MBSE-based climate adaptability agricultural machine whole life cycle depreciation rate accounting method

Abstract

The invention discloses an MBSE-based climate adaptability agricultural machine whole life cycle depreciation rate accounting method. The method comprises the following steps: S1, establishing a scatter data sequence; s2, establishing a working condition function of illumination and climate adaptive agricultural machinery operation; s3, establishing a mechanical dynamics model; s4, establishing a mechanical-electrical-thermal coupling simulation model; s5, loading the working condition function into the mechanical-electrical-thermal coupling simulation model; s6, establishing a control model of the climate adaptability agricultural machinery; s7, establishing an agricultural machinery degradation rate function based on MBSE; and S8, establishing a depreciation function. According to the method, a digital physical model with depreciation rate accounting as a core target is adopted, and the characteristics of the climate-adaptive agricultural machine body can be accurately restored; the full-life-cycle data loading of the climate adaptability agricultural machine based on the climate data model is closer to the real working condition; and high-precision calculation and evaluation of the depreciation rate of the agricultural machinery can be realized for the specific type of climate adaptability agricultural machinery with any working duration.

Description

Technical Field

[0001] This invention relates to a method for calculating the life-cycle depreciation rate of climate-adaptive agricultural machinery, specifically a method for calculating the life-cycle depreciation rate of climate-adaptive agricultural machinery based on MBSE (Model-Based Systems Engineering). Background Technology

[0002] Depreciation rate estimation, especially for climate-adaptive agricultural machinery, is inextricably linked to climate influences and the actual working and degradation status of the machinery itself. Compared to ordinary agricultural machinery, climate-adaptive machinery such as anti-winding tracks, micro-tillers, and water pumps have significant characteristics such as specialized climate response and high-efficiency, specialized operational adaptability. Their annual working hours are short and intensive, with less time spent on non-climate-adaptive or non-corresponding land operations. Therefore, their depreciation rate estimation is significantly affected by intermittent work schedules. Traditional constant or linear degradation rate depreciation estimation methods are difficult to accurately estimate the depreciated portion, resulting in low accuracy in depreciation rate estimation.

[0003] To address the above issues, based on existing meteorological data from multiple years, a degradation model for climate-adaptive agricultural machinery is established using digital methods. This model more accurately simulates the degradation process during normal downtime and operation, which will help improve the accuracy of depreciation estimation and more effectively estimate the overall depreciation rate of climate-adaptive agricultural machinery. Summary of the Invention

[0004] The purpose of this invention is to provide a method for calculating the life-cycle depreciation rate of climate-adaptive agricultural machinery based on MBSE. This method establishes a digital physical model with depreciation rate calculation as the main objective, maps the loading of the model load to climate time, transforms it into a time-series intensity sequence through a neural network algorithm, evaluates the life-cycle degradation process, and calculates and evaluates the depreciation rate of a specific type of climate-adaptive agricultural machinery with arbitrary working time, thus solving the problem of inaccurate depreciation rate estimation.

[0005] The objective of this invention is achieved through the following technical solution:

[0006] A method for calculating the life-cycle depreciation rate of climate-adaptive agricultural machinery based on MBSE includes the following steps:

[0007] Step S1: Based on the historical statistical data of a certain place, the parameters corresponding to a certain climate characteristic of the region are converted into thermal parameters, and a scatter data sequence with time as the sequence is established.

[0008] Step S2: Establish the working condition function for agricultural machinery operation based on light and climate adaptability, and extract the working segments corresponding to temperature and precipitation based on the scatter data sequence processed by Matlab.

[0009] Step S3: Extract the mechanical structure characteristics of climate-adaptive agricultural machinery and establish a mechanical dynamics model of the climate-adaptive agricultural machinery based on ANSYS Motion;

[0010] Step S4: Extract the electrical structural characteristics of the climate-adaptive agricultural machinery, establish an electrical model of the climate-adaptive agricultural machinery based on ANSYS Workbench, and combine it with the mechanical dynamics model to establish a mechanical-electrical-thermal coupled simulation model of the climate-adaptive agricultural machinery.

[0011] Step S5: Use the operating condition function as the load factor (temperature), establish its coupling relationship with the mechanical-electrical-thermal coupled simulation model, and load the operating condition function into the mechanical-electrical-thermal coupled simulation model;

[0012] Step S6: Establish a control model for climate-adaptive agricultural machinery based on Matlab Simulink, and simulate and analyze the losses of the climate-adaptive agricultural machinery;

[0013] Step S7: Establish an agricultural machinery degradation rate function based on MBSE. Using calculated data on mechanical wear, electrical wear, and climate corrosion, establish degradation data output. The expression for the agricultural machinery degradation rate function based on MBSE is:

[0014]

[0015] in, For the first Annual comprehensive degradation rate of agricultural machinery As the baseline degradation rate, This is the mechanical loss weighting coefficient. For the first Quantitative value of annual mechanical loss This is the electrical loss weighting coefficient. For the first Annual electrical loss quantification value This is the climate corrosion weighting coefficient. For the first Annual climate corrosion quantification value, For the first Annual assignment probability factor;

[0016] Step S8: Combine the degradation data with the initial values ​​of climate-adaptive agricultural machinery to establish a depreciation function:

[0017]

[0018] In the formula, The basic constant term, i.e., the depreciation basis, Total years of use For years, The physical regression coefficients of climate-adaptive agricultural machinery for the corresponding year, calculated for the MBSE degradation model. As a climate error variable, The probability of operation for climate-adaptive agricultural machinery is calculated based on past operating data. For stages involving only agricultural machinery, For all stages, This is random error.

[0019] Compared with the prior art, the present invention has the following advantages:

[0020] This invention employs a digital physical model with depreciation rate calculation as its core objective, which can more accurately reproduce the characteristics of climate-adaptive agricultural machinery. The loading of climate-adaptive agricultural machinery's full life cycle data based on the climate data model is more in line with real working conditions. For specific types of climate-adaptive agricultural machinery with any working time, it can achieve high-precision calculation and evaluation of their depreciation rate. Attached Figure Description

[0021] Figure 1 A flowchart illustrating the life-cycle depreciation rate calculation method for climate-adaptive agricultural machinery based on MBSE;

[0022] Figure 2 This is scatter plot data of climate-time series for a certain region;

[0023] Figure 3 A schematic diagram of the mechanical structure of a core component of a typical climate-adaptive agricultural machine;

[0024] Figure 4 This is a schematic diagram of the electrical structure of a core component of a typical climate-adaptive agricultural machine.

[0025] Figure 5 This is a schematic diagram of a climate-adaptive agricultural machinery coupled simulation control model.

[0026] Figure 6 This is the depreciation function and its typical calculation results. Detailed Implementation

[0027] The technical solution of the present invention will be further described below with reference to the accompanying drawings, but it is not limited thereto. Any modifications or equivalent substitutions to the technical solution of the present invention that do not depart from the spirit and scope of the technical solution of the present invention should be covered within the protection scope of the present invention.

[0028] This invention provides a method for calculating the life-cycle depreciation rate of climate-adaptive agricultural machinery based on MBSE, such as... Figure 1 As shown, the method includes the following steps:

[0029] Step S1: Based on the historical statistical data (10 years) of a certain place, the parameters (temperature, heat, precipitation, sunshine, etc.) corresponding to a certain climate characteristic of the region are converted into thermal parameters, and a scatter data sequence with time as the sequence is established.

[0030] Step S2: Establish operating condition functions for agricultural machinery operations adapted to light and climate, and extract the corresponding temperature and precipitation operating segments based on Matlab processing of scattered data sequences. Figure 2 ).

[0031] Step S3: Extract the mechanical structural characteristics of climate-adaptive agricultural machinery ( Figure 3 A mechanical dynamics model of climate-adaptive agricultural machinery was established based on ANSYS Motion, in which the material property values ​​were assigned as curves of temperature variation.

[0032] Step S4: Extract the electrical structural characteristics of climate-adaptive agricultural machinery ( Figure 4 , Figure 5 An electrical model of climate-adaptive agricultural machinery was established based on ANSYS Workbench. The core circuit was extracted, and the material properties of the drive motor coil, core circuit components, etc. were assigned as curves that change with temperature. Combined with the mechanical dynamics model, a mechanical-electrical-thermal coupled simulation model of climate-adaptive agricultural machinery was established.

[0033] Step S5: Use the operating condition function as the load factor (temperature), establish its coupling relationship with the mechanical-electrical-thermal coupled simulation model, and load the operating condition function into the mechanical-electrical-thermal coupled simulation model.

[0034] Step S6: Establish a control model for climate-adaptive agricultural machinery based on Matlab Simulink, and simulate and analyze the losses of climate-adaptive agricultural machinery (mechanical wear, structural deformation and fatigue, drive motor brush loss, corrosion and aging loss, etc.).

[0035] Step S7: Establish an agricultural machinery degradation rate function based on MBSE, and output degradation data by calculating mechanical loss, electrical loss, and climate corrosion data.

[0036]

[0037] in, For the first The annual comprehensive degradation rate of agricultural machinery reflects the degree of overall performance degradation of agricultural machinery; the higher the value, the faster the degradation. The baseline degradation rate, also known as the initial degradation coefficient of a new machine, is a fixed constant determined by the factory performance specification of the agricultural machinery. This is the mechanical loss weighting coefficient, representing the contribution of mechanical loss to degradation; For the first The annual mechanical loss quantification value is obtained by normalizing indicators such as wear amount and fatigue damage; This is the electrical loss weighting coefficient, representing the contribution of electrical loss to degradation; For the first The annual electrical loss quantification value is obtained by normalizing indicators such as contact resistance and insulation performance; This is the climate corrosion weighting coefficient, which characterizes the contribution of climate corrosion to degradation; For the first The annual climate corrosion quantification value is obtained by normalizing environmental indicators such as temperature, humidity, and salt spray corrosion. For the first Annual assignment probability factor.

[0038] Step S8: Combine the degradation data with the initial values ​​of climate-adaptive agricultural machinery to establish a depreciation function:

[0039]

[0040] In the formula, The basic constant term, i.e., the depreciation basis, Total years of use For years, The physical regression coefficients of climate-adaptive agricultural machinery for the corresponding year, calculated for the MBSE degradation model. As a climate error variable, The probability of operation for climate-adaptive agricultural machinery is calculated based on past operating data. For stages involving only agricultural machinery, For all stages, This is due to random error. The depreciation rate calculation results are as follows: Figure 6 As shown.

[0041] This invention uses digital methods to more accurately simulate the degradation process of climate-adaptive agricultural machinery during normal idle and working periods, improving the accuracy of depreciation estimation and enabling more effective estimation of the overall depreciation rate of climate-adaptive agricultural machinery.

Claims

1. A method for calculating the life-cycle depreciation rate of climate-adaptive agricultural machinery based on MBSE, characterized in that... The method includes the following steps: Step S1: Based on the historical statistical data of a certain place, the parameters corresponding to a certain climate characteristic of the region are converted into thermal parameters, and a scatter data sequence with time as the sequence is established. Step S2: Establish the working condition function for agricultural machinery operation based on light and climate adaptability, and extract the working segments corresponding to temperature and precipitation based on the scatter data sequence processed by Matlab. Step S3: Extract the mechanical structure characteristics of climate-adaptive agricultural machinery and establish a mechanical dynamics model of the climate-adaptive agricultural machinery based on ANSYS Motion; Step S4: Extract the electrical structural characteristics of the climate-adaptive agricultural machinery, establish an electrical model of the climate-adaptive agricultural machinery based on ANSYS Workbench, and combine it with the mechanical dynamics model to establish a mechanical-electrical-thermal coupled simulation model of the climate-adaptive agricultural machinery. Step S5: Use the operating condition function as a load coefficient to establish its coupling relationship with the mechanical-electrical-thermal coupled simulation model, and load the operating condition function into the mechanical-electrical-thermal coupled simulation model; Step S6: Establish a control model for climate-adaptive agricultural machinery based on Matlab Simulink, and simulate and analyze the losses of the climate-adaptive agricultural machinery; Step S7: Establish an agricultural machinery degradation rate function based on MBSE, and output degradation data using the calculated data of mechanical loss, electrical loss, and climate corrosion; Step S8: Combine the degradation data with the initial values ​​of climate-adaptive agricultural machinery to establish a depreciation function.

2. The method for calculating the life-cycle depreciation rate of climate-adaptive agricultural machinery based on MBSE as described in claim 1, characterized in that... In the aforementioned mechanical dynamics model, the material property assignments change with temperature.

3. The method for calculating the life-cycle depreciation rate of climate-adaptive agricultural machinery based on MBSE according to claim 1, characterized in that... In the electrical model, the material properties of the drive motor coil, core circuit components, etc., are all assigned as curves that change with temperature.

4. The method for calculating the life-cycle depreciation rate of climate-adaptive agricultural machinery based on MBSE as described in claim 1, characterized in that... The wear and tear on the climate-adaptive agricultural machinery includes mechanical wear, structural deformation and fatigue, wear of the drive motor brushes, and wear and tear due to corrosion and aging.

5. The method for calculating the life-cycle depreciation rate of climate-adaptive agricultural machinery based on MBSE according to claim 1, characterized in that... The expression for the MBSE-based agricultural machinery degradation rate function is as follows: in, For the first Annual comprehensive degradation rate of agricultural machinery As the baseline degradation rate, This is the mechanical loss weighting coefficient. For the first Quantitative value of annual mechanical loss This is the electrical loss weighting coefficient. For the first Annual electrical loss quantification value This is the climate corrosion weighting coefficient. For the first Annual climate corrosion quantification value, For the first Annual assignment probability factor.

6. The method for calculating the life-cycle depreciation rate of climate-adaptive agricultural machinery based on MBSE according to claim 1, characterized in that... The expression for the depreciation function is: In the formula, The basic constant term, i.e., the depreciation basis, Total years of use For years, The physical regression coefficients of climate-adaptive agricultural machinery for the corresponding year, calculated for the MBSE degradation model. As a climate error variable, The probability of operation for climate-adaptive agricultural machinery is calculated based on past operating data. For stages involving only agricultural machinery, For all stages, This is random error.

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