WO2023019947A1 - 生产过程质量控制方法、电子设备和计算机可读存储介质 - Google Patents

生产过程质量控制方法、电子设备和计算机可读存储介质 Download PDF

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WO2023019947A1
WO2023019947A1 PCT/CN2022/082865 CN2022082865W WO2023019947A1 WO 2023019947 A1 WO2023019947 A1 WO 2023019947A1 CN 2022082865 W CN2022082865 W CN 2022082865W WO 2023019947 A1 WO2023019947 A1 WO 2023019947A1
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eigenvalues
products
batch
production process
distribution
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French (fr)
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宋俊颖
张加民
卜有照
崔巍
邱传麒
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中兴通讯股份有限公司
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/10—Complex mathematical operations
    • G06F17/18—Complex mathematical operations for evaluating statistical data, e.g. average values, frequency distributions, probability functions, regression analysis
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0639—Performance analysis of employees; Performance analysis of enterprise or organisation operations
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
    • G06Q50/04—Manufacturing

Definitions

  • the embodiments of the present application relate to the technical field of production quality control, and in particular to a production process quality control method, electronic equipment, and a computer-readable storage medium.
  • SPC Statistical Process Control
  • the main tool of SPC is the control chart, which can be used to monitor the quality characteristics of each stage, process and test item in the production process.
  • the SPC control chart contains two aspects: one is to use the control chart to analyze the stability of the process, and to give early warning to the abnormal factors existing in the process; Calculate the process capability index to analyze whether the process capability meets the technical requirements and evaluate the process quality.
  • the SPC control chart includes the non-conforming product rate control chart (p control chart) which is widely used to effectively monitor the change of the defective rate in the process, and the single-value control chart with simple calculation and easy data collection.
  • the p control chart is mainly used to judge the production process. Whether the unqualified product rate p is maintained in the required statistically controlled state, the single-value control chart is a control chart plotting a single observation value within a period of time, and the measured values are plotted one by one in the control chart, and the process is implemented QC.
  • the traditional single-value control chart requires that the collected sample data must obey the independent and same normal distribution, which is difficult to achieve in the actual production process, so the reliability and accuracy of quality control in the production process based on the traditional control chart are not good High, unable to meet the actual production needs of users.
  • the embodiment of the present application provides a method for quality control of the production process, the method comprising: calculating the defective product rate of each batch of products in k batches of products and the average defective product rate of the k batches of products; wherein, the k It is an integer greater than 1; according to the average defective product rate, the defective product rate of each batch of products is processed to obtain k eigenvalues respectively corresponding to the k batches of products; wherein, the eigenvalues are Characterize the dimensionless value of the defective product rate; calculate the statistic of the k eigenvalues; wherein, the statistic includes the mean value of the k eigenvalues; according to the statistic, determine the k The distribution state of each eigenvalue; wherein, the distribution state includes approximately normal distribution and skewed distribution; according to the mean value and the distribution state, determine the control line, and draw the control chart of the production process.
  • the embodiment of the present application also provides an electronic device, including: at least one processor; and a memory connected in communication with the at least one processor; wherein, the memory stores information that can be executed by the at least one processor. Instructions, the instructions are executed by the at least one processor, so that the at least one processor can execute the above-mentioned production process quality control method.
  • the embodiment of the present application also provides a computer-readable storage medium storing a computer program, and when the computer program is executed by a processor, the above method for quality control of the production process is realized.
  • Fig. 1 is a flowchart one of a production process quality control method according to an embodiment of the present application
  • Fig. 2 is a control diagram of a production process provided in an embodiment according to the present application.
  • Fig. 3 is according to one embodiment of the present application, the flowchart of performing U test on k eigenvalues
  • Fig. 4 is a flow chart of determining the degree of skewness of the skewed distribution of k eigenvalues according to one embodiment of the present application
  • FIG. 5 is a flow chart of determining the control line by the server according to the mean value of k eigenvalues and the distribution state of k eigenvalues according to an embodiment of the present application;
  • Fig. 6 is a flow chart of judging the control chart according to one embodiment of the present application.
  • FIG. 7 is a second flow chart of a production process quality control method according to another embodiment of the present application.
  • Fig. 8 is a schematic structural diagram of an electronic device according to another embodiment of the present application.
  • the main purpose of the embodiment of the present application is to provide a production process quality control method, electronic equipment and computer-readable storage medium, which can more comprehensively monitor abnormal data fluctuations in the production process, and effectively improve the production process quality control method. Accuracy and universality are conducive to improving product quality and better meeting the actual production needs of users.
  • An embodiment of the present application relates to a production process quality control method, which is applied to an electronic device, where the electronic device may be a terminal or a server.
  • the electronic device is described by taking the server as an example.
  • the implementation details of the production process quality control method of this embodiment are described in detail below. The following content is only the implementation details provided for the convenience of understanding, and is not necessary for the implementation of this solution.
  • the application scenarios of the embodiments of the present application may include, but are not limited to: the processing process of industrial products, agricultural products, by-products, etc.; the design process of mechanical parts, buildings, and handicrafts, the management process of enterprises, and the service process of e-commerce services.
  • the specific flow of the production process quality control method of the present embodiment can be as shown in Figure 1, including:
  • Step 101 calculating the defective product rate of each batch of products in k batches of products and the average defective product rate of k batches of products.
  • k is an integer greater than 1.
  • the test data of a certain test item of the product within a certain period of time can be selected as the research object, and k batches of products can be collected as the research object.
  • the quantity of each batch of products can be the same or different
  • the number of products in the i-th batch is recorded as n i
  • the number of unqualified products in the i-th batch of products is recorded as d i
  • the server is at
  • the unqualified product rate of each batch of products in k batches of products and the average unqualified product rate of these k batches of products can be calculated.
  • the average rate of defective products Can pass the formula: calculated.
  • Step 102 according to the average defective product rate, process the defective product rate of each batch of products, and obtain k feature values corresponding to k batches of products respectively.
  • the server can calculate the unqualified product rate of each batch of products according to the average unqualified product rate Processing is performed to obtain k eigenvalues corresponding to k batches of products, and the calculation process of the processing of the non-conforming product rate of the i-th batch of products is as follows:
  • Z i is the eigenvalue corresponding to the i-th batch of products
  • the k eigenvalues corresponding to k batches of products are dimensionless values that can represent the rate of defective products of the product.
  • eigenvalues can eliminate the dimension and order of magnitude and other factors, it is convenient to make a comprehensive judgment on each point in the control chart.
  • the embodiments of the present application do not require the product quantity of each batch of products to be consistent, that is, the embodiments of the present application can be applied to different sample sizes and different sample sizes.
  • the distributed production process does not limit the sampling method at the same time. It can monitor the production process of various products, which is closer to the real production situation and further meets the production needs of users.
  • Step 103 calculating the statistics of the k eigenvalues, the statistic including the mean value of the k eigenvalues.
  • the server needs to perform statistical analysis on the k eigenvalues corresponding to the obtained k batches of products, and calculate the statistics of the k eigenvalues, where the statistics include the mean value of the k eigenvalues, denoted as
  • Step 104 determine the distribution status of the k feature values.
  • the server can determine the distribution status of the k eigenvalues according to the statistics.
  • the distribution status of the k eigenvalues includes an approximate normal distribution (including the standard normal distribution ) and a skewed distribution.
  • the server may perform T test, U test, chi-square test, etc. on the k eigenvalues according to the statistics of the k eigenvalues to determine the distribution status of the k eigenvalues.
  • Step 105 determine the control line according to the mean value and the distribution state, and draw a control chart of the production process.
  • the server determines the distribution state of the k eigenvalues according to the statistics, and can estimate the standard deviation ⁇ of the k eigenvalues according to the mean value and distribution state of the k eigenvalues, and determine the control line.
  • control lines include a central line (Central Line, referred to as: CL), a first upper control line (First Upper Control Line, referred to as: 1UCL), a second upper control line 2UCL, a third upper control line 3UCL, a first upper control line First Lower Control Line (1LCL for short), second lower control line 2LCL, third lower control line 3LCL, CL is 1UCL is 2UCL is 3UCL is 1LCL is 2LCL is 3LCL is The server draws each control line as a corresponding solid line, and marks k points in the control chart in order of time, and connects each data point with a broken line in turn.
  • the abscissa of the control chart is time, and the ordinate is An eigenvalue representing the rate of defective products.
  • control line can also be represented by the center line, ⁇ 1SL, ⁇ 2SL and ⁇ 3SL, and the drawn control chart of the production process can be shown in Figure 2.
  • the embodiment of the present application calculates the unqualified product rate of each batch of products in k batches of products and the average unqualified product rate of k batches of products, And according to the average unqualified product rate, the unqualified product rate of each batch of products is processed to obtain k eigenvalues corresponding to k batches of products, and then calculate the statistics of k eigenvalues, so as to determine k according to the statistics Whether the distribution state of the characteristic value is approximately normal distribution or skewed distribution, finally, according to the mean value and distribution state in the statistics, determine the control line, and draw the control chart of the production process.
  • the product rate is processed to obtain k eigenvalues corresponding to k batches of products.
  • the obtained eigenvalues are dimensionless values that can represent the unqualified product rate of the product, and can eliminate the influence of factors such as dimension and order of magnitude, so that
  • the subsequent determined control line is unified and definite, which facilitates a comprehensive judgment on each point in the control chart.
  • the embodiment of the present application does not require the collected sample data to obey an independent and same normal distribution, and can be applied to In a variety of production processes, so as to conduct more comprehensive monitoring of abnormal data fluctuations in the production process, effectively improve the accuracy and universality of the quality control methods in the production process, help improve product quality, and better meet the actual needs of users production needs.
  • the statistics of the k eigenvalues calculated by the server also include the variance of the k eigenvalues, the moving range of the eigenvalues corresponding to two adjacent batches of products, and the mean square difference of the moving range, the server
  • the distribution state of k eigenvalues can be determined by performing U-test on k eigenvalues, and the server can perform U-test on k eigenvalues according to the steps shown in Figure 3, specifically including:
  • Step 201 according to the mean value, variance, moving range and mean square difference, perform U-test on k eigenvalues, and obtain U-test values of k eigenvalues.
  • the statistics of the k eigenvalues calculated by the server include the mean value of the k eigenvalues The variance S 2 of the k eigenvalues, the moving range MR of the eigenvalues corresponding to two adjacent batches of products, and the mean square difference MSSD of the moving range, where,
  • the server can use the following formula to perform a U test on the k eigenvalues according to the mean, variance, moving range, and mean squared deviation, and obtain the U test values of the k eigenvalues:
  • i is an integer greater than 0 and less than or equal to k
  • Z i is the characteristic value corresponding to the i-th batch of products
  • S 2 is the variance of k eigenvalues
  • MR i is the moving range between the eigenvalues corresponding to the i-th batch of products and the eigenvalues corresponding to the i+1-th batch of products
  • MR i+ 1 is the moving range between the eigenvalues corresponding to the i+1 batch of products and the eigenvalues corresponding to the i+2 batch of products
  • MSSD is the mean square difference
  • U is the U test value.
  • the MSSD value is very close to the S2 value, the process belongs to an approximate normal distribution.
  • T test chi-square test, etc.
  • U test can support The larger the number of samples, the more accurate the process of judging the distribution state of the eigenvalues corresponding to each batch of products.
  • Step 202 judging whether the U test value is within the preset first threshold range, if yes, execute step 203 , otherwise, execute step 204 .
  • the server performs the U test on the k eigenvalues according to the mean value, variance, moving range and mean square difference, and after obtaining the U test values of the k eigenvalues, it can determine whether the U test value is in the preset Within the first threshold range, wherein the preset first threshold range may be -3 ⁇ U ⁇ 3.
  • Step 203 determining that the distribution state of the k eigenvalues is an approximate normal distribution.
  • the preset first threshold range is -3 ⁇ U ⁇ 3, and the server determines that the U test value of the k feature values is 2.1, then the server determines that the distribution state of the k feature values is an approximately normal distribution.
  • Step 204 determining that the distribution state of the k eigenvalues is a skewed distribution.
  • the server may determine that the distribution state of the k feature values is a skewed distribution.
  • the preset first threshold range is -3 ⁇ U ⁇ 3, and the server determines that the U test value of the k feature values is 3.2, then the server determines that the distribution state of the k feature values is a skewed distribution.
  • the server may also determine the degree of skewness of the skewed distribution of the k eigenvalues according to the steps shown in Figure 4, specifically including:
  • Step 301 determine the skewness of k eigenvalues according to the mean and variance.
  • the server can further refine the skewed distribution by calculating the skewness. Variance, which determines the skewness of the k eigenvalues.
  • the server can determine the skewness of the k eigenvalues according to the mean value of the k eigenvalues and the variance of the k eigenvalues according to the following formula:
  • i is an integer greater than 0 and less than or equal to k
  • Z i is the characteristic value corresponding to the i-th batch of products
  • S 3 is the cube of the arithmetic square root of the variance of k eigenvalues
  • Sk is the skewness of k eigenvalues
  • Step 302 judging whether the skewness is within the preset second threshold range, if yes, execute step 303 , otherwise, execute step 304 .
  • the server determines the skewness of the k eigenvalues according to the mean value of the k eigenvalues and the variance of the k eigenvalues, it can determine whether the skewness of the k eigenvalues is within the preset second threshold range , wherein the preset second threshold range may be -1 ⁇ S k ⁇ 1.
  • Step 303 determining that the distribution state of the k eigenvalues is a slightly skewed distribution.
  • the server may determine that the distribution state of the k feature values is a slightly skewed distribution.
  • the preset first threshold range is -1 ⁇ S k ⁇ 1
  • the server determines that the skewness S k of the k eigenvalues is 0.657, then the server determines that the distribution state of the k eigenvalues is slightly skewed distributed.
  • Step 304 determining that the distribution state of the k eigenvalues is a heavily skewed distribution.
  • the server may determine that the distribution state of the k feature values is a highly skewed distribution.
  • the preset first threshold range is -1 ⁇ S k ⁇ 1
  • the server determines that the skewness S k of the k eigenvalues is -1.27, then the server determines that the distribution state of the k eigenvalues is heavily skewed distributed.
  • the server determines the control line according to the mean value of the k eigenvalues and the distribution state of the k eigenvalues, which can be implemented by the steps shown in Figure 5, specifically including:
  • Step 401 Estimate the standard deviations of the k feature values according to the distribution state and the pre-stored correspondence, the pre-stored correspondence being the correspondence between the distribution state and the way of estimating the standard deviation.
  • the server After the server determines the distribution state of the k eigenvalues, it can estimate the standard deviation of the k eigenvalues according to the pre-stored corresponding relationship.
  • the pre-stored corresponding relationship is the corresponding relationship between the distribution state and the method of estimating the standard deviation, and the pre-stored corresponding relationship can be set by those skilled in the art according to actual needs, and stored in the internal memory of the server in advance.
  • the corresponding relationship between the distribution state and the method of estimating the standard deviation includes: the method of estimating the standard deviation corresponding to the approximate normal distribution is to estimate the standard deviation according to the mean square gradient; the method of estimating the standard deviation corresponding to the slightly skewed distribution is The method is to estimate the standard deviation based on the mean of the moving range; the method of estimating the standard deviation corresponding to the heavily skewed distribution is to estimate the standard deviation based on the median of the moving range.
  • the server determines that the distribution state of the k eigenvalues is a slightly skewed distribution, and the server can estimate the standard deviation based on the average of the moving ranges of the eigenvalues corresponding to two adjacent batches of products by the following formula:
  • ⁇ is the standard deviation
  • ⁇ is the average value of the moving range.
  • the server determines that the distribution state of the k eigenvalues is a heavily skewed distribution, and the server can determine the median of the k-1 moving ranges based on the calculated and measured k-1 moving ranges, according to The estimated standard deviation of the median of k-1 moving ranges, denoted as in, is the median of k-1 moving ranges.
  • the estimated standard deviation of the median of k-1 moving ranges denoted as in, is the median of k-1 moving ranges.
  • the server determines that the distribution state of the k eigenvalues is an approximately normal distribution, and the server can estimate the standard deviation based on the calculated mean squared deviation of the moving range by the following formula: Among them, MSSD is the mean square difference of the moving range. When the distribution state of the k eigenvalues is approximately normal, the production process is in a controllable state, and the mean square difference of the moving range can be used to more accurately predict The status of the production process.
  • Step 402 determine the control line according to the mean value and standard deviation.
  • the control line can be determined according to the mean of the k eigenvalues and the estimated standard deviation.
  • the server draws the control chart of the production process based on the drawing method of the Shewhart control chart, it can differentiate the control chart through the steps shown in Figure 6, specifically including:
  • Step 501 check whether there are any points in the control map that meet the judgment rules.
  • the server after the server draws the control diagram of the production process, it can detect whether there are points in the control diagram that meet the judgment rules according to the preset judgment rules. Technicians select and set from eight discrimination criteria according to actual needs. Due to the universality and high accuracy of the control chart, discrimination can be performed according to the preset discrimination rules and control charts, and any reason in the production process can be identified. The abnormal fluctuations caused by the comprehensive monitoring are conducive to the improvement of product quality in the process of batch processing.
  • control diagram of the production process drawn by the server is shown in Figure 2.
  • the control lines include the center line, ⁇ 1SL, ⁇ 2SL and ⁇ 3SL, the area between +3SL and +2SL and the area between -3SL and -2SL
  • the area between is marked as area A
  • the area between +2SL and +1SL and the area between -2SL and -1SL is marked as area B
  • the area is recorded as C area
  • the default judgment rules are 8 kinds of judgment rules stipulated in GB/T 4091-2001, including:
  • Rule 3 if 6 consecutive data points are increasing or decreasing, output the last data point of the 6 consecutive data points;
  • Rule 8 if 8 consecutive data points are located on both sides of the center line and the 8 consecutive data points are located outside the area of C, output the last data point of the 8 consecutive data points.
  • Step 502 if there are points in the control map that conform to the discriminant rules, then output the points that conform to the discriminant rules.
  • the preset discrimination rules are the eight discrimination rules stipulated in GB/T 4091-2001, and the control chart of the production process drawn by the server is shown in Figure 2, in which the second data point, the first 10 data points, the 11th data point and the 12th data point are located outside the area of A, the server will output the 2nd data point, the 10th data point, the 11th data point and the 12th data point , for those skilled in the art to analyze, determine that serious defects occur in the production process, and stop production.
  • the preset discrimination rules are the eight discrimination rules stipulated in GB/T 4091-2001, and the control chart of the production process drawn by the server is shown in Figure 2, in which: the 25th data point to the 27 data points, if at least 2 of the 3 consecutive data points are located in areas other than Area B on the same side of the center line, the server outputs the 27th data point; the 26th to 28th data points are continuous If at least 2 of the 3 data points are located in areas other than Area B on the same side of the center line, the server outputs the 28th data point. Similarly, the server outputs the 29th data point, the 30th data point, and the 31st data point.
  • a data point is provided for those skilled in the art to analyze, and it is determined that only the rate of defective products is significantly reduced, the production process is good, and the production is continued.
  • FIG. 7 is a flow chart of the production process quality control method described in this embodiment, including:
  • Step 601 collect k batches of sample data.
  • the server determines that the same product has been processed in the corresponding process, it can select the test data of a certain test item of the product within a certain period of time as the research object according to the actual monitoring needs, and collect k batches of products as samples .
  • Table 1 Statistical table of sample data collected by the server
  • Step 602 calculating the defective product rate of the i-th batch of samples and the average defective product rate of the k batch of samples.
  • the 31 batches of sample data collected by the server can be shown in Table 1.
  • the server calculates the non-conforming product rate of the i-th batch of samples and the average non-conforming product rate of the 31 batches of samples, wherein, The defective product rate of the first batch of samples is:
  • the non-conforming product rate of each batch of samples calculated by the server and the average non-conforming product rate of the samples can be shown in Table 2:
  • Table 2 Statistical table of the non-conforming product rate of each batch of samples and the average non-conforming product rate of the samples calculated by the server
  • Step 603 process the defective rate of each batch of samples to obtain k feature values respectively corresponding to k batches of samples.
  • Step 604 calculate the mean value, U test value and skewness of the k eigenvalues, and determine the distribution status of the k eigenvalues.
  • the non-conforming product rate of each batch of samples calculated by the server and the average non-conforming product rate of the samples can be shown in Table 2, and the server calculates according to the formula:
  • the unqualified product rate of each batch of samples is processed, and 31 eigenvalues corresponding to 31 batches of samples are obtained, among which, the eigenvalues corresponding to the first batch of samples are:
  • the eigenvalues corresponding to the second batch of samples are:
  • 5.4009, and the eigenvalues corresponding to each batch of samples calculated by the server and
  • the moving range between the eigenvalues corresponding to two adjacent batches of samples can be shown in Table 3:
  • Table 3 Statistical table of eigenvalues and moving ranges corresponding to each batch of samples calculated by the server
  • Step 605 according to the distribution state and the corresponding relationship between the pre-stored distribution state and the way of estimating the standard deviation, estimate the standard deviation of the k feature values.
  • the eigenvalues and moving range statistical tables corresponding to each batch of samples calculated by the server are shown in Table 3.
  • the server determines that the distribution state of these 31 eigenvalues is a heavily skewed distribution, and the prestored distribution state and estimated
  • the corresponding relationship of the standard deviation method includes: the method of estimating the standard deviation corresponding to the heavily skewed distribution is to estimate the standard deviation based on the median of the moving range, and the server determines that the median of the 30 moving ranges is 1.08, that is
  • Step 606 according to the mean value and standard deviation, determine the control line, and draw the control chart of the production process.
  • Step 607 according to the preset judgment rules, check whether there are any points in the control diagram that meet the judgment rules.
  • Step 608 if there are points in the control map that meet the judgment rules, output the points that meet the judgment rules.
  • steps 607 to 608 are substantially the same as steps 501 to 502, and will not be repeated here.
  • FIG. 8 Another embodiment of the present application relates to an electronic device, as shown in FIG. 8 , including: at least one processor 701; and a memory 702 communicatively connected to the at least one processor 701; wherein, the memory 702 stores Instructions that can be executed by the at least one processor 701, the instructions are executed by the at least one processor 701, so that the at least one processor 701 can execute the production process quality control method in the foregoing embodiments.
  • the memory and the processor are connected by a bus
  • the bus may include any number of interconnected buses and bridges, and the bus connects one or more processors and various circuits of the memory together.
  • the bus may also connect together various other circuits such as peripherals, voltage regulators, and power management circuits, all of which are well known in the art and therefore will not be further described herein.
  • the bus interface provides an interface between the bus and the transceivers.
  • a transceiver may be a single element or multiple elements, such as multiple receivers and transmitters, providing means for communicating with various other devices over a transmission medium.
  • the data processed by the processor is transmitted on the wireless medium through the antenna, further, the antenna also receives the data and transmits the data to the processor.
  • the processor is responsible for managing the bus and general processing, and can also provide various functions, including timing, peripheral interface, voltage regulation, power management, and other control functions. Instead, memory can be used to store data that the processor uses when performing operations.
  • Another embodiment of the present application relates to a computer-readable storage medium storing a computer program.
  • the above method embodiments are implemented when the computer program is executed by the processor.
  • a storage medium includes several instructions to make a device ( It may be a single-chip microcomputer, a chip, etc.) or a processor (processor) to execute all or part of the steps of the methods described in the various embodiments of the present application.
  • the aforementioned storage media include: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), magnetic disk or optical disc, etc., which can store program codes. .

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Abstract

一种生产过程质量控制方法、电子设备和计算机可读存储介质，涉及生产质量控制技术领域。上述生产过程质量控制方法包括：计算k批产品中各批产品的不合格品率和k批产品的平均不合格品率（101）；根据平均不合格品率，对各批产品的不合格品率进行处理，得到k批产品分别对应的k个特征值（102）；计算k个特征值的统计量，统计量包括k个特征值的均值（103）；根据统计量，确定k个特征值的分布状态（104）；根据均值和分布状态，确定控制线，并绘制生产过程的控制图（105）。

Description

生产过程质量控制方法、电子设备和计算机可读存储介质
相关申请的交叉引用
本申请基于申请号为“202110949969.3”、申请日为2021年08月18日的中国专利申请提出，并要求该中国专利申请的优先权，该中国专利申请的全部内容在此以引入方式并入本申请。
技术领域
本申请实施例涉及生产质量控制技术领域，特别涉及一种生产过程质量控制方法、电子设备和计算机可读存储介质。
背景技术
统计过程控制(Statistical Process Control，简称：SPC)是一种借助数理统计方法的过程控制工具，SPC的主要工具是控制图，控制图可用于监控生产过程中各阶段、工序以及测试项的质量特性，根据图中各点的分布状况，来分析质量特性的变化趋势，SPC控制图包含两个方面的内容：一是利用控制图分析过程的稳定性，对过程存在的异常因素进行预警；二是计算过程能力指数来分析过程能力是否满足技术要求，对过程质量进行评价，为了能很好地控制并改进生产过程中的各类质量问题，越来越多的企业引进了SPC技术。
SPC控制图包括应用较广泛的有效监控过程不良率变化的不合格品率控制图(p控制图)和计算简单、数据便于收集的单值控制图等，p控制图主要用于判断生产过程中的不合格品率p是否保持在所要求的统计受控状态，单值控制图为标绘一段时间内单个观测值的控制图，把逐个测定值一个一个地在控制图中打点，对过程实施质量控制。
然而，传统的p控制图在确定控制线时，由于量纲、数量级等因素的影响，确定的控制线可能不统一，这导致难以对p控制图界内的点的波动情况进行全面的判断，而传统的单值控制图要求采集的样本数据要服从独立且同一个正态分布，这在实际生产过程中很难达到，因此基于传统控制图对生产过程中的质量控制可靠性、准确性不高，不能满足用户实际的生产需求。
发明内容
本申请实施例提供了一种生产过程质量控制方法，所述方法包括：计算k批产品中各批产品的不合格品率和所述k批产品的平均不合格品率；其中，所述k为大于1的整数；根据所述平均不合格品率，对所述各批产品的不合格品率进行处理，得到所述k批产品分别对应的k个特征值；其中，所述特征值为表征所述不合格品率的无量纲的值；计算所述k个特征值的统计量；其中，所述统计量包括所述k个特征值的均值；根据所述统计量，确定所述k个特征值的分布状态；其中，所述分布状态包括近似正态分布和偏态分布；根据所述均值和所述分布状态，确定控制线，并绘制生产过程的控制图。
本申请实施例还提供了一种电子设备，包括：至少一个处理器；以及，与所述至少一个处理器通信连接的存储器；其中，所述存储器存储有可被所述至少一个处理器执行的指令， 所述指令被所述至少一个处理器执行，以使所述至少一个处理器能够执行上述的生产过程质量控制方法。
本申请实施例还提供了一种计算机可读存储介质，存储有计算机程序，所述计算机程序被处理器执行时实现上述的生产过程质量控制方法。
附图说明
图1是根据本申请一个实施例的生产过程质量控制方法的流程图一；
图2是根据本申请一个实施例中提供的一种生产过程的控制图；
图3是根据本申请一个实施例中，对k个特征值进行U检验的流程图；
图4是根据本申请一个实施例中，确定k个特征值的偏态分布的偏态程度的流程图；
图5是根据本申请一个实施例中，服务器根据k个特征值的均值和k个特征值的分布状态，确定控制线的流程图；
图6是根据本申请一个实施例中，对控制图进行判异的流程图；
图7是根据本申请另一个实施例的生产过程质量控制方法的流程图二；
图8是根据本申请另一个实施例的电子设备的结构示意图。
具体实施方式
本申请实施例的主要目的在于提出一种生产过程质量控制方法、电子设备和计算机可读存储介质，可以对生产过程中的数据异常波动进行更全面的监控，有效提升了生产过程质量控制方法的准确性和普适性，有利于提升产品质量，更好地满足用户实际的生产需求。
为使本申请实施例的目的、技术方案和优点更加清楚，下面将结合附图对本申请的各实施例进行详细的阐述。然而，本领域的普通技术人员可以理解，在本申请各实施例中，为了使读者更好地理解本申请而提出了许多技术细节。但是，即使没有这些技术细节和基于以下各实施例的种种变化和修改，也可以实现本申请所要求保护的技术方案。以下各个实施例的划分是为了描述方便，不应对本申请的具体实现方式构成任何限定，各个实施例在不矛盾的前提下可以相互结合相互引用。
本申请的一个实施例涉及一种生产过程质量控制方法，应用于电子设备，其中，电子设备可以为终端或服务器，本实施例以及以下各个实施例中电子设备以服务器为例进行说明。下面对本实施例的生产过程质量控制方法的实现细节进行具体的说明，以下内容仅为方便理解提供的实现细节，并非实施本方案的必须。
本申请的实施例的应用场景可以包括但不限于：工业产品、农业产品、副产品等的加工过程；机械配件、建筑、工艺品的设计过程、企业的管理过程、电商服务业的服务过程等。
本实施例的生产过程质量控制方法的具体流程可以如图1所示，包括：
步骤101，计算k批产品中各批产品的不合格品率和k批产品的平均不合格品率。
具体而言，k为大于1的整数。
在具体实现中，服务器在确定相同的产品在相应的工序加工完成后，可以根据实际的监控需要，选择某个时间段内产品的某个测试项的测试数据作为研究对象，采集k批产品作为样本，各批产品的数量(即各批样本的样本大小)可以相同，也可以不同，第i批产品的数量记为n i，第i批产品的不合格产品数记作d i，服务器在采集到k批产品后，可以计算k批产 品中各批产品的不合格品率和这k批产品的平均不合格品率，第i批产品的不合格品率P i可以通过公式：P i＝d i/n i，计算得到，平均不合格品率
Figure PCTCN2022082865-appb-000001
可以通过公式：
Figure PCTCN2022082865-appb-000002
计算得到。
步骤102，根据平均不合格品率，对各批产品的不合格品率进行处理，得到k批产品分别对应的k个特征值。
在具体实现中，服务器在计算得到k批产品中各批产品的不合格品率和k批产品的平均不合格品率后，可以根据平均不合格品率，对各批产品的不合格品率进行处理，得到k批产品分别对应的k个特征值，第i批产品的不合格品率的处理的计算过程如下：
Figure PCTCN2022082865-appb-000003
式中，Z i为第i批产品对应的特征值，k批产品分别对应的k个特征值，是可以表征产品的不合格品率的无量纲的值，使用特征值能够消除量纲、数量级等因素的影响，便于对控制图内的各点进行全面的判断，同时，本申请的实施例不要求每批产品的产品数量一致，即本申请的实施例可适用于不同样本大小、不同样本分布的生产过程，同时也不限制抽样方法，可以对多种产品的生产过程进行监控，更贴近真实的生产情况，进一步满足用户的生产需求。
步骤103，计算k个特征值的统计量，统计量包括k个特征值的均值。
在具体实现中，考虑到服务器选取的k批产品是按照时间顺序随机抽取的，并不一定都完全服从标准的正态分布，经过处理之后得到的k个特征值也并不一定服从标准的正态分布，服务器需要对得到的k批产品分别对应的k个特征值进行统计分析，计算k个特征值的统计量，其中，统计量包括k个特征值的均值，记作
Figure PCTCN2022082865-appb-000004
步骤104，根据统计量，确定k个特征值的分布状态。
在具体实现中，服务器在计算k个特征值的统计量后，可以根据统计量，确定k个特征值的分布状态，k个特征值的分布状态包括近似正态分布(包括标准的正态分布)和偏态分布。
在一个例子中，服务器可以根据k个特征值的统计量，对k个特征值进行T检验、U检验、卡方检验等，确定k个特征值的分布状态。
步骤105，根据均值和分布状态，确定控制线，并绘制生产过程的控制图。
具体而言，服务器在根据统计量，确定k个特征值的分布状态，可以根据k个特征值的均值和分布状态，估算k个特征值的标准差δ，确定控制线。
在具体实现中，控制线包括中心线(Central Line，简称：CL)、第一上控制线(First Upper Control Line，简称：1UCL)、第二上控制线2UCL、第三上控制线3UCL、第一下控制线(First Lower Control Line，简称：1LCL)、第二下控制线2LCL、第三下控制线3LCL，CL为
Figure PCTCN2022082865-appb-000005
1UCL为
Figure PCTCN2022082865-appb-000006
2UCL为
Figure PCTCN2022082865-appb-000007
3UCL为
Figure PCTCN2022082865-appb-000008
1LCL为
Figure PCTCN2022082865-appb-000009
2LCL为
Figure PCTCN2022082865-appb-000010
3LCL为
Figure PCTCN2022082865-appb-000011
服务器将各控制线绘制成的对应的实线，并将k个按照时间的顺序依次打点标示在控制图中，依次将各数据点用折线连接起来，控制图的横坐标是时间，纵坐标是表示不合格品率的特征值。
在一个例子中，控制线也可以用中心线、±1SL、±2SL和±3SL表示，绘制好的生产过程的控制图可以如图2所示。
本实施例，相较于传统的p控制图和单值控制图而言，本申请的实施例，计算k批产品 中各批产品的不合格品率和k批产品的平均不合格品率，并根据平均不合格品率，对各批产品的不合格品率进行处理，得到k批产品分别对应的k个特征值，再计算k个特征值的统计量，从而根据统计量，确定k个特征值的分布状态是近似正态分布还是偏态分布，最后根据统计量中的均值和分布状态，确定控制线，并绘制生产过程的控制图，本申请的实施例对各批产品的不合格品率进行处理，以得到k批产品分别对应的k个特征值，得到的特征值是可以表征产品的不合格品率的无量纲的值，能够消除量纲、数量级等因素的影响，从而使后续确定的控制线是统一的、确定的，便于对控制图内的各点进行全面的判断，同时，本申请的实施例不要求采集的样本数据要服从独立且同一个正态分布，可以适用于各种各样生产过程，从而对生产过程中的数据异常波动进行更全面的监控，有效提升生产过程质量控制方法的准确性和普适性，有利于提升产品质量，更好地满足用户实际的生产需求。
在一个实施例中，服务器计算得到的k个特征值的统计量还包括k个特征值的方差、相邻两批产品对应的特征值的移动极差和移动极差的均方递差，服务器可以通过对k个特征值进行U检验来确定k个特征值的分布状态，服务器可以根据如图3所示的各步骤对k个特征值进行U检验，具体包括：
步骤201，根据均值、方差、移动极差和均方递差，对k个特征值进行U检验，得到k个特征值的U检验值。
具体而言，服务器计算得到的k个特征值的统计量，包括k个特征值的均值
Figure PCTCN2022082865-appb-000012
k个特征值的方差S 2、相邻两批产品对应的特征值的移动极差MR和移动极差的均方递差MSSD，其中，
Figure PCTCN2022082865-appb-000013
第i批产品对应的特征值与第i+1批产品对应的特征值之间的移动极差记作MR i，MR i＝|Z i+1-Z i|，
Figure PCTCN2022082865-appb-000014
在具体实现中，服务器可以通过下面的公式，根据均值、方差、移动极差和均方递差，对k个特征值进行U检验，得到k个特征值的U检验值：
Figure PCTCN2022082865-appb-000015
式中，i为大于0且小于或等于k的整数，Z i为第i批产品对应的特征值，
Figure PCTCN2022082865-appb-000016
为k个特征值的均值，S 2为k个特征值的方差，MR i为第i批产品对应的特征值和第i+1批产品对应的特征值之间的移动极差，MR i+1为第i+1批产品对应的特征值和第i+2批产品对应的特征值之间的移动极差，MSSD为均方递差，U为U检验值。在U检验公式中，MSSD值表示过程中没有非随机因素，只有随机因素带来的变化，而S 2值包含了引起过程变化的所有因素，所以当MSSD＝S 2时，表示引起过程变化的只有随机因素，过程为受控状态，属于正态分布，当MSSD值与S 2值很接近时，过程即属于近似正态分布，相较于T检验、卡方检验等，U检验可支持的样本数量更大，判断各批产品对应的特征值的分布状态的过程更加准确。
步骤202，判断U检验值是否位于预设的第一阈值范围内，如果是，执行步骤203，否则，执行步骤204。
在具体实现中，服务器根据均值、方差、移动极差和均方递差，对k个特征值进行U检验，得到k个特征值的U检验值后，可以判断U检验值是否位于预设的第一阈值范围内，其中，预设的第一阈值范围可以为-3≤U≤3。
步骤203，确定k个特征值的分布状态为近似正态分布。
在具体实现中，服务器若确定U检验值位于第一阈值范围内，则服务器可以确定k个特征值的分布状态为近似正态分布，特别地，当U＝0时，服务器可以确定k个特征值的分布状态为标准正态分布。
在一个例子中，预设的第一阈值范围为-3≤U≤3，服务器确定k个特征值的U检验值为2.1，则服务器确定k个特征值的分布状态为近似正态分布。
步骤204，确定k个特征值的分布状态为偏态分布。
在具体实现中，服务器若确定U检验值位于第一阈值范围外，则服务器可以确定k个特征值的分布状态为偏态分布。
在一个例子中，预设的第一阈值范围为-3≤U≤3，服务器确定k个特征值的U检验值为3.2，则服务器确定k个特征值的分布状态为偏态分布。
在一个实施例中，服务器在确定k个特征值的分布状态为偏态分布之后，还可以根据如图4所示的各步骤确定k个特征值的偏态分布的偏态程度，具体包括：
步骤301，根据均值和方差，确定k个特征值的偏度。
具体而言，服务器在确定k个特征值的分布状态为偏态分布之后，可以进一步地通过计算偏度来对偏态分布进行细化，服务器根据k个特征值的均值和k个特征值的方差，确定k个特征值的偏度。
在具体实现中，服务器可以通过以下公式，根据k个特征值的均值和k个特征值的方差，确定k个特征值的偏度：
Figure PCTCN2022082865-appb-000017
式中，i为大于0且小于或等于k的整数，Z i为第i批产品对应的特征值，
Figure PCTCN2022082865-appb-000018
为k个特征值的均值，S 3为k个特征值的方差的算术平方根的三次方，S k为k个特征值的偏度，本申请的实施例对不属于近似正态分布的分布状态，即偏态分布可以进一步地通过计算偏度来细化，进一步提升生产过程质量控制方法的准确性和普适性。
步骤302，判断偏度是否位于预设的第二阈值范围内，如果是，执行步骤303，否则，执行步骤304。
在具体实现中，服务器在根据k个特征值的均值和k个特征值的方差，确定k个特征值的偏度后，可以判断k个特征值的偏度是否位于预设的第二阈值范围内，其中，预设的第二阈值范围可以为-1<S k<1。
步骤303，确定k个特征值的分布状态为轻度偏态分布。
在具体实现中，服务器若确定偏度S k位于预设的第二阈值范围内，则服务器可以确定k个特征值的分布状态为轻度偏态分布。
在一个例子中，预设的第一阈值范围为-1<S k<1，服务器确定k个特征值的偏度S k为0.657，则服务器确定k个特征值的分布状态为轻度偏态分布。
步骤304，确定k个特征值的分布状态为重度偏态分布。
在具体实现中，服务器若确定偏度S k位于预设的第二阈值范围外，则服务器可以确定k个特征值的分布状态为重度偏态分布。
在一个例子中，预设的第一阈值范围为-1<S k<1，服务器确定k个特征值的偏度S k为 -1.27，则服务器确定k个特征值的分布状态为重度偏态分布。
在一个实施例中，服务器根据k个特征值的均值和k个特征值的分布状态，确定控制线可以由如图5所示的各步骤实现，具体包括：
步骤401，根据分布状态和预存的对应关系，估算k个特征值的标准差，预存的对应关系为分布状态与估算标准差的方式的对应关系。
具体而言，服务器在确定k个特征值的分布状态后，可以根据预存的对应关系，估算k个特征值的标准差。其中，预存的对应关系为分布状态与估算标准差的方式的对应关系，预存的对应关系可以由本领域的技术人员根据实际需要进行设定，并预先存储在服务器内部的存储器中。
在具体实现中，分布状态与估算标准差的方式的对应关系包括：近似正态分布对应的估算标准差的方式为根据均方递差估算标准差；轻度偏态分布对应的估算标准差的方式为根据移动极差的均值估算标准差；重度偏态分布对应的估算标准差的方式为根据移动极差的中位数估算标准差。
在一个例子中，服务器确定k个特征值的分布状态为轻度偏态分布，服务器可以通过以下公式根据相邻两批产品对应的特征值的移动极差的平均值估算标准差：
Figure PCTCN2022082865-appb-000019
式中，δ为标准差，
Figure PCTCN2022082865-appb-000020
为移动极差的平均值，当k个特征值的分布状态为轻度偏态分布状态时，使用移动极差的均值更能准确地预估生产过程的状态。
在一个例子中，服务器确定k个特征值的分布状态为重度偏态分布，服务器可以根据计算的测到的k-1个移动极差，确定k-1个移动极差的中位数，根据k-1个移动极差的中位数估算标准差，记作
Figure PCTCN2022082865-appb-000021
其中，
Figure PCTCN2022082865-appb-000022
为k-1个移动极差的中位数，当k个特征值的分布状态为重度偏态分布状态时，使用移动极差的中位数更能准确地预估生产过程的状态。
在一个例子中，服务器确定k个特征值的分布状态为近似正态分布，服务器可以通过以下公式根据计算出的移动极差的均方递差来估算标准差：
Figure PCTCN2022082865-appb-000023
其中，MSSD为移动极差的均方递差，当k个特征值的分布状态为近似正态分布时，生产过程属于可控状态，使用移动极差的均方递差更能准确地预估生产过程的状态。
步骤402，根据均值和标准差，确定控制线。
在具体实现中，服务器在根据分布状态和预存的对应关系，估算k个特征值的标准差后，可以根据k个特征值的均值和估算出的标准差，确定控制线。
在一个实施例中，服务器在基于休哈特控制图的绘制方法，绘制得到生产过程的控制图之后，可以通过如图6所示的各步骤，对控制图进行判异，具体包括：
步骤501，根据预设的判异规则，检测控制图中是否有符合判异规则的点。
在具体实现中，服务器在绘制得到生产过程的控制图之后，可以根据预设的判异规则，检测控制图中是否有符合判异规则的点，其中，预设的判异规则可以由本领域的技术人员根据实际需要从八种判异准则里面选择、设置，由于控制图的普适性、准确性非常高，根据预设的判异规则和控制图进行判异，可对生产过程中任何原因导致的异常波动进行全面的监控，有利于批量加工过程中对产品质量的改进。
在一个例子中，服务器绘制得到的生产过程的控制图如图2所示，控制线包括中心线、±1SL、±2SL和±3SL，+3SL与+2SL之间的区域和-3SL与-2SL之间的区域记作A区，+2SL与+1SL之间的区域和-2SL与-1SL之间的区域记作B区，+1SL与中心线之间的区域和-SL与中心线之间的区域记作C区，预设的判异规则为GB/T 4091-2001规定的8种判异规则，包括：
规则1，若数据点位于A区之外的区域，输出该数据点；
规则2，若连续9个数据点位于中心线同一侧，输出该连续9个数据点的最后一个数据点；
规则3，若连续6个数据点呈递增或递减，输出该连续6个数据点的最后一个数据点；
规则4，若连续14个数据点相邻点交替上下，输出该连续14个数据点的最后一个数据点；
规则5，若连续3个数据点中有至少2个数据点位于中心线同一侧的B区以外的区域，输出该连续3个数据点的最后一个数据点；
规则6，若连续5个数据点中有至少4个数据点位于中心线同一侧的C区以外的区域，输出该连续5个数据点的最后一个数据点；
规则7，若连续15个数据点位于中心线两侧的C区以内的区域，输出该连续15个数据点的最后一个数据点；
规则8，若连续8个数据点位于中心线两侧且该连续8个数据点都位于C区以外的区域，输出该连续8个数据点的最后一个数据点。
步骤502，若控制图中有符合判异规则的点，则输出符合判异规则的点。
在一个例子中，预设的判异规则为GB/T 4091-2001规定的8种判异规则，服务器绘制得到的生产过程的控制图如图2所示，其中，第2个数据点、第10个数据点，第11个数据点和第12个数据点位于A区之外的区域，服务器将第2个数据点、第10个数据点，第11个数据点和第12个数据点输出，供本领域的技术人员进行分析，确定生产过程出现严重缺陷，停止生产。
在一个例子中，预设的判异规则为GB/T 4091-2001规定的8种判异规则，服务器绘制得到的生产过程的控制图如图2所示，其中：第25个数据点至第27个数据点，连续3个数据点中有至少2个数据点位于中心线同一侧的B区以外的区域，服务器输出第27个数据点；第26个数据点至第28个数据点，连续3个数据点中有至少2个数据点位于中心线同一侧的B区以外的区域，服务器输出第28个数据点，同理，服务器输出第29个数据点、第30个数据点和第31个数据点，供本领域的技术人员进行分析，确定只是不合格品率显著降低，生产过程良好，继续进行生产。
本申请的另一个实施例涉及一种生产过程质量控制方法，下面对本实施例的生产过程质量控制方法的实现细节进行具体的说明，以下内容仅为方便理解提供的实现细节，并非实施本方案的必须，图7是本实施例所述的生产过程质量控制方法的流程图，包括：
步骤601，采集k批样本数据。
具体而言，服务器在确定相同的产品在相应的工序加工完成后，可以根据实际的监控需要，选择某个时间段内产品的某个测试项的测试数据作为研究对象，采集k批产品作为样本。
表1：服务器采集的样本数据统计表
批次 日期 样本大小(产品数) 不合格产品数
1 7.1 395 2
2 7.2 32 3
3 7.3 363 6
4 7.4 422 4
5 7.5 581 3
6 7.6 91 0
7 7.7 261 3
8 7.8 478 4
9 7.9 753 21
10 7.10 1039 42
11 7.11 619 28
12 7.12 447 26
13 7.13 959 5
14 7.14 658 10
15 7.15 467 3
16 7.16 858 6
17 7.17 910 10
18 7.18 820 8
19 7.19 598 10
20 7.20 513 4
21 7.21 790 10
22 7.22 872 18
23 7.23 757 18
24 7.24 432 12
25 7.25 712 11
26 7.26 1221 8
27 7.27 1707 13
28 7.28 1797 10
29 7.29 2081 10
30 7.30 1847 13
31 7.31 1764 15
步骤602，计算第i批样本的不合格品率和k批样本的平均不合格品率。
在一个例子中，服务器采集的31批样本数据可以如表1所示，服务器在采集到样本数据后，计算第i批样本的不合格品率和31批样本的平均不合格品率，其中，第1批样本的不合格品率为：
Figure PCTCN2022082865-appb-000024
服务器计算的各批样本的不合格品率和样本的平均不合格品率可以如表2所示：
表2：服务器计算的各批样本的不合格品率和样本的平均不合格品率统计表
Figure PCTCN2022082865-appb-000025
步骤603，对各批样本的不合格品率进行处理，得到k批样本分别对应的k个特征值。
步骤604，计算k个特征值的均值、U检验值和偏度，确定k个特征值的分布状态。
在一个例子中，服务器计算的各批样本的不合格品率和样本的平均不合格品率可以如表2所示，服务器根据公式：
Figure PCTCN2022082865-appb-000026
对各批样本的不合格品率进行处理，得到31批样本分别对应的31个特征值，其中，第1批样本对应的特征值为：
Figure PCTCN2022082865-appb-000027
第2批样本对应的特征值为：
Figure PCTCN2022082865-appb-000028
服务器计算第1批样本对应的特征值与第2批样本对应的特征值之间的移动极差为：MR 1＝|3.9707+1.4302|＝5.4009，服务器计算的各批样本分别对应的特征值和相邻两批样本对应的特征值之间的移动极差可以如表3所示：
表3：服务器计算的各批样本分别对应的特征值和移动极差统计表
批次 特征值Z MR
1 -1.4302 5.4009
2 3.9707 3.4355
3 0.5351 1.2220
4 -0.6868 1.0267
5 -1.7135 0.6055
6 -1.1080 0.8520
7 -0.2560 0.6868
8 -0.9428 4.4336
9 3.4908 4.1354
10 7.6262 0.6954
11 6.9308 1.3446
12 8.2754 10.4632
13 -2.1878 2.6103
14 0.4225 1.7210
15 -1.2985 0.3161
16 -1.6147 1.0037
17 -0.6110 0.2771
18 -0.8881 1.6162
19 0.7281 1.8177
20 -1.0896 0.9297
21 -0.1599 2.0492
22 1.8893 0.6239
23 2.5132 0.1108
24 2.6240 2.1259
25 0.4981 2.5588
26 -2.0606 0.0077
27 -2.0530 0.8121
28 -2.8650 0.5204
29 -3.3854 1.0335
30 -2.3520 0.5904
31 -1.7616 /
服务器根据公式：
Figure PCTCN2022082865-appb-000029
对31个特征值进行U检验，计算得到U＝3.3，预设的第一阈值范围为-3≤U≤3，服务器确定这31个特征值属于偏态分布，并根据公式：
Figure PCTCN2022082865-appb-000030
计算这31个特征值的偏度为S k＝1.39，预设的第二阈值范围为-1<S k<1，服务器确定这31个特征值的分布状态为重度偏态分布。
步骤605，根据分布状态和预存的分布状态与估算标准差的方式的对应关系，估算k个特征值的标准差。
在一个例子中，服务器计算的各批样本分别对应的特征值和移动极差统计表如表3所示，服务器确定这31个特征值的分布状态为重度偏态分布，预存的分布状态与估算标准差的方式的对应关系包括：重度偏态分布对应的估算标准差的方式为根据移动极差的中位数估算标准差，服务器确定30个移动极差的中位数为1.08，即
Figure PCTCN2022082865-appb-000031
步骤606，根据均值和标准差，确定控制线，并绘制生产过程的控制图。
在一个例子中，服务器计算31个特征值的均值
Figure PCTCN2022082865-appb-000032
标准差δ＝1.08，服务器确定控制线为如下：中心线为0.36，1SL为0.36+1.08＝1.44，-1SL为0.36-1.08＝-0.72，2SL为0.36+2.16＝2.52，-2SL为0.36-2.16＝-1.80，3SL为0.36+3.24＝3.60，-3SL为0.36-3.24＝-2.88。
步骤607，根据预设的判异规则，检测控制图中是否有符合判异规则的点。
步骤608，若控制图中有符合判异规则的点，则输出符合判异规则的点。
其中，步骤607至步骤608与步骤501至步骤502大致相同，此处不再赘述。
本申请另一个实施例涉及一种电子设备，如图8所示，包括：至少一个处理器701；以及，与所述至少一个处理器701通信连接的存储器702；其中，所述存储器702存储有可被所述至少一个处理器701执行的指令，所述指令被所述至少一个处理器701执行，以使所述至少一个处理器701能够执行上述各实施例中的生产过程质量控制方法。
其中，存储器和处理器采用总线方式连接，总线可以包括任意数量的互联的总线和桥，总线将一个或多个处理器和存储器的各种电路连接在一起。总线还可以将诸如外围设备、稳压器和功率管理电路等之类的各种其他电路连接在一起，这些都是本领域所公知的，因此，本文不再对其进行进一步描述。总线接口在总线和收发机之间提供接口。收发机可以是一个元件，也可以是多个元件，比如多个接收器和发送器，提供用于在传输介质上与各种其他装置通信的单元。经处理器处理的数据通过天线在无线介质上进行传输，进一步，天线还接收数据并将数据传送给处理器。
处理器负责管理总线和通常的处理，还可以提供各种功能，包括定时，外围接口，电压调节、电源管理以及其他控制功能。而存储器可以被用于存储处理器在执行操作时所使用的数据。
本申请另一个实施例涉及一种计算机可读存储介质，存储有计算机程序。计算机程序被处理器执行时实现上述方法实施例。
即，本领域技术人员可以理解，实现上述实施例方法中的全部或部分步骤是可以通过程序来指令相关的硬件来完成，该程序存储在一个存储介质中，包括若干指令用以使得一个设备(可以是单片机，芯片等)或处理器(processor)执行本申请各个实施例所述方法的全部 或部分步骤。而前述的存储介质包括：U盘、移动硬盘、只读存储器(ROM，Read-Only Memory)、随机存取存储器(RAM，Random Access Memory)、磁碟或者光盘等各种可以存储程序代码的介质。
本领域的普通技术人员可以理解，上述各实施例是实现本申请的具体实施例，而在实际应用中，可以在形式上和细节上对其作各种改变，而不偏离本申请的精神和范围。

Claims (10)

  1. 一种生产过程质量控制方法，包括：
    计算k批产品中各批产品的不合格品率和所述k批产品的平均不合格品率；其中，所述k为大于1的整数；
    根据所述平均不合格品率，对所述各批产品的不合格品率进行处理，得到所述k批产品分别对应的k个特征值；其中，所述特征值为表征所述不合格品率的无量纲的值；
    计算所述k个特征值的统计量；其中，所述统计量包括所述k个特征值的均值；
    根据所述统计量，确定所述k个特征值的分布状态；其中，所述分布状态包括近似正态分布和偏态分布；
    根据所述均值和所述分布状态，确定控制线，并绘制生产过程的控制图。
  2. 根据权利要求1所述的生产过程质量控制方法，其中，通过以下公式，根据所述平均不合格品率，对所述各批产品的不合格品率进行处理，得到所述各批产品对应的特征值：
    Figure PCTCN2022082865-appb-100001
    其中，i为大于0且小于或等于k的整数，p i为第i批产品的不合格品率，
    Figure PCTCN2022082865-appb-100002
    为所述平均不合格品率，n i为所述第i批产品的产品总数，Z i为所述第i批产品对应的特征值。
  3. 根据权利要求1至2中任一项所述的生产过程质量控制方法，其中，所述统计量还包括：所述k个特征值的方差、相邻两批产品对应的特征值的移动极差和所述移动极差的均方递差；
    所述根据所述统计量，确定所述k个特征值的分布状态，包括：
    根据所述均值、所述方差、所述移动极差和所述均方递差，对所述k个特征值进行U检验，得到所述k个特征值的U检验值；
    若所述U检验值位于预设的第一阈值范围内，则确定所述k个特征值的分布状态为近似正态分布；
    若所述U检验值位于所述预设的第一阈值范围外，则确定所述k个特征值的分布状态为偏态分布。
  4. 根据权利要求3所述的生产过程质量控制方法，其中，通过以下公式，根据所述均值、所述方差、所述移动极差和所述均方递差，对所述k个特征值进行U检验，得到所述k个特征值的U检验值：
    Figure PCTCN2022082865-appb-100003
    其中，i为大于0且小于或等于k的整数，Z i为第i批产品对应的特征值，
    Figure PCTCN2022082865-appb-100004
    为所述均值，S 2为所述方差，MR i为第i批产品对应的特征值和第i+1批产品对应的特征值之间的移动极差，MR i+1为第i+1批产品对应的特征值和第i+2批产品对应的特征值之间的移动极差，MSSD为所述均方递差，U为所述U检验值。
  5. 根据权利要求3所述的生产过程质量控制方法，其中，在所述确定所述k个特征值的 分布状态为偏态分布之后，还包括：
    根据所述均值和所述方差，确定所述k个特征值的偏度；
    若所述偏度位于预设的第二阈值范围内，则确定所述k个特征值的分布状态为轻度偏态分布；
    若所述偏度位于所述预设的第二阈值范围外，则确定所述k个特征值的分布状态为重度偏态分布。
  6. 根据权利要求5所述的生产过程质量控制方法，其中，通过以下公式，根据所述均值和所述方差，确定所述k个特征值的偏度：
    Figure PCTCN2022082865-appb-100005
    其中，i为大于0且小于或等于k的整数，Z i为第i批产品对应的特征值，
    Figure PCTCN2022082865-appb-100006
    为所述均值，S 3为所述方差的算术平方根的三次方，S k为所述偏度。
  7. 根据权利要求5所述的生产过程质量控制方法，其中，所述根据所述均值和所述分布状态，确定控制线，包括：
    根据所述分布状态和预存的对应关系，估算所述k个特征值的标准差；
    根据所述均值和所述标准差，确定控制线；
    其中，所述对应关系为分布状态与估算标准差的方式的对应关系，所述分布状态与估算标准差的方式的对应关系，包括：
    所述近似正态分布对应的估算标准差的方式为根据所述均方递差估算标准差；
    所述轻度偏态分布对应的估算标准差的方式为根据所述移动极差的均值估算标准差；
    所述重度偏态分布对应的估算标准差的方式为根据所述移动极差的中位数估算标准差。
  8. 根据权利要求1至7中任一项所述的生产过程质量控制方法，其中，在所述绘制生产过程的控制图之后，还包括：
    根据预设的判异规则，检测所述控制图中是否有符合所述判异规则的点；
    若所述控制图中有符合所述判异规则的点，则输出所述符合所述判异规则的点。
  9. 一种电子设备，包括：
    至少一个处理器；以及，
    与所述至少一个处理器通信连接的存储器；其中，
    所述存储器存储有可被所述至少一个处理器执行的指令，所述指令被所述至少一个处理器执行，以使所述至少一个处理器能够执行如权利要求1至8中任一项所述的生产过程质量控制方法。
  10. 一种计算机可读存储介质，存储有计算机程序，所述计算机程序被处理器执行时实现权利要求1至8中任一项所述的生产过程质量控制方法。
PCT/CN2022/082865 2021-08-18 2022-03-24 生产过程质量控制方法、电子设备和计算机可读存储介质 WO2023019947A1 (zh)

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