Forest management data processing method driven by large language model

Abstract

The invention discloses a forest management data processing method driven by a large language model, and belongs to the technical field of forest resource management and artificial intelligence data processing crossing. The method comprises the steps of multi-source data acquisition and preprocessing, forest management knowledge graph construction, large language model fine adjustment and retrieval enhancement generation, data semantic fusion and understanding and management strategy reasoning, wherein the large language model performs automatic reasoning based on fused data, management intention and industry knowledge to form a management strategy conforming to a forest growth law and multi-target balance; operation plan text generation: on the basis of completing strategy and space matching, compiling a forest operation plan text meeting forestry industry specifications and management requirements, and man-machine interaction and continuous optimization: collecting feedback information of forestry workers on forest operation plans through a man-machine interaction interface, and systematic evaluation is carried out on the generated operation scheme from forest resource sustainability, ecological function improvement, operation goal achievement degree, risk controllability and policy compliance.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical 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

[0002] With the increasing requirements of data refinement and decision-making intelligence for forest resource management, the preparation and management of traditional forest management plans often rely on manual experience and rule-based models, and the fusion ability of multi-source heterogeneous data (such as remote sensing images, forestry survey data, policy texts, and meteorological data) is insufficient, making it difficult to meet the actual needs of dynamic and complex forestry production.

[0003] The knowledge reasoning and text generation capabilities of large language models provide a new path for efficient data processing, personalized generation, and dynamic updating of forest management plans during the preparation process, and become an important support for promoting the intelligent transformation of forest management. It is necessary to study how to use large language models to realize efficient data processing during the preparation process of forest management plans to solve the problems of long preparation period, reliance on experience, and difficulty in fine adaptation. With the development of large language models (such as DeepSeek, Wenxin Yiyang, ChatGPT, etc.), it is expected to realize efficient processing of forest management data, reduce costs, and improve quality by using the language understanding and generation capabilities of large models.

[0004] When preparing a forest plan, the prior art needs to first select a typical forest type stand from the forest as a sample plot, and obtain a stand volume and carbon storage estimation model based on the sample plot data. On this basis, an initial forest carbon sink management plan is manually written, and the carbon sink plan is optimized through a simulated annealing algorithm. Although this plan can generate a forest carbon sink management plan, it still relies on manual data processing during the preparation process of the forest management plan, which is inefficient and cannot effectively utilize multi-source and multi-modal data. In addition, it does not achieve intelligent generation of forest management plans based on forest types, structures, compositions, site conditions, health, biodiversity, and other conditions. Moreover, relying on manual writing of management plans does not achieve intelligent generation of forest management plans.

[0005] From the demand for forest management development and the current situation of relying on manual experience and low efficiency in traditional management data processing, it can be found that the realization of intelligent processing of forest management data is the inevitable trend of the intelligent development of forest management. To realize the automatic processing of forest management data, a key problem needs to be solved, which is how to integrate the professional knowledge of forest management on the basis of existing large language models and automatically process data according to the business logic of forest management standards. At present, the preparation and management of traditional forest management plans often rely on manual experience and rule-based models, and the fusion ability of multi-source heterogeneous data (such as remote sensing images, forestry survey data, policy texts, and meteorological data) is insufficient, and there are few reports on efficient processing of forest management data using large language models. SUMMARY

[0006] The present application provides a large language model driven forest management data processing method.

[0007] A large language model driven forest management data processing method, comprising the following steps: multi-source data acquisition 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 the fusion data, management intention and industry knowledge, the large language model automatically reasons to form a management strategy that conforms to the forest growth rule and multi-objective balance, management scheme text generation: based on the completion of strategy and space matching, relying on the text generation ability of the large language model and the industry knowledge in the knowledge base, automatically preparing a forest management plan text that conforms to the forestry industry specifications and management requirements, human-computer interaction and continuous optimization: collecting the feedback information of forestry workers on the forest management plan through the human-computer interaction interface, and systematically evaluating the generated management plan from the aspects of forest resource sustainability, ecological function improvement, management target achievement, risk controllability, and policy compliance.

[0008] A large language model driven forest management data processing method, compared with the traditional forest management data processing method relying on manual experience and rule-based models, has the following significant technical effects and advantages:

[0009] (1) Improve the data processing efficiency and intelligent level of forest management plan preparation

[0010] By introducing a large language model with strong knowledge understanding and reasoning ability, multi-source and multi-modal data of forest management can be quickly and automatically processed according to different regions, different forest types and different management objectives, and a forest management plan can be generated, significantly improving the efficiency of plan preparation, reducing the dependence on manual experience and reducing labor costs.

[0011] (2) Promote standardization, scientization and standardization of plans

[0012] Through the deep learning and fusion of existing forest management technical regulations, laws and regulations, and industry standards by the large language model, the generated scheme can be more in line with national and industry standards, avoiding human errors and inconsistencies, and improving the standardization, scientificity and operability of the preparation results.

[0013] (3) Enhance the sustainability and dynamic updating ability of the scheme

[0014] The present application has the ability of continuous learning and knowledge updating, which can dynamically adjust the management strategy according to the changes of external data (such as climate, pests and diseases, market demand, etc.) and the latest research progress in forestry, ensuring the foresight, scientificity and sustainability of the forest management scheme.

[0015] (4) Assist in the construction of digital and intelligent forestry

[0016] The present application can be deeply integrated with existing forest resource monitoring systems, remote sensing data, geographic information systems (GIS) and other digital platforms, further promoting the construction of smart forestry and digital forestry, and providing technical support for precise forest management and "double carbon" goals.

[0017] In summary, the present application can effectively break through the technical bottlenecks of weak data processing ability, low efficiency and lack of dynamicity in the existing forest management scheme preparation process, and has good application prospect and popularization value.

[0018] By introducing a large language model, the unified analysis and semantic fusion of structured and unstructured forestry data are realized, significantly improving the processing efficiency and intelligent level of forest management data. This method can automatically complete spatial registration, coordinate system unification, semantic recognition, index reasoning, target matching and data standardization, etc. complex tasks, solving the problems of strong data heterogeneity, difficult semantic alignment and multiple manual interventions in traditional methods. Technically, it realizes efficient fusion and semantic standardization output of multi-source heterogeneous forestry data, effectively improving the automation, accuracy and scalability of data processing, and providing a solid data foundation for large-scale forest management intelligent decision-making. BRIEF DESCRIPTION OF DRAWINGS

[0019] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the drawings needed to be used in the embodiment or prior art description will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present application, and those skilled in the art can obtain other drawings according to these drawings without creating any inventive labor. As shown in the drawings:

[0020] Figure 1 A large language model driven forest management data processing method flowchart.

[0021] Figure 2A large language model driven forest management data processing key technology flowchart of the present application.

[0022] Figure 3 A large language model driven forest management data preprocessing, standardization and fusion flowchart of the present application.

[0023] Figure 4 A large language model driven forest management data preprocessing, standardization and fusion flowchart of the present application.

[0024] Figure 5 A large language model driven forest management data processing method terminal block diagram of the present application. DETAILED DESCRIPTION

[0025] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0026] In order for those skilled in the art to better understand the technical solutions of the present application, the embodiments of the present application will be further described in detail below with reference to the drawings and specific embodiments, but not as a limitation of the present application. If there is no necessity for the relationship between the steps described in the present application, the order described as an example in the present application should not be regarded as a limitation, and those skilled in the art should know that the order can be adjusted, as long as it does not destroy the logic between them and lead to the impossibility of the whole process.

[0027] Embodiment 1: as shown in Figure 1 , Figure 2 , Figure 3 , Figure 4 and Figure 5 , a large language model driven forest management data processing method is used to improve the intelligent level of data processing in the process of forest management scheme compilation.

[0028] In order to overcome the problem that the existing forest management data processing method relies on expert experience and rule-based model, and has insufficient fusion ability for multi-source heterogeneous data (such as remote sensing image, forestry survey data, policy text, meteorological data, etc.), and is difficult to meet the actual needs of dynamic and complex forestry production, the present application provides a large language model driven forest management data processing method.

[0029] A large language model driven forest management data processing method includes:

[0030] First step, multi-source data collection and preprocessing: Collect multi-dimensional, multi-source, heterogeneous data such as remote sensing images, aerial and unmanned aerial vehicle images, LiDAR data, field ground survey data, forest stand archives, management history data, climate and environment data, terrain data, soil data, biodiversity data, pest monitoring data, and related policy and regulation standards required for forest management plan compilation. Through data standardization, cleaning, spatio-temporal registration, multi-source fusion, etc., a unified data resource system is formed;

[0031] Second step, construction of forest management knowledge graph: Based on expert knowledge in the field of forest management, combined with multi-source heterogeneous data such as papers, patents, standards, policies and regulations, management plans in the field of forest management, natural language processing and information extraction techniques are used to construct a forest management knowledge dataset and knowledge base covering semantic understanding and structured representation. Through key technologies such as entity recognition, relationship extraction, and event modeling, a forest management knowledge graph is constructed to realize the structured representation of forest and grass field professional terms, concept relationships and business logic as the core knowledge support module for large model driven forest management intelligent decision-making;

[0032] Third step, fine-tuning and retrieval enhancement generation of large language model: Two technologies of fine-tuning and retrieval enhancement generation are used to process the open source large language model DeepSeek to integrate professional knowledge in the field of forest management and make it more professional in reasoning forest management problems. On this basis, the fine-tuned DeepSeek model accurately analyzes the management needs and goals of management subjects (such as forest farms, state-owned forestry bureaus, and local forestry departments) and converts them into structured and computable management intent information. The fine-tuned DeepSeek model extracts periodic targets (such as forestation, conservation, carbon sink, and revenue), management constraints (policies and regulations, capital investment, and ecological protection red lines), and regional characteristics (forest type and climate) based on existing industry knowledge to form a clear management target system and priority;

[0033] Fourth step, data semantic fusion and understanding: Structured and unstructured forestry data are input into the large language model, which is guided by the prompt engineering to complete data understanding, index reasoning, and management target matching tasks to generate semantically consistent management element descriptions;

[0034] Fifth step, management strategy reasoning: The fine-tuned DeepSeek model automatically reasons to form a management strategy that conforms to the laws of forest growth and the balance of multiple targets based on the integrated data, management intent, and industry knowledge, including: reasonable division of management periods, formulation of technical measures (afforestation, updating, tending, felling, and utilization), pest control, etc. in different periods, clear expected results (carbon sink, accumulation, and economic value), and combination of regional characteristics and industry regulations to output specific and executable management measures;

[0035] Step 6: Business plan text generation: Based on the completion of strategy and space matching, relying on the text generation and industry knowledge reserve ability of the fine-tuned DeepSeek model, automatically prepare the forest management plan text (including background analysis, resource status, overall goal, stage goal, zoning layout, technical measures, annual task, fund budget, effectiveness estimation, risk prevention and control, policy compliance explanation, etc.) that meets the forestry industry specifications and management requirements. The generated results can be directly used for recordation and approval or implementation guidance, improving the efficiency and standardization of plan preparation.

[0036] Step 7: Human-computer interaction and continuous optimization: Collect feedback information from forestry workers on the forest management plan through the human-computer interaction interface, and systematically evaluate the generated management plan from the aspects of forest resource sustainability (accumulation dynamics, carbon sink dynamics), ecological function improvement (biodiversity, water conservation, etc.), management goal achievement (yield, economic benefit), risk controllability (fire, pest, etc.), and policy compliance. Quantitative scoring is conducted by comparing the industry index system, model reasoning results, and historical data. If the evaluation result is not up to standard, the system automatically adjusts the management strategy, spatial configuration, cycle arrangement, etc., and iteratively optimizes the fine-tuned DeepSeek model, finally outputting the forest management plan with the best comprehensive benefit.

[0037] Embodiment 2: as shown in Figure 1 、 Figure 2 、 Figure 3 、 Figure 4 and Figure 5 , a large language model driven forest management data processing method, its key technical process diagram is shown in Figure 2 , including:

[0038] Step 101: Multi-source data collection of forest management area. Collect multi-source heterogeneous data required for preparing forest management plan, such as remote sensing image data (multi-temporal optical remote sensing image, radar image (SAR), LiDAR point cloud data, unmanned aerial vehicle image, etc.), forestry survey data (forest inventory data, sample plot monitoring data, phenology observation data, etc.), meteorological environment data (temperature, precipitation, wind speed, humidity, etc. Time series data), policy and regulation documents (laws and regulations, industry standards, historical management archives, regional policies, etc.).

[0039] Step 102: Data preprocessing and standardization. With text, remote sensing and sensor data as input, the data processing capability of the fine-tuned DeepSeek model is used to perform spatial registration, format unification, coordinate system unification and data standardization on different sources of forest data, to realize the unification of spatial resolution, time scale, data format, etc.

[0040] Step 103: Data fusion. As shown in 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 main modal features are residual fused with the cross-attention output and nonlinearly transformed:

[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) is the final representation of the remote sensing modal after fusing the context information from the text and the sensor, F (t) is the final representation of the sensor after fusing the context information from the text and the remote sensing, F (s) is the final representation of the text modal after fusing the context information from the remote sensing and the sensor, and ReLU represents the ReLU activation function.

[0054] Finally, the fusion features of all the modals are unified by using a Transformer fusioner, and the specific formula is as follows:

[0055] F fusion = TransformerFusion([F (v) ; F (t) ; F (s) ])

[0056] In the formula, F fusion is the joint representation of the fused multi-modal features.

[0057] Step 201, forest management knowledge dataset. Based on historical forest management plans, forest management data, papers, patents, standards, policies and regulations in the field of forest and grass, a forest management knowledge dataset for fine-tuning the DeepSeek model is built.

[0058] Step 202, forest management plan knowledge base. Based on the forest management core data and materials of the forest management plan unit to be done, a forest management plan knowledge base is constructed.

[0059] Step 301, fine-tuning. Based on the built forest management knowledge dataset, QLoRA technology is used to fine-tune DeepSeek to integrate professional knowledge in the field of forest management, making its reasoning and answering of forest management knowledge more professional and accurate.

[0060] Step 302, retrieval augmentation generation. Based on the fine-tuned DeepSeek model, retrieval augmentation generation technology (RAG) is used to realize real-time retrieval and dynamic calling of forest management plans in the knowledge base, such as Figure 4 The retrieval augmentation generation of forest management plan knowledge base mainly includes three steps: data preparation, data retrieval and plan generation. In the data preparation stage, based on the knowledge data in the field of forest and grass such as papers, patents and forest management plans, an external knowledge base is constructed. Then, the document is divided into appropriate size fragments by using large language model, so as to facilitate subsequent retrieval. On this basis, the embedded model Chinese-Alpaca-2 is used to convert the divided text into semantic vector and store it in the vector knowledge base.

[0061] In the retrieval stage, the retrieval augmentation generation technology will first convert the user input question (such as "please help me generate a forest management plan for Gao Feng Forest Farm") into a query vector by using the embedded model Chinese-Alpaca-2, and input the query vector into the vector database for similarity retrieval based on the query vector to find the most relevant text fragments in the database. Then, the retrieval results are sorted according to relevance, and the most relevant content is selected as the input of the generation stage.

[0062] In the plan generation stage, the user's input question and the content obtained in the retrieval stage are packaged as prompt words, and they are used as the input of the fine-tuned DeepSeek model in the generation stage. The fine-tuned DeepSeek model will generate the final forest management plan for Gao Feng Forest Farm based on the augmented context.

[0063] Step 303, forest management plan generation workflow. Based on the fine-tuned DeepSeek model optimized by fine-tuning and retrieval augmentation generation technology, the natural language understanding and domain knowledge reasoning ability of the optimized model are used to accurately analyze the management needs and goals of management subjects (such as forest farms, state-owned forestry bureaus, local forestry departments, etc.), and convert them into structured and calculable management intent information. Through the optimized fine-tuned DeepSeek model, the periodic goals (such as forest cultivation, conservation, carbon sink, income, etc.), management constraints (policies and regulations, capital investment, ecological protection red line, etc.), and regional characteristics (forest type, climate, etc.) are extracted to form a clear management goal system and priority, providing a basis for subsequent multi-scale reasoning.

[0064] Step 401, structured data (such as forest resource inventory table, management index data) and unstructured text (such as investigation report, remote sensing image) are taken as input data. Through format conversion and field alignment, unified expression of multi-source heterogeneous data is realized. Unstructured text extracts key information through natural language preprocessing and constructs standard input format.

[0065] Step 402, combining forest management objectives and business scenarios, design field knowledge enhanced prompt template to guide the fine-tuned DeepSeek model to complete data analysis, management logic reasoning and target matching tasks. The prompt explicitly includes forest stand attributes, management objectives, current status indicators and other elements to guide the model to generate standardized management descriptions.

[0066] Step 403, input the preprocessed data and prompts into the fine-tuned DeepSeek model. Based on the understanding of forestry knowledge and language, the fine-tuned DeepSeek model completes semantic association construction between data, logical reasoning between indicators, and matching degree analysis of management objectives, and outputs semantic consistent management element description and matching suggestions.

[0067] Step 404, the output of the fine-tuned DeepSeek model is parsed into structured semantic units, including small class management status, key limiting factors, recommended measures and matching score information. The results are directly connected to the forest management plan generation module.

[0068] Step 501, based on the analysis of management intent, the fine-tuned DeepSeek model optimized by fine-tuning and retrieval enhancement generation technology is used to reasonably divide the management cycle (short-term, medium-term, long-term), formulate technical measures in different cycles (afforestation, tending, intermediate cutting, protection, harvesting, pest control, etc.), and clarify the expected results (carbon sink, stock, economic value, etc.).

[0069] Step 502, based on the fine-tuned DeepSeek model management content reasoning, combined with the characteristics of forest management area and industry regulations, multi-objective (economic benefit, ecological benefit, social benefit) collaborative optimization technology is used to generate forest management strategy.

[0070] Step 601, based on the existing forest management plan, a complete forest management plan Word template is constructed, including background analysis, resource status, overall goal, stage goal, zoning layout, technical measures, annual task, fund budget, performance estimation, policy compliance explanation, etc.

[0071] Step 602, based on the fine-tuned DeepSeek model optimized by fine-tuning and retrieval enhancement generation technology and the forest management plan Word template, a forest management plan generation workflow is constructed to realize the automatic generation of forest management plan.

[0072] Step 701, a forest management plan system evaluation optimization system is constructed, and the forest management plan is evaluated from the aspects of forest resource sustainability, ecological function improvement, management target achievement degree, risk controllability, policy compliance and the like.

[0073] Step 702, based on the constructed forest management plan system evaluation system, combined with the comparison of industry index system, model reasoning result and historical data, the multi-attribute decision analysis technology is used to quantitatively score and evaluate the forest management plan.

[0074] Step 703, based on the evaluation result of the forest management plan, the genetic algorithm is used to automatically adjust the management strategy, spatial configuration, cycle arrangement and the like and perform multiple rounds of iteration optimization, and finally generate a forest management plan with optimal comprehensive benefits.

[0075] Step 704, based on a large language model driven forest management data processing method, through a cloud edge combination mode, a forest management data automatic processing terminal is designed, as shown in Figure 5 The forest management data automatic processing terminal includes a forest management data acquisition module, an edge device, a cloud server and a display module. The forest management data acquisition module is mainly used to acquire stand information, diameter at breast height, forest microclimate and other sensor data of the forest management area; then the edge module sends the collected data to the server end, performs multi-source heterogeneous data preprocessing on the server end, then performs standardization, forest management knowledge graph construction, data semantic fusion and understanding module processing, and inputs the corresponding data into the large language model generated after fine-tuning and retrieval enhancement, generates a forest management plan, and displays the forest management plan by calling the display module of the forest management data automatic processing terminal.

[0076] The above only describes the preferred embodiments of the present application and does not limit the present application. Any modification, equivalent replacement and improvement within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A forest management data processing method driven by a large language model, characterized in that, The process includes the following steps: multi-source data collection and preprocessing, construction of a forest management knowledge graph, fine-tuning and enhanced generation of a large language model, data semantic fusion and understanding, management strategy reasoning: the large language model automatically infers management strategies that conform to forest growth patterns and multi-objective balance based on fused data, management intentions, and industry knowledge, management plan text generation: based on strategy and spatial matching, and relying on the text generation capabilities of the large language model and industry knowledge in the knowledge base, automatically compiles forest management plan texts that conform to forestry industry standards and management requirements, and human-computer interaction and continuous optimization: through a human-computer interaction interface, feedback information from forestry workers on forest management plans is collected, and the generated management plans are systematically evaluated from multiple aspects such as forest resource sustainability, ecological function enhancement, 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, It also includes the following steps: The first step is the collection and preprocessing of multi-source data for forest management areas: collecting multi-dimensional, multi-source, and heterogeneous data such as 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 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.

3. The forest management data processing method driven by a large language model according to claim 2, characterized in that, The data standardization process includes the following steps: analyzing the standardized data using a large language model, and based on the analysis of forest management objectives and data characteristics, constructing a multimodal cross-attention fusion network to build a unified, spatiotemporally consistent, and structured forest resource data foundation. For remote sensing image data, forestry text data, and time-series sensor data, the input fusion representation is as follows: 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.

4. The forest management data processing method driven by a large language model according to claim 3, characterized in that, With remote sensing image mode H (v)) As the primary modality, cross-attention mechanisms are applied to both the text and sensor modalities: Attention operations on the text modality are as follows: z (v←t) =A (v←t) (H (t) W V (t) ) The attention operation for the sensor mode is as follows: Z (v←s) =A (v-s) (H (s) W V (s) ) 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 text / sensor input. V (t) W V (s) A is a value matrix of text / sensor data. (v←t) Let A be the attention weight matrix of the remote sensing modality to the text modality. (v←s) This is the attention weight matrix of the remote sensing mode on the sensor mode. All are cross-attention learnable projection parameters, z (v←t) Z represents the feature representation of remote sensing modalities after weighted information from text. (v←s) The feature representation of the weighted information from the sensor after incorporating the remote sensing mode.

5. The forest management data processing method driven by a large language model according to claim 4, characterized in that, The original dominant modality features are combined with the cross-attention output through residual fusion and nonlinear transformation: F (v) =ReLU(H (v) +Z (v←t) +Z (v←s) ) F (t) =ReLU(H (t) +Z (t←v) +Z (t←s) ) F (s) =ReLU(H (s) +Z (s←v) +Z (s←t) ) 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 sensor modalities 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.

6. The forest management data processing method driven by a large language model according to claim 5, characterized in that, The Transformer fusion machine is used to unify and integrate the fusion features of all modalities. The specific formula is as follows: F fusion =TransformerFusion([F (v) ;F (t) ;F (s) ]) In the formula, F fusion This is a joint representation of the fused multimodal features.

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