Land cultivation direction dynamic adjustment system based on illumination monitoring

Abstract

The invention discloses a land cultivation direction dynamic adjustment system based on illumination monitoring, relates to the technical field of intelligent agriculture and intelligent equipment, and solves the problem of uneven macroscopic distribution of slope illumination resources through a self-adaptive illumination feature zoning method executed by a data processing and feature zoning module. Based on a high-precision digital elevation model and meteorological data, theoretical cumulative illumination income of each grid unit is simulated, on this basis, a region growing algorithm is adopted, the illumination income is taken as a benchmark, a slope with continuously changing terrain is automatically divided into a plurality of illumination characteristic bands with uniform internal illumination conditions, and the illumination characteristic bands are used as the basis. The process realizes the recognition and classification of the internal subtle illumination gradient of the'yin and yang slopes', so that the continuous and non-uniform problem which is originally difficult to process is converted into the discretization management problem of several areas with clear illumination characteristics, and the system can get rid of the dependence on the unified direction of the whole field.

Description

Technical Field

[0001] This invention relates to the field of smart agriculture technology and intelligent equipment technology, specifically a dynamic adjustment system for land cultivation direction based on light monitoring. Background Technology

[0002] In agricultural production, the direction of cultivation (i.e., the orientation of crop rows) is a key agronomic parameter affecting field light environment and crop yield. Traditional cultivation direction is often determined based on experience or a rough north-south orientation, neglecting the dynamic changes in the sun's trajectory and the micro-topographical differences within the plot. Especially in hilly and sloping terrain, uniform cultivation direction can lead to severely uneven distribution of light resources due to the "sunny slope" effect. Excessive sunlight on the sunny side may cause stress, while insufficient sunlight on the shady side directly limits yield, resulting in low overall light energy utilization.

[0003] While some existing technologies have attempted to optimize tillage direction, they still have significant drawbacks: Firstly, existing solutions are mostly based on historical meteorological data or average field light intensity for static planning, which cannot respond to real-time weather changes and the dynamic light requirements of crops at different growth stages, resulting in delayed decision-making and poor flexibility. Secondly, even if some solutions take into account the slope and aspect, they can only provide a single recommended direction for the entire plot, or can only make rough zoning. They cannot achieve fine-grained zoning management based on the subtle gradient changes in sunlight on continuous terrain, which means that the problem of uneven sunlight in undulating terrain areas cannot be fundamentally solved. Third, the decision-making models of existing solutions are often disconnected from agronomic constraints and subsequent precise implementation. The optimization results are often difficult to implement because they do not conform to actual operating conditions, and thus become theoretical calculations.

[0004] Therefore, existing technologies lack a method that can deeply integrate high-precision terrain data, predict illumination information in real time, and achieve refined and adaptive zonal decision-making on complex terrains, with the decision results seamlessly integrated with the dynamic tillage direction adjustment scheme of agricultural machinery automatic execution systems. This has become a key technological bottleneck restricting the improvement of quality and efficiency in agriculture on complex terrains such as slopes. Summary of the Invention

[0005] The purpose of this invention is to overcome the shortcomings of existing technologies and provide a dynamic adjustment system for land cultivation direction based on light monitoring. This system solves the problem of uneven macroscopic distribution of light resources on slopes through an adaptive light feature zoning method executed by a data processing and feature zoning module. Based on a high-precision digital elevation model and meteorological data, it simulates the theoretical cumulative light benefit of each grid cell. On this basis, a region growing algorithm is used to automatically divide the continuously changing slope into multiple light feature zones with uniform internal light conditions, using light benefit as a benchmark. This process enables the identification and classification of subtle light gradients within "slopes with varying light intensity," thus transforming the originally difficult problem of continuous unevenness into a discrete management problem of several regions with clear light characteristics. This allows the system to break free from dependence on a uniform direction across the entire field, laying a precise spatial foundation for subsequent implementation of truly differentiated regulation.

[0006] To solve the above-mentioned technical problems, the present invention provides the following technical solution: a dynamic adjustment system for land cultivation direction based on light monitoring, the system comprising: a multi-source data acquisition module, a data processing and feature zoning module, a zoning independent decision-making module, a control execution and coordination module, and an agronomic constraint integration module; The multi-source data acquisition module is used to acquire multi-dimensional input data, including high-precision digital elevation model data, real-time illumination monitoring data, meteorological and astronomical forecast data, and crop growth information data. The data processing and feature zoning module is used to simulate the spatiotemporal distribution of sunlight in the field within a preset time period based on the high-precision digital elevation model data and the meteorological and astronomical prediction data, and to divide the continuously changing terrain of the field into multiple sunlight feature zones with differentiated sunlight characteristics based on the simulation results. The independent decision-making module for each light feature zone is used to independently calculate the optimal tillage direction vector that matches the light conditions of that light feature zone. The control execution and coordination module is used to convert the optimal tillage direction vector corresponding to each of the light feature bands into executable agricultural machinery control commands, and drive the agricultural machinery to perform tillage operations in different directions of the bands; The agronomic constraint integration module is used to provide agronomic division constraints to the data processing and feature zoning module, and to provide boundary constraints for tillage direction decision-making to the zoning independent decision-making module.

[0007] Furthermore, the multi-source data acquisition module includes: The terrain data unit is used to acquire the high-precision digital elevation model data through UAV aerial surveying and lidar scanning. The high-precision digital elevation model data is used to characterize the terrain elevation and slope aspect information of the target cultivated area. The real-time light monitoring unit consists of a distributed network of light sensors deployed in the field, used to measure the total solar irradiance, photosynthetically active radiation intensity and sunshine duration at each monitoring point in real time, forming the real-time light monitoring data. The meteorological and astronomical data unit is used to access external weather forecast data sources to obtain the meteorological and astronomical forecast data containing cloud cover and visibility information, and to acquire solar azimuth and elevation angle data. The crop information unit is used to receive user input and obtain crop growth information data from the farm management system. The crop growth information data includes crop variety, current growth stage and corresponding light saturation point and light compensation point parameters.

[0008] Furthermore, the data processing and feature zoning module includes a terrain lighting simulation unit and an adaptive zoning unit; The terrain lighting simulation unit, based on the slope and aspect information described by the high-precision digital elevation model data, and the solar azimuth, elevation, and cloud cover data provided by the meteorological and astronomical data unit, calculates the illumination of each grid cell within the field during a preset time period using a ray tracing model. Theoretical cumulative light gain within The theoretical cumulative light gain ,in,( Indicates the geographic coordinates of the grid cells within the field. For time variables, This indicates that under cloudless conditions, the grid cells are in The theoretical direct solar irradiance received at any given time. yes Cloud transmittance coefficient at any given time. express Sky diffuse irradiance received by the grid cell at any given time. It is the crop light response function, representing the crop's response to the angle of incident light. Photosynthetic efficiency; The adaptive zoning unit is used to analyze the theoretical cumulative illumination gain output by the terrain lighting simulation unit. The spatial distribution is based on the preset regional growth strategy and the agronomic division constraints provided by the agronomic constraint integration module, dividing the entire field into multiple light characteristic zones.

[0009] Furthermore, the regional growth strategy is as follows: accumulating light benefits based on the theoretical values. Grid cells with values ​​higher than a first preset threshold are used as seed points, and those adjacent to the seed points and whose theoretical cumulative illumination gains are... Grid cells with values ​​differing within a second preset threshold are merged into the same region. This process is repeated until all grid cells are assigned to a specific region, and each resulting continuous region is a lighting feature band.

[0010] Furthermore, the constraints for the photoagronomic partitioning are as follows: the area and width of each ultimately formed illumination feature band must satisfy: and ,in, The area of ​​the illumination characteristic band. This is the minimum threshold for agricultural machinery operation area. The width of the illumination feature band. The maximum effective working width threshold for agricultural machinery .

[0011] Furthermore, the process of the zonal independent decision module independently calculating the optimal tillage direction vector for each of the light characteristic zones involves solving an optimization problem with the objective function of maximizing the total effective photosynthetic radiation intercepted by the crop canopy within the light characteristic zone, wherein: The objective function aims to maximize the total effective photosynthetic radiation intercepted by all crop plants within the defined illumination characteristic band throughout their entire growth period, while simultaneously satisfying the boundary constraints provided by the agronomic constraint integration module. ,in, This represents the total amount of effective photosynthetic radiation intercepted. Indicates the azimuth angle in the direction of cultivation. At that time, the crop canopy was The intensity of incident photosynthetically active radiation received at any given time. It is the canopy light interception efficiency function. This refers to the azimuth angle of the cultivation direction. For crop row spacing, LAI is the angle between the crop row direction and the main slope direction of the light characteristic zone. It is the leaf area index; The optimization problem satisfies the following constraints: ,in, and For the minimum and maximum allowed line spacing, This represents the main slope direction of the illumination characteristic zone. It is the minimum safe angle threshold set to prevent soil erosion caused by downhill farming; Solving by combining boundary constraints yields the result that... Maximize azimuth angle This refers to the optimal tillage direction vector of the light characteristic zone.

[0012] Furthermore, the boundary constraint is that the absolute value of the angle difference between the optimal tillage direction vectors between adjacent light feature bands must be less than or equal to a preset maximum transition angle. This is to ensure the smoothness and safety of agricultural machinery operation when transitioning between different belts.

[0013] Furthermore, the control execution and coordination module includes: The path planning unit is used to receive the optimal tillage direction vector of each of the light feature zones, and combine it with the width of the agricultural machinery to generate a continuous operation path whose direction changes with the light feature zone. When the path crosses different light feature zones, the path planning unit controls the travel direction to smoothly transition at the boundary between the zones. An actuator drive unit is used to convert the work path generated by the path planning unit into steering control commands for the agricultural machinery navigation system. The human-computer interaction and verification unit is used to visualize the land cultivation process, the results of the light feature zone division, the optimal cultivation direction vector for each light feature zone, and to trigger the operation after receiving confirmation instructions.

[0014] Furthermore, in the human-computer interaction and verification unit, the land cultivation process includes: Simultaneously acquire high-precision digital elevation model data, real-time light monitoring data, meteorological and astronomical forecast data, and crop growth information data for the target field; Based on the acquired data, the terrain lighting simulation engine is run to calculate the theoretical cumulative lighting benefits for a preset period. Spatial distribution, and by calling adaptive zoning units, the field is divided into multiple light characteristic zones under the condition of satisfying agronomic zoning constraints; For each defined illumination feature band, operate independently to maximize the total effective photosynthetic radiation interception. The system performs optimization calculations for the objective and outputs the optimal tillage direction vector under the condition of satisfying boundary constraints. The system receives the optimal tillage direction vector from all light characteristic zones, generates a specific operation path through the path planning unit, and controls the intelligent agricultural machinery to perform tillage through the actuator drive unit.

[0015] Compared with existing technologies, this land cultivation direction dynamic adjustment system based on light monitoring has the following advantages: I. This invention solves the problem of uneven macroscopic distribution of light resources on slopes by implementing an adaptive light feature zoning method through data processing and feature zoning modules. Based on a high-precision digital elevation model and meteorological data, the theoretical cumulative light benefit of each grid cell is simulated. On this basis, a region growing algorithm is used to automatically divide the continuously changing slope into multiple light feature zones with uniform internal light conditions, based on the light benefit. This process enables the identification and classification of subtle light gradients within "slopes with varying light and shade," thereby transforming the originally difficult problem of continuous unevenness into a discretized management problem of several regions with clear light characteristics. This allows the system to break free from dependence on a uniform direction across the entire field, laying a precise spatial foundation for subsequent implementation of truly differentiated regulation.

[0016] Second, by establishing a constraint and coordination mechanism that runs through the entire process of zoning, decision-making, and execution, this invention ensures the feasibility and efficient coordination of the optimization scheme from theoretical model to field operation. The multiple constraints embedded in the agronomic constraint integration module and the zoning independent decision-making module ensure that the output of each technical link conforms to agronomic standards and agricultural machinery operation capabilities. This series of constraints deeply binds agronomic principles and engineering constraints, eliminating the drawback of the disconnect between decision-making and execution in traditional optimization models, and forming a reliable and automatically executable perception, decision-making, and control system.

[0017] Other advantages, objectives and features of the invention will be set forth in part in the description which follows, and in part will be apparent to those skilled in the art from the following examination or study, or may be learned from the practice of the invention. Attached Figure Description

[0018] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are merely some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without any creative effort.

[0019] Figure 1 A flowchart of a land cultivation direction dynamic adjustment system based on light monitoring; Figure 2 This is a block diagram of the modules of a land cultivation direction dynamic adjustment system based on light monitoring; Figure 3 This is a flowchart of the land cultivation process in a land cultivation direction dynamic adjustment system based on light monitoring. Detailed Implementation

[0020] To better understand the above technical solutions, a detailed description of the solutions will be provided below in conjunction with the accompanying drawings and specific embodiments. Obviously, the described embodiments are merely some, not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0021] The terminology used in the embodiments of this invention is for the purpose of describing particular embodiments only and is not intended to limit the invention. The singular forms “a,” “the,” and “the” as used in the embodiments of this invention and the appended claims are also intended to include the plural forms, and “multiple” generally includes at least two unless the context clearly indicates otherwise.

[0022] To address the problems of reliance on experience, neglect of spatial and temporal differences in terrain and sunlight, and disconnect between decision-making and execution in existing land cultivation direction decisions, this invention provides a dynamic adjustment system for land cultivation direction based on sunlight monitoring. It aims to achieve refined utilization of sunlight resources and dynamic optimization of cultivation direction in complex terrains through a collaborative mechanism of high-precision terrain and sunlight data fusion, adaptive zoned decision-making, and precise execution by agricultural machinery. This invention is primarily applicable to smart agriculture operations in hilly and sloping areas, and is especially suitable for the planting and management of high-value-added crops that are sensitive to sunlight. By constructing a complete technological closed loop from data perception and intelligent decision-making to automatic execution, it solves the core problems of uneven sunlight distribution, low light energy utilization, and disconnect between agronomy and mechanical execution in traditional cultivation methods.

[0023] Specifically, such as Figure 2 As shown, the land cultivation direction dynamic adjustment system based on light monitoring includes: a multi-source data acquisition module, a data processing and feature zoning module, a zoning independent decision-making module, a control execution and coordination module, and an agronomic constraint integration module. The multi-source data acquisition module is used to acquire multi-dimensional input data, including high-precision digital elevation model data, real-time illumination monitoring data, meteorological and astronomical forecast data, and crop growth information data. The data processing and feature zoning module is used to simulate the spatiotemporal distribution of sunlight in the field within a preset time period based on the high-precision digital elevation model data and the meteorological and astronomical prediction data, and to divide the continuously changing terrain of the field into multiple sunlight feature zones with differentiated sunlight characteristics based on the simulation results. The independent decision-making module for each light feature zone is used to independently calculate the optimal tillage direction vector that matches the light conditions of that light feature zone. The control execution and coordination module is used to convert the optimal tillage direction vector corresponding to each of the light feature bands into executable agricultural machinery control commands, and drive the agricultural machinery to perform tillage operations in different directions of the bands; The agronomic constraint integration module is used to provide agronomic division constraints to the data processing and feature zoning module, and to provide boundary constraints for tillage direction decision-making to the zoning independent decision-making module.

[0024] In its specific implementation, the multi-source data acquisition module serves as the data foundation for the system's illumination analysis and decision-making. Through the synchronous acquisition and integration of heterogeneous multi-source data, it provides comprehensive, real-time, and high-precision input information for subsequent processing. This multi-source data acquisition module includes a terrain data unit, a real-time illumination monitoring unit, a meteorological and astronomical data unit, and a crop information unit. The specific implementation methods of each unit are as follows: The terrain data unit is responsible for acquiring high-precision digital elevation model data of the target cultivated area. It uses drones equipped with lidar for aerial surveying, and generates a high-precision digital elevation model through point cloud data processing and surface modeling. This high-precision digital elevation model accurately represents the terrain information such as the elevation, slope, and aspect of the field, providing a terrain basis for illumination simulation.

[0025] The real-time light monitoring unit consists of a distributed network of light sensors deployed in the field. The sensor nodes are arranged in a grid, and each node is equipped with a total irradiance sensor and a photosynthetically active radiation sensor to measure the total solar irradiance and photosynthetically active radiation intensity in real time. The sensor data is transmitted to the base station via a wireless LoRa network, and then uploaded to the cloud platform via a 4G / 5G network to form a real-time light monitoring data stream covering the entire field.

[0026] The meteorological and astronomical data unit connects to weather forecast data sources via an API interface to obtain real-time weather forecast information for the next 7 days, including cloud cover, visibility, and sunshine duration. Simultaneously, based on astronomical algorithms, it calculates the solar azimuth and altitude angles for the current and future periods in real time, providing astronomical driving parameters for illumination simulation.

[0027] The crop information unit supports two data input methods: one is to receive information such as crop variety, current growth stage, and planting density from the user through the human-computer interaction and verification unit's interface; the other is to obtain crop growth data from the farm management system through a standard data interface, including light response parameters such as leaf area index, light saturation point, and light compensation point. The system has a built-in light response function library for common crops and supports user-defined crop types and parameters.

[0028] The data processing and feature zoning module is responsible for simulating field illumination distribution based on multi-source data and performing adaptive zoning according to illumination characteristics. This module includes a topographic illumination simulation unit and an adaptive zoning unit, specifically: The terrain lighting simulation unit, based on slope and aspect information described by a high-precision digital elevation model, and combined with solar azimuth, elevation, and cloud cover data provided by meteorological and astronomical data units, uses a ray tracing model to simulate the theoretical cumulative lighting gain of each grid cell within a field over a preset time period T. The theoretical cumulative lighting gain... , among which, among which, This represents the geographic coordinates of the raster cells within the field. For time variables, This represents the theoretical direct solar irradiance received by a grid cell at time t. It is calculated using the cosine projection law based on the solar altitude angle, azimuth angle, and terrain slope and aspect. ,in The solar irradiance constant is the solar radiation incident perpendicularly from outside the atmosphere. Let be the angle between the incident sunlight ray and the normal vector of the grid cell. It is the cloud transmittance coefficient at time t, obtained from cloud cover data through... Calculations show that CloudCover represents cloud coverage. This represents the sky diffuse irradiance received by the grid cell at time t. It is the crop light response function, representing the crop's response to the angle of incident light. The photosynthetic efficiency.

[0029] The adaptive zoning unit is based on the spatial distribution of the theoretical cumulative illumination gain output by the terrain illumination simulation unit. It uses a region growing algorithm to perform illumination feature zoning. The steps of the zoning process are as follows: Grid cells with theoretical cumulative illumination gains exceeding a preset threshold are used as seed points; Starting from each seed point, examine its eight neighboring raster cells. If the difference between the illumination gain value of the neighboring cells and the seed point does not exceed the second preset threshold, then merge them into the same region. The region growth process is iteratively executed until all grid cells are assigned to a certain region; The generated initial region is post-processed, and regions are merged or segmented according to the constraints provided by the agronomic constraint integration module to ensure that the area of ​​each final light feature band is not less than the minimum agricultural machinery operation area threshold. And the bandwidth is not greater than the maximum effective operating width threshold of agricultural machinery. ,Right now and ,in, The area of ​​the illumination characteristic band. This is the minimum threshold for agricultural machinery operation area. The width of the illumination feature band. The maximum effective working width threshold for agricultural machinery .

[0030] The independent decision-making module for each light characteristic zone independently solves the optimal tillage direction vector. This process is transformed into an optimization problem with the objective of maximizing the total effective photosynthetic radiation intercepted by the crop canopy within the characteristic zone.

[0031] The objective function is defined as follows: ,in, This represents the total amount of effective photosynthetic radiation intercepted. Indicates the azimuth angle in the direction of cultivation. At that time, the crop canopy was The intensity of incident photosynthetically active radiation received at any given time. It is the canopy light interception efficiency function, calculated from the layer structure. It is the canopy light interception efficiency function, and its expression is: LAI ,in The extinction coefficient of the canopy. Let LA be the angle between the sunlight and the normal direction of the crop row, and LA(t) be the time function of the leaf area index, provided by the crop growth model. This refers to the azimuth angle of the cultivation direction. For crop row spacing, It is the angle between the crop row direction and the main slope direction of the light characteristic zone.

[0032] The optimization problem must satisfy the following constraints: Line spacing constraints: ,in and The minimum and maximum row spacing allowed by agronomical rules; Soil and water conservation constraints: ,in This represents the main slope direction of the illumination characteristic zone. It is the minimum safe angle threshold set to prevent soil erosion caused by downhill farming; Transition smoothing constraint: The absolute value of the angle difference between the optimal tillage direction between adjacent light feature bands must be less than or equal to the preset maximum transition angle. This is to ensure the smoothness and safety of agricultural machinery operation when transitioning between different belts.

[0033] The above constrained optimization problem is solved using a genetic algorithm to obtain the result. Maximize the azimuth angle of the tillage direction This is the optimal tillage direction vector for this illumination characteristic zone. The specific steps of the genetic algorithm to solve this constrained optimization problem are as follows: Encoding and initialization: The decision variable (azimuth of tillage direction) is encoded and initialized. line spacing The encoding is a binary string, the population size is set to 50, and the initial population is within the feasible region. Randomly generated; Fitness assessment: Calculate fitness for each individual (i.e., a group of individuals). The corresponding objective function value At the same time, check whether the soil and water conservation constraints and transition smoothness constraints are met; Selection, crossover, and mutation: For the selected parent, with probability... Perform a single-point crossover operation on offspring individuals, using probability. , to mutate gene loci; Iteration and Termination: Repeated fitness evaluation - selection, crossover, and mutation are used for iterative optimization until the fitness of the optimal individual changes less than a certain value over 20 consecutive generations. Output the optimal solution; Decode the individual with the highest fitness in the final population to obtain... Maximize the azimuth angle of the tillage direction and line spacing ,in This is the optimal tillage direction vector for that light characteristic zone.

[0034] The control execution and coordination module is responsible for converting the decision results into executable operation instructions for the agricultural machinery and controlling the agricultural machinery to complete the strip-shaped and opposite-direction tillage. The control execution and coordination module includes a path planning unit, an actuator driving unit, and a human-machine interaction and verification unit.

[0035] The path planning unit receives the optimal tillage direction vector of each light characteristic zone, and combines it with the width of the agricultural machinery to generate a continuous operation path whose direction changes with the light characteristic zone, ensuring that the agricultural machinery travels in a straight line along the optimal direction within the zone.

[0036] The actuator drive unit converts the operation path generated by the path planning unit into steering control commands for the agricultural machinery navigation system, and sends them to the agricultural machinery automatic driving system via the bus to control the steering hydraulic valve and throttle opening, thereby achieving high-precision path tracking.

[0037] The human-computer interaction and verification unit provides a visual operating interface that dynamically displays the entire land cultivation process, including: real-time display of multi-source data acquisition status; visual presentation of terrain and illumination simulation results and illumination feature zone division maps; arrow marking of the optimal cultivation direction for each illumination feature zone; and dynamic display of the real-time location and planned path of agricultural machinery. Users can adjust parameters and preview operation plans on the interface, and must trigger operation commands after final confirmation to ensure human-machine collaboration and operational safety. The land cultivation process is as follows: Figure 3 As shown, specifically: Simultaneously acquire high-precision digital elevation model data, real-time light monitoring data, meteorological and astronomical forecast data, and crop growth information data for the target field; Based on the acquired data, the terrain lighting simulation engine is run to calculate the theoretical cumulative lighting benefits for a preset period. Spatial distribution, and by calling adaptive zoning units, the field is divided into multiple light characteristic zones under the condition of satisfying agronomic zoning constraints; For each defined illumination feature band, operate independently to maximize the total effective photosynthetic radiation interception. The system performs optimization calculations for the objective and outputs the optimal tillage direction vector under the condition of satisfying boundary constraints. The system receives the optimal tillage direction vector from all light characteristic zones, generates a specific operation path through the path planning unit, and controls the intelligent agricultural machinery to perform tillage through the actuator drive unit.

[0038] The agronomic constraint integration module incorporates multiple constraint rules, including those related to crop cultivation, agricultural machinery and agronomy integration, and soil and water conservation. This module exists in the form of a knowledge base, providing agronomic division constraints (minimum operating area, maximum bandwidth) for the data processing and feature zoning module, and boundary constraints (row spacing range, slope angle limit, and inter-zone transition angle limit) for the zoning independent decision module. The constraint parameters can be flexibly configured and updated by users according to actual agronomic requirements and agricultural machinery configurations.

[0039] like Figure 1 As shown below, the specific workflow of this system will be explained in detail using a smart agriculture operation scenario in a sloping, uneven terrain area. The specific steps of this workflow are as follows: (1) Data collection and preparation Using drones equipped with lidar to conduct aerial surveys of target fields and obtain high-precision digital elevation model data.

[0040] A distributed light sensor network is deployed in the field to monitor the total solar radiation and photosynthetically active radiation at each point in real time.

[0041] Connect to the meteorological data platform to obtain weather forecast information (cloud cover, visibility, etc.) for the next period of time.

[0042] Enter or retrieve crop information from the farm management system, including variety, growth period, light saturation point, light compensation point, etc.

[0043] (2) Simulation of spatiotemporal distribution of illumination Based on digital elevation models and meteorological data, a ray tracing model was used to simulate the light intensity distribution of each grid cell in a field over a future period of time.

[0044] Based on the crop's light response characteristics, the theoretical cumulative light gain for each grid cell is calculated.

[0045] (3) Division of illumination characteristic zones Grid cells with high theoretical cumulative illumination gains are used as seed points.

[0046] A region growing algorithm is used to merge adjacent grids with similar lighting conditions into the same region.

[0047] Based on the operational capabilities of agricultural machinery, the areas are merged or divided to form several light characteristic zones.

[0048] (4) Decision on the direction of independent cultivation in separate zones For each illumination feature band, an optimization problem is established with the goal of maximizing canopy light energy interception.

[0049] Agronomic constraints, such as row spacing and soil and water conservation requirements, should be considered.

[0050] The optimal tillage direction for each zone is determined by solving the problem.

[0051] Coordinate the tillage direction between adjacent strips to ensure a smooth transition for agricultural machinery.

[0052] (5) Operation path planning and smoothing Based on the optimal tillage direction and the width of the agricultural machinery in each strip, a parallel straight-line operation path is generated within the strip.

[0053] Generate continuous operation paths covering the entire field and optimize turning paths at field ends.

[0054] (6) Agricultural machinery control and operation execution The planned path is converted into control commands that the agricultural machinery autopilot system can recognize.

[0055] The monitoring terminal displays information such as light characteristic zones, optimal cultivation direction, and planned paths for operators to confirm.

[0056] After confirming that everything is correct, the work instruction is issued, and the agricultural machinery automatically performs zoned and directional tillage according to the planned path.

[0057] The system monitors the location and operating status of agricultural machinery in real time and supports manual intervention.

[0058] (7) Work records and effect evaluation After the work is completed, the system automatically records information such as the actual cultivation direction and the area of ​​work in each zone.

[0059] The effect of improving light utilization efficiency can be evaluated by combining subsequent crop growth monitoring data.

[0060] In summary, this invention achieves spatial optimization of light resources and dynamic and precise control of cultivation direction in complex terrain farmland through the synergy of multi-source data fusion, refined illumination simulation, adaptive zoned decision-making, and precise execution by agricultural machinery. The system can significantly alleviate the problem of uneven illumination on "sunny slopes" and improve the light energy interception efficiency of crop canopies. At the same time, by embedding agronomic constraints throughout the entire process, it ensures that the optimization scheme conforms to agronomic standards and agricultural machinery operation capabilities, realizing a management system from perception to execution, and providing technical support for improving the quality and efficiency of slope agriculture and intelligent operation.

[0061] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some modifications or alterations to the above-disclosed technical content to create equivalent embodiments without departing from the scope of the present invention. Any simple modifications, equivalent changes and alterations made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the scope of the present invention.

Claims

1. A dynamic adjustment system for land cultivation direction based on light monitoring, characterized in that, The system includes: a multi-source data acquisition module, a data processing and feature segmentation module, a segmentation independent decision-making module, a control execution and coordination module, and an agronomic constraint integration module; The multi-source data acquisition module is used to acquire multi-dimensional input data, including high-precision digital elevation model data, real-time illumination monitoring data, meteorological and astronomical forecast data, and crop growth information data. The data processing and feature zoning module is used to simulate the spatiotemporal distribution of sunlight in the field within a preset time period based on the high-precision digital elevation model data and the meteorological and astronomical prediction data, and to divide the continuously changing terrain of the field into multiple sunlight feature zones with differentiated sunlight characteristics based on the simulation results. The independent decision-making module for each light feature zone is used to independently calculate the optimal tillage direction vector that matches the light conditions of that light feature zone. The control execution and coordination module is used to convert the optimal tillage direction vector corresponding to each of the light feature bands into executable agricultural machinery control commands, and drive the agricultural machinery to perform tillage operations in different directions of the bands; The agronomic constraint integration module is used to provide agronomic division constraints to the data processing and feature zoning module, and to provide boundary constraints for tillage direction decision-making to the zoning independent decision-making module.

2. The land cultivation direction dynamic adjustment system based on light monitoring according to claim 1, characterized in that, The multi-source data acquisition module includes: The terrain data unit is used to acquire the high-precision digital elevation model data through UAV aerial surveying and lidar scanning. The high-precision digital elevation model data is used to characterize the terrain elevation and slope aspect information of the target cultivated area. The real-time light monitoring unit consists of a distributed network of light sensors deployed in the field, used to measure the total solar irradiance, photosynthetically active radiation intensity and sunshine duration at each monitoring point in real time, forming the real-time light monitoring data. The meteorological and astronomical data unit is used to access external weather forecast data sources to obtain the meteorological and astronomical forecast data containing cloud cover and visibility information, and to acquire solar azimuth and elevation angle data. The crop information unit is used to receive user input and obtain crop growth information data from the farm management system. The crop growth information data includes crop variety, current growth stage and corresponding light saturation point and light compensation point parameters.

3. The land cultivation direction dynamic adjustment system based on light monitoring according to claim 1, characterized in that, The data processing and feature segmentation module includes a terrain lighting simulation unit and an adaptive segmentation unit; The terrain lighting simulation unit, based on the slope and aspect information described by the high-precision digital elevation model data, and the solar azimuth, elevation, and cloud cover data provided by the meteorological and astronomical data unit, calculates the illumination of each grid cell within the field during a preset time period using a ray tracing model. Theoretical cumulative light gain within The theoretical cumulative light gain ,in,( Indicates the geographic coordinates of the grid cells within the field. For time variables, This indicates that under cloudless conditions, the grid cells are in The theoretical direct solar irradiance received at any given time. yes Cloud transmittance coefficient at any given time. express Sky diffuse irradiance received by the grid cell at any given time. It is the crop light response function, representing the crop's response to the angle of incident light. Photosynthetic efficiency; The adaptive zoning unit is used to analyze the theoretical cumulative illumination gain output by the terrain lighting simulation unit. The spatial distribution is based on the preset regional growth strategy and the agronomic division constraints provided by the agronomic constraint integration module, dividing the entire field into multiple light characteristic zones.

4. The land cultivation direction dynamic adjustment system based on light monitoring according to claim 3, characterized in that, The regional growth strategy is as follows: accumulating light benefits based on the theoretical values. Grid cells with values ​​higher than a first preset threshold are used as seed points, and those adjacent to the seed points and whose theoretical cumulative illumination gains are... Grid cells with values ​​differing within a second preset threshold are merged into the same region. This process is repeated until all grid cells are assigned to a specific region, and each resulting continuous region is a lighting feature band.

5. The land cultivation direction dynamic adjustment system based on light monitoring according to claim 3, characterized in that, The agronomic partitioning constraint is that the area and width of each final light feature band must satisfy the following conditions: and ,in, The area of ​​the illumination characteristic band. This is the minimum threshold for agricultural machinery operation area. The width of the illumination feature band. The maximum effective working width threshold for agricultural machinery .

6. The land cultivation direction dynamic adjustment system based on light monitoring according to claim 1, characterized in that, The process by which the independent decision-making module for each light characteristic zone independently calculates the optimal tillage direction vector involves solving an optimization problem with the objective function of maximizing the total effective photosynthetic radiation intercepted by the crop canopy within the light characteristic zone, wherein: The objective function aims to maximize the total effective photosynthetic radiation intercepted by all crop plants within the defined illumination characteristic band throughout their entire growth period, while simultaneously satisfying the boundary constraints provided by the agronomic constraint integration module. ,in, This represents the total amount of effective photosynthetic radiation intercepted. Indicates the azimuth angle in the direction of cultivation. At that time, the crop canopy was The intensity of incident photosynthetically active radiation received at any given time. It is the canopy light interception efficiency function. This refers to the azimuth angle of the cultivation direction. For crop row spacing, LAI is the angle between the crop row direction and the main slope direction of the light characteristic zone. It is the leaf area index; The optimization problem satisfies the following constraints: ,in, and For the minimum and maximum allowed line spacing, This represents the main slope direction of the illumination characteristic zone. It is the minimum included angle safety threshold; Solving by combining boundary constraints yields the result that... Maximize azimuth angle This refers to the optimal tillage direction vector of the light characteristic zone.

7. The land cultivation direction dynamic adjustment system based on light monitoring according to claim 6, characterized in that, The boundary constraint is that the absolute value of the angle difference between the optimal tillage direction vectors between adjacent light feature bands must be less than or equal to a preset maximum transition angle. .

8. The land cultivation direction dynamic adjustment system based on light monitoring according to claim 1, characterized in that, The control execution and coordination module includes: The path planning unit is used to receive the optimal tillage direction vector of each of the light feature zones, and combine it with the width of the agricultural machinery to generate a continuous operation path whose direction changes with the light feature zone. When the path crosses different light feature zones, the path planning unit controls the travel direction to smoothly transition at the boundary between the zones. An actuator drive unit is used to convert the work path generated by the path planning unit into steering control commands for the agricultural machinery navigation system. The human-computer interaction and verification unit is used to visualize the land cultivation process, the results of the light feature zone division, the optimal cultivation direction vector for each light feature zone, and to trigger the operation after receiving confirmation instructions.

9. The land cultivation direction dynamic adjustment system based on light monitoring according to claim 8, characterized in that, The human-computer interaction and verification unit includes the following land cultivation process: Simultaneously acquire high-precision digital elevation model data, real-time light monitoring data, meteorological and astronomical forecast data, and crop growth information data for the target field; Based on the acquired data, the terrain lighting simulation engine is run to calculate the theoretical cumulative lighting benefits for a preset period. Spatial distribution, and by calling adaptive zoning units, the field is divided into multiple light characteristic zones under the condition of satisfying agronomic zoning constraints; For each defined illumination feature band, operate independently to maximize the total effective photosynthetic radiation interception. The system performs optimization calculations for the objective and outputs the optimal tillage direction vector under the condition of satisfying boundary constraints. The system receives the optimal tillage direction vector from all light characteristic zones, generates a specific operation path through the path planning unit, and controls the intelligent agricultural machinery to perform tillage through the actuator drive unit.

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