A multi-mode working condition-based hybrid tractor load spectrum effective stress identification method

Abstract

The present application relates to hybrid tractor technical field, specifically relates to a kind of based on the effective stress identification method of hybrid tractor load spectrum of multi-mode operating condition, comprising the following steps: S1, the load signal of power output shaft, suspension mechanism and battery management system under multi-mode operation is collected;S2, according to the segment of operating mode switching feature, extract peak value, change rate and energy proportion to generate load feature matrix;S3, combined with engine speed and motor torque curve, calculate dynamic torque distribution coefficient;S4, according to distribution coefficient and energy proportion, resolve and synthesize load spectrum into mechanical and electric drive load spectrum;S5, introduce slope angle and soil adhesion coefficient, correct load amplitude;S6, input equivalent damage model, output component stress nephogram.The present application realizes the effective stress identification and fatigue damage assessment of key components of hybrid tractor under complex working conditions, improves the accuracy of stress calculation and the scientificity of equipment operation and maintenance.

Description

Technical Field

[0001] This invention relates to the field of hybrid tractor technology, and in particular to a method for identifying the effective stress of the load spectrum of a hybrid tractor based on multi-mode operating conditions. Background Technology

[0002] With the continuous improvement of agricultural mechanization, hybrid tractors have been widely used in complex farmland operation environments due to their fuel economy and power response advantages. In order to improve the reliability of the whole system and the adaptability of structural design, it is necessary to conduct in-depth analysis of the load characteristics of hybrid tractors under various operation modes (such as rotary tillage, transportation, idling, etc.) and then assess the stress level and fatigue risk of its key components. In engineering practice, load spectrum analysis and equivalent stress identification technology have become important means of structural life assessment and fault early warning.

[0003] Existing load identification methods for agricultural machinery are mostly based on single operating conditions or idealized load conditions, ignoring the non-stationary characteristics of loads caused by frequent switching of operating modes and varying terrain and soil conditions in actual operations. They cannot fully reflect the load coupling behavior of each subsystem under multi-mode operating conditions. At the same time, traditional stress assessment methods mostly use empirical parameters or standard operating condition mapping, which makes it difficult to accurately characterize the energy coordination path of mechanical transmission and electric drive systems. They also fail to establish a closed-loop mapping from the original load signal to the effective stress of the component, resulting in large deviations in the fatigue damage assessment results of key structures, which affects the system optimization design and operation and maintenance strategy formulation. Summary of the Invention

[0004] This invention provides a method for identifying the effective stress of the load spectrum of a hybrid tractor based on multi-mode operating conditions. By constructing a feature matrix including peak load, load change rate, and energy ratio, and combining the torque distribution behavior of the power system with environmental-operating condition coupling factors, it achieves fine decomposition and correction of the load spectrum of the mechanical and electric drive subsystems. Furthermore, based on the equivalent damage model, it outputs the effective stress distribution map of the components, thereby improving the accuracy of structural life assessment and environmental adaptability, and meeting the reliability assessment needs of hybrid agricultural machinery under high-intensity and multi-scenario operating conditions.

[0005] A method for identifying effective stress in the load spectrum of a hybrid tractor based on multi-mode operating conditions includes the following steps: S1, through sensors installed on the power output shaft, suspension mechanism, and battery management system, synchronously collects load time-domain signals under multiple operating modes; S2, based on the characteristics of the operation mode switching, the load time domain signal is divided into independent segments, and the peak load, load change rate, and energy ratio parameters of each segment are extracted to generate a load feature matrix; S3. Based on the peak load and load change rate in the load characteristic matrix, combined with the engine speed curve and motor torque curve, calculate the dynamic torque distribution coefficient under the current working condition to characterize the energy distribution ratio between the mechanical transmission and electric drive systems. S4. Based on the dynamic torque distribution coefficient and combined with the energy proportion parameter in the load characteristic matrix, the synthesized load spectrum is decomposed into the mechanical subsystem load spectrum and the electric drive subsystem load spectrum. S5 introduces an environmental-working condition coupling correction factor, including slope angle and soil adhesion coefficient, to correct the amplitude of the decomposed synthetic load spectrum. S6 inputs the corrected and decomposed synthetic load spectrum into the equivalent damage superposition model and outputs the effective stress distribution cloud map of the specified component.

[0006] Optionally, S1 includes: S11, torque sensors, strain gauge force measuring devices and voltage and current acquisition units are respectively installed on the power output shaft (PTO), suspension mechanism and battery management system (BMS) of the hybrid tractor to obtain the original electrical signals of different working parts. Through static or dynamic calibration, the original electrical signals are converted into a time-domain physical quantity sequence to form basic load data, including power output shaft torque, suspension load and electric power. S12, using the main controller clock on the hybrid tractor or an external PPS (pulses per second) as a reference, aligns the base load data to a unified time grid by estimating the time offset and frequency drift of the power output shaft torque, suspension load, and electric power channel. S13, the aligned base load data is filtered to obtain the load time-domain data, including the power output shaft torque sequence. Suspension load sequence and electric power sequence .

[0007] Optionally, S2 includes: S21, based on the switching characteristics of the working mode, the load time domain data is segmented, and the load time domain data is divided into multiple load segments according to the changes in working conditions. S22. Extract peak load, load change rate and energy percentage parameters for each load segment, and organize them in chronological order to generate the corresponding load feature matrix.

[0008] Optionally, S21 includes: S211, On the unified time grid corresponding to the load time domain data, extract the operation mode label corresponding to the sampling point to form an operation mode indication sequence. ; S212, based on the work mode indication sequence, determine whether the labels of the two time points before and after have changed. If they have changed, record them as switching points. All switching points are used as segment boundaries to form a set of work mode switching time points. S213, using the time points in the set of work mode switching time points as boundaries, divides the synchronized load time domain data into multiple sub-segments according to time continuity, forming a set of load segments with working condition boundaries.

[0009] Optionally, S22 includes: S221, for each load segment, extract the maximum amplitude value of the power output shaft torque, suspension load, and electric power channel respectively, as the peak load of the load segment, reflecting the maximum load stress state under the working condition; S222, within each load segment, calculate the rate of change of power output shaft torque, suspension load, and electric power channel signal to reflect the intensity of load fluctuation in the corresponding operating condition range; S223. Calculate the total power consumption of each load segment and use the proportion of total power consumption in the total power consumption of all load segments as an energy weight index to reflect its contribution to the overall load process's energy consumption. Finally, organize the peak load, load change rate, and energy proportion parameters of each load segment in chronological order to form a load characteristic matrix. .

[0010] Optionally, S3 includes: S31, Based on the peak torque and the output shaft torque change rate in the load characteristic matrix, calculate the instantaneous comprehensive torque demand value corresponding to the current segment, which is used as a reference load index for the power system output; S32 uses the engine speed and motor speed under the current working conditions to obtain the corresponding maximum output torque, so as to determine its output capability under the current torque demand value; S33 combines the instantaneous comprehensive torque demand value with the maximum output torque output capacity of the engine and motor, and proportionally allocates the output tasks undertaken by the engine and motor to obtain the dynamic torque distribution coefficient between the mechanical system and the electric drive system.

[0011] Optionally, S4 includes: S41, constructing the synthetic load spectrum using the energy proportion of the load segments as weights. The distribution intensity function on each segment enables a weighted mapping of the total spectral distribution of each segment; S42, combined with the dynamic torque distribution coefficient of each segment, decomposes the load spectrum of the corresponding segment into mechanical subsystem components and electric drive subsystem components, reflecting the load proportion borne by each system; S43, all moments within the segment The decomposition results are summarized to form the load spectrum of the mechanical subsystem and the load spectrum of the electric drive subsystem.

[0012] Optionally, S5 includes: S51 obtains the slope angle and soil adhesion coefficient corresponding to each load segment through sensors, topographic maps or operation records, which is used to reflect the modulation effect of different operation conditions on the actual load response. S52, Construct a coupled correction function that combines slope angle and soil adhesion coefficient. The coupled correction function is used to calculate the coupling correction factor to reflect the actual impact of complex terrain and soil on the load transfer path. S53, apply the coupling correction factor corresponding to each load segment to the decomposed mechanical subsystem load spectrum and electric drive subsystem load spectrum to obtain the corrected spectrum after terrain and soil coupling.

[0013] Optionally, S6 includes: S61, based on the mechanical properties of the component material and the force transmission path at the installation location, converts the modified decomposed composite load spectrum into the stress response acting on the component. S62 inputs the calculated stress response into the equivalent damage model (Miner linear cumulative damage criterion) to calculate the cumulative damage caused by each stress level throughout the entire load cycle and estimate the equivalent fatigue load of the component material. S63 generates a stress visualization map to characterize the fatigue risk distribution at different locations of the component based on the spatial location mapping of stress response in the time domain and the results of cumulative damage. The output is a stress distribution cloud map.

[0014] Optionally, S62 includes: S621, components The stress response is counted by rainflow, and the repeated cycle information of different stress amplitude levels in the load cycle is extracted and statistically classified according to the amplitude. S622, based on the S-N curve of the component material, find the fatigue life corresponding to each stress amplitude level (i.e. the maximum number of cycles allowed at that stress level), and calculate the local damage factor of each stress amplitude level in combination with the number of stress cycles. S623 uses the Miner linear cumulative damage criterion to accumulate the damage factors for all stress amplitude levels, obtaining the component... Total equivalent fatigue damage value ,like This indicates that the failure threshold has been reached, and replacement or maintenance is recommended.

[0015] The beneficial effects of this invention are: This invention collects the power output shaft torque, suspension load, and electric power signals of a hybrid tractor in multi-mode operation, and combines this with operation mode switching information for refined segmentation and feature extraction. This enables the accurate construction of a load feature matrix, achieving a comprehensive characterization of load variation intensity, energy consumption distribution, and peak impact during operation.

[0016] This invention constructs an output capability model of the engine and motor, calculates the dynamic torque distribution coefficient by combining load characteristics, and further decomposes the synthetic load spectrum into independent load spectra of the mechanical subsystem and the electric drive subsystem. This enables the identification of the actual load contribution of multiple power sources in the hybrid system. At the same time, it introduces environmental-operating condition coupling factors such as slope angle and soil adhesion coefficient for amplitude correction, effectively reflecting the actual modulation effect of complex terrain on the load and improving the accuracy and adaptability of stress calculation.

[0017] This invention constructs an equivalent damage analysis path based on a modified load spectrum, utilizes rainflow counting, stress amplitude classification, and S-N fatigue curve lookup tables, and combines the Miner linear cumulative damage model to output the fatigue risk level and effective stress distribution cloud map of the target component under complex load cycles. This provides refined data support for the assessment of the remaining life of key structural components and the formulation of maintenance strategies, and has significant engineering practical value. Attached Figure Description

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

[0019] Figure 1 This is a schematic diagram of the identification method flow according to an embodiment of the present invention; Figure 2 This is a schematic diagram of the load feature extraction process according to an embodiment of the present invention. Detailed Implementation

[0020] The present invention will now be described in detail with reference to the accompanying drawings and specific embodiments. Those skilled in the art may employ other alternative methods to implement some well-known technologies; moreover, the accompanying drawings are only for more specific description of the embodiments and are not intended to specifically limit the present invention.

[0021] like Figures 1-2 As shown, a method for identifying the effective stress of a hybrid tractor load spectrum based on multi-mode operating conditions includes the following steps: S1, through sensors installed on the power output shaft, suspension mechanism, and battery management system, synchronously collects load time-domain signals under multiple operating modes; S2, based on the characteristics of the operation mode switching, the load time domain signal is divided into independent segments, and the peak load, load change rate, and energy ratio parameters of each segment are extracted to generate a load feature matrix; S3, based on the peak load and load change rate in the load characteristic matrix, combined with the engine speed curve and motor torque curve, calculates the dynamic torque distribution coefficient under the current working condition, which characterizes the energy distribution ratio between the mechanical transmission and electric drive systems; S4. Based on the dynamic torque distribution coefficient and the energy proportion parameter in the load characteristic matrix, the composite load spectrum is decomposed into the mechanical subsystem load spectrum and the electric drive subsystem load spectrum. S5 introduces an environmental-working condition coupling correction factor, including slope angle and soil adhesion coefficient, to correct the amplitude of the decomposed synthetic load spectrum. S6 inputs the corrected and decomposed synthetic load spectrum into the equivalent damage superposition model and outputs the effective stress distribution cloud map of the specified component.

[0022] S1 includes: S11, torque sensors, strain gauges, and voltage / current acquisition units are respectively installed on the power take-off shaft (PTO), suspension mechanism, and battery management system (BMS) of the hybrid tractor to acquire raw electrical signals from different operating parts. Through static or dynamic calibration, the raw electrical signals are converted into a time-domain physical quantity sequence to form basic load data, including power take-off shaft torque, suspension load, and electrical power, expressed as: ; ; ; in, The time-domain torque of the power output shaft. The original signal voltage of the torque sensor. , This is the torque channel calibration coefficient. For the suspension mechanism to bear the force, For strain signals, The elastic modulus of the component. For the cross-sectional area under stress, The electrical power measured by the battery management system. , These are the original output signals from the voltage and current sensors, respectively. , , , These are the calibration coefficients for the voltage and current channels, respectively. S12, to achieve synchronous acquisition of basic load data, using the main controller clock on the hybrid tractor or an external PPS (pulses per second) as a reference, the basic load data is aligned to a unified time grid by estimating the time offset and frequency drift of the power output shaft torque, suspension load, and electric power channel, ensuring that all data streams are accurately synchronized under the same time reference, as shown in: ; ; in, This represents the original sequence of physical quantities for the power output shaft torque, suspension load, and electrical power path. For the time after alignment The synchronization value, For channel The fixed time offset For channel clock drift ratio, For interpolation functions, To unify the global time grid One sampling point, The start time, For time step; S13, the aligned base load data is filtered to obtain the load time-domain data, including the power output shaft torque sequence. Suspension load sequence and electric power sequence , is represented as: ; in, For channel At any moment The filtered values This is the length of the sliding window.

[0023] S2 includes: S21, based on the switching characteristics of the working mode, the load time domain data is segmented, and the load time domain data is divided into multiple load segments according to the changes in working conditions. S22. Extract peak load, load change rate and energy percentage parameters for each load segment, and organize them in chronological order to generate the corresponding load feature matrix.

[0024] S21 includes: S211, On the unified time grid corresponding to the load time domain data, extract the operation mode label corresponding to the sampling point to form an operation mode indication sequence. , is represented as: ; ; in, For a moment The corresponding job mode label, A predefined set of operating modes (including rotary tillage, transportation, and idling). This represents the total number of sampling points; S212, based on the work mode indication sequence, determine whether the labels of two consecutive time points have changed. If they have changed, record them as switching points. All switching points form a set of work mode switching time points as segment boundaries, represented as: ; in, This is a set of time points for switching work modes. , Labels for work modes at adjacent time points; S213, using the time points in the set of work mode switching time points as boundaries, divides the synchronized load time-domain data into multiple sub-segments according to time continuity, forming a set of load segments with working condition boundaries, represented as: ; in, For the first One load segment, For a moment Load time-domain data, , The first The start and end times of each load segment. The total number of payload segments that were divided into segments.

[0025] S22 includes: S221, for each load segment, extract the maximum amplitude values ​​of the power output shaft torque, suspension load, and electric power channel, respectively, as the peak load of the load segment, reflecting the maximum load stress state under this working condition, expressed as: ; ; ; in, For fragments The collection of all time indices included in it. For fragments PTO peak torque, For fragments Peak suspension stress For fragments Peak electrical power; S222, within each load segment, calculate the rate of change of the power output shaft torque, suspension load, and electric power channel signal, reflecting the intensity of load fluctuation in the corresponding operating condition range, expressed as: ; ; ; in, , , The first The average rate of change of the power output shaft torque, suspension load, and electric power channel signals in each segment. For a time series, a uniform sampling interval is used. This excludes the last index (to prevent out-of-bounds errors). S223. Calculate the total power consumption of each load segment and use the proportion of total power consumption in the total power consumption of all load segments as an energy weight index to reflect its contribution to the overall load process's energy consumption. Finally, organize the peak load, load change rate, and energy proportion parameters of each load segment in chronological order to form a load characteristic matrix. , is represented as: ; ; in, For load segments Total energy consumption For load segments The proportion of energy; ; ; in, For the first Feature vectors of each load segment .

[0026] S3 includes: S31, based on the peak torque and output shaft torque change rate in the load characteristic matrix, calculate the instantaneous comprehensive torque demand value corresponding to the current segment, which is used as a reference load index for the power system output, expressed as: ; in, For the first The instantaneous combined torque demand value for each segment. , These are the corresponding weight coefficients; S32 uses the engine speed and motor speed under the current operating conditions to obtain the corresponding maximum output torque, in order to determine its output capability under the current torque demand value, expressed as: ; ; in, This refers to the engine's maximum torque output capability at the current engine speed. This refers to the maximum torque output capability of the motor at the current speed. , The current engine speed and motor speed are... , These are the torque output capability curve functions for the engine and the electric motor, respectively. ; in, , , , These are the fitting coefficients for the engine torque-speed curves; ; in, The rated torque of the motor. This refers to the rated power of the motor. This is the reference speed of the motor; S33, combining the instantaneous comprehensive torque demand value with the maximum output torque capacity of the engine and motor, proportionally allocates the output task undertaken by the engine and motor to obtain the dynamic torque distribution coefficient between the mechanical system and the electric drive system, expressed as: ; ; in, Assign coefficients to the mechanical transmission system (engine). Assign coefficients to the electric drive system (motor).

[0027] S4 includes: S41, constructing the synthetic load spectrum using the energy proportion of the load segments as weights. The distribution intensity function on each segment performs a weighted mapping of the total spectral distribution of each segment, expressed as: ; in, For the synthesis of the loading spectrum at time points The value on; S42, combining the dynamic torque distribution coefficient of each segment, decomposes the load spectrum of the corresponding segment into mechanical subsystem components and electric drive subsystem components, reflecting the load proportion borne by each system, as shown in: ; ; in, For a moment The load spectrum values ​​borne by the upper mechanical subsystem, For a moment The load spectrum values ​​borne by the electric drive subsystem; S43, all moments within the segment The decomposition results are summarized to form the load spectrum of the mechanical subsystem and the load spectrum of the electric drive subsystem, as follows: ; ; in, , These are the load spectra of the mechanical subsystem and the load spectra of the electric drive subsystem, respectively.

[0028] S5 includes: S51 obtains the slope angle and soil adhesion coefficient corresponding to each load segment through sensors, topographic maps or operation records, which is used to reflect the modulation effect of different operation conditions on the actual load response. S52, Construct a coupled correction function that combines slope angle and soil adhesion coefficient. The coupled correction function is used to calculate the coupling correction factor to reflect the actual impact of complex terrain and soil on the load transfer path, expressed as: ; in, For the first The coupling correction factor for each segment. The slope angle, Soil adhesion coefficient, This is the slope response weighting coefficient. Soil adhesion weighting coefficient; S53, the coupling correction factor corresponding to each load segment is applied to the decomposed mechanical subsystem load spectrum and electric drive subsystem load spectrum to obtain the corrected spectrum after terrain and soil coupling, as follows: ; ; in, The corrected load spectrum of the mechanical subsystem. This is the corrected load spectrum of the electric drive subsystem.

[0029] S6 includes: S61, based on the mechanical properties of the component material and the force transmission path at the installation location, the corrected decomposed composite load spectrum is converted into the stress response acting on the component, expressed as: ; in, For the first Each component at time Stress response, The corrected composite load spectrum after decomposition includes the corrected mechanical subsystem load spectrum and the corrected electric drive subsystem load spectrum. S62 inputs the calculated stress response into the equivalent damage model (Miner linear cumulative damage criterion) to calculate the cumulative damage caused by each stress level throughout the entire load cycle and estimate the equivalent fatigue load of the component material. S63 generates a stress visualization map to characterize the fatigue risk distribution at different locations of the component based on the spatial location mapping of stress response in the time domain and the results of cumulative damage. The output is a stress distribution cloud map.

[0030] S62 includes: S621, components The stress response was analyzed by rainflow counting, and the repeated cycle information of different stress amplitude levels in the load cycle was extracted and statistically classified according to the amplitude, as follows: ; in, For components The stress amplitude versus the number of cycles for the set, For the first Each stress amplitude level, For the first Number of cycles at each stress level This represents the total number of stress grades. Total number of stress grades Determined by the rainflow count, and expressed as: (1) Extracting stress cycles by rainflow counting: stress time history of the target component The rainflow counting method was applied to extract all stress cycles and record the corresponding stress amplitudes. (2) Determine the stress level: according to the preset stress interval All stress amplitudes are classified into multiple stress level ranges; (3) Count the number of non-zero stress levels: Count the actual number of stress levels with a cycle count greater than zero, and use this as the total number of stress levels. ; S622, based on the S-N curve of the component material, find the fatigue life (i.e., the maximum number of cycles allowed at that stress level) corresponding to each stress amplitude level, and calculate the local damage factor for each stress amplitude level in combination with the number of stress cycles, expressed as: ; in, For the first Local damage factors corresponding to each stress amplitude level For the first The actual number of cycles for each stress amplitude level For the first The fatigue life (permissible number of cycles) given by the S-N curve of the material at each stress amplitude level. S623 uses the Miner linear cumulative damage criterion to accumulate the damage factors for all stress amplitude levels, obtaining the component... Total equivalent fatigue damage value ,like This indicates that the failure threshold has been reached, and replacement or maintenance is recommended. It is represented as: .

[0031] This invention encompasses any substitutions, modifications, equivalent methods, and solutions made within the spirit and scope of this invention. To provide the public with a thorough understanding of this invention, specific details are described in detail in the following preferred embodiments; however, those skilled in the art will fully understand the invention even without these details. Furthermore, to avoid unnecessary misunderstanding of the essence of this invention, well-known methods, processes, procedures, components, and circuits are not described in detail.

[0032] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A method for identifying effective stress in the load spectrum of a hybrid tractor based on multi-mode operating conditions, characterized in that, Includes the following steps: S1, through sensors installed on the power output shaft, suspension mechanism, and battery management system, synchronously collects load time-domain signals under multiple operating modes; S2, based on the characteristics of the operation mode switching, the load time domain signal is divided into independent segments, and the peak load, load change rate, and energy ratio parameters of each segment are extracted to generate a load feature matrix; S3. Based on the peak load and load change rate in the load characteristic matrix, combined with the engine speed curve and motor torque curve, calculate the dynamic torque distribution coefficient under the current working condition to characterize the energy distribution ratio between the mechanical transmission and electric drive systems. S4. Based on the dynamic torque distribution coefficient and combined with the energy proportion parameter in the load characteristic matrix, the synthesized load spectrum is decomposed into the mechanical subsystem load spectrum and the electric drive subsystem load spectrum. S5 introduces an environmental-working condition coupling correction factor, including slope angle and soil adhesion coefficient, to correct the amplitude of the decomposed synthetic load spectrum. S6 inputs the corrected and decomposed synthetic load spectrum into the equivalent damage superposition model and outputs the effective stress distribution cloud map of the specified component.

2. The method for identifying effective stress of load spectrum of a hybrid tractor based on multi-mode operating conditions according to claim 1, characterized in that, S1 includes: S11, torque sensors, strain gauge force measuring devices and voltage and current acquisition units are respectively installed on the power output shaft, suspension mechanism and battery management system of the hybrid tractor to obtain the original electrical signals of different working parts. Through static or dynamic calibration, the original electrical signals are converted into a time-domain physical quantity sequence to form basic load data, including power output shaft torque, suspension load and electric power. S12, using the main controller clock on the hybrid tractor or an external PPS as a reference, aligns the base load data to a unified time grid by estimating the time offset and frequency drift of the power output shaft torque, suspension load, and electric power channel. S13, the aligned base load data is filtered to obtain the load time-domain data, including the power output shaft torque sequence. Suspension load sequence and electric power sequence .

3. The method for identifying effective stress of load spectrum of a hybrid tractor based on multi-mode operating conditions according to claim 2, characterized in that, S2 includes: S21, based on the switching characteristics of the working mode, the load time domain data is segmented, and the load time domain data is divided into multiple load segments according to the changes in working conditions. S22. Extract peak load, load change rate and energy percentage parameters for each load segment, and organize them in chronological order to generate the corresponding load feature matrix.

4. The method for identifying effective stress of load spectrum of a hybrid tractor based on multi-mode operating conditions according to claim 3, characterized in that, S21 includes: S211, On the unified time grid corresponding to the load time domain data, extract the operation mode label corresponding to the sampling point to form an operation mode indication sequence. ; S212, based on the work mode indication sequence, determine whether the labels of the two time points before and after have changed. If they have changed, record them as switching points. All switching points are used as segment boundaries to form a set of work mode switching time points. S213, using the time points in the set of work mode switching time points as boundaries, divides the synchronized load time domain data into multiple sub-segments according to time continuity, forming a set of load segments with working condition boundaries.

5. The method for identifying effective stress of load spectrum of a hybrid tractor based on multi-mode operating conditions according to claim 4, characterized in that, S22 includes: S221, for each load segment, extract the maximum amplitude value of the power output shaft torque, suspension load, and electric power channel respectively, as the peak load of the load segment, reflecting the maximum load stress state under the working condition; S222, within each load segment, calculate the rate of change of power output shaft torque, suspension load, and electric power channel signal to reflect the intensity of load fluctuation in the corresponding operating condition range; S223. Calculate the total power consumption of each load segment and use the proportion of total power consumption in the total power consumption of all load segments as an energy weight index to reflect its contribution to the overall load process's energy consumption. Finally, organize the peak load, load change rate, and energy proportion parameters of each load segment in chronological order to form a load characteristic matrix. .

6. The method for identifying effective stress of load spectrum of a hybrid tractor based on multi-mode operating conditions according to claim 5, characterized in that, S3 includes: S31, Based on the peak torque and the output shaft torque change rate in the load characteristic matrix, calculate the instantaneous comprehensive torque demand value corresponding to the current segment, which is used as a reference load index for the power system output; S32 uses the engine speed and motor speed under the current working conditions to obtain the corresponding maximum output torque, so as to determine its output capability under the current torque demand value; S33 combines the instantaneous comprehensive torque demand value with the maximum output torque output capacity of the engine and motor, and proportionally allocates the output tasks undertaken by the engine and motor to obtain the dynamic torque distribution coefficient between the mechanical system and the electric drive system.

7. The method for identifying effective stress of load spectrum of a hybrid tractor based on multi-mode operating conditions according to claim 6, characterized in that, S4 includes: S41, constructing the synthetic load spectrum using the energy proportion of the load segments as weights. The distribution intensity function on each segment enables a weighted mapping of the total spectral distribution of each segment; S42, combined with the dynamic torque distribution coefficient of each segment, decomposes the load spectrum of the corresponding segment into mechanical subsystem components and electric drive subsystem components, reflecting the load proportion borne by each system; S43, all moments within the segment The decomposition results are summarized to form the load spectrum of the mechanical subsystem and the load spectrum of the electric drive subsystem.

8. The method for identifying effective stress of load spectrum of a hybrid tractor based on multi-mode operating conditions according to claim 7, characterized in that, S5 includes: S51 obtains the slope angle and soil adhesion coefficient corresponding to each load segment through sensors, topographic maps or operation records, which is used to reflect the modulation effect of different operation conditions on the actual load response. S52, Construct a coupled correction function that combines slope angle and soil adhesion coefficient. The coupled correction function is used to calculate the coupling correction factor to reflect the actual impact of complex terrain and soil on the load transfer path. S53, apply the coupling correction factor corresponding to each load segment to the decomposed mechanical subsystem load spectrum and electric drive subsystem load spectrum to obtain the corrected spectrum after terrain and soil coupling.

9. The method for identifying effective stress of load spectrum of a hybrid tractor based on multi-mode operating conditions according to claim 8, characterized in that, S6 includes: S61, based on the mechanical properties of the component material and the force transmission path at the installation location, converts the modified decomposed composite load spectrum into the stress response acting on the component. S62 inputs the calculated stress response into the equivalent damage model to calculate the cumulative damage caused by each stress level throughout the entire load cycle and estimate the equivalent fatigue load of the component material. S63 generates a stress visualization map to characterize the fatigue risk distribution at different locations of the component based on the spatial location mapping of stress response in the time domain and the results of cumulative damage. The output is a stress distribution cloud map.

10. The method for identifying effective stress of load spectrum of a hybrid tractor based on multi-mode operating conditions according to claim 9, characterized in that, S62 includes: S621, components The stress response is counted by rainflow, and the repeated cycle information of different stress amplitude levels in the load cycle is extracted and statistically classified according to the amplitude. S622, based on the S-N curve of the component material, finds the fatigue life corresponding to each stress amplitude level, and calculates the local damage factor of each stress amplitude level in combination with the number of stress cycles. S623 uses the Miner linear cumulative damage criterion to accumulate the damage factors for all stress amplitude levels, obtaining the component... Total equivalent fatigue damage value ,like This indicates that the failure threshold has been reached, and replacement or maintenance is recommended.

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