis vegetation coverage obtained from the preset database to obtain a Phragmites australis vegetation coverage comparison coefficient; Comparing the soil microbial biomass carbon with the preset soil microbial biomass carbon obtained from the preset database, a soil microbial biomass carbon comparison coefficient is obtained; Performing inverse contrast processing on the soil respiration rate and a preset soil respiration rate obtained from a preset database to obtain an inverse contrast coefficient of the soil respiration rate; Comparing the Arundo donax carbon sequestration rate with a preset Arundo donax carbon sequestration rate obtained from a preset database to obtain a Arundo donax carbon sequestration rate comparison coefficient; Carbon sequestration pre-assessment adjustment parameters were introduced, and the Phragmites australis vegetation coverage contrast coefficient, soil microbial biomass carbon contrast coefficient, soil respiration rate inverse contrast coefficient, and Phragmites australis carbon sequestration rate contrast coefficient were assigned and coupled to obtain a carbon sequestration pre-assessment index, which was used to quantitatively assess the carbon sequestration potential of a preset assessment area in the Phragmites australis carbon sequestration monitoring area.

3. The method for dynamic assessment of carbon sequestration in Phragmites australis based on big data as claimed in claim 2, characterized in that: The specific process for determining whether to conduct a dynamic assessment of the carbon sequestration of Phragmites australis is as follows: If the carbon sequestration pre-assessment index is greater than the preset threshold of the reed bamboo carbon sequestration pre-assessment result obtained from the preset database, the rationality of the dynamic assessment of the reed bamboo carbon sequestration is determined; otherwise, the reed bamboo carbon sequestration pre-assessment optimization is performed; The preliminary assessment and optimization of the Arundo donax carbon sequestration includes the optimization of Arundo donax vegetation coverage and the optimization and control of environmental interference; If the carbon sequestration pre-assessment index after the optimization of the Phragmites australis vegetation coverage is greater than the preset Phragmites australis carbon sequestration pre-assessment result threshold obtained from the preset database, the rationality of the Phragmites australis carbon sequestration dynamic assessment is determined, otherwise the environmental interference optimization control is performed; If the carbon sequestration pre-assessment index after the optimization of the Phragmites australis vegetation coverage is greater than the preset Phragmites australis carbon sequestration pre-assessment result threshold obtained from the preset database, the rationality of the Phragmites australis carbon sequestration dynamic assessment is determined, otherwise feedback is given.

4. The method for dynamic assessment of carbon sequestration in Phragmites australis based on big data as claimed in claim 3, characterized in that: The optimization of the Phragmites australis vegetation coverage refers to the preliminary assessment of the Phragmites australis carbon sink based on the modified Phragmites australis vegetation coverage; The corrected Phragmites australis vegetation coverage is obtained by correcting the Phragmites australis vegetation coverage in a preset assessment area based on the Phragmites australis area classification error; The classification error of the Phragmites australis area is obtained by performing relative deviation processing on the classification area of Phragmites australis obtained in the standard assessment area and the standard Phragmites australis area; The specific process of the environmental interference optimization control is as follows: Inputting environmental parameters and preliminary assessment results of the carbon sequestration of Phragmites australis within a preset time period into an environmental compensation mapping set to obtain an environmental compensation factor, wherein the environmental parameters include average ambient temperature, average ambient humidity, and average light intensity, and the environmental compensation mapping set is a set obtained from a preset database that represents a mapping relationship between environmental parameters, preliminary assessment results of the carbon sequestration of Phragmites australis within a preset time period, and environmental compensation factors; The carbon sink pre-assessment index is corrected according to the environmental compensation factor to obtain a corrected carbon sink pre-assessment index; It is determined whether the revised carbon sink pre-assessment index is greater than a preset threshold of the reed bamboo carbon sink pre-assessment result obtained from a preset database. If so, a rationality judgment of the dynamic assessment of the reed bamboo carbon sink is performed; otherwise, feedback is given.

5. The method for dynamic assessment of carbon sequestration in Phragmites australis based on big data as claimed in claim 1, characterized in that: The specific method for determining the rationality of the dynamic assessment of the carbon sequestration of Phragmites australis based on the carbon sequestration monitoring parameters is as follows: performing relative deviation processing on the carbon absorption-collection correlation coefficient and the preset carbon absorption-collection correlation coefficient obtained from the preset database to obtain a carbon absorption-collection relative deviation coefficient; Performing relative deviation processing on the monitoring data representative coefficient and the preset monitoring data representative coefficient obtained from the preset database to obtain the monitoring data representative relative deviation coefficient; Comparing the root mean square error of the Arundo donax carbon sink with the root mean square error of the preset Arundo donax carbon sink obtained from the preset database, a root mean square error comparison coefficient of the Arundo donax carbon sink is obtained; A rationality judgment compensation value was introduced, and after assignment and coupling processing of the carbon absorption-collection relative deviation coefficient, the monitoring data representative relative deviation coefficient, and the root mean square error comparison coefficient of the Phragmites australis carbon sink, an inverse proportional operation was performed to obtain the rationality index of the dynamic assessment of the carbon sink. The rationality index of the dynamic assessment of the carbon sink was used to quantitatively evaluate the rationality of the dynamic assessment results of the Phragmites australis carbon sink.

6. The method for dynamic assessment of carbon sequestration in Phragmites australis based on big data as claimed in claim 5, characterized in that: The specific process of determining whether to perform monitoring data collection optimization is as follows: If the carbon sequestration dynamic assessment rationality index is greater than the preset carbon sequestration dynamic assessment rationality threshold obtained from the preset database, then the subsequent dynamic assessment of the carbon sequestration of Phragmites australis will continue; otherwise, the monitoring data collection optimization will be carried out; The monitoring data collection optimization includes first data collection frequency optimization, second data collection frequency optimization and monitoring data filling; Determine whether the carbon sequestration dynamic assessment rationality index after the first data collection frequency optimization is greater than a preset carbon sequestration dynamic assessment rationality threshold obtained from a preset database; if so, continue with the subsequent dynamic assessment of the carbon sequestration of Phragmites australis; otherwise, perform the second data collection frequency optimization; Determine whether the carbon sequestration dynamic assessment rationality index after the second data collection frequency optimization is greater than a preset carbon sequestration dynamic assessment rationality threshold obtained from a preset database; if so, continue with the subsequent dynamic assessment of the austria carbon sequestration; otherwise, perform monitoring data filling; If the reasonable index of dynamic carbon sequestration assessment after filling in the monitoring data is greater than the preset reasonable threshold of dynamic carbon sequestration assessment obtained from the preset database, the subsequent dynamic assessment of carbon sequestration of Phragmites australis will continue, otherwise feedback will be given.

7. The method for dynamic assessment of the carbon sequestration of Phragmites australis based on big data as claimed in claim 6, characterized in that: The specific process of optimizing the first data acquisition frequency is as follows: Data is collected according to a first modified monitoring data collection frequency corresponding to each growth stage, and the rationality of the dynamic assessment of the Phragmites australis carbon sequestration is re-determined; the first modified monitoring data collection frequency corresponding to each growth stage is obtained by modifying the monitoring data collection frequency of each growth stage using a monitoring data representative coefficient; The specific process of optimizing the second data acquisition frequency is as follows: Data collection is performed according to the second revised monitoring data collection frequency, and the second revised monitoring data collection frequency is obtained by correcting the current monitoring data collection frequency through the carbon sink dynamic assessment deviation coefficient, and the carbon sink dynamic assessment deviation coefficient is obtained by relative deviation processing of the carbon sink dynamic assessment reasonable index and the preset carbon sink dynamic assessment reasonable threshold.

8. The method for dynamic assessment of carbon sequestration in Phragmites australis based on big data as claimed in claim 1, characterized in that: The specific process of determining whether to perform dynamic assessment optimization of the Arundo donax carbon sink based on the result of the dynamic assessment of the Arundo donax carbon sink is as follows: A1: If the first carbon sink standard deviation within the preset number of preset time periods in the preset assessment area is greater than the first preset carbon sink standard deviation obtained from the preset database, then perform dynamic assessment and optimization of the Arundo donax carbon sink; otherwise, execute A2; A2: If the second carbon sequestration standard deviation of each preset assessment area in the Arundo donax carbon sequestration monitoring area is greater than the second preset carbon sequestration standard deviation obtained from the preset database, prompt the preset personnel to adjust the Arundo donax planting density; The optimization of the dynamic assessment of the carbon sequestration of Phragmites australis includes increasing the frequency of data collection and regional inspections; Increasing the data collection frequency means gradually increasing the data collection frequency of the corresponding monitoring points; The regional inspection indicates prompting preset personnel to use mobile monitoring equipment to conduct regional inspections on preset assessment areas.

9. The method for dynamic assessment of carbon sequestration in Phragmites australis based on big data as claimed in claim 1, characterized in that: The specific method for dynamically evaluating the carbon sink of Phragmites australis based on the optimized carbon sink evaluation parameters collected from monitoring data is as follows: The parameters for assessing the carbon sequestration of Phragmites australis include meteorological data, soil data, Phragmites australis growth data and model parameters; Inputting the obtained Arundo donax carbon sequestration assessment parameters into a Arundo donax carbon sequestration dynamic assessment model to obtain model output data, wherein the Arundo donax carbon sequestration dynamic assessment model is used to quantitatively assess the carbon sequestration capacity of Arundo donax; The model output data was processed for deviation to obtain the carbon sink of Phragmites australis.

10. A big data-based dynamic assessment system for the carbon sink of Phragmites australis, characterized by: include: Phragmites australis carbon sink pre-assessment and determination module, monitoring data collection optimization module and Phragmites australis carbon sink dynamic assessment optimization module; Wherein, the reed bamboo carbon sink pre-assessment determination module is used to perform a reed bamboo carbon sink pre-assessment on a preset assessment area according to the reed bamboo carbon sink pre-assessment parameters within a preset time period, and determine whether to perform a rationality determination of a dynamic assessment of the reed bamboo carbon sink; if so, perform a rationality determination of a dynamic assessment of the reed bamboo carbon sink according to the carbon sink monitoring parameters, and determine whether to perform monitoring data collection optimization; otherwise, perform a reed bamboo carbon sink pre-assessment optimization; The monitoring data acquisition optimization module is used to perform a dynamic assessment of the carbon sink of Phragmites australis according to the carbon sink assessment parameters of the Phragmites australis after the monitoring data acquisition optimization is performed, otherwise the dynamic assessment of the carbon sink of Phragmites australis is performed directly according to the carbon sink assessment parameters of the Phragmites australis; The dynamic assessment and optimization module for the austria carbon sink is used to determine whether to perform dynamic assessment and optimization of the austria carbon sink based on the result of the dynamic assessment of the austria carbon sink. If so, feedback is given after the dynamic assessment and optimization of the austria carbon sink; otherwise, feedback is given directly.

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