Quantum neural network-based motor electromagnetic design method, device, and medium

Abstract

This invention discloses a method, device, and medium for electromagnetic design of a motor based on a quantum neural network. The method includes: inputting permanent magnet structure and battery parameters into an electromagnetic parameter model and a permanent magnet structure model to obtain an electromagnetic parameter output layer and a permanent magnet structure output layer; performing quantum entanglement calculations on the electromagnetic parameter output layer and the permanent magnet structure output layer to obtain a comprehensive evaluation function; learning the influence of random variable fluctuations based on the quantum neural network to obtain a reliability function; performing a weighted summation of the comprehensive evaluation function and the reliability function to obtain an objective function; and solving the objective function based on the quantum neural network to obtain the optimal permanent magnet structure and battery parameters. This application can comprehensively consider the overall performance, stability, and safety of the motor, and solve the objective function to obtain the optimal permanent magnet structure and battery parameters.

Description

Technical Field

[0001] This invention relates to the field of computer technology, and in particular to a method, device and medium for electromagnetic design of motors based on quantum neural networks. Background Technology

[0002] Electromagnetic design of motors refers to determining the electromagnetic parameters and structural dimensions of the motor, as well as the data of components such as windings and permanent magnets, based on the motor's technical requirements and performance indicators, thereby achieving the optimized design of the motor.

[0003] Electromagnetic design of electric motors is a complex nonlinear, multivariable, multi-constraint, and multi-objective optimization problem that involves the coupled analysis of multiple physical fields such as electromagnetic field, thermal field, and force field.

[0004] Traditional electromagnetic design methods for electric motors mainly include empirical formulas, analytical methods, numerical methods, and intelligent optimization algorithms.

[0005] Among them, the electromagnetic design method for motors based on deep reinforcement learning utilizes deep neural networks and reinforcement learning algorithms to model and optimize different types of motors, realizing an adaptive and intelligent design process.

[0006] The electromagnetic design method for motors based on fusion computing or fuzzy computing utilizes fusion computing or fuzzy computing technology to comprehensively evaluate and optimize different types of motors, enabling the selection of design schemes under multiple objectives and constraints.

[0007] Electromagnetic design of motors based on probabilistic analysis methods and optimization algorithms: By using probabilistic analysis methods and optimization algorithms, reliability analysis and optimization are performed on different types of motors, and design schemes are determined considering the influence of random variable fluctuations.

[0008] In the process of implementing the technical methods of the embodiments of the present invention, the inventors of this application have discovered at least the following technical problems in the prior art:

[0009] The electromagnetic design method for electric motors based on deep reinforcement learning requires a large amount of data and computing resources, has a long training time, and is difficult to handle nonlinear, high-dimensional, and dynamic problems.

[0010] Electromagnetic design methods for motors based on fusion computing or fuzzy computing require manually setting evaluation indicators and weights, which involves subjectivity and uncertainty, and makes it difficult to utilize relevant information between models.

[0011] Electromagnetic design methods for motors based on probabilistic analysis and optimization algorithms require the assumption that random variables follow a certain distribution, which introduces errors and biases, and makes it difficult to find the global optimal solution.

[0012] In summary, existing electromagnetic design methods for motors cannot meet the requirements of motor electromagnetic design. Summary of the Invention

[0013] This invention provides a method, device, and medium for electromagnetic design of motors based on quantum neural networks, which solves the problem that existing electromagnetic design methods cannot meet the requirements of electromagnetic design of motors.

[0014] One embodiment of the present invention provides an electromagnetic design method for motors based on quantum neural networks, applied to a quantum neural network-based electromagnetic design system for motors. The quantum neural network-based electromagnetic design system has an electromagnetic parameter model and a permanent magnet structure model based on a quantum neural network. The electromagnetic design method for motors based on quantum neural networks includes: inputting the permanent magnet structure and battery parameters into the electromagnetic parameter model and the permanent magnet structure model to obtain an electromagnetic parameter output layer and a permanent magnet structure output layer; performing quantum entanglement calculation on the electromagnetic parameter output layer and the permanent magnet structure output layer to obtain a comprehensive evaluation function; learning the influence of random variable fluctuations based on the quantum neural network to obtain a reliability function; performing a weighted summation of the comprehensive evaluation function and the reliability function to obtain an objective function; and solving the objective function based on the quantum neural network to obtain the optimal permanent magnet structure and battery parameters.

[0015] Optionally, before inputting the permanent magnet structure and battery parameters into the electromagnetic parameter model and the permanent magnet structure model, the method further includes: S1: preparing a training dataset, including input variables and output variables; S2: defining a loss function; S3: using an optimization algorithm to adjust the parameters of the two quantum neural network models to minimize the loss function; S4: using a validation method to evaluate the generalization ability and accuracy of the two quantum neural network models; S5: repeating steps S1 to S4 until the termination condition is met to generate the electromagnetic parameter model and the permanent magnet structure model.

[0016] Optionally, the loss function is defined specifically as the mean squared error (MSE):

[0017] Where N is the number of samples, y i It is the expected output of the i-th sample. It is the model output of the i-th sample.

[0018] Optionally, the step of using an optimization algorithm to adjust the parameters of the two quantum neural network models to minimize the loss function specifically involves using gradient descent or a variable quantum eigenvalue solver to adjust the parameters of the two quantum neural network models to minimize the loss function.

[0019] Optionally, the method of using verification to evaluate the generalization ability and accuracy of the two quantum neural network models specifically involves using cross-validation or leave-one-out method to evaluate the generalization ability and accuracy of the two quantum neural network models.

[0020] Optionally, the step of performing quantum entanglement calculation on the electromagnetic parameter output layer and the permanent magnet structure output layer to obtain a comprehensive evaluation function specifically includes: representing the electromagnetic parameter model and the permanent magnet structure model as two quantum neural networks respectively; performing quantum entanglement on the output layers of the two quantum neural networks to establish a correlation between the two quantum neural networks; and using the quantum entangled output layer as input, obtaining the comprehensive evaluation function through a quantum linear regression model.

[0021] Optionally, the comprehensive evaluation function adopts a linear or nonlinear weighted combination method.

[0022] Optionally, the fluctuation of the random variable may specifically be at least one of material properties, processing technology, and external load.

[0023] On the other hand, embodiments of the present invention also provide a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of the electromagnetic design method for motors based on quantum neural networks in the foregoing embodiments.

[0024] This invention also provides a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the steps of the motor electromagnetic design method based on quantum neural networks in the foregoing embodiments.

[0025] One or more technical solutions provided in the embodiments of the present invention have at least the following technical effects or advantages:

[0026] An electromagnetic design method for electric motors based on quantum neural networks is applied to an electromagnetic design system for electric motors based on quantum neural networks. The system includes an electromagnetic parameter model and a permanent magnet structure model based on quantum neural networks. The method comprises: inputting the permanent magnet structure and battery parameters into the electromagnetic parameter model and the permanent magnet structure model to obtain an electromagnetic parameter output layer and a permanent magnet structure output layer; performing quantum entanglement calculations on the electromagnetic parameter output layer and the permanent magnet structure output layer to obtain a comprehensive evaluation function; learning the influence of random variable fluctuations based on the quantum neural network to obtain a reliability function; performing a weighted summation of the comprehensive evaluation function and the reliability function to obtain an objective function; and solving the objective function based on the quantum neural network to obtain the optimal permanent magnet structure and battery parameters. The electromagnetic parameter model and permanent magnet structure model of this application are quantum neural network models. Using quantum neural network models instead of traditional deep reinforcement learning models can improve the accuracy, efficiency, stability, and reliability of motor electromagnetic design, enabling an adaptive and intelligent design process. This application uses quantum entanglement computation instead of traditional fusion computation or fuzzy computation, effectively utilizing the relevant information between the electromagnetic parameter model and the permanent magnet structure model, avoiding subjectivity and uncertainty, and improving the comprehensive evaluation and optimization capabilities of motor electromagnetic design. This application uses quantum neural networks instead of traditional probabilistic analysis methods and optimization algorithms, accurately estimating the impact of random variable fluctuations and quickly finding the optimal solution, enabling timely output of the optimal permanent magnet structure and... Battery parameters; the comprehensive evaluation function of this application can output a comprehensive evaluation value based on the input permanent magnet structure and electromagnetic parameters, which can reflect the overall performance of the motor; the reliability function of this application can output a reliability value based on the input permanent magnet structure and electromagnetic parameters, which can reflect the stability and safety of the motor; the objective function of this application is a weighted sum of the comprehensive evaluation function and the reliability function, which can comprehensively consider the overall performance, stability and safety of the motor, solve the objective function to obtain the optimal permanent magnet structure and battery parameters, realize the optimal design of the motor, improve the performance and quality of the motor, reduce the cost of motor electromagnetic design, shorten the time of motor electromagnetic design, and improve the efficiency and competitiveness of motor electromagnetic design.

[0027] Furthermore, before inputting the permanent magnet structure and battery parameters into the electromagnetic parameter model and the permanent magnet structure model, the method further includes: S1: preparing a training dataset, including input and output variables; S2: defining a loss function; S3: using an optimization algorithm to adjust the parameters of the two quantum neural network models to minimize the loss function; S4: using a validation method to evaluate the generalization ability and accuracy of the two quantum neural network models; S5: repeating steps S1 to S4 until the termination condition is met, generating the electromagnetic parameter model and the permanent magnet structure model. Through the parallel processing of quantum computing, the electromagnetic parameter model and the permanent magnet structure model can be trained and generated simultaneously, improving efficiency.

[0028] Furthermore, the loss function is defined specifically as the mean squared error (MSE):

[0029] Where N is the number of samples, y i It is the expected output of the i-th sample. This represents the model output for the i-th sample. The loss function measures the difference between the model output and the expected output; mean squared error is a commonly used loss function with a wide range of applications.

[0030] Furthermore, the optimization algorithm used to adjust the parameters of the two quantum neural network models to minimize the loss function specifically involves using gradient descent or a variable quantum eigenvalue solver to adjust the parameters of the two quantum neural network models until the loss function reaches its minimum. This allows for the selection of a suitable optimization algorithm based on the specific application requirements.

[0031] Furthermore, the verification method used to evaluate the generalization ability and accuracy of the two quantum neural network models specifically involves using cross-validation or leave-one-out validation to assess the generalization ability and accuracy of the two quantum neural network models. This allows for the selection of an appropriate verification method based on the specific application requirements.

[0032] Furthermore, the step of performing quantum entanglement calculations on the electromagnetic parameter output layer and the permanent magnet structure output layer to obtain a comprehensive evaluation function specifically includes: representing the electromagnetic parameter model and the permanent magnet structure model as two quantum neural networks; quantum entanglement of the output layers of the two quantum neural networks, establishing a correlation between them; and using the quantum entangled output layers as input, obtaining the comprehensive evaluation function through a quantum linear regression model. By representing the electromagnetic parameter model and the permanent magnet structure model as quantum neural networks, which are quantum circuits composed of several qubits and quantum gates, quantum entanglement calculations are easily implemented. This effectively utilizes the relevant information between the two models, improving the efficiency and accuracy of information processing and avoiding the uncertainties and subjectivity present in traditional calculations.

[0033] Furthermore, the comprehensive evaluation function employs a linear or nonlinear weighted combination method. This allows for the selection of a suitable weighted combination method based on actual application requirements, such as motor type and design specifications.

[0034] Furthermore, the fluctuation of the random variable specifically refers to at least one of material properties, processing technology, and external load. This allows for the consideration of fluctuations in multiple random variables, improving reliability. Attached Figure Description

[0035] Figure 1 This is a flowchart of a quantum neural network-based motor electromagnetic design method according to an embodiment of the present invention;

[0036] Figure 2 This is a flowchart illustrating the overall process of a quantum neural network-based electromagnetic design method for motors in one embodiment of the present invention.

[0037] Figure 3 This is an architectural diagram of a two-layer quantum neural network model in one embodiment of the present invention;

[0038] Figure 4 This is a quantum circuit representation diagram of the first layer of a quantum neural network in one embodiment of the present invention. Detailed Implementation

[0039] This invention provides a method, device, and medium for electromagnetic design of motors based on quantum neural networks, which solves the problem that existing electromagnetic design methods cannot meet the requirements of electromagnetic design of motors.

[0040] The technical solution of one embodiment of the present invention is to solve the above-mentioned problems, and the overall idea is as follows:

[0041] An electromagnetic design method for electric motors based on quantum neural networks is applied to an electromagnetic design system based on quantum neural networks. The system comprises an electromagnetic parameter model and a permanent magnet structure model based on quantum neural networks. The method includes: inputting the permanent magnet structure and battery parameters into the electromagnetic parameter model and the permanent magnet structure model to obtain an electromagnetic parameter output layer and a permanent magnet structure output layer; performing quantum entanglement calculations on the electromagnetic parameter output layer and the permanent magnet structure output layer to obtain a comprehensive evaluation function; learning the influence of random variable fluctuations based on the quantum neural network to obtain a reliability function; performing a weighted summation of the comprehensive evaluation function and the reliability function to obtain an objective function; and solving the objective function based on the quantum neural network to obtain the optimal permanent magnet structure and battery parameters. The electromagnetic parameter model and permanent magnet structure model of this application are quantum neural network models. Using quantum neural network models instead of traditional deep reinforcement learning models can improve the accuracy, efficiency, stability, and reliability of motor electromagnetic design, enabling an adaptive and intelligent design process. This application uses quantum entanglement computation instead of traditional fusion computation or fuzzy computation, effectively utilizing the relevant information between the electromagnetic parameter model and the permanent magnet structure model, avoiding subjectivity and uncertainty, and improving the comprehensive evaluation and optimization capabilities of motor electromagnetic design. This application uses quantum neural networks instead of traditional probabilistic analysis methods and optimization algorithms, accurately estimating the impact of random variable fluctuations and quickly finding the optimal solution, enabling timely output of the optimal permanent magnet structure and... Battery parameters; the comprehensive evaluation function of this application can output a comprehensive evaluation value based on the input permanent magnet structure and electromagnetic parameters, which can reflect the overall performance of the motor; the reliability function of this application can output a reliability value based on the input permanent magnet structure and electromagnetic parameters, which can reflect the stability and safety of the motor; the objective function of this application is a weighted sum of the comprehensive evaluation function and the reliability function, which can comprehensively consider the overall performance, stability and safety of the motor, solve the objective function to obtain the optimal permanent magnet structure and battery parameters, realize the optimal design of the motor, improve the performance and quality of the motor, reduce the cost of motor electromagnetic design, shorten the time of motor electromagnetic design, and improve the efficiency and competitiveness of motor electromagnetic design.

[0042] To better understand the above technical solutions, a detailed description of the solutions will be provided below in conjunction with the accompanying drawings and specific embodiments. Obviously, the embodiments described in this invention are only some, not all, of the embodiments of this invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0043] Quantum neural networks are neural network models based on the principles of quantum mechanics. They can utilize properties such as qubits, quantum superposition, and quantum entanglement to enhance information processing capabilities. Quantum neural networks have broad application prospects in machine learning, optimization, and control. This application uses quantum neural networks to solve complex problems in the electromagnetic design of electric motors.

[0044] To implement quantum neural network models, quantum gates are needed to manipulate qubits. A quantum gate is a linear transformation that preserves unitary property and can be represented by a unitary matrix. Commonly used quantum gates include:

[0045] Pauli X-gates can realize the flipping of qubits, and their matrix form is as follows:

[0046]

[0047] Pauli Y gates can realize phase flipping of qubits, and their matrix form is as follows:

[0048]

[0049] Pauli Z-gates can achieve phase reversal of qubits, and their matrix form is as follows:

[0050]

[0051] The Hadamard H-gate can realize the generation of superposition states of qubits, and its matrix form is as follows:

[0052]

[0053] The phase S-gate can realize the phase rotation of a qubit by π / 2 radians, and its matrix form is as follows:

[0054]

[0055] A π / 8T gate can achieve a phase rotation of π / 4 radians for a qubit, and its matrix form is as follows:

[0056]

[0057] Controlled non-CONT gates can generate entangled states between two qubits, and their matrix form is as follows:

[0058]

[0059] Quantum neural network models can be constructed using quantum gates. A quantum neural network model is a quantum circuit composed of several qubits and quantum gates, which can realize operations such as quantum superposition, quantum entanglement, and quantum measurement. Figure 3The diagram shows a two-layer neural network model, where each layer consists of several qubits and quantum gates. Figure 4 As shown, the first layer can be represented by a quantum circuit, where H, T, and CNOT represent the Hadamard gate, π / 8 gate, and controlled NOT gate, respectively. This allows for the transformation and entanglement of the input layer qubits.

[0060] This application provides a quantum neural network-based electromagnetic design method for electric motors, applied to a quantum neural network-based electromagnetic design system. The system comprises an electromagnetic parameter model and a permanent magnet structure model based on a quantum neural network. The electromagnetic parameter model and the permanent magnet structure model utilize the quantum neural network model to fit the relationship between the motor's electromagnetic parameters and the permanent magnet structure and battery parameters.

[0061] This method employs a quantum neural network model to learn the electromagnetic parameters of different types of motors, resulting in an electromagnetic parameter model. This model can then output corresponding performance indicators such as torque, efficiency, and temperature rise based on the input permanent magnet structure and battery parameters.

[0062] A quantum neural network model is used to learn different types of permanent magnet structures, resulting in a permanent magnet structure model. This model can then output a suitable permanent magnet structure based on input requirements such as motor size, power, and speed.

[0063] Please refer to the following as well. Figure 1 and Figure 2 The electromagnetic design method for motors based on quantum neural networks in the embodiments of the present invention will be described in detail.

[0064] Step 101: Input the permanent magnet structure and battery parameters into the electromagnetic parameter model and the permanent magnet structure model to obtain the electromagnetic parameter output layer and the permanent magnet structure output layer;

[0065] Step 102: Perform quantum entanglement calculations on the electromagnetic parameter output layer and the permanent magnet structure output layer to obtain a comprehensive evaluation function;

[0066] Step 103: Based on the quantum neural network, learn the impact of random variable fluctuations to obtain a reliability function;

[0067] Step 104: Weight the comprehensive evaluation function and the reliability function to obtain an objective function;

[0068] Step 105: Based on the quantum neural network, solve the objective function to obtain the optimal permanent magnet structure and battery parameters.

[0069] When there is a need for electromagnetic design of an electric motor, step 101 is executed: input the permanent magnet structure and battery parameters into the electromagnetic parameter model and the permanent magnet structure model to obtain the electromagnetic parameter output layer and the permanent magnet structure output layer.

[0070] In the specific implementation process of step 101, for example, the permanent magnet structure and battery parameters are the input layer of the electromagnetic parameter model and the permanent magnet structure model. The input layer is represented by a binary vector, for example, x = (x1, x2, ..., xn), where Xi ∈ {0, 1} represents the value of the i-th bit and n represents the number of input bits.

[0071] The electromagnetic parameters of the motor output from the electromagnetic parameter model and the permanent magnet structure model are represented by a real number vector, for example, y = (y1, y2, ..., ym), where yj ∈ R represents the value of the j-th parameter and m represents the number of output parameters.

[0072] The electromagnetic parameter output layer and the permanent magnet structure output layer can be represented by a complex vector, such as z = (z1, z2, ..., zk), where zi ∈ C represents the value of the i-th complex number and k represents the number of complex numbers in the output layer.

[0073] After obtaining the electromagnetic parameter output layer and the permanent magnet structure output layer, step 102 is executed: quantum entanglement calculation is performed on the electromagnetic parameter output layer and the permanent magnet structure output layer to obtain a comprehensive evaluation function.

[0074] Step 102, in its specific implementation, for example: Quantum entanglement computing is a technique that utilizes the phenomenon of quantum entanglement for information processing. It can realize non-local interactions between two or more quantum systems. Quantum entanglement refers to a special correlation between two or more quantum systems, such that their states cannot be described individually but only by the overall wave function. The advantage of quantum entanglement computing is that it can effectively utilize the relevant information between two or more quantum systems, improving the efficiency and accuracy of information processing, while avoiding the uncertainty and subjectivity inherent in traditional computing.

[0075] The comprehensive evaluation function is a function used to evaluate the overall performance of a motor. It is obtained through quantum entanglement calculations using an electromagnetic parameter model and a permanent magnet structure model. The input to the comprehensive evaluation function is the permanent magnet structure and battery parameters, and the output is a comprehensive evaluation value that reflects the overall performance level of the motor, including torque, efficiency, and temperature rise. The comprehensive evaluation function can be represented by a real number function, such as f(x) = g(z), where x represents the binary vector of the input layer, z represents the complex vector of the output layer, and g(.) represents a nonlinear transformation function.

[0076] To facilitate quantum entanglement calculations, effectively utilize the relevant information between the two models, improve the efficiency and accuracy of information processing, and avoid the uncertainties and subjectivity inherent in traditional calculations, step 102 involves performing quantum entanglement calculations on the electromagnetic parameter output layer and the permanent magnet structure output layer to obtain a comprehensive evaluation function. Specifically, this includes: representing the electromagnetic parameter model and the permanent magnet structure model as two quantum neural networks, each composed of several qubits and quantum gates, enabling quantum superposition, quantum entanglement, quantum measurement, and other operations; quantum entanglement of the output layers of the two quantum neural networks to establish a correlation between them; and using the quantum entangled output layer as input, obtaining the comprehensive evaluation function through a quantum linear regression model. This comprehensive evaluation function outputs a comprehensive evaluation value based on the input permanent magnet structure and battery parameters, reflecting the overall performance of the motor.

[0077] To achieve quantum entanglement computation, a special correlation needs to be established between the electromagnetic parameter model and the permanent magnet structure model. One possible approach is to use a controlled SWAP gate (CSWAP), whose matrix form is $$CSWAP=

[0078] \begin{bmatrix}1&0&0&0&0&0&0&0\0&1&0&0&0&0&0&0\0&0&1&0&0&0&0&0\ 0&0&0&1&0&0&0&0\0&0&0&0&1&0&0&0\0&0&0&0&0&0&1&0\0&0&0&0&0&1&0&0\

[0079] To select the appropriate weighted combination method based on actual application requirements, such as motor type and design requirements, the comprehensive evaluation function adopts either a linear or nonlinear weighted combination method, for example:

[0080] Overall evaluation value = w1 * torque + w2 * efficiency + w3 * temperature rise

[0081] Overall evaluation value = w1*log(torque) + w2*log(efficiency) + w3*log(temperature rise)

[0082] Overall evaluation value = w1*exp(torque) + w2*exp(efficiency) + w3*exp(temperature rise)

[0083] Among them, w1, w2, and w3 are weight coefficients for different performance indicators, which can be set according to different optimization objectives.

[0084] For example, suppose there are two different permanent magnet structures and battery parameter schemes, namely:

[0085] Option 1: The permanent magnet structure is a four-pole tile type, and the battery parameters are 12V voltage and 10A current;

[0086] Option 2: The permanent magnet has a six-pole circular structure, and the battery parameters are 10V voltage and 12A current.

[0087] Using electromagnetic parameter models and permanent magnet structure models, the performance indicators such as torque, efficiency, and temperature rise for the two schemes were calculated respectively, with the following assumptions:

[0088] Option 1: Torque is 10 Nm, efficiency is 80%, and temperature rise is 20 °C;

[0089] Option 2: Torque is 12 Nm, efficiency is 75%, and temperature rise is 25 °C;

[0090] Using a comprehensive evaluation function, the comprehensive evaluation values ​​for the two schemes are calculated respectively. Assuming the weight coefficients are w1 = 0.4, w2 = 0.3, and w3 = 0.3, and a linear weighted combination method is used, the results are as follows:

[0091] Option 1: Overall evaluation value = 0.4*10 + 0.3*80 + 0.3*20 = 38;

[0092] Option 2: Overall evaluation value = 0.4*12 + 0.3*75 + 0.3*25 = 39.5;

[0093] Based on the magnitude of the comprehensive evaluation value, it can be determined that the motor performance of Scheme 2 is superior to that of Scheme 1.

[0094] After obtaining the comprehensive evaluation function, step 103 is executed: based on the quantum neural network, the influence of random variable fluctuations is learned to obtain a reliability function.

[0095] In the specific implementation process, step 103 involves, for example, learning the impact of fluctuations in different random variables based on a quantum neural network to obtain a reliability function. This reliability function can output a reliability value based on the input permanent magnet structure and battery parameters, reflecting the stability and safety of the motor.

[0096] To account for fluctuations in multiple random variables and improve reliability, the fluctuations in random variables specifically include at least one of material properties, processing technology, and external loads. This application does not impose restrictions on the selection of a single random variable fluctuation or a combination of two or more random variable fluctuations, depending on the actual application requirements.

[0097] After obtaining the comprehensive evaluation function and the reliability function, step 104 is executed: the comprehensive evaluation function and the reliability function are weighted and summed to obtain an objective function.

[0098] In the specific implementation process of step 104, for example, weighted summation refers to assigning different weight coefficients to different performance indicators based on their importance, and then performing a summation operation. The comprehensive evaluation function and the reliability function can be weighted and summed according to actual application requirements to obtain an objective function that meets the actual application needs.

[0099] After obtaining the objective function, step 105 is executed: based on the quantum neural network, the objective function is solved to obtain the optimal permanent magnet structure and battery parameters.

[0100] In step 105, during the specific implementation process, for example, the objective function outputs a target value based on the input permanent magnet structure and battery parameters, reflecting the degree of optimization of the motor. Based on the target value, a quantum neural network is used to solve the objective function to obtain the optimal permanent magnet structure and battery parameters.

[0101] In order to improve efficiency by simultaneously training and generating electromagnetic parameter models and permanent magnet structure models through parallel processing of quantum computing, the following steps are included before step 101:

[0102] S1: Prepare the training dataset, including input and output variables. Input variables refer to the permanent magnet structure and battery parameters, while output variables refer to performance indicators such as torque, efficiency, and temperature rise, or permanent magnet structure schemes that meet the conditions.

[0103] S2: Define the loss function. The loss function measures the difference between the model output and the expected output. To broaden its application, the commonly used loss function is the mean squared error (MSE), defined as:

[0104]

[0105] Where N is the number of samples, y i It is the expected output of the i-th sample. It is the model output of the i-th sample.

[0106] S3: Use optimization algorithms to adjust the parameters of the two quantum neural network models to minimize the loss function. To select a suitable optimization algorithm based on the specific application requirements, gradient descent or a variable quantum eigenvalue solver can be used to adjust the parameters of the two quantum neural network models to minimize the loss function. For example, calculate the gradient of the loss function with respect to the parameters:

[0107]

[0108] Here, θ is a parameter in the quantum neural network model, such as the rotation angle of a quantum gate. Utilizing the properties of parameterized quantum circuits (PQC), and The relational expression is as follows:

[0109]

[0110] in <z> θ This represents the expected value of the Z observation of the output layer qubit when the parameter is θ. The gradient can be estimated and the parameter updated using a quantum computer.

[0111] S4: Use validation methods to evaluate the generalization ability and accuracy of the two quantum neural network models. To select an appropriate validation method based on practical application requirements, cross-validation or leave-one-out validation can be used to evaluate the generalization ability and accuracy of the two quantum neural network models.

[0112] S5: Repeat steps S1 to S4 until the termination condition is met, generating the electromagnetic parameter model and the permanent magnet structure model. To allow for the selection of appropriate verification methods based on actual application requirements, the termination condition can be model convergence or other preset termination conditions.

[0113] Another embodiment of the present invention provides a computer device, including a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the steps of the motor electromagnetic design method based on quantum neural networks in the foregoing embodiments.

[0114] This invention also provides a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the steps of the motor electromagnetic design method based on a quantum neural network as described in the foregoing embodiments.

[0115] One or more technical solutions provided in the embodiments of the present invention have at least the following technical effects or advantages:

[0116] An electromagnetic design method for electric motors based on quantum neural networks is applied to an electromagnetic design system based on quantum neural networks. The system comprises an electromagnetic parameter model and a permanent magnet structure model based on quantum neural networks. The method includes: inputting the permanent magnet structure and battery parameters into the electromagnetic parameter model and the permanent magnet structure model to obtain an electromagnetic parameter output layer and a permanent magnet structure output layer; performing quantum entanglement calculations on the electromagnetic parameter output layer and the permanent magnet structure output layer to obtain a comprehensive evaluation function; learning the influence of random variable fluctuations based on the quantum neural network to obtain a reliability function; performing a weighted summation of the comprehensive evaluation function and the reliability function to obtain an objective function; and solving the objective function based on the quantum neural network to obtain the optimal permanent magnet structure and battery parameters. The electromagnetic parameter model and permanent magnet structure model of this application are quantum neural network models. Using quantum neural network models instead of traditional deep reinforcement learning models can improve the accuracy, efficiency, stability, and reliability of motor electromagnetic design, enabling an adaptive and intelligent design process. This application uses quantum entanglement computation instead of traditional fusion computation or fuzzy computation, effectively utilizing the relevant information between the electromagnetic parameter model and the permanent magnet structure model, avoiding subjectivity and uncertainty, and improving the comprehensive evaluation and optimization capabilities of motor electromagnetic design. This application uses quantum neural networks instead of traditional probabilistic analysis methods and optimization algorithms, accurately estimating the impact of random variable fluctuations and quickly finding the optimal solution, enabling timely output of the optimal permanent magnet structure and... Battery parameters; the comprehensive evaluation function of this application can output a comprehensive evaluation value based on the input permanent magnet structure and electromagnetic parameters, which can reflect the overall performance of the motor; the reliability function of this application can output a reliability value based on the input permanent magnet structure and electromagnetic parameters, which can reflect the stability and safety of the motor; the objective function of this application is a weighted sum of the comprehensive evaluation function and the reliability function, which can comprehensively consider the overall performance, stability and safety of the motor, solve the objective function to obtain the optimal permanent magnet structure and battery parameters, realize the optimal design of the motor, improve the performance and quality of the motor, reduce the cost of motor electromagnetic design, shorten the time of motor electromagnetic design, and improve the efficiency and competitiveness of motor electromagnetic design.

[0117] Furthermore, before inputting the permanent magnet structure and battery parameters into the electromagnetic parameter model and the permanent magnet structure model, the process includes: S1: preparing the training dataset, including input and output variables; S2: defining the loss function; S3: using an optimization algorithm to adjust the parameters of the two quantum neural network models to minimize the loss function; S4: using a validation method to evaluate the generalization ability and accuracy of the two quantum neural network models; S5: repeating steps S1 to S4 until the termination condition is met, generating the electromagnetic parameter model and the permanent magnet structure model. Through the parallel processing of quantum computing, the electromagnetic parameter model and the permanent magnet structure model can be trained and generated simultaneously, improving efficiency.

[0118] Furthermore, we define the loss function, specifically the mean squared error (MSE):

[0119] Where N is the number of samples, y i It is the expected output of the i-th sample. This represents the model output for the i-th sample. The loss function measures the difference between the model output and the expected output; mean squared error is a commonly used loss function with a wide range of applications.

[0120] Furthermore, optimization algorithms are used to adjust the parameters of the two quantum neural network models to minimize the loss function. Specifically, gradient descent or a variable quantum eigenvalue solver is used to adjust the parameters of the two quantum neural network models to minimize the loss function. Appropriate optimization algorithms can be selected based on the specific application requirements.

[0121] Furthermore, validation methods are used to evaluate the generalization ability and accuracy of the two quantum neural network models. Specifically, cross-validation or leave-one-out method is used to evaluate the generalization ability and accuracy of the two quantum neural network models. Appropriate validation methods can be selected based on the specific application requirements.

[0122] Furthermore, quantum entanglement calculations are performed on the electromagnetic parameter output layer and the permanent magnet structure output layer to obtain a comprehensive evaluation function. Specifically, this involves: representing the electromagnetic parameter model and the permanent magnet structure model as two separate quantum neural networks; quantum entanglement of the output layers of the two quantum neural networks to establish a correlation between them; and using the quantum entangled output layers as input, applying a quantum linear regression model to obtain the comprehensive evaluation function. By representing the electromagnetic parameter model and the permanent magnet structure model as quantum neural networks—quantum circuits composed of several qubits and quantum gates—quantum entanglement calculations are easily implemented, effectively utilizing the correlation between the two models, improving the efficiency and accuracy of information processing, and avoiding the uncertainties and subjectivity inherent in traditional calculations.

[0123] Furthermore, the comprehensive evaluation function employs a linear or nonlinear weighted combination method. This allows for the selection of an appropriate weighted combination method based on actual application requirements, such as motor type and design specifications.

[0124] Furthermore, the random variable fluctuations specifically include at least one of the following: material properties, processing technology, and external loads. This allows for the consideration of fluctuations from multiple random variables, thus improving reliability.

[0125] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage and optical storage) containing computer-usable program code.

[0126] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0127] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0128] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0129] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.< / z>

Claims

1. A quantum neural network-based electromagnetic design method for electric motors, applied to a quantum neural network-based electromagnetic design system for electric motors, characterized in that, The quantum neural network-based motor electromagnetic design system has an electromagnetic parameter model and a permanent magnet structure model based on a quantum neural network. The quantum neural network-based motor electromagnetic design method includes: Electromagnetic parameters and permanent magnet structure are input into the electromagnetic parameter model and the permanent magnet structure model, respectively, to obtain the electromagnetic parameter output layer and the permanent magnet structure output layer; A comprehensive evaluation function is obtained by performing quantum entanglement calculations on the electromagnetic parameter output layer and the permanent magnet structure output layer. Based on quantum neural networks, the impact of random variable fluctuations is learned to obtain a reliability function; A weighted sum of the comprehensive evaluation function and the reliability function is obtained to obtain an objective function. Based on a quantum neural network, the objective function is solved to obtain the optimal permanent magnet structure and electromagnetic parameters.

2. The method as described in claim 1, characterized in that, Before inputting the electromagnetic parameters and the permanent magnet structure into the electromagnetic parameter model and the permanent magnet structure model respectively, the method further includes: S1: Prepare the training dataset, including input and output variables; S2: Define the loss function; S3: Use optimization algorithms to adjust the parameters of the two quantum neural network models to minimize the loss function; S4: Use validation methods to evaluate the generalization ability and accuracy of the two quantum neural network models; S5: Repeat steps S1 to S4 until the termination condition is met, and generate the electromagnetic parameter model and the permanent magnet structure model.

3. The method as described in claim 2, characterized in that, The loss function is defined specifically as the mean squared error (MSE). in, N It is the sample size. y i It is the first i The expected output of each sample y ^ i It is the first i The model output for each sample.

4. The method as described in claim 2, characterized in that, The optimization algorithm is used to adjust the parameters of the two quantum neural network models to minimize the loss function. Specifically, the gradient descent method or a variable quantum eigenvalue solver is used to adjust the parameters of the two quantum neural network models to minimize the loss function.

5. The method as described in claim 2, characterized in that, The method of using verification to evaluate the generalization ability and accuracy of the two quantum neural network models specifically involves using cross-validation to evaluate the generalization ability and accuracy of the two quantum neural network models.

6. The method as described in claim 1, characterized in that, The step of performing quantum entanglement calculations on the electromagnetic parameter output layer and the permanent magnet structure output layer to obtain a comprehensive evaluation function specifically includes: The electromagnetic parameter model and the permanent magnet structure model are respectively represented as two quantum neural networks; The output layers of the two quantum neural networks are quantum entangled to create a correlation between them. Using the quantum entangled output layer as input, the comprehensive evaluation function is obtained through a quantum linear regression model.

7. The method as described in claim 1, characterized in that, The comprehensive evaluation function adopts a linear or nonlinear weighted combination method.

8. The method as described in claim 1, characterized in that, The fluctuation of the random variable specifically refers to at least one of the following: material properties, processing technology, and external load.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1-8.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1-8.

Images