Method, system and apparatus for optimizing product spray processes

Abstract

The present disclosure relates to methods, systems and apparatuses for optimizing a product painting process. A computer-implemented method includes receiving data related to a plurality of groups of products to be painted, the products of a same group having a same color to be painted, the products of different groups having different colors to be painted; generating a product painting sequencing queue for the plurality of groups of products based at least on the received data and based on a set of product painting color sequencing patterns and corresponding set of defect risk coefficients, each product painting color sequencing pattern having a corresponding defect risk coefficient and indicating an ordered arrangement of a plurality of colors to be painted in succession by a spray gun; and the corresponding defect risk coefficient of a product painting color sequencing pattern characterizing a likelihood of a painting defect occurring for a product when an ordered relationship between a color to be painted by the product and one or more ordered painting colors immediately preceding the product painting color sequencing pattern matches the product painting color sequencing pattern.

Description

Technical Field

[0001] This disclosure relates to process optimization, specifically to methods, systems, and equipment for optimizing product spraying sequence during the product spraying process. Background Technology

[0002] Automotive painting workshops can typically receive painting orders well in advance. Painting orders typically include information such as the number of vehicles to be painted, vehicle model, color, arrival time in the painting workshop, and departure time from the painting workshop.

[0003] Based on the information from the pre-received painting orders, the painting tasks for the vehicles to be painted can be prioritized, taking into account various factors such as delivery time, painting time, painting cost, and painting efficiency.

[0004] In related technologies, when considering painting costs and efficiency, it is generally advisable to group car bodies with the same color to be painted together to minimize the number of times the paint needs to be changed at the spray gun. This is because each time the paint needs to be changed at the spray gun, the gun needs to be cleaned and reloaded with the new paint. The paint loss and replacement work incur costs, and the time spent on the replacement process also reduces production efficiency. Summary of the Invention

[0005] According to embodiments of this disclosure, a computer-implemented method is provided, comprising: receiving data relating to multiple groups of products to be sprayed, wherein products in the same group have the same color to be sprayed, and products in different groups have different colors to be sprayed; generating a product spraying sorting queue for the multiple groups of products based at least on the received data and based on a set of product spraying color sorting patterns and a corresponding set of defect risk coefficients, wherein each product spraying color sorting pattern is associated with a corresponding defect risk coefficient, the product spraying color sorting pattern indicating an ordered arrangement of multiple colors sprayed consecutively by a spray gun; a defect risk coefficient corresponding to a product spraying color sorting pattern characterizes the probability that a product will have a spraying defect when the order relationship between the product's color to be sprayed and one or more ordered colors immediately preceding the product's spraying matches the product spraying color sorting pattern.

[0006] According to some embodiments, the method may further include: receiving the set of product coating color sorting patterns and the corresponding set of defect risk coefficients.

[0007] According to some embodiments, the method may further include: determining the set of product spraying sorting patterns and the corresponding set of defect risk coefficients based on historical data within a predetermined time period.

[0008] According to some embodiments, the defect risk factor can be based on at least one of the following: the proportion of minor painting defects and the proportion of serious painting defects.

[0009] According to some embodiments, the step of generating a product coating sorting queue for the multiple groups of products based on received data and a set of product coating color sorting patterns and a corresponding set of defect risk coefficients may further include: generating at least one set of all colors of the multiple groups of products based on the set of product coating color sorting patterns and the corresponding set of defect risk coefficients, wherein each set in the at least one set matches a product coating color sorting pattern, and the step of generating at least one set prioritizes matching patterns with low defect risk coefficients from low to high defect risk coefficients; and generating a product coating sorting queue for the multiple groups of products by utilizing the generated sets.

[0010] According to some embodiments, the position of the at least one set in the product coating sorting queue of the multiple product groups is determined based on the product coating color sorting pattern of the group and the corresponding defect risk coefficient of the group.

[0011] According to some embodiments, the step of generating a product painting sorting queue for the multiple groups of products based on received data and a set of product painting color sorting patterns and a corresponding set of defect risk coefficients may further include: for the preceding painting color, determining colors from all colors of the multiple groups of products that can be combined with the preceding painting color to form a set based on the set of product painting color sorting patterns, each set matching a product painting color sorting pattern; selecting colors from the determined colors that match the set of product painting color sorting patterns with the lowest defect risk coefficient based on the set of defect risk coefficients; and arranging the painting task of the group of products corresponding to the selected color into the product painting sorting queue for the multiple groups of products.

[0012] According to some embodiments, the step of generating a product coating sorting queue for the multiple groups of products based on received data and a set of product coating color sorting patterns and a corresponding set of defect risk coefficients may further include: generating a first product coating sorting queue for the multiple groups of products based on received data; and optimizing the first product coating sorting queue for the multiple groups of products based on the set of product coating color sorting patterns and the corresponding set of defect risk coefficients, thereby generating the product coating sorting queue for the multiple groups of products.

[0013] According to some embodiments, the step of optimizing the first product coating sorting queue of the multiple product groups based on the set of product coating color sorting patterns and the corresponding set of defect risk coefficients may further include: determining the colors of the first multiple product groups involved in at least a portion of the first product coating sorting queue of the multiple product groups; generating at least one set of colors for the first product groups based on the set of product coating color sorting patterns and the corresponding set of defect risk coefficients, wherein each set in the at least one set matches a product coating color sorting pattern, and the step of generating at least one set follows a pattern of prioritizing the matching of defect risk coefficients from low to high; and adjusting the at least a portion of the first product coating sorting queue using the generated at least one set.

[0014] According to embodiments of the present disclosure, a computer system is provided, including: one or more processors, and a memory coupled to the one or more processors, the memory storing computer-readable program instructions that, when executed by the one or more processors, cause the one or more processors to perform the method as described above.

[0015] According to embodiments of the present disclosure, a computer-readable storage medium is provided that stores computer-readable program instructions thereon, which, when executed by a processor, cause the processor to perform the method described above.

[0016] According to embodiments of the present disclosure, a computer program product is provided, including computer-readable program instructions that, when executed by a processor, cause the processor to perform the method described above. Attached Figure Description

[0017] Figure 1 A table showing product coating color sorting patterns and corresponding defect risk coefficients according to embodiments of the present disclosure is provided.

[0018] Figure 2 An exemplary flowchart of a method for optimizing a product coating process according to an embodiment of the present disclosure is shown.

[0019] Figure 3 An exemplary flowchart is shown for another method for optimizing a product coating process according to an embodiment of this disclosure.

[0020] Figure 4A A schematic diagram of six sets of products according to embodiments of the present disclosure is shown.

[0021] Figure 4B A schematic diagram of a product coating sorting queue according to an embodiment of the present disclosure is shown.

[0022] Figure 5An exemplary flowchart is shown for another method for optimizing a product coating process according to an embodiment of this disclosure.

[0023] Figure 6 An exemplary flowchart is shown for another method for optimizing a product coating process according to an embodiment of this disclosure.

[0024] Figure 7 This is a schematic diagram illustrating a general hardware environment in which a device according to an embodiment of the present disclosure can be implemented. Detailed Implementation

[0025] The following description is provided to enable those skilled in the art to implement and use the embodiments, and the description is provided in the context of a particular application and its requirements. Various modifications will be apparent to those skilled in the art, and the general principles defined herein can be applied to other embodiments and applications without departing from the spirit and scope of the embodiments. Therefore, the embodiments are not limited to the embodiments shown, but are to be given the widest scope consistent with the principles and features disclosed herein.

[0026] This application relates to methods, systems, and apparatus for optimizing product painting sequence during the product painting process. Embodiments of this application analyze historical data related to vehicle body painting over a predetermined time period (e.g., one year) to determine a set of product painting color sorting patterns and a corresponding set of defect risk coefficients. A defect risk coefficient corresponding to a product painting color sorting pattern represents the probability of a painting defect occurring when the order of the color to be painted on a product matches the sorting pattern with one or more preceding ordered colors. Embodiments of this application can optimize the product painting sequence queue of multiple products based on this set of product painting color sorting patterns and the corresponding set of defect risk coefficients, thereby reducing defect rates, saving production costs, and improving production efficiency.

[0027] In this document, the product is, for example, an automobile. Those skilled in the art will understand that the product can be any product that requires painting. Embodiments of this application can be applied to similar production scenarios.

[0028] Figure 1 Table 100 shows a product coating color sorting pattern and corresponding defect risk coefficients according to an embodiment of the present disclosure.

[0029] like Figure 1As shown in Table 100, there are 21 product painting color sorting patterns. Each pattern includes the color to be painted and the prior painting sequence. For example, pattern 2 includes the color to be painted, A96, and the prior painting sequence "C2X, M475, WC3C". A96, C2X, M475, and WC3C are codes for different colors. The alphanumeric combinations in Table 100 are codes for different painting colors. Table 100 also lists the defect risk coefficient corresponding to each product painting color sorting pattern. For example, the defect risk coefficient for pattern 2 is "8.33%", meaning that when the spray gun is used to paint according to the color sorting of pattern 2, the probability of a painting defect appearing on the A96 body is "8.33%".

[0030] Table 100, for example, is generated based on historical data from the paint shop. Historical data generated by the paint shop within a predetermined time period (e.g., one year) can be obtained. Historical data may include, for example, one or more of the following: spray gun number; the number, model, color, start and end times of the vehicle body painted by each spray gun; whether any paint defects occurred on any vehicle body; the type of defect on the vehicle body with paint defects (e.g., minor defects requiring local touch-ups, or serious defects requiring a complete repaint), etc.

[0031] The defect risk factor can be determined based on at least one of the number of vehicle bodies with serious defects and the number of vehicle bodies with minor defects.

[0032] Multiple product painting color sorting patterns can be extracted from historical data. As shown in Table 100, the product painting color sorting pattern can be the ordered color sequence sprayed by the spray gun during actual operation. Taking Pattern 1 as an example, relevant historical data matching this pattern can be retrieved, and the total number of car bodies painted in the A96 color under Pattern 1 and the number of defective car bodies among them can be calculated. The proportion of defective car bodies to the total number is calculated as the defect risk coefficient corresponding to Pattern 1. Specifically, the total number of A96 car bodies painted in the historical order of C10, A96 is counted, and the number of defective car bodies among the A96 car bodies painted in the order of C10, A96 is counted. Then, the percentage of defective car bodies to the total number is calculated to obtain the defect risk coefficient for Pattern 1.

[0033] The defect risk factor can be determined based on both the number of vehicle bodies with serious defects and the number of vehicle bodies with minor defects. In some embodiments, the defect risk factor can be a set of two factors, for example, a defect risk factor (2%; 10%) could represent that the number of vehicle bodies with serious defects accounts for 2% of the total number and the number of vehicle bodies with minor defects accounts for 10% of the total number. In some embodiments, the defect risk factor can be the sum or weighted sum of these two percentages.

[0034] Those skilled in the art will understand that Figure 1 Table 100 shown is merely an example and can be modified as needed. For example, the defect risk coefficient can be calculated by considering only two consecutive sequences of paint colors, or by considering sequences of more paint colors.

[0035] Figure 2 An exemplary flowchart of a method 200 for optimizing a product coating process according to an embodiment of the present disclosure is shown.

[0036] like Figure 2 As shown, method 200 may include step 201, in which data relating to multiple groups of products to be sprayed is received, wherein products in the same group have the same color to be sprayed, and products in different groups have different colors to be sprayed.

[0037] In some embodiments, multiple groups of products can be obtained by grouping the products to be sprayed only according to their colors; that is, the products are grouped into different groups based on the color to be sprayed. Products in the same group have the same color to be sprayed, while products in different groups have different colors to be sprayed.

[0038] In some embodiments, each group of products can be further subdivided into multiple subgroups based on factors such as model number, delivery time, etc. For example, products in different subgroups within the same group may have the same color but different models. Continuous painting of the same model of vehicle body can improve painting efficiency because the painting procedures followed by the spray guns may differ for different models of vehicle bodies. As another example, products in different subgroups within the same group may have the same color but different delivery times, and therefore need to be positioned at different points in the product painting sequence queue. This allows for finer-grained optimization of the product painting sequence queue.

[0039] Painting workshops typically receive painting orders in advance, allowing them to analyze and process order-related data before painting begins. Data related to multiple product sets to be painted is often based on such orders. In some cases, painting workshops also need to handle car bodies that require repainting due to painting defects; this car body-related information can also serve as a basis for obtaining data related to the multiple product sets to be painted.

[0040] The vehicles to be painted could be, for example, all vehicles scheduled to be painted on a certain day according to the production plan, or all vehicles scheduled to be painted in a certain week according to the production plan.

[0041] Method 200 may further include step 203, in which a product coating sorting queue of the multiple sets of products is generated based at least on the received data and based on a set of product coating color sorting patterns and a corresponding set of defect risk coefficients.

[0042] For ease of explanation, the product coating color sorting pattern and the corresponding defect risk coefficient are as follows: Figure 1 As shown in Table 100.

[0043] When the painting workshop receives a painting task for multiple car bodies, it can generate a sorting queue for these painting tasks based on a predetermined set of product painting color sorting patterns and corresponding defect risk coefficients. This set of product painting color sorting patterns and corresponding defect risk coefficients are, for example, based on historical data as described above.

[0044] As can be seen from Table 100, for the same spray gun, when changing the color to spray different sets of products, the combination of the color to be sprayed with the previous color spraying sequence is different, and the probability of defects appearing on the car body after changing the pigment is different.

[0045] Therefore, from the perspective of the spray gun, for an existing prior color spraying sequence, it is preferable to replace it with a subsequent color that forms a product spraying color sorting pattern with a lower defect risk coefficient when the combination of the subsequent color and the prior color spraying sequence of the spray gun is matched.

[0046] From the perspective of a set of products to be painted, for a known color to be painted, it is preferable to place it after such a prior color painting sequence, and the combination of it with the prior color painting sequence has a lower defect risk coefficient in the product painting color sorting pattern.

[0047] In other words, the goal of optimizing the product painting process is to make the final product painting sequence queue utilize as many product painting color sorting patterns with low defect risk coefficients as possible, while meeting the production plan.

[0048] Therefore, the method according to the embodiments of this disclosure can reduce the coating defect rate, improve production efficiency, and reduce production costs.

[0049] The following discussion considers several different scenarios and explores several specific methods for generating product spraying sorting queues for multiple products.

[0050] Figure 3 An exemplary flowchart of another method 300 for optimizing a product coating process according to an embodiment of the present disclosure is shown.

[0051] like Figure 3As shown, method 300 includes step 301, in which data related to multiple groups of products to be sprayed is received, wherein products in the same group have the same color to be sprayed, and products in different groups have different colors to be sprayed. Step 301 is similar to step 201, and will not be described again here.

[0052] For ease of explanation, let's assume we received data for 6 product groups, which correspond to colors WB66, WC4F, A96, C08, WC1X, and B53, respectively. Figure 4A A schematic diagram of six groups of products according to an embodiment of the present disclosure is shown. Each group of products may have a different quantity (each cell represents one product), and different groups of products may have different colors.

[0053] Method 300 further includes step 303, in which at least one set is generated based on the product spraying color sorting pattern and the corresponding defect risk coefficient of the multiple sets of products, each set in the at least one set being matched with a product spraying color sorting pattern, and the step of generating at least one set prioritizing matching patterns with low defect risk coefficients from low to high defect risk coefficients.

[0054] You can retrieve all patterns in Table 100 that contain at least one of the six colors. For example, in Table 100, you can retrieve patterns 1-19.

[0055] Then, patterns with low defect risk coefficients are prioritized for matching, from lowest to highest. For example, as shown in Table 100, patterns 4 and 17 have the lowest defect risk coefficients, both at 0.00%, making them the most preferred product coating color sorting patterns and should be used first. Therefore, among these 6 sets of product colors, two sets can be generated based on patterns 4 and 17: the first set "WB66, A96" and the second set "C08, WC1X". Excluding patterns 4 and 17, pattern 18 has the lowest defect risk coefficient, at 0.01%, so a third set "WC4F, B53" can be generated based on pattern 18. Those skilled in the art will understand that if more colors are involved, potentially generating more sets, this process can be repeated sequentially.

[0056] The method further includes step 305, generating a product coating sorting queue for the multiple product groups by utilizing the generated at least one set. This generated product coating sorting queue minimizes the risk of coating defects, improves production efficiency, and reduces production costs.

[0057] In some embodiments, the final product spraying sorting queue is formed by grouping the products corresponding to each of the six product groups together.

[0058] Figure 4B A schematic diagram of a product coating sorting queue according to an embodiment of the present disclosure is shown. "WB66, A96" are grouped together, "C08, WC1X" are grouped together, and "WC4F, B53" are grouped together.

[0059] In some embodiments, the order of the sets can be random. For example, a queue could be generated in the order of “C08, WC1X”, “WC4F, B53”, and “WB66, A96”.

[0060] In other embodiments, the position of each set in the product coating sorting queue of the multiple product groups is further determined based on the product coating color sorting pattern of that group and the corresponding defect risk coefficient of that group.

[0061] For example, the sets can be further sorted based on, for example, Table 100. For instance, WB66 in the first set might be queued after "WC1X" or "B53", "C08" might be queued after "A96" or "B53", and "WC4F" might be queued after "A96" or "WC1X". Then, a search can be performed in Table 100 for these possible patterns, and the matching patterns found can be prioritized based on their defect risk coefficient, from low to high, using patterns with lower defect risk coefficients.

[0062] For example, when a color that is not in any set appears after the set is generated, we can consider, based on Table 100, whether the possible combinations of these colors with the last color of the previous set and / or the first color of the next set match the low defect risk coefficient pattern in Table 100. If they match, such combinations can be preferred.

[0063] Furthermore, when a product set comprises multiple subgroups, one or more sets can be generated for each subgroup using a similar approach. This provides finer-grained optimization.

[0064] Figure 5 An exemplary flowchart of another method 500 for optimizing a product coating process according to an embodiment of the present disclosure is shown.

[0065] like Figure 5 As shown, method 500 includes step 501, in which data related to multiple groups of products to be sprayed is received, wherein products in the same group have the same color to be sprayed, and products in different groups have different colors to be sprayed. Step 501 is similar to steps 201 and 301, and will not be described again here.

[0066] Method 500 further includes step 503, in which, for the preceding paint color, colors that can be combined with the preceding paint color to form a set are determined from all colors of the multiple sets of products based on the product paint color sorting pattern, each set being matched with a product paint color sorting pattern.

[0067] Taking Table 100 as an example, and assuming the immediate preceding paint color is A83, based on the colors of the 6 product groups and Table 100, it can be determined that the colors of the 6 product groups that can form combinations with A83 as the immediate preceding paint color are C08 and WC1X, with the corresponding patterns as follows:

[0068] Pattern 9: The defect risk coefficient for A83 C08 is 0.41%.

[0069] Mode 13: The defect risk coefficient for A83 WC1X is 0.61%.

[0070] Method 500 further includes step 505, in which color is selected from the determined colors based on the set of defect risk coefficients and the set of colors that match the product spraying color sorting pattern with the lowest defect risk coefficient.

[0071] For example, if you select mode 9 from mode 9 and mode 13, the selected color will be C08.

[0072] Method 500 further includes step 507, in which the painting tasks of a group of products corresponding to the selected color are arranged into the product painting sorting queue of the multiple groups of products.

[0073] An embodiment of method 500 is for selecting a color that satisfies a low defect risk coefficient for the spray gun when the spray gun has completed a previous spraying task and is about to perform a different spraying task, and selecting the group of products corresponding to that color.

[0074] Figure 6 An exemplary flowchart of another method 600 for optimizing a product coating process according to an embodiment of the present disclosure is shown.

[0075] like Figure 6 As shown, method 600 includes step 601, in which data relating to multiple groups of products to be sprayed is received, wherein products in the same group have the same color to be sprayed, and products in different groups have different colors to be sprayed. Step 601 is similar to steps 201, 301, and 501, and will not be described again here.

[0076] Method 600 further includes step 603, in which a first product coating sorting queue is generated based on the received data for the multiple groups of products.

[0077] The first product painting sorting queue is, for example, sorted based on factors such as the delivery dates of multiple product groups, but without considering the different defect risks associated with different painting colors. Under the condition of meeting the production plan, products of the same color are grouped together.

[0078] Method 600 further includes step 605, optimizing the first product coating sorting queue of the multiple groups of products based on the set of product coating color sorting patterns and the corresponding set of defect risk coefficients, thereby generating the second product coating sorting queue of the multiple groups of products.

[0079] In some cases, adjusting the order of products within a portion of the first product painting sequence queue does not affect the implementation of the production plan. For example, suppose the first product painting sequence queue involves painting tasks for 600 vehicles, where adjusting the order of vehicles 300-450 in the queue does not affect an embodiment of the production plan. In this case, the sequencing of these vehicles can be optimized.

[0080] Step 605 may include, for example, the following sub-steps:

[0081] Sub-step 6051: Determine the color of the first multi-group product involved in at least a portion of the first product spraying sorting queue of the multi-group products.

[0082] Assume the first group of products is Figure 4A The six product groups shown are arranged randomly in the first product spraying sorting queue without considering the influence of the spraying color sorting, provided that the production plan is met.

[0083] Sub-step 6053: Based on the product coating color sorting pattern and the corresponding defect risk coefficient, generate at least one set of colors for the first group of products. Each set in the at least one set matches a product coating color sorting pattern, and the step of generating at least one set prioritizes matching defect risk coefficients from low to high. Sub-step 6053 is similar to step 303 and will not be described again here.

[0084] Sub-step 6055: Adjust at least a portion of the first product spraying sorting queue using the generated at least one set. Sub-step 6055 is similar to step 305 and will not be described again here.

[0085] Through steps 6053-6055, at least a portion of the first product spraying sorting queue is, for example, adjusted as follows: Figure 4B As shown.

[0086] The above embodiments are all described assuming that a matching pattern can be found. Those skilled in the art will understand that if a matching pattern cannot be found, conventional methods can be used for processing.

[0087] Those skilled in the art will also understand that when a product group comprises multiple subgroups, the spraying sorting queue can be optimized at a finer granular level based on the product spraying color sorting pattern and the corresponding defect risk coefficient of the group, on a subgroup basis.

[0088] This disclosure embodiment determines the product coating color sorting pattern and defect risk coefficient based on historical data, and uses the determined product coating color sorting pattern and defect risk coefficient to optimize the product coating color sorting, thereby reducing the defect rate, reducing production costs, and improving production efficiency.

[0089] Figure 7 This is a schematic diagram illustrating a general hardware environment in which a device according to an embodiment of the present disclosure can be implemented.

[0090] Now for reference Figure 7 The diagram illustrates an example of a compute node 700. Compute node 700 is merely one example of a suitable compute node and is not intended to imply any limitation on the scope of use or functionality of the embodiments described herein. In any case, compute node 700 is capable of implementing and / or performing any of the functions set forth above.

[0091] Within compute node 700, there is a computer system / server 6012 that can operate with a wide variety of other general-purpose or special-purpose computing system environments or configurations. Examples of well-known computing systems, environments, and / or configurations suitable for use with computer system / server 6012 include, but are not limited to: personal computer systems, server computer systems, thin clients, thick clients, handheld or laptop devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronics, network PCs, minicomputer systems, mainframe computer systems, and distributed cloud computing environments that include any of the aforementioned systems or devices, etc.

[0092] The computer system / server 6012 can be described in the general context of computer system executable instructions (such as program modules) executed by the computer system. Generally, program modules can include routines, programs, objects, components, logic, data structures, etc., that perform specific tasks or implement specific abstract data types. The computer system / server 6012 can be implemented in a distributed cloud computing environment, where tasks are performed by remote processing devices linked via a communication network. In a distributed cloud computing environment, program modules can reside on both local and remote computer system storage media, including memory storage devices.

[0093] like Figure 7 As shown, the computer system / server 6012 in compute node 700 is illustrated in the form of a general-purpose computing device. The components of the computer system / server 6012 may include, but are not limited to: one or more processors or processing units 6016, system memory 6028, and a bus 6018 that couples the various system components, including the system memory 6028, to the processing unit 6016.

[0094] Bus 6018 represents any one or more of several types of bus architectures, including memory buses or memory controllers, peripheral buses, accelerated graphics ports, processors, or local buses using any of the various bus architectures. By way of example and not limitation, these architectures include, but are not limited to, Industry Standard Architecture (ISA) buses, Microchannel Architecture (MAC) buses, Enhanced ISA buses, Video Electronics Standards Association (VESA) local buses, Peripheral Component Interconnect (PCI) buses, Peripheral Component Interconnect High Speed ​​(PCIe) buses, and Advanced Microcontroller Bus Architecture (AMBA).

[0095] Computer system / server 6012 typically includes various computer system readable media. These media can be any available media accessible by computer system / server 6012, including volatile and non-volatile media, removable and non-removable media.

[0096] System memory 6028 may include computer system readable media in the form of volatile memory, such as random access memory (RAM) 30 and / or cache memory 6032. Computer system / server 6012 may also include other removable / non-removable, volatile / non-volatile computer system storage media. By way of example only, a storage system 6034 may be provided for reading from and writing to a non-removable non-volatile magnetic medium (not shown, and generally referred to as a "hard disk drive"). Although not shown, a disk drive may be provided for reading from and writing to a removable non-volatile disk (e.g., a "floppy disk"), and an optical disc drive may be provided for reading from and writing to a removable non-volatile optical disc (such as a CD-ROM, DVD-ROM, or other optical media). In these cases, each may be connected to bus 6018 via one or more data media interfaces. As will be further described and illustrated below, memory 6028 may include at least one program product having a set (e.g., at least one) of program modules configured to perform the functions of embodiments of this disclosure.

[0097] By way of example and not limitation, a program / utility 6040 having a set (at least one) of program modules 6042, along with an operating system, one or more application programs, other program modules, and program data, may be stored in memory 6028. Each of the operating system, one or more application programs, other program modules, and program data, or some combination thereof, may include an implementation of a network environment. Program modules 6042 generally perform functions and / or methods as described in the embodiments herein.

[0098] The computer system / server 6012 can also communicate with one or more external devices 6014 (such as a keyboard, indicating device, display 6024, etc.), one or more devices that enable a user to interact with the computer system / server 6012, and / or any device that enables the computer system / server 6012 to communicate with one or more other computing devices (e.g., a network card, modem, etc.). This communication can occur via input / output (I / O) interface 22. Furthermore, the computer system / server 6012 can communicate with one or more networks (such as a local area network (LAN), a general area network (WAN), and / or a public network (e.g., the Internet)) via network adapter 20. As depicted, network adapter 20 communicates with other components of the computer system / server 6012 via bus 6018. It should be understood that, although not shown, other hardware and / or software components can be used in conjunction with the computer system / server 6012. Examples include, but are not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data archiving storage systems.

[0099] This disclosure can be implemented as a system, method, and / or computer program product. The computer program product may include one or more computer-readable storage media having computer-readable program instructions thereon for causing a processor to perform aspects of this disclosure.

[0100] Computer-readable storage media can be tangible devices capable of holding and storing instructions for use by an instruction execution device. Computer-readable storage media can be, for example (but not limited to), electronic storage devices, magnetic storage devices, optical storage devices, electromagnetic storage devices, semiconductor storage devices, or any suitable combination of the foregoing. A non-exhaustive list of more specific examples of computer-readable storage media includes the following: portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), static random access memory (SRAM), portable compact disc read-only memory (CD-ROM), digital universal disc (DVD), memory sticks, floppy disks, mechanical encoding devices (such as punch cards or recessed protrusions storing instructions thereon), and any suitable combination of the foregoing. As used herein, computer-readable storage media is not to be construed as transient signals themselves, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through waveguides or other transmission media (e.g., light pulses through fiber optic cables), or electrical signals transmitted through wires.

[0101] The computer-readable program instructions described herein can be downloaded from computer-readable storage media to various computing / processing devices, or downloaded via a network (e.g., the Internet, local area network, wide area network, and / or wireless network) to an external computer or external storage device. The network may include copper cables, fiber optic cables, wireless transmission, routers, firewalls, switches, gateway computers, and / or edge servers. A network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards them to computer-readable storage media within the respective computing / processing device.

[0102] Computer-readable program instructions used to perform the operations of this disclosure may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-dependent instructions, microcode, firmware instructions, state setting data, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages ​​(such as Smalltalk, C++, etc.) and conventional procedural programming languages ​​(such as the "C" programming language or similar programming languages). The computer-readable program instructions may execute entirely on the user's computer, partially on the user's computer, as a stand-alone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer may be connected to the user's computer via any type of network (including a local area network (LAN) or a wide area network (WAN)), or may be connected to an external computer (e.g., via the Internet using an Internet service provider). In some embodiments, electronic circuitry, including, for example, programmable logic circuitry, field-programmable gate arrays (FPGAs), or programmable logic arrays (PLAs), may be personalized by utilizing state information from the computer-readable program instructions to perform aspects of this disclosure.

[0103] This document describes aspects of the present disclosure with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the present disclosure. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer-readable program instructions.

[0104] These computer-readable program instructions may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing apparatus to produce a machine, such that, when executed by the processor of the computer or other programmable data processing apparatus, these instructions create means for implementing the functions / behaviors specified in one or more blocks of the flowchart and / or block diagram. These computer-readable program instructions may also be stored in a computer-readable storage medium that can instruct a computer, programmable data processing apparatus, and / or other device to operate in a particular manner, thereby including an article of manufacture comprising instructions for implementing aspects of the functions / behaviors specified in one or more blocks of the flowchart and / or block diagram.

[0105] Computer-readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device to cause a series of operational steps to be performed on the computer, other programmable apparatus, or other device to produce a computer-implemented process, thereby causing the instructions to be executed on the computer, other programmable apparatus, or other device to perform the functions / behaviors specified in one or more boxes of a flowchart and / or block diagram.

[0106] The flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present disclosure. In this regard, each block in a flowchart or block diagram may represent a portion of a module, segment, or instruction containing one or more executable instructions for implementing the specified logical function(s). In some alternative implementations, the functions marked in the blocks may occur in a different order than those marked in the figures. For example, depending on the functions involved, two consecutive blocks may actually be executed substantially in parallel, or these blocks may sometimes be executed in reverse order. It will also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, may be implemented by a dedicated hardware-based system that performs the specified function or behavior or executes a combination of dedicated hardware and computer instructions.

[0107] Those skilled in the art should also understand that the various operations illustrated in sequence in the embodiments of this disclosure do not necessarily have to be performed in the illustrated order. Those skilled in the art can adjust the order of operations as needed. They can also add more operations or omit some operations as needed.

[0108] Various embodiments of this disclosure have been described for illustrative purposes, but are not intended to be exhaustive or limiting of the disclosed embodiments. Many modifications and variations will be apparent to those skilled in the art without departing from the scope and spirit of the described embodiments. The terminology used herein is chosen to best explain the principles of the embodiments, their practical application, or technical improvements to technologies found in the market, or to enable others skilled in the art to understand the embodiments disclosed herein.

Claims

1. A method for optimizing a product coating process implemented by a computer, comprising: Receive data related to multiple groups of products to be sprayed, wherein products in the same group have the same color to be sprayed, and products in different groups have different colors to be sprayed. The product coating sorting queue for the multiple sets of products is generated based at least on the received data and a set of product coating color sorting patterns and a corresponding set of defect risk coefficients. Each product's paint color sorting pattern has a corresponding defect risk coefficient. The product spraying color sorting mode indicates the orderly arrangement of multiple colors sprayed consecutively by a spray gun; The defect risk coefficient corresponding to a product's paint color sorting pattern represents the probability of a paint defect occurring when the sorting relationship between the color to be painted and one or more preceding ordered paint colors matches the product's paint color sorting pattern. The step of generating the product coating sorting queue of the multiple sets of products based at least on the received data and based on a set of product coating color sorting patterns and a corresponding set of defect risk coefficients further includes: At least one set is generated by generating all colors of the multiple product groups based on the product painting color sorting pattern and the corresponding set of defect risk coefficients, wherein each set in the at least one set matches a product painting color sorting pattern, and the step of generating at least one set prioritizes matching patterns with low defect risk coefficients from low to high. The resulting set is used to generate a product coating sorting queue for the multiple product groups.

2. The method of claim 1, further comprising: Receive the set of product coating color sorting patterns and the corresponding set of defect risk coefficients.

3. The method of claim 1, further comprising: The sorting pattern of the product coating colors and the corresponding set of defect risk coefficients are determined based on historical data within a predetermined time period.

4. The method of claim 1, wherein, The defect risk factor is based on at least one of the following: The proportion of minor spraying defects, and The proportion of serious painting defects.

5. The method of claim 1, wherein, The position of the at least one set in the product coating sorting queue of the multiple product groups is determined based on the product coating color sorting pattern of the group and the corresponding set of defect risk coefficients.

6. The method of claim 1, wherein the step of generating the product coating sorting queue of the plurality of products based at least on the received data and based on a set of product coating color sorting patterns and a corresponding set of defect risk coefficients further comprises: For the preceding paint color, based on the product paint color sorting pattern, colors that can be combined with the preceding paint color to form a set are determined from all colors of the multiple product groups. Each set matches a product paint color sorting pattern. Based on the set of defect risk coefficients, select colors from the determined colors and colors that match the product spraying color sorting pattern with the lowest defect risk coefficient. The painting task of a group of products corresponding to the selected color is placed into the product painting sorting queue of the multiple groups of products.

7. A method for optimizing a product coating process implemented by a computer, comprising: Receive data related to multiple groups of products to be sprayed, wherein products in the same group have the same color to be sprayed, and products in different groups have different colors to be sprayed. The product coating sorting queue for the multiple sets of products is generated based at least on the received data and a set of product coating color sorting patterns and a corresponding set of defect risk coefficients. Each product's paint color sorting pattern has a corresponding defect risk coefficient. The product spraying color sorting mode indicates the orderly arrangement of multiple colors sprayed consecutively by a spray gun; The defect risk coefficient corresponding to a product's paint color sorting pattern represents the probability of a paint defect occurring when the sorting relationship between the color to be painted and one or more preceding ordered paint colors matches the product's paint color sorting pattern. The step of generating the product coating sorting queue of the multiple sets of products based at least on the received data and based on a set of product coating color sorting patterns and a corresponding set of defect risk coefficients further includes: A first product coating sorting queue is generated based on the received data; and Based on the product coating color sorting pattern and the corresponding set of defect risk coefficients, the first product coating sorting queue of the multiple product groups is optimized to generate the product coating sorting queue of the multiple product groups. The step of optimizing the first product coating sorting queue of the multiple product groups based on the set of product coating color sorting patterns and the corresponding set of defect risk coefficients further includes: Determine the color of at least a portion of the first multi-group products involved in the first product spraying sorting queue of the multi-group products; Based on the set of product spraying color sorting patterns and the corresponding set of defect risk coefficients, at least one set of colors for the first set of products is generated. Each set in the at least one set is matched with a product spraying color sorting pattern, and the step of generating at least one set prioritizes matching patterns with low defect risk coefficients from low to high defect risk coefficients. The at least one portion of the first product spraying sorting queue is adjusted using the generated at least one set.

8. The method of claim 7, further comprising: Receive the set of product coating color sorting patterns and the corresponding set of defect risk coefficients.

9. The method of claim 7, further comprising: The sorting pattern of the product coating colors and the corresponding set of defect risk coefficients are determined based on historical data within a predetermined time period.

10. The method of claim 7, wherein, The defect risk factor is based on at least one of the following: The proportion of minor spraying defects, and The proportion of serious painting defects.

11. The method of claim 7, wherein the step of generating the product coating sorting queue of the plurality of products based at least on the received data and based on a set of product coating color sorting patterns and a corresponding set of defect risk coefficients further comprises: For the preceding paint color, based on the product paint color sorting pattern, colors that can be combined with the preceding paint color to form a set are determined from all colors of the first plurality of product groups. Each set matches a product paint color sorting pattern. Based on the set of defect risk coefficients, select colors from the determined colors and colors that match the product spraying color sorting pattern with the lowest defect risk coefficient. The painting task of a group of products corresponding to the selected color is placed into the product painting sorting queue of the multiple groups of products.

12. A computer system, comprising: One or more processors, and A memory coupled to the one or more processors, the memory storing computer-readable program instructions that, when executed by the one or more processors, cause the one or more processors to perform the method as described in any one of claims 1-11.

13. A computer-readable storage medium storing computer-readable program instructions thereon, which, when executed by a processor, cause the processor to perform the method as described in any one of claims 1-11.

14. A computer program product comprising computer-readable program instructions that, when executed by a processor, cause the processor to perform the method as described in any one of claims 1-11.

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