A method for selecting samples for random inspection

Abstract

The present invention relates to the technical field of drill bit detection, and specifically to a method for selecting samples for random inspection, the method comprising the following steps: S100. Determine the basic parameters of the product to be inspected, the parameters including product specifications S, usage time T and number of uses N; S200. Obtain model coefficients by analyzing historical data, construct a mathematical model, and input the coefficients and the parameters in step S100 into the mathematical model; S300. Calculate the frequency F of random inspection of the product according to the mathematical model, and inspect the product at the corresponding frequency of production of the product. The purpose of the present invention is to provide a method for selecting samples for random inspection, according to parameters such as product specifications, usage time of sharpening, number of products ground, etc., calculate the frequency of random inspection samples according to a mathematical model, ensure that the production of the product is positively correlated with the random inspection of the product, and effectively improve the robustness of the production of the product.

Description

Technical Field

[0001] The invention relates to the technical field of drill bit detection, and in particular to a method for selecting samples for random inspection. Background Art

[0002] At present, drill bits need to be sampled after production to obtain the yield rate of the current batch. When testing drill bits, random sampling is often used, but this sampling method is relatively random and cannot determine the relationship between the sample and the actual production based on the actual use of the product, and cannot make positive adjustments to the production of the product, causing trouble. Summary of the invention

[0003] In order to overcome the shortcomings and deficiencies in the prior art, the purpose of the present invention is to provide a method for selecting random inspection samples. According to parameters such as product specifications, knife sharpening usage time, and number of products ground, the frequency of random inspection samples is calculated according to a mathematical model to ensure that product production is positively correlated with product random inspection, thereby effectively improving the robustness of product production.

[0004] The present invention is achieved through the following technical solutions:

[0005] In a first aspect, the present invention provides a method for selecting samples for random inspection, which comprises the following steps:

[0006] S100. Determine the basic parameters of the product to be sampled, including product specifications S, usage time T, and number of uses N;

[0007] S200. Obtain model coefficients by analyzing historical data, construct a mathematical model, and input the coefficients and the parameters in step S100 into the mathematical model;

[0008] S300. Calculate the sampling frequency F of the product according to the mathematical model, and perform sampling inspection on the product at the corresponding frequency of production of the product.

[0009] In combination with the first aspect, further, in step S300, the calculation formula for sampling frequency is:

[0010] f(S,T,N)=F=aS+bT+cN

[0011] Among them, a, b, and c are model coefficients and are all constants. a represents the influence of product specification S on the sampling frequency, b represents the influence of usage time T on the sampling frequency, and c represents the influence of usage times N on the sampling frequency.

[0012] Combined with the first aspect, further, the calculation formula of a is:

[0013]

[0014] The calculation formula of b is:

[0015]

[0016] The calculation formula of b is:

[0017]

[0018] Among them, S i is the specification of the i-th product randomly selected from the historical data, F i is the frequency of the i-th product randomly selected from the historical data, T i is the usage time of the i-th product randomly selected from the historical data, N i is the number of times the i-th product is randomly selected from the historical data. is the average specification of the product in the historical data, is the average extraction frequency of products in historical data, is the average usage time of the product in historical data, is the average number of times the product is used in historical data.

[0019] In combination with the first aspect, further, the method further includes step S401: setting a, b, c, and F in step S300 to a old , b old 、c old 、F old , and the above a old , b old 、c old The calculated F old Input the historical data and repeat step S200.

[0020] In combination with the first aspect, further, the method of the present invention also includes step S402: collecting a set of sampling frequency numbers and a corresponding set of product yield numbers within a certain unit time, converting each sampling frequency and the corresponding yield rate in the set of sampling frequency numbers and the corresponding product yield rate into a grade, setting the two grades as a pair of observation values, and calculating the difference in grades between the pair of observation values, and calculating the correlation between the detection frequency and the corresponding product yield based on the difference in grades.

[0021] Combined with the first aspect, further, the correlation equation between the detection frequency and the product yield is:

[0022]

[0023] Among them, d n is the difference in rank between the observations of each team, and n is the logarithm of the observations.

[0024] In a second aspect, the present invention provides a computer device, including a processor and a storage medium;

[0025] The storage medium is used to store instructions;

[0026] The processor is used to operate according to the instructions to execute the steps of any one of the above methods.

[0027] Beneficial effects of the present invention:

[0028] A method for selecting random inspection samples of the present invention can calculate the frequency of random inspection samples according to parameters such as product specifications, knife sharpening usage time, and the number of products ground, based on a mathematical model, to ensure that product production is positively correlated with product random inspection, thereby effectively improving the robustness of product production. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] The present invention is further described using the accompanying drawings, but the embodiments in the accompanying drawings do not constitute any limitation to the present invention. A person skilled in the art can obtain other drawings based on the following drawings without creative work.

[0030] Figure 1 The flowchart of the selection method of the present invention is shown in FIG. DETAILED DESCRIPTION

[0031] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific embodiments of the present invention are described in detail below in conjunction with the accompanying drawings. In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention can be implemented in many other ways different from those described herein, and those skilled in the art can make similar improvements without violating the connotation of the present invention, so the present invention is not limited by the specific embodiments disclosed below.

[0032] At present, drill bits need to be sampled after production to obtain the yield rate of the current batch. When testing drill bits, random sampling is often used, but this sampling method is relatively random and cannot determine the relationship between the sample and the actual production based on the actual use of the product, and cannot make positive adjustments to the production of the product, causing trouble.

[0033] Example 1

[0034] In order to solve the above problems, this embodiment provides a method for selecting samples for random inspection, which includes the following steps:

[0035] S100. Determine the basic parameters of the product to be sampled. In this embodiment, the parameters include product specifications S, usage time T, and number of uses N. The units of product specifications S and usage time T can be determined according to production requirements, and can be length, weight, etc. The usage time T is generally days or hours, or minutes or seconds. By analyzing these basic parameters, the variables in the production process can be better understood, and a sampling plan can be formulated based on these variables;

[0036] S200. Obtain model coefficients by analyzing historical data, construct a mathematical model, and input the coefficients and the parameters in step S100 into the mathematical model;

[0037] S300. Calculate the sampling frequency F of the product according to the mathematical model, and perform sampling inspection on the product at the corresponding frequency of production of the product.

[0038] In order to more clearly illustrate the method for selecting samples for random inspection of the present invention, the following is combined with Figure 1 Each step in the embodiment of the present invention is described in detail.

[0039] Furthermore, in step S300, the calculation formula for sampling frequency is:

[0040] F=aS+bT+cN

[0041] Among them, a, b, and c are model coefficients and are all constants. a represents the influence of product specification S on the sampling frequency, b represents the influence of usage time T on the sampling frequency, and c represents the influence of usage times N on the sampling frequency.

[0042] Combined with the first aspect, further, the calculation formula of a is:

[0043]

[0044] The calculation formula of b is:

[0045]

[0046] The calculation formula of b is:

[0047]

[0048] Among them, S i is the specification of the i-th product randomly selected from the historical data, F i is the frequency of the i-th product randomly selected from the historical data, T i is the usage time of the i-th product randomly selected from the historical data, N i is the number of times the i-th product is randomly selected from the historical data. is the average specification of the product in the historical data, is the average extraction frequency of products in historical data, is the average usage time of the product in historical data, is the average number of times the product is used in historical data.

[0049] Assume the following historical data:

[0050] <![CDATA[Product Specification S i > <![CDATA[Usage duration T i > <![CDATA[Number of uses N i > <![CDATA[Sampling inspection frequency F i > 10 5 100 20 20 3 150 30 30 8 200 40

[0051] First, calculate the mean of each parameter:

[0052]

[0053] These averages are then used to calculate the coefficients:

[0054]

[0055] a=1

[0056]

[0057] b≈2.37

[0058]

[0059] c=0.2

[0060] Finally, we get the coefficients a = 1, b ≈ 2.37, and c = 0.2. These coefficients can be substituted into the model formula F = aS + bT + cN to calculate the new sampling frequency.

[0061] Furthermore, assuming that the product specification S of this drill bit product is 15, the usage time T is 6, and the number of uses N is 120, after substituting into the model formula, it is calculated that F = 53.22. Therefore, for this new drill bit product, according to the model, the sampling frequency F is about 53.22 times. This means that during the production process, it may be necessary to conduct a sampling inspection every 53 to 54 products to ensure quality control.

[0062] Furthermore, the steps of this method further include step S401: setting a, b, c, and F in step S300 to a old , b old 、c old 、F old , and the above a old , b old 、c old The calculated F old Input the historical data and repeat step S200.

[0063] One possible example is to use the method that minimizes the predicted sampling frequency F and the historical average sampling frequency The coefficients a, b, and c are optimized by the sum of the square differences between them.

[0064] Specifically, the objective function can be expressed as:

[0065]

[0066] Here, m is the number of samples.

[0067] In order to find the coefficients a, b, c that minimize the objective function J(a, b, c), we take partial derivatives for each coefficient and set them equal to zero to find the optimal solution.

[0068]

[0069]

[0070] The optimal values ​​of coefficients a, b, and c are obtained by setting these partial derivatives equal to zero and then solving the above system of equations. After obtaining the optimal values, an iterative method such as gradient descent is used to gradually update the coefficients until the coefficient values ​​that minimize the objective function are found:

[0071]

[0072] Among them, α is the learning rate, which determines the step size of the coefficient update in each iteration. Through this process, the parameters of the model can be optimized so that the sampling frequency predicted by the model is more consistent with the actual production sampling frequency in historical data.

[0073] In combination with the first aspect, further, the method of the present invention also includes step S402: collecting a set of sampling frequency numbers and a corresponding set of product yield numbers within a certain unit time, converting each sampling frequency and the corresponding yield rate in the set of sampling frequency numbers and the corresponding product yield rate into a grade, setting the two grades as a pair of observation values, and calculating the difference in grades between the pair of observation values, and calculating the correlation between the detection frequency and the corresponding product yield based on the difference in grades.

[0074] Combined with the first aspect, further, the correlation equation between the detection frequency and the product yield is:

[0075]

[0076] Among them, d n is the difference in rank between the observations of each team, and n is the logarithm of the observations.

[0077] In this embodiment, the number of levels can be set according to the number of observations. For example, if there are five observations of a variable, they will be assigned levels 1 to 5 according to their size. Finally, the value of ρ is substituted into the correlation equation. If ρ is close to 1, it means that there is a strong positive correlation between the variables; if ρ is close to -1, it means that there is a strong negative correlation; if ρ is close to 0, it means that there is no linear correlation. According to the result of ρ, the coefficients or historical data in the sampling frequency F can be checked for errors to improve the accuracy and robustness of the sampling.

[0078] Example 2

[0079] This embodiment provides a computer device, including a processor and a storage medium;

[0080] The storage medium is used to store instructions;

[0081] The processor is used to operate according to the instructions to execute the steps of any method described in Example 1.

[0082] Example 3

[0083] An embodiment of the present invention further provides a computer-readable storage medium on which a computer program is stored. When the program is executed by a processor, the steps of the method described in Embodiment 1 are implemented.

[0084] In summary, a method for selecting random inspection samples in this embodiment can calculate the frequency of random inspection samples according to parameters such as product specifications, knife sharpening usage time, and the number of products ground, based on a mathematical model, to ensure that product production is positively correlated with product random inspection, thereby effectively improving the robustness of product production.

[0085] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include computer-usable program code.

[0086] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0087] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0088] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0089] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention, rather than to limit the scope of protection of the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solution of the present invention can be modified or replaced by equivalents without departing from the essence and scope of the technical solution of the present invention.

Claims

1. A method for selecting samples for random inspection, characterized in that: The following steps are involved: S100. Determine the basic parameters of the product to be sampled, including product specifications S, usage time T, and number of uses N; S200. Obtain model coefficients by analyzing historical data, construct a mathematical model, and input the coefficients and the parameters in step S100 into the mathematical model; S300. Calculate the sampling frequency F of the product according to the mathematical model, and perform sampling inspection on the product at the corresponding frequency of production of the product.

2. A method for selecting random samples according to claim 1, characterized in that: In step S300, the calculation formula for sampling frequency is: f(S,T,N)=F=aS+bT+cN Among them, a, b, and c are model coefficients and are all constants. a represents the influence of product specification S on the sampling frequency, b represents the influence of usage time T on the sampling frequency, and c represents the influence of usage times N on the sampling frequency.

3. A method for selecting samples for random inspection according to claim 2, characterized in that: The calculation formula of a is: The calculation formula of b is: The calculation formula of b is: Among them, S i is the specification of the i-th product randomly selected from the historical data, F i is the frequency of the i-th product randomly selected from the historical data, T i is the usage time of the i-th product randomly selected from the historical data, N i is the number of times the i-th product is randomly selected from the historical data. is the average specification of the product in the historical data, is the average extraction frequency of products in historical data, is the average usage time of the product in historical data, is the average number of times the product is used in historical data.

4. A method for selecting samples for random inspection according to claim 2, characterized in that: The step S401 is also included: a, b, c, F in step S300 are set to a old 、b old 、c old 、F old , and the above a old 、b old 、c old The calculated F old Input the historical data and repeat step S200.

5. A method for selecting random samples according to claim 1, characterized in that: It also includes step S402: collecting a set of sampling frequency numbers and a corresponding set of product yield numbers within a certain unit time, converting each sampling frequency and the corresponding yield rate in the set of sampling frequency numbers and the corresponding product yield rate into a grade, setting the two grades as a pair of observation values, and calculating the grade difference between the pair of observation values, and calculating the correlation between the detection frequency and the corresponding product yield based on the grade difference.

6. A method for selecting random samples according to claim 5, characterized in that: The correlation equation between detection frequency and product yield is: Among them, d n is the difference in rank between the observations of each team, and n is the logarithm of the observations.

7. A computer device, characterized in that: including processor and storage medium; The storage medium is used to store instructions; The processor is used to operate according to the instructions to execute the steps of the method according to any one of claims 1 to 6.

Images