Motor multi-physical field agent model construction method and related device

Abstract

The invention relates to the technical field of model construction, and discloses a motor multi-physics field agent model construction method and a related device, the motor multi-physics field agent model construction method comprises the following steps: obtaining field operation condition data of a target motor; dividing the sample three-dimensional space into a plurality of key sampling intervals and a plurality of non-key sampling intervals based on the data density of the sample three-dimensional space, setting a first sampling number for the key sampling intervals, and setting a second sampling number for the non-key sampling intervals; sampling the field operation condition data in the key sampling interval and the non-key sampling interval according to the first sampling number and the second sampling number by using a Latin hypercube sampling method to obtain a first condition sample set; and performing model training on the initial agent model based on the first working condition sample set until an average error meets an error requirement, and obtaining a motor multi-physics field target agent model. The method can improve the sample rationality and improve the accuracy of the agent model.

Description

Technical Field

[0001] The present invention relates to the technical field of model construction, and in particular to a method for constructing a multi-physics field proxy model of a motor and related devices. Background Art

[0002] As the complexity of the model increases, the amount of calculation in multi-physics simulation increases. In the field of digital twin intelligent operation and maintenance of hydropower and pumped storage unit motors, different methods need to be used to construct real-time simulation models for different models, so as to achieve dimensionality reduction or data fitting of the numerical calculation order, and realize the real-time display of the multi-physics fields of each component through visualization. The proxy model is an efficient mathematical approximation model. Because it can significantly improve the calculation efficiency, it has gradually developed into a new calculation method. The sampling method is to study how to construct a fitting function with the least error with the least sample points, and can take into account the random error of the data, while the accuracy of the proxy model depends on the distribution of the sample points.

[0003] Existing sample point collection often relies on common sampling methods for one-time sampling, and the distribution of sampling often has errors and uncertainties. The proxy model constructed after the initial sampling may not meet the needs of the site, so secondary sampling is required, which is often time-consuming and labor-intensive. There are also repeated sampling points in the sampling results, and the deduplication work is also time-consuming and labor-intensive. At the same time, the actual daily operating conditions of power stations are often concentrated in a certain area. The sample data obtained by uniform sampling is not universal, and the trained proxy model has low accuracy and does not fit the actual operating conditions. Summary of the invention

[0004] In view of this, the present invention provides a method and related devices for constructing a motor multi-physics field proxy model to improve sample rationality and improve the accuracy of the proxy model.

[0005] In a first aspect, the present invention provides a method for constructing a multi-physics proxy model of a motor, and the method for constructing a multi-physics proxy model of a motor includes: obtaining on-site operating condition data of a target motor; dividing the sample three-dimensional space into a plurality of key sampling intervals and a plurality of non-key sampling intervals based on the data density of the sample three-dimensional space, and setting a first sampling number for the key sampling interval and a second sampling number for the non-key sampling interval, wherein the data density of the key sampling interval is greater than the data density of the non-key sampling interval; using a Latin hypercube sampling method, sampling the on-site operating condition data in the key sampling interval and the non-key sampling interval according to the first sampling number and the second sampling number, respectively, to obtain a first condition sample set; training the initial proxy model based on the first condition sample set until the average error meets the error requirement, thereby obtaining a multi-physics target proxy model of the motor.

[0006] In this implementation, the sampling intervals are divided according to the data density in the sample three-dimensional space. The ones with high density are key sampling intervals, and the ones with low density are non-key sampling intervals. Considering that the actual operating conditions of the power station are often concentrated in a certain area, that is, the key sampling intervals with high density, different sampling numbers are set for different sampling intervals. In practical applications, the number of samples per unit area in the key sampling interval is greater than that in the non-key interval, which optimizes the sample space in the process of constructing the motor temperature field proxy model, so that the sample data collected subsequently is universal and can fit the actual operating data situation, and improves the accuracy of the sample space point collection and the fit with the field data. Furthermore, the initial proxy model is trained until the average error meets the error requirements, thereby improving the accuracy of the motor multi-physics field target proxy model.

[0007] In an optional embodiment, the on-site operating condition data includes inlet wind speed data, air temperature data and current data, and dividing the sample three-dimensional space into multiple key sampling intervals and multiple non-key sampling intervals based on the data density of the sample three-dimensional space includes: establishing a three-dimensional coordinate system with the inlet wind speed, air temperature and current to obtain the sample three-dimensional space, and dividing the sample three-dimensional space into multiple sample three-dimensional subspaces; determining point elements in the sample three-dimensional subspace based on the inlet wind speed data, air temperature data and current data; selecting a first preset number of sample three-dimensional subspaces with the largest number of point elements as key sampling intervals, and using other sample three-dimensional subspaces as non-key sampling intervals.

[0008] In this implementation, a coordinate system is constructed using the input features of the model, and the resulting sample three-dimensional space can accurately reflect the distribution of the data. By reflecting the spatial data density with the number of feature points, the difficulty of density calculation can be reduced and the efficiency and accuracy of interval division can be improved.

[0009] In an optional embodiment, the on-site operating condition data also includes the stator average temperature, and the first operating condition sample set includes the first operating condition sample characteristics and the first operating condition sample label of each sampling point. The on-site operating condition data is sampled in the key sampling interval and the non-key sampling interval according to the first sampling number and the second sampling number, and the first operating condition sample set includes: sampling in each sample three-dimensional subspace in the key sampling interval according to the first sampling number, and sampling in each sample three-dimensional subspace in the non-key sampling interval according to the second sampling number to obtain multiple sampling points; obtaining the inlet wind speed data, air temperature data and current data corresponding to each sampling point to obtain the first operating condition sample characteristics; when the sampling point has a corresponding stator average temperature, the stator average temperature is used as the first operating condition sample label corresponding to the sampling point; when the sampling point does not have a corresponding stator average temperature, the inlet wind speed data, air temperature data and current data are simulated based on the stator periodic boundary three-dimensional fluid-thermal coupling model to generate a simulated average temperature, and the simulated average temperature is used as the first operating condition sample label corresponding to the sampling point.

[0010] In this implementation, sampling is performed in each sample stereo subspace, and the number of samples corresponding to each sample stereo subspace is small, which can reduce the difficulty of sample sampling and make the sample distribution more uniform. Furthermore, the accurate stator average temperature that can be collected is preferentially used as the sample label. When the corresponding stator average temperature does not exist at the sampling point, the data simulation model is used for simulation to fill the sample set, which can improve the accuracy of the model in subsequent training.

[0011] In an optional embodiment, the initial proxy model is trained based on the first operating condition sample set until the average error meets the error requirement, and the motor multi-physics field target proxy model is obtained, including: performing sample quality assessment on the first operating condition sample set, and when the sample quality does not meet the quality requirement, expanding the first operating condition sample set; training the initial proxy model based on the first operating condition sample set, and using the first operating condition sample set to judge the average error of the trained initial proxy model, when the average error does not meet the error requirement, expanding the first operating condition sample set until the average error meets the error requirement, and obtaining the motor multi-physics field target proxy model.

[0012] In this implementation, sample expansion is performed twice, before and after model training. When the sample quality does not meet the quality requirements, sample expansion is performed; when the model accuracy is low, sample expansion is performed. This application only performs sample sampling once, and only performs sample expansion later, which can reduce the difficulty of secondary sampling, reduce the steps of sample deduplication, and improve the efficiency of sample addition. Repeating the quality assessment and model evaluation steps until the requirements are met can further improve the accuracy of the model.

[0013] In an optional embodiment, sample expansion of the first operating condition sample set includes: obtaining a second preset number of target sampling points with the worst quality or the largest error; arranging the target sampling points in sequence along the X-axis direction of the sample three-dimensional space, forming adjacent target sampling points into a target sampling point group, and calculating the X-axis distance of the target sampling points in the target sampling point group; obtaining a third preset number of target sampling point groups with the largest X-axis distance as a newly added sampling point group, and setting a new sample point between two target sampling points in the newly added sampling point group; using the inlet wind speed data, air temperature data and current data corresponding to the newly added sample points as new operating condition sample features of the newly added sample points; performing data simulation on the inlet wind speed data, air temperature data and current data of the newly added sample points based on the three-dimensional fluid-thermal coupling model of the stator periodic boundary, generating a newly added average temperature, and using the newly added average temperature as a new operating condition sample label for the newly added sampling point.

[0014] In an optional implementation, setting a new sample point between two target sampling points of the new sampling point group includes: determining a target X-axis coordinate at 1 / 2 of the X-axis direction of the two target sampling points of the new sampling point group; and determining a point with the largest distance from other sampling points and other new sample points on the plane of the target X-axis coordinate as the new sample point.

[0015] In this implementation, adding sampling points between sampling points that are far away can ensure the uniformity of the distribution of sampling points, avoid the occurrence of repeated sampling points, and ensure the quality of the newly added sampling points. At the same time, this application first determines a coordinate axis and then determines the final sampling point information, which can improve the efficiency of adding sampling points.

[0016] In the second aspect, the present invention provides a motor multi-physics field proxy model construction device, which includes: an acquisition module, used to obtain the on-site operating condition data of the target motor; a division module, used to divide the sample three-dimensional space into multiple key sampling intervals and multiple non-key sampling intervals based on the data density of the sample three-dimensional space, and set a first sampling number for the key sampling interval, and set a second sampling number for the non-key sampling interval, and the data density of the key sampling interval is greater than the data density of the non-key sampling interval; a sampling module, used to use the Latin hypercube sampling method to sample in the key sampling interval and the non-key sampling interval according to the first sampling number and the second sampling number, respectively, to obtain a first working condition sample set; a training module, used to perform model training on the initial proxy model based on the first working condition sample set until the average error meets the error requirement, and obtain the motor multi-physics field target proxy model.

[0017] In a third aspect, the present invention provides a computer device, comprising: a memory and a processor, the memory and the processor being communicatively connected to each other, the memory storing computer instructions, and the processor executing the method for constructing a multi-physics proxy model of a motor according to the first aspect or any corresponding embodiment thereof by executing the computer instructions.

[0018] In a fourth aspect, the present invention provides a computer-readable storage medium having computer instructions stored thereon, the computer instructions being used to enable a computer to execute the method for constructing a motor multi-physics field proxy model according to the first aspect or any corresponding embodiment thereof.

[0019] In a fifth aspect, the present invention provides a computer program product, comprising computer instructions for causing a computer to execute the method for constructing a motor multi-physics proxy model according to the first aspect or any corresponding embodiment thereof. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] In order to more clearly illustrate the specific implementation methods of the present invention or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some implementation methods of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0021] Figure 1 is a flow chart of a method for constructing a multi-physics proxy model of a motor according to an embodiment of the present invention; Figure 2 is a first flow chart of another method for constructing a multi-physics proxy model of a motor according to an embodiment of the present invention; Figure 3 is a second flow chart of another method for constructing a motor multi-physics proxy model according to an embodiment of the present invention; Figure 4 is a structural block diagram of a device for constructing a multi-physics proxy model of a motor according to an embodiment of the present invention; Figure 5 It is a schematic diagram of the hardware structure of a computer device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0022] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.

[0023] The multi-physics simulation of power equipment requires hardware conditions with many cores, high frequency and large memory. A single simulation takes a long time. The existing common sampling method for one-time sampling often has errors and uncertainties in the distribution of sampling. The proxy model constructed after the initial sampling may not be able to meet the needs of the site, so secondary sampling is required, and secondary sampling is often time-consuming and laborious. There are also repeated sampling points in the sampling results, and the deduplication work is also time-consuming and laborious. At the same time, the disordered and random sample points in the sampling process will lead to low efficiency problems. The present application proposes a method for constructing a multi-physics proxy model of a motor, which divides the sampling interval according to the data density in the sample three-dimensional space, and the high density is the key sampling interval, and the low density is the non-key sampling interval. Considering that the working conditions of the actual daily operation of the power station are often concentrated in a certain area, that is, the high density key sampling interval, different sampling numbers are set for different sampling intervals. In actual application, the number of samples per unit area in the key sampling interval is greater than that in the non-key interval, and the sample space in the construction process of the motor temperature field proxy model is optimized, so that the sample data collected subsequently is universal and can fit the actual operation data, and the accuracy of the sample space point collection and the fit with the field data are improved. Furthermore, the initial proxy model is trained until the average error meets the error requirement, thereby improving the accuracy of the motor multi-physics field target proxy model.

[0024] According to an embodiment of the present invention, an embodiment of a method for constructing a multi-physics proxy model of a motor is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0025] In this embodiment, a method for constructing a multi-physics proxy model of a motor is provided. Figure 1 is a flow chart of a method for constructing a multi-physics proxy model of a motor according to an embodiment of the present invention. It should be noted that if there are substantially the same results, this embodiment does not use Figure 1 The process sequence shown is limited. Figure 1 As shown, the process includes the following steps: Step S101, obtaining on-site operating condition data of a target motor.

[0026] Among them, the on-site operating condition data includes inlet wind speed data, air temperature data, stator average temperature data and stator current data.

[0027] Among them, according to different application scenarios, temperature data and current data can be obtained for the stator winding or the stator core.

[0028] In a possible implementation, inlet wind speed data V, air temperature data T1, stator winding average temperature data T2, and stator winding current data of the target motor for one year are obtained.

[0029] In another possible implementation, the inlet wind speed data V, air temperature data T1, stator core average temperature data T3 and stator core current data of the target motor for one year are obtained.

[0030] Among them, the on-site operating condition data is usually recorded in a time series format, and each operating condition data includes a corresponding value and a timestamp.

[0031] Step S102 : dividing the sample stereoscopic space into a plurality of key sampling intervals and a plurality of non-key sampling intervals based on the data density of the sample stereoscopic space, setting a first sampling number for the key sampling intervals and a second sampling number for the non-key sampling intervals.

[0032] The three-dimensional sample space is constructed with inlet wind speed, air temperature and stator current, and further divided into multiple key sampling intervals and multiple non-key sampling intervals according to the density of point elements corresponding to the field operating condition data in the sample space. Among them, the data density of the key sampling interval is greater than that of the non-key sampling interval.

[0033] Specifically, in the constructed sample three-dimensional space, the inlet wind speed data, air temperature data, and stator current data corresponding to the same timestamp are used as a three-dimensional coordinate to determine the point element. The area with a large density of element points is divided into a key sampling interval, and the area with a small density of element points is divided into a non-key sampling interval.

[0034] For example, if the X-axis of the sample stereoscopic space represents the inlet wind speed, the Y-axis represents the air temperature, and the Z-axis represents the stator current, the inlet wind speed data at time t1 is v1, the air temperature data is t1, and the stator current data is i1, then the coordinates of the element point corresponding to time t1 are determined to be (v1, t1, i1).

[0035] Furthermore, sampling data is set in key sampling intervals and non-key intervals according to the density of different point elements.

[0036] Specifically, the first unit sampling number of the key sampling interval in the unit space is determined, and the first sampling number is determined according to the spatial volume of the key sampling interval. Similarly, the second unit sampling number of the non-key sampling interval in the unit space is determined, and the second sampling number is determined according to the spatial volume of the non-key sampling interval. Among them, the first unit sampling number is greater than the second unit sampling number. That is, the greater the density of feature points, the greater the number of samplings in the area.

[0037] Step S103 , using the Latin hypercube sampling method, sampling the on-site operating condition data in the key sampling interval and the non-key sampling interval according to the first sampling quantity and the second sampling quantity, respectively, to obtain a first operating condition sample set.

[0038] Specifically, a first sampling number of sampling points is collected from multiple point elements in a key sampling interval by using a Latin hypercube sampling method, and a second sampling number of sampling points is collected from multiple point elements in a non-key sampling interval.

[0039] In a possible implementation, the entire key sampling interval is sampled once, and the entire non-key sampling interval is sampled once.

[0040] In another possible implementation, the key sampling interval is divided into multiple unit spaces, and the Latin hypercube sampling method is used to sample in the unit space according to the first unit sampling quantity; similarly, the non-key sampling interval is divided into multiple unit spaces, and the Latin hypercube sampling method is used to sample in the unit space according to the second unit sampling quantity.

[0041] Furthermore, the stator average temperature data corresponding to the inlet wind speed data, air temperature data and stator current data at each sampling point is obtained, and the inlet wind speed data, air temperature data, stator average temperature data and stator current data at each sampling point are used as the first operating condition sample set.

[0042] Step S104, performing model training on the initial proxy model based on the first operating condition sample set until the average error meets the error requirement, thereby obtaining a motor multi-physics field target proxy model.

[0043] Specifically, the first operating condition sample set is divided into a training set and a validation set, and an initial proxy model of the motor multi-physics field is constructed. The inlet wind speed data, air temperature data and stator current data of the training set are used as input features, and the stator average temperature data is used as a label feature to train the initial proxy model.

[0044] The inlet wind speed data, air temperature data and stator current data of the validation set are input into the initial proxy model to obtain the predicted value of the proxy model, and the average error value between the stator average temperature data of the validation set and the predicted value of the proxy model is calculated. When the error value is greater than or equal to the error threshold, the model training of the initial proxy model is repeated. When the error value is less than the error threshold, the trained motor multi-physics field target proxy model is output.

[0045] The method for constructing a multi-physics proxy model of a motor provided in this embodiment divides the sampling intervals according to the data density in the sample three-dimensional space, with the high-density intervals being key sampling intervals and the low-density intervals being non-key sampling intervals. Considering that the actual operating conditions of the power station are often concentrated in a certain area, namely, the key sampling intervals with high density, different sampling numbers are set for different sampling intervals. In actual application, the number of samples per unit area in the key sampling intervals is greater than that in the non-key intervals, and the sample space in the process of constructing the motor temperature field proxy model is optimized, so that the sample data collected subsequently is universal and can fit the actual operating data situation, thereby improving the accuracy of the sample space point collection and the fit with the field data. Furthermore, the initial proxy model is trained until the average error meets the error requirements, thereby improving the accuracy of the motor multi-physics target proxy model.

[0046] In this embodiment, a method for constructing a multi-physics proxy model of a motor is provided. Figure 2 and Figure 3 , Figure 2 is a first flow chart of another method for constructing a motor multi-physics proxy model according to an embodiment of the present invention, Figure 3 is a second flow chart of another method for constructing a multi-physics proxy model of a motor according to an embodiment of the present invention. It should be noted that if there are substantially the same results, this embodiment does not use Figure 2 The process sequence shown is limited. Figure 2 As shown, the process includes the following steps: Step S201, obtaining on-site operating condition data of the target motor.

[0047] For details, please see Figure 1 Step S101 of the illustrated embodiment will not be described in detail here.

[0048] Step S202: dividing the sample stereoscopic space into a plurality of key sampling intervals and a plurality of non-key sampling intervals based on the data density of the sample stereoscopic space, setting a first sampling number for the key sampling intervals and a second sampling number for the non-key sampling intervals.

[0049] Specifically, the above step S202 includes: Step S2021 , a three-dimensional coordinate system is established with the inlet wind speed, air temperature and current to obtain a sample three-dimensional space, and the sample three-dimensional space is divided into a plurality of sample three-dimensional subspaces.

[0050] Specifically, the inlet wind speed, air temperature and current are used as the X-axis, Y-axis and Z-axis respectively to establish a three-dimensional coordinate system, and a sample three-dimensional space is constructed according to the data range of the inlet wind speed, air temperature and current. Among them, the inlet wind speed, air temperature and current are not limited to specific axes and can be set according to actual conditions.

[0051] Exemplarily, a three-dimensional coordinate system is constructed with the inlet wind as the X-axis, the air temperature as the Y-axis, and the current as the Z-axis. The numerical ranges of the inlet wind speed data, air temperature data, and stator current data are [X0, X1], [Y0, Y1], and [Z0, Z1], respectively, to construct a sample three-dimensional space.

[0052] Furthermore, the sample three-dimensional space is divided into a plurality of D×D×D sample three-dimensional subspaces of equal size.

[0053] Step S2022: determining point elements in the sample stereo subspace based on the inlet wind speed data, the air temperature data and the current data.

[0054] Specifically, in the divided sample three-dimensional subspace, a point element is determined according to the inlet wind speed data, air temperature data and stator current data corresponding to the same timestamp, and the coordinates of the point element are the corresponding three data values.

[0055] For details, please see Figure 1 The example of step S102 of the illustrated embodiment will not be described in detail here.

[0056] Step S2023 , selecting a first preset number of sample stereo subspaces with the largest number of point elements as key sampling intervals, and using other sample stereo subspaces as non-key sampling intervals.

[0057] According to the density of point elements corresponding to the field operation condition data in the sample three-dimensional space, the sample three-dimensional space is divided into multiple key sampling intervals and multiple non-key sampling intervals. Among them, the data density of the key sampling interval is greater than the data density of the non-key sampling interval.

[0058] In this implementation, the number of point elements is used as a parameter to measure density, the number of point elements in each sample stereo subspace is obtained, and the sample stereo subspaces are sorted according to the number of point elements. The sample stereo subspaces with the first preset number of point elements, which are arranged at the front, are taken as key sampling intervals, and other sample stereo subspaces are taken as non-key sampling intervals.

[0059] For example, select D that is ranked first 2 The sample stereo subspace is used as the key sampling interval.

[0060] Step S2024, setting a first sampling quantity for the key sampling interval and setting a second sampling quantity for the non-key sampling interval.

[0061] In a possible implementation, a first unit sampling number of a key sampling interval in a sample stereo subspace is determined, and the first sampling number is determined according to the number of sample stereo subspaces included in the key sampling interval. Similarly, a second unit sampling number of a non-key sampling interval in a sample stereo subspace is determined, and the second sampling number is determined according to the number of sample stereo subspaces included in the non-key sampling interval. The first unit sampling number is greater than the second unit sampling number.

[0062] In another possible implementation, the first unit sampling number of the key sampling interval is determined to be m1, and the first sampling number is evenly divided into each sample stereo subspace; the second unit sampling number of the non-key sampling interval is determined to be m2, and the second sampling number is evenly divided into each sample stereo subspace. Wherein, m1 is greater than m2.

[0063] In another possible implementation, the second unit sampling quantity of the non-important sampling interval is determined to be M: .

[0064] Wherein, N is the number of variables. In this implementation, the features include inlet wind speed data, air temperature data and stator current data, so N is 3.

[0065] Determine the first unit sampling quantity of the key sampling interval as: .

[0066] Among them, the sampling density of the key sampling interval is: The number of samples in each corresponding sample stereo subspace is .

[0067] in, is the total spatial volume of the key sampling interval, is the spatial volume of the sample stereo subspace.

[0068] in, .

[0069] Determine the number of sample points for initial sampling .

[0070] In this implementation, a coordinate system is constructed using the input features of the model, and the resulting sample three-dimensional space can accurately reflect the distribution of the data. By reflecting the spatial data density with the number of feature points, the difficulty of density calculation can be reduced and the efficiency and accuracy of interval division can be improved.

[0071] Step S203 , using the Latin hypercube sampling method, sampling is performed in the key sampling interval and the non-key sampling interval according to the first sampling quantity and the second sampling quantity, respectively, to obtain a first operating condition sample set.

[0072] In this implementation, the key sampling interval is divided into multiple unit spaces, and the Latin hypercube sampling method is used to sample in the unit space according to the first unit sampling quantity; similarly, the non-key sampling interval is divided into multiple unit spaces, and the Latin hypercube sampling method is used to sample in the unit space according to the second unit sampling quantity.

[0073] Specifically, the above step S203 includes: Step S2031 , sampling is performed in each sample stereo subspace of the key sampling interval according to the first sampling quantity, and sampling is performed in each sample stereo subspace of the non-key sampling interval according to the second sampling quantity, to obtain a plurality of sampling points.

[0074] Among them, the maximum and minimum distance Latin hypercube is used for sampling, and the maximum and minimum criteria are expressed as: .

[0075] in, is the distance between two sampling points in the sample stereo subspace.

[0076] in, . t=1 or t=2.

[0077] According to the above-mentioned Latin hypercube sampling method, sampling is performed in the key sampling interval and the non-key sampling interval to obtain multiple sampling points.

[0078] Step S2032, obtaining the first operating condition sample characteristics and the first operating condition sample label of each sampling point.

[0079] Specifically, the corresponding inlet wind speed data, air temperature data and stator current data at each sampling point are obtained as the first operating condition sample characteristics of the sampling point.

[0080] If the on-site operating condition data of the target motor contains stator average temperature data corresponding to the inlet wind speed data, air temperature data and stator current data of the sampling point, the stator average temperature data corresponding to the inlet wind speed data, air temperature data and stator current data at each sampling point is obtained as the first operating condition sample label corresponding to the sampling point.

[0081] If the stator average temperature data corresponding to the inlet wind speed data, air temperature data and stator current data of the sampling point does not exist in the field operating condition data of the target motor, the inlet wind speed data, air temperature data and current data are simulated based on the three-dimensional fluid-thermal coupling model of the stator periodic boundary to generate a simulated average temperature, and the simulated average temperature is used as the first operating condition sample label corresponding to the sampling point.

[0082] For example, for each sample point m, based on the periodic boundary three-dimensional fluid-heat coupling model of the stator winding and the stator core, the numerical simulation of the operating point is realized by using commercial software, and the average temperature of the stator winding and the stator core under different operating conditions is generated to generate the corresponding data set .

[0083] .

[0084] Among them, P1 is the stator winding heat source, and P2 is the stator core heat source.

[0085] The above method is used to generate the average stator temperature. When the stator winding is simulated, the average stator winding temperature is used as the first working condition sample label. When the stator core is simulated, the average stator core temperature is used as the first working condition sample label.

[0086] In this implementation, sampling is performed in each sample stereo subspace, and the number of samples corresponding to each sample stereo subspace is small, which can reduce the difficulty of sample sampling and make the sample distribution more uniform. Furthermore, the accurate stator average temperature that can be collected is preferentially used as the sample label. When the corresponding stator average temperature does not exist at the sampling point, the data simulation model is used for simulation to fill the sample set, which can improve the accuracy of the model in subsequent training.

[0087] Step S204, training the initial proxy model based on the first operating condition sample set until the average error meets the error requirement, thereby obtaining a motor multi-physics field target proxy model.

[0088] Specifically, the above step S204 includes: Step S2041, performing sample quality assessment on the first working condition sample set, and when the sample quality does not meet the quality requirement, performing sample expansion on the first working condition sample set.

[0089] Specifically, a sampling evaluation is performed on the first working condition sample set. The sampling result sample set B is: .

[0090] The sample quality of the first working condition sample set is evaluated using the following formula: .

[0091] in, The smaller the value, the better the uniformity of the sampling results. When the value is greater than the threshold, it means that the sample quality does not meet the quality requirements, and the first working condition sample set is expanded until the sample quality meets the quality requirements.

[0092] Step S2042: training the initial proxy model based on the first operating condition sample set.

[0093] Specifically, the above step S2042 specifically includes: Step a1: construct a processing agent model.

[0094] Specifically, based on the sampling Kriging proxy model, the basic function of the initial proxy model is: .

[0095] Where F represents the trend and z is the residual. F is usually simulated by a certain linear function: .

[0096] Where f is the basis function and β is the fitting coefficient.

[0097] Step a2: training the initial proxy model.

[0098] The first operating condition sample set is divided into a training set and a validation set, and an initial proxy model of the motor multi-physics field is constructed. The inlet wind speed data, air temperature data and stator current data of the training set are used as input features, and the stator average temperature data is used as a label feature to train the initial proxy model.

[0099] Step S2043, using the first working condition sample set to judge the average error of the trained initial proxy model, when the average error does not meet the error requirement, the first working condition sample set is expanded until the average error meets the error requirement, and the motor multi-physics field target proxy model is obtained.

[0100] For the n sample points of the validation set, the inlet wind speed data, air temperature data and stator current data are input into the initial proxy model to obtain the predicted value of the proxy model. , and calculate the stator average temperature data T of the validation set n The predicted value of the surrogate model The average error value of .

[0101] Among them, the stator average temperature data T i The predicted value of the surrogate model The error value is: .

[0102] Stator average temperature data T n The predicted value of the surrogate model The average error value is: .

[0103] When the error value is greater than or equal to the error threshold, the model training of the initial proxy model is repeated. When the error value is less than the error threshold, the trained motor multi-physics field target proxy model is output.

[0104] In one implementation, the error threshold is 10%.

[0105] In the above step S2041 and the above step S2043, the method for expanding the sample set of the first working condition is the same, and the sample set is expanded by increasing the sampling points.

[0106] Specifically, the method for expanding the sample set of the first working condition includes: Step b1, obtaining a second preset number of target sampling points with the worst quality or the largest error.

[0107] Get t target sampling points with the worst quality or the largest error.

[0108] Step b2, arranging the target sampling points in sequence along the X-axis direction of the sample stereoscopic space, grouping adjacent target sampling points into a target sampling point group, and calculating the X-axis distance of the target sampling points in the target sampling point group.

[0109] Arrange the t target sampling points in order of size along the X-axis direction of the sample stereo space to obtain the sample set X new ={X1,…,X t}. The adjacent target sampling points are taken as the target sampling point group {(X1,X2),…,(X t-1 ,X t )}. Further, the X-axis distance between two target sampling points in the target sampling point group is calculated, and the distance set is obtained as D={d1,…,d t+1}.

[0110] Step b3: obtaining a target sampling point group with a third preset number of the largest X-axis distance as a newly added sampling point group, and setting a newly added sampling point between two target sampling points in the newly added sampling point group.

[0111] Select L target sampling point groups with the largest X-axis distance as the newly added sampling point groups, and add new sampling points in the interval where the newly added sampling point groups are located. Specifically, place the newly added sampling point {t+1,…,t+L} between two sampling points in the newly added sampling point group.

[0112] Specifically, the target X-axis coordinate is determined at 1 / 2 of the X-axis direction of the two target sampling points of the newly added sampling point group, and the point with the largest distance from other sampling points and other newly added sampling points is determined on the plane of the target X-axis coordinate as the newly added sample point.

[0113] Step b4, taking the inlet wind speed data, air temperature data and current data corresponding to the newly added sample point as the newly added working condition sample features of the newly added sample point.

[0114] Step b5, based on the three-dimensional fluid-thermal coupling model of the stator periodic boundary, the inlet wind speed data, air temperature data and current data of the newly added sample points are simulated to generate a newly added average temperature, and the newly added average temperature is used as the newly added operating condition sample label of the newly added sampling point.

[0115] Specifically, the data simulation method of step S2032 is used to generate the newly added average temperature, which will not be described in detail here.

[0116] Since multi-physics field simulation of power equipment requires hardware conditions with many cores, high frequency and large memory, a single simulation takes a long time. This application achieves targeted optimization and improvement by optimizing the sample space in the process of building the motor temperature field proxy model, avoiding the inefficiency of disorderly and randomly added sample points, accelerating the improvement rate and efficiency of the proxy model, and reducing the computer hardware storage requirements and the time for calculating disorderly and randomly added sample points during the proxy model training process.

[0117] The motor multi-physics field proxy model construction method provided in this embodiment performs two sample expansions before and after model training. When the sample quality does not meet the quality requirements, sample expansion is performed; when the model accuracy is low, sample expansion is performed. This application only performs sample sampling once, and only performs sample expansion later, which can reduce the difficulty of secondary sampling, reduce the steps of sample deduplication, and improve the efficiency of sample addition.

[0118] Specifically, adding new sampling points between sampling points that are far away can ensure the uniformity of the distribution of sampling points, avoid duplicate sampling points, and ensure the quality of the newly added sampling points. At the same time, this application first determines a coordinate axis and then determines the final sampling point information, which can improve the efficiency of adding sampling points. Repeating the quality assessment and model assessment steps until the requirements are met can further improve the accuracy of the model.

[0119] In this embodiment, a motor multi-physics proxy model construction device is also provided, which is used to implement the above-mentioned embodiments and preferred implementation modes, and the descriptions that have been made will not be repeated. As used below, the term "module" can be a combination of software and / or hardware that implements a predetermined function. Although the device described in the following embodiments is preferably implemented in software, the implementation of hardware, or a combination of software and hardware, is also possible and conceivable.

[0120] This embodiment provides a motor multi-physics field proxy model construction device, Figure 4 is a structural block diagram of a motor multi-physics field proxy model building device according to an embodiment of the present invention. Figure 4As shown, the motor multi-physics field proxy model building device includes: The acquisition module 401 is used to acquire the on-site operating condition data of the target motor.

[0121] The division module 402 is used to divide the sample three-dimensional space into multiple key sampling intervals and multiple non-key sampling intervals based on the data density of the sample three-dimensional space, and set a first sampling number for the key sampling interval and a second sampling number for the non-key sampling interval, and the data density of the key sampling interval is greater than the data density of the non-key sampling interval.

[0122] The sampling module 403 is used to use the Latin hypercube sampling method to perform sampling in the key sampling interval and the non-key sampling interval according to the first sampling quantity and the second sampling quantity, respectively, to obtain a first operating condition sample set.

[0123] The training module 404 is used to perform model training on the initial proxy model based on the first operating condition sample set until the average error meets the error requirement, thereby obtaining a motor multi-physics field target proxy model.

[0124] In some optional implementations, the partitioning module 402 includes: The division unit is used to establish a three-dimensional coordinate system based on the inlet wind speed, air temperature and current to obtain a sample three-dimensional space, and divide the sample three-dimensional space into a plurality of sample three-dimensional subspaces.

[0125] The determination unit is used to determine point elements in the sample stereo subspace based on the inlet wind speed data, the air temperature data and the current data.

[0126] The allocation unit is used to select a first preset number of sample stereo subspaces with the largest number of point elements as key sampling intervals, and use other sample stereo subspaces as non-key sampling intervals.

[0127] In some optional implementations, the sampling module 403 includes: The sampling unit is used to perform sampling in each sample stereo subspace of the key sampling interval according to the first sampling quantity, and perform sampling in each sample stereo subspace of the non-key sampling interval according to the second sampling quantity, so as to obtain multiple sampling points.

[0128] The feature selection unit is used to obtain the inlet wind speed data, air temperature data and current data corresponding to each sampling point to obtain the first working condition sample characteristics.

[0129] The first label selection unit is used to use the stator average temperature as the first operating condition sample label corresponding to the sampling point when there is a corresponding stator average temperature at the sampling point.

[0130] The second label selection unit, when there is no corresponding stator average temperature at the sampling point, performs data simulation on the inlet wind speed data, air temperature data and current data based on the stator periodic boundary three-dimensional fluid-thermal coupling model to generate a simulated average temperature, and uses the simulated average temperature as the first operating condition sample label corresponding to the sampling point.

[0131] In some optional implementations, the training module 404 includes: The expansion unit is used to evaluate the sample quality of the first working condition sample set, and when the sample quality does not meet the quality requirements, expand the samples of the first working condition sample set.

[0132] A training unit is used to train an initial proxy model based on a first operating condition sample set, and use the first operating condition sample set to perform average error judgment on the trained initial proxy model. When the average error does not meet the error requirement, the first operating condition sample set is expanded until the average error meets the error requirement, thereby obtaining a motor multi-physics field target proxy model.

[0133] In some optional implementations, the expansion unit includes: The acquisition subunit is used to acquire a second preset number of target sampling points with the worst quality or the largest error.

[0134] The calculation subunit is used to sequentially arrange the target sampling points along the X-axis direction of the sample stereo space, group the adjacent target sampling points into a target sampling point group, and calculate the X-axis distance of the target sampling points in the target sampling point group.

[0135] The setting subunit is configured to obtain a target sampling point group with a third preset number of the largest X-axis distance as a newly added sampling point group, and set a newly added sampling point between two target sampling points in the newly added sampling point group.

[0136] A new feature subunit is added, which is used to use the inlet wind speed data, air temperature data and current data corresponding to the newly added sample point as the new working condition sample features of the newly added sample point.

[0137] A new label subunit is added to simulate the inlet wind speed data, air temperature data and current data of the newly added sample points based on the three-dimensional fluid-thermal coupling model of the stator periodic boundary, generate a new average temperature, and use the new average temperature as the new operating condition sample label for the new sampling point.

[0138] In some optional embodiments, setting the subunit includes: The first determining subunit is used to determine the target X-axis coordinates at 1 / 2 of the X-axis direction of the two target sampling points of the newly added sampling point group.

[0139] The second determination subunit is used to determine the point with the largest distance from other sampling points and other newly added sample points on the plane of the target X-axis coordinate as the newly added sample point.

[0140] The further functional description of each of the above modules and units is the same as that of the above corresponding embodiments and will not be repeated here.

[0141] The motor multi-physics field proxy model building device in this embodiment is presented in the form of a functional unit, where the unit refers to an ASIC (Application Specific Integrated Circuit) circuit, a processor and memory that executes one or more software or fixed programs, and / or other devices that can provide the above functions.

[0142] The embodiment of the present invention also provides a computer device having the above Figure 4 The apparatus for building a multiphysics proxy model of an electric motor is shown.

[0143] See also Figure 5 , Figure 5 is a schematic diagram of the structure of a computer device provided by an optional embodiment of the present invention, such as Figure 5 As shown, the computer device includes: one or more processors 10, a memory 20, and interfaces for connecting various components, including high-speed interfaces and low-speed interfaces. Various components are connected to each other using different buses for communication, and can be installed on a common mainboard or installed in other ways as needed. The processor can process instructions executed in the computer device, including instructions stored in or on the memory to display the graphical information of the GUI on an external input / output device (such as a display device coupled to the interface). In some optional embodiments, if necessary, multiple processors and / or multiple buses can be used together with multiple memories. Similarly, multiple computer devices can be connected, and each device provides some necessary operations (for example, as a server array, a group of blade servers, or a multi-processor system). Figure 5 A processor 10 is taken as an example.

[0144] The processor 10 may be a central processing unit, a network processor or a combination thereof. The processor 10 may further include a hardware chip. The hardware chip may be a dedicated integrated circuit, a programmable logic device or a combination thereof. The programmable logic device may be a complex programmable logic device, a field programmable gate array, a general purpose array logic or any combination thereof.

[0145] The memory 20 stores instructions executable by at least one processor 10, so that the at least one processor 10 executes the method shown in the above embodiment.

[0146] The memory 20 may include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application required for at least one function; the data storage area may store data created according to the use of the computer device, etc. In addition, the memory 20 may include a high-speed random access memory, and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some optional embodiments, the memory 20 may optionally include a memory remotely arranged relative to the processor 10, and these remote memories may be connected to the computer device via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0147] The memory 20 may include a volatile memory, such as a random access memory; the memory may also include a non-volatile memory, such as a flash memory, a hard disk or a solid state drive; the memory 20 may also include a combination of the above types of memory.

[0148] The computer device also includes an input device 30 and an output device 40. The processor 10, the memory 20, the input device 30 and the output device 40 may be connected via a bus or other means. Figure 5 The example of connecting through bus is taken in the following.

[0149] The input device 30 can receive input digital or character information, and generate key signal input related to the user settings and function control of the computer device, such as a touch screen, a keypad, a mouse, a track pad, a touch pad, an indicator bar, one or more mouse buttons, a trackball, a joystick, etc. The output device 40 may include a display device, an auxiliary lighting device (e.g., an LED) and a tactile feedback device (e.g., a vibration motor), etc. The above-mentioned display device includes but is not limited to a liquid crystal display, a light emitting diode, a display and a plasma display. In some optional embodiments, the display device may be a touch screen.

[0150] The embodiment of the present invention also provides a computer-readable storage medium. The method according to the embodiment of the present invention can be implemented in hardware, firmware, or can be implemented as a computer code that can be recorded in a storage medium, or can be implemented as a computer code that is originally stored in a remote storage medium or a non-temporary machine-readable storage medium and will be stored in a local storage medium through a network download, so that the method described herein can be stored in such software processing on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. Among them, the storage medium can be a magnetic disk, an optical disk, a read-only storage memory, a random access memory, a flash memory, a hard disk or a solid-state hard disk, etc.; further, the storage medium can also include a combination of the above types of memories. It can be understood that a computer, a processor, a microprocessor controller, or programmable hardware includes a storage component that can store or receive software or computer code. When the software or computer code is accessed and executed by a computer, a processor, or hardware, the method shown in the above embodiment is implemented.

[0151] A part of the present invention may be applied as a computer program product, such as a computer program instruction, which, when executed by a computer, can call or provide the method and / or technical solution according to the present invention through the operation of the computer. Those skilled in the art should understand that the existence of the computer program instruction in a computer-readable medium includes, but is not limited to, a source file, an executable file, an installation package file, etc., and accordingly, the way in which the computer program instruction is executed by the computer includes, but is not limited to: the computer directly executes the instruction, or the computer compiles the instruction and then executes the corresponding compiled program, or the computer reads and executes the instruction, or the computer reads and installs the instruction and then executes the corresponding installed program. Here, the computer-readable medium may be any available computer-readable storage medium or communication medium accessible to the computer.

[0152] Although the embodiments of the present invention have been described in conjunction with the accompanying drawings, those skilled in the art may make various modifications and variations without departing from the spirit and scope of the present invention, and such modifications and variations are all within the scope defined by the appended claims.

Claims

1. A method for constructing a multi-physics proxy model of a motor, characterized in that: The method comprises: Obtaining on-site operating condition data of the target motor; Dividing the sample three-dimensional space into a plurality of key sampling intervals and a plurality of non-key sampling intervals based on the data density of the sample three-dimensional space, setting a first sampling number for the key sampling intervals, and setting a second sampling number for the non-key sampling intervals, wherein the data density of the key sampling intervals is greater than the data density of the non-key sampling intervals; Using a Latin hypercube sampling method, sampling the on-site operating condition data in the key sampling interval and the non-key sampling interval according to the first sampling quantity and the second sampling quantity, respectively, to obtain a first operating condition sample set; The initial proxy model is trained based on the first operating condition sample set until the average error meets the error requirement, thereby obtaining a motor multi-physics field target proxy model.

2. The method for constructing a motor multi-physics field proxy model according to claim 1, characterized in that: The on-site operating condition data includes inlet wind speed data, air temperature data and current data. The data density of the sample three-dimensional space is used to divide the sample three-dimensional space into a plurality of key sampling intervals and a plurality of non-key sampling intervals, including: A three-dimensional coordinate system is established based on the inlet wind speed, air temperature and current to obtain the sample three-dimensional space, and the sample three-dimensional space is divided into a plurality of sample three-dimensional subspaces; Determine a point element in the sample stereo subspace based on the inlet wind speed data, the air temperature data and the current data; The sample stereo subspaces with a first preset number of the largest number of point elements are selected as the key sampling intervals, and the other sample stereo subspaces are used as the non-key sampling intervals.

3. The method for constructing a motor multi-physics field proxy model according to claim 2, characterized in that: The field operating condition data also includes an average stator temperature, the first operating condition sample set includes a first operating condition sample feature and a first operating condition sample label of each sampling point, and the field operating condition data is sampled in the key sampling interval and the non-key sampling interval according to the first sampling quantity and the second sampling quantity to obtain a first operating condition sample set, including: Sampling is performed in each sample stereo subspace of the key sampling interval according to the first sampling quantity, and sampling is performed in each sample stereo subspace of the non-key sampling interval according to the second sampling quantity, to obtain a plurality of sampling points; Acquire the inlet wind speed data, the air temperature data and the current data corresponding to each sampling point to obtain the first operating condition sample characteristics; When the sampling point has a corresponding stator average temperature, the stator average temperature is used as the first operating condition sample label corresponding to the sampling point; When the sampling point does not have the corresponding stator average temperature, the inlet wind speed data, the air temperature data and the current data are simulated based on the stator periodic boundary three-dimensional fluid-thermal coupling model to generate a simulated average temperature, and the simulated average temperature is used as the first operating condition sample label corresponding to the sampling point.

4. The method for constructing a motor multi-physics field proxy model according to claim 1, characterized in that: The initial proxy model is trained based on the first operating condition sample set until the average error meets the error requirement to obtain the motor multi-physics field target proxy model, including: Performing sample quality evaluation on the first working condition sample set, and when the sample quality does not meet the quality requirement, performing sample expansion on the first working condition sample set; The initial proxy model is trained based on the first operating condition sample set, and the first operating condition sample set is used to perform average error judgment on the trained initial proxy model. When the average error does not meet the error requirement, the first operating condition sample set is expanded until the average error meets the error requirement, thereby obtaining a motor multi-physics field target proxy model.

5. The method for constructing a motor multi-physics field proxy model according to claim 4, characterized in that: The expanding the first working condition sample set includes: Acquire a second preset number of target sampling points with the worst quality or the largest error; The target sampling points are sequentially arranged along the X-axis direction of the sample stereoscopic space, adjacent target sampling points are grouped as a target sampling point group, and the X-axis distances of the target sampling points in the target sampling point group are calculated; Acquire the target sampling point group with the third preset number of the largest X-axis distance as a newly added sampling point group, and set a newly added sampling point between two target sampling points in the newly added sampling point group; The inlet wind speed data, air temperature data and current data corresponding to the newly added sample point are used as the newly added working condition sample features of the newly added sample point; Based on the three-dimensional fluid-thermal coupling model of the stator periodic boundary, data simulation is performed on the inlet wind speed data, the air temperature data and the current data of the newly added sample point to generate a newly added average temperature, and the newly added average temperature is used as a new operating condition sample label for the newly added sampling point.

6. The method for constructing a motor multi-physics proxy model according to claim 5, characterized in that: The step of setting a new sampling point between two target sampling points in the new sampling point group includes: Determine the target X-axis coordinate at 1 / 2 of the X-axis direction of the two target sampling points of the newly added sampling point group; A point with the largest distance from other sampling points and other newly added sample points is determined on the plane of the target X-axis coordinate as the newly added sample point.

7. A motor multi-physics field proxy model construction device, characterized in that: The device comprises: An acquisition module, used to acquire the on-site operating condition data of the target motor; A division module, for dividing the sample three-dimensional space into a plurality of key sampling intervals and a plurality of non-key sampling intervals based on the data density of the sample three-dimensional space, and setting a first sampling number for the key sampling interval and a second sampling number for the non-key sampling interval, wherein the data density of the key sampling interval is greater than the data density of the non-key sampling interval; A sampling module, configured to perform sampling in the key sampling interval and the non-key sampling interval according to the first sampling quantity and the second sampling quantity respectively by using a Latin hypercube sampling method to obtain a first operating condition sample set; A training module is used to perform model training on the initial proxy model based on the first operating condition sample set until the average error meets the error requirement, thereby obtaining a motor multi-physics field target proxy model.

8. A computer device, characterized in that: include: A memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the motor multi-physics field proxy model construction method according to any one of claims 1 to 6 by executing the computer instructions.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a computer to execute the motor multi-physics field proxy model construction method according to any one of claims 1 to 6.

10. A computer program product, characterized in that The method comprises computer instructions, wherein the computer instructions are used to cause a computer to execute the method for constructing a multi-physics proxy model of a motor according to any one of claims 1 to 6.

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