Python-based INCA measurement parameter response speed calculation method

Abstract

The invention discloses a Python-based INCA measurement parameter response speed calculation method. The method comprises the following steps: S1, configuring a Python environment, and importing a Python module necessary for the whole calculation process; s2, INCA measurement data are read; s3, calculating the probability density of the measurement data of each point in the whole test process by adopting a kernel density estimation (KDE) algorithm, and finding extreme points of upper and lower thresholds of the measurement data according to a solved probability density function; s4, identifying a steady state stage in the response process of the whole test; and S5, filtering out the test data in the steady-state stage in the test data, and reserving other test data in the response process as dynamic data. According to the method, the problems of tedious process and low efficiency of manually calculating the response speed of the INCA test data in the prior art are solved, and the response speed of the specified measurement parameter in the INCA test data can be automatically, quickly and accurately calculated.

Description

Technical Field

[0001] The present invention relates to the field of vehicle powertrain testing, and particularly to a calculation method for the response speed of INCA measurement parameters based on Python. Background Art

[0002] Common automotive calibration tools mainly include INCA, CANape, ECU, etc. INCA is a basic product under ETAS, which can achieve interface interaction with other test platforms, HIL systems, etc., has comprehensive test and calibration functions, supports protocols such as CCP or XCP, can manage calibration data, can be used for data acquisition, calibration, ECU flash programming ProF integration, can be used for an oscilloscope with graphical strategy data display, and has powerful functions such as self-programming of the interface.

[0003] In engine development, INCA is an important tool for powertrain calibration testing. The test data of INCA is required to test the response speed of powertrain components (such as the engine oil pump) under actual working conditions to verify whether the part design meets the design requirements under system integration conditions. However, currently, calculating the response speed requires a dedicated INCA data reading software. When calculating the response speed of measurement parameters, it is necessary to manually find the start and end times of the measurement parameters and perform manual calculations. This process is cumbersome and inefficient. Summary of the Invention

[0004] The present invention aims to provide a calculation method for the response speed of INCA measurement parameters based on Python to solve the problems of cumbersome process and low efficiency in manually calculating the response speed of INCA test data in the prior art, and to achieve automatic, fast and accurate calculation of the response speed of specified measurement parameters in INCA test data.

[0005] To achieve the above object, the present invention provides a calculation method for the response speed of INCA measurement parameters based on Python, including the following steps.

[0006] S1. Configure the Python environment and import the necessary python modules for the entire calculation process;

[0007] S2. Read the INCA measurement data;

[0008] S3. Use the kernel density estimation algorithm (KDE) to calculate the probability density of the measurement data at each point during the entire test process. The formula is:

[0009]

[0010] h = 1.06σn -1 / 5 (2)

[0011] Where f(x) represents the probability density function, n represents the number of measurement data during the entire test process, and x i (i = 1, 2…n) is the true value of the measurement data, and the value of σ is the standard deviation of the measurement data;

[0012] According to the obtained probability density function, find the extreme points of the upper and lower thresholds of the measurement data;

[0013] S4. Identify the steady-state stage in the response process of the entire test: Using the extreme point of the upper threshold of the measurement data as the upper threshold boundary, when the true value x of the measurement data i is greater than or less than the preset value A of the upper threshold boundary, the test process is the steady-state stage of the upper threshold; using the extreme point of the lower threshold of the measurement data as the lower threshold boundary, when the true value x of the measurement data i is greater than or less than the preset value B of the upper threshold boundary, the test process is the steady-state stage of the lower threshold;

[0014] S5. Filter out the test data in the steady-state stage from the test data, and retain other test data in the response process as dynamic data;

[0015] S6. Determine the start and end of each response stage:

[0016] Store the time information of the dynamic data in the response process into the array time_list, and perform difference calculation on the adjacent time data in the time_list array: y(i) = time_list[i] - time_list[i - 1],

[0017] y(i) is the time difference of the i-th point, and [i] is the time of the i-th point;

[0018] If y(i) is greater than the time difference between adjacent two points, and, greater than or equal to the minimum measurement time interval of the steady-state stage, then, time_list[i - 1] and time_list[i] are the start / end times of the response stage in this test process;

[0019] Store the start / end values of time_list and the start / end times obtained by the difference method into the array s in the time series. In the array s, s[2n] is the start time of the response stage, and s[2n + 1] is the end time of the response stage;

[0020] S7. Calculate the response speed of the measurement parameter:

[0021] Let the measurement data value corresponding to the time s[2n] be u[2n], and the measurement data value corresponding to the time s[2n + 1] be u[2n + 1]. The response speed g of the measurement parameter is calculated according to the following formula:

[0022] g = [u(2n) - u(2n + 1)] / [s(2n) - s(2n + 1)].

[0023] Further, the Python module includes: an asammdf module for reading INCA measurement data; a pandas module for subsequent calculations.

[0024] Further, the preset value A is not equal to the preset value B.

[0025] Further, the test data of INCA are time-sequential continuous data, with the time intervals of each measurement point being consistent and inversely proportional to the test frequency set by INCA.

[0026] A calculation method for the response speed of INCA measurement parameters based on Python according to the present invention obtains steady-state / dynamic response data through INCA measurement, uses the KDE method combined with extreme points to filter static data, then identifies the start / end moments of the dynamic response by the difference method, and finally calculates the response speed of the INCA measurement parameters.

[0027] This method can realize the reading and calculation of INCA measurement data in a Python environment, without the need for other relevant third-party commercial software, reducing costs and dependence on specific software.

[0028] This method can effectively distinguish between the steady-state and dynamic data of INCA measurement data, improving the accuracy of data processing.

[0029] This method can effectively identify the start / end moments, providing key support for accurately calculating the response speed.

[0030] This method can realize the rapid calculation of the response of INCA measurement parameters, with good operability and passability, greatly improving work efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] To better understand the above and other objects, features, advantages, and functions of the present invention, reference may be made to the embodiments shown in the drawings. Those skilled in the art should understand that the drawings are intended to schematically illustrate the preferred embodiments of the present invention and have no restrictive effect on the scope of the present invention. The components in the drawings are not drawn to scale.

[0032] Figure 1 It is a flowchart of a calculation method for the response speed of INCA measurement parameters based on Python according to the present invention.

[0033] Figure 2 It is a sample of INCA test data, showing the situation of steady-state data and dynamic data included in the INCA test data.

[0034] Figure 3 It is a flowchart for calculating the response speed of INCA measurement parameters, clearly presenting the various step processes of the method of the present invention.

[0035] Figure 4 It is the probability density and extreme points of the test sample, intuitively showing the probability density and extreme points obtained by the KDE algorithm, and is used to determine the upper and lower boundaries of the steady-state threshold.

[0036] Figure 5 It is the data after screening, reflecting the result of screening the dynamic-phase data according to the upper and lower boundaries obtained by the KDE algorithm.

[0037] Figure 6 It is a schematic diagram of identifying the start / end time of dynamic data by difference, explaining the principle and process of identifying the start / end time of dynamic data by the difference method. Specific Embodiments

[0038] The following describes exemplary embodiments of the present disclosure with reference to the accompanying drawings. Various details of the embodiments of the present disclosure are included to facilitate understanding, and they should be considered merely exemplary. Therefore, those of ordinary skill in the art should recognize that various changes and modifications can be made to the embodiments described herein without departing from the scope and spirit of the present disclosure. Similarly, descriptions of well-known functions and structures are omitted below for clarity and conciseness.

[0039] As used herein, the term "including" and its variants mean open inclusion, that is, "including but not limited to". Unless otherwise stated, the term "or" means "and / or". The term "based on" means "at least partially based on". The terms "an exemplary embodiment" and "an embodiment" mean "at least one exemplary embodiment". The term "another embodiment" means "at least one additional embodiment". The terms "first", "second", etc. may refer to different or the same objects. There may be other explicit and implicit definitions below.

[0040] To at least partially solve one or more of the above problems and other potential problems, embodiments of the present disclosure propose a calculation method for the response speed of INCA measurement parameters based on Python, as Figure 1 shown, and the method includes the following steps.

[0041] S1. Configure the Python environment and import the necessary Python modules for the entire calculation process. The Python modules include: the asammdf module for reading INCA measurement data; the pandas module for subsequent calculations;

[0042] S2. Read the INCA measurement data; as Figure 2As shown, it is a sample of INCA test data, demonstrating the steady-state data and dynamic data included in the INCA test data.

[0043] S3. Use the kernel density estimation algorithm (KDE) to calculate the probability density of the measurement data at each point during the entire test. The formula is:

[0044]

[0045] h = 1.06σn -1 / 5 (2)

[0046] In the formula, f(x) represents the probability density function, n represents the number of measurement data during the entire test, x i (i = 1, 2…n) is the true value of the measurement data, and the σ value is the standard deviation of the measurement data;

[0047] Based on the obtained probability density function, find the extreme points of the upper and lower thresholds of the measurement data;

[0048] As Figure 3 shown, it is the probability density and extreme points of the test sample, intuitively showing that there are extreme points near the upper threshold of 23 and also near the lower threshold of -58 for the probability density obtained by the KDE algorithm.

[0049] S4. Identify the steady-state stage in the response process of the entire test: Take the extreme point near the upper threshold of 23 of the measurement data as the upper threshold boundary. The test process when the true value x of the measurement data i is greater than or less than the upper threshold boundary by 5 (the preset value A) is the steady-state stage of the upper threshold; Take the extreme point of the lower threshold of the measurement data as the lower threshold boundary. The test process when the true value x of the measurement data i is greater than or less than the upper threshold boundary by 5 (the preset value B) is the steady-state stage of the lower threshold; That is, the response processes in the two stages where the measured value is between 18 and 28, and between -53 and -63 are the steady-state stages, and the measured values are the steady-state values.

[0050] Figure 3 In the example shown, the preset values A and B are equal, both being 5, or they can also be unequal. For example, the preset value A = 6 and the preset value B = 4, which can be corrected according to the specific test situation.

[0051] S5. Filter out the test data in the steady-state stage from the test data, and retain the other test data in the response process as dynamic data; As Figure 4 shown, it is the filtered data, reflecting the result of filtering the dynamic stage data according to the upper and lower boundaries obtained by the KDE algorithm.

[0052] S5. Determine the start and end of each response stage:

[0053] Store the moment information of the dynamic data during the response process into the array time_list, and perform differential calculation on the adjacent moment data in the time_list array: y(i) = time_list[i] - time_list[i - 1].

[0054] y(i) is the time difference at the i-th point, and [i] is the moment at the i-th point.

[0055] If y(i) is greater than the time difference between adjacent two points and greater than or equal to the minimum measurement time interval in the steady state stage, then time_list[i - 1] and time_list[i] are the start / end moments of the response stage in this test process.

[0056] Store the start / end values of time_list and the start / end moments obtained by the differential method into the array s in time series. In the array s, s[2n] is the start moment of the response stage, and s[2n + 1] is the end moment of the response stage.

[0057] As Figure 5 shown, it is a schematic diagram of identifying the start / end moments of dynamic data by differential. The test frequency is 50Hz, and the test time t between adjacent two points is 0.02s. This figure illustrates the principle and process of identifying the start / end moments of dynamic data by the differential method.

[0058] S6. Calculate the response speed of the measurement parameter:

[0059] Let the measured data value corresponding to the moment s[2n] be u[2n], and the measured data value corresponding to the moment s[2n + 1] be u[2n + 1]. The response speed g of the measurement parameter is calculated according to the following formula:

[0060] g = [u(2n) - u(2n + 1)] / [s(2n) - s(2n + 1)].

[0061] As Figure 6 shown, it is the overall flow chart for calculating the response speed of INCA measurement parameters, clearly presenting each step process of the response speed calculation.

[0062] A calculation method for the response speed of INCA measurement parameters based on Python according to the present invention obtains steady state / dynamic response data through INCA measurement, uses the KDE method combined with extreme points to filter static data, then identifies the start / end moments of dynamic response by the differential method, and finally calculates the response speed of INCA measurement parameters.

[0063] This method can realize the reading and calculation of INCA measurement data in a Python environment without other relevant third-party commercial software, reducing costs and dependence on specific software.

[0064] This method can effectively distinguish between the steady-state and dynamic data of INCA measurement data, improving the accuracy of data processing.

[0065] This method can effectively identify the start / end moments, providing key support for accurately calculating the response speed.

[0066] This method can achieve rapid calculation of the response of INCA measurement parameters, with good operability and passability, greatly improving work efficiency.

Claims

1. A Python-based method for calculating the response speed of INCA measurement parameters, characterized in that: The steps include: S1. Configure the Python environment and import the necessary Python modules for the entire calculation process; S2, read INCA measurement data; S3. The kernel density estimation algorithm (KDE) is used to calculate the probability density of the measurement data at each point during the entire test process. The formula is: h=1.06σn -1 / 5 (2) In the formula, f(x) represents the probability density function, n represents the number of measured data in the entire test process, and x i (i=1,2…n) is the true value of the measured data, and σ is the standard deviation of the measured data; According to the obtained probability density function, find the extreme points of the upper and lower thresholds of the measured data; S4, identify the steady-state stage in the response process of the entire test: take the extreme point of the upper threshold of the measured data as the upper threshold boundary, and measure the true value of the data x i The test process when it is greater than or less than the preset value A of the upper threshold boundary is the steady-state stage of the upper threshold; the extreme point of the lower threshold of the measured data is the lower threshold boundary, and the true value x of the measured data is i The test process when the value is greater than or less than the preset value B of the upper threshold boundary is the steady-state stage of the lower threshold; S5. Filter out the test data in the steady state phase, and retain the other test data in the response process as dynamic data; S6. Determine the start and end of each response phase: The time information of the dynamic data in the response process is stored in the array time_list, and the difference calculation is performed on the adjacent time data in the time_list array: y(i) = time_list[i]-time_list[i-1], y(i) is the time difference of the i-th point, [i] is the moment of the i-th point; If y(i) is greater than the time difference between two adjacent points and greater than or equal to the minimum measurement time interval in the steady-state phase, then time_list[i-1] and time_list[i] are the start / end time of the response phase in this test process; The start / end values ​​of time_list and the start / end times obtained by the difference method are stored in array s according to the time series. In array s, s[2n] is the start time of the response phase, and s[2n+1] is the end time of the response phase. S7. Calculate the response speed of the measurement parameters: Assume that the measured data value corresponding to time s[2n] is u[2n], and the measured data value corresponding to time s[2n+1] is u[2n+1]. The response speed g of the measured parameter is calculated as follows: g=[u(2n)-u(2n+1)] / [s(2n)-s(2n+1)].

2. A method for calculating the response speed of INCA measurement parameters based on Python as claimed in claim 1, characterized in that: The python module includes: an asammdf module for reading INCA measurement data; and a pandas module for subsequent calculations.

3. A method for calculating the response speed of INCA measurement parameters based on Python as claimed in claim 1, characterized in that: The preset value A is not equal to the preset value B.

4. A method for calculating the response speed of INCA measurement parameters based on Python as claimed in claim 1, characterized in that: INCA's test data is time-series continuous data. The time intervals between each measurement point are consistent and inversely proportional to the test frequency set by INCA.

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