A large language model driven forest management data processing method
The forest management data processing method driven by a large language model solves the problem of insufficient multi-source heterogeneous data fusion capability in the traditional forest management plan preparation, realizes efficient and intelligent forest management plan generation, and supports dynamic updates and digital forestry construction.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- RES INST OF FOREST RESOURCE INFORMATION TECHN CHINESE ACADEMY OF FORESTRY
- Filing Date
- 2025-08-13
- Publication Date
- 2026-07-28
AI Technical Summary
Traditional forest management plans rely on human experience and rule-based models, which makes it difficult to effectively integrate multi-source heterogeneous data, resulting in low efficiency and an inability to achieve intelligent generation, thus failing to meet the dynamic and complex needs of forestry production.
A forest management data processing method driven by a large language model is adopted, including multi-source data collection and preprocessing, construction of forest management knowledge graph, fine-tuning of large language model and enhanced retrieval generation, data semantic fusion and understanding, management strategy reasoning and management plan text generation, and continuous optimization combined with human-computer interaction.
It significantly improves the efficiency and intelligence of forest management plan development. The generated plans comply with industry standards, have dynamic update capabilities, and support the construction of digital and intelligent forestry.
Smart Images
Figure CN120994908B_ABST
Abstract
Description
Technical Field
[0001] This invention belongs to the interdisciplinary field of forest resource management and artificial intelligence data processing, and relates to a forest management data processing method driven by a large language model. Background Technology
[0002] As forest resource management increasingly demands more refined data and intelligent decision-making, the formulation and management of traditional forest management plans often rely on human experience and rule-based models. They lack the ability to integrate multi-source heterogeneous data (such as remote sensing images, forestry survey data, policy texts, meteorological data, etc.) and are unable to meet the dynamic and complex actual needs of forestry production.
[0003] The knowledge reasoning and text generation capabilities of large language models provide a new path for efficient data processing, personalized plan generation, and dynamic updates in the forest management plan development process, becoming an important support for promoting the intelligent transformation of forest management. Efforts should be focused on how to utilize large language models to achieve efficient data processing in the forest management plan development process, in order to solve the current problems of long plan development cycles, reliance on experience, and difficulty in fine-grained adaptation. With the development of large language models (such as DeepSeek, Wenxin Yiyan, and ChatGPT), leveraging their language understanding and generation capabilities, it is expected that efficient processing of forest management data can be achieved, reducing costs and improving quality.
[0004] Current technologies for developing forest management plans require selecting typical forest types as sample plots. Based on the sample plot data, models for predicting forest stand volume and carbon storage are derived. An initial forest carbon sequestration management plan is then manually drafted, and this plan is optimized using simulated annealing algorithms. While this approach can generate a forest carbon sequestration management plan, the process still relies on manual data processing, resulting in low efficiency and an inability to effectively utilize multi-source, multi-modal data. Furthermore, it fails to intelligently generate forest management plans based on forest management objectives, considering factors such as forest type, structure, composition, site conditions, health, and biodiversity. Moreover, relying on manual drafting of management plans prevents the realization of intelligent forest management plan generation.
[0005] Given the needs of forest management development and the inefficiency of traditional data processing relying on manual experience, it is clear that intelligent processing of forest management data is an inevitable trend in the intelligent development of forest management. A key issue to address in achieving automated processing of forest management data is how to integrate professional forest management knowledge with existing large-scale language models and automatically process data according to the business logic of forest management standards. Currently, the formulation and management of traditional forest management plans often rely on manual experience and rule-based models, lacking the ability to integrate multi-source heterogeneous data (such as remote sensing imagery, forestry survey data, policy texts, and meteorological data), and there are few reports on the efficient processing of forest management data using large-scale language models. Summary of the Invention
[0006] This invention addresses the problems of existing technologies by providing a forest management data processing method driven by a large language model.
[0007] A large language model-driven forest management data processing method includes the following steps: multi-source data collection and preprocessing, forest management knowledge graph construction, large language model fine-tuning and retrieval enhancement generation, data semantic fusion and understanding, management strategy reasoning: based on fused data, management intentions, and industry knowledge, the large language model automatically infers management strategies that conform to forest growth patterns and multi-objective balance; management plan text generation: based on strategy and spatial matching, relying on the text generation capabilities of the large language model and industry knowledge in the knowledge base, the method automatically compiles forest management plan texts that conform to forestry industry standards and management requirements; human-computer interaction and continuous optimization: through a human-computer interaction interface, feedback information from forestry workers on the forest management plan is collected, and the generated management plan is systematically evaluated from multiple aspects such as forest resource sustainability, ecological function enhancement, achievement of management goals, risk controllability, and policy compliance.
[0008] A large language model-driven forest management data processing method, compared with existing traditional forest management data processing methods that rely on human experience and rule-based models, has the following significant technical effects and advantages:
[0009] (1) Improve the data processing efficiency and intelligence level of forest management plan preparation.
[0010] By introducing a large language model with strong knowledge understanding and reasoning capabilities, it is possible to quickly and automatically process multi-source and multi-modal data on forest management based on different regions, forest stand types, and management objectives, and generate forest management plans that meet the requirements. This significantly improves the efficiency of plan development, reduces reliance on human experience, and lowers labor costs.
[0011] (2) Promote the standardization, scientification and normalization of the program.
[0012] By deeply learning and integrating existing forest management technical regulations, laws and regulations, and industry standards through a large language model, the generated solutions can better comply with national and industry norms, avoid human oversights and inconsistencies, and improve the standardization, scientificity, and operability of the compilation results.
[0013] (3) Enhance the sustainability and dynamic updating capability of the plan.
[0014] This invention possesses continuous learning and knowledge updating capabilities, enabling it to dynamically adjust management strategies based on changes in external data (such as climate, pests and diseases, market demand, etc.) and the latest research progress in forestry, thereby ensuring the forward-looking, scientific, and sustainable nature of forest management plans.
[0015] (4) Support the development of digital and intelligent forestry
[0016] This invention can be deeply integrated with existing forest resource monitoring systems, remote sensing data, geographic information systems (GIS) and other digital platforms to further promote the construction of smart forestry and digital forestry, and provide technical support for precise forest management and "dual carbon" targets.
[0017] In summary, this invention can effectively overcome the technical bottlenecks in the existing forest management plan preparation process, such as weak data processing capabilities, low efficiency, and lack of dynamism, and has good application prospects and promotion value.
[0018] By introducing a large language model, this method achieves unified parsing and semantic fusion of structured and unstructured forestry data, significantly improving the processing efficiency and intelligence level of forest management data. This method can automatically complete complex tasks such as spatial registration, coordinate system unification, semantic recognition, index reasoning, target matching, and data standardization, solving the problems of strong data heterogeneity, difficult semantic alignment, and excessive manual intervention in traditional methods. Technically, it achieves efficient fusion and semantically standardized output of multi-source heterogeneous forestry data, effectively improving the automation, accuracy, and scalability of data processing, providing a solid data foundation for large-scale intelligent decision-making in forest management. Attached Figure Description
[0019] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort. As shown in the figures:
[0020] Figure 1 This is a flowchart of a forest management data processing method driven by a large language model according to the present invention.
[0021] Figure 2This is a flowchart illustrating the key technologies for forest management data processing driven by a large language model, as described in this invention.
[0022] Figure 3 This is a flowchart illustrating the preprocessing, standardization, and fusion process of forest management data driven by a large language model, as described in this invention.
[0023] Figure 4 This is a flowchart illustrating the technical process of this invention, which uses large language model fine-tuning and retrieval enhancement generation techniques to process data and generate forest management plans.
[0024] Figure 5 This is a terminal block diagram of a forest management data processing method driven by a large language model according to the present invention. Detailed Implementation
[0025] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.
[0026] To enable those skilled in the art to better understand the technical solutions of the present invention, the embodiments of the present invention will be further described in detail below with reference to the accompanying drawings and specific examples, but this is not intended to limit the present invention. If there is no necessary sequential relationship between the various steps described in the present invention, the order in which they are described as examples should not be considered a limitation. Those skilled in the art should understand that the order can be adjusted, as long as it does not disrupt the logical consistency between them and render the entire process impossible.
[0027] Example 1: As Figure 1 , Figure 2 , Figure 3 , Figure 4 and Figure 5 As shown, a large language model-driven forest management data processing method is proposed to improve the intelligence level of data processing in the process of forest management plan preparation.
[0028] To overcome the shortcomings of existing forest management data processing methods that rely on expert experience and rule-based models, which are insufficient in their ability to integrate multi-source heterogeneous data (such as remote sensing images, forestry survey data, policy texts, meteorological data, etc.) and thus cannot meet the dynamic and complex actual needs of forestry production, this invention provides a forest management data processing method driven by a large language model.
[0029] A large language model-driven method for forest management data processing includes:
[0030] The first step is multi-source data acquisition and preprocessing: Collect multi-dimensional, multi-source, and heterogeneous data, including remote sensing images, aerial and UAV images, lidar data, field ground survey data, forest stand archives, management history data, climate and environmental data, topographic data, soil data, biodiversity data, pest and disease monitoring data, and relevant policies, regulations, and standards, as required for compiling forest management plans. Through data standardization, cleaning, spatiotemporal registration, and multi-source fusion, a unified data resource system is formed.
[0031] The second step is the construction of a forest management knowledge graph. Based on expert knowledge in the field of forest management, and combined with multi-source heterogeneous data such as papers, patents, standards, policies and regulations, and management plans in the field, natural language processing and information extraction technologies are used to construct a forest management knowledge dataset and knowledge base that covers semantic understanding and structured representation. Through key technologies such as entity recognition, relation extraction, and event modeling, a forest management knowledge graph is constructed to realize the structured representation of professional terms, conceptual relationships, and business logic in the forestry and grassland field, serving as the core knowledge support module for large-scale models to drive intelligent decision-making in forest management.
[0032] The third step involves fine-tuning the large language model and enhancing retrieval generation: Two techniques, fine-tuning and retrieval enhancement generation, are used to process the open-source large language model DeepSeek, integrating professional knowledge from the forest management field to make its reasoning on forest management issues more professional. Based on this, the fine-tuned DeepSeek model accurately analyzes the management needs and objectives of management entities (such as forest farms, state-owned forestry bureaus, and local forestry authorities), transforming them into structured, computable management intent information. The fine-tuned DeepSeek model, combined with existing industry knowledge, extracts cyclical objectives (such as afforestation, conservation, carbon sequestration, and revenue), management constraints (policies and regulations, capital investment, and ecological protection red lines), and regional characteristics (forest type, climate, etc.), forming a clear management objective system and priorities.
[0033] The fourth step is data semantic fusion and understanding: structured and unstructured forestry data are input into a large language model, and the model is guided by prompts and engineering to complete tasks such as data understanding, indicator reasoning, and management objective matching, generating semantically consistent descriptions of management elements;
[0034] The fifth step is the reasoning behind the management strategy: The fine-tuned DeepSeek model automatically reasons based on integrated data, management intentions, and industry knowledge to form management strategies that conform to the laws of forest growth and the balance of multiple objectives. These strategies include: rationally dividing the management cycle, formulating technical measures for different cycles (afforestation and regeneration, tending and thinning, enclosure and protection, harvesting and utilization, pest and disease control, etc.), clarifying expected results (carbon sequestration, stock volume, economic output, etc.), and combining regional characteristics and industry regulations to output specific and executable combinations of management measures.
[0035] Step 6: Management Plan Text Generation: Based on the completion of strategy and spatial matching, and relying on the text generation and industry knowledge reserves of the fine-tuned DeepSeek model, the forest management plan text that meets the forestry industry norms and management requirements is automatically generated (including background analysis, resource status, overall goals, phase goals, zoning layout, technical measures, annual tasks, budget, performance forecast, risk prevention and control, policy compliance explanation, etc.). The generated results can be directly used for filing and approval or implementation guidance, improving the efficiency and standardization of plan preparation.
[0036] Step 7, Human-Computer Interaction and Continuous Optimization: Feedback from forestry workers on forest management plans is collected through a human-computer interface. The generated management plans are then systematically evaluated from multiple perspectives, including forest resource sustainability (stock dynamics, carbon sequestration dynamics), ecological function enhancement (biodiversity, water conservation, etc.), achievement of management goals (yield, economic benefits), risk controllability (fire, pests, etc.), and policy compliance. A quantitative score is calculated by comparing industry indicator systems, model inference results, and historical data. If the evaluation results are unsatisfactory, the system automatically adjusts management strategies, spatial configurations, and cycle arrangements, iterating and optimizing the fine-tuned DeepSeek model multiple times to ultimately output the forest management plan with the optimal overall benefits.
[0037] Example 2: As Figure 1 , Figure 2 , Figure 3 , Figure 4 and Figure 5 As shown, a large language model-driven forest management data processing method has the following key technology flowchart: Figure 2 As shown, it includes:
[0038] Step 101: Multi-source data collection for forest management areas. Collect multi-source heterogeneous data required for compiling forest management plans, including remote sensing imagery (multi-temporal optical remote sensing imagery, radar imagery (SAR), lidar (LiDAR) point cloud data, UAV imagery, etc.), forestry survey data (stand survey data, sample plot monitoring data, phenological observation data, etc.), meteorological and environmental data (time-series data such as temperature, precipitation, wind speed, and humidity), and policy and regulatory documents (laws and regulations, industry standards, historical management archives, regional policies, etc.).
[0039] Step 102, Data Preprocessing and Standardization. Using text, remote sensing, and sensor data as input, the data processing capabilities of the finely tuned DeepSeek model are utilized to perform spatial registration, format unification, coordinate system unification, and data standardization on forest data from different sources, in order to achieve uniformity in spatiotemporal resolution, time scale, and data format.
[0040] Step 103, data fusion. For example... Figure 3As shown, based on data preprocessing and standardization, a fine-tuned DeepSeek model is used to analyze the standardized data. Furthermore, based on the analysis of forest management objectives and data characteristics, a multimodal cross-attention fusion network is constructed to build a unified, spatiotemporally consistent, and structured forest resource data foundation. Taking remote sensing data, text data, and sensor data as examples, the input fusion can be represented as:
[0041]
[0042] In the formula, the remote sensing image features are represented as follows: Forestry text features are represented as Time-series sensor data is represented as T represents the time step or sequence length (number of image blocks, number of words, and number of time points) of each modal input sequence, and b represents the original modal feature dimension.
[0043] Next, using remote sensing image mode H (v) As the primary modality, cross-attention mechanisms are applied to both the text and sensor modalities:
[0044] Attention operations on the text modality are as follows:
[0045]
[0046] The attention operation for the sensor mode is as follows:
[0047]
[0048] In the formula, W Q (v) W is the query matrix for remote sensing modes. K (t) W K (s) W is the key matrix for the sensor / text. V (t) W V (s) A is the value matrix of the sensor / text. (v←t) Let A be the attention weight matrix of the remote sensing mode on the sensor mode. (v←s) W is the attention weight matrix of the remote sensing modality to the text modality. Q (v) W K (·) , All are projection parameters that can be learned through cross-attention. Z (v←t) Z represents the feature representation after weighted information from sensors is fused with remote sensing modes. (v←s) The feature representation of the weighted information from the text is incorporating the remote sensing modality.
[0049] Then, the original master modality features and the cross-attention output are subjected to residual fusion and nonlinear transformation:
[0050] F (v) =ReLU(H (v) +Z (v←t) +Z (v←s) )
[0051] F (t) =ReLU(H (t) +Z (t←v) +Z (t←s) )
[0052] F (s) =ReLU(H (s) +Z (s←v) +Z (s←t) )
[0053] In the formula, F (v) For the final representation of remote sensing modalities after fusing contextual information from text and sensors, F (t) For the final representation of the sensor after fusing contextual information from text and remote sensing, F (s) ReLU represents the ReLU activation function as the final representation of text modalities after fusing contextual information from remote sensing and sensors.
[0054] Finally, the Transformer fusion machine is used to unify and integrate the fusion features of all modalities. The specific formula is as follows:
[0055] F fusion =TransformerFusion([F (v) ;F (t) ;F (s) ])
[0056] In the formula, F fusion This is a joint representation of the fused multimodal features.
[0057] Step 201, Forest Management Knowledge Dataset. Based on historical forest management plans, forest management data, and textual data such as papers, patents, standards, policies and regulations in the field of forestry and grassland, a forest management knowledge dataset is built for fine-tuning the DeepSeek model.
[0058] Step 202, Forest Management Plan Knowledge Base. Based on the core forest management data and information within the organization developing the forest management plan, a forest management plan knowledge base is constructed.
[0059] Step 301, Fine-tuning. Based on the established forest management knowledge dataset, QLoRA technology is used to efficiently fine-tune DeepSeek to integrate professional knowledge in the field of forest management, making its reasoning and question-answering of forest management knowledge more professional and accurate.
[0060] Step 302, Retrieval Enhancement Generation. Based on the fine-tuned DeepSeek model, retrieval enhancement generation technology (RAG) is used to achieve real-time retrieval and dynamic retrieval of forest management plans in the knowledge base, such as... Figure 4 The enhanced generation of the forest management plan knowledge base mainly consists of three steps: data preparation, data retrieval, and plan generation. In the data preparation stage, an external knowledge base is constructed based on knowledge data in the forestry and grassland field, such as papers, patents, and forest management plans. Then, a large language model is used to segment the documents into appropriately sized fragments for subsequent retrieval. Based on this, the Chinese-Alpaca-2 embedding model is used to convert the segmented text into semantic vectors, which are then stored in a vector knowledge base.
[0061] In the retrieval phase, the retrieval enhancement generation technology first converts the user's input question (e.g., "Please help me generate a forest management plan for Gaofeng Forest Farm") into a query vector using the Chinese-Alpaca-2 embedding model. This query vector is then used as the basis for inputting into a similarity database to find the most relevant text fragments to the question. Next, the search results are sorted according to relevance, and the most relevant input is selected for the content generation phase.
[0062] In the solution generation phase, the user's input question and the content obtained in the retrieval phase are packaged into prompts, which are then used as input to the fine-tuned DeepSeek model in the generation phase. The fine-tuned DeepSeek model will generate the final forest management solution for Gaofeng Forest Farm based on the enhanced context.
[0063] Step 303: Forest Management Plan Generation Workflow. Based on a finely tuned DeepSeek model optimized using fine-tuning and retrieval enhancement generation techniques, the optimized model's natural language understanding and domain knowledge reasoning capabilities are utilized to accurately analyze the management needs and objectives of management entities (such as forest farms, state-owned forestry bureaus, and local forestry authorities), transforming them into structured and computable management intent information. The optimized DeepSeek model extracts cyclical objectives (such as afforestation, conservation, carbon sequestration, and revenue), management constraints (policies and regulations, capital investment, and ecological protection red lines), and regional characteristics (forest type, climate, etc.), forming a clear management objective system and priorities, providing a basis for subsequent multi-scale reasoning.
[0064] Step 401 involves using structured data (such as forest resource inventory forms and management indicator data) and unstructured text (such as survey reports and remote sensing images) as input data. Through format conversion and field alignment, a unified representation of multi-source heterogeneous data is achieved. Key information is extracted from the unstructured text through natural language preprocessing to construct a standard input format.
[0065] Step 402: Combining forest management objectives with business scenarios, design domain-knowledge-enhanced prompt templates to guide the fine-tuned DeepSeek model in completing data parsing, management logic reasoning, and objective matching tasks. The prompts explicitly include elements such as stand attributes, management objectives, and current status indicators, guiding the model to generate standardized management descriptions.
[0066] Step 403: Input the preprocessed data and prompts into the fine-tuned DeepSeek model. Based on its understanding of forestry knowledge and language, the fine-tuned DeepSeek model completes the construction of semantic relationships between data, logical reasoning between indicators, and analysis of the fit between management objectives, outputting semantically consistent descriptions of management elements and matching suggestions.
[0067] Step 404: The output of the fine-tuned DeepSeek model is parsed into structured semantic units, including information such as the management status of small-scale forests, key limiting factors, recommended measures, and matching scores. The results are directly integrated into the forest management plan generation module.
[0068] Step 501: Based on the analysis of business intentions, the DeepSeek model is optimized through fine-tuning and retrieval enhancement techniques to reasonably divide the business cycle (short-term, medium-term, long-term), formulate technical measures for different cycles (afforestation and regeneration, thinning, enclosure and protection, logging and utilization, pest and disease control, etc.), and clarify the expected results (carbon sink, stock volume, economic output, etc.).
[0069] Step 502: Based on the reasoning of the management content in the fine-tuned DeepSeek model, and combined with the characteristics of the forest management area and industry regulations, a multi-objective (economic benefits, ecological benefits, and social benefits) collaborative optimization technique is used to generate forest management strategies.
[0070] Step 601: Based on the existing forest management plan, construct a complete forest management plan Word template, including background analysis, current resource status, overall objectives, phased objectives, zoning layout, technical measures, annual tasks, budget, expected results, and explanation of policy compliance.
[0071] Step 602: Based on the finely tuned DeepSeek model optimized by fine-tuning and retrieval enhancement generation technology and the Word template of the forest management plan, a workflow for generating forest management plans is constructed to realize the automatic generation of forest management plans.
[0072] Step 701: Construct a systematic evaluation and optimization framework for forest management plans, and evaluate the forest management plans from multiple aspects such as the sustainability of forest resources, the improvement of ecological functions, the achievement of management objectives, the controllability of risks, and policy compliance.
[0073] Step 702: Based on the constructed systematic evaluation system for forest management plans, and combined with industry indicator systems, model reasoning results, and historical data comparison, multi-attribute decision analysis technology is used to quantitatively score and evaluate forest management plans.
[0074] Step 703: Based on the evaluation results of the forest management plan, a genetic algorithm is used to automatically adjust the management strategy, spatial configuration, and cycle arrangement, and to perform multiple rounds of iterative optimization, ultimately generating a forest management plan with the best overall benefits.
[0075] Step 704: Based on a large language model-driven forest management data processing method, an automatic forest management data processing terminal is designed using a cloud-edge combination approach, such as... Figure 5 As shown, the automatic forest management data processing terminal includes a forest management data acquisition module, edge devices, a cloud server, and a display module. The forest management data acquisition module is mainly used to collect sensor data such as standing tree information, diameter at breast height (DBH), and forest microclimate in the area to be managed. Then, the edge module sends the collected data to the server, where multi-source heterogeneous data preprocessing is performed, followed by standardization, forest management knowledge graph construction, and data semantic fusion and understanding. The corresponding data is then input into a fine-tuned and enhanced large language model to generate a forest management plan. After the forest management plan is generated, the display module of the automatic forest management data processing terminal displays the plan.
[0076] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the protection scope of the present invention.
Claims
1. A forest management data processing method driven by a large language model, characterized in that, Includes the following steps: The first step is the collection and preprocessing of multi-source data for forest management areas: collecting remote sensing images, aerial and UAV images, lidar data, field ground survey data, forest stand archives, management history data, climate and environmental data, topographic data, soil data, biodiversity data, pest and disease monitoring data, and relevant policy, regulation and standard data for forest management areas. Through data standardization, cleaning, spatiotemporal registration and multi-source fusion, a unified data resource system is formed. The second step is to construct a forest management knowledge graph: based on expert knowledge in the field of forest management, and combined with multi-source heterogeneous data such as papers, patents, standards, policies and regulations, and management plans in the field of forest management, natural language processing and information extraction technologies are used to construct a forest management knowledge dataset and knowledge base that covers semantic understanding and structured representation. The third step is to fine-tune the large language model and enhance the retrieval generation: the open-source large language model DeepSeek is processed using two techniques: fine-tuning and retrieval enhancement generation. The optimized large language model accurately analyzes the forest management needs and goals of the management entities and transforms them into structured and computable management intention information. The large language model combines existing industry knowledge to extract cyclical goals, management constraints, and regional characteristics, forming a clear management goal system and priorities. The fourth step is data semantic fusion and understanding: structured and unstructured forest data are input into a large language model, and the model is guided by prompting engineering to complete tasks such as data understanding, indicator reasoning, and management goal matching, and generate semantically consistent descriptions of forest management elements. The fifth step is business strategy reasoning: Based on integrated data, business intentions, and industry knowledge, the big language model automatically reasons to form business strategies that conform to the laws of forest growth and the balance of multiple objectives. Step 6: Management plan text generation: Based on the completion of strategy and spatial matching, and relying on the text generation capabilities of the large language model and industry knowledge reserves, the forest management plan text that meets the forestry industry standards and management requirements is automatically generated. Step 7, Human-computer interaction and continuous optimization: Collect feedback from forestry workers on the forest management plan through the human-computer interaction interface, and conduct a systematic evaluation of the generated management plan from multiple aspects such as forest resource sustainability, ecological function improvement, achievement of management goals, risk controllability, and policy compliance.
2. The forest management data processing method driven by a large language model according to claim 1, characterized in that, The data standardization process includes the following steps: analyzing the standardized data using a large language model, and constructing a multimodal cross-attention fusion network based on the analysis of forest management objectives and data characteristics to build a unified, spatiotemporally consistent, and structured forest resource data foundation, and performing input fusion on remote sensing image data, forestry text data, and time-series sensor data.
3. The forest management data processing method driven by a large language model according to claim 2, characterized in that, The original master modality features and cross-attention output are subjected to residual fusion and nonlinear transformation.
4. The forest management data processing method driven by a large language model according to claim 3, characterized in that, The Transformer fusion machine is used to unify and integrate the fusion features of all modalities.