Product quality monitoring method and device, computer equipment and storage medium

Abstract

The invention relates to a product quality monitoring method and device, computer equipment and a storage medium. The method comprises the following steps: according to a statistical hypothesis test model, calculating a minimum sampling detection frequency for sampling detection of spare and accessory parts provided by a supplier under a given confidence coefficient and a nominal value; carrying out sampling detection on the spare and accessory parts by utilizing the minimum sampling detection frequency, determining whether the spare and accessory parts of a supplier are accepted or not according to a sampling scheme, carrying out electronic product production according to the accepted spare and accessory parts, and determining an optimal strategy for detecting, disassembling and exchanging the spare and accessory parts and finished products in the production process through cost-benefit analysis; carrying out sensitivity analysis on the optimal strategy by considering a defective rate and a sampling detection error in a sampling detection process, and optimizing the sampling detection process by utilizing a Bayesian inference method according to an analysis result; determining an optimized optimal strategy according to the optimized sampling scheme; and carrying out product quality monitoring by using the optimized optimal strategy. By adopting the method, the product quality can be improved.

Description

Technical Field

[0001] The present application relates to the field of data processing technology, and in particular to a product quality monitoring method, device, computer equipment and storage medium. Background Art

[0002] With the rapid development of the electronic product manufacturing industry, the market's requirements for product quality are increasing day by day, and electronic product manufacturers rely on key components to ensure the quality and performance of their products. Therefore, when faced with the components provided by suppliers, sampling inspection must be performed to determine whether to accept these components. Now a strategy needs to be developed to improve product quality. Summary of the invention

[0003] Based on this, it is necessary to provide a product quality monitoring method, device, computer equipment and storage medium that can improve product quality in response to the above technical problems.

[0004] A product quality monitoring method, the method comprising: Calculate the minimum number of sampling tests for the spare parts provided by the supplier at a given confidence level and nominal value based on the statistical hypothesis testing model; Perform sampling inspection on spare parts using the minimum number of sampling inspections to obtain a sampling plan; Determine whether to accept the supplier's parts and components based on the sampling plan and produce electronic products based on the accepted parts and components. Determine the optimal strategy for testing, disassembling and replacing parts and finished products during the production process through cost-benefit analysis; Considering the defective rate and sampling detection error in the sampling detection process, a sensitivity analysis is conducted on the optimal strategy. According to the analysis results, the sampling detection process is optimized using the Bayesian inference method to obtain the optimized sampling scheme; the optimized optimal strategy is determined based on the optimized sampling scheme; and the optimized optimal strategy is used to monitor product quality.

[0005] In one embodiment, sampling inspection is performed on the spare parts provided by the supplier according to the statistical hypothesis testing model to obtain the minimum number of sampling inspections, including: According to the statistical hypothesis testing model, the spare parts provided by the supplier are sampled and tested, and the minimum number of sampling tests is obtained as ; in, The supplier's nominal defective rate, represents the increment of the sample rate, represents the test statistic, Indicates the significance level.

[0006] In one embodiment, a cost-benefit analysis is performed to determine the best strategy for testing, disassembling, and replacing parts and finished products during the production process, including: Define decision nodes, event nodes and key parameters, build a decision tree, and build a main decision tree and a sub-decision tree. The main decision tree includes the decision of whether to inspect spare parts and finished products. The sub-decision tree specifically handles the disassembly and reuse of unqualified finished products, connecting the two decision trees. Taking the expected profit maximization as the objective function and the limited defective rate entering the market as the constraint condition, the optimal strategy model is established; The optimal strategy model is solved using a dynamic programming algorithm, which gradually calculates the best strategy for each situation.

[0007] In one embodiment, the optimal strategy model is established with the expected profit maximization as the objective function and the limited defective rate entering the market as the constraint condition, including: Taking the expected profit maximization as the objective function and the limited defective rate entering the market as the constraint condition, the optimal strategy model is established as Objective function: ; ; ; ; ; ; ; Constraints: ; in, Indicates the purchase cost, represents the detection cost, represents the assembly cost, represents the switching cost, represents the dismantling cost, Indicates spare parts i The testing cost, represents the sample size, represents the market price of the finished product, represents the total income, represents the maximum defective rate, Indicates k Whether the second cycle detects component 1, represents the inspection cost of part 1, Indicates k Whether the second cycle detects the spare part 2, represents the inspection cost of part 2, Indicatesk Whether the finished product is tested in the second cycle, represents the assembly cost of the finished product, Indicates the replacement loss of unqualified finished products, Indicates The rate of defective products entering the market in the second cycle, Indicates the cost of dismantling unqualified finished products, Indicates whether unqualified finished products are disassembled in the kth cycle.

[0008] In one embodiment, the confidence interval of the defective rate is ; in, represents the estimated defective rate obtained by sampling, Represents a normal distribution Quantile, is the sample size.

[0009] In one embodiment, a sensitivity analysis is performed on the optimal strategy by considering the defective rate and sampling detection error in the sampling detection process, and the sampling detection process is optimized using the Bayesian inference method according to the analysis results, including: The mean standard deviation of the maximum profit under different confidence levels is greater than 0.1. It is believed that the error in defective rate detection has a great impact on the calculation result of the maximum profit and has high sensitivity. The Bayesian inference method is used to continuously update the estimate of the defective rate based on historical detection data and current sampling detection data, and the optimal balance point between detection accuracy and detection cost is found to optimize the sampling detection process.

[0010] A product quality monitoring device, comprising: The sampling inspection module is used to calculate the minimum number of sampling inspections for sampling inspections of spare parts provided by suppliers under given confidence levels and nominal values ​​according to the statistical hypothesis testing model; the spare parts are sampled and inspected using the minimum number of sampling inspections to obtain a sampling plan; The optimal strategy determination module is used to determine whether to accept the supplier's spare parts according to the sampling plan and produce electronic products based on the accepted spare parts, and to determine the optimal strategy for testing, disassembling and replacing spare parts and finished products during the production process through cost-benefit analysis; The product quality monitoring module is used to conduct sensitivity analysis on the optimal strategy by considering the defective rate and sampling detection error in the sampling detection process, and optimize the sampling detection process using the Bayesian inference method based on the analysis results to obtain the optimized sampling plan; determine the optimized optimal strategy based on the optimized sampling plan; and use the optimized optimal strategy to monitor product quality.

[0011] A computer device comprises a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the following steps are implemented: Calculate the minimum number of sampling tests for the spare parts provided by the supplier at a given confidence level and nominal value based on the statistical hypothesis testing model; Perform sampling inspection on spare parts using the minimum number of sampling inspections to obtain a sampling plan; Determine whether to accept the supplier's parts and components based on the sampling plan and produce electronic products based on the accepted parts and components. Determine the optimal strategy for testing, disassembling and replacing parts and finished products during the production process through cost-benefit analysis; Considering the defective rate and sampling detection error in the sampling detection process, a sensitivity analysis is conducted on the optimal strategy. According to the analysis results, the sampling detection process is optimized using the Bayesian inference method to obtain the optimized sampling scheme; the optimized optimal strategy is determined based on the optimized sampling scheme; and the optimized optimal strategy is used to monitor product quality.

[0012] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the following steps: Calculate the minimum number of sampling tests for the spare parts provided by the supplier at a given confidence level and nominal value based on the statistical hypothesis testing model; Perform sampling inspection on spare parts using the minimum number of sampling inspections to obtain a sampling plan; Determine whether to accept the supplier's parts and components based on the sampling plan and produce electronic products based on the accepted parts and components. Determine the optimal strategy for testing, disassembling and replacing parts and finished products during the production process through cost-benefit analysis; Considering the defective rate and sampling detection error in the sampling detection process, a sensitivity analysis is conducted on the optimal strategy. According to the analysis results, the sampling detection process is optimized using the Bayesian inference method to obtain the optimized sampling scheme; the optimized optimal strategy is determined based on the optimized sampling scheme; and the optimized optimal strategy is used to monitor product quality.

[0013] The above-mentioned product quality monitoring method, device, computer equipment and storage medium, this application can effectively identify the quality level of spare parts provided by suppliers at a given confidence level and reduce risks by determining the minimum number of sampling inspections. Then, through a specific sampling plan, it is possible to systematically evaluate whether the spare parts meet the standards to ensure that only qualified parts enter production. By analyzing the costs of testing, disassembly and replacement, and formulating the optimal strategy, production costs can be effectively controlled while ensuring product quality. Finally, considering the impact of defective rate and detection error, the sampling plan is optimized to improve the accuracy of the detection process and reduce the misjudgment rate. Monitoring using the optimized strategy can ensure that high quality standards are maintained during the production process and that the strategy is adjusted in time to cope with changes. This application can significantly improve the control of spare parts quality, thereby improving the overall quality of the final product. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Figure 1 A schematic diagram of a process of a product quality monitoring method in an embodiment; Figure 2 is a schematic diagram of a decision tree in one embodiment; Figure 3 is a structural block diagram of a product quality monitoring device in one embodiment; Figure 4 FIG. 4 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0015] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0016] In one embodiment, Figure 1 As shown, a product quality monitoring method is provided, comprising the following steps: Step 102, calculating the minimum number of sampling tests for sampling the spare parts provided by the supplier under a given confidence level and nominal value based on the statistical hypothesis testing model; sampling the spare parts using the minimum number of sampling tests to obtain a sampling plan.

[0017] Common distribution types of defective spare parts include normal distribution, binomial distribution, hypergeometric distribution, etc. Therefore, a smaller sample experiment can be used to first assume the distribution type, and then perform the corresponding fitting test to determine the distribution type of each defective spare part. Based on the statistical hypothesis test model (two-sided), the minimum number of tests under a given reliability and nominal value is determined. The minimum number of sampling tests is used to sample and test spare parts, and a sampling plan is formed based on the sampling results of each time.

[0018] Through the statistical hypothesis testing model, the minimum sample size for sampling and testing supplier parts can be calculated under the set confidence level and nominal value. This process helps manufacturers minimize testing costs and time while ensuring statistical validity. Through hypothesis testing, the risk of defective products entering the production line can be identified and reduced, ensuring that only parts that meet quality standards are accepted.

[0019] Step 104, determine whether to accept the supplier's spare parts according to the sampling plan and produce electronic products based on the accepted spare parts, and determine the optimal strategy for testing, disassembling and replacing spare parts and finished products during the production process through cost-benefit analysis.

[0020] Statistical methods are used to determine specific sampling plans, such as random sampling, stratified sampling, etc., to ensure that the samples are representative and can effectively reflect the overall quality of the batch of spare parts. Deciding whether to accept the supplier's spare parts based on the sampling results is a data-based decision-making method that can improve the objectivity and scientificity of decision-making.

[0021] A systematic analysis of the costs of inspection, disassembly and replacement can help manufacturers identify the optimal return on investment and avoid unnecessary expenses while ensuring that product quality is not compromised.

[0022] Based on the results of the cost-benefit analysis, manufacturers can allocate resources more rationally, focus testing on high-risk parts and components, and improve overall efficiency.

[0023] Step 106, performing sensitivity analysis on the optimal strategy by considering the defective rate and sampling detection error in the sampling detection process, optimizing the sampling detection process using the Bayesian inference method based on the analysis results, and obtaining an optimized sampling scheme; determining the optimized optimal strategy based on the optimized sampling scheme; and using the optimized optimal strategy to monitor product quality.

[0024] Consider the defective rate of sampling detection error and calculate the confidence interval of the defective rate; use Monte Carlo to simulate the defective rate fluctuation caused by sampling detection error and generate the defective rate distribution under different sample sizes to simulate sampling detection error. Analyze the impact of detection error on the optimal strategy under different defective rate estimates; recalculate the optimal strategy under different confidence intervals; perform sensitivity analysis. Determine which parameters have the greatest impact on model output and decision results, which helps focus resources and attention for more precise measurement and control.

[0025] Through sensitivity analysis, we evaluate the impact of different defective rates and detection errors on the strategy, thereby identifying potential improvement points. Based on previous data and experience, we continuously update our understanding of spare parts quality through the Bayesian method, achieve dynamic adjustments, and improve the flexibility and adaptability of the detection plan.

[0026] Utilize optimized sampling plans and strategies to continuously monitor the quality of spare parts and finished products during the production process to ensure that quality standards are always met. According to the monitoring results, timely adjust the testing plan and production strategy to form a closed-loop quality management system and achieve dynamic optimization.

[0027] Through this application, manufacturers can not only effectively improve the quality control level of spare parts, but also achieve a balance between cost and efficiency, ultimately achieving the production of high-quality electronic products. They can also adapt to the ever-changing market demand and production environment, and ensure the long-term stability of product quality.

[0028] The above-mentioned product quality monitoring method, this application can effectively identify the quality level of spare parts provided by suppliers at a given confidence level and reduce risks by determining the minimum number of sampling inspections. Then, through a specific sampling plan, it is possible to systematically evaluate whether the spare parts meet the standards to ensure that only qualified parts enter production. By analyzing the costs of testing, disassembly and replacement, and formulating the optimal strategy, production costs can be effectively controlled while ensuring product quality. Finally, considering the impact of defective rate and detection error, the sampling plan is optimized to improve the accuracy of the detection process and reduce the misjudgment rate. Monitoring using the optimized strategy can ensure that high quality standards are maintained during the production process and that the strategy is adjusted in time to cope with changes. This application can significantly improve the control of spare parts quality, thereby improving the overall quality of the final product.

[0029] In one embodiment, sampling inspection is performed on the spare parts provided by the supplier according to the statistical hypothesis testing model to obtain the minimum number of sampling inspections, including: According to the statistical hypothesis testing model, the spare parts provided by the supplier are sampled and tested, and the minimum number of sampling tests is obtained as ; in, The supplier's nominal defective rate, represents the increment of the sample rate, represents the test statistic, Indicates the significance level.

[0030] In a specific embodiment, it is assumed that , N=1000, the calculation results are as follows.

[0031] (1) Scenario 1: If the defective rate of spare parts exceeds 10% at a confidence level of 95%, the parts will be rejected.

[0032] ①Normal distribution (binomial distribution): ; ②Hypergeometric distribution: .

[0033] If 139 spare parts are sampled from the population for testing, and the result is 17 defective, then the defective rate is 0.1223. Substituting into the formula The test statistic Z=2.351 is calculated. According to the standard normal distribution table, it is greater than the critical value of 1.96. Therefore, the original hypothesis can be rejected and it is determined that the defective rate exceeds 10%. The company should reject this batch of parts.

[0034] Verification by confidence interval: According to the formula , substituting the data into the calculation, the 95% confidence interval is [0.0412, 0.1588]. Since the upper limit of the confidence interval is 0.1588, which is greater than the nominal value of 0.10, it means that the defective rate exceeds the nominal value, so this batch of spare parts should be rejected.

[0035] (2) Case 2: With a confidence level of 90%, if the defective rate of spare parts does not exceed 10%, they are accepted.

[0036] ①Normal distribution (binomial distribution): ; ②Hypergeometric distribution:

[0037] If 98 spare parts samples are drawn from the population for testing, and the results show that 6 are defective, then the defective rate is 0.0612. Substituting into the formula The test statistic Z=-1.89 is calculated. According to the standard normal distribution table, it is less than the critical value of 1.645. Therefore, the original hypothesis cannot be rejected. It is believed that the defective rate is not significantly greater than 10%, and the company should accept this batch of parts.

[0038] Verification by confidence interval: According to the formula , substituting the data into the calculation, the 90% confidence interval is [0.0745, 0.0954]. Since the upper limit of the confidence interval 0.0845 is less than the nominal value 0.10, this batch of spare parts should be accepted.

[0039] Generally speaking, if the machining process is caused by the combined effect of many independent random errors, and there is no error factor with a significant advantage, the defective rate of processed parts will be normally distributed. When the distribution type is unknown and cannot be fitted and verified temporarily, the conclusion of the normal distribution can be used as a reference.

[0040] In one embodiment, a cost-benefit analysis is performed to determine the best strategy for testing, disassembling, and replacing parts and finished products during the production process, including: Define decision nodes, event nodes and key parameters, build a decision tree, and build a main decision tree and a sub-decision tree. The main decision tree includes the decision of whether to inspect spare parts and finished products. The sub-decision tree specifically handles the disassembly and reuse of unqualified finished products, connecting the two decision trees. Taking the expected profit maximization as the objective function and the limited defective rate entering the market as the constraint condition, the optimal strategy model is established; The optimal strategy model is solved using a dynamic programming algorithm, which gradually calculates the best strategy for each situation.

[0041] In a specific embodiment, by defining decision nodes (such as whether to inspect spare parts and finished products) and event nodes (such as inspection results), a decision tree of the system is formed, which helps to clearly present the consequences of each decision step.

[0042] The main decision tree focuses on the preliminary inspection decision, while the sub-decision tree focuses on the disassembly and reuse of unqualified finished products. This hierarchical structure facilitates system analysis and the formulation of optimization strategies. Taking the expected profit maximization as the objective function ensures that the ultimate goal of all decisions is to maximize economic benefits. This not only focuses on cost control, but also takes into account market competitiveness. The defective rate entering the market is used as a constraint to ensure that while pursuing profits, product quality is maintained and the negative impact of unqualified products is avoided. Through the dynamic programming algorithm, the best strategy in different situations is gradually calculated. This method can effectively handle complex decision-making processes and ensure that each decision is optimized based on the previous results. Dynamic programming can cope with market fluctuations and changes in the quality of spare parts, so that strategies can be adjusted in time according to actual conditions and improve the flexibility of decision-making.

[0043] In addition, through clear decision-making processes and data-based analysis, manufacturers can allocate resources more effectively, focus on high-risk links, and reduce unnecessary testing and disassembly costs. This strategy ensures timely processing of non-conforming products, reduces defective rates, and improves the quality of final products.

[0044] The specific process includes: defining decision nodes and event nodes as well as key parameters: ① Decision nodes. Enterprises need to make a series of decisions in multiple links of their production process. These decisions not only involve the input of production costs in the production process, but also directly affect the final profit of the enterprise. These decisions include: : Whether to detect component 1 in the kth cycle (yes = 1, no = 0), k = 1, 2, ..., n; : Whether to detect component 2 in the kth cycle (yes = 1, no = 0); : Whether to detect finished products in the kth cycle (yes = 1, no = 0); : Whether to dismantle unqualified finished products in the kth cycle (yes = 1, no = 0); : Whether the customer exchanges defective products in the kth cycle (yes = 1, no = 0).

[0045] Based on the pessimistic criterion, it is assumed here that users will choose to exchange defective products after purchasing them. That is, as long as defective products flow into the market, the manufacturer will bear this part of the exchange cost, so that the best action plan can be chosen among the worst results.

[0046] ② Event nodes. In a decision tree, event nodes (also called random nodes or chance nodes) are nodes used to represent uncertainty or random events in the decision-making process. These nodes represent various situations or results that may occur after a specific decision, including: : The status of component 1 in the kth cycle (qualified = 1, unqualified = 0); : The status of component 2 in the kth cycle (qualified = 1, unqualified = 0); : The status of the finished product in the kth cycle (qualified = 1, unqualified = 0).

[0047] ③Key parameters. Including: : Defective rate of spare part 1; : Defective rate of spare part 2; : The defective rate of finished products; : Purchase price of spare part 1; : Purchase price of spare part 2; : Assembly cost of finished product; : Testing cost of spare part 1; : Testing cost of spare part 2; : Testing cost of finished products; : Market price of finished product; : Losses from replacement of unqualified finished products; : Dismantling costs of unqualified finished products; : Considering the social reputation of the enterprise, the maximum acceptable defective rate that can flow into the society; : Number of loop recursions, =1, 2, 3, 4….

[0048] (2) Construct a decision tree such as Figure 2 As shown in Figure 1, it contains a main decision tree and a sub-decision tree. The sub-decision tree specifically handles the disassembly and reuse of unqualified finished products and connects the two decision trees.

[0049] (3) Objective function and constraints. The objective is to maximize expected profit, and the limited defective rate entering the market is used as a constraint.

[0050] ①Purchase cost: ; ②Testing cost: ; ③Assembly cost: ; ④ Losses caused by replacement of unqualified finished products: ; in represents the defective rate entering the market in the kth cycle; is an indicator function, which is 1 if the condition is met and 0 otherwise.

[0051] ⑤ Disassembly cost: It includes two parts: the defective products that flow into the market after being disassembled and the defective products that are detected after testing. ; in Indicates the defective rate found in the kth cycle after finished product testing ⑥Total income: ; Objective function: ; Constraints: ; (4) Dynamic programming Dynamic programming is a method for solving complex problems by breaking down the original problem into relatively simple sub-problems. It avoids repeated calculations by breaking down complex problems into simpler sub-problems and storing the solutions to these sub-problems (called memoization), ensuring that each sub-problem is solved only once. This method relies on the overlapping sub-problems and optimal sub-structure characteristics of the problem, that is, the optimal solution to the problem can be constructed from the optimal solutions to its sub-problems. Dynamic programming usually adopts a bottom-up strategy to calculate all sub-problems in sequence and gradually build a solution to the final problem. This method can significantly improve the efficiency of the algorithm while ensuring accuracy, and is especially suitable for problems with obvious sub-structures. The optimal solution and optimal decision plan for each case are gradually calculated using the dynamic programming algorithm, as shown in Table 1.

[0052] Table 1

[0053] ① In case 1, neither spare parts nor finished products are tested, and the testing cost is the lowest, but it leads to a higher defective rate of finished products, the highest replacement and disassembly costs, but the overall profit is not low. In the actual production process, since testing will occupy production capacity, if the company has a large number of orders, the company can choose this decision.

[0054] ② In case 2, the inspection cost is high because the quality control of spare parts and finished products is relatively strict. The defective rate in this case is more effectively controlled, so that the disassembly cost and replacement cost are kept at the lowest level.

[0055] ③ In situation 3, since only the quality of the finished product is strictly controlled, the cost of parts inspection can be saved, and production capacity can be saved in actual production. In addition, since the defective rate is controlled, the replacement cost and disassembly cost will not be too high, and the overall profit is considerable. This is a compromise solution.

[0056] ④ In case 4, due to the strict control of the quality of spare parts and finished products, the testing cost is relatively the highest. In this case, the defective rate is effectively controlled, so that the disassembly cost and replacement cost are kept at a low level, and the final profit is the highest. In the actual production process, since a large number of tests will occupy production capacity, if the company has a small order volume, this decision can be selected.

[0057] ⑤ In scenario 5, similar to scenario 3, it is a compromise solution that can be used in daily production with appropriate production capacity.

[0058] ⑥In situation 6, the decision is the same as in situation 1.

[0059] Compare the costs of each part horizontally: ① Purchase cost: In all scenarios, purchase cost accounts for an important proportion of the total cost, ranging from 20% to 40%. Scenario 6 shows the highest purchase cost ratio (40%), which may mean that the cost of spare parts used in this scenario is high.

[0060] ② Inspection cost: Inspection cost varies greatly in different scenarios, ranging from 13.4% to 27.9%. The inspection cost in scenarios 1 and 6 is lower, which may be because fewer parts or finished products need to be inspected in these scenarios.

[0061] ③ Assembly cost: The proportion of assembly cost in each scenario is relatively stable, but the proportion of assembly cost in scenario 6 is the highest (27.9%), which may reflect a more complex assembly process or higher labor costs.

[0062] ④ Replacement loss of unqualified finished products: The replacement loss is the highest in scenario 3 (20.7%), which is consistent with the higher replacement loss of unqualified finished products mentioned in the problem description. The replacement loss in scenario 6 is also high (18.5%), which may require further analysis of its causes.

[0063] ⑤ Disassembly cost: Scenario 6 has the highest disassembly cost (28.1%), which may be related to the higher disassembly cost in this scenario, as mentioned in the problem description.

[0064] In one embodiment, the optimal strategy model is established with the maximum expected profit as the objective function and the limited defective rate entering the market as a constraint condition, including: Taking the expected profit maximization as the objective function and the limited defective rate entering the market as the constraint condition, the optimal strategy model is established as Objective function: ; ; ; ; ; ; ; Constraints: ; in, Indicates the purchase cost, represents the detection cost, represents the assembly cost, represents the switching cost, represents the dismantling cost, Indicates spare parts i The testing cost, represents the sample size, represents the market price of the finished product, represents the total income, represents the maximum defective rate, Indicates k Whether the second cycle detects component 1, represents the inspection cost of part 1, Indicates k Whether the second cycle detects the spare part 2, represents the inspection cost of part 2, Indicates k Whether the finished product is tested in the second cycle, represents the assembly cost of the finished product, Indicates the replacement loss of unqualified finished products, Indicates The rate of defective products entering the market in the second cycle, Indicates the cost of dismantling unqualified finished products, Indicates whether unqualified finished products are disassembled in the kth cycle.

[0065] In one embodiment, the confidence interval of the defective rate is ; in, represents the estimated defective rate obtained by sampling, Represents a normal distribution Quantile, is the sample size.

[0066] In one embodiment, a sensitivity analysis is performed on the optimal strategy by considering the defective rate and sampling detection error in the sampling detection process, and the sampling detection process is optimized using the Bayesian inference method according to the analysis results, including: The mean standard deviation of the maximum profit under different confidence levels is greater than 0.1. It is believed that the error in defective rate detection has a great impact on the calculation result of the maximum profit and has high sensitivity. The Bayesian inference method is used to continuously update the estimate of the defective rate based on historical detection data and current sampling detection data, and the optimal balance point between detection accuracy and detection cost is found to optimize the sampling detection process.

[0067] It should be understood that although Figure 1 The steps in the flowchart are shown in sequence as indicated by the arrows, but these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified in this document, there is no strict order restriction for the execution of these steps, and these steps can be executed in other orders. Moreover, Figure 1 At least part of the steps may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily executed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be executed in turn or alternately with other steps or at least part of the sub-steps or stages of other steps.

[0068] In one embodiment, Figure 3 As shown, a product quality monitoring device is provided, including: a sampling detection module 302, an optimal strategy determination module 304 and a product quality monitoring module 306, wherein: The sampling inspection module 302 is used to calculate the minimum number of sampling inspections for sampling inspections of spare parts provided by suppliers under a given confidence level and nominal value according to a statistical hypothesis testing model; and to perform sampling inspections on spare parts using the minimum number of sampling inspections to obtain a sampling plan; The optimal strategy determination module 304 is used to determine whether to accept the spare parts of the supplier according to the sampling plan and produce the electronic products according to the accepted spare parts, and to determine the optimal strategy for testing, disassembling and replacing the spare parts and finished products during the production process through cost-benefit analysis; The product quality monitoring module 306 is used to perform sensitivity analysis on the optimal strategy by considering the defective rate and sampling detection error in the sampling detection process, and optimize the sampling detection process using the Bayesian inference method based on the analysis results to obtain an optimized sampling plan; determine the optimized optimal strategy based on the optimized sampling plan; and use the optimized optimal strategy to monitor product quality.

[0069] For the specific definition of a product quality monitoring device, please refer to the definition of a product quality monitoring method above, which will not be repeated here. Each module in the above-mentioned product quality monitoring device can be implemented in whole or in part by software, hardware and a combination thereof. The above-mentioned modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory of the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above modules.

[0070] In one embodiment, a computer device is provided. The computer device may be a terminal, and its internal structure diagram may be as follows: Figure 4 As shown. The computer device includes a processor, a memory, a network interface, a display screen and an input device connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The network interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a product quality monitoring method is implemented. The display screen of the computer device can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device can be a touch layer covering the display screen, or a key, trackball or touchpad set on the computer device housing, or an external keyboard, touchpad or mouse, etc.

[0071] Those skilled in the art will understand that Figure 4 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0072] Those of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be implemented by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0073] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0074] The above-described embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the invention. It should be pointed out that, for a person of ordinary skill in the art, several modifications and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims

1. A product quality monitoring method, characterized in that: The method comprises: Calculate the minimum number of sampling tests for the spare parts provided by the supplier at a given confidence level and nominal value based on the statistical hypothesis testing model; Performing sampling inspection on spare parts using the minimum number of sampling inspections to obtain a sampling plan; Determine whether to accept the supplier's spare parts according to the sampling plan and produce electronic products based on the accepted spare parts, and determine the optimal strategy for testing, disassembling and replacing spare parts and finished products during the production process through cost-benefit analysis; A sensitivity analysis is performed on the optimal strategy by considering the defective rate and sampling detection error in the sampling detection process. The sampling detection process is optimized using the Bayesian inference method based on the analysis results to obtain an optimized sampling scheme. The optimized optimal strategy is determined based on the optimized sampling scheme; and the optimized optimal strategy is used to monitor product quality.

2. The method according to claim 1, characterized in that According to the statistical hypothesis testing model, the spare parts provided by the supplier are sampled and tested to obtain the minimum number of sampling tests, including: According to the statistical hypothesis testing model, the spare parts provided by the supplier are sampled and tested, and the minimum number of sampling tests is obtained as in, The supplier's nominal defective rate, represents the increment of the sample rate, represents the test statistic, Indicates the significance level.

3. The method according to claim 1, characterized in that Determine the best strategy for testing, disassembling and replacing parts and finished products during production through cost-benefit analysis, including: Define decision nodes, event nodes and key parameters, build a decision tree, and build a main decision tree and a sub-decision tree. The main decision tree includes the decision of whether to inspect spare parts and finished products. The sub-decision tree specifically handles the disassembly and reuse of unqualified finished products, connecting the two decision trees. Taking the maximum expected profit as the objective function and the limited defective rate entering the market as the constraint condition, the optimal strategy model is established; The optimal strategy model is solved using a dynamic programming algorithm to gradually calculate the optimal strategy in each case.

4. The method according to claim 3, characterized in that Taking the expected profit maximization as the objective function and the limited defective rate entering the market as the constraint condition, the optimal strategy model is established, including: Taking the expected profit maximization as the objective function and the limited defective rate entering the market as the constraint condition, the optimal strategy model is established as Objective function: ; ； ； ； ； ； ； Constraints: ; in, Indicates the purchase cost, represents the detection cost, represents the assembly cost, represents the switching cost, represents the dismantling cost, Indicates spare parts i The testing cost, represents the sample size, represents the market price of the finished product, represents the total income, represents the maximum defective rate, Indicates k Whether the second cycle detects component 1, represents the inspection cost of part 1, Indicates k Whether the second cycle detects the spare part 2, represents the inspection cost of part 2, Indicates k Whether the finished product is tested in the second cycle, represents the assembly cost of the finished product, Indicates the replacement loss of unqualified finished products, Indicates The rate of defective products entering the market in the second cycle, Indicates the cost of dismantling unqualified finished products, Indicates whether unqualified finished products are disassembled in the kth cycle.

5. The method according to claim 1, characterized in that The confidence interval of the defective rate is ； in, represents the estimated defective rate obtained by sampling, Represents a normal distribution Quantile, is the sample size.

6. The method according to claim 1, characterized in that Considering the defective rate and sampling detection error in the sampling detection process, a sensitivity analysis is conducted on the optimal strategy. Based on the analysis results, the Bayesian inference method is used to optimize the sampling detection process, including: The mean standard deviation of the maximum profit under different confidence levels is greater than 0.

1. It is believed that the error in defective rate detection has a great impact on the calculation result of the maximum profit and has high sensitivity. The Bayesian inference method is used to continuously update the estimate of the defective rate based on historical detection data and current sampling detection data, and the optimal balance point between detection accuracy and detection cost is found to optimize the sampling detection process.

7. A product quality monitoring device, characterized in that: The device comprises: A sampling inspection module is used to calculate the minimum number of sampling inspections for sampling inspections of spare parts provided by suppliers under a given confidence level and nominal value according to a statistical hypothesis testing model; and to perform sampling inspections on spare parts using the minimum number of sampling inspections to obtain a sampling plan; An optimal strategy determination module, used to determine whether to accept the supplier's spare parts according to the sampling plan and to produce electronic products according to the accepted spare parts, and to determine the optimal strategy for testing, disassembling and replacing spare parts and finished products during the production process through cost-benefit analysis; The product quality monitoring module is used to perform sensitivity analysis on the optimal strategy by considering the defective rate and sampling detection error in the sampling detection process, and optimize the sampling detection process using the Bayesian inference method based on the analysis results to obtain an optimized sampling plan; determine the optimized optimal strategy based on the optimized sampling plan; and use the optimized optimal strategy to monitor product quality.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

Images