Optimized output method and device of motor efficiency, computer equipment and storage medium

Abstract

The invention relates to an optimized output method and device for motor efficiency, computer equipment and a storage medium. The method comprises the steps that N pieces of original efficiency data of a current collection cycle are acquired; determining an efficiency reference value of the current collection cycle according to the N pieces of original efficiency data; determining an efficiency deviation according to the efficiency reference value and a basic value of effective data, and when the efficiency deviation is smaller than a deviation threshold value, newly adding the original efficiency data of the current collection cycle to the effective data; and according to the newly added effective data, fitting to obtain an output model of the motor efficiency, and obtaining an output value of the motor efficiency based on the output model. By adopting the method provided by the invention, the problem of inaccurate efficiency evaluation caused by data fluctuation in the prior art can be improved.

Description

Technical Field

[0001] The present application relates to the field of testing technology, and in particular to a method, device, computer equipment and storage medium for optimizing output of motor efficiency. Background Art

[0002] For automotive motors, efficiency is a very important performance indicator. The measurement method and process of motor efficiency have a profound impact on the size of the measured data.

[0003] The general calculation method is to continuously record 20 or 30 different efficiency data from the beginning of the dot and then calculate the average value as the final output of the motor efficiency. This method ignores the efficiency deviation that may be caused by the change of working conditions during the operation of the motor. Moreover, in the actual operation of the motor, even under stable operating conditions, the real-time changes of the motor speed and torque, current and voltage will cause fluctuations in efficiency. In this way, the results of the efficiency calculation at different dot times may deviate greatly, which will have a bad impact on the performance evaluation of the motor. Summary of the invention

[0004] Based on this, a method, device, computer equipment and storage medium for optimizing output of motor efficiency are provided to improve the problem of inaccurate efficiency evaluation caused by data fluctuation in the prior art.

[0005] On the one hand, a method for optimizing output of motor efficiency is provided, comprising:

[0006] Get N raw efficiency data of the current acquisition cycle, where N is greater than or equal to 2;

[0007] Determine an efficiency reference value of a current acquisition cycle according to the N original efficiency data, the efficiency reference value being a value or an average value in the original efficiency data;

[0008] determining the efficiency deviation according to the efficiency reference value and the base value of the valid data, wherein the valid data is obtained according to the original efficiency of the historical collection cycle, and the base value of the valid data is the last value of the valid data;

[0009] Performing a threshold judgment based on the efficiency deviation, and when the efficiency deviation is less than the deviation threshold, adding the original efficiency data of the current acquisition cycle to the valid data;

[0010] According to the newly added effective data, an output model of the motor efficiency is obtained by fitting, and an output value of the motor efficiency is obtained based on the output model.

[0011] In one embodiment, N is greater than or equal to 3, and determining the efficiency reference value of the current acquisition cycle includes:

[0012] Obtaining a fluctuation amplitude according to the N original efficiency data;

[0013] When the fluctuation amplitude is less than or equal to a first fluctuation threshold, determining that the final value of the N original efficiency data is the efficiency reference value;

[0014] When the fluctuation amplitude is greater than the first fluctuation threshold, the interference extreme values ​​in the N original efficiency data are excluded, and the average is calculated to obtain the efficiency reference value;

[0015] The newly added valid data include:

[0016] The original efficiency data after eliminating the interference extreme value is added to the effective data.

[0017] In one embodiment, determining the efficiency reference value of the current acquisition cycle further includes:

[0018] When the fluctuation amplitude is greater than the second fluctuation threshold, the interference extreme values ​​in the N original efficiency data are excluded, and the number of original efficiency data is filled to N, and the efficiency reference value is obtained by averaging, wherein the second fluctuation threshold is greater than the first fluctuation threshold;

[0019] The newly added valid data include:

[0020] The original efficiency data after eliminating interference extreme values ​​and completing the quantity is added to the effective data.

[0021] In one embodiment, N is greater than or equal to 4, and determining the efficiency reference value of the current acquisition cycle further includes:

[0022] When the fluctuation amplitude is greater than the third fluctuation threshold, after excluding the maximum value, the second largest value, the second smallest value, and the minimum value of the N original efficiency data and padding the number of original efficiency data to N, the efficiency reference value is obtained by averaging, wherein the third fluctuation threshold is greater than the second fluctuation threshold;

[0023] The newly added valid data include:

[0024] Add the original efficiency data after excluding the maximum value, the second largest value, the second smallest value, the minimum value and filling in the quantity to the valid data.

[0025] In one embodiment, after obtaining the fluctuation amplitude, the method further includes:

[0026] When the fluctuation amplitude is greater than a fourth fluctuation threshold, the N original efficiency data of the current acquisition cycle are deleted, and the efficiency reference value is determined according to the N original efficiency data of the next acquisition cycle, wherein the fourth fluctuation threshold is greater than the third fluctuation threshold.

[0027] In one embodiment, after obtaining N original efficiency data of the current acquisition cycle, the method further includes:

[0028] It is determined whether the original efficiency data is greater than or equal to 100%. If so, the original efficiency data greater than or equal to 100% is deleted.

[0029] In one embodiment, the fitting to obtain an output model of motor efficiency includes:

[0030] Establish an initial model with time as the independent variable and motor efficiency as the dependent variable;

[0031] According to the effective data, based on the least square method, the coefficients in the initial model are determined to obtain the output model of the motor efficiency.

[0032] On the other hand, a device for optimizing output of motor efficiency is provided, the device comprising:

[0033] An acquisition module, used to acquire N raw efficiency data of the current acquisition cycle, where N is greater than or equal to 2;

[0034] An estimation module, configured to determine an efficiency reference value of a current acquisition cycle according to the N original efficiency data, wherein the efficiency reference value is a value or an average value in the original efficiency data;

[0035] a calculation module, for determining an efficiency deviation according to the efficiency reference value and a base value of valid data, wherein the valid data is obtained according to an original efficiency of a historical collection cycle, and the base value of the valid data is a final value of the valid data;

[0036] A supervision module, configured to perform a threshold judgment based on the efficiency deviation, and when the efficiency deviation is less than the deviation threshold, add the original efficiency data of the current acquisition cycle to the valid data;

[0037] The fitting module is used to fit the output model of the motor efficiency according to the newly added effective data, and obtain the output value of the motor efficiency based on the output model.

[0038] In another aspect, a computer device is provided, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the method is implemented when the processor executes the computer program.

[0039] A computer-readable storage medium is also provided, on which a computer program is stored, and when the computer program is executed by a processor, the method described above is implemented.

[0040] The above-mentioned motor efficiency optimization output method, device, computer equipment and storage medium obtain an efficiency reference value from N original efficiency data, obtain an efficiency deviation by comparing the efficiency reference value with the basic value of the effective data, and perform a threshold judgment on the efficiency deviation. When the efficiency deviation is less than the deviation threshold, the original efficiency data of the current acquisition cycle is added to the effective data. Through this process, data with abnormal fluctuations are excluded, so when fitting is performed based on the effective data, the impact of abnormally fluctuating data is reduced. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] Figure 1 An application environment diagram of a method for optimizing output of motor efficiency in one embodiment;

[0042] Figure 2 A schematic flow chart of a method for optimizing output of motor efficiency in one embodiment;

[0043] Figure 3 A structural block diagram of a gantry control system for implementing an optimization output method in one embodiment;

[0044] Figure 4 is a structural block diagram of an optimized output device for motor efficiency in one embodiment;

[0045] Figure 5 FIG. 4 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0046] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0047] As the core equipment of electric vehicles, the efficiency of motors directly affects energy consumption and vehicle endurance. Currently, the test methods for the efficiency of automotive motors can be roughly divided into the following three categories:

[0048] ① Direct test method: Install the motor on the test bench and determine the efficiency of the motor by measuring the input power and output power of the motor.

[0049] ② Indirect test method: By placing the powertrain system on a test bench or placing the whole vehicle on a wheel hub test bench, the efficiency of the motor is calculated based on the collected powertrain output shaft or wheel side output power and the estimated transmission system efficiency.

[0050] ③. Theoretical calculation method (also called calorimetry): The motor efficiency is calculated based on the loss theory by measuring the heat converted from the internal loss of the motor to the cooling medium.

[0051] The direct test method is intuitive, simple and relatively accurate, and can be used as the preferred choice for motor efficiency testing. However, even the direct test method with the highest measurement accuracy, whether it is a full-condition test or a comprehensive test condition based on NEDC (New Europe Drive Cycle), WLTC (World Light Vehicle Test Cycle) or CLTC (China Light Vehicle Test Cycle) or different weight ratios set by the OEM, its efficiency marking and calculation are based on the same time interval benchmark. After the power analyzer collects current, voltage, speed, torque and other signals at the set sampling frequency, it immediately performs real-time power and efficiency calculations and transmits the relevant data to the test bench data acquisition system. The calculated real-time data is not directly output. The test bench control system can start the output efficiency calculation by manual or program-controlled automatic marking.

[0052] The general calculation method is to continuously record 20 or 30 different efficiency data from the beginning of the dot and then calculate the average value as the final output of the motor efficiency value. This method ignores the efficiency deviation that may be caused by the change of working conditions during the operation of the motor. Moreover, in the actual operation of the motor, even under stable operating conditions, the real-time changes of the motor speed and torque, current and voltage will cause fluctuations in efficiency. In this way, the results of the efficiency calculation at different dot moments may have large deviations. There may even be some efficiency mutations or even points with efficiency greater than 100% in the test results, which will have a bad impact on the performance evaluation of the motor.

[0053] The present application provides a method for optimizing the output of motor efficiency, which improves the accuracy of efficiency calculation by excluding efficiency data with abnormal fluctuations and performing fitting calculations.

[0054] In one embodiment, the method for optimizing the output of motor efficiency is as follows: Figure 1 The test system shown is implemented, in which the battery simulator simulates the working state of the vehicle power battery and powers the motor controller. The motor controller provides three-phase AC power to the motor. The power analyzer collects the DC bus voltage and bus current of the motor controller under test, the three-phase AC voltage and three-phase AC current of the motor, and the speed and torque data output from the dynamometer shaft end, and sends them to the test bench control system through serial communication. The test bench control system records and calculates the output efficiency in real time.

[0055] It can be understood that the raw data sampled and calculated by the power analyzer is continuously sent to the bench control system, and the bench control system processes the raw data and outputs it.

[0056] The method for optimizing the output of the motor efficiency is as follows: Figure 2 As shown, the following steps are included:

[0057] Step 110, obtaining N original efficiency data of the current acquisition cycle.

[0058] During the actual implementation process, the power analyzer collects the DC voltage and DC current on the DC bus between the battery simulator and the motor controller, the three-phase AC voltage and AC current between the motor controller and the motor, and the speed and torque signals from the dynamometer at the set sampling frequency, and then operates on these data to calculate the real-time efficiency data. After receiving the real-time efficiency data transmitted by the power analyzer, the test bench control system uses it as the original efficiency data and determines whether the data is adopted and involved in the calculation of the final efficiency output value of the operating point based on the fluctuation of the original efficiency data.

[0059] It is worth mentioning that after the test bench control system obtains N original efficiency data of the current acquisition cycle, it determines whether the original efficiency data is greater than or equal to 100%, and if so, deletes it.

[0060] The original efficiency data is divided into batches according to time series, and N continuously collected data is regarded as a collection cycle, where N is greater than or equal to 2. For example, 10 original efficiency data are taken for processing in each collection cycle.

[0061] Step 120: Determine the efficiency reference value of the current acquisition cycle according to the N original efficiency data.

[0062] In the same acquisition cycle, there may still be certain fluctuations between the original efficiency data. The typical efficiency value that can represent the current acquisition cycle is selected as a reference for subsequent calculations.

[0063] For example, if the raw efficiency data fluctuates slightly in the current collection cycle, a value in the raw efficiency data can be selected as the efficiency reference value; if the raw efficiency data fluctuates greatly, an average value can be calculated as the efficiency reference value.

[0064] In one embodiment, the following efficiency reference value determination process is provided:

[0065] Step 201: Obtain the fluctuation amplitude Δeff according to 10 original efficiency data.

[0066] Exemplarily, the calculation of the fluctuation amplitude adopts the percentage change method or the standard deviation method, for example, the minimum value and the maximum value of 10 original efficiency data are selected, and the fluctuation amplitude Δeff=(maximum value-minimum value) / minimum value×100%.

[0067] Step 202: Select a method for determining an efficiency reference value based on the fluctuation amplitude.

[0068] For example, when the fluctuation amplitude is less than or equal to the first fluctuation threshold, the last value of 10 original efficiency data is determined as the efficiency reference value.

[0069] The first fluctuation threshold is, for example, 10%.

[0070] The fluctuation amplitude Δeff≤10% indicates that in the current acquisition cycle, the original efficiency data is relatively concentrated and stable, and the data quality is high. Therefore, one of the values ​​can be selected as the efficiency reference value. In this implementation, the last value of the 10 original efficiency data is used as the efficiency reference value.

[0071] When the fluctuation amplitude Δeff>10%, the average value can be used as the efficiency reference value. In particular, when calculating the average value, the interference extreme values ​​(maximum value, minimum value) in the 10 original efficiency data are excluded, and the remaining 8 original efficiency data are averaged to obtain the efficiency reference value.

[0072] By eliminating interfering extreme values ​​and reducing the impact of individual extreme data on the overall average, the average can more truly reflect the central trend of the data.

[0073] Furthermore, a second fluctuation threshold is set, and the second fluctuation threshold is greater than the first fluctuation threshold, for example 20%. When Δeff>20%, the data fluctuation in the acquisition cycle is greater. At this time, after excluding the maximum value and the minimum value, 2 data are sequentially added from the real-time power data provided by the power analyzer to make up the number to 10, and then the average value is calculated based on the newly combined 10 original efficiency data as the efficiency reference value.

[0074] In the actual implementation process, when 10%<Δeff≤20%, the deviation is relatively small. Removing the maximum and minimum values ​​and taking the average value can reduce the amount of calculation; when Δeff>20%, removing the extreme value and then entering two data and taking the average value can reduce the cumulative error a little.

[0075] It is understandable that when the 10 new combinations of original efficiency data are obtained, step 202 is executed again, the fluctuation amplitude Δeff is recalculated, and then a threshold judgment is performed on the new fluctuation amplitude Δeff to determine whether to exclude extreme values ​​again and whether to add new data after excluding extreme values.

[0076] It can be understood that in the above process, N is configured to be ≥3.

[0077] Furthermore, a third fluctuation threshold is set, and the third fluctuation threshold is greater than the second fluctuation threshold, for example 30%. When Δeff>30%, the data fluctuation in the acquisition cycle is greater. At this time, after excluding the maximum value, the second largest value, the second smallest value, and the minimum value, 4 data are sequentially added from the real-time power data provided by the power analyzer to make up the number to 10, and then the average value is calculated based on the newly combined 10 original efficiency data as the efficiency reference value.

[0078] It can be understood that the application condition of the above process includes configuring N≥4.

[0079] When the fluctuation amplitude is larger within the same collection cycle, the impact of the fluctuating data can be reduced by excluding the maximum value, the second largest value, the second smallest value, and the minimum value.

[0080] Furthermore, a fourth fluctuation threshold is set, which is greater than the third fluctuation threshold, for example, 50%. The fourth fluctuation threshold can be regarded as a critical value for evaluating whether the data in the acquisition cycle is valid or invalid. When Δeff>50%, it means that the fluctuation in the current acquisition cycle is too large, and all the data in the acquisition cycle are unreliable, then the 10 original efficiency data of the current acquisition cycle are deleted. The efficiency reference value is re-determined based on the 10 original efficiency data of the next acquisition cycle.

[0081] In summary, step 202 uses different calculation methods to determine the efficiency reference value based on different ranges of the fluctuation amplitude, and the process includes the following:

[0082] When Δeff≤10%, the last value among the 10 original efficiency data is used as the efficiency reference value;

[0083] When 10%<Δeff≤20%, remove the maximum and minimum values ​​and take the average value as the efficiency reference value;

[0084] When 20%<Δeff≤30%, remove the maximum and minimum values ​​and fill in two data, and calculate the average value as the efficiency reference value;

[0085] When 30%<Δeff≤50%, exclude the maximum value, the second largest value, the second smallest value, and the minimum value, and then fill in the 4 data to calculate the average value as the efficiency reference value;

[0086] When Δeff>50%, the original efficiency data of the current acquisition cycle is deleted.

[0087] In the above process, abnormal fluctuation points within the same acquisition cycle are eliminated by comparing the fluctuation amplitudes.

[0088] Step 130 , determining the efficiency deviation according to the efficiency reference value and the basic value of the valid data, wherein the basic value of the valid data is the last value of the valid data.

[0089] Among them, the valid data is obtained according to the original efficiency of the historical collection cycle. For example, after the historical collection cycle is carried out according to step 202 and abnormal fluctuation points are excluded, the remaining original efficiency data is recorded and stored to obtain valid data, and the last value entered in the last batch is used as the basic value of the valid data.

[0090] The efficiency deviation is obtained by subtracting the efficiency reference value from the basic value of the effective data. The efficiency deviation reflects the degree of fluctuation between the current collection cycle and the historical collection cycle.

[0091] Step 140 , performing a threshold determination based on the efficiency deviation, and when the efficiency deviation is less than the deviation threshold, adding the original efficiency data of the current acquisition cycle to the valid data.

[0092] The deviation threshold is set based on the actual situation. When the efficiency deviation is greater than or equal to the deviation threshold, it indicates that the original efficiency data of the current acquisition cycle fluctuates greatly compared with the existing valid data, which is not conducive to subsequent calculations. Therefore, when the efficiency deviation is less than the deviation threshold, the original efficiency data of the current acquisition cycle is added to the valid data.

[0093] Based on the aforementioned step 202 , if the original efficiency data of the current acquisition cycle has data exclusion or data addition, the newly combined original efficiency data is added to the valid data.

[0094] The above steps 110, 120, 130, and 140 may be executed repeatedly for several times, and valid data may be accumulated, and each time the last data of the newly entered data is used as the basic value of the valid value.

[0095] Step 150: fitting and obtaining an output model of the motor efficiency according to the newly added effective data, and obtaining an output value of the motor efficiency based on the output model.

[0096] In the actual implementation process, after the efficiency dot instruction is obtained, all the recorded and stored valid data are extracted, and the output model of the motor efficiency is calculated by linear fitting. For example, the output model is obtained by linear fitting using the least squares method. The least squares method finds a set of estimated values ​​so that the actual value and the estimated value are as similar as possible and the distance is minimized. The process is exemplified as follows:

[0097] S1, establish an initial model with time as the independent variable and motor efficiency as the dependent variable.

[0098] S2, determining the coefficients in the initial model according to the effective data based on the least square method, and obtaining the output model of the motor efficiency.

[0099] For step S1, the initial model is exemplarily a linear system of equations yk =θ1+θ2x k , where the independent variable x k represents time, the dependent variable y k represents the estimator of efficiency, and θ1 and θ2 are the coefficients of the initial model.

[0100] For step S2, calculate the variance of the linear equation system f(θ1,θ2)=Σ(yy k )^2, minimize the sum of squares of errors, where y is the actual value.

[0101] For example, there are five data points in the valid data: (10, 0.23), (11, 0.21), (12, 0.25), (13, 0.27), (14, 0.26). We hope to find a straight line with the shortest distance between the five points. The mathematical expression of the straight line is defined as y = ax + b, where a and b are equivalent to the above θ1 and θ2. The least squares method requires the variance value S to be minimized, that is, the following mathematical expression is minimized:

[0102] S(a,b)=[0.23-y1] 2 +[0.21-y2] 2 +[0.25-y3] 2 +[0.27-y4] 2 +[0.26-y5] 2

[0103] Among them, y1=10a+b, y2=11a+b, y3=12a+b, y4=13a+b, y5=14a+b.

[0104] The minimum value can be obtained by taking the partial derivative of S(a,b) and making its partial derivative Then we can get the quadratic equation for solving unknown variables a and b:

[0105]

[0106] By calculating the above two-variable linear equation, the values ​​of a and b can be obtained, that is, the coefficients of the initial model can be obtained, and the output model of the motor efficiency can be obtained.

[0107] After fitting and obtaining the output model of the motor efficiency, the closest next efficiency data can be calculated, and the efficiency data at the next moment can be used as the output value of the motor efficiency.

[0108] In one embodiment, the gantry control system is configured as follows Figure 3The system structure shown in the figure includes a controller Controller, a memory Plant, an estimator Estimator and a supervisor. The memory Plant stores the original efficiency data calculated and the valid data to be output after receiving the dot instruction. It is divided into three areas: 1. Collection area, used to store the original efficiency data sent by the controller; 2. Observation area deadzone, used to temporarily store data whose validity is not clear (used to identify noise interference); 3. Valid area, used to store valid data.

[0109] The controller completes the following tasks: ①, receiving data from the power analyzer and sending the efficiency data to the collection area of ​​the memory Plant; ②, judging the validity of the data; ③, determining whether to delete the previous data based on the calculation of subsequent data; ④, after obtaining the efficiency dot instruction, outputting data from the Plant effective area to perform least squares efficiency calculation and determine the final output value.

[0110] The estimator is a computational model constructed under different circumstances. It is divided into 5 computational models according to the different fluctuations of the 10 original efficiency data sent by the controller. For example, Estimator1 is applied when the fluctuation amplitude Δeff≤10%, Estimator2 is applied when the fluctuation amplitude 10%<Δeff≤20%..., and the output Y^ is the efficiency reference value calculated under different computational models. It will be compared with the basic value Y of the previous valid data output by Plant to obtain an efficiency deviation ε. A deviation threshold is set to compare with the efficiency deviation ε to determine whether the data in the collection area directly enters the valid area or enters the observation area deadzone.

[0111] The system operates as follows:

[0112] Through the power analyzer, the controller Controller obtains the original efficiency data, such as C10, C9, ..., C1. The original efficiency data will be transmitted to the collection area of ​​the memory Plant, and will also be transmitted to the estimator Estimator. The estimator will first determine the effectiveness of the efficiency, and directly delete the efficiency value ≥100%, and continue to input the subsequent data.

[0113] Each time 10 data are received, they are divided into 5 calculation models according to the fluctuation amplitude of the 10 efficiency data:

[0114] 1. When the fluctuation amplitude Δeff of the 10 data sent by the Controller is ≤10%, Y^ uses the last original efficiency data as the efficiency reference value and compares it with the basic value Y of the effective data. The efficiency deviation ε is sent to the supervisor, and the supervisor will generate a judgment instruction δ to indicate whether the data sent to the Plant collection area will enter the deadzone or the effective zone. If the efficiency deviation ε is greater than or equal to the deviation threshold, it will indicate that the data in the collection area will be sent to the deadzone (for example, the original efficiency data B10, B9, ..., B1 of the historical collection cycle), and continue to observe the fluctuation of the efficiency data in the next data collection cycle. If the efficiency deviation ε is less than the deviation threshold, the data in the collection area will be moved into the effective zone (for example, the original efficiency data A10, A9, ..., A1 of the historical collection cycle). At the same time, the last data C10 will be output as Y for comparison with the next batch of data to generate the efficiency deviation ε and identify the noise interference.

[0115] 2. If the fluctuation range of 10 data is 10%<Δeff≤20%, Y^ will take the average value of 8 data after removing the maximum and minimum values ​​as Y ε , it will be compared with the previous Y, and the efficiency deviation ε is sent to the Supervisor to generate the judgment instruction δ to confirm whether the input data is valid data. If valid, the data without the maximum and minimum values ​​is moved into the valid area, otherwise it is judged as data to be observed entering the deadzone.

[0116] 3. If the fluctuation amplitude of the 10 data is 20%<Δeff≤30%, Y^ will remove the maximum and minimum values ​​and continue to add 2 data and calculate the average value Y^ of the 10 data again. The efficiency deviation ε is sent to the Supervisor to generate a judgment instruction δ to confirm whether the data sent is valid data. If the data is valid, the data sent to the efficiency collection area (similarly remove the maximum and minimum values ​​and continue to add 2 data) will be moved to the data valid area; otherwise, it will be moved to the deadzone observation area.

[0117] 4. If the fluctuation amplitude of the 10 data is 30%<Δeff≤50%, the Estimator will remove the maximum value, the second largest value, the minimum value, and the second smallest value and continue to input 4 efficiency data, and calculate the average value of these 10 values ​​to generate Y^, which is compared with the basic value Y calculated in the previous cycle. The efficiency deviation ε is sent to the supervisor to generate a judgment instruction δ, which is used to determine whether the data enters the deadzone.

[0118] 5. If the fluctuation amplitude Δeff of the 10 data is greater than 50%, the 10 data sent in will be deleted and the next 10 data will be directly sent to the collection area of ​​Plant.

[0119] In the above process, data in the effective area is continuously accumulated. After the controller receives the efficiency management instruction, the controller inputs the data from the effective area and calculates the output value of the motor efficiency corresponding to this working condition by the least squares linear fitting method.

[0120] It should be understood that although Figure 2 The steps in the flowchart are shown in sequence as indicated by the arrows, but these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified in this document, there is no strict order restriction for the execution of these steps, and these steps can be executed in other orders. Moreover, Figure 2 At least part of the steps may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily executed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be executed in turn or alternately with other steps or at least part of the sub-steps or stages of other steps.

[0121] In one embodiment, Figure 4 As shown, a device for optimizing output of motor efficiency is provided, comprising: an acquisition module 301, an estimation module 302, a calculation module 303, a supervision module 304 and a fitting module 305, wherein:

[0122] An acquisition module 301 is used to acquire N original efficiency data of a current acquisition cycle, where N is greater than or equal to 2;

[0123] An estimation module 302 is used to determine an efficiency reference value of a current acquisition cycle according to the N original efficiency data, wherein the efficiency reference value is a value or an average value in the original efficiency data;

[0124] A calculation module 303, configured to determine an efficiency deviation according to the efficiency reference value and a base value of valid data, wherein the valid data is obtained according to the original efficiency of a historical collection cycle, and the base value of the valid data is the last value of the valid data;

[0125] A monitoring module 304 is used to perform a threshold judgment based on the efficiency deviation, and when the efficiency deviation is less than the deviation threshold, the original efficiency data of the current acquisition cycle is added to the valid data;

[0126] The fitting module 305 is used to fit the output model of the motor efficiency according to the newly added effective data, and obtain the output value of the motor efficiency based on the output model.

[0127] The above-mentioned device obtains an efficiency reference value from N original efficiency data, obtains an efficiency deviation by comparing the efficiency reference value with the basic value of the effective data, and performs a threshold judgment on the efficiency deviation. When the efficiency deviation is less than the deviation threshold, the original efficiency data of the current acquisition cycle is added to the effective data. Through this process, data with abnormal fluctuations are excluded, so when fitting is performed based on the effective data, the impact of abnormally fluctuating data is reduced.

[0128] In one embodiment, N is greater than or equal to 3, and the estimation module 302 obtains the fluctuation amplitude according to the N original efficiency data; when the fluctuation amplitude is less than or equal to the first fluctuation threshold, the final value of the N original efficiency data is determined as the efficiency reference value; when the fluctuation amplitude is greater than the first fluctuation threshold, the interference extreme value in the N original efficiency data is excluded, and the efficiency reference value is obtained by averaging. The supervision module 304 adds the original efficiency data after excluding the interference extreme value to the valid data.

[0129] In one embodiment, when the fluctuation amplitude is greater than the second fluctuation threshold, the interference extreme values ​​in the N original efficiency data are excluded, the number of original efficiency data is padded to N, and the efficiency reference value is obtained by averaging, wherein the second fluctuation threshold is greater than the first fluctuation threshold. The supervision module 304 adds the original efficiency data after excluding the interference extreme values ​​and padded to the valid data.

[0130] In one embodiment, N is greater than or equal to 4. When the fluctuation amplitude is greater than the third fluctuation threshold, after excluding the maximum value, the second largest value, the second smallest value, and the minimum value of the N original efficiency data and padding the number of original efficiency data to N, the efficiency reference value is obtained by averaging, wherein the third fluctuation threshold is greater than the second fluctuation threshold, and the supervision module 304 adds the original efficiency data after excluding the maximum value, the second largest value, the second smallest value, and the minimum value and padding the number to the valid data.

[0131] The estimation module 302 also deletes N original efficiency data of the current acquisition cycle when the fluctuation amplitude is greater than a fourth fluctuation threshold, and determines the efficiency reference value according to N original efficiency data of the next acquisition cycle, wherein the fourth fluctuation threshold is greater than the third fluctuation threshold.

[0132] In one embodiment, the estimation module 302 determines whether the original efficiency data is greater than or equal to 100%, and if so, deletes it.

[0133] The fitting module 305 establishes an initial model with time as the independent variable and motor efficiency as the dependent variable; determines the coefficients in the initial model based on the least square method according to the effective data, and obtains the output model of the motor efficiency.

[0134] The specific definition of the optimized output device of motor efficiency can be found in the definition of the optimized output method of motor efficiency mentioned above, which will not be repeated here. Each module in the above-mentioned optimized output device of motor efficiency can be implemented in whole or in part by software, hardware and a combination thereof. The above-mentioned modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory of the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above modules.

[0135] In one embodiment, a computer device is provided. The computer device may be a terminal, and its internal structure diagram may be as follows: Figure 5 As shown. The computer device includes a processor, a memory, a network interface, a display screen and an input device connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The network interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, an optimized output method for motor efficiency is realized. The display screen of the computer device can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device can be a touch layer covered on the display screen, or a key, trackball or touchpad set on the computer device housing, or an external keyboard, touchpad or mouse, etc.

[0136] Those skilled in the art will understand that Figure 5 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0137] In one embodiment, a computer device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the following steps when executing the computer program:

[0138] Get N raw efficiency data of the current acquisition cycle, where N is greater than or equal to 2;

[0139] Determine an efficiency reference value of a current acquisition cycle according to the N original efficiency data, the efficiency reference value being a value or an average value in the original efficiency data;

[0140] determining the efficiency deviation according to the efficiency reference value and the base value of the valid data, wherein the valid data is obtained according to the original efficiency of the historical collection cycle, and the base value of the valid data is the last value of the valid data;

[0141] Performing a threshold judgment based on the efficiency deviation, and when the efficiency deviation is less than the deviation threshold, adding the original efficiency data of the current acquisition cycle to the valid data;

[0142] According to the newly added effective data, an output model of the motor efficiency is obtained by fitting, and an output value of the motor efficiency is obtained based on the output model.

[0143] In one embodiment, when the processor executes the computer program, the following steps are also implemented:

[0144] N is greater than or equal to 3, and determining the efficiency reference value of the current acquisition cycle includes:

[0145] Obtaining a fluctuation amplitude according to the N original efficiency data;

[0146] When the fluctuation amplitude is less than or equal to a first fluctuation threshold, determining that the final value of the N original efficiency data is the efficiency reference value;

[0147] When the fluctuation amplitude is greater than the first fluctuation threshold, the interference extreme values ​​in the N original efficiency data are excluded, and the average is calculated to obtain the efficiency reference value;

[0148] The newly added valid data include:

[0149] The original efficiency data after eliminating the interference extreme value is added to the effective data.

[0150] In one embodiment, when the processor executes the computer program, the following steps are also implemented:

[0151] The step of determining the efficiency reference value of the current acquisition cycle further includes:

[0152] When the fluctuation amplitude is greater than the second fluctuation threshold, the interference extreme values ​​in the N original efficiency data are excluded, and the number of original efficiency data is filled to N, and the efficiency reference value is obtained by averaging, wherein the second fluctuation threshold is greater than the first fluctuation threshold;

[0153] The newly added valid data include:

[0154] The original efficiency data after eliminating interference extreme values ​​and completing the quantity is added to the effective data.

[0155] In one embodiment, when the processor executes the computer program, the following steps are also implemented:

[0156] N is greater than or equal to 4, and determining the efficiency reference value of the current acquisition cycle further includes:

[0157] When the fluctuation amplitude is greater than the third fluctuation threshold, after excluding the maximum value, the second largest value, the second smallest value, and the minimum value of the N original efficiency data and padding the number of original efficiency data to N, the efficiency reference value is obtained by averaging, wherein the third fluctuation threshold is greater than the second fluctuation threshold;

[0158] The newly added valid data include:

[0159] Add the original efficiency data after excluding the maximum value, the second largest value, the second smallest value, the minimum value and filling in the quantity to the valid data.

[0160] In one embodiment, when the processor executes the computer program, the processor further implements the following steps:

[0161] After obtaining the fluctuation amplitude, the method further includes:

[0162] When the fluctuation amplitude is greater than a fourth fluctuation threshold, the N original efficiency data of the current acquisition cycle are deleted, and the efficiency reference value is determined according to the N original efficiency data of the next acquisition cycle, wherein the fourth fluctuation threshold is greater than the third fluctuation threshold.

[0163] In one embodiment, when the processor executes the computer program, the processor further implements the following steps:

[0164] It is determined whether the original efficiency data is greater than or equal to 100%. If so, the original efficiency data greater than or equal to 100% is deleted.

[0165] In one embodiment, when the processor executes the computer program, the processor further implements the following steps:

[0166] Establish an initial model with time as the independent variable and motor efficiency as the dependent variable;

[0167] According to the effective data, based on the least square method, the coefficients in the initial model are determined to obtain the output model of the motor efficiency.

[0168] In one embodiment, a computer readable storage medium is provided, on which a computer program is stored, and when the computer program is executed by a processor, the following steps are implemented:

[0169] Get N raw efficiency data of the current acquisition cycle, where N is greater than or equal to 2;

[0170] Determine an efficiency reference value of a current acquisition cycle according to the N original efficiency data, the efficiency reference value being a value or an average value in the original efficiency data;

[0171] determining the efficiency deviation according to the efficiency reference value and the base value of the valid data, wherein the valid data is obtained according to the original efficiency of the historical collection cycle, and the base value of the valid data is the last value of the valid data;

[0172] Performing a threshold judgment based on the efficiency deviation, and when the efficiency deviation is less than the deviation threshold, adding the original efficiency data of the current acquisition cycle to the valid data;

[0173] According to the newly added effective data, an output model of the motor efficiency is obtained by fitting, and an output value of the motor efficiency is obtained based on the output model.

[0174] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:

[0175] N is greater than or equal to 3, and determining the efficiency reference value of the current acquisition cycle includes:

[0176] Obtaining a fluctuation amplitude according to the N original efficiency data;

[0177] When the fluctuation amplitude is less than or equal to a first fluctuation threshold, determining that the final value of the N original efficiency data is the efficiency reference value;

[0178] When the fluctuation amplitude is greater than the first fluctuation threshold, the interference extreme values ​​in the N original efficiency data are excluded, and the average is calculated to obtain the efficiency reference value;

[0179] The newly added valid data include:

[0180] The original efficiency data after eliminating the interference extreme value is added to the effective data.

[0181] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:

[0182] The step of determining the efficiency reference value of the current acquisition cycle further includes:

[0183] When the fluctuation amplitude is greater than the second fluctuation threshold, the interference extreme values ​​in the N original efficiency data are excluded, and the number of original efficiency data is filled to N, and the efficiency reference value is obtained by averaging, wherein the second fluctuation threshold is greater than the first fluctuation threshold;

[0184] The newly added valid data include:

[0185] The original efficiency data after eliminating interference extreme values ​​and completing the quantity is added to the effective data.

[0186] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:

[0187] N is greater than or equal to 4, and determining the efficiency reference value of the current acquisition cycle further includes:

[0188] When the fluctuation amplitude is greater than the third fluctuation threshold, after excluding the maximum value, the second largest value, the second smallest value, and the minimum value of the N original efficiency data and padding the number of original efficiency data to N, the efficiency reference value is obtained by averaging, wherein the third fluctuation threshold is greater than the second fluctuation threshold;

[0189] The newly added valid data include:

[0190] Add the original efficiency data after excluding the maximum value, the second largest value, the second smallest value, the minimum value and filling in the quantity to the valid data.

[0191] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:

[0192] After obtaining the fluctuation amplitude, the method further includes:

[0193] When the fluctuation amplitude is greater than a fourth fluctuation threshold, the N original efficiency data of the current acquisition cycle are deleted, and the efficiency reference value is determined according to the N original efficiency data of the next acquisition cycle, wherein the fourth fluctuation threshold is greater than the third fluctuation threshold.

[0194] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:

[0195] After obtaining the N original efficiency data of the current acquisition cycle, the method further includes:

[0196] It is determined whether the original efficiency data is greater than or equal to 100%. If so, the original efficiency data greater than or equal to 100% is deleted.

[0197] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:

[0198] The fitting to obtain the output model of the motor efficiency includes:

[0199] Establish an initial model with time as the independent variable and motor efficiency as the dependent variable;

[0200] According to the effective data, based on the least square method, the coefficients in the initial model are determined to obtain the output model of the motor efficiency.

[0201] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0202] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0203] The above-mentioned embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the invention patent. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the patent of the present application shall be subject to the attached claims.

Claims

1. A method for optimizing output of motor efficiency, characterized in that: include: Get N raw efficiency data of the current acquisition cycle, where N is greater than or equal to 2; Determine an efficiency reference value of a current acquisition cycle according to the N original efficiency data, the efficiency reference value being a value or an average value in the original efficiency data; determining the efficiency deviation according to the efficiency reference value and the base value of the valid data, wherein the valid data is obtained according to the original efficiency of the historical collection cycle, and the base value of the valid data is the last value of the valid data; Performing a threshold judgment based on the efficiency deviation, and when the efficiency deviation is less than the deviation threshold, adding the original efficiency data of the current acquisition cycle to the valid data; According to the newly added effective data, an output model of the motor efficiency is obtained by fitting, and an output value of the motor efficiency is obtained based on the output model.

2. The method for optimizing output of motor efficiency according to claim 1, characterized in that: N is greater than or equal to 3, and determining the efficiency reference value of the current acquisition cycle includes: Obtaining a fluctuation amplitude according to the N original efficiency data; When the fluctuation amplitude is less than or equal to a first fluctuation threshold, determining that the final value of the N original efficiency data is the efficiency reference value; When the fluctuation amplitude is greater than the first fluctuation threshold, the interference extreme values ​​in the N original efficiency data are excluded, and the average is calculated to obtain the efficiency reference value; The newly added valid data include: The original efficiency data after eliminating the interference extreme value is added to the effective data.

3. The method for optimizing output of motor efficiency according to claim 2, characterized in that: The step of determining the efficiency reference value of the current acquisition cycle further includes: When the fluctuation amplitude is greater than the second fluctuation threshold, the interference extreme values ​​in the N original efficiency data are excluded, and the number of original efficiency data is filled to N, and the efficiency reference value is obtained by averaging, wherein the second fluctuation threshold is greater than the first fluctuation threshold; The newly added valid data include: The original efficiency data after eliminating interference extreme values ​​and completing the quantity is added to the effective data.

4. The method for optimizing output of motor efficiency according to claim 3, characterized in that: N is greater than or equal to 4, and determining the efficiency reference value of the current acquisition cycle further includes: When the fluctuation amplitude is greater than the third fluctuation threshold, after excluding the maximum value, the second largest value, the second smallest value, and the minimum value of the N original efficiency data and padding the number of original efficiency data to N, the efficiency reference value is obtained by averaging, wherein the third fluctuation threshold is greater than the second fluctuation threshold; The newly added valid data include: Add the original efficiency data after excluding the maximum value, the second largest value, the second smallest value, the minimum value and filling in the quantity to the valid data.

5. The method for optimizing output of motor efficiency according to claim 4, characterized in that: After obtaining the fluctuation amplitude, the method further includes: When the fluctuation amplitude is greater than a fourth fluctuation threshold, the N original efficiency data of the current acquisition cycle are deleted, and the efficiency reference value is determined according to the N original efficiency data of the next acquisition cycle, wherein the fourth fluctuation threshold is greater than the third fluctuation threshold.

6. The method for optimizing output of motor efficiency according to claim 1, characterized in that: After obtaining the N original efficiency data of the current acquisition cycle, the method further includes: It is determined whether the original efficiency data is greater than or equal to 100%. If so, the original efficiency data greater than or equal to 100% is deleted.

7. The method for optimizing output of motor efficiency according to claim 1, characterized in that: The fitting to obtain the output model of the motor efficiency includes: Establish an initial model with time as the independent variable and motor efficiency as the dependent variable; According to the effective data, based on the least square method, the coefficients in the initial model are determined to obtain the output model of the motor efficiency.

8. A device for optimizing output of motor efficiency, characterized in that: The device comprises: An acquisition module, used to acquire N raw efficiency data of the current acquisition cycle, where N is greater than or equal to 2; An estimation module, used to determine an efficiency reference value of a current acquisition cycle according to the N original efficiency data, wherein the efficiency reference value is a value or an average value in the original efficiency data; a calculation module, for determining an efficiency deviation according to the efficiency reference value and a base value of valid data, wherein the valid data is obtained according to an original efficiency of a historical collection cycle, and the base value of the valid data is a final value of the valid data; A supervision module, configured to perform a threshold judgment based on the efficiency deviation, and when the efficiency deviation is less than the deviation threshold, add the original efficiency data of the current acquisition cycle to the valid data; The fitting module is used to fit the output model of the motor efficiency according to the newly added effective data, and obtain the output value of the motor efficiency based on the output model.

9. A computer device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the method according to any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 7 is implemented.

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