Unmanned agricultural vehicle and cloud platform system

Abstract

The invention discloses a system based on an unmanned agricultural vehicle and a cloud platform. The system comprises the following steps: a data acquisition module for acquiring data through a multi-source sensor of the unmanned agricultural vehicle; the data preprocessing module is used for preprocessing the data; the feature analysis module is used for performing multi-dimensional feature analysis by using a non-Abeth geometric calculation network to generate a preliminary operation model; the dynamic optimization module is used for optimizing a geometric structure based on historical and real-time data and generating an optimized feature vector; the operation model construction module is used for constructing a self-adaptive operation model through a quantum state geometric fusion algorithm; the operation instruction generation module is used for generating operation instructions of path planning, speed control and resource allocation and transmitting the operation instructions to the agricultural vehicle; according to the operation execution module, the agricultural vehicle adjusts and executes an operation task in real time according to the instruction; and the result visualization module is used for displaying the operation result through a cloud platform for a manager to monitor and adjust. The agricultural production efficiency and the operation precision are improved through automatic and intelligent management.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent agriculture, and in particular to a system based on an unmanned agricultural vehicle and a cloud platform. Background Art

[0002] With the continuous changes in global agricultural production methods, intelligent and automated technologies have gradually been widely used in the agricultural field. As the core components of modern agriculture, unmanned agricultural vehicles and cloud platform systems play an important role in farmland management, crop production and other aspects. However, existing agricultural vehicles and agricultural management systems still face many technical challenges and shortcomings in practical applications, which restricts the further improvement of agricultural production efficiency.

[0003] First, most existing unmanned agricultural vehicle systems rely on a single data source, such as environmental sensor data or operation record data. This single data source acquisition method results in the system lacking sufficient data support when facing complex farmland environments, making it difficult to accurately analyze and predict changes in the operating environment. For example, during crop growth, environmental factors such as soil moisture, temperature, and light intensity will continue to fluctuate with seasonal and climate changes, and traditional systems are often unable to obtain and comprehensively process these multi-dimensional data in real time, resulting in low accuracy in operational decisions.

[0004] Secondly, existing technologies also have obvious deficiencies in data processing and analysis capabilities. Traditional farmland management systems mostly rely on simple data processing algorithms and cannot effectively deal with large amounts of multi-source data, especially when dealing with nonlinear, complex and multidimensional data. This inefficient data processing method limits the system's comprehensive understanding of the farmland environment, resulting in inaccurate construction of the operation model and the inability to dynamically adjust to different crop growth stages and different farmland conditions.

[0005] In addition, the current unmanned agricultural vehicle system also has limitations in the construction and optimization of operation models. Traditional operation models are mostly based on fixed parameters and rules, lacking adaptive capabilities and difficult to cope with the complexity and variability of farmland environments. Especially in farmlands with complex and changeable terrain, traditional path planning and operation instruction generation methods often show problems such as slow response and lack of flexibility, resulting in low operation efficiency and even possible interruption or erroneous operation.

[0006] Furthermore, the existing unmanned agricultural vehicle system lacks an effective real-time adjustment mechanism during the operation. Since the traditional system lacks the ability to quickly process and feedback real-time data, when the farmland environment changes, such as sudden weather or sudden changes in soil moisture, the system cannot quickly adjust the operation path, speed and resource allocation strategy, thus affecting the continuity and accuracy of the operation.

[0007] Finally, traditional farmland management systems usually only focus on data collection and operation execution, and pay less attention to the display and visualization of results. This makes it difficult for farmland managers to understand the actual effects of operations and changes in farmland environment in a timely and intuitive manner, which leads to deviations and misjudgments in the decision-making process.

[0008] Therefore, how to provide a system based on an unmanned agricultural vehicle and a cloud platform is an urgent problem that technicians in this field need to solve. Summary of the invention

[0009] One purpose of the present invention is to propose a system based on unmanned agricultural vehicles and cloud platforms. The present invention describes in detail how to realize intelligent operation management in a farmland environment by combining multi-source sensor data acquisition, non-Abelian geometric computing networks, multi-dimensional quantum state geometric fusion algorithms and other technologies. The present invention includes steps such as data acquisition, preprocessing, feature analysis, dynamic optimization, operation model construction, instruction generation, execution and result visualization. Through these steps, the system can realize real-time perception and operation adjustment of the farmland environment, and has the advantages of accurate data processing, adaptive operation model, flexible real-time adjustment and management visualization. The system not only improves the efficiency of agricultural operations, but also effectively reduces production costs and manual intervention, making agricultural production move towards intelligence and automation.

[0010] According to an embodiment of the present invention, a system based on an unmanned agricultural vehicle and a cloud platform includes:

[0011] Data acquisition module: collects farmland environment data and operation data through multi-source sensors installed on unmanned agricultural vehicles, and uploads the collected data to the cloud platform through wireless communication modules;

[0012] Data preprocessing module: cleans, normalizes and extracts preliminary features of the uploaded data in the cloud platform. The processed data is used for subsequent feature analysis and job model construction;

[0013] Multi-input feature analysis module: Use the non-Abelian geometric computing network to extract and analyze multi-dimensional features of the pre-processed data, map the extracted multi-dimensional feature data to high-dimensional non-Abelian geometric space, analyze the geometric symmetry and topological structure of the data, and generate a preliminary operation model;

[0014] Dynamic feature optimization module: Based on the interaction between historical operation data and real-time operation data, the geometric symmetry and topological structure are further optimized to generate optimized feature vectors;

[0015] Operation model building module: introduces a multi-dimensional quantum state geometric fusion algorithm, further optimizes the preliminary operation model based on the optimized feature vector, and builds an adaptive operation model. The model adaptively adjusts the operation path, speed and resource allocation strategy according to the changes in the current and future environment;

[0016] Operation instruction generation module: Based on the adaptive operation model, the operation instructions of the unmanned agricultural vehicle are generated in the cloud platform. The operation instructions include path planning, speed control and resource allocation, and are transmitted to the central control system of the agricultural vehicle through the wireless communication module;

[0017] Operation execution module: After receiving the operation instruction, the unmanned agricultural vehicle dynamically adjusts the path planning and operation tasks based on the microgravity sensing navigation system and dynamic terrain adaptive optimization algorithm to adapt to the changes in the farmland environment and actual operation needs, and performs operations including sowing, fertilization, irrigation, and pest control;

[0018] Result visualization module: The system presents the operation execution results and the optimized operation path, speed and resource allocation on the cloud platform interface through visualization technology for farmland managers to monitor and adjust.

[0019] According to an embodiment of the present invention, a method based on an unmanned agricultural vehicle and a cloud platform includes the following steps:

[0020] S1, collect real-time data of farmland environment through multi-source sensors to form a multi-source data set, and upload the data to the cloud platform through the wireless communication module;

[0021] S2. Use the topological quantum field data processing method to process the sensor data of the unmanned agricultural vehicle in real time, generate operation decisions by mapping the sensor data into a high-dimensional topological space, and control and dynamically adjust the unmanned agricultural vehicle;

[0022] S3. In the cloud platform, high-dimensional data analysis methods driven by quantum field theory are used to process uploaded multi-source data sets, and optimization suggestions for agricultural operations, including sowing, fertilization, irrigation, and pest control operations, are generated by capturing complex patterns and correlations in the data.

[0023] S4, transmit the operation suggestions generated by the cloud platform to the unmanned agricultural vehicle, use the microgravity sensing navigation system to plan the path and execute the operation tasks, and adjust the operation parameters to adapt to the complex changes in the farmland environment by sensing the terrain undulations and ground changes;

[0024] S5. During the operation, the optical quantum metrology perception network is used to monitor the operation status of agricultural vehicles and changes in the farmland environment, measure small changes in the environment, and make real-time parameter adjustments based on these monitoring data;

[0025] S6. Encrypted storage and distributed management of farmland data, operation records and analysis results are carried out through four-dimensional photonic crystal storage technology. By manipulating the behavior of photons in four-dimensional space, high-density storage and security protection are provided, and fast access and retrieval in the cloud platform are supported.

[0026] S7. Use non-Abelian geometric computing networks to analyze historical and real-time operation data, dynamically optimize operation paths, speeds, and resource allocations, and adaptively adjust operation models by processing complex geometric symmetries and topological structures;

[0027] S8. Superconducting optical fiber interactive display technology is used to provide farm managers with ultra-high-definition visual display of operation status and farmland data. Superconducting optical fiber is used to transmit high-frequency optical signals for virtual interaction and adjustment operations, supporting on-site or remote operation management.

[0028] Optionally, S2 includes the following steps:

[0029] S21, collecting multi-source data of farmland environment in real time through various types of sensors installed on unmanned agricultural vehicles, and transmitting the data to the central processing unit through a wireless communication module;

[0030] S22. In the central processing unit, the received sensor data is pre-processed using an innovative topological quantum field data processing algorithm, by mapping multi-source data into a high-dimensional topological quantum field, and filtering and denoising the data based on topological invariants to generate a multi-dimensional feature vector set;

[0031] S23, after mapping the multidimensional feature vector set generated by the topological quantum field data processing algorithm to the high-dimensional topological space, a segmentation processing mechanism is applied to divide the data in the high-dimensional topological space into multiple subspaces T according to different dimensions or features. j , each subspace T j Corresponding to a feature or dimension, and independently processed and optimized;

[0032] S24, based on each segmented processed subspace T j , respectively calculate the operation decision vector D of the unmanned agricultural vehicle j :

[0033]

[0034] Among them, W j is the weight matrix corresponding to the subspace, R j is the expected job result matrix for each subspace, representing the target state of the job, α k is the weight coefficient of different feature dimensions, ||T j ×W j-R j || 2 It represents the sum of squares of the difference between the currently calculated job result and the expected result. β and γ are adjustment factors. Represents the weight matrix W j The expected result matrix for a specific state i After that, the sum of squares of the errors is calculated, ||T j || F is the Frobenius norm, which measures the overall degree of change of the data in the subspace. represents the expected result matrix under different states, exp(-γ·||X j || 2 ) is an exponential decay term, which is used to suppress excessive influence between data features;

[0035] S25, the operation decision vector D calculated in each subspace j The data are summarized and synthesized into a global operation decision vector, and then input into the control system of the unmanned agricultural vehicle. The control system controls the agricultural vehicle's travel path, operation speed and tool use in real time according to the synthesized operation decision vector. Through dynamic adjustment, the agricultural vehicle can perform various operation tasks accurately and efficiently in the complex and changeable farmland environment.

[0036] Optionally, S3 includes the following steps:

[0037] S31. In the cloud platform, pre-process the uploaded existing multi-source data sets, including filling in missing data, filtering noisy data, and standardizing the data format;

[0038] S32. Use a high-dimensional data analysis method driven by quantum field theory to extract high-dimensional features from the preprocessed multi-source data set, map the data set to a high-dimensional feature space, and generate a feature matrix;

[0039] S33, based on the feature matrix and the obtained job decision vector D j , generate the analysis matrix A:

[0040]

[0041] Among them, P j is the agricultural operation parameter matrix, and the operation decision vector D j Related, α j is the weight coefficient, which controls the influence of each job decision vector on the analysis matrix, γ j is a regulating factor used to adjust the influence of different job decision vectors, β i is the adjustment coefficient used to control the weight of the Frobenius norm term, ||T j|| F It represents the overall change degree of the data in the subspace, and δ is the attenuation factor, which controls the influence of the data characteristics;

[0042] S34. Generate optimization suggestions O for agricultural operations based on the analysis matrix A:

[0043]

[0044] in, It means that we use the optimization algorithm to find the optimization suggestion moment that minimizes the objective function. k is the analysis matrix for the kth operation scenario, Q k is the adjusted agricultural operation parameter matrix, G k is the target operation matrix, which indicates the expected operation effect. λ is the regularization parameter, which controls the complexity of the optimization result. represents the Frobenius norm square of the optimization suggestion matrix, which is used to measure the overall strength of the optimization suggestion. l is the adjustment coefficient, which is used to adjust the correlation between the optimization suggestions and historical data. l is the historical data matrix, ||O l ×H l || represents the optimization suggestion matrix O l With the historical data matrix H l The product of

[0045] S35. The generated optimization suggestion O is transmitted to the unmanned agricultural vehicle. The on-board processing unit generates the final operation instruction based on the received optimization suggestion and the real-time sensor data, and executes operations including sowing, fertilizing, irrigation, and pest control.

[0046] Optionally, S4 includes the following steps:

[0047] S41, the operation suggestion matrix O generated by the cloud platform is transmitted to the central control system of the unmanned agricultural vehicle. The central control system analyzes and decomposes the operation suggestion in combination with the real-time data of the vehicle-mounted sensor to generate an operation instruction matrix C. The operation instruction matrix C includes the path planning instruction C p and job parameter command C a ;

[0048] S42, using the microgravity sensing navigation system and dynamic terrain adaptive optimization algorithm, the path planning instruction C p Perform optimization and generate the path matrix P:

[0049]

[0050] Among them, P j is the path parameter matrix, αj is the weight coefficient, controlling the impact of the operation decision vector on path planning, γ j is the adjustment factor used to balance the weights of different job decision vectors in path planning, T j represents the terrain feature matrix, W j is the weight matrix, R j is the target path matrix, A k is the analysis matrix, Q k is the job parameter adjustment matrix, G k is the target operation matrix, ζ is the terrain adaptation adjustment factor, and the function f(T current ,T predicted ) is used to evaluate the current terrain T current With the predicted terrain T predicted The difference between

[0051] S43, in the process of path planning, the path matrix P and the operation parameter instruction C are adjusted according to the terrain fluctuations and environmental changes perceived in real time by using the dynamic terrain adaptive optimization algorithm. a To make adaptive adjustments:

[0052]

[0053] Among them, Q is the operation parameter adjustment matrix, G is the target operation effect matrix, λ is the regularization parameter used to control the complexity of the operation parameters, ξ is the environmental adaptation adjustment factor, and the function h(E real-time ,E expected ) is used to evaluate real-time environmental data E real-time With expected environmental conditions E expected The difference between

[0054] S44. During the operation, the central control system dynamically adjusts the path planning and operation parameters through dynamic terrain adaptive optimization algorithms and real-time sensor data to cope with terrain undulations and environmental changes, enabling the unmanned agricultural vehicle to perform operation tasks in the farmland environment.

[0055] Optionally, the S6 includes the following steps:

[0056] S61. Use four-dimensional photonic crystal storage technology to encrypt farmland environmental data, operation records of unmanned agricultural vehicles, and analysis results generated by the cloud platform. The encryption process maps data to multi-dimensional photon states by manipulating the behavior of photons in four-dimensional space, thereby performing multi-level data protection.

[0057] S62, distributing and storing the encrypted data in multiple independent photonic crystal storage units, each storage unit is located on a different physical node, maintaining data consistency between nodes through a photon state synchronization mechanism, and managing data reliability by using a redundant storage method;

[0058] S63. During the data storage process, the photon state adaptive control algorithm is used to adjust the photon behavior in real time to manage storage density and data security:

[0059]

[0060] Among them, D0 is the initial storage density matrix, D j is the job decision vector, and the path planning matrix P j is associated with, used to adjust storage density, α j is the weight coefficient, which controls the impact of the job decision vector on the storage density, A is the analysis matrix, G is the target job effect matrix, β is the adjustment factor, which is used to control the relationship between different influencing factors in the optimization process, γ is the control coefficient, ψ(H,F real-time ) is the photon state historical data H and real-time data F real-time The matching function between

[0061] S64. In a distributed storage network, the stored data is accessed and retrieved through a photon state adaptive control algorithm. When accessing, the photon state switching mechanism is used to switch the photon from the storage state to the access state, and the data is read and retrieved in parallel through the quantum entanglement effect between photons.

[0062] S65. The encrypted data in distributed storage is provided to authorized users through the access interface of the cloud platform. A dynamic photon decryption mechanism is used during the access process. The photon state is restored to its original state during decryption to maintain the integrity and security of the data during transmission and access.

[0063] Optionally, the S7 includes the following steps:

[0064] S71. Perform feature extraction and fusion processing on historical operation data and real-time operation data through a non-Abelian geometric computing network, map the extracted multi-dimensional feature data into a high-dimensional non-Abelian geometric space, analyze the geometric symmetry and topological structure of the data, and generate a preliminary operation model;

[0065] S72. In high-dimensional non-Abelian geometric space, based on the interaction between historical and real-time data, the geometric symmetry and topological structure are analyzed to generate the optimized feature vector V k :

[0066]

[0067] Among them, D j is the job decision vector, combined with the path planning matrix P j Adjust the feature vector, A is the analysis matrix, combined with the operation parameter adjustment matrix Q, to reflect the difference in operation data, G is the target operation matrix, ψ(H,F real-time ) is the matching function between the photon state historical data and the real-time data, Υ(T current ,T future ) is the transformation function between the current and future topological states, α j , β, γ, and λ are adjustment factors, which respectively control the influence of different features on the feature vector;

[0068] S73, introduce the multi-dimensional quantum state geometric fusion algorithm, and generate the feature vector V k Further optimization and fusion are performed to optimize the generated preliminary operation model into an adaptive operation model M opt :

[0069]

[0070] Among them, Q k is the quantum state characteristic matrix, and the characteristic vector V k fusion, exp(·) represents the mapping of quantum state geometric transformation to the operation model through matrix exponential operation, Ω(Ψ current ,Ψ future ) is the transformation function between the current and future quantum state geometric states, Φ(A,G) is the fusion function between the analysis matrix and the target matrix, which is used to adjust the geometric adaptability of the model, θ and δ are control parameters, which control the overall impact of quantum state and geometric transformation on the model;

[0071] S74, Adaptive operation model M generated based on multi-dimensional quantum state geometric fusion algorithm opt , dynamically generate the operation path, speed and resource allocation strategy of the unmanned agricultural vehicle, and adjust the operation strategy in real time through the feedback mechanism of the non-Abelian geometric computing network to generate the final operation instruction C to guide the unmanned agricultural vehicle to perform farmland operation tasks.

[0072] The beneficial effects of the present invention are:

[0073] (1) The present invention significantly enhances the system's ability to process complex farmland environmental data by combining a multi-source data acquisition module and a non-Abelian geometric computing network. In particular, in terms of multidimensional data fusion and geometric structure analysis, the present invention overcomes the limitations of traditional single data sources and can efficiently process and generate accurate operation models.

[0074] (2) The present invention effectively realizes the real-time optimization and adaptive adjustment of the operation model by introducing a dynamic feature optimization module and a multi-dimensional quantum state geometric fusion algorithm. When facing complex environments and real-time data changes, the system can dynamically adjust the operation path, speed, and resource allocation.

[0075] (3) The present invention improves the data security and storage efficiency of the system by adopting four-dimensional photonic crystal storage technology and photon state adaptive control algorithm. In particular, in terms of data encryption storage and real-time control, the present invention provides strong technical support to ensure that data remains intact and secure during transmission and access. BRIEF DESCRIPTION OF THE DRAWINGS

[0076] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0077] Figure 1 A flow chart of an unmanned agricultural vehicle and cloud platform system proposed by the present invention;

[0078] Figure 2 A flow chart of performing multi-dimensional feature analysis on the non-Abelian geometric computing network proposed by the present invention;

[0079] Figure 3 Schematic diagram of the application of the photon state adaptive control algorithm proposed in the present invention in four-dimensional photonic crystal storage. DETAILED DESCRIPTION

[0080] The present invention will now be described in further detail with reference to the accompanying drawings. These drawings are simplified schematic diagrams, which only illustrate the basic structure of the present invention in a schematic manner, and therefore only show the components related to the present invention.

[0081] refer to Figure 1-3 , a system based on unmanned agricultural vehicles and cloud platform, including:

[0082] Data acquisition module: collects farmland environment data and operation data through multi-source sensors installed on unmanned agricultural vehicles, and uploads the collected data to the cloud platform through wireless communication modules;

[0083] Data preprocessing module: cleans, normalizes and extracts preliminary features of the uploaded data in the cloud platform. The processed data is used for subsequent feature analysis and job model construction;

[0084] Multi-input feature analysis module: Use the non-Abelian geometric computing network to extract and analyze multi-dimensional features of the pre-processed data, map the extracted multi-dimensional feature data to high-dimensional non-Abelian geometric space, analyze the geometric symmetry and topological structure of the data, and generate a preliminary operation model;

[0085] Dynamic feature optimization module: Based on the interaction between historical operation data and real-time operation data, the geometric symmetry and topological structure are further optimized to generate optimized feature vectors;

[0086] Operation model building module: introduces a multi-dimensional quantum state geometric fusion algorithm, further optimizes the preliminary operation model based on the optimized feature vector, and builds an adaptive operation model. The model adaptively adjusts the operation path, speed and resource allocation strategy according to the changes in the current and future environment;

[0087] Operation instruction generation module: Based on the adaptive operation model, the operation instructions of the unmanned agricultural vehicle are generated in the cloud platform. The operation instructions include path planning, speed control and resource allocation, and are transmitted to the central control system of the agricultural vehicle through the wireless communication module;

[0088] Operation execution module: After receiving the operation instruction, the unmanned agricultural vehicle dynamically adjusts the path planning and operation tasks based on the microgravity sensing navigation system and dynamic terrain adaptive optimization algorithm to adapt to the changes in the farmland environment and actual operation needs, and performs operations including sowing, fertilization, irrigation, and pest control;

[0089] Result visualization module: The system presents the operation execution results and the optimized operation path, speed and resource allocation on the cloud platform interface through visualization technology for farmland managers to monitor and adjust.

[0090] According to an embodiment of the present invention, a method based on an unmanned agricultural vehicle and a cloud platform includes the following steps:

[0091] S1, collect real-time data of farmland environment through multi-source sensors to form a multi-source data set, and upload the data to the cloud platform through the wireless communication module;

[0092] S2. Use the topological quantum field data processing method to process the sensor data of the unmanned agricultural vehicle in real time, generate operation decisions by mapping the sensor data into a high-dimensional topological space, and control and dynamically adjust the unmanned agricultural vehicle;

[0093] S3. In the cloud platform, high-dimensional data analysis methods driven by quantum field theory are used to process uploaded multi-source data sets, and optimization suggestions for agricultural operations, including sowing, fertilization, irrigation, and pest control operations, are generated by capturing complex patterns and correlations in the data.

[0094] S4, transmit the operation suggestions generated by the cloud platform to the unmanned agricultural vehicle, use the microgravity sensing navigation system to plan the path and execute the operation tasks, and adjust the operation parameters to adapt to the complex changes in the farmland environment by sensing the terrain undulations and ground changes;

[0095] S5. During the operation, the optical quantum metrology perception network is used to monitor the operation status of agricultural vehicles and changes in the farmland environment, measure small changes in the environment, and make real-time parameter adjustments based on these monitoring data;

[0096] S6. Encrypted storage and distributed management of farmland data, operation records and analysis results are carried out through four-dimensional photonic crystal storage technology. By manipulating the behavior of photons in four-dimensional space, high-density storage and security protection are provided, and fast access and retrieval in the cloud platform are supported.

[0097] S7. Use non-Abelian geometric computing networks to analyze historical and real-time operation data, dynamically optimize operation paths, speeds, and resource allocations, and adaptively adjust operation models by processing complex geometric symmetries and topological structures;

[0098] S8. Superconducting optical fiber interactive display technology is used to provide farm managers with ultra-high-definition visual display of operation status and farmland data. Superconducting optical fiber is used to transmit high-frequency optical signals for virtual interaction and adjustment operations, supporting on-site or remote operation management.

[0099] In this implementation, S2 includes the following steps:

[0100] S21, collecting multi-source data of farmland environment in real time through various types of sensors installed on unmanned agricultural vehicles, and transmitting the data to the central processing unit through a wireless communication module;

[0101] S22. In the central processing unit, the received sensor data is pre-processed using an innovative topological quantum field data processing algorithm, by mapping multi-source data into a high-dimensional topological quantum field, and filtering and denoising the data based on topological invariants to generate a multi-dimensional feature vector set;

[0102] S23, after mapping the multidimensional feature vector set generated by the topological quantum field data processing algorithm to the high-dimensional topological space, a segmentation processing mechanism is applied to divide the data in the high-dimensional topological space into multiple subspaces T according to different dimensions or features. j , each subspace T j Corresponding to a feature or dimension, and independently processed and optimized;

[0103] S24, based on each segmented processed subspace T j, respectively calculate the operation decision vector D of the unmanned agricultural vehicle j :

[0104]

[0105] Among them, W j is the weight matrix corresponding to the subspace, R j is the expected job result matrix for each subspace, representing the target state of the job, α k is the weight coefficient of different feature dimensions, ||T j ×W j -R j || 2 It represents the sum of squares of the difference between the currently calculated job result and the expected result. β and γ are adjustment factors. Represents the weight matrix W j The expected result matrix for a specific state i After that, the sum of squares of the errors is calculated, ||T j || F is the Frobenius norm, which measures the overall degree of change of the data in the subspace. represents the expected result matrix under different states, exp(-γ·||X j || 2 ) is an exponential decay term, which is used to suppress excessive influence between data features;

[0106] S25, the operation decision vector D calculated in each subspace j The data are summarized and synthesized into a global operation decision vector, and then input into the control system of the unmanned agricultural vehicle. The control system controls the agricultural vehicle's travel path, operation speed and tool use in real time according to the synthesized operation decision vector. Through dynamic adjustment, the agricultural vehicle can perform various operation tasks accurately and efficiently in the complex and changeable farmland environment.

[0107] In this implementation, S3 includes the following steps:

[0108] S31. In the cloud platform, pre-process the uploaded existing multi-source data sets, including filling in missing data, filtering noisy data, and standardizing the data format;

[0109] S32. Use a high-dimensional data analysis method driven by quantum field theory to extract high-dimensional features from the preprocessed multi-source data set, map the data set to a high-dimensional feature space, and generate a feature matrix;

[0110] S33, based on the feature matrix and the obtained job decision vector D j , generate the analysis matrix A:

[0111]

[0112] Among them, P j is the agricultural operation parameter matrix, and the operation decision vector D j Related, α j is the weight coefficient, which controls the influence of each job decision vector on the analysis matrix, γ j is a regulating factor used to adjust the influence of different job decision vectors, β i is the adjustment coefficient used to control the weight of the Frobenius norm term, ||T j || F It represents the overall change degree of the data in the subspace, and δ is the attenuation factor, which controls the influence of the data characteristics;

[0113] S34. Generate optimization suggestions O for agricultural operations based on the analysis matrix A:

[0114]

[0115] in, It means that we use the optimization algorithm to find the optimization suggestion moment that minimizes the objective function. k is the analysis matrix for the kth operation scenario, Q k is the adjusted agricultural operation parameter matrix, G k is the target operation matrix, which indicates the expected operation effect. λ is the regularization parameter, which controls the complexity of the optimization result. represents the Frobenius norm square of the optimization suggestion matrix, which is used to measure the overall strength of the optimization suggestion. l is the adjustment coefficient, which is used to adjust the correlation between the optimization suggestions and historical data. l is the historical data matrix, ||O l ×H l || represents the optimization suggestion matrix O l With the historical data matrix H l The product of

[0116] S35. The generated optimization suggestion O is transmitted to the unmanned agricultural vehicle. The on-board processing unit generates the final operation instruction based on the received optimization suggestion and the real-time sensor data, and executes operations including sowing, fertilizing, irrigation, and pest control.

[0117] In this implementation manner, S4 includes the following steps:

[0118] S41, the operation suggestion matrix O generated by the cloud platform is transmitted to the central control system of the unmanned agricultural vehicle. The central control system analyzes and decomposes the operation suggestion in combination with the real-time data of the vehicle-mounted sensor to generate an operation instruction matrix C. The operation instruction matrix C includes the path planning instruction C p and job parameter command C a ;

[0119] S42, using the microgravity sensing navigation system and dynamic terrain adaptive optimization algorithm, the path planning instruction C p Perform optimization and generate the path matrix P:

[0120]

[0122] Among them, P j is the path parameter matrix, α j is the weight coefficient, controlling the impact of the operation decision vector on path planning, γ j is the adjustment factor used to balance the weights of different job decision vectors in path planning, T j represents the terrain feature matrix, W j is the weight matrix, R j is the target path matrix, A k is the analysis matrix, Q k is the job parameter adjustment matrix, G k is the target operation matrix, ζ is the terrain adaptation adjustment factor, and the function f(T current ,T predicted ) is used to evaluate the current terrain T current With the predicted terrain T predicted The difference between

[0123] S43, in the process of path planning, the path matrix P and the operation parameter instruction C are adjusted according to the terrain fluctuations and environmental changes perceived in real time by using the dynamic terrain adaptive optimization algorithm. a To make adaptive adjustments:

[0124]

[0125] Among them, Q is the operation parameter adjustment matrix, G is the target operation effect matrix, λ is the regularization parameter used to control the complexity of the operation parameters, ξ is the environmental adaptation adjustment factor, and the function h(E real-time ,E expected ) is used to evaluate real-time environmental data E real-time With expected environmental conditions E expected The difference between

[0126] S44. During the operation, the central control system dynamically adjusts the path planning and operation parameters through dynamic terrain adaptive optimization algorithms and real-time sensor data to cope with terrain undulations and environmental changes, enabling the unmanned agricultural vehicle to perform operation tasks in the farmland environment.

[0127] In this implementation manner, S6 includes the following steps:

[0128] S61. Use four-dimensional photonic crystal storage technology to encrypt farmland environmental data, operation records of unmanned agricultural vehicles, and analysis results generated by the cloud platform. The encryption process maps data to multi-dimensional photon states by manipulating the behavior of photons in four-dimensional space, thereby performing multi-level data protection.

[0129] S62, distributing and storing the encrypted data in multiple independent photonic crystal storage units, each storage unit is located on a different physical node, maintaining data consistency between nodes through a photon state synchronization mechanism, and managing data reliability by using a redundant storage method;

[0130] S63. During the data storage process, the photon state adaptive control algorithm is used to adjust the photon behavior in real time to manage storage density and data security:

[0131]

[0132] Among them, D0 is the initial storage density matrix, D j is the job decision vector, and the path planning matrix P j is associated with, used to adjust storage density, α j is the weight coefficient, which controls the impact of the job decision vector on the storage density, A is the analysis matrix, G is the target job effect matrix, β is the adjustment factor, which is used to control the relationship between different influencing factors in the optimization process, γ is the control coefficient, ψ(H,F real-time ) is the photon state historical data H and real-time data F real-time The matching function between

[0133] S64. In a distributed storage network, the stored data is accessed and retrieved through a photon state adaptive control algorithm. When accessing, the photon state switching mechanism is used to switch the photon from the storage state to the access state, and the data is read and retrieved in parallel through the quantum entanglement effect between photons.

[0134] S65. The encrypted data in distributed storage is provided to authorized users through the access interface of the cloud platform. A dynamic photon decryption mechanism is used during the access process. The photon state is restored to its original state during decryption to maintain the integrity and security of the data during transmission and access.

[0135] In this implementation manner, the S7 includes the following steps:

[0136] S71. Perform feature extraction and fusion processing on historical operation data and real-time operation data through a non-Abelian geometric computing network, map the extracted multi-dimensional feature data into a high-dimensional non-Abelian geometric space, analyze the geometric symmetry and topological structure of the data, and generate a preliminary operation model;

[0137] S72. In high-dimensional non-Abelian geometric space, based on the interaction between historical and real-time data, the geometric symmetry and topological structure are analyzed to generate the optimized feature vector V k :

[0138]

[0139] Among them, D j is the job decision vector, combined with the path planning matrix P j Adjust the feature vector, A is the analysis matrix, combined with the operation parameter adjustment matrix Q, to reflect the difference in operation data, G is the target operation matrix, ψ(H,F real-time ) is the matching function between the photon state historical data and the real-time data, Υ(T current ,T future ) is the transformation function between the current and future topological states, α j , β, γ, and λ are adjustment factors, which respectively control the influence of different features on the feature vector;

[0140] S73, introduce the multi-dimensional quantum state geometric fusion algorithm, and generate the feature vector V k Further optimization and fusion are performed to optimize the generated preliminary operation model into an adaptive operation model M opt :

[0141]

[0142] Among them, Q k is the quantum state characteristic matrix, and the characteristic vector V k fusion, exp(·) represents the mapping of quantum state geometric transformation to the operation model through matrix exponential operation, Ω(Ψ current ,Ψ future ) is the transformation function between the current and future quantum state geometric states, Φ(A,G) is the fusion function between the analysis matrix and the target matrix, which is used to adjust the geometric adaptability of the model, θ and δ are control parameters, which control the overall impact of quantum state and geometric transformation on the model;

[0143] S74, Adaptive operation model M generated based on multi-dimensional quantum state geometric fusion algorithm opt, dynamically generate the operation path, speed and resource allocation strategy of the unmanned agricultural vehicle, and adjust the operation strategy in real time through the feedback mechanism of the non-Abelian geometric computing network to generate the final operation instruction C to guide the unmanned agricultural vehicle to perform farmland operation tasks.

[0144] Embodiment 1:

[0145] In order to verify the feasibility of the present invention in implementation, the present invention was applied to the "Pingxiang Cherry Development Demonstration Garden" of Yingzi Agricultural Technology Hebei Co., Ltd., which covers an area of ​​316 acres and mainly grows cherries. The demonstration garden has typical problems in traditional farmland management, such as high labor costs, low operating efficiency, waste of resources, and unstable operating quality. In order to solve these problems, we introduced a system based on unmanned agricultural vehicles and cloud platforms, aiming to improve the efficiency and quality of agricultural production in an intelligent and automated way.

[0146] During the implementation process, we first deployed unmanned agricultural vehicles in the demonstration park and installed multi-source sensors on the vehicles to collect farmland environmental data and operation data in real time. These sensors can detect soil moisture, temperature, light intensity, crop growth status and changes in the surrounding environment. The collected data is uploaded to the cloud platform in real time through the wireless communication module. The cloud platform uses the data preprocessing module to clean and normalize the data and extract preliminary feature data to ensure the accuracy of subsequent analysis.

[0147] Next, the cloud platform uses a non-Abelian geometric computing network to perform high-dimensional analysis on the preprocessed multidimensional feature data, maps the data into a high-dimensional non-Abelian geometric space, analyzes the geometric symmetry and topological structure of the data, and generates a preliminary operation model. Subsequently, the model combines historical operation data and real-time data, and is further optimized through a dynamic optimization module to generate an optimized feature vector for current farmland conditions.

[0148] After obtaining the optimized feature vector, the operation model construction module further optimizes and constructs the preliminary operation model using the quantum state geometry fusion algorithm, and finally generates an adaptive operation model. This model can adaptively adjust the operation path, operation speed, and resource allocation strategy according to current and future environmental changes. After that, the cloud platform generates specific operation instructions, including path planning, speed control, and resource allocation, which are transmitted to the central control system of the unmanned agricultural vehicle through the wireless communication module.

[0149] During the operation execution phase, after receiving the operation instructions, the unmanned agricultural vehicle uses the microgravity sensing navigation system and dynamic terrain adaptive optimization algorithm to make real-time adjustments to the path planning and operation tasks to adapt to the changes in the farmland environment. The entire operation process covers multiple tasks such as precision sowing, fertilization, irrigation, and pest control. Each operation is optimized through intelligent algorithms to ensure the continuity and efficiency of the operation process.

[0150] Table 1: Comparison of the effects of the system of the present invention and the traditional method

[0151]

[0152] After a growing season of testing, the system has demonstrated significant advantages and effects in the Pingxiang Cherry Demonstration Garden. First, through automatic path planning and precise operation, the average daily operating area of ​​the park has increased to 50 mu, which is 66.7% higher than the operating efficiency of traditional diesel mist sprayers. In terms of cost control, automated operation reduces the use of pesticides and fertilizers by about 20%, and reduces the operating cost per mu by 2 yuan. At the same time, battery-powered unmanned vehicles not only have low energy consumption, but also have maintenance costs far lower than traditional agricultural machinery and equipment. Their service life of more than 10 years has greatly reduced the frequency of equipment replacement and maintenance.

[0153] More importantly, the quality of operations has been significantly improved. Through the operation model generated by high-dimensional non-Abelian geometry analysis and quantum state geometry fusion algorithm, crop management in the park has become more refined, and the pesticide coverage rate has reached 98%, effectively reducing the incidence of pests and diseases. In addition, using the dynamic terrain adaptive optimization algorithm, the vehicle's adaptability to complex terrain during operation has been greatly enhanced, avoiding the omissions and duplications common in traditional mechanical operations.

[0154] Overall, the successful application of the present invention in the "Pingxiang Cherry Development Demonstration Garden" has verified its feasibility and superiority in actual agricultural production. This system not only effectively solves the problems of low efficiency, high cost and unstable operation quality in traditional farmland management, but also provides a strong technical support for the development of smart agriculture in the future. These actual data and effects show that the present invention has significant advantages in improving agricultural production efficiency, reducing costs, improving operation quality, etc., and has laid a solid foundation for its promotion and application in other agricultural fields.

[0155] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical scheme and inventive concept of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. A system based on unmanned agricultural vehicles and cloud platforms, characterized in that: include: Data acquisition module: collects farmland environment data and operation data through multi-source sensors installed on unmanned agricultural vehicles, and uploads the collected data to the cloud platform through wireless communication modules; Data preprocessing module: cleans, normalizes and extracts preliminary features of the uploaded data in the cloud platform. The processed data is used for subsequent feature analysis and job model construction; Multi-input feature analysis module: Use the non-Abelian geometric computing network to extract and analyze multi-dimensional features of the pre-processed data, map the extracted multi-dimensional feature data to high-dimensional non-Abelian geometric space, analyze the geometric symmetry and topological structure of the data, and generate a preliminary operation model; Dynamic feature optimization module: Based on the interaction between historical operation data and real-time operation data, the geometric symmetry and topological structure are further optimized to generate optimized feature vectors; Operation model building module: introduces a multi-dimensional quantum state geometric fusion algorithm, further optimizes the preliminary operation model based on the optimized feature vector, and builds an adaptive operation model. The model adaptively adjusts the operation path, speed and resource allocation strategy according to the changes in the current and future environment; Operation instruction generation module: Based on the adaptive operation model, the operation instructions of the unmanned agricultural vehicle are generated in the cloud platform. The operation instructions include path planning, speed control and resource allocation, and are transmitted to the central control system of the agricultural vehicle through the wireless communication module; Operation execution module: After receiving the operation instruction, the unmanned agricultural vehicle dynamically adjusts the path planning and operation tasks based on the microgravity sensing navigation system and dynamic terrain adaptive optimization algorithm to adapt to the changes in the farmland environment and actual operation needs, and performs operations including sowing, fertilization, irrigation, and pest control; Result visualization module: The system presents the operation execution results and the optimized operation path, speed and resource allocation on the cloud platform interface through visualization technology for farmland managers to monitor and adjust.

2. A method based on an unmanned agricultural vehicle and a cloud platform, characterized in that: The steps include: S1, collect real-time data of farmland environment through multi-source sensors to form a multi-source data set, and upload the data to the cloud platform through the wireless communication module; S2. Use the topological quantum field data processing method to process the sensor data of the unmanned agricultural vehicle in real time, generate operation decisions by mapping the sensor data into a high-dimensional topological space, and control and dynamically adjust the unmanned agricultural vehicle; S3. In the cloud platform, high-dimensional data analysis methods driven by quantum field theory are used to process uploaded multi-source data sets, and optimization suggestions for agricultural operations, including sowing, fertilization, irrigation, and pest control operations, are generated by capturing complex patterns and correlations in the data. S4, transmit the operation suggestions generated by the cloud platform to the unmanned agricultural vehicle, use the microgravity sensing navigation system to plan the path and execute the operation tasks, and adjust the operation parameters to adapt to the complex changes in the farmland environment by sensing the terrain undulations and ground changes; S5. During the operation, the optical quantum metrology perception network is used to monitor the operation status of agricultural vehicles and changes in the farmland environment, measure small changes in the environment, and make real-time parameter adjustments based on these monitoring data; S6. Encrypted storage and distributed management of farmland data, operation records and analysis results are carried out through four-dimensional photonic crystal storage technology. By manipulating the behavior of photons in four-dimensional space, high-density storage and security protection are provided, and fast access and retrieval in the cloud platform are supported. S7. Use non-Abelian geometric computing networks to analyze historical and real-time operation data, dynamically optimize operation paths, speeds, and resource allocations, and adaptively adjust operation models by processing complex geometric symmetries and topological structures; S8. Superconducting optical fiber interactive display technology is used to provide farm managers with ultra-high-definition visual display of operation status and farmland data. Superconducting optical fiber is used to transmit high-frequency optical signals for virtual interaction and adjustment operations, supporting on-site or remote operation management.

3. The method based on unmanned agricultural vehicle and cloud platform according to claim 1, characterized in that: The S2 specifically includes: S21, collecting multi-source data of farmland environment in real time through various types of sensors installed on unmanned agricultural vehicles, and transmitting the data to the central processing unit through a wireless communication module; S22. In the central processing unit, the received sensor data is pre-processed using an innovative topological quantum field data processing algorithm, by mapping multi-source data into a high-dimensional topological quantum field, and filtering and denoising the data based on topological invariants to generate a multi-dimensional feature vector set; S23, after mapping the multidimensional feature vector set generated by the topological quantum field data processing algorithm to the high-dimensional topological space, a segmentation processing mechanism is applied to divide the data in the high-dimensional topological space into multiple subspaces T according to different dimensions or features. j , each subspace T j Corresponding to a feature or dimension, and independently processed and optimized; S24, based on each segmented processed subspace T j , respectively calculate the operation decision vector D of the unmanned agricultural vehicle j : Among them, W j is the weight matrix corresponding to the subspace, R j is the expected job result matrix for each subspace, representing the target state of the job, α k is the weight coefficient of different feature dimensions, ∥T j ×W j -R j ∥ 2 It represents the sum of squares of the difference between the currently calculated job result and the expected result. β and γ are adjustment factors. Represents the weight matrix W j The expected result matrix for a specific state i After that, the sum of squares of the calculated errors is ∥T j ∥ F is the Frobenius norm, which measures the overall degree of change of the data in the subspace. Represents the expected result matrix under different states, exp(-γ·∥X j ∥ 2 ) is an exponential decay term, which is used to suppress excessive influence between data features; S25, the operation decision vector D calculated in each subspace j The data are summarized and synthesized into a global operation decision vector, and then input into the control system of the unmanned agricultural vehicle. The control system controls the agricultural vehicle's travel path, operation speed and tool use in real time according to the synthesized operation decision vector. Through dynamic adjustment, the agricultural vehicle can perform various operation tasks accurately and efficiently in the complex and changeable farmland environment.

4. The method based on unmanned agricultural vehicle and cloud platform according to claim 1, characterized in that: The S3 specifically includes: S31. In the cloud platform, pre-process the uploaded existing multi-source data sets, including filling in missing data, filtering noisy data, and standardizing the data format; S32. Use a high-dimensional data analysis method driven by quantum field theory to extract high-dimensional features from the preprocessed multi-source data set, map the data set to a high-dimensional feature space, and generate a feature matrix; S33, based on the feature matrix and the obtained job decision vector D j , generate the analysis matrix A: Among them, P j is the agricultural operation parameter matrix, and the operation decision vector D j Related, α j is the weight coefficient, which controls the influence of each job decision vector on the analysis matrix, γ j is a regulating factor used to adjust the influence of different job decision vectors, β i is the adjustment coefficient used to control the weight of the Frobenius norm term, ∥T j ∥ F It represents the overall change degree of the data in the subspace, and δ is the attenuation factor, which controls the influence of the data characteristics; S34. Generate optimization suggestions O for agricultural operations based on the analysis matrix A: in, It means that we use the optimization algorithm to find the optimization suggestion moment that minimizes the objective function. k is the analysis matrix for the kth operation scenario, Q k is the adjusted agricultural operation parameter matrix, G k is the target operation matrix, which indicates the expected operation effect. λ is the regularization parameter, which controls the complexity of the optimization result. represents the Frobenius norm square of the optimization suggestion matrix, which is used to measure the overall strength of the optimization suggestion. l is the adjustment coefficient, which is used to adjust the correlation between the optimization suggestions and historical data. l is the historical data matrix, ∥O l ×H l ∥ represents the optimization suggestion matrix O l With the historical data matrix H l The product of S35. The generated optimization suggestion O is transmitted to the unmanned agricultural vehicle. The on-board processing unit generates the final operation instruction based on the received optimization suggestion and the real-time sensor data, and executes operations including sowing, fertilizing, irrigation, and pest control.

5. The method based on unmanned agricultural vehicle and cloud platform according to claim 1, characterized in that: The S4 specifically includes: S41, the operation suggestion matrix O generated by the cloud platform is transmitted to the central control system of the unmanned agricultural vehicle. The central control system analyzes and decomposes the operation suggestion in combination with the real-time data of the vehicle-mounted sensor to generate an operation instruction matrix C. The operation instruction matrix C includes the path planning instruction C p and job parameter command C a ; S42, using the microgravity sensing navigation system and dynamic terrain adaptive optimization algorithm, the path planning instruction C p Perform optimization and generate the path matrix P: Among them, P j is the path parameter matrix, α j is the weight coefficient, controlling the impact of the operation decision vector on path planning, γ j is the adjustment factor used to balance the weights of different job decision vectors in path planning, T j represents the terrain feature matrix, W j is the weight matrix, R j is the target path matrix, A k is the analysis matrix, Q k is the job parameter adjustment matrix, G k is the target operation matrix, ζ is the terrain adaptation adjustment factor, and the function f(T current ,T predicted ) is used to evaluate the current terrain T current With the predicted terrain T predicted The difference between S43, in the process of path planning, the path matrix P and the operation parameter instruction C are adjusted according to the terrain fluctuations and environmental changes perceived in real time by using the dynamic terrain adaptive optimization algorithm. a To make adaptive adjustments: Among them, Q is the operation parameter adjustment matrix, G is the target operation effect matrix, λ is the regularization parameter used to control the complexity of the operation parameters, ξ is the environmental adaptation adjustment factor, and the function h(E real-time ,E expected ) is used to evaluate real-time environmental data E real-time With expected environmental conditions E expected The difference between S44. During the operation, the central control system dynamically adjusts the path planning and operation parameters through dynamic terrain adaptive optimization algorithms and real-time sensor data to cope with terrain undulations and environmental changes, enabling the unmanned agricultural vehicle to perform operation tasks in the farmland environment.

6. The method based on unmanned agricultural vehicle and cloud platform according to claim 1, characterized in that: The S6 specifically includes: S61. Use four-dimensional photonic crystal storage technology to encrypt farmland environmental data, operation records of unmanned agricultural vehicles, and analysis results generated by the cloud platform. The encryption process maps data to multi-dimensional photon states by manipulating the behavior of photons in four-dimensional space, thereby performing multi-level data protection. S62, distributing and storing the encrypted data in multiple independent photonic crystal storage units, each storage unit is located on a different physical node, maintaining data consistency between nodes through a photon state synchronization mechanism, and managing data reliability by using a redundant storage method; S63. During the data storage process, the photon state adaptive control algorithm is used to adjust the photon behavior in real time to manage storage density and data security: Among them, D0 is the initial storage density matrix, D j is the job decision vector, and the path planning matrix P j is associated with, used to adjust storage density, α j is the weight coefficient, which controls the impact of the job decision vector on the storage density, A is the analysis matrix, G is the target job effect matrix, β is the adjustment factor, which is used to control the relationship between different influencing factors in the optimization process, γ is the control coefficient, ψ(H,F real-time ) is the photon state historical data H and real-time data F real-time The matching function between S64. In a distributed storage network, the stored data is accessed and retrieved through a photon state adaptive control algorithm. When accessing, the photon state switching mechanism is used to switch the photon from the storage state to the access state, and the data is read and retrieved in parallel through the quantum entanglement effect between photons. S65. The encrypted data in distributed storage is provided to authorized users through the access interface of the cloud platform. A dynamic photon decryption mechanism is used during the access process. The photon state is restored to its original state during decryption to maintain the integrity and security of the data during transmission and access.

7. The method based on unmanned agricultural vehicle and cloud platform according to claim 1, characterized in that: The S7 specifically includes: S71. Perform feature extraction and fusion processing on historical operation data and real-time operation data through a non-Abelian geometric computing network, map the extracted multi-dimensional feature data into a high-dimensional non-Abelian geometric space, analyze the geometric symmetry and topological structure of the data, and generate a preliminary operation model; S72. In high-dimensional non-Abelian geometric space, based on the interaction between historical and real-time data, the geometric symmetry and topological structure are analyzed to generate the optimized feature vector V k : Among them, D j is the job decision vector, combined with the path planning matrix P j Adjust the feature vector, A is the analysis matrix, combined with the operation parameter adjustment matrix Q, to reflect the difference in operation data, G is the target operation matrix, ψ(H,F real-time ) is the matching function between the photon state historical data and the real-time data, Υ(T current ,T future ) is the transformation function between the current and future topological states, α j , β, γ, and λ are adjustment factors, which respectively control the influence of different features on the feature vector; S73, introduce the multi-dimensional quantum state geometric fusion algorithm, and generate the feature vector V k Further optimization and fusion are performed to optimize the generated preliminary operation model into an adaptive operation model M opt : Among them, Q k is the quantum state characteristic matrix, and the characteristic vector V k fusion, exp(·) represents the mapping of quantum state geometric transformation to the operation model through matrix exponential operation, Ω(Ψ current ,Ψ future ) is the transformation function between the current and future quantum state geometric states, Φ(A,G) is the fusion function between the analysis matrix and the target matrix, which is used to adjust the geometric adaptability of the model, θ and δ are control parameters, which control the overall impact of quantum state and geometric transformation on the model; S74, Adaptive operation model M generated based on multi-dimensional quantum state geometric fusion algorithm opt , dynamically generate the operation path, speed and resource allocation strategy of the unmanned agricultural vehicle, and adjust the operation strategy in real time through the feedback mechanism of the non-Abelian geometric computing network to generate the final operation instruction C to guide the unmanned agricultural vehicle to perform farmland operation tasks.

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