Automobile part intelligent management method based on big data and SaaS platform

Abstract

The invention relates to an automobile part intelligent management method based on big data and an SaaS platform, and relates to the technical field of automobile part management. The method comprises the following steps: collecting automobile part demand information of each region through an automobile part application program, and analyzing automobile part demand differentiation information of each region based on the automobile part demand information. And determining a management strategy of the automobile parts based on the automobile part demand information and the corresponding differentiation information through a digital twin simulation technology. And issuing the management strategy to an automobile part factory and a dealer or a store, so that the automobile part factory, the dealer or the store performs corresponding automobile part management according to the management strategy. According to the technical scheme, a complete link among an automobile part factory, an automobile part application program and an automobile part seller is broken through, automobile part management based on an industrial chain can be realized, and the intelligence and high efficiency of automobile part management are improved.

Description

Technical Field

[0001] The present disclosure relates to the technical field of automobile parts management, and in particular, to an automobile parts intelligent management method and SaaS platform based on big data. Background Art

[0002] With the development of automobile technology, automobile parts technology has also developed. For automobile manufacturers, it is necessary to manage automobile parts reasonably to ensure that automobile parts can be supplied to the market.

[0003] In the related art, the management of auto parts is usually carried out on the sales platform or the factory, but the management of the industrial chain is not opened up, which makes the management of auto parts less intelligent and efficient. Summary of the invention

[0004] The purpose of the present invention is to provide an intelligent management method and SaaS platform for automobile parts based on big data, which realizes automobile parts management based on the industrial chain and improves the intelligence and efficiency of automobile parts management.

[0005] In order to achieve the above-mentioned objectives, in a first aspect, the present disclosure provides an intelligent management method for auto parts based on big data, comprising: obtaining auto parts demand information corresponding to multiple regions respectively collected through an auto parts application; determining differentiated auto parts demand information based on the auto parts demand information corresponding to the multiple regions respectively, the differentiated information being used to characterize the differentiated auto parts demand of the multiple regions; determining target simulation parameters corresponding to an auto parts management model based on the auto parts demand information corresponding to the multiple regions and the differentiated information; determining an auto parts management strategy by simulating the auto parts management model according to the target simulation parameters; and executing the auto parts management strategy through an auto parts factory and an auto parts sales platform.

[0006] Optionally, the automobile parts demand information includes: regional location information, automobile parts type demand information, automobile parts quantity demand information and automobile parts supply cycle demand information; the determination of automobile parts demand differentiation information based on the automobile parts demand information corresponding to the multiple regions includes: for any region among the multiple regions, determining the first differentiation information based on the difference between the regional location information corresponding to the region and the regional location information corresponding to other regions; determining the second differentiation information based on the difference between the automobile parts type demand information corresponding to the region and the automobile parts type demand information corresponding to other regions; determining the third differentiation information based on the difference between the automobile parts quantity demand information corresponding to the region and the automobile parts quantity demand information corresponding to other regions; determining the fourth differentiation information based on the difference between the automobile parts supply cycle demand information corresponding to the region and the automobile parts supply cycle demand information corresponding to other regions; determining the automobile parts demand differentiation information based on the first differentiation information, the second differentiation information, the third differentiation information and the fourth differentiation information corresponding to each region.

[0007] Optionally, the method of determining the differentiated information on demand for auto parts according to the first differentiated information, the second differentiated information and the third differentiated information respectively corresponding to each region includes: determining a first differentiation matrix according to the first differentiated information and the fourth differentiated information respectively corresponding to each region, the first differentiation matrix being used to characterize the differences in demand for sales of auto parts; determining a second differentiation matrix according to the second differentiated information and the third differentiated information respectively corresponding to each region, the second differentiation matrix being used to characterize the differences in demand for production of auto parts; and determining the differentiated information on demand for auto parts according to the first differentiation matrix and the second differentiation matrix.

[0008] Optionally, the auto parts management model includes an auto parts factory model and an auto parts sales platform model, and the target simulation parameters corresponding to the auto parts management model are determined according to the auto parts demand information corresponding to the multiple regions and the differentiation information, including: determining the production simulation parameters of the auto parts factory model according to the auto parts demand information corresponding to the multiple regions; determining the sales simulation parameters of the auto parts sales platform model according to the differentiation information; determining the industrial chain simulation parameters between the auto parts factory model and the auto parts sales platform model according to the differentiation information, the industrial chain simulation parameters being used to affect the number of auto parts interactions between the auto parts factory model and the auto parts sales platform model; and determining the target simulation parameters according to the production simulation parameters, the sales simulation parameters and the industrial chain simulation parameters.

[0009] Optionally, the differentiated information on automobile parts demand includes a first differentiated matrix characterizing the differences in automobile parts sales demand, and determining the sales simulation parameters of the automobile parts sales platform model based on the differentiated information includes: obtaining initial sales simulation parameters of the automobile parts sales platform model; adjusting the initial sales simulation parameters multiple times according to each matrix element in the first differentiated matrix to obtain sales simulation parameters that have been adjusted multiple times, wherein the adjustment method of two adjacent sales simulation parameters matches the change rule between the corresponding matrix elements; and determining the sales simulation parameters of the automobile parts sales platform model based on the initial sales simulation parameters and the sales simulation parameters that have been adjusted multiple times.

[0010] Optionally, the differentiated information on auto parts demand includes a first differentiation matrix characterizing the differences in auto parts sales demand and a second differentiation matrix characterizing the differences in auto parts production. The method of determining the industrial chain simulation parameters between the auto parts factory model and the auto parts sales platform model based on the differentiation information includes: obtaining initial industrial chain simulation parameters between the auto parts factory model and the auto parts sales platform model; adjusting the initial industrial chain simulation parameters multiple times according to the matrix elements at the same position in the first differentiation matrix and the second differentiation matrix to obtain industrial chain simulation parameters adjusted multiple times, wherein the difference between the industrial chain simulation parameters adjusted each time and the industrial chain simulation parameters adjusted previously matches the difference between the matrix elements at the same position in the first differentiation matrix and the second differentiation matrix; determining the industrial chain simulation parameters between the auto parts factory model and the auto parts sales platform model according to the initial industrial chain simulation parameters and the industrial chain simulation parameters adjusted multiple times.

[0011] Optionally, the method of determining the auto parts management strategy by simulating according to the target simulation parameters through the auto parts management model includes: performing simulation according to the target simulation parameters through the auto parts management model to obtain simulation results, the simulation results including an auto parts conversion rate for characterizing the conversion of auto parts produced by the auto parts factory in the industrial chain; determining, according to the simulation results, simulation parameters corresponding to the highest auto parts conversion rate from the target simulation parameters; determining, according to the simulation parameters corresponding to the highest auto parts conversion rate, the auto parts factory production strategy, the auto parts sales platform sales strategy and the auto parts interaction strategy; determining the auto parts management strategy according to the auto parts factory production strategy, the auto parts sales platform sales strategy and the auto parts interaction strategy.

[0012] Optionally, executing the automobile management strategy through the automobile parts factory and the automobile parts sales platform includes: executing the automobile parts factory production strategy through the automobile parts factory; executing the automobile parts sales strategy through the automobile parts sales platform; executing the automobile parts interaction strategy through the automobile parts factory and the automobile parts sales platform.

[0013] Optionally, the auto parts intelligent management method also includes: obtaining auto parts after-sales demand information corresponding to the multiple regions respectively collected through the auto parts application; determining an auto parts after-sales management strategy based on the auto parts after-sales demand information corresponding to the multiple regions respectively; and executing the auto parts after-sales management strategy through the auto parts sales platform.

[0014] In the second aspect, the present disclosure provides an automobile parts intelligent management SaaS platform based on big data, including: an automobile parts application, an automobile parts factory end, an automobile parts sales platform end and an automobile parts intelligent management end; wherein, the automobile parts intelligent management end is used to execute the automobile parts intelligent management method based on big data as described in the first aspect of the present disclosure.

[0015] Through the above technical solution, the demand information of auto parts in various regions is collected through the auto parts application, and the differentiated information of auto parts demand in various regions is analyzed based on the auto parts demand information. Through the digital twin simulation technology, the management strategy of auto parts is determined based on the auto parts demand information and the corresponding differentiated information. The management strategy is issued to the auto parts factory and the auto parts sales platform, so that the auto parts factory and the auto parts sales platform can perform corresponding auto parts management according to the management strategy. This technical solution opens up the complete link of auto parts factories, auto parts applications and auto parts sellers, and can realize auto parts management based on the industrial chain, improving the intelligence and efficiency of auto parts management.

[0016] Other features and advantages of the present disclosure will be described in detail in the following detailed description. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The accompanying drawings are used to provide a further understanding of the present disclosure and constitute a part of the specification. Together with the following specific embodiments, they are used to explain the present disclosure but do not constitute a limitation of the present disclosure. In the accompanying drawings: Figure 1 It is a block diagram of an automobile parts intelligent management SaaS platform based on big data according to an exemplary embodiment.

[0018] Figure 2 The present invention is a flowchart of a method for intelligent management of automobile parts based on big data according to an exemplary embodiment.

[0019] Figure 3 The diagram is a schematic diagram of a user interface of an automobile accessories application according to an exemplary embodiment.

[0020] Figure 4 It is a block diagram of an automobile parts management model according to an exemplary embodiment.

[0021] Figure 5 The present invention is a block diagram of an intelligent management device for automobile parts based on big data according to an exemplary embodiment.

[0022] Figure 6 It is a block diagram of an electronic device according to an exemplary embodiment. DETAILED DESCRIPTION

[0023] The specific implementation of the present disclosure is described in detail below in conjunction with the accompanying drawings. It should be understood that the specific implementation described herein is only used to illustrate and explain the present disclosure, and is not used to limit the present disclosure.

[0024] With the development of automobile technology, automobile parts technology has also developed. For automobile manufacturers, it is necessary to manage automobile parts reasonably to ensure that automobile parts can be supplied to the market.

[0025] For example, automobile manufacturers need to manage the production of automobile parts, or the sales of automobile parts, etc.

[0026] In the related art, the management of auto parts is usually carried out on the sales platform or the factory, but the management of the industrial chain is not opened up, which makes the management of auto parts less intelligent and efficient.

[0027] Based on this, the disclosed embodiment provides a technical solution, which collects automobile parts demand information from various regions through automobile parts applications, and analyzes differentiated information on automobile parts demand from various regions based on the automobile parts demand information. Through digital twin simulation technology, the management strategy for automobile parts is determined based on the automobile parts demand information and the corresponding differentiated information. The management strategy is issued to automobile parts factories and automobile parts sales platforms, so that the automobile parts factories and automobile parts sales platforms can perform corresponding automobile parts management according to the management strategy. This technical solution opens up a complete link between automobile parts factories, automobile parts applications and automobile parts sellers, and can realize automobile parts management based on the industrial chain, improving the intelligence and efficiency of automobile parts management.

[0028] In some embodiments, the technical solution can be implemented based on a SaaS (Software as a Service) platform, which enables users to use cloud-based applications through an Internet connection.

[0029] Therefore, on the user side, information feedback can be achieved through the auto parts application. On the back end, auto parts intelligent management can be performed based on the information fed back by users.

[0030] Figure 1 is a block diagram of an automobile parts intelligent management SaaS platform based on big data according to an exemplary embodiment, such as Figure 1 As shown, the platform includes: auto parts application, auto parts factory end, auto parts sales platform end and auto parts intelligent management end.

[0031] Among them, the auto parts application, auto parts factory and auto parts sales platform can be regarded as the front end of the platform, and the auto parts intelligent management end can be regarded as the back end of the platform.

[0032] On the auto parts application side, users who have auto parts needs can use cloud-based applications to provide auto parts demand information.

[0033] The auto parts factory can be controlled by the auto parts intelligent management end to implement different auto parts production strategies.

[0034] The auto parts sales platform end may be the end where the dealer or store is located, and may be controlled by the auto parts intelligent management end to implement different auto parts sales strategies.

[0035] The intelligent management terminal of automobile parts, as the backend of the entire platform, is used to implement backend management. The management terminal can integrate digital twin simulation technology to determine the management strategy of automobile parts.

[0036] Figure 2 is a flowchart of a method for intelligent management of automobile parts based on big data according to an exemplary embodiment. The method can be applied to an intelligent management terminal of automobile parts, such as Figure 2 As shown, the method comprises the following steps: Step S21, obtaining automobile parts demand information corresponding to a plurality of regions respectively collected through an automobile parts application program.

[0037] Step S22, determining differentiated information on the demand for automobile parts according to the automobile parts demand information corresponding to the multiple regions, where the differentiated information is used to characterize differentiated situations of the demand for automobile parts in the multiple regions.

[0038] Step S23, determining target simulation parameters corresponding to the automobile parts management model according to automobile parts demand information and differentiation information corresponding to the plurality of regions.

[0039] Step S24, performing simulation according to target simulation parameters through the automobile parts management model to determine the automobile parts management strategy.

[0040] Step S25, executing the automobile management strategy through the automobile parts factory and the automobile parts sales platform.

[0041] In step S21, the auto parts application can collect auto parts demand information from users in different regions and integrate it into auto parts demand information corresponding to multiple regions. The auto parts demand information corresponding to each region can be distinguished by region identifiers. Thus, the auto parts intelligent management terminal can obtain the auto parts demand information to perform auto parts intelligent management.

[0042] In some embodiments, when collecting information, the auto parts application may organize and normalize the information to obtain more uniform information that is easier to process.

[0043] In some embodiments, the auto parts application may have multiple forms and may have multiple different interface designs.

[0044] In some embodiments, the automobile parts demand information includes: regional location information, automobile parts type demand information, automobile parts quantity demand information, and automobile parts supply cycle demand information.

[0045] Based on these types of auto parts demand information, Figure 3 is a schematic diagram of a user interface of an automobile accessories application according to an exemplary embodiment. Figure 3 As shown, in the user interface, there are: regional location acquisition controls, auto parts type demand acquisition controls, auto parts quantity demand acquisition controls and auto parts supply cycle demand acquisition controls.

[0046] Users can input different requirements by triggering different controls, so that the application can collect the required information.

[0047] Among them, the demand for automobile parts supply cycle can represent the user's automobile parts purchasing cycle. For example, there may be a demand for automobile parts purchase every other month.

[0048] In some embodiments, the above-mentioned controls may be implemented in different forms or implementations, and specific reference may be made to mature human-computer interaction technologies in the art, which will not be described in detail here.

[0049] In step S22, based on the automobile parts demand information corresponding to the plurality of regions, differentiated information representing differentiated situations of automobile parts demand may be determined.

[0050] As an optional implementation, step S22 includes: for any area among the multiple areas, determining first differentiation information according to the difference between the regional location information corresponding to the area and the regional location information corresponding to other areas; determining second differentiation information according to the difference between the automobile parts type demand information corresponding to the area and the automobile parts type demand information corresponding to other areas; determining third differentiation information according to the difference between the automobile parts quantity demand information corresponding to the area and the automobile parts quantity demand information corresponding to other areas; determining fourth differentiation information according to the difference between the automobile parts supply cycle demand information corresponding to the area and the automobile parts supply cycle demand information corresponding to other areas; determining automobile parts demand differentiation information according to the first differentiation information, second differentiation information, third differentiation information and fourth differentiation information corresponding to each area respectively.

[0051] In this implementation, for each region, the first differentiation information, the second differentiation information, the third differentiation information and the fourth differentiation information need to be determined respectively.

[0052] The first differentiation information may be the distance difference (difference) between the regional locations. Therefore, assuming there are 10 regions, for any region, the distance difference between the region and the other 9 regions is determined based on the regional location information, and then the 9 distance differences are averaged, weighted averaged, or the standard deviation is determined, and the result is used as the first differentiation information.

[0053] The second differentiation information may be the difference between the types of automobile parts. Therefore, assuming there are 10 regions, for any region, the similarity / correlation between the automobile parts type and the other 9 regions is determined according to the automobile parts type, and then the 9 similarities / correlations are averaged, weighted averaged, or the standard deviation is determined, and the result is used as the second differentiation information. Among them, the similarity / correlation of automobile parts types can be determined according to the pre-configured association / similarity relationship of automobile parts types, for example: the association / similarity of automobile parts belonging to the same drive system is high, and the association / similarity of automobile parts belonging to the drive system and the automobile media system is low.

[0054] The third differentiation information may be the difference (difference) between the numbers of automobile accessories. Therefore, assuming there are 10 regions, for any region, the difference between the numbers of automobile accessories and those of the other 9 regions is determined based on the number of automobile accessories. The 9 differences are then averaged, weighted averaged, or the standard deviation is determined, and the result is used as the third differentiation information.

[0055] The fourth differentiation information may be the difference between automobile parts supply cycles. Therefore, assuming there are 10 regions, for any region, the difference between the automobile parts supply cycles with the other 9 regions is determined based on the automobile parts supply cycle. Then, the 9 differences are averaged, weighted averaged, or the standard deviation is determined, and the result is used as the fourth differentiation information.

[0056] Furthermore, by combining the first differentiation information, the second differentiation information, the third differentiation information and the fourth differentiation information corresponding to each region, the differentiation information of the demand for automobile parts can be determined.

[0057] In some embodiments, determining differentiated information on demand for auto parts based on first differentiated information, second differentiated information, and third differentiated information corresponding to each region may include: determining a first differentiation matrix based on the first differentiated information and fourth differentiated information corresponding to each region, the first differentiation matrix being used to characterize differences in demand for auto parts sales; determining a second differentiation matrix based on the second differentiated information and third differentiated information corresponding to each region, the second differentiation matrix being used to characterize differences in demand for auto parts production; and determining differentiated information on demand for auto parts based on the first differentiation matrix and the second differentiation matrix.

[0058] In this embodiment, the first differentiation information and the fourth differentiation information can be converted into a first differentiation matrix to characterize the difference in sales demand for auto parts. It can be understood that the regional location and supply cycle have a greater impact on sales, so they are converted into the first differentiation matrix.

[0059] And, the second differentiation information and the third differentiation information are converted into a second differentiation matrix to characterize the difference in demand for automobile parts production. It can be understood that since quantity and type have a greater impact on automobile parts production, they are converted into the second differentiation matrix.

[0060] In combination with the introduction of the above-mentioned embodiment, each region corresponds to the first differential information and the fourth differential information, and the first differential information and the fourth differential information are both corresponding differential values.

[0061] Therefore, assuming that there are 10 regions, the first differentiation information corresponding to these 10 regions is A1~A10, and the fourth differentiation information is B1~B10, then A1~A10 can be the 10 matrix elements of the first row of the matrix, and B1~B10 can be the 10 elements of the second row of the matrix, forming a 2×10 first differentiation matrix.

[0062] Furthermore, each region corresponds to the second differential information and the third differential information, and the second differential information and the third differential information are both corresponding differential values.

[0063] Therefore, assuming that there are 10 regions, the second differentiation information corresponding to these 10 regions is C1~C10, and the third differentiation information is D1~D10, then, C1~C10 can be the 10 matrix elements of the first row of the matrix, and D1~D10 can be the 10 elements of the second row of the matrix, forming a 2×10 second differentiation matrix.

[0064] Furthermore, the first differentiation matrix and the second differentiation matrix may be used as differentiation information of automobile parts demand.

[0065] In the embodiments of the present disclosure, digital twin simulation technology is also involved. With regard to this technology, full use is made of data such as physical models, sensors, and operation history, and multi-disciplinary, multi-physical quantity, multi-scale, and multi-probability simulation processes are integrated to complete mapping in virtual space, thereby reflecting the entire life cycle of the corresponding physical equipment. Digital twin is a concept that transcends reality and can be regarded as a digital mapping system of one or more important, interdependent equipment systems.

[0066] Therefore, the industrial chain management of auto parts can be mapped into a virtual model, and the actual industrial chain management strategy can be formulated through simulation of the virtual model.

[0067] Therefore, in step S23, target simulation parameters corresponding to the automobile parts management model may be determined according to automobile parts demand information and differentiation information corresponding to the plurality of regions.

[0068] Figure 4 is a block diagram of an automobile parts management model according to an exemplary embodiment. Figure 4 As shown in the figure, the auto parts management model includes the auto parts factory model and the auto parts sales platform model. The interaction between the auto parts factory model and the auto parts sales platform model involves auto parts, which can be called a data interaction in the virtual model. The auto parts factory model and the auto parts sales platform model can form a virtual model of the industrial chain.

[0069] In some embodiments, the auto parts factory model and the auto parts sales platform model correspond to some initial simulation parameters, and the initial simulation parameters can realize some basic simulation functions.

[0070] In some embodiments, an automobile parts management model may be pre-built, and when simulation is needed, the model may be directly applied.

[0071] For differentiated information, the production simulation parameters and sales simulation parameters of the auto parts factory model can be determined, so that the auto parts factory model can simulate the factory's production, and the auto parts sales platform model can simulate the sales of the sales platform, which can be a dealer or store, etc.

[0072] Among them, regarding the specific construction method of the model, reference can be made to the mature digital twin simulation technology in the field, which will not be introduced in detail here. The present disclosure mainly involves the configuration of the simulation parameters therein.

[0073] As an optional implementation, step S23 includes: determining the production simulation parameters of the auto parts factory model according to the auto parts demand information corresponding to multiple regions; determining the sales simulation parameters of the auto parts sales platform model according to the differentiated information; determining the industrial chain simulation parameters between the auto parts factory model and the auto parts sales platform model according to the differentiated information, the industrial chain simulation parameters being used to influence the number of auto parts interactions between the auto parts factory model and the auto parts sales platform model; determining the target simulation parameters according to the production simulation parameters, the sales simulation parameters and the industrial chain simulation parameters.

[0074] In some embodiments, determining the production simulation parameters of the auto parts factory model according to the auto parts demand information corresponding to the multiple regions may include: integrating the auto parts demand information corresponding to the multiple regions to obtain the types of auto parts to be generated and the quantities of auto parts to be generated as production simulation parameters. Also, integrating the supply cycle requirements corresponding to the multiple regions to obtain a suitable production cycle, which can also be used as a production simulation parameter.

[0075] It can be understood that one factory is responsible for the supply of auto parts in these multiple regions, so by integrating these demands, production simulation parameters can be directly obtained. Thus, the auto parts factory model can produce corresponding types and quantities of auto parts according to the production cycle.

[0076] Furthermore, based on the differentiated information, the sales simulation parameters of the automobile parts sales platform model are determined, including: obtaining the initial sales simulation parameters of the automobile parts sales platform model; adjusting the initial sales simulation parameters multiple times according to each matrix element in the first differentiation matrix to obtain the sales simulation parameters that have been adjusted multiple times, wherein the adjustment method of two adjacent sales simulation parameters matches the change rule between the corresponding matrix elements; determining the sales simulation parameters of the automobile parts sales platform model based on the initial sales simulation parameters and the sales simulation parameters that have been adjusted multiple times.

[0077] In this embodiment, the auto parts sales platform model corresponds to initial sales simulation parameters, and the first differentiation matrix can be used to generate a variety of sales simulation parameters. The sales simulation parameters may include sales quantity, sales type, etc.

[0078] Therefore, the initial sales simulation parameters can be adjusted multiple times using the matrix elements in the first differentiation matrix, wherein the adjustment method of two adjacent sales simulation parameters matches the change rule between the corresponding matrix elements.

[0079] For example, during the first adjustment, the change between the first row and first column element and the first row and second column element is determined. If the element increases, the sales quantity in the initial sales simulation parameters can be increased, and the sales type can also be increased. Then continue to determine the change between the first row and second column element and the first row and third column element, and continue to adjust the sales simulation parameters adjusted last time according to the change rule, and repeat the process until multiple sales simulation parameters are obtained. It can be understood that each sales simulation parameter obtained needs to be retained.

[0080] Furthermore, in this way, the differentiation information can be combined to generate a variety of sales simulation parameters that characterize the differences.

[0081] In some embodiments, the industrial chain simulation parameters between the automobile parts factory model and the automobile parts sales platform model are determined based on the differentiation information, including: obtaining the initial industrial chain simulation parameters between the automobile parts factory model and the automobile parts sales platform model; adjusting the initial industrial chain simulation parameters multiple times based on the matrix elements at the same position in the first differentiation matrix and the second differentiation matrix to obtain the industrial chain simulation parameters adjusted multiple times, wherein the difference between the industrial chain simulation parameters adjusted each time and the industrial chain simulation parameters adjusted the previous time matches the difference between the matrix elements at the same position in the first differentiation matrix and the second differentiation matrix; determining the industrial chain simulation parameters between the automobile parts factory model and the automobile parts sales platform model based on the initial industrial chain simulation parameters and the industrial chain simulation parameters adjusted multiple times.

[0082] In this embodiment, the industry chain simulation parameters may include: the type of auto parts, the quantity of auto parts, and the auto parts delivery cycle from the auto parts factory model to the auto parts sales platform model.

[0083] Correspondingly, the initial industrial chain simulation parameters can be configured in the model, and two differentiation matrices can be used to generate a variety of industrial chain simulation parameters.

[0084] In some embodiments, each time the initial industrial chain simulation parameters are adjusted, the adjustment is performed in combination with the difference between the matrix elements at the same position in the first differentiation matrix and the second differentiation matrix.

[0085] For example, during the first adjustment, the difference between the first row and first column elements of the first differentiation matrix and the second differentiation matrix is ​​determined, and based on the difference and the initial industrial chain simulation parameters, the industrial chain simulation parameters adjusted for the first time are obtained. For example, if the difference is negative, the type of auto parts, the number of auto parts, and the auto parts delivery cycle are reduced; if the difference is positive, the type of auto parts, the number of auto parts, and the auto parts delivery cycle are increased. Alternatively, other adjustment methods may be used, which are not limited here. Through multiple adjustments, the industrial chain simulation parameters that have been adjusted multiple times can be obtained in combination with the differences in different matrix elements.

[0086] Furthermore, the initial industrial chain simulation parameters and the industrial chain simulation parameters adjusted multiple times can be used as the final industrial chain simulation parameters.

[0087] Furthermore, in this model, there are three simulation parameters that need to be determined in combination with demand information: production simulation parameters, sales simulation parameters and industrial chain simulation parameters. For other basic simulation parameters of the model, existing model configurations can be used.

[0088] Furthermore, in step S24, the automobile parts management model is used to perform simulation according to the target simulation parameters to determine the automobile parts management strategy.

[0089] In some embodiments, step S24 includes: performing simulation according to the target simulation parameters through the automobile parts management model to obtain simulation results, the simulation results including the automobile parts conversion rate used to characterize the conversion of automobile parts produced by the automobile parts factory in the industrial chain; according to the simulation results, determining the simulation parameters corresponding to the highest automobile parts conversion rate from the target simulation parameters; according to the simulation parameters corresponding to the highest automobile parts conversion rate, determining the automobile parts factory production strategy, the automobile parts sales platform sales strategy and the automobile parts interaction strategy; according to the automobile parts factory production strategy, the automobile parts sales platform sales strategy and the automobile parts interaction strategy, determining the automobile parts management strategy.

[0090] In this implementation, simulation can be performed based on the target simulation parameters to obtain corresponding simulation results, which can characterize the auto parts conversion rate. Based on the auto parts conversion rate, the simulation parameters corresponding to the highest auto parts conversion rate are determined from the target simulation parameters, and then the simulation parameters are converted into auto parts factory production strategies, auto parts sales platform sales strategies, and auto parts interaction strategies to obtain the final auto parts management strategy.

[0091] In some embodiments, the simulation results obtained by simulating the auto parts management model according to the target simulation parameters may include: producing according to the production parameters through the auto parts factory model, interacting according to the industrial chain interaction parameters through the auto parts factory model and the auto parts sales platform model, and selling according to the sales parameters through the auto parts sales platform model. Finally, the auto parts conversion rate is calculated.

[0092] In some embodiments, the auto parts conversion rate may be a ratio of the total sales volume of the auto parts sales platform model to the total production volume of the auto parts factory model.

[0093] In some embodiments, the corresponding strategy can be obtained by converting various data of the simulation parameters into actual data, for example, the production type and production quantity in the production strategy can be used as the actual production strategy of the auto parts factory.

[0094] Furthermore, in step S25, executing automobile management strategies through automobile parts factories and automobile parts sales platforms may include: executing automobile parts factory production strategies through automobile parts factories; executing automobile parts sales strategies through automobile parts sales platforms; and executing automobile parts interaction strategies through automobile parts factories and automobile parts sales platforms.

[0095] In some embodiments, the auto parts factory production strategy is sent to the auto parts factory end so that the auto parts factory executes the auto parts factory production strategy. Also, the auto parts sales strategy is sent to the corresponding auto parts sales platform end so that the auto parts sales platform end executes the corresponding auto parts sales strategy. Similarly, the auto parts interaction strategy is sent to the corresponding auto parts factory end and the auto parts sales platform end so that the auto parts factory and the auto parts sales platform execute the corresponding auto parts interaction strategy.

[0096] In some embodiments, the method may also include: obtaining automobile parts after-sales demand information corresponding to multiple regions respectively collected through the automobile parts application; determining the automobile parts after-sales management strategy based on the automobile parts after-sales demand information corresponding to the multiple regions respectively; and executing the automobile parts after-sales management strategy through the automobile parts sales platform.

[0097] In this implementation, the auto parts application program may also collect auto parts after-sales demand information, and the demand information may include: auto parts quality information and auto parts warranty information, etc.

[0098] Therefore, based on the after-sales demand information of automobile parts corresponding to multiple regions, the after-sales management strategy of automobile parts can be determined, and then the after-sales management strategy of automobile parts can be implemented through the automobile parts sales platform.

[0099] For example, if the after-sales demand information for auto parts collected in a certain area involves warranty needs, a corresponding warranty strategy can be formulated through the auto parts sales platform in the corresponding area to meet the warranty needs of the area.

[0100] In the disclosed embodiment, the auto parts sales platform may include multiple dealers or stores, different dealers or stores correspond to different regions, and the above-mentioned related simulation parameters or sales strategies may involve simulation parameters and sales strategies corresponding to different dealers or stores.

[0101] Figure 5 : is a block diagram of an intelligent management device for automobile parts based on big data according to an exemplary embodiment, the device comprising: The acquisition module 501 acquires automobile parts demand information corresponding to multiple regions respectively collected through the automobile parts application program.

[0102] The determination module 502 is used to determine the differentiated information of automobile parts demand according to the automobile parts demand information corresponding to the multiple regions, wherein the differentiated information is used to characterize the differentiated conditions of automobile parts demand in the multiple regions. According to the automobile parts demand information corresponding to the multiple regions and the differentiated information, the target simulation parameters corresponding to the automobile parts management model are determined. The automobile parts management model is used to perform simulation according to the target simulation parameters to determine the automobile parts management strategy.

[0103] The execution module 503 is used to execute the automobile management strategy through the automobile parts factory and the automobile parts sales platform.

[0104] Regarding the device in the above embodiment, the specific manner in which each module performs operations has been described in detail in the embodiment of the method, and will not be elaborated here.

[0105] Figure 6 FIG. 6 is a block diagram of an electronic device 600 according to an exemplary embodiment. Figure 6As shown, the electronic device 600 may include: a processor 601 , a memory 602 . The electronic device 600 may also include one or more of a multimedia component 603 , an input / output (I / O) interface 604 , and a communication component 605 .

[0106] The processor 601 is used to control the overall operation of the electronic device 600 to complete all or part of the steps in the above-mentioned intelligent management method for automobile accessories based on big data. The memory 602 is used to store various types of data to support the operation of the electronic device 600, and these data may include, for example, instructions for any application or method used to operate on the electronic device 600, and application-related data, such as contact data, sent and received messages, pictures, audio, video, etc. The memory 602 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (Static Random Access Memory, referred to as SRAM), electrically erasable programmable read-only memory (Electrically Erasable Programmable Read-Only Memory, referred to as EEPROM), erasable programmable read-only memory (Erasable Programmable Read-Only Memory, referred to as EPROM), programmable read-only memory (Programmable Read-Only Memory, referred to as PROM), read-only memory (Read-Only Memory, referred to as ROM), magnetic memory, flash memory, magnetic disk or optical disk. The multimedia component 603 may include a screen and an audio component. The screen may be, for example, a touch screen, and the audio component is used to output and / or input audio signals. For example, the audio component may include a microphone, which is used to receive external audio signals. The received audio signal may be further stored in the memory 602 or sent through the communication component 605. The audio component also includes at least one speaker for outputting audio signals. The I / O interface 604 provides an interface between the processor 601 and other interface modules, and the above-mentioned other interface modules may be a keyboard, a mouse, buttons, etc. These buttons may be virtual buttons or physical buttons. The communication component 605 is used for wired or wireless communication between the electronic device 600 and other devices. Wireless communication, such as Wi-Fi, Bluetooth, Near Field Communication (NFC), 2G, 3G or 4G, or a combination of one or more of them, so the corresponding communication component 605 may include: Wi-Fi module, Bluetooth module, NFC module.

[0107] In an exemplary embodiment, the electronic device 600 can be implemented by one or more application specific integrated circuits (ASIC), digital signal processors (DSP), digital signal processing devices (DSPD), programmable logic devices (PLD), field programmable gate arrays (FPGA), controllers, microcontrollers, microprocessors or other electronic components to execute the above-mentioned big data-based intelligent management method for automotive parts.

[0108] In another exemplary embodiment, a computer-readable storage medium including program instructions is also provided, and when the program instructions are executed by a processor, the steps of the above-mentioned intelligent management method for automobile parts based on big data are implemented. For example, the computer-readable storage medium can be the above-mentioned memory 602 including program instructions, and the above-mentioned program instructions can be executed by the processor 601 of the electronic device 600 to complete the above-mentioned intelligent management method for automobile parts based on big data.

[0109] In another exemplary embodiment, a computer program product is also provided, which includes a computer program that can be executed by a processor, and when the computer program is executed by the processor, the steps of the above-mentioned method for intelligent management of automobile parts based on big data are implemented.

[0110] The preferred embodiments of the present disclosure are described in detail above in conjunction with the accompanying drawings; however, the present disclosure is not limited to the specific details in the above embodiments. Within the technical concept of the present disclosure, a variety of simple modifications can be made to the technical solution of the present disclosure, and these simple modifications all fall within the protection scope of the present disclosure.

[0111] It should also be noted that the various specific technical features described in the above specific embodiments can be combined in any suitable manner without contradiction. In order to avoid unnecessary repetition, the present disclosure will not further describe various possible combinations.

[0112] In addition, various embodiments of the present disclosure may be arbitrarily combined, and as long as they do not violate the concept of the present disclosure, they should also be regarded as the contents disclosed by the present disclosure.

Claims

1. An intelligent management method for automobile parts based on big data, characterized in that: include: Obtaining automobile parts demand information corresponding to multiple regions respectively collected through an automobile parts application; Determining differentiated information on demand for automobile parts according to the automobile parts demand information corresponding to the plurality of regions, wherein the differentiated information is used to characterize differentiated conditions of demand for automobile parts in the plurality of regions; Determining target simulation parameters corresponding to an automobile parts management model according to automobile parts demand information corresponding to the plurality of regions and the differentiation information; Determine the automobile parts management strategy by performing simulation according to the target simulation parameters through the automobile parts management model; The automobile management strategy is implemented through automobile parts factories and automobile parts sales platforms.

2. The intelligent management method for automobile parts according to claim 1, characterized in that: The automobile parts demand information includes: regional location information, automobile parts type demand information, automobile parts quantity demand information and automobile parts supply cycle demand information; the automobile parts demand differentiation information is determined according to the automobile parts demand information corresponding to the multiple regions, including: For any area among the multiple areas, determining first differential information according to a difference between area position information corresponding to the area and area position information corresponding to other areas; Determining second differentiation information according to a difference between the automobile parts type demand information corresponding to the region and the automobile parts type demand information corresponding to other regions; determining third differentiation information according to a difference between the automobile parts quantity demand information corresponding to the area and the automobile parts quantity demand information corresponding to other areas; Determining fourth differentiation information according to a difference between the automobile parts supply cycle demand information corresponding to the region and the automobile parts supply cycle demand information corresponding to other regions; The differentiated information of automobile parts demand is determined according to the first differentiated information, the second differentiated information, the third differentiated information and the fourth differentiated information corresponding to each region.

3. The intelligent management method for automobile parts according to claim 2, characterized in that: The step of determining the differentiated information on demand for automobile parts according to the first differentiated information, the second differentiated information, and the third differentiated information respectively corresponding to each region includes: Determine a first differentiation matrix according to the first differentiation information and the fourth differentiation information corresponding to each region, wherein the first differentiation matrix is ​​used to characterize the difference in sales demand for auto parts; Determine a second differentiation matrix according to the second differentiation information and the third differentiation information corresponding to each region, wherein the second differentiation matrix is ​​used to characterize the difference in production demand for automobile parts; According to the first differentiation matrix and the second differentiation matrix, the differentiation information of automobile parts demand is determined.

4. The intelligent management method for automobile parts according to claim 1, characterized in that: The automobile parts management model includes an automobile parts factory model and an automobile parts sales platform model, and determining target simulation parameters corresponding to the automobile parts management model according to the automobile parts demand information corresponding to the multiple regions and the differentiation information, includes: Determining production simulation parameters of the auto parts factory model according to the auto parts demand information corresponding to the multiple regions; Determining sales simulation parameters of the auto parts sales platform model according to the differentiation information; Determine, according to the differentiation information, an industrial chain simulation parameter between the auto parts factory model and the auto parts sales platform model, wherein the industrial chain simulation parameter is used to influence the number of auto parts interactions between the auto parts factory model and the auto parts sales platform model; The target simulation parameters are determined according to the production simulation parameters, the sales simulation parameters and the industrial chain simulation parameters.

5. The intelligent management method for automobile parts according to claim 4, characterized in that: The automobile parts demand differentiation information includes a first differentiation matrix representing the differences in automobile parts sales demand. The determining of sales simulation parameters of the automobile parts sales platform model based on the differentiation information includes: Acquiring initial sales simulation parameters of the auto parts sales platform model; According to each matrix element in the first differentiation matrix, the initial sales simulation parameter is adjusted multiple times to obtain the sales simulation parameter adjusted multiple times, wherein the adjustment mode of two adjacent sales simulation parameters matches the change rule between the corresponding matrix elements; The sales simulation parameters of the auto parts sales platform model are determined according to the initial sales simulation parameters and the sales simulation parameters adjusted multiple times.

6. The method for intelligent management of automobile parts according to claim 4, characterized in that: The automotive parts demand differentiation information includes a first differentiation matrix representing the automotive parts sales demand differences and a second differentiation matrix representing the automotive parts production differences. The industrial chain simulation parameters between the automotive parts factory model and the automotive parts sales platform model are determined based on the differentiation information, including: Acquire initial industry chain simulation parameters between the auto parts factory model and the auto parts sales platform model; According to the matrix elements at the same position in the first differentiation matrix and the second differentiation matrix, the initial industrial chain simulation parameters are adjusted multiple times to obtain the industrial chain simulation parameters adjusted multiple times, wherein the difference between the industrial chain simulation parameters adjusted each time and the industrial chain simulation parameters adjusted the previous time matches the difference between the matrix elements at the same position in the first differentiation matrix and the second differentiation matrix; The industrial chain simulation parameters between the auto parts factory model and the auto parts sales platform model are determined according to the initial industrial chain simulation parameters and the industrial chain simulation parameters adjusted multiple times.

7. The intelligent management method for automobile parts according to claim 1, characterized in that: The step of performing simulation according to the target simulation parameters by the automobile parts management model to determine the automobile parts management strategy includes: Performing simulation according to the target simulation parameters by the automobile parts management model to obtain simulation results, wherein the simulation results include an automobile parts conversion rate used to characterize the conversion status of the automobile parts produced by the automobile parts factory in the industrial chain; According to the simulation results, determining the simulation parameters corresponding to the highest automobile parts conversion rate from the target simulation parameters; Determining, according to the simulation parameters corresponding to the highest auto parts conversion rate, the auto parts factory production strategy, the auto parts sales platform sales strategy, and the auto parts interaction strategy; An auto parts management strategy is determined based on the auto parts factory production strategy, the auto parts sales platform sales strategy and the auto parts interaction strategy.

8. The method for intelligent management of automobile parts according to claim 7, characterized in that: The automobile management strategy is implemented through automobile parts factories and automobile parts sales platforms, including: Executing the automobile parts factory production strategy through the automobile parts factory; Executing the auto parts sales strategy through the auto parts sales platform; The auto parts interaction strategy is executed through the auto parts factory and the auto parts sales platform.

9. The intelligent management method for automobile parts according to claim 1, characterized in that: The automobile parts intelligent management method further comprises: Acquire the automobile parts after-sales demand information corresponding to the plurality of regions respectively collected through the automobile parts application program; Determining an after-sales management strategy for automobile parts according to after-sales demand information for automobile parts corresponding to the plurality of regions; The auto parts after-sales management strategy is executed through the auto parts sales platform.

10. An automotive parts intelligent management SaaS platform based on big data, characterized in that: include: An automobile parts application, an automobile parts factory end, an automobile parts sales platform end and an automobile parts intelligent management end; wherein the automobile parts intelligent management end is used to execute the automobile parts intelligent management method based on big data as described in any one of claims 1 to 9.

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