Commercial inspection data analysis method and system suitable for air conditioner

Abstract

The invention mainly relates to the technical field of air conditioner control. In order to obtain the quality analysis condition in the air conditioner commodity inspection process in time, the commodity inspection data analysis method and system suitable for the air conditioner are provided, and operation parameters, collected by a commodity inspection machine, of the air conditioner under the refrigeration working condition and the heating working condition in a set time period are collected; the method comprises the steps of analyzing operation parameters, collected by a commodity inspection machine, of an air conditioner under a refrigeration working condition and a heating working condition within a set time period, obtaining process precision and process capability indexes within the set time period, analyzing the normal distribution condition of the operation parameters of the air conditioner, obtaining the Sigma level of the commodity inspection process within the set time period, and obtaining the Sigma level of the commodity inspection process within the set time period; the overall process quality condition of each time period and the parameter deviation condition of each single machine can be known in time, and meanwhile, the data analysis result can be subjected to chart visualization through a large screen.

Description

Technical Field

[0001] The present invention mainly relates to the technical field of air-conditioning control, and in particular to a commercial inspection data analysis method and system suitable for air-conditioning. Background Art

[0002] Commodity inspection is the last comprehensive test that air-conditioning products undergo before leaving the factory, and it is also one of the most important links in the quality inspection process. The commodity inspection process for air-conditioning outdoor units is to send the outdoor units to be tested on each AGV (Automated Guided Vehicle) to a special commodity inspection room in turn, connect them to the test indoor units, and complete the operation test under specific working conditions and for a specific duration. In this process, the measured values ​​of multiple parameters of the model product are obtained through real-time communication with the test machine and multiple sensor devices and instruments; then the system compares the measured values ​​with the upper and lower limit specifications of each parameter to complete the quality measurement of each outdoor unit. After a long period of recording in the commodity inspection system, the staff obtained a large data set of the commodity inspection process for this model product, which includes the barcode, AGV number, test time, test conditions, production line power parameters, operating parameters of each working condition, test results, etc. of each tested machine. Such detailed big data provides an analytical basis for the "smart transformation and digital transformation" and quality improvement of air-conditioning companies. With the advent of the era of digital production and big data, a series of analysis and processing of massive production big data for the purpose of "improving quality and increasing efficiency" has become increasingly important. However, due to the wide range and diverse content of SPC (Statistical Process Control) in the production process, there is currently no process control method specifically for the air-conditioning commercial inspection process in the industry. Therefore, it is urgent to combine actual needs and summarize a set of analysis systems and methods suitable for the big data of the air-conditioning commercial inspection process, so as to timely understand the quality of the key processes of the production line, effectively improve production efficiency, and enable staff to promptly discover the hidden dangers and risks hidden behind the data, thereby accelerating the intelligent manufacturing and new quality productivity level of the company's air-conditioning products. Summary of the invention

[0003] Technical Problems to be Solved by the Invention

[0004] Provided is a commodity inspection data analysis method and system applicable to air conditioners, aiming to analyze the quality of the commodity inspection process of air conditioners and understand the quality of each process of air conditioner production.

[0005] The technical solution adopted by the present invention to solve the above technical problems

[0006] A general intelligent commodity inspection data analysis method applicable to air conditioners, comprising:

[0007] Collect the operating parameters of the air conditioner under cooling and heating conditions in the set time period collected by the commodity inspection machine;

[0008] The collected operating parameters under cooling and heating conditions are analyzed to obtain the process precision and process capability index within a set time period, analyze the normal distribution of the air-conditioning operating parameters, and obtain the Sigma level of the commercial inspection process within the set time period.

[0009] Furthermore, the operating parameters under the cooling and heating conditions specifically include: air-conditioning compressor three-phase voltage, three-phase current, power, air inlet temperature, air outlet temperature, indoor ambient temperature, exhaust temperature, outdoor coil temperature, outdoor coil pressure, compressor module temperature, compressor frequency, fan speed, and the maximum value, average value, median value, mode, variance, Sigma, Aver±1*Sigma, Aver±2*Sigma, Aver±3*Sigm corresponding to the above parameters.

[0010] Furthermore, obtaining the process precision and process capability index within a set time period specifically includes: the process precision The process capability index Among them, USL is the upper limit value allowed for the operating parameters under the corresponding refrigeration condition or heating condition, LSL is the lower limit value allowed for the operating parameters under the corresponding refrigeration condition or heating condition, when the calculated process capability index Cpk is less than the first set value, the quality control personnel are reminded to adjust the threshold of the operating parameters under the corresponding refrigeration condition or heating condition, and when the calculated process capability index Cpk is less than the second set value, the quality control personnel are reminded to stop the production task of the production line, wherein the second set value is less than the first set value.

[0011] Furthermore, analyzing the normal distribution of the air conditioner operating parameters specifically includes: for the operating parameters under the cooling condition and the heating condition that conform to the normal distribution, obtaining the proportion of the number of air conditioners within the range of [Aver-2*Sigma, Aver+2*Sigma].

[0012] Furthermore, the Sigma level of the commodity inspection process within the set time period is specifically: Z≈3×Cpk.

[0013] Furthermore, the method also includes recording a machine barcode of the corresponding air conditioner if the number of operating parameters under cooling conditions and heating conditions that reach set limit values ​​exceeds a set number.

[0014] Furthermore, the method also includes: obtaining the average output, qualified rate, and rework rate of the air conditioner within the set time period, and plotting the average output, qualified rate, and rework rate into a scatter plot and a histogram.

[0015] Furthermore, the method also includes: summarizing the process precision and process capability index within the set time period, the normal distribution of air conditioning operating parameters, and the Sigma level of the commodity inspection process within the set time period on a daily, monthly, and annual basis, and formulating the summarized data into scatter plots and histograms.

[0016] Based on the above commodity inspection data analysis method applicable to air conditioners, the present invention also provides a commodity inspection data analysis system applicable to air conditioners, including a data acquisition board, a commodity inspection machine, a processor and a display;

[0017] The data acquisition board is used to collect the operating parameters of the air conditioner under the cooling and heating conditions in the set time period collected by the commodity inspection machine, and transmit them to the main processor;

[0018] The main processor is used to analyze the operating parameters of the air conditioner under cooling and heating conditions within a set time period collected by the commodity inspection machine, obtain the process precision and process capability index within the set time period, analyze the normal distribution of the air conditioner operating parameters, and obtain the Sigma level of the commodity inspection process within the set time period;

[0019] The display is used to display the analysis results of the main processor on the operating parameters of the air conditioner under cooling conditions and heating conditions within a set time period collected by the commercial inspection machine.

[0020] Beneficial Effects of the Invention

[0021] The commercial inspection data analysis method and system for air conditioners described in the present invention are suitable for commercial inspection big data analysis and processing of air conditioner products of various types and models. By collecting the operating parameters of the air conditioner under the cooling condition and heating condition in the set time period collected by the commercial inspection machine; analyzing the collected operating parameters under the cooling condition and heating condition, obtaining the process precision and process capability index within the set time period, analyzing the normal distribution of the air conditioner operating parameters, and obtaining the Sigma level of the commercial inspection process within the set time period, the entire air conditioner commercial inspection data analysis process can realize automated process control and result judgment, through statistical calculation and analysis of the commercial inspection data of a certain period, it can not only timely understand the overall process quality of each period, but also refine to know the parameter deviation of each single machine, and can master the quality of the key process of the production line, effectively improve production efficiency, and allow staff to timely discover hidden dangers and risks hidden behind the data, thereby accelerating the intelligent manufacturing and new quality productivity level of the company's air conditioning products. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 It is a schematic diagram of a universal intelligent commodity inspection data analysis system according to the present invention;

[0023] Figure 2It is a visualized line graph of the air inlet temperature Tin, the exhaust temperature Tdis, and the compressor temperature Tcomp during a specific period of time in the embodiment. DETAILED DESCRIPTION

[0024] like Figure 1 As shown, the commercial inspection data analysis system suitable for air conditioners described in the present invention includes a data acquisition board, a commercial inspection machine, a processor and a display; the data acquisition board is used to collect the three-phase voltage, three-phase current, power, air inlet temperature, air outlet temperature, indoor ambient temperature, exhaust temperature, outdoor coil temperature, outdoor coil pressure, compressor module temperature, compressor frequency, fan speed of the air conditioner compressor under heating conditions collected by the commercial inspection machine through a program, and the maximum value, average value, median value, mode, variance, Sigma, Aver±1*Sigma, Aver±2*Sigma, Aver±3*Sigm and other corresponding values ​​of the above parameters are transmitted to the main processor. The main processor analyzes the above operating parameters and calculates the process precision of each corresponding operating parameter respectively. Process Capability Index Among them, USL is the upper limit value allowed for the operating parameters under the corresponding cooling condition or heating condition, and LSL is the lower limit value allowed for the operating parameters under the corresponding cooling condition or heating condition. When the calculated process capability index Cpk is less than the first set value, the quality control personnel are reminded to adjust the threshold value of the operating parameters under the corresponding cooling condition or heating condition. When the calculated process capability index Cpk is less than the second set value, the quality control personnel are reminded to stop the production task of the production line, wherein the second set value is less than the first set value; the main processor simultaneously obtains the proportion of the number of air conditioners in the range of [Aver-2*Sigma, Aver+2*Sigma] for the operating parameters under the cooling condition and heating condition that conform to the normal distribution, and calculates the Sigma level of the commercial inspection process within the set time period as follows: Z≈3×Cpk, so as to timely understand the overall process quality of each time period and the parameter deviation of each single machine. The display displays the analysis results of the above operating parameters. This embodiment takes the commercial inspection process of a certain brand of 1.5P variable frequency hanging outdoor unit as an example. Figure 1 As shown in the figure, the data acquisition board uses a single-chip microcomputer as the main MCU, and completes two-way communication with the main processor, the outdoor unit of the air conditioner under test, and the production line pressure gauge, power monitor and other instruments through three-way Modbus serial port communication. The display uses a 24-27-inch IPS touch screen panel with a wide viewing angle. Due to the analysis task of large data sets, higher stability and security are required, and long-term operation and high-load environment are supported. Therefore, the main processor uses Intel Core i7 main chip, Windows Server operating system, 16GB or 32GB memory configuration, and through its built-in dedicated data analysis program, the following analysis and processing is performed on the commodity inspection data from 9:00 to 11:30 on a certain weekday morning.

[0025] The operating parameters of the air conditioner under cooling and heating conditions in a specific time period are collected respectively, mainly including the three-phase voltage (Ua, Ub, Uc), three-phase current (Ia, Ib, Ic), power (P), air inlet temperature (Tin), air outlet temperature (Tout), indoor ambient temperature (Troom), exhaust temperature (Tdis), external coil temperature (Tcoil), pressure (Press), compressor temperature (Tcomp), compressor frequency (Fcomp), and fan speed (FanSpeed) of the air conditioner under cooling and heating conditions. The line graphs of air inlet temperature (Tin), exhaust temperature (Tdis) and compressor temperature (Tcomp) in heating mode are shown in the following figure: Figure 2 As shown. The above operating parameters are preprocessed, mainly including the removal of obviously abnormal data in the current and temperature data and the filling of missing data, including the use of the average, mode and median to supplement individual missing data, and the use of interpolation to fill in parameters such as temperature and pressure that are suitable for time series or linear data sets. After preprocessing the above parameters, the maximum value, Aver, median, mode, Sigma, variance, and upper and lower limits of Aver±1*Sigma, Aver±2*Sigma, and Aver±3*Sigma corresponding to each parameter are calculated respectively.

[0026] Calculate the precision of the above parameters inspection process during this period and process capability index Among them, USL is the upper limit value allowed by the parameter, and LSL is the lower limit value allowed by the parameter. The larger the Cp, the better the technical capability of the process. When the calculated process capability index is lower than the first set value, the system reminds the quality control personnel of the production line to adjust the relevant thresholds. If the calculated process capability index is lower than the second set value, the system will immediately issue an alarm to remind the quality control personnel to stop the line as soon as possible for rectification and check the processing.

[0027] Statistical calculations are performed on parameters that conform to or basically conform to the normal distribution, such as Ia, Tin, Tdis, Tcomp, Icomp, etc., to obtain the percentage of air conditioners within the range of [Aver-2*Sigma, Aver+2*Sigma] within the time period.

[0028] Get the Sigma level of the commodity inspection process during this period, that is, the Z value. The higher the Z value, the better the process quality and the lower the defect rate. To simplify the calculation process, it is assumed that the parameters of the commodity inspection process follow a normal distribution, and the Z value is estimated according to the following formula: Z≈3×Cpk.

[0029] Draw scatter plots and histograms for important parameters and the average output, qualified rate (yield rate), and rework rate during the analysis period. The horizontal axis of the scatter plot uses the detection time or machine barcode, and the vertical axis is the parameter value. The scatter plot can intuitively understand the discreteness of important parameters in the production of the day; the horizontal axis of the histogram is the parameter value, and the vertical axis is the number of occurrences. The histogram can be used to promptly discover abnormal fluctuations in the process and take corresponding measures. The histogram can also be used to compare the distribution of two or more parameter sets to understand the differences between them, which helps analysts find the commonalities and differences between them and provide clues for solving quality problems in the process.

[0030] Extract the barcodes of air conditioner products with three or more operating parameters reaching the limit value for record keeping so as to compare with after-sales maintenance data in the future, so as to further understand the correlation between the calibration machine and subsequent maintenance failure conditions.

[0031] In order to analyze and display commodity inspection data on a digital large screen, the above analysis and processing can be summarized. In addition to the chart presentation, it also includes whether the quality control requirements are met; whether there are machines with large parameter dispersion under multiple working conditions, and the cumulative number of times to capture machines with potential failure risks; whether it is necessary to adjust the parameter limit value to meet quality control while ensuring production efficiency, etc. It is summarized in a report together with the visual chart and presented.

[0032] Based on the content and duration of the summary, daily, weekly, monthly, quarterly and annual summaries can be conducted respectively, and the summary data can be formulated into scatter plots and histograms.

Claims

1. A commercial inspection data analysis method applicable to air conditioners, characterized in that: include: Collect the operating parameters of the air conditioner under cooling and heating conditions in the set time period collected by the commodity inspection machine; The collected operating parameters under cooling and heating conditions are analyzed to obtain the process precision and process capability index within a set time period, analyze the normal distribution of the air-conditioning operating parameters, and obtain the Sigma level of the commercial inspection process within the set time period.

2. A general intelligent commodity inspection data analysis method applicable to air conditioners according to claim 1, characterized in that: The operating parameters under the cooling and heating conditions specifically include: air-conditioning compressor three-phase voltage, three-phase current, power, air inlet temperature, air outlet temperature, indoor ambient temperature, exhaust temperature, outdoor coil temperature, outdoor coil pressure, compressor module temperature, compressor frequency, fan speed, and the maximum value, average value, median value, mode, variance, Sigma, Aver±1*Sigma, Aver±2*Sigma, Aver±3*Sigm corresponding to the above parameters.

3. A commercial inspection data analysis method applicable to air conditioners according to claim 2, characterized in that: Obtaining the process precision and process capability index within a set time period specifically includes: the process precision The process capability index Among them, USL is the upper limit value allowed for the operating parameters under the corresponding refrigeration condition or heating condition, LSL is the lower limit value allowed for the operating parameters under the corresponding refrigeration condition or heating condition, when the calculated process capability index Cpk is less than the first set value, the quality control personnel are reminded to adjust the threshold of the operating parameters under the corresponding refrigeration condition or heating condition, and when the calculated process capability index Cpk is less than the second set value, the quality control personnel are reminded to stop the production task of the production line, wherein the second set value is less than the first set value.

4. A commercial inspection data analysis method applicable to air conditioners according to claim 2, characterized in that: The analyzing the normal distribution of the air conditioner operating parameters specifically includes: for the operating parameters under the cooling condition and the heating condition that conform to the normal distribution, obtaining the proportion of the number of air conditioners within the range of [Aver-2*Sigma, Aver+2*Sigma].

5. The commercial inspection data analysis method applicable to air conditioners according to claim 3, characterized in that: The Sigma level of the commodity inspection process within the set time period is specifically: Z≈3×Cpk.

6. A commodity inspection data analysis method applicable to air conditioners according to any one of claims 1 to 5, characterized in that: The method further includes recording a machine barcode of the corresponding air conditioner if the number of operating parameters under the cooling condition and the heating condition that reach the set limit value exceeds a set number.

7. A commodity inspection data analysis method applicable to air conditioners according to any one of claims 1 to 5, characterized in that: The method further includes: obtaining the average output, qualified rate, and rework rate of the air conditioner within the set time period, and plotting the average output, qualified rate, and rework rate into a scatter plot and a histogram.

8. A commodity inspection data analysis method applicable to air conditioners according to any one of claims 1 to 5, characterized in that: The method also includes: summarizing the process precision and process capability index within a set time period, the normal distribution of air conditioning operating parameters, and the Sigma level of the commodity inspection process within the set time period on a daily, monthly, and annual basis, and formulating the summarized data into a scatter plot and a histogram.

9. A commodity inspection data analysis system applicable to air conditioners, used to implement a commodity inspection data analysis method applicable to air conditioners as claimed in any one of claims 1 to 8, characterized in that: Including data acquisition board, commodity inspection machine, processor and display; The data acquisition board is used to collect the operating parameters of the air conditioner under the cooling and heating conditions in the set time period collected by the commodity inspection machine, and transmit them to the main processor; The main processor is used to analyze the operating parameters of the air conditioner under cooling and heating conditions within a set time period collected by the commodity inspection machine, obtain the process precision and process capability index within the set time period, analyze the normal distribution of the air conditioner operating parameters, and obtain the Sigma level of the commodity inspection process within the set time period; The display is used to display the analysis results of the main processor on the operating parameters of the air conditioner under cooling conditions and heating conditions within a set time period collected by the commercial inspection machine.

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