A product quality data analysis and feedback method and system based on the entire life cycle

Abstract

The present disclosure belongs to the technical field of product life cycle management, and specifically relates to a product quality data analysis feedback method and system based on the entire life cycle, wherein the entire life cycle includes a design stage, a manufacturing stage, and a service stage, and includes: obtaining an association mapping relationship between product objects in the design stage and the manufacturing stage, and between the manufacturing stage and the service stage; calculating the probability of occurrence of product quality problems in different stages according to the obtained association mapping relationship between product objects; judging the source of the product quality problem based on the obtained probability; and completing the product quality analysis according to the judged source of the product quality problem.

Description

Technical Field

[0001] The present disclosure belongs to the technical field of product lifecycle management, and specifically relates to a product quality data analysis and feedback method and system based on the entire lifecycle. Background Art

[0002] The statements in this section merely provide background information related to the present disclosure and do not necessarily constitute prior art.

[0003] After the product is designed and manufactured, it needs to be used for a long time and undergo maintenance, repair and overhaul during the operation and maintenance process, so as to maintain the product's use value and extend its life. Therefore, the product's operation and maintenance service has important value in the product's life cycle. For complex products, the level of operation and maintenance service directly determines the product market.

[0004] Product quality originates from the design and manufacturing stages, but quality problems are often exposed in the service stage. The cost of repairing quality problems is very high, thousands of times higher than the cost of solving them in the design and manufacturing stages, or even impossible to repair. Therefore, the quality data of the product service stage is fed back to the product design and manufacturing stages, which can help eliminate quality problems and reduce the cost of the product throughout its life cycle.

[0005] The design and manufacturing stages of a product determine the product value and 70%-90% of the life cycle cost, and are the two most important stages in the product life cycle. When a product is updated or upgraded, quality data support from the product service stage is needed to improve product design and manufacturing, improve product quality, and reduce the cost of the product throughout its life cycle.

[0006] According to the inventors, some studies use statistical analysis for quality data management. Statistical analysis can be used in the manufacturing process to ensure process quality and find manufacturing defects. In the service stage, because the product working conditions are random, it is impossible to establish a stable statistical analysis model to find the source of the problem.

[0007] Some studies have used data mining to identify the source of quality problems, but for the design stage, the relevant factors for each component are the same, with fewer options and no appropriate differentiation. Summary of the invention

[0008] In order to solve the above problems, this paper proposes a product quality data analysis and feedback method and system based on the whole life cycle. Product quality data is fed back from the service stage to the product design and manufacturing stage to effectively eliminate product quality problems. First, the data of the design stage, the manufacturing stage and the service stage are integrated together; second, the quality data of the service stage is analyzed to find the source of quality problems; finally, the quality data is fed back to the source of the problem to support the improvement of quality problems.

[0009] According to some embodiments, the first solution of the present disclosure provides a product quality data analysis and feedback method based on the entire life cycle, using the following technical solutions:

[0010] A product quality data analysis and feedback method based on the entire life cycle, wherein the entire life cycle includes the design stage, the manufacturing stage, and the service stage, including:

[0011] Obtain the association mapping relationship between product objects in the design stage and the manufacturing stage, and between the manufacturing stage and the service stage;

[0012] Calculate the probability of product quality problems occurring at different stages based on the associated mapping relationship between the obtained product objects;

[0013] Determine the source of product quality problems based on the obtained probabilities;

[0014] Complete the product quality analysis based on the identified source of the product quality problem.

[0015] As a further technical limitation, a product object in the design stage maps to multiple product objects in the manufacturing stage, the product object in the manufacturing stage is related to a manufacturing batch of the product, and the manufacturing batch is related to the man-machine-material-method-environment factors in the manufacturing process.

[0016] As a further technical limitation, one product object in the manufacturing stage maps to a plurality of product objects in the service stage, and the product objects in the service stage are related to the time, space and working conditions of the product.

[0017] As a further technical limitation, in the process of calculating the probability of occurrence of product quality problems in different manufacturing batches, a uniquely determined serial number is used to represent the product object, and the variance of the probability of occurrence of product quality problems in different stages is calculated. The obtained variance is compared with a preset threshold. If the variance is within the preset threshold range, the product quality problem occurs in the design stage. The product quality data is fed back to the design stage, and the product quality problem is analyzed in the design stage to support the improvement of product design.

[0018] Furthermore, if the variance is not within a preset threshold range, data mining analysis of the product quality data at the manufacturing stage is performed, and it is determined whether the product quality problem occurs at the manufacturing stage based on the confidence level difference obtained from the data mining analysis.

[0019] Furthermore, in the process of data mining and analysis, the product quality data of the manufacturing stage and the man-machine-material-method-environment factors of the manufacturing stage are combined into an item set, and data mining is performed on the obtained item set based on association rule mining, and the rule sequence obtained by data mining is arranged in descending order of confidence to obtain a confidence sequence; the difference is obtained for the obtained confidence sequence, and the maximum difference, the mean difference and the variance of the difference are calculated based on the obtained difference, and the difference deviation threshold is calculated through the difference variance, and it is determined whether the sum of the mean difference and the difference deviation threshold is less than the maximum difference. If so, the product quality problem occurs in the manufacturing stage, otherwise, the product quality problem does not occur in the design stage and the manufacturing stage.

[0020] Furthermore, if the product quality problem occurs in the manufacturing stage, the man-machine-material-method-environment factors of different production lines in the manufacturing stage are matched according to the quality problem association rules. If the match is successful, the product quality problem occurs in the manufacturing stage. The product quality is analyzed in the manufacturing stage, and the man-machine-material-method-environment factors on different production lines in the product manufacturing stage are improved according to the product quality.

[0021] According to some embodiments, the second solution of the present disclosure provides a product quality data analysis and feedback system based on the entire life cycle, which adopts the following technical solutions:

[0022] A product quality data analysis and feedback system based on the entire life cycle, wherein the entire life cycle includes the design stage, the manufacturing stage, and the service stage, and is characterized by comprising:

[0023] An acquisition module is configured to acquire an association mapping relationship between product objects in the design stage and the manufacturing stage, and between the manufacturing stage and the service stage;

[0024] A calculation module, configured to calculate the probability of occurrence of product quality problems at different stages according to the acquired association mapping relationship between product objects;

[0025] A judgment module is configured to judge the source of the product quality problem based on the obtained probability;

[0026] The analysis module is configured to complete the product quality analysis based on the source of the determined product quality problem.

[0027] According to some embodiments, a third solution of the present disclosure provides a computer-readable storage medium, which adopts the following technical solution:

[0028] A computer-readable storage medium stores a program, which, when executed by a processor, implements the steps in the product quality data analysis and feedback method based on the entire life cycle as described in the first aspect of the present disclosure.

[0029] According to some embodiments, a fourth solution of the present disclosure provides an electronic device, which adopts the following technical solution:

[0030] An electronic device comprises a memory, a processor and a program stored in the memory and executable on the processor, wherein when the processor executes the program, the steps in the product quality data analysis and feedback method based on the entire life cycle as described in the first aspect of the present disclosure are implemented.

[0031] Compared with the prior art, the present invention has the following beneficial effects:

[0032] The present invention establishes a product quality data analysis path with the three stages of design-manufacturing-service as the entire life cycle. Through the combination of statistical analysis and data mining, the source of quality problems is analyzed, and the quality data is fed back to the source; if the quality problems are caused by the design stage, it is necessary to return to the design stage to improve the design; if the quality problems are caused by the manufacturing stage, it is necessary to feed back to the manufacturing stage to improve factors such as man-machine-material-method-environment; it solves the problem of being unable to effectively distinguish whether the source of product quality problems is the design link or the manufacturing factor, and uses service quality data to improve product design and manufacturing, improve product quality, and reduce product life cycle costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] The accompanying drawings constituting a part of the present disclosure are used to provide a further understanding of the present disclosure. The illustrative embodiments of the present disclosure and their descriptions are used to explain the present disclosure and do not constitute an improper limitation on the present disclosure.

[0034] Figure 1 is a flowchart of a product quality data analysis and feedback method based on the entire life cycle in Embodiment 1 of the present disclosure;

[0035] Figure 2 It is a schematic diagram of a specific process of the product quality data analysis and feedback method based on the entire life cycle in the first embodiment of the present disclosure;

[0036] Figure 3 is a schematic diagram of the association mapping relationship in the first embodiment of the present disclosure;

[0037] Figure 4 It is a structural block diagram of a product quality data analysis and feedback system based on the entire life cycle in the second embodiment of the present disclosure. DETAILED DESCRIPTION

[0038] The present disclosure is further described below in conjunction with the accompanying drawings and embodiments.

[0039] It should be noted that the following detailed descriptions are all illustrative and are intended to provide further explanation of the present disclosure. Unless otherwise specified, all technical and scientific terms used herein have the same meanings as those commonly understood by those skilled in the art to which the present disclosure belongs.

[0040] It should be noted that the terms used herein are only for describing specific embodiments and are not intended to limit the exemplary embodiments according to the present disclosure. As used herein, unless the context clearly indicates otherwise, the singular form is also intended to include the plural form. In addition, it should be understood that when the terms "comprising" and / or "including" are used in this specification, it indicates the presence of features, steps, operations, devices, components and / or combinations thereof.

[0041] In the absence of conflict, the embodiments in the present disclosure and the features in the embodiments may be combined with each other.

[0042] Glossary:

[0043] Product Data Management: A technology for managing all product-related data (including engineering specifications, electronic documents, scanned images, CAD / CAPP / CAM files, product structure, product orders, supply status, etc.) and processes (including workflow, approval / issuance process, engineering change orders, etc.).

[0044] Product life cycle management: is an enterprise information strategy that describes and specifies the process and methods of creating, managing, distributing and using product information during the product life cycle, provides an information infrastructure, integrates and manages related technologies and application systems, and enables users to collaboratively develop, manufacture and manage products during the product life cycle. It is a new management model created to meet the manufacturing industry's demand for product life cycle information management.

[0045] Product service: refers to the installation, commissioning, maintenance, repair, recycling and remanufacturing services of products before, during and after sales, reflecting the integration of products and services.

[0046] Man, Machine, Material, Method, Environment and Measurement: It is the abbreviation of the six major factors that affect quality in total quality management. It originated from the field of product manufacturing quality management. Among them, "man" refers to the personnel who manufacture products, including their understanding of quality, technical proficiency, physical condition, etc.; "machine" refers to the machines used to manufacture products, including the accuracy of machinery and tools, maintenance status, etc.; "material" refers to the raw materials used to manufacture products, including the composition, physical and chemical properties of the materials, etc.; "method" refers to the method used to manufacture products, including processing technology, tooling selection and operating procedures, etc.; "environment" refers to the environment in which the product is manufactured, including the temperature, humidity, lighting and sanitary conditions of the workplace.

[0047] BOM: Bill of Material (Bom), using computers to assist enterprise production management, first of all, the computer must be able to read the product composition and all the materials involved in the enterprise. In order to facilitate computer recognition, the product structure expressed in diagrams must be converted into a certain data format. This file that describes the product structure in data format is the bill of materials, that is, Bom. It is a technical file that defines the product structure, so it is also called a product structure table or a product structure tree.

[0048] Embodiment 1

[0049] Embodiment 1 of the present disclosure introduces a product quality data analysis and feedback method based on the entire life cycle.

[0050] During the design, manufacturing and service stages, products are in different forms and different time and space, from digital form to physical form, from manufacturing enterprises to users. Product design data is in the design institute or enterprise, manufacturing data is in the enterprise, and service data is in the user and service provider. The evolution relationship of the product life cycle is unclear, resulting in the inability to feed back the quality data of the product service stage to the upstream of the product life cycle; product quality data is in the downstream of the product life cycle, and it is impossible to determine the source of quality problems through data analysis.

[0051] This embodiment is oriented to products and parts, and establishes a clear evolution path for products or parts. From product design to multi-batch manufactured products, to countless physical products, the evolution process is analyzed, a mapping relationship is established, and an evolution path for the product life cycle is formed, which builds a path for data feedback.

[0052] This embodiment combines statistical analysis with association rules to analyze whether the source of the quality problem is the design stage or the manufacturing stage, and then builds a path through data feedback to feed back the quality problem to the relevant stage.

[0053] like Figure 1 A product quality data analysis and feedback method based on the entire life cycle is shown, wherein the entire life cycle includes a design stage, a manufacturing stage, and a service stage, including:

[0054] Obtain the association mapping relationship between product objects in the design stage and the manufacturing stage, and between the manufacturing stage and the service stage;

[0055] Calculate the probability of occurrence of quality problems in different batches of products based on the associated mapping relationship between the obtained product objects;

[0056] Determine the source of product quality problems based on the obtained probabilities;

[0057] Complete the product quality analysis based on the identified source of the product quality problem.

[0058] As one or more implementation methods, in the design stage, the probability of quality problems in each batch of a design object is counted. The variance of the probability of quality problems in each batch is calculated. If the variance is less than a certain threshold, the source of the quality problem is in the design stage; the variance threshold is selected according to different industries and products. Otherwise, facing the manufacturing stage, data mining and analysis are performed on all batches of product data; the quality problems of each physical product and the factors of man-machine-material-method-environment in its production process together constitute an item set, and the item set is mined for association rules; the rules mined with quality problems as the latter item are sorted in descending order according to confidence. If there is a previous rule whose confidence is obviously higher than that of the latter rule by a certain threshold, the source of the quality problem is in the manufacturing stage, and the former item of the previous rule is the source of the problem; the quality data of the service stage is fed back to the factors such as man-machine-material-method-environment in the design stage or the manufacturing stage through the established data feedback path to support design and manufacturing improvements.

[0059] As one or more implementations, the product has a specific hierarchical structure that describes the composition relationship between products, components and parts, and is managed using a product BOM or product structure tree. A product consists of multiple components and multiple parts, a component consists of multiple subcomponents and multiple parts, and a subcomponent consists of multiple parts.

[0060] In quality management, quality data analysis can be performed on the entire product, or quality data management can be performed on components / subcomponents and parts. Therefore, objects are used to describe products, components and parts.

[0061] The product life cycle mainly studies the product design, manufacturing and service stages, covering both virtual digital products and physical physical products. In the design stage, a design object D is an abstraction of a product or component or part. The same design object D will produce multiple batches of products B, resulting in differences in factors such as man-machine-material-method-environment. The same batch object B will produce multiple physical products P. Different physical products are located in different time and space and different working conditions, resulting in different quality problems. The descriptions of the design object, batch object and physical object include the corresponding product, component and part data. When analyzing product quality data, data analysis and quality data feedback are performed for each type of quality problem.

[0062] In this embodiment, if Figure 2 As shown, taking a specific quality problem Q as an example, the details of the product quality data analysis and feedback method provided in this embodiment are expanded:

[0063] First, establish Figure 3 The design object D in the design phase and the batch object B in the production phase are shown. i (i=1,2,…M) and the physical object P in the service phasei.j (i=1,2,…M;j=1,2,…,N),wherein the physical object P i.j =(SN i,j ,isFlt i,j ). SN i,j is a physical object P i.j The serial number identifies a unique physical object; isFlt i,j =1 indicates physical object P i.j A quality problem Q occurred, isFlt i,j =0 indicates physical object P i.j No quality issues occurred.

[0064] Secondly, in the design stage, the probability of quality problems in each batch of products is counted. The variance of the probability of quality problems in each batch is calculated. If the variance is less than a certain threshold, the source of the quality problem is in the design stage. The selection of variance threshold varies according to different industries and products.

[0065] For batch object B i (i=1,2,…M), and count the occurrence probability Pb ​​of the quality problem Q of the corresponding physical object i for:

[0066]

[0067] Calculate batch object B i The probability of quality problems occurring Pb i The variance σ B for:

[0068]

[0069] If σ B ≤σ T , then the design object D is the source of quality data feedback, where σ T is the probability variance threshold.

[0070] Again, during the manufacturing phase, for batch object B i (i=1,2,…M) data for data mining analysis. The quality problem of each physical product and the factors of man-machine-material-method-environment in the manufacturing process together form an item set for association rule mining. The mined rules with the quality problem as the successor are sorted in descending order according to the confidence. If the confidence of the first n rules is obviously higher than that of the n+1th rule by a certain threshold, the source of the quality problem is in the manufacturing stage, and the predecessor of the n rules is the source of the problem. The selection of thresholds varies according to different industries and products.

[0071] Specifically, each physical object P i.jThrough the mapping relationship between physical objects and batch objects, the data of the human-machine-material-method loop during manufacturing can be tracked; each physical object P i.j The human-machine-material-method-environment data and quality issues form an item set:

[0072] IS i.j =(Mn i.j ,Mc i.j ,Mt i.j ,Md i.j ,E i.j ,isFlt i.j ), (i=1,2,…,M; j=1,2,…,N)

[0073] Among them, Mn i.j ,Mc i.j ,Mt i.j ,Md i.j ,E i.j Respectively represent P i.j The human-machine-material-method-environment factor or a combination of factors. isFlt i.j =1 means P i.j Quality problem Q;isFlt i.j =0 means P i.j No quality issues occurred.

[0074] For all IS i.j Perform association rule mining and take the quality problem Q as the last item (isFlt i.j =1) according to the confidence level C l (l=1,2,…,S) are sorted in descending order to obtain the regular sequence SR={R l ,(l=1,2,…,S;S=∑∑i,j)}, the corresponding confidence sequence SC={C l ,(l=1,2,…,S)}.

[0075] For the confidence SC sequence, find the difference:

[0076] Df l =C l -C l+1 ,(l=1,2,…,S-1)

[0077] Find the difference Df l The maximum value of the difference Df Max Sum difference mean Df Avg , variance σ Df , there are:

[0078] Df Max ≥Df l ,(l=1,2,…,S-1)

[0079]

[0080]

[0081] If Df Max ≥Df Avg +cst·σ Df , then rule R l (l=1,2,…,Max) is the quality problem association rule, and its rule antecedent corresponds to P i.j Factors are the source of quality problems Src l (l=1,2,…,Max), where cst is a constant that takes different values ​​depending on the industry and product.

[0082] Feedback product problem data to the source of the problem.

[0083] If the source of the quality problem is in design object D, then:

[0084] All physical objects P i.j The quality data is fed back to the corresponding batch object B i ;

[0085] All batch objects B i The quality data is fed back to the design object D.

[0086] If the source of the quality problem is in batch object B i ,but:

[0087] Traversing the quality problem association rules R l Each rule in (l=1,2,…,Max);

[0088] Using Rule R l The previous item matches P i.j Man-machine-material-method-environment factors (Mn i.j ,Mc i.j ,Mt i.j ,Md i.j ,E i.j );

[0089] If the match is successful, then P i.j The quality data is fed back to R l The man-machine-material-method-environment factors of the manufacturing stage corresponding to the previous item.

[0090] This embodiment establishes a product quality data analysis path with the three stages of design-manufacturing-service as the entire life cycle. Through the combination of statistical analysis and data mining, the source of quality problems is analyzed, and the quality data is fed back to the source; if the quality problems are caused by the design stage, it is necessary to return to the design stage to improve the design; if the quality problems are caused by the manufacturing stage, it is necessary to feed back to the manufacturing stage to improve factors such as man-machine-material-method-environment; it solves the problem of being unable to effectively distinguish whether the source of product quality problems is the design link or the manufacturing factor, and uses service quality data to improve product design and manufacturing, improve product quality, and reduce product life cycle costs.

[0091] Embodiment 2

[0092] Embodiment 2 of the present disclosure introduces a product quality data analysis and feedback system based on the entire life cycle.

[0093] like Figure 4 A product quality data analysis and feedback system based on the entire life cycle is shown, wherein the entire life cycle includes the design stage, the manufacturing stage, and the service stage, including:

[0094] An acquisition module is configured to acquire an association mapping relationship between product objects in the design stage and the manufacturing stage, and between the manufacturing stage and the service stage;

[0095] A calculation module, configured to calculate the probability of occurrence of product quality problems at different stages according to the acquired association mapping relationship between product objects;

[0096] A judgment module is configured to judge the source of the product quality problem based on the obtained probability;

[0097] The analysis module is configured to complete the product quality analysis based on the source of the determined product quality problem.

[0098] The detailed steps are the same as the product quality data analysis and feedback method based on the entire life cycle provided in Example 1, and will not be repeated here.

[0099] Embodiment 3

[0100] A third embodiment of the present disclosure provides a computer-readable storage medium.

[0101] A computer-readable storage medium stores a program, which, when executed by a processor, implements the steps of the product quality data analysis and feedback method based on the entire life cycle as described in the first embodiment of the present disclosure.

[0102] The detailed steps are the same as the product quality data analysis and feedback method based on the entire life cycle provided in Example 1, and will not be repeated here.

[0103] Embodiment 4

[0104] A fourth embodiment of the present disclosure provides an electronic device.

[0105] An electronic device comprises a memory, a processor and a program stored in the memory and executable on the processor, wherein when the processor executes the program, the steps in the product quality data analysis and feedback method based on the entire life cycle as described in the first embodiment of the present disclosure are implemented.

[0106] The detailed steps are the same as the product quality data analysis and feedback method based on the entire life cycle provided in Example 1, and will not be repeated here.

[0107] The above description is only a preferred embodiment of the present disclosure and is not intended to limit the present disclosure. For those skilled in the art, the present disclosure may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present disclosure shall be included in the protection scope of the present disclosure.

Claims

1. A product quality data analysis and feedback method based on the entire life cycle, wherein the entire life cycle includes the design stage, the production stage, and the service stage, characterized in that: include: Obtain the association mapping relationship between the product objects in the design stage and the production stage, and between the production stage and the service stage; Calculate the probability of product quality problems occurring at different stages based on the associated mapping relationship between the obtained product objects; Determine the source of product quality problems based on the obtained probabilities; Complete product quality analysis based on the source of the identified product quality issues; In the process of calculating the probability of occurrence of product quality problems at different stages, a uniquely determined serial number is used to represent the product object, and the variance of the probability of occurrence of product quality problems at different stages is calculated. The obtained variance is compared with the preset threshold. If the variance is within the preset threshold, the product quality problem occurs in the design stage. Feedback is given to the design stage based on the obtained variance, and the product quality is analyzed in the design stage, and the product design plan is improved based on the product quality. If the variance is not within the preset threshold, data mining analysis of the product quality data in the production stage is performed, and the confidence level difference obtained from the data mining analysis is used to determine whether the product quality problem occurred in the production stage; Specifically, each physical object P i.j Through the mapping relationship between physical objects and batch objects, the data of the human-machine-material-method loop during manufacturing can be tracked; each physical object P i.j The human-machine-material-method-environment data and quality issues form an item set: IS i.j =(Mn i.j ,Mc i.j ,Mt i.j ,Md i.j ,E i.j ,isFlt i.j ), where i = 1, 2, ..., M; j = 1, 2, ..., N Among them, Mn i.j ,Mc i.j ,Mt i.j ,Md i.j ,E i.j Respectively represent P i.j Human-machine-material-method-environment factor or factor set; isFlt i.j =1 means P i.j Quality problem Q;isFlt i.j =0 means P i.j No quality problems occurredQ; For all IS i.j Perform association rule mining and use the mined quality problem Q as the subsequent item isFlt i.j = 1 according to the confidence level C l Sort in descending order to obtain the regular sequence SR = {R l ,}, the corresponding confidence sequence SC={C l ,}; where l=1,2,…,S, S=∑∑i,j; For the confidence SC sequence, find the difference: Df l =C l -C l+1 , where l = 1, 2, ..., S-1 Find the difference Df l The maximum value of the difference Df Max Sum difference mean Df Avg , variance σ Df , there are: Df Max ≥Df l , where l = 1, 2, ..., S-1 If Df Max ≥Df Avg +cst·σ Df , then rule R l , is the quality problem association rule, where l = 1, 2, ..., Max, and the P corresponding to the rule antecedent i.j Factors are the source of quality problems Src l , where l = 1, 2, ..., Max, cst is a constant that takes different values ​​depending on the industry and product; Feedback product problem data to the source of the problem; If the source of the quality problem is in design object D, then: All physical objects P i.j The quality data is fed back to the corresponding batch object B i ; All batch objects B i Quality data is fed back to the design object D; If the source of the quality problem is in batch object B i ,but: Traversing the quality problem association rules R l For each rule in, l = 1, 2, ..., Max; Using Rule R l The previous item matches P i.j Man-machine-material-method-environment factors (Mn i.j ,Mc i.j ,Mt i.j ,Md i.j ,E i.j ); If the match is successful, then P i.j The quality data is fed back to R l The man-machine-material-method-environment factors of the production stage corresponding to the previous item.

2. A product quality data analysis and feedback method based on the entire life cycle as described in claim 1, characterized in that: One product object in the design stage maps to a plurality of product objects in the production stage, the product object in the production stage is related to the production batch of the product, and the production batch is related to the man-machine-material-method-environment factors in the production process.

3. A product quality data analysis and feedback method based on the entire life cycle as described in claim 1, characterized in that: One product object in the production stage maps to a plurality of product objects in the service stage, and the product objects in the service stage are related to the time, space and working conditions of the products.

4. A product quality data analysis and feedback system based on the entire life cycle, wherein the entire life cycle includes a design phase, a production phase, and a service phase, and is based on a product quality data analysis and feedback method based on the entire life cycle as described in any one of claims 1 to 3, characterized in that: include: An acquisition module is configured to acquire an association mapping relationship between product objects in the design stage and the production stage, and between the production stage and the service stage; A calculation module, configured to calculate the probability of occurrence of product quality problems at different stages according to the acquired association mapping relationship between product objects; A judgment module is configured to judge the source of the product quality problem based on the obtained probability; The analysis module is configured to complete the product quality analysis based on the source of the determined product quality problem.

5. A computer-readable storage medium having a program stored thereon, characterized in that: When the program is executed by a processor, the steps in the product quality data analysis and feedback method based on the entire life cycle as described in any one of claims 1 to 3 are implemented.

6. An electronic device comprising a memory, a processor, and a program stored in the memory and executable on the processor, characterized in that: When the processor executes the program, the steps in the product quality data analysis and feedback method based on the entire life cycle as described in any one of claims 1 to 3 are implemented.

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