Selective assembly addition rate confirmation method and device and electronic equipment

Abstract

The invention provides an optional component addition rate confirmation method and device and electronic equipment, and the method comprises the steps: building a target model based on the independent demands, component types and attribute groups of optional products, and the non-independent demands of optional components corresponding to the optional products; based on the target model, at least determining the neat set number and the related cost of the non-independent demand of the selected assembly; determining a weighting target based on the number of the neat sets and the related cost; based on the sum of the addition rates of a pair of complementary attribute groups of the selected and matched components in the same component category and the sum of the addition rates of the selected and matched components of different component categories in the same attribute group, constraining the weighting target; and determining a corresponding additional factor value when the value of the weighting target meets the condition, wherein the additional factor value is used for determining the demand quantity of each selected assembly.

Description

Technical Field

[0001] The present disclosure relates to the field of supply chain technology, and in particular to a method, device, and electronic device for confirming the additional rate of an optional component. Background Art

[0002] In the demand planning field of discrete manufacturing supply chain, a key link in the demand planning process for optional products is the calculation of the non-independent demand conversion factor of optional components relative to the optional main products (i.e., the optional component attachment rate). The role of the optional component attachment rate is to convert the independent demand of the optional main product into the non-independent demand at the optional component level. The optional component attachment rate needs to meet the rationality constraints of the optional proportion of the optional component category and multi-dimensional attribute combination. This is a common link and method for optional component demand planning, which can solve the component demand forecasting problem in scenarios where optional products and optional components do not have a static bill of materials. Summary of the Invention

[0003] The present disclosure provides a method, device and electronic device for confirming the additional rate of an optional component.

[0004] According to a first aspect of the present disclosure, a method for confirming an optional component attachment rate is provided, comprising:

[0005] Building a target model based on the independent requirements, component categories, and attribute groups of the optional products and the dependent requirements of the optional components corresponding to the optional products;

[0006] Based on the target model, at least determining the complete set quantity and related costs of the dependent requirements of the optional components;

[0007] Determining a weighted target based on the complete set quantity and the relevant costs; constraining the weighted target based on the sum of the attach rates of a pair of complementary attribute groups of optional components in the same component category, and the sum of the attach rates of optional components in different component categories in the same attribute group;

[0008] An additional factor value corresponding to when the value of the weighted target meets a condition is determined, and the additional factor value is used to determine the required quantity of each optional component.

[0009] In the above solution, the target model is constructed based on the independent requirements, component categories, attribute groups of the optional products and the dependent requirements of the optional components corresponding to the optional products, including:

[0010] The independent requirement of the optional product is used as the starting node, and at least one component category node is used as the child node of the starting node;

[0011] At least one attribute grouping node is used as a child node of at least one component category, and the weight of the connection line between the attribute grouping node and the component category node represents the attribute grouping attachment rate;

[0012] The node of the dependent demand of any optional component serves as a child node of at least one attribute group, and the weight of the connection line between the node of the dependent demand of the optional component and the node of the attribute group represents the optional component attachment rate.

[0013] In the above solution, the relevant costs include the total value cost of the optional components and the inventory backlog cost of the optional components. The determination of at least the complete set quantity and relevant costs of the non-independent requirements of the optional components based on the target model includes:

[0014] Determining the complete set of non-independent requirements for optional components of the optional product based on the independent demand nodes and optional component attachment rates of the optional product in the target model;

[0015] Determining the total value cost of the optional components of the optional products based on the independent demand of the optional products, the value cost coefficient of the optional components, and the optional component attachment rate in the target model;

[0016] Based on the inventory quantity of the optional components, the independent demand of the optional products, the optional component attachment rate, and the inventory backlog cost coefficient of the optional components in the target model, the inventory backlog cost of the optional components of the optional products is determined.

[0017] In the above solution, the determination of the non-independent required complete set quantity of optional components of the optional product based on the independent demand nodes of the optional product and the optional component attachment rate in the target model includes:

[0018] Determine the dependent demand for the oth optional component based on the attachment rate of the oth optional component in the jth component category of the ith optional product and the independent demand for the ith optional product;

[0019] Determine the non-independent demand for all optional components in all component categories corresponding to the optional product, which is the complete set quantity of the non-independent demand for the optional components of the optional product.

[0020] In the above solution, the determination of the total value cost of the optional components of the optional products based on the independent demand of the optional products, the value cost coefficient of the optional components, and the optional component attachment rate in the target model includes:

[0021] Determine the attach rate of the oth optional component in the jth component category of the ith optional product, the independent demand of the ith optional product, and the value-cost coefficient of the oth optional component to determine the value-cost of the oth optional component;

[0022] Determine the sum of the value costs of all optional components in all component categories corresponding to the optional product, which is the total value cost of the optional components of the optional product.

[0023] In the above solution, determining the inventory backlog cost of optional components of optional products based on the inventory quantity of optional components, the independent demand of optional products, the optional component attachment rate, and the inventory backlog cost coefficient of the optional components in the target model includes:

[0024] Determine the inventory backlog quantity of the oth optional component based on the inventory quantity of the oth component and the dependent demand of the oth optional component;

[0025] determining an inventory backlog cost of the oth optional component based on the inventory backlog quantity of the oth optional component and the inventory backlog cost coefficient of the oth optional component;

[0026] Based on the sum of the inventory backlog costs of all optional components in all component categories corresponding to the optional products, the minimum optional component inventory backlog cost of the optional products is determined.

[0027] In the above solution, the relevant costs include the total value cost of the optional components and the inventory backlog cost of the optional components. The weighted target is determined based on the complete set quantity and the relevant costs, including:

[0028] Determine the first coefficient corresponding to the non-independent required complete set quantity of the optional components of the optional product, the second coefficient of the total value cost of the optional components of the optional product, and the third coefficient of the inventory backlog cost of the optional components of the optional product;

[0029] A weighted target is obtained by performing a weighted summation based on the non-independent demand set quantity of the optional components of the optional product, the total value cost of the optional components, the inventory backlog cost of the optional components, the first coefficient, the second coefficient and the third coefficient.

[0030] In the above solution, the weighted target is constrained based on the sum of the additional rates of a pair of complementary attribute groups of optional components in the same component category, and the sum of the additional rates of optional components of different component categories in the same attribute group, including:

[0031] Determining a first constraint function based on a pair of complementary attribute grouping attachment rates of optional components under the same component category and a target ratio of component category to optional products;

[0032] The second constraint function is determined based on optional component attachment rates of different component categories within the same attribute group.

[0033] According to a second aspect of the present disclosure, a device for confirming an optional component attachment rate is provided, the device comprising:

[0034] A model building unit, configured to build a target model based on independent requirements, component categories, attribute groups of optional products and dependent requirements of optional components corresponding to the optional products;

[0035] A first determining unit is configured to determine at least the complete set quantity and related costs of the dependent requirements of the optional components based on the target model;

[0036] a second determining unit configured to determine a weighted target based on the complete set quantity and the relevant cost; and constrain the weighted target based on a sum of additional rates of a pair of complementary attribute groups of optional components in the same component category, and a sum of additional rates of optional components of different component categories in the same attribute group;

[0037] The third determining unit is configured to determine an additional factor value corresponding to when the value of the weighted target meets a condition, wherein the additional factor value is used to determine a required quantity of each of the optional components.

[0038] According to a third aspect of the present disclosure, there is provided an electronic device, including:

[0039] At least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method described in the present disclosure.

[0040] It should be understood that the contents described in this section are not intended to identify the key or important features of the embodiments of the present disclosure, nor are they intended to limit the scope of the present disclosure. Other features of the present disclosure will become readily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] The above and other objects, features and advantages of the exemplary embodiments of the present disclosure will become readily understood by reading the detailed description below with reference to the accompanying drawings, in which several embodiments of the present disclosure are shown by way of example and not limitation, wherein:

[0042] In the drawings, the same or corresponding reference numerals denote the same or corresponding parts.

[0043] Figure 1 A first optional flow chart of a method for confirming the additional rate of an optional component provided by an embodiment of the present disclosure is shown;

[0044] Figure 2 A second optional flow chart of the method for confirming the additional rate of optional components provided by an embodiment of the present disclosure is shown;

[0045] Figure 3A first optional schematic diagram of a target model provided by an embodiment of the present disclosure is shown;

[0046] Figure 4 A third optional flow chart of the method for confirming the additional rate of optional components provided in an embodiment of the present disclosure is shown;

[0047] Figure 5 A schematic diagram of a flow chart of a method for confirming an optional component attachment rate according to an embodiment of the present disclosure is shown;

[0048] Figure 6 A second optional schematic diagram of a target model provided by an embodiment of the present disclosure is shown;

[0049] Figure 7 A schematic diagram of an optional structure of a device for confirming the attachment rate of an optional component provided in an embodiment of the present disclosure is shown;

[0050] Figure 8 A schematic diagram of the structure of an electronic device according to an embodiment of the present disclosure is shown. DETAILED DESCRIPTION

[0051] To make the purposes, features, and advantages of the present disclosure more apparent and understandable, the technical solutions in the embodiments of the present disclosure will be clearly and completely described below in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present disclosure, not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of the present disclosure without creative work shall fall within the scope of protection of the present disclosure.

[0052] In the following description, reference is made to “some embodiments”, which describes a subset of all possible embodiments, but it will be understood that “some embodiments” may be the same subset or different subsets of all possible embodiments and may be combined with each other without conflict.

[0053] In the following description, the terms "first\second" are merely used to distinguish similar objects and do not represent a specific ordering of the objects. It is understandable that "first\second" can be interchanged with a specific order or sequence where permitted, so that the embodiments of the present disclosure described herein can be implemented in an order other than that illustrated or described herein.

[0054] Unless otherwise defined, all technical and scientific terms used in this disclosure have the same meaning as commonly understood by those skilled in the art in the art of this disclosure. The terms used in this disclosure are only for the purpose of describing the embodiments of this disclosure and are not intended to limit this disclosure.

[0055] It should be understood that in the various embodiments of the present disclosure, the size of the serial number of each implementation process does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present disclosure.

[0056] Before further describing the embodiments of the present disclosure in detail, the nouns and terms involved in the embodiments of the present disclosure are explained. The nouns and terms involved in the embodiments of the present disclosure are subject to the following interpretations.

[0057] 1) Optional products.

[0058] A specific product that users demand, such as a certain model of electronic equipment.

[0059] 2) Independent requirements for optional products.

[0060] Direct demand for a specific product is independent of other products. For example, if a customer directly demands a specific model of computer (i.e., the quantity of a specific model of computer required), the demand quantity is clear and does not change due to changes in demand for other products.

[0061] 3) Component category.

[0062] A collection of components with the same function, such as the display assembly, keyboard assembly, fingerprint module assembly, etc. of a laptop computer.

[0063] 4)Component properties.

[0064] Component attributes such as function and color. For example, the display component of a laptop computer has functional attributes such as black or non-black color and Extended Graphics Array (XGA), Wide Extended Graphics Array (WXGA), and Wide Ultra Extended Graphics Array (WUXGA) resolutions. The fingerprint module component also has functional attributes such as color and whether it is compatible with the Wireless Wide Area Network (WWAN) function.

[0065] 5) Attribute grouping.

[0066] A collection of components with the same properties, such as the display components of a laptop computer, where those with black color are grouped together and those with non-black color are grouped together.

[0067] 6) Attribute grouping attachment rate.

[0068] The sum of the optional component add-on rates under each attribute group. The attribute group add-on rate corresponds to the add-on rate of different optional component categories under each attribute group. For example, for the component category Display (DPY), the add-on rate for non-black components is 0.1, and the add-on rate for black components is 0.9. For the component category Power Delivery (PD), the add-on rate for non-black components is 0.1, and the add-on rate for black components is 0.9.

[0069] 7) Optional components corresponding to optional products

[0070] The product components that can be configured and selected have specific component attributes. For example, for a notebook, a black WUXGA resolution display component, a black fingerprint module component adapted to WWAN function, etc.

[0071] 8) Optional component additional rate

[0072] The optional component attach rate (or optional component attach rate) is used to convert demand. Once the independent demand for an optional product is known, the optional component attach rate can be used to calculate the dependent demand for the optional component. For example, if the optional component attach rate is 0.2 and the independent demand for the optional product is 100 units, the demand for the optional component can be calculated as 100 x 0.2 = 20 units. This converts the independent demand for the optional product into dependent demand at the optional component level.

[0073] 9) Non-independent requirements for optional components

[0074] Dependent demand, also known as dependent demand, is based on independent demand. For example, if a computer is an independent demand, the demand for memory modules is based on the purchase of a computer. If you don't have a computer, you don't need memory modules, so the demand for memory modules is non-independent demand. Non-independent demand for optional components = independent demand for optional products × optional component attachment rate.

[0075] 10) Raw materials.

[0076] Materials required to produce optional components; dependent raw material demand = dependent demand for optional components × BOM usage.

[0077] 11) Maximum number of complete sets of components required for non-independent use.

[0078] The optional components must meet the maximum match of non-independent requirements under the combination dimension of component attributes and categories. For example, for components with color attributes, the non-independent requirements of black and non-black for display screens and keyboards must meet a 1:1 ratio, and the sum of the non-independent requirements of black and non-black for each component category must meet a 1:1 ratio. As a key component, it must meet a 1:1 ratio with the independent requirements of the product.

[0079] 12) Inventory cost of obsolete components.

[0080] Redundant inventory costs caused by incomplete non-independent demand for components. For example, for a laptop computer, the independent demand for the black color option is 100. For components with color attributes, the non-independent demand for the black display and keyboard needs to meet a 1:1 ratio. As key components, they need to meet a 1:1 ratio with the independent demand of the product, each of which is 100. If the non-independent demand for the black keyboard is calculated to be 120, then the demand of 20 will become obsolete inventory due to the incomplete set, and the obsolete inventory cost will be calculated based on the value of the component.

[0081] 13) Grouping of complementary attributes.

[0082] This means that multiple attributes correspond to the same category. For example, the component category of black shell and non-black shell is both shell, but they correspond to different attribute groups.

[0083] 14) Value of optional components.

[0084] Or component value, which refers to the cost price of optional components.

[0085] 15) User configuration parameters.

[0086] Users can adjust the set parameters to control the independent demand range of products involved in the calculation, such as specifying parameter configurations such as model, product life cycle, region, and customer.

[0087] In order to achieve the accuracy of the non-independent demand plan for optional components, the calculation of the optional component attachment rate is the key point and the complexity. It is necessary to meet the rationality constraints of the optional component ratio in the category and multi-dimensional attribute combination.

[0088] Currently, in the field of supply chain demand planning, solutions for calculating the attachment rate ratio of optional components include: 1) offline estimation based on the planner's accumulated experience, 2) statistical calculation methods for calculating the attachment rate based on historical statistical data, and 3) based on the planner's manual prediction of the attachment rate, using the statistical attachment rate as a baseline value, and combining it with business-specified business rules, the system logic makes incremental adjustments based on the attachment rate baseline value to correct unreasonable values of the optional component attachment rate ratio relative to the business rules, as an auxiliary solution for the planner's attachment rate prediction. While these existing solutions can help improve the efficiency of planners' manual calculations and make system corrections to improve the rationality of attachment rate calculations, solution 1) relies entirely on the planner's experience calculations, solution 2) relies on historical statistical data and data completeness, and solution 3) the system's execution of the correction of the attachment rate rationality depends on the input of business rules. For many optional models, especially complex optional models, it is impossible to extract universal business rules. This solution has limited coverage of demand planning scenarios and accuracy improvement. None of the above existing solutions can achieve a true multi-objective optimization solution calculation that fully satisfies the rationality constraint of the optional ratio of the additional rate, and no method has been found to perform a constrained multi-objective optimization solution calculation for the additional rate through an optimization algorithm.

[0089] In response to the defects existing in the relevant technology, the optional component attachment rate confirmation method provided by the embodiment of the present disclosure is based on a multi-objective constrained optimization algorithm to achieve the optimal solution for calculating the attachment rate of optional components for optional products, thereby serving as a conversion factor for calculating the non-independent demand between optional components and main products, thereby improving the accuracy of demand planning for optional products.

[0090] Figure 1 A first optional flow chart of the method for confirming the additional rate of an optional component provided in an embodiment of the present disclosure is shown, and will be explained according to each step.

[0091] Step S101 : constructing a target model based on the independent requirements, component categories, attribute groups of optional products and the dependent requirements of optional components corresponding to the optional products.

[0092] In some embodiments, the target model can be a network topology model, which includes multiple nodes; the carrier that implements the optional component attachment rate confirmation method (hereinafter referred to as the carrier) constructs the target model with the independent requirements of optional products, component categories, attribute groupings and non-independent requirements of optional components corresponding to optional products as nodes in the target model.

[0093] In some embodiments, the target model includes multiple types of nodes, such as nodes corresponding to independent requirements of optional products, nodes corresponding to component categories, nodes corresponding to attribute groups, and nodes corresponding to non-independent requirements of optional components. Nodes of the same type are not connected; nodes of different types may or may not have a connection line between them. If a connection line exists between two nodes, the weight on the connection line is the attachment rate. For example, if a node corresponding to a component category is connected to a node corresponding to an attribute group, the weight on the connection line is the attribute group attachment rate; or if a node corresponding to an attribute group is connected to a node corresponding to a non-independent requirement of an optional component, the weight on the connection line is the optional component attachment rate.

[0094] In some embodiments, the carrier can be a computer program, electronic circuit, database, mobile application, electronic device, cloud computing platform, distributed system, artificial intelligence framework, mathematical model, automation tool and microcontroller, etc., which can implement software or hardware of algorithm and method process.

[0095] Step S102: Based on the target model, at least the complete set quantity and related costs of the non-independent requirements of the optional components are determined.

[0096] In some embodiments, the related costs include at least the total value cost of the optional components and the inventory backlog cost of the optional components.

[0097] In some embodiments, the carrier determines the complete set quantity of non-independent requirements of optional components based on the nodes corresponding to the independent requirements of optional products in the target model and the optional component attachment rate; determines the total value cost of optional components based on the nodes corresponding to the independent requirements of optional products in the target model, the optional component attachment rate and the value cost coefficient of optional components; determines the inventory backlog cost of optional components based on the nodes corresponding to the independent requirements of optional products in the target model, the optional component attachment rate and the inventory quantity of optional components.

[0098] Step S103: determining a weighted target based on the complete set quantity and the related costs.

[0099] In some embodiments, the weighted target is determined based on a weighted sum of the complete set quantity of the dependent requirements of the optional components, the total value cost of the optional components, and the inventory backlog cost of the optional components.

[0100] Step S104 : constraining the weighted target based on the sum of the addition rates of a pair of complementary attribute groups of optional components in the same component category and the sum of the addition rates of optional components of different component categories in the same attribute group.

[0101] In some embodiments, a first constraint function of the weighted target is determined based on the sum of the attachment rates of a pair of complementary attribute groupings of optional components in the same component category; a second constraint function is determined based on the sum of the attachment rates of optional components of different component categories in the same attribute grouping; and the weighted target is constrained based on the first constraint function and the second constraint function.

[0102] The first and second constraint functions are set based on facts and rationality. For example, the sum of the add-on rates for a pair of complementary attribute groups of optional components within the same component category should be within a reasonable range. If it exceeds the first preset threshold, it indicates that there are surplus optional components, resulting in waste. If it is less than the second preset threshold, it indicates that there are insufficient optional components to assemble the optional product's independent requirements. Furthermore, the sum of the add-on rates for optional components across different component categories within the same attribute group should be equal. The first and second preset thresholds can be set based on actual needs.

[0103] Step S105 : determining an additional factor value corresponding to when the value of the weighted target meets a condition, wherein the additional factor value is used to determine the required quantity of each optional component.

[0104] In some embodiments, the weighted target is solved and the corresponding additional factor value is determined when the weighted target value meets the conditions, which is the additional rate of the optional component. The additional rate of the optional component is used to determine the required quantity of the optional component in combination with the independent demand of the optional product, i.e., the dependent demand for the optional component.

[0105] The conditions may include satisfying a first constraint function, satisfying a second constraint function, and the weighted target value being within a preset range. The preset range may be set according to actual needs, and the preset range may be a target value, such as the weighted target value being the minimum value of the weighted target.

[0106] In this way, through the optional component attachment rate confirmation method provided by the embodiment of the present disclosure, an intuitive and highly interpretable target model is constructed, weighted objectives and constraint functions are constructed based on the target model, and multi-objective optimization is solved based on the constraint rules to obtain the optional component attachment rate, thereby improving the accuracy and generalization of the optional component demand plan for optional products and improving the efficiency of calculating the optional product demand in the discrete manufacturing industry.

[0107] Figure 2 A second optional flow chart of the method for confirming the additional rate of optional components provided in an embodiment of the present disclosure is shown, and will be explained according to each step.

[0108] Step S201 : constructing a target model based on the independent requirements, component categories, attribute groups of optional products and the dependent requirements of optional components corresponding to the optional products.

[0109] In some embodiments, specifically, the carrier takes the independent demand for an optional product as the starting node, and the node of at least one component category as the child node of the starting node; takes the node of at least one attribute grouping as the child node of at least one component category, and the weight of the connection line between the node of the attribute grouping and the node of the component category represents the attribute grouping attachment rate; takes the node of the non-independent demand for any optional component as the child node of at least one attribute grouping, and the weight of the connection line between the node of the non-independent demand for the optional component and the node of the attribute grouping represents the optional component attachment rate.

[0110] Figure 3 A first optional schematic diagram of a target model provided by an embodiment of the present disclosure is shown.

[0111] like Figure 3 As shown, a corresponding target model is constructed for any optional product, with the independent demand of the optional product as the starting node, and the node of at least one component category corresponding to the optional product as the child node of the starting node; the child nodes of each component category are connected to the starting node. The nodes of the attribute groupings corresponding to different component categories are used as child nodes of the component category, and the weight of the connection between the two nodes represents the attribute grouping attachment rate. For example, in the component category, category 1 corresponds to group 1 and group 2, then group 1 and group 2 are used as child nodes of category 1; at the same time, category 2 corresponds to group 1, group 2, group 3 and group 4, then group 1, group 2, group 3 and group 4 are used as child nodes of category 2. The nodes of the non-independent demand of the optional component are used as child nodes of the attribute grouping, and the weight of the connection line between the node of the non-independent demand of the optional component and the node of the attribute grouping represents the optional component attachment rate. Furthermore, the node of the raw material is used as the child node of the non-independent demand of the optional component.

[0112] Step S202 : Based on the target model, determine the non-independent required complete set quantity of optional components of the optional product, the total value cost of the optional components, and the inventory backlog cost of the optional components.

[0113] In some embodiments, the carrier determines the number of complete sets of optional component dependent requirements for the optional product based on the independent requirement nodes of the optional product and the optional component attachment rates of all optional components in the target model. Specifically, the optional component attachment rate of each optional component dependent requirement node is determined based on the weight of the connection line between the optional component dependent requirement node and the attribute grouping node in the target model. The optional component attachment rate is an unknown parameter that needs to be solved.

[0114] Specifically, the carrier can determine the non-independent demand for the oth optional component based on the additional rate of the oth optional component in the jth component category of the i-th optional product and the independent demand of the i-th optional product, and determine the non-independent demand for all optional components in all component categories corresponding to the optional product, which is the complete set of non-independent demand quantities for the optional components of the optional product.

[0115] In some embodiments, the carrier determines the total value cost of the optional components of the optional products based on the independent demand of the optional products in the target model, the value cost coefficient of the optional components, and the optional component attachment rate.

[0116] Specifically, the carrier can determine the additional rate of the oth optional component in the j component categories of the i-th optional product, the independent demand of the i-th optional product, and the value cost coefficient of the oth optional component to determine the value cost of the oth optional component; determine the sum of the value costs of all optional components in all component categories corresponding to the optional product as the total value cost of the optional components of the optional product.

[0117] In some embodiments, the carrier determines the optional component inventory backlog cost of all optional products based on the inventory quantity of optional components in the target model, the independent demand of optional products, the optional component attachment rate, and the inventory backlog cost coefficient of the optional components.

[0118] Specifically, the carrier can determine the inventory backlog quantity of the oth optional component based on the inventory quantity of the oth component and the non-independent demand of the oth optional component; determine the inventory backlog cost of the oth optional component based on the inventory backlog quantity of the oth optional component and the inventory backlog cost coefficient of the oth optional component; determine the minimum optional component inventory backlog cost of the optional product based on the sum of the inventory backlog costs of all optional components in all component categories corresponding to the optional product

[0119] Step S203 , performing weighted summation on the non-independent required complete set quantity of optional components of the optional product, the total value cost of the optional components, and the inventory backlog cost of the optional components to obtain a weighted target.

[0120] In some embodiments, the sum of the first coefficient for the number of optional components required for a complete set of optional products, the second coefficient for the total value cost of the optional components, and the third coefficient for the inventory backlog cost of the optional components is 1. The first coefficient, second coefficient, and third coefficient can be set based on the needs of different business scenarios. For example, a higher first coefficient can be set to obtain more optional products.

[0121] In some embodiments, when constructing the weighted target, parameters can be restricted according to the business scenario, such as maximizing the number of non-independent demand sets of optional components, minimizing the total value cost of optional components, and minimizing the inventory backlog cost of optional components, so that the final optional component attachment rate can achieve the lowest cost, the most optional products and the least inventory backlog.

[0122] Step S204 : constraining the weighted target based on the sum of the attachment rates of a pair of complementary attribute groups of optional components in the same component category and the sum of the attachment rates of optional components of different component categories in the same attribute group.

[0123] In some embodiments, the carrier determines a first constraint function based on a pair of complementary attribute grouping attachment rates of optional components under the same component category and a target ratio of component category to optional products; wherein, the pair of complementary attribute grouping attachment rates of optional components under the same component category and the target ratio of component category to optional products are equal.

[0124] In some embodiments, the carrier determines the second constraint function based on the optional component attachment rates of different component categories within the same attribute group; wherein the optional component attachment rates of different component categories within the same attribute group are equal.

[0125] In some embodiments, the carrier constrains the weighted objective based on the first constraint function and the second constraint function.

[0126] Step S205 , determining the additional factor value corresponding to the minimum value of the weighted target, which is the set of optional component additional rates of the optional product.

[0127] In some embodiments, the additional factor value is the optional component addition rate; when the value of the weighted target solved by the carrier based on the first constraint function and the second constraint function is the minimum, the corresponding optional component addition rate is the set of all optional component addition rates of the optional product.

[0128] In some embodiments, the carrier can construct a target model for each type of optional product; that is, the type of optional product corresponds to the target model one-to-one; the non-independent demand for each optional component is determined based on the number of all optional products and the corresponding optional component attachment rate. For example, the non-independent demand for each optional component can be obtained by summing the product of the number of each type of optional products and the optional component attachment rate corresponding to the type of optional products. If optional product A and optional product B both use optional component C, in optional product A, the optional component attachment rate of optional component C is a; in optional product B, the optional component attachment rate of optional component C is b, then the non-independent demand for optional component C = the number of optional products A × a + the number of optional products B × b.

[0129] In this way, through the optional component attachment rate confirmation method provided by the embodiment of the present disclosure, an intuitive and highly interpretable target model is constructed, weighted objectives and constraint functions are constructed based on the target model, and multi-objective optimization is solved based on the constraint rules to obtain the optional component attachment rate, thereby improving the accuracy and generalization of the optional component demand plan for optional products and improving the efficiency of calculating the optional product demand in the discrete manufacturing industry.

[0130] Figure 4 FIG. 3 is a schematic diagram showing a third optional flow chart of the method for confirming the additional rate of optional components provided in an embodiment of the present disclosure. Figure 5 The flow chart of the method for confirming the additional rate of optional components provided by the embodiment of the present disclosure is shown. Figure 4 and Figure 5 Provide explanation.

[0131] In some embodiments, when determining the non-independent requirements of optional components, the non-independent requirements of the optional components are usually determined based on the independent requirements of each optional product in the optional product independent requirement set, and finally, the non-independent requirement set of optional components corresponding to the optional product independent requirement set is determined based on the sum of the non-independent requirements of at least one optional component corresponding to each optional product independent requirement.

[0132] like Figure 5As shown, the carrier traverses each independent demand of optional products based on the set of independent demands of optional products, the value cost coefficients of all optional components, the inventory quantity of all optional components, and user configuration parameters. During the traversal, the carrier determines the weighted target of the current optional product based on the complete set of non-independent demands of optional components of the current optional product, the total value cost of optional components, the inventory backlog cost of optional components, the corresponding first coefficient, second coefficient and third coefficient, and solves the weighted target based on the first constraint function and the second constraint function of the current optional product to obtain the optional component attachment rate of each optional component corresponding to the current optional product, and the non-independent demand of each optional component; after traversing the set of independent demands of optional products to determine the optional component attachment rate of each optional component corresponding to all optional products, and the non-independent demand of each optional component, the optional component attachment rate result set and the optional component non-independent demand result set are obtained. The optional component attachment rate result set includes the optional component attachment rate of each optional component, and the optional component attachment rate of each optional component is determined based on the optional component attachment rate corresponding to each optional product; the optional component dependent demand result set includes the sum of the dependent demands of each optional component. The specific steps include:

[0133] Step S401 : constructing a target model with optional products, optional components, attribute groups, and component categories as nodes.

[0134] The specific steps of step S401 are the same as those of step S201 and will not be repeated here.

[0135] Step S402 : adjusting the target model based on the set of independent requirements for optional products, the value cost coefficients of all optional components, the inventory quantities of all optional components, and user configuration parameters.

[0136] In some embodiments, the carrier adjusts the starting node of the target model based on each optional product in the independent demand set of optional products, adjusts the quantity and attributes corresponding to the nodes of the component category in the target model based on the component category corresponding to the optional product; adjusts the quantity and attributes corresponding to the nodes of the attribute grouping in the target model based on the attribute grouping corresponding to the optional product; adjusts the quantity and attributes of the nodes of the optional components in the target model based on the optional components and raw materials corresponding to the optional products.

[0137] Specifically, the attributes of the node include the information referred to by the node. For example, if the optional product is a laptop computer, the component category may include a display component, a keyboard component, a fingerprint module component, etc.; if the optional product is a mobile phone, the component category may include a screen component, a back cover component, a camera component, etc.; accordingly, different optional products have different attributes of component categories and the number of component categories.

[0138] Step S403 : determining the complete set quantity of optional components required independently of the optional products based on the independent requirement nodes and the optional component attachment rate of the optional products in the target model.

[0139] In some embodiments, the carrier may be based on the additional rate x of the oth optional component in the jth component category of the i-th optional product. i,j,o Independent demand QTY for the i-th optional product i , determine the non-independent requirements of the oth optional component, including:

[0140] QTYi i *x i,j,o

[0141] In some embodiments, the carrier determines the dependent requirements of all optional components in all component categories corresponding to the optional product, which is the complete set quantity of the dependent requirements of the optional components of the optional product, specifically including:

[0142]

[0143] In some embodiments, the carrier can maximize the number of optional components that require a complete set of dependent components, namely:

[0144]

[0145] Where f1′ is the maximum number of complete sets of optional components with dependent requirements, i is the optional product identifier, i = 1, 2, ..., I, I is the total number of optional products; j is the component category identifier, j = 1, 2, ..., n i , n i Characterizes the total number of component categories of the i-th optional product; G i,j Characterizes the jth component category of the i-th optional product; o Characterizes component category G i,j The identification of each optional component, o=1,2,…,g i,j , g i,j Represents the total number of optional components in the jth component category of the i-th optional product; QTY i Represents the independent demand of the i-th optional product; the set of independent demand of optional products is D={D1,D2,…,D I}, h is the optional component identifier, h=1,2,…,H, H is the total number of optional components, that is, there are H optional components in total, g i,j ≤H,M h Represents the number of h-th optional components, and the optional component set is M={M1,M2,…,M H}.

[0146] In the embodiment of the present disclosure, The number of complete sets of optional components required for optional products is treated as a negative value in order to minimize the total value cost of optional components and minimize the inventory backlog cost of optional components for optional products. The maximum number of complete sets of optional components required for optional products is treated as a negative value, and the original maximum number of complete sets of optional components required is treated as a minimum number. The maximum number of complete sets of optional components required after the negative value treatment is:

[0147]

[0148] Step S404 : determining the total value cost of the optional components of the optional products based on the independent demand of the optional products, the value cost coefficient of the optional components, and the optional component attachment rate in the target model.

[0149] In some embodiments, the carrier may determine the additional rate x of the oth optional component in the jth component category of the i-th optional product. i,j,o , the independent demand QTY of the i-th optional product i , and the value cost coefficient v1 of the oth optional component o , determine the value cost of the oth optional component, including:

[0150] QTY i *v1 o *x i,j,o

[0151] In some embodiments, the carrier determines the sum of the value costs of all optional components in all component categories corresponding to the optional product as the total value cost of the optional components of the optional product, specifically including:

[0152]

[0153] In some embodiments, the carrier can minimize the total value cost of optional components, specifically including:

[0154]

[0155] Among them, f2 is the minimum total value cost of optional components.

[0156] Step S405 , determining the optional component inventory backlog cost of the optional product based on the inventory quantity of the optional component, the independent demand of the optional product, the optional component attachment rate, and the optional component inventory backlog cost coefficient in the target model.

[0157] In some embodiments, the carrier is based on the inventory quantity INV of the oth component. o and the dependent demand QTYi of the oth optional component i*x i,j,o , determine the inventory backlog quantity of the oth optional component, including:

[0158] INV o -QTY i *x i,j,o

[0159] In some embodiments, the carrier is based on the inventory backlog quantity of the oth optional component and the inventory backlog cost coefficient v2 of the oth optional component. h , determine the inventory backlog cost of the oth optional component, including:

[0160] v2 h *(INV o -QTY i *x i,j,o )

[0161] In some embodiments, the carrier determines the inventory backlog cost of optional components of the optional product based on the sum of the inventory backlog costs of all optional components in all component categories corresponding to the optional product, specifically including:

[0162]

[0163] In some embodiments, the carrier can minimize the inventory backlog cost of optional components of optional products, specifically including:

[0164]

[0165] Among them, f3 is to minimize the inventory backlog cost of optional components of optional products.

[0166] Step S406 , performing weighted summation based on maximizing the number of complete sets of non-independent requirements for optional components of the optional product, minimizing the total value cost of the optional components, and minimizing the inventory backlog cost of the optional components to obtain a weighted target.

[0167] In some embodiments, the carrier performs a weighted summation of maximizing the number of optional components of the optional product that are not independent of independent requirements f1, minimizing the total value cost f2 of the optional components, and minimizing the inventory backlog cost f3 of the optional components based on the first coefficient w1, the second coefficient w2, and the third coefficient w3 to obtain a weighted target:

[0168] f ws (x)=min(w1f1(x)+w2f2(x)+w3f3(x))

[0169] Among them, w1+w2+w3=1.

[0170] In some optional embodiments, the carrier may further perform normalization processing on maximizing the number of complete sets of dependent requirements for optional components, minimizing the total value cost of optional components, and minimizing the inventory backlog cost of optional components, and determine a weighted objective based on the normalized function, specifically including:

[0171]

[0172] Among them, c = 1, 2, 3, d = 1, 2, 3 are used to represent the identifier for maximizing the number of complete sets of non-independent requirements of optional components, the identifier for minimizing the total value cost of optional components, and the identifier for minimizing the inventory backlog cost of optional components.

[0173] Step S407: determine the constraint function.

[0174] In some embodiments, the carrier groups the attach rates of a pair of complementary attributes of optional components under the same component category and the target ratio r of the component category to the optional products. i,j , determine the first constraint function. Specifically, the sum of the attach rates of a pair of complementary attribute groups of optional components under the same component category should be equal to the target ratio of the component category to the optional products, including:

[0175]

[0176] Where a is the attribute group number of the hth optional component, a=1,2,…,A h (A is an even number, adjacent odd and even numbers are a pair of complementary attribute groups, and so on), a1=1,2,…,A h -1; a2=1,2,…,A h .

[0177] In some embodiments, the carrier determines the second constraint function based on the optional component attachment rates of different component categories within the same attribute group. Specifically, the sum of the component attachment rates of different component categories within the same attribute group should be equal, specifically including:

[0178]

[0179] Where j1, j2 = 1, 2, ..., n i ;j1≠j2.

[0180] Step S408 , calculating the optional component attachment rate of each optional component when the weighted target value is the minimum value.

[0181] In some embodiments, when the value of the weighted target solved by the carrier based on the first constraint function and the second constraint function is minimized, the corresponding optional component attachment rate is the set of all optional component attachment rates of the optional product.

[0182] In some embodiments, after all optional products are traversed, a result set of optional component attachment rates required for each optional product is output.

[0183] In this way, the optional component attachment rate confirmation method provided by the embodiment of the present disclosure realizes the intuitive expression and structured description of complex optional relationships by constructing optional products, optional components and their attributes, categories and optional component attachment rates into a concrete topological network model. This model not only has strong interpretability and is easy to test and verify, but also significantly improves the efficiency of algorithm execution and can quickly handle the computing needs in large-scale optional scenarios. Secondly, the embodiment of the present disclosure adopts a multi-objective constrained optimization algorithm to achieve the optimal solution for the optional component attachment rate, which is significantly better than the traditional calculation method that relies on manual experience or historical statistical data. Under the premise of meeting the multi-dimensional proportional rationality constraints such as optional component categories and attribute combinations, the accuracy of the optional product component demand plan is effectively improved, and the demand forecasting problem in the scenario without a static bill of materials is solved. In addition, the solution has strong generalization capabilities and can be widely used in optional product demand planning scenarios in discrete manufacturing industries such as laptops, home appliances, and automobiles, and is suitable for demand forecasting of complex optional models. Through automated and intelligent calculation of the attachment rate, the reliance on manual experience is reduced, human errors are reduced, and the limitations brought by incomplete historical statistical data are avoided, providing scientific and reliable decision support for supply chain demand planning.

[0184] Next, we will take the specific discrete manufacturing scenario of laptop computers as an example to illustrate.

[0185] Figure 6 A second optional schematic diagram of the target model provided by the embodiment of the present disclosure is shown.

[0186] like Figure 6 As shown, the optional product is a certain model of laptop with an independent demand of 1000. Its component categories include at least DPY (Key, i.e., key component category), PD, BASE2, and SLT. The attribute groups corresponding to DPY include non-black and black. For DPY, non-black and black are a pair of complementary attribute groups. The attribute groups corresponding to PD include non-black, black, no WWAN, and WWAN. For PD, non-black and black are a pair of complementary attribute groups, and no WWAN and WWAN are a pair of complementary attribute groups. The attribute groups corresponding to BASE2 include no WWAN and WWAN. The attribute groups corresponding to SLT include non-black or black. Due to space constraints, not all attribute groups are shown.

[0187] The optional components corresponding to the non-black attribute grouping include:

[0188] Component 1: T14 G3 WUXG MT FHD IR GY FS, which is the display module of a laptop computer with model T14. G3 is the version identifier, WUXG refers to the screen resolution or display technology, MT indicates multi-touch or installation method, FHD refers to full HD resolution, IR is an infrared camera, GY is gray (not black), and FS is the design method, including borderless or full surface.

[0189] Component 3: FINGERPRINT NO NFC GY NWW, which is the fingerprint recognition module of the laptop. FINGERPRINT refers to the fingerprint recognition module, NO NFC means that this optional component does not include NFC function, GY is gray (not black), and NWW logo does not support wireless wide area network.

[0190] The optional components corresponding to the black attribute group include:

[0191] Component 2: T14 G3 WUXGA FHD FS, which is a notebook computer display module with model number T14. G3 is the version identifier, WUXGA refers to the screen resolution or display technology, MT indicates multi-touch or installation method, FHD refers to full high-definition resolution, and FS is the design method, including borderless or full-surface.

[0192] Component 4: FINGERPRINT NO NFC BK NWW, which is the fingerprint recognition module of the laptop. FINGERPRINT refers to the fingerprint recognition module, NO NFC means that this optional component does not include NFC function, BK is black, and NWW means that wireless wide area network is not supported.

[0193] Optional components corresponding to the Non-WWAN attribute group are not supported:

[0194] FINGERP RINT NO NFC GY NWW; FINGERP RINT NO NFC BK NWW; Component 5: D Coverw / SIM GY, bottom cover with SIM card slot, gray; Component 6: D Cover w / SIM BK, bottom cover with SIM card slot, black.

[0195] After calculation based on the above steps S101 to S105, steps S201 to S205, or steps S401 to S408, the carrier obtains the additional rate of each optional component of the laptop computer, where the additional rate of the optional component of T14 G3 WUXG MT FHD IRGY FS is 0.1, and the dependent demand is 100; the additional rate of the optional component of T14 G3 WUXGA FHD FS is 0.9, and the dependent demand is 900; the additional rate of the optional component of FINGERP RINT NO NFC GY NWW is 0.1, and the dependent demand is 100; the additional rate of the optional component of FINGERP RINT NO NFC BK NWW T14 G3 WUXGA FHD FS is 0.9, and the dependent demand is 900; the additional rate of the optional component of D Cover w / SIM GY is 0.1, and the dependent demand is 100; and the additional rate of the optional component of DCover w / SIM BK is 0.9, and the dependent demand is 900.

[0196] Compared with the existing technology, the method for confirming the attach rate of optional components provided by the embodiment of the present disclosure is estimated to be much more efficient than the current offline manual calculation combined with system correction and adjustment planning efficiency. For weekly demand forecasts of optional products, the time required for attach rate planning is expected to be reduced from more than one day of manual calculation to within 10 minutes for complex optional models and within 5 minutes for common optional models by the optimization algorithm. Compared with the existing solution that corrects attach rate plans based on business rules, the method provided by the embodiment of the present disclosure can be expanded from the existing solution's EOL locked-in volume models (1 to 5 products / month) to cover the entire product line (~100 products / week) for configuration models. The method solves multiple objectives based on constraints, and the algorithm has strong universality, breaking the scenario applicability limitations of existing solutions. Based on the average 26-week WW forecast statistics for notebook products of a certain brand's notebook business unit, the method provided by the embodiment of the present disclosure estimates that within the weekly demand forecast cycle, 74 optional product models and 3,535K independent optional product demands can be processed. Units, the number of optional components required in sets is 126,705K Pcs; it is estimated that the risk cost of obsolete material inventory can be reduced by 10%, and the E&O hit rate can be reduced to 4.5M Pcs.

[0197] Figure 7 A schematic diagram of an optional structure of a device for confirming the additional rate of an optional component provided in an embodiment of the present disclosure is shown, and will be described according to each part.

[0198] In some embodiments, the optional component attachment rate confirmation device 700 includes: a model building unit 701 , a first determination unit 702 , a second determination unit 703 and a third determination unit 704 .

[0199] The model building unit 701 is used to build a target model based on the independent requirements, component categories, attribute groups of the optional products and the dependent requirements of the optional components corresponding to the optional products;

[0200] The first determining unit 702 is configured to determine at least the complete set quantity and related costs of the dependent requirements of the optional components based on the target model;

[0201] The second determining unit 703 is configured to determine a weighted target based on the complete set quantity and the relevant cost; and constrain the weighted target based on the sum of the additional rates of a pair of complementary attribute groups of optional components in the same component category, and the sum of the additional rates of optional components of different component categories in the same attribute group;

[0202] The third determining unit 704 is configured to determine an additional factor value corresponding to when the value of the weighted target meets a condition, and the additional factor value is used to determine the required quantity of each optional component.

[0203] The model building unit 701 is specifically configured to use the independent requirement of the optional product as a starting node and at least one component category node as a child node of the starting node;

[0204] At least one attribute grouping node is used as a child node of at least one component category, and the weight of the connection line between the attribute grouping node and the component category node represents the attribute grouping attachment rate;

[0205] The node of the dependent demand of any optional component serves as a child node of at least one attribute group, and the weight of the connection line between the node of the dependent demand of the optional component and the node of the attribute group represents the optional component attachment rate.

[0206] In some embodiments, the relevant costs include the total value cost of optional components and the inventory backlog cost of optional components. The first determining unit 702 is specifically configured to determine the number of optional component non-independent requirements of the optional product based on the independent demand nodes of the optional product and the optional component attachment rate in the target model.

[0207] Determining the total value cost of the optional components of the optional products based on the independent demand of the optional products, the value cost coefficient of the optional components, and the optional component attachment rate in the target model;

[0208] Based on the inventory quantity of the optional components, the independent demand of the optional products, the optional component attachment rate, and the inventory backlog cost coefficient of the optional components in the target model, the inventory backlog cost of the optional components of the optional products is determined.

[0209] The first determining unit 702 is specifically configured to determine the dependent demand for the oth optional component based on the attachment rate of the oth optional component in the jth component category of the ith optional product and the independent demand of the ith optional product;

[0210] Determine the non-independent demand for all optional components in all component categories corresponding to the optional product, which is the complete set quantity of the non-independent demand for the optional components of the optional product.

[0211] The first determining unit 702 is specifically configured to determine the attaching rate of the oth optional component in the jth component category of the ith optional product, the independent demand of the ith optional product, and the value-cost coefficient of the oth optional component, and determine the value-cost of the oth optional component;

[0212] Determine the sum of the value costs of all optional components in all component categories corresponding to the optional product, which is the total value cost of the optional components of the optional product.

[0213] The first determining unit 702 is specifically configured to determine an inventory backlog quantity of the oth optional component based on the inventory quantity of the oth component and the dependent demand of the oth optional component;

[0214] determining an inventory backlog cost of the oth optional component based on the inventory backlog quantity of the oth optional component and the inventory backlog cost coefficient of the oth optional component;

[0215] The inventory backlog cost of optional components of the optional products is determined based on the sum of the inventory backlog costs of all optional components in all component categories corresponding to the optional products.

[0216] The second determining unit 703 is specifically configured to determine a first coefficient corresponding to the non-independent required complete set quantity of optional components of the optional product, a second coefficient corresponding to the total value cost of the optional components of the optional product, and a third coefficient corresponding to the inventory backlog cost of the optional components of the optional product;

[0217] A weighted target is obtained by performing a weighted summation based on the non-independent demand set quantity of the optional components of the optional product, the total value cost of the optional components, the inventory backlog cost of the optional components, the first coefficient, the second coefficient and the third coefficient.

[0218] The second determining unit 703 is specifically configured to determine a first constraint function based on a pair of complementary attribute grouping attachment rates of optional components under the same component category and a target ratio of component category to optional products;

[0219] The second constraint function is determined based on optional component attachment rates of different component categories within the same attribute group.

[0220] According to an embodiment of the present disclosure, the present disclosure also provides an electronic device and a readable storage medium.

[0221] Figure 8 A schematic block diagram of an example electronic device 800 that can be used to implement embodiments of the present disclosure is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital assistants, cellular phones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are provided as examples only and are not intended to limit the implementation of the present disclosure described and / or claimed herein.

[0222] like Figure 8 As shown, the electronic device 800 includes a computing unit 801, which can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) 802 or a computer program loaded from a storage unit 808 into a random access memory (RAM) 803. In the RAM 803, various programs and data required for the operation of the electronic device 800 can also be stored. The computing unit 801, the ROM 802, and the RAM 803 are connected to each other via a bus 804. An input / output (I / O) interface 805 is also connected to the bus 804.

[0223] Multiple components in the electronic device 800 are connected to the I / O interface 805, including an input unit 806, such as a keyboard, a mouse, etc.; an output unit 807, such as various types of displays, speakers, etc.; a storage unit 808, such as a magnetic disk, an optical disk, etc.; and a communication unit 809, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 809 allows the electronic device 800 to exchange information / data with other devices via a computer network such as the Internet and / or various telecommunication networks.

[0224] The computing unit 801 can be any general-purpose and / or specialized processing component with processing and computing capabilities. Some examples of the computing unit 801 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various specialized artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. The computing unit 801 performs the various methods and processes described above, such as the optional component attachment rate confirmation method. For example, in some embodiments, the optional component attachment rate confirmation method can be implemented as a computer software program tangibly embodied in a machine-readable medium, such as the storage unit 808. In some embodiments, part or all of the computer program can be loaded and / or installed onto the electronic device 800 via the ROM 802 and / or the communication unit 809. When the computer program is loaded into the RAM 803 and executed by the computing unit 801, one or more steps of the optional component attachment rate confirmation method described above can be performed. Alternatively, in other embodiments, the computing unit 801 may be configured to execute the optional component attachment rate confirmation method in any other appropriate manner (for example, by means of firmware).

[0225] Various embodiments of the systems and techniques described herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), system-on-chip systems (SOCs), programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments can include being implemented in one or more computer programs that are executable and / or interpreted on a programmable system that includes at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0226] The program code for implementing the method of the present disclosure can be written in any combination of one or more programming languages. These program codes can be provided to a processor or controller of a general-purpose computer, a special-purpose computer, or other programmable data processing device so that when the program code is executed by the processor or controller, the functions / operations specified in the flow chart and / or block diagram are implemented. The program code can be executed entirely on the machine, partially on the machine, as a stand-alone software package, partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0227] In the context of the present disclosure, a machine-readable medium can be a tangible medium that can contain or store a program for use by or in conjunction with an instruction execution system, device or equipment. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium can include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or equipment, or any suitable combination of the foregoing. A more specific example of a machine-readable storage medium can include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0228] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user can provide input to the computer. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0229] The systems and techniques described herein can be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such back-end components, middleware components, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network (LAN), a wide area network (WAN), and the Internet.

[0230] A computer system may include a client and a server. The client and server are generally remote from each other and typically interact through a communication network. The client-server relationship arises through computer programs running on the respective computers and having a client-server relationship with each other. The server may be a cloud server, a server in a distributed system, or a server integrated with a blockchain.

[0231] It should be understood that the various forms of the processes shown above can be used to reorder, add, or delete steps. For example, the steps described in this disclosure can be performed in parallel, sequentially, or in a different order, as long as the desired results of the technical solutions disclosed in this disclosure can be achieved. This is not a limitation herein.

[0232] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features being referred to. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one such feature. Throughout the present disclosure, "plurality" means two or more, unless otherwise specifically defined.

[0233] The above description is merely a specific embodiment of the present disclosure, but the scope of protection of the present disclosure is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this disclosure should be included in the scope of protection of the present disclosure. Therefore, the scope of protection of the present disclosure should be based on the scope of protection of the claims.

Claims

1. A method for determining an optional component attachment rate, the method comprising: Building a target model based on the independent requirements, component categories, and attribute groups of the optional products and the dependent requirements of the optional components corresponding to the optional products; Based on the target model, at least determining the complete set quantity and related costs of the dependent requirements of the optional components; Determining a weighted target based on the complete set quantity and the relevant costs; constraining the weighted target based on the sum of the attach rates of a pair of complementary attribute groups of optional components in the same component category, and the sum of the attach rates of optional components in different component categories in the same attribute group; An additional factor value corresponding to when the value of the weighted target meets a condition is determined, and the additional factor value is used to determine the required quantity of each optional component.

2. The method according to claim 1, wherein constructing a target model based on the independent requirements, component categories, attribute groups of optional products and the dependent requirements of optional components corresponding to the optional products comprises: The independent requirement of the optional product is used as the starting node, and at least one component category node is used as the child node of the starting node; At least one attribute grouping node is used as a child node of at least one component category, and the weight of the connection line between the attribute grouping node and the component category node represents the attribute grouping attachment rate; The node of the dependent demand of any optional component serves as a child node of at least one attribute group, and the weight of the connection line between the node of the dependent demand of the optional component and the node of the attribute group represents the optional component attachment rate.

3. The method according to claim 1, wherein the relevant costs include the total value cost of the optional components and the inventory backlog cost of the optional components, and the determining, based on the target model, at least the complete set quantity and relevant costs of the dependent requirements of the optional components comprises: Determining the complete set of non-independent requirements for optional components of the optional product based on the independent demand nodes and optional component attachment rates of the optional product in the target model; Determining the total value cost of the optional components of the optional products based on the independent demand of the optional products, the value cost coefficient of the optional components, and the optional component attachment rate in the target model; Based on the inventory quantity of the optional components, the independent demand of the optional products, the optional component attachment rate, and the inventory backlog cost coefficient of the optional components in the target model, the inventory backlog cost of the optional components of the optional products is determined.

4. The method according to claim 3, wherein determining the number of optional component dependent requirements of the optional product based on the independent requirement nodes and optional component attachment rates of the optional product in the target model comprises: Determine the dependent demand for the oth optional component based on the attachment rate of the oth optional component in the jth component category of the ith optional product and the independent demand for the ith optional product; Determine the non-independent demand for all optional components in all component categories corresponding to the optional product, which is the complete set quantity of the non-independent demand for the optional components of the optional product.

5. The method according to claim 3, wherein determining the total value cost of optional components of an optional product based on the independent demand of the optional product, the value cost coefficient of the optional component, and the optional component attachment rate in the target model comprises: Determine the attach rate of the oth optional component in the jth component category of the ith optional product, the independent demand of the ith optional product, and the value-cost coefficient of the oth optional component to determine the value-cost of the oth optional component; Determine the sum of the value costs of all optional components in all component categories corresponding to the optional product, which is the total value cost of the optional components of the optional product.

6. The method according to claim 3, wherein determining the inventory backlog cost of optional components of optional products based on the inventory quantity of optional components, the independent demand of optional products, the optional component attachment rate, and the inventory backlog cost coefficient of the optional components in the target model comprises: Determine the inventory backlog quantity of the oth optional component based on the inventory quantity of the oth component and the dependent demand of the oth optional component; determining an inventory backlog cost of the oth optional component based on the inventory backlog quantity of the oth optional component and the inventory backlog cost coefficient of the oth optional component; The inventory backlog cost of optional components of the optional products is determined based on the sum of the inventory backlog costs of all optional components in all component categories corresponding to the optional products.

7. The method according to claim 1, wherein the relevant costs include the total value cost of optional components and the inventory backlog cost of optional components, and determining the weighted target based on the complete set quantity and the relevant costs comprises: Determine the first coefficient corresponding to the non-independent required complete set quantity of the optional components of the optional product, the second coefficient of the total value cost of the optional components of the optional product, and the third coefficient of the inventory backlog cost of the optional components of the optional product; A weighted target is obtained by performing a weighted summation based on the non-independent demand set quantity of the optional components of the optional product, the total value cost of the optional components, the inventory backlog cost of the optional components, the first coefficient, the second coefficient and the third coefficient.

8. The method according to claim 1, constraining the weighted target based on the sum of the attach rates of a pair of complementary attribute groups of optional components in the same component category, and the sum of the attach rates of optional components in different component categories in the same attribute group, comprises: Determining a first constraint function based on a pair of complementary attribute grouping attachment rates of optional components under the same component category and a target ratio of component category to optional products; The second constraint function is determined based on optional component attachment rates of different component categories within the same attribute group.

9. A device for confirming an optional component attachment rate, the device comprising: A model building unit, configured to build a target model based on independent requirements, component categories, attribute groups of optional products and dependent requirements of optional components corresponding to the optional products; A first determining unit is configured to determine at least the complete set quantity and related costs of the dependent requirements of the optional components based on the target model; a second determining unit configured to determine a weighted target based on the complete set quantity and the relevant cost; and constrain the weighted target based on a sum of additional rates of a pair of complementary attribute groups of optional components in the same component category, and a sum of additional rates of optional components of different component categories in the same attribute group; The third determining unit is configured to determine an additional factor value corresponding to when the value of the weighted target meets a condition, wherein the additional factor value is used to determine a required quantity of each of the optional components.

10. An electronic device, characterized in that: include: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute the method according to any one of claims 1 to 8.

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