Optimization method of built-in permanent magnetic mechanism

Abstract

The invention provides an optimization method for a built-in permanent magnetic mechanism, and relates to the technical field of built-in permanent magnetic mechanisms, and the method achieves the dynamic and precise temperature compensation of the magnetic induction intensity of the permanent magnetic mechanism through testing the temperature change of the permanent magnetic mechanism in an actual working environment, generating a PWM control signal based on the time sequence analysis, and employing a compensation coil to achieve the dynamic and precise temperature compensation of the magnetic induction intensity of the permanent magnetic mechanism. The temperature coefficient is obtained through a temperature gradient test, the compensation coil parameters are determined through simulation modeling, and the composite constraint optimization design is utilized, so that the compensation precision and the system stability are remarkably improved. Meanwhile, by considering the production cost and the use cost, the weight is dynamically adjusted, and the design economy and the market competitiveness are optimized. Through the improvements, the problems of inaccurate temperature compensation, high cost and poor market adaptability in the prior art are effectively solved, so that the permanent magnetic mechanism can keep the optimal performance in various working environments, and the permanent magnetic mechanism has remarkable engineering application value and market popularization prospect.

Description

Technical Field

[0001] The invention relates to the technical field of built-in permanent magnet mechanisms, and in particular to an optimization method for built-in permanent magnet mechanisms. Background Art

[0002] In the application of permanent magnet mechanisms, permanent magnet motors are a relatively common type. When a permanent magnet motor is working, temperature changes have a certain impact on the magnetic properties of the permanent magnets inside it, resulting in a decrease in working efficiency and stability. Traditional methods usually rely on fixed temperature compensation strategies, which cannot adapt to temperature fluctuations in the actual working environment, have limited compensation accuracy, and generally compensate in the direction of the motor's torque rather than the magnetic induction intensity of the permanent magnet. This method is more suitable for motors with faster speeds, but is not so suitable in certain specific scenarios or special motors that require lower speeds. In addition, in the design and optimization process of compensation coils, the existing technology often ignores the impact of cost-effectiveness and market price fluctuations, resulting in high production costs and poor economic benefits. Therefore, how to achieve dynamic and accurate temperature compensation, while taking into account cost-effectiveness and market adaptability, has become a technical problem that needs to be urgently solved in the practical application of permanent magnet mechanisms.

[0003] The above information disclosed in this Background section is only for enhancement of understanding of the background of the present disclosure and therefore it may contain information that does not constitute the prior art that is already known to one of ordinary skill in the art. Summary of the invention

[0004] The object of the present invention is to provide an optimization method for a built-in permanent magnet mechanism to solve the problems raised in the above background technology.

[0005] To achieve the above object, the present invention provides the following technical solutions:

[0006] An optimization method for a built-in permanent magnet mechanism, the specific steps comprising:

[0007] S1: Randomly select at least 3 groups of test samples from the permanent magnet mechanism to conduct temperature gradient test, obtain the temperature coefficient of each group of test samples, and take the average value as the temperature coefficient of the permanent magnet mechanism of this batch;

[0008] S2: Simulate and model the permanent magnet mechanism, set a compensation coil in the permanent magnet mechanism, simulate the theoretical temperature of the permanent magnet mechanism in the actual working environment, calculate the magnetic induction intensity of the permanent magnet mechanism at the theoretical temperature, and construct an optimization function for the process parameters of the compensation coil;

[0009] S3: Taking the volume and power consumption of the compensation coil as constraint conditions, weighting the volume and power consumption to construct a composite constraint, and then using the composite constraint to solve the optimization function, the process parameters of the compensation coil are optimized by dynamically adjusting the weights of the volume and power consumption in the composite constraint calculation, and the compensation coil is set with the optimized process parameters;

[0010] S4: Place the permanent magnet mechanism in an actual working environment for testing, collect the actual temperature of the permanent magnet mechanism during the test, generate a curve of the actual temperature change with respect to the test time, perform a time series analysis on the change curve, divide the analysis results into intervals, set the duty cycle of the compensation current according to each interval, and generate the final PWM control signal.

[0011] Preferably, when the test sample is subjected to a temperature gradient test, the temperature range covered is set to -20°C to 80°C, with 5°C as the temperature step, and the test temperature and test magnetic induction intensity of the permanent magnet mechanism at each temperature step are collected, and then the test temperature and the test magnetic induction intensity are fitted to obtain the temperature coefficient of each test sample, and the fitting formula is:

[0012] B i (T) = B (T0) [1-a i (T-T0)]

[0013] Where B i (T) represents the test magnetic induction intensity of the i-th test sample at the test temperature T, T0 represents the preset reference temperature, B(T0) represents the standard magnetic induction intensity at the reference temperature, a i represents the temperature coefficient of the i-th test sample, the subscript i represents the sample number of the test sample, i = 1, 2, 3, ..., M, where M is the number of test samples, and then the temperature coefficient a of the M test samples is calculated. i Find the mean And the mean As the temperature coefficient of this batch of permanent magnet mechanisms.

[0014] Preferably, the process parameters of the compensation coil include the number of coil turns n, the coil diameter D, and the resistance wire diameter d. When determining the initial process parameters of the compensation coil, firstly, the magnetic induction intensity ΔB that needs to be compensated by the compensation coil is calculated theoretically according to the theoretical temperature Tl of the permanent magnet mechanism in the actual working environment during simulation, ΔB=B(Tl)-B(T0), where B(Tl) represents the magnetic induction intensity of the permanent magnet mechanism at the theoretical temperature Tl;

[0015] The Lagrangian function is defined according to the composite constraints and the optimization function, and then the partial derivatives of the parameters in the Lagrangian function are calculated and set equal to zero, and the numerical method is used to solve it, so as to obtain the optimal process parameters.

[0016] Preferably, constructing an optimization function for the compensation coil process parameters includes:

[0017]

[0018] Where I, P, and R represent the compensation current, power, and total resistance of the compensation coil, respectively; l, A, ρ, and V represent the length, cross-sectional area, resistivity, and volume of the resistance wire of the compensation coil, respectively; and μ0 represents the vacuum permeability, where l = πDn, A = πd 2 / 4;

[0019] The constructed composite constraint is expressed as:

[0020] F(D,n,A)=δ1V+δ2P

[0021] Wherein F(D, n, A) represents the constraint function, δ1 and δ2 represent the first weight and the second weight respectively, and both are greater than 0 and less than 1, and δ1+δ2=1.

[0022] Preferably, the Lagrangian function is expressed as:

[0023]

[0024] In the formula represents the Lagrangian function, λ represents the Lagrangian multiplier, P0 represents the preset target power. When calculating the partial derivative, D, n, A, and λ are derived respectively to obtain the optimal solution of D, n, and A. Then, the diameter d of the resistance wire is calculated according to the cross-sectional area of ​​the resistance wire to obtain the final process parameters.

[0025] Preferably, the first weight and the second weight are determined by market fluctuations, and the market fluctuations are price fluctuations of the resistance wire. The specific logic is:

[0026] Collect the resistance wire in the past period of time T g Historical price data within M l and real-time price data M0, and then calculate the first weight based on the real-time price data and historical price data, the calculation method is:

[0027]

[0028] Where M l (max), M l (min) represent the maximum and minimum values ​​in the historical price data respectively.

[0029] Preferably, after performing a time series analysis on the variation curve of the actual temperature with respect to the test time, the time series fluctuation period T Z As the observation window, divide the time series fluctuation period into k intervals and calculate the average actual temperature in each interval The subscript j represents the interval number, j = 1, 2, 3, ..., k;

[0030] The magnetic induction intensity ΔB that actually needs to be compensated by the compensation coil is calculated based on the average actual temperature in each interval. ′ , in Indicates the average actual temperature of the permanent magnet mechanism The magnetic induction intensity under the condition of ΔB ′ Calculate the actual compensation current I ′ , calculated as:

[0031]

[0032] Where n Z Indicates the number of coil turns after optimization;

[0033] Then according to the actual compensation current I ′ and the preset peak current I P Calculate the duty cycle D corresponding to this interval j , D j =I ′ / I P , connect the duty cycle corresponding to each interval according to the interval number to obtain the final PWM control signal.

[0034] Compared with the prior art, the present invention has the following beneficial effects:

[0035] The present invention tests the temperature change of the permanent magnet mechanism under the actual working environment, generates a PWM control signal based on the timing analysis, and uses the compensation coil to realize dynamic and accurate temperature compensation for the magnetic induction intensity of the permanent magnet mechanism. The temperature coefficient is obtained through the temperature gradient test, the compensation coil parameters are determined by simulation modeling, and the composite constraint optimization design is used to significantly improve the compensation accuracy and system stability. At the same time, by considering the production cost and the use cost, the weight is dynamically adjusted to optimize the design economy and market competitiveness. These improvements effectively solve the problems of inaccurate temperature compensation, high cost and poor market adaptability in the prior art, so that the permanent magnet mechanism can maintain the best performance under various working environments, and has significant engineering application value and market promotion prospects. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Figure 1 It is a schematic diagram of the overall method flow of the present invention. DETAILED DESCRIPTION

[0037] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with specific embodiments.

[0038] It should be noted that, unless otherwise defined, the technical terms or scientific terms used in the present invention should be understood by people with ordinary skills in the field to which the present invention belongs. The words "first", "second" and similar words used in the present invention do not indicate any order, quantity or importance, but are only used to distinguish different components. "Include" or "comprise" and similar words mean that the elements or objects appearing before the word include the elements or objects listed after the word and their equivalents, without excluding other elements or objects. "Connect" or "connected" and similar words are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. "Up", "down", "left", "right" and the like are only used to indicate relative positional relationships. When the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0039] Example:

[0040] See also Figure 1 , the present invention provides a technical solution:

[0041] An optimization method for a built-in permanent magnet mechanism, the specific steps comprising:

[0042] S1: Randomly select at least 3 groups from the permanent magnet mechanism as test samples for temperature gradient test, obtain the temperature coefficient of each group of test samples, and take the average as the temperature coefficient of the permanent magnet mechanism of this batch.

[0043] When the test sample is subjected to the temperature gradient test, the temperature range covered is set to -20℃~80℃, with 5℃ as the temperature step. The test temperature and test magnetic induction intensity of the permanent magnet mechanism at each temperature step are collected, and then the test temperature and test magnetic induction intensity are fitted to obtain the temperature coefficient of each test sample. The fitting formula is:

[0044] B i (T) = B (T0) [1-a i (T-T0)]

[0045] Where B i (T) represents the test magnetic induction intensity of the i-th test sample at the test temperature T, T0 represents the preset reference temperature, B(T0) represents the standard magnetic induction intensity at the reference temperature, a i represents the temperature coefficient of the i-th test sample, the subscript i represents the sample number of the test sample, i = 1, 2, 3, ..., M, where M is the number of test samples, and then the temperature coefficient a of the M test samples is i Find the mean And the mean As the temperature coefficient of this batch of permanent magnet mechanisms.

[0046] In this step, the temperature gradient test is used to obtain the change law of the magnetic induction intensity of each batch of permanent magnet mechanisms at different temperatures, and the temperature coefficient is calculated. This not only facilitates the accurate simulation and compensation of the magnetic property changes caused by temperature changes when optimizing the compensation coil parameters in the subsequent process, but also avoids the problem of temperature coefficient differences between batches after different batches of permanent magnet raw materials are assembled into permanent magnet mechanisms. In addition, multi-point temperature measurement can fully cover the actual working temperature range and obtain a more accurate temperature coefficient, thereby improving the design and optimization accuracy of the compensation coil.

[0047] S2: Simulate and model the permanent magnet mechanism, set a compensation coil in the permanent magnet mechanism, simulate the theoretical temperature of the permanent magnet mechanism in the actual working environment, calculate the magnetic induction intensity of the permanent magnet mechanism at the theoretical temperature, and construct an optimization function for the process parameters of the compensation coil.

[0048] The process parameters of the compensation coil include the number of coil turns n, the coil diameter D, and the resistance wire diameter d. When determining the initial process parameters of the compensation coil, the magnetic induction intensity ΔB that theoretically needs to be compensated by the compensation coil is first calculated based on the theoretical temperature Tl of the permanent magnet mechanism in the actual working environment during simulation, ΔB=B(Tl)-B(T0), where B(Tl) represents the magnetic induction intensity of the permanent magnet mechanism at the theoretical temperature Tl.

[0049] The optimization function of the compensation coil process parameters is constructed as follows:

[0050]

[0051] Where I, P, and R represent the compensation current, power, and total resistance of the compensation coil, respectively; l, A, ρ, and V represent the length, cross-sectional area, resistivity, and volume of the resistance wire of the compensation coil, respectively; and μ0 represents the vacuum permeability, where l = πDn, A = πd 2 / 4.

[0052] In this step, through simulation modeling and calculation of magnetic induction intensity at theoretical temperature, the magnetic induction intensity that needs to be compensated by the compensation coil can be accurately determined, which can more accurately reflect the changes in magnetic properties of the permanent magnet mechanism under the actual working environment, thereby improving the accuracy of compensation. At the same time, by constructing an optimization function, the various process parameters of the compensation coil (such as the number of coil turns n, coil diameter D, resistance wire diameter d) are associated with the magnetic induction intensity ΔB, power P, resistance value R, etc. that need to be compensated, forming a systematic optimization framework, which helps to comprehensively consider the mutual influence between various parameters when designing the compensation coil, thereby achieving a more optimized design.

[0053] S3: Taking the volume and power consumption of the compensation coil as constraints, weighted processing is performed on the volume and power consumption to construct a composite constraint, and then the composite constraint is used to solve the optimization function. The process parameters of the compensation coil are optimized by dynamically adjusting the weights of the volume and power consumption in the composite constraint calculation, and the compensation coil is set with the optimized process parameters.

[0054] Define the Lagrangian function based on the composite constraints and the optimization function,

[0055] The constructed composite constraint is expressed as:

[0056] F(D,n,A)=δ1V+δ2P

[0057] Wherein F(D, n, A) represents the constraint function, δ1 and δ2 represent the first weight and the second weight respectively, and both are greater than 0 and less than 1, and δ1+δ2=1.

[0058] The first weight and the second weight are determined by market fluctuations, which are price fluctuations of the resistance wire. The specific logic is:

[0059] Collect the resistance wire in the past period of time T g Historical price data within M l and real-time price data M0, and then calculate the first weight based on the real-time price data and historical price data, the calculation method is:

[0060]

[0061] Where M l (max), M l (min) represent the maximum and minimum values ​​in the historical price data respectively.

[0062] The composite constraint here is mainly based on cost considerations. The cost is mainly divided into production cost and use cost. When winding the compensation coil, the volume of the resistance wire in the compensation coil is proportional to the weight of the material consumed, that is, it is positively correlated with the production cost. Similarly, the power of the compensation coil is positively correlated with the use cost. Therefore, for cost considerations, the composite constraint needs to be as small as possible, which not only helps to reduce production costs and increase economic benefits, but also facilitates market promotion and increases market competitiveness. Among the first weight and the second weight, the first weight is mainly analyzed. This is because the price of the resistance wire in the production cost often fluctuates, while the use cost, that is, the fluctuation of the electricity fee is relatively small and relatively stable. Therefore, the weight is mainly analyzed and set from the price of the resistance wire that fluctuates more frequently. This method of dynamically adjusting the weight is more flexible and responsive, which significantly improves the economy and market competitiveness of the design. These advantages jointly promote the economic benefits, design efficiency and market adaptability of the overall solution.

[0063] The Lagrangian function is expressed as:

[0064]

[0065] In the formula represents the Lagrangian function, λ represents the Lagrangian multiplier, and P0 represents the preset target power. The Lagrangian function is constructed to minimize the composite constraint, so it is introduced into the Lagrangian function. In addition, since a power constraint needs to be satisfied, that is, the actual power P should be equal to the required target power P0. We introduce the Lagrangian multiplier to handle this constraint. That is to say, the last term λ(P-P0) in the Lagrangian function ensures that the power constraint is satisfied while the composite function is minimized.

[0066] When calculating partial derivatives, we take the derivatives of D, n, A, and λ respectively and set them equal to zero. The specific partial derivatives are expressed as follows:

[0067]

[0068] Since this set of equations is relatively complex, numerical methods (such as SciPy's optimization function) can be used to solve it, so as to obtain the optimal solution of D, n, and A, and then calculate the resistance wire diameter d based on the cross-sectional area of ​​the resistance wire to obtain the final process parameters.

[0069] In this step, by considering the production cost and the use cost to construct the composite constraint, the material consumption in the production process is minimized, and the energy consumption in the use process is reduced, thereby achieving multi-objective optimization. At the same time, the objective weights can be flexibly adjusted to meet the actual constraints, and the solution efficiency is high. These advantages jointly promote the economy, design efficiency and applicability of the overall solution, and have significant engineering application value and market competitiveness.

[0070] S4: Place the permanent magnet mechanism in an actual working environment for testing, collect the actual temperature of the permanent magnet mechanism during the test, generate a curve of the actual temperature change with respect to the test time, perform a time series analysis on the change curve, divide the analysis results into intervals, set the duty cycle of the compensation current according to each interval, and generate the final PWM control signal.

[0071] After analyzing the actual temperature variation curve with respect to the test time, according to the timing fluctuation period T Z As the observation window, divide the time series fluctuation period into k intervals and calculate the average actual temperature in each interval Subscript j represents the interval number, j = 1, 2, 3, ..., k. The magnetic induction intensity ΔB that actually needs to be compensated by the compensation coil is calculated based on the average actual temperature in each interval. ′ , in Indicates the average actual temperature of the permanent magnet mechanism The magnetic induction intensity under the condition of ΔB ′ Calculate the actual compensation current I ′ , calculated as:

[0072]

[0073] Where n Z Indicates the number of coil turns after optimization;

[0074] Then according to the actual compensation current I ′ and the preset peak current I P Calculate the duty cycle D corresponding to this interval j , D j =I ′ / I P , connect the duty cycle corresponding to each interval according to the interval number to obtain the final PWM control signal.

[0075] In this step, the corresponding duty cycle is calculated according to the compensation current of each interval, and the final PWM control signal is generated. This method simplifies the control process, avoids manual intervention, and ensures the accuracy and consistency of the compensation signal. At the same time, the PWM signal can flexibly control the current by adjusting the duty cycle, thereby achieving precise control of the compensation current. At the same time, by setting the PWM control signal in advance, the response time of the system can be reduced, which has the effect of improving the response speed.

[0076] In summary, the present invention tests the temperature change of the permanent magnet mechanism under the actual working environment, generates a PWM control signal based on the timing analysis, and uses the compensation coil to achieve dynamic and accurate temperature compensation for the magnetic induction intensity of the permanent magnet mechanism. The temperature coefficient is obtained through the temperature gradient test, the compensation coil parameters are determined by simulation modeling, and the composite constraint optimization design is used to significantly improve the compensation accuracy and system stability. At the same time, by considering the production cost and the use cost, the weight is dynamically adjusted to optimize the design economy and market competitiveness. These improvements effectively solve the problems of inaccurate temperature compensation, high cost and poor market adaptability in the prior art, so that the permanent magnet mechanism can maintain optimal performance under various working environments, and has significant engineering application value and market promotion prospects.

[0077] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0078] The above embodiments may be implemented in whole or in part by software, hardware, firmware or any other combination thereof. When implemented by software, the above embodiments may be implemented in whole or in part in the form of a computer program product. Those skilled in the art may appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein may be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed by hardware or software methods depends on the specific application and design constraints of the technical solution.

[0079] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, and may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0080] The above description is only a specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application.

Claims

1. A method for optimizing a built-in permanent magnet mechanism, characterized in that: The specific steps include: S1: Randomly select at least 3 groups of test samples from the permanent magnet mechanism to conduct temperature gradient test, obtain the temperature coefficient of each group of test samples, and take the average value as the temperature coefficient of the permanent magnet mechanism of this batch; S2: Simulate and model the permanent magnet mechanism, set a compensation coil in the permanent magnet mechanism, simulate the theoretical temperature of the permanent magnet mechanism in the actual working environment, calculate the magnetic induction intensity of the permanent magnet mechanism at the theoretical temperature, and construct an optimization function for the process parameters of the compensation coil; S3: Taking the volume and power consumption of the compensation coil as constraint conditions, weighting the volume and power consumption to construct a composite constraint, and then using the composite constraint to solve the optimization function, the process parameters of the compensation coil are optimized by dynamically adjusting the weights of the volume and power consumption in the composite constraint calculation, and the compensation coil is set with the optimized process parameters; S4: Place the permanent magnet mechanism in an actual working environment for testing, collect the actual temperature of the permanent magnet mechanism during the test, generate a curve of the actual temperature change with respect to the test time, perform a time series analysis on the change curve, divide the analysis results into intervals, set the duty cycle of the compensation current according to each interval, and generate the final PWM control signal.

2. The optimization method of a built-in permanent magnet mechanism according to claim 1, characterized in that: When the test sample is subjected to the temperature gradient test, the temperature range covered is set to -20℃~80℃, with 5℃ as the temperature step. The test temperature and test magnetic induction intensity of the permanent magnet mechanism at each temperature step are collected, and then the test temperature and test magnetic induction intensity are fitted to obtain the temperature coefficient of each test sample. The fitting formula is: B i (T)=B(T0)[1-a i (T-T0)] Where B i (T) represents the test magnetic induction intensity of the i-th test sample at the test temperature T, T0 represents the preset reference temperature, B(T0) represents the standard magnetic induction intensity at the reference temperature, a i represents the temperature coefficient of the i-th test sample, the subscript i represents the sample number of the test sample, i = 1, 2, 3, ..., M, where M is the number of test samples, and then the temperature coefficient a of the M test samples is i Find the mean And the mean As the temperature coefficient of this batch of permanent magnet mechanisms.

3. The optimization method of a built-in permanent magnet mechanism according to claim 1, characterized in that: The process parameters of the compensation coil include the number of coil turns n, the coil diameter D, and the resistance wire diameter d. When determining the initial process parameters of the compensation coil, firstly, the magnetic induction intensity ΔB that needs to be compensated by the compensation coil is calculated according to the theoretical temperature Tl of the permanent magnet mechanism in the actual working environment during simulation, ΔB=B(Tl)-B(T0), where B(Tl) represents the magnetic induction intensity of the permanent magnet mechanism at the theoretical temperature Tl; The Lagrangian function is defined according to the composite constraints and the optimization function, and then the partial derivatives of the parameters in the Lagrangian function are calculated and set equal to zero, and the numerical method is used to solve it, so as to obtain the optimal process parameters.

4. The optimization method of a built-in permanent magnet mechanism according to claim 3, characterized in that: The optimization function of the compensation coil process parameters is constructed as follows: Where I, P, and R represent the compensation current, power, and total resistance of the compensation coil, respectively; l, A, ρ, and V represent the length, cross-sectional area, resistivity, and volume of the resistance wire of the compensation coil, respectively; and μ0 represents the vacuum permeability, where l = πDn, A = πd 2 / 4; The constructed composite constraint is expressed as: F(D,n,A)=δ1V+δ2P Wherein F(D,n,A) represents the constraint function, δ1 and δ2 represent the first weight and the second weight respectively, and both are greater than 0 and less than 1, and δ1+δ2=1.

5. The optimization method of a built-in permanent magnet mechanism according to claim 4, characterized in that: The Lagrangian function is expressed as: In the formula represents the Lagrangian function, λ represents the Lagrangian multiplier, P0 represents the preset target power. When calculating the partial derivative, D, n, A, and λ are derived respectively to obtain the optimal solution of D, n, and A. Then, the diameter d of the resistance wire is calculated according to the cross-sectional area of ​​the resistance wire to obtain the final process parameters.

6. The optimization method of a built-in permanent magnet mechanism according to claim 5, characterized in that: The first weight and the second weight are determined by market fluctuations, which are price fluctuations of the resistance wire. The specific logic is: Collect the resistance wire in the past period of time T g Historical price data within M l and real-time price data M0, and then calculate the first weight based on the real-time price data and historical price data, the calculation method is: Where M l (max), M l (min) represent the maximum and minimum values ​​in the historical price data respectively.

7. The optimization method of a built-in permanent magnet mechanism according to claim 6, characterized in that: After analyzing the actual temperature variation curve with respect to the test time, according to the timing fluctuation period T Z As the observation window, divide the time series fluctuation period into k intervals and calculate the average actual temperature in each interval The subscript j represents the interval number, j = 1, 2, 3, ..., k; Calculate the magnetic induction intensity ΔB that actually needs to be compensated by the compensation coil based on the average actual temperature in each interval ′ , in Indicates the average actual temperature of the permanent magnet mechanism The magnetic induction intensity under ΔB ′ Calculate the actual compensation current I ′ , calculated as: Where n Z Indicates the number of coil turns after optimization; Then according to the actual compensation current I ′ and the preset peak current I P Calculate the duty cycle D corresponding to this interval j , D j =I ′ / I P , connect the duty cycle corresponding to each interval according to the interval number to obtain the final PWM control signal.

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