Internet-based production order placing and tracing system and method

Abstract

The invention discloses a production order placing and tracing system and method based on the Internet, and relates to the technical field of production data analysis, and the system comprises a purchase order synchronization module, an order code management module, a code production module, an order binding module and an order analysis module. In a business system, demand order information is established according to demand quantity, order coding is carried out based on the types of parts, a purchaser verifies and confirms related information after receiving a mail and submits and confirms purchase, a code production module allocates a code for each part, and an order binding module allocates a part production demand to an entrusted production system. The order analysis module collects part reject ratio information data in the production process, the part production speed is synthesized to participate in prediction of the next production order, the next order progress is effectively predicted according to the production speed and the reject ratio of the parts in the order, and data guarantee is provided for users.

Description

Technical Field

[0001] The present invention relates to the technical field of production data analysis, and in particular to an Internet-based production order placing and tracing system and method. Background Art

[0002] The company's existing consumables business is outsourced for production, and the annual production volume of a single product is at the KK level. The current production method is to place an order directly to the other party in the company's procurement system. The entrusting party will produce and calculate according to the paper order. After production is completed, the product will be packaged, loaded into outer boxes, and shipped back to the company. This method cannot accurately control whether the entrusting party actually produces according to the order quantity, cannot monitor the entrusting party's production defect rate, and cannot provide forecast information for the next production order by effectively utilizing production process data.

[0003] Therefore, people need an Internet-based production order placement and traceability system and method to solve the above problems. Summary of the invention

[0004] The purpose of the present invention is to provide an Internet-based production order placing and tracing system and method to solve the problems raised in the above-mentioned background technology.

[0005] In order to solve the above technical problems, the present invention provides the following technical solutions: an Internet-based production order placing and tracing system, the system comprising: a purchase order synchronization module, an order coding management module, a coding production module, an order binding module and an order analysis module; After predicting the demand volume based on historical sales data through the purchase order synchronization module, the demand order information is established in the business system based on the demand volume; The order coding management module codes the order based on the type of parts. After receiving the email, the purchaser verifies and confirms the relevant information and submits the confirmation purchase. Assigning a code to each component through the coding production module; Allocate parts production requirements to the entrusted production system through the order binding module; The order analysis module collects information data on the defective rate of parts in the production process, and comprehensively considers the production speed of parts to participate in the prediction of the next production order.

[0006] Furthermore, the order information mainly includes: order number, order quantity, batch code, sub-component code set and packaging information.

[0007] A production order placing and tracing method based on the Internet comprises the following steps: S1: Collect and synchronize the orders from the business system, and use the product code PC of the business order as the order subject object through relationship mapping; S2: After adjusting and structuring the business system data, an order is automatically created in the procurement system and the procurement staff is notified by email. The procurement staff adjusts the purchase order quantity and creates an approval process based on the historical production yield. S3: Collect the approved procurement system data, and create a unique coding set based on the smallest unit according to the sub-component set under the PC; S4: Data sets are distributed and issued to the entrusted production system according to the request. After each component is produced, the production system returns the component inspection information; S5: After the order production is completed, the system will format the data and convert it into the business order system, produce the data structure and rules that match the order system, and notify personnel in different directions of the completion status of this order production; S6: Collect production parts time and defective parts data during the production process to provide completion progress forecast information for the next order.

[0008] Furthermore, in step S1, business orders are synchronized: after the company's business receives external orders or predicts the future demand for consumables according to sales leads, previous years' and last month's sales data, a demand order is established in the business system; the order of the sales system CRM (hereinafter collectively referred to as the CRM system) is synchronized to the procurement system in this system, and the order subject is adjusted to the product code PC (Product Code) as the order subject object through the relationship mapping table; a purchase order for procurement is automatically created, and the information is sent to the procurement-related personnel; Further, in step S2: Purchase order synchronization system: after receiving the email, the purchaser verifies and confirms the relevant information, submits and confirms the purchase structure, and the purchase order information mainly includes: order number, order quantity, batch code and child item code set (Child Item Code Set), packaging information, etc. are summarized and synchronized; Further, in step S3: the approved procurement system data is collected, and a unique code set CICs->{CIC1, CIC2, CIC3, ..., CICX} based on the components is generated and created according to the sub-component set under the PC; S310: Perform data encryption and packaging processing on the PC and CICs packaging information PI to assign a code to each component. Each component corresponds to a unique data label PC-sn and component data set CICs-sn; S320: Extract the unique code set of parts CICs=(CIC1,CIC2,CIC3,…,CICX), and perform encryption mapping with the unique data label PC-sn of the product according to the set data encoding rules: secret(PC-sn,CICx)->{CIC1->S_CIC1,CIC2->S_CIC2,…,CICx->S_CICx}; Generate a two-dimensional data list according to the order quantity; S330: Serialize the encoding matrix and convert it into Json format data, perform signature verification and upload it to the database of this system for storage; The specific steps of S330 are: Step S331: The guid of the target data requested each time is the workstation information duid of the entrusting production party, and the entrusting production party fuid is obtained through secret (duid, fuid), which is agreed to be 32-bit character length, the parameter value is the current Unix timestamp, and the nonce parameter value is a 32-bit random string. The hash value is calculated for the target; Step S332: The client uses its own private key to encrypt the calculated hash value using asymmetric encryption to generate a signature strA; Step S333: The signature strA is attached to the data packet and sent to the service as a field of the request. When processing multi-end production concurrency, all transmission request data are sorted from small to large according to the ASCII code of the parameter name, and case-sensitive; Step S334: extracting the guid information and timestamp imestamp information in the target data transmission request to lock the target data, so as to avoid data request conflicts and repeated requests; Step S335: extract the routing information C in the target data transmission request, and map it to the target execution program funR of the specified interface through the routing information mapping table {C1->R1, C2->R2, ..., Cx->Rx}, and the execution program funR processes the request data according to the preset regulations; Step S336: Execute the program funR to deserialize the encoding matrix and store it in the system database; Step S337: Add a status label SC_SL for each component; the label includes the following statuses {not produced, produced, quality inspection completed, assembly completed}.

[0009] Furthermore, in step S4: the data set is distributed and issued to the entrusted production system according to the request, the data set is updated each time production and inspection are performed, and multiple production stations request the data at the same time, and confirm it according to production, quality inspection and assembly.

[0010] Further, in step S5: the number of PC-sns Number (PC-sn) with the status of "Quality Inspection Pass" under the order in the statistical system is compared with the actual number (OrderNumber) in the order. If Number (PC-sn) is equal to OrderNumber, the order is completed. Further, after the order status is "Completed", the request data interface directly returns "This order has been completed and production cannot continue."

[0011] Further, in step S6: for any type of component H i Analyze and find out that the parts H in this order i The quantity is e, record the component H i The defect rate, H i Represents the i-th component in this order.

[0012] Furthermore, the component H i The defective rate includes the defective rate of parts production O i , Parts quality inspection defect rate P i and the final defect rate Q i The production defect rate indicates the proportion of parts that do not meet the requirements in the production process to the total production; the quality inspection defect rate indicates the proportion of parts that do not meet the requirements in the quality inspection process to the total production; the final defect rate indicates the proportion of parts that do not meet the requirements after the parts are delivered to the user; for parts H i The production process was analyzed and a total of N parts H were produced, including qualified parts and defective parts. i , sorted by the production time of the parts, marked as {h1,h2,…,h N}, where the production time of the nth component is J n , the first successfully produced part is the nth c The nth produced part c The production time of the produced parts is J n_c , analyze the first Y parts sorted by time and obtain the defect rate R of the first Y parts i_Y =P i_Y +Q i_Y , Y=Y min ,Y min +1,…,N, where Y min The minimum sample size set for the system; Substitute Y=Y min ,Y min +1,…,N, when there exists Y such that Ri_Y ≤R i_max When the record satisfies R i_Y ≤R i_max The minimum Y value is denoted as Y=y, where R i_max For system settings of parts H i The maximum tolerable defective rate, each qualified component in the system has been assigned a code and marked with a timestamp, and the production time point J when the yth component is produced can be obtained. y , new order parts H i In the presence of Y such that R i_Y ≤R i_max The production time in this case is t i , construct t i With the new order parts H i The demand quantity h i The relationship is: ; Among them J N For the production time point of the Nth component, substitute Y=Y min ,Y min +1,…,N, when there is no Y such that R i_Y ≤R i_max When e qualified parts H i Sort by time, marked as {M1,M2,…,M e}, where the production time of the uth component is T u , then the average production speed of e qualified parts is V i =(T e -T1) / e, new order parts H i In the absence of Y, R i_Y ≤R i_max The production time in this case is t ’ i , construct t ’ i With the new order parts H i The demand quantity h i The relationship is: ; Then, all the parts in the order are analyzed to obtain the relationship between the quantity of any part in the new order and the time required, so that users can specifically predict the production status of any part in the new order and change the part usage plan in time when the part production speed is lower than expected.

[0013] Compared with the prior art, the beneficial effects achieved by the present invention are: accurately controlling whether the client actually produces according to the order quantity, obtaining the client's production defect rate in real time, and effectively predicting the progress of the next order through the production speed and defect rate of the parts of this order, providing data protection for users, which is beneficial for users to understand production quality, control production conditions, and thus adjust the use plan of parts in time. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings: Figure 1 It is a structural diagram of an Internet-based production order placing and tracing system of the present invention; Figure 2 It is a flow chart of a production order placing and tracing method based on the Internet of the present invention; Figure 3 It is a two-dimensional data list generated by the present invention according to the order quantity; Figure 4 It is a flow chart of the production situation distributed to the workstations by the present invention. DETAILED DESCRIPTION

[0015] The preferred embodiments of the present invention are described below in conjunction with the accompanying drawings. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present invention, and are not used to limit the present invention.

[0016] See also Figure 1 , Figure 2 , Figure 3 and Figure 4 ,The present invention provides a technical solution: a production order placing and tracing system based on the Internet, the system includes: a purchase order synchronization module, an order coding management module, a coding production module, an order binding module and an order analysis module; After predicting the demand volume based on historical sales data through the purchase order synchronization module, the demand order information is established in the business system based on the demand volume; The order coding management module codes the order based on the type of parts. After receiving the email, the purchaser verifies and confirms the relevant information and submits the confirmation purchase. Assigning a code to each component through the coding production module; Allocate parts production requirements to the entrusted production system through the order binding module; The order analysis module collects information data on the defective rate of parts in the production process, and comprehensively considers the production speed of parts to participate in the prediction of the next production order.

[0017] Order information mainly includes: order number, order quantity, batch code, sub-component code set and packaging information.

[0018] A production order placing and tracing method based on the Internet comprises the following steps: S1: Collect and synchronize the orders from the business system, and use the product code PC of the business order as the order subject object through relationship mapping; S2: After adjusting and structuring the business system data, an order is automatically created in the procurement system and the procurement staff is notified by email. The procurement staff adjusts the purchase order quantity and creates an approval process based on the historical production yield. S3: Collect the approved procurement system data, and create a unique coding set based on the smallest unit according to the sub-component set under the PC; S4: Data sets are distributed and issued to the entrusted production system according to the request. After each component is produced, the production system returns the component inspection information; S5: After the order production is completed, the system will format the data and convert it into the business order system, produce the data structure and rules that match the order system, and notify personnel in different directions of the completion status of this order production; S6: Collect production parts time and defective parts data during the production process to provide completion progress forecast information for the next order.

[0019] In step S1, business orders are synchronized: after the company's business receives external orders or predicts the future demand for consumables based on sales leads, previous years' and last month's sales data, a demand order is established in the business system; the orders of the sales system CRM (hereinafter referred to as the CRM system) are synchronized to the procurement system in this system, and the order subject is adjusted to the product code PC (Product Code) as the order subject object through the relationship mapping table; a purchase order for procurement is automatically created, and the information is sent to procurement-related personnel.

[0020] In step S2: Purchase order synchronization system: after receiving the email, the purchasing staff verifies and confirms the relevant information and submits the confirmed purchase structure. The purchase order information mainly includes: order number, order quantity, batch code and child item code set (Child Item Code Set), packaging information, etc. are summarized and synchronized.

[0021] In step S3: the approved procurement system data is collected, and a unique code set CICs->{CIC1, CIC2, CIC3,…, CICX} based on the components is generated according to the sub-component set under the PC; S310: Perform data encryption and packaging processing on the PC and CICs packaging information PI to assign a code to each component. Each component corresponds to a unique data label PC-sn and component data set CICs-sn; S320: Extract the unique code set of parts CICs=(CIC1,CIC2,CIC3,…,CICX), and perform encryption mapping with the unique data label PC-sn of the product according to the set data encoding rules: secret(PC-sn,CICx)->{CIC1->S_CIC1,CIC2->S_CIC2,…,CICx->S_CICx}; Generate a two-dimensional data list according to the order quantity; S330: Serialize the encoding matrix and convert it into Json format data, perform signature verification and upload it to the database of this system for storage; The specific steps of S330 are: Step S331: The guid of the target data requested each time is the workstation information duid of the entrusting production party, and the entrusting production party fuid is obtained through secret (duid, fuid), which is agreed to be 32-bit character length, the parameter value is the current Unix timestamp, and the nonce parameter value is a 32-bit random string. The hash value is calculated for the target; Step S332: The client uses its own private key to encrypt the calculated hash value using asymmetric encryption to generate a signature strA; Step S333: The signature strA is attached to the data packet and sent to the service as a field of the request. When processing multi-end production concurrency, all transmission request data are sorted from small to large according to the ASCII code of the parameter name, and case-sensitive; Step S334: extracting the guid information and timestamp imestamp information in the target data transmission request to lock the target data, so as to avoid data request conflicts and repeated requests; Step S335: extract the routing information C in the target data transmission request, and map it to the target execution program funR of the specified interface through the routing information mapping table {C1->R1, C2->R2, ..., Cx->Rx}, and the execution program funR processes the request data according to the preset regulations; Step S336: Execute the program funR to deserialize the encoding matrix and store it in the system database; Step S337: Add a status label SC_SL for each component; the label includes the following statuses {not produced, produced, quality inspection completed, assembly completed}.

[0022] In step S4: the data set is distributed and issued to the entrusted production system according to the request, and the data set is updated each time production and inspection are performed. Multiple production stations request the data at the same time, and the data is confirmed according to production, quality inspection and assembly.

[0023] In step S5: the number of PC-sns with the status of "Quality Inspection Pass" under the order in the statistical system is compared with the actual number (OrderNumber) in the order. If Number(PC-sn) is equal to OrderNumber, the order is completed. Furthermore, after the order status is "Completed", the request data interface directly returns "This order has been completed and production cannot continue."

[0024] In step S6: for any type of component H i Analyze and find out that the parts H in this order i The quantity is e, record the component H i The defect rate, H i represents the i-th component in this order; 11. The component H i The defective rate includes the defective rate of parts production O i , Parts quality inspection defect rate P i and the final defect rate Q i The production defect rate indicates the proportion of parts that do not meet the requirements in the production process to the total production; the quality inspection defect rate indicates the proportion of parts that do not meet the requirements in the quality inspection process to the total production; the final defect rate indicates the proportion of parts that do not meet the requirements after the parts are delivered to the user; for parts H i The production process was analyzed and a total of N parts H were produced, including qualified parts and defective parts. i , sorted by the production time of the parts, marked as {h1,h2,…,h N}, where the production time of the nth component is J n , the first successfully produced part is the nth c The nth produced part c The production time of the produced parts is J n_c , analyze the first Y parts sorted by time and obtain the defect rate R of the first Y parts i_Y =P i_Y +Q i_Y , Y=Y min ,Y min +1,…,N, where Y minThe minimum sample size set for the system; Substitute Y=Y min ,Y min +1,…,N, when there exists Y such that R i_Y ≤R i_max When the record satisfies R i_Y ≤R i_max The minimum Y value is denoted as Y=y, where R i_max For system settings of parts H i The maximum tolerable defective rate, each qualified component in the system has been assigned a code and marked with a timestamp, and the production time point J when the yth component is produced can be obtained. y , new order parts H i In the presence of Y such that R i_Y ≤R i_max The production time in this case is t i , construct t i With the new order parts H i The demand quantity h i The relationship is: ; Among them J N For the production time point of the Nth component, substitute Y=Y min ,Y min +1,…,N, when there is no Y such that R i_Y ≤R i_max When e qualified parts H i Sort by time, marked as {M1,M2,…,M e}, where the production time of the uth component is T u , then the average production speed of e qualified parts is V i =(T e -T1) / e, new order parts H i In the absence of Y, R i_Y ≤R i_max The production time in this case is t ’ i , construct t ’ i With the new order parts H i The demand quantity h i The relationship is: ; Then, all the parts in the order are analyzed to obtain the relationship between the quantity of any part in the new order and the time required, so that users can specifically predict the production status of any part in the new order and change the part usage plan in time when the part production speed is lower than expected.

[0025] Embodiment 1: The component H i The defective rate includes the defective rate of parts production O i =0.05, parts quality inspection defect rate P i =0.04 and the final defect rate Q i =0.01, the production defect rate means: the proportion of parts that do not meet the requirements in the production process to the total production; the production defect rate means: the proportion of parts that do not meet the requirements in the production process to the total production; the quality inspection defect rate means: the proportion of parts that do not meet the requirements in the quality inspection process to the total production; the final defect rate means: the proportion of parts that do not meet the requirements after the parts are delivered to the user to the total production; for parts H i The production process was analyzed and a total of N = 110,000 parts H were produced, including qualified parts and defective parts. i , sorted by the production time of the parts, marked as {h1,h2,…,h N}, where the production time of the nth component is J n , the first successfully produced part is the nth c and the production time is J n_c , analyze the first Y parts sorted by time, Y min The minimum sample size set for the system is set to 2000, and the defect rate R of the first Y parts is obtained. i_Y =P i_Y +Q i_Y , Y=2000,2001,…,N; Substitute Y = 2000, 2001, ..., N one by one. When there exists Y such that R i_Y ≤R i_max When the minimum Y value is recorded, Y=y=6000, where R i_max For system settings of parts H i The maximum tolerable defect rate, each qualified component in the system has been assigned a code and marked with a timestamp, and the time point J at this time can be obtained. y , Parts H in the order i In the presence of Y such that R i_Y ≤R i_max The time required is t i , construct t iWith the new order parts H i Quantity i The relationship is: ; Substitute Y=Y min ,Y min +1,…,N, when there is no Y such that R i_Y ≤R i_max When e qualified parts H i Sort by time, marked as {M1,M2,…,M e}, where the production time of the uth component is T u , then the average production speed of e qualified parts is V i =(T e -T1) / e, Parts H in order i In the absence of Y, R i_Y ≤R i_max The time required is t ’ i , construct t ’ i With the new order parts H i Quantity i The relationship is: .

[0026] Finally, it should be noted that the above description is only a preferred example of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art can still modify the technical solutions described in the aforementioned embodiments or replace some of the technical features therein by equivalents. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. An Internet-based production order placing and tracing system, characterized by: The system includes: a purchase order synchronization module, an order coding management module, a coding production module, an order binding module and an order analysis module; After predicting the demand volume based on historical sales data through the purchase order synchronization module, the demand order information is established in the business system based on the demand volume; The order coding management module codes the order based on the type of parts. After receiving the email, the purchaser verifies and confirms the relevant information and submits the confirmation purchase. Assigning a code to each component through the coding production module; Allocate parts production requirements to the entrusted production system through the order binding module; The order analysis module collects information data on the defective rate of parts in the production process, and comprehensively considers the production speed of parts to participate in the prediction of the next production order.

2. The Internet-based production order placing and tracing system according to claim 1, characterized in that: The order information mainly includes: order number, order quantity, batch code, sub-component code set and packaging information.

3. A production order placing and tracing method based on the Internet, characterized by: The following steps are involved: S1: collect and synchronize the orders of the business system, and use the product code PC of the business order as the order subject object through relationship mapping; S2: After adjusting and structuring the business system data, an order is automatically created in the procurement system and the procurement staff is notified by email. The procurement staff adjusts the purchase order quantity and creates an approval process based on the historical production yield. S3: Collect the approved procurement system data, and create a unique coding set based on the smallest unit according to the sub-component set under the PC; S4: Data sets are distributed and issued to the entrusted production system according to the request. After each component is produced, the production system returns the component inspection information; S5: After the order production is completed, the system will format the data and convert it into the business order system, the data structure and rules that match the production order system, and notify personnel in different directions of the completion status of this order production; S6: Collect production parts time and defective parts data during the production process to provide completion progress forecast information for the next order.

4. The Internet-based production order placing and tracing method according to claim 3 is characterized by: In step S1, synchronize business orders: after the company's business receives external orders or predicts the future demand for consumables based on sales leads, previous years' and last month's sales data, a demand order is established in the business system; the order of the sales system CRM is synchronized to the procurement system in this system, and the order subject is adjusted to the product code PC as the order subject object through the relationship mapping table; a purchase order for procurement is automatically created, and the information is sent to procurement-related personnel.

5. The Internet-based production order placing and tracing method according to claim 4 is characterized by: In step S2: Purchase order synchronization system: after receiving the email, the purchasing staff verifies and confirms the relevant information and submits the confirmed purchase structure. The purchase order information mainly includes: order number, order quantity, batch code, sub-component code set and packaging information are summarized and synchronized.

6. The Internet-based production order placing and tracing method according to claim 5, characterized in that: In step S3: the approved procurement system data is collected, and a unique code set CICs->{CIC1, CIC2, CIC3,…, CICX} based on the components is generated according to the sub-component set under the PC; S310: Perform data encryption and packaging processing on the PC and CICs packaging information PI to assign a code to each component. Each component corresponds to a unique data label PC-sn and component data set CICs-sn; S320: Extract the unique code set of components CICs={CIC1,CIC2,CIC3,…,CICX}, and perform encryption mapping with the unique data label of the product PC-sn according to the set data encoding rules: secret(PC-sn,CICx)->{CIC1->S_CIC1,CIC2->S_CIC2,…,CICx->S_CICx}; generate a two-dimensional data list according to the order quantity; S330: Serialize the encoding matrix and convert it into Json format data, perform signature verification and upload it to the database of this system for storage; The specific steps of S330 are: Step S331: The guid of the target data requested each time is the workstation information duid of the entrusting production party, and the entrusting production party fuid is obtained through secret (duid, fuid), which is agreed to be 32-bit character length, the parameter value is the current Unix timestamp, and the nonce parameter value is a 32-bit random string. The hash value is calculated for the target; Step S332: The client uses its own private key to encrypt the calculated hash value using asymmetric encryption to generate a signature strA; Step S333: The signature strA is attached to the data packet and sent to the service as a field of the request. When processing multi-end production concurrency, all transmission request data are sorted from small to large according to the ASCII code of the parameter name, and case-sensitive; Step S334: extracting the guid information and timestamp imestamp information in the target data transmission request to lock the target data, so as to avoid data request conflicts and repeated requests; Step S335: extract the routing information C in the target data transmission request, and map it to the target execution program funR of the specified interface through the routing information mapping table {C1->R1, C2->R2, ..., Cx->Rx}, and the execution program funR processes the request data according to the preset regulations; Step S336: Execute the program funR to deserialize the encoding matrix and store it in the system database; Step S337: Add a status label SC_SL for each component; the label includes the following statuses {not produced, produced, quality inspection completed, assembly completed}.

7. The Internet-based production order placing and tracing method according to claim 6 is characterized by: In step S4: the data set is distributed and issued to the entrusted production system according to the request, and the data set is updated each time production and inspection are performed. Multiple production stations request the data at the same time, and the data is confirmed according to production, quality inspection and assembly. When production starts, the production time is recorded.

8. The Internet-based production order placing and tracing method according to claim 7, characterized in that: In step S5: the number Number of PC-sns with the status of "Quality Inspection Pass" under the order in the statistical system is compared with the actual number in the order. If Number is equal to OrderNumber, the order is completed.

9. The Internet-based production order placing and tracing method according to claim 8, characterized in that: In step S6: for any type of component H i Analyze and find out that the parts H in this order i The quantity is e, record the component H i The defect rate, H i Represents the i-th component in this order.

10. The Internet-based production order placing and tracing method according to claim 9, characterized in that: The component H i The defective rate includes the defective rate of parts production O i , Parts quality inspection defect rate P i and the final defect rate Q i The production defect rate indicates the proportion of parts that do not meet the requirements in the production process to the total production; the quality inspection defect rate indicates the proportion of parts that do not meet the requirements in the quality inspection process to the total production; the final defect rate indicates the proportion of parts that do not meet the requirements after the parts are delivered to the user; for parts H i The production process was analyzed and a total of N parts H were produced, including qualified parts and defective parts. i , sorted by the production time of the parts, marked as {h1,h2,…,h N }, where the production time of the nth component is J n , the first successfully produced part is the nth c The nth produced part c The production time of the produced parts is J n_c , analyze the first Y parts sorted by time and obtain the defect rate R of the first Y parts i_Y =P i_Y +Q i_Y , Y=Y min ,Y min +1,…,N, where Y min The minimum sample size set for the system; Substitute Y=Y min ,Y min +1,…,N, when there exists Y such that R i_Y ≤R i_max When the record satisfies R i_Y ≤R i_max The minimum Y value is denoted as Y=y, where R i_max For system settings of parts H i The maximum tolerable defective rate, each qualified component in the system has been assigned a code and marked with a timestamp, and the production time point J when the yth component is produced can be obtained. y , new order parts H i In the presence of Y such that R i_Y ≤R i_max The production time in this case is t i , construct t i With the new order parts H i The demand quantity h i The relationship is: ； Among them J N For the production time point of the Nth component, substitute Y=Y min ,Y min +1,…,N, when there is no Y such that R i_Y ≤R i_max When e qualified parts H i Sort by time, marked as {M1,M2,…,M e }, where the production time of the uth component is T u , then the average production speed of e qualified parts is V i =(T e -T1) / e, new order parts H i In the absence of Y, R i_Y ≤R i_max The production time in this case is t ’ i , construct t ’ i With the new order parts H i The demand quantity h i The relationship is: 。

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