Product data management method based on digital image

Abstract

The present invention relates to the technical field of digital image management, and in particular to a product data management method based on digital images, comprising: obtaining historical digital image information of all products and storing it in an information storage unit of corresponding products, obtaining historical production data and product inventory data, determining obsolescence characterization parameters of corresponding products, arranging the products in ascending order of the obsolescence characterization parameters, and determining products with optimal element distribution; determining the difference in obsolescence characterization parameters of sorted adjacent products to determine whether the sorted adjacent products are adjacent selected products and determine the same component digital image information; determining a first selected component in the non-identical component digital image information, and determining a data management method for the corresponding product according to the type of its component digital image information, thereby improving the efficiency and accuracy of product data management and effectively reducing inventory costs and product obsolescence risks.

Description

Technical Field

[0001] The present invention relates to the technical field of digital image management, and in particular to a product data management method based on digital images. Background Art

[0002] Currently, many companies use image acquisition and preprocessing technologies, using high-resolution cameras and professional image processing software to capture, crop, and adjust product images to improve image quality. They then establish image storage and classification systems, utilizing cloud and local storage technologies to store images by category, model, and other factors for easy retrieval. Simultaneously, image recognition technology is incorporated to automatically identify product features within images, enabling classification and labeling, thereby improving management efficiency. Furthermore, image display and update mechanisms ensure that platform images are updated and accurately displayed in a timely manner. However, these technologies also have some shortcomings in practical applications. For example, during the image acquisition phase, problems such as poor shooting angles and suboptimal lighting conditions may affect image quality; image storage and classification systems may face data security risks, such as hacker attacks and data leaks; the accuracy of image recognition technology in complex scenarios needs to be improved; and the image display and update mechanisms may also lead to display delays or errors due to improper operation or system failures.

[0003] Existing product data management methods present high data security risks in storage and classification systems, potentially leading to data leakage or loss due to cyberattacks or poor internal management. Image recognition technology lacks accuracy for complex patterns and multi-component products, hindering classification efficiency. Furthermore, display and update mechanisms lack real-time monitoring, preventing timely detection of faults or errors. Summary of the Invention

[0004] To this end, the present invention provides a product data management method based on digital images to overcome the problem in the prior art that data updating is slow, resulting in a high product obsolescence rate and affecting product iterative production.

[0005] To achieve the above-mentioned purpose, as a preferred technical solution of a digital image-based product data management method, the present invention provides a digital image-based product data management method, comprising:

[0006] Step S1, obtaining historical digital image information of all products and storing it in the information storage unit of the corresponding products, wherein the digital image information includes overall digital image information and component digital image information;

[0007] Step S2: Obtain historical production data and product inventory data to determine the slow-moving characteristic parameters of the corresponding products;

[0008] Step S3, arranging the products in order from small to large according to the sluggishness characterization parameters to determine the product with the optimal element distribution;

[0009] Step S4, determining the difference in the sluggishness characterization parameter of the adjacent products in the sorting order to determine whether the adjacent products in the sorting order are adjacent selected products and determine the same component digital image information;

[0010] Step S5, determine the first selected component in the non-identical component digital image information, and determine the data management method of the corresponding product according to its component digital image information type, determine the iteration order of the corresponding product according to the sluggish characterization parameter, and determine the product data storage accuracy according to the storage accuracy of the first selected component.

[0011] As a preferred technical solution of the product data management method based on digital images, in step S1, the component digital image information is the image information of the components that need to be assembled during the production process of each product.

[0012] As a preferred technical solution of the product data management method based on digital images, in step S2, determining the slowness characterization parameter of each product includes:

[0013] Obtain production data for various products within the same production period and inventory data at the same inventory reduction start time to determine the inventory reduction amount of the product;

[0014] Calculate the ratio of the total inventory reduction of the product to the corresponding production data, record it as the stagnation factor, and calculate the total stagnation factor;

[0015] The sluggishness average value and the sluggishness average deviation of all sluggishness factors are calculated, and the sluggishness characterization parameter is the ratio of the sluggishness average deviation to the sluggishness average value.

[0016] As a preferred technical solution of the product data management method based on digital images, in step S4, the adjacent products to be ranked are determined based on the difference in the sluggishness characterization parameter of the adjacent products, and the determination of whether the adjacent products to be ranked are adjacent selected products includes:

[0017] If the difference in the sluggishness characterization parameter is within the adjacent parameter threshold range, the currently sorted adjacent product is determined to be the adjacent selected product.

[0018] As a preferred technical solution of the product data management method based on digital images, in step S4, determining the digital image information of the same components of adjacent selected products includes:

[0019] Performing overlap comparison and color comparison on the overall digital image information of the adjacent selected products;

[0020] If the comparison results are all the same, the digital image information of the components inside the product will be compared one by one, and the components with exactly the same comparison results will be determined to be the same digital image information;

[0021] If the comparison results are different, the component digital image information of the adjacent selected products will be overlapped and compared one by one, and the component digital image information with the same comparison results will be determined to be the same.

[0022] As a preferred technical solution of the digital image-based product data management method, the types of component digital image information include appearance component types and functional component types.

[0023] As a preferred technical solution of the digital image-based product data management method, determining the first selection component includes:

[0024] Determining the sluggishness influence weight of each non-identical component according to the sluggishness characterization parameter difference and the digital image information of each non-identical component;

[0025] The non-identical component with the largest slowdown influence weight among all non-identical components of adjacent products is determined as the first selected component.

[0026] As a preferred technical solution of the digital image-based product data management method, in step S5, determining the data management method of the corresponding product according to the component digital image information type of the first selected component includes:

[0027] If the first selected component is an appearance component type, the data management method of the corresponding product is to determine the iteration order of the corresponding product based on the sluggish characterization parameter;

[0028] If the first selection component is a functional component type, the data management method of the corresponding product is to determine the product data storage accuracy according to the storage accuracy of the first selection component.

[0029] As a preferred technical solution of the product data management method based on digital images, determining the iteration order of corresponding products according to the sluggishness characterization parameter includes:

[0030] Determine the last selected component, and iterate in reverse order from the last selected component to the first selected component;

[0031] The iteration time interval is determined based on the ratio of the sluggish influence weight of the currently selected component to the adjacent parameter threshold range combined with the default iteration time.

[0032] As the preferred technical solution for product data management method based on digital image,

[0033] The beneficial effects of the present invention are:

[0034] By obtaining the historical digital image information and related production and inventory data of the product, calculating the obsolescence characterization parameters and sorting them accordingly, it is possible to accurately identify products with a higher risk of obsolescence, conduct comparative analysis on adjacent products, and determine the digital image information of the same components, which helps to reduce duplicate data storage and management and optimize resource utilization. In addition, by determining the first-selected components and their obsolescence impact weights, key components can be optimized in a targeted manner to improve product iteration efficiency. In addition, different data management methods are determined according to component type to achieve refined management and improve the effectiveness of data storage and use. The present invention improves the efficiency and accuracy of product data management, effectively reducing inventory costs and product obsolescence risks.

[0035] In particular, by first performing an overlap comparison and color comparison on the overall digital images of adjacent selected products, a preliminary judgment is made on the consistency of the product appearance. If the overall image comparison results are the same, only the digital images of the components inside the product are overlapped and compared one by one, reducing unnecessary image comparisons and wasting the system's computing power. If there are differences in the overall image comparison results, the digital image information of all components is overlapped and compared one by one, and the completely identical ones are also determined to be the same component digital image information. This hierarchical and gradually refined comparison method can efficiently and accurately identify the same and different component digital image information in adjacent selected products, thereby providing accurate data support for product production management, quality control, and product production data storage. This not only helps to reduce repeated design and production and reduce costs, but also improves the consistency and reliability of product quality, while optimizing inventory structure and reducing inventory costs.

[0036] In particular, by quantitatively analyzing the obsolescence impact weights of each non-identical component, the component that contributes most to the obsolescence condition, namely the first-selected component, is accurately identified. The selection order of non-identical components is determined based on the weight, and the last-selected component is prioritized for iterative optimization. This can effectively focus on the improvement of key components, thereby reducing the obsolescence risk of products in a targeted manner. At the same time, through the precise screening and priority sorting of components, resource utilization efficiency is improved, blind optimization of non-critical components is avoided, and unnecessary cost investment is reduced. In addition, combining the digital image information of components with obsolescence characterization parameters not only provides intuitive data support for product design and production, but also provides a scientific basis for the company's inventory management and product iteration strategy.

[0037] In particular, by analyzing the type of the first-selected component, the data management method of the corresponding product is determined in a targeted manner, thereby achieving refined and differentiated data management. When the first-selected component is an appearance component, the iteration order is determined based on the sluggish characterization parameters, aiming to optimize the product from the perspective of appearance design, enhance product competitiveness, reduce inventory backlogs caused by unsalable appearance, and accelerate product iteration speed. If it is a functional component, the product data storage accuracy is determined according to its storage accuracy requirements to ensure that the key details and characteristics of the functional component are accurately preserved and utilized. The data management strategy based on component type and importance not only improves the efficiency and accuracy of data management, but also further optimizes product life cycle management.

[0038] In particular, the product iteration process is effectively optimized by reversing the order of the iterative order from the last selected component to the first selected component, combined with the dynamic calculation of the iteration time interval. The determination of the iteration time interval comprehensively considers the ratio of the stagnation impact weight to the adjacent parameter threshold range, making the iteration time more reasonable and avoiding unnecessary iteration waiting or overly frequent iterations. The data-driven iteration strategy helps prioritize the components with the greatest impact on stagnation, thereby reducing the risk of product stagnation. At the same time, by combining it with the default iteration time, it ensures the stability and executability of the iteration plan, avoiding production disruptions or resource waste caused by too short or too long iteration time intervals. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Figure 1 This is a flowchart of a product data management method based on digital images according to an embodiment of the present invention;

[0040] Figure 2 A flow chart for determining slowness characterization parameters for each product according to an embodiment of the present invention;

[0041] Figure 3 A logic diagram for determining whether adjacent products are adjacent to selected products in accordance with an embodiment of the present invention. DETAILED DESCRIPTION

[0042] In order to make the objects and advantages of the present invention more clearly understood, the present invention is further described below in conjunction with embodiments; it should be understood that the specific embodiments described herein are merely used to explain the present invention and are not intended to limit the present invention.

[0043] The preferred embodiments of the present invention are described below with reference to the accompanying drawings. It should be understood by those skilled in the art that these embodiments are only used to explain the technical principles of the present invention and are not intended to limit the scope of protection of the present invention.

[0044] It should be noted that, in the description of the present invention, terms such as "up", "down", "left", "right", "inside", and "outside" indicating directions or positional relationships are based on the directions or positional relationships shown in the accompanying drawings. This is only for the convenience of description and does not indicate or imply that the device or element must have a specific orientation, be constructed and operated in a specific orientation. Therefore, it cannot be understood as a limitation on the present invention.

[0045] See also Figure 1 As shown, it is a flow chart of a product data management method based on digital images according to an embodiment of the present invention. The present invention provides a product data management method based on digital images, comprising:

[0046] Step S1, obtaining historical digital image information of all products and storing it in the information storage unit of the corresponding products, wherein the digital image information includes overall digital image information and component digital image information;

[0047] Step S2: Obtain historical production data and product inventory data to determine the slow-moving characteristic parameters of the corresponding products;

[0048] Step S3, arranging the products in order from small to large according to the sluggishness characterization parameters to determine the product with the optimal element distribution;

[0049] Step S4, determining the difference in the sluggishness characterization parameter of the adjacent products in the sorting order to determine whether the adjacent products in the sorting order are adjacent selected products and determine the same component digital image information;

[0050] Step S5, determine the first selected component in the non-identical component digital image information, and determine the data management method of the corresponding product according to its component digital image information type, determine the iteration order of the corresponding product according to the sluggish characterization parameter, and determine the product data storage accuracy according to the storage accuracy of the first selected component.

[0051] In implementation, the total amount of digital image information of a product is determined according to parts that need to be assembled during the production process of the product.

[0052] Understandably, these various products belong to the same product series, which share a high degree of similarity in design, function, and structure. This ensures the consistency and comparability of digital image information, production, and inventory data, enabling more accurate calculation of obsolete characterization parameters and product sorting. Furthermore, the common components and assemblies across products in the same series enable identification of identical parts through image overlap and color comparison, supporting the optimization of key components and improving production efficiency. Furthermore, the fact that products in the same series target similar user groups facilitates the reduction of obsolete inventory and optimizes resource allocation, ensuring the effectiveness and practicality of this method in practical applications.

[0053] By obtaining the historical digital image information and related production and inventory data of the product, calculating the obsolescence characterization parameters and sorting them accordingly, it is possible to accurately identify products with a higher risk of obsolescence, conduct comparative analysis on adjacent products, and determine the digital image information of the same components, which helps to reduce duplicate data storage and management and optimize resource utilization. In addition, by determining the first-selected components and their obsolescence impact weights, key components can be optimized in a targeted manner to improve product iteration efficiency. In addition, different data management methods are determined according to component type to achieve refined management and improve the effectiveness of data storage and use. The present invention improves the efficiency and accuracy of product data management, effectively reducing inventory costs and product obsolescence risks.

[0054] Specifically, in step S1, the component digital image information is image information of components that need to be assembled during the production process of each product.

[0055] In practice, the components of the product may not be disassembled during actual use, as long as they meet the requirements of separate production and assembly during the production process.

[0056] It is understandable that the appearance and shape information of the product will affect the product's slow-moving characterization parameters, the product's appearance or usage function will affect the product's remaining inventory, and the product's physical properties will have an impact on consumer decisions. Timely identification of the reasons for this impact can avoid the lag in the identification of slow-moving products, which will affect product production volume.

[0057] See also Figure 2 As shown, it is a flow chart of determining the sluggishness characterization parameters of each product according to an embodiment of the present invention. In the step S2, determining the sluggishness characterization parameters of each product includes:

[0058] Obtain production data for various products within the same production period and inventory data at the same inventory reduction start time to determine the inventory reduction amount of the product;

[0059] Calculate the ratio of the total inventory reduction of the product to the corresponding production data, record it as the stagnation factor, and calculate the total stagnation factor;

[0060] The sluggishness average value and the sluggishness average deviation of all sluggishness factors are calculated, and the sluggishness characterization parameter is the ratio of the sluggishness average deviation to the sluggishness average value.

[0061] In implementation, the production time is determined according to the product type and is selected within [1,6] months.

[0062] It is understandable that the inventory reduction and iteration cycle of different types of products vary greatly. If the extraction time is too short, it may not accurately reflect the actual inventory reduction of the product during the entire production cycle, resulting in the calculation result of the stagnation factor being too high or too low, and unable to truly reflect the inventory reduction performance of the product; and if the extraction time is too long, it may include factors such as too many product iterations, which will reduce the timeliness and pertinence of the data, and affect the accuracy and effectiveness of the stagnation characterization parameters. Therefore, determining the production time and inventory reduction time interval according to the adaptability of the product type is crucial for accurately measuring the degree of product stagnation, which can help companies more effectively manage inventory and adjust product strategies.

[0063] See also Figure 3 As shown, it is a logic diagram for determining whether the sorted adjacent products are adjacent selected products according to an embodiment of the present invention. In the step S4, the sorted adjacent products are determined according to the difference in the sluggishness characteristic parameter of the adjacent products. Determining whether the sorted adjacent products are adjacent selected products includes:

[0064] If the difference in the sluggishness characterization parameter is within the adjacent parameter threshold range, the currently sorted adjacent product is determined to be the adjacent selected product.

[0065] In implementation, the proximity parameter threshold range is determined based on the maximum value of the sluggish characterization parameter difference corresponding to the number of iterations not exceeding 3 in historical production data.

[0066] Understandably, products with fewer iterations in their production history are often in the early stages of their lifecycles, having not yet undergone multiple iterations of optimization for inventory reduction and production adjustments. The maximum difference in the slowdown parameter at this point reflects the reasonable fluctuation range of slowdown risk between adjacent products before the products reach maturity. Therefore, using this maximum value as the proximity parameter threshold effectively identifies products with similar characteristics in terms of slowdown risk, thereby determining them as near-selection products.

[0067] Specifically, in step S4, determining the digital image information of the same component adjacent to the selected product includes:

[0068] Performing overlap comparison and color comparison on the overall digital image information of the adjacent selected products;

[0069] If the comparison results are all the same, the digital image information of the components inside the product will be compared one by one, and the components with exactly the same comparison results will be determined to be the same digital image information;

[0070] If the comparison results are different, the component digital image information of the adjacent selected products will be overlapped and compared one by one, and the component digital image information with the same comparison results will be determined to be the same.

[0071] In practice, the coincidence alignment is achieved by the SSIM structural similarity algorithm.

[0072] In the present invention, by first performing an overlap comparison and color comparison on the overall digital images of adjacent selected products, a preliminary judgment is made on the consistency of the product appearance. If the overall image comparison results are the same, only the digital images of the components inside the product are overlapped and compared one by one, reducing unnecessary image comparisons and wasting the system's computing power. If there are differences in the overall image comparison results, all the component digital image information is overlapped and compared one by one, and the completely identical ones are also determined to be the same component digital image information. This hierarchical and gradually refined comparison method can efficiently and accurately identify the same and different component digital image information in adjacent selected products, thereby providing accurate data support for product production management, quality control, and product production data storage. This not only helps to reduce repeated design and production and reduce costs, but also improves the consistency and reliability of product quality, while optimizing inventory structure and reducing inventory costs.

[0073] Specifically, the types of component digital image information include appearance component types and function component types.

[0074] It is understandable that images of appearance parts can intuitively present the product's appearance design, color, surface processing, etc., meeting the needs of product appearance evaluation in the design and purchase process; images of functional parts focus on the product's internal structure, mechanical principles, electrical connections and other functional characteristics, which facilitate technical operations, maintenance and improvements. This classification method can more accurately meet the diverse needs of different links for product information, and improve the level of refinement and practicality of product data management.

[0075] Specifically, determining the first selection component includes:

[0076] Determining the sluggishness influence weight of each non-identical component according to the sluggishness characterization parameter difference and the digital image information of each non-identical component;

[0077] The non-identical component with the largest slowdown influence weight among all non-identical components of adjacent products is determined as the first selected component.

[0078] In implementation, the selection order of each non-identical component is determined according to this order, and the last selected component is iterated first.

[0079] In the present invention, by quantitatively analyzing the stagnation impact weights of various non-identical components, the components that contribute most to the stagnation condition, namely the first-selected components, are accurately identified, and the selection order of non-identical components is determined according to the weights. The last-selected components are prioritized for iterative optimization, which can effectively focus on the improvement of key components, thereby reducing the stagnation risk of products in a targeted manner. At the same time, through the precise screening and priority sorting of components, the efficiency of resource utilization is improved, the blind optimization of non-critical components is avoided, and unnecessary cost investment is reduced. In addition, combining the digital image information of components with stagnation characterization parameters not only provides intuitive data support for product design and production, but also provides a scientific basis for the company's inventory management and product iteration strategy.

[0080] Specifically, in step S5, determining the data management method of the corresponding product according to the component digital image information type of the first selected component includes:

[0081] If the first selected component is an appearance component type, the data management method of the corresponding product is to determine the iteration order of the corresponding product based on the sluggish characterization parameter;

[0082] If the first selection component is a functional component type, the data management method of the corresponding product is to determine the product data storage accuracy according to the storage accuracy of the first selection component.

[0083] In the present invention, by analyzing the type of the first-selected component, the data management method of the corresponding product is determined in a targeted manner, thereby achieving refinement and differentiation of data management. When the first-selected component is an appearance component, the iteration order is determined based on the sluggish characterization parameters, aiming to optimize the product from the perspective of appearance design, enhance product competitiveness, reduce inventory backlogs caused by unsalable appearance, and accelerate product iteration speed. If it is a functional component, the product data storage accuracy is determined according to its storage accuracy requirements to ensure that the key details and characteristics of the functional component are accurately preserved and utilized. The data management strategy based on component type and importance not only improves the efficiency and accuracy of data management, but also further optimizes product life cycle management.

[0084] Specifically, determining the iteration order of corresponding products according to the sluggishness characterization parameter includes:

[0085] Determine the last selected component, and iterate in reverse order from the last selected component to the first selected component;

[0086] The iteration time interval is determined based on the ratio of the sluggish influence weight of the currently selected component to the adjacent parameter threshold range combined with the default iteration time.

[0087] In implementation, the default iteration time is the initial iteration time of product production planning.

[0088] Iteration time interval = (1-stagnation impact weight / nearby parameter threshold range) × default iteration time.

[0089] In the present invention, the product iteration process is effectively optimized by reversing the order of the iterative order from the last selected component to the first selected component, combined with the dynamic calculation of the iteration time interval. The determination of the iteration time interval comprehensively considers the ratio of the stagnation impact weight to the adjacent parameter threshold range, making the iteration time more reasonable and avoiding unnecessary iteration waiting or overly frequent iterations. The data-driven iteration strategy helps to prioritize the components that have the greatest impact on stagnation, thereby reducing the risk of product stagnation. At the same time, by combining with the default iteration time, it ensures the stability and executability of the iteration plan, avoiding production chaos or resource waste caused by too short or too long iteration time intervals.

[0090] If the digital image-based product data management method of the present invention is implemented as a software functional unit and sold or used as a standalone product, it can be stored on a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the portion that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product, stored on a storage medium, includes instructions for causing a computer device (such as a personal computer, server, or network device) to execute all or part of the steps of the method described in various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, mobile hard drives, read-only memories (ROMs), random access memories (RAMs), magnetic disks, or optical disks.

[0091] The flowcharts and block diagrams in the accompanying drawings illustrate the possible architectures, functions and operations of the devices, methods and computer program products according to various embodiments of the present application. In this regard, each box in the flowchart or block diagram can represent a module, program segment or part of the code, which contains one or more executable instructions for implementing the specified logical functions. It should also be noted that in some alternative implementations, the functions marked in the boxes can also occur in an order different from that marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flowchart, as well as the combination of boxes in the block diagram and / or flowchart, can be implemented using a dedicated hardware-based device that performs the specified function or operation, or can be implemented using a combination of dedicated hardware and computer instructions.

[0092] Thus far, the technical solutions of the present invention have been described in conjunction with the preferred embodiments shown in the accompanying drawings. However, it will be readily understood by those skilled in the art that the scope of protection of the present invention is obviously not limited to these specific embodiments. Without departing from the principles of the present invention, those skilled in the art may make equivalent changes or substitutions to the relevant technical features, and the technical solutions after such changes or substitutions will fall within the scope of protection of the present invention.

[0093] The foregoing description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Those skilled in the art will readily appreciate that the present invention is susceptible to various modifications and variations. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of the present invention are intended to be within the scope of protection of the present invention.

Claims

1. A product data management method based on digital images, characterized in that: include: Step S1, obtaining historical digital image information of all products and storing it in the information storage unit of the corresponding products, wherein the digital image information includes overall digital image information and component digital image information; Step S2: Obtain historical production data and product inventory data to determine the slow-moving characteristic parameters of the corresponding products; Step S3, arranging the products in order from small to large according to the sluggishness characterization parameters to determine the product with the optimal element distribution; Step S4, determining the difference in the sluggishness characterization parameter of the adjacent products in the sorting order to determine whether the adjacent products in the sorting order are adjacent selected products and determine the same component digital image information; Step S5, determining a first selected component from the non-identical component digital image information, and determining a data management method for the corresponding product based on the component digital image information type, determining an iteration order for the corresponding product based on the slowness characterization parameter, and determining a product data storage accuracy based on the storage accuracy of the first selected component; In step S2, determining the sluggishness characterization parameters of each product includes: Obtain production data for various products within the same production period and inventory data at the same inventory reduction start time to determine the inventory reduction amount of the product; Calculate the ratio of the total inventory reduction of the product to the corresponding production data, record it as the stagnation factor, and calculate the total stagnation factor; Calculating the sluggishness average value and the sluggishness average deviation of all sluggishness factors, wherein the sluggishness characterization parameter is the ratio of the sluggishness average deviation to the sluggishness average value; Determining the first selection component includes: Determining the sluggishness influence weight of each non-identical component according to the sluggishness characterization parameter difference and the digital image information of each non-identical component; Determine the non-identical component with the largest slowdown impact weight among all non-identical components of adjacent products as the first selected component; In step S5, determining the data management method of the corresponding product according to the component digital image information type of the first selected component includes: If the first selected component is an appearance component type, the data management method of the corresponding product is to determine the iteration order of the corresponding product based on the sluggish characterization parameter; If the first selected component is a functional component type, the data management method for the corresponding product is to determine the product data storage accuracy according to the storage accuracy of the first selected component; Determining the iteration order of the corresponding product according to the sluggishness characterization parameter includes: Determine the last selected component, and iterate in reverse order from the last selected component to the first selected component; The iteration time interval is determined based on the ratio of the sluggish influence weight of the currently selected component to the adjacent parameter threshold range combined with the default iteration time.

2. The product data management method based on digital images according to claim 1, characterized in that: In step S1 , the component digital image information is image information of components that need to be assembled during the production process of each product.

3. The product data management method based on digital images according to claim 1, characterized in that: In step S4, the adjacent products are determined according to the difference in the sluggishness characterization parameters of the adjacent products, and the determination of whether the adjacent products are adjacent selected products includes: If the difference in the sluggishness characterization parameter is within the adjacent parameter threshold range, the currently sorted adjacent product is determined to be the adjacent selected product.

4. The product data management method based on digital images according to claim 3, characterized in that: In step S4, determining the digital image information of the same component adjacent to the selected product includes: Performing overlap comparison and color comparison on the overall digital image information of the adjacent selected products; If the comparison results are all the same, the digital image information of the components inside the product will be compared one by one, and the components with exactly the same comparison results will be determined to be the same digital image information; If the comparison results are different, the component digital image information of the adjacent selected products will be overlapped and compared one by one, and the component digital image information with the same comparison results will be determined to be the same.

5. The product data management method based on digital images according to claim 4, characterized in that: The types of component digital image information include appearance component types and functional component types.

Images