Ploughing quality intelligent evaluation and decision support system based on AI large model

Abstract

The invention belongs to the cross field of intelligent agriculture and information technology, and provides an AI large model-based cultivated land quality intelligent evaluation and decision support system, which comprises a data acquisition and access module, a data processing platform, an AI intelligent engine core module, an application service module and a dynamic feedback adjustment module, the data acquisition and access module is used for acquiring multi-source heterogeneous cultivated land data and improvement effect feedback data, and acquiring soil microscopic data through quantum dot marking and a Raman spectrum technology to generate soil physical and chemical fusion data; the data processing platform performs parallel processing and feature fusion on the multi-source data; the AI intelligent engine core module generates a personalized cultivated land improvement scheme based on a plurality of intelligent models; the application service module realizes scheme landing through a mobile application and intelligent agricultural machine interface; and the dynamic feedback adjustment module optimizes knowledge graph weights and model parameters based on the improved effect data. According to the invention, farmland quality evaluation and improvement scheme optimization are realized, and the intelligent level of farmland quality management can be improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of smart agriculture and information technology, and particularly relates to a cultivated land quality intelligent evaluation and decision support system based on an AI large model. BACKGROUND

[0002] Cultivated land is the basic resource of agricultural production, and its quality status is directly related to national food security, agricultural product quality and safety, and the sustainable development of agriculture. Traditional cultivated land quality evaluation methods mainly rely on manual field sampling and laboratory chemical analysis. This method not only has a long data collection period and high labor and material costs, but also has insufficient spatial representativeness of data due to the limited number of sampling points, making it difficult to fully reflect the quality heterogeneity of large-area cultivated land. In addition, traditional evaluation often focuses on a single or a few indicators such as soil physical and chemical properties, and the comprehensive evaluation of cultivated land quality is not comprehensive enough to meet the needs of the transformation of modern agriculture to precision, dynamic, and fine management.

[0003] In recent years, with the development of Internet of Things, big data and artificial intelligence technology, some cultivated land quality evaluation systems have begun to introduce Internet of Things data collection methods and machine learning evaluation models, which have improved the evaluation efficiency and objectivity to a certain extent. However, the existing technology still faces many challenges in practical application, which can be summarized as follows: First, the integration and analysis capabilities of current multi-source data need to be strengthened. Although soil physical and chemical data (such as pH value, organic matter content, nitrogen, phosphorus, and potassium nutrients), remote sensing image data (such as vegetation coverage, crop growth), and meteorological data (such as rainfall, temperature) can be obtained, the fusion of these data is mostly limited to simple spatio-temporal matching or feature splicing, lacking deep correlation analysis and information mining. More importantly, key information such as the occurrence form of heavy metals (effective state, residual state, etc., which has higher biological availability and migration risk than total amount), the functional group structure of soil organic carbon (which affects its stability and adsorption capacity for pollutants), and the structure and diversity of soil microbial community (which drives soil material circulation and nutrient transformation) have not been effectively integrated into the evaluation system, which limits the precision of the evaluation model in describing the intrinsic physical and chemical properties, biological activity, and potential risks of pollutants of the soil, and makes it difficult to reveal the deep mechanism of cultivated land quality formation and degradation.

[0004] Second, traditional machine learning models (such as logistic regression, support vector machines, shallow neural networks, etc.) often lack the ability to learn and express complex nonlinear relationships, multi-factor interactions, and high-dimensional feature spaces in arable land systems. More importantly, most existing models are static and lack effective dynamic knowledge updating mechanisms. They are difficult to adapt to long-term trends in arable land quality, the effectiveness of agricultural management measures, and changes in external environmental conditions (such as climate change and new pollutant inputs). This makes it difficult for them to generate arable land improvement programs or management recommendations that are regionally applicable but lack individuality and specificity, making it difficult to precisely match unique soil conditions and production goals for specific plots.

[0005] Third, existing arable land quality evaluation and decision support systems often present a one-way linear model of data input, model evaluation, and decision output. Once the arable land quality rating is complete and preliminary improvement or management recommendations are made, the system stops tracking and feedback, and lacks an effective channel for continuously optimizing and iteratively upgrading evaluation model parameters and decision logic based on actual improvement results or management measures. The lack of this process loop management mechanism makes it difficult for the system to learn from practice and improve the accuracy of evaluation and the effectiveness of decision recommendations, limiting the system's ability to evolve and its long-term application value.

[0006] Fourth, the precise connection between evaluation results and actual arable land management measures is a key step in improving arable land quality. However, the evaluation results and improvement programs output by existing systems are mostly presented in the form of reports, charts, or grade divisions, lacking standardized interfaces and data conversion protocols for operation instructions of modern agricultural intelligent equipment (such as variable rate fertilizer applicators, precision seeders, unmanned aerial vehicle plant protection systems, etc.). This makes it difficult to directly and efficiently convert evaluation results and decision recommendations into specific operation parameters (such as spatial variation prescription maps for fertilizer amount, pesticide amount, and seeding depth) for intelligent agricultural machinery, affecting the practical application effect and conversion efficiency of technical achievements and restricting the scale-up of precision agriculture technology.

[0007] Therefore, to effectively solve the above problems in existing technology and break through the limitations of traditional evaluation methods, it is urgent to build an intelligent arable land quality evaluation and decision support system that can deeply integrate multi-source heterogeneous data, have dynamic learning and self-evolution capabilities, support precise decision generation, and achieve full-process closed-loop optimization. This system should be able to comprehensively perceive arable land quality factors, deeply mine data value, accurately predict quality changes, intelligently generate individualized improvement programs, and effectively guide field precision operations, thereby providing strong technical support for the scientific protection, precision management, and sustainable use of arable land resources. SUMMARY

[0008] The purpose of this invention is to overcome the shortcomings of the prior art and provide an intelligent evaluation and decision support system for arable land quality based on an AI large model. Through deep fusion of multi-source data, collaborative reasoning of AI models, and dynamic feedback optimization, it realizes the intelligent generation of accurate evaluation of arable land quality and personalized improvement plans.

[0009] To achieve the above objectives, the present invention relates to an intelligent evaluation and decision support system for farmland quality based on an AI large model, comprising a data acquisition and access module, a data processing platform, an AI intelligent engine core module, an application service module, and a dynamic feedback adjustment module. The data acquisition and access module is configured to collect and access multi-source heterogeneous cultivated land data and cultivated land improvement effect feedback data. Multi-source heterogeneous cultivated land data includes soil physicochemical data, soil physicochemical fusion data, remote sensing data, meteorological environment data, agricultural activity data and geospatial data. Cultivated land improvement effect feedback data includes soil index change data after the application of improvement materials, crop growth status data and yield and quality data. The data processing platform, which connects the data acquisition and access module, is configured to perform parallel processing on multi-source heterogeneous farmland data and farmland improvement effect feedback data to generate processed data. The core module of the AI ​​intelligent engine connects the data processing platform and the dynamic feedback adjustment module. It is configured to perform comprehensive analysis and intelligent reasoning on the processed data based on dynamic knowledge graphs, multimodal prediction models, anomaly diagnosis models, and generative decision-making models, and generate personalized farmland improvement plans for specific farmland units. The application service module, connected to the core module of the AI ​​intelligent engine, is configured to present personalized farmland improvement plans to end users through mobile applications and intelligent agricultural machinery control interfaces, convert personalized farmland improvement plans into operation instructions that can be executed by intelligent agricultural machinery, and collect feedback data on farmland improvement effects. The dynamic feedback adjustment module connects the core module of the AI ​​intelligent engine and the application service module. It is configured to evaluate the feedback data on the effect of farmland improvement, update the collaborative relationship weights in the dynamic knowledge graph, and optimize the parameters of the multimodal prediction model, the anomaly diagnosis model, and the generative decision-making model.

[0010] Furthermore, multi-source heterogeneous farmland data and farmland improvement effect feedback data are collected and accessed, including: obtaining data through file import or application programming interface (API) connection, adding a unique traceability tag to each type of accessed data, the unique traceability tag including collection time, geographical location coordinates and accuracy, data source credibility score, and using blockchain for encrypted storage.

[0011] Furthermore, the data acquisition and access module includes a farmland micro-data acquisition and processing sub-module, which is used to specifically label soil colloidal particles in farmland using quantum dot labeling technology, acquire soil micro-data of the specifically labeled farmland soil using a portable Raman spectrometer, and correlate the soil micro-data with soil physicochemical data to generate soil physicochemical fusion data; the soil micro-data includes at least the heavy metal speciation and organic carbon functional group structure.

[0012] Furthermore, the data processing platform is used to perform the following operations: For structured data, perform data cleaning, data alignment, and data standardization to generate the first processed data; Unstructured data undergoes feature extraction, format conversion, and semantic annotation to generate second-processed data. Specifically, the U-Net++ model is used for image segmentation, and feature data is extracted. The feature data is then concatenated and combined to generate bimodal feature vectors. A pre-trained language model based on a Transformer encoder is used to perform semantic analysis and recognition on the bimodal feature vectors, and corresponding semantic labels are added. The bimodality includes visual and textual modalities. The first and second processed data are subjected to spatiotemporal alignment and spatial interpolation to generate processed data. The processed data includes continuous raster data, labeled data based on historical evaluation results, and bimodal feature fusion data.

[0013] Furthermore, the core modules of the AI ​​intelligent engine include a knowledge graph construction submodule, a multimodal prediction submodule, an anomaly diagnosis submodule, a farmland quality assessment submodule, a decision generation submodule, and a model update submodule; The knowledge graph construction submodule is used to construct a dynamic knowledge graph containing the synergistic relationship between soil, crops and environment based on an ontology library in the agricultural field. Based on the dynamic knowledge graph, entity matching and association retrieval are performed on the processed data to generate a structured data analysis context. The multimodal prediction submodule is used to input the feature data of the structured data analysis context into the large multimodal prediction model and output the prediction results. The feature data includes soil indicators, remote sensing time series features, meteorological and environmental indicators, historical events of agricultural activities, geospatial data and feedback data indicators of farmland improvement effects. The prediction results include the current quality level of farmland and the trend of quality level change under specific climate and management measures in the next 1-3 months. The anomaly diagnosis submodule is used to diagnose anomalies in arable land quality by using an improved isolated forest algorithm based on the feature data of the structured data analysis context. The arable land quality assessment submodule is used to evaluate and generate a comprehensive evaluation index of arable land quality based on three indicators: arable land ecological resilience, arable land production potential, and arable land restoration feasibility. It adopts the TOPSIS algorithm by introducing a dynamic weight adjustment mechanism and a grey relational degree correction term to analyze the characteristic data in the structured data analysis context. Arable land ecological resilience is used to characterize the ability of arable land to recover from damage caused by natural disasters. Arable land production potential is used to characterize the upper limit of the optimal yield of different crop varieties. Arable land restoration feasibility is used to characterize the technical difficulty and economic input of restoring arable land to a healthy state under different degrees of degradation. The decision generation submodule is used to generate personalized farmland improvement plans for specific farmland units based on structured knowledge graphs, prediction results, diagnostic results, and comprehensive evaluation indices, by calling the improvement measures knowledge base and combining crop yield information. The personalized farmland improvement plan includes at least the following: the specific types of improvement materials recommended for application, the precise dosage of each improvement material, the application method for each improvement material, and the corresponding tillage suggestions. The model update submodule is used to receive new evaluation criteria, diagnostic rules and improvement experience through the expert experience input interface, and to perform incremental training and parameter optimization on the multimodal prediction model, anomaly diagnosis model and generative decision model based on the updated multi-source heterogeneous farmland data and improvement effect feedback data.

[0014] Furthermore, the large multimodal prediction model is further configured as follows: A multimodal feature fusion model is used to encode soil physicochemical data, remote sensing data, meteorological environmental data, geospatial data, and farmland improvement effect feedback data. Then, feature-level fusion of each modality feature is performed to complete the farmland quality prediction task based on the fused features. Configure a dynamic weight allocation unit to dynamically adjust the weight coefficients of feature data according to the type of cultivated land. The dynamic weight allocation unit is further configured to: input microscale features as prior knowledge into the multimodal prediction model to correct the evaluation bias based on soil indicators, and combine the extreme weather probability prediction of the regional climate model to dynamically adjust the weight allocation of micro- and meso-scale features through an attention mechanism; microscale features are extracted from soil micro data through the improved 3DU-Net model.

[0015] Furthermore, the generative decision model is configured as follows: The factors affecting farmland quality are analyzed using a causal inference algorithm to pinpoint the causes of farmland quality degradation and obtain inference results. Based on multi-agent simulation technology, a dynamic model of farmland ecosystem is constructed to simulate the long-term impact of different improvement measures on farmland ecosystem, identify potential ecological risks, and obtain simulation results. The results of the inference and simulation are verified by transfer learning with the improvement cases stored in the improvement measures knowledge base. By comparing and analyzing the feedback data of improvement effects under similar geological and climatic conditions, the personalized farmland improvement plan is optimized to ensure the effectiveness of the personalized farmland improvement plan under similar geological and climatic conditions.

[0016] Furthermore, the application service module includes a mobile application submodule, a quality evaluation report generation submodule, and an intelligent agricultural machinery control interface submodule; The mobile application submodule is configured to present personalized farmland improvement plans to end users in a visual interface, supporting plan query, historical data review, and manual entry of effect feedback data; The quality evaluation report generation submodule is configured with built-in report templates that conform to national standards, and can generate evaluation reports that include data, charts, analysis conclusions and AI suggestions. The intelligent agricultural machinery control interface submodule is configured to parse personalized farmland improvement schemes into operation instructions that conform to the ISO11783 agricultural machinery communication protocol, including parameters for applying improvement materials, tillage path planning, and operation timing control instructions, and transmit them to the intelligent agricultural machinery control system via CAN bus or wireless communication module.

[0017] Furthermore, the dynamic feedback adjustment module is configured as follows: Establish a quality assessment mechanism for farmland improvement effect feedback data, perform integrity verification and credibility classification on farmland improvement effect feedback data, and filter out outliers and low credibility data; An incremental learning algorithm is used to dynamically adjust the decision boundary of the anomaly diagnosis model. The incremental learning algorithm includes model parameter transfer based on knowledge distillation and a catastrophic forgetting suppression mechanism based on elastic weight consolidation (EWC). By using a comparative learning algorithm to process unlabeled farmland improvement practice data, the implicit association rules are mined. These association rules include the impact of different farming methods on soil microbial communities. Based on the mined association rules, the association weights of the soil-crop-environment synergistic relationship in the dynamic knowledge graph are updated. The feedback data of the improvement effect is used as a reward signal to be input into the deep reinforcement learning model; the parameters of the multimodal prediction model and the generative decision-making model are optimized based on the reward signal using the deep reinforcement learning model.

[0018] Furthermore, it also includes a module for early warning and restoration planning of farmland degradation risks, which is configured as follows: Based on integrated long-term monitoring data of soil physicochemical properties, topographic data, land use change data, and climate change data, a risk assessment index system for arable land degradation is constructed. Using a Long Short-Term Memory (LSTM) network model, we can predict the trends of key indicators such as the rate of organic matter decay in arable land, soil erosion modulus, and the rate of salinization expansion, and identify potential arable land degradation risk areas and their degradation levels. Automatically match soil remediation technology models from a pre-set soil remediation technology model library to the identified different types and levels of degradation risk; By combining the cost-benefit analysis model of restoration, the costs and benefits of different restoration schemes are evaluated, and a medium- and long-term planning scheme for farmland quality restoration is generated, which includes restoration objectives, key technical measures, implementation steps, expected effect assessment and estimated resource input.

[0019] Compared with existing technologies, this invention has the following advantages and beneficial effects: it achieves deep fusion of multi-source heterogeneous data and synergistic driving of dynamic knowledge graphs, breaking through the limitations of single data dimension and isolated model reasoning in traditional farmland evaluation; it collects soil micro-data through quantum dot labeling-Raman spectroscopy and combines it with multi-scale information such as remote sensing and meteorology to construct a full-element data chain from micro to macro, and through U-Net++ image segmentation and Transformer semantic analysis on the data processing platform, it forms dual-modal feature fusion data with both spatial continuity and semantic depth, laying a data foundation for accurate evaluation; the core module of the AI ​​intelligent engine relies on dynamic... The knowledge graph-based soil-crop-environment synergy guides a multimodal prediction model to integrate multidimensional data such as soil indicators and remote sensing time-series features, outputting the current and future 1-3 month trends in arable land quality. Simultaneously, an improved isolated forest algorithm in the anomaly diagnosis submodule identifies quality anomalies. The arable land quality assessment submodule, based on three original dimensions—arable land ecological resilience, production potential, and restoration feasibility—uses an improved TOPSIS algorithm to generate a comprehensive evaluation index. Finally, the decision generation submodule combines causal inference and multi-agent simulation to generate personalized solutions including the types and amounts of improvement materials and cultivation suggestions, achieving intelligent processing from data input to decision output.

[0020] Other features and advantages of the invention will be set forth in the description which follows, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention may be realized and obtained by means of the structures particularly pointed out in the written description and drawings.

[0021] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0022] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used in conjunction with embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings: Figure 1 This is a schematic diagram of the structure of an intelligent evaluation and decision support system for farmland quality based on an AI large model. Figure 2 This is a schematic diagram of the core module structure of the AI ​​intelligent engine; Figure 3 This is a schematic diagram of the application service module structure. Detailed Implementation

[0023] The preferred embodiments of the present invention will be described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are for illustration and explanation only and are not intended to limit the present invention.

[0024] This invention provides an intelligent evaluation and decision support system for arable land quality based on a large AI model, such as... Figure 1 As shown, it includes a data acquisition and access module, a data processing platform, an AI intelligent engine core module, an application service module, and a dynamic feedback adjustment module; The data acquisition and access module is configured to collect and access multi-source heterogeneous cultivated land data and cultivated land improvement effect feedback data. Multi-source heterogeneous cultivated land data includes soil physicochemical data, soil physicochemical fusion data, remote sensing data, meteorological environment data, agricultural activity data and geospatial data. Cultivated land improvement effect feedback data includes soil index change data after the application of improvement materials, crop growth status data and yield and quality data. The data processing platform, which connects the data acquisition and access module, is configured to perform parallel processing on multi-source heterogeneous farmland data and farmland improvement effect feedback data to generate processed data. The core module of the AI ​​intelligent engine connects the data processing platform and the dynamic feedback adjustment module. It is configured to perform comprehensive analysis and intelligent reasoning on the processed data based on dynamic knowledge graphs, multimodal prediction models, anomaly diagnosis models, and generative decision-making models, and generate personalized farmland improvement plans for specific farmland units. The application service module, connected to the core module of the AI ​​intelligent engine, is configured to present personalized farmland improvement plans to end users through mobile applications and intelligent agricultural machinery control interfaces, convert personalized farmland improvement plans into operation instructions that can be executed by intelligent agricultural machinery, and collect feedback data on farmland improvement effects. The dynamic feedback adjustment module connects the core module of the AI ​​intelligent engine and the application service module. It is configured to evaluate the feedback data of farmland improvement effect, update the collaborative relationship weights in the dynamic knowledge graph, and optimize the parameters of the multimodal prediction model, the anomaly diagnosis model, and the generative decision model. In specific applications, for example, in a provincial-level agricultural demonstration zone, to address prominent issues such as soil acidification, uneven organic matter content, and large fluctuations in crop yields, an intelligent evaluation and decision support system for farmland quality based on an AI-powered large-scale model will be applied. The specific implementation is as follows: Firstly, through the data acquisition and access module, multi-source heterogeneous data were comprehensively integrated. Specifically, 100 deployed smart soil sensors were used to acquire real-time soil physicochemical data such as pH value (4.5-5.8, average 5.2) and organic matter content (12-28g / kg, average 18g / kg) in the 0-20cm soil layer; 30m resolution satellite imagery was used to extract farmland vegetation cover (65%-85%) and crop growth index (NDVI average 0.62); and regional meteorological stations were connected to obtain rainfall (average 1100mm per year), temperature (average 16℃ per year), and records of extreme weather events (such as 20...) over the past three years. (The system was developed based on data collected from various sources.) The data included: 1) July 2025 rainstorms causing 30% of farmland to be flooded; 2) integrating farmers' agricultural machinery operation records, including fertilizer application rates (average 220 kg / mu) and straw return area (60%) from 2023-2024; 3) overlaying a digital elevation model (DEM) to identify low-lying, flood-prone areas (approximately 800 mu); 4) data on the effects of applying quicklime (100 kg / mu) to a 500 mu pilot improvement area in 2023, which showed an average soil pH increase to 5.8, a 15 cm increase in rice plant height, and a yield increase from 550 kg / mu to 620 kg / mu. This data provided a foundation for the system's subsequent operation. After the data enters the processing platform, it undergoes an efficient parallel processing flow. First, the data is cleaned to remove data from three faulty sensors whose pH values ​​jumped to 7.0, and missing values ​​in the meteorological data are filled in using the neighboring site interpolation method. Next, the data is fused to combine soil pH values, remote sensing NDVI, and geospatial low-lying area data to output a standardized dataset containing 100,000 samples, laying a high-quality data foundation for the operation of the AI ​​intelligent engine. As the core of the system, the AI ​​intelligent engine first constructs and infers a dynamic knowledge graph of "soil-crop-environment". This graph includes entities such as "quicklime", "straw return to the field", and "rice", as well as relationships such as "quicklime-pH increase-correlation coefficient 0.82". For the target cultivated land unit numbered G302 (pH=4.8, organic matter 15g / kg, NDVI=0.55), the graph inference concludes that the dominant factor of acidification is long-term excessive application of nitrogen fertilizer (weight 0.75), requiring priority adjustment of pH and supplementation of organic matter. Subsequently, a multimodal prediction model is input with processed soil pH, meteorological data, and topographic features to predict yield changes for different improvement schemes. The predicted results are: Scheme A (quicklime 120kg / mu + straw return to the field) is expected to yield 630kg / mu, with pH increased to 5.7, which is accurate. The accuracy rate was 92%; Plan B (800 kg / mu of organic fertilizer) is expected to yield 590 kg / mu, with the pH value increased to 5.3, and the accuracy rate is 88%; the anomaly diagnosis model also found that there is a risk of "hidden acidification" in unit G302, that is, the pH of the topsoil is 4.8, while the pH of the subsoil (20-40cm) is 4.5, suggesting that traditional improvement that only focuses on the topsoil may lead to a rebound in the effect. Based on these analyses, the generative decision model outputs a personalized plan for unit G302: apply 100 kg / mu of quicklime + 300 kg / mu of decomposed straw to the topsoil (0-20cm) to adjust the pH to 5.5; after deep loosening (30cm) of the subsoil (20-40cm), apply 50 kg / mu of dolomite powder to reduce aluminum ion toxicity; the recommended supporting plant is the acid-resistant variety "Xiangzaoxian 45", and foliar fertilizer containing humic acid is applied during the growth period; The application service module effectively implements the decision-making plan. Farmers receive the plan through a mobile application and can view 3D visualizations such as soil pH change curves before and after improvement and yield prediction comparison charts. They can also obtain key operation node annotations such as "quicklime should be applied 15 days before transplanting". The system also connects to the intelligent agricultural machinery control interface, converting the plan into operation instructions and sending them to the autonomous driving tractor. The fertilizer applicator automatically adjusts the spreading amount according to the ratio of "100kg / mu quicklime + 300kg / mu straw". The subsoiler adjusts the tillage depth to 35cm based on the 30cm clay layer thickness data. After the operation is completed, the mobile application prompts farmers to upload a soil test report (pH value 5.4) one month after improvement and photos of crop seedlings (plant height 25cm, 3cm taller than the control area). The data is automatically transmitted back to the system, forming a closed loop. The dynamic feedback adjustment module evaluates and optimizes the improvement effect. Comparing the data before and after the improvement of unit G302, the actual pH value was 5.4 (slightly lower than the predicted value of 5.5), and the yield was 625 kg / mu (close to the predicted value of 630 kg / mu). The overall scheme is effective, but the improvement effect of the subsurface layer did not meet expectations (the subsurface pH only increased to 4.8). Based on this, the system updated the dynamic knowledge graph, increasing the weight of the correlation between "subsurface acidification and insufficient deep tillage depth" from 0.6 to 0.78. At the same time, the parameters of the multimodal prediction model were optimized, and the coefficient of "deep tillage depth-subsurface pH response" was adjusted from 0.5 to 0.65, which improved the prediction accuracy of subsurface improvement of subsequent schemes from 85% to 91%.

[0025] The working principle of the above technical solution is as follows: The system aggregates soil physicochemical data (such as pH value, organic matter content, and other data reflecting basic soil properties), soil physicochemical fusion data (comprehensive data formed by integrating multiple soil testing indicators), remote sensing data (spatial information such as farmland surface cover and vegetation growth obtained through satellites or drones), meteorological environmental data (environmental parameters affecting crop growth such as temperature, precipitation, and light), agricultural activity data (records of field management behaviors such as fertilization, sowing, and irrigation), geospatial data (spatial attribute data such as the geographical location and topography of farmland), and farmland improvement effect feedback data (data on changes in soil indicators after the application of improvement materials, crop growth status data, and yield and quality data); the data processing platform processes these multi-source heterogeneous data in parallel to improve data processing efficiency and quality, generating standardized and structured processed data; the AI ​​intelligent engine core module, as the core of the system, relies on a dynamic knowledge graph (a structured knowledge base that stores and associates various types of knowledge and collaborative relationships related to farmland quality) and uses a multimodal prediction model ( An AI model capable of processing and analyzing various types of data and predicting trends predicts the development trend of arable land quality. An anomaly diagnosis model (a model that identifies abnormal indicators and causes of arable land quality) identifies existing problems. A generative decision-making model (a model that proactively generates solutions based on analysis results) then integrates analysis and intelligent reasoning to produce personalized arable land improvement plans for specific arable land units. The application service module presents these plans to end users through a mobile application and converts them into executable instructions for intelligent agricultural machinery to guide actual improvement operations. Simultaneously, it collects feedback data on the effectiveness of arable land improvement after implementation. The dynamic feedback adjustment module evaluates this feedback data, updates the weights of collaborative relationships between various knowledge items in the dynamic knowledge graph based on the evaluation results, and optimizes the parameters of the multimodal prediction model, the anomaly diagnosis model, and the generative decision-making model. This continuously improves the accuracy and applicability of the system's generated improvement plans, forming a closed-loop workflow of "data collection - processing and analysis - plan generation - implementation feedback - model optimization," achieving intelligent evaluation and precise decision support for arable land quality.

[0026] The beneficial effects of the above technical solution are as follows: By adopting the solution provided in this embodiment, through the deep fusion of multi-source heterogeneous data and the continuous iteration of dynamic knowledge graph, the evaluation of arable land quality has achieved a leap from experience-based judgment to data-driven and from static analysis to dynamic early warning; by integrating the self-supervised-reinforcement learning dual-loop dynamic optimization engine into the core module of the AI ​​intelligent engine, the model can autonomously learn the complex laws of the arable land system without large-scale labeled data, and continuously optimize decision-making strategies through reinforcement learning, which significantly improves the generation efficiency and implementation accuracy of personalized improvement schemes.

[0027] In one embodiment, collecting and accessing multi-source heterogeneous arable land data and arable land improvement effect feedback data includes: obtaining data through file import or application programming interface (API) connection; adding a unique traceability tag to each type of accessed data; the unique traceability tag includes collection time, geographical location coordinates and accuracy, and data source credibility score; and storing the data using blockchain encryption. The encryption structure of the unique traceability tag includes: the first layer is a combination of the SHA-256 hash value of the data collection device number and the collection time; the second layer is a combination of the SHA-256 hash value of the original data and the data source credibility. The blockchain adopts a consortium blockchain architecture, whose nodes include data collection party nodes, laboratory nodes, agricultural authority nodes, and end-user nodes, and only authorized nodes can query traceability information to ensure data security and traceability.

[0028] The working principle of the above technical solution is as follows: First, the system extensively collects and accesses multi-source heterogeneous farmland data and farmland improvement effect feedback data through two methods: file import or application programming interface (API) connection, to ensure the diversity and comprehensiveness of data sources. During the data access process, a unique traceability tag is added to each type of accessed data. This tag contains the data collection time, geographical coordinates and accuracy information, and also assigns a credibility score to the data source. These information together constitute the data's identity and quality description. To ensure data security and immutability, the system employs blockchain technology to encrypt and store data with unique traceability tags. The encryption structure of these tags uses a two-layer SHA-256 hash value combination. The first layer is a SHA-256 hash value calculated by combining the data collection device number with the collection time; this layer primarily identifies the data collection entity and timestamp, ensuring traceability of the data collection process. The second layer is a SHA-256 hash value calculated by combining the original data with a data source credibility score; this layer further links the data content itself to the reliability assessment of its source. Regarding the blockchain architecture, the system uses a consortium blockchain, with nodes strictly limited to data collection nodes, laboratory nodes, agricultural authority nodes, and end-user nodes. This consortium blockchain architecture ensures that only authorized nodes are allowed to query data traceability information, effectively preventing unauthorized access while achieving data traceability and guaranteeing data security and privacy throughout its entire lifecycle.

[0029] The beneficial effects of the above technical solution are as follows: by adopting the solution provided in this embodiment, a complete closed loop is formed from data collection and access, tag generation and encryption, to secure storage and authorized query based on consortium blockchain, which ensures the security, reliability and traceability of farmland data and improvement effect feedback data.

[0030] In one embodiment, the data acquisition and access module includes a farmland micro-data acquisition and processing submodule, used to specifically label soil colloidal particles in farmland using quantum dot labeling technology, acquire soil micro-data of the specifically labeled farmland soil using a portable Raman spectrometer, and correlate the soil micro-data with soil physicochemical data to generate fused soil physicochemical data; the soil micro-data includes at least the speciation of heavy metals and the structure of organic carbon functional groups; the implementation of quantum dot labeling technology and portable Raman spectrometer is as follows: firstly, quantum dot nanoprobes with specific emission wavelengths are prepared by hydrothermal synthesis, and the surface of the probe is modified with specific ligands targeting specific components (such as heavy metal ions and organic carbon active sites) in soil colloidal particles. The probe solution was then mixed with the soil suspension at a volume ratio of 1:100 and reacted in a 37°C constant-temperature shaker for 30 minutes. This allowed the quantum dot probe to form a stable labeled complex with soil colloidal particles through ligand-acceptor interactions. After the reaction was complete, unbound free probes were removed by filtration through a 0.22 μm filter membrane. The labeled complex on the filter membrane was then transferred to a glass slide, and a portable Raman spectrometer was used to acquire spectra under 532 nm laser excitation. Spectral data from five different regions were collected for each sample, and the average value was taken. Finally, a quantitative correlation model was established between the Raman characteristic peak intensity and the content of soil micro-components using a spectral analysis algorithm, enabling high-precision detection of heavy metal speciation pathways and changes in the structure of organic carbon functional groups.

[0031] The working principle of the above technical solution is as follows: A micro-data acquisition and processing submodule for cultivated land enables precise acquisition and fusion of micro-data on cultivated land soil. First, quantum dot labeling technology is used to specifically label soil colloidal particles. Quantum dots, as nanomaterials with unique optical properties, can specifically bind to specific target substances (such as heavy metal ions and organic carbon functional groups) in soil colloidal particles, thereby achieving precise labeling and tracing of these micro-components. Next, a portable Raman spectrometer is used to detect the specifically labeled cultivated land soil samples. Raman spectroscopy, based on the interaction between light and molecules, analyzes the characteristic Raman scattering spectra generated by molecular vibrational energy level transitions to identify the molecular structure and chemical composition of substances. Since the soil colloidal particles have been specifically labeled with quantum dots... Portable Raman spectrometers can more accurately capture characteristic spectral signals related to heavy metal speciation and organic carbon functional group structure, thereby acquiring soil micro-data. This data includes at least heavy metal speciation (detailed existence forms such as valence and binding states of heavy metals) and organic carbon functional group structure (the composition and distribution of specific chemical groups such as hydroxyl, carboxyl, and carbonyl groups). Finally, the acquired soil micro-data is correlated and integrated with soil physicochemical data (such as soil pH, organic matter content, texture, bulk density, and other conventional physicochemical properties). Through data fusion algorithms, the intrinsic relationship between micro-data and macro-physicochemical properties is established, thereby generating soil physicochemical fusion data that comprehensively reflects soil characteristics. This provides more scientific and accurate data support for subsequent farmland quality assessment, soil pollution remediation, and precision agricultural management.

[0032] The beneficial effects of the above technical solution are as follows: The solution provided in this embodiment overcomes the limitations of traditional soil data acquisition, which relies solely on macroscopic physicochemical indicators. By combining quantum dot labeling with Raman spectroscopy, it achieves highly specific and sensitive detection of soil micro-components. Quantum dot labeling technology solves the problem of accurately identifying trace targets in soil colloidal particles, and its unique optical stability and fluorescence properties ensure the reliability and repeatability of the labeling process. The portable Raman spectrometer, with its rapid and non-destructive detection advantages, can directly acquire soil micro-data in the field, avoiding data deviations caused by sample transportation and processing in traditional laboratory testing. By linking micro-data with soil physicochemical data to generate fused data, it not only compensates for the lack of information in a single data dimension but also reveals the potential influence mechanism of micro-characteristics such as heavy metal speciation and organic carbon functional group structure on macroscopic physicochemical properties such as soil pH and organic matter content. This provides multi-scale, multi-dimensional data support for a deeper understanding of the intrinsic characteristics and quality change patterns of arable soil, thereby improving the accuracy of subsequent arable land quality assessments and the scientific nature of decision-making recommendations.

[0033] In one embodiment, the data processing platform is used to perform the following operations: For structured data, perform data cleaning, data alignment, and data standardization to generate the first processed data; Unstructured data undergoes feature extraction, format conversion, and semantic annotation to generate second-processed data. Specifically, the U-Net++ model is used for image segmentation, and feature data is extracted. The feature data is then concatenated and combined to generate bimodal feature vectors. A pre-trained language model based on a Transformer encoder is used to perform semantic analysis and recognition on the bimodal feature vectors, and corresponding semantic labels are added. The bimodality includes visual and textual modalities. The first and second processed data are subjected to spatiotemporal alignment and spatial interpolation to generate processed data. The processed data includes continuous raster data, labeled data based on historical evaluation results, and bimodal feature fusion data.

[0034] The working principle of the above technical solution is as follows: First, the data processing platform adopts differentiated preprocessing strategies for different types of data. For structured data, data cleaning is performed in sequence to remove noise and outliers, data alignment is performed to ensure the consistency of data from different sources, and data standardization is performed to unify data format and units, and finally, regular and usable first-processed data is generated. For unstructured data, the U-Net++ model is first used to perform accurate image segmentation on image data. Key visual feature data is extracted from the segmentation results. Then, these visual feature data are concatenated and combined with potentially related text data to construct a bimodal feature vector that contains both visual and text modal information. Next, a pre-trained language model based on the Transformer encoder is used to perform in-depth semantic analysis and recognition on the bimodal feature vector to accurately understand its meaning and add corresponding semantic labels to it, thereby generating second-processed data rich in semantic information. After obtaining the first and second processed data respectively, in order to achieve effective integration and unified representation of different types and modalities of data, the platform performs spatiotemporal alignment processing on both to ensure the consistency and correlation of data in time and space dimensions; at the same time, spatial interpolation processing is performed to fill the gaps in the spatial distribution of data or to achieve the unification of spatial scale, and finally generate processed data; the processed data is a comprehensive dataset, specifically including continuous raster data (used to represent information on continuous spatial distribution), labeled data based on historical evaluation results (providing reference evaluation information), and dual-modal feature fusion data (a comprehensive feature representation that integrates visual and text features).

[0035] The beneficial effects of the above technical solution are as follows: by adopting the solution provided in this embodiment, through a series of processing steps, the data processing platform can transform the original structured and unstructured data into high-quality, multi-dimensional, semantically rich, and easily processed data for subsequent analysis and application.

[0036] In one embodiment, such as Figure 2 As shown, the core modules of the AI ​​intelligent engine include a knowledge graph construction submodule, a multimodal prediction submodule, an anomaly diagnosis submodule, a farmland quality assessment submodule, a decision generation submodule, and a model update submodule; The knowledge graph construction submodule is used to construct a dynamic knowledge graph containing the synergistic relationship between soil, crops and environment based on an ontology library in the agricultural field. Based on the dynamic knowledge graph, entity matching and association retrieval are performed on the processed data to generate a structured data analysis context. The multimodal prediction submodule is used to input the feature data of the structured data analysis context into the large multimodal prediction model and output the prediction results. The feature data includes soil indicators, remote sensing time series features, meteorological and environmental indicators, historical events of agricultural activities, geospatial data and feedback data indicators of farmland improvement effects. The prediction results include the current quality level of farmland and the trend of quality level change under specific climate and management measures in the next 1-3 months. The anomaly diagnosis submodule is used to diagnose anomalies in farmland quality based on feature data from a structured data analysis context using an improved Isolation Forest algorithm. The improved Isolation Forest algorithm incorporates a farmland type penalty factor to optimize anomaly scoring; the improved anomaly scoring formula is as follows: ,in, This represents the improved anomaly score. Anomaly scores representing traditional isolated forests, The farmland type penalty factor is obtained by constructing a farmland type feature matrix and training it with historical anomaly data. Farmland types include saline-alkali land, red soil, and black soil, with different penalty factors corresponding to different farmland types. Values ​​corresponding to saline-alkali land The value ranges from 0.3 to 0.5, corresponding to red soil. The value ranges from 0.1 to 0.3, corresponding to black soil. The value range is 0.05-0.15; when A value ≥0.8 is considered a serious abnormality in farmland quality; 0.5≤ An S < 0.8 value indicates moderately abnormal farmland quality, while an S < 0.5 value indicates normal farmland quality. The arable land quality assessment submodule uses a TOPSIS algorithm that incorporates a dynamic weight adjustment mechanism and a grey relational analysis term to evaluate and generate a comprehensive evaluation index of arable land quality based on three indicators: arable land ecological resilience, arable land production potential, and arable land restoration feasibility. This is achieved through the analysis of structured data contextual features. Arable land ecological resilience characterizes the ability of arable land to recover from damage caused by natural disasters; arable land production potential characterizes the optimal yield ceiling for different crop varieties; and arable land restoration feasibility characterizes the technical difficulty and economic investment required to restore arable land to a healthy state under different degrees of degradation. The improved TOPSIS algorithm specifically introduces a dynamic weight adjustment mechanism and a grey relational analysis term. The state weight adjustment mechanism is based on the ecological vulnerability level and agricultural production priority of the cultivated land area. It uses a combination of entropy weight method and analytic hierarchy process to calibrate the basic weights of the three indicators of cultivated land ecological resilience, production potential and restoration feasibility in real time. The grey relational degree correction term addresses the fuzziness and uncertainty of some indicators in the feature data. It uses grey system theory to calculate the correlation between each evaluation object and the ideal solution and negative ideal solution, and supplements and corrects the Euclidean distance in the traditional TOPSIS algorithm. This makes the comprehensive evaluation index more in line with the complex characteristics of the actual cultivated land system. The final output comprehensive evaluation index ranges from 0 to 100 points, which can intuitively reflect the quality of cultivated land. The decision generation submodule is used to generate personalized farmland improvement plans for specific farmland units based on structured knowledge graphs, prediction results, diagnostic results, and comprehensive evaluation indices, by calling the improvement measures knowledge base and combining crop yield information. The personalized farmland improvement plan includes at least the following: the specific types of improvement materials recommended for application, the precise dosage of each improvement material, the application method for each improvement material, and the corresponding tillage suggestions. The model update submodule is used to receive new evaluation criteria, diagnostic rules and improvement experience through the expert experience input interface, and to perform incremental training and parameter optimization on the multimodal prediction model, anomaly diagnosis model and generative decision model based on the updated multi-source heterogeneous farmland data and improvement effect feedback data.

[0037] The working principle of the above technical solution is as follows: First, the knowledge graph construction sub-module relies on the agricultural domain ontology to construct a knowledge graph that can dynamically reflect the synergistic relationship between soil, crops and environment. This sub-module performs entity matching and association retrieval on the input processed data, thereby generating a structured data analysis context, providing a unified and semantically rich data foundation for the accurate analysis of subsequent modules. Next, the multimodal prediction submodule inputs multi-dimensional feature data extracted from the structured data analysis context, including soil indicators, remote sensing time series features, meteorological and environmental indicators, historical events of agricultural activities, geospatial data, and feedback data indicators of farmland improvement effects, into a pre-trained multimodal prediction model. Through deep fusion and learning of these multi-source heterogeneous data, the model outputs two key prediction results: the current farmland quality level, and the trend of farmland quality level changes in the next 1-3 months under specific climatic conditions and management measures, providing a forward-looking judgment for the dynamic understanding of farmland quality. At the same time, the anomaly diagnosis submodule is also based on the feature data of the structured data analysis context. It uses an anomaly diagnosis model built with an improved isolated forest algorithm for analysis. This model can effectively identify anomaly patterns in the data, thereby accurately diagnosing whether there are anomalies in the quality of cultivated land and the specific manifestations of the anomalies, providing technical support for timely detection of cultivated land problems. The arable land quality assessment submodule focuses on comprehensively evaluating arable land quality from three core dimensions: arable land ecological resilience, which characterizes the recovery capacity of arable land after natural disasters; arable land production potential, i.e., the upper limit of the optimal yield that different crop varieties can achieve on that arable land; and arable land restoration feasibility, reflecting the technical difficulty and economic investment required to restore arable land to a healthy state under different degrees of degradation. This submodule uses an improved TOPSIS algorithm to comprehensively evaluate the feature data in the context of structured data analysis, ultimately generating a quantitative comprehensive arable land quality evaluation index that fully reflects the quality of arable land. The decision generation submodule integrates semantic association information provided by the structured knowledge graph, prediction results output by the multimodal prediction submodule, diagnostic results from the anomaly diagnosis submodule, and comprehensive evaluation index generated by the arable land quality assessment submodule. It then calls upon the built-in improvement measures knowledge base and combines it with specific crop yield information to perform deep reasoning and matching. Its goal is to generate personalized arable land improvement plans for each specific arable land unit. These plans are detailed and include at least the specific types of improvement materials recommended for application, the precise dosage of each improvement material, the specific application methods for each type of improvement material, and corresponding tillage suggestions to ensure the operability and relevance of the plan. Finally, the model update submodule is responsible for the continuous optimization and iteration of the entire system. Through an expert experience input interface, it receives new evaluation criteria, diagnostic rules, and improvement experiences from domain experts. Simultaneously, this submodule regularly collects updated multi-source heterogeneous farmland data and feedback data on farmland improvement effects. Using this new data, it incrementally trains and optimizes the parameters of the system's multimodal prediction model, anomaly diagnosis model, and generative decision-making model, continuously improving the accuracy, adaptability, and decision support capabilities of each model. This ensures that the entire technical solution can keep pace with the times and continuously meet the needs of practical applications.

[0038] The beneficial effects of the above technical solution are as follows: The solution provided in this embodiment can construct a dynamic knowledge graph containing the synergistic relationship between soil, crops, and the environment, providing a structured semantic association data foundation for farmland quality evaluation and decision-making, and effectively integrating multi-source heterogeneous information; the multimodal prediction submodule, with the help of a large multimodal prediction model, can accurately predict the current farmland quality level and its changing trend over the next 1-3 months, providing a forward-looking basis for dynamically grasping farmland quality; the anomaly diagnosis submodule, using an improved isolated forest algorithm, can accurately identify abnormal patterns in farmland quality and promptly discover potential problems; the farmland quality assessment submodule, from three dimensions—ecological resilience, production potential, and restoration feasibility—generates a comprehensive evaluation index using an improved TOPSIS algorithm, comprehensively and realistically reflecting the quality of farmland; the decision generation submodule can integrate information from multiple aspects to generate personalized improvement plans including the type, amount, application method, and cultivation suggestions of improvement materials, enhancing the operability and pertinence of the plans; and the model update submodule, by receiving expert experience and feedback data, incrementally trains and optimizes the parameters of relevant models, ensuring the system continuously improves its accuracy, adaptability, and decision support capabilities.

[0039] In one embodiment, the large multimodal prediction model is further configured as follows: A multimodal feature fusion model is employed to encode soil physicochemical data, remote sensing data, meteorological and environmental data, geospatial data, and farmland improvement effect feedback data. Then, feature-level fusion is performed on the features of each modality to predict farmland quality based on the fused features. Specifically, a gradient boosting tree model is used to process soil physicochemical data; a Transformer encoder is used to encode remote sensing and meteorological and environmental data; a CNN encoder is used to encode geospatial data; and an attention mechanism and Bidirectional Long Short-Term Memory (BiLSTM) fusion model is used to encode farmland improvement effect feedback data. Subsequently, feature-level fusion is performed on the features obtained from the gradient boosting tree model, the Transformer encoder, the CNN encoder, and the BiLSTM fusion model. Configure a dynamic weight allocation unit to dynamically adjust the weight coefficients of feature data according to the type of cultivated land. The dynamic weight allocation unit is further configured to: input microscale features as prior knowledge into the multimodal prediction model to correct evaluation biases based on soil indicators, and dynamically adjust the weight allocation of micro- and mesoscale features through an attention mechanism, in conjunction with extreme weather probability predictions from regional climate models; microscale features are extracted from soil microdata using an improved 3DU-Net model, referring to soil pore structure, organic matter distribution, and spatial heterogeneity of microbial communities, capable of capturing dynamic changes in the micron-level soil microenvironment; mesoscale features refer to the spatial variability of soil physicochemical properties and the spatiotemporal characteristics of crop growth at the plot scale. Information such as dynamic patterns and the effects of regional agricultural management measures can be obtained by fusing multispectral remote sensing inversion with ground sampling data. When adjusting the weight allocation of micro- and meso-scale features, the dynamic weight allocation unit first conducts a preliminary assessment of the importance of micro-scale and meso-scale features. Then, combined with the historical improvement effect dataset corresponding to the current cultivated land type, an initial weight matrix is ​​generated through a Bayesian optimization algorithm. Subsequently, the extreme weather probability prediction results are used as perturbation factors and input into the attention mechanism network to dynamically correct the initial weight matrix. Finally, dynamic weight coefficients that can accurately reflect the degree of influence of different scale features on cultivated land quality are obtained. The improved 3DU-Net model, based on the original model, introduces a multi-scale attention gating mechanism and a residual connection enhancement module. The multi-scale attention gating mechanism can dynamically adjust the weight allocation of feature maps at different depths according to the spatial distribution characteristics of soil microstructure, focusing on pore connectivity regions and organic matter-enriched microdomains that significantly affect arable land quality. The residual connection enhancement module effectively alleviates the gradient vanishing problem during deep network training by using skip connections and feature fusion operations, improving the extraction accuracy of spatial heterogeneity features of soil microbial communities. During the model training phase, a hybrid loss function is used for optimization. This loss function is composed of a weighted combination of mean squared error loss, structural similarity loss, and focal loss. The focal loss is used to address the problem of uneven distribution of samples from different soil types. By dynamically adjusting the weights of difficult-to-classify samples, the model's ability to capture micro-features under extreme soil conditions is further improved.

[0040] The working principle of the above technical solution is as follows: The multimodal prediction model first performs targeted encoding processing on various types of input data; for soil physicochemical data, a gradient boosting tree model is used to effectively capture its nonlinear relationships and important features; remote sensing data and meteorological environmental data are encoded through a Transformer encoder, which uses its self-attention mechanism to capture long-distance dependencies and temporal dynamic changes; geospatial data is processed using a CNN encoder to extract its spatial local features and texture information; and farmland improvement effect feedback data is encoded through a fusion model of attention mechanism and bidirectional long short-term memory network (BiLSTM). BiLSTM can capture the temporal features of the feedback data, while the attention mechanism can focus on feedback information fragments that are more critical to the impact on farmland quality. After completing the encoding of each modality of data, the model performs feature-level fusion of these features from different encoders, integrating multiple aspects of information to serve the subsequent farmland quality prediction task. Meanwhile, the model is equipped with a dynamic weight allocation unit to dynamically adjust the weight coefficients of each feature data according to the type of cultivated land. This unit first extracts microscale features from soil microdata using an improved 3DU-Net model. These features include soil pore structure, organic matter distribution, and spatial heterogeneity of microbial communities, reflecting the dynamic changes of the micron-level soil microenvironment. Mesoscale features cover information such as the spatial variability of soil physicochemical properties at the plot scale, the spatiotemporal dynamics of crop growth, and the effects of regional agricultural management measures. These features are obtained through the fusion of multispectral remote sensing inversion and ground sampling data. When adjusting the weight allocation of micro- and mesoscale features, the dynamic weight allocation unit first performs a preliminary assessment of the importance of these two types of features, and then combines the current cultivated land... The historical improvement effect dataset corresponding to each type is used to generate an initial weight matrix using a Bayesian optimization algorithm. Subsequently, the extreme weather probability prediction results provided by the regional climate model are used as perturbation factors input into the attention mechanism network to dynamically correct the initial weight matrix. Finally, dynamic weight coefficients that can accurately reflect the degree of influence of features at different scales on arable land quality are obtained. These dynamic weight coefficients will be used as prior knowledge input into the multimodal prediction model to correct the evaluation bias based on soil indicators and dynamically adjust the weights of all feature data according to the arable land type. After obtaining the fused modal features and dynamic weight coefficients, the multimodal prediction model completes the arable land quality prediction task based on this information, so that the prediction results can more accurately reflect the comprehensive impact of different factors on arable land quality.

[0041] The beneficial effects of the above technical solution are as follows: By adopting the solution provided in this embodiment, multi-modal feature fusion model is used to encode and fuse multi-source heterogeneous data in a targeted manner, effectively breaking the limitations of traditional single-modal data processing and realizing deep collaboration of multi-dimensional information such as soil physicochemical properties, remote sensing, and meteorology; The introduction of dynamic weight allocation unit, guided by prior knowledge of micro-scale features and dynamic correction of extreme weather probability prediction, combined with the weight matrix generated by Bayesian optimization algorithm, enables the model to adaptively adjust the weights of each feature according to the type of cultivated land, significantly improving the adaptability of cultivated land quality prediction to complex environmental conditions and micro-soil characteristics, and effectively correcting the bias caused by relying only on macro-soil indicators in traditional evaluation.

[0042] In one embodiment, the generative decision model is configured as follows: The factors affecting farmland quality are analyzed using a causal inference algorithm to pinpoint the causes of farmland quality degradation and obtain inference results. Based on multi-agent simulation technology, a dynamic model of farmland ecosystem is constructed to simulate the long-term impact of different improvement measures on farmland ecosystem, identify potential ecological risks, and obtain simulation results. The results of the inference and simulation are verified by transfer learning with the improvement cases stored in the improvement measures knowledge base. By comparing and analyzing the feedback data of improvement effects under similar geological and climatic conditions, the personalized farmland improvement plan is optimized to ensure the effectiveness of the personalized farmland improvement plan under similar geological and climatic conditions.

[0043] The working principle of the above technical solution is as follows: The generative decision-making model first uses a causal inference algorithm to deeply analyze the influencing factors of farmland quality degradation. By constructing a causal relationship network between variables, it accurately locates the key driving factors leading to the decline in farmland quality, forming a systematic inference result. The causal inference algorithm provides interpretable causal path analysis for the model, avoiding the limitations of traditional statistical methods that only focus on correlation and ignore causal mechanisms, making the location of degradation causes more scientific and reliable. On this basis, relying on multi-agent simulation technology, a dynamic model that can reflect the complex interactive relationships of the farmland ecosystem is built. This model covers multiple subsystems such as soil microbial community, crop growth cycle, and water resource cycle, and can simulate different improvement measures (such as soil conditioner application, planting structure adjustment, and water-saving irrigation technology application) in... The system assesses the impacts on various elements of the arable land ecosystem on short-, medium-, and long-term timescales, simultaneously identifying potential ecological risks such as increased soil salinization and biodiversity loss during the improvement process, and generating comprehensive simulation results. Subsequently, the system inputs these simulation results and the above-mentioned extrapolation results into the improvement measures knowledge base, and performs transfer learning verification with the massive number of improvement cases stored in the database. It focuses on comparing the feedback data of improvement effects in areas with similar geological structures and climatic characteristics, strengthens the weight of key influencing factors by introducing an attention mechanism, and dynamically adjusts the technical parameters and implementation steps in the improvement plan. Finally, it optimizes and forms a personalized arable land improvement plan that is both scientific and targeted, fundamentally ensuring that the plan can stably achieve the expected improvement effect under similar geological and climatic conditions, and realize the precise improvement and sustainable maintenance of arable land quality.

[0044] The beneficial effects of the above technical solution are as follows: By adopting the solution provided in this embodiment, the limitations of traditional farmland improvement schemes relying on experience-based judgment can be overcome through the deep integration of causal inference and multi-agent simulation, enabling accurate localization of degradation causes and forward-looking prediction of improvement effects; the causal inference algorithm ensures the logic and interpretability of degradation factor analysis, avoiding the risk of data correlation misleading decision-making; the multi-agent dynamic model reveals the implicit correlation between improvement measures and ecological risks through simulation of complex ecosystems, providing a scientific basis for scheme optimization; the transfer learning verification mechanism further integrates the practical experience of historical improvement cases, and through effect comparison of similar scenarios, enables personalized schemes to be both innovative and practically feasible, improving the intelligence level and success rate of farmland improvement decision-making.

[0045] In one embodiment, such as Figure 3 As shown, the application service module includes a mobile application submodule, a quality evaluation report generation submodule, and an intelligent agricultural machinery control interface submodule; The mobile application submodule is configured to present personalized farmland improvement plans to end users in a visual interface, supporting plan query, historical data review, and manual entry of effect feedback data; The quality evaluation report generation submodule is configured with built-in report templates that conform to national standards. It can generate evaluation reports that include data, charts, analysis conclusions, and AI suggestions. The report templates reference specific national standards (such as "Grades of Cultivated Land" GB / T33469-2016). The intelligent agricultural machinery control interface submodule is configured to parse personalized farmland improvement schemes into operation instructions that conform to the ISO11783 agricultural machinery communication protocol, including parameters for applying improvement materials, tillage path planning, and operation timing control instructions, and transmit them to the intelligent agricultural machinery control system via CAN bus or wireless communication module.

[0046] The working principle of the above technical solution is as follows: The application service module, through its included mobile application sub-module, quality evaluation report generation sub-module, and intelligent agricultural machinery control interface sub-module, works collaboratively to present the farmland improvement plan, generate the evaluation report, and precisely control the intelligent agricultural machinery. Firstly, the mobile application sub-module, as the core window for user interaction, displays the personalized farmland improvement plan generated by the system to the end user in an intuitive and visual interface, facilitating user viewing and understanding of the plan content. Simultaneously, this sub-module allows users to query historical plans, trace back relevant historical data, and allows users to manually input feedback on the effects of farmland improvement. Feedback data serves as a crucial basis for subsequent scheme optimization and effect evaluation. Secondly, the quality evaluation report generation submodule incorporates standardized report templates compliant with national standards. It integrates various data from the farmland improvement process, automatically generating a quality evaluation report containing detailed data, intuitive charts, professional analysis conclusions, and improvement suggestions based on AI algorithms, providing users with a comprehensive and objective evaluation document of the improvement effect. Finally, the intelligent agricultural machinery control interface submodule plays a key role in the implementation of the scheme. It parses the various parameters and requirements in the personalized farmland improvement scheme and transforms them into specifications strictly compliant with ISO11783 agricultural machinery standards. The system uses standardized operational instructions based on a machine communication protocol. These instructions specifically cover application parameters for modified materials (such as fertilizers and soil conditioners), including application rate, concentration, and location; tillage path planning for intelligent agricultural machinery (ensuring accuracy and efficiency of operational coverage); and precise operational timing control instructions (coordinating the sequence and time intervals of different agricultural machines or different operational stages). The generated operational instructions are then transmitted to the intelligent agricultural machinery's control system via a CAN bus or wireless communication module (such as 4G, 5G, LoRa, etc., suitable for long-distance or mobile communication scenarios), thereby guiding the intelligent agricultural machinery to complete tasks accurately and efficiently according to a predetermined plan. Farmland improvement operations achieve a seamless transition from digital solutions to mechanized and intelligent operations. The intelligent agricultural machinery control interface submodule translates the upper-level farmland improvement plan into instructions that the agricultural machinery can understand and execute, and transmits these instructions to the machinery through specific communication methods. The ISO 11783 agricultural machinery communication protocol refers to a series of standards numbered 11783 developed by the International Organization for Standardization (ISO). These standards specifically regulate data communication between agricultural machinery and vehicles, as well as information exchange with external systems (such as management systems and dispatch centers), aiming to achieve interconnectivity and data sharing between different brands and types of agricultural machinery.Soil amendment application parameters refer to the specific technical parameters that need to be followed when applying various soil amendments (such as organic fertilizers, chemical fertilizers, soil conditioners, lime, gypsum, etc.) during the process of soil improvement. These parameters directly affect the improvement effect and material utilization rate. Examples include the type of material, application rate (per unit area or total amount), application depth, application uniformity, application location (such as near the roots, general application, etc.), and possible application concentration. Tillage path planning refers to planning an optimal driving and operating route for intelligent agricultural machinery based on the shape, size, obstacle distribution, and operational requirements (such as row spacing, plant spacing, and coverage). Its purpose is to ensure that the agricultural machinery can efficiently and completely complete the tillage tasks while reducing unnecessary empty runs. Energy consumption; Operation timing control instructions refer to instructions used to specify the order of execution, start time, duration, and time intervals between different operation stages of intelligent agricultural machinery. Through timing control, the collaborative work of multiple agricultural machines or different functional modules of the same agricultural machine can be coordinated to ensure a smooth and orderly operation process; Intelligent agricultural machinery control system refers to the electronic control unit (ECU) and its related software and hardware installed on intelligent agricultural machinery to receive and parse external instructions (such as operation instructions from application service modules), and control the coordinated work of various components such as the engine, transmission system, actuators (such as seeding mechanism, fertilizing mechanism, tillage mechanism, etc.), and navigation system to complete the predetermined operation tasks.

[0047] The beneficial effects of the above technical solution are as follows: The solution provided in this embodiment enables closed-loop management of the entire process of farmland quality evaluation and decision support through multi-dimensional application service sub-modules; the mobile application sub-module enhances the user interaction experience through visualization, allowing end-users to easily access and participate in the implementation process of farmland improvement schemes, and manually entered effect feedback data provides real field practice evidence for continuous system optimization; the quality evaluation report generation sub-module relies on national standard templates to ensure the standardization and authority of the output reports, providing standardized quality assessment documents for government regulatory departments, agricultural technology extension agencies, and planting entities, thereby enhancing the credibility of decision-making basis; the intelligent agricultural machinery control interface sub-module achieves seamless integration with intelligent agricultural machinery through standardized protocols, transforming abstract improvement schemes into directly executable mechanical operation instructions. This not only avoids errors and lags in manual operation but also further improves the efficiency and accuracy of farmland improvement measures by precisely controlling the application parameters of improvement materials, tillage paths, and operation sequences.

[0048] In one embodiment, the dynamic feedback adjustment module is configured as follows: Establish a quality assessment mechanism for farmland improvement effect feedback data, perform integrity verification and credibility classification on farmland improvement effect feedback data, and filter out outliers and low credibility data; An incremental learning algorithm is used to dynamically adjust the decision boundary of the anomaly diagnosis model. The incremental learning algorithm includes model parameter transfer based on knowledge distillation and a catastrophic forgetting suppression mechanism based on elastic weight consolidation (EWC). By using a comparative learning algorithm to process unlabeled farmland improvement practice data, the implicit association rules are mined. These association rules include the impact of different farming methods on soil microbial communities. Based on the mined association rules, the association weights of the soil-crop-environment synergistic relationship in the dynamic knowledge graph are updated. The feedback data of the improvement effect is used as a reward signal to be input into the deep reinforcement learning model; the parameters of the multimodal prediction model and the generative decision-making model are optimized based on the reward signal using the deep reinforcement learning model.

[0049] The working principle of the above technical solution is as follows: The dynamic feedback adjustment module first conducts a comprehensive integrity check on the collected farmland improvement effect feedback data by constructing a scientific and rigorous quality assessment mechanism to ensure that the data is complete in terms of recording dimensions and necessary information. At the same time, it performs credibility classification and evaluates the reliability of the data based on factors such as the authority of the data source, the standardization of the collection method, and consistency with other relevant data. On this basis, it effectively filters out outliers that deviate from the normal range and data with low credibility, thereby ensuring the high quality of subsequent analysis and model training data and laying a solid data foundation for the entire adjustment process. Next, the module employs an incremental learning algorithm to dynamically adjust the decision boundary of the anomaly diagnosis model. In this process, the incremental learning algorithm integrates model parameter transfer technology based on knowledge distillation and catastrophic forgetting suppression mechanism of Elastic Weight Consolidation (EWC). Knowledge distillation technology can effectively transfer the knowledge of a mature, trained model (teacher model) to a new model (student model), enabling the new model to inherit the generalization ability and decision-making experience of the teacher model while learning new data. The EWC mechanism effectively suppresses the forgetting of old knowledge when learning new knowledge by penalizing important parameters in the model (i.e., parameters that are crucial to the performance of previous tasks). This ensures that the anomaly diagnosis model can adapt to new data distributions and potential patterns while maintaining the ability to accurately identify anomaly patterns in historical data when continuously receiving new farmland improvement feedback data. This dynamically optimizes its decision boundary and improves the accuracy and timeliness of diagnosing anomalies in the farmland improvement process. Meanwhile, the module utilizes a contrastive learning algorithm to deeply process a large amount of unlabeled farmland improvement practice data. By constructing similarity and difference relationships between data samples, contrastive learning can learn the deep features and internal structure of the data in an unsupervised manner, thereby uncovering the implicit association rules in these practice data. For example, by comparing the various physicochemical properties, crop growth status, and environmental factors of soil samples under different farming methods, it can reveal the specific impacts of different farming methods (such as crop rotation, no-till, and deep tillage) on the soil microbial community, including the associations of species composition, diversity, abundance changes, and functional activities of the microbial community. These uncovered association rules are used to update the association weights of the soil-crop-environment synergistic relationship in the dynamic knowledge graph. The dynamic knowledge graph stores the complex relationships between soil, crops, and environmental elements in the form of a graph structure. Updating the association weights means adjusting the intensity and degree of interaction between different elements based on the newly discovered association rules, so that the knowledge graph can more accurately reflect the actual dynamics of the farmland ecosystem and provide richer and more accurate knowledge reserves for subsequent decision support. Finally, the improved effect feedback data, after quality assessment and screening, is used as a reward signal input to the deep reinforcement learning model. The core of the deep reinforcement learning model lies in the agent (which can be understood here as the decision-maker of the control strategy) learning how to take optimal actions to maximize cumulative rewards through interaction with the environment (i.e., the farmland improvement system). In this technical solution, the improved effect feedback data (such as positive effects like increased soil fertility, changes in crop yield, and reduced frequency of pests and diseases, or the opposite negative effects) constitute a quantitative reward for the implementation effect of the current farmland improvement strategy (jointly formulated by the multimodal prediction model and the generative decision-making model). Based on these reward signals, the deep reinforcement learning model, through continuous trial and error and strategy adjustment, improves the multimodal prediction model (using...) The parameters of the multimodal prediction model (which predicts multi-dimensional results of soil, crops, and environment under different improvement measures) and the generative decision-making model (used to generate specific farmland improvement schemes and control strategies) are optimized. Specifically, if the improvement strategy generated under a certain set of parameters brings positive reward signals (i.e., good improvement effects), the model will strengthen the influence of these parameters; conversely, if the reward signal is negative (i.e., poor improvement effects), the relevant parameters will be adjusted to avoid the regeneration of similar strategies. Through this continuous feedback and optimization cycle, the prediction accuracy of the multimodal prediction model is continuously improved. The generative decision-making model can formulate improvement decisions that are more in line with the actual farmland conditions and are more targeted and effective, thereby achieving dynamic, intelligent, and precise closed-loop regulation of the farmland improvement process.

[0050] The beneficial effects of the above technical solution are as follows: By constructing a scientific feedback data quality assessment mechanism, the reliability of the data foundation used for model optimization is ensured, avoiding interference from abnormal or low-reliability data on model tuning; the incremental learning algorithm enables dynamic adjustment of the decision boundary of the anomaly diagnosis model, and combined with knowledge distillation and elastic weight consolidation mechanisms, effectively balancing new knowledge learning and old knowledge retention, overcoming the catastrophic forgetting problem of traditional models in dynamic data environments, and improving the model's ability to continuously identify abnormal farmland conditions; comparative learning is used to mine implicit association rules in unlabeled practical data and update the knowledge graph weights, enhancing the system's understanding of the complex collaborative relationship between soil, crops, and the environment, and enriching the knowledge reserves for decision support; feedback data is used as a reward signal to drive the deep reinforcement learning model to optimize prediction and decision model parameters, forming a closed-loop adjustment mechanism, enabling the system to dynamically iterate according to the actual improvement effect, continuously improving the accuracy of farmland quality evaluation and the pertinence of improvement decisions.

[0051] In one embodiment, a farmland degradation risk early warning and restoration planning module is also included, which is configured as follows: Based on integrated long-term monitoring data of soil physicochemical properties, topographic data, land use change data, and climate change data, a risk assessment index system for arable land degradation is constructed. Using a Long Short-Term Memory (LSTM) network model, we can predict the trends of key indicators such as the rate of organic matter decay in arable land, soil erosion modulus, and the rate of salinization expansion, and identify potential arable land degradation risk areas and their degradation levels. Automatically match soil remediation technology models from a pre-set soil remediation technology model library to the identified different types and levels of degradation risk; By combining the cost-benefit analysis model of restoration, the costs and benefits of different restoration schemes are evaluated, and a medium- and long-term planning scheme for farmland quality restoration is generated, which includes restoration objectives, key technical measures, implementation steps, expected effect assessment and estimated resource input.

[0052] The working principle of the above technical solution is as follows: First, it integrates long-term monitoring data of soil physicochemical properties, topographic data, land use change data, and climate change data to construct a comprehensive indicator system for assessing farmland degradation risks, providing a scientific standard for subsequent risk assessments. Next, it uses a Long Short-Term Memory (LSTM) network model, a special type of recurrent neural network, to effectively process and learn long-term dependencies in time-series data. This model predicts the future trends of key indicators such as the rate of organic matter decay in farmland soil, soil erosion modulus, and the rate of salinization expansion. By analyzing the prediction results, it identifies potential farmland degradation risk areas and determines their degradation status. The system first identifies the types and levels of degradation risk to pinpoint the exact problem. Then, based on these identified risks, it automatically retrieves and matches suitable soil remediation technologies from a pre-defined database, ensuring the remediation technology's relevance and applicability. Finally, using a cost-benefit analysis model, the system comprehensively evaluates the matched remediation schemes from both economic input and expected benefits. After considering various factors, a medium- to long-term plan for farmland quality restoration is generated. This plan details restoration objectives, key technical measures, specific implementation steps, expected effect assessments, and estimated resource inputs, providing a complete and feasible action guide for the systematic governance of farmland degradation.

[0053] The beneficial effects of the above technical solution are as follows: By adopting the solution provided in this embodiment, early and accurate identification and graded early warning of farmland degradation risks can be achieved through deep integration of multi-source heterogeneous data and spatiotemporal dynamic modeling, thus buying time for farmland protection decisions; the phased implementation steps and expected effect evaluation mechanism can track the restoration process in real time and dynamically adjust the strategy to ensure the scientific nature and sustainability of restoration measures, effectively curb the trend of farmland degradation, and provide strong technical support for the long-term protection and rational utilization of farmland resources.

[0054] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims and their equivalents, this invention is also intended to include these modifications and variations.

Claims

1. An AI large model-based cultivated land quality intelligent evaluation and decision support system, characterized in that, The system comprises a data collection and access module, a data processing platform, an AI intelligent engine core module, an application service module and a dynamic feedback adjustment module. The data collection and access module is configured to collect and access multi-source heterogeneous cultivated land data and cultivated land improvement effect feedback data, the multi-source heterogeneous cultivated land data including soil physicochemical data, soil physicochemical fusion data, remote sensing data, meteorological environment data, farming activity data and geographic spatial data, and the cultivated land improvement effect feedback data including soil index change data after application of improvement materials, crop growth state data and yield quality data. The data processing platform is connected to the data collection and access module and is configured to perform parallel processing on the multi-source heterogeneous cultivated land data and the cultivated land improvement effect feedback data to generate processed data. The AI intelligent engine core module is connected to the data processing platform and the dynamic feedback adjustment module and is configured to perform comprehensive analysis and intelligent reasoning on the processed data based on a dynamic knowledge graph, a multi-modal prediction large model, an abnormal diagnosis model and a generative decision model to generate an individualized cultivated land improvement scheme for a specific cultivated land unit. The application service module is connected to the AI intelligent engine core module and is configured to present the individualized cultivated land improvement scheme to an end user through a mobile application and an intelligent agricultural machine control interface, convert the individualized cultivated land improvement scheme into executable operation instructions for intelligent agricultural machines, and collect cultivated land improvement effect feedback data. The dynamic feedback adjustment module is connected to the AI intelligent engine core module and the application service module and is configured to evaluate the cultivated land improvement effect feedback data, update the coordination relationship weights in the dynamic knowledge graph, and optimize the parameters of the multi-modal prediction large model, the abnormal diagnosis model and the generative decision model. 2.The AI large model-based cultivated land quality intelligent evaluation and decision support system according to claim 1, characterized in that, The multi-source heterogeneous cultivated land data and the cultivated land improvement effect feedback data are collected and accessed by means of file import or application programming interface (API) connection, and a unique traceability label is added to each type of accessed data, the unique traceability label including collection time, geographic location coordinates and precision, and data source credibility score, and the data is stored by means of blockchain encryption. 3.The AI large model-based cultivated land quality intelligent evaluation and decision support system according to claim 1, characterized in that, The data collection and access module comprises a cultivated land microcosm data collection and processing sub-module, which is used for specifically marking the soil colloid particles of the cultivated land by means of quantum dot marking technology, acquiring soil microcosm data of the specifically marked cultivated land soil by means of a portable Raman spectrometer, and associating the soil microcosm data with the soil physicochemical data to generate soil physicochemical fusion data; the soil microcosm data at least includes heavy metal speciation and organic carbon functional group structure. 4.The AI large model-based cultivated land quality intelligent evaluation and decision support system according to claim 1, characterized in that, The data processing platform is used to perform the following operations: For structured data, data cleaning, data alignment and data standardization processing are performed to generate first processed data; For unstructured data, feature extraction, format conversion and semantic annotation processing are performed to generate second processed data, specifically: a U-Net++ model is used to perform image segmentation operation and extract feature data, the feature data is spliced and combined to generate a dual-modal feature vector, a pre-trained language model based on a Transformer encoder is used to perform semantic analysis and recognition on the dual-modal feature vector and add corresponding semantic labels; the dual-modal includes a visual modal and a text modal; The first processing data and the second processing data are subjected to spatio-temporal alignment and spatial interpolation processing to generate processed data; the processed data includes continuous grid data, labeled data based on historical evaluation results, and dual-modal feature fusion data. 5.The AI large model-based cultivated land quality intelligent evaluation and decision support system according to claim 1, characterized in that, The AI intelligent engine core module includes a knowledge graph construction submodule, a multi-modal prediction submodule, an anomaly diagnosis submodule, a cultivated land quality evaluation submodule, a decision generation submodule, and a model updating submodule; The knowledge graph construction submodule is configured to construct a dynamic knowledge graph containing soil-crop-environment collaborative relationships based on an agricultural domain ontology library, and perform entity matching and association retrieval on the processed data based on the dynamic knowledge graph to generate a structured data analysis context. The multi-modal prediction submodule is configured to input feature data of the structured data analysis context into a multi-modal prediction large model to output prediction results; the feature data includes soil indicators, remote sensing time series features, meteorological environment indicators, historical events of farming activities, geographic spatial data, and cultivated land improvement effect feedback data indicators; the prediction results include a current quality grade of the cultivated land and a quality grade change trend of the cultivated land under specific climate and management measures in the next 1-3 months; The anomaly diagnosis submodule is configured to use an anomaly diagnosis model improved from an isolation forest algorithm to diagnose based on the feature data of the structured data analysis context to obtain a diagnosis result of cultivated land quality anomaly. The cultivated land quality evaluation submodule is configured to use a TOPSIS algorithm with a dynamic weight adjustment mechanism and a gray correlation degree correction term to evaluate the feature data of the structured data analysis context based on three indicators of cultivated land ecological resilience, cultivated land production potential, and cultivated land repair feasibility to generate a comprehensive evaluation index of cultivated land quality; the cultivated land ecological resilience is used to represent the recovery ability of the cultivated land to damage caused by natural disasters, the cultivated land production potential is used to represent the upper limit of the optimal yield of different crop varieties, and the cultivated land repair feasibility is used to represent the technical difficulty and economic investment of restoring the cultivated land to a healthy state under different degradation degrees. The decision generation submodule is configured to call an improvement measure knowledge base and combine crop yield information based on the structured knowledge graph, the prediction results, the diagnosis results, and the comprehensive evaluation index to generate an individualized cultivated land improvement scheme for a specific cultivated land unit; the individualized cultivated land improvement scheme at least includes the following contents: specific types of recommended improvement materials, accurate dosages of each improvement material, application methods for each improvement material, and supporting tillage suggestions. The model updating submodule is configured to receive new evaluation standards, diagnosis rules, and improvement experiences through an expert experience input interface, and perform incremental training and parameter optimization on the multi-modal prediction large model, the anomaly diagnosis model, and the generative decision model based on updated multi-source heterogeneous cultivated land data and improvement effect feedback data. 6.The AI big model-based cultivated land quality intelligent evaluation and decision support system according to claim 5, characterized in that, The multi-modal prediction large model is further configured to: use a multi-modal feature fusion model to encode soil physicochemical data, remote sensing data, meteorological environment data, geographic spatial data, and cultivated land improvement effect feedback data, and then perform feature-level fusion on the modal features to complete the cultivated land quality prediction task based on the fused features; The dynamic weight allocation unit is configured to dynamically adjust the weight coefficients of the characteristic data according to the types of cultivated land; The dynamic weight allocation unit is further configured to input micro-scale features as prior knowledge into the multi-modal prediction large model to correct the evaluation deviation based on soil indicators, and combine the extreme weather probability prediction of the regional climate model to dynamically adjust the weight allocation of micro-macro features through an attention mechanism; the micro-scale features are extracted from soil micro-data through an improved 3DU-Net model. 7.The AI big model-based cultivated land quality intelligent evaluation and decision support system according to claim 5, characterized in that, The generative decision model is configured to: analyze the factors affecting the quality of cultivated land through a causal inference algorithm, locate the causes of the degradation of the quality of cultivated land, and obtain a deduction result; based on multi-agent simulation technology, a dynamic model of the cultivated land ecosystem is constructed to simulate the long-term impact of different improvement measures on the cultivated land ecosystem, identify potential ecological risks, and obtain simulation results; migrate learning verification is performed on the deduction result, the simulation result and the improvement cases stored in the improvement measure knowledge base, the improvement effect feedback data under similar geological-climatic conditions is compared and analyzed to optimize the individualized cultivated land improvement scheme and ensure the effectiveness of the individualized cultivated land improvement scheme under similar geological-climatic conditions. 8.The AI large model-based cultivated land quality intelligent evaluation and decision support system according to claim 1, characterized in that, The application service module includes a mobile application sub-module, a quality evaluation report generation sub-module, and an intelligent agricultural machine control interface sub-module; The mobile application sub-module is configured to present the individualized cultivated land improvement scheme to the end user in a visual interface, support scheme query, historical data backtracking and manual entry of effect feedback data; The quality evaluation report generation sub-module is configured to have built-in report templates that meet national standards, and can generate evaluation reports including data, charts, analysis conclusions and AI suggestions; The intelligent agricultural machine control interface sub-module is configured to parse the individualized cultivated land improvement scheme into operation instructions that meet the ISO11783 agricultural machinery communication protocol, including improvement material application parameters, tillage path planning and operation timing control instructions, and transmit them to the intelligent agricultural machine control system through CAN bus or wireless communication module. 9.The AI large model-based cultivated land quality intelligent evaluation and decision support system according to claim 1, characterized in that, The dynamic feedback adjustment module is configured to: build a quality evaluation mechanism for cultivated land improvement effect feedback data, perform integrity verification and credibility classification on the cultivated land improvement effect feedback data, and filter abnormal values and low credibility data; an incremental learning algorithm is used to dynamically adjust the decision boundary of the anomaly diagnosis model, wherein the incremental learning algorithm includes model parameter migration based on knowledge distillation and an elastic weight consolidation (EWC) catastrophic forgetting suppression mechanism; correlation rules are mined from unannotated cultivated land improvement practice data through a contrastive learning algorithm, including the influence of different tillage methods on soil microbial communities, and based on the mined correlation rules, the correlation weights of the soil-crop-environment synergistic relationship in the dynamic knowledge graph are updated; the improvement effect feedback data is input as a reward signal into a deep reinforcement learning model; the parameters of the multi-modal prediction large model and the generative decision model are optimized based on the reward signal using the deep reinforcement learning model. 10.The AI large model-based cultivated land quality intelligent evaluation and decision support system according to claim 1, characterized in that, It also includes a cultivated land degradation risk early warning and repair planning module, which is configured to: Based on the integrated long-term monitoring data of soil physicochemical properties, topographic data, land use change data and climate change data, an index system for assessing the risk of cultivated land degradation is constructed; Using the long short-term memory (LSTM) model, the trend of key indicators such as the degradation rate of soil organic matter, soil erosion modulus, and the expansion speed of salinization is predicted to identify potential areas of cultivated land degradation risk and their degradation levels. From the pre-set soil remediation technology mode library, the soil remediation technology mode for different types and levels of identified degradation risk is automatically matched. Combined with the cost-benefit analysis model, the cost and benefit of different remediation schemes are evaluated to generate a medium and long-term planning scheme for cultivated land quality remediation, which includes remediation targets, key technical measures, implementation steps, expected effect evaluation, and required resource input estimation.

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