An intelligent management method and SaaS platform for auto parts based on big data

Abstract

The present disclosure relates to an intelligent management method and SaaS platform for auto parts based on big data, and relates to the technical field of auto parts management. Through an auto parts application, auto parts demand information from various regions is collected, and based on the auto parts demand information, differentiated information on auto parts demand in various regions is analyzed. Through digital twin simulation technology, an auto parts management strategy is determined based on auto parts demand information and corresponding differentiated information. The management strategy is issued to auto parts factories and dealers or stores, so that the auto parts factories, dealers or stores can perform corresponding auto parts management in accordance with the management strategy. This technical solution opens up a complete link between auto parts factories, auto parts applications and auto parts sellers, and can realize auto parts management based on the industrial chain, thereby improving the intelligence and efficiency of auto parts management.

Description

Technical Field

[0001] The present disclosure relates to the technical field of automobile parts management, and in particular, to an intelligent management method and SaaS platform for automobile parts 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 related technologies, the management of auto parts is usually carried out on sales platforms or factories, but the management of the entire industrial chain is not integrated, resulting in poor intelligence and efficiency in the management of auto parts. Summary of the Invention

[0004] The purpose of the present disclosure 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 automobile parts based on big data, comprising: obtaining automobile parts demand information corresponding to multiple regions respectively collected through an automobile parts application; determining automobile parts demand differentiation information based on the automobile parts demand information corresponding to the multiple regions respectively, and the differentiation information is used to characterize the differentiation of automobile parts demand in the multiple regions; determining target simulation parameters corresponding to an automobile parts management model based on the automobile parts demand information corresponding to the multiple regions and the differentiation information; determining an automobile parts management strategy by simulating the automobile parts management model according to the target simulation parameters; and executing the automobile management strategy through automobile parts factories and automobile parts sales platforms.

[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 automobile parts demand differentiation information is determined based on the automobile parts demand information corresponding to the multiple regions, including: 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, second differentiation information, third differentiation information and fourth differentiation information corresponding to each region.

[0007] Optionally, determining the differentiated information on demand for auto parts based on the first differentiated information, second differentiated information, and third differentiated information corresponding to each region includes: determining a first differentiation matrix based on the first differentiation information and the fourth differentiation 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 differentiation information and the third differentiation information corresponding to each region, the second differentiation matrix being used to characterize differences in demand for auto parts production; and determining the differentiated information on demand for auto parts based on 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, and the industrial chain simulation parameters are 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 differentiation 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 differentiation information includes: 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 multiple adjusted sales simulation parameters, 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 multiple adjusted sales simulation parameters.

[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. 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 the 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 based on the matrix elements at the same position in the first differentiation matrix and the second differentiation matrix to obtain multiple adjusted industrial chain simulation parameters, 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 based on the initial industrial chain simulation parameters and the multiple adjusted industrial chain simulation parameters.

[0011] Optionally, the auto parts management model is used to perform simulation according to the target simulation parameters to determine the auto parts management strategy, including: performing simulation according to the target simulation parameters by 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; according to the simulation results, determining the simulation parameters corresponding to the highest auto parts conversion rate from the target simulation parameters; according to the simulation parameters corresponding to the highest auto parts conversion rate, determining the auto parts factory production strategy, the auto parts sales platform sales strategy and the auto parts interaction strategy; and 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 intelligent management method for auto parts also includes: obtaining auto parts after-sales demand information corresponding to the multiple regions 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; 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] The above technical solution collects regional auto parts demand information through an auto parts application. Based on this information, it analyzes regional differences in auto parts demand. Using digital twin simulation technology, an auto parts management strategy is determined based on this demand information and the corresponding differentiation. This management strategy is then distributed to auto parts manufacturers and auto parts sales platforms, enabling them to manage auto parts accordingly. This technical solution connects auto parts manufacturers, auto parts applications, and auto parts sellers, enabling integrated auto parts management across the industry chain and 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 detailed description, they are used to explain the present disclosure but do not constitute a limitation of the present disclosure. In the accompanying drawings:

[0018] Figure 1 The present invention is a block diagram of a big data-based intelligent management SaaS platform for automotive parts according to an exemplary embodiment.

[0019] Figure 2The present invention is a flowchart showing a method for intelligent management of automobile parts based on big data according to an exemplary embodiment.

[0020] Figure 3 The figure is a schematic diagram showing a user interface of an automobile accessories application according to an exemplary embodiment.

[0021] Figure 4 The figure is a block diagram showing an automobile parts management model according to an exemplary embodiment.

[0022] Figure 5 The present invention is a block diagram showing an intelligent management device for automobile parts based on big data according to an exemplary embodiment.

[0023] Figure 6 It is a block diagram of an electronic device according to an exemplary embodiment. DETAILED DESCRIPTION

[0024] The following describes the specific embodiments of the present disclosure in detail with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are only used to illustrate and explain the present disclosure and are not intended to limit the present disclosure.

[0025] 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.

[0026] For example, automobile manufacturers need to manage the production of auto parts, or the sales of auto parts, etc.

[0027] In related technologies, the management of auto parts is usually carried out on sales platforms or factories, but the management of the entire industrial chain is not integrated, resulting in poor intelligence and efficiency in the management of auto parts.

[0028] Based on this, the embodiments of the present disclosure provide a technical solution that uses an auto parts application to collect auto parts demand information from various regions, and based on this auto parts demand information, analyzes the differentiated information on auto parts demand in various regions. Through digital twin simulation technology, the management strategy for auto parts is determined based on the auto parts demand information and the corresponding differentiated information. The management strategy is distributed to auto parts factories and auto parts sales platforms so that they can manage auto parts accordingly in accordance with the management strategy. This technical solution opens up a complete link between auto parts factories, auto parts applications, and auto parts sellers, and can realize auto parts management based on the industrial chain, thereby improving the intelligence and efficiency of auto parts management.

[0029] 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.

[0030] Therefore, on the user side, information feedback can be achieved through the auto parts application. On the back end, intelligent management of auto parts can be carried out based on the information fed back by users.

[0031] Figure 1 FIG. 1 is a block diagram of a big data-based intelligent management SaaS platform for auto parts according to an exemplary embodiment. 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.

[0032] 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.

[0033] On the auto parts application side, users who have auto parts needs can use cloud-based applications to provide auto parts demand information.

[0034] The auto parts factory can be controlled by the auto parts intelligent management end to implement different auto parts production strategies.

[0035] The auto parts sales platform end can be the end where the dealer or store is located, and can be controlled by the auto parts intelligent management end to implement different auto parts sales strategies.

[0036] The intelligent auto parts management terminal, serving as the backend of the entire platform, is used for back-end management. This terminal can integrate digital twin simulation technology to determine auto parts management strategies.

[0037] Figure 2 This is a flow chart showing 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 includes the following steps:

[0038] Step S21 : obtaining automobile parts demand information corresponding to a plurality of regions respectively collected through an automobile parts application program.

[0039] Step S22 : determining differentiated information on auto parts demand based on the auto parts demand information corresponding to the multiple regions, where the differentiated information is used to characterize differentiated situations of the auto parts demand in the multiple regions.

[0040] Step S23 : determining target simulation parameters corresponding to the automobile parts management model according to the automobile parts demand information and differentiation information corresponding to the plurality of regions.

[0041] Step S24 , performing simulation according to target simulation parameters through the automobile parts management model to determine the automobile parts management strategy.

[0042] Step S25: executing the automobile management strategy through the automobile parts factory and the automobile parts sales platform.

[0043] 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 identifier. Thus, the auto parts intelligent management terminal can obtain the auto parts demand information for intelligent auto parts management.

[0044] In some embodiments, when collecting information, the auto parts application may organize and normalize the information to obtain more unified and easier-to-process information.

[0045] In some embodiments, the auto parts application may have multiple forms and may have multiple different interface designs.

[0046] 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.

[0047] Based on these types of auto parts demand information, Figure 3 FIG. 1 is a schematic diagram of a user interface of an automobile accessories application according to an exemplary embodiment. Figure 3 As shown, the user interface includes: regional location acquisition controls, auto parts type demand acquisition controls, auto parts quantity demand acquisition controls, and auto parts supply cycle demand acquisition controls.

[0048] Users can input different requirements by triggering different controls, so that the application can collect the required information.

[0049] 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.

[0050] 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.

[0051] 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.

[0052] As an optional implementation, step S22 includes: for any area among the multiple areas, determining first differentiation information based on the difference between the area location information corresponding to the area and the area location information corresponding to other areas; determining second differentiation information based on 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 based on 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 based on 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 based on the first differentiation information, second differentiation information, third differentiation information and fourth differentiation information corresponding to each area respectively.

[0053] In this embodiment, for each region, the first differential information, the second differential information, the third differential information, and the fourth differential information need to be determined respectively.

[0054] The first differentiation information can be the distance difference (difference) between area locations. For example, assuming there are 10 areas, for each area, the distance difference between it and the other nine areas is determined based on the area's location information. The nine distance differences are then averaged, weighted averaged, or standard deviation is calculated, and the resulting result is used as the first differentiation information.

[0055] The second differentiation information can be differences between auto part types. For example, assuming there are 10 regions, for each region, the auto part type similarity / correlation between the region and the other 9 regions is determined based on the auto part type. The resulting 9 similarities / correlations are then averaged, weighted averaged, or standard deviation is calculated, and the resulting result is used as the second differentiation information. The auto part type similarity / correlation can be determined based on pre-configured auto part type association / similarity relationships. For example, auto parts belonging to the same drive system have a high correlation / similarity, while auto parts belonging to both the drive system and the vehicle media system have a low correlation / similarity.

[0056] The third differentiation information may be the difference (difference) between the numbers of auto parts. Therefore, assuming there are 10 regions, for any region, the difference between the number of auto parts in the region and that in the other 9 regions is determined based on the number of auto parts. The 9 obtained differences are then averaged, weighted averaged, or the standard deviation is determined, and the result is used as the third differentiation information.

[0057] 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 of the other 9 regions is determined based on the automobile parts supply cycle. The 9 differences are then averaged, weighted averaged, or the standard deviation is determined, and the result is used as the fourth differentiation information.

[0058] Furthermore, by combining the first differentiation information, the second differentiation information, the third differentiation information, and the fourth differentiation information corresponding to each region, differentiation information on demand for auto parts may be determined.

[0059] In some embodiments, determining the differentiated information on demand for auto parts based on the 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 the 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 the differences in demand for auto parts production; and determining the differentiated information on demand for auto parts based on the first differentiation matrix and the second differentiation matrix.

[0060] In this embodiment, the first differentiation information and the fourth differentiation information can be converted into a first differentiation matrix to represent the differences in auto parts sales demand. It can be understood that regional location and supply cycle have a greater impact on sales, so they are converted into the first differentiation matrix.

[0061] Furthermore, the second differentiation information and the third differentiation information are converted into a second differentiation matrix to represent the difference in demand for auto parts production. It can be understood that since quantity and type have a greater impact on auto parts production, they are converted into the second differentiation matrix.

[0062] With reference to the description of the foregoing 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.

[0063] Therefore, assuming there are 10 regions, the first differentiation information corresponding to these 10 regions is A1 to A10, and the fourth differentiation information is B1 to B10. Then, A1 to A10 can be the 10 matrix elements in the first row of the matrix, and B1 to B10 can be the 10 elements in the second row of the matrix, forming a 2×10 first differentiation matrix.

[0064] 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.

[0065] Therefore, assuming there are 10 regions, the second differentiation information corresponding to these 10 regions is C1 to C10, and the third differentiation information is D1 to D10. Then, C1 to C10 can be the 10 matrix elements in the first row of the matrix, and D1 to D10 can be the 10 elements in the second row of the matrix, forming a 2×10 second differentiation matrix.

[0066] Furthermore, the first differentiation matrix and the second differentiation matrix may be used as differentiation information of automobile parts demand.

[0067] The disclosed embodiments also involve digital twin simulation technology. This technology leverages data from physical models, sensors, operational histories, and other sources, integrating multidisciplinary, multi-physical, multi-scale, and multi-probability simulation processes to map the entire lifecycle of the corresponding physical equipment in a virtual space. A digital twin is a concept that transcends reality and can be viewed as a digital mapping system for one or more important, interdependent equipment systems.

[0068] 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.

[0069] Therefore, in step S23 , target simulation parameters corresponding to the automobile parts management model may be determined according to the automobile parts demand information and differentiation information corresponding to the plurality of regions.

[0070] Figure 4 is a block diagram of an automobile parts management model according to an exemplary embodiment. Figure 4 As shown, the auto parts management model includes an auto parts factory model and an auto parts sales platform model. The interaction between the auto parts factory and the auto parts sales platform models involves auto parts. In the virtual model, this can be considered data interaction. Together, the auto parts factory and auto parts sales platform models form a virtual model of the industrial chain.

[0071] 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.

[0072] In some embodiments, an automobile parts management model may be pre-built, and when simulation is needed, the model may be directly applied.

[0073] 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.

[0074] Among them, regarding the specific construction method of the model, you can refer to the mature digital twin simulation technology in this field, which will not be introduced in detail here. This disclosure mainly involves configuring the simulation parameters therein.

[0075] As an optional implementation, step S23 includes: determining the production simulation parameters of the auto parts factory model based on the auto parts demand information corresponding to multiple regions; determining the sales simulation parameters of the auto parts sales platform model based on the differentiation information; determining the industrial chain simulation parameters between the auto parts factory model and the auto parts sales platform model based on the differentiation 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; and determining the target simulation parameters based on the production simulation parameters, the sales simulation parameters and the industrial chain simulation parameters.

[0076] In some embodiments, determining production simulation parameters for an auto parts factory model based on auto parts demand information corresponding to multiple regions may include: integrating the auto parts demand information corresponding to the multiple regions to obtain the types and quantities of auto parts to be produced, which serve as production simulation parameters; and integrating the supply cycle requirements corresponding to the multiple regions to obtain an appropriate production cycle, which may also serve as a production simulation parameter.

[0077] As you can see, a single factory is responsible for supplying auto parts to multiple regions, so integrating these demands directly yields production simulation parameters. This allows the auto parts factory model to produce the appropriate types and quantities of auto parts according to the production cycle.

[0078] 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 multiple adjusted sales simulation parameters, wherein the adjustment method of two adjacent sales simulation parameters matches the change law 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 multiple adjusted sales simulation parameters.

[0079] 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 multiple sales simulation parameters, such as sales quantity, sales type, etc.

[0080] Therefore, the initial sales simulation parameters can be adjusted multiple times using each matrix element in the first differentiation matrix, wherein the adjustment method of two adjacent sales simulation parameters matches the change pattern between the corresponding matrix elements.

[0081] For example, during the first adjustment, the change between the element in the first row, first column and the element in the first row, second column is determined. If the element increases, the sales quantity and sales type in the initial sales simulation parameters can be increased. Then, the change between the element in the first row, second column and the element in the first row, third column is determined. The sales simulation parameters adjusted last are adjusted based on the change pattern. This process is repeated until multiple sales simulation parameters are obtained. It is understood that each sales simulation parameter obtained needs to be retained.

[0082] Furthermore, in this way, the differentiation information can be combined to generate a variety of sales simulation parameters that represent the differences.

[0083] In some embodiments, based on differentiation information, the industrial chain simulation parameters between the auto parts factory model and the auto parts sales platform model are determined, including: obtaining the 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 multiple adjusted industrial chain simulation parameters, 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 auto parts factory model and the auto parts sales platform model according to the initial industrial chain simulation parameters and the multiple adjusted industrial chain simulation parameters.

[0084] In this embodiment, the industry chain simulation parameters may include: auto parts types, auto parts quantities, and auto parts delivery cycles from the auto parts factory model to the auto parts sales platform model.

[0085] 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.

[0086] In some embodiments, each time the initial industrial chain simulation parameters are adjusted, the adjustment is performed based on the difference between the matrix elements at the same position in the first differentiation matrix and the second differentiation matrix.

[0087] 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 industry chain simulation parameters, the first adjusted industry chain simulation parameters 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 can be used, which are not limited here. Through multiple adjustments, the multiple adjusted industry chain simulation parameters can be obtained by combining the differences in different matrix elements.

[0088] 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.

[0089] Furthermore, in this model, there are three types of 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.

[0090] Furthermore, in step S24, the automobile parts management model is simulated according to the target simulation parameters to determine the automobile parts management strategy.

[0091] 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, automobile parts sales platform sales strategy and automobile parts interaction strategy; according to the automobile parts factory production strategy, automobile parts sales platform sales strategy and automobile parts interaction strategy, determining the automobile parts management strategy.

[0092] In this embodiment, simulation can be performed based on the target simulation parameters to obtain corresponding simulation results, which can represent 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. Furthermore, these 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.

[0093] In some embodiments, the auto parts management model performs simulations based on target simulation parameters to obtain simulation results, which may include: performing production based on production parameters using the auto parts factory model; interacting with the auto parts factory model and the auto parts sales platform model based on industry chain interaction parameters; and performing sales based on sales parameters using the auto parts sales platform model. Finally, the auto parts conversion rate is calculated.

[0094] 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.

[0095] In some embodiments, the simulation parameters are converted into actual data to obtain corresponding strategies, 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.

[0096] 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.

[0097] In some embodiments, the auto parts factory production policy is distributed to the auto parts factory, causing the auto parts factory to implement the policy. Furthermore, the auto parts sales policy is distributed to the corresponding auto parts sales platform, causing the auto parts sales platform to implement the policy. Similarly, the auto parts interaction policy is distributed to the corresponding auto parts factory and auto parts sales platform, causing the auto parts factory and auto parts sales platform to implement the policy.

[0098] In some embodiments, the method may further include: obtaining automobile parts after-sales demand information corresponding to multiple regions collected through an automobile parts application; determining an automobile parts after-sales management strategy based on the automobile parts after-sales demand information corresponding to multiple regions; and executing the automobile parts after-sales management strategy through an automobile parts sales platform.

[0099] In this embodiment, the auto parts application program may also collect auto parts after-sales demand information, which may include: auto parts quality information and auto parts warranty information, etc.

[0100] Therefore, based on the after-sales demand information of auto parts corresponding to multiple regions, an after-sales management strategy for auto parts can be determined and then implemented through the auto parts sales platform.

[0101] 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.

[0102] In the disclosed embodiment, the auto parts sales platform may include multiple dealers or stores, and different dealers or stores correspond to different regions. The above-mentioned related simulation parameters or sales strategies may involve simulation parameters and sales strategies corresponding to different dealers or stores.

[0103] Figure 5 This is a block diagram of an intelligent management device for automobile parts based on big data according to an exemplary embodiment. The device includes:

[0104] The acquisition module 501 acquires automobile parts demand information corresponding to multiple regions collected through the automobile parts application program.

[0105] Determination module 502 is configured to determine differentiated auto parts demand information based on the auto parts demand information corresponding to the multiple regions, wherein the differentiated information is used to characterize the differentiated auto parts demand in the multiple regions. Target simulation parameters corresponding to an auto parts management model are determined based on the auto parts demand information corresponding to the multiple regions and the differentiated information. The auto parts management model is configured to perform simulations based on the target simulation parameters to determine an auto parts management strategy.

[0106] The execution module 503 is used to execute the automobile management strategy through the automobile parts factory and the automobile parts sales platform.

[0107] Regarding the apparatus 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.

[0108] 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 , and may further include one or more of a multimedia component 603 , an input / output (I / O) interface 604 , and a communication component 605 .

[0109] 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 method for intelligent management of automotive parts based on big data. The memory 602 is used to store various types of data to support the operation of the electronic device 600. This data may include, for example, instructions for any application or method operating on the electronic device 600, as well as application-related data such as contact information, sent and received messages, images, 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 (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, 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 for receiving external audio signals. The received audio signals may be further stored in the memory 602 or transmitted via 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. The aforementioned 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 thereof, and therefore the corresponding communication component 605 may include: a Wi-Fi module, a Bluetooth module, an NFC module.

[0110] In an exemplary embodiment, the electronic device 600 may be implemented by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components to execute the above-mentioned big data-based intelligent management method for automotive parts.

[0111] In another exemplary embodiment, a computer-readable storage medium including program instructions is also provided. When executed by a processor, the program instructions implement the steps of the aforementioned method for intelligent management of automotive parts based on big data. For example, the computer-readable storage medium may be the aforementioned memory 602 including the program instructions. The program instructions may be executed by the processor 601 of the electronic device 600 to implement the aforementioned method for intelligent management of automotive parts based on big data.

[0112] In another exemplary embodiment, a computer program product is further provided. The computer program product includes a computer program that can be executed by a processor. 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.

[0113] 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 of the above embodiments. Within the technical concept of the present disclosure, various simple modifications can be made to the technical solutions of the present disclosure, and these simple modifications all fall within the scope of protection of the present disclosure.

[0114] It should also be noted that the various specific technical features described in the above specific embodiments can be combined in any appropriate manner without contradiction. In order to avoid unnecessary repetition, the present disclosure will not further describe various possible combinations.

[0115] In addition, the 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. A method for intelligent management of automobile parts based on big data, characterized in that: include: Obtaining automobile parts demand information corresponding to multiple regions collected through an automobile parts application; Determining differentiated information on demand for auto parts based on the auto parts demand information corresponding to the plurality of regions, wherein the differentiated information is used to characterize differentiated conditions of demand for auto parts in the plurality of regions; determining target simulation parameters corresponding to an automobile parts management model according to the automobile parts demand information corresponding to the plurality of regions and the differentiation information; Determine an automobile parts management strategy by performing simulation based on the target simulation parameters using the automobile parts management model; Execute the auto parts management strategy through auto parts factories and auto parts sales platforms; The auto parts management model includes an auto parts factory model and an auto parts sales platform model. The target simulation parameters include: industry chain simulation parameters for influencing the number of auto parts interactions between the auto parts factory model and the auto parts sales platform model. The auto parts demand differentiation information includes a first differentiation matrix representing differences in auto parts sales demand and a second differentiation matrix representing differences in auto parts production. The first differentiation matrix is ​​obtained by converting distance differences between regional locations and differences between auto parts supply cycles. The second differentiation matrix is ​​obtained by converting differences between auto parts types and differences between auto parts quantities. Determining the industrial chain simulation parameters, including: Acquiring 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 previously matches the difference between the matrix elements at the same position in the first differentiation matrix and the second differentiation matrix; during the first adjustment process of the initial industrial chain simulation parameters, the difference between the first row and first column elements of each of the first differentiation matrix and the second differentiation matrix is ​​determined, and when the difference is negative, the type of auto parts, the number of auto parts, and the issuance period of auto parts in the initial industrial chain simulation parameters are reduced, and when the difference is positive, the type of auto parts, the number of auto parts, and the issuance period of auto parts in the initial industrial chain simulation parameters are increased; 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 multiple adjusted industrial chain simulation parameters; 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 auto parts management model to obtain simulation results, wherein the simulation results include an auto parts conversion rate used to characterize the conversion status of auto parts produced by the auto parts factory in the industrial chain; Determining simulation parameters corresponding to the highest automobile parts conversion rate from the target simulation parameters according to the simulation results; Determining, based on the simulation parameters corresponding to the highest auto parts conversion rate, an auto parts factory production strategy, an auto parts sales platform sales strategy, and an 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.

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 based on 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 location information corresponding to the area and area location information corresponding to other areas; determining second differentiation information based on 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 differential information based on a difference between the automobile parts quantity demand information corresponding to the region and the automobile parts quantity demand information corresponding to other regions; determining fourth differentiation information based on a difference between the demand information on the supply cycle of automobile parts corresponding to the region and the demand information on the supply cycle of automobile parts corresponding to other regions; Differentiation information on demand for auto parts is determined based on the first differentiation information, the second differentiation information, the third differentiation information, and the fourth differentiation 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 auto parts based on the first differentiated information, the second differentiated information, and the third differentiated information corresponding to each region includes: Determining a first differentiation matrix based on the first differentiation information and the fourth differentiation information corresponding to each region, wherein the first differentiation matrix is ​​used to characterize differences in sales demand for auto parts; Determining a second differentiation matrix based on the second differentiation information and the third differentiation information corresponding to each region, wherein the second differentiation matrix is ​​used to characterize differences in production demand for auto parts; Determine differentiated information on auto parts demand based on the first differentiation matrix and the second differentiation matrix.

4. The intelligent management method for automobile parts according to claim 1, characterized in that: The determining target simulation parameters corresponding to the automobile parts management model according to the automobile parts demand information corresponding to the plurality of 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 based on the differentiation information; Determining, based on the differentiation information, industry chain simulation parameters 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: Determining the sales simulation parameters of the auto parts sales platform model based on the differentiation information includes: Obtaining initial sales simulation parameters of the auto parts sales platform model; Adjusting the initial sales simulation parameters multiple times according to each matrix element in the first differentiation matrix to obtain multiple adjusted sales simulation parameters, wherein the adjustment methods of two adjacent sales simulation parameters match the change patterns 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 intelligent management method for automobile parts according to claim 1, characterized in that: The implementation of the auto parts management strategy through the auto parts factory and the auto parts sales platform includes: executing the automobile parts factory production strategy through the automobile parts factory; Executing the auto parts sales platform 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.

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

8. A big data-based intelligent management SaaS platform for auto parts, characterized by: 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 7.

Images