Digital device quality evaluation method and system

Abstract

The invention provides a digital device quality evaluation method and system, and belongs to the technical field of digital device evaluation, and the method comprises the steps: obtaining the historical supply data of devices of all suppliers in to-be-evaluated digital devices in the same field in a fixed time period, and analyzing different product supply chains in which the devices participate. And according to the assembly process data, determining the technical specification change of the digital device in each product, and calculating the fluctuation value of the technical specification change so as to deduce the product suitability demand in the field. Meanwhile, according to the associated data of the supplies, the type selection range and the allowable reprocessing range of the supplies are determined. And calculating the coverage rate of the ranges on the adaptability demand to obtain an adaptability quality index. Besides, on the basis of historical working states and service life data, the replacement frequency, replacement frequency and similar replacement rate of the supplies are analyzed, and performance quality indexes are generated. And finally, integrating the suitability quality index and the performance quality index to obtain a comprehensive quality evaluation result of the products of each supplier in the digital products. According to the method, accurate quality evaluation is realized by investigating the practical application suitability and stability of the supplies in multiple products.

Description

Technical Field

[0001] This invention relates to the field of digital product evaluation technology, specifically to a method and system for evaluating the quality of digital products. Background Technology

[0002] Digital products, as a crucial component of modern industrial manufacturing and supply chain management, have become a vital carrier of digital transformation. Presented in digital models and data form and integrated into digital supply chain platforms, they provide robust technical support and a data foundation for product design, production, procurement, and management. Each digital product is not merely a simple digitization of a physical part or device, but rather contains a rich dataset including model number, specifications, performance parameters, and material information. This data provides a complete basis for product design, selection, optimization, and subsequent maintenance. Therefore, digital products play a vital role in modern industry and are a key tool for product lifecycle management. The creation and parametric selection of digital products are among their core functions. Through these functions, companies can quickly select parts and components that meet their needs during the design phase, shortening product development cycles and reducing development costs. More importantly, the existence of digital products breaks down information barriers between enterprises, enabling collaborative work across design, procurement, and manufacturing stages on the same digital platform. This collaboration not only improves work efficiency but also effectively reduces errors and delays caused by poor information transmission, ensuring the efficient operation of the entire supply chain.

[0003] In the use of digital tools, although the sets of digital tools provided by various suppliers offer basic resource support for the production of the target product, there are often differences in technology, quality, and applicability among the tools provided by different suppliers. Therefore, in practical applications, enterprises need to evaluate and screen the tools from different suppliers corresponding to these digital tools to ensure that the selected parts can meet the needs of the target product to the greatest extent. To achieve this goal, enterprises usually need to compare the performance of tools from various suppliers and evaluate their performance under specific production conditions. This involves not only comparing the basic data of the parts but also evaluating their adaptability and reliability under different operating conditions. However, existing technologies mostly rely on publicly available related data of digital tools for selection and judgment, or on simple performance reviews based on their usage. While these methods can provide some reference value, their accuracy and reference value are low because they do not fully integrate the tools from various suppliers in the digital tools with the specific requirements of the target product and the production environment. The lack of a comprehensive evaluation mechanism may lead to mismatches in the actual production process, affecting production efficiency and product quality. Summary of the Invention

[0004] To address the aforementioned issues, this invention provides a method for evaluating the quality of digital products. This method comprehensively assesses the application of each digital product in a product based on the adaptability and replaceability of various supplier products in different products, thereby enabling the comprehensive and accurate acquisition of the quality evaluation results of each supplier product in the digital product.

[0005] To achieve the above objectives, the present invention provides the following technical solution.

[0006] A method for evaluating the quality of digital products includes the following steps: The historical supply data of each supplier's products in the digital products to be evaluated within a fixed time period is obtained, and the products of multiple production supply chains in which any product participates are obtained; the digital products include digital models and their corresponding specification parameters and material information associated data; Acquire assembly process data, historical working status data, and historical lifespan data of each supplier's tools in multiple products; Based on assembly process data, determine the variation data of technical specifications of digital tools involved in production across multiple products; based on the variation data of technical specifications across multiple products, determine the fluctuation value of technical specifications, and then determine the product adaptability requirements in this field. Based on the correlation data of the products from various suppliers in this field, determine the selection range and allowable reprocessing range of each supplier's products; obtain the coverage rate of the selection and allowable reprocessing range of each supplier's products in terms of product adaptability requirements in this field, and determine the adaptability quality indicators of each supplier's products in digital products. Based on historical working status data and historical lifespan data of multiple suppliers' products, determine the replacement frequency data of multiple suppliers' products in the same field, obtain replacement rate, replacement frequency and similar replacement rate, and determine the performance and quality indicators of each supplier's products in multiple products. The quality evaluation results of each supplier's products in the digital product are determined based on the adaptability quality index and the performance quality index.

[0007] Preferably, obtaining products from multiple production supply chains involving any workpiece includes the following steps: Identify the areas to be evaluated, select historical supply data of all suppliers' products within the target area for a fixed time period, and ensure that all products within the area are related to digital products. Based on the acquired historical supply data, the model information, specifications, and material information of supplier products are associated with the products to form a bipartite graph mapping supplier products—supply chain—products: ; In the formula, For supplier products gather, For products in the supply chain gather, This refers to the use of supplier components in the corresponding processes and bill of materials for a product; each edge in the bipartite graph records supply chain-related information, including supplier, process route, site, batch, and timestamp.

[0008] Preferably, the acquisition of assembly process data, historical working status data, and historical lifespan data of each supplier's tools in multiple products includes the following steps: Assembly process data for multiple products is extracted from the product manufacturer's production scheduling system, quality control system, and production process management platform. The assembly process data includes the production process flow, process requirements, and process optimization measures for each product. Based on historical operating data extracted from sensors, equipment monitoring systems, and production databases, the working status data of multiple products during the production and use processes are determined, including load data, runtime, and fault data of each component during product operation. Based on the product's maintenance records, production reports, historical performance tests, and historical parts replacements, determine the supplier's historical lifespan data for the products.

[0009] Preferably, obtaining the product compatibility requirements includes the following steps: Based on the assembly process flow, extract the technical specifications of each supplier's workpiece in multiple products, including information on changes in process requirements; Compare the technical specifications of different products to obtain the range of specification variations for each product; quantify the fluctuation value of each product's technical specifications by calculating the standard deviation of the product specification variations, and sort them from smallest to largest. By merging the ranges of technical specification changes with relatively small preset quantity fluctuations, the final range of technical specification changes is determined as the adaptation requirement.

[0010] Preferably, obtaining the adaptability quality index includes the following steps: Based on the technical specifications, parameter ranges, and material information of each supplier's products, calculate the selection range and allowable reprocessing range; Compare the selection range and permissible reprocessing range of each supplier's products with the adaptability requirements to calculate their coverage rate: ; ; In the formula, To define the selection range, For the scope of reprocessing, For the total scope of adaptation requirements; Based on the coverage results, adaptability quality indicators are defined, specifically including selection range coverage and reprocessing range coverage indicators. The adaptability quality of supplier products is comprehensively evaluated through a weighted average method.

[0011] Preferably, obtaining the performance quality indicators includes the following steps: Based on historical operating status data and historical lifespan data of multiple suppliers' products, the lifespan distribution of the corresponding components of the supplier's products is fitted to obtain the mean lifespan MTTF and the failure probability within the product's design life, i.e., the replacement rate: ; In the formula, The reliability function is derived from the fitted lifetime distribution, i.e., the Weiber distribution. get, For product design lifespan, For proportional parameters, k For shape parameters; Based on historical work status data, the average number of times the same supplier's products were replaced due to failure within the life cycle of a single product is calculated to determine the replacement frequency. Based on historical work status data, determine the ratio of products replaced by the same model to products replaced by different models in the replacement records of this supplier's products; The replacement rate, replacement frequency, and similar replacement rate are normalized and weighted to obtain the performance quality index.

[0012] This invention also proposes a digital product quality evaluation system, the system comprising: The data retrieval module is used to obtain historical supply data of each supplier's products in the digital products to be evaluated within a fixed time period, and to obtain products from multiple production supply chains in which any product participates; the digital products include digital models and their corresponding specification parameters and material information associated data; it is also used to obtain assembly process data, historical working status data and historical lifespan data of each supplier's products in multiple products; The compatibility evaluation module is used to determine the changes in technical specifications of digital tools involved in production across multiple products based on assembly process data; to determine the fluctuation value of technical specifications based on the changes in technical specifications across multiple products, and thus determine the compatibility requirements of products in this field; it is also used to determine the selection range and allowable reprocessing range of each supplier's tools based on the correlation data of tools from various suppliers in this field; to obtain the coverage rate of the selection and allowable reprocessing range of each supplier's tools in covering the compatibility requirements of products in this field, and to determine the compatibility quality indicators of each supplier's tools in the digital tools. The performance evaluation module is used to determine the replacement frequency data of multiple supplier products in the same field based on historical working status data and historical lifespan data of multiple supplier products, to obtain replacement rate, replacement frequency and similar replacement rate, and to determine the performance quality indicators of each supplier product in multiple products. The comprehensive quality evaluation output module is used to determine the quality evaluation results of each supplier's products in multiple products based on the adaptability quality index and performance quality index.

[0013] The present invention also proposes a computer-readable storage medium storing a data processing program, which, when executed by a processor, implements the digital product quality evaluation method.

[0014] The present invention also proposes a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the digital product quality evaluation method.

[0015] The beneficial effects of this invention are: This invention proposes a digital product quality evaluation method. Addressing the limitations and inaccuracies of traditional, singular quality evaluations of individual supplier products within the digital product ecosystem, this method proposes a comprehensive quality assessment based on data analysis of the corresponding products from each supplier at the process and application levels. It considers multiple perspectives, including the target product's historical supply, process, application, and maintenance, to comprehensively determine the quality of each product within a specific domain. This method fully integrates the target product's specific requirements with the production environment, improving the accuracy and reliability of the evaluation results. Furthermore, this method introduces assembly process data analysis to determine the compatibility quality indicators of each supplier's product with the target product. Based on the product's historical operating status data and the historical lifespan data of multiple products, it determines the performance quality indicators of each product, deeply exploring the applicability and substitutability of each product and forming a multi-directional evaluation reference model. This provides better data support for achieving optimal production supply chain solutions. Attached Figure Description

[0016] Figure 1 This is a flowchart of a method according to an embodiment of the present invention.

[0017] Figure 2 This is a flowchart of the method for obtaining adaptability quality indicators according to an embodiment of the present invention.

[0018] Figure 3 This is a flowchart of the method for obtaining performance quality indicators according to an embodiment of the present invention. Detailed Implementation

[0019] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0020] Example 1 Existing technologies largely rely on publicly available data related to digital products for selection and evaluation, or on simple performance reviews based on usage. While these methods offer some reference value, their accuracy and reliability are low because they don't fully integrate the various supplier products within the digital product pool with the specific requirements and production environment of the target product. The lack of a comprehensive evaluation mechanism can lead to mismatches in actual production, impacting production efficiency and product quality. Therefore, this embodiment proposes a digital product quality evaluation method, with the specific steps as follows: Figure 1 As shown, it includes: S1: Obtain historical supply data of each supplier's products in the digital products to be evaluated within a fixed time period, and obtain products from multiple production supply chains in which any product participates; the digital products include digital models and their corresponding specification parameters and material information associated data.

[0021] S2: Obtain assembly process data, historical working status data, and historical lifespan data of each supplier's tools in multiple products.

[0022] S3: Based on the assembly process data, determine the changes in the technical specifications of the digital tools involved in production across multiple products; based on the changes in the technical specifications across multiple products, determine the fluctuation value of the technical specifications, and then determine the product adaptability requirements in this field.

[0023] S4: Based on the correlation data of the products from various suppliers in this field, determine the selection range and allowable reprocessing range of each supplier's products; obtain the coverage rate of the selection and allowable reprocessing range of each supplier's products in terms of product adaptability requirements in this field, and determine the adaptability quality indicators of each supplier's products in digital products.

[0024] S5: Based on historical working status data and historical lifespan data of multiple supplier products, determine the replacement frequency data of multiple supplier products in the same field, obtain the replacement rate, replacement frequency and similar replacement rate, and determine the performance quality indicators of each supplier product in multiple products.

[0025] S6: Determine the quality evaluation results of each supplier's products in multiple products based on the adaptability quality index and performance quality index.

[0026] Specifically, the acquisition of historical supply data in S1 relies on the supply chain database interface, which calls relevant information about the corresponding products of each supplier in the digital product database. Specifically: S1.1: Select a fixed time period (such as the past 12 months, 24 months, etc.) to collect historical supply data of all supplier products in the target field.

[0027] S1.2: Identify the areas to be evaluated, ensuring that all products within these areas are related to digital products (including digital models, specifications, and material information). Data acquisition may rely on supply chain database interfaces, APIs, or log systems. Alternatively, the corresponding products can be obtained from the statistical structure of the supply chain platform.

[0028] Extract supply data from historical supply data for multiple supplier products in the area to be evaluated, including information on the production processes in which the supplier products participate in multiple supply chains.

[0029] For each supplier's product, extract its associated digital model, technical specifications, parameters, and material information. Simultaneously, ensure the accuracy and completeness of the data.

[0030] S1.3: Clean the acquired supply chain data, remove redundant information, and verify the accuracy of the data. Associate the model information, specifications, and material information of supplier products with the products to form a unified data structure, such as a bipartite graph mapping supplier products—supply chain—products, facilitating subsequent analysis. ; In the formula, For supplier products gather, For products in the supply chain gather, This refers to the use of supplier components in the corresponding processes and bill of materials for a product; each edge in the bipartite graph records supply chain-related information, including supplier, process, site, batch, timestamp, etc.

[0031] Furthermore, the present invention evaluates the quality of supplier components in digital components based on assembly process data of multiple products, historical working status data, and historical lifespan data of each supplier component.

[0032] The assembly process data for multiple products includes the production process flow, process requirements, and process optimization measures for each product. This data can be extracted from the manufacturer's production scheduling system, quality control system, or production process management platform. Historical operating status data refers to the operating status data of multiple products during the production process, including load data, runtime, and fault data. This data can be extracted from sensors, equipment monitoring systems, or production databases. Historical lifespan data for supplier tools includes full lifecycle information from product input to decommissioning. Data sources can include product maintenance records, production reports, historical performance tests, and historical parts replacements. All of the above data sources originate from the manufacturer.

[0033] Based on the above data, this invention proposes to quantify the quality of digital products into two indicators: adaptability quality indicator and performance quality indicator, and provides the following analysis method: (1) Adaptability quality indicators, flowchart as follows Figure 2 As shown: Based on assembly process data, determine the variation data of technical specifications of supplier tools involved in production across multiple products; based on the variation data of technical specifications across multiple products, determine the product compatibility requirements in this field. Specifically: S3.1: Based on the assembly process flow, extract the different technical specifications of each supplier's workpiece in multiple products, including information on changes in process requirements, such as dimensions and tolerances.

[0034] S3.2: Compare the technical specifications of different products to obtain the range of specification variations for each product. Quantify the fluctuation value of each product's technical specifications by calculating the standard deviation of the specification variations, and sort them from smallest to largest.

[0035] S3.3: Merge the technical specification variation ranges with small preset quantity fluctuation values ​​to determine the final technical specification variation range as the adaptability requirement.

[0036] This invention uses fluctuation values ​​to determine the specific application of supplier products in this field, eliminating the possibility that significant variations in supplier product technical specifications due to special forms or requirements of individual products could affect the accuracy and universality of the evaluation method.

[0037] Furthermore, based on the correlation data of the products from various suppliers in this field, the selection range and permissible reprocessing range of each supplier's products are determined; the coverage rate of each supplier's product selection and permissible reprocessing range with the product adaptability requirements of this field is obtained, and adaptability quality indicators are determined. Specifically: S4.1: Based on the technical specifications, parameter ranges, and material information of each supplier's products, calculate the selection range and allowable reprocessing range. For example, define the allowable variation range of product parameters and limit it to the range of production requirements and material properties.

[0038] S4.2: Compare the selection range and permissible reprocessing range of each supplier's products with the adaptability requirements and calculate their coverage. Use overlap or coverage as a metric to evaluate the applicability of the supplier's product selection and reprocessing range to the adaptability requirements: ; ; In the formula, To define the selection range, For the scope of reprocessing, This refers to the total scope of adaptation requirements.

[0039] S4.3: Based on the coverage results, define compatibility quality indicators, specifically including selection range coverage, reprocessing range coverage, etc. A weighted average method is used to comprehensively evaluate the compatibility quality of supplier products.

[0040] (2) Performance quality indicators Based on historical operating status data and historical lifespan data of multiple suppliers' products, the replacement frequency data of multiple suppliers' products in the same field is determined. Replacement rate, replacement frequency, and similar replacement rate are obtained. Performance and quality indicators for each supplier's product are then determined. The flowchart is as follows: Figure 3 As shown, the specific steps include: S5.1: Based on historical operating status data and historical lifespan data of multiple suppliers' products, fit the lifespan distribution of the corresponding components of the supplier's products to obtain the mean lifespan MTTF and the failure probability within the product's design life, i.e., the replacement rate. ; In the formula, The reliability function is derived from the fitted lifetime distribution, i.e., the Weiber distribution. get, For product design lifespan, For proportional parameters, k For shape parameters.

[0041] S5.2: Based on historical working status data, calculate the average number of times the same supplier's products were replaced due to failure within the life cycle of a single product, and determine the replacement frequency.

[0042] S5.3: Based on historical work status data, determine the ratio of replacements of the same model to replacements of different models in the supplier's product replacement records.

[0043] S5.4: Normalize and weight the replacement rate, replacement frequency, and similar replacement rate to obtain the performance quality index.

[0044] Finally, the two indicators can be output by normalizing and weighting the sum, or by using "fitness indicator + performance indicator" to indicate the results of the supplier's products in different quality evaluation directions. This invention does not impose any limitations.

[0045] This invention addresses the limitations and inaccuracies of existing digital product quality evaluation methods by proposing a data analysis approach based on the technological and application aspects of the corresponding digital products. This approach aims to achieve a more comprehensive and accurate quality assessment. Traditional evaluation methods often rely solely on publicly available parameters or historical usage data of supplier products within the digital product category, neglecting the specific requirements and production environment of the target product. This results in low accuracy and reliability of the evaluation results. Therefore, this method comprehensively analyzes historical supply data, technological information, application scenarios, and maintenance records of the target product to assess the quality of supplier products from multiple dimensions. This comprehensive analysis not only focuses on the technical performance of the supplier products themselves but also closely integrates them with the actual technological conditions, operating environment, and long-term usage requirements of the target product. This makes the evaluation results more aligned with practical application needs and provides a reliable basis for enterprises to select the most suitable digital products on digital supply chain platforms. This method effectively identifies the strengths and weaknesses of supplier products in various digital products within specific fields, providing scientific support for optimizing component selection and production planning, thereby improving overall production efficiency and product quality.

[0046] This method incorporates assembly process data analysis to determine the suitability and quality indicators of each supplier's tools within the target product. This means not only examining the basic performance parameters of the parts but also analyzing their compatibility and process adaptability during the assembly of the target product, ensuring the smooth application of each supplier's tool in actual production. Simultaneously, this method combines historical product operating status data with historical lifespan data from multiple supplier tools to construct performance quality indicators, evaluating the reliability and durability of each supplier's tool. By deeply exploring the applicability and substitutability of digital tools, a multi-directional, multi-dimensional evaluation reference model is formed, enabling enterprises to fully consider the comprehensive performance of various supplier tools when formulating production supply chain solutions. This not only improves the accuracy and reference value of digital tool evaluation but also provides solid data support for achieving optimal production supply chain solutions, ensuring a more efficient, stable, and predictable production process, while laying the foundation for future intelligent supply chain management and refined production decision-making.

[0047] The above is one embodiment of the digital product quality evaluation method provided in this example. Based on the same idea, this example also provides a corresponding digital product quality evaluation system: The data retrieval module is used to obtain historical supply data of each supplier's products in the digital products to be evaluated within a fixed time period, and to obtain products from multiple production supply chains in which any product participates; the digital products include digital models and their corresponding specification parameters and material information associated data; it is also used to obtain assembly process data, historical working status data and historical lifespan data of each supplier's products in multiple products; The compatibility evaluation module is used to determine the changes in technical specifications of digital tools involved in production across multiple products based on assembly process data; to determine the fluctuation value of technical specifications based on the changes in technical specifications across multiple products, and thus determine the compatibility requirements of products in this field; it is also used to determine the selection range and allowable reprocessing range of each supplier's tools based on the correlation data of tools from various suppliers in this field; to obtain the coverage rate of the selection and allowable reprocessing range of each supplier's tools in covering the compatibility requirements of products in this field, and to determine the compatibility quality indicators of each supplier's tools in the digital tools. The performance evaluation module is used to determine the replacement frequency data of multiple supplier products in the same field based on historical working status data and historical lifespan data of multiple supplier products, to obtain replacement rate, replacement frequency and similar replacement rate, and to determine the performance quality indicators of each supplier product in multiple products. The comprehensive quality evaluation output module is used to determine the quality evaluation results of each supplier's products in multiple products based on the adaptability quality index and performance quality index.

[0048] This embodiment also provides a computer-readable storage medium storing a computer program that can be used to execute the above-described... Figure 1 The provided method for evaluating the quality of digital products.

[0049] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, or optical storage, etc. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc.

[0050] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for evaluating the quality of digital products, characterized in that, Includes the following steps: The historical supply data of each supplier's products in the digital products to be evaluated within a fixed time period is obtained, and the products of multiple production supply chains in which any product participates are obtained; the digital products include digital models and their corresponding specification parameters and material information associated data; Acquire assembly process data, historical working status data, and historical lifespan data of each supplier's tools in multiple products; Based on assembly process data, determine the variation data of technical specifications of digital tools involved in production across multiple products; based on the variation data of technical specifications across multiple products, determine the fluctuation value of technical specifications, and then determine the product adaptability requirements in this field. Based on the correlation data of the products from various suppliers in this field, determine the selection range and allowable reprocessing range of each supplier's products; obtain the coverage rate of the selection and allowable reprocessing range of each supplier's products in terms of product adaptability requirements in this field, and determine the adaptability quality indicators of each supplier's products in digital products. Based on historical working status data and historical lifespan data of multiple suppliers' products, determine the replacement frequency data of multiple suppliers' products in the same field, obtain replacement rate, replacement frequency and similar replacement rate, and determine the performance and quality indicators of each supplier's products in multiple products. The quality evaluation results of each supplier's products in the digital product are determined based on the adaptability quality index and the performance quality index.

2. The method for evaluating the quality of digital products according to claim 1, characterized in that, The product that obtains participation in multiple production supply chains for any workpiece includes the following steps: Identify the areas to be evaluated, select historical supply data of all suppliers' products within the target area for a fixed time period, and ensure that all products within the area are related to digital products. Based on the acquired historical supply data, the model information, specifications, and material information of supplier products are associated with the products to form a bipartite graph mapping supplier products—supply chain—products: ； In the formula, For supplier products gather, For products in the supply chain gather, This refers to the use of supplier components in the corresponding processes and bill of materials for a product; each edge in the bipartite graph records supply chain-related information, including supplier, process route, site, batch, and timestamp.

3. The method for evaluating the quality of digital products according to claim 2, characterized in that, The acquisition of assembly process data, historical working status data, and historical lifespan data of each supplier's tools in multiple products includes the following steps: Assembly process data for multiple products is extracted from the product manufacturer's production scheduling system, quality control system, and production process management platform. The assembly process data includes the production process flow, process requirements, and process optimization measures for each product. Based on historical operating data extracted from sensors, equipment monitoring systems, and production databases, the working status data of multiple products during the production and use processes are determined, including load data, runtime, and fault data of each component during product operation. Based on the product's maintenance records, production reports, historical performance tests, and historical parts replacements, determine the supplier's historical lifespan data for the products.

4. The method for evaluating the quality of digital products according to claim 1, characterized in that, The acquisition of product compatibility requirements includes the following steps: Based on the assembly process flow, extract the technical specifications of each supplier's workpiece in multiple products, including information on changes in process requirements; Compare the technical specifications of different products to obtain the range of specification variations for each product; quantify the fluctuation value of each product's technical specifications by calculating the standard deviation of the product specification variations, and sort them from smallest to largest. By merging the ranges of technical specification changes with relatively small preset quantity fluctuations, the final range of technical specification changes is determined as the adaptation requirement.

5. The method for evaluating the quality of digital products according to claim 4, characterized in that, The acquisition of the compatibility quality indicators includes the following steps: Based on the technical specifications, parameter ranges, and material information of each supplier's products, calculate the selection range and allowable reprocessing range; Compare the selection range and permissible reprocessing range of each supplier's products with the adaptability requirements to calculate their coverage rate: ； ; In the formula, To define the selection range, For the scope of reprocessing, For the total scope of adaptation requirements; Based on the coverage results, adaptability quality indicators are defined, specifically including selection range coverage and reprocessing range coverage indicators. The adaptability quality of supplier products is comprehensively evaluated through a weighted average method.

6. The method for evaluating the quality of digital products according to claim 1, characterized in that, The acquisition of the performance quality indicators includes the following steps: Based on historical operating status data and historical lifespan data of multiple suppliers' products, the lifespan distribution of the corresponding components of the supplier's products is fitted to obtain the mean lifespan MTTF and the failure probability within the product's design life, i.e., the replacement rate: ； In the formula, The reliability function is derived from the fitted lifetime distribution, i.e., the Weiber distribution. get, For product design lifespan, For proportional parameters, k For shape parameters; Based on historical work status data, the average number of times the same supplier's products were replaced due to failure within the life cycle of a single product is calculated to determine the replacement frequency. Based on historical work status data, determine the ratio of products replaced by the same model to products replaced by different models in the replacement records of this supplier's products; The replacement rate, replacement frequency, and similar replacement rate are normalized and weighted to obtain the performance quality index.

7. A digital product quality evaluation system, characterized in that, The system includes: The data retrieval module is used to obtain historical supply data of each supplier's products in the digital products to be evaluated within a fixed time period, and to obtain products from multiple production supply chains in which any product participates; the digital products include digital models and their corresponding specification parameters and material information associated data; it is also used to obtain assembly process data, historical working status data and historical lifespan data of each supplier's products in multiple products; The compatibility evaluation module is used to determine the changes in technical specifications of digital tools involved in production across multiple products based on assembly process data; to determine the fluctuation value of technical specifications based on the changes in technical specifications across multiple products, and thus determine the compatibility requirements of products in this field; it is also used to determine the selection range and allowable reprocessing range of each supplier's tools based on the correlation data of tools from various suppliers in this field; to obtain the coverage rate of the selection and allowable reprocessing range of each supplier's tools in covering the compatibility requirements of products in this field, and to determine the compatibility quality indicators of each supplier's tools in the digital tools. The performance evaluation module is used to determine the replacement frequency data of multiple supplier products in the same field based on historical working status data and historical lifespan data of multiple supplier products, to obtain replacement rate, replacement frequency and similar replacement rate, and to determine the performance quality indicators of each supplier product in multiple products. The comprehensive quality evaluation output module is used to determine the quality evaluation results of each supplier's products in multiple products based on the adaptability quality index and performance quality index.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a data processing program, which, when executed by a processor, implements the digital product quality evaluation method as described in any one of claims 1 to 6.

9. A computer device, characterized in that, The method includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the program, implements the digital product quality evaluation method according to any one of claims 1 to 6.

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