CN118962438A - Motor speed detection method, detection system, and motor device - Google Patents
Motor speed detection method, detection system, and motor device Download PDFInfo
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Abstract
本发明涉及电机技术领域，具体为一种电机转速检测方法、检测系统、电机装置。包括：首先，在预处理过程通过计算获取的数据与标准电机转速数据之间的变化差值，并评估出每个特征参数对电机转速变化的贡献度以此进行权，提升了电机转速检测的准确度；其次，提出一种电机转速检测模型，该模型在训练过程中对样本进行聚类，并根据聚类结果得出多个转速检测子模型；对于相似的特征分布的数据子模型具有准确的检测作用；最后，提出一种电机转速优化方法，通过设定电机转速优化目标和约束条件对电机转速进行调整，根据电机转速与所述目标汽车的性能之间的数学模型获取各项性能，通过优化算法优化关于电机转速的特征参数，以获得最优的电机调整转速。
The present invention relates to the field of motor technology, specifically to a motor speed detection method, detection system, and motor device. The method comprises: first, in the preprocessing process, the difference between the acquired data and the standard motor speed data is calculated, and the contribution of each characteristic parameter to the motor speed change is evaluated to perform weighting, thereby improving the accuracy of motor speed detection; second, a motor speed detection model is proposed, the model clusters samples during the training process, and multiple speed detection sub-models are obtained according to the clustering results; the sub-models have an accurate detection effect on data with similar feature distributions; finally, a motor speed optimization method is proposed, the motor speed is adjusted by setting the motor speed optimization target and constraint conditions, various performances are obtained according to the mathematical model between the motor speed and the performance of the target vehicle, and the characteristic parameters about the motor speed are optimized by the optimization algorithm to obtain the optimal motor adjustment speed.
Description
Technical Field
The invention relates to the technical field of motors, in particular to a motor rotating speed detection method, a motor rotating speed detection system and a motor device.
Background
An electric motor is a device for converting electric energy into mechanical energy, and is widely used in various industrial, commercial and household devices. The operation is realized mainly by an electromagnetic induction principle, and can be divided into a direct current motor and an alternating current motor according to the working principle and the structural characteristics.
The running state of the motor refers to various performances and characteristics of the motor in the working process; the method comprises the following steps: parameters and indexes of current, temperature, vibration, rotating speed and the like; in detecting the running state of the motor, the rotating speed of the motor is taken as one of key parameters, and the stability and the precision of the motor directly influence the performance and the running speed of the equipment. The rotational speed of the motor depends on factors such as the frequency of the power supply, the voltage, the load and the design specifications. If the rotating speed does not meet certain requirements in the running process of the motor, the conditions of efficiency reduction, output power reduction, overload, overheat and the like can be caused. Therefore, the rotation speed of the motor needs to be detected in the running process of the motor so as to be convenient for timely adjusting the rotation speed of the motor.
Motors have a wide range of applications in modern industry and modern life, particularly in the new energy automotive industry. The motor not only bears the transmission function of driving force in modern automobiles, but also plays a vital role in the aspects of automobile performance, energy efficiency and environmental protection. With the development of electric technology, the application of electric motors in the automotive industry will continue to expand and deepen. The motor rotation speed not only directly affects the power output and energy efficiency of the vehicle, but also plays a key role in driving control, stability and comfort of the vehicle and overall energy utilization efficiency. Therefore, detection and optimization of motor speed is an important technical challenge and area of research in the design and operation of electric vehicles.
There are many methods in the prior art to study the rotational speed of automotive motors; wherein, include: as disclosed in the chinese patent with the prior art application number of cn202110569953.X, a motor rotational speed redundancy detection method, a circuit and a motor controller are disclosed, wherein the detection method determines a first rotational speed of a motor by obtaining a phase current signal of the motor, filtering the phase current signal, and performing zero-crossing comparison; acquiring a line voltage signal of the motor, filtering the line voltage signal, performing zero-crossing comparison processing, and determining a second rotating speed of the motor; determining an actual rotation speed of the motor according to the first rotation speed and the second rotation speed; the method can simulate the automobile work to vibrate the detection motor, increase the accuracy of rotation speed test, and then test the rotation speed of the detection motor under two different test environments of load and no-load, so that the range of test results is wider. The above mentioned prior art realizes the rotation speed detection of the motor, but the consideration factors about the rotation speed of the motor in the detection process are less, and the direct detection method is adopted to generate deviation for the rotation speed detection, which is not beneficial to the adjustment of the rotation speed of the motor of the automobile.
Therefore, the invention provides a motor rotating speed detection method, a motor rotating speed detection system and a motor device.
Disclosure of Invention
The invention provides a motor rotating speed detection method, a motor rotating speed detection system and a motor device, which are used for detecting and adjusting the rotating speed of an automobile motor; the content of the invention mainly comprises: firstly, sample data of a motor of an automobile at different rotating speeds are obtained, and the obtained data are preprocessed; in order to accurately detect the rotating speed of the motor subsequently, a data enhancement method is provided in the preprocessing process, the method evaluates the contribution degree of each characteristic parameter to the rotating speed change of the motor according to the obtained change difference value by calculating the change difference value between the obtained data and the standard rotating speed data of the motor, and weights each characteristic parameter according to the contribution degree so as to illustrate different influence degrees in the rotating speed detection of the motor; secondly, the invention provides a motor rotation speed detection model which clusters through rotation speed distribution characteristics of motors in a training set; training a plurality of rotation speed detection sub-models according to the clustering result, wherein different sub-models have accurate detection effect on data of similar characteristic distribution; finally, the invention provides an automobile motor rotating speed optimization method, which adjusts the motor rotating speed by setting a motor rotating speed optimization target and a motor rotating speed constraint condition, and optimizes characteristic parameters related to the motor rotating speed by adopting an optimization algorithm through a mathematical model between the motor rotating speed and the performance of the target automobile so as to obtain the optimal motor adjusting rotating speed.
Accordingly, an object of the present invention is to provide a motor rotation speed detecting method, detecting system, and motor device, so as to overcome the problems in the prior art.
In a first aspect, an embodiment of the present application provides a method for detecting a rotational speed of a motor, including:
Acquiring data samples of a motor of a target automobile at different rotating speeds; wherein the data samples comprise: motor rotation speed data, motor power data, current data, voltage data, motor vibration data and motor temperature data;
further, marking the data samples according to actual requirements, and constructing motor data; wherein the motor data is output with the rotation speed of the motor as an output target;
Further, preprocessing the motor data to obtain standard motor data;
the motor data are subjected to data cleaning to obtain first motor data; wherein the data cleansing comprises: missing value filling, outlier processing and noise filtering;
normalizing the first motor data to obtain second motor data;
performing data enhancement on the second motor data to obtain third motor data;
Wherein the data enhancement comprises:
establishing a second motor data matrix according to the second motor data; wherein the second motor data matrix is expressed as: wherein, F NM is represented as the Mth characteristic parameter in the Nth second motor data; v N is the motor rotation speed value in the nth second television data;
Further, calculating a variation difference value of each piece of data in the second motor data matrix; the variation difference value is obtained by subtracting a standard motor rotating speed parameter vector from data in the second motor data matrix; the standard motor rotating speed parameter vector is recorded as: (s 1,s2,…,sM,sv); s M is represented as an mth parameter value in the standard motor rotating speed parameter vector; s v is expressed as a motor rotation speed value in the standard motor rotation speed parameter vector;
Further, according to the variation difference, a decision tree model is adopted to evaluate the contribution degree of each characteristic parameter to the variation difference of the motor rotation speed;
Wherein the variation difference is expressed as E NM is represented as a motor parameter variation difference value between an Mth characteristic parameter of the Nth data in the second motor data matrix and an Mth parameter value in the standard motor rotating speed parameter vector; e Nv is expressed as the motor rotation speed variation difference value between the motor rotation speed value of the Nth data in the second motor data matrix and the motor rotation speed value in the standard motor rotation speed parameter vector;
Further, the second motor data is weighted according to the contribution degree, and the third motor data is obtained.
Further, the third motor data are subjected to distributed processing, and the standard motor data are obtained.
Further, dividing the motor standard data into a training set and a testing set according to a certain proportion;
further, a motor rotating speed detection model is built;
Further, inputting the training set into the motor rotation speed detection model until a model training stopping condition is reached, and obtaining an initial motor rotation speed detection model;
The training process of the motor rotation speed detection model comprises the following steps of:
inputting the training set to a clustering layer to obtain a plurality of clustering clusters; the clustering layer clusters according to the motor rotation speed distribution characteristics in the training set;
further, each data sample is distributed to the corresponding clustering cluster according to the clustering result;
further, inputting each cluster to a corresponding rotation speed detection sub-model; the rotational speed detection sub-model is subjected to parameter updating according to a loss function;
the loss function is expressed as: Wherein ω is represented as a weight vector of the rotational speed detection submodel; b represents an offset term of the rotational speed detection submodel; x is represented as the training set; c is expressed as a punishment parameter of the rotational speed detection sub-model; x i is represented as the ith training data in the cluster corresponding to the rotation speed detection sub-model, and X i epsilon X;
Setting a constraint on the loss function is: Wherein y i represents the motor rotation speed corresponding to the ith training data; sigma is expressed as a delivery variable of the rotational speed detection submodel; xi i is the boundary width corresponding to the ith training data;
Further, iterating the training process of the motor rotation speed detection model, and judging whether the model training stopping condition is met;
when model training conforming to the model training stopping condition is finished, obtaining the initial motor rotating speed detection model; continuing training when the model training stopping condition model is not met; the model training stopping condition is set to enable the loss of the motor rotating speed detection model to reach convergence.
Further, inputting the test set into the initial motor rotation speed detection model for testing, and optimizing the initial motor rotation speed detection model according to a test result to obtain an optimal motor rotation speed detection model;
Further, detecting the motor speed of the target automobile by adopting the optimal motor speed detection model to obtain a motor speed detection result;
Further, performing rotation speed optimization according to the motor rotation speed detection result to obtain the optimal rotation speed of the motor of the target automobile;
the rotational speed optimization process comprises the following steps:
Setting a motor rotation speed optimization target and a motor rotation speed constraint condition of the target automobile;
Wherein, the motor rotation speed optimization target is expressed as: The motor rotation speed constraint condition is expressed as:
further, acquiring corresponding power output, power consumption and running state data by adopting a mathematical model according to the motor of the target automobile at different rotating speeds;
Further, optimizing the characteristic parameters of the motor by adopting an optimization algorithm, and detecting the rotating speed by using the motor rotating speed detection model;
further, when the motor rotation speed optimization target is reached, the optimal rotation speed of the motor is obtained.
In a second aspect, the present invention proposes a motor rotation speed detection system, the system comprising: the motor speed sensor comprises a sensor and data acquisition unit, a motor data receiving unit, a data processing unit, a motor speed detection unit and a motor speed adjustment unit;
the sensor and data acquisition unit is used for acquiring various data of the target automobile through the current sensor, the voltage sensor, the vibration sensor and the temperature sensor;
the motor data receiving unit is used for receiving the collected data;
the data processing unit is used for preprocessing the received data; wherein the data processing unit comprises:
carrying out data cleaning on the motor data to obtain first motor data; wherein the data cleansing comprises: missing value filling, outlier processing and noise filtering;
normalizing the first motor data to obtain second motor data;
Performing data enhancement on the second motor data to obtain third motor data; wherein the data enhancement comprises:
establishing a second motor data matrix according to the second motor data; wherein the second motor data matrix is expressed as: wherein, F NM is represented as the Mth characteristic parameter in the Nth second motor data; v N is the motor rotation speed value in the nth second television data;
Further, calculating a variation difference value of each piece of data in the second motor data matrix; the variation difference value is obtained by subtracting a standard motor rotating speed parameter vector from data in the second motor data matrix; the standard motor rotating speed parameter vector is recorded as: (s 1,s2,…,sM,sv); s M is represented as an mth parameter value in the standard motor rotating speed parameter vector; s v is expressed as a motor rotation speed value in the standard motor rotation speed parameter vector;
Further, according to the variation difference, a decision tree model is adopted to evaluate the contribution degree of each characteristic parameter to the variation difference of the motor rotation speed;
Wherein the variation difference is expressed as E NM is represented as a motor parameter variation difference value between an Mth characteristic parameter of the Nth data in the second motor data matrix and an Mth parameter value in the standard motor rotating speed parameter vector; e Nv is expressed as the motor rotation speed variation difference value between the motor rotation speed value of the Nth data in the second motor data matrix and the motor rotation speed value in the standard motor rotation speed parameter vector;
Further, the second motor data is weighted according to the contribution degree, and the third motor data is obtained.
Further, the third motor data is subjected to distributed processing to obtain standard motor data.
The motor rotation speed detection unit is used for detecting the motor rotation speed of the target automobile; the motor rotation speed detection unit adopts a motor rotation speed detection model;
the training process of the motor rotation speed detection model comprises the following steps:
inputting the training set into a clustering layer to obtain a plurality of clustering clusters; the clustering layer clusters according to the motor rotation speed distribution characteristics in the training set;
further, each data sample is distributed to the corresponding clustering cluster according to the clustering result;
further, inputting each cluster to a corresponding rotation speed detection sub-model; the rotational speed detection sub-model is subjected to parameter updating according to a loss function;
the loss function is expressed as: Wherein ω is represented as a weight vector of the rotational speed detection submodel; b represents an offset term of the rotational speed detection submodel; x is represented as the training set; c is expressed as a punishment parameter of the rotational speed detection sub-model; x i is represented as the ith training data in the cluster corresponding to the rotation speed detection sub-model, and X i epsilon X;
Setting a constraint on the loss function is: Wherein y i represents the motor rotation speed corresponding to the ith training data; sigma is expressed as a delivery variable of the rotational speed detection submodel; xi i is the boundary width corresponding to the ith training data;
Further, iterating the training process of the motor rotation speed detection model, and judging whether the model training stopping condition is met;
further, when model training conforming to the model training stopping condition is finished, an initial motor rotating speed detection model is obtained; continuing training when the model training stopping condition model is not met; the model training stopping condition is set to be that the loss of the motor rotation speed detection model is converged;
and the motor rotating speed adjusting unit is used for optimally adjusting the motor rotating speed.
In a third aspect, the present invention provides a motor apparatus; wherein the device comprises: the device comprises a sensing module, an acquisition module, a data processing module, a rotation speed detection module and a rotation speed adjustment module; and the device can be realized by any step of a motor rotation speed detection method; wherein, the rotational speed detection module includes:
acquiring a current motor rotating speed related characteristic parameter of a target automobile;
Further, inputting the characteristic parameters related to the current motor rotation speed to a clustering layer to obtain a clustering result;
further, selecting a corresponding rotation speed detection submodel according to the clustering result; the rotational speed detection submodel adopts a loss function to update parameters;
the loss function is expressed as: Wherein ω is represented as a weight vector of the rotational speed detection submodel; b represents an offset term of the rotational speed detection submodel; x is denoted as training set; c is expressed as a punishment parameter of the rotational speed detection sub-model; x i is represented as the ith training data in the cluster corresponding to the rotation speed detection sub-model, and X i epsilon X;
Setting a constraint on the loss function is: Wherein y i represents the motor rotation speed corresponding to the ith training data; sigma is expressed as a delivery variable of the rotational speed detection submodel; xi i is the boundary width corresponding to the ith training data;
Further, the motor rotating speed of the target automobile is output according to the rotating speed detection submodel.
Further, the rotating speed adjusting module of the motor device adjusts the rotating speed; wherein, the rotational speed adjustment module includes:
Setting a motor rotation speed optimization target and a motor rotation speed constraint condition of the target automobile;
Wherein, the motor rotation speed optimization target is expressed as: The motor rotation speed constraint condition is expressed as:
further, acquiring corresponding power output, power consumption and running state data by adopting a mathematical model according to the motor of the target automobile at different rotating speeds;
Further, optimizing the characteristic parameters of the motor by adopting an optimization algorithm, and detecting the rotating speed by using the motor rotating speed detection model;
further, when the motor rotation speed optimization target is reached, the optimal rotation speed of the motor is obtained.
Compared with the prior art, the invention has the beneficial effects that:
1. the invention provides a data enhancement method, which comprises the steps of calculating a variation difference value between acquired data and standard motor rotating speed data, evaluating the contribution degree of each characteristic parameter to motor rotating speed variation according to the obtained variation difference value, and weighting each characteristic parameter by the contribution degree so as to explain different influence degrees in motor rotating speed detection; the method can improve the accuracy of the subsequent motor rotation speed detection, and is beneficial to the optimization and adjustment of the motor rotation speed of the automobile.
2. The invention provides a motor rotation speed detection model, which is characterized in that a training set is clustered in the training process, and a plurality of corresponding rotation speed detection sub-models are trained according to a clustering result; setting a limiting condition for a training sample for a loss function by minimizing parameters of the loss function training model in the training process; therefore, the training process of the motor rotation speed detection model can be effectively optimized, so that the motor rotation speed detection model is more suitable for actual application requirements; therefore, the performance of the motor rotation speed detection model is improved, and an accurate detection result is provided for the subsequent motor rotation speed adjustment.
3. The invention provides an automobile motor rotating speed adjusting method, which adjusts the motor rotating speed by setting a motor rotating speed optimizing target and a motor rotating speed constraint condition, obtains various performance values through a mathematical model between the motor rotating speed and the performance of the target automobile, and optimizes characteristic parameters related to the motor rotating speed by adopting an optimizing algorithm so as to obtain the optimal motor adjusting rotating speed.
Drawings
Fig. 1 is a flowchart of a motor rotation speed detection method according to an embodiment of the present invention;
fig. 2 is a block diagram of a motor rotation speed detection system according to an embodiment of the present invention;
fig. 3 is a structural diagram of a motor device according to an embodiment of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
As a device for converting electric energy into mechanical energy, a motor has a wide range of applications in modern industry and daily life, particularly playing a key role in the new energy automobile industry;
For various manifestations and features during operation of the motor; the method comprises the following steps: parameters and indexes of current, temperature, vibration, rotating speed and the like; in detecting the running state of the motor, the rotating speed of the motor is taken as one of key parameters, and the stability and the precision of the motor directly influence the performance and the running speed of the equipment. The rotational speed of the motor depends on factors such as the frequency of the power supply, the voltage, the load and the design specifications. If the rotating speed does not meet certain requirements in the running process of the motor, the conditions of efficiency reduction, output power reduction, overload, overheat and the like can be caused. Therefore, the rotation speed of the motor needs to be detected in the running process of the motor so as to be convenient for timely adjusting the rotation speed of the motor.
There are many studies on motor speed detection in the prior art, but there are some drawbacks; in order to solve the defects in the prior art, the invention provides a motor rotating speed detection method, a motor rotating speed detection system and a motor device; the following will explain in detail two embodiments.
Example 1
In the embodiment of the application, the method and the system provided by the application are combined, and the method corresponds to each unit of the system.
Firstly, the invention provides a motor rotation speed detection method, as shown in fig. 1; the method comprises the following steps: s10, acquiring data samples of motors of a target automobile at different rotating speeds; s20, marking the data samples according to actual requirements, and constructing motor data; s30, preprocessing the motor data to obtain standard motor data; s40, dividing the motor standard data into a training set and a testing set according to a certain proportion; s50, building a motor rotation speed detection model; s60, training a motor rotation speed detection model; s70, testing a motor rotation speed detection model; s80, detecting the motor rotation speed of the target automobile; s90, adjusting the motor rotating speed of the target automobile. The method is realized by combining the system unit in the figure 2 in the process of detecting the rotating speed of the automobile motor; as shown in fig. 2, the system includes: the motor speed sensor comprises a sensor and data acquisition unit, a motor data receiving unit, a data processing unit, a motor speed detection unit and a motor speed adjustment unit;
further, in conjunction with the content of fig. 1 and 2, the specific procedure is as follows:
Acquiring data samples of a motor of a target automobile at different rotating speeds by a sensor and a data acquisition unit of the system, wherein the data samples correspond to the step S10 of FIG. 1; the sensor and the data acquisition unit are respectively provided with a current sensor, a voltage sensor, a vibration sensor and a temperature sensor, and are used for acquiring motor power data, current data, voltage data, motor vibration data and motor temperature data;
further, the data acquired by the sensor and the data acquisition unit are received through a motor data receiving unit of the system;
Further, marking the acquired data samples according to the step S20; wherein, the rotating speed of the motor is used as a mark and also used as an output target;
further, preprocessing the data sample by a data processing unit of the system to obtain standard motor data, corresponding to step S30 in fig. 1; among them, the pretreatment mentioned in the embodiment of the present application includes:
Performing data cleaning on the motor data to obtain first motor data; wherein the data cleansing comprises: missing value filling, outlier processing and noise filtering;
normalizing the first motor data to obtain second motor data;
performing data enhancement on the second motor data to obtain third motor data;
Wherein the data enhancement comprises:
establishing a second motor data matrix according to the second motor data; wherein the second motor data matrix is expressed as: wherein, F NM is represented as the Mth characteristic parameter in the Nth second motor data; v N is the motor rotation speed value in the nth second television data;
Further, calculating a variation difference value of each piece of data in the second motor data matrix; the variation difference value is obtained by subtracting a standard motor rotating speed parameter vector from data in the second motor data matrix; the standard motor rotating speed parameter vector is recorded as: (s 1,s2,…,sM,sv); s M is represented as an mth parameter value in the standard motor rotating speed parameter vector; s v is expressed as a motor rotation speed value in the standard motor rotation speed parameter vector;
Further, according to the variation difference, a decision tree model is adopted to evaluate the contribution degree of each characteristic parameter to the variation difference of the motor rotation speed;
Wherein the variation difference is expressed as E NM is represented as a motor parameter variation difference value between an Mth characteristic parameter of the Nth data in the second motor data matrix and an Mth parameter value in the standard motor rotating speed parameter vector; e Nv is expressed as the motor rotation speed variation difference value between the motor rotation speed value of the Nth data in the second motor data matrix and the motor rotation speed value in the standard motor rotation speed parameter vector;
Obtaining contribution degree scores of all characteristic parameters after evaluation by using a decision tree model in application implementation; see table 1:
TABLE 1 contribution of motor speed characteristic parameters
| Characteristic parameter | Contribution degree |
| Motor power | 0.30 |
| Electric current | 0.25 |
| Voltage (V) | 0.20 |
| Motor vibration | 0.15 |
| Motor temperature | 0.10 |
From table 1, it can be found that the contribution of motor power is 0.30, the contribution of current is 0.25, the contribution of voltage is 0.20, the contribution of motor vibration is 0.15, and the contribution of motor temperature is 0.10.
Further, weighting the second motor data according to the contribution degree to obtain third motor data;
The embodiment of the application provides a data enhancement method in the data preprocessing process, which comprises the steps of calculating a variation difference value between acquired data and standard motor rotating speed data, evaluating the contribution degree of each characteristic parameter to motor rotating speed variation according to the obtained variation difference value, and weighting each characteristic parameter by the contribution degree so as to explain different influence degrees in motor rotating speed detection; the method can improve the accuracy of the subsequent motor rotation speed detection, and is beneficial to the optimization and adjustment of the motor rotation speed of the automobile.
Further, the third motor data are subjected to distributed processing, and the standard motor data are obtained.
In the embodiment of the application, a series of data processing methods are adopted to preprocess sample data related to the motor rotation speed, and the method comprises the following steps: data cleaning, normalization processing, data enhancement and distribution processing; the data cleaning can help to remove noise, abnormal values and inconsistencies in the data, so that the quality and accuracy of the data are improved; in motor rotation speed detection, accurate input data is the basis for ensuring effective operation of a model and an algorithm; errors and deviations can be reduced by cleaning the data, so that a subsequent motor rotation speed detection model can be trained and predicted based on more reliable and consistent data; the normalization processing improves the consistency and stability of data, accelerates the training process of the model, improves the characteristic distribution and the data analysis effect, and improves the generalization capability of the model; the data enhancement can effectively improve the utilization efficiency of the data under the condition of not increasing the data acquisition cost, and a more robust and accurate motor rotation speed detection model can be trained by utilizing a limited original data set; the distribution process may adjust or optimize the data distribution, possibly helping to further improve the effect of data analysis or the performance of the model.
Further, the motor standard data is divided into a training set and a testing set according to a certain proportion, which corresponds to the step S40 in FIG. 1; the embodiment of the application divides the data set into a training set and a testing set according to the proportion of 8:2;
The main implementation process of the motor rotation speed detection model comprises the following steps:
constructing a motor rotation speed detection model, and correspondingly S50;
Further, in the step S60, the training set is input to the motor rotation speed detection model until a model training stop condition is reached, so as to obtain an initial motor rotation speed detection model;
The training process of the motor rotation speed detection model comprises the following steps of:
inputting the training set into a clustering layer to obtain a plurality of clustering clusters; the clustering layer clusters according to the motor rotation speed distribution characteristics in the training set;
further, each data sample is distributed to the corresponding clustering cluster according to the clustering result;
Further, inputting each cluster to a corresponding rotation speed detection sub-model; the rotational speed detection sub-model is subjected to parameter updating according to a loss function; the rotational speed detection sub-model can use a multi-layer perceptron as a basic model, and can effectively learn the nonlinear relation between input features;
the loss function is expressed as: Wherein ω is represented as a weight vector of the rotational speed detection submodel; b represents an offset term of the rotational speed detection submodel; x is represented as the training set; c is expressed as a punishment parameter of the rotational speed detection sub-model; x i is represented as the ith training data in the cluster corresponding to the rotation speed detection sub-model, and X i epsilon X;
Setting a constraint on the loss function is: Wherein y i represents the motor rotation speed corresponding to the ith training data; sigma is expressed as a delivery variable of the rotational speed detection submodel; xi i is the boundary width corresponding to the ith training data;
Further, iterating the training process of the motor rotation speed detection model, and judging whether the model training stopping condition is met;
further, when model training conforming to the model training stopping condition is finished, an initial motor rotating speed detection model is obtained; continuing training when the model training stopping condition model is not met; the model training stopping condition is set to be that the loss of the motor rotation speed detection model is converged;
In the embodiment of the application, a motor rotation speed detection model is provided, wherein the model clusters a training set in the training process and trains a plurality of corresponding rotation speed detection sub-models according to the clustering result; setting a limiting condition for a training sample for a loss function by minimizing parameters of the loss function training model in the training process; therefore, the training process of the motor rotation speed detection model can be effectively optimized, so that the motor rotation speed detection model is more suitable for actual application requirements; therefore, the performance of the motor rotating speed detection model is improved.
Further, according to the step S70, the test set is input into the initial motor rotation speed detection model for testing, and the initial motor rotation speed detection model is optimized according to a test result, so that an optimal motor rotation speed detection model is obtained;
further, the motor rotation speed detection unit of the system detects the motor rotation speed, corresponding to the step S80; the motor rotating speed detection unit adopts a motor rotating speed detection model for detection;
Further, the motor rotation speed of the target automobile is adjusted by a motor rotation speed adjusting unit of the system, and the step S90 is correspondingly performed; the specific adjustment process comprises the following steps:
Setting a motor rotation speed optimization target and a motor rotation speed constraint condition of the target automobile;
Wherein, the motor rotation speed optimization target is expressed as: The motor rotation speed constraint condition is expressed as:
further, acquiring corresponding power output, power consumption and running state data by adopting a mathematical model according to the motor of the target automobile at different rotating speeds;
the mathematical model is expressed as: p motor = T (θ) x θ; wherein P motor is denoted as output performance; t (θ) is expressed as a function of rotational speed; θ is the rotational speed;
the rotational speed can be expressed as: wherein MSDM () is represented as the rotation speed detection model; the characteristic parameter vector is expressed as a motor rotating speed characteristic parameter vector;
Further, optimizing motor rotating speed characteristic parameters by adopting an optimization algorithm, and detecting the rotating speed by using the motor rotating speed detection model; the optimization algorithm can update the characteristic parameters of the motor rotation speed by adopting the ant colony algorithm, and obtain the optimized motor rotation speed according to the updated parameters.
Further, when the motor rotation speed optimization target is reached, the optimal rotation speed of the motor is obtained.
According to the method, the motor speed is adjusted by setting a motor speed optimization target and a motor speed constraint condition, various performance values are obtained through a mathematical model between the motor speed and the performance of the target automobile, and characteristic parameters about the motor speed are optimized by adopting an optimization algorithm so as to obtain the optimal motor adjustment speed.
In the first embodiment, the system and the method of the invention are combined to realize the detection and adjustment of the motor rotation speed of the target automobile; the invention mainly starts from the following aspects: firstly, sample data of a motor of an automobile at different rotating speeds are obtained, and the obtained data are preprocessed; in order to accurately detect the rotating speed of the motor subsequently, a data enhancement method is provided in the preprocessing process, the method evaluates the contribution degree of each characteristic parameter to the rotating speed change of the motor according to the obtained change difference value by calculating the change difference value between the obtained data and the standard rotating speed data of the motor, and the different influence degrees in the rotating speed detection of the motor are illustrated by weighting each characteristic parameter by the contribution degree; secondly, the invention provides a motor rotation speed detection model which clusters through rotation speed distribution characteristics of motors in a training set; training a plurality of rotation speed detection sub-models according to the clustering result, wherein different sub-models have accurate detection effect on data of similar characteristic distribution; finally, the invention provides an automobile motor rotating speed optimization method, which adjusts the motor rotating speed by setting a motor rotating speed optimization target and a motor rotating speed constraint condition, obtains various performance values through a mathematical model between the motor rotating speed and the performance of the target automobile, and optimizes characteristic parameters related to the motor rotating speed by adopting an optimization algorithm so as to obtain the optimal motor adjusting rotating speed.
Example two
In the first embodiment, the motor rotation speed detection method is combined with the detection system, so that the rotation speed detection and the subsequent rotation speed adjustment of the automobile motor are realized; the embodiment of the application will explain a motor rotation speed detection method again, and the specific process is the same as the implementation process of the embodiment, and includes the following steps:
acquiring data samples of a motor of a target automobile at different rotating speeds;
Further, marking the data samples according to actual requirements, and constructing motor data;
Further, preprocessing the motor data to obtain standard motor data;
Wherein, the preprocessing process comprises the enhancement operation on the data; the specific data enhancement process is as follows:
establishing a second motor data matrix according to the second motor data; wherein the second motor data matrix is expressed as: wherein, F NM is represented as the Mth characteristic parameter in the Nth second motor data; v N is the motor rotation speed value in the nth second television data;
Further, calculating a variation difference value of each piece of data in the second motor data matrix; the variation difference value is obtained by subtracting a standard motor rotating speed parameter vector from data in the second motor data matrix; the standard motor rotating speed parameter vector is recorded as: (s 1,s2,…,sM,sv); s M is represented as an mth parameter value in the standard motor rotating speed parameter vector; s v is expressed as a motor rotation speed value in the standard motor rotation speed parameter vector;
Further, according to the variation difference, a decision tree model is adopted to evaluate the contribution degree of each characteristic parameter to the variation difference of the motor rotation speed;
Wherein the variation difference is expressed as E NM is represented as a motor parameter variation difference value between an Mth characteristic parameter of the Nth data in the second motor data matrix and an Mth parameter value in the standard motor rotating speed parameter vector; e Nv is expressed as the motor rotation speed variation difference value between the motor rotation speed value of the Nth data in the second motor data matrix and the motor rotation speed value in the standard motor rotation speed parameter vector;
Further, the second motor data is weighted according to the contribution degree, and the third motor data is obtained.
Further, dividing the motor standard data into a training set and a testing set according to a certain proportion;
further, a motor rotating speed detection model is built;
Further, inputting the training set into the motor rotation speed detection model until a model training stopping condition is reached, and obtaining an initial motor rotation speed detection model;
Further, inputting the test set into the initial motor rotation speed detection model for testing, and optimizing the initial motor rotation speed detection model according to a test result to obtain an optimal motor rotation speed detection model;
Further, detecting the motor speed of the target automobile by adopting the optimal motor speed detection model to obtain a motor speed detection result;
Further, performing rotation speed optimization according to the motor rotation speed detection result to obtain the optimal rotation speed of the motor of the target automobile;
the rotational speed optimization process comprises the following steps:
Setting a motor rotation speed optimization target and a motor rotation speed constraint condition of the target automobile;
Wherein, the motor rotation speed optimization target is expressed as: The motor rotation speed constraint condition is expressed as:
further, acquiring corresponding power output, power consumption and running state data by adopting a mathematical model according to the motor of the target automobile at different rotating speeds;
Further, optimizing the characteristic parameters of the motor by adopting an optimization algorithm, and detecting the rotating speed by using the motor rotating speed detection model;
further, when the motor rotation speed optimization target is reached, the optimal rotation speed of the motor is obtained.
For the detection method in the embodiment of the present application, referring to fig. 3, an embodiment of the present application further provides a motor apparatus, where the motor apparatus includes: the device comprises a sensing module, an acquisition module, a data processing module, a rotation speed detection module and a rotation speed adjustment module; wherein the motor device execution can be realized by the motor rotation speed detection method provided by the above;
wherein, the rotational speed detection module includes:
acquiring a current motor rotating speed related characteristic parameter of a target automobile;
Further, inputting the characteristic parameters related to the current motor rotation speed to a clustering layer to obtain a clustering result;
further, selecting a corresponding rotation speed detection submodel according to the clustering result; the rotational speed detection submodel adopts a loss function to update parameters;
the loss function is expressed as: Wherein ω is represented as a weight vector of the rotational speed detection submodel; b represents an offset term of the rotational speed detection submodel; x is denoted as training set; c is expressed as a punishment parameter of the rotational speed detection sub-model; x i is represented as the ith training data in the cluster corresponding to the rotation speed detection sub-model, and X i epsilon X;
Setting a constraint on the loss function is: Wherein y i represents the motor rotation speed corresponding to the ith training data; sigma is expressed as a delivery variable of the rotational speed detection submodel; xi i is the boundary width corresponding to the ith training data;
Further, the motor rotating speed of the target automobile is output according to the rotating speed detection submodel.
Further, the rotating speed adjusting module of the motor device adjusts the rotating speed; wherein, the rotational speed adjustment module includes:
Setting a motor rotation speed optimization target and a motor rotation speed constraint condition of the target automobile;
Wherein, the motor rotation speed optimization target is expressed as: The motor rotation speed constraint condition is expressed as:
further, acquiring corresponding power output, power consumption and running state data by adopting a mathematical model according to the motor of the target automobile at different rotating speeds;
Further, optimizing the characteristic parameters of the motor by adopting an optimization algorithm, and detecting the rotating speed by using the motor rotating speed detection model;
further, when the motor rotation speed optimization target is reached, the optimal rotation speed of the motor is obtained.
Although embodiments of the present invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made therein without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.
Claims (10)
1. A motor rotation speed detection method, characterized by comprising: acquiring data samples of a motor of a target automobile at different rotating speeds; marking the data samples according to actual requirements, and constructing motor data; preprocessing the motor data to obtain standard motor data; dividing the motor standard data into a training set and a testing set according to a certain proportion; constructing a motor rotating speed detection model; inputting the training set into the motor rotation speed detection model until the training set meets the model training stop condition, and obtaining an initial motor rotation speed detection model; inputting the test set into the initial motor rotation speed detection model for testing, and optimizing the initial motor rotation speed detection model according to a test result to obtain an optimal motor rotation speed detection model; detecting the motor speed of the target automobile by adopting the optimal motor speed detection model to obtain a motor speed detection result; performing rotation speed optimization according to the motor rotation speed detection result to obtain the optimal rotation speed of the motor of the target automobile; the rotational speed optimization process comprises the following steps:
Setting a motor rotation speed optimization target and a motor rotation speed constraint condition of the target automobile;
Wherein, the motor rotation speed optimization target is expressed as: The motor rotation speed constraint condition is expressed as:
obtaining corresponding power output, power consumption and running state data by adopting a mathematical model according to the motor of the target automobile at different rotating speeds;
optimizing motor characteristic parameters by adopting an optimization algorithm, and detecting the rotating speed by using the motor rotating speed detection model;
And when the motor rotating speed optimization target is reached, obtaining the optimal rotating speed of the motor.
2. The motor rotation speed detection method according to claim 1, wherein the preprocessing includes:
Performing data cleaning on the motor data to obtain first motor data; wherein the data cleansing comprises: missing value filling, outlier processing and noise filtering;
normalizing the first motor data to obtain second motor data;
performing data enhancement on the second motor data to obtain third motor data;
and carrying out distributed processing on the third motor data to obtain the standard motor data.
3. A motor speed detection method according to claim 2, wherein the data enhancement includes: establishing a second motor data matrix according to the second motor data; wherein the second motor data matrix is expressed as: wherein, F NM is represented as the Mth characteristic parameter in the Nth second motor data; v N is the motor rotation speed value in the nth second television data;
calculating a variation difference value of each piece of data in the second motor data matrix; the variation difference value is obtained by subtracting a standard motor rotating speed parameter vector from data in the second motor data matrix; the standard motor rotating speed parameter vector is recorded as: (s 1,s2,…,sM,sv); s M is represented as an mth parameter value in the standard motor rotating speed parameter vector; s v is expressed as a motor rotation speed value in the standard motor rotation speed parameter vector;
According to the variation difference, a decision tree model is adopted to evaluate the contribution degree of each characteristic parameter to the variation difference of the motor rotation speed;
Wherein the variation difference is expressed as E NM is represented as a motor parameter variation difference value between an Mth characteristic parameter of the Nth data in the second motor data matrix and an Mth parameter value in the standard motor rotating speed parameter vector; e Nv is expressed as the motor rotation speed variation difference value between the motor rotation speed value of the Nth data in the second motor data matrix and the motor rotation speed value in the standard motor rotation speed parameter vector;
and weighting the second motor data according to the contribution degree to obtain the third motor data.
4. The motor speed detection method according to claim 1, wherein the training process of the motor speed detection model includes:
inputting the training set to a clustering layer to obtain a plurality of clustering clusters; the clustering layer clusters according to the motor rotation speed distribution characteristics in the training set;
distributing each data sample to the corresponding cluster according to the clustering result;
Inputting each cluster to a corresponding rotation speed detection sub-model; the rotational speed detection sub-model is subjected to parameter updating according to a loss function;
the loss function is expressed as: Wherein ω is represented as a weight vector of the rotational speed detection submodel; b represents an offset term of the rotational speed detection submodel; x is represented as the training set; c is expressed as a punishment parameter of the rotational speed detection sub-model; x i is represented as the ith training data in the cluster corresponding to the rotation speed detection sub-model, and X i epsilon X;
Setting a constraint on the loss function is: Wherein y i represents the motor rotation speed corresponding to the ith training data; sigma is expressed as a delivery variable of the rotational speed detection submodel; xi i is the boundary width corresponding to the ith training data;
Iterating the training process of the motor rotation speed detection model, and judging whether the model training stopping condition is met;
when model training conforming to the model training stopping condition is finished, obtaining the initial motor rotating speed detection model; continuing training when the model training stopping condition model is not met; the model training stopping condition is set to enable the loss of the motor rotating speed detection model to reach convergence.
5. A motor speed detection system, the system comprising: the motor speed sensor comprises a sensor and data acquisition unit, a motor data receiving unit, a data processing unit, a motor speed detection unit and a motor speed adjustment unit;
The sensor and data acquisition unit is used for acquiring various data of the target automobile through the current sensor, the voltage sensor, the vibration sensor and the temperature sensor; the motor data receiving unit is used for receiving the collected data; the data processing unit is used for preprocessing the received data; the motor rotation speed detection unit is used for detecting the motor rotation speed of the target automobile; and the motor rotating speed adjusting unit is used for optimally adjusting the motor rotating speed.
6. The motor speed detection system according to claim 5, wherein the data processing unit includes:
carrying out data cleaning on the motor data to obtain first motor data; wherein the data cleansing comprises: missing value filling, outlier processing and noise filtering;
normalizing the first motor data to obtain second motor data;
performing data enhancement on the second motor data to obtain third motor data;
And carrying out distributed processing on the third motor data to obtain standard motor data.
7. The motor speed detection system according to claim 6, wherein the data enhancement includes:
establishing a second motor data matrix according to the second motor data; wherein the second motor data matrix is expressed as: wherein, F NM is represented as the Mth characteristic parameter in the Nth second motor data; v N is the motor rotation speed value in the nth second television data;
calculating a variation difference value of each piece of data in the second motor data matrix; the variation difference value is obtained by subtracting a standard motor rotating speed parameter vector from data in the second motor data matrix; the standard motor rotating speed parameter vector is recorded as: (s 1,s2,…,sM,sv); s M is represented as an mth parameter value in the standard motor rotating speed parameter vector; s v is expressed as a motor rotation speed value in the standard motor rotation speed parameter vector;
According to the variation difference, a decision tree model is adopted to evaluate the contribution degree of each characteristic parameter to the variation difference of the motor rotation speed;
Wherein the variation difference is expressed as E NM is represented as a motor parameter variation difference value between an Mth characteristic parameter of the Nth data in the second motor data matrix and an Mth parameter value in the standard motor rotating speed parameter vector; e Nv is expressed as the motor rotation speed variation difference value between the motor rotation speed value of the Nth data in the second motor data matrix and the motor rotation speed value in the standard motor rotation speed parameter vector;
and weighting the second motor data according to the contribution degree to obtain the third motor data.
8. The motor speed detection system according to claim 5, wherein the motor speed detection unit employs a motor speed detection model; the training process of the motor rotation speed detection model comprises the following steps:
inputting the training set into a clustering layer to obtain a plurality of clustering clusters; the clustering layer clusters according to the motor rotation speed distribution characteristics in the training set;
distributing each data sample to the corresponding cluster according to the clustering result;
Inputting each cluster to a corresponding rotation speed detection sub-model; the rotational speed detection sub-model is subjected to parameter updating according to a loss function;
the loss function is expressed as: Wherein ω is represented as a weight vector of the rotational speed detection submodel; b represents an offset term of the rotational speed detection submodel; x is represented as the training set; c is expressed as a punishment parameter of the rotational speed detection sub-model; x i is represented as the ith training data in the cluster corresponding to the rotation speed detection sub-model, and X i epsilon X;
Setting a constraint on the loss function is: wherein y i represents the motor rotation speed corresponding to the ith training data; sigma is expressed as a relaxation variable of the rotational speed detection submodel; xi i is the boundary width corresponding to the ith training data;
iterating the training process of the motor rotation speed detection model, and judging whether the model training stopping condition is met;
When model training conforming to the model training stopping condition is finished, an initial motor rotating speed detection model is obtained; continuing training when the model training stopping condition model is not met; the model training stopping condition is set to enable the loss of the motor rotating speed detection model to reach convergence.
9. An electrical machine apparatus, comprising: the device comprises a sensing module, an acquisition module, a data processing module, a rotation speed detection module and a rotation speed adjustment module;
Wherein, the rotational speed detection module includes: acquiring a current motor rotating speed related characteristic parameter of a target automobile; inputting the relevant characteristic parameters of the current motor rotation speed to a clustering layer to obtain a clustering result; selecting a corresponding rotation speed detection sub-model according to the clustering result; the rotational speed detection submodel adopts a loss function to update parameters;
the loss function is expressed as: Wherein ω is represented as a weight vector of the rotational speed detection submodel; b represents an offset term of the rotational speed detection submodel; x is denoted as training set; c is expressed as a punishment parameter of the rotational speed detection sub-model; x i is represented as the ith training data in the cluster corresponding to the rotation speed detection sub-model, and X i epsilon X;
Setting a constraint on the loss function is: Wherein y i represents the motor rotation speed corresponding to the ith training data; sigma is expressed as a delivery variable of the rotational speed detection submodel; xi i is the boundary width corresponding to the ith training data;
and outputting the motor rotating speed of the target automobile according to the rotating speed detection submodel.
10. The motor apparatus of claim 9, wherein the rotational speed adjustment module comprises:
Setting a motor rotation speed optimization target and a motor rotation speed constraint condition of the target automobile;
Wherein, the motor rotation speed optimization target is expressed as: The motor rotation speed constraint condition is expressed as:
obtaining corresponding power output, power consumption and running state data by adopting a mathematical model according to the motor of the target automobile at different rotating speeds;
optimizing motor characteristic parameters by adopting an optimization algorithm, and detecting the rotating speed by using the motor rotating speed detection model;
And when the motor rotating speed optimization target is reached, obtaining the optimal rotating speed of the motor.
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