Coffee global ecological intelligent management system and method based on AI vertical large model

Abstract

The invention relates to the field of intelligent agricultural management and optimization, and discloses an AI vertical large model-based coffee global ecological intelligent management system, which comprises a data acquisition and fusion module, an AI vertical large model module, a state modeling and embedding module, a multi-objective optimization module, a multi-agent game control module and a control execution and feedback learning module, the invention also discloses a coffee global ecological intelligent management method based on the AI vertical large model, and the method comprises the following steps: data collection and fusion: collecting farmland meteorology, soil environment and crop growth multi-dimensional ecological data through the Internet of Things equipment, carrying out the cleaning, denoising and time sequence alignment of the collected original data, and carrying out the fusion of the data; and forming a multi-modal data stream which can be processed by the AI model. Through multi-target optimization, intelligent agent game control and a real-time feedback learning mechanism, collaborative optimization of ecology, economy and supply chain targets is realized, and the flexibility, adaptability and execution precision of the system in a complex environment are improved.

Description

Technical Field

[0001] The present invention relates to the field of intelligent agricultural management and optimization, and specifically to a coffee whole-region ecological intelligent management system and method based on an AI vertical large model. Background Art

[0002] In today's society, with global climate change, resource tension, and market demand fluctuations, how to achieve the dual optimization of agricultural production and the ecological environment has become an important issue in the agricultural field. The production of crops such as coffee cultivation not only concerns farmers' income but also poses higher requirements for the ecological environment and supply chain management. Traditional production methods often ignore the interrelationships between different fields and rely solely on fixed rules for production scheduling, making it difficult to effectively balance the goals of all parties in a complex system.

[0003] In the prior art, the optimization control methods in the agricultural field usually focus on the optimization of a single goal, such as increasing crop yield, reducing production costs, or reducing resource consumption, etc.; scheduling and control are carried out through simple rules or experience-based models, which can solve problems in a certain aspect to a certain extent. Many existing technologies also adopt multi-objective optimization techniques in certain application scenarios, which can incorporate multiple goals into the decision-making framework for preliminary resource allocation optimization. These technologies can help improve production efficiency and reduce waste in some cases, especially when facing simple environmental conditions, and the effect is more significant.

[0004] However, there are many deficiencies in the prior art and it cannot fully meet the requirements of multi-objective collaborative optimization in modern agricultural production. First of all, most traditional optimization methods rely on fixed rules and static parameter settings, lacking sufficient flexibility and adaptability, and unable to cope with the influence of uncertain factors such as the ecological environment and market demand. Secondly, most of the multi-objective optimization schemes in the prior art do not consider the game behaviors among different participants, which makes the resource allocation among different participants in the system often not optimized. Finally, the existing feedback control mechanisms are often simple and static, and it is difficult to adjust the optimization strategy in real time to cope with external disturbances or changes in system feedback, resulting in a large deviation between the execution result and the expected goal. Summary of the Invention

[0005] Aiming at the deficiencies of the prior art, the present invention provides a coffee whole-region ecological intelligent management system and method based on an AI vertical large model, which solves the problems of insufficient multi-objective optimization, poor dynamic adaptability, unconsidered game among agents, and inflexible feedback control mechanism in the prior art.

[0006] To achieve the above object, the present invention is realized through the following technical solutions: A coffee whole-region ecological intelligent management system based on an AI vertical large model, comprising: Data acquisition and fusion module, which is used to obtain multi-dimensional ecological data. The multi-dimensional ecological data includes data on farmland meteorology, soil environment, crop growth, market supply and demand, and warehousing and transportation, and cleans, denoises and aligns the data in time series to form a multi-modal input data stream; AI vertical large model module, which constructs and runs an AI vertical large model based on the multi-modal input data stream provided by the data acquisition and fusion module. This model predicts the state evolution trajectory of the coffee ecosystem through deep learning algorithms and adaptively adjusts the model parameters according to historical data and system feedback data; State modeling and embedding module, which further embeds the multi-objective state variables of the coffee ecosystem into the Riemannian manifold space based on the prediction results output by the AI vertical large model, constructs a high-dimensional non-linear state space model, and makes accurate state predictions for the dynamic evolution of the future ecosystem; Multi-objective optimization module, which constructs optimization functions for multiple objectives of ecology, economy and supply chain based on the state information generated by the state modeling and embedding module, and uses the Hamiltonian-Pontryagin system to solve the optimal control trajectory, and combines multi-objective optimization algorithms to achieve the coordinated optimization of multi-dimensional objectives; Multi-agent game control module, which, based on the control strategy solved by the multi-objective optimization module, models coffee growers, warehousers and logistics nodes as agents, and analyzes the decision-making behaviors of different agents through the mean-field game model; Control execution and feedback learning module, which transforms the optimal control strategy obtained by the multi-agent game control module into actual execution instructions. The execution instructions include precise irrigation, fertilization and warehousing scheduling, and real-time monitors the system response through the control instruction execution feedback mechanism.

[0007] Preferably, the data acquisition and fusion module includes: Internet of Things data access unit, which is used to collect raw ecological data from farmland meteorological sensors, soil monitoring devices, crop image collectors and logistics node terminals; Data preprocessing unit, which is used to fill in missing values, remove outliers and filter out noise from the raw data; Time series alignment unit, which is used to calibrate the time axis of data from different sources and unify the data format; Multi-modal fusion unit, which is used to extract and fuse features from image, time series, structured and unstructured data to form a standardized multi-modal input data stream.

[0008] Preferably, the AI vertical large model module includes: Feature encoding unit, which is used to perform vector quantization encoding on the fused multi-modal input data and extract key feature representations; A state evolution modeling unit for modeling and predicting the state evolution of the coffee ecosystem through time series modeling and graph neural networks; A parameter adaptive adjustment unit for online updating of model parameters based on historical system performance and current feedback data.

[0009] Preferably, the state modeling and embedding module includes: A state variable extraction unit for extracting key system state variables from the output of the AI vertical large model, where the state variables include yield prediction, soil humidity, and pest index; A manifold embedding unit for embedding multi-objective state variables into a Riemannian manifold space, preserving the non-linear relationship between the original variables; A non-linear dynamic modeling unit for constructing a non-linear state space model in the embedding space and performing dynamic prediction of future state evolution.

[0010] Preferably, the multi-objective optimization module includes: An objective function construction unit for setting corresponding optimization functions based on the goals of ecological balance, economic benefits, and supply chain efficiency; A control trajectory solving unit for calculating the optimal control path based on the optimization function and the state model through the Hamilton-Pontryagin principle; A collaborative optimization execution unit for dynamically adjusting the weights among multi-objective functions.

[0011] Preferably, the multi-agent game control module includes: An agent modeling unit for respectively modeling the participants of coffee growers, warehousing nodes, and logistics service providers as decision-making agents; A game strategy generation unit for generating initial strategies for each agent based on its own revenue function; An average field game solving unit for solving the game equilibrium solution in the average field framework through an iterative mechanism.

[0012] Preferably, the control execution and feedback learning module includes: A control instruction generation unit for generating operation control instructions based on the results of multi-agent game solving, where the operation control instructions include instructions for planting plans, irrigation regulation, and logistics scheduling; An execution interface unit for transmitting the control instructions to agricultural machinery equipment and Internet of Things nodes to achieve control execution at the physical level; A feedback data collection and learning unit for collecting system response data and comparing it with the expected goals, and updating control strategies and model parameters through machine learning algorithms.

[0013] Preferably, the feature encoding unit includes: A data preprocessing unit for receiving multi-modal data from a data acquisition and fusion module and performing preliminary processing such as format conversion, missing value filling, and outlier handling on this data; A feature extraction unit for extracting meaningful features from the processed data. The features include climate parameters, soil properties, and crop growth status, and converting them into a unified feature vector; An embedding layer unit that further embeds the extracted features using a deep learning model to generate large-scale feature representations suitable for input into an AI model; A feature fusion unit that fuses different types of data features through a feature selection algorithm to generate a final input feature set for further state evolution modeling.

[0014] Preferably, the state variable extraction unit includes: A state recognition unit for identifying and extracting key system state variables from the output results of the AI vertical large model; A dynamic adjustment unit for dynamically adjusting the extracted state variables according to system feedback information and actual monitoring results; A multi-objective state extraction unit for extracting relevant multi-objective state variables according to different target requirements; A time series analysis unit for performing time series analysis on the extracted state variables, identifying and predicting the change trends of future states, and providing support for the long-term prediction of system states.

[0015] The present invention also provides a coffee whole-region ecological intelligent management method based on an AI vertical large model, including the following steps: Data acquisition and fusion, collecting multi-dimensional ecological data of farmland meteorology, soil environment, and crop growth through Internet of Things devices, and cleaning, denoising, and time series alignment of the collected raw data to form a multi-modal data stream for processing by an AI model.

[0016] AI model training and state prediction, using the AI vertical large model to train the multi-modal data stream, establishing a state evolution model of the coffee ecosystem, and predicting future ecological states and system evolution trajectories based on this model.

[0017] State modeling and embedding, extracting key system state variables from the output results of the AI model, embedding them based on the Riemannian manifold space, constructing a non-linear state space model, and performing dynamic prediction of future states.

[0018] Multi-objective optimization, calculating the optimal control trajectory according to the multi-objective optimization function using the Hamilton-Pontryagin principle, and realizing the dynamic optimization of multiple objectives in combination with the multi-objective collaborative optimization algorithm.

[0019] Multi-agent game control, based on the multi-objective optimization control strategy, models different participants as agents and solves the optimal decision-making strategies of each agent through a game model.

[0020] Control execution and feedback optimization, generate operation control instructions according to the game control results, transmit the instructions to the actual actuator, and monitor the execution effect in real time through the system feedback mechanism.

[0021] The present invention provides a coffee whole-domain ecological intelligent management system and method based on an AI vertical large model. It has the following beneficial effects: 1. The present invention adopts multi-objective optimization technology, incorporates ecological, economic, and supply chain objectives into the system optimization framework, and achieves the coordinated optimization effect of multi-dimensional objectives through precise objective function design and control strategy solution. Compared with the existing technology that optimizes a single objective, the solution of the present invention can comprehensively consider the interests of all parties, solves the technical problem of the inability to effectively coordinate the contradictions between different objectives in the multi-objective optimization process, and thus improves the overall efficiency and sustainability of the system operation.

[0022] 2. Through multi-agent game control technology, the present invention simulates the interaction behaviors of participants such as coffee growers, warehousers, and logistics nodes, and achieves the effect of behavior coordination among agents. Different from the common single decision-making models in the existing technology, the present invention can dynamically adjust the strategies of each agent, enabling all parties to achieve the optimal resource allocation during the game process. This not only improves the operation flexibility of the system but also solves the deficiency in the existing solutions that lack dynamic adaptability and cannot cope with complex environmental changes.

[0023] 3. The present invention combines a control execution and feedback learning module, and through a real-time monitoring and feedback mechanism, ensures that the execution instructions are accurately and efficiently implemented in actual operations. Compared with the technical solutions with a weak feedback link in traditional methods, the feedback learning mechanism of the present invention can adjust the control strategy in real time during the execution process, timely correct the execution deviation, thereby significantly improving the execution accuracy and system stability, and reducing resource waste and operation errors caused by improper execution.

[0024] 4. Through the solution of the control strategy based on the Hamiltonian-Pontryagin optimal control theory, the present invention realizes precise control in a complex and uncertain environment. Different from the control methods that mostly rely on experience or rules in the existing technology, the present invention provides a clear control path through a mathematical optimization model, solves the problem of inaccurate decision-making in traditional methods due to the lack of mathematical basis, and improves the reliability and adaptability of the system under different operating conditions. Brief Description of the Drawings

[0025] Figure 1 It is a schematic diagram of the system construction of the present invention; Figure 2 This is the framework diagram of the data acquisition and fusion module of the present invention; Figure 3 This is the framework diagram of the AI vertical large model module of the present invention; Figure 4 This is the framework diagram of the state modeling and embedding module of the present invention; Figure 5 This is the framework diagram of the multi-objective optimization module of the present invention; Figure 6 This is the framework diagram of the multi-agent game control module of the present invention; Figure 7 This is the framework diagram of the control execution and feedback learning module of the present invention; Figure 8 This is the schematic diagram of the method flow of the present invention. Detailed implementation manners

[0026] Next, in combination with the accompanying drawings of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0027] Please refer to the attached Figure 1 - attached Figure 7 , the embodiment of the present invention provides a coffee global ecological intelligent management system based on an AI vertical large model, including: A data acquisition and fusion module, which is used to obtain multi-dimensional ecological data. The multi-dimensional ecological data includes data on farmland meteorology, soil environment, crop growth, market supply and demand, and warehousing and transportation, and cleans, denoises, and aligns the time series of this data to form a multi-modal input data stream; The data acquisition and fusion module is a key component in the system, mainly responsible for obtaining multi-dimensional ecological data and performing preprocessing to ensure that high-quality data that has been cleaned, denoised, and time-aligned can be used in subsequent links. The multi-dimensional ecological data includes various data such as farmland meteorology, soil environment, crop growth, market supply and demand, and warehousing and transportation. These data are collected in real time through multiple sensors and data acquisition devices, and are cleaned, denoised, and time-aligned through corresponding data processing modules to generate a multi-modal data stream suitable for input to the AI model. The design of the data acquisition and fusion module can ensure that the data input of the system has high quality and high precision, and ensure that the subsequent AI model can provide accurate predictions and optimization suggestions.

[0028] Under normal circumstances, this module adopts advanced Internet of Things (IoT) devices and sensor technologies to obtain various types of ecological data in real time. The collected data comes from multiple sources, including weather monitoring stations, soil sensors, crop growth image acquisition devices, market data platforms, and warehousing and logistics systems. These data are diverse in type and vary in spatial and temporal distribution. Therefore, it is necessary to effectively fuse and process the collected data to ensure data consistency and availability.

[0029] Specifically, the data acquisition and fusion module performs real-time data acquisition through different hardware devices and sensors. In terms of farmland weather monitoring, weather stations and weather sensor devices are used to monitor key meteorological variables such as air temperature and humidity, wind speed, precipitation, and sunshine duration in real time. These meteorological data are of great significance for predicting crop growth, irrigation requirements, etc.

[0030] In terms of soil environment monitoring, devices such as soil moisture sensors, temperature sensors, and pH sensors are used to collect information such as soil moisture, temperature, and acidity. These data directly affect crop growth and the biological environment.

[0031] Crop growth data is collected through image recognition technology. Using drones, ground cameras, or remote sensing satellite images, the growth status of farmland crops is regularly photographed to obtain crop growth cycle information. Further combined with farmland management data, a time series of crop growth is formed.

[0032] Market supply and demand data is obtained by real-time data docking with e-commerce platforms, wholesale markets, industry data providers, etc., to obtain information such as the changing trends of market demand for agricultural products such as coffee and price fluctuations, helping farmland production to conduct effective market forecasts.

[0033] Warehousing and transportation data are connected to the warehousing system and transportation vehicles through Internet of Things technology to collect data such as inventory information, transportation routes, and arrival times, ensuring the transparency and efficient operation of the supply chain.

[0034] In this embodiment, the data preprocessing process includes data cleaning, denoising, and time series alignment. First, the cleaning module eliminates outliers from the collected raw data to remove noise caused by sensor failures or data transmission errors, ensuring data quality. The goal of data cleaning is to remove extreme values that do not conform to the actual situation and ensure data accuracy.

[0035] Secondly, for data from different sources and types, denoising algorithms are used for processing. For example, for meteorological data, time series denoising methods are used to remove fluctuations caused by meteorological mutations or equipment problems; for soil and crop growth data, smoothing filtering techniques are used to reduce random fluctuations in the data.

[0036] In terms of time series alignment, considering the differences in the acquisition frequencies and time delays of different sensor data, this embodiment adopts an interpolation algorithm and a timestamp alignment method to unify data from different sources onto the same time scale, forming a standardized time series data stream. In this process, interpolation methods such as linear interpolation and spline interpolation are used to better preserve the changing trends of the data.

[0037] In a possible implementation, the data time series alignment algorithm is as follows: ; where is the data value after time series alignment, is the original data value of the th sensor at time point , is the number of sensors participating in the alignment. This algorithm ensures that the data after time series alignment has high accuracy by performing weighted averaging on data from different sources.

[0038] After the data preprocessing is completed, the system will fuse various multi-dimensional data (meteorological, soil, crop, market, warehousing, etc.) into a unified multi-modal data stream through a data fusion module. The fusion process combines the weighted average method and a deep fusion network to ensure the complementarity and coordination between different types of data.

[0039] In some embodiments, meteorological data and soil data can be fused through weighted averaging and combined into a comprehensive environmental factor; crop growth data is used to extract features through image recognition and deep learning models and then fused with other data. Market data and warehousing and transportation data are integrated through association rule mining and pattern recognition methods to improve the accuracy of market demand prediction and supply chain optimization.

[0040] Through the above data cleaning, denoising, time series alignment, and fusion processing, the finally formed multi-modal input data stream can provide high-quality and accurate data input for the subsequent AI vertical large model, supporting intelligent decision-making and optimized control of the coffee production process.

[0041] The AI vertical large model module constructs and runs an AI vertical large model based on the multi-modal input data stream provided by the data acquisition and fusion module. This model predicts the state evolution trajectory of the coffee ecosystem through deep learning algorithms and adaptively adjusts the model parameters according to historical data and system feedback data; The AI vertical large model module constructs and runs a deep learning algorithm model based on the multi-modal input data stream provided by the data collection and fusion module. This model aims to predict the state evolution trajectory of the coffee ecosystem. The multi-modal input data stream contains real-time data from multiple data sources such as meteorology, soil, crop growth, market supply and demand, and warehousing and transportation. Through these data, the AI vertical large model can simulate various dynamic changes in the coffee production process, predict the future evolution trend of the system state, and make adaptive adjustments based on historical data and system feedback to continuously optimize the model performance and improve the prediction accuracy and system response ability.

[0042] Supported by the aforementioned data collection and fusion module, the AI vertical large model module constructs an accurate and efficient prediction model through deep learning algorithms, combined with multi-dimensional ecological data, to help the managers of the coffee ecosystem make scientific decisions. Specifically, this model can not only predict the environmental changes during coffee planting, but also evaluate the impact of different factors on the ecosystem, providing a theoretical basis for precise management and scheduling.

[0043] Specifically, in this embodiment, the AI vertical large model uses a deep neural network (DNN) as the core algorithm to process multi-modal data through a multi-layer network structure. The deep learning algorithm contains multiple hidden layers, and these layers extract data features through non-linear activation functions (such as ReLU or Tanh) to identify the potential relationships and patterns between different input data.

[0044] In terms of data input, the model receives the time-series aligned multi-modal data stream provided by the data collection and fusion module. The input data stream includes meteorological data, soil data, crop growth data, market demand data, and warehousing and logistics data, etc. Through this multi-modal data stream, the model can comprehensively understand the state and its evolution law of the coffee ecosystem.

[0045] The training process of the model adopts the supervised learning method, in which the model is trained through a historical data set, and the mean square error (MSE) is used as the loss function. The specific definition of the loss function is as follows: ; where, represents the mean square error, is the true label of the th sample, is the model prediction result, is the total number of samples. By minimizing the mean square error, the model can gradually adjust its weights to optimize the prediction result.

[0046] After the model training is completed, the AI vertical large model can predict the future state evolution of the coffee ecosystem by inputting real-time multi-modal data streams. Specifically, the model first processes and encodes the input data, then maps the data to the hidden layer through a feedforward network, and finally outputs the prediction results.

[0047] The predicted outputs include data in multiple dimensions, such as temperature, soil humidity, crop growth index, etc. within a future period of time. These output data can not only be used for predicting the state of the coffee ecosystem, but also provide strong support for subsequent decision-making. Through state prediction, managers can respond to potential risks in advance, such as predicting the occurrence of drought or pests and diseases, and thus take corresponding prevention and control measures.

[0048] In a possible implementation, the AI vertical large model generates a state prediction formula through a time series regression algorithm: ; where, represents the system state at time point , represents the system state at time point , is the input ecological data (such as meteorology, soil, etc.), is the target output (such as crop growth index, yield, etc.), is the manifold mapping function. This formula is fitted and predicted through a deep learning network to provide accurate predictions for the system state at future moments.

[0049] To improve the prediction accuracy of the model and ensure its good adaptability under different environmental changes, the AI vertical large model module introduces an adaptive learning mechanism. During the operation of the system, the model continuously obtains new information from historical data and real-time feedback, and adjusts the model parameters based on this information.

[0050] The adaptive adjustment process is achieved through a model feedback loop. Specifically, the system adjusts the weights of the model using the gradient descent algorithm or other optimization algorithms according to the error between the prediction result and the actual feedback to reduce the prediction error. For example, in the loss function, a feedback error term is added so that each prediction can be corrected according to the actual result. The adjustment process can be expressed as: ; where, is the model parameter at the th iteration, is the model parameter at the th iteration, is the learning rate, is the gradient of the loss function, is the model predicted value, is the true value, is the loss function. Through such adaptive adjustment, the model can maintain a high prediction accuracy in a changing ecological environment.

[0051] In some embodiments, the adjustment mechanism can also be combined with a reinforcement learning algorithm. By setting a reward and punishment mechanism, the model can continuously optimize its decision-making strategy in actual production. For example, the system can reward according to the final economic benefit of a certain production cycle, so as to prompt the model to adjust the strategy to achieve the optimal production and management effect.

[0052] To ensure that the model can adapt to new data and environmental changes in a timely manner, the AI vertical large model module also includes a real-time data monitoring and updating mechanism. This mechanism retrains the model regularly according to the newly collected real-time data to ensure that the model can always accurately reflect the state of the current ecosystem.

[0053] Specifically, in some embodiments, every certain period of time (such as weekly or monthly), the model will perform incremental learning on the newly collected data and update the model parameters. This process can be carried out through fine-tuning technology, using new data to make local adjustments to the existing model, thus saving computing resources and improving the efficiency of model updating.

[0054] The state modeling and embedding module, based on the prediction results output by the AI vertical large model, further embeds the multi-objective state variables of the coffee ecosystem into the Riemannian manifold space, constructs a high-dimensional non-linear state space model, and makes accurate state predictions for the dynamic evolution of the future ecosystem; The state modeling and embedding module, based on the output results of the AI vertical large model, further embeds the multi-objective state variables in the coffee ecosystem into the Riemannian manifold space, constructs a high-dimensional non-linear state space model, so as to achieve accurate state predictions for the dynamic evolution of the future ecosystem. The core task of this module is to use the various state variables predicted by the AI model to construct a mathematical model that adapts to the complex changes of the ecosystem, and capture the dynamic characteristics of the system through non-linear state space modeling. Through this process, the state parameters of the coffee ecosystem at different future time nodes can be accurately predicted, and a basis can be provided for the optimization and scheduling of the system.

[0055] Connected to the aforementioned AI vertical large model module, the prediction results output by the AI model will be used as the input data of this module, forming state variables including multiple dimensions such as climate, soil, crop growth, and market demand. After these variables enter the state modeling and embedding module, they will be converted into points on the Riemannian manifold through complex geometric embedding methods, constructing a high-dimensional non-linear state space model, thereby effectively simulating the evolution trajectory and future state of the coffee ecosystem.

[0056] Specifically, in this embodiment, the state modeling and embedding module first maps multi-dimensional predictive variables to the Riemannian manifold space based on the output results of the AI vertical large model. As a surface with a local Euclidean structure, the Riemannian manifold can well describe the state changes of non-linear systems. On the Riemannian manifold, the mutual relationships between state variables are presented through geometric structures, thus enabling the capture of more complex dynamic evolution laws.

[0057] In this process, first, each target variable (such as meteorological data, soil data, crop growth data, etc.) is standardized so that they can be compared on the same scale. Then, embedding techniques on the Riemannian manifold are adopted, and methods such as manifold learning and manifold mapping are used to embed these high-dimensional state variables into a low-dimensional Riemannian manifold space, obtaining a non-linear model adapted to system changes.

[0058] In some embodiments, the embedding of state variables can be achieved through manifold learning algorithms. For example, techniques such as Isomap and LLE (Locally Linear Embedding) are used. These methods can preserve the non-linear structure of the data and effectively reduce the dimension. For example, assume we have a multi-dimensional state variable vector , which is mapped to a point on the Riemannian manifold. We hope to embed it into a low-dimensional manifold through the following transformation: ; where is the input data, is the manifold mapping function, and is the low-dimensional representation mapped to the Riemannian manifold. This process can capture the complex non-linear relationships between system states.

[0059] After completing the embedding of state variables, in this embodiment, a high-dimensional non-linear state space model is further constructed. This model forms a multi-dimensional non-linear system model by integrating multiple embedded state variables. Specifically, the evolution of the system state can be described by the following state space equation: ; where is the system state vector at time , is the control input, and is the noise term, representing external perturbations or measurement errors. is the manifold mapping function, describing the state evolution process of the system from time to time .

[0060] In a possible implementation, the non-linear state space model is modeled using a deep neural network (DNN). By adding multiple hidden layers to the neural network, the model can automatically learn the complex non-linear relationships between system states and capture the dynamic change trends of these states. The non-linear activation functions of the neural network (such as ReLU, Sigmoid, etc.) enable the model to process high-dimensional, non-linear data inputs, thus achieving accurate dynamic evolution prediction.

[0061] In this embodiment, through the constructed high-dimensional non-linear state space model, the state prediction of the future coffee ecosystem can be realized. Specifically, the system iterates using the trained state transition function according to historical data and the current state input to predict the system state within a future period of time.

[0062] Assume that the system state at the current moment is \(\mathbf{x}(t)\), then the predicted state at a future moment can be calculated through the following formula: ; where, is the predicted time step, is the total number of samples, is the manifold mapping function, represents the predicted future state, is the predicted time step, is the control input at time step . Through iterative prediction, the model can simulate the state of the coffee ecosystem in the next few days, weeks or months, providing a scientific basis for production managers.

[0063] As an option, the state modeling and embedding module can also be optimized by combining with a reinforcement learning algorithm. Through reinforcement learning, the model can continuously adjust the state space model according to the feedback information of the ecosystem to optimize the prediction effect. For example, the system can set up a reward mechanism to prompt the model to make optimal decisions when facing different ecological environments, so as to achieve the purpose of improving the overall production efficiency.

[0064] In some embodiments, in order to improve the robustness and generalization ability of the model, a model integration method can be adopted. By training multiple different models and performing weighted averaging on their prediction results, the model error can be further reduced and the prediction accuracy can be improved.

[0065] The multi-objective optimization module constructs optimization functions for multiple objectives of ecology, economy and supply chain based on the state information generated by the state modeling and embedding module, solves the optimal control trajectory using the Hamiltonian-Pontryagin system, and realizes the collaborative optimization of multi-dimensional objectives by combining with the multi-objective optimization algorithm; Based on the dynamic state vector output by the state modeling and embedding module, the multi-objective optimization module constructs a set of objective functions, which involve ecological sustainability objectives (such as carbon emissions, soil quality maintenance, etc.), economic benefit objectives (such as revenue per unit yield, total profit margin), and supply chain stability objectives (such as inventory volatility, transportation cost). This module takes multi-objective optimization as the theoretical core and combines the Hamiltonian-Pontryagin system theory to design a mathematical framework for obtaining the optimal control strategy, so as to solve the optimal operation trajectory of the ecosystem in different objective dimensions.

[0066] Generally, the multi-objective optimization model needs to comprehensively consider the coupling relationship between state variables and control variables. In this system, the control variables usually include fertilization rate, irrigation strategy, harvest time node, logistics scheduling parameters, etc. The state variables are generated by the aforementioned state modeling module, including but not limited to crop growth index, biomass accumulation value, soil humidity, market price dynamics, etc.

[0067] In this embodiment, the constructed set of objective functions can be expressed in the following form: ; where, is the control input, is the system state vector at time , represents the economic objective function, is the ecological objective function, is the supply chain objective function.

[0068] : Quantify the revenue per unit land area, total crop sales profit or input-output ratio; : Constrain or minimize carbon emissions, pesticide residue risk or water resource usage; : Reflect inventory holding cost, logistics transportation cost or supply-demand matching efficiency.

[0069] To solve the above multi-objective optimization problem, this embodiment introduces the Hamiltonian-Pontryagin optimal control theory. First, define the Hamiltonian of the system: ; where, is the control input, is the system state vector at time , is the objective function at time , representing the function value of the system state, control input and Lagrange multiplier , is the conjugate variable vector, is the manifold mapping function, is the immediate cost function, usually the manifestation of the objective function at time specific performance.

[0070] In some embodiments, to further adapt to the dynamic environment and uncertainty factors, this module can introduce multi-objective evolutionary algorithms (such as NSGA-II, MOEA / D, etc.) to globally explore the search space of control variables. Compared with the traditional gradient method, this type of algorithm is insensitive to the initial value and has strong robustness and global convergence ability. By evolving the objective value performance of individuals within the population, a Pareto optimal solution set is finally obtained, providing multiple optimization alternative solutions for decision-making.

[0071] As an option, this module also supports the introduction of a mechanism based on multi-agent collaborative learning to achieve distributed optimization of ecosystem sub-modules (such as farm units, logistics nodes). Each subsystem independently optimizes based on local state information and achieves global optimality at the system level through an information interaction mechanism.

[0072] In addition, considering possible external disturbance factors such as environmental mutations and drastic demand fluctuations during the implementation process, in some embodiments, this module further integrates a dynamic replanning mechanism. This mechanism can re-evaluate the current state at each time step, combine the predicted trajectory, and dynamically correct the control strategy to enhance the system's adaptability.

[0073] The multi-agent game control module, based on the control strategy solved by the multi-objective optimization module, models each participant such as coffee growers, warehousers, and logistics nodes as agents, and analyzes the decision-making behaviors of different agents through the mean-field game model; In the aforementioned multi-objective optimization module, a set of control strategies has been obtained by optimizing the multi-dimensional objectives of the coffee ecosystem. This strategy considers the comprehensive objectives of ecology, economy, and supply chain. On this basis, a multi-agent game model is further introduced to simulate the decision-making behaviors of various participants such as coffee growers, warehousers, and logistics nodes. Each participant (agent) makes an optimal decision according to its own objectives and constraints, thereby achieving global collaborative optimization of the system. For this purpose, this embodiment designs an agent interaction mechanism based on mean-field game to achieve the balance of multi-party interests and the efficient allocation of resources by simulating the mutual influence and game behaviors among multiple agents.

[0074] In this embodiment, based on the control strategy provided by the multi-objective optimization module, coffee growers, warehousers, and logistics nodes are regarded as agents. Each agent represents its goal by defining a specific utility function, which in turn affects its decision-making. The goal of coffee growers may be to maximize crop yields and profits, the goal of warehousers may focus on inventory management and minimizing storage costs, while the goal of logistics nodes may be to reduce transportation costs and improve transportation efficiency. The decision-making of each agent is not only affected by its own goal but also interacts with the behaviors of other agents.

[0075] Specifically, in this embodiment, the decision-making behaviors of different agents are analyzed through the mean-field game model. In this model, it is assumed that the decision-making process of each agent is affected by the behaviors of other agents within its neighborhood. The strategy of an agent can be represented by the following utility function: ; where is the utility function of agent , is the action strategy of agent , is the set of action strategies of other agents, is the reward function when agent adopts strategy , is the cost that agent faces when adopting strategy .

[0076] In some embodiments, the reward function can be defined according to the goals of different agents. For example, the reward function of coffee growers may involve factors such as crop yields and market price fluctuations; the reward function of warehousers may be related to inventory levels and storage times; and the reward function of logistics nodes may involve transportation times and transportation costs.

[0077] The cost function considers the interaction between agents. For example, the cost of coffee growers may be related to the inventory of warehousers and the transportation efficiency of logistics nodes, the cost of warehousers may be related to the transportation capacity of logistics nodes and the receiving quantity of coffee growers, and the cost of logistics nodes may be related to factors such as transportation routes and transportation volumes.

[0078] In a possible implementation, each agent maximizes its own utility function by continuously adjusting its strategy. During this game process, it is assumed that the interaction between agents gradually reaches the Nash Equilibrium. That is, each agent selects its optimal strategy given the strategies of other agents, so that the utility function reaches its maximum value. Specifically, the Nash equilibrium condition can be expressed as: ; where, is the agent when its strategy is , is the action strategy of agent , is the optimal strategy of agent , is the optimal strategy of other agents.

[0079] As an option, considering that there may be multiple equilibrium solutions in multi-agent games, a mean-field game model is introduced in this embodiment to handle this situation. In mean-field games, it is assumed that the decision-making of each agent depends not only on the individual behaviors of other agents but also on the influence of the behavior of the entire group. The key of the mean-field model is to simplify the complexity by introducing the influence of the group average behavior. The decision-making process of the agent can be described by the following dynamic equation: ; where, is the decision variable of agent at time , represents the average decision of other agents except agent , is the dynamic function related to the utility function.

[0080] In some embodiments, the mean-field game model can be solved by reinforcement learning techniques. In this context, the agent adjusts its strategy according to the environmental feedback and continuously optimizes its decision-making process to achieve a locally optimal or globally optimal game result. For example, using the Deep Q-Network (DQN) algorithm, each agent learns how to select the optimal strategy by interacting with other agents, thus promoting the collaborative optimization of the entire system.

[0081] In addition, considering the uncertainty and complexity of the ecosystem, the game model in this embodiment also supports dynamic adjustment. In practice, as the external environment changes (such as market demand fluctuations, climate condition changes, etc.), the strategies of the agents may need to be adjusted in real time. For this purpose, the module also integrates a real-time feedback mechanism, which can update the strategy according to the actual situation.

[0082] The control execution and feedback learning module converts the optimal control strategy obtained by the multi-agent game control module into actual execution instructions. The execution instructions include precise irrigation, fertilization, and warehousing scheduling, and the system response is monitored in real time through the control instruction execution feedback mechanism. In the aforementioned multi-agent game control module, the optimal control strategies among multiple agents have been obtained by modeling and optimizing the behaviors of various participants such as coffee growers, warehousing merchants, and logistics nodes. These strategies cover key operations such as precise irrigation, fertilization, and warehousing scheduling, and can effectively promote the collaborative optimization of ecological, economic, and supply chain goals. In order to implement these theoretically optimized strategies into actual operations, the optimal control strategy is further converted into specific execution instructions through the control execution and feedback learning module, and the response of the system is monitored in real time through the feedback mechanism to ensure that the system achieves the expected effect during the execution process.

[0083] In this embodiment, the core task of the control execution and feedback learning module is to convert the optimal control strategy obtained by the multi-agent game control module into specific and operable execution instructions. These instructions cover fields such as irrigation, fertilization, and warehousing scheduling, and specifically include: precise irrigation instructions, fertilization amount instructions, and warehousing scheduling plans. Each execution instruction is generated according to the optimal control strategy and is automatically executed through control devices. Specifically, the precise irrigation instructions are generated based on the water requirements of the crops, soil humidity, and meteorological data, the fertilization instructions are adjusted according to the growth stage of the crops, soil nutrient content, and climate change, and the warehousing scheduling instructions consider the inventory situation of coffee, transportation requirements, and market sales forecasts.

[0084] During the actual control execution process, the execution instructions are executed through automated devices (such as intelligent irrigation systems, fertilization robots, warehousing management systems, etc.). Specifically: The precise irrigation instructions generate the irrigation amount and time arrangement based on real-time soil humidity, crop requirements, and climate data. These data are dynamically monitored through sensors and data processing systems and fed back to the irrigation equipment to ensure that the irrigation operation meets the real-time needs of the crops.

[0085] The fertilization instructions generate the appropriate amount of fertilizer and fertilization method based on the nutrient conditions of the soil and the growth stage of the crops, and are precisely applied through the fertilization system.

[0086] The warehousing scheduling instructions generate warehousing management and logistics scheduling instructions based on market demand forecasts, warehouse inventory, and transportation plans to ensure the efficient utilization of the storage and transportation capabilities of the warehouse.

[0087] In some embodiments, the feedback mechanism for executing instructions can monitor the system response during the execution process in real time to ensure the accuracy and effectiveness of the executed instructions. By setting up a feedback loop, the system can monitor operations such as irrigation volume, fertilization amount, and warehousing scheduling in real time after the instructions are executed. If the execution effect of a certain link is found to be inconsistent with the expectation, the feedback mechanism will adjust the execution strategy. For example, if the irrigation system detects that the soil humidity fails to reach the set standard, the system will automatically adjust the irrigation volume or fertilization frequency. Similarly, if the warehousing system finds that the inventory exceeds the standard or the transportation is lagging behind, the system will re-schedule according to the demand.

[0088] As an option, the feedback mechanism can also be self-optimized by combining machine learning algorithms. The system can learn from the historical data of the execution process and continuously adjust the control strategy to adapt to possible future changes. For example, based on historical crop growth conditions, soil humidity changes, and climate data, the system can predict future irrigation requirements and adjust the irrigation strategy accordingly. In addition, the feedback mechanism can also make dynamic adjustments for the special needs of different crops and different regions, making the executed instructions more accurate.

[0089] Specifically, the control equation of the feedback mechanism can be expressed as: ; where, represents the feedback result, is the control input, is the system state vector at time , is the manifold mapping function, is the feedback function, is the noise term, and the system can reduce the feedback error to make the execution result more accurate.

[0090] In a possible implementation, this module adaptively adjusts the feedback system by setting the objective function and constraint conditions and using optimization algorithms (such as gradient descent method, genetic algorithm, etc.). For example, the irrigation control system may automatically adjust the irrigation volume according to the change of real-time soil humidity to achieve the expected soil moisture balance. Similarly, the warehousing scheduling system can dynamically adjust the inventory level according to the change of market demand, thereby reducing the risk of overstock or shortage.

[0091] In addition, to improve the robustness of the system, this embodiment also considers the uncertain factors in the system, such as weather changes, market fluctuations, etc. For this reason, the system will introduce predictive control technology. By modeling and predicting the uncertain factors, the control strategy is adjusted in advance to ensure efficient operation under uncertain conditions.

[0092] The coffee full-domain ecological intelligent management method based on the AI vertical large model described below can be correspondingly referred to the coffee full-domain ecological intelligent management system based on the AI vertical large model described above.

[0093] Please refer to the appendix Figure 8 , the present invention also provides a coffee full-domain ecological intelligent management method based on the AI vertical large model, including the following steps: S1. Data collection and fusion: Collect multi-dimensional ecological data of farmland meteorology, soil environment, and crop growth through Internet of Things devices, and clean, denoise, and align the time series of the collected raw data to form a multi-modal data stream for AI model processing.

[0094] S2. AI model training and state prediction: Use the AI vertical large model to train the multi-modal data stream, establish a state evolution model of the coffee ecosystem, and predict the future ecological state and system evolution trajectory based on this model.

[0095] S3. State modeling and embedding: Extract key system state variables from the results output by the AI model, embed them based on the Riemannian manifold space, construct a non-linear state space model, and perform dynamic prediction of future states.

[0096] S4. Multi-objective optimization: According to the multi-objective optimization function, calculate the optimal control trajectory using the Hamilton-Pontryagin principle, and combine the multi-objective collaborative optimization algorithm to achieve the dynamic optimization of multiple objectives.

[0097] S5. Multi-agent game control: Based on the multi-objective optimization control strategy, model different participants as agents, and solve the optimal decision-making strategies of each agent through the game model.

[0098] S6. Control execution and feedback optimization: Generate operation control instructions according to the game control results, transmit the instructions to the actual actuator, and monitor the execution effect in real time through the system feedback mechanism.

[0099] The method of this embodiment can be used to execute the above system embodiment, and its principle and technical effects are similar, so they will not be elaborated here.

[0100] Although the embodiments of the present invention have been shown and described, for those of ordinary skill in the art, it can be understood that various changes, modifications, substitutions, and variations can be made to these embodiments without departing from the principle and spirit of the present invention. The scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. The coffee full-domain ecological intelligent management system based on the AI vertical large model is characterized in that Including: A data acquisition and fusion module, which is used to obtain multi-dimensional ecological data. The multi-dimensional ecological data includes data on farmland meteorology, soil environment, crop growth, market supply and demand, and warehousing and transportation, and cleans, denoises, and aligns the data in time series to form a multi-modal input data stream; An AI vertical large model module, which constructs and runs an AI vertical large model based on the multi-modal input data stream provided by the data acquisition and fusion module. This model predicts the state evolution trajectory of the coffee ecosystem through deep learning algorithms and adaptively adjusts the model parameters according to historical data and system feedback data; A state modeling and embedding module, which further embeds the multi-objective state variables of the coffee ecosystem into the Riemannian manifold space based on the prediction results output by the AI vertical large model, constructs a high-dimensional non-linear state space model, and makes accurate state predictions for the dynamic evolution of the future ecosystem; A multi-objective optimization module, which constructs optimization functions for multiple objectives of ecology, economy, and supply chain based on the state information generated by the state modeling and embedding module, and uses the Hamiltonian-Pontryagin system to solve the optimal control trajectory, and combines multi-objective optimization algorithms to achieve the collaborative optimization of multi-dimensional objectives; A multi-agent game control module, which, based on the control strategy solved by the multi-objective optimization module, models coffee growers, warehousers, and logistics nodes as agents, and analyzes the decision-making behaviors of different agents through an average field game model; A control execution and feedback learning module, which converts the optimal control strategy obtained by the multi-agent game control module into actual execution instructions. The execution instructions include precise irrigation, fertilization, and warehousing scheduling, and real-time monitors the system response through a control instruction execution feedback mechanism.

2. The coffee global ecological intelligent management system based on the AI vertical large model according to claim 1, characterized in that, The data acquisition and fusion module includes: An Internet of Things data access unit, which is used to collect raw ecological data from farmland meteorological sensors, soil monitoring devices, crop image collectors, and logistics node terminals; A data preprocessing unit, which is used to fill in missing values, remove outliers, and filter out noise from the raw data; A time series alignment unit, which is used to calibrate the time axis of data from different sources and unify the data format; A multi-modal fusion unit, which is used to extract and fuse features of image, time series, structured, and unstructured data to form a standardized multi-modal input data stream.

3. The coffee full-domain ecological intelligent management system based on the AI vertical large model according to claim 1, characterized in that, The AI vertical large model module includes: A feature encoding unit, which is used to perform vector quantization encoding on the fused multi-modal input data to extract key feature representations; A state evolution modeling unit, which is used to model and predict the state evolution of the coffee ecosystem through time series modeling and graph neural networks; A parameter adaptive adjustment unit, which is used to update the model parameters online according to historical system performance and current feedback data.

4. The coffee global ecological intelligent management system based on the AI vertical large model according to claim 1, characterized in that, The state modeling and embedding module includes: A state variable extraction unit, which is used to extract key system state variables from the output of the AI vertical large model. The state variables include yield estimation, soil humidity, and pest and disease index; A manifold embedding unit, which is used to embed multi-objective state variables into the Riemannian manifold space and retain the non-linear relationship between the original variables; A non-linear dynamic modeling unit for constructing a non-linear state space model in the embedding space and performing dynamic prediction of future state evolution.

5. The coffee global ecological intelligent management system based on the AI vertical large model according to claim 1, characterized in that, The multi-objective optimization module includes: An objective function construction unit for setting corresponding optimization functions based on the objectives of ecological balance, economic benefits, and supply chain efficiency; A control trajectory solving unit for calculating the optimal control path based on the optimization function and the state model through the Hamilton-Pontryagin principle; A collaborative optimization execution unit for dynamically adjusting the weights among multiple objective functions.

6. The coffee full-domain ecological intelligent management system based on the AI vertical large model according to claim 1, characterized in that, The multi-agent game control module includes: An agent modeling unit for respectively modeling the participants of coffee growers, warehousing nodes, and logistics service providers as decision-making agents; A game strategy generation unit for generating initial strategies for each agent based on its own revenue function; An average field game solving unit for solving the game equilibrium solution in the average field framework through an iterative mechanism.

7. The coffee global ecological intelligent management system based on the AI vertical large model according to claim 1, characterized in that, The control execution and feedback learning module includes: A control instruction generation unit for generating operation control instructions according to the results of multi-agent game solving. The operation control instructions include instructions for planting plans, irrigation regulation, and logistics scheduling; An execution interface unit for transmitting the control instructions to agricultural machinery equipment and Internet of Things nodes to achieve control execution at the physical level; A feedback data collection and learning unit for collecting system response data and comparing it with the expected target, and updating the control strategy and model parameters through machine learning algorithms.

8. The coffee global ecological intelligent management system based on the AI vertical large model according to claim 3, characterized in that The feature encoding unit includes: A data preprocessing unit for receiving multi-modal data from the data collection and fusion module and performing preliminary processing such as format conversion, missing value filling, and outlier processing on these data; A feature extraction unit for extracting meaningful features from the processed data. The features include climate parameters, soil properties, and crop growth status, and converting them into a unified feature vector; An embedding layer unit for further embedding the extracted features using a deep learning model to generate a large-scale feature representation suitable for input into the AI model; A feature fusion unit for fusing different types of data features through a feature selection algorithm to generate a final input feature set for further state evolution modeling.

9. The coffee global ecological intelligent management system based on the AI vertical large model according to claim 4, wherein, The state variable extraction unit includes: A state recognition unit for identifying and extracting key system state variables from the output results of the AI vertical large model; A dynamic adjustment unit for dynamically adjusting the extracted state variables according to system feedback information and actual monitoring results; A multi-objective state extraction unit for extracting relevant multi-objective state variables according to different target requirements; A time series analysis unit for performing time series analysis on the extracted state variables, identifying and predicting the change trend of future states, and providing support for the long-term prediction of system states.

10. A coffee global ecological intelligent management method based on an AI vertical large model, and a coffee global ecological intelligent management system based on an AI vertical large model according to any one of claims 1-9, characterized in that, It includes the following steps: Data collection and fusion: Collect multi-dimensional ecological data of farmland meteorology, soil environment, and crop growth through Internet of Things devices, and clean, denoise, and align the collected raw data in time series to form a multi-modal data stream for AI model processing. AI model training and state prediction: Use an AI vertical large model to train the multi-modal data stream, establish a state evolution model of the coffee ecosystem, and predict the future ecological state and system evolution trajectory based on this model. State modeling and embedding: Extract key system state variables from the results output by the AI model, embed them based on the Riemannian manifold space, construct a non-linear state space model, and conduct dynamic prediction of future states. Multi-objective optimization: According to the multi-objective optimization function, use the Hamilton-Pontryagin principle to calculate the optimal control trajectory, and combine the multi-objective collaborative optimization algorithm to achieve dynamic optimization of multiple objectives. Multi-agent game control: Based on the multi-objective optimization control strategy, model different participants as agents, and solve the optimal decision-making strategies of each agent through the game model. Control execution and feedback optimization: Generate operation control instructions according to the game control results, transmit the instructions to the actual actuator, and monitor the execution effect in real time through the system feedback mechanism.

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