Electromagnetic range energy efficiency test method and system

Abstract

The invention discloses an electromagnetic range energy efficiency test method and system, and the method comprises the steps: carrying out the energy efficiency test in a simulation test environment chamber, and obtaining environment data, temperature monitoring data, energy consumption data and load data; obtaining energy efficiency data according to the temperature monitoring data, the energy consumption data and the load data, and performing feature selection on the environment data, the temperature monitoring data and the energy consumption data based on the energy efficiency data to obtain energy efficiency associated features; performing feature correlation analysis on the energy efficiency correlation features to obtain energy efficiency influence data, and constructing an electromagnetic range energy efficiency test model based on the energy efficiency influence data; and optimizing the electromagnetic range energy efficiency test model through a quantum particle swarm algorithm, inputting to-be-tested data into the electromagnetic range energy efficiency test model, and outputting a test result. The method not only can improve the precision of the electromagnetic range energy efficiency test, but also has good interpretability, and can be directly applied to an electromagnetic range energy efficiency test system.

Description

Technical Field

[0001] The present invention relates to the technical field of electromagnetic cooker energy efficiency detection, and in particular to an electromagnetic cooker energy efficiency testing method and a system thereof. Background Art

[0002] With the increasing prominence of energy issues and the continuous enhancement of environmental awareness, the energy efficiency requirements for household appliances are getting higher and higher. As a common kitchen appliance, the energy efficiency test of induction cookers is of great significance for energy conservation and emission reduction. Traditional induction cooker energy efficiency test methods mostly rely on manual operation and simple data recording, and have problems such as low test accuracy, low efficiency, and great influence of environmental factors. For example, during the test process, changes in ambient temperature, humidity, etc. will affect the actual working state and energy efficiency performance of the induction cooker, and the errors in manually recorded data will further reduce the accuracy of the test results. In addition, traditional testing methods lack in-depth analysis and mining of test data, and cannot accurately identify the key factors affecting the energy efficiency of induction cookers, making it difficult to provide strong support for the company's product optimization and quality control.

[0003] Therefore, there is an urgent need for an electromagnetic cooker energy efficiency testing method and system that can improve test accuracy and efficiency and has strong environmental adaptability and interpretability. Summary of the invention

[0004] The purpose of the present invention is to provide an electromagnetic cooker energy efficiency testing method and system.

[0005] To achieve the above object, the present invention is implemented according to the following technical solutions: The present invention comprises the following steps: Conduct energy efficiency tests in a simulated test environment chamber to obtain environmental data, temperature monitoring data, energy consumption data, and load data; Obtaining energy efficiency data according to the temperature monitoring data, the energy consumption data and the load data, and performing feature selection on the environment data, the temperature monitoring data and the energy consumption data based on the energy efficiency data to obtain energy efficiency related features; Performing feature correlation analysis on the energy efficiency related features to obtain energy efficiency impact data, and constructing an electromagnetic cooker energy efficiency test model based on the energy efficiency impact data; The electromagnetic cooker energy efficiency test model is optimized by using a quantum particle swarm algorithm, and the data to be tested is input into the electromagnetic cooker energy efficiency test model to output a test result.

[0006] Furthermore, the method of performing energy efficiency testing in a simulated test environment chamber and obtaining environmental data, temperature monitoring data, energy consumption data and load data includes: The simulation data of the simulated test environment chamber include temperature, humidity, air pressure and electromagnetic disturbance data. The air pressure is stabilized at standard atmospheric pressure. The environmental data include temperature, humidity and electromagnetic disturbance data of the simulated test environment chamber. The temperature monitoring data include pot bottom temperature and load monitoring temperature. The energy consumption data include the power and power consumption of the induction cooker. The load data include load mass, load type and load initial temperature. Liquid load is selected for testing.

[0007] Furthermore, the method for obtaining energy efficiency data according to the temperature monitoring data, the energy consumption data and the load data includes: The load temperature difference is obtained by the initial load temperature and the highest load monitoring temperature within 1 minute after stopping heating. The effective energy consumption is calculated based on the load temperature difference. The energy efficiency data in the heating stage is obtained through the effective energy consumption and energy consumption data.

[0008] Furthermore, the method of performing feature selection on the environmental data, the temperature monitoring data and the energy consumption data based on the energy efficiency data to obtain energy efficiency related features includes: Extract the maximum value, average value, fluctuation range and change rate of the pot bottom temperature; extract the maximum value, change rate, change gradient, temperature uniformity and heating time of the load monitoring temperature; extract the change rate of the set power and actual power of the energy consumption data; take the extracted features and environmental data as the first feature, and perform standardization preprocessing on the first feature. The LGB algorithm is used to establish a regression model of the first feature and energy efficiency data. The energy efficiency data is the dependent variable of the first feature, and the objective function is: , in, is the objective function, n is the number of objective values, is the i-th target value, is the pair obtained in the first t-1 iterations The predicted value of is the loss function, is the decision tree added in the tth iteration The value of is the i-th feature data, is the initial weight, is the decay coefficient, K is the number of decision trees, It is A decision tree, is a regularization term used to control the complexity of the prediction model. is the depth weight, D is the maximum depth of the leaf nodes in the decision tree, The calculation formula for split gain is: , in, is the sample set of the current node, and are the sample sets of the left child node and the right child node after splitting, is the regularization parameter, is the weight coefficient, For the uncertainty of division, The recursive feature elimination method is used to select the first feature whose feature importance is greater than a preset importance threshold to obtain the energy efficiency correlation feature.

[0009] Furthermore, a method for performing feature correlation analysis on the energy efficiency related features to obtain energy efficiency impact data includes: Calculate the information gain rate of energy efficiency related features: , in Features The information gain rate is is the number of features of historical data, is the energy efficiency related feature, is the kth energy efficiency related feature, is the number of energy efficiency related features, and They are energy efficiency related features and The number of It is a feature Middle A feature subset of eigenvalues, Features The indices of the eigenvalues ​​in , for The conditional entropy of the energy efficiency correlation feature is selected using the information gain rate to obtain the gain feature. is the influence weight of feature redundancy, Features The feature redundancy of Calculate the variance inflation factor between the gain features, remove the gain features with variance inflation factor greater than 10 to obtain the second feature, perform principal component analysis on the second feature, and select the first feature with the largest eigenvalue. The principal components and their eigenvalues ​​are used as energy efficiency impact data.

[0010] Furthermore, a method for constructing an electromagnetic cooker energy efficiency test model based on the energy efficiency impact data includes: A data set was established using energy efficiency impact data and energy efficiency data. The data set was randomly sampled into a training set and a validation set. The test set was used to train the neural network model to learn the mapping pattern from energy efficiency impact data to energy efficiency data. The quantum particle swarm algorithm was used to optimize the parameters of the neural network model through the validation set to establish an induction cooker energy efficiency test model.

[0011] Furthermore, the method for optimizing the electromagnetic cooker energy efficiency test model by using a quantum particle swarm algorithm comprises: Initialize the particle swarm. The particle position is the parameter of the electromagnetic cooker energy efficiency test model. The inverse of the objective function of the electromagnetic cooker energy efficiency test model is the particle fitness value. The particle position update formula is: , in and are the d-dimensional position coordinates of the ith particle at the t+1th iteration and the tth iteration, respectively. , is the d-th dimension coordinate of the potential center point of the ith particle in the t-th iteration, and are the optimal position of the ith particle in the tth iteration and the d-th dimension coordinate of the global optimal position of the particle swarm, respectively. g and G are random numbers. , , is the absolute value function, represents a Gaussian distribution random number with a mean of 0 and a variance of 1, k is a random parameter, , is the adaptive diversity weight, is a random disturbance term, and the contraction and expansion coefficient is: , in, is the contraction-expansion coefficient of the tth iteration, is the maximum value of the contraction-expansion coefficient, is the minimum value of the shrinkage expansion coefficient, and the shrinkage expansion coefficient ranges from 1.0 to 0.5. is the preset maximum number of iterations, , represents the particle aggregation degree, and are the fitness corresponding to the global optimal position at the tth iteration and the average fitness of all particles’ current positions, is the variance of the particle swarm fitness at the tth iteration, is the maximum variance of the particle swarm fitness, the global optimal position: , in is the average optimal position of the dth dimension at the tth iteration, is the optimal position fitness of the ith particle at the tth iteration, is the d-th dimension component of the optimal position of the ith particle at the t-th iteration, is the total number of particles, The position update is performed iteratively until the maximum number of iterations is reached.

[0012] An electromagnetic cooker energy efficiency testing system, characterized by comprising: The acquisition module performs energy efficiency testing in a simulated test environment chamber to obtain environmental data, temperature monitoring data, energy consumption data, and load data; a calculation module, which obtains energy efficiency data according to the temperature monitoring data, the energy consumption data and the load data, performs feature selection on the environment data, the temperature monitoring data and the energy consumption data based on the energy efficiency data to obtain energy efficiency related features, performs feature correlation analysis on the energy efficiency related features to obtain energy efficiency impact data, and constructs an electromagnetic cooker energy efficiency test model based on the energy efficiency impact data; An optimization module, which optimizes the electromagnetic cooker energy efficiency test model by using a quantum particle swarm algorithm; The output module inputs the data to be tested into the electromagnetic cooker energy efficiency test model to output the test results.

[0013] In a second aspect, an embodiment of the present application further provides an electronic device, including: A processor; and a memory arranged to store computer executable instructions, which when executed cause the processor to perform the method steps described in the first aspect.

[0014] In a third aspect, an embodiment of the present application further provides a computer-readable storage medium, which stores one or more programs. When the one or more programs are executed by an electronic device including multiple applications, the electronic device executes the method steps described in the first aspect.

[0015] The beneficial effects of the present invention are: The present invention controls the test conditions precisely by simulating the test environment chamber, extracts key energy efficiency features and builds an optimization model using advanced data analysis methods, and significantly improves the accuracy and efficiency of the electromagnetic cooker energy efficiency test. The system has a high degree of automation, reduces manual intervention, reduces labor intensity, and has good interpretability. It can provide strong support for the product development and quality control of electromagnetic cookers and has important practical application value. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 A flowchart of the steps of an electromagnetic cooker energy efficiency testing method and system thereof according to the present invention; Figure 2It is a schematic diagram of the structure of an electronic device in an embodiment of this specification. DETAILED DESCRIPTION

[0017] The present invention is further described below by means of specific embodiments. The illustrative embodiments and descriptions of the present invention are used to explain the present invention but are not intended to limit the present invention.

[0018] The present invention provides an electromagnetic cooker energy efficiency testing method and system thereof, comprising the following steps: like Figure 1 As shown, in this embodiment, the following steps are included: Conduct energy efficiency tests in a simulated test environment chamber to obtain environmental data, temperature monitoring data, energy consumption data, and load data; In the actual evaluation, the temperature simulation range of the simulated test environment chamber is 0-40℃, the humidity range is 30-90%, the frequency range of the electromagnetic interference level is 150kHz-80MHz, and the field strength range is 1-3V / m. The smallest standard pot covering the electromagnetic cooker coil is used for testing, and 1kg of water is used as the load. The load is placed in the simulated test environment chamber for 2 hours, and the test is carried out after the temperature stabilizes. Obtaining energy efficiency data according to the temperature monitoring data, the energy consumption data and the load data, and performing feature selection on the environment data, the temperature monitoring data and the energy consumption data based on the energy efficiency data to obtain energy efficiency related features; In actual evaluation, energy efficiency-related features include: the temperature and humidity of the simulated test environment chamber, the maximum value, average value and change rate of the pot bottom temperature, the maximum value, change rate, change gradient, uniformity and heating time of the load temperature, and energy consumption data characteristics; Performing feature correlation analysis on the energy efficiency related features to obtain energy efficiency impact data, and constructing an electromagnetic cooker energy efficiency test model based on the energy efficiency impact data; The electromagnetic cooker energy efficiency test model is optimized by using a quantum particle swarm algorithm, and the data to be tested is input into the electromagnetic cooker energy efficiency test model to output a test result.

[0019] In this embodiment, the method of performing energy efficiency testing in a simulated test environment chamber and obtaining environmental data, temperature monitoring data, energy consumption data, and load data includes: The simulation data of the simulated test environment chamber include temperature, humidity, air pressure and electromagnetic disturbance data. The air pressure is stabilized at standard atmospheric pressure. The environmental data include temperature, humidity and electromagnetic disturbance data of the simulated test environment chamber. The temperature monitoring data include pot bottom temperature and load monitoring temperature. The energy consumption data include the power and power consumption of the induction cooker. The load data include load mass, load type and load initial temperature. Liquid load is selected for testing.

[0020] In this embodiment, the method for obtaining energy efficiency data according to the temperature monitoring data, the energy consumption data and the load data includes: The load temperature difference is obtained by the initial load temperature and the highest load monitoring temperature within 1 minute after stopping heating. The effective energy consumption is calculated based on the load temperature difference. The energy efficiency data in the heating stage is obtained through the effective energy consumption and energy consumption data.

[0021] In this embodiment, the method for obtaining energy efficiency related features by performing feature selection on the environmental data, the temperature monitoring data and the energy consumption data based on the energy efficiency data includes: Extract the maximum value, average value, fluctuation range and change rate of the pot bottom temperature; extract the maximum value, change rate, change gradient, temperature uniformity and heating time of the load monitoring temperature; extract the change rate of the set power and actual power of the energy consumption data; take the extracted features and environmental data as the first feature, and perform standardization preprocessing on the first feature. The LGB algorithm is used to establish a regression model of the first feature and energy efficiency data. The energy efficiency data is the dependent variable of the first feature, and the objective function is: , in, is the objective function, n is the number of objective values, is the i-th target value, is the pair obtained in the first t-1 iterations The predicted value of is the loss function, is the decision tree added in the tth iteration The value of is the i-th feature data, is the initial weight, is the decay coefficient, K is the number of decision trees, It is A decision tree, is a regularization term used to control the complexity of the prediction model. is the depth weight, D is the maximum depth of the leaf nodes in the decision tree, The calculation formula for split gain is: , in, is the sample set of the current node, and are the sample sets of the left child node and the right child node after splitting, is the regularization parameter, is the weight coefficient, For the uncertainty of division, The recursive feature elimination method is used to select the first feature whose feature importance is greater than a preset importance threshold to obtain the energy efficiency correlation feature.

[0022] In this embodiment, the method of performing feature correlation analysis on the energy efficiency related features to obtain energy efficiency impact data includes: Calculate the information gain rate of energy efficiency related features: , in Features The information gain rate is is the number of features of historical data, is the energy efficiency related feature, is the kth energy efficiency related feature, is the number of energy efficiency related features, and They are energy efficiency related features and The number of It is a feature Middle A feature subset of eigenvalues, Features The indices of the eigenvalues ​​in , for The conditional entropy of the energy efficiency correlation feature is selected using the information gain rate to obtain the gain feature. is the influence weight of feature redundancy, Features The feature redundancy of Calculate the variance inflation factor between the gain features, remove the gain features with variance inflation factor greater than 10 to obtain the second feature, perform principal component analysis on the second feature, and select the first feature with the largest eigenvalue. The principal components and their eigenvalues ​​are used as energy efficiency impact data.

[0023] In this embodiment, the method for constructing an electromagnetic cooker energy efficiency test model based on the energy efficiency impact data includes: A data set was established using energy efficiency impact data and energy efficiency data. The data set was randomly sampled into a training set and a validation set. The test set was used to train the neural network model to learn the mapping pattern from energy efficiency impact data to energy efficiency data. The quantum particle swarm algorithm was used to optimize the parameters of the neural network model through the validation set to establish an induction cooker energy efficiency test model.

[0024] In this embodiment, the method for optimizing the electromagnetic cooker energy efficiency test model by using a quantum particle swarm algorithm includes: Initialize the particle swarm. The particle position is the parameter of the electromagnetic cooker energy efficiency test model. The inverse of the objective function of the electromagnetic cooker energy efficiency test model is the particle fitness value. The particle position update formula is: , in and are the d-dimensional position coordinates of the ith particle at the t+1th iteration and the tth iteration, respectively. , is the d-th dimension coordinate of the potential center point of the ith particle in the t-th iteration, and are the optimal position of the ith particle in the tth iteration and the d-th dimension coordinate of the global optimal position of the particle swarm, respectively. g and G are random numbers. , , is the absolute value function, represents a Gaussian distribution random number with a mean of 0 and a variance of 1, k is a random parameter, , is the adaptive diversity weight, is a random disturbance term, and the contraction and expansion coefficient is: , in, is the contraction-expansion coefficient of the tth iteration, is the maximum value of the contraction-expansion coefficient, is the minimum value of the shrinkage expansion coefficient, and the shrinkage expansion coefficient ranges from 1.0 to 0.5. is the preset maximum number of iterations, , represents the particle aggregation degree, and are the fitness corresponding to the global optimal position at the tth iteration and the average fitness of all particles’ current positions, is the variance of the particle swarm fitness at the tth iteration, is the maximum variance of the particle swarm fitness, the global optimal position: , in is the average optimal position of the dth dimension at the tth iteration, is the optimal position fitness of the ith particle at the tth iteration, is the d-th dimension component of the optimal position of the ith particle at the t-th iteration, is the total number of particles, The position update is performed iteratively until the maximum number of iterations is reached.

[0025] An electromagnetic cooker energy efficiency testing system, characterized by comprising: The acquisition module performs energy efficiency testing in a simulated test environment chamber to obtain environmental data, temperature monitoring data, energy consumption data, and load data; a calculation module, which obtains energy efficiency data according to the temperature monitoring data, the energy consumption data and the load data, performs feature selection on the environment data, the temperature monitoring data and the energy consumption data based on the energy efficiency data to obtain energy efficiency related features, performs feature correlation analysis on the energy efficiency related features to obtain energy efficiency impact data, and constructs an electromagnetic cooker energy efficiency test model based on the energy efficiency impact data; An optimization module, which optimizes the electromagnetic cooker energy efficiency test model by using a quantum particle swarm algorithm; The output module inputs the data to be tested into the electromagnetic cooker energy efficiency test model to output the test results.

[0026] Figure 2 This is a schematic diagram of the structure of an electronic device according to an embodiment of the present application. Figure 2 At the hardware level, the electronic device includes a processor, and optionally also includes an internal bus, a network interface, and a memory. The memory may include a memory, such as a high-speed random access memory (RAM), and may also include a non-volatile memory (non-volatile memory), such as at least one disk storage. Of course, the electronic device may also include hardware required for other services.

[0027] The processor, network interface and memory can be interconnected through an internal bus, which can be an ISA (Industry Standard Architecture) bus, a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 2 Only one bidirectional arrow is used in the diagram, but this does not mean that there is only one bus or only one type of bus.

[0028] The memory is used to store the program. Specifically, the program may include a program code, and the program code includes a computer operation instruction. The memory may include a memory and a non-volatile memory, and provides instructions and data to the processor.

[0029] The processor reads the corresponding computer program from the non-volatile memory into the memory and then runs it, forming an electromagnetic cooker energy efficiency test device at the logical level. The processor executes the program stored in the memory and is specifically used to execute any of the aforementioned electromagnetic cooker energy efficiency test methods and systems.

[0030] The above application Figure 1 The electromagnetic cooker energy efficiency test method and system disclosed in the illustrated embodiment can be applied to a processor or implemented by a processor. The processor may be an integrated circuit chip with signal processing capabilities. In the implementation process, each step of the above method can be completed by an integrated logic circuit of hardware in the processor or instructions in the form of software. The above processor can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components. The methods, steps and logic block diagrams disclosed in the embodiments of the present application can be implemented or executed. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor, etc. The steps of the method disclosed in the embodiments of the present application can be directly embodied as being executed by a hardware decoding processor, or can be executed by a combination of hardware and software modules in the decoding processor. The software module can be located in a storage medium mature in the art such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory, or an electrically erasable programmable memory, a register, etc. The storage medium is located in the memory, and the processor reads the information in the memory and completes the steps of the above method in combination with its hardware.

[0031] The electronic device may also perform Figure 1 Method and system for testing energy efficiency of electromagnetic cooker Figure 1 The functions of the illustrated embodiment will not be described in detail in the embodiments of the present application.

[0032] An embodiment of the present application also proposes a computer-readable storage medium, which stores one or more programs, and the one or more programs include instructions. When the instructions are executed by an electronic device including multiple application programs, any of the aforementioned induction cooker energy efficiency testing methods and systems are executed.

[0033] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present application may adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes.

[0034] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0035] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0036] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0037] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0038] The memory may include non-permanent storage in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0039] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0040] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0041] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems or computer program products. Therefore, the present application may take the form of a complete hardware embodiment, a complete software embodiment or an embodiment combining software and hardware. Moreover, the present application may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes.

[0042] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for testing the energy efficiency of an electromagnetic cooker, characterized in that: The following steps are involved: Conduct energy efficiency tests in a simulated test environment chamber to obtain environmental data, temperature monitoring data, energy consumption data, and load data; Obtaining energy efficiency data according to the temperature monitoring data, the energy consumption data and the load data, and performing feature selection on the environment data, the temperature monitoring data and the energy consumption data based on the energy efficiency data to obtain energy efficiency related features; Performing feature correlation analysis on the energy efficiency related features to obtain energy efficiency impact data, and constructing an electromagnetic cooker energy efficiency test model based on the energy efficiency impact data; The electromagnetic cooker energy efficiency test model is optimized by using a quantum particle swarm algorithm, and the data to be tested is input into the electromagnetic cooker energy efficiency test model to output a test result.

2. The electromagnetic cooker energy efficiency testing method according to claim 1, characterized in that: The method of performing energy efficiency testing in a simulated test environment chamber and obtaining environmental data, temperature monitoring data, energy consumption data and load data includes: The simulation data of the simulated test environment chamber include temperature, humidity, air pressure and electromagnetic disturbance data. The air pressure is stabilized at standard atmospheric pressure. The environmental data include temperature, humidity and electromagnetic disturbance data of the simulated test environment chamber. The temperature monitoring data include pot bottom temperature and load monitoring temperature. The energy consumption data include the power and power consumption of the induction cooker. The load data include load mass, load type and load initial temperature. Liquid load is selected for testing.

3. The electromagnetic cooker energy efficiency testing method according to claim 1, characterized in that: The method for obtaining energy efficiency data according to the temperature monitoring data, the energy consumption data and the load data comprises: The load temperature difference is obtained by the initial load temperature and the highest load monitoring temperature within 1 minute after stopping heating. The effective energy consumption is calculated based on the load temperature difference. The energy efficiency data in the heating stage is obtained through the effective energy consumption and energy consumption data.

4. The electromagnetic cooker energy efficiency testing method according to claim 1, characterized in that: The method for obtaining energy efficiency related features by performing feature selection on the environmental data, the temperature monitoring data and the energy consumption data based on the energy efficiency data includes: Extract the maximum value, average value, fluctuation range and change rate of the pot bottom temperature; extract the maximum value, change rate, change gradient, temperature uniformity and heating time of the load monitoring temperature; extract the change rate of the set power and actual power of the energy consumption data; take the extracted features and environmental data as the first feature, and perform standardization preprocessing on the first feature. The LGB algorithm is used to establish a regression model of the first feature and energy efficiency data. The energy efficiency data is the dependent variable of the first feature, and the objective function is: ， in, is the objective function, n is the number of objective values, is the i-th target value, is the pair obtained in the first t-1 iterations The predicted value of is the loss function, is the decision tree added in the tth iteration The value on is the i-th feature data, is the initial weight, is the decay coefficient, K is the number of decision trees, It is A decision tree, is a regularization term used to control the complexity of the prediction model. is the depth weight, D is the maximum depth of the leaf nodes in the decision tree, The calculation formula for split gain is: ， in, is the sample set of the current node, and are the sample sets of the left child node and the right child node after splitting, is the regularization parameter, is the weight coefficient, For the uncertainty of division, The recursive feature elimination method is used to select the first feature whose feature importance is greater than a preset importance threshold to obtain the energy efficiency correlation feature.

5. The electromagnetic cooker energy efficiency testing method according to claim 1, characterized in that: The method of performing feature correlation analysis on the energy efficiency related features to obtain energy efficiency impact data includes: Calculate the information gain rate of energy efficiency related features: ， in Features The information gain rate, is the number of features of historical data, is the energy efficiency related feature, is the kth energy efficiency related feature, is the number of energy efficiency related features, and They are energy efficiency related features and The number of It is a feature Middle A feature subset of eigenvalues, Features The indices of the eigenvalues ​​in , for The conditional entropy of the energy efficiency correlation feature is selected using the information gain rate to obtain the gain feature. is the influence weight of feature redundancy, Features The feature redundancy of Calculate the variance inflation factor between the gain features, remove the gain features with variance inflation factor greater than 10 to obtain the second feature, perform principal component analysis on the second feature, and select the first feature with the largest eigenvalue. The principal components and their eigenvalues ​​are used as energy efficiency impact data.

6. The electromagnetic cooker energy efficiency testing method according to claim 1, characterized in that: The method for constructing an electromagnetic cooker energy efficiency test model based on the energy efficiency impact data comprises: A data set was established using energy efficiency impact data and energy efficiency data. The data set was randomly sampled into a training set and a validation set. The test set was used to train the neural network model to learn the mapping pattern from energy efficiency impact data to energy efficiency data. The quantum particle swarm algorithm was used to optimize the parameters of the neural network model through the validation set to establish an induction cooker energy efficiency test model.

7. The electromagnetic cooker energy efficiency testing method according to claim 1, characterized in that: The method for optimizing the electromagnetic cooker energy efficiency test model by using a quantum particle swarm algorithm comprises: Initialize the particle swarm. The particle position is the parameter of the electromagnetic cooker energy efficiency test model. The inverse of the objective function of the electromagnetic cooker energy efficiency test model is the particle fitness value. The particle position update formula is: ， in and are the d-dimensional position coordinates of the ith particle at the t+1th iteration and the tth iteration, respectively. , is the d-th dimension coordinate of the potential center point of the ith particle in the t-th iteration, and are the optimal position of the ith particle in the tth iteration and the d-th dimension coordinate of the global optimal position of the particle swarm, respectively. g and G are random numbers. , , is the absolute value function, represents a Gaussian distribution random number with a mean of 0 and a variance of 1, k is a random parameter, , is the adaptive diversity weight, is the random disturbance term, the shrinkage and expansion coefficient: ， in, is the contraction-expansion coefficient of the tth iteration, is the maximum value of the contraction-expansion coefficient, is the minimum value of the shrinkage expansion coefficient, and the shrinkage expansion coefficient ranges from 1.0 to 0.

5. is the preset maximum number of iterations, , represents the particle aggregation degree, and are the fitness corresponding to the global optimal position at the tth iteration and the average fitness of all particles’ current positions, is the variance of the particle swarm fitness at the tth iteration, is the maximum variance of the particle swarm fitness, the global optimal position: ， in is the average optimal position of the dth dimension at the tth iteration, is the optimal position fitness of the ith particle at the tth iteration, is the d-th dimension component of the optimal position of the ith particle at the t-th iteration, is the total number of particles, The position update is performed iteratively until the maximum number of iterations is reached.

8. An electromagnetic cooker energy efficiency testing system, used to execute the electromagnetic cooker energy efficiency testing method according to any one of claims 1 to 7, characterized in that: include: The acquisition module performs energy efficiency testing in a simulated test environment chamber to obtain environmental data, temperature monitoring data, energy consumption data, and load data; a calculation module, which obtains energy efficiency data according to the temperature monitoring data, the energy consumption data and the load data, performs feature selection on the environment data, the temperature monitoring data and the energy consumption data based on the energy efficiency data to obtain energy efficiency related features, performs feature correlation analysis on the energy efficiency related features to obtain energy efficiency impact data, and constructs an electromagnetic cooker energy efficiency test model based on the energy efficiency impact data; An optimization module, which optimizes the electromagnetic cooker energy efficiency test model by using a quantum particle swarm algorithm; The output module inputs the data to be tested into the electromagnetic cooker energy efficiency test model to output the test results.

9. An electronic device, comprising: processor; as well as A memory arranged to store computer executable instructions, which when executed cause the processor to perform the method of any one of claims 1 to 7.

10. A computer-readable storage medium storing one or more programs, which, when executed by an electronic device including a plurality of application programs, enables the electronic device to execute any one of the methods of claims 1 to 7.

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