An Adaptive Parameter Control System and Method for Terraced Rice Transplanters Based on Full Hydraulic Transmission

Abstract

This invention discloses an adaptive parameter control system and method for a terraced rice transplanter based on full hydraulic transmission, specifically relating to the field of hydraulic parameter control technology. It addresses the technical problem of dynamic power competition between the walking system and the transplanting device in a fully hydraulically driven rice transplanter operating in terraced fields. By real-time monitoring of the working pressure signals of the walking motor and the transplanting device, the divergence of the envelopes of the two pressure signals is calculated to identify the power competition state of the system. When power competition is detected, the required power of the two actuators is dynamically calculated, and a hydraulic pump displacement distribution coefficient is generated. Furthermore, by integrating the interaction characteristics of the main pump outlet pressure and output flow signals and the transmission characteristics of the actuator pressure signals, a dynamic balance of the system is generated, and the displacement distribution coefficient is corrected. Finally, based on the corrected coefficient, the opening of the proportional valves of the walking motor and the transplanting device is synchronously adjusted to achieve precise power distribution and dynamic balance control of the hydraulic system.

Description

Technical Field

[0001] This invention relates to the field of hydraulic parameter control technology, and more specifically, to an adaptive parameter control system and method for a terraced rice transplanter based on full hydraulic transmission. Background Technology

[0002] Fully hydraulic transmission systems are widely used in terraced field machinery, achieving power transmission and control of the working device through the coordinated action of hydraulic pumps, valves, and actuators. In existing fully hydraulic transmission designs for rice transplanters, the hydraulic parameters of each actuator are typically adjusted independently based on fixed working conditions or simple logic. For example, the flow rate of the travel motor is controlled according to a preset speed, or the pressure of the planting unit is adjusted based on soil resistance. This control method can maintain basic operations in flat fields, but in the complex terrain of terraced fields, due to drastic load fluctuations and the coupling of multiple mechanism actions, existing technology struggles to achieve system-level dynamic coordination.

[0003] In existing technologies, when fully hydraulic rice transplanters operate in terraced fields, there is a dynamic power competition problem between the walking system and the transplanting device. When the machine climbs a slope or encounters significant resistance, the walking motor of the walking system needs to increase hydraulic power to maintain stable movement. Meanwhile, the transplanting device is in the high-load phase of its work cycle. The superposition of the instantaneous power demands of the two can easily lead to a sudden drop in system pressure and disordered flow distribution. This not only causes operational quality problems such as walking instability and uneven transplanting depth, but may also lead to frequent overflows, efficiency degradation, and component overloads in the hydraulic system. Existing control strategies based on fixed priorities or local feedback cannot fundamentally solve this kind of global energy scheduling conflict. Summary of the Invention

[0004] In order to overcome the above-mentioned defects of the prior art, the present invention provides an adaptive parameter control system and method for a terraced rice transplanter based on full hydraulic transmission to solve the problems mentioned in the background art.

[0005] To achieve the above objectives, the present invention provides the following technical solution:

[0006] An adaptive parameter control method for a terraced rice transplanter based on full hydraulic transmission includes:

[0007] S1. Real-time monitoring of the working pressure of the walking motor and the working pressure of the insertion device;

[0008] S2. Calculate the divergence of the envelopes of the two pressure signals based on the working pressure of the walking motor and the working pressure of the insertion device. When the divergence exceeds the preset divergence threshold, it is determined that the full hydraulic transmission system has entered a power competition state.

[0009] S3. Under power competition conditions, calculate the required power of the walking motor and the required power of the insertion device respectively.

[0010] S4. Generate the hydraulic pump displacement distribution coefficient based on the ratio of the power required by the walking motor to the power required by the planting device.

[0011] S5. By integrating the interaction characteristics of the main pump outlet pressure and output flow signals and the transmission characteristics of the pressure signals of the travel motor and the insertion device, the dynamic balance of the full hydraulic transmission system is generated, thereby correcting the hydraulic pump displacement distribution coefficient.

[0012] S6. Based on the corrected hydraulic pump displacement distribution coefficient, synchronously adjust the opening of the proportional valve of the walking motor and the proportional valve of the insertion device.

[0013] Furthermore, real-time monitoring of the operating pressure of the walking motor and the operating pressure of the insertion device includes:

[0014] The working pressure of the walking motor is collected in real time by a first pressure sensor installed on the inlet oil circuit of the walking motor, and the working pressure of the planting device is collected in real time by a second pressure sensor installed on the working oil circuit of the planting device. The working pressure of the walking motor and the working pressure of the planting device are converted into electrical signals for output.

[0015] Furthermore, based on the working pressure of the walking motor and the working pressure of the insertion device, the divergence of the envelopes of the two pressure signals is calculated. When the divergence exceeds a preset divergence threshold, the fully hydraulic transmission system is determined to have entered a power competition state, including:

[0016] Envelope extraction was performed on the time-series data of the pressure signals of the walking motor working pressure and the implantation device working pressure to obtain the envelope sequence of the walking motor working pressure and the envelope sequence of the implantation device working pressure.

[0017] The working pressure envelope sequence of the walking motor and the working pressure envelope sequence of the insertion device are dynamically time-normalized and aligned to eliminate the effect of phase lag. The cumulative Euclidean distance between the two envelope sequences after alignment is calculated as a quantitative index characterizing their divergence.

[0018] The quantitative index is compared with a preset divergence threshold. When the quantitative index exceeds the preset divergence threshold for multiple consecutive sampling cycles, the fully hydraulic transmission system is determined to have entered a power competition state.

[0019] Furthermore, under power competition conditions, the required power of the walking motor and the required power of the insertion device are calculated separately, including:

[0020] The real-time working pressure of the walking motor and the real-time working pressure of the planting device are calculated based on the working pressure envelope sequence of the walking motor and the working pressure envelope sequence of the planting device, respectively.

[0021] The real-time speed of the walking motor and the real-time speed of the insertion device are obtained respectively.

[0022] The required flow rate of the walking motor is calculated by multiplying the geometric value of the displacement of the walking motor by the real-time speed value, and the required flow rate of the planting device is calculated by multiplying the geometric value of the displacement of the planting device by the real-time speed value.

[0023] The required power of the walking motor is obtained by multiplying its real-time operating pressure by its required flow rate, and the required power of the planting device is obtained by multiplying its real-time operating pressure by its required flow rate.

[0024] Furthermore, based on the ratio of the power required by the walking motor to the power required by the insertion device, a hydraulic pump displacement distribution coefficient is generated, including:

[0025] The total system power requirement is obtained by summing the power demand of the walking motor and the power demand of the insertion device.

[0026] Calculate the first proportion of the power demand of the walking motor to the total power demand of the system and the second proportion of the power demand of the insertion device to the total power demand of the system.

[0027] The larger of the first and second proportional values ​​is determined as the base value of the hydraulic pump displacement distribution coefficient.

[0028] The base value of the hydraulic pump displacement allocation coefficient is smoothed and filtered based on the stability of the main pump outlet pressure signal to generate the final hydraulic pump displacement allocation coefficient.

[0029] Furthermore, by integrating the interaction characteristics of the main pump outlet pressure and output flow signals with the transmission characteristics of the pressure signals from the travel motor and the insertion device, a dynamic balance of the entire hydraulic transmission system is generated, thereby correcting the hydraulic pump displacement distribution coefficient, including:

[0030] Analyze the fluctuation range of the main pump outlet pressure signal and the change trend of the output flow signal within the same evaluation period. When the fluctuation range decreases and the change trend is stable, it is determined that the energy supply stability is in a good state.

[0031] The mutual following characteristics of the working pressure signal of the walking motor and the working pressure signal of the insertion device within an evaluation cycle were analyzed. When the pressure changes of the two showed a significant alternating rise and fall pattern, it was determined that the disturbance intensity between the actuators was in a high state.

[0032] Based on the combination of the energy supply stability assessment results and the inter-agency disturbance intensity assessment results, the corresponding correction mode is selected from a variety of preset correction modes.

[0033] The hydraulic pump displacement allocation coefficient is corrected using the algorithm corresponding to the selected correction mode, and the corrected hydraulic pump displacement allocation coefficient is generated.

[0034] Furthermore, based on the combined state of the energy supply stability assessment result and the disturbance intensity assessment result between the actuators, the selection of the corresponding correction mode from a variety of preset correction modes is achieved in the following way:

[0035] A two-dimensional decision table is predefined, which contains combinations of different energy supply stability states and different perturbation intensity states between different actuators. Each state combination cell in the two-dimensional decision table is associated with a preset correction mode.

[0036] In actual operation, the energy supply stability status and the disturbance intensity status between the actuators obtained in real time are used as input query conditions. The corrective mode to be adopted is selected from the two-dimensional decision table through a lookup operation.

[0037] Furthermore, the hydraulic pump displacement distribution coefficient is corrected using the algorithm corresponding to the selected correction mode in the following way:

[0038] Each preset correction mode corresponds to a set of pre-set correction parameters and calculation rules. The calculation rules are mathematical operations that linearly scale or superimpose a fixed adjustment amount on the hydraulic pump displacement distribution coefficient.

[0039] After selecting the correction mode, the corresponding correction parameters and calculation rules are called to perform the corresponding mathematical operations on the current hydraulic pump displacement allocation coefficient, thereby generating the corrected hydraulic pump displacement allocation coefficient.

[0040] Furthermore, based on the corrected hydraulic pump displacement distribution coefficient, the opening of the proportional valve of the travel motor and the proportional valve of the insertion device are adjusted synchronously, including:

[0041] The corrected hydraulic pump displacement distribution coefficient is mapped to the target opening reference value of the travel motor proportional valve and the target opening reference value of the insertion device proportional valve.

[0042] Based on the current main pump outlet pressure, the target opening reference values ​​of the travel motor proportional valve and the insertion device proportional valve are finely adjusted by pressure compensation to generate the final target opening command of the travel motor proportional valve and the final target opening command of the insertion device proportional valve.

[0043] The final target opening command of the walking motor proportional valve and the final target opening command of the insertion device proportional valve are synchronously output to the corresponding proportional valve driver to drive the opening changes of the walking motor proportional valve and the insertion device proportional valve.

[0044] On the other hand, the present invention provides an adaptive parameter control system for a terraced rice transplanter based on full hydraulic transmission, comprising:

[0045] The status monitoring module is used to monitor the working pressure of the walking motor and the working pressure of the insertion device in real time.

[0046] The competition identification module is used to calculate the divergence of the envelopes of the two pressure signals based on the working pressure of the walking motor and the working pressure of the insertion device. When the divergence exceeds the preset divergence threshold, it is determined that the full hydraulic transmission system has entered a power competition state.

[0047] The power calculation module is used to calculate the power required by the walking motor and the power required by the insertion device under power competition conditions.

[0048] The coefficient generation module is used to generate the hydraulic pump displacement distribution coefficient based on the ratio of the power required by the walking motor to the power required by the insertion device.

[0049] The coefficient correction module is used to integrate the interaction characteristics of the main pump outlet pressure and output flow signals and the transmission characteristics of the pressure signals of the travel motor and the insertion device to generate the dynamic balance of the full hydraulic transmission system, thereby correcting the hydraulic pump displacement distribution coefficient.

[0050] The valve control adjustment module is used to synchronously adjust the opening of the proportional valve of the travel motor and the proportional valve of the insertion device based on the corrected hydraulic pump displacement distribution coefficient.

[0051] Compared with the prior art, the present invention has the following beneficial effects:

[0052] 1. By monitoring the working pressure of the walking motor and the planting device in real time, and dynamically identifying the power competition state of the system based on the divergence of the two pressure signal envelopes, the hydraulic power competition phenomenon between the walking system and the planting device can be captured in a timely manner under the complex working conditions of terraced fields. By calculating the required power of the two actuators and generating the hydraulic pump displacement distribution coefficient, the system-level dynamic energy scheduling is realized. This effectively avoids the problems of sudden drop in system pressure and disordered flow distribution caused by fixed parameters or simple priority strategies in traditional control. It can adaptively adjust the power distribution strategy according to the real-time working conditions, significantly improving the coordination and response capability of the all-hydraulic transmission system under variable load conditions.

[0053] 2. By integrating the multi-parameter interactive characteristics of the main pump outlet pressure, output flow signal, and actuator pressure signal, a dynamic balance of the system is generated and the displacement distribution coefficient is corrected. This enables the system to comprehensively consider the stability of energy supply and the disturbance intensity between actuators. Based on the corrected coefficient, the opening of the proportional valve of the walking motor and the planting device is adjusted synchronously, realizing the precise distribution of hydraulic power and the coordinated control of the actuators. This not only improves the system pressure stability and flow control accuracy, but also significantly reduces the overflow loss and energy consumption of the hydraulic system, extends the service life of components, and provides a more reliable power guarantee for rice planting operations in terraced fields. Attached Figure Description

[0054] Figure 1 This is a flowchart of an adaptive parameter control method for a terraced rice transplanter based on full hydraulic transmission, according to the present invention.

[0055] Figure 2 This is a schematic diagram of the adaptive parameter control system for a terraced rice transplanter based on full hydraulic transmission, according to the present invention. Detailed Implementation

[0056] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0057] Example 1: Figure 1 The flowchart of an adaptive parameter control method for a terraced rice transplanter based on full hydraulic transmission according to the present invention is given, including:

[0058] S1. Real-time monitoring of the working pressure of the walking motor and the working pressure of the insertion device;

[0059] S2. Calculate the divergence of the envelopes of the two pressure signals based on the working pressure of the walking motor and the working pressure of the insertion device. When the divergence exceeds the preset divergence threshold, it is determined that the full hydraulic transmission system has entered a power competition state.

[0060] S3. Under power competition conditions, calculate the required power of the walking motor and the required power of the insertion device respectively.

[0061] S4. Generate the hydraulic pump displacement distribution coefficient based on the ratio of the power required by the walking motor to the power required by the planting device.

[0062] S5. By integrating the interaction characteristics of the main pump outlet pressure and output flow signals and the transmission characteristics of the pressure signals of the travel motor and the insertion device, the dynamic balance of the full hydraulic transmission system is generated, thereby correcting the hydraulic pump displacement distribution coefficient.

[0063] S6. Based on the corrected hydraulic pump displacement distribution coefficient, synchronously adjust the opening of the proportional valve of the walking motor and the proportional valve of the insertion device.

[0064] To achieve real-time monitoring of the operating pressure of the travel motor and the insertion device, the following specific method is adopted. A first pressure sensor is installed on the inlet oil line of the travel motor. This first pressure sensor is preferably a piezoresistive pressure sensor, and its installation position should be as close as possible to the oil inlet of the travel motor to accurately sense the hydraulic oil pressure entering the travel motor and avoid the significant impact of pipeline pressure loss on the measured value. The first pressure sensor converts the sensed hydraulic pressure signal into a weak electrical signal at the millivolt level through its internal sensitive element (such as a silicon strain gauge). After the electrical signal is initially amplified and conditioned by the built-in amplifier circuit of the sensor, the output is a standardized analog voltage signal, such as a signal of 0 volts to 5 volts or 4 mA to 20 mA. This signal corresponds to the real-time operating pressure of the travel motor.

[0065] Similarly, a second pressure sensor is installed in the working oil circuit of the implantation device to monitor the hydraulic oil pressure driving the implantation action. The selection and installation principles of the second pressure sensor are the same as those of the first pressure sensor, ensuring that it can respond quickly and accurately to pressure changes within the working chamber of the implantation device. The second pressure sensor also converts the detected working pressure of the implantation device into a standard electrical signal output proportional to it.

[0066] The analog electrical signals output by the two pressure sensors are transmitted to the analog input port of the controller. The integrated analog-to-digital converter (ADC) within the controller synchronously samples and quantizes these two continuous analog voltage signals at a pre-set sampling frequency, such as 1000 Hz, converting them into discrete digital sequences. This sampling frequency must be at least twice the highest frequency component of the hydraulic system pressure fluctuations to satisfy the Nyquist sampling theorem and ensure that dynamic pressure information is not lost. After the analog-to-digital conversion, the working pressure of the travel motor and the working pressure of the insertion device are represented as a series of digital quantities that change over time. These digital quantities are stored in the controller's memory buffer, providing the raw data basis for signal processing and analysis in subsequent steps. Through this method, high-frequency, high-precision synchronous monitoring and data acquisition of the working pressure of the two key actuators are achieved.

[0067] The range selection of the first and second pressure sensors must cover the possible range of their respective working pressures. For example, if the working pressure of the walking motor may vary between 5 MPa and 25 MPa, then the range of the first pressure sensor should be selected from 0 MPa to 40 MPa, leaving a certain overload margin. Their accuracy class should be no less than 0.5% to ensure measurement accuracy. Shielded cables must be used for signal transmission lines to reduce the impact of on-site electromagnetic interference on weak sensor signals.

[0068] The analog input ports of the controller typically have programmable gain amplifiers that can be adjusted according to the range of the sensor output signal to fully utilize the resolution of the analog-to-digital converter (ADC). For example, a 16-bit ADC can convert an input voltage of 0 to 5 volts into a digital value between 0 and 65535. The controller can perform preliminary digital filtering on the sampled digital sequence, such as using a first-order low-pass filter to remove high-frequency noise introduced during sampling. The cutoff frequency of the filter can be set according to the main operating frequency bandwidth of the hydraulic system, for example, 100 Hz, thereby suppressing noise interference while retaining effective pressure fluctuation information. The digital sequence of the walking motor's working pressure and the digital sequence of the insertion device's working pressure, after the above processing, become reliable data sources for subsequent calculations and analyses.

[0069] The installation of pressure sensors requires ensuring full contact between the sensing surface and the hydraulic oil to prevent air bubbles or impurities from affecting measurement accuracy. During installation, specialized tools must be used to tighten the sensors according to the torque requirements provided by the sensor manufacturer. For example, a torque wrench should be used to install the sensor at a torque of 20 N·m to ensure a tight seal and prevent overtightening that could damage the sensor threads. A damping connector or miniature accumulator is typically installed between the sensor and the hydraulic circuit to mitigate damage to the sensor caused by pressure surges in the hydraulic system; for example, a miniature accumulator with a volume of 1 cubic centimeter may be used.

[0070] The controller's sampling program needs to be configured with an appropriate sampling hold time, such as 2 microseconds, to ensure that the input voltage remains stable during sampling. The analog-to-digital converter's reference voltage needs to remain stable, with fluctuations controlled within ±0.1%, for example, using a 2.5V reference voltage source with 0.05% accuracy. Zero-point and full-scale calibrations are required for the sampled data. Zero-point calibration can be performed with the system depressurized, adjusting the sensor output value to zero. Full-scale calibration uses a standard pressure source to apply a known pressure value; for example, using a 0.1-accuracy pressure calibrator to apply a standard pressure of 20 MPa, adjusting the gain to match the standard value.

[0071] For data storage, the controller needs to be configured with a sufficiently large data buffer, such as a circular buffer of size 1024, to store pressure data collected within the last 1.024 seconds. The data storage format can use 16-bit integers to save storage space while ensuring sufficient data accuracy. When long-term data recording is required, the data can be transmitted to an external storage device via a communication interface, for example, at a rate of 1000 samples per second via the CAN bus to the onboard data logger for storage.

[0072] Based on the digital sequences of the working pressure of the walking motor and the insertion device obtained in the aforementioned steps, the divergence of the pressure signal envelope and the determination of the power competition state are performed. First, envelope extraction is performed on the time-series data of the two pressure signals to obtain envelope sequences representing the signal change trends. Envelope extraction is implemented using the moving extremum method. The specific process is as follows: For each pressure signal sequence, a sliding time window of length N is set, for example, N is 50 sampling points (corresponding to a 50-millisecond time window). Local maxima and local minima of the pressure are found within the window. Then, these extrema are connected using linear interpolation to form upper and lower envelopes respectively. Finally, the arithmetic mean of the upper and lower envelopes is taken as the envelope value at that moment. The sliding window moves forward with a fixed step size, for example, moving one sampling point each time, repeating the above extremum search and interpolation process, ultimately generating complete working pressure envelope sequences for the walking motor and the insertion device.

[0073] After obtaining the two envelope sequences, dynamic time warping alignment is required to eliminate potential phase lag between the two signals. Dynamic time warping is achieved by constructing a cumulative distance matrix between the two sequences. The rows of the matrix correspond to the time points of the travel motor working pressure envelope sequence, and the columns correspond to the time points of the insertion device working pressure envelope sequence. Each element of the matrix represents the Euclidean distance between the two sequences at that point. The optimal alignment path is found by identifying the path from the top left corner to the bottom right corner of the matrix, minimizing the sum of distances between points along the path. The two sequences are then resampled and matched along this path to align their time axes and eliminate the phase difference caused by the hydraulic system response delay. The slope of the path is limited to between 0.5 and 2 to ensure that the alignment does not involve excessive stretching or compression.

[0074] After alignment, the cumulative Euclidean distance between the two envelope sequences is calculated as a quantitative indicator of divergence. In the calculation, the two aligned envelope sequences are treated as two vectors, and the sum of the squares of the Euclidean distances between their corresponding points is calculated. The square root of this sum is then taken to obtain the total cumulative distance. A larger value indicates a greater difference in the changing trends of the two pressure signals, i.e., a higher degree of divergence. This quantitative indicator comprehensively reflects the intensity of load competition between the two actuators. To further improve the comparability of the indicators, the cumulative Euclidean distance can be divided by the sequence length to obtain the average Euclidean distance, which serves as a standardized indicator of divergence.

[0075] The preset divergence threshold is determined based on statistical analysis of a large amount of historical operating data. Under normal system conditions without power competition, multiple sets of working pressure envelope sequences of the walking motor and the insertion device are collected, and their cumulative Euclidean distance is calculated to obtain a set of benchmark values. The average of these benchmark values ​​plus twice the standard deviation is taken as the preset divergence threshold. For example, by analyzing 100 sets of normal data, the average distance is 150 and the standard deviation is 25, so the preset divergence threshold can be set to 200. This threshold setting method ensures that under normal operating conditions, the probability of the quantified index exceeding the threshold is less than 5%. To adapt to changes in system characteristics, a threshold update mechanism can be established, for example, by re-collecting pressure data under normal conditions every 100 hours of operation and updating the threshold benchmark.

[0076] The system employs a continuous threshold-exceeding logic to improve reliability in determining the state. The controller continuously monitors the comparison between a quantized indicator and a preset divergence threshold. Only when the quantized indicator exceeds the preset divergence threshold for multiple consecutive sampling periods (e.g., 5 sampling periods, corresponding to 5 milliseconds) is the fully hydraulic transmission system determined to have entered a power competition state. This design avoids misjudgments caused by instantaneous pressure fluctuations and improves the accuracy of state identification. The determination result is output as a Boolean flag; a true value indicates that the system has entered a power competition state, triggering subsequent power calculation and allocation adjustment processes.

[0077] During envelope extraction, special attention must be paid to handling boundary points. When the sliding window is located at the beginning or end of the signal sequence, a mirror expansion method is used to handle the boundaries, that is, symmetrical data points are copied at both ends of the signal to ensure that the window can calculate extreme values ​​normally at the signal boundaries. The size of the sliding window needs to be adjusted according to the characteristics of the pressure signal, and is generally 1 to 2 times the main fluctuation period of the system. For hydraulic systems, the main fluctuation frequency is usually between 10 Hz and 50 Hz, so the window size can be set in the range of 20 to 100 sampling points, for example, 50 sampling points can be selected according to the actual system characteristics.

[0078] The dynamic time warping algorithm is implemented using dynamic programming, with the recursive formula: the cumulative distance of the current grid point equals the Euclidean distance between the current points plus the minimum cumulative distance among adjacent grid points. The optimal path backtracking starts from the bottom right corner of the matrix and gradually moves towards the top left corner, selecting the adjacent grid point with the minimum cumulative distance each time, until the top left corner of the matrix is ​​reached. To ensure computational efficiency, a maximum curvature window limit can be set, for example, limiting the maximum deviation of the path from the diagonal to no more than one-quarter of the sequence length.

[0079] The calculation of quantitative indicators requires consideration of dimensional consistency. Both the working pressure envelope sequence of the walking motor and the working pressure envelope sequence of the insertion device have pressure dimensions, measured in megapascals (MPA). Therefore, the calculated Euclidean distance also has pressure dimensions, ensuring consistency in physical meaning. To further improve calculation accuracy, the two envelope sequences can be normalized before calculating the Euclidean distance, ensuring their values ​​are both between 0 and 1, thus eliminating the influence of amplitude differences on distance calculation.

[0080] The anomaly handling mechanism includes checking the validity of input data, such as whether the pressure value is within a reasonable range (0-40 MPa) and whether there are any abnormal jumps. When abnormal data is detected, the previous valid value can be used to replace it or the data can be re-acquired. For the dynamic time warping algorithm, when the length difference between two sequences is too large, preliminary coarse alignment can be performed first, followed by precise warping, to improve computational efficiency.

[0081] Once the system is determined to be in a power competition state, the calculation process for the required power of the walking motor and the insertion device is executed. First, based on the working pressure envelope sequences of the walking motor and the insertion device obtained in the previous steps, their real-time working pressure values ​​are calculated. The real-time working pressure is calculated using a moving window weighted average method. The most recent data points in the envelope sequence are selected for weighted average calculation; for example, the most recent 20 sampling points are selected. Data points closer to the current time are assigned higher weights, and the weight distribution adopts a linear decreasing method, with the nearest point having a weight of 1, the farthest point having a weight of 0.5, and intermediate points having weights determined by linear interpolation. This weighted averaging process reflects the real-time trend of pressure changes and effectively smooths random fluctuations, obtaining accurate and reliable real-time working pressure values ​​for the walking motor and the insertion device.

[0082] Real-time rotational speed is acquired through speed sensors mounted on the corresponding actuators. The real-time rotational speed of the travel motor is measured by an incremental encoder mounted on its output shaft. This encoder outputs a certain number of pulses per revolution, for example, 1024 pulses per revolution. The rotational speed is calculated by measuring the number of pulses per unit time, with a sampling frequency set to 1000 Hz. The real-time rotational speed of the insertion device is measured by a Hall effect sensor mounted on the insertion drive shaft. The sensor detects the frequency of magnets passing over the rotating component. Four magnets are evenly distributed on the rotating component. The rotational speed is calculated by measuring the time interval between the passage of two adjacent magnets. Both rotational speed signals are processed by a signal conditioning circuit before being sent to the controller's digital input port, with the sampling frequency synchronized with the pressure signal.

[0083] The calculation of required flow rate is based on the fundamental principle of hydraulic power transmission. The required flow rate of the travel motor is calculated by multiplying the geometric value of the travel motor's displacement by its real-time rotational speed. The geometric value of the travel motor's displacement refers to the theoretical oil displacement per revolution, a fixed parameter determined by the motor's design. For example, the geometric value of a certain model of travel motor is 28 ml per revolution. It is crucial to maintain unit consistency during calculation: convert the rotational speed to revolutions per second and the displacement to cubic meters per revolution, resulting in a flow rate in cubic meters per second. The required flow rate of the insertion device is calculated using the same method, multiplying the geometric value of the insertion device's displacement by its real-time rotational speed. For example, the geometric value of the insertion hydraulic motor's displacement is 16 ml per revolution. For ease of subsequent calculations, the flow rate unit is usually standardized to liters per minute (L / min), which can be converted by multiplying by 60,000.

[0084] The required power is calculated by multiplying the real-time operating pressure by the required flow rate. The required power of the traveling motor equals the product of its real-time operating pressure and its required flow rate, and the required power of the insertion device equals the product of its real-time operating pressure and its required flow rate. Special attention must be paid to the consistency of units during the calculation: pressure is measured in Pascals, flow rate in cubic meters per second, and the calculated power is measured in watts. In practical engineering applications, the power unit is usually converted to kilowatts, which can be done by dividing by 1000. To ensure calculation accuracy, all parameters are calculated using floating-point arithmetic, with pressure values ​​accurate to 0.1 MPa, flow rate values ​​accurate to 0.1 liters per minute, and power values ​​accurate to 0.1 kilowatts.

[0085] The efficiency of the hydraulic system needs to be considered during the calculation. The actual power demand also needs to take into account the volumetric efficiency and mechanical efficiency of the hydraulic components. A compensation coefficient greater than 1 can be multiplied by the final power value, for example, a value between 1.1 and 1.3. This coefficient is obtained through experimental determination. The method for determining the compensation coefficient is as follows: measure the ratio of the actual output power to the calculated power under standard operating conditions, and take the average value from multiple experiments as the compensation coefficient. For example, after conducting 10 experiments, if the measured ratios are 1.12, 1.15, 1.18, etc., take the arithmetic mean of 1.15 as the compensation coefficient.

[0086] All calculated parameters require range checks and validity verification. Real-time operating pressure values ​​should be between 0 and the system's rated pressure, e.g., 0 to 25 MPa; speed values ​​should be between 0 and the maximum permissible speed, e.g., 0 to 2000 rpm; calculated flow and power values ​​also have corresponding reasonable ranges. When any parameter is detected to be outside its reasonable range, an exception handling procedure should be triggered, replacing it with the previously valid value or a preset default value. For example, if the speed value exceeds 2000 rpm, the previously valid speed value should be used for calculation.

[0087] Data update and storage mechanisms ensure computational continuity. The required power value needs to be recalculated for each sampling period, and the results are stored in a circular buffer. The buffer size should be able to hold data for at least one working cycle, for example, storing data from the most recent 10 seconds, with 1000 sampling points per second, totaling 10,000 data points. The timestamp of the power calculation also needs to be recorded for time-aligned analysis with other parameters. Data storage is structured, with each data point containing a timestamp, the required power value for the walking motor, the required power value for the implantation device, and the intermediate parameter values ​​used in the calculation.

[0088] Based on the power requirements of the travel motor and the insertion device calculated in the preceding steps, the process of generating the hydraulic pump displacement allocation coefficient is executed. First, the total system power requirement is calculated by adding the power requirements of the travel motor and the insertion device. During the calculation, it is necessary to ensure that the units of the two power values ​​are consistent, for example, both converted to kilowatts before addition. The total system power requirement represents the total power output required by the hydraulic system at the current moment, providing a benchmark value for subsequent proportional calculations. When the sum of the two power requirements is detected to be less than a certain minimum threshold, for example, less than 0.5 kilowatts, it can be determined that the system is in an idling state. In this case, the default power allocation ratio is used, for example, both ratio values ​​are set to 0.5.

[0089] After obtaining the total system power demand, the proportions of the two power requirements are calculated separately. The first proportion is calculated by dividing the power demand of the walking motor by the total system power demand, and the second proportion is calculated by dividing the power demand of the insertion device by the total system power demand. A division-by-zero protection mechanism is incorporated into the calculation process. If the total system power demand is less than a certain minimum value, such as less than 0.1 kW, both proportions are set to the default value of 0.5. The calculated proportions are decimals between 0 and 1, representing the proportion of each actuator's power demand in the total demand. After calculation, the proportions need to be checked for reasonableness to ensure that each proportion is within the range of 0 to 1, and that the sum of the two proportions equals 1. A small calculation error is allowed, for example, within a range of ±0.001.

[0090] When determining the base value for the hydraulic pump displacement allocation coefficient, the first proportional value and the second proportional value are compared, and the larger one is selected as the base value. This selection logic ensures that the hydraulic pump displacement allocation prioritizes the actuators with higher power demands, avoiding insufficient power supply. For example, when the power demand of the travel motor accounts for 0.7 and the power demand of the insertion device accounts for 0.3, 0.7 is selected as the base value. This selection process needs to be executed once in each sampling period to ensure that the allocation coefficient can respond promptly to changes in power demand. Historical data also needs to be considered during the selection process. When the difference between the two proportional values ​​is very small, for example, less than 0.05, the previous base value is kept unchanged to avoid frequent switching.

[0091] When smoothing the baseline values, an adaptive filtering method based on the stability of the main pump outlet pressure signal is adopted. First, the fluctuation characteristics of the main pump outlet pressure signal over a recent period are analyzed; for example, the pressure variance of the most recent 100 sampling points is calculated as a stability index. The pressure variance is calculated using the standard deviation formula: first, the average of the 100 pressure values ​​is calculated; then, the sum of the squares of the differences between each pressure value and the average is calculated; finally, this is divided by 99 to obtain the variance value. When the pressure variance is small, it indicates that the system pressure is stable, and the filtering intensity can be appropriately reduced, with a smaller filtering time constant, such as 0.1 seconds. When the pressure variance is large, it indicates that the system pressure fluctuates significantly, requiring enhanced filtering; therefore, a larger filtering time constant is used, such as 0.5 seconds.

[0092] The smoothing filter employs a first-order low-pass filter algorithm. The calculation formula is: the current output value equals the previous output value plus the filter coefficient multiplied by the difference between the current input value and the previous output value. The filter coefficient is calculated based on the time constant and sampling period. For example, with a sampling period of 0.001 seconds and a time constant of 0.1 seconds, the filter coefficient equals 0.001 divided by 0.1, which equals 0.01. The filter coefficient is dynamically adjusted based on pressure stability, which is quantified by calculating the standard deviation of the main pump outlet pressure signal within a sliding time window. The calculation window length for the standard deviation is set to 2 to 3 times the main fluctuation period of the system; for example, for a 10 Hz pressure fluctuation, the window length is 200 sampling points.

[0093] When mapping the standard deviation to the filtering time constant, a piecewise linear function is used. For example, when the standard deviation is less than 0.5 MPa, the time constant is 0.1 seconds; when the standard deviation is between 0.5 MPa and 1.5 MPa, the time constant increases linearly from 0.1 seconds to 0.5 seconds; when the standard deviation is greater than 1.5 MPa, the time constant remains at 0.5 seconds. This adaptive mechanism ensures that the allocation coefficient responds quickly when the pressure is stable and changes smoothly when the pressure fluctuates. The initial value also needs to be handled during the filtering process. A default initial value, such as 0.5, is used when the system starts up, and a transition period is set during which the filtering intensity is gradually reduced.

[0094] The final hydraulic pump displacement allocation coefficient needs to be limited to a reasonable range, for example, a minimum of 0.3 and a maximum of 0.8. This range is determined based on the working characteristics of the hydraulic pump. When the calculated coefficient exceeds this range, a limiting process is used to restrict it to the boundary value. Simultaneously, the rate of change of the coefficient also needs to be limited, ensuring that the change within each sampling period does not exceed a certain maximum value, such as 0.05, to avoid drastic changes in the allocation coefficient causing system shocks. The rate of change is limited by comparing the current calculated value with the previous output value. When the difference exceeds the limit, the output value is equal to the previous output value plus the upper limit multiplied by the sign of the difference.

[0095] To ensure system reliability, an exception handling mechanism needs to be added to the calculation process. When an abnormal input parameter is detected, such as a negative or abnormally large demand power value, the previous valid value is used for calculation. Simultaneously, a coefficient validity check mechanism is established; if the allocation coefficients calculated for multiple consecutive cycles are the same and the system power demand changes, a recalculation process is triggered. All intermediate calculation results must be recorded in the data log, including the proportion value, base value, filter parameters, and final coefficient value, for subsequent analysis and debugging. The data log uses a circular buffer with a buffer size of 1000 records; each record contains a timestamp and all relevant parameter values.

[0096] Based on the hydraulic pump displacement allocation coefficient obtained from the aforementioned steps, and the real-time monitored main pump outlet pressure signal, output flow signal, travel motor working pressure signal, and insertion device working pressure signal, a dynamic equilibrium generation and coefficient correction process is performed. First, the fluctuation characteristics of the main pump outlet pressure signal within an evaluation period are analyzed. The evaluation period length is determined based on the system response characteristics; for example, 200 sampling points correspond to a 0.2-second time window. The fluctuation amplitude is quantified by calculating the standard deviation of the pressure signal within this time period. The standard deviation is calculated using an unbiased estimation formula: first, the arithmetic mean of 200 pressure values ​​is calculated; then, the sum of squared deviations of each pressure value from the mean is calculated; finally, this is divided by 199 to obtain the variance, and the square root is taken to obtain the standard deviation value. The stability of the trend is evaluated by calculating the autocorrelation coefficient of the first-order difference sequence of the pressure signal. An autocorrelation coefficient close to 0 indicates a stable trend, while a significant deviation from 0 indicates a trend change. When the pressure fluctuation amplitude is less than a set threshold, for example, a standard deviation less than 0.3 MPa, and the autocorrelation coefficient is within ±0.2, the energy supply stability is considered to be in a good state.

[0097] When analyzing the tracking characteristics of the working pressure signals of the walking motor and the insertion device, a cross-correlation coefficient analysis method is used. First, the two pressure signal sequences are time-aligned to eliminate the influence of measurement delay. Then, their cross-correlation coefficients at multiple time offsets are calculated. The cross-correlation coefficient is calculated using a standard formula, dividing the covariance of the two sequences by the product of their respective standard deviations. When the cross-correlation coefficients of the two pressure signals show a significant peak at zero time delay, and the peak coefficient value is greater than 0.7, they are considered to have good synchronization. When the cross-correlation coefficients show bimodal peaks at both positive and negative time delays, and the absolute values ​​of the peaks are both large, it indicates the existence of an alternating rise and fall pattern. The significance of the alternating rise and fall pattern is quantified by calculating the difference between the two peak coefficients. When the difference is greater than 0.3, it is determined to be a significant alternating rise and fall pattern, indicating that the disturbance intensity between the actuators is high.

[0098] Based on the combined state of the energy supply stability assessment and the inter-agency disturbance intensity assessment, a corresponding correction mode is selected from a set of preset correction modes. The selection of the correction mode is achieved through a two-dimensional decision table. The rows of this table correspond to the energy supply stability state (divided into good and poor levels), and the columns correspond to the inter-agency disturbance intensity state (divided into high and low levels), forming a 2×2 decision matrix. Each cell is associated with a preset correction mode number. For example, mode 1 is used when energy supply stability is good and disturbance intensity is low; mode 2 is used when stability is good but disturbance intensity is high; mode 3 is used when stability is poor but disturbance intensity is low; and mode 4 is used when both are poor. The state classification thresholds in the decision table are determined through statistical analysis of extensive experimental data. For example, the standard deviation threshold for good energy supply stability is set at 0.3 MPa, and the cross-correlation coefficient difference threshold for high disturbance intensity is set at 0.3.

[0099] Each correction mode corresponds to a set of pre-defined correction parameters and calculation rules. The correction parameters include a scaling factor and an adjustment amount. For example, mode 1 has a scaling factor of 1.0 and an adjustment amount of 0, meaning the original coefficient remains unchanged; mode 2 has a scaling factor of 0.9 and an adjustment amount of -0.02; mode 3 has a scaling factor of 1.1 and an adjustment amount of +0.03; and mode 4 has a scaling factor of 1.2 and an adjustment amount of +0.05. The calculation rules involve linearly scaling or superimposing a fixed adjustment amount on the hydraulic pump displacement distribution coefficient. The formula for linear scaling is that the new coefficient value equals the original coefficient value multiplied by the scaling factor plus the adjustment amount. The scaling factor and adjustment amount are determined based on system characteristic analysis and experimental verification. By testing the effects of different parameter values ​​under different operating conditions, the optimal parameter combination that restores system stability is selected.

[0100] When performing correction calculations, boundary condition handling must also be considered. The corrected coefficient values ​​need to be limited to an effective range, for example, a minimum of 0.3 and a maximum of 0.8. When the calculation result exceeds this range, amplitude limiting is used to restrict it to the boundary values. Simultaneously, the rate of change of the coefficients also needs to be limited, with the change within each sampling period not exceeding 0.05 to avoid excessive changes causing system shocks. The rate of change is limited by comparing the difference between the new calculated value and the value of the previous period. When the difference exceeds the limit, the previous period value plus the upper limit multiplied by the sign of the difference is used as the new output value.

[0101] To ensure the stability of the correction effect, a feedback adjustment mechanism is also required. After each correction operation, changes in the system state need to be monitored. If the system state does not improve or even deteriorates after the correction, the correction parameters need to be adjusted or a different correction mode needs to be switched. The improvement in state is evaluated by comparing the changes in pressure fluctuation amplitude and cross-correlation coefficient before and after the correction. For example, if the pressure standard deviation decreases and the peak value of the cross-correlation coefficient increases after the correction, the improvement is considered effective. Detailed logs of each correction operation are also recorded, including the coefficient values ​​before correction, the correction mode used, the correction parameters, the coefficient values ​​after correction, and changes in system state indicators. This log data is used for subsequent analysis and optimization of the correction strategy.

[0102] All correction parameters require regular calibration and updates to adapt to changes in system characteristics. The calibration cycle is determined based on system usage, for example, once every 100 hours of operation. During calibration, the effectiveness of each correction mode is tested under different operating conditions, and the correction parameter values ​​are adjusted based on the test results to achieve optimal correction. Calibration data processing employs least squares fitting to find the parameter combination that optimizes system stability. The calibration process includes establishing a test condition matrix covering different load conditions and operating modes, recording system response data under each test condition, analyzing the impact of different parameter combinations on system stability, and ultimately determining the optimal parameter values.

[0103] The construction of a two-dimensional decision table needs to consider the system's operating characteristics. The granularity of state classification can be adjusted according to actual needs. For example, energy supply stability can be divided into four levels: excellent, good, medium, and poor; disturbance intensity can be divided into four levels: none, weak, medium, and strong, forming a 4×4 decision matrix. The threshold for each state level is determined through cluster analysis. A large amount of state indicator data during normal operation is collected, and the k-means clustering algorithm is used to divide the data into several categories, with the category boundaries serving as the threshold for state classification. The correction mode settings in the decision table need to be fully validated to ensure that appropriate correction strategies are provided under various state combinations.

[0104] The implementation of correction operations needs to ensure real-time performance, and the computational complexity must be kept within the processor's capabilities. For linear scaling operations, fixed-point arithmetic is used to improve computational efficiency, and the coefficient values ​​are converted to Q-format fixed-point numbers for calculation. The adjustment is applied gradually, breaking down large adjustments into multiple small steps and applying them step by step to avoid impacting the system. After each correction operation, a stabilization waiting time needs to be set, for example, waiting for 10 sampling periods before performing the next correction, to ensure that the system has sufficient time to respond to the adjustment.

[0105] The anomaly handling mechanism includes checking the validity of input data. When a sensor malfunction or data anomaly is detected, the correction operation is paused and the system runs with default parameters. Fault detection is achieved by monitoring the rate of change and amplitude range of the signal; for example, a pressure signal with a rate of change exceeding 10 MPa / s or an amplitude exceeding 40 MPa is considered an anomaly. The system also includes a correction effect evaluation timeout mechanism. If the system status does not improve after multiple consecutive corrections, an expert intervention mode is triggered, requiring manual inspection of the system status and adjustment of the correction strategy.

[0106] Based on the obtained corrected hydraulic pump displacement distribution coefficient, a synchronous adjustment control process is executed for the proportional valves of the travel motor and the implantation device. First, the corrected hydraulic pump displacement distribution coefficient is mapped to the target opening reference values ​​of the two proportional valves. The mapping relationship is achieved using a piecewise linear function. Specifically, a correspondence is established between the hydraulic pump displacement distribution coefficient and the target opening reference value of the travel motor proportional valve. When the distribution coefficient is at its minimum value of 0.3, the corresponding opening reference value is 20%. When the distribution coefficient is at its maximum value of 0.8, the corresponding opening reference value is 80%. Intermediate values ​​are calculated using linear interpolation; for example, a distribution coefficient of 0.55 corresponds to an opening reference value of 50%. Similarly, a correspondence is established between the hydraulic pump displacement distribution coefficient and the target opening reference value of the implantation device proportional valve, but with the opposite mapping trend. When the distribution coefficient is 0.3, the corresponding opening reference value is 80%. When the distribution coefficient is 0.8, the corresponding opening reference value is 20%. Intermediate values ​​are also calculated using linear interpolation. This inverse mapping relationship ensures a reasonable distribution of hydraulic pump power between the two actuators. When the distribution coefficient increases, the travel motor receives more flow distribution, while the insertion device receives less flow distribution accordingly.

[0107] When fine-tuning the target opening reference value based on the current main pump outlet pressure, a pressure compensation coefficient is used to adjust the reference value. The pressure compensation coefficient is calculated based on the ratio of the main pump outlet pressure to the system's rated pressure, which is determined according to the hydraulic pump's specifications, for example, 25 MPa. When the main pump outlet pressure is lower than the rated pressure, the compensation coefficient is greater than 1, and the opening command is appropriately increased to compensate for insufficient pressure. When the main pump outlet pressure is higher than the rated pressure, the compensation coefficient is less than 1, and the opening command is appropriately decreased to avoid overload. The specific value of the compensation coefficient is determined by a lookup table. A pressure-compensation coefficient lookup table is pre-established based on experimental data of the hydraulic system's flow-pressure characteristics. For example, by testing the opening value required to achieve the rated flow at different pressures, the correspondence between pressure and compensation coefficient is established. For instance, when the pressure is 80% of the rated pressure, the compensation coefficient is 1.05; when the pressure is 100% of the rated pressure, the compensation coefficient is 1.0; and when the pressure is 120% of the rated pressure, the compensation coefficient is 0.95. This pressure compensation mechanism ensures that appropriate flow distribution is obtained under different working pressures.

[0108] When generating the final target opening command, the dynamic response characteristics of the proportional valve also need to be considered. A first-order inertial filter is applied to the compensated opening value. The filter time constant is determined based on the proportional valve's response speed. This is achieved by measuring the proportional valve's step response characteristics; for example, by applying a step signal to the proportional valve and measuring the response time required to increase the opening from 10% to 90%, and taking one-third of that response time as the filter time constant, for example, 0.05 seconds. This filtering process smooths changes in the opening command, preventing shocks to the proportional valve. Simultaneously, the opening command also needs to be limited to ensure its value remains within the proportional valve's effective operating range; for example, the minimum opening should not be less than 5%, and the maximum opening should not exceed 95%. The limiting value is determined based on the specific model and operating characteristics of the proportional valve, typically retaining a certain margin to ensure safe operation. The size of this margin is determined experimentally, for example, by testing the flow characteristics of the proportional valve at different openings to find the operating range with good flow control linearity.

[0109] When outputting the final target opening command to the proportional valve driver, a synchronous output mechanism is used to ensure that the two proportional valves operate simultaneously. The controller sends control commands to both proportional valve drivers simultaneously via the CAN bus or analog output module, with the command transmission interval controlled within 1 millisecond to ensure synchronization accuracy. The proportional valve driver generates a corresponding drive current based on the received opening command value. The drive current has a linear relationship with the opening command; for example, an opening command of 0% corresponds to a drive current of 4 mA, and an opening command of 100% corresponds to a drive current of 20 mA. The specific value of the drive current is determined based on the current-displacement characteristics of the proportional valve. By measuring the valve core displacement of the proportional valve under different drive currents, the correspondence between current and opening is established.

[0110] To ensure control accuracy, a closed-loop feedback mechanism is also required. A displacement sensor installed on the proportional valve continuously monitors the actual position of the valve core. The displacement sensor is an LVDT linear displacement sensor with a measurement accuracy of 0.1 mm. The detected value is compared with the target opening command to form a position closed-loop control. When the deviation between the actual position and the target position exceeds the allowable range, for example, exceeding 2%, the drive current output is adjusted to eliminate the deviation. The adjustment parameters of the position closed-loop control are tuned based on the dynamic characteristics of the proportional valve, using a PID control algorithm. The proportional coefficient, integral time, and derivative time are determined experimentally. For example, using the step response method, a step signal is given to the proportional valve, the response curve is observed, and the PID parameters are adjusted based on the overshoot and settling time. Ultimately, the proportional coefficient is set to 0.8, the integral time to 0.1 seconds, and the derivative time to 0.01 seconds.

[0111] The system also features an anomaly protection mechanism. When proportional valve sticking or abnormal response is detected, it automatically switches to safety mode. Anomaly detection is achieved by monitoring the relationship between valve spool position and drive current. A fault is identified when the drive current changes but the valve spool position remains unchanged or changes abnormally. Specific fault criteria are: a sticking fault is identified when the drive current change exceeds 10% while the valve spool position change is less than 1% for 100 milliseconds; an abnormal response is identified when the relationship between drive current and valve spool position deviates from the normal range by more than 20%. In safety mode, the opening command is gradually reduced to a safe position, for example, adjusting both proportional valves to 50% opening and issuing an alarm signal. Simultaneously, various parameters at the time of the fault are recorded, including the opening command value, drive current value, valve spool position value, and main pump pressure value, providing data support for fault analysis.

[0112] Example 2: Figure 2 A schematic diagram of an adaptive parameter control system for a terraced rice transplanter based on full hydraulic transmission is provided. The adaptive parameter control system for a terraced rice transplanter based on full hydraulic transmission includes:

[0113] The status monitoring module is used to monitor the working pressure of the walking motor and the working pressure of the insertion device in real time.

[0114] The competition identification module is used to calculate the divergence of the envelopes of the two pressure signals based on the working pressure of the walking motor and the working pressure of the insertion device. When the divergence exceeds the preset divergence threshold, it is determined that the full hydraulic transmission system has entered a power competition state.

[0115] The power calculation module is used to calculate the power required by the walking motor and the power required by the insertion device under power competition conditions.

[0116] The coefficient generation module is used to generate the hydraulic pump displacement distribution coefficient based on the ratio of the power required by the walking motor to the power required by the insertion device.

[0117] The coefficient correction module is used to integrate the interaction characteristics of the main pump outlet pressure and output flow signals and the transmission characteristics of the pressure signals of the travel motor and the insertion device to generate the dynamic balance of the full hydraulic transmission system, thereby correcting the hydraulic pump displacement distribution coefficient.

[0118] The valve control adjustment module is used to synchronously adjust the opening of the proportional valve of the travel motor and the proportional valve of the insertion device based on the corrected hydraulic pump displacement distribution coefficient.

[0119] All calculations involved in the embodiments are dimensionless numerical calculations, and the preset parameters and thresholds in the calculations are set by those skilled in the art according to the actual situation.

[0120] It should be noted that this invention can be deployed on the device itself to realize embedded applications, or it can run on a PC or other terminal with a user interface, thereby meeting various hardware environments and usage requirements.

[0121] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, as a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wireless or wired transmission; wired transmission methods include optical fiber, twisted pair, coaxial cable, etc.; wireless transmission includes infrared, microwave, etc. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center containing one or more sets of available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium. A semiconductor medium can be a solid-state drive.

[0122] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and modules described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0123] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of modules is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple modules or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or modules may be electrical, mechanical, or other forms.

[0124] The modules described as separate components may or may not be physically separate. The components shown as modules may or may not be physical modules; they may be located in one place or distributed across multiple network modules. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0125] In addition, the functional modules in the various embodiments of this application can be integrated into one processing module, or each module can exist physically separately, or two or more modules can be integrated into one module.

[0126] If the aforementioned functions are implemented as software functional modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0127] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0128] In conclusion, the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A self-adaptive parameter control method for a full-hydraulic transmission-based bench field transplanter, characterized in that, Comprise: S1, real-time monitoring of walking motor working pressure and implant device working pressure; S2, based on walking motor working pressure and implant device working pressure, the divergence degree of the envelope line of two pressure signals is calculated, when the divergence degree exceeds the preset divergence threshold, it is judged that the power competition state of the full hydraulic transmission system is entered, including: The pressure signal time series data of walking motor working pressure and implant device working pressure are enveloped respectively to obtain walking motor working pressure envelope line sequence and implant device working pressure envelope line sequence; The walking motor working pressure envelope line sequence and the implant device working pressure envelope line sequence are aligned by dynamic time warping to eliminate the influence of phase lag, and the cumulative Euclidean distance of the two envelope line sequences after alignment is calculated as a quantitative index representing the divergence degree; The quantitative index is compared with the preset divergence threshold, when the quantitative index exceeds the preset divergence threshold for several sampling periods, it is judged that the power competition state of the full hydraulic transmission system is entered; S3, in the power competition state, the walking motor demand power and the implant device demand power are calculated respectively; S4, according to the ratio of walking motor demand power and implant device demand power, the hydraulic pump displacement distribution coefficient is generated; S5, fusion of the interactive characteristics of main pump outlet pressure and output flow signal and the transmission characteristics of walking motor and implant device pressure signal to generate the dynamic balance degree of full hydraulic transmission system, so as to correct the hydraulic pump displacement distribution coefficient; S6, based on the corrected hydraulic pump displacement distribution coefficient, the walking motor proportional valve opening and the implant device proportional valve opening are adjusted synchronously.

2. The self-adaptive parameter control method for a full-hydraulic transmission-based bench field transplanter according to claim 1, characterized in that, Real-time monitoring of walking motor working pressure and implant device working pressure, including: The walking motor working pressure is collected in real time by the first pressure sensor installed on the walking motor inlet oil way, and the implant device working pressure is collected in real time by the second pressure sensor installed on the implant device working oil way, and the walking motor working pressure and the implant device working pressure are converted into electrical signal output.

3. The self-adaptive parameter control method for a full-hydraulic transmission-based bench field transplanter according to claim 1, characterized in that, In the power competition state, the walking motor demand power and the implant device demand power are calculated respectively, including: Based on the walking motor working pressure envelope line sequence and the implant device working pressure envelope line sequence, the walking motor real-time working pressure and the implant device real-time working pressure are calculated respectively; The walking motor real-time speed and the implant device real-time speed are obtained respectively; The walking motor demand flow is calculated by the product of walking motor displacement geometric value and real-time speed value, and the implant device demand flow is calculated by the product of implant device displacement geometric value and real-time speed value; The walking motor demand power is obtained by multiplying the walking motor real-time working pressure and the walking motor demand flow, and the implant device demand power is obtained by multiplying the implant device real-time working pressure and the implant device demand flow.

4. The self-adaptive parameter control method for a full-hydraulic transmission based bench field transplanter according to claim 1, characterized in that, According to the ratio of walking motor demand power and implant device demand power, the hydraulic pump displacement distribution coefficient is generated, including: The sum of walking motor demand power and implant device demand power is calculated to obtain system total demand power; The first proportion value of walking motor demand power to system total demand power and the second proportion value of implant device demand power to system total demand power are calculated respectively; Determine the larger one of the first and second proportional values as a base value of the hydraulic pump displacement distribution coefficient; Smooth filtering processing is performed on the base value of the hydraulic pump displacement distribution coefficient based on the stability of the main pump outlet pressure signal to generate a final hydraulic pump displacement distribution coefficient.

5. The self-adaptive parameter control method for a full-hydraulic transmission-based bench field transplanter according to claim 1, characterized in that, The dynamic balance degree of the full hydraulic transmission system is generated by fusing the interaction characteristics of the main pump outlet pressure and the output flow signal and the transmission characteristics of the walking motor and the implant device pressure signal, thereby correcting the hydraulic pump displacement distribution coefficient, including: Analyze the fluctuation amplitude of the main pump outlet pressure signal and the change trend of the output flow signal in an evaluation period. When the fluctuation amplitude decreases and the change trend is stable, it is determined that the energy supply stability is in a good state; Analyze the mutual following characteristics of the walking motor working pressure signal and the implant device working pressure signal in an evaluation period. When the pressure changes of the two present a significant alternating mode, it is determined that the disturbance intensity between the actuators is in a high state; According to the combination state of the energy supply stability determination result and the disturbance intensity determination result between the actuators, select the corresponding correction mode from the preset multiple correction modes; The selected correction mode is used to correct the hydraulic pump displacement distribution coefficient, and a corrected hydraulic pump displacement distribution coefficient is generated.

6. The self-adaptive parameter control method for a full-hydraulic transmission based bench field transplanter according to claim 5, characterized in that, According to the combination state of the energy supply stability determination result and the disturbance intensity determination result between the actuators, select the corresponding correction mode from the preset multiple correction modes by the following method: A two-dimensional decision table containing different energy supply stability states and different disturbance intensity states between actuators is defined in advance. Each state combination cell in the two-dimensional decision table is associated with a preset correction mode; During actual operation, the energy supply stability state and the disturbance intensity state between the actuators determined in real time are used as input query conditions to match and select the correction mode to be used currently from the two-dimensional decision table through table lookup operation.

7. The self-adaptive parameter control method for a full-hydraulic transmission based bench field transplanter according to claim 5, characterized in that, The selected correction mode is used to correct the hydraulic pump displacement distribution coefficient by the following method: Each preset correction mode corresponds to a set of pre-set correction parameters and calculation rules. The calculation rule is a mathematical operation of linear scaling or adding a fixed adjustment amount to the hydraulic pump displacement distribution coefficient; After selecting the correction mode, the corresponding correction parameters and calculation rules are called to perform the corresponding mathematical operation on the current hydraulic pump displacement distribution coefficient, thereby generating the corrected hydraulic pump displacement distribution coefficient.

8. The self-adaptive parameter control method for a full-hydraulic transmission based bench field transplanter according to claim 1, characterized in that, Based on the corrected hydraulic pump displacement distribution coefficient, the walking motor proportional valve opening and the implant device proportional valve opening are adjusted synchronously, including: Map the corrected hydraulic pump displacement distribution coefficient to the target opening reference value of the walking motor proportional valve and the target opening reference value of the implant device proportional valve; According to the current main pump outlet pressure, the target opening reference value of the walking motor proportional valve and the target opening reference value of the implant device proportional valve are pressure compensated and fine tuned to generate the final target opening command of the walking motor proportional valve and the final target opening command of the implant device proportional valve; Synchronize the final target opening degree command of the walking motor proportional valve and the final target opening degree command of the planting device proportional valve to the corresponding proportional valve driver, and drive the opening degree change of the walking motor proportional valve and the planting device proportional valve.

9. A self-adaptive parameter control system for a full-hydraulic transmission-based bench field transplanter, for implementing the self-adaptive parameter control method for a full-hydraulic transmission-based bench field transplanter according to any one of claims 1-8, characterized in that, Comprise: A state monitoring module for real-time monitoring of walking motor working pressure and planting device working pressure; A competition identification module for calculating the divergence degree of the two pressure signal envelopes based on the walking motor working pressure and the planting device working pressure, and determining that the full hydraulic transmission system enters the power competition state when the divergence degree exceeds the preset divergence threshold; A power calculation module for calculating the walking motor demand power and the planting device demand power respectively in the power competition state; A coefficient generation module for generating a hydraulic pump displacement distribution coefficient according to the ratio of the walking motor demand power and the planting device demand power; A coefficient correction module for generating the dynamic balance degree of the full hydraulic transmission system by fusing the interaction characteristics of the main pump outlet pressure and the output flow signal and the transmission characteristics of the walking motor and the planting device pressure signal, thereby correcting the hydraulic pump displacement distribution coefficient; A valve control and adjustment module for synchronously adjusting the walking motor proportional valve opening degree and the planting device proportional valve opening degree based on the corrected hydraulic pump displacement distribution coefficient.

Images