Artificial forest carbon sink monitoring report automatic generation system and method

Abstract

The invention relates to the field of man-made forest carbon sequestration, and particularly discloses a man-made forest carbon sequestration monitoring report automatic generation system and method.The method comprises the steps that firstly, carbon dioxide values of different environment heights, needle-leaved man-made forest growth environment data and needle-leaved man-made forest ecological productivity text data collected by a carbon dioxide monitor are obtained; and then feature extraction and correlation analysis are performed on the three by using a deep learning technology, and finally a man-made forest carbon sink monitoring report is generated through a generator, so that more accurate and effective support is provided for man-made forest management and environmental protection.

Description

Technical Field

[0001] The present application relates to the field of plantation carbon sinks, and more specifically, to a system and method for automatically generating plantation carbon sink monitoring reports. Background Art

[0002] Plantations are artificially planted and managed forest areas, with the main purpose of achieving the desired forest land effect under specific spatial layout and management measures through artificially selected and cultivated tree species. These forests are usually established through technical means such as seeding, planting or cuttings to ensure that the individual trees have good genetic quality and adaptability. The individual trees in the plantation are usually of the same age and are evenly distributed throughout the forest, making the overall forest stand structure uniform and reasonable.

[0003] However, current technologies have some challenges in monitoring carbon sinks in plantations. Existing methods mainly measure carbon dioxide content directly in the air of plantations. It is impossible to accurately assess the carbon dioxide level in a specific space based on a single monitoring data, thus limiting the accuracy and comprehensiveness of the assessment of the carbon sink effect of plantations.

[0004] Therefore, a system and method for automatically generating a plantation carbon sink monitoring report is desired. Summary of the invention

[0005] In order to solve the above technical problems, this application is proposed. The embodiment of this application provides a system and method for automatically generating a plantation carbon sink monitoring report, which first obtains the carbon dioxide values ​​at different environmental heights collected by a carbon dioxide monitor, the coniferous plantation growth environment data, and the coniferous plantation ecological productivity text data, and then uses deep learning technology to perform feature extraction and correlation analysis on the three, and finally generates a plantation carbon sink monitoring report through a generator, thereby providing more accurate and effective support for plantation management and environmental protection.

[0006] According to one aspect of the present application, a system for automatically generating a plantation carbon sink monitoring report is provided, which comprises:

[0007] The plantation carbon sink data acquisition module is used to obtain the carbon dioxide values ​​at different environmental heights collected by the carbon dioxide monitor, the coniferous plantation growth environment data and the coniferous plantation ecological productivity text data;

[0008] A plantation carbon sink data extraction module is used to extract a coniferous plantation carbon dioxide feature vector and a plantation growth state multimodal correlation feature vector from the carbon dioxide values ​​at different environmental heights collected by the carbon dioxide monitor, the coniferous plantation growth environment data, and the coniferous plantation ecological productivity text data;

[0009] The carbon sink monitoring report generating module is used to generate a plantation carbon sink monitoring report based on the coniferous plantation carbon dioxide feature vector and the multimodal correlation feature vector of the plantation growth state.

[0010] According to another aspect of the present application, a method for automatically generating a plantation carbon sink monitoring report is provided, which comprises:

[0011] Obtain the carbon dioxide values ​​at different environmental altitudes collected by the carbon dioxide monitor, the growth environment data of coniferous plantations, and the text data of the ecological productivity of coniferous plantations;

[0012] Extracting a coniferous plantation carbon dioxide feature vector and a multimodal correlation feature vector of the plantation growth state from the carbon dioxide values ​​at different environmental heights collected by the carbon dioxide monitor, the coniferous plantation growth environment data, and the coniferous plantation ecological productivity text data;

[0013] Based on the coniferous plantation carbon dioxide feature vector and the multimodal correlation feature vector of the plantation growth status, a plantation carbon sink monitoring report is generated.

[0014] Compared with the prior art, the present application provides an automatic generation system and method for a plantation carbon sink monitoring report, which first obtains the carbon dioxide values ​​at different environmental altitudes collected by a carbon dioxide monitor, the growth environment data of coniferous plantations, and the ecological productivity text data of coniferous plantations, and then uses deep learning technology to perform feature extraction and correlation analysis on the three, and finally generates a plantation carbon sink monitoring report through a generator, thereby providing more accurate and effective support for plantation management and environmental protection. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] By describing the embodiments of the present application in more detail in conjunction with the accompanying drawings, the above and other purposes, features and advantages of the present application will become more apparent. The accompanying drawings are used to provide a further understanding of the embodiments of the present application and constitute a part of the specification. Together with the embodiments of the present application, they are used to explain the present application and do not constitute a limitation of the present application. In the accompanying drawings, the same reference numerals generally represent the same components or steps.

[0016] Figure 1 It is a block diagram of a system for automatically generating a plantation carbon sink monitoring report according to an embodiment of the present application.

[0017] Figure 2 It is a block diagram of a plantation carbon sink data extraction module in the system for automatically generating a plantation carbon sink monitoring report according to an embodiment of the present application.

[0018] Figure 3 It is a block diagram of the plantation growth environment feature extraction unit in the plantation carbon sink monitoring report automatic generation system according to the embodiment of the present application.

[0019] Figure 4 It is a block diagram of a carbon sink monitoring report generation module in the automatic generation system of plantation carbon sink monitoring reports according to an embodiment of the present application.

[0020] Figure 5 The present invention is a flowchart of a method for automatically generating a plantation carbon sink monitoring report according to an embodiment of the present application.

[0021] Figure 6 is a block diagram of an electronic device according to an embodiment of the present application. DETAILED DESCRIPTION

[0022] Below, the exemplary embodiments according to the present application will be described in detail with reference to the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments of the present application, and it should be understood that the present application is not limited to the exemplary embodiments described here.

[0023] Figure 1 This is a block diagram of the system for automatically generating a plantation carbon sink monitoring report according to an embodiment of the present application. Figure 1 As shown, according to the automatic generation system 100 of the plantation carbon sink monitoring report of the embodiment of the present application, it includes: a plantation carbon sink data acquisition module 110, which is used to obtain the carbon dioxide values ​​at different environmental heights collected by the carbon dioxide monitor, the coniferous plantation growth environment data and the coniferous plantation ecological productivity text data; a plantation carbon sink data extraction module 120, which is used to extract the coniferous plantation carbon dioxide feature vector and the plantation growth state multimodal correlation feature vector from the carbon dioxide values ​​at different environmental heights collected by the carbon dioxide monitor, the coniferous plantation growth environment data and the coniferous plantation ecological productivity text data; a carbon sink monitoring report generation module 130, which is used to generate a plantation carbon sink monitoring report based on the coniferous plantation carbon dioxide feature vector and the coniferous plantation growth state multimodal correlation feature vector.

[0024] In the above-mentioned plantation carbon sink monitoring report automatic generation system 100, the plantation carbon sink data acquisition module 110 is used to obtain carbon dioxide values ​​at different environmental heights collected by a carbon dioxide monitor, coniferous plantation growth environment data and coniferous plantation ecological productivity text data. It should be understood that a plantation refers to a forest area designed and managed by humans, and its main purpose is to achieve the expected ecological effect under specific layout and management measures through carefully selected and cultivated tree species. These woodlands are generally established by methods such as sowing, planting or cuttings to ensure that the trees have excellent genetic characteristics and the ability to adapt to the environment. The trees in the plantation are usually of the same age and are evenly distributed throughout the area, so that the overall structure of the forest is uniform and reasonable. However, in terms of plantation carbon sink monitoring, there are currently some technical challenges. The current monitoring method usually evaluates the carbon sink effect by directly measuring the carbon dioxide content in the air. However, this method relies on a single data source and is difficult to accurately reflect the carbon dioxide concentration in a specific area. Therefore, there are limitations on the accuracy and comprehensiveness of the evaluation of the carbon sink benefits of plantations. Therefore, in the technical solution of the present application, by obtaining the carbon dioxide values ​​at different environmental altitudes collected by the carbon dioxide monitor, the coniferous plantation growth environment data and the coniferous plantation ecological productivity text data, and combining with deep learning technology, a plantation carbon sink monitoring report is generated, which can provide more accurate and efficient support for plantation management and environmental protection.

[0025] Specifically, the carbon dioxide values ​​at different environmental heights collected by the carbon dioxide monitor, the coniferous plantation growth environment data, and the coniferous plantation ecological productivity text data are obtained in order to comprehensively evaluate the carbon sink capacity and ecological health of the plantation. Among them, the carbon dioxide monitoring data provides information on carbon dioxide concentrations at different heights in the plantation area. This is the basis for evaluating the carbon sink function of the forest, because changes in carbon dioxide concentrations directly reflect the absorption and release of carbon by plants. By monitoring carbon dioxide values ​​at different heights, we can understand the carbon exchange at different levels within the forest, thereby more comprehensively evaluating the carbon fixation capacity of the forest. The plantation growth environment data covers ecological and environmental factors such as soil quality, climatic conditions, and water supply. These data are crucial to understanding the growth status and ecological functions of the forest. The coniferous plantation ecological productivity text data includes detailed information on tree growth, yield, stand productivity, and biomass. These data provide productivity indicators for forest ecosystems and can illustrate the growth efficiency of forests under different conditions. By analyzing these text data, the carbon storage potential of forests can be quantified and its productivity changes under different environmental conditions can be evaluated. The comprehensive use of these three types of data can provide comprehensive basic information for monitoring the carbon sink capacity of plantations. This not only helps to accurately assess the role of forests in carbon reduction, but also provides a scientific basis for forest management and protection strategies and supports the implementation of sustainable forestry practices. In a specific embodiment of the present application, the coniferous plantation growth environment data includes climate data and soil data, and the coniferous plantation ecological productivity text data includes stand productivity data and biomass data.

[0026] In a specific embodiment of the application, low-efficiency coniferous forests (such as aerial seeding of Masson pine forests and fir plantations) are used as research objects, the impact of transformation measures on their forest characteristics is analyzed, and the succession process of forests before and after transformation is simulated using a model. The forest characteristics, environmental factors, and human interference are integrated to analyze the changing characteristics of carbon sinks at different succession stages and their impact on the carbon cycle process, and to clarify the driving mechanism of the spatiotemporal changes in carbon sinks of two types of coniferous low-conversion forests. Specifically, by conducting a survey and calibration of model tree species parameters, collecting environmental data such as climate and soil required by the model, simulating the growth and carbon sink dynamics of typical artificial forests in the study area in a specific time period, and then collecting verification data such as forest productivity and biomass (remote sensing or field survey) in the study area, the accuracy of the simulation results is verified, and finally, the annual value data set of the simulation results (forest productivity, biomass, ecosystem carbon budget) is provided, and a report text is formed.

[0027] In the above-mentioned plantation carbon sink monitoring report automatic generation system 100, the plantation carbon sink data extraction module 120 is used to extract the coniferous plantation carbon dioxide feature vector and the plantation growth state multimodal correlation feature vector from the carbon dioxide values ​​at different environmental heights collected by the carbon dioxide monitor, the coniferous plantation growth environment data and the coniferous plantation ecological productivity text data. In this way, it can help researchers and managers better understand the carbon sink capacity of plantations and the complexity of their growth status. This multi-dimensional information integration can provide more accurate carbon sink assessments and more effective forest management strategies, thereby supporting the realization of sustainable forest management and climate change mitigation goals.

[0028] Figure 2 FIG. 1 is a block diagram of a plantation carbon sink data extraction module in a plantation carbon sink monitoring report automatic generation system according to an embodiment of the present application. Figure 2 As shown, in a specific embodiment of the present application, the plantation carbon sink data extraction module 120 includes: a plantation carbon dioxide feature extraction unit 121, which is used to perform feature extraction on the carbon dioxide values ​​at different environmental heights collected by the carbon dioxide monitor to obtain the coniferous plantation carbon dioxide feature vector; a plantation growth environment feature extraction unit 122, which is used to perform feature extraction on the coniferous plantation growth environment data to obtain a plantation growth environment associated feature vector; an ecological productivity text feature extraction unit 123, which is used to perform feature extraction on the coniferous plantation ecological productivity text data to obtain a plantation ecological productivity semantic associated feature vector; a growth state multimodal feature association unit 124, which is used to associate the plantation growth environment associated feature vector with the plantation ecological productivity semantic associated feature vector to obtain the plantation growth state multimodal associated feature vector.

[0029] It should be understood that the spatial distribution of carbon dioxide concentration is a key factor in assessing the carbon sink capacity of forests. Since carbon dioxide concentrations at different altitudes within the forest may vary significantly, these differences directly affect the carbon storage and exchange process. By extracting features from these data, carbon dioxide values ​​at different altitudes can be converted into meaningful feature vectors that capture the characteristics of carbon concentration at each altitude level. This helps to understand the specific situation of the carbon cycle within the forest, such as the difference in carbon absorption and release between the canopy layer and the surface layer. The feature extraction process can simplify complex, raw carbon dioxide measurements into a form that is easy to analyze by converting raw data into feature vectors. Through feature extraction methods, such as a combination of convolutional layers and fully connected layers, key features that are representative of the forest carbon sink function can be extracted. This simplification not only helps to reduce the complexity of data processing, but also improves the accuracy and efficiency of analysis.

[0030] Furthermore, feature extraction of coniferous plantation growth environment data is performed to transform complex environmental information into a form that is easy to analyze and apply, in order to support in-depth research and management of forest ecosystems. This process helps to reveal the impact of environmental factors on the growth and health of plantations, thereby optimizing forest management strategies. The complexity of environmental data usually involves multiple variables, such as soil type, climatic conditions, moisture content, etc. The interaction of these factors and their combined impact on forest growth are complex. Through feature extraction, these raw data can be simplified into representative feature vectors. These feature vectors condense the key information in the environmental data, making it easier to analyze and model, thereby better understanding the impact of environmental factors on plantations.

[0031] Furthermore, feature extraction of coniferous plantation ecological productivity text data is to transform text information into a numerical form with high information density and operability, so as to more effectively analyze and utilize ecological productivity data. This process is crucial for a deeper understanding of forest productivity, optimizing management strategies and promoting scientific research. The complexity of text data usually contains a large amount of descriptive and quantitative information, such as tree growth rate, biomass, productivity indicators, etc. Since these data exist in the form of natural language or unstructured text, it may be difficult to directly analyze and extract useful information. Through feature extraction, these raw text data can be transformed into structured feature vectors, from which key information can be extracted to capture the ecological productivity information hidden in the text. Through natural language processing (NLP) techniques, such as word embedding models (WordEmbeddings) or transformers (Transformers), the semantic information in text data can be transformed into vectors in a high-dimensional feature space. These vectors capture the semantic relationships and contextual information in the text, thus providing deep insights for analysis.

[0032] In particular, the purpose of associating the feature vector associated with the growth environment of plantations with the semantically associated feature vector of ecological productivity of plantations is to comprehensively consider environmental factors and productivity data, so as to more comprehensively and accurately evaluate and understand the growth status of plantations. This multimodal feature vector integrates information from different data sources, which helps to provide more in-depth ecological analysis and management decision support. It should be understood that the relationship between the growth environment and ecological productivity is complex and multidimensional. Analyzing environmental data or productivity data alone often cannot fully reveal their mutual influence and comprehensive effects. Among them, the feature vector associated with the growth environment includes the external conditions of forest growth, such as climate, soil and water, while the feature vector associated with ecological productivity semantics covers the results and performance of forest growth, such as biomass, stand productivity, tree growth rate, etc. By associating these two types of feature vectors, environmental conditions and growth results can be comprehensively considered to obtain a more comprehensive description of the growth status and further understand the actual performance of forests under different environmental conditions.

[0033] In a specific embodiment of the present application, the plantation carbon dioxide feature extraction unit 121 includes: arranging the carbon dioxide values ​​at different ambient altitudes collected by the carbon dioxide monitor into a coniferous plantation carbon dioxide input vector; passing the coniferous plantation carbon dioxide input vector through a coniferous plantation carbon dioxide feature encoder comprising a one-dimensional convolutional layer and a fully connected layer to obtain the coniferous plantation carbon dioxide feature vector.

[0034] It should be understood that the spatial distribution of carbon dioxide is of great significance in forest ecosystems. The concentration of carbon dioxide at different heights reflects the carbon exchange at different levels within the forest. There may be significant differences in carbon dioxide concentrations in the canopy, subcanopy and surface layers, and these differences affect the carbon absorption and release processes of the forest. In practical applications, the original carbon dioxide measurement data is usually unstructured, and arranging it as an input vector can integrate the concentration data at different heights into a unified format. This arrangement not only facilitates data storage and management, but also lays the foundation for the application of machine learning algorithms and data analysis models.

[0035] Furthermore, the coniferous plantation CO2 input vector is processed through a feature encoder consisting of a one-dimensional convolutional layer and a fully connected layer in order to effectively extract and represent the key features in the CO2 data, thereby improving the performance of data analysis and model prediction. Among them, the one-dimensional convolutional layer is used to extract local features in the input vector. The one-dimensional convolutional layer slides on different parts of the data through convolution operations and filters the data to capture the spatial pattern and structural characteristics in the input vector. In CO2 monitoring data, such local features may include concentration trends at different height levels or short-term fluctuations in CO2 concentration in a time series. The role of the fully connected layer is to further integrate and map the features extracted by the convolutional layer into a lower-dimensional feature space. The fully connected layer flattens the feature map output by the convolutional layer and linearly combines it with the activation function through a weight matrix to finally obtain a compact feature vector. By using deep learning models, such as convolutional neural networks, feature encoders can automatically learn and extract the most useful features for analysis and decision-making, improving the prediction ability and accuracy of the model. Specifically, the coniferous forest carbon dioxide input vector is fully-connected using the fully-connected layer of the coniferous forest carbon dioxide feature encoder comprising a one-dimensional convolutional layer and a fully-connected layer to extract high-dimensional implicit features of the eigenvalues ​​at each position in the coniferous forest carbon dioxide input vector; and, the coniferous forest carbon dioxide input vector is one-dimensionally encoded using the one-dimensional convolutional layer of the coniferous forest carbon dioxide feature encoder comprising a one-dimensional convolutional layer and a fully-connected layer to extract high-dimensional implicit correlation features of the correlation between the eigenvalues ​​at each position in the coniferous forest carbon dioxide input vector.

[0036] Figure 3 FIG. 1 is a block diagram of a plantation growth environment feature extraction unit in a plantation carbon sink monitoring report automatic generation system according to an embodiment of the present application. Figure 3 As shown, in a specific embodiment of the present application, the artificial forest growth environment feature extraction unit 122 includes: a growth environment data preprocessing subunit 1221, used to perform data preprocessing on the coniferous artificial forest growth environment data to obtain an artificial forest growth environment data word feature matrix; a growth environment data convolution encoding subunit 1222, used to pass the artificial forest growth environment data word feature matrix through an artificial forest growth environment data feature encoder based on a convolutional neural network to obtain the artificial forest growth environment associated feature vector.

[0037] It should be understood that the necessity of data preprocessing is that the original growth environment data usually contains noise, missing values ​​and inconsistencies, which will affect the accuracy of the analysis. Through data preprocessing, erroneous data can be cleaned, missing values ​​can be filled, and the data can be standardized. This process makes the data more consistent and reliable, thus providing a clear basis for further analysis. Here, the growth environment data may include multiple features, such as climate conditions, soil type, and moisture content. Converting these features into a word feature matrix can structure the complex environmental data into an easy-to-handle matrix form. By encoding and vectorizing environmental features, unstructured or semi-structured data can be converted into numerical form, making it suitable for calculation and analysis.

[0038] Furthermore, the feature matrix of the artificial forest growth environment data is processed by a feature encoder based on a convolutional neural network in order to automatically extract and represent the key features in the data through deep learning technology, thereby improving the ability and accuracy of data analysis. Convolutional neural networks are particularly suitable for processing and analyzing data with spatial or temporal structures, such as local patterns and features in growth environment data. Among them, the advantage of convolutional neural networks lies in their powerful feature extraction capabilities. Convolutional neural networks can capture local dependencies and spatial features in data through one-dimensional or two-dimensional convolutional layers. When processing artificial forest growth environment data, convolutional neural networks can effectively identify characteristic patterns under different environmental conditions, such as temperature changes, humidity distribution, etc. The convolutional layer filters the feature matrix through sliding convolution kernels to extract representative features, which help to reveal key patterns and relationships in the data. The automated nature of the convolution operation eliminates the need for manual feature design in the feature extraction process. In traditional data analysis, features need to be manually selected and constructed, which may miss important information. The use of convolutional neural networks can automatically learn and extract the most relevant features, thereby improving the comprehensiveness and accuracy of the analysis. Specifically, each layer of the artificial forest growth environment data feature encoder based on convolutional neural network performs convolution processing, mean pooling processing based on local feature matrix and nonlinear activation processing on the input data in the forward pass of the layer, so that the last layer of the artificial forest growth environment data feature encoder based on convolutional neural network outputs the artificial forest growth environment associated feature vector, wherein the input of the artificial forest growth environment data feature encoder based on convolutional neural network is the artificial forest growth environment data word feature matrix.

[0039] In a specific embodiment of the present application, the growth environment data preprocessing subunit 1221 includes: passing the coniferous plantation growth environment data through a plantation growth environment word embedding module to obtain a plantation growth environment numerical word vector sequence; arranging the plantation growth environment numerical word vector sequence in two dimensions to obtain the plantation growth environment data word feature matrix.

[0040] It should be understood that the basic concept of word embedding is to convert discrete, non-numerical data into continuous, dense vector representation. In the growth environment data, multiple different features may be included, such as temperature, humidity, soil type, etc. The original data of these features are usually categorical or have different dimensions. Through the word embedding module, these features can be converted into low-dimensional, dense numerical vectors. The numerical vector of each feature can capture the similarities and relationships between features, making the representation of the data more meaningful and easy to process. Specifically, the coniferous plantation growth environment data is segmented to obtain a growth environment word sequence; the embedding layer of the plantation growth environment word embedding module is used to map each growth environment word in the growth environment word sequence to a word embedding vector to obtain a plantation growth environment numerical word vector sequence.

[0041] Furthermore, the basic idea of ​​two-dimensional arrangement is to reorganize the numerical information in the one-dimensional vector sequence into a two-dimensional matrix. This process usually involves dividing the vector sequence into blocks or reshaping it into a matrix form with a fixed number of rows and columns. In this way, the structured information of the data can be expressed more clearly, and each row or column may correspond to a specific environmental feature or time point, thereby retaining the local relationship and structural characteristics of the data. The advantage of a two-dimensional feature matrix is ​​that it can capture spatial or temporal patterns in the data. For example, when processing the growth environment data of artificial forests, the two-dimensional matrix can arrange the values ​​of different environmental variables (such as temperature, humidity, etc.) at different time points or different spatial locations in different positions of the matrix. In this way, the rows and columns of the matrix can represent different features and time steps, enabling the model to recognize and learn the time series characteristics and spatial distribution patterns in the environmental data.

[0042] In a specific embodiment of the present application, the ecological productivity text feature extraction unit 123 includes: passing the coniferous plantation ecological productivity text data through a plantation ecological productivity text editor to obtain a plurality of plantation ecological productivity text understanding feature vectors; passing the plurality of plantation ecological productivity text understanding feature vectors through a sample dimension-based plantation ecological productivity multi-scale neighborhood feature extraction module to obtain the plantation ecological productivity semantic association feature vector.

[0043] It should be understood that the role of a text editor is to convert unstructured text data into structured feature representations. In the ecological productivity text, the descriptions usually involve different ecological indicators, environmental conditions, and productivity data. Through the text editor, these descriptions are parsed and encoded into feature vectors. Each feature vector captures key information and patterns in the text, such as productivity levels, the impact of environmental factors, and so on. By generating multiple feature vectors, multi-level information and complex relationships in the text can be captured, thereby providing richer contextual information for subsequent analysis. Specifically, the embedding layer of the artificial forest ecological productivity text editor is used to convert the coniferous plantation ecological productivity text data features into embedding vectors to obtain a sequence of ecological productivity embedding vectors; the converter-based Bert model of the artificial forest ecological productivity text editor is used to perform global context semantic encoding on the sequence of ecological productivity embedding vectors to obtain multiple artificial forest ecological productivity text understanding feature vectors.

[0044] Furthermore, the feature vectors of the text understanding of multiple plantation ecological productivity are processed by a multi-scale neighborhood feature extraction module based on the sample dimension in order to deeply explore and extract the potential semantic relationships and multi-level features in the text data. This process can capture the complex patterns and relationships in the text at different scales and ranges through the multi-scale neighborhood feature extraction technology, thereby improving the understanding and prediction capabilities of ecological productivity. Among them, the core of the multi-scale neighborhood feature extraction module is to analyze data from different scales and perspectives. This module can identify local and global patterns in different ranges by performing multi-scale analysis on feature vectors. For example, for the feature vectors in the ecological productivity text, multi-scale neighborhood analysis can capture the short-term and long-term relationships between features and identify the micro and macro factors that affect productivity. Such a method can more comprehensively understand the information in the text data and discover the potential correlation patterns therein. Feature extraction in the sample dimension makes the processing of feature vectors more detailed. By performing feature extraction on the sample dimension, the features of each sample can be deeply analyzed and the specific factors affecting productivity in each sample can be identified. In this way, not only can the global correlation features be extracted, but also the local information of each sample can be refined to obtain a more accurate feature representation. Specifically, each layer of the multi-scale neighborhood feature extraction module of artificial forest ecological productivity based on sample dimension performs the following operations on the input data in the forward transmission of the layer: performing convolution processing on the input data based on the first convolution kernel to obtain a first convolution feature map; performing convolution processing on the input data based on the second convolution kernel to obtain a second convolution feature map; performing convolution processing on the input data based on the third convolution kernel to obtain a third convolution feature map; performing convolution processing on the input data based on the fourth convolution kernel to obtain a fourth convolution feature map, wherein the first convolution kernel, the second convolution kernel, and the fourth convolution kernel are respectively used as the input data. The convolution kernel, the third convolution kernel and the fourth convolution kernel have different sizes; the first convolution feature map, the second convolution feature map, the third convolution feature map and the fourth convolution feature map are cascaded to obtain a multi-scale convolution feature map; the multi-scale convolution feature map is subjected to mean pooling processing along the channel dimension to obtain a pooled feature map; and the pooled feature map is subjected to nonlinear activation processing to obtain an activated feature map; wherein the output of the last layer of the convolution neural network model with a multi-scale convolution structure is the semantic association feature vector of the artificial forest ecological productivity.

[0045] In the above-mentioned plantation carbon sink monitoring report automatic generation system 100, the carbon sink monitoring report generation module 130 is used to generate a plantation carbon sink monitoring report based on the coniferous plantation carbon dioxide feature vector and the plantation growth state multimodal association feature vector. It should be understood that the carbon dioxide feature vector reflects the concentration and absorption capacity of carbon dioxide in the plantation. Coniferous plantations absorb carbon dioxide through photosynthesis, which is crucial to mitigating climate change. The carbon dioxide feature vector provides data on the distribution of carbon dioxide in different time and space, helping to assess the carbon absorption and emission of forests. By analyzing these features, the effectiveness and potential improvement points of forest carbon sinks can be identified. The multimodal association feature vector of the plantation growth state integrates different growth information of the forest, including the growth rate, health status, coverage, etc. of trees. These features can provide a comprehensive perspective on the health and productivity of forest ecosystems. The association analysis of multimodal data allows comprehensive consideration of the impact of growth status on carbon sink capacity, such as how tree density and growth rate affect the absorption and storage of carbon dioxide. Combining these two types of feature vectors can generate a comprehensive plantation carbon sink monitoring report. The report integrates multiple aspects of information on carbon dioxide absorption capacity and growth status, providing a comprehensive assessment of the forest carbon sink function.

[0046] Figure 4 FIG. 1 is a block diagram of a carbon sink monitoring report generation module in a system for automatically generating a plantation carbon sink monitoring report according to an embodiment of the present application. Figure 4 As shown, in a specific embodiment of the present application, the carbon sink monitoring report generation module 130 includes: a coniferous plantation carbon sink feature fusion unit 131, which is used to fuse the coniferous plantation carbon dioxide feature vector and the multimodal correlation feature vector of the plantation growth state to obtain a coniferous plantation carbon sink monitoring feature vector; a coniferous plantation carbon sink feature optimization unit 132, which is used to perform target parameter space-guided topological space constraints on the coniferous plantation carbon sink monitoring feature vector to obtain an optimized coniferous plantation carbon sink monitoring feature vector; and a plantation carbon sink monitoring report generation unit 133, which is used to pass the optimized coniferous plantation carbon sink monitoring feature vector through a generator to generate a plantation carbon sink monitoring report.

[0047] It should be understood that by fusing the coniferous plantation carbon dioxide feature vector and the plantation growth state multimodal correlation feature vector, the carbon absorption capacity and growth state of the forest can be combined to form a more comprehensive feature representation. This comprehensive information can more accurately reflect the actual status of forest carbon sinks, rather than relying solely on a single data source.

[0048] In particular, in the technical solution of the present application, the coniferous plantation carbon sink monitoring generation feature vector is obtained by integrating information from multiple aspects such as carbon dioxide data, growth environment data, and ecological productivity data. Among them, these multiple aspects of information are very high and complex in dimension, and may contain a lot of different, intertwined information. When the weights of the generator are adapted to the high-dimensional feature vector obtained from these multiple aspects of information, it may not be possible to accurately capture the uniqueness of each feature, resulting in overfitting of certain features or ignoring other features, thereby introducing interference in the generation process, resulting in quasi-coherent interference in the generated plantation carbon sink monitoring report. Therefore, in the technical solution of the present application, the coniferous plantation carbon sink monitoring generation feature vector is subjected to topological space constraints guided by the target parameter space to obtain an optimized coniferous plantation carbon sink monitoring generation feature vector.

[0049] Among them, the coniferous forest carbon sink monitoring generation feature vector is subjected to target parameter space-oriented topological space constraints to obtain an optimized coniferous forest carbon sink monitoring generation feature vector, including: extracting the coniferous forest carbon sink monitoring generation target parameter matrix of the generator; node-decomposing the coniferous forest carbon sink monitoring generation target parameter matrix in units of row vectors to obtain a set of coniferous forest carbon sink monitoring generation target parameter node encoding vectors; using each coniferous forest carbon sink monitoring generation target parameter node encoding vector in the set of coniferous forest carbon sink monitoring generation target parameter node encoding vectors as the wandering topological space, and topologically constraining the coniferous forest carbon sink monitoring generation feature vector respectively to obtain a set of coniferous forest carbon sink monitoring generation feature vectors after constraints; calculating the positional mean vector of the set of coniferous forest carbon sink monitoring generation feature vectors after constraints to obtain the optimized coniferous forest carbon sink monitoring generation feature vector.

[0050] Among them, each coniferous forest carbon sink monitoring target parameter node encoding vector in the set of coniferous forest carbon sink monitoring target parameter node encoding vectors is used as a walking topological space, and the coniferous forest carbon sink monitoring feature vectors are topologically constrained to obtain a set of constrained coniferous forest carbon sink monitoring feature vectors, including: multiplying the coniferous forest carbon sink monitoring feature vector and the transposed vector of the coniferous forest carbon sink monitoring target parameter node encoding vector, and calculating the natural exponential function value of the multiplication result to obtain the coniferous forest carbon sink monitoring weighted index response weight; calculating the coniferous forest carbon sink monitoring Generate a characteristic vector and the Euclidean distance between the target parameter node coding vector of the coniferous plantation carbon sink monitoring to obtain the node coding distance value of the coniferous plantation carbon sink monitoring; perform point multiplication on the node coding distance value of the coniferous plantation carbon sink monitoring and the characteristic vector of the coniferous plantation carbon sink monitoring, and calculate the natural exponential function value for each eigenvalue of the vector after the point multiplication to obtain the distance guidance index characteristic vector of the coniferous plantation carbon sink monitoring; perform point multiplication on the weighted index response weight of the coniferous plantation carbon sink monitoring and the distance guidance index characteristic vector of the coniferous plantation carbon sink monitoring to obtain the constrained coniferous plantation carbon sink monitoring characteristic vector.

[0051] In particular, considering that when constructing a coniferous plantation carbon sink monitoring system, data from multiple sources such as carbon dioxide monitors and growth environment data are processed. Each data source has its own unique information and structure, such as the changing pattern of carbon dioxide concentration over time and altitude, and the historical records of coniferous forest growth conditions (such as soil type and moisture content). When these different data features are extracted and fused into a coniferous plantation carbon sink monitoring generating feature vector, the goal of this application is to create a feature vector that can reflect the current state of the forest ecosystem and capture its long-term evolution pattern. This means that the fused data should not only accurately reflect the growth status of coniferous plantations under existing environmental conditions (such as instant carbon dioxide concentration, soil moisture and temperature, etc.), but also be able to reveal stable characteristics formed based on the accumulation of historical data. To ensure this, after the process of generating the coniferous plantation carbon sink monitoring generating feature vector, it is also necessary to pay attention to how to keep the main characteristics of the original data from being blurred. Based on this, in the technical solution of the present application, the coniferous plantation carbon sink monitoring generating feature vector is subjected to a topological space constraint guided by the target parameter space to obtain an optimized coniferous plantation carbon sink monitoring generating feature vector.

[0052] In an embodiment of the present application, specifically, the coniferous plantation carbon sink monitoring generated feature vector is subjected to a target parameter space-oriented topological space constraint to obtain an optimized coniferous plantation carbon sink monitoring generated feature vector, which is used to: process the coniferous plantation carbon sink monitoring generated feature vector using the following optimization formula to obtain the optimized coniferous plantation carbon sink monitoring generated feature vector; wherein the optimization formula is:

[0053]

[0054]

[0055]

[0056] in, represents the target parameter matrix generated by coniferous plantation carbon sink monitoring, The first, second, and third nodes of the set of target parameter node encoding vectors for coniferous plantation carbon sink monitoring are represented. , The coniferous plantation carbon sink monitoring generates the target parameter node encoding vector, represents the transpose of a vector, represents the natural exponential function, represents the feature vector generated by coniferous plantation carbon sink monitoring, represents matrix multiplication, It means point multiplication by position. Represents the calculation vector and vector The Euclidean distance between The first one represents the set of feature vectors generated by the coniferous plantation carbon sink monitoring after constraints. The feature vector of carbon sink monitoring of coniferous plantations after constraints is generated. Represents the total number of sets of feature vectors generated by coniferous plantation carbon sink monitoring after constraints, Represents the generated feature vector for optimized coniferous plantation carbon sink monitoring.

[0057] That is, in response to the above technical problems, in the technical solution of the present application, the coniferous plantation carbon sink monitoring generates characteristic vectors that are subject to topological space constraints guided by the target parameter space. The process first extracts the key parameters used for decision-making from the trained generator. These parameters form a matrix in a high-dimensional space, and each row represents the weight or influencing factor on a different dimension. The target parameter matrix generated by the coniferous plantation carbon sink monitoring can provide insight into the location and shape of the model's decision boundary, and then infer which input features are most critical to the prediction results.

[0058] Next, the coniferous plantation carbon sink monitoring target parameter matrix is ​​node-decomposed in units of row vectors to obtain a set of node encoding vectors of coniferous plantation carbon sink monitoring target parameters. Here, each row vector is a node in graph theory, which means that each set of parameters is now regarded as an entity with potential connectivity. This transformation allows the application of methods from graph theory and network science to explore the interactions between features. The node encoding vector not only carries the information of the original parameters, but also implies knowledge about the topological structure of the entire system. Node decomposition further reveals the intrinsic connection pattern or structure of the data, so that the optimized feature vector can better adapt to new task requirements.

[0059] Then, each coniferous plantation carbon sink monitoring target parameter node encoding vector in the set of the coniferous plantation carbon sink monitoring target parameter node encoding vector is used as the walking topological space, and the coniferous plantation carbon sink monitoring feature vector is subjected to topological space constraints to obtain a set of coniferous plantation carbon sink monitoring feature vectors after constraints. "Walking" in the topological space defined by the node encoding vector is actually simulating an exploratory process, the purpose of which is to find feature transformations that can best maintain the structural characteristics of the original data. Each step determines the position of the next step based on the probability distribution of the current state. The topological space constraints ensure that even in different contexts, the feature representation retains certain invariances. At the same time, it can also promote cross-domain transfer learning because it emphasizes the universal relationship between features rather than the details of a specific field. In this way, the reconstruction of the feature space is achieved, making the optimized feature vector more compact and having better generalization capabilities.

[0060] Finally, the positional mean vector of the set of the constrained coniferous plantation carbon sink monitoring generated feature vectors is calculated to obtain the optimized coniferous plantation carbon sink monitoring generated feature vectors. Calculating the mean vector is a statistical aggregation method used to integrate the optimal solution from multiple perspectives. The idea behind this step is to reduce the deviation caused by a single estimate by fusing the information provided by different sample points. The averaging process is equivalent to performing a soft voting, which enhances the expressiveness of common features and makes the optimized feature vectors more stable and reliable.

[0061] Furthermore, the function of the generator is to convert the optimized coniferous plantation carbon sink monitoring generated feature vectors into a structured report. The optimized coniferous plantation carbon sink monitoring generated feature vectors usually contain a large amount of numerical data and complex pattern information, and it may be difficult for non-professional users to directly interpret these data. By mapping the optimized coniferous plantation carbon sink monitoring generated feature vectors into a specific report template, the generator can present the data in the form of charts, images and text descriptions, making the report content more intuitive and easy to understand. In a specific embodiment of the present application, the plantation carbon sink monitoring report includes stand productivity, biomass, and ecosystem carbon budget.

[0062] In summary, the embodiment of the present application first obtains the carbon dioxide values ​​at different environmental altitudes collected by the carbon dioxide monitor, the coniferous plantation growth environment data and the coniferous plantation ecological productivity text data, and then uses deep learning technology to perform feature extraction and correlation analysis on the three, and finally generates a plantation carbon sink monitoring report through a generator, thereby providing more accurate and effective support for plantation management and environmental protection.

[0063] As described above, the system 100 for automatically generating a report on carbon sink monitoring in plantations according to the embodiment of the present application can be implemented in various terminal devices. In one example, the system 100 for automatically generating a report on carbon sink monitoring in plantations can be integrated into a terminal device as a software module and / or a hardware module. For example, the system 100 for automatically generating a report on carbon sink monitoring in plantations can be a software module in the operating system of the terminal device, or can be an application developed for the terminal device; of course, the system 100 for automatically generating a report on carbon sink monitoring in plantations can also be one of the many hardware modules of the terminal device.

[0064] Alternatively, in another example, the plantation forest carbon sink monitoring report automatic generation system 100 and the terminal device may also be separate devices, and the plantation forest carbon sink monitoring report automatic generation system 100 may be connected to the terminal device via a wired and / or wireless network and transmit interactive information in accordance with an agreed data format.

[0065] Figure 5 Flow chart of the method for automatically generating a plantation carbon sink monitoring report according to an embodiment of the present application. Figure 5As shown, according to the method for automatically generating a plantation carbon sink monitoring report of an embodiment of the present application, it includes: S110, obtaining carbon dioxide values ​​at different environmental heights collected by a carbon dioxide monitor, coniferous plantation growth environment data and coniferous plantation ecological productivity text data; S120, extracting a coniferous plantation carbon dioxide feature vector and a multimodal correlation feature vector of the plantation growth state from the carbon dioxide values ​​at different environmental heights collected by the carbon dioxide monitor, the coniferous plantation growth environment data and the coniferous plantation ecological productivity text data; S130, generating a plantation carbon sink monitoring report based on the coniferous plantation carbon dioxide feature vector and the multimodal correlation feature vector of the plantation growth state.

[0066] Here, those skilled in the art can understand that the specific operations of each step in the above-mentioned method for automatically generating a plantation carbon sink monitoring report have been referred to above. Figures 1 to 4 The description of the automatic generation system of the plantation carbon sink monitoring report has been introduced in detail, and therefore, its repeated description will be omitted.

[0067] Below, reference Figure 6 To describe an electronic device according to an embodiment of the present application.

[0068] like Figure 6 As shown, the electronic device 10 includes an input device 11, an input interface 12, a central processing unit 13, a memory 14, an output interface 15, an output device 16, and a bus 17. The input interface 12, the central processing unit 13, the memory 14, and the output interface 15 are connected to each other through the bus 17, and the input device 11 and the output device 16 are connected to the bus 17 through the input interface 14 and the output interface 15, respectively, and then connected to other components of the electronic device 10.

[0069] Specifically, the input device 11 receives input information from the outside and transmits the input information to the central processing unit 13 through the input interface 12; the central processing unit 13 processes the input information based on the computer executable instructions stored in the memory 14 to generate output information, temporarily or permanently stores the output information in the memory 14, and then transmits the output information to the output device 16 through the output interface 15; the output device 16 outputs the output information to the outside of the electronic device 10 for user use.

[0070] In one embodiment, Figure 6 The electronic device 10 shown can be implemented as a network device, which may include: a memory configured to store programs; a processor configured to run the programs stored in the memory to execute any one of the methods for automatically generating a plantation forest carbon sink monitoring report described in the above embodiments.

[0071] According to an embodiment of the present application, the process described above with reference to the flowchart can be implemented as a computer software program. For example, an embodiment of the present application includes a computer program product, which includes a computer program tangibly contained on a machine-readable medium, and the computer program includes a program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network, and / or installed from a removable storage medium.

[0072] It will be appreciated by those skilled in the art that all or some of the steps, systems, and functional modules / units in the methods disclosed above may be implemented as software, firmware, hardware, and appropriate combinations thereof. In hardware implementations, the division between the functional modules / units mentioned in the above description does not necessarily correspond to the division of physical components; for example, a physical component may have multiple functions, or a function or step may be performed by several physical components in cooperation. Some or all physical components may be implemented as software executed by a processor, such as a central processing unit, a digital signal processor, or a microprocessor, or implemented as hardware, or implemented as an integrated circuit, such as an application-specific integrated circuit. Such software may be distributed on a computer-readable medium, which may include a computer storage medium (or non-transitory medium) and a communication medium (or temporary medium). As known to those skilled in the art, the term computer storage medium includes volatile and non-volatile, removable and non-removable media implemented in any method or technology for storing information (such as computer-readable instructions, data structures, program modules, or other data). Computer storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital versatile disks (DVD) or other optical disk storage, magnetic cassettes, magnetic tapes, magnetic disk storage or other magnetic storage devices, or any other medium that can be used to store the desired information and which can be accessed by a computer.

[0073] It is to be understood that the above embodiments are merely exemplary embodiments used to illustrate the principles of the present application, but the present application is not limited thereto. For those skilled in the art, various modifications and improvements can be made without departing from the spirit and substance of the present application, and these modifications and improvements are also considered to be within the scope of protection of the present application.

Claims

1. A system for automatically generating plantation carbon sink monitoring reports, characterized in that: include: The plantation carbon sink data acquisition module is used to obtain the carbon dioxide values ​​at different environmental heights collected by the carbon dioxide monitor, the coniferous plantation growth environment data and the coniferous plantation ecological productivity text data; A plantation carbon sink data extraction module is used to extract a coniferous plantation carbon dioxide feature vector and a plantation growth state multimodal correlation feature vector from the carbon dioxide values ​​at different environmental heights collected by the carbon dioxide monitor, the coniferous plantation growth environment data, and the coniferous plantation ecological productivity text data; The carbon sink monitoring report generating module is used to generate a carbon sink monitoring report for the artificial forest based on the carbon dioxide feature vector of the coniferous artificial forest and the multimodal correlation feature vector of the growth state of the artificial forest.

2. The automatic generation system of plantation carbon sink monitoring report according to claim 1 is characterized in that: The plantation carbon sink data extraction module comprises: A plantation carbon dioxide feature extraction unit, used for extracting features from the carbon dioxide values ​​at different environmental heights collected by the carbon dioxide monitor to obtain a carbon dioxide feature vector for the coniferous plantation; A plantation forest growth environment feature extraction unit, used for extracting features from the coniferous plantation forest growth environment data to obtain a plantation forest growth environment-related feature vector; An ecological productivity text feature extraction unit, used for extracting features from the coniferous plantation ecological productivity text data to obtain a semantically associated feature vector of the plantation ecological productivity; The growth state multimodal feature association unit is used to associate the artificial forest growth environment association feature vector with the artificial forest ecological productivity semantic association feature vector to obtain the artificial forest growth state multimodal association feature vector.

3. The automatic generation system of plantation carbon sink monitoring report according to claim 2 is characterized in that: The plantation carbon dioxide feature extraction unit comprises: Arranging the carbon dioxide values ​​at different environmental heights collected by the carbon dioxide monitor as a coniferous plantation carbon dioxide input vector; The coniferous forest carbon dioxide input vector is passed through a coniferous forest carbon dioxide feature encoder comprising a one-dimensional convolutional layer and a fully connected layer to obtain the coniferous forest carbon dioxide feature vector.

4. The automatic generation system of plantation carbon sink monitoring report according to claim 3 is characterized in that: The artificial forest growth environment feature extraction unit comprises: A growth environment data preprocessing subunit, used for preprocessing the coniferous plantation growth environment data to obtain a plantation growth environment data word feature matrix; The growth environment data convolution encoding subunit is used to obtain the artificial forest growth environment associated feature vector by passing the artificial forest growth environment data word feature matrix through an artificial forest growth environment data feature encoder based on a convolutional neural network.

5. The automatic generation system of plantation carbon sink monitoring report according to claim 4 is characterized in that: The growth environment data preprocessing subunit includes: The coniferous plantation growth environment data is passed through a plantation growth environment word embedding module to obtain a plantation growth environment word vector sequence; The artificial forest growth environment numeral word vector sequence is arranged in two dimensions to obtain the artificial forest growth environment data word feature matrix.

6. The automatic generation system of plantation carbon sink monitoring report according to claim 5 is characterized in that: The ecological productivity text feature extraction unit comprises: The coniferous plantation ecological productivity text data is passed through a plantation ecological productivity text editor to obtain a plurality of plantation ecological productivity text understanding feature vectors; The multiple plantation forest ecological productivity text comprehension feature vectors are passed through a sample dimension-based plantation forest ecological productivity multi-scale neighborhood feature extraction module to obtain the plantation forest ecological productivity semantic association feature vector.

7. The automatic generation system of plantation carbon sink monitoring report according to claim 6 is characterized in that: The carbon sink monitoring report generation module includes: A coniferous plantation carbon sink feature fusion unit, used for fusing the coniferous plantation carbon dioxide feature vector and the plantation growth state multimodal correlation feature vector to obtain a coniferous plantation carbon sink monitoring generation feature vector; A coniferous plantation carbon sink characteristic optimization unit, used for performing a target parameter space-oriented topological space constraint on the coniferous plantation carbon sink monitoring and generating characteristic vector to obtain an optimized coniferous plantation carbon sink monitoring and generating characteristic vector; The plantation forest carbon sink monitoring report generating unit is used to pass the optimized coniferous plantation forest carbon sink monitoring generating feature vector through a generator to generate a plantation forest carbon sink monitoring report.

8. The automatic generation system of plantation carbon sink monitoring report according to claim 7 is characterized in that: The coniferous plantation carbon sink characteristic optimization unit is used to: Extracting a coniferous plantation carbon sink monitoring generation target parameter matrix of the generator; Decomposing the coniferous plantation carbon sink monitoring generation target parameter matrix into nodes in units of row vectors to obtain a set of coniferous plantation carbon sink monitoring generation target parameter node encoding vectors; Taking each coniferous plantation carbon sink monitoring target parameter node encoding vector in the set of coniferous plantation carbon sink monitoring target parameter node encoding vectors as the walking topological space, topological space constraints are respectively performed on the coniferous plantation carbon sink monitoring feature vectors to obtain a set of coniferous plantation carbon sink monitoring feature vectors after constraints; The positional mean vector of the set of the constrained coniferous plantation carbon sink monitoring generated feature vectors is calculated to obtain the optimized coniferous plantation carbon sink monitoring generated feature vectors.

9. The automatic generation system of plantation carbon sink monitoring report according to claim 8 is characterized in that: Taking each coniferous plantation carbon sink monitoring target parameter node encoding vector in the set of the coniferous plantation carbon sink monitoring target parameter node encoding vector as the walking topological space, topological space constraints are respectively performed on the coniferous plantation carbon sink monitoring feature vector to obtain a set of constrained coniferous plantation carbon sink monitoring feature vectors, including: After multiplying the coniferous plantation carbon sink monitoring generation feature vector and the transposed vector of the coniferous plantation carbon sink monitoring generation target parameter node encoding vector, calculating the natural exponential function value of the multiplication result to obtain the coniferous plantation carbon sink monitoring generation weighted index response weight; Calculating the Euclidean distance between the coniferous plantation carbon sink monitoring generation feature vector and the coniferous plantation carbon sink monitoring generation target parameter node coding vector to obtain the coniferous plantation carbon sink monitoring generation node coding distance value; Performing a dot multiplication on the coniferous plantation carbon sink monitoring generation node encoding distance value and the coniferous plantation carbon sink monitoring generation characteristic vector, and calculating a natural exponential function value for each eigenvalue of the vector after the dot multiplication to obtain a coniferous plantation carbon sink monitoring generation distance guidance index characteristic vector; The response weight of the weighted index generated by the coniferous plantation carbon sink monitoring and the characteristic vector of the distance guidance index generated by the coniferous plantation carbon sink monitoring are point-multiplied to obtain the constrained coniferous plantation carbon sink monitoring and characteristic vector.

10. A method for automatically generating a plantation carbon sink monitoring report, characterized in that: include: Obtain the carbon dioxide values ​​at different environmental altitudes collected by the carbon dioxide monitor, the growth environment data of coniferous plantations, and the text data of the ecological productivity of coniferous plantations; Extracting a coniferous plantation carbon dioxide feature vector and a multimodal correlation feature vector of the plantation growth state from the carbon dioxide values ​​at different environmental heights collected by the carbon dioxide monitor, the coniferous plantation growth environment data, and the coniferous plantation ecological productivity text data; Based on the coniferous plantation carbon dioxide feature vector and the multimodal correlation feature vector of the plantation growth status, a plantation carbon sink monitoring report is generated.

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