Ploughing machine operation width self-adaptive adjusting method and system based on soil moisture content

Abstract

The application belongs to the field of agricultural machinery, and discloses a plowing machine operation width self-adaptive adjusting method and system based on soil moisture content, aiming to solve the technical problem that the prior art lacks soil water content sensing parameters and cannot realize dynamic adjustment of operation width. The method comprises the following steps: collecting soil water content data in real time through a soil moisture content sensor array arranged in front of a plow body; sending the data to a control system through a wireless transmission module; calling a stored soil moisture content-operation width mapping relationship model to calculate a target operation width value; adjusting the width by a dynamic adjusting mechanism; triggering a width reduction control strategy to reduce the risk of soil compaction when the soil water content is too high, and triggering a width increase control strategy to improve the tillage efficiency when the soil water content is too low. The application can dynamically adjust the operation width according to the real-time soil moisture content, realize optimal matching of tillage parameters and soil conditions, significantly reduce the risk of soil compaction, and improve the operation efficiency.

Description

Technical Field

[0001] This invention belongs to the field of agricultural machinery, specifically relating to a method and system for adaptive adjustment of the working width of a plow based on soil moisture. Background Technology

[0002] Agricultural mechanization is a crucial technological support for the development of modern agriculture. Plowing, as the primary step in soil cultivation, directly impacts soil improvement and farming efficiency through the scientific setting of its operating parameters. Plowing machines achieve soil loosening, weed control, and soil structure optimization by turning the soil over; the appropriate selection of the working width is a key factor in ensuring cultivation quality. In actual operation, soil conditions exhibit significant spatial heterogeneity and temporal variability; therefore, dynamic adjustment of the working width is essential for adapting to complex and changing field environments.

[0003] Soil moisture, as a key agronomic parameter reflecting soil water content, directly determines the physical and mechanical properties of the soil, including soil cohesion, hardness, compressibility modulus, and the bonding state between soil particles. When soil moisture content is high, the soil is in a plastic state, and the lubrication between particles is enhanced. At this time, tillage resistance is reduced, but soil compaction sensitivity increases significantly. When soil moisture content is low, the soil is in a hard state, tillage resistance increases, and energy consumption increases. Given the intrinsic relationship between soil moisture content and soil physical properties, using it as a decision-making basis for adjusting the work width can achieve optimal matching between tillage parameters and soil conditions.

[0004] However, existing tillage machine sensing parameter systems have significant shortcomings. Known adaptive adjustment technologies for tillage machines primarily rely on external environmental parameters such as plot shape sensing, soil breaking resistance sensing, and slope sensing. Plot shape sensing is used to determine the boundaries of the work area, soil breaking resistance sensing is used to detect changes in tillage load, and slope sensing is used to adjust the machine's posture and balance. While these existing technologies achieve the sensing and response to some environmental factors, none of them incorporate soil moisture content into the sensing parameter system. Due to the lack of real-time soil moisture sensing capabilities, existing technologies cannot obtain soil moisture conditions—a key parameter directly affecting tillage quality and operational efficiency—and therefore cannot establish a linkage adjustment mechanism between soil condition and work width. This technological deficiency makes it difficult for existing tillage machines to achieve precise and adaptive operation under complex soil conditions. Operating at the conventional width when soil moisture content is too high will exacerbate the risk of soil compaction, while operating at a fixed width when soil moisture content is too low will lead to energy waste and low operational efficiency. Therefore, how to incorporate soil moisture information into the parameter adjustment system of plowing operations and construct a dynamic adaptive method for operation width based on soil moisture content is a technical problem that urgently needs to be solved in the field of agricultural mechanization. Summary of the Invention

[0005] The purpose of this invention is to provide a method and system for adaptive adjustment of the working width of a ploughing machine based on soil moisture conditions, which can effectively solve the technical problems of lacking soil moisture content sensing parameters and being unable to achieve dynamic adjustment of the working width in the prior art.

[0006] To achieve the above objectives, the technical solution adopted by the present invention is as follows: a method and system for adaptive adjustment of the working width of a ploughing machine based on soil moisture conditions, the method including a soil moisture perception step, a soil moisture data transmission step, a mapping model calling step, a working width adjustment step, a high moisture content adjustment step, and a low moisture content adjustment step.

[0007] In the soil moisture perception step, the system collects soil moisture data in real time through a soil moisture sensor array deployed in front of the plow body. The sensor array contains multiple soil moisture sensor nodes, which are arranged linearly or in a matrix on the working path at the front of the plow body to obtain soil moisture information in a predetermined area in front of the plow body.

[0008] In the soil moisture data transmission step, the soil moisture content data collected by the soil moisture sensor array is transmitted to the control system in real time through the wireless transmission module. The wireless transmission module adopts a low-power wide area network communication protocol to realize the real-time transmission of sensor node data to the control system, ensuring the timeliness and reliability of data transmission.

[0009] In the mapping model invocation step, the control system internally stores a soil moisture operation width mapping relationship model. This mapping relationship model contains the functional relationship between soil moisture content threshold range and corresponding operation width. The control system invokes the mapping relationship model based on the received real-time soil moisture content data to calculate the target operation width value corresponding to the current soil conditions.

[0010] In the operation width adjustment step, the control system sends an adjustment command to the dynamic adjustment mechanism of the plow body operation width based on the target operation width value calculated by the mapping model. The dynamic adjustment mechanism is driven by the control system to achieve width adjustment. The actuator drives the plow body to adjust to the target operation width according to the adjustment command, thus completing the dynamic adaptive adjustment of the operation width.

[0011] In the high moisture content adjustment step, when the soil moisture content data exceeds the preset high moisture content threshold, the control system triggers the width reduction control strategy, calculates the width reduction range based on the high moisture content threshold range, and issues a width reduction command to the dynamic adjustment mechanism to reduce the working width of the plow body, thereby reducing the risk of soil compaction and protecting the soil structure.

[0012] In the low moisture content adjustment step, when the soil moisture content data is lower than the preset low moisture content threshold, the control system triggers the widening control strategy, calculates the widening range based on the low moisture content threshold range, and issues a widening command to the dynamic adjustment mechanism to increase the working width of the plow body, thereby improving tillage efficiency and reducing energy consumption.

[0013] Preferably, the soil moisture sensor array has no fewer than 5 sensor nodes, which are arranged linearly at equal intervals along the plowing direction. The spacing between the sensor nodes is determined based on the plowing width and the spatial heterogeneity of soil moisture, ensuring that representative soil moisture content data can be obtained.

[0014] Preferably, in the soil moisture data transmission step, the data acquisition frequency of the wireless transmission module is not less than once per second, and the data transmission adopts a cyclic redundancy check mechanism to verify the data, ensuring the integrity and accuracy of the transmitted data and avoiding erroneous adjustments caused by data transmission errors.

[0015] Preferably, in the soil moisture operation width mapping relationship model, the soil moisture content threshold range is divided into three levels: low moisture content range, moderate moisture content range, and high moisture content range. Each range corresponds to a different operation width benchmark value. The mapping relationship model also includes operation width smoothing transition logic at the range boundary to avoid the impact of sudden changes in operation width on tillage quality.

[0016] Preferably, the dynamic working width adjustment mechanism is driven by an electric drive or a hydraulic drive. The adjustment mechanism is equipped with a position feedback device to monitor the actual working width of the plow body in real time and feed the position information back to the control system to form a closed-loop control, ensuring that the accuracy and response speed of the working width adjustment meet the requirements of field operations.

[0017] Preferably, in the high moisture content adjustment step, the width reduction range is directly proportional to the degree to which the soil moisture content exceeds the high moisture content threshold. The width reduction range is calculated and determined according to a preset mapping relationship. When the soil moisture content reaches or exceeds the preset maximum moisture content threshold, the working width is adjusted to the minimum allowable value to minimize the risk of soil compaction.

[0018] Preferably, in the low moisture content adjustment step, the widening range is directly proportional to the degree to which the soil moisture content is lower than the low moisture content threshold. The widening range is calculated and determined according to a preset mapping relationship. When the soil moisture content is too low, the working width is adjusted to the maximum allowable value so as to make full use of the soil characteristics under low moisture content conditions and improve working efficiency.

[0019] In summary, this application includes at least one of the following beneficial technical effects: 1. This invention introduces soil moisture content as the core sensing parameter for plowing operations, and constructs a linkage adjustment mechanism between soil moisture and working width. This solves the technical bottleneck of the lack of real-time soil moisture sensing capability in the prior art, enabling the plowing machine to dynamically adjust the operating parameters according to the real-time soil moisture status, and achieving the optimal matching between tillage parameters and soil conditions.

[0020] 2. This invention achieves refined adaptive adjustment of the operation width by establishing and dynamically calling a soil moisture operation width mapping relationship model. The mapping model establishes a clear quantitative relationship between the soil moisture content threshold range and the corresponding operation width, and can accurately calculate the target operation width value according to different soil moisture conditions, avoiding the problems of rough parameter adjustment and poor adaptability in the prior art.

[0021] 3. This invention effectively reduces the risk of soil compaction by using a width reduction control strategy when the soil moisture content is too high. When the soil moisture content is too high, the working width is automatically reduced, which reduces the compaction effect on the soil with high moisture content, protects the soil structure and water and soil conservation capacity, and improves the quality of cultivation and the level of sustainable soil utilization.

[0022] 4. This invention significantly improves tillage efficiency and reduces energy consumption through a widening control strategy under low moisture content. When the soil moisture content is too low, the working width is automatically increased, making full use of the characteristic of low soil resistance under low moisture content conditions. This improves working efficiency while ensuring tillage effect, and achieves the unity of economic efficiency and environmental protection in agricultural production. Attached Figure Description

[0023] Figure 1 This is a schematic diagram of the overall technical solution architecture of the adaptive adjustment method for the working width of a ploughing machine based on soil moisture proposed in this invention. Figure 2 This is a schematic diagram of the core principle framework of the soil moisture operation width mapping relationship model in this invention; Figure 3 This is a logical flowchart of the soil moisture sensor array sensing and moisture data transmission in this invention; Figure 4 This is a flowchart illustrating the control execution process of the dynamic adjustment mechanism for working width in this invention. Figure 5 This is a framework diagram of the adaptive adjustment strategy of reducing the width at high water content and increasing the width at low water content in this invention. Detailed Implementation

[0024] To make the objectives, technical solutions, and advantages of this invention clearer, the following description is provided in conjunction with the appendix. Figure 1 To be continued Figure 5 The present invention will be further described in detail below with reference to specific embodiments.

[0025] Example 1 This embodiment applies to the field of adaptive adjustment technology for plowing machine operating width in agricultural tillage scenarios, specifically involving an automatic adjustment method and supporting system for plowing operating width based on real-time soil moisture perception and dynamic decision-making. In actual field tillage, soil conditions exhibit significant spatial heterogeneity and time-varying characteristics. Traditional plowing machines have a fixed operating width and cannot adaptively adjust according to changes in soil moisture content, leading to increased soil compaction when soil moisture content is too high and reduced operating efficiency when soil moisture content is too low. This embodiment introduces soil moisture content as a core sensing parameter, constructs a soil moisture-operating width mapping model, and achieves refined dynamic adjustment of plowing operating width, solving the technical problem of existing technologies that cannot achieve adaptive operation due to a lack of soil moisture perception capabilities.

[0026] First, a detailed description is given from the system architecture perspective. The adaptive adjustment system for the plowing machine's working width in this embodiment mainly consists of five core parts: a soil moisture sensor array, a data acquisition and wireless transmission module, a control system, a soil moisture-working width mapping model, and a dynamic adjustment mechanism for the plow body's working width. These parts interact with each other and transmit commands via wired or wireless communication, forming a complete closed-loop control system of perception, decision-making, and execution.

[0027] A soil moisture sensor array is deployed in front of the plow, comprising multiple soil moisture content sensor nodes arranged linearly or in a matrix in front of the tilled soil area along the plow's forward direction. The specific layout of the sensor nodes needs to consider the actual needs of field operations, typically using a linear arrangement with equal spacing. The spacing between sensor nodes is determined based on the adjustable width of the plow and the representativeness requirements of the spatial distribution of soil moisture, generally set to 20 to 50 centimeters. In one specific embodiment, the soil moisture sensor array contains 5 to 15 sensor nodes, evenly distributed along the working path directly in front of the plow, covering a soil area 2 to 6 meters wide. Each sensor node integrates a soil moisture content measuring element. The measurement principle can employ mature technologies such as frequency domain reflectometry, time domain reflectometry, or microwave methods, achieving a measurement accuracy better than ±2% volumetric water content and a response time of less than 1 second. The sensor node's outer shell is made of high-strength engineering plastic or stainless steel, possessing excellent sealing performance and corrosion resistance, enabling long-term stable operation in humid field environments. The sensor node has a probe structure at the bottom. The length of the probe is determined according to the measurement depth requirements, generally 10 to 30 centimeters, which can penetrate deep into the soil tillage layer to measure the moisture content.

[0028] The data acquisition and wireless transmission module enables real-time transmission of sensor data to the control system. This module includes a data acquisition circuit, a signal conditioning circuit, a microprocessor, and a wireless communication unit. The data acquisition circuit collects analog or digital signals output from each sensor node. The signal conditioning circuit amplifies, filters, and performs analog-to-digital conversion on the acquired signals. The microprocessor preprocesses the converted digital signals, including data verification, outlier removal, and average value calculation. The wireless communication unit uses low-power wireless communication protocols such as ZigBee or LoRa to transmit the processed soil moisture data to the control system. The data acquisition frequency is designed to be 1 to 10 times per second, meeting the real-time monitoring requirements for dynamic changes in soil moisture during tillage operations. The data verification mechanism employs a cyclic redundancy check algorithm to ensure the reliability and integrity of data transmission.

[0029] The control system is the core decision-making unit of the entire adaptive regulation system, responsible for receiving soil moisture data, executing mapping model calculations, and issuing regulation commands. The control system hardware uses an embedded industrial control computer or programmable logic controller, including a central processing unit (CPU), memory, input / output interfaces, and communication interfaces. The CPU's computing power should meet the requirements of real-time data processing and complex algorithm execution, with a clock frequency of no less than 100 MHz. The memory stores soil moisture-operation width mapping model parameters, historical operation data, and system configuration parameters. The input / output interfaces connect to the data acquisition module, drive actuators, and human-machine interface. The communication interface supports industrial communication protocols such as CAN bus, RS485 bus, or Ethernet to achieve data interaction with agricultural machinery management systems or remote monitoring platforms. The control system internally runs a soil moisture-operation width mapping model, which establishes a quantitative mapping relationship between soil moisture content and operation width, including the division of moisture content threshold intervals and the corresponding operation width calculation logic.

[0030] The soil moisture-operation width mapping model is the core algorithmic foundation for adaptive adjustment. The basic principle of this model is to divide soil moisture content into preset threshold ranges, with different moisture content ranges corresponding to different operation width values. Specific parameters of the model include the boundary values ​​of the moisture content threshold ranges, the baseline operation width value within each range, and the operation width adjustment coefficient. In a specific implementation, soil moisture content is divided into three threshold ranges: when the soil volumetric moisture content is below 20%, it is defined as a low moisture content range, with the corresponding baseline operation width being the maximum value; when the soil volumetric moisture content is between 20% and 35%, it is defined as a medium moisture content range, with the corresponding baseline operation width being the middle value; and when the soil volumetric moisture content is above 35%, it is defined as a high moisture content range, with the corresponding baseline operation width being the minimum value. The specific working width is determined based on the model of the tiller and the operational requirements. For example, for tillers with an adjustable working width range of 1.5 to 3.5 meters, the baseline working width corresponds to 3.5 meters for low moisture content, 2.5 meters for medium moisture content, and 1.5 meters for high moisture content. The introduction of an adjustment coefficient allows for continuous and smooth adjustment of the working width based on the baseline value. The adjustment coefficient is calculated through linear interpolation based on the specific soil moisture content within the threshold range.

[0031] The dynamic width adjustment mechanism for the plow body is driven by the control system to achieve width adjustment. The adjustment mechanism is either electrically or hydraulically driven and includes a drive motor or hydraulic cylinder, a reduction gear, a lead screw and nut transmission mechanism, and a position feedback device. The drive motor is a servo motor or stepper motor, possessing precise position control capabilities and rapid response characteristics. The reduction gear converts the high-speed rotation of the motor into a low-speed, high-torque output, driving the lead screw and nut transmission mechanism to achieve horizontal displacement of the plow body. The position feedback device uses a rotary encoder or linear displacement sensor to detect the actual position of the plow body in real time and feeds the position information back to the control system, forming a closed-loop position control. The response speed of the adjustment mechanism should meet the dynamic adjustment requirements; the time from receiving the adjustment command to completing the width adjustment should not exceed 2 seconds.

[0032] Regarding the connections between the various parts of the system, the soil moisture sensor array is connected to the data acquisition module via signal cables, and each sensor node is connected to the data acquisition circuit in parallel. The wireless communication unit of the data acquisition and wireless transmission module is connected to the wireless communication interface of the control system to achieve wireless data transmission. The output interface of the control system is connected to the drive controller of the dynamic adjustment mechanism for the plow body's working width, and the control system controls the operation of the drive motor by sending pulse signals or analog voltage signals. The signal line of the position feedback device is connected to the input interface of the control system, forming a position closed-loop control circuit.

[0033] Next, the system's operation process will be described in detail from a workflow perspective. The workflow of this embodiment includes three main stages: soil moisture perception and data acquisition, data processing and decision calculation, and operation width adjustment execution. Each stage is executed sequentially according to a time sequence, forming a complete adaptive adjustment cycle.

[0034] During the soil moisture perception and data acquisition phase, each sensor node in the soil moisture sensor array simultaneously measures the soil moisture content in front of the plow. The measuring elements inside the sensor nodes sense changes in the soil dielectric constant and convert this physical quantity into an electrical signal proportional to the soil moisture content. Multiple sensor nodes are distributed in different spatial locations, enabling the acquisition of spatial distribution information of soil moisture content along the working width. The data acquisition and wireless transmission module sequentially scans each sensor node according to a set acquisition frequency, reading the measurement data output by each node. The microprocessor preprocesses the acquired raw data, including data format conversion, outlier removal, and moving average calculation. After preprocessing, the wireless communication unit sends the encapsulated data frame to the control system. The data frame contains information such as the sensor node number, measured value, acquisition timestamp, and checksum. In a complete work cycle, the data acquisition frequency is set to 5 times per second, meaning a full sensor array data acquisition is completed every 200 milliseconds.

[0035] During the data processing and decision-making stage, the control system receives soil moisture content data frames from the data acquisition module. The control system first performs integrity verification on the received data, determining whether the data frame was transmitted correctly by comparing the checksum. If the data frame verification fails, the frame is discarded and a retransmission is requested. If the data frame verification succeeds, the data frame is parsed to extract the measurement values ​​from each sensor node. The control system further performs spatial aggregation processing on the measurement values ​​from multiple sensor nodes to calculate the representative value of soil moisture content within the current operating area. Spatial aggregation processing can employ methods such as arithmetic mean, weighted average, or regional integration. In a preferred embodiment, a weighted average algorithm is used, assigning higher weights to sensor nodes closer to the center of the plow and lower weights to sensor nodes closer to the edges. The formula for calculating the weighted average is: The weighted average soil moisture content is equal to the sum of the measured values ​​of each sensor node multiplied by their corresponding weight coefficients, and then divided by the sum of the weight coefficients.

[0036] After spatial aggregation processing, the control system uses the calculated weighted average soil moisture content as a representative indicator of the current soil moisture state and inputs it into the soil moisture-operating width mapping model to calculate the operating width. The mapping model first compares the input soil moisture content with a preset threshold interval boundary to determine the threshold interval in which the current soil moisture content falls. Then, it reads the corresponding baseline operating width value based on the threshold interval and calculates an adjustment coefficient based on the specific soil moisture content within the interval. Finally, it multiplies the baseline operating width value by the adjustment coefficient to obtain the target operating width value. In a specific calculation example, assuming the weighted average soil moisture content is 28%, falling within the medium moisture content range of 20% to 35%, the corresponding baseline operating width is 2.5 meters. The adjustment coefficient, calculated based on the moisture content's position within the interval, is 0.8, so the target operating width is 2.5 multiplied by 0.8, equal to 2.0 meters. The control system compares the calculated target operating width value with the currently measured operating width of the plow. If the difference between the two exceeds a preset adjustment threshold, the operating width adjustment process is triggered.

[0037] During the working width adjustment phase, the control system sends adjustment commands to the dynamic adjustment mechanism of the plow body. These commands include parameters such as the target working width value, adjustment direction, and adjustment speed. Upon receiving the commands, the drive controller starts the drive motor or hydraulic cylinder, driving the plow body to adjust its horizontal position according to the commands. The position feedback device detects the actual position of the plow body in real time and feeds the position signal back to the control system. The control system uses a PID control algorithm or a fuzzy control algorithm to calculate the control quantity based on the position deviation, dynamically adjusting the speed of the drive motor or the flow rate of the hydraulic cylinder to achieve smooth and precise movement of the plow body. When the position feedback device detects that the actual position of the plow body has reached the target working width value, the control system stops sending drive signals, and the adjustment mechanism remains in its current position, completing the working width adjustment process. After adjustment, the control system records the adjustment time, the working width values ​​before and after adjustment, and the corresponding soil moisture content data for subsequent data analysis and system optimization.

[0038] The entire workflow is continuously cyclical, constantly sensing soil moisture, processing data for decision-making, and adjusting the working width during tillage operations to achieve real-time adaptation to changes in soil conditions. Through this technical solution, when soil moisture content is too high, the system automatically reduces the working width to lower the risk of soil compaction and protect soil structure; when soil moisture content is too low, the system automatically increases the working width to improve tillage efficiency and reduce energy consumption.

[0039] Example 2 Based on Example 1, this example provides a modified implementation scheme for complex plots and extreme soil conditions, focusing on solving the problem of uneven spatial distribution of soil moisture and adaptive adjustment under extreme water content conditions.

[0040] In terms of system architecture, the soil moisture sensor array in this embodiment adopts a matrix distribution layout instead of the linear layout in Embodiment 1. The sensor array contains multiple sensor nodes, increasing the number of nodes to 15 to 30, covering a larger area in front of the work site. The sensor nodes are arranged in a row-column matrix, with row and column spacing set according to the characteristics of the plot and operational requirements, generally 30 to 60 centimeters. The matrix layout can acquire more comprehensive spatial distribution information of soil moisture, providing data support for zonal adjustment decisions. In one specific implementation, the sensor array adopts a 6-row, 5-column matrix layout, containing a total of 30 sensor nodes, covering a soil area with a width of 6 meters and a depth of 3 meters.

[0041] In terms of data processing and decision-making algorithms, this embodiment introduces a spatial distribution feature analysis module to extract and analyze the spatial distribution characteristics of soil moisture. The spatial distribution feature analysis module first performs gridding processing on the raw data collected by the sensor array, forming a two-dimensional grid map of the spatial distribution of soil moisture content. Then, it calculates statistical characteristic indicators such as the mean, variance, maximum, minimum, and gradient in the grid map. Based on these characteristic indicators, the system can determine whether the soil moisture distribution in the current working area is uniform or non-uniform, and the specific pattern of non-uniform distribution. The system adopts different adjustment strategies according to different distribution characteristics: when the spatial distribution of soil moisture is relatively uniform, the single-width overall adjustment strategy in Embodiment 1 is adopted; when the spatial distribution of soil moisture exhibits obvious gradient characteristics, the system calculates the corresponding target working width according to the actual moisture content of different areas, forming a zonal adjustment scheme.

[0042] The specific implementation of the zonal adjustment scheme is as follows: The control system divides the area covered by the sensor array into multiple sub-regions, each corresponding to a portion of the plow body's width. For example, the plow body's working width is divided into three sections: front, middle, and rear, each corresponding to a sensor node at a specific position in the sensor array. The control system calculates the representative soil moisture content value for each section, and then uses this representative value to look up a table or calculate the target working width for that section. Finally, the control system issues zonal adjustment commands to the dynamic adjustment mechanism for the plow body's working width, allowing each section to adjust its width independently, thus achieving fine-grained adjustment of different working widths for different positions on the plow body. This zonal adjustment scheme is particularly suitable for fields with significant spatial variations in soil moisture and can better adapt to complex and changing field environments.

[0043] In handling extreme moisture content conditions, this embodiment adds an extreme condition protection module. When the soil moisture content exceeds a preset safety upper limit threshold, the system determines that the current working conditions are not suitable for continued cultivation, issues an alarm signal, and automatically reduces the working speed or suspends the operation, waiting for the soil conditions to improve before resuming work. When the soil moisture content is below a preset drought threshold, the system activates a dust prevention protection mechanism, spraying an appropriate amount of mist water in the working area to improve soil moisture while reducing dust. When the soil moisture content is within the normal range but the rate of change is too rapid, the system uses a smoothing filtering algorithm to preprocess the measurement data, avoiding frequent adjustments caused by instantaneous fluctuations and improving system stability.

[0044] Furthermore, the control system in this embodiment incorporates a self-learning optimization module. This module records soil moisture content data, operation width adjustment data, and operation effect evaluation data for each operation. Through analysis and mining of historical data, the system can automatically optimize the parameters of the soil moisture-operation width mapping model, improving the model's adaptability and accuracy. The self-learning optimization module employs machine learning algorithms, including linear regression, decision trees, or neural networks, to continuously update model parameters based on new operation data. The introduction of this self-learning optimization mechanism enables the system to adapt to the unique characteristics of different plots, achieving long-term continuous performance optimization.

[0045] Example 3 This embodiment provides an integrated implementation plan for the needs of large-scale farms and intelligent agricultural management, focusing on the integration of the system with an agricultural information management platform and the functions of remote monitoring and intelligent scheduling.

[0046] In terms of system architecture, this embodiment adds a remote communication module and cloud management platform access functionality to the existing embodiments one and two. The remote communication module uses 4G or 5G mobile communication networks or satellite communication to achieve real-time data communication between the ploughing machine and the remote server. The remote communication module uploads soil moisture content data, working width data, working efficiency data, and equipment status data to the cloud management platform, while simultaneously receiving work task instructions and parameter configuration updates from the cloud management platform. The hardware platform of the control system is upgraded to an intelligent controller with edge computing capabilities, enabling it to perform data processing and decision calculations locally while simultaneously synchronizing key data to the cloud for in-depth analysis and long-term storage.

[0047] The cloud-based management platform is a comprehensive management software for large-scale farms and agricultural service enterprises, providing functions such as plot management, operation planning, real-time monitoring, data analysis, and decision support. The plot management function establishes a spatial information database for farmland, recording soil type, historical tillage records, soil fertility data, and historical soil moisture changes for each plot. The operation planning function generates mechanical operation tasks based on plot information and crop planting plans, and distributes these tasks to the tillage machine control system at the work site. The real-time monitoring function displays key parameters such as the location, operating status, soil moisture, and working width of all tillage machines in operation through a data visualization interface, allowing managers to monitor field operations in real time from a remote monitoring center. The data analysis function performs statistical, trend, and correlation analysis on massive amounts of operational data to uncover the inherent patterns between soil moisture and operational effectiveness, providing data support for optimizing operational parameters and developing scientific farming plans. The decision support function, based on machine learning and artificial intelligence algorithms, automatically generates operational parameter optimization suggestions and anomaly warning information to assist managers in making scientific decisions.

[0048] Regarding the adaptive adjustment function of work width, this embodiment combines a regional adjustment strategy with global optimization scheduling. The regional adjustment strategy refers to implementing differentiated work width adjustments within the same plot based on the spatial distribution characteristics of soil moisture; this strategy has been described in detail in Embodiment 2. Global optimization scheduling refers to coordinating and optimizing among multiple plots or work units to maximize overall work efficiency. The specific implementation is as follows: The cloud management platform aggregates and analyzes soil moisture prediction data, crop growth cycle data, and agricultural machinery scheduling data for each plot to generate a globally optimal work scheduling plan. The scheduling plan includes the optimal work time window, recommended work width range, and work priority ranking for each plot. After receiving the scheduling plan from the cloud, the control system dynamically adjusts the work width within the recommended range, combining it with locally sensed real-time soil moisture data. If there is a significant deviation between the locally sensed data and the cloud prediction data, the system adjusts in real-time based on the locally sensed data to ensure that work quality is not affected by prediction errors.

[0049] This embodiment also introduces a multi-machine collaborative operation function, supporting multiple ploughing machines equipped with adaptive adjustment systems to work collaboratively on the same or adjacent plots. In the multi-machine collaborative operation mode, the system realizes information exchange between agricultural implements through vehicle-to-everything (V2X) communication technology. Each ploughing machine shares its working position, working width, and soil moisture data with neighboring machines in real time. Based on the position and status information of each machine, the collaborative operation controller calculates a path planning scheme to avoid collisions and achieve optimal work coverage, guiding each machine to work collaboratively according to the optimal path and optimal spacing. In terms of soil moisture perception, the multi-machine collaborative operation mode combines the sensor arrays of multiple ploughing machines to form a larger-scale soil moisture monitoring network, obtaining more comprehensive plot moisture information. The collaborative operation controller fuses the moisture data from multiple ploughing machines to generate a high-resolution soil moisture distribution map, providing more accurate data support for refined operations.

[0050] Regarding data security and system reliability, this embodiment employs a multi-layered protection mechanism to ensure the safe and stable operation of the system. Communication data uses an encrypted transmission protocol to prevent data theft or tampering during transmission. The control system is equipped with a watchdog timer and anomaly detection mechanism, automatically restarting or entering safe mode when a program crash or hardware failure is detected. Redundant verification and confirmation mechanisms are implemented for critical operations to prevent security incidents caused by misoperation. System operation logs and operation records are automatically saved to local storage and backed up in the cloud, supporting fault tracing and responsibility determination.

[0051] Through the description of the above three embodiments, the technical solution of the adaptive adjustment method and system for tillage machine operating width based on soil moisture proposed in this invention can be fully understood. This invention introduces soil moisture content as a core sensing parameter, constructs a soil moisture-operating width mapping model, and realizes dynamic adaptive adjustment of tillage operating width, effectively solving the technical problem that existing technologies cannot achieve refined and adaptive operation due to a lack of soil moisture sensing capabilities. Embodiment 1 describes the basic technical solution and core workflow; Embodiment 2 provides a modified implementation scheme for complex plots and extreme conditions; and Embodiment 3 provides an integrated implementation scheme for large-scale farms and intelligent agricultural management needs. The three embodiments complement each other and gradually expand to form a complete technical solution system that can meet the needs of different application scenarios.

[0052] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention. Therefore, the embodiments should be regarded as exemplary and non-limiting in all respects.

[0053] Furthermore, it should be understood that although this specification describes embodiments, not every embodiment contains only one independent technical solution. This narrative style is merely for clarity. Those skilled in the art should consider the specification as a whole, and the technical solutions in each embodiment can also be appropriately combined to form other embodiments that can be understood by those skilled in the art.

Claims

1. A method for adaptive adjustment of the working width of a ploughing machine based on soil moisture, characterized in that, Includes the following steps: S1. Soil moisture perception steps: Soil moisture content data is collected in real time through an array of soil moisture sensors deployed in front of the plow body; S2. Soil moisture data transmission step: The soil moisture content data is transmitted to the control system in real time via a wireless transmission module; S3. Mapping Model Calling Step: The control system calls the internally stored soil moisture operation width mapping relationship model based on the received soil moisture content data to calculate the target operation width value; S4. Working width adjustment step: The control system sends an adjustment command to the plow body working width dynamic adjustment mechanism to drive the plow body to adjust to the target working width; S5. High moisture content adjustment step: When the soil moisture content data exceeds the preset high moisture content threshold, the control system triggers the width reduction control strategy, calculates the width reduction range, and issues a width reduction command. S6. Low moisture content adjustment step: When the soil moisture content data is lower than the preset low moisture content threshold, the control system triggers the widening control strategy, calculates the widening range, and issues a widening command.

2. A tillage machine operating width adaptive adjustment system based on soil moisture, characterized in that, include: A soil moisture sensor array is set in front of the plow body to collect soil moisture content data in real time; A wireless transmission module, connected to the soil moisture sensor array, is used to send soil moisture content data to the control system in real time; a control system, connected to the wireless transmission module, internally stores a soil moisture operation width mapping model, used to calculate the target operation width value based on the soil moisture content data and issue adjustment commands; a plow body operation width dynamic adjustment mechanism, connected to the control system, is used to drive the plow body to adjust the operation width according to the adjustment commands.

3. The method as described in claim 1, characterized in that, The soil moisture sensor array includes multiple soil moisture content sensor nodes, which are arranged linearly or in a matrix on the working path at the front end of the plow.

4. The method or system as described in claim 1 or 2, characterized in that, The soil moisture sensor array has no fewer than 5 sensor nodes, which are arranged linearly at equal intervals along the plowing direction.

5. The method as described in claim 1, characterized in that, The wireless transmission module uses a low-power wide-area network communication protocol to realize real-time transmission of sensor node data to the control system.

6. The method or system as claimed in claim 1 or 2, characterized in that, The soil moisture operation width mapping model includes a functional relationship between soil moisture content threshold range and corresponding operation width. The soil moisture content threshold range is divided into low moisture content range, moderate moisture content range and high moisture content range.

7. The method or system as claimed in claim 1 or 2, characterized in that, The plow body working width dynamic adjustment mechanism is driven by electricity or hydraulically and is equipped with a position feedback device to monitor the actual working width of the plow body in real time and feed the position information back to the control system to form a closed-loop control.

8. The method as described in claim 1, characterized in that, In step S5, the reduction in width is directly proportional to the degree to which the soil moisture content exceeds the high moisture content threshold. When the soil moisture content reaches or exceeds the preset maximum moisture content threshold, the working width is adjusted to the minimum allowable value.

9. The method as described in claim 1, characterized in that, In step S6, the widening range is directly proportional to the degree to which the soil moisture content is below the low moisture content threshold. When the soil moisture content is too low, the working width is adjusted to the maximum allowable value.

10. The system as described in claim 2, characterized in that, The control system also includes a data verification module, which uses a cyclic redundancy check mechanism to verify the integrity of the transmitted data.

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