Magnetic levitation compressor and control method, device, storage medium and program product thereof

Abstract

The present invention discloses a magnetic levitation compressor and its control method, device, storage medium, and computer program product. The method includes: introducing one or more disturbances and adjusting the control parameters of a displacement current dual closed-loop control system; obtaining the rotor displacement and stator coil current of the magnetic bearing, obtaining the control parameters, rotor displacement, and stator coil current corresponding to all disturbances, and recording them as a first data set; training and testing the first data set using two or more preset training models to obtain an initial control parameter prediction model; obtaining compressor operating parameters and unit operating parameters, and recording the input and output of the initial control parameter prediction model, the compressor operating parameters, and the unit operating parameters as a second data set; and training and testing the second data set again to obtain an updated control parameter prediction model. This solution improves bearing control performance and ensures operational stability by using two or more preset training models to obtain a control parameter prediction model.

Description

Technical Field

[0001] The present invention belongs to the technical field of magnetic levitation compressors, and specifically relates to a control method, device, magnetic levitation compressor, storage medium and computer program product for a magnetic levitation compressor, and more particularly to a method, device, magnetic levitation compressor, storage medium and computer program product for adaptive optimal parameter control of a magnetic levitation compressor based on machine learning. Background Art

[0002] Magnetic levitation compressors utilize active magnetic bearing (AMB)-rotor systems. These systems have attracted considerable attention due to their advantages over mechanical bearing-rotor systems, such as frictionlessness and low energy consumption. However, bearing control in AMB-rotor systems involves a control algorithm that requires adjusting control parameters to ensure excellent bearing control performance. Manually finding optimal control parameters is often impossible, making it difficult to guarantee optimal bearing control performance.

[0003] The above content is only used to assist in understanding the technical solution of the present invention and does not constitute an admission that the above content is prior art. Summary of the Invention

[0004] The object of the present invention is to provide a control method, device, magnetic levitation compressor, storage medium and computer program product for a magnetic levitation compressor, so as to solve the problem that it is usually impossible to manually find the optimal bearing control parameters and it is difficult to ensure the optimal bearing control performance in the bearing control of an active magnetic levitation bearing-rotor system, so as to achieve the effect of improving the bearing control performance and ensuring the operating stability of the magnetic levitation compressor by using an extreme learning machine model, a support vector machine model and a random forest model to predict the control parameter prediction model of the magnetic levitation compressor.

[0005] The present invention provides a control method for a magnetic levitation compressor, wherein the magnetic levitation compressor has a magnetic bearing, and the control system of the magnetic bearing adopts a displacement current double closed-loop control system; the control method for the magnetic levitation compressor comprises: when the magnetic levitation compressor is running, introducing one or more preset disturbances into the displacement current double closed-loop control system; and adjusting the control parameters of the displacement current double closed-loop control system more than once; for each of the one or more preset disturbances introduced, when the magnetic bearing is running based on the control parameters of the displacement current double closed-loop control system adjusted each time, obtaining the rotor displacement of the magnetic bearing and obtaining the stator coil current of the magnetic bearing; thereby, obtaining the control parameters, rotor displacement and stator coil current of the displacement current double closed-loop control system corresponding to all disturbances in the one or more preset disturbances introduced, which are recorded as a first data group; based on the The first data group is trained and tested using two or more preset training models to obtain an initial control parameter prediction model of the magnetic levitation compressor, so as to control the operation of the magnetic bearing according to the initial control parameter prediction model of the magnetic levitation compressor; when the displacement current dual closed-loop control system operates according to the initial control parameter prediction model, the operating parameters of the magnetic levitation compressor are obtained, which are recorded as compressor operating parameters; and the operating parameters of the unit where the magnetic levitation compressor is located are obtained, which are recorded as unit operating parameters; the input and output of the initial control parameter prediction model, the compressor operating parameters and the unit operating parameters are recorded as the second data group; based on the second data group, training and testing are performed using two or more preset training models to obtain an updated control parameter prediction model of the magnetic levitation compressor, so as to control the operation of the magnetic bearing according to the updated control parameter prediction model of the magnetic levitation compressor.

[0006] In some embodiments, when the magnetic levitation compressor is running, one or more preset disturbances are introduced into the displacement current dual closed-loop control system, including: adding one or more interference sources to the feedback value of the rotor displacement of the displacement loop of the displacement current dual closed-loop control system to achieve the introduction of one or more preset disturbances into the displacement current dual closed-loop control system; wherein the one or more interference sources include: at least one of Gaussian white noise of a set frequency, sinusoidal interference signals of different amplitudes, and random pulse signals of different amplitudes; and / or, two or more preset training models, including: at least two of an extreme learning machine model, a support vector machine model, and a random forest model.

[0007] In some embodiments, based on the first data group, training and testing are performed using two or more preset training models to obtain an initial control parameter prediction model of the magnetic levitation compressor, including: preprocessing the first data group based on each training model in the two or more preset training models to obtain first sample data under each training model; training and testing each training model using the first sample data under each training model to obtain a control parameter prediction model under each training model; thereby, obtaining a control parameter prediction model under each training model in the two or more preset training models; and performing weighted averaging processing on the control parameter prediction model under each training model in the two or more preset training models to obtain a weighted average control parameter prediction model of the two or more preset training models as the initial control parameter prediction model of the magnetic levitation compressor.

[0008] In some embodiments, based on each training model in more than two preset training models, the first data group is preprocessed to obtain the first sample data under each training model, including: based on the first data group, determining the control parameters corresponding to the minimum rotor displacement and the minimum stator coil current under each disturbance in more than one preset disturbance introduced, as the optimal control parameters under each disturbance; thereby, obtaining the optimal control parameters, minimum rotor displacement and minimum stator coil current corresponding to all disturbances in more than one preset disturbance introduced, which are recorded as the initial data group; for the initial data group, based on the input and output requirement relationship of each training model in more than two preset training models, constructing input and output data pairs to obtain the first sample data under each training model.

[0009] In some embodiments, based on the second data group, two or more preset training models are used for training and testing to obtain an updated control parameter prediction model for the magnetic levitation compressor, including: when the displacement current dual closed-loop control system operates according to the initial control parameter prediction model, obtaining the operating parameters of the magnetic levitation compressor, recorded as compressor operating parameters; and obtaining the operating parameters of the unit where the magnetic levitation compressor is located, recorded as unit operating parameters; recording the input and output of the initial control parameter prediction model, the compressor operating parameters and the unit operating parameters as the second data group; based on each training model in the two or more preset training models, the second data group is preprocessed to obtain second sample data under each training model; using the second sample data under each training model to train and test each training model to obtain an updated control parameter prediction model under each training model; thereby, an updated control parameter prediction model under each training model in the two or more preset training models is obtained; the updated control parameter prediction model under each training model in the two or more preset training models is weighted averaged to obtain a weighted average updated control parameter prediction model of the two or more preset training models as the updated control parameter prediction model of the magnetic levitation compressor.

[0010] In some embodiments, in the second data group, the compressor operating parameters include at least one of the following: the current of the motor in the compressor, the operating frequency of the motor in the compressor, and the bus voltage of the motor controller in the compressor; the unit where the magnetic levitation compressor is located includes an air-conditioning unit, and the unit operating parameters include at least one of the following: the shutdown or running state of the air-conditioning unit, the operating condition of the air-conditioning unit, the state of the cooling water flow switch in the air-conditioning unit, the state of the freezing water flow switch in the air-conditioning unit, the opening of the throttle valve in the air-conditioning unit, the load of the air-conditioning unit, the cold water inlet pressure in the air-conditioning unit, the freezing water outlet pressure in the air-conditioning unit, the current heat exchange rate of the air-conditioning unit, the condensing pressure of the condenser in the air-conditioning unit, and the evaporating pressure of the evaporator in the air-conditioning unit; based on each training model in more than two preset training models, the second data group is preprocessed. , obtaining the second sample data under each training model, including: for the compressor operating parameters in the second data group, screening according to the first corresponding set parameter range, obtaining the corresponding operating parameters within the first corresponding set parameter range, recorded as the first screening operating parameters; for the unit operating parameters in the second data group, screening according to the second corresponding set parameter range, obtaining the corresponding operating parameters within the second corresponding set parameter range, recorded as the second screening operating parameters; taking the input and output of the initial control parameter prediction model, the first screening operating parameters and the second screening operating parameters as the screening data group; for the screening data group, constructing the input and output data pairs based on the demand relationship between the input and output of each training model in more than two preset training models, and obtaining the second sample data under each training model.

[0011] Matching the above method, the present invention provides a control device for a magnetic levitation compressor on the other hand. The magnetic levitation compressor has a magnetic bearing, and the control system of the magnetic bearing adopts a displacement current double closed-loop control system; the control device of the magnetic levitation compressor includes: a control unit, configured to introduce one or more preset disturbances into the displacement current double closed-loop control system when the magnetic levitation compressor is running; and adjust the control parameters of the displacement current double closed-loop control system more than once; an acquisition unit, configured to obtain the rotor displacement of the magnetic bearing and the stator coil current of the magnetic bearing for each disturbance among the one or more preset disturbances introduced, when the magnetic bearing operates based on the control parameters of the displacement current double closed-loop control system adjusted each time; thereby, the control parameters, rotor displacement and stator coil current of the displacement current double closed-loop control system corresponding to all disturbances among the one or more preset disturbances introduced are obtained, which are recorded as a first data group; the control unit , is also configured to perform training and testing based on the first data group using two or more preset training models to obtain an initial control parameter prediction model of the magnetic levitation compressor, so as to control the operation of the magnetic bearing according to the initial control parameter prediction model of the magnetic levitation compressor; the control unit is also configured to obtain the operating parameters of the magnetic levitation compressor when the displacement current dual closed-loop control system operates according to the initial control parameter prediction model, which are recorded as compressor operating parameters; and obtain the operating parameters of the unit where the magnetic levitation compressor is located, which are recorded as unit operating parameters; the input and output of the initial control parameter prediction model, the compressor operating parameters and the unit operating parameters are recorded as a second data group; the control unit is also configured to perform training and testing based on the second data group using two or more preset training models to obtain an updated control parameter prediction model of the magnetic levitation compressor, so as to control the operation of the magnetic bearing according to the updated control parameter prediction model of the magnetic levitation compressor.

[0012] In some embodiments, the control unit introduces one or more preset disturbances into the displacement current dual closed-loop control system when the magnetic levitation compressor is running, including: adding one or more interference sources to the feedback value of the rotor displacement of the displacement loop of the displacement current dual closed-loop control system to achieve the introduction of one or more preset disturbances into the displacement current dual closed-loop control system; wherein the one or more interference sources include: at least one of Gaussian white noise of a set frequency, sinusoidal interference signals of different amplitudes, and random pulse signals of different amplitudes; and / or, two or more preset training models, including: at least two of an extreme learning machine model, a support vector machine model, and a random forest model.

[0013] In some embodiments, the control unit, based on the first data group, uses two or more preset training models for training and testing to obtain an initial control parameter prediction model of the magnetic levitation compressor, including: preprocessing the first data group based on each training model in the two or more preset training models to obtain first sample data under each training model; training and testing each training model using the first sample data under each training model to obtain a control parameter prediction model under each training model; thereby, obtaining a control parameter prediction model under each training model in the two or more preset training models; performing weighted averaging processing on the control parameter prediction model under each training model in the two or more preset training models to obtain a weighted average control parameter prediction model of the two or more preset training models as the initial control parameter prediction model of the magnetic levitation compressor.

[0014] In some embodiments, the control unit preprocesses the first data group based on each of the two or more preset training models to obtain the first sample data under each training model, including: based on the first data group, determining the control parameters corresponding to the minimum rotor displacement and the minimum stator coil current under each of the one or more preset disturbances introduced, as the optimal control parameters under each disturbance; thereby, obtaining the optimal control parameters, minimum rotor displacement and minimum stator coil current corresponding to all disturbances in the one or more preset disturbances introduced, which are recorded as the initial data group; for the initial data group, constructing the input and output data pairs based on the input and output requirement relationship of each training model in the two or more preset training models to obtain the first sample data under each training model.

[0015] In some embodiments, the control unit, based on the second data group, uses two or more preset training models for training and testing to obtain an updated control parameter prediction model for the magnetic levitation compressor, including: when the displacement current dual closed-loop control system operates according to the initial control parameter prediction model, obtaining the operating parameters of the magnetic levitation compressor, recorded as compressor operating parameters; and obtaining the operating parameters of the unit where the magnetic levitation compressor is located, recorded as unit operating parameters; recording the input and output of the initial control parameter prediction model, the compressor operating parameters, and the unit operating parameters as the second data group; based on each training model in the two or more preset training models, preprocessing the second data group to obtain second sample data under each training model; using the second sample data under each training model to train and test each training model to obtain an updated control parameter prediction model under each training model; thereby obtaining an updated control parameter prediction model under each training model in the two or more preset training models; performing weighted averaging processing on the updated control parameter prediction model under each training model in the two or more preset training models to obtain a weighted average updated control parameter prediction model of the two or more preset training models as the updated control parameter prediction model of the magnetic levitation compressor.

[0016] In some embodiments, in the second data group, the compressor operating parameters include at least one of the following: the current of the motor in the compressor, the operating frequency of the motor in the compressor, and the bus voltage of the motor controller in the compressor; the unit where the magnetic levitation compressor is located includes an air-conditioning unit, and the unit operating parameters include at least one of the following: the shutdown or running state of the air-conditioning unit, the operating condition of the air-conditioning unit, the state of the cooling water flow switch in the air-conditioning unit, the state of the freezing water flow switch in the air-conditioning unit, the opening of the throttle valve in the air-conditioning unit, the load of the air-conditioning unit, the cold water inlet pressure in the air-conditioning unit, the freezing water outlet pressure in the air-conditioning unit, the current heat exchange rate of the air-conditioning unit, the condensing pressure of the condenser in the air-conditioning unit, and the evaporating pressure of the evaporator in the air-conditioning unit; the control unit performs training on the second data group based on each training model in more than two preset training models. Preprocessing is performed to obtain second sample data under each training model, including: for the compressor operating parameters in the second data group, screening is performed according to the first corresponding set parameter range, and the corresponding operating parameters within the first corresponding set parameter range are obtained, which are recorded as first screened operating parameters; for the unit operating parameters in the second data group, screening is performed according to the second corresponding set parameter range, and the corresponding operating parameters within the second corresponding set parameter range are obtained, which are recorded as second screened operating parameters; the input and output of the initial control parameter prediction model, the first screened operating parameters and the second screened operating parameters are used as a screened data group; for the screened data group, based on the demand relationship between the input and output of each training model in more than two preset training models, an input and output data pair is constructed to obtain the second sample data under each training model.

[0017] Matching the above device, the present invention provides a magnetic levitation compressor on another aspect, including: the control device of the magnetic levitation compressor described above.

[0018] In accordance with the above method, the present invention provides a storage medium on another aspect, wherein the storage medium includes a stored program, wherein when the program is running, the device where the storage medium is located is controlled to execute the steps of the above-mentioned method for controlling the magnetic levitation compressor.

[0019] In accordance with the above method, the present invention further provides a computer program product, comprising a computer program, which implements the steps of the above method for controlling a magnetic levitation compressor when executed by a processor.

[0020] Therefore, the solution of the present invention obtains a control parameter prediction model of the magnetic levitation compressor by performing two model trainings on the magnetic bearing control system; wherein, in the first model training, the displacement feedback value of the displacement loop and the current feedback value of the current loop in the displacement current double closed-loop control system of the magnetic bearing control system are used as input, and the control parameters of the control mode of the magnetic bearing control system (such as the PID control mode) are used as output, and various possible disturbances that may occur during the actual operation of the magnetic levitation compressor are added to the displacement current double closed-loop control system (such as adding disturbances to the displacement feedback value in the displacement loop), and the extreme learning machine model, the support vector machine model and the random forest model are used for training to obtain the initial control parameters of the magnetic levitation compressor. number prediction model; in the second model training, the input and output of the initial control parameter prediction model, the operating parameters of the magnetic levitation compressor, and the operating parameters of the unit where the magnetic levitation compressor is located (such as an air-conditioning unit) are used as input, and the output of the initial control parameter prediction model is used as output. The extreme learning machine model, the support vector machine model and the random forest model are used for training to obtain an updated control parameter prediction model of the magnetic levitation compressor, which is used as the control parameter prediction model of the required magnetic levitation compressor; thus, the control parameter prediction model of the magnetic levitation compressor is obtained by using the extreme learning machine model, the support vector machine model and the random forest model to predict, which is beneficial to improving the bearing control performance and ensuring the operating stability of the magnetic levitation compressor.

[0021] Other features and advantages of the present invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the present invention.

[0022] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 1. It is a flow chart of an embodiment of a method for controlling a magnetic levitation compressor according to the present invention;

[0024] Figure 2 1. A flow chart of an embodiment of the method of the present invention for performing training and testing based on the first data set using two or more preset training models;

[0025] Figure 3 is a flow chart of an embodiment of preprocessing the first data set in the method of the present invention;

[0026] Figure 4 1. A flow chart of an embodiment of the method of the present invention for performing training and testing based on the second data set using two or more preset training models;

[0027] Figure 51 is a flow chart of an embodiment of preprocessing the second data set in the method of the present invention;

[0028] Figure 6 Schematic diagram of the structure of an embodiment of a control device for a magnetic levitation compressor of the present invention;

[0029] Figure 7 This is a schematic diagram of the workflow of the control parameter prediction model for the magnetic levitation compressor;

[0030] Figure 8 This is a schematic diagram of the implementation process of the extreme learning machine model;

[0031] Figure 9 Schematic diagram of the solution process of the random forest control parameter prediction model;

[0032] Figure 10 This is a schematic diagram of the regression solution process of the support vector machine model;

[0033] Figure 11 Schematic diagram of the process of solving machine learning problems.

[0034] In conjunction with the accompanying drawings, the reference numerals in the embodiments of the present invention are as follows:

[0035] 102 - acquisition unit; 104 - control unit. DETAILED DESCRIPTION

[0036] To make the objectives, technical solutions, and advantages of the present invention more clear, the technical solutions of the present invention will be clearly and completely described below in conjunction with specific embodiments of the present invention and corresponding drawings. Obviously, the embodiments described are only some of the embodiments of the present invention, not all of them. Based on the embodiments of the solutions of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0037] Considering the difficulty of controlling active magnetic bearing-rotor systems, the parameters of the bearing controller significantly influence the dynamic response speed, control bandwidth, and system stability of the bearing control system. This makes tuning these parameters challenging when using control methods such as PID, active disturbance rejection, and model prediction. In practice, manually finding the optimal control parameters is often impossible, making it difficult to guarantee optimal control performance.

[0038] Therefore, the solution of the present invention proposes a control method for a magnetic levitation compressor, specifically a magnetic levitation compressor adaptive optimal parameter control method based on machine learning. Through optimization and model training, the optimal parameters within a certain parameter range are found, the control reliability is enhanced, the bearing control performance is improved, and the stability of the magnetic levitation compressor system is greatly improved.

[0039] According to an embodiment of the present invention, a control method for a magnetic levitation compressor is provided. Figure 1 The flow chart of an embodiment of the method of the present invention is shown in FIG. The magnetic levitation compressor has a magnetic bearing, and the control system of the magnetic bearing adopts a displacement current double closed loop control system, that is, a double closed loop control system in which the outer loop is a displacement loop and the inner loop is a current loop. In the solution of the present invention, Figure 1 As shown, the control method of the magnetic levitation compressor includes: steps S110 to S150.

[0040] At step S110, when the magnetic levitation compressor is running, one or more preset disturbances are introduced into the displacement current dual closed-loop control system, such as simulating various disturbances that may occur during the actual operation of the magnetic levitation compressor, for example: preset disturbance parameters are introduced into the displacement current dual closed-loop control system, there are more than one type of preset disturbance parameters, and the number of each type of disturbance parameters in the preset disturbance parameters is more than one; and the control parameters of the displacement current dual closed-loop control system are adjusted more than once, specifically, the control parameters of the displacement current dual closed-loop control system can be adjusted according to a set period, such as adjusting the PID control parameters of the displacement current dual closed-loop control system.

[0041] In some embodiments, in step S110, when the magnetic levitation compressor is running, one or more preset disturbances are introduced into the displacement current dual closed-loop control system, including: adding one or more interference sources to the feedback value of the rotor displacement of the displacement loop of the displacement current dual closed-loop control system to achieve the introduction of one or more preset disturbances into the displacement current dual closed-loop control system; wherein the one or more interference sources include: at least one of: Gaussian white noise of a set frequency, sinusoidal interference signals of different amplitudes, and random pulse signals of different amplitudes.

[0042] Figure 7The figure is a workflow diagram of the control parameter prediction model for the magnetic levitation compressor. In the actual process of designing the bearing parameters of the magnetic levitation compressor, it is usually impossible to manually find the optimal control parameters, and it is difficult to ensure the optimal control performance. Therefore, the solution of the present invention provides a method for predicting the optimal control parameters based on previous data. To this end, the present invention provides a method for obtaining initial control parameter prediction model training data, a method for installing the entire control parameter prediction model, and a structural device for optimizing the control parameter prediction model through actual operation data. For example, using 5G communication as a real-time data transmission method, the remote PC trains the control parameter prediction model according to the specific training method of the control parameter prediction model given in the solution of the present invention, which can specifically be the use of extreme learning machines, random forests and support vector machines to weightedly predict control parameters.

[0043] like Figure 7 As shown in FIG, the workflow of the control parameter prediction model of the magnetic levitation compressor includes:

[0044] Step 11: Initial training data acquisition: On the experimental unit, when the magnetic bearing rotor is in static suspension, high-frequency Gaussian white noise A is added to the bearing control loop. j +Sinusoidal interference of different amplitudes B j +Large random pulse signals of different amplitudes C j As the interference source, by determining a control mode (such as PID control mode), randomly changing the control parameters K1, K2, K3, the current value D is obtained. j and displacement accuracy E j The magnetic bearing control loop is a closed-loop system. When adding interference sources to the bearing control loop, they are added based on needs and convenience, such as adding them to the displacement sampling value.

[0045] In the solution of the present invention, by introducing one or more preset disturbances into the displacement current dual closed-loop control system to simulate various possible disturbances that may occur during the actual operation of the magnetic levitation compressor, the optimal parameters under each disturbance are found in advance; thereby, all the data found in advance can be trained into an initial control parameter prediction model through machine learning, and the initial control parameter prediction model can be integrated into the magnetic bearing controller. The optimal control parameters given by the control parameter prediction model are used under each operating condition or disturbance of the magnetic bearing compressor system, thereby improving the control effect and making the magnetic bearing rotor stably and levitated with high displacement accuracy.

[0046] At step S120, for each of the one or more preset disturbances introduced, the rotor displacement of the magnetic bearing is obtained, and the stator coil current of the magnetic bearing is obtained when the magnetic bearing operates based on the control parameters of the displacement current dual closed-loop control system adjusted each time; in this way, the control parameters, rotor displacement and stator coil current of the displacement current dual closed-loop control system corresponding to all disturbances among the one or more preset disturbances introduced are obtained, which are recorded as the first data group.

[0047] At step S130, based on the first data group, two or more preset training models are used for training and testing to obtain an initial control parameter prediction model of the magnetic levitation compressor, so as to control the operation of the magnetic bearing according to the initial control parameter prediction model of the magnetic levitation compressor.

[0048] In some embodiments, the specific process of obtaining the initial control parameter prediction model of the magnetic levitation compressor by training and testing using two or more preset training models based on the first data set in step S130 is described in the following exemplary embodiments.

[0049] The following combination Figure 2 The flowchart of an embodiment of the method of the present invention for training and testing based on the first data group using more than two preset training models is shown, further illustrating the specific process of training and testing based on the first data group using more than two preset training models in step S130, including: steps S210 to S230.

[0050] In step S210, the first data group is preprocessed based on each of two or more preset training models to obtain first sample data under each training model; that is, the first data group is preprocessed based on each of two or more preset training models to obtain first sample data under each training model.

[0051] In some embodiments, in step S210, the first data group is preprocessed based on each of two or more preset training models to obtain the specific process of first sample data under each training model. Please refer to the following exemplary description.

[0052] The following combination Figure 3 The flowchart of an embodiment of preprocessing the first data group in the method of the present invention further illustrates the specific process of preprocessing the first data group in step S210, including: steps S310 to S320.

[0053] Step S310: Based on the first data group, determine the control parameters corresponding to the minimum rotor displacement and the minimum stator coil current under each disturbance among the one or more preset disturbances introduced, as the optimal control parameters under each disturbance; in this way, the optimal control parameters, minimum rotor displacement and minimum stator coil current corresponding to all disturbances among the one or more preset disturbances introduced are obtained, which are recorded as the initial data group.

[0054] Step S320: For the initial data group, based on the required relationship between input and output of each training model in more than two preset training models, construct input and output data pairs to obtain first sample data under each training model.

[0055] like Figure 7 As shown, the workflow of the control parameter prediction model of the magnetic levitation compressor further includes: in step 11, by analyzing data, such as analyzing control parameters K1, K2, K3, obtaining the current value D j and displacement accuracy E j Under each disturbance, the smaller the displacement accuracy, the better, and the smaller the current, the better; that is, when the control current and displacement accuracy are the smallest, it is determined to be the optimal control parameter, and sufficient initial data is obtained through this method. Among them, [K1, K2, K3, D j 、E j ] is a set of data.

[0056] Step 12: Data preprocessing:

[0057] According to the input and output requirements of different types of prediction models, corresponding input-output data pairs are constructed, and the sorted input-output data pairs are divided into model training sets and final model test sets according to a certain ratio. For example: in the input-output data pair, the current value D j and displacement accuracy E j is the input data, and the control parameters K1, K2, and K3 are the output data; for a set of data [K1, K2, K3, D j 、E j ], if there are 1000 sets of data, set 800 sets as training data and the remaining 200 sets as test data.

[0058] ① Determine the control target: Determine the optimal control parameters to minimize the displacement accuracy of the magnetic bearing and achieve high-precision and stable operation of the bearing rotor.

[0059] ② Determine the magnetic bearing control parameters K1, K2, and K3 as output parameters and the other parameters as input parameters. The control parameters K1, K2, and K3 have different values ​​for different control modes.

[0060] ③ Perform parameter correlation analysis to determine the main input parameters, such as current value D j and displacement accuracy E j The input is used as the main input for the model of different prediction methods. Among them, through correlation analysis, we can know the correlation between the input data and the output data. The higher the correlation, the more suitable they are as a set of training data pairs.

[0061] In the solution of the present invention, based on each of two or more preset training models, the first data group obtained by introducing disturbance is preprocessed to obtain the first sample data under each training model, and then training and testing are performed using two or more preset training models based on the first sample data to more accurately obtain the initial control parameter prediction model of the magnetic levitation compressor, so as to find the optimal parameters under each disturbance in advance; and then all the data found in advance can be trained into the initial control parameter prediction model through machine learning, and integrated into the magnetic bearing controller, and the optimal control parameters given by the control parameter prediction model are used under each working condition or disturbance of the magnetic bearing compressor system, thereby improving the control effect, so that the magnetic bearing rotor is stably and suspended with high displacement accuracy.

[0062] In step S220, each training model is trained and tested using the first sample data under each training model to obtain a control parameter prediction model under each training model; thereby, a control parameter prediction model under each training model in more than two preset training models is obtained.

[0063] In step S230, the control parameter prediction model under each of the two or more preset training models is weighted averaged to obtain a weighted average control parameter prediction model of the two or more preset training models as the initial control parameter prediction model of the magnetic levitation compressor.

[0064] like Figure 7 As shown, the workflow of the control parameter prediction model for a magnetic levitation compressor further includes: in step 12, performing a correlation analysis to determine the correlation between the input data and the output data; the higher the correlation, the more suitable the two are for serving as a training data pair; obtaining a total of N = 4000 processed data items, dividing the data into 3800 training sets and 200 test sets. Training is performed using the training sets, and testing is performed using the test sets. When the accuracy of the test results meets a preset accuracy requirement, training and testing are stopped, thereby determining that the initial control parameter prediction model for the magnetic levitation compressor has been obtained.

[0065] exist Figure 7 In the example shown, in the magnetic bearing control loop, the rotor displacement X detected by the displacement sensor is fb(k), respectively input to the first comparator and the control parameter prediction module, the control parameter prediction module starts the control parameter prediction function under the control of the bearing host computer to change the control parameters K1, K2, K3; the reference displacement X ref (k) and rotor displacement X fb (k outputs reference displacement X after passing through the first comparator ref (k) and rotor displacement X fb The difference between the displacement error and the control parameters K1, K2 and K3 is the displacement error e(k). The controller outputs the current adjustment parameter u(k) based on the displacement error e(k) and the control parameters K1, K2 and K3. The reference current I is obtained based on the current adjustment parameter u(k). ref (k), the current sensor detects the coil current I fb (k); reference current I ref (k) and coil current I fb (k) The reference current I is obtained after the second comparator ref (k) and coil current I fb The difference between (k) and the current error is the current error. This current error is fed into a current controller to generate a PWM signal. This PWM signal is then fed into a power amplifier to generate a coil current control signal. The control parameter prediction module and the controller form a control parameter prediction model. This control parameter prediction model uses machine learning to predict and adjust the control parameters.

[0066] In the solution of the present invention, based on the first data group obtained by introducing disturbance, training and testing are performed using two or more preset training models to obtain an initial control parameter prediction model of the magnetic levitation compressor, so as to find the optimal parameters under each disturbance in advance; then all the data found in advance can be trained into an initial control parameter prediction model through machine learning, and the model can be integrated into the magnetic bearing controller, and the optimal control parameters given by the control parameter prediction model can be used under each working condition or disturbance of the magnetic bearing compressor system, thereby improving the control effect and making the magnetic bearing rotor stably and suspended with high displacement accuracy.

[0067] At step S140, when the displacement current dual closed-loop control system operates according to the initial control parameter prediction model, the operating parameters of the magnetic levitation compressor are obtained and recorded as compressor operating parameters; and the operating parameters of the unit in which the magnetic levitation compressor is located are obtained and recorded as unit operating parameters; the input and output of the initial control parameter prediction model, the compressor operating parameters and the unit operating parameters are recorded as a second data group.

[0068] At step S150, based on the second data group, two or more preset training models are used for training and testing to obtain an updated control parameter prediction model of the magnetic levitation compressor, so as to control the operation of the magnetic bearing according to the updated control parameter prediction model of the magnetic levitation compressor.

[0069] Bearing control involves a control algorithm, and control parameters need to be adjusted to ensure excellent bearing control performance. It is difficult to manually adjust the parameters to the optimal parameters, and it is impossible to ensure optimal control within a certain parameter range. The scheme of the present invention provides a scheme for adaptive optimal parameter control of magnetic bearings, which simulates various possible disturbances in the actual operation of the magnetic levitation compressor and finds the optimal parameters under each disturbance in advance; all the data found in advance are trained into an initial control parameter prediction model through machine learning, which is integrated into the magnetic bearing controller, and the optimal control parameters given by the control parameter prediction model are used under each working condition or disturbance of the magnetic bearing compressor system, thereby improving the control effect and making the magnetic bearing rotor stable and suspended with high displacement accuracy. At the same time, the displacement accuracy, impact force, control parameters used, etc. of each magnetic bearing compressor system in the actual operating conditions on site are obtained through the fifth generation mobile communication technology (5G) module, and the data set is fed back to the aforementioned control parameter prediction model to further train the control parameter prediction model to achieve optimal control.

[0070] In some embodiments, in step S150, based on the second data group, two or more preset training models are used for training and testing to obtain the updated control parameter prediction model of the magnetic levitation compressor. Please refer to the following exemplary description for the specific process.

[0071] The following combination Figure 4 The flowchart of an embodiment of the method of the present invention for training and testing based on the second data group using more than two preset training models is shown, further illustrating the specific process of training and testing based on the second data group using more than two preset training models in step S150, including: steps S410 to S440.

[0072] Step S410, when the displacement current dual closed-loop control system operates according to the initial control parameter prediction model, obtain the operating parameters of the magnetic levitation compressor, which are recorded as compressor operating parameters; and obtain the operating parameters of the unit where the magnetic levitation compressor is located, which are recorded as unit operating parameters; record the input and output of the initial control parameter prediction model, the compressor operating parameters, and the unit operating parameters as a second data group.

[0073] In step S420, the second data group is preprocessed based on each of two or more preset training models to obtain second sample data under each training model; that is, the second data group is preprocessed based on each of two or more preset training models to obtain second sample data under each training model.

[0074] In some embodiments, in the second data group, the compressor operating parameters include at least one of the following: the current of the motor in the compressor, the operating frequency of the motor in the compressor, and the bus voltage of the motor controller in the compressor; the unit where the magnetic levitation compressor is located includes an air-conditioning unit, and the unit operating parameters include at least one of the following: the shutdown or running status of the air-conditioning unit, the operating conditions of the air-conditioning unit, the status of the cooling water flow switch in the air-conditioning unit, the status of the chilled water flow switch in the air-conditioning unit, the opening of the throttle valve in the air-conditioning unit, the load of the air-conditioning unit, the cold water inlet pressure in the air-conditioning unit, the chilled water outlet pressure in the air-conditioning unit, the current heat exchange capacity of the air-conditioning unit, the condensing pressure of the condenser in the air-conditioning unit, and the evaporating pressure of the evaporator in the air-conditioning unit.

[0075] In step S420, based on each of the two or more preset training models, the second data group is preprocessed to obtain the specific process of second sample data under each training model. Please refer to the following exemplary description.

[0076] The following combination Figure 5 The flowchart of an embodiment of preprocessing the second data group in the method of the present invention further illustrates the specific process of preprocessing the second data group in step S420, including steps S510 to S540.

[0077] Step S510: Filter the compressor operating parameters in the second data group according to a first corresponding set parameter range to obtain corresponding operating parameters within the first corresponding set parameter range, which are recorded as first filtered operating parameters.

[0078] Step S520: filtering the unit operating parameters in the second data group according to a second corresponding set parameter range to obtain corresponding operating parameters within the second corresponding set parameter range, which are recorded as second filtered operating parameters.

[0079] Step S530: taking the input and output of the initial control parameter prediction model, the first screening operation parameter, and the second screening operation parameter as a screening data set.

[0080] Step S540: for the screening data group, based on the required relationship between input and output of each training model in more than two preset training models, construct input and output data pairs to obtain second sample data under each training model.

[0081] To achieve the best effect of the control parameter prediction model, such as Figure 7 As shown, the current operating data of the unit system is obtained in real time through the 5G module of the unit system controller via a remote PC host computer. Each data includes the current state of the unit, the operating condition of the unit, the cooling water flow switch state, the chilled water flow switch state, the throttle valve conduction opening, the unit load, the chilled water inlet pressure, the chilled water outlet pressure, the current cooling capacity, the condensing pressure, the evaporating pressure, the magnetic levitation compressor current, the magnetic levitation compressor motor operating frequency, the magnetic levitation controller bus voltage, the magnetic levitation bearing displacement accuracy, the magnetic levitation bearing current, and the magnetic bearing control parameters K1, K2, and K3, for a total of 19 feature categories.

[0082] Among them, the current state of the unit refers to the state of the unit being shut down or running, and the operating condition of the unit refers to the cooling water flow switch state, the chilled water flow switch state, the throttle valve conduction opening, the unit load, the chilled water inlet pressure, the chilled water outlet pressure, the current cooling capacity, the condensing pressure, the evaporating pressure, the magnetic levitation compressor current, the magnetic levitation compressor motor operating frequency, the magnetic levitation controller bus voltage, the magnetic levitation bearing displacement accuracy, the magnetic levitation bearing current, and the set values ​​of the magnetic bearing control parameters K1, K2, and K3 under each operating condition.

[0083] Data from the magnetic bearing units during operation was collected. Each data entry contained the 19 characteristic categories of the unit's operating data. The data was preprocessed as described above, for example, to remove abnormal data, including data that was clearly incorrect or inconsistent with reality, data with manually discovered data record issues, and data with missing information.

[0084] In the solution of the present invention, based on each of more than two preset training models, the second data group obtained based on the initial control parameter model is preprocessed so that the second sample data obtained under each training model is more accurate, and then the control parameter prediction model is further trained to achieve optimal control.

[0085] In step S430, each training model is trained and tested using the second sample data under each training model to obtain an updated control parameter prediction model under each training model; thereby, an updated control parameter prediction model under each of the two or more preset training models is obtained.

[0086] In step S440, the updated control parameter prediction model under each of the two or more preset training models is weighted averaged to obtain a weighted average updated control parameter prediction model of the two or more preset training models as the updated control parameter prediction model of the magnetic levitation compressor.

[0087] See also Figure 7 In the example shown, the main controller of the magnetic levitation compressor system communicates with the magnetic levitation compressor system via a 5G communication module. The system receives data on the real-time operating current and displacement accuracy of the magnetic bearings transmitted by the magnetic levitation compressor system, transmits data on the compressor system's current operating conditions and pressure differential to the bearing controller, and updates the latest control parameter prediction model to the bearing main control chip. The main controller of the magnetic levitation compressor system exchanges data with a remote PC host (including the unit operation data monitoring system and the control parameter prediction model training system). The remote PC transmits the latest control parameter prediction model to the main controller of the magnetic levitation compressor system, which then feeds back the pressure differential, operating conditions, and received magnetic bearing data of the magnetic levitation compressor system to the remote PC host.

[0088] See also Figure 7 In the example shown, while preprocessing the acquired data, the training architectures of the three models are updated simultaneously, such as by re-introducing the newly acquired data into the model for training:

[0089] a. The number of nodes in the hidden layer of the extreme learning machine is 10.

[0090] b. The number of random features of random forest is mtry=4.

[0091] c. The support vector machine inputs 16 values ​​and outputs 3 values. The 16 input values ​​include: the unit's current state, cooling water flow switch status under each operating condition, chilled water flow switch status, throttle valve guide vane opening, unit load, chilled water inlet pressure, chilled water outlet pressure, current cooling capacity, condensing pressure, evaporating pressure, magnetic levitation compressor current, magnetic levitation compressor motor operating frequency, magnetic levitation controller bus voltage, magnetic levitation bearing displacement accuracy, and magnetic levitation bearing current; and the output values ​​include: magnetic bearing control parameters K1, K2, and K3.

[0092] The data is divided into training and test sets, and the previous generation of control parameter prediction models is further optimized and iterated. This cycle continues until each magnetic levitation compressor achieves the desired operating performance under different operating conditions. The control parameter prediction model can be iterated again if components of the unit are damaged and parameter re-adjustment is required.

[0093] The adaptive control scheme and control parameter training device for magnetic levitation compressors constructed by the present invention finds the optimal parameters within a certain parameter range through optimization and model training. This enhances control reliability, improves bearing control performance, and significantly improves the stability of the magnetic levitation compressor system. The magnetic bearing control parameter prediction method provided by the present invention can be used not only in magnetic levitation bearing systems but also in other control scenarios.

[0094] In some embodiments, the two or more preset training models include at least two of an extreme learning machine model, a support vector machine model, and a random forest model. Of course, preferably, the two or more preset training models include an extreme learning machine model, a support vector machine model, and a random forest model.

[0095] Figure 8 This is a schematic diagram of the implementation flow of the extreme learning machine model structure. Figure 8 As shown in Figure 2, the implementation process of the Extreme Learning Machine model structure is as follows: The Extreme Learning Machine (ELM) is also a neural network, belonging to a feedforward model with a single hidden layer. The ELM randomly initializes the weights of the input and hidden layers and the node biases, and ultimately calculates the weights of the hidden and output layers.

[0096] Assume there are n sample points (x j ,t j ), then the extreme learning machine model can be written as follows:

[0097]

[0098] Among them, g(x) is the activation function, W i =[W i,1 ,W i,2 ,..,W i,n ] T is the input weight, β i is the weight of the output layer (beta), b i is the bias of the i-th hidden node. i *X j W i and X j The inner product of O. j Indicates output, X j represents the input, and N represents the number of neural network layers.

[0099] The learning goal of the extreme learning machine is to minimize the error between the predicted output value and the actual output value:

[0100]

[0101] Among them, t j Indicates actual value.

[0102] That is, there is β i 、W i and b i , such that:

[0103]

[0104] It can be expressed as a matrix:

[0105] H*β=T (4).

[0106] Among them, H is the output of the hidden layer node, β is the weight of the connection between the hidden layer and the output layer, and T is the output label in the training data pair, which is specifically expressed as follows:

[0107]

[0108]

[0109] In order to train a single hidden layer neural network, we want to get b i and β i , such that:

[0110]

[0111] Among them, H represents the output of the hidden layer node, β represents the weight of the connection between the hidden layer and the output layer, T represents the output label in the training data pair, b i represents the bias of the i-th hidden node, W i =[W i,1 ,W i,2 ,..,W i,n ] T represents the input weight. i=1,...,L, which is equivalent to minimizing the loss function E:

[0112]

[0113] In the ELM algorithm, when we randomly determine the input weights of the input layer and the hidden layer, W i and bias, b i , we can uniquely determine the output matrix H of the hidden layer. Solving the neural network model with a single hidden layer can be transformed into solving a linear equation:

[0114] H*β=T (8).

[0115] Wherein, formula (8) is the same as formula (4).

[0116] And the output weight ③β can be determined:

[0117]

[0118] where H + is the generalized inverse of the matrix H, and it can be proved that the solution has the minimum and unique norm; β represents the weight connecting the hidden layer and the output layer, and T represents the output label in the training data pair.

[0119] The implementation steps of the ELM algorithm are as follows:

[0120] (1) Select the number of neurons in the hidden layer. The number of neurons is calculated according to the formula where m is the number of neurons in the hidden layer, n is the number of input nodes, l is the number of output nodes, and α is a constant between 1 and 10.

[0121] In the solution of the present invention, the number of nodes in the input layer of the initial control parameter prediction model is 5, and the number of nodes in the output layer is 3. After repeated adjustment, the number of nodes in the hidden layer is finally selected as 8.

[0122] (2) Randomly set the connection weights weight (0 < weight < 1) between the input layer and the hidden layer and the biases bias (0 < bias < 1) of the neurons.

[0123] (3) Select an activation function. Due to the good performance of the sigmoid function, the function H = 1 / (1 + exp(-H)) is selected in the solution of the present invention. Calculate the matrix H between the hidden layer and the output layer, and calculate the output layer weight β using the formula.

[0124] Through multiple calculations and tests, the number of neurons is finally selected as 8. Import 3800 groups of processed data to solve the prediction model of the extreme learning machine.

[0125] Set the model parameters well and test with 200 groups of test data. If the accuracy rate reaches 90%, integrate it into the upper computer.

[0126] Figure 9 It is a schematic flow chart for solving the random forest control parameter prediction model. As Figure 9 shown, the implementation steps of the random forest regression prediction model structure are as follows:

[0127] ① A random forest has n trees, and each regression tree corresponds to a training set. To construct n regression trees, a corresponding number of training sets are required. The random forest algorithm primarily uses bagging sampling to generate n training subsets from the original training set. Each sampling is random and with replacement.

[0128] ② Then, for each new dataset, a decision tree base learner is trained using random attribute selection. Assuming the number of independent variables in the original sample is p, a subset of mtry independent variables is randomly selected from the independent variable set of each decision tree, and then the optimal independent variable is selected as the partitioning attribute. In the random forest regression algorithm, mtry is generally = log2p.

[0129] ③ Thus, n tree regression decision trees are obtained. Each tree starts to recursively generate child nodes from top to bottom. The decision tree is determined to terminate the split by setting a minimum threshold of the number of samples contained in the leaf node (5 for the regression tree).

[0130] ④ From ntree decision trees, ntree prediction values ​​can be obtained. The weighted average of ntree prediction values ​​is taken using the ensemble learning idea as the regression prediction result, which will be the final output of the algorithm.

[0131] The regression tree mathematical model is:

[0132]

[0133] Where: M is the number of samples in the subset of the regression tree model; c m is the mean response of the data samples in each subset; R m are the subsets divided; I(x∈Rm) is the characteristic function, when x∈R m The value is 1 when it is set, otherwise the value is 0.

[0134] From the original training sample set of 3800 random forest (RF) groups, 3800 training samples were randomly sampled with replacement to obtain one subsample set, denoted as M = 3800, and one regression tree was constructed for each sample; the experiment was repeated 300 times to obtain 500 subsample sets (the 500 subsample sets are independent of each other and samples can be repeated), and 500 regression decision trees were constructed, denoted as ntree = 300.

[0135] Since the initial control parameter prediction model in the present invention uses a variable p = 5, a regression tree learner is trained by randomly selecting a feature number. Generally, the random feature number mtry = log2p, so mtry = 2 in the present invention. 500 regression decision trees are trained, and the results of the 500 regression subtrees are finally averaged as the prediction result.

[0136] The accuracy of the established random forest regression model is verified by using the remaining 200 groups of validation samples. If the accuracy exceeds 90%, the random forest model is integrated into the host computer.

[0137] Figure 10 Schematic diagram of the process of solving support vector machine regression. Figure 10 In the example shown, the Support Vector Machine (SVM) is considered to be a very beautiful, practical and powerful learning algorithm. SVM is a classification algorithm. The basic model is a linear classifier with the maximum margin in the feature space, that is, maximizing the minimum margin, and finally converting it into a convex optimization problem to solve. Simply put, it is to find a segmentation surface, that is, a hyperplane, in an n-dimensional space to correctly classify the feature points in the space. The distance between the points on both sides of the hyperplane and the hyperplane also indicates the accuracy of the segmentation. The points closest to the hyperplane on both sides are called support vectors (SV), and the distance between these points and the hyperplane is called the margin. SVM is to maximize this margin value. In other words, the segmentation problem can essentially be converted into a regression problem.

[0138] like Figure 10 As shown in , the implementation steps of the support vector machine structure are as follows: Support Vector Machine (SVM) is a two-class classification model that maps the feature vector of an instance (taking two dimensions as an example) to some points in space, such as Figure 10 The red and black points in the image belong to two different categories. The goal of SVM is to draw a line that "best" separates the two categories of points, so that if new points appear in the future, this line can also make a good classification. There are countless lines that can be drawn, and the difference lies in the effectiveness. Each line can be called a dividing hyperplane. The best line we hope to find is the "partitioning hyperplane with the largest margin." Figure 10 It can be seen that the distances from the points on the dotted line to the dividing hyperplane are the same. In fact, only these points jointly determine the position of the hyperplane, so they are called "support vectors".

[0139] The partitioning hyperplane can be defined as a linear equation:

[0140] ωT x + b = y;

[0141] Among them, ω={ω1;ω2;...;ω d} is a normal vector that determines the direction of the hyperplane; d is the number of eigenvalues; x is a training example; b is a displacement term that determines the distance between the hyperplane and the origin. Once the normal vector ω and the displacement b are determined, a unique partitioning hyperplane can be determined. x represents the input of the training example, y represents the output of the training example, and the superscript T represents the transpose symbol.

[0142] Since the SVM regression model (SVR) is used in the solution of the present invention, the hyperplane decision boundary obtained when the support vector is used for classification is the regression model when the support vector is used for regression:

[0143] y i =ω T x i +b (11).

[0144] The distance between any point on the partitioning hyperplane and the marginal hyperplanes on both sides of it is So the minimum margin is

[0145] The constraint condition in SVR regression is to make the points in each training set as concentrated as possible near the support vector; in SVR, in order to obtain the distance between all feature sample points and the hyperplane, the sample points closest to the hyperplane are found at the same time.

[0146] The function interval is defined: for the selected training data set T and hyperplane (ω, b), the hyperplane and the feature sample point (x i ,y i ) is:

[0147] r i =y i (ω T x i +b) (12).

[0148] In order to find the support vector, we can calculate the minimum distance r between all sample points. min The one that:

[0149] r min =min(r) (13).

[0150] There is a serious problem with the function interval, that is, when ω and b increase or decrease at a given ratio, the hyperplane equation does not change, but the function interval becomes a multiple of the original ratio. In order to avoid this situation, a concept of geometric interval is introduced, namely:

[0151] r i =y i ((ω T / ||ω||)x i +b / ||ω||) (14).

[0152] This ensures that if ω and b change proportionally, the hyperplane will also change according to the given proportional multiple. Since the learning goal of SVM is to maximize the corresponding geometric interval, the goal is to maximize the geometric interval, which is expressed as follows:

[0153]

[0154] In terms of optimizing the objective function, SVR and SVM are consistent. However, under the constraint condition, SVM is to make the points in each training set as far away as possible from the support vector of its own category (sign); for the regression model, our indicator is to make each feature sample point (x i ,y i ), try to fit a linear model as much as possible:

[0155] f(X)=ω T X+b.

[0156] In ε-SVR, if a sample point is close enough to the regression model and falls within the interval of the regression model, no loss is calculated for that sample point. The loss function is defined as follows:

[0157]

[0158] Therefore, the objective function of SVR can be expressed as follows:

[0159]

[0160] By introducing slack variables and Lagrange multipliers, the problem is transformed into a dual problem. The coefficient ω of the support vector machine model is solved using the Karush-Kuhn-Tucker conditions (KKT) and the sequential minimal optimization algorithm (SMO). T b. Determine the support vector machine's five input values ​​and three output values. The five input values ​​are: unit load, magnetic levitation compressor current, magnetic levitation compressor motor operating frequency, magnetic levitation bearing displacement accuracy, and magnetic levitation bearing current; the three output values ​​are: magnetic bearing control parameters K1, K2, and K3.

[0161] The 3800 groups of original training sample sets are divided to train the SVR model, and the remaining 200 groups are used as validation samples. If the validation accuracy reaches 90%, they are integrated into the host computer as a regression prediction model.

[0162] Figure 11 A flow chart for machine learning solutions. Figure 11 Figure 2 shows a schematic diagram of a heterogeneous (i.e., different types of training models) ensemble learning solution. The previously established extreme learning machine, support vector machine, and random forest regressor are integrated into a host computer data monitoring system. Real-time data is fed into the three previously trained models to generate three prediction results. Based on the prediction accuracy of the previous models, weights are assigned and the average is calculated, which serves as the control parameter for predicting the output. The weighted average prediction performs better. For example, 0.1*extreme learning machine prediction value + 0.5*support vector machine prediction value + 0.4*random forest regressor prediction value = the final output value.

[0163] In the solution of the present invention, at least two, preferably three, of the extreme learning machine model, the support vector machine model, and the random forest model are used for training and testing, and the three prediction results corresponding to the three previously trained models are input into real-time data and weighted averaged to be used as the control parameter prediction output, so that the training is more comprehensive, thereby making the output more accurate and stable.

[0164] By adopting the technical solution of this embodiment, a control parameter prediction model of the magnetic levitation compressor is obtained by performing two model trainings on the magnetic bearing control system; wherein, in the first model training, the displacement feedback value of the displacement loop and the current feedback value of the current loop in the displacement current dual closed-loop control system of the magnetic bearing control system are used as input, and the control parameters of the control mode of the magnetic bearing control system (such as the PID control mode) are used as output, and various possible disturbances that may occur during the actual operation of the magnetic levitation compressor are added to the displacement current dual closed-loop control system (such as adding disturbances to the displacement feedback value in the displacement loop), and the extreme learning machine model, the support vector machine model and the random forest model are used for training to obtain the initial control of the magnetic levitation compressor. Parameter prediction model; in the second model training, the input and output in the initial control parameter prediction model, the operating parameters of the magnetic levitation compressor, and the operating parameters of the unit where the magnetic levitation compressor is located (such as an air-conditioning unit) are used as input, and the output in the initial control parameter prediction model is used as output. The extreme learning machine model, the support vector machine model and the random forest model are used for training to obtain an updated control parameter prediction model of the magnetic levitation compressor, which is used as the control parameter prediction model of the required magnetic levitation compressor; thus, the control parameter prediction model of the magnetic levitation compressor is obtained by using the extreme learning machine model, the support vector machine model and the random forest model to predict, which is beneficial to improving the bearing control performance and ensuring the operating stability of the magnetic levitation compressor.

[0165] According to an embodiment of the present invention, a control device for a magnetic levitation compressor corresponding to the control method for the magnetic levitation compressor is also provided. Figure 6 The structure diagram of an embodiment of the device of the present invention is shown in FIG. The magnetic levitation compressor has a magnetic bearing, and the control system of the magnetic bearing adopts a displacement current double closed loop control system, that is, a double closed loop control system in which the outer loop is a displacement loop and the inner loop is a current loop. In the solution of the present invention, Figure 6 As shown, the control device of the magnetic levitation compressor includes: an acquisition unit 102 and a control unit 104.

[0166] The control unit 104 is configured to introduce one or more preset disturbances into the displacement current dual closed-loop control system when the magnetic levitation compressor is operating, such as simulating various disturbances that may occur during the actual operation of the magnetic levitation compressor. For example, the control unit 104 introduces preset disturbance parameters into the displacement current dual closed-loop control system, wherein the preset disturbance parameters are of one or more types, and each type of the preset disturbance parameters is at least one; and adjust the control parameters of the displacement current dual closed-loop control system at least once. Specifically, the control parameters of the displacement current dual closed-loop control system may be adjusted according to a set period, such as adjusting the PID control parameters of the displacement current dual closed-loop control system. The specific functions and processing of the control unit 104 are shown in step S110.

[0167] In some embodiments, the control unit 104 introduces one or more preset disturbances into the displacement current dual closed-loop control system when the magnetic levitation compressor is running, including: the control unit 104 is specifically further configured to add one or more interference sources to the feedback value of the rotor displacement of the displacement loop of the displacement current dual closed-loop control system to achieve the introduction of one or more preset disturbances into the displacement current dual closed-loop control system; wherein the one or more interference sources include: at least one of: Gaussian white noise of a set frequency, sinusoidal interference signals of different amplitudes, and random pulse signals of different amplitudes.

[0168] Figure 7The figure is a workflow diagram of the control parameter prediction model for the magnetic levitation compressor. In the actual process of designing the bearing parameters of the magnetic levitation compressor, it is usually impossible to manually find the optimal control parameters, and it is difficult to ensure the optimal control performance. Therefore, the solution of the present invention provides a method for predicting the optimal control parameters based on previous data. To this end, the present invention provides a method for obtaining initial control parameter prediction model training data, a method for installing the entire control parameter prediction model, and a structural device for optimizing the control parameter prediction model through actual operation data. For example, using 5G communication as a real-time data transmission method, the remote PC trains the control parameter prediction model according to the specific training method of the control parameter prediction model given in the solution of the present invention, which can specifically be the use of extreme learning machines, random forests and support vector machines to weightedly predict control parameters.

[0169] like Figure 7 As shown in FIG, the workflow of the control parameter prediction model of the magnetic levitation compressor includes:

[0170] Step 11: Initial training data acquisition: On the experimental unit, when the magnetic bearing rotor is in static suspension, high-frequency Gaussian white noise A is added to the bearing control loop. j +Sinusoidal interference of different amplitudes B j +Large random pulse signals of different amplitudes C j As the interference source, by determining a control mode (such as PID control mode), randomly changing the control parameters K1, K2, K3, the current value D is obtained. j and displacement accuracy E j The magnetic bearing control loop is a closed-loop system. When adding interference sources to the bearing control loop, they are added based on needs and convenience, such as adding them to the displacement sampling value.

[0171] In the solution of the present invention, by introducing one or more preset disturbances into the displacement current dual closed-loop control system to simulate various possible disturbances that may occur during the actual operation of the magnetic levitation compressor, the optimal parameters under each disturbance are found in advance; thereby, all the data found in advance can be trained into an initial control parameter prediction model through machine learning, and the initial control parameter prediction model can be integrated into the magnetic bearing controller. The optimal control parameters given by the control parameter prediction model are used under each operating condition or disturbance of the magnetic bearing compressor system, thereby improving the control effect and making the magnetic bearing rotor stably and levitated with high displacement accuracy.

[0172] The acquisition unit 102 is configured to, for each of the one or more preset disturbances introduced, acquire the rotor displacement of the magnetic bearing and the stator coil current of the magnetic bearing while the magnetic bearing operates based on the control parameters of the displacement current dual closed-loop control system adjusted at each time. In this way, the control parameters of the displacement current dual closed-loop control system, the rotor displacement, and the stator coil current corresponding to all of the one or more preset disturbances introduced are obtained and recorded as a first data set. The specific functions and processing of the acquisition unit 102 are described in step S120.

[0173] The control unit 104 is further configured to train and test the first data set using two or more preset training models to obtain an initial control parameter prediction model for the magnetic levitation compressor, thereby controlling the operation of the magnetic bearing according to the initial control parameter prediction model for the magnetic levitation compressor. The specific functions and processing of the control unit 104 are further described in step S130.

[0174] In some embodiments, the control unit 104 performs training and testing based on the first data set using two or more preset training models to obtain an initial control parameter prediction model for the magnetic levitation compressor, including:

[0175] The control unit 104 is further configured to preprocess the first data set based on each of the two or more preset training models to obtain first sample data under each training model. In other words, the control unit 104 preprocesses the first data set based on each of the two or more preset training models to obtain first sample data under each training model. The specific functions and processing of the control unit 104 are further described in step S210.

[0176] In some embodiments, the control unit 104 preprocesses the first data group based on each of the two or more preset training models to obtain first sample data under each training model, including:

[0177] The control unit 104 is further configured to, based on the first data set, determine, for each of the one or more preset disturbances, the control parameters corresponding to the minimum rotor displacement and the minimum stator coil current, as the optimal control parameters for each disturbance. This determines the optimal control parameters, minimum rotor displacement, and minimum stator coil current corresponding to all of the one or more preset disturbances, which are recorded as the initial data set. The specific functions and processing of the control unit 104 are further described in step S310.

[0178] The control unit 104 is further configured to construct input and output data pairs for the initial data set based on the input and output requirements of each of the two or more preset training models, thereby obtaining first sample data for each training model. The specific functions and processing of the control unit 104 are further described in step S320.

[0179] like Figure 7 As shown, the workflow of the control parameter prediction model of the magnetic levitation compressor further includes: in step 11, by analyzing data, such as analyzing control parameters K1, K2, K3, obtaining the current value D j and displacement accuracy E j Under each disturbance, the smaller the displacement accuracy, the better, and the smaller the current, the better; that is, when the control current and displacement accuracy are the smallest, it is determined to be the optimal control parameter, and sufficient initial data is obtained through this method. Among them, [K1, K2, K3, D j 、E j ] is a set of data.

[0180] Step 12: Data preprocessing:

[0181] According to the input and output requirements of different types of prediction models, corresponding input-output data pairs are constructed, and the sorted input-output data pairs are divided into model training sets and final model test sets according to a certain ratio. For example: in the input-output data pair, the current value D j and displacement accuracy E j is the input data, and the control parameters K1, K2, and K3 are the output data; for a set of data [K1, K2, K3, D j 、E j ], if there are 1000 sets of data, set 800 sets as training data and the remaining 200 sets as test data.

[0182] ① Determine the control target: Determine the optimal control parameters to minimize the displacement accuracy of the magnetic bearing and achieve high-precision and stable operation of the bearing rotor.

[0183] ② Determine the magnetic bearing control parameters K1, K2, and K3 as output parameters and the other parameters as input parameters. The control parameters K1, K2, and K3 have different values ​​for different control modes.

[0184] ③ Perform parameter correlation analysis to determine the main input parameters, such as current value D j and displacement accuracy E j The input is used as the main input for the model of different prediction methods. Among them, through correlation analysis, we can know the correlation between the input data and the output data. The higher the correlation, the more suitable they are as a set of training data pairs.

[0185] In the solution of the present invention, based on each of two or more preset training models, the first data group obtained by introducing disturbance is preprocessed to obtain the first sample data under each training model, and then training and testing are performed using two or more preset training models based on the first sample data to more accurately obtain the initial control parameter prediction model of the magnetic levitation compressor, so as to find the optimal parameters under each disturbance in advance; and then all the data found in advance can be trained into the initial control parameter prediction model through machine learning, and integrated into the magnetic bearing controller, and the optimal control parameters given by the control parameter prediction model are used under each working condition or disturbance of the magnetic bearing compressor system, thereby improving the control effect, so that the magnetic bearing rotor is stably and suspended with high displacement accuracy.

[0186] The control unit 104 is further configured to train and test each training model using the first sample data for each training model to obtain a control parameter prediction model for each training model. This results in a control parameter prediction model for each of the two or more preset training models. The specific functions and processing of the control unit 104 are further described in step S220.

[0187] The control unit 104 is further configured to perform a weighted average of the control parameter prediction models for each of the two or more preset training models to obtain a weighted average control parameter prediction model of the two or more preset training models, and use the weighted average control parameter prediction model as the initial control parameter prediction model for the magnetic levitation compressor. The specific functions and processing of the control unit 104 are further described in step S230.

[0188] like Figure 7 As shown, the workflow of the control parameter prediction model for a magnetic levitation compressor further includes: in step 12, performing a correlation analysis to determine the correlation between the input data and the output data; the higher the correlation, the more suitable the two are for serving as a training data pair; obtaining a total of N = 4000 processed data items, dividing the data into 3800 training sets and 200 test sets. Training is performed using the training sets, and testing is performed using the test sets. When the accuracy of the test results meets a preset accuracy requirement, training and testing are stopped, thereby determining that the initial control parameter prediction model for the magnetic levitation compressor has been obtained.

[0189] exist Figure 7 In the example shown, in the magnetic bearing control loop, the rotor displacement X detected by the displacement sensor is fb (k), respectively input to the first comparator and the control parameter prediction module, the control parameter prediction module starts the control parameter prediction function under the control of the bearing host computer to change the control parameters K1, K2, K3; the reference displacement X ref (k) and rotor displacement Xfb (k outputs reference displacement X after passing through the first comparator ref (k) and rotor displacement X fb The difference between the displacement error and the control parameters K1, K2 and K3 is the displacement error e(k). The controller outputs the current adjustment parameter u(k) based on the displacement error e(k) and the control parameters K1, K2 and K3. The reference current I is obtained based on the current adjustment parameter u(k). ref (k), the current sensor detects the coil current I fb (k); reference current I ref (k) and coil current I fb (k) The reference current I is obtained after the second comparator ref (k) and coil current I fb The difference between (k) and the current error is the current error. This current error is fed into a current controller to generate a PWM signal. This PWM signal is then fed into a power amplifier to generate a coil current control signal. The control parameter prediction module and the controller form a control parameter prediction model. This control parameter prediction model uses machine learning to predict and adjust the control parameters.

[0190] In the solution of the present invention, based on the first data group obtained by introducing disturbance, training and testing are performed using two or more preset training models to obtain an initial control parameter prediction model of the magnetic levitation compressor, so as to find the optimal parameters under each disturbance in advance; then all the data found in advance can be trained into an initial control parameter prediction model through machine learning, and the model can be integrated into the magnetic bearing controller, and the optimal control parameters given by the control parameter prediction model can be used under each working condition or disturbance of the magnetic bearing compressor system, thereby improving the control effect and making the magnetic bearing rotor stably and suspended with high displacement accuracy.

[0191] The control unit 104 is further configured to, when the displacement current dual closed-loop control system operates according to the initial control parameter prediction model, obtain operating parameters of the magnetic levitation compressor, recording them as compressor operating parameters; obtain operating parameters of the unit in which the magnetic levitation compressor is located, recording them as unit operating parameters; and record the input and output of the initial control parameter prediction model, the compressor operating parameters, and the unit operating parameters as a second data set. The specific functions and processing of the control unit 104 are further described in step S140.

[0192] The control unit 104 is further configured to train and test the second data set using two or more preset training models to obtain an updated control parameter prediction model for the magnetic levitation compressor, thereby controlling the operation of the magnetic bearing according to the updated control parameter prediction model for the magnetic levitation compressor. The specific functions and processing of the control unit 104 are further described in step S150.

[0193] Bearing control involves a control algorithm, and control parameters need to be adjusted to ensure excellent bearing control performance. It is difficult to manually adjust the parameters to the optimal parameters, and it is impossible to ensure optimal control within a certain parameter range. The scheme of the present invention provides a scheme for adaptive optimal parameter control of magnetic bearings, which simulates various possible disturbances in the actual operation of the magnetic levitation compressor and finds the optimal parameters under each disturbance in advance; all the data found in advance are trained into an initial control parameter prediction model through machine learning, which is integrated into the magnetic bearing controller, and the optimal control parameters given by the control parameter prediction model are used under each working condition or disturbance of the magnetic bearing compressor system, thereby improving the control effect and making the magnetic bearing rotor stable and suspended with high displacement accuracy. At the same time, the displacement accuracy, impact force, control parameters used, etc. of each magnetic bearing compressor system in the actual operating conditions on site are obtained through the fifth generation mobile communication technology (5G) module, and the data set is fed back to the aforementioned control parameter prediction model to further train the control parameter prediction model to achieve optimal control.

[0194] In some embodiments, the control unit 104 performs training and testing based on the second data set using two or more preset training models to obtain an updated control parameter prediction model for the magnetic levitation compressor, including:

[0195] The control unit 104 is further configured to, when the displacement current dual closed-loop control system operates according to the initial control parameter prediction model, obtain operating parameters of the magnetic levitation compressor, recording them as compressor operating parameters; obtain operating parameters of the unit in which the magnetic levitation compressor is located, recording them as unit operating parameters; and record the input and output of the initial control parameter prediction model, the compressor operating parameters, and the unit operating parameters as a second data set. The specific functions and processing of the control unit 104 are further described in step S410.

[0196] The control unit 104 is further configured to preprocess the second data set based on each of the two or more preset training models to obtain second sample data under each training model. In other words, the second data set is preprocessed based on each of the two or more preset training models to obtain second sample data under each training model. The specific functions and processing of the control unit 104 are further described in step S420.

[0197] In some embodiments, in the second data group, the compressor operating parameters include at least one of the following: the current of the motor in the compressor, the operating frequency of the motor in the compressor, and the bus voltage of the motor controller in the compressor; the unit where the magnetic levitation compressor is located includes an air-conditioning unit, and the unit operating parameters include at least one of the following: the shutdown or running status of the air-conditioning unit, the operating conditions of the air-conditioning unit, the status of the cooling water flow switch in the air-conditioning unit, the status of the chilled water flow switch in the air-conditioning unit, the opening of the throttle valve in the air-conditioning unit, the load of the air-conditioning unit, the cold water inlet pressure in the air-conditioning unit, the chilled water outlet pressure in the air-conditioning unit, the current heat exchange capacity of the air-conditioning unit, the condensing pressure of the condenser in the air-conditioning unit, and the evaporating pressure of the evaporator in the air-conditioning unit.

[0198] The control unit 104 pre-processes the second data group based on each of the two or more preset training models to obtain second sample data under each training model, including:

[0199] The control unit 104 is further configured to filter the compressor operating parameters in the second data set according to a first corresponding set parameter range, and obtain corresponding operating parameters within the first corresponding set parameter range, which are recorded as first filtered operating parameters. The specific functions and processing of the control unit 104 are further described in step S510.

[0200] The control unit 104 is further configured to filter the unit operating parameters in the second data set according to a second corresponding set parameter range, and obtain corresponding operating parameters within the second corresponding set parameter range, which are recorded as second filtered operating parameters. The specific functions and processing of the control unit 104 are further described in step S520.

[0201] The control unit 104 is further configured to use the input and output of the initial control parameter prediction model, the first screening operation parameter, and the second screening operation parameter as a screening data set. The specific functions and processing of the control unit 104 are further described in step S530.

[0202] The control unit 104 is further configured to construct input and output data pairs for the screening data set based on the input and output requirements of each of the two or more preset training models, thereby obtaining second sample data for each training model. The specific functions and processing of the control unit 104 are further described in step S540.

[0203] To achieve the best effect of the control parameter prediction model, such as Figure 7As shown, the current operating data of the unit system is obtained in real time through the 5G module of the unit system controller via a remote PC host computer. Each data includes the current state of the unit, the operating condition of the unit, the cooling water flow switch state, the chilled water flow switch state, the throttle valve conduction opening, the unit load, the chilled water inlet pressure, the chilled water outlet pressure, the current cooling capacity, the condensing pressure, the evaporating pressure, the magnetic levitation compressor current, the magnetic levitation compressor motor operating frequency, the magnetic levitation controller bus voltage, the magnetic levitation bearing displacement accuracy, the magnetic levitation bearing current, and the magnetic bearing control parameters K1, K2, and K3, for a total of 19 feature categories.

[0204] Among them, the current state of the unit refers to the state of the unit being shut down or running, and the operating condition of the unit refers to the cooling water flow switch state, the chilled water flow switch state, the throttle valve conduction opening, the unit load, the chilled water inlet pressure, the chilled water outlet pressure, the current cooling capacity, the condensing pressure, the evaporating pressure, the magnetic levitation compressor current, the magnetic levitation compressor motor operating frequency, the magnetic levitation controller bus voltage, the magnetic levitation bearing displacement accuracy, the magnetic levitation bearing current, and the set values ​​of the magnetic bearing control parameters K1, K2, and K3 under each operating condition.

[0205] Data from the magnetic bearing units during operation was collected. Each data entry contained the 19 characteristic categories of the unit's operating data. The data was preprocessed as described above, for example, to remove abnormal data, including data that was clearly incorrect or inconsistent with reality, data with manually discovered data record issues, and data with missing information.

[0206] In the solution of the present invention, based on each of more than two preset training models, the second data group obtained based on the initial control parameter model is preprocessed so that the second sample data obtained under each training model is more accurate, and then the control parameter prediction model is further trained to achieve optimal control.

[0207] The control unit 104 is further configured to train and test each training model using the second sample data for each training model to obtain an updated control parameter prediction model for each training model. This results in an updated control parameter prediction model for each of the two or more preset training models. The specific functions and processing of the control unit 104 are further described in step S430.

[0208] The control unit 104 is further configured to perform a weighted average of the updated control parameter prediction models for each of the two or more preset training models to obtain a weighted average updated control parameter prediction model of the two or more preset training models, and use the weighted average updated control parameter prediction model as the updated control parameter prediction model for the magnetic levitation compressor. The specific functions and processing of the control unit 104 are further described in step S440.

[0209] See also Figure 7 In the example shown, the main controller of the magnetic levitation compressor system communicates with the magnetic levitation compressor system via a 5G communication module. The system receives data on the real-time operating current and displacement accuracy of the magnetic bearings transmitted by the magnetic levitation compressor system, transmits data on the compressor system's current operating conditions and pressure differential to the bearing controller, and updates the latest control parameter prediction model to the bearing main control chip. The main controller of the magnetic levitation compressor system exchanges data with a remote PC host (including the unit operation data monitoring system and the control parameter prediction model training system). The remote PC transmits the latest control parameter prediction model to the main controller of the magnetic levitation compressor system, which then feeds back the pressure differential, operating conditions, and received magnetic bearing data of the magnetic levitation compressor system to the remote PC host.

[0210] See also Figure 7 In the example shown, while preprocessing the acquired data, the training architectures of the three models are updated simultaneously, such as by re-introducing the newly acquired data into the model for training:

[0211] a. The number of nodes in the hidden layer of the extreme learning machine is 10.

[0212] b. The number of random features of random forest is mtry=4.

[0213] c. The support vector machine inputs 16 values ​​and outputs 3 values. The 16 input values ​​include: the unit's current state, cooling water flow switch status under each operating condition, chilled water flow switch status, throttle valve guide vane opening, unit load, chilled water inlet pressure, chilled water outlet pressure, current cooling capacity, condensing pressure, evaporating pressure, magnetic levitation compressor current, magnetic levitation compressor motor operating frequency, magnetic levitation controller bus voltage, magnetic levitation bearing displacement accuracy, and magnetic levitation bearing current; and the output values ​​include: magnetic bearing control parameters K1, K2, and K3.

[0214] The data is divided into training and test sets, and the previous generation of control parameter prediction models is further optimized and iterated. This cycle continues until each magnetic levitation compressor achieves the desired operating performance under different operating conditions. The control parameter prediction model can be iterated again if components of the unit are damaged and parameter re-adjustment is required.

[0215] The adaptive control scheme and control parameter training device for magnetic levitation compressors constructed by the present invention finds the optimal parameters within a certain parameter range through optimization and model training. This enhances control reliability, improves bearing control performance, and significantly improves the stability of the magnetic levitation compressor system. The magnetic bearing control parameter prediction method provided by the present invention can be used not only in magnetic levitation bearing systems but also in other control scenarios.

[0216] In some embodiments, the two or more preset training models include at least two of an extreme learning machine model, a support vector machine model, and a random forest model. Of course, preferably, the two or more preset training models include an extreme learning machine model, a support vector machine model, and a random forest model.

[0217] Figure 8 This is a schematic diagram of the implementation flow of the extreme learning machine model structure. Figure 8 As shown in Figure 2, the implementation process of the Extreme Learning Machine model structure is as follows: The Extreme Learning Machine (ELM) is also a neural network, belonging to a feedforward model with a single hidden layer. The ELM randomly initializes the weights of the input and hidden layers and the node biases, and ultimately calculates the weights of the hidden and output layers.

[0218] Assume there are n sample points (x j ,t j ), then the extreme learning machine model can be written as follows:

[0219]

[0220] Among them, g(x) is the activation function, W i =[W i,1 ,W i,2 ,..,W i,n ] T is the input weight, β i is the weight of the output layer (beta), b i is the bias of the i-th hidden node. i *X j W i and X j The inner product of O. j Indicates output, X j represents the input, and N represents the number of neural network layers.

[0221] The learning goal of the extreme learning machine is to minimize the error between the predicted output value and the actual output value:

[0222]

[0223] Among them, t j Indicates actual value.

[0224] That is, there is β i 、W i and b i , such that:

[0225]

[0226] It can be expressed as a matrix:

[0227] H*β=T (4).

[0228] Among them, H is the output of the hidden layer node, β is the weight of the connection between the hidden layer and the output layer, and T is the output label in the training data pair, which is specifically expressed as follows:

[0229]

[0230]

[0231] In order to train a single hidden layer neural network, we want to get b i and β i , such that:

[0232]

[0233] Among them, H represents the output of the hidden layer node, β represents the weight of the connection between the hidden layer and the output layer, T represents the output label in the training data pair, and b i represents the bias of the i-th hidden node, W i =[W i,1 ,W i,2 ,..,W i,n ] T represents the input weight. i=1,...,L, which is equivalent to minimizing the loss function E:

[0234]

[0235] In the ELM algorithm, when we randomly determine the input weights of the input layer and the hidden layer, W i and bias, b i , we can uniquely determine the output matrix H of the hidden layer. Solving the neural network model with a single hidden layer can be transformed into solving a linear equation:

[0236] H*β=T (8).

[0237] Wherein, formula (8) is the same as formula (4).

[0238] And the output weight ③β can be determined:

[0239]

[0240] Among them, H + is the generalized inverse of the matrix H. And it can be proved that the solution The norm is the smallest and unique; β represents the weight connecting the hidden layer and the output layer, and T represents the output label in the training data pair.

[0241] The implementation steps of the ELM algorithm are as follows:

[0242] (1) Select the number of neurons in the hidden layer. The number of neurons is calculated according to the formula where m is the number of neurons in the hidden layer, n is the number of input nodes, l is the number of output nodes, and α is a constant between 1 and 10.

[0243] In the solution of the present invention, the number of nodes in the input layer of the initial control parameter prediction model is 5, and the number of nodes in the output layer is 3. After repeated adjustment, the number of nodes in the hidden layer is finally selected as 8.

[0244] (2) Randomly set the connection weights weight (0 < weight < 1) between the input layer and the hidden layer and the biases bias (0 < bias < 1) of the neurons.

[0245] (3) Select an activation function. Due to the good performance of the sigmoid function, the function H = 1 / (1 + exp(-H)) is selected in the solution of the invention. Calculate the matrix H of the hidden layer output layer, and calculate the weight β of the output layer using the formula.

[0246] Finally, the number of neurons is selected as 8 through multiple calculations and tests. Import 3800 groups of processed data to solve the prediction model of the extreme learning machine.

[0247] Set the model parameters well and test with 200 groups of test data. If the accuracy rate reaches 90%, integrate it into the upper computer.

[0248] Figure 9 It is a schematic flow chart for solving the random forest control parameter prediction model. As Figure 9 shown, the implementation steps of the random forest regression prediction model structure are as follows:

[0249] ① There are ntree trees in the random forest. Each regression tree corresponds to a training set. To construct ntree regression trees, the corresponding number of training sets need to be generated. In the process of generating the random forest algorithm, the bagging sampling technique is mainly used to generate ntree training subsets from the original training set, and each sampling is random and with replacement.

[0250] ② Then, for each new dataset, a decision tree base learner is trained using random attribute selection. Assuming the number of independent variables in the original sample is p, a subset of mtry independent variables is randomly selected from the independent variable set of each decision tree, and then the optimal independent variable is selected as the partitioning attribute. In the random forest regression algorithm, mtry is generally = log2p.

[0251] ③ Thus, n tree regression decision trees are obtained. Each tree starts to recursively generate child nodes from top to bottom. The decision tree is determined to terminate the split by setting a minimum threshold of the number of samples contained in the leaf node (5 for the regression tree).

[0252] ④ From ntree decision trees, ntree prediction values ​​can be obtained. The weighted average of ntree prediction values ​​is taken using the ensemble learning idea as the regression prediction result, which will be the final output of the algorithm.

[0253] The regression tree mathematical model is:

[0254]

[0255] Where: M is the number of samples in the subset of the regression tree model; c m is the mean response of the data samples in each subset; R m are the subsets divided; I(x∈Rm) is the characteristic function, when x∈R m The value is 1 when it is set, otherwise the value is 0.

[0256] From the original training sample set of 3800 random forest (RF) groups, 3800 training samples were randomly sampled with replacement to obtain one subsample set, denoted as M = 3800, and one regression tree was constructed for each sample; the experiment was repeated 300 times to obtain 500 subsample sets (the 500 subsample sets are independent of each other and samples can be repeated), and 500 regression decision trees were constructed, denoted as ntree = 300.

[0257] Since the initial control parameter prediction model in the present invention uses a variable p = 5, a regression tree learner is trained by randomly selecting a feature number. Generally, the random feature number mtry = log2p, so mtry = 2 in the present invention. 500 regression decision trees are trained, and the results of the 500 regression subtrees are finally averaged as the prediction result.

[0258] The accuracy of the established random forest regression model is verified by using the remaining 200 groups of validation samples. If the accuracy exceeds 90%, the random forest model is integrated into the host computer.

[0259] Figure 10 This is a flowchart of the support vector machine regression solution. Figure 10 As shown in , the implementation steps of the support vector machine structure are as follows: Support Vector Machine (SVM) is a two-class classification model that maps the feature vector of an instance (taking two dimensions as an example) to some points in space, such as Figure 10 The red and black points in the image belong to two different categories. The goal of SVM is to draw a line that "best" separates the two categories of points, so that if new points appear in the future, this line can also make a good classification. There are countless lines that can be drawn, and the difference lies in the effectiveness. Each line can be called a dividing hyperplane. The best line we hope to find is the "partitioning hyperplane with the largest margin." Figure 10 It can be seen that the distances from the points on the dotted line to the dividing hyperplane are the same. In fact, only these points jointly determine the position of the hyperplane, so they are called "support vectors".

[0260] The partitioning hyperplane can be defined as a linear equation:

[0261] ω T x + b = y;

[0262] Among them, ω={ω1;ω2;...;ω d} is a normal vector that determines the direction of the hyperplane; d is the number of eigenvalues; x is a training example; b is a displacement term that determines the distance between the hyperplane and the origin. Once the normal vector ω and the displacement b are determined, a unique partitioning hyperplane can be determined. x represents the input of the training example, y represents the output of the training example, and the superscript T represents the transpose symbol.

[0263] Since the SVM regression model (SVR) is used in the solution of the present invention, the hyperplane decision boundary obtained when the support vector is used for classification is the regression model when the support vector is used for regression:

[0264] y i =ω T x i +b (11).

[0265] The distance between any point on the partitioning hyperplane and the marginal hyperplanes on both sides of it is So the minimum margin is

[0266] The constraint condition in SVR regression is to make the points in each training set as concentrated as possible near the support vector; in SVR, in order to obtain the distance between all feature sample points and the hyperplane, the sample points closest to the hyperplane are found at the same time.

[0267] The function interval is defined: for the selected training data set T and hyperplane (ω, b), the hyperplane and the feature sample point (x i ,y i ) is:

[0268] r i =y i (ω T x i +b) (12).

[0269] In order to find the support vector, we can calculate the minimum distance r between all sample points. min The one that:

[0270] r min =min(r) (13).

[0271] There is a serious problem with the function interval, that is, when ω and b increase or decrease at a given ratio, the hyperplane equation does not change, but the function interval becomes a multiple of the original ratio. In order to avoid this situation, a concept of geometric interval is introduced, namely:

[0272] r i =y i ((ω T / ||ω||)x i +b / ||ω||) (14).

[0273] This ensures that if ω and b change proportionally, the hyperplane will also change according to the given proportional multiple. Since the learning goal of SVM is to maximize the corresponding geometric interval, the goal is to maximize the geometric interval, which is expressed as follows:

[0274]

[0275] In terms of optimizing the objective function, SVR and SVM are consistent. However, under the constraint condition, SVM is to make the points in each training set as far away as possible from the support vector of its own category (sign); for the regression model, our indicator is to make each feature sample point (x i ,y i ), try to fit a linear model as much as possible:

[0276] f(X)=ω T X+b.

[0277] In ε-SVR, if a sample point is close enough to the regression model and falls within the interval of the regression model, no loss is calculated for that sample point. The loss function is defined as follows:

[0278]

[0279] Therefore, the objective function of SVR can be expressed as follows:

[0280]

[0281] By introducing slack variables and Lagrange multipliers, the problem is transformed into a dual problem. The coefficient ω of the support vector machine model is solved using the Karush-Kuhn-Tucker conditions (KKT) and the sequential minimal optimization algorithm (SMO). T b. Determine the support vector machine's five input values ​​and three output values. The five input values ​​are: unit load, magnetic levitation compressor current, magnetic levitation compressor motor operating frequency, magnetic levitation bearing displacement accuracy, and magnetic levitation bearing current; the three output values ​​are: magnetic bearing control parameters K1, K2, and K3.

[0282] The 3800 groups of original training sample sets are divided to train the SVR model, and the remaining 200 groups are used as validation samples. If the validation accuracy reaches 90%, they are integrated into the host computer as a regression prediction model.

[0283] Figure 11 A flow chart for machine learning solutions. Figure 11 Figure 2 shows a schematic diagram of a heterogeneous (i.e., different types of training models) ensemble learning solution. The previously established extreme learning machine, support vector machine, and random forest regressor are integrated into a host computer data monitoring system. Real-time data is fed into the three previously trained models to generate three prediction results. Based on the prediction accuracy of the previous models, weights are assigned and the average is calculated, which serves as the control parameter for predicting the output. The weighted average prediction performs better. For example, 0.1*extreme learning machine prediction value + 0.5*support vector machine prediction value + 0.4*random forest regressor prediction value = the final output value.

[0284] In the solution of the present invention, at least two, preferably three, of the extreme learning machine model, the support vector machine model, and the random forest model are used for training and testing, and the three prediction results corresponding to the three previously trained models are input into real-time data and weighted averaged to be used as the control parameter prediction output, so that the training is more comprehensive, thereby making the output more accurate and stable.

[0285] Since the processing and functions implemented by the device of this embodiment basically correspond to the embodiments, principles and examples of the aforementioned method, for any details not fully described in this embodiment, please refer to the relevant descriptions in the aforementioned embodiments and will not be repeated here.

[0286] According to an embodiment of the present invention, a magnetic levitation compressor corresponding to the control device of the magnetic levitation compressor is also provided. The magnetic levitation compressor may include: the control device of the magnetic levitation compressor described above.

[0287] Since the processing and functions implemented by the magnetic levitation compressor of this embodiment basically correspond to the embodiments, principles and examples of the aforementioned device, for any details not fully described in this embodiment, please refer to the relevant descriptions in the aforementioned embodiments and will not be repeated here.

[0288] According to an embodiment of the present invention, a computer program product corresponding to a magnetic levitation compressor is further provided, comprising a computer program. When the computer program is executed by a processor, the steps of the control method of the magnetic levitation compressor described above are implemented.

[0289] Since the processing and functions implemented by the product of this embodiment basically correspond to the embodiments, principles and examples of the aforementioned magnetic levitation compressor, for any details not fully described in this embodiment, please refer to the relevant descriptions in the aforementioned embodiments and will not be repeated here.

[0290] According to an embodiment of the present invention, a storage medium corresponding to the control method of the magnetic levitation compressor is also provided, wherein the storage medium includes a stored program, wherein when the program is running, the device where the storage medium is located is controlled to execute the steps of the control method of the magnetic levitation compressor described above.

[0291] Since the processing and functions implemented by the storage medium of this embodiment basically correspond to the embodiments, principles and examples of the aforementioned method, for any details not fully described in this embodiment, please refer to the relevant descriptions in the aforementioned embodiments and will not be repeated here.

[0292] In summary, it is easy for those skilled in the art to understand that, under the premise of no conflict, the above-mentioned advantageous methods can be freely combined and superimposed.

[0293] The foregoing description is merely an embodiment of the present invention and is not intended to limit the present invention. Those skilled in the art will readily appreciate that various modifications and variations of the present invention are possible. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of the present invention are intended to be within the scope of the claims.

Claims

1. A control method for a magnetic levitation compressor, characterized in that: The magnetic levitation compressor has a magnetic bearing, and the control system of the magnetic bearing adopts a displacement current double closed-loop control system; The control method of the magnetic levitation compressor includes: In the case where the magnetic levitation compressor is running, introducing one or more preset disturbances into the displacement current double closed-loop control system; and adjusting the control parameters of the displacement current double closed-loop control system more than once; For each of the one or more preset disturbances introduced, while the magnetic bearing operates based on the control parameters of the displacement current dual closed-loop control system adjusted each time, the rotor displacement of the magnetic bearing and the stator coil current of the magnetic bearing are obtained; thereby, the control parameters of the displacement current dual closed-loop control system, the rotor displacement, and the stator coil current corresponding to all disturbances of the one or more preset disturbances introduced are obtained, and recorded as a first data group; Based on the first data set, training and testing are performed using two or more preset training models to obtain an initial control parameter prediction model for the magnetic levitation compressor, so as to control the operation of the magnetic bearing according to the initial control parameter prediction model for the magnetic levitation compressor; When the displacement current double closed-loop control system operates according to the initial control parameter prediction model, the operating parameters of the magnetic levitation compressor are obtained and recorded as compressor operating parameters; the operating parameters of the unit in which the magnetic levitation compressor is located are also obtained and recorded as unit operating parameters; the input and output of the initial control parameter prediction model, the compressor operating parameters, and the unit operating parameters are recorded as a second data set; Based on the second data group, two or more preset training models are used for training and testing to obtain an updated control parameter prediction model of the magnetic levitation compressor, so as to control the operation of the magnetic bearing according to the updated control parameter prediction model of the magnetic levitation compressor.

2. The control method of the magnetic levitation compressor according to claim 1, characterized in that: in, When the magnetic levitation compressor is running, one or more preset disturbances are introduced into the displacement current double closed-loop control system, including: Adding one or more interference sources to the feedback value of the rotor displacement of the displacement loop of the displacement current dual closed-loop control system to introduce one or more preset disturbances into the displacement current dual closed-loop control system; wherein the one or more interference sources include at least one of: Gaussian white noise of a set frequency, sinusoidal interference signals of different amplitudes, and random pulse signals of different amplitudes; and / or, Two or more preset training models, including at least two of an extreme learning machine model, a support vector machine model, and a random forest model.

3. The control method of the magnetic levitation compressor according to claim 1, characterized in that: Based on the first data set, training and testing are performed using two or more preset training models to obtain an initial control parameter prediction model for the magnetic levitation compressor, including: Preprocessing the first data group based on each of the two or more preset training models to obtain first sample data under each training model; Using the first sample data under each training model, each training model is trained and tested to obtain a control parameter prediction model under each training model; thereby, a control parameter prediction model under each training model of the two or more preset training models is obtained; The control parameter prediction model under each training model of the two or more preset training models is weighted averaged to obtain a weighted average control parameter prediction model of the two or more preset training models as the initial control parameter prediction model of the magnetic levitation compressor.

4. The control method of the magnetic levitation compressor according to claim 3, characterized in that: Preprocessing the first data group based on each of the two or more preset training models to obtain first sample data under each training model includes: Based on the first data set, determining control parameters corresponding to a minimum rotor displacement and a minimum stator coil current under each of the one or more introduced preset disturbances, as optimal control parameters under each disturbance; thereby obtaining optimal control parameters, minimum rotor displacement, and minimum stator coil current corresponding to all of the one or more introduced preset disturbances, which are recorded as an initial data set; For the initial data group, based on the required relationship between the input and output of each training model in more than two preset training models, a data pair of input and output is constructed to obtain the first sample data under each training model.

5. The control method of the magnetic levitation compressor according to any one of claims 1 to 4, characterized in that: Based on the second data set, training and testing are performed using two or more preset training models to obtain an updated control parameter prediction model for the magnetic levitation compressor, including: When the displacement current double closed-loop control system operates according to the initial control parameter prediction model, the operating parameters of the magnetic levitation compressor are obtained and recorded as compressor operating parameters; the operating parameters of the unit in which the magnetic levitation compressor is located are also obtained and recorded as unit operating parameters; the input and output of the initial control parameter prediction model, the compressor operating parameters, and the unit operating parameters are recorded as a second data set; Preprocessing the second data group based on each of the two or more preset training models to obtain second sample data under each training model; Using the second sample data under each training model, each training model is trained and tested to obtain an updated control parameter prediction model under each training model; thereby, an updated control parameter prediction model under each of the two or more preset training models is obtained; The updated control parameter prediction model under each training model of the two or more preset training models is weighted averaged to obtain the weighted average updated control parameter prediction model of the two or more preset training models as the updated control parameter prediction model of the magnetic levitation compressor.

6. The control method of the magnetic levitation compressor according to claim 5, characterized in that: In the second data group, the compressor operating parameters include at least one of the following: the current of the motor in the compressor, the operating frequency of the motor in the compressor, and the bus voltage of the motor controller in the compressor; the unit in which the magnetic levitation compressor is located includes an air-conditioning unit, and the unit operating parameters include at least one of the following: the shutdown or running state of the air-conditioning unit, the operating condition of the air-conditioning unit, the state of the cooling water flow switch in the air-conditioning unit, the state of the chilled water flow switch in the air-conditioning unit, the opening of the throttle valve in the air-conditioning unit, the load of the air-conditioning unit, the chilled water inlet pressure in the air-conditioning unit, the chilled water outlet pressure in the air-conditioning unit, the current heat exchange capacity of the air-conditioning unit, the condensing pressure of the condenser in the air-conditioning unit, and the evaporating pressure of the evaporator in the air-conditioning unit; Preprocessing the second data group based on each of the two or more preset training models to obtain second sample data under each training model includes: The compressor operating parameters in the second data group are screened according to a first corresponding set parameter range to obtain corresponding operating parameters within the first corresponding set parameter range, which are recorded as first screened operating parameters; The unit operating parameters in the second data group are screened according to a second corresponding set parameter range to obtain corresponding operating parameters within the second corresponding set parameter range, which are recorded as second screened operating parameters; using the input and output of the initial control parameter prediction model, the first screening operation parameter, and the second screening operation parameter as a screening data set; For the screening data group, based on the required relationship between the input and output of each training model in more than two preset training models, a data pair of input and output is constructed to obtain second sample data under each training model.

7. A control device for a magnetic levitation compressor, characterized in that: The magnetic levitation compressor has a magnetic bearing, and the control system of the magnetic bearing adopts a displacement current double closed-loop control system; The control device of the magnetic levitation compressor includes: a control unit configured to introduce one or more preset disturbances into the displacement current double closed-loop control system when the magnetic levitation compressor is in operation; and adjust control parameters of the displacement current double closed-loop control system at least once; an acquisition unit configured to, for each of the one or more introduced preset disturbances, acquire a rotor displacement of the magnetic bearing and a stator coil current of the magnetic bearing when the magnetic bearing operates based on the control parameters of the displacement current dual closed-loop control system adjusted each time; thereby obtaining the control parameters of the displacement current dual closed-loop control system, the rotor displacement, and the stator coil current corresponding to all disturbances of the one or more introduced preset disturbances, and recording them as a first data group; The control unit is further configured to perform training and testing based on the first data set using two or more preset training models to obtain an initial control parameter prediction model for the magnetic levitation compressor, so as to control the operation of the magnetic bearing according to the initial control parameter prediction model for the magnetic levitation compressor; The control unit is further configured to, when the displacement current dual closed-loop control system operates according to the initial control parameter prediction model, obtain operating parameters of the magnetic levitation compressor, which are recorded as compressor operating parameters; and obtain operating parameters of the unit in which the magnetic levitation compressor is located, which are recorded as unit operating parameters; and record the input and output of the initial control parameter prediction model, the compressor operating parameters, and the unit operating parameters as a second data set; The control unit is also configured to perform training and testing based on the second data group using two or more preset training models to obtain an updated control parameter prediction model of the magnetic levitation compressor, so as to control the operation of the magnetic bearing according to the updated control parameter prediction model of the magnetic levitation compressor.

8. A magnetic levitation compressor, characterized in that: include: The control device for the magnetic levitation compressor according to claim 7.

9. A storage medium, characterized in that: The storage medium includes a stored program, wherein when the program is running, the device where the storage medium is located is controlled to execute the control method of the magnetic levitation compressor according to any one of claims 1 to 6.

10. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method for controlling a magnetic levitation compressor according to any one of claims 1 to 6 are implemented.

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