Self-adaptive parameter control system and method for halfpace field rice transplanter based on full-hydraulic transmission

Abstract

The invention discloses a self-adaptive parameter control system and method for a halfpace field rice transplanter based on full-hydraulic transmission, particularly relates to the technical field of hydraulic parameter control, and aims to solve the technical problem of dynamic power competition between a walking system and a planting device when the full-hydraulic transmission rice transplanter works in a halfpace field. Working pressure signals of a walking motor and a planting device are monitored in real time, the divergence degree of envelope lines of the two pressure signals is calculated so as to recognize the power competition state of a system, and when power competition is detected, the required power of two execution mechanisms is dynamically calculated, and a hydraulic pump displacement distribution coefficient is generated; and then the interaction characteristics of the outlet pressure of the main pump and the output flow signal and the transmission characteristics of the pressure signal of the executing mechanism are fused, the dynamic balance degree of the system is generated, the displacement distribution coefficient is corrected, and finally the opening degree of the walking motor and the proportional valve of the planting device is synchronously adjusted based on the corrected coefficient. Accurate distribution and dynamic balance control of the power of the hydraulic system are achieved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of hydraulic parameter control, more particularly, the present application relates to a self-adaptive parameter control system and method for a full-hydraulic transmission-based terraced field transplanter. BACKGROUND

[0002] The full-hydraulic transmission system is widely used in the field of terraced field working machines, and power transmission and working device control are achieved through the coordinated action of hydraulic pumps, valves and actuators. In the full-hydraulic transmission design of the existing transplanter, the hydraulic parameters of each actuator are usually independently adjusted according to fixed working conditions or simple logic, for example, the flow of the walking motor is controlled according to the preset speed, or the pressure of the planting unit is adjusted based on the soil resistance. This control method can maintain basic work in flat fields, but in complex terraced fields, due to the severe load fluctuation and the coupling of multiple mechanism actions, the existing technology is difficult to achieve dynamic coordination at the system level.

[0003] In the prior art, when the full-hydraulic transmission transplanter works in the terraced field, there is a dynamic power competition problem between the walking system and the planting device. When the machine climbs a slope or encounters a large resistance, the walking motor of the walking system needs to increase the hydraulic power to maintain stable progress, while the planting device is in the high load stage of the working cycle, and the instantaneous power demand of the two is superimposed, which easily leads to sudden drop of system pressure and disorder of flow distribution, which not only causes walking instability, uneven planting depth and other work quality problems, but also may cause frequent overflow of the hydraulic system, efficiency decay and overload of the elements. The control strategy based on fixed priority or local feedback cannot fundamentally solve such global energy scheduling conflicts. SUMMARY

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

[0005] To achieve the above-mentioned purpose, the present application provides the following technical scheme: A self-adaptive parameter control method for a full-hydraulic transmission-based terraced field transplanter, comprising: S1, real-time monitoring of walking motor working pressure and planting device working pressure; S2, calculating the divergence degree of the envelope line of the two pressure signals based on the walking motor working pressure and the planting device working pressure, and determining that the full-hydraulic transmission system enters a power competition state when the divergence degree exceeds a preset divergence threshold; S3, calculating the walking motor demand power and the planting device demand power respectively under the power competition state; S4, generating a hydraulic pump displacement distribution coefficient according to the ratio of the walking motor demand power and the planting device demand power; S5, the interaction characteristics of the fusion 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 generate the dynamic balance degree of the full hydraulic transmission system, so as to correct the displacement distribution coefficient of the hydraulic pump; S6, based on the corrected hydraulic pump displacement distribution coefficient, synchronously adjusting the walking motor proportional valve opening and the implant device proportional valve opening.

[0006] Further, real-time monitoring of the walking motor working pressure and the implant device working pressure, comprising: 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.

[0007] Further, based on the walking motor working pressure and the implant device working pressure, the divergence degree of the envelope line of the two pressure signals is calculated, and when the divergence degree exceeds the preset divergence threshold, it is judged that the full hydraulic transmission system enters the power competition state, comprising: The pressure signal time sequence data of the walking motor working pressure and the implant device working pressure are respectively enveloped to obtain the envelope line sequence of the walking motor working pressure and the envelope line sequence of the implant device working pressure; The envelope line sequence of the walking motor working pressure and the envelope line sequence of the implant device working pressure are dynamically time-warped to eliminate the phase lag effect, 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, and when the quantitative index exceeds the preset divergence threshold for multiple sampling periods, it is judged that the full hydraulic transmission system enters the power competition state.

[0008] Further, in the power competition state, the walking motor demand power and the implant device demand power are calculated respectively, comprising: Based on the envelope line sequence of the walking motor working pressure and the envelope line sequence of the implant device working pressure, the real-time working pressure of the walking motor and the real-time working pressure of the implant device are calculated respectively; The real-time speed of the walking motor and the real-time speed of the implant device are obtained respectively; The walking motor demand flow is calculated by multiplying the walking motor displacement geometric value and the real-time speed value, and the implant device demand flow is calculated by multiplying the implant device displacement geometric value and the 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.

[0009] Further, generate a hydraulic pump displacement distribution coefficient according to the ratio of the walking motor demand power and the implant device demand power, including: Calculate the sum of the walking motor demand power and the implant device demand power to obtain the total system demand power; Calculate the first proportion value of the walking motor demand power to the total system demand power and the second proportion value of the implant device demand power to the total system demand power, respectively; Determine the larger one of the first proportion value and the second proportion value as the base value of the hydraulic pump displacement distribution coefficient; Smoothly filter the base value of the hydraulic pump displacement distribution coefficient based on the stability of the main pump outlet pressure signal to generate the final hydraulic pump displacement distribution coefficient.

[0010] Further, generate 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 implant device pressure signal, thereby correcting the hydraulic pump displacement distribution coefficient, including: Analyze the fluctuation amplitude of the main pump outlet pressure signal within an evaluation period and the change trend of the output flow signal within the same 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 within an evaluation period. When the pressure changes of the two signals present a significant alternating rising and falling 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 actuator disturbance intensity determination result, select the corresponding correction mode from the pre-set multiple correction modes; Correct the hydraulic pump displacement distribution coefficient using the algorithm corresponding to the selected correction mode to generate the corrected hydraulic pump displacement distribution coefficient.

[0011] Further, according to the combination state of the energy supply stability determination result and the actuator disturbance intensity determination result, select the corresponding correction mode from the pre-set multiple correction modes by the following way: Predefine a two-dimensional decision table containing different energy supply stability states and different actuator disturbance intensity state combinations. Each state combination cell in the two-dimensional decision table is associated with a pre-set correction mode; In actual operation, the energy supply stability state and the actuator disturbance intensity state 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.

[0012] Further, the correction of the hydraulic pump displacement distribution coefficient by using the algorithm corresponding to the selected correction mode is realized by the following way: Each preset correction mode corresponds to a set of pre-set correction parameters and calculation rules, and the calculation rule is a mathematical operation of linear scaling or superimposing a fixed adjustment amount on 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.

[0013] Further, based on the corrected hydraulic pump displacement distribution coefficient, the walking motor proportional valve opening degree and the planting device proportional valve opening degree are synchronously adjusted, including: mapping the corrected hydraulic pump displacement distribution coefficient to the target opening degree reference value of the walking motor proportional valve and the target opening degree reference value of the planting device proportional valve; performing pressure compensation fine adjustment on the target opening degree reference value of the walking motor proportional valve and the target opening degree reference value of the planting device proportional valve according to the current main pump outlet pressure, to generate 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; synchronously outputting 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 to drive the opening degree change of the walking motor proportional valve and the planting device proportional valve.

[0014] On the other hand, the present application provides a self-adaptive parameter control system for a full-hydraulic transmission rice transplanter, comprising: a state monitoring module for real-time monitoring of the walking motor working pressure and the 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 under the power competition state; a coefficient generation module for generating the 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 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.

[0015] Compared with the prior art, the present application has the following beneficial effects: 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 degree of the envelope lines of the two pressure signals, the hydraulic power competition phenomenon between the walking system and the planting device under complex working conditions of the terrace field can be captured in time, the demand power of the two actuators is calculated, and the hydraulic pump displacement distribution coefficient is generated, realizing the dynamic scheduling of the system level energy, effectively avoiding the problems of system pressure drop and flow distribution disorder caused by fixed parameters or simple priority strategy in traditional control, and the power distribution strategy can be adaptively adjusted according to the real-time working condition, which significantly improves the coordination and response ability of the full-hydraulic transmission system under variable load conditions.

[0016] 2. By fusing the multi-parameter interaction features of the main pump outlet pressure, the output flow signal and the actuator pressure signal, the system dynamic balance degree is generated and the displacement distribution coefficient is corrected, so that the system can comprehensively consider the energy supply stability and the disturbance intensity between the actuators, and the walking motor and the planting device proportional valve opening degree are synchronously adjusted based on the corrected coefficient, realizing the precise distribution of hydraulic power and the cooperative control of actuators, not only improving the system pressure stability and flow control accuracy, but also significantly reducing the hydraulic system overflow loss and energy consumption, prolonging the service life of components, and providing more reliable power support for terrace field transplanting operation. BRIEF DESCRIPTION OF DRAWINGS

[0017] Figure 1 The flowchart of the adaptive parameter control method of the terrace field transplanter based on full-hydraulic transmission of the present application is given. Figure 2 The structure schematic diagram of the adaptive parameter control system of the terrace field transplanter based on full-hydraulic transmission of the present application is given. DETAILED DESCRIPTION

[0018] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0019] Embodiment 1: Figure 1 The flow of the adaptive parameter control method of the terrace field transplanter based on full-hydraulic transmission of the present application is given, including: S1, real-time monitoring of walking motor working pressure and planting device working pressure; S2, calculate the divergence degree of the envelope lines of the two pressure signals based on the walking motor working pressure and the implant device working pressure, and determine that the full hydraulic transmission system enters the power competition state when the divergence degree exceeds the preset divergence threshold; S3, calculate the walking motor demand power and the implant device demand power respectively under the power competition state; S4, generate a hydraulic pump displacement distribution coefficient according to the ratio of the walking motor demand power and the implant device demand power; S5, generate 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 implant device pressure signal, so as to correct the hydraulic pump displacement distribution coefficient; S6, based on the corrected hydraulic pump displacement distribution coefficient, synchronously adjust the walking motor proportional valve opening degree and the implant device proportional valve opening degree.

[0020] In order to realize the real-time monitoring of the walking motor working pressure and the implant device working pressure, the following specific ways are adopted. A first pressure sensor is installed on the inlet oil circuit of the walking motor, which is preferably a piezoresistive pressure sensor. The installation position should be as close to the oil inlet of the walking motor as possible to accurately sense the hydraulic oil pressure entering the walking motor and avoid significant influence of pipeline pressure loss on the measured value. The first pressure sensor converts the sensed hydraulic pressure signal into a weak electric signal of millivolt level through its internal sensitive element (such as a silicon strain gauge). After preliminary amplification and conditioning by the built-in amplifier circuit of the sensor, the output is a standardized analog voltage signal, such as 0 volts to 5 volts or 4 milliamperes to 20 milliamperes, which corresponds to the real-time walking motor working pressure.

[0021] Similarly, a second pressure sensor is installed on the working oil circuit of the implant device to monitor the hydraulic oil pressure driving the implanting action. The selection and installation principle of the second pressure sensor is the same as that of the first pressure sensor, which needs to ensure that it can quickly and accurately respond to the pressure change in the working cavity of the implant device. The second pressure sensor also converts the detected implant device working pressure into a standard electric signal output proportional to it.

[0022] The analog electrical signals outputted by the two pressure sensors are transmitted to the analog input ports of the controller. The analog-to-digital converter integrated inside the controller samples and quantizes the two continuous analog voltage signals synchronously at a pre-set sampling frequency, for example 1000 Hz, and converts them into discrete digital quantity sequences. The sampling frequency should be set to be more than twice the highest frequency component of the pressure fluctuation in the hydraulic system to satisfy the Nyquist sampling theorem, ensuring that the dynamic information of the pressure is not lost. After analog-to-digital conversion, the working pressure of the walking motor and the working pressure of the implanting device are represented as a series of digital quantities that change over time, which are stored in the memory buffer of the controller, providing the original data basis for subsequent signal processing and analysis. Through the above method, high-frequency and high-precision synchronous monitoring and data acquisition of the working pressures of the two key actuators are realized.

[0023] The range of the first pressure sensor and the second pressure sensor should cover the possible range of their respective working pressures, for example, the working pressure of the walking motor may vary between 5 MPa and 25 MPa, so the range of the first pressure sensor should be selected as 0 MPa to 40 MPa with a certain overload margin. The accuracy level should not be less than 0.5% to ensure the accuracy of the measurement. The signal transmission line should use a shielded cable to reduce the influence of electromagnetic interference on the weak sensor signal.

[0024] The analog input port of the controller usually has a programmable gain amplifier that can be adjusted according to the range of the sensor output signal to fully utilize the resolution of the analog-to-digital converter, for example, a 16-bit precision analog-to-digital converter can convert an input voltage of 0 volts to 5 volts into a digital quantity between 0 and 65535. The controller can perform preliminary digital filtering on the digital quantity sequence obtained by sampling, for example, using a first-order low-pass filter to filter out high-frequency noise introduced during sampling. The cutoff frequency of the filter can be set according to the main working frequency bandwidth of the hydraulic system, for example, set to 100 Hz, thereby retaining the effective pressure fluctuation information while suppressing noise interference. The walking motor working pressure digital quantity sequence and the implanting device working pressure digital quantity sequence after the above processing are reliable data sources for subsequent calculation and analysis.

[0025] The installation of the pressure sensor needs to ensure that its sensing surface is in full contact with the hydraulic oil to avoid bubbles or impurities affecting the measurement accuracy. During installation, a special tool should be used according to the torque requirements provided by the sensor manufacturer, for example, a torque wrench is used to install the sensor with a torque of 20 Nm to ensure the sealing and prevent damage to the sensor threads due to overtightening. A damping joint or a micro accumulator is usually installed between the sensor and the oil line to suppress the damage to the sensor caused by pressure shocks in the hydraulic system, for example, a micro accumulator with a volume of 1 cubic centimeter is installed.

[0026] The sampling program of the controller needs to configure a proper hold time, for example, set to 2 microseconds, to ensure that the input voltage remains stable during sampling. The reference voltage of the analog-to-digital converter needs to remain stable, and its fluctuation range should be controlled within ±0.1%, for example, a reference voltage source with an accuracy of 0.05% is used to provide a reference voltage of 2.5 volts. For the sampled data, zero point and full scale calibration is required, zero point calibration can be performed in the system depressurization state, adjusting the sensor output value to zero point value, full scale calibration can use a standard pressure source to apply a known pressure value, for example, use a pressure calibrator with an accuracy of 0.1 level to apply a standard pressure of 20 megapascals, adjust the gain to make the reading value consistent with the standard value.

[0027] In terms of data storage, the controller needs to configure a data buffer with sufficient size, for example, configure a ring buffer with a size of 1024, to store the pressure data collected in 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 through a communication interface, for example, transmitted to a vehicle data recorder through a CAN bus at a rate of 1000 samples per second for storage.

[0028] Based on the digital quantity sequences of the walking motor working pressure and the implant device working pressure obtained in the foregoing steps, the calculation of the divergence degree of the pressure signal envelope and the determination of the power competition state are performed. First, the envelope extraction is performed on the two pressure signal time series data respectively to obtain the envelope line sequence representing the signal change trend. The envelope extraction is realized by using the moving extreme value method, and the specific process is as follows: for each pressure signal sequence, a sliding time window with a length of N is set, for example, N is 50 sampling points (corresponding to a time window of 50 milliseconds), and the local maximum and local minimum of the pressure in the window are found; then the extreme value points are connected by linear interpolation method to form the upper envelope line and the lower envelope line respectively; finally, the arithmetic mean value of the upper and lower envelope lines is taken as the envelope line value at this moment. The sliding window moves forward with a fixed step, for example, 1 sampling point each time, and the above-mentioned extreme value finding and interpolation process is repeated, and finally the complete walking motor working pressure envelope line sequence and the implant device working pressure envelope line sequence are generated.

[0029] After obtaining the two envelope sequences, dynamic time warping alignment is needed to eliminate the phase lag between the two signals. Dynamic time warping is achieved by constructing a cumulative distance matrix of the two sequences, the rows of the matrix correspond to the time points of the walking motor working pressure envelope sequence, and the columns correspond to the time points of the implant device working pressure envelope sequence. The value of each element of the matrix represents the Euclidean distance between the two sequences at that position. By finding the optimal path from the top left corner to the bottom right corner of the matrix, the sum of the distances of the points on the path is minimized, which is the best alignment path. Along this path, the two sequences are resampled and matched to align their time axes and eliminate the phase difference caused by the response delay of the hydraulic system. The slope of the path is limited to between 0.5 and 2 to ensure that the alignment does not appear excessive stretching or compression.

[0030] After alignment, the cumulative Euclidean distance of the two envelope sequences is calculated as a quantitative indicator of divergence. When calculating, the aligned two envelope sequences are regarded as two vectors, the sum of the squared Euclidean distances between their corresponding points is calculated, and the square root is taken to get the total cumulative distance. The larger the value, the greater the difference between the two pressure signals, i.e. the higher the divergence. This quantitative indicator reflects the load competition intensity between the two actuators. To further improve the comparability of the indicator, the cumulative Euclidean distance can be divided by the sequence length to obtain the average Euclidean distance as the standardized divergence indicator.

[0031] The determination of the preset divergence threshold is based on statistical analysis of a large amount of historical operation data. In the normal state of the system without power competition, multiple sets of walking motor working pressure envelope sequences and implant device working pressure envelope sequences are collected, and their cumulative Euclidean distances are calculated to obtain a set of reference values. The average of these reference values plus 2 times the standard deviation is taken as the preset divergence threshold, for example, by analyzing 100 sets of normal data, the average distance value is 150, and the standard deviation is 25, then the preset divergence threshold can be set to 200. This threshold setting method ensures that the probability of the quantitative indicator exceeding the threshold is less than 5% in normal working conditions. To adapt to the changes in system characteristics, a threshold updating mechanism can be established, for example, to collect pressure data in normal state every 100 hours to update the threshold reference.

[0032] The state determination uses a sustained over-threshold logic to improve reliability. The controller continuously monitors the comparison result of the quantitative indicator and the preset divergence threshold. Only when the quantitative indicator exceeds the preset divergence threshold for a number of consecutive sampling periods (for example, 5 sampling periods, corresponding to 5 milliseconds), does it determine that the full hydraulic transmission system enters the power competition state. This design avoids false positives caused by transient pressure fluctuations, improving the accuracy of state recognition. The determination result is output in the form of a Boolean flag, which is true when the system enters the power competition state, triggering the subsequent power calculation and distribution adjustment process.

[0033] In the envelope extraction process, special attention should be paid to the treatment of boundary points. When the sliding window is located at the beginning or end of the signal sequence, mirror extension method is used to handle the boundary, that is, symmetric data points are copied at both ends of the signal to ensure that the window can normally calculate the extreme value at the signal boundary. The selection of the size of the sliding window needs to be adjusted according to the characteristics of the pressure signal, and generally takes 1 to 2 times of 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 are selected according to the actual system characteristics.

[0034] The implementation of dynamic time warping algorithm adopts dynamic programming method, and the recursive formula is: the cumulative distance of the current grid point is equal to the Euclidean distance between the current points plus the minimum value of the cumulative distance of the adjacent grid points. The optimal path backtracking starts from the right lower corner of the matrix, and moves to the left upper corner step by step, and selects the adjacent grid point with the minimum cumulative distance each time until it reaches the left upper corner of the matrix. In order to ensure the calculation efficiency, the maximum bending window limit can be set, for example, the maximum deviation of the path from the diagonal line is limited to not more than one quarter of the sequence length.

[0035] The calculation of the quantitative index needs to consider the dimensional consistency. The envelope sequence of the walking motor working pressure and the envelope sequence of the implant device working pressure both have the dimension of pressure, with the unit of megapascal, so the Euclidean distance calculated also has the dimension of pressure, which ensures the consistency of physical meaning. In order to further improve the calculation accuracy, the two envelope sequences can be normalized before calculating the Euclidean distance, so that their numerical range is between 0 and 1, eliminating the influence of amplitude difference on distance calculation.

[0036] The abnormal processing mechanism includes checking the validity of the input data, such as checking whether the pressure value is within a reasonable range (0-40 megapascal) and whether there is an abnormal jump. When abnormal data is detected, the previous valid value can be replaced or data reacquisition can be triggered. For dynamic time warping algorithm, when the length difference between two sequences is too large, preliminary coarse alignment processing can be performed first, and then accurate warping is performed to improve the calculation efficiency.

[0037] When the system is determined to enter the power competition state, the walking motor demand power and the implant device demand power are calculated. First, based on the walking motor working pressure envelope sequence and the implant device working pressure envelope sequence obtained in the foregoing steps, the real-time working pressure values thereof are calculated respectively. The calculation of the real-time working pressure adopts a moving window weighted average method, and the latest several data points in the envelope sequence are taken for weighted average calculation, for example, the last 20 sampling points are taken, and the data points closer to the current time are given higher weights, and the weight distribution adopts a linear decreasing manner, and the weight of the nearest point is 1, and the weight of the farthest point is 0.5, and the weight values of the intermediate points are determined by linear interpolation. Through such weighted average processing, the real-time change trend of the pressure can be reflected, and the random fluctuations can be effectively smoothed, and accurate and reliable walking motor real-time working pressure values and implant device real-time working pressure values are obtained.

[0038] The real-time speed is obtained through a speed sensor installed on the corresponding actuator. The walking motor real-time speed is measured through an incremental encoder installed on the walking motor output shaft, the encoder outputs a certain number of pulses per revolution, for example, 1024 pulses per revolution, and the speed value is calculated by measuring the number of pulses per unit time, and the sampling frequency is set to 1000 Hz. The implant device real-time speed is measured through a Hall sensor installed on the implant transmission shaft, the sensor detects the passing frequency of the magnet on the rotating part, and the number of magnets is 4, which are uniformly distributed on the rotating part, and the speed is calculated by measuring the time interval of two adjacent magnets passing. Both speed signals are sent to the digital input port of the controller after being processed by the signal conditioning circuit, and the sampling frequency is synchronized with the pressure signal.

[0039] The demand flow is calculated based on the basic principle of hydraulic power transmission. The walking motor demand flow is calculated by multiplying the walking motor displacement geometric value and the real-time speed value, wherein the walking motor displacement geometric value refers to the theoretical oil displacement per revolution of the motor, which is a fixed parameter determined by the design structure of the motor, for example, the displacement geometric value of a certain type of walking motor is 28 milliliters per revolution. When calculating, attention should be paid to the unit uniformity, and the speed unit is converted into revolutions per second, and the displacement unit is converted into cubic meters per revolution, so that the flow unit calculated is cubic meters per second. The implant device demand flow is calculated in the same way, and the product of the implant device displacement geometric value and the real-time speed value is used, for example, the displacement geometric value of the implant hydraulic motor is 16 milliliters per revolution. For the convenience of subsequent calculation, the flow unit is usually unified as liters per minute, which can be converted by multiplying by 60000.

[0040] The calculation of the required power is achieved by multiplying the real-time working pressure by the required flow rate. The walking motor required power is equal to the product of the walking motor real-time working pressure and the walking motor required flow rate, and the planting device required power is equal to the product of the planting device real-time working pressure and the planting device required flow rate. Special attention should be paid to the unification of dimensions during the calculation process. The pressure unit is in pascal, and the flow rate unit is in cubic meters per second, so that the power unit calculated is in watt. In actual engineering applications, the power unit is usually converted into kilowatt, which can be converted by dividing by 1000. In order to ensure the calculation accuracy, all parameters are calculated by using floating point numbers. The pressure value is accurate to 0.1 megapascal, the flow rate value is accurate to 0.1 liter per minute, and the power value is accurate to 0.1 kilowatt.

[0041] The efficiency factor of the hydraulic system needs to be considered during the calculation process. The actual required power also needs to consider 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, which is obtained by test. The method for determining the compensation coefficient is as follows: measure the ratio of actual output power to calculated power under standard working conditions, and take the average value of multiple tests as the compensation coefficient, for example, perform 10 tests, and measure the ratio of 1.12, 1.15, 1.18, etc. Take the arithmetic mean value 1.15 as the compensation coefficient.

[0042] All calculation parameters need to be checked for range and reasonableness. The real-time working pressure value should be between 0 and the rated pressure of the system, for example, 0 to 25 megapascal; the rotational speed value should be between 0 and the maximum allowable rotational speed, for example, 0 to 2000 revolutions per minute; the calculated flow rate value and power value also have corresponding reasonable ranges. When any parameter is detected to be out of the reasonable range, an exception handling program should be triggered to replace it with the last valid value or a pre-set default value, for example, when the rotational speed value exceeds 2000 revolutions per minute, the last valid rotational speed value is used for calculation.

[0043] The data updating and storage mechanism ensures the continuity of the calculation. The required power value needs to be recalculated every sampling period, and the calculation result is stored in a circular buffer. The buffer size should be able to accommodate at least one working period of data, for example, it can store the last 10 seconds of data, 1000 sampling points per second, a total of 10000 data points. At the same time, the time stamp of the power calculation needs to be recorded for time alignment analysis with other parameters. The data storage is in a structured manner, each data point contains a time stamp, a walking motor required power value, a planting device required power value, and intermediate parameter values used in the calculation process.

[0044] Based on the walking motor demand power and the planting device demand power calculated in the previous steps, the generation process of the hydraulic pump displacement distribution coefficient is performed. First, the total system demand power is calculated, and the walking motor demand power value and the planting device demand power value are added to obtain the total. When calculating, ensure that the units of the two power values are unified, for example, both are converted to kilowatts before addition. The total system demand power represents the total power output required by the hydraulic system at the current time, providing a reference value for subsequent proportional calculation. When the sum of the two demand power values is less than a certain minimum threshold, for example, less than 0.5 kilowatts, it can be determined that the system is in an idle state, and the default power distribution ratio is used, for example, both the two ratio values are set to 0.5.

[0045] After obtaining the total system demand power, the proportion of the two powers is calculated respectively. The first proportion value is calculated by dividing the walking motor demand power value by the total system demand power value, and the second proportion value is calculated by dividing the planting device demand power value by the total system demand power value. During the calculation process, zero protection mechanism needs to be added, when the total system demand power value is less than a certain minimum value, for example, less than 0.1 kilowatts, both the two ratio values are set to the default value 0.5. The calculation result of the ratio value is a decimal between 0 and 1, indicating the proportion of each actuator power demand in the total demand. After calculation, the ratio value needs to be checked for reasonableness to ensure that each ratio value is within the range of 0 to 1, and the sum of the two ratio values is equal to 1, allowing a small calculation error, for example, within the error range of plus or minus 0.001.

[0046] When determining the base value of the hydraulic pump displacement distribution coefficient, the first proportion value and the second proportion value are compared, and the larger one is selected as the base value. This selection logic ensures that the displacement distribution of the hydraulic pump prioritizes the actuator with greater power demand, avoiding insufficient power supply. For example, when the walking motor demand power proportion is 0.7 and the planting device demand power proportion is 0.3, 0.7 is selected as the base value. This selection process needs to be performed every sampling period to ensure that the distribution coefficient can respond to power demand changes in time. In the selection process, historical data also needs to be considered, when the difference between the two proportion values is small, for example, less than 0.05, the last base value is kept unchanged to avoid frequent switching.

[0047] When performing the smoothing filtering on the base value, 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 in the recent period of time are analyzed, such as calculating the pressure variance value of the last 100 sampling points as the stability index. The calculation of the pressure variance adopts the standard variance formula, first calculates the average value of 100 pressure values, then calculates the square sum of the difference between each pressure value and the average value, and finally divides by 99 to obtain the variance value. When the pressure variance value is small, it indicates that the system pressure is stable, the filtering strength can be appropriately reduced, and the filtering time constant is taken as a small value, such as 0.1 seconds; when the pressure variance value is large, it indicates that the system pressure fluctuates greatly, and the filtering effect needs to be enhanced, and the filtering time constant is taken as a large value, such as 0.5 seconds.

[0048] The smoothing filtering is realized by using a first-order low-pass filtering algorithm, and the calculation formula is that the current output value is equal to the last output value plus the filtering coefficient multiplied by the difference between the current input value and the last output value. The filtering coefficient is calculated according to the time constant and the sampling period, for example, when the sampling period is 0.001 seconds and the time constant is 0.1 seconds, the filtering coefficient is equal to 0.001 divided by 0.1, which is equal to 0.01. The determination of the filtering coefficient is dynamically adjusted according to the pressure stability, and the pressure stability is quantified by calculating the standard deviation of the main pump outlet pressure signal in the sliding time window. The length of the standard deviation calculation window 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 taken as 200 sampling points.

[0049] When mapping the standard deviation to the filtering time constant, a piecewise linear function is adopted, for example, when the standard deviation is less than 0.5 MPa, the time constant is taken as 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 0.5 seconds. Through this adaptive mechanism, it is ensured that the distribution coefficient responds quickly when the pressure is stable, and the distribution coefficient changes smoothly when the pressure fluctuates. During the filtering process, the initial value needs to be processed, the default initial value is used when the system starts, for example, 0.5, and a transition period is set, and the filtering strength is gradually reduced during the transition period.

[0050] The final generated hydraulic pump displacement distribution coefficient needs to be limited within a reasonable range, for example, the minimum value is 0.3 and the maximum value is 0.8, this range is determined according to the working characteristics of the hydraulic pump. When the calculated coefficient exceeds this range, it is limited to the boundary value by using the amplitude limiting processing. At the same time, the change rate of the coefficient also needs to be limited, the change amount in each sampling period does not exceed a certain maximum value, for example, does not exceed 0.05, to avoid the impact of the system caused by the sharp change of the distribution coefficient. The change rate limit is realized by comparing the difference between the current calculated value and the last output value, when the difference exceeds the limit value, the output value is equal to the last output value plus the limit value multiplied by the sign of the difference.

[0051] To ensure the reliability of the system, an exception handling mechanism needs to be added to the calculation process. When an input parameter exception is detected, such as a negative or abnormally large value of the required power, the last valid value is used for calculation. At the same time, a coefficient validity check mechanism is established, which triggers a recalculation process when the allocation coefficient calculated in multiple consecutive periods is the same and the system power demand changes. All intermediate calculation results, including the scaling value, the base value, the filtering parameters, and the final coefficient value, are recorded in a data log for subsequent analysis and debugging. The data log is stored in a circular buffer with a size of 1000 records, each record containing a timestamp and all related parameter values.

[0052] Based on the hydraulic pump displacement allocation coefficient obtained from the previous steps and the real-time monitoring of the main pump outlet pressure signal, output flow signal, walking motor working pressure signal, and implanting device working pressure signal, the dynamic balance degree generation and coefficient correction process is performed. First, analyze the fluctuation characteristics of the main pump outlet pressure signal within an evaluation period, the evaluation period length is determined according to the system response characteristics, for example, take 200 sampling points corresponding to a 0.2 second time window. The fluctuation amplitude is quantified by calculating the standard deviation of the pressure signal in this time period, the standard deviation calculation uses the unbiased estimation formula, first calculate the arithmetic mean of the 200 pressure values, then calculate the square sum of the deviation of each pressure value from the mean, finally divide by 199 to get the variance, and then take the square root to get the standard deviation value. The trend stability 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, and a significant deviation from 0 indicates a trend change. When the pressure fluctuation amplitude is less than the set threshold, for example, the standard deviation is less than 0.3 MPa, and the autocorrelation coefficient is within the range of plus or minus 0.2, it is determined that the energy supply stability is in good condition.

[0053] When analyzing the mutual following characteristics of the walking motor working pressure signal and the implanting device working pressure signal, the cross-correlation coefficient analysis method is used. First, align the two pressure signal sequences in time to eliminate measurement delay effects, then calculate their cross-correlation coefficients at multiple time offsets. The calculation of the cross-correlation coefficient uses the standard formula, which divides the covariance of the two sequences by the product of their standard deviations. When the cross-correlation coefficient of the two pressure signals appears a significant peak at zero time delay, and the peak coefficient value is greater than 0.7, it is considered that the two have good synchronicity; when the cross-correlation coefficient appears a double peak at positive and negative time delays, and the absolute values of the peaks are large, it indicates that there is an alternating rise and fall mode. The significance of the alternating rise and fall mode is quantified by calculating the difference between the two peak coefficients, and when the difference is greater than 0.3, it is determined to be a significant alternating rise and fall mode, at which point it is determined that the disturbance intensity between the actuators is in a high state.

[0054] According to the combination state of the energy supply stability determination result and the actuator disturbance intensity determination result, a corresponding correction mode is selected from a plurality of preset correction modes. The selection of the correction mode is realized by a two-dimensional decision table, the rows of the decision table correspond to the energy supply stability state (divided into two levels of good and poor), and the columns correspond to the actuator disturbance intensity state (divided into two levels of high and low), forming a 2x2 decision matrix. Each cell is associated with a preset correction mode number, for example, mode 1 is used when the energy supply stability is good and the disturbance intensity is low, mode 2 is used when the stability is good but the disturbance intensity is high, mode 3 is used when the stability is poor but the disturbance intensity is low, and mode 4 is used when both are poor. The state classification threshold in the decision table is determined by statistical analysis of a large amount of experimental data, for example, the standard deviation threshold of good energy supply stability is set to 0.3 MPa, and the mutual correlation coefficient difference threshold of high disturbance intensity is set to 0.3.

[0055] Each correction mode corresponds to a set of pre-set correction parameters and calculation rules. The correction parameters include scaling coefficients and adjustment amounts, for example, the scaling coefficient of mode 1 is 1.0 and the adjustment amount is 0, that is, the original coefficient remains unchanged; the scaling coefficient of mode 2 is 0.9 and the adjustment amount is -0.02; the scaling coefficient of mode 3 is 1.1 and the adjustment amount is +0.03; and the scaling coefficient of mode 4 is 1.2 and the adjustment amount is +0.05. The calculation rule is a mathematical operation of linear scaling or superimposing a fixed adjustment amount on the hydraulic pump displacement distribution coefficient. The calculation formula of linear scaling is that the new coefficient value is equal to the original coefficient value multiplied by the scaling coefficient and then added to the adjustment amount. The determination of the scaling coefficient and the adjustment amount is based on system characteristic analysis and experimental verification, and the best parameter combination that can restore the stability of the system is selected by testing the effects of different parameter values under different working conditions.

[0056] When implementing the correction operation, the boundary condition processing also needs to be considered. The corrected coefficient value needs to be limited within the effective range, for example, the minimum value is 0.3 and the maximum value is 0.8. When the calculation result exceeds this range, amplitude limiting processing is used to limit it to the boundary value. At the same time, the change rate of the coefficient also needs to be limited, and the change amount in each sampling period should not exceed 0.05, so as to avoid system impact caused by excessive changes. The change rate limit is realized by comparing the difference between the new calculation value and the last period value, and when the difference exceeds the limit value, the last period value plus the limit value multiplied by the difference sign is used as the new output value.

[0057] To ensure the stability of the correction effect, a feedback adjustment mechanism needs to be established. After each correction operation, the change in system state needs to be monitored. If the system state does not improve or even worsens after correction, the correction parameters need to be adjusted or switched to other correction modes. The evaluation of state improvement is achieved by comparing the changes in pressure fluctuation amplitude and cross-correlation coefficient before and after correction. For example, if the standard deviation of pressure decreases and the peak value of cross-correlation coefficient increases after correction, it is considered to be effective improvement. Detailed logs of each correction operation are recorded, including the coefficient values before correction, the correction mode used, the correction parameters, the coefficient values after correction, and the changes in system state indicators. These log data are used for subsequent analysis and optimization of correction strategies.

[0058] All correction parameters need to be calibrated and updated regularly to adapt to changes in system characteristics. The calibration period is determined according to system usage, such as once every 100 hours of work. During the calibration process, the effects of each correction mode are tested under different working conditions, and the correction parameter values are adjusted based on the test results to achieve optimal correction effect. The calibration data is processed using least squares fitting to find the optimal parameter combination that maximizes system stability indicators. The calibration process includes establishing a test condition matrix that covers different load conditions and operation modes, recording system response data under each test condition, analyzing the impact of different parameter combinations on system stability, and finally determining the optimal parameter values.

[0059] The construction of the two-dimensional decision table needs to consider the working characteristics of the system. The granularity of state classification can be adjusted according to actual needs, such as dividing energy supply stability into four levels: excellent, good, medium, and poor, and dividing disturbance intensity into four levels: none, weak, medium, and strong, forming a 4x4 decision matrix. The threshold values for each state level are determined through cluster analysis methods. 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 values for state classification. The correction mode settings in the decision table need to be thoroughly verified to ensure that appropriate correction strategies are provided under various state combinations.

[0060] The implementation of correction operations needs to ensure real-time performance, and the computational complexity needs to be controlled within the processor's capability range. For linear scaling operations, fixed-point number operations are used to improve computational efficiency, and coefficient values are converted to Q-format fixed-point numbers for calculation. The application of adjustment amounts uses a gradual change method, which decomposes large adjustment amounts into multiple small steps and applies them gradually to avoid shocks to the system. A stable waiting time needs to be set after each correction operation, such as waiting for 10 sampling periods before the next correction, to ensure that the system has enough time to respond to adjustments.

[0061] The abnormality handling mechanism includes validity check of input data, suspension of correction operation and running with default parameters when sensor failure or data abnormality is detected. The failure detection is achieved by monitoring the rate of change and amplitude range of signals, for example, the pressure signal is determined to be abnormal when the rate of change exceeds 10 MPa / s or the amplitude exceeds 40 MPa. The system also sets a correction effect evaluation timeout mechanism, if the system state does not improve after multiple corrections, expert intervention mode is triggered, manual inspection of system state and adjustment of correction strategy are required.

[0062] Based on the obtained corrected hydraulic pump displacement distribution coefficient, the synchronization adjustment control process of the walking motor proportional valve and the implant device proportional valve is performed. First, the corrected hydraulic pump displacement distribution coefficient is mapped to the target opening reference value of the two proportional valves, and the mapping relationship is achieved by using a piecewise linear function. Specifically, a corresponding relationship between the hydraulic pump displacement distribution coefficient and the target opening reference value of the walking motor proportional valve is established, when the distribution coefficient is the minimum value 0.3, the corresponding opening reference value is 20%, when the distribution coefficient is the maximum value 0.8, the corresponding opening reference value is 80%, and the intermediate value is calculated by linear interpolation, for example, when the distribution coefficient is 0.55, the corresponding opening reference value is 50%. Similarly, a corresponding relationship between the hydraulic pump displacement distribution coefficient and the target opening reference value of the implant device proportional valve is established, but the mapping trend is opposite, 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%, and the intermediate value is also calculated by linear interpolation. This opposite mapping relationship ensures the reasonable distribution of hydraulic pump power between the two actuators, when the distribution coefficient increases, the walking motor obtains more flow distribution, and the implant device correspondingly reduces the flow distribution.

[0063] When the target opening reference value is adjusted by pressure compensation according to the current main pump outlet pressure, the reference value is adjusted by a pressure compensation coefficient. The calculation of the pressure compensation coefficient is based on the ratio of the main pump outlet pressure value to the system rated pressure, and the system rated pressure is determined according to the specifications of the hydraulic pump, 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 the lack of 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 reduced to avoid overload. The specific value of the compensation coefficient is determined by table lookup method, and a pressure-compensation coefficient lookup table is established in advance, which is based on the experimental data of the flow-pressure characteristics of the hydraulic system, for example, by testing the opening value required to reach the rated flow at different pressures, the corresponding relationship between pressure and compensation coefficient is established. For example, 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. Through this pressure compensation mechanism, appropriate flow distribution can be obtained under different working pressures.

[0064] When generating the final target opening command, the dynamic response characteristics of the proportional valve also need to be considered. The compensated opening value is subjected to first-order inertia filtering processing, and the filtering time constant is determined according to the response speed of the proportional valve. By measuring the step response characteristics of the proportional valve, for example, by giving a step signal to the proportional valve, the response time required from 10% opening to 90% opening is measured, and 1 / 3 of the response time is taken as the filtering time constant, for example, 0.05 seconds. Filtering processing can smooth the changes of the opening command and avoid impacting the proportional valve. At the same time, the opening command also needs to be subjected to limiting processing to ensure that its value is within the effective working range of the proportional valve, for example, the minimum opening is not less than 5% and the maximum opening is not more than 95%. The limiting value is determined according to the specific model and working characteristics of the proportional valve, and usually a certain margin is reserved to ensure safe operation, and the size of the margin is determined through experiments, for example, by testing the flow characteristics of the proportional valve at different openings to find the working range with better flow control linearity.

[0065] When outputting the final target opening command to the proportional valve driver, a synchronous output mechanism is adopted to ensure that the two proportional valves act simultaneously. The controller sends control commands to the two proportional valve drivers simultaneously through the CAN bus or analog output module, and the command sending interval is controlled within 1 millisecond to ensure synchronization accuracy. The proportional valve driver generates a corresponding driving current according to the received opening command value, and the driving current and the opening command are in a linear relationship, for example, an opening command of 0% corresponds to a driving current of 4 milliamperes, and an opening command of 100% corresponds to a driving current of 20 milliamperes. The specific value of the driving current is determined according to the current-displacement characteristics of the proportional valve, and by measuring the spool displacement of the proportional valve under different driving currents, the corresponding relationship between current and opening is established.

[0066] In order to ensure control accuracy, a closed-loop feedback mechanism also needs to be established. The actual position of the spool is detected in real time by the displacement sensor installed on the proportional valve, and the displacement sensor adopts an LVDT linear displacement sensor with a measurement accuracy of 0.1 millimeters. The detected value is compared with the target opening command to form a position closed-loop control. When the actual position deviates from the target position by more than the allowed range, for example, by more than 2%, the driving current output is adjusted to eliminate the deviation. The adjustment parameters of the position closed-loop control are set according to the dynamic characteristics of the proportional valve, and a PID control algorithm is adopted, and the proportional coefficient, integral time and derivative time are determined through experiments, for example, by 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 according to the overshoot and adjustment time, and finally the proportional coefficient is determined to be 0.8, the integral time is determined to be 0.1 seconds, and the derivative time is determined to be 0.01 seconds.

[0067] 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.

[0068] 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: 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. 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. 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. 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. 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. 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.

[0069] 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.

[0070] 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.

[0071] The above-described embodiments can be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented by software, the above-described embodiments can be implemented in whole or in part in the form of 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, the processes or functions described in the embodiments of the present application are wholly or partially generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium or transferred from one computer-readable storage medium to another computer-readable storage medium, for example, the computer instructions can be transferred from one website, computer, server, or data center to another website, computer, server, or data center through wireless or wired transmission. The wired transmission includes optical fiber, twisted pair, coaxial cable, etc. The wireless transmission includes infrared, microwave, etc. The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server, data center, etc. containing one or more available medium collections. The available medium can be a magnetic medium (for example, a floppy disk, a hard disk, a magnetic tape), an optical medium (for example, a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state disk.

[0072] Those skilled in the art can clearly understand that, for the convenience and brevity of the description, the specific working processes of the above-described system, device and module can refer to the corresponding processes in the foregoing method embodiments, which will not be described here.

[0073] In several embodiments provided in the present application, it should be understood that the disclosed system, device and method can be implemented in other ways. For example, the above-described device embodiments are only schematic, for example, the division of the modules is only a logical function division, and actual implementation can have another division manner, for example, a plurality of modules or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the coupling or direct coupling or communication connection between the displayed or discussed each other can be indirect coupling or communication connection through some interfaces, devices or modules, which can be electrical, mechanical or other forms.

[0074] The modules described as separate components can or can not be physically separated, and the components displayed as modules can or can not be physical modules, which can be located in one place or distributed on a plurality of network modules. Some or all of the modules can be selected according to actual needs to achieve the purpose of the embodiments.

[0075] In addition, each functional module in the various embodiments of the present application can be integrated in one processing module, or each module can exist physically independently, or two or more modules can be integrated in one module.

[0076] If the functions are implemented in the form of software function modules and sold or used as independent products, they can be stored in a computer readable storage medium. Based on this understanding, the technical solutions of the present application can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions to make a computer device (which can be a personal computer, a server or a network device, etc.) execute all or part of the steps of the methods described in the various embodiments of the present application. The aforementioned storage medium includes: a U disk, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, and various media that can store program codes.

[0077] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art can easily think of changes or replacements within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

[0078] Finally: the above is only the preferred embodiment of the present application, and is not used to limit the present application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principles of the present application should be included in the protection scope of the present application.

Claims

1. An adaptive parameter control method for a terraced rice transplanter based on full hydraulic transmission, characterized in that, include: S1. Real-time monitoring of the working pressure of the walking motor and the working pressure of the insertion device; 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. S3. Under power competition conditions, calculate the required power of the walking motor and the required power of the insertion device respectively. 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. 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. 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.

2. The adaptive parameter control method for a terraced rice transplanter based on full hydraulic transmission according to claim 1, characterized in that, Real-time monitoring of the operating pressure of the walking motor and the planting device, including: 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.

3. The adaptive parameter control method for a terraced rice transplanter based on full hydraulic transmission according to claim 1, characterized in that, The divergence of the envelopes of the two pressure signals—the working pressure of the walking motor and the working pressure of the insertion device—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: Envelope extraction was performed on the time-series data of the working pressure of the walking motor and the working pressure of the implantation device to obtain the envelope sequence of the working pressure of the walking motor and the working pressure of the implantation device. 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. 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.

4. The adaptive parameter control method for a terraced rice transplanter based on full hydraulic transmission according to claim 1, characterized in that, Under power competition conditions, the required power of the walking motor and the required power of the insertion device are calculated separately, including: 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. The real-time speed of the walking motor and the real-time speed of the insertion device are obtained respectively. 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. 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.

5. The adaptive parameter control method for a terraced rice transplanter based on full hydraulic transmission according to claim 1, characterized in that, Based on the ratio of the power required by the walking motor to the power required by the planting device, a hydraulic pump displacement distribution coefficient is generated, including: The total system power requirement is obtained by summing the power demand of the walking motor and the power demand of the insertion device. 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. The larger of the first and second proportional values ​​is determined as the base value of the hydraulic pump displacement distribution coefficient. 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.

6. The adaptive parameter control method for a terraced rice transplanter based on full hydraulic transmission according to claim 1, characterized in that, 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: 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. Analyze 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. When the pressure changes of the two show a significant alternating rise and fall pattern, it is determined that the disturbance intensity between the actuators is in a high state. 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. 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.

7. The adaptive parameter control method for a terraced rice transplanter based on full hydraulic transmission according to claim 6, characterized in that, Based on the combined state of the energy supply stability assessment results and the inter-agency disturbance intensity assessment results, the selection of the corresponding correction mode from a variety of preset correction modes is achieved in the following way: 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. 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.

8. The adaptive parameter control method for a terraced rice transplanter based on full hydraulic transmission according to claim 6, characterized in that, The correction of the hydraulic pump displacement distribution coefficient using the algorithm corresponding to the selected correction mode is achieved in the following way: 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. 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.

9. The adaptive parameter control method for a terraced rice transplanter based on full hydraulic transmission according to claim 1, characterized in that, 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: 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. 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. 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.

10. An adaptive parameter control system for a terraced rice transplanter based on full hydraulic transmission, used to implement the adaptive parameter control method for a terraced rice transplanter based on full hydraulic transmission as described in any one of claims 1-9, characterized in that, include: 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. 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. 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. 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. 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. 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.

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