Automobile battery box size measurement analysis method and device, storage medium and computer equipment

Abstract

The invention relates to the technical field of automobile battery box measurement, in particular to an automobile battery box size measurement analysis method and device, a storage medium and computer equipment, and the purposes of real-time feedback and correction can be achieved by performing visualization and predictive analysis on the measurement error of a single detection point. In the measurement process of the laser radar, an error prediction result is obtained, and the previous production process can be fed back and corrected when an abnormality occurs, so that the size of the subsequently produced automobile battery box is more accurate. Besides, by analyzing the relevance of the measurement results of the multiple detection points, similar influence factors can be found out in a concentrated and targeted mode, and therefore the preorder production process is further subjected to joint correction. Meanwhile, by researching the relation between the laser radar measurement result and the temperature and introducing an error compensation function, the influence of thermal expansion caused by the temperature on the size measurement value can be effectively solved, errors are reduced, and the measurement precision is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of automobile battery box measurement, and in particular to an automobile battery box size measurement and analysis method, device, storage medium and computer equipment. Background Art

[0002] With the rapid development of the new energy vehicle industry, the sales of new energy vehicles have also increased significantly, resulting in an increasing demand for power batteries. As a device that carries and protects power batteries, the battery box is an important component of electric vehicles, and the measurement and analysis of its related process production data has become increasingly important.

[0003] Currently, manufacturers usually rely on traditional measurement technology to measure battery boxes. Traditional measurement technology often faces problems of insufficient measurement accuracy and low efficiency when dealing with complex shapes of battery boxes. LiDAR, with its non-contact, fast and high-precision measurement characteristics, has become an effective means to solve this problem. However, the measurement results of LiDAR are affected by many factors, such as ambient temperature or previous production processes. When using LiDAR to measure battery boxes, the measurement results are still not accurate enough due to the lack of real-time data feedback, insufficient data analysis depth and lack of comprehensive data correlation. Summary of the invention

[0004] In view of the shortcomings of the prior art mentioned above, the purpose of the present invention is to provide a method, device, storage medium and computer equipment for measuring and analyzing the size of a vehicle battery box, so as to solve the problem that when measuring the battery box in the prior art, the measurement results are still not accurate enough due to the lack of real-time data feedback, insufficient data analysis depth and lack of comprehensive data correlation.

[0005] To achieve the above objectives and other related objectives, the present invention provides, on one hand, a method for measuring and analyzing the size of an automobile battery box based on a laser radar, the method comprising:

[0006] Obtain the measurement data of the dimensions of each inspection point on the car battery box;

[0007] Single-point measurement trend analysis: analyze the measurement data of each detection point and establish an ARIMA model to predict the change trend;

[0008] Correlation analysis of multi-point measurement data, using Pearson correlation coefficient to measure the linear relationship between each detection point and using Spearman correlation coefficient to analyze the monotonic relationship between each detection point;

[0009] Temperature error compensation analysis determines the relationship between the measurement data and temperature of the detection point and obtains the error compensation function.

[0010] Optionally, the single-point measurement trend analysis includes:

[0011] Visualize the measurement data of the detection points, draw a time series diagram and use the ADF test to perform stationarity analysis, obtain a stationary data sequence, and determine the value of the difference coefficient d;

[0012] Carry out autocorrelation analysis on the stationary data series, estimate the autoregressive order p and moving average order q based on the graphical representation of the autocorrelation graph ACF and partial autocorrelation graph PACF, and establish the ARIMA (p, d, q) model;

[0013] Based on the ARIMA (p, d, q) model, obtain the corresponding AIC value, and then select the optimal model based on the AIC value;

[0014] Based on the optimal model, the measurement data change trend of each detection point in the future is predicted.

[0015] Optionally, the calculation formula of the Pearson correlation coefficient is: Where Xi and Yi are the values ​​of the measurement data at the two detection points, and is the mean of the measured data. The r value ranges from -1 to 1, which means that the detection points are completely positively correlated to completely negatively correlated. The r value is 0, which means that there is no linear correlation between the detection points.

[0016] Optionally, the calculation formula of the Spearman correlation coefficient is: Where di is the ranking difference of the measurement data of each pair of detection points, n is the number of detection points, and the ρ value ranges from -1 to 1, indicating that the detection points are completely positively correlated to completely negatively correlated. A ρ value of 0 indicates that there is no monotonic relationship between the detection points.

[0017] Optionally, the obtaining of the error compensation function includes: using regression analysis to fit data according to the trend of the measurement data and the temperature, and verifying through continuous fitting data to find the optimal regression model parameters to obtain the error compensation function.

[0018] Optionally, the error compensation function is: Lcompensated=Lmeasured-ΔL(T), wherein Lcompensated is the compensated measurement value, Lmeasured is the uncompensated measurement value, and ΔL(T) is the measurement deviation at temperature T.

[0019] Optionally, the obtaining of the measurement results of the dimensions of each detection point on the automobile battery box includes: using a laser radar to perform high-precision scanning of the automobile battery box at regular intervals.

[0020] Another aspect of the present invention provides a device for measuring and analyzing the dimensions of a vehicle battery box, comprising:

[0021] A measurement result acquisition unit, used to acquire the measurement results of the dimensions of each detection point on the automobile battery box;

[0022] Single-point measurement trend analysis unit, used to analyze the measurement results of each detection point and establish an ARIMA model to predict the change trend;

[0023] A multi-point measurement result correlation analysis unit, used to measure the linear relationship between various detection points using the Pearson correlation coefficient and to analyze the monotonic relationship between various detection points using the Spearman correlation coefficient;

[0024] The temperature error compensation analysis unit is used to determine the relationship between the measurement result of the detection point and the temperature and obtain the error compensation function.

[0025] Another aspect of the present invention provides a machine-readable storage medium having a machine-executable program stored thereon, and when the machine-executable program is executed by a processor, the method for measuring and analyzing the size of a vehicle battery box as described above is implemented.

[0026] On the other hand, the present invention provides a computer device, including a memory, a processor, and a machine executable program stored in the memory and running on the processor, and when the processor executes the machine executable program, it implements the above-mentioned automobile battery box size measurement and analysis method.

[0027] In summary, the beneficial effects of the present invention are:

[0028] By visualizing and predicting the measurement data of a single inspection point, the previous production process can be fed back and corrected when errors occur, so that the size of the subsequent production of automotive battery boxes is more accurate. In addition, by analyzing the correlation of the measurement data of multiple inspection points, similar influencing factors can be found in a centralized and targeted manner, so as to further jointly correct the previous production process. At the same time, by studying the relationship between measurement data and temperature and introducing an error compensation function, the influence of thermal expansion caused by temperature on the dimensional measurement value can be effectively solved to reduce errors and improve measurement accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Figure 1 It is an overall flow chart of a method for measuring and analyzing the dimensions of a vehicle battery box according to an embodiment of the present invention;

[0030] Figure 2 is a flow chart of single point measurement trend analysis in one embodiment of the present invention;

[0031] Figure 3 This is a timing diagram of one of the detection points in the car battery box;

[0032] Figure 4 It is the autocorrelation plot ACF and partial autocorrelation plot PACF;

[0033] Figure 5 This is a forecast analysis chart of the measurement data change trend of one of the inspection points of the car battery box;

[0034] Figure 6 This is a forecast analysis chart of the measurement data of one of the inspection points of the car battery box in the future;

[0035] Figure 7 This is a correlation chart of the results between three test points in the car battery box;

[0036] Figure 8 This is a flow chart for error compensation caused by temperature;

[0037] Figure 9 A schematic diagram of a machine-readable storage medium according to an embodiment of the present invention;

[0038] Figure 10 is a schematic diagram of a computer device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0039] Refer to the following Figures 1 to 10 To describe a method, device, storage medium and computer equipment for measuring and analyzing the size of an automobile battery box of the present invention. In the description of this embodiment, the description with reference to the terms "one embodiment", "some embodiments", "illustrative embodiment", "example", "specific example", or "some examples" means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner.

[0040] like Figure 1 As shown, an embodiment of the present invention provides a method for measuring and analyzing the size of a vehicle battery box, and the method comprises the following steps:

[0041] Step 1: Obtain the measurement data of the dimensions of each inspection point of the car battery box.

[0042] Furthermore, a laser radar is used to periodically scan the car battery box with high precision to obtain the measurement data of the dimensions of each detection point of the car battery box. By using a laser radar for measurement, the measurement is more accurate and more efficient.

[0043] Optionally, the measured data can be transmitted to an external receiving unit via a signal transmission unit of the laser radar scanner for subsequent data analysis operations, for example, can be transmitted to a computer via a communication line.

[0044] Step 2: Single-point measurement trend analysis. That is, analyze the measurement data of each detection point and establish an ARIMA model to predict the change trend of the measurement data of each detection point in the future, so as to obtain the prediction results of subsequent measurement data. By obtaining the prediction results of measurement data, the previous production process can be fed back and corrected, achieving the purpose of real-time feedback and correction.

[0045] Furthermore, if Figure 2 As shown, single point measurement trend analysis includes:

[0046] Step 21: Visualize the measured data of the detection points. That is, draw a time series diagram. Through the time series diagram, you can visually observe the data characteristics to determine whether the data is stable. For a stable data series, the time series diagram often shows a constant fluctuation around the mean. If it is a non-stationary series, the time series diagram often shows different means in different time periods, such as continuous rise or continuous decline. Figure 3 As shown in the figure, it is a timing diagram of one of the detection points of the car battery box. However, the use of timing diagrams is highly subjective, so it is necessary to combine the ADF test for stationarity analysis to obtain a more accurate judgment. The use of the ADF test for stationarity analysis is to determine whether there is a unit root in the data sequence. If there is a unit root, it means that it is a non-stationary data sequence. For non-stationary data sequences, data differentiation is required until the data sequence is proved to be a stationary data sequence through the ADF test. At this time, the differential order corresponding to the stationary data sequence is the value of the differential coefficient d.

[0047] Step 22: Perform autocorrelation analysis on the stationary data sequence. Draw the autocorrelation graph ACF and partial autocorrelation graph PACF based on the stationary data sequence after the stationarity test. Then, based on the graphical representation of the autocorrelation graph ACF and the partial autocorrelation graph PACF, the autoregressive order p and the moving average order q can be estimated. Estimate the autoregressive order p and the moving average order q based on the autocorrelation graph ACF and the partial autocorrelation graph PACF, that is, to determine the order of truncation of the autocorrelation graph ACF and the partial autocorrelation graph PACF, and the value corresponding to the order is the value of the autoregressive order p and the moving average order q. Among them, after being greater than the k order, ACF and PACF quickly tend to 0, which indicates k-order truncation. For example Figure 4 As shown, they are the ACF and PACF of the autocorrelation diagram. After knowing the difference coefficient d, the autoregressive order p and the moving average order q, the ARIMA (p, d, q) model can be established.

[0048] Step 23: Based on the ARIMA (p, d, q) model, obtain the corresponding AIC value, and then select the optimal model through the AIC value. The model corresponding to the minimum AIC value is the optimal model. The calculation formula of the AIC value is: AIC = 2*p-2*ln(L). Among them, p is usually the sum of the number of AR (autoregressive term) and MA (moving average term) contained in the model. L is the maximum likelihood function value of the model.

[0049] Step 24: Based on the optimal model, predict the change trend of the measurement data of each detection point in the future. Figure 5 and Figure 6 As shown, there are respectively a forecast analysis chart of the measurement data change trend of one of the inspection points of the automobile battery box and a forecast analysis chart of the measurement data in the future period of time.

[0050] Step 3: Correlation analysis of multi-point measurement data. That is, use the Pearson correlation coefficient to measure the linear relationship between each detection point. If the measurement data between certain detection points show a significant linear correlation, it is inferred that they may be affected by similar systematic errors or environmental factors, and then the measurement data of these detection points are combined for optimization during the error correction process. At the same time, the Spearman correlation coefficient is used to analyze the monotonic relationship between each detection point. By sorting the measurement data of each detection point and calculating the correlation between the rankings, the nonlinear monotonic relationship between the detection points can be identified. If there is a monotonic increase or decrease trend between certain detection points, it can be inferred that they are affected by similar factors, thereby further jointly correcting the previous production process. If Figure 7 Shown is a correlation chart of the results between three test points in a car battery box.

[0051] Furthermore, the calculation formula of the Pearson correlation coefficient is: Where Xi and Yi are the values ​​of the measurement data at the two detection points, and is the mean of the measured data. r values ​​range from -1 to 1, indicating that the test points are completely positively correlated to completely negatively correlated. r values ​​of 0 indicate that there is no linear correlation between the test points.

[0052] Furthermore, the calculation formula of the Spearman correlation coefficient is: Where di is the ranking difference of the measured data of each pair of detection points, and n is the number of detection points. The ρ value ranges from -1 to 1, indicating that the detection points are completely positively correlated to completely negatively correlated. A ρ value of 0 indicates that there is no monotonic relationship between the detection points.

[0053] Step 4: Temperature error compensation analysis, that is, determine the relationship between the measurement data and temperature of the detection point, and obtain the error compensation function. Affected by temperature, the size and shape of the parts may change with the change of temperature, resulting in deviations in the measured size position. Therefore, the regression analysis method is used to fit the data according to the trend of the measurement data and temperature, and then the data is continuously fitted for verification to find the optimal regression model parameters and obtain the error compensation function. Finally, the error compensation function is used to compensate and correct the measurement results, so that the compensated measurement value is closer to the true value. Reference Figure 8 , is the flow chart of error compensation caused by temperature.

[0054] Furthermore, the error compensation function is: Lcompensated=Lmeasured-ΔL(T), wherein Lcompensated is the compensated measurement value, Lmeasured is the uncompensated measurement value, and ΔL(T) is the measurement deviation at temperature T.

[0055] In summary, by visualizing and predicting the measurement data of a single detection point, feedback and correction can be made to the previous production process when errors occur, so that the size of the subsequent production of automotive battery boxes is more accurate. In addition, by analyzing the correlation of the measurement data of multiple detection points, similar influencing factors can be found in a centralized and targeted manner, so as to further jointly correct the previous production process. At the same time, by studying the relationship between measurement data and temperature and introducing an error compensation function, the influence of thermal expansion caused by temperature on the dimensional measurement value can be effectively solved to reduce errors and improve measurement accuracy.

[0056] The present invention also provides a device for measuring and analyzing the size of a vehicle battery box, comprising a measurement result acquisition unit, a single-point measurement trend analysis unit, a multi-point measurement result correlation analysis unit, and a temperature error compensation analysis unit. The measurement result acquisition unit is used to obtain the measurement results of the size of each detection point on the vehicle battery box. The single-point measurement trend analysis unit is used to analyze the measurement results of each detection point and establish an ARIMA model to predict the change trend. The multi-point measurement result correlation analysis unit is used to measure the linear relationship between each detection point using the Pearson correlation coefficient and to analyze the monotonic relationship between each detection point using the Spearman correlation coefficient. The temperature error compensation analysis unit is used to determine the relationship between the measurement result of the detection point and the temperature and obtain the error compensation function.

[0057] refer to Figure 9 The present invention further provides a machine-readable storage medium on which a machine executable program is stored. When the machine executable program is executed by a processor, the method for measuring and analyzing the size of a vehicle battery box according to the above embodiment is implemented.

[0058] refer toFigure 10 The present invention further provides a computer device, including a memory, a processor, and a machine executable program stored in the memory and running on the processor. When the processor executes the machine executable program, the automobile battery box size measurement and analysis method according to the above embodiment is implemented.

[0059] It should be noted that the logic and / or steps represented in the flowchart or described in other ways herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be specifically implemented in any machine-readable storage medium for use by an instruction execution system, device or equipment (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or equipment and execute instructions), or used in combination with these instruction execution systems, devices or equipment.

[0060] For the description of this embodiment, the machine-readable storage medium 400 can be any device that can contain, store, communicate, propagate or transmit a program for use with an instruction execution system, device or equipment or in conjunction with these instruction execution systems, devices or equipment. More specific examples (non-exhaustive list) of the machine-readable storage medium 400 include the following: an electrical connection portion (electronic device) with one or more wirings, a portable computer disk box (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and editable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the machine-readable storage medium 400 can even be paper or other suitable media on which the program can be printed, because the program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting or processing in other suitable ways as necessary, and then stored in a computer memory.

[0061] It should be understood that each part of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system.

[0062] Computer device 500 may be, for example, a server, a desktop computer, a notebook computer, a tablet computer, or a smart phone. In some examples, computer device 500 may be a cloud computing node. Computer device 500 may be described in the general context of computer system executable instructions (such as program modules) executed by a computer system. Typically, a program module may include routines, programs, target programs, components, logic, data structures, etc. that perform specific tasks or implement specific abstract data types. Computer device 500 may be implemented in a distributed cloud computing environment where remote processing devices linked via a communication network perform tasks. In a distributed cloud computing environment, program modules may be located on a local or remote computing system storage medium including a storage device.

[0063] The computer device 500 may include a processor 510 adapted to execute stored instructions, and a memory 520 providing temporary storage space for the operation of the instructions during operation. The processor 510 may be a single-core processor, a multi-core processor, a computing cluster, or any number of other configurations. The memory 520 may include a random access memory (RAM), a read-only memory, a flash memory, or any other suitable storage system.

[0064] The processor 510 may be connected to an I / O interface (input / output interface) suitable for connecting the computer device 500 to one or more I / O devices (input / output devices) through a system interconnect (e.g., PCI, PCI-Express, etc.). The I / O devices may include, for example, a keyboard and a pointing device, wherein the pointing device may include a touch pad or a touch screen, etc. The I / O devices may be built-in components of the computer device 500, or may be devices externally connected to the computing device.

[0065] Processor 510 can also be linked to a display interface suitable for connecting computer device 500 to a display device through a system interconnection. Display device can include a display screen as a built-in component of computer device 500. Display device can also include a computer monitor, a television or a projector, etc., which are externally connected to computer device 500. In addition, a network interface controller (NIC) can be suitable for connecting computer device 500 to a network through a system interconnection. In some embodiments, NIC can use any suitable interface or protocol (such as Internet Small Computer System Interface, etc.) to transmit data. The network can be a cellular network, a radio network, a wide area network (WAN), a local area network (LAN) or the Internet, etc. A remote device can be connected to a computing device through a network.

[0066] The flow chart provided by the present embodiment is not intended to indicate that the operation of the method will be performed in any particular order, or that all operations of the method are included in all every case. In addition, the method may include additional operations. Within the scope of the technical thinking provided by the present embodiment method, additional changes may be made to the above method.

[0067] The above embodiments are merely illustrative of the principles and effects of the present invention, and are not intended to limit the present invention. Anyone familiar with the art may modify or alter the above embodiments without departing from the spirit and scope of the present invention. Therefore, all equivalent modifications or alterations made by a person of ordinary skill in the art without departing from the spirit and technical concept disclosed by the present invention shall still be covered by the claims of the present invention.

Claims

1. A method for measuring and analyzing the size of a car battery box, characterized in that: The measurement and analysis method comprises: Obtain the measurement data of the dimensions of each inspection point on the car battery box; Single-point measurement trend analysis: analyze the measurement data of each detection point and establish an ARIMA model to predict the change trend; Correlation analysis of multi-point measurement data, using Pearson correlation coefficient to measure the linear relationship between each detection point and using Spearman correlation coefficient to analyze the monotonic relationship between each detection point; Temperature error compensation analysis determines the relationship between the measurement data and temperature of the detection point and obtains the error compensation function.

2. The automotive battery box size measurement and analysis method according to claim 1, characterized in that: The single-point measurement trend analysis includes: Visualize the measurement data of the detection points, draw a time series diagram and use the ADF test to perform stationarity analysis, obtain a stationary data sequence, and determine the value of the difference coefficient d; Carry out autocorrelation analysis on the stationary data series, estimate the autoregressive order p and moving average order q based on the graphical representation of the autocorrelation graph ACF and partial autocorrelation graph PACF, and establish the ARIMA (p, d, q) model; Based on the ARIMA (p, d, q) model, obtain the corresponding AIC value, and then select the optimal model based on the AIC value; Based on the optimal model, the measurement data change trend of each detection point in the future is predicted.

3. The automotive battery box size measurement and analysis method according to claim 1, characterized in that: The calculation formula of the Pearson correlation coefficient is: Where Xi and Yi are the values ​​of the measurement data at the two detection points, and is the mean of the measured data. The r value ranges from -1 to 1, which means that the detection points are completely positively correlated to completely negatively correlated. The r value is 0, which means that there is no linear correlation between the detection points.

4. The automotive battery box size measurement and analysis method according to claim 1, characterized in that: The calculation formula of the Spearman correlation coefficient is: Where di is the ranking difference of the measurement data of each pair of detection points, n is the number of detection points, and the ρ value ranges from -1 to 1, indicating that the detection points are completely positively correlated to completely negatively correlated. A ρ value of 0 indicates that there is no monotonic relationship between the detection points.

5. The automotive battery box size measurement and analysis method according to claim 1, characterized in that: The method of obtaining the error compensation function includes: using regression analysis to fit data according to the trend of the measured data and the temperature, and verifying through continuous fitting data to find the optimal regression model parameters to obtain the error compensation function.

6. The automotive battery box size measurement and analysis method according to claim 5, characterized in that: The error compensation function is: Lcompensated=Lmeasured-ΔL(T), wherein Lcompensated is the compensated measurement value, Lmeasured is the uncompensated measurement value, and ΔL(T) is the measurement deviation at temperature T.

7. The automotive battery box size measurement and analysis method according to claim 1, characterized in that: The method of obtaining the measurement results of the dimensions of each detection point on the automobile battery box includes: using a laser radar to perform high-precision scanning on the automobile battery box at regular intervals.

8. A device for measuring and analyzing the dimensions of a car battery box, characterized in that: include: A measurement result acquisition unit, used to acquire the measurement results of the dimensions of each detection point on the automobile battery box; Single-point measurement trend analysis unit, used to analyze the measurement results of each detection point and establish an ARIMA model to predict the change trend; A multi-point measurement result correlation analysis unit is used to measure the linear relationship between various detection points using the Pearson correlation coefficient and to analyze the monotonic relationship between various detection points using the Spearman correlation coefficient; The temperature error compensation analysis unit is used to determine the relationship between the measurement result of the detection point and the temperature and obtain the error compensation function.

9. A machine-readable storage medium, characterized in that: A machine executable program is stored thereon, and when the machine executable program is executed by a processor, the method for measuring and analyzing the size of a vehicle battery box according to any one of claims 1 to 7 is implemented.

10. A computer device, characterized in that: The invention comprises a memory, a processor and a machine executable program stored in the memory and running on the processor, and the processor implements the automobile battery box size measurement and analysis method according to any one of claims 1 to 7 when executing the machine executable program.

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