Supply chain collaborative management method and system based on artificial intelligence

Abstract

The invention provides a supply chain collaborative management method and system based on artificial intelligence, and is used for improving the reliability and stability of supply chain collaborative management through a neural network model. The method is applied to a management device, and comprises the steps that the management device obtains supply data of accessories provided by N supply chains in different time periods and obtains sales data of products in different time periods, N is an integer larger than 1, the products comprise the accessories provided by the N supply chains, and the sales data of the products comprise the accessories provided by the N supply chains; the time periods in which the N supply chains provide the accessories are related to the product sales time; the management equipment fuses the supply data and the sales data to obtain fused data; and the management equipment analyzes the fused data through a neural network model to obtain supply data of the accessories needing to be provided by the N supply chains in the future time period.

Description

Technical Field

[0001] The present application relates to the field of artificial intelligence, and in particular to a supply chain collaborative management method and system based on artificial intelligence. Background Art

[0002] With the acceleration of globalization and increasingly fierce market competition, enterprises are facing huge challenges in production management and supply chain optimization. The traditional supply chain and production line coordination management model mainly relies on manual judgment and decision-making.

[0003] Specifically, in the traditional coordination management of supply chains and production lines, managers usually coordinate the supply of parts in the supply chain based on the production capacity and sales data of the production line. In this model, managers need to have strong professional knowledge and experience to accurately judge changes in market demand, production capacity limitations, and supply chain response speed. However, this approach that relies on manual decision-making has the following major problems: Slow response speed: The speed of manual information processing is limited, and it is difficult to respond to market changes and production needs in real time. Limited decision-making accuracy: Manual judgment is easily affected by personal experience and subjective judgment, which may lead to wrong decisions. Difficult data integration: The phenomenon of information islands between different departments is prevalent, resulting in inefficient data integration and analysis. Insufficient risk management: The lack of a systematic risk assessment and early warning mechanism makes it difficult to detect and respond to potential risks in a timely manner.

[0004] In this regard, how to improve the reliability and stability of supply chain collaborative management is a current research issue. Summary of the invention

[0005] The embodiments of the present application provide a supply chain collaborative management method and system based on artificial intelligence, which are used to improve the reliability and stability of supply chain collaborative management through a neural network model.

[0006] In order to achieve the above objectives, this application adopts the following technical solutions: In a first aspect, an embodiment of the present application provides a supply chain collaborative management method based on artificial intelligence, which is applied to a management device, and the method includes: the management device obtains supply data of accessories provided by N supply chains in different time periods, and obtains sales data of products in different time periods, N is an integer greater than 1, and the product includes accessories provided by N supply chains, and the time period in which the N supply chains provide the accessories is related to the time of product sales; the management device fuses the supply data with the sales data to obtain fused data; the management device analyzes the fused data through a neural network model to obtain supply data of accessories that each of the N supply chains needs to provide in a future time period.

[0007] Optionally, N=1, N supply chains are the first supply chain; the supply data of accessories provided by the first supply chain in different time periods include: the first supply data of accessories provided by the first supply chain in the first time period and the second supply data of accessories provided by the first supply chain in the second time period, the sales data of the product at different times: the sales data of the product sold at different times in the third time period; the first time period, the second time period, and the third time period are adjacent time periods in chronological order; accordingly, the management device merges the supply data with the sales data to obtain the merged data, including: the management device merges the first supply data and the second supply data with the sales data to obtain the merged data.

[0008] Optionally, the first time period includes K1 unit times, K1 is an integer greater than 1, the first supply data includes the number of accessories provided by the first supply chain at each unit time in the K1 unit times, and a total of K1 accessory number values; the second time period includes K2 unit times, K2 is an integer greater than 1, the second supply data includes the number of accessories provided by the first supply chain at each unit time in the K2 unit times, and a total of K2 accessory number values; the third time period includes K3 unit times, K3 is an integer greater than 1, and the sales data includes the sales number value of the product at each unit time in the K3 unit times; accordingly, the management device merges the first supply data and the second supply data with the sales data to obtain merged data, including: the management device determines the K1 accessory number The total number of values ​​is M1, and the total number of K2 accessory number values ​​is M2, and both M1 and M2 are integers greater than 2; the management device randomly selects [X1 / (M1+M2)]*K3 sales number values ​​from the K3 sales number values ​​and randomly inserts them into the K1 accessory number values ​​to obtain a first fused data set. At this time, K3-[X / (M1+M2)]*K3 sales number values ​​remain in the K3 sales number values, [ ] is rounded down, if M1 is less than M2, then X1=M1, if M1 is greater than M2, then X1=M2; the management device randomly inserts K3-[X1 / (M1+M2)]*K3 sales number values ​​into the K2 accessory number values ​​to obtain a second fused data set, and the fused data includes the first fused data set and the second fused data set.

[0009] Optionally, the management device analyzes the fused data through a neural network model to obtain supply data of accessories that each of the N supply chains needs to provide in a future time period, including: the management device inputs the first fused data set and the second fused data set into the neural network model analysis to obtain the supply data of accessories that the first supply chain needs to provide in a fourth time period output by the neural network model analysis.

[0010] Optionally, N=2, the N supply chains are the first supply chain and the second supply chain, the supply data of accessories provided by the first supply chain in different time periods include: the first supply data of accessories provided by the first supply chain in the first time period, the second supply data of accessories provided by the first supply chain in the second time period, the supply data of accessories provided by the second supply chain in different time periods include: the third supply data of accessories provided by the second supply chain in the first time period, and the fourth supply data of accessories provided by the second supply chain in the second time period; sales data of products sold at different times in the third time period; the first time period, the second time period, and the third time period are time periods adjacent to each other in chronological order; accordingly, the management device merges the supply data with the sales data to obtain merged data, including: the management device merges the first supply data with the third supply data to obtain first merged supply data, and merges the second supply data with the fourth supply data to obtain second merged supply data; the management device merges the first merged supply data and the second merged supply data with the sales data to obtain the merged data.

[0011] Optionally, the first time period includes K1 unit times, K1 is an integer greater than 1, the first supply data includes the number of first accessories provided by the first supply chain at each unit time in the K1 unit times, and a total of K1 first accessories number values; the second time period includes K2 unit times, K2 is an integer greater than 1, the second supply data includes the number of first accessories provided by the first supply chain at each unit time in the K2 unit times, and a total of K2 first accessories number values; the third supply data includes the number of second accessories provided by the second supply chain at each unit time in the K1 unit times, and a total of K1 second accessories number values; the fourth supply data includes the number of second accessories provided by the second supply chain at each unit time in the K2 unit times, and a total of K2 second accessories number values; the third time period includes K3 unit times, K3 is an integer greater than 1, and the sales data includes the sales number value of the product at each unit time in the K3 unit times; the management device merges the first supply data with the third supply data to obtain a first fusion data. The supply data of the present invention is provided, and the second supply data is merged with the fourth supply data to obtain the second merged supply data, including: the management device maps K1 first accessory number values ​​into K1 first vectors in a one-to-one correspondence, and maps K2 first accessory number values ​​into K2 first vectors in a one-to-one correspondence; and the management device maps K1 second accessory number values ​​into K1 second vectors in a one-to-one correspondence, and maps K2 second accessory number values ​​into K2 second vectors in a one-to-one correspondence; the management device merges the i-th first vector among the K1 first vectors with the i-th second vector among the K1 second vectors to obtain the i-th fused vector, and when i traverses 1 to K1, K1 fused vectors are obtained; the management device merges the j-th first vector among the K2 first vectors with the j-th second vector among the K2 second vectors to obtain the j-th fused vector, and when j traverses 1 to K2, K2 fused vectors are obtained; wherein, the first fused supply includes K1 fused vectors, and the second fused supply data includes K2 fused vectors.

[0012] Optionally, the management device fuses the first fused supply data and the second fused supply data with the sales data to obtain fused data, including: the management device maps K3 sales quantity values ​​into K3 vectors in a one-to-one correspondence; the management device determines that the total number of K1 first accessory quantity values ​​is M1, the total number of K2 first accessory quantity values ​​is M2, the total number of K1 second accessory quantity values ​​is M3, and the total number of K2 second accessory quantity values ​​is M4, and M1, M2, M3 and M4 are all integers greater than 2; the management device randomly selects [X2 / (M1+M2+M3+M4) from the K3 vectors 】*K3 vectors are randomly inserted into the K1 fused vectors to obtain a first fused vector set. At this time, K3-

X2 / (M1+M2+M3+M4)

X2 / (M1+M2+M3+M4)

[0013] Optionally, the management device analyzes the fused data through a neural network model to obtain supply data of accessories that each of the N supply chains needs to provide in a future time period, including: the management device inputs the first fusion vector set and the second fusion vector set into the neural network model for analysis, and obtains the supply data of accessories that the first supply chain and the second supply chain need to provide in a fourth time period, respectively, output by the neural network model analysis.

[0014] Optionally, the neural network model is a deep neural network model.

[0015] In the second aspect, an embodiment of the present application provides a supply chain collaborative management system based on artificial intelligence, which is applied to a management device; the management device obtains the supply data of accessories provided by K supply chains in different time periods, and obtains the sales data of products in different time periods, K is an integer greater than 1, and the product includes accessories provided by K supply chains, and the time period in which the K supply chains provide accessories is related to the time period in which the product is sold; the management device fuses the supply data with the sales data to obtain the fused data; the management device analyzes the fused data through a neural network model to obtain the supply data of accessories that the K supply chains need to provide in the future time period.

[0016] The system is specifically used to execute the method described in the first aspect, which will not be described in detail here.

[0017] In a third aspect, an embodiment of the present application provides a computer-readable storage medium having program code stored thereon. When the program code is executed by the computer, the method described in the first aspect is executed.

[0018] In summary, the above method and system have the following technical effects: The management device obtains the supply data of accessories provided by N supply chains in different time periods, as well as the sales data of products in different time periods, and fuses the supply data with the sales data to obtain the fused data, so that the features between the data can be fitted. In this way, the management device can analyze the fused data through the neural network model to obtain the supply data of accessories that each of the N supply chains needs to provide in the future time period, that is, to achieve early estimation, thereby improving the reliability and stability of supply chain collaborative management. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 A schematic diagram of the architecture of a production system provided in an embodiment of the present application; Figure 2 A flowchart of a supply chain collaborative management method based on artificial intelligence provided in an embodiment of the present application; Figure 3 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0020] 1. Deep neural network (DNN): DNN is a specific implementation of machine learning. According to the universal approximation theorem, neural networks can theoretically approximate any continuous function, thus enabling neural networks to learn any mapping. According to the way the network is constructed, DNN can be divided into convolutional neural networks (CNN) and recurrent neural networks (RNN).

[0021] CNN is a neural network that is specifically designed to process data with a grid-like structure. For example, time series data (discrete sampling on the time axis) and image data (discrete sampling on two dimensions) can be considered to be data with a grid-like structure. CNN does not use all the input information for calculations at once, but uses a fixed-size window to intercept part of the information for convolution operations, which greatly reduces the amount of calculation of model parameters. In addition, depending on the type of information intercepted by the window (for example, people and objects in a picture are different types of information), each window can use different convolution kernel operations, which enables CNN to better extract the features of the input data.

[0022] RNN is a type of DNN network that uses feedback time series information. Its input includes the new input value at the current moment and its own output value at the previous moment. RNN is suitable for obtaining sequence features that are correlated in time, and is particularly suitable for applications such as speech recognition and channel coding.

[0023] In the embodiment of the present invention, "indication" may include direct indication and indirect indication, and may also include explicit indication and implicit indication. The information indicated by a certain information is called information to be indicated. In the specific implementation process, there are many ways to indicate the information to be indicated, such as but not limited to, the information to be indicated can be directly indicated, such as the information to be indicated itself or the index of the information to be indicated. The information to be indicated can also be indirectly indicated by indicating other information, wherein there is an association relationship between the other information and the information to be indicated. It is also possible to indicate only a part of the information to be indicated, while the other parts of the information to be indicated are known or agreed in advance. For example, the indication of specific information can also be achieved by means of the arrangement order of each information agreed in advance (for example, specified by the protocol), thereby reducing the indication overhead to a certain extent. At the same time, the common parts of each information can also be identified and indicated uniformly to reduce the indication overhead caused by indicating the same information separately.

[0024] In addition, the specific indication method may also be various existing indication methods, such as but not limited to the above-mentioned indication methods and various combinations thereof. The specific details of the various indication methods can refer to the prior art and will not be repeated herein. As can be seen from the above, for example, when it is necessary to indicate multiple information of the same type, different indication methods may be used for different information. In the specific implementation process, the desired indication method can be selected according to specific needs. The embodiment of the present invention does not limit the selected indication method. In this way, the indication method involved in the embodiment of the present invention should be understood to cover various methods that can enable the party to be indicated to obtain the information to be indicated.

[0025] It should be understood that the information to be indicated can be sent as a whole or divided into multiple sub-information and sent separately, and the sending period and / or sending time of these sub-information can be the same or different. The specific sending method is not limited in the embodiment of the present invention. Among them, the sending period and / or sending time of these sub-information can be pre-defined, for example, pre-defined according to a protocol, or can be configured by the sending end device by sending configuration information to the receiving end device.

[0026] "Pre-definition" or "pre-configuration" can be implemented by pre-saving corresponding codes, tables or other methods that can be used to indicate relevant information in the device, and the embodiments of the present invention do not limit the specific implementation method. Among them, "saving" can mean saving in one or more memories. The one or more memories can be set separately or integrated in an encoder or decoder, a processor, or an electronic device. The one or more memories can also be partially set separately and partially integrated in a decoder, a processor, or an electronic device. The type of memory can be any form of storage medium, which is not limited by the embodiments of the present invention.

[0027] The "protocol" involved in the embodiments of the present invention may refer to a protocol family in the communication field, a standard protocol with a similar protocol family frame structure, or a related protocol in a reliable access method system for future Internet of Things devices, and the embodiments of the present invention do not specifically limit this.

[0028] In the embodiments of the present invention, descriptions such as "when...", "in the case of...", "if" and "if" all mean that the device will perform corresponding processing under certain objective circumstances, but do not limit the time, nor do they require the device to perform judgment actions when implementing, nor do they mean the existence of other limitations.

[0029] In the description of the embodiments of the present invention, unless otherwise specified, " / " indicates that the objects associated before and after are in an "or" relationship. For example, A / B can represent A or B; "and / or" in the embodiments of the present invention is only a kind of association relationship describing the associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone, where A and B can be singular or plural. In addition, in the description of the embodiments of the present invention, unless otherwise specified, "multiple" refers to two or more than two. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single items or plural items. For example, at least one of a, b or c can represent: a, b, c, ab, ac, bc, or abc, where a, b, c can be single or multiple. In addition, in order to facilitate the clear description of the technical solution of the embodiments of the present invention, in the embodiments of the present invention, the words "first" and "second" are used to distinguish the same or similar items with basically the same functions and effects. Those skilled in the art will appreciate that words such as "first" and "second" do not limit the quantity and execution order, and words such as "first" and "second" do not necessarily limit the difference. At the same time, in the embodiments of the present invention, words such as "exemplary" or "for example" are used to indicate examples, illustrations or explanations. Any embodiment or design described as "exemplary" or "for example" in the embodiments of the present invention should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of words such as "exemplary" or "for example" is intended to present related concepts in a concrete way for easy understanding.

[0030] The network architecture and business scenarios described in the embodiments of the present invention are intended to more clearly illustrate the technical solutions of the embodiments of the present invention, and do not constitute a limitation on the technical solutions provided by the embodiments of the present invention. A person of ordinary skill in the art can appreciate that with the evolution of the network architecture and the emergence of new business scenarios, the technical solutions provided by the embodiments of the present invention are equally applicable to similar technical problems.

[0031] The technical solution in this application will be described below in conjunction with the accompanying drawings.

[0032] See also Figure 1 , an embodiment of the present application provides a production system, which may include: management equipment and supply chain service equipment.

[0033] Among them, both the management equipment and the service equipment can be understood as terminals, and the terminal can be a terminal with communication function, or can be a chip or chip system set in the terminal. The terminal device can also be called user equipment (UE), access terminal, user unit, user station, mobile station, mobile station, remote station, remote terminal, mobile device, user terminal, terminal, wireless communication equipment, user agent or user device. The terminal device in the embodiment of the present application can be a mobile phone, a tablet computer (Pad), a computer with wireless transceiver function, a virtual reality (VR) terminal device, an augmented reality (AR) terminal device, a wireless terminal in industrial control, a wireless terminal in self driving, a wireless terminal in remote medical, a wireless terminal in smart grid, a wireless terminal in transportation safety, a wireless terminal in smart city, a wireless terminal in smart home, a vehicle-mounted terminal, an RSU with terminal function, etc.

[0034] Management devices and service devices can be connected.

[0035] In this system, the management device obtains the supply data of accessories provided by N supply chains in different time periods, as well as the sales data of products in different time periods, and fuses the supply data with the sales data to obtain the fused data, so that the features between the data can be fitted. In this way, the management device can analyze the fused data through the neural network model to obtain the supply data of accessories that each of the N supply chains needs to provide in the future time period, that is, to achieve early estimation, thereby improving the reliability and stability of supply chain collaborative management.

[0036] The following will describe in detail the interaction of the devices in the above production system in combination with the method.

[0037] See also Figure 2 , the embodiment of the present application provides a supply chain collaborative management method based on artificial intelligence. The method can be applied to the interaction between the management device and the service device. The process of the method includes: S201, the management device obtains the supply data of accessories provided by N supply chains in different time periods, and obtains the sales data of products in different time periods, where N is an integer greater than 1.

[0038] The product includes accessories provided by N supply chains, and the time period in which each of the N supply chains provides accessories is related to the time of product sales. The service device is connected to the N supply chains, or manages / serves the N supply chains, so that the management device can obtain the supply data of accessories provided by each of the N supply chains in different time periods from the service device. In addition, the management device can also obtain the sales data of the product in different time periods from the product sales platform.

[0039] The value of N can be 1 or 2, which is described below.

[0040] Case 1: N=1, N supply chains are the first supply chain; the supply data of accessories provided by the first supply chain in different time periods include: the first supply data of accessories provided by the first supply chain in the first time period and the second supply data of accessories provided by the first supply chain in the second time period, the sales data of products in different time periods: the sales data of products sold at different times in the third time period; the first time period, the second time period, and the third time period are adjacent time periods in chronological order. Among them, the first time period, the second time period, and the third time period are time periods of equal length, such as 1 week or 2 weeks.

[0041] Specifically, the first time period includes K1 unit times, K1 is an integer greater than 1, such as the first time period is 1 week, the unit time is 1 day, that is, K1=7, the first supply data includes the number of accessories provided by the first supply chain at each unit time in the K1 unit time, K1 accessory number values, such as 7 days in 1 week, that is, K1=7, each accessory number value is the number of accessories provided by the first supply chain every day, that is, 7 accessory number values, specifically, 120 on the first day, 80 on the second day, 84 on the third day, 130 on the fourth day, 92 on the fifth day, 79 on the sixth day, and 101 on the seventh day. Similarly, the second time period includes K2 unit times, K2 is an integer greater than 1, the second supply data includes the number of accessories provided by the first supply chain at each unit time in the K2 unit times, a total of K2 accessory number values, the specific implementation is similar to the above-mentioned K1 accessory number value, and will not be repeated. The third time period includes K3 unit time, K3 is an integer greater than 1, and the sales data includes the sales number value of the product in each unit time of the K3 unit time, and a total of K3 sales number values. For example, the third time period is also 1 week, and the unit time is 0.5 days, that is, K3=14, K3 sales number values ​​are 14 sales number values, and each sales number value is the number of products sold every half day.

[0042] Case 2: N=2, N supply chains are the first supply chain and the second supply chain. The first supply chain and the second supply chain are two supply chains that provide different accessories respectively. The supply data of accessories provided by the first supply chain in different time periods include: the first supply data of accessories provided by the first supply chain in the first time period, the second supply data of accessories provided by the first supply chain in the second time period, and the supply data of accessories provided by the second supply chain in different time periods include: the third supply data of accessories provided by the second supply chain in the first time period, and the fourth supply data of accessories provided by the second supply chain in the second time period; sales data of products sold at different times in the third time period; the first time period, the second time period, and the third time period are time periods that are adjacent in chronological order.

[0043] Specifically, the first time period includes K1 unit times, K1 is an integer greater than 1, the first supply data includes the number value of first accessories provided by the first supply chain at each unit time in the K1 unit times, and a total of K1 first accessories number values; the second time period includes K2 unit times, K2 is an integer greater than 1, the second supply data includes the number value of first accessories provided by the first supply chain at each unit time in the K2 unit times, and a total of K2 first accessories number values; the third supply data includes the number value of second accessories provided by the second supply chain at each unit time in the K1 unit times, and a total of K1 second accessories number values; the fourth supply data includes the number value of second accessories provided by the second supply chain at each unit time in the K2 unit times, and a total of K2 second accessories number values; the third time period includes K3 unit times, K3 is an integer greater than 1, and the sales data includes the sales number value of the product at each unit time in the K3 unit times, and a total of K3 sales number values.

[0044] The principle of case 2 is similar to that of case 1 above. Please refer to it for understanding and I will not repeat it here.

[0045] It can be understood that the "first" and "second" in Case 2, such as the first accessory number value and the second accessory number value, are only used for distinction, that is, they represent the number values ​​of different accessories and have no other special meaning.

[0046] It can also be understood that the embodiments of the present application are not limited to the specific implementation of products and accessories. For example, the product may be a mobile phone, and the accessories may be related accessories, such as an antenna, a screen, a motherboard, a processor, etc.; or the product may be a car, and the accessories may be related accessories, such as a battery, a suspension, a motor, etc.

[0047] S202, the management device merges the supply data with the sales data to obtain merged data.

[0048] For situation 1: The management device may merge the first supply data and the second supply data with the sales data to obtain merged data, which may be specifically as follows.

[0049] The management device can determine that the total number of K1 accessory number values ​​is M1, that is, the sum of the K1 accessory number values, and the total number of K2 accessory number values ​​is M2, and the sum of the K2 accessory number values, M1 and M2 are both integers greater than 2. The management device randomly selects [X1 / (M1+M2)]*K3 sales number values ​​from the K3 sales number values ​​and randomly inserts them into the K1 accessory number values. For example, a sales number value is randomly inserted between two adjacent accessory number values ​​to obtain a first fused data set. At this time, K3-[X / (M1+M2)]*K3 sales number values ​​remain in the K3 sales number values, [ ] is rounded down, if M1 is less than M2, then X1=M1, if M1 is greater than M2, then X1=M2. That is to say, considering that sales have a certain lag, the accessories provided in the first time period have a relatively smaller impact on sales in the third time period than the accessories provided in the second time period. Therefore, it is necessary to set X1 to a relatively smaller value to ensure that more K3 sales quantity values ​​are inserted into the K2 accessories quantity values. For example, the management device randomly inserts K3-[M2 / (M1+M2)]*K3 sales quantity values ​​into the K2 accessories quantity values ​​to obtain the second fused data set, and the fused data includes the first fused data set and the second fused data set.

[0050] For situation 2: The management device may merge the first supply data with the third supply data to obtain first merged supply data, and merge the second supply data with the fourth supply data to obtain second merged supply data.

[0051] For example, the management device maps K1 first accessory number values ​​into K1 first vectors one-to-one, and maps K2 first accessory number values ​​into K2 first vectors one-to-one; and, the management device maps K1 second accessory number values ​​into K1 second vectors one-to-one, and maps K2 second accessory number values ​​into K2 second vectors one-to-one; the management device fuses (such as multiplying) the i-th first vector among the K1 first vectors with the i-th second vector among the K1 second vectors to obtain the i-th fused vector, and when i traverses 1 to K1, K1 fused vectors are obtained; the management device fuses (such as multiplying) the j-th first vector among the K2 first vectors with the j-th second vector among the K2 second vectors to obtain the j-th fused vector, and when j traverses 1 to K2, K2 fused vectors are obtained; wherein, the first fused supply includes K1 fused vectors, and the second fused supply data includes K2 fused vectors.

[0052] The numerical mapping / conversion into vectors may be achieved by existing technologies without limitation, and after conversion into vectors, fusion may be performed through vector calculation to retain the characteristics of the data.

[0053] The management device may fuse the first fused supply data and the second fused supply data with the sales data to obtain fused data.

[0054] For example, the management device may map K3 sales quantity values ​​into K3 vectors in a one-to-one correspondence.

[0055] The management device can determine that the total number of K1 first accessory number values ​​is M1, the total number of K2 first accessory number values ​​is M2, the total number of K1 second accessory number values ​​is M3, and the total number of K2 second accessory number values ​​is M4, and M1, M2, M3 and M4 are all integers greater than 2.

[0056] The management device can randomly select [X2 / (M1+M2+M3+M4)]*K3 vectors from the K3 vectors and randomly insert them into the K1 fused vector to obtain a first fused vector set. At this time, K3-[X2 / (M1+M2+M3+M4)]*K3 vectors remain in the K3 vectors, [ ] is rounded down, if M1+M3 is less than M2+M4, then X2=M1+M3, if M1+M3 is greater than M2+M4, then X2=M2+M4; the management device randomly inserts K3-[X2 / (M1+M2+M3+M4)]*K3 vectors into the K2 fused vectors to obtain a second fused vector set, and the fused data includes the first fused vector set and the second fused vector set.

[0057] The above principle is similar to the principle in the above situation 1, which can be understood by reference and will not be repeated here.

[0058] S203, the management device analyzes the fused data through a neural network model to obtain supply data of the accessories that each of the N supply chains needs to provide in a future time period.

[0059] The neural network model is a deep neural network model.

[0060] For possible situation 1: The management device inputs the first fused data set and the second fused data set into the neural network model analysis to obtain the supply data of accessories that the first supply chain needs to provide within the fourth time period output by the neural network model analysis, which may specifically be the total number within the fourth time period.

[0061] For situation 2: The management device can input the first fusion vector set and the second fusion vector set into the neural network model analysis to obtain the supply data of accessories that the first supply chain and the second supply chain need to provide respectively within the fourth time period output by the neural network model analysis, which can specifically be the total number within the fourth time period.

[0062] The fourth time period may be a next time period adjacent to the third time period.

[0063] In summary, the management device obtains the supply data of accessories provided by N supply chains in different time periods, as well as the sales data of products at different times, and fuses the supply data with the sales data to obtain the fused data, so that the features between the data can be fitted. In this way, the management device can analyze the fused data through the neural network model to obtain the supply data of accessories that each of the N supply chains needs to provide in the future time period, that is, to achieve early estimation, thereby improving the reliability and stability of supply chain collaborative management.

[0064] Combination of the above Figure 2 The method provided by the embodiment of the present application is described in detail. The following introduces an artificial intelligence-based supply chain collaborative management system for executing the method provided by the embodiment of the present application, which is applied to a management device, specifically as follows: the management device obtains the supply data of accessories provided by K supply chains in different time periods, and obtains the sales data of the product in different time periods, K is an integer greater than 1, and the product includes accessories provided by K supply chains, and the time period in which the K supply chains provide accessories is related to the time period in which the product is sold; the management device fuses the supply data with the sales data to obtain the fused data; the management device analyzes the fused data through a neural network model to obtain the supply data of accessories that each of the K supply chains needs to provide in a future time period.

[0065] The system is specifically used to perform Figure 2 Please refer to the described method for your understanding and will not repeat it here.

[0066] Combine the following Figure 3 The components of the electronic device 500 are described in detail: The processor 501 is the control center of the electronic device 500, and may be a processor or a general term for multiple processing elements. For example, the processor 501 is one or more central processing units (CPUs), or may be application specific integrated circuits (ASICs), or may be one or more integrated circuits configured to implement the embodiments of the present application, such as one or more microprocessors (digital signal processors, DSPs), or one or more field programmable gate arrays (FPGAs).

[0067] Optionally, the processor 501 can execute various functions of the electronic device 500 by running or executing the software program stored in the memory 502 and calling the data stored in the memory 502, as described above. Figure 3 Functionality in the method shown.

[0068] In a specific implementation, as an embodiment, the processor 501 may include one or more CPUs, such as Figure 3 CPU0 and CPU1 are shown in FIG.

[0069] In a specific implementation, as an embodiment, the electronic device 500 may also include multiple processors. Each of these processors may be a single-core processor (single-CPU) or a multi-core processor (multi-CPU). The processor here may refer to one or more devices, circuits, and / or processing cores for processing data (such as computer program instructions).

[0070] Among them, the memory 502 is used to store the software program for executing the solution of the present application, and the execution is controlled by the processor 501. The specific implementation method can refer to the above method embodiment and will not be repeated here.

[0071] Optionally, the memory 502 may be a read-only memory (ROM) or other types of static storage devices that can store static information and instructions, a random access memory (RAM) or Other types of dynamic storage devices that can store information and instructions may also be electrically erasable programmable read-only memory (EEPROM), compact disc read-only memory (CD-ROM) or other optical disc storage, optical disc storage (including compressed optical disc, laser disc, optical disc, digital versatile disc, Blu-ray disc, etc.), magnetic disk storage medium or other magnetic storage device, or any other medium that can be used to carry or store the desired program code in the form of instructions or data structures and can be accessed by a computer, but is not limited thereto. The memory 502 may be integrated with the processor 501 or exist independently and access the processor 501 through the interface circuit ( Figure 3 (not shown) is coupled to the processor 501, which is not specifically limited in this embodiment of the present application.

[0072] The transceiver 503 is used for communication with other devices. For example, if the multi-beam based positioning device is a terminal, the transceiver 503 can be used for communication with a network device or another terminal.

[0073] Optionally, the transceiver 503 may include a receiver and a transmitter ( Figure 3 The receiver is used to implement a receiving function, and the transmitter is used to implement a sending function.

[0074] Optionally, the transceiver 503 may be integrated with the processor 501, or may exist independently and communicate with the processor 501 through the interface circuit ( Figure 3 (not shown) is coupled to the processor 501, which is not specifically limited in this embodiment of the present application.

[0075] It should be noted that Figure 3 The structure of the electronic device 500 shown in the figure does not constitute a limitation on the device, and the actual electronic device 500 may include more or less components than those shown in the figure, or combine certain components, or arrange the components differently.

[0076] In addition, the technical effects based on the electronic device 500 can refer to the technical effects of the method in the above method embodiment, which will not be repeated here.

[0077] It should be understood that the processor in the embodiments of the present application may be a central processing unit (CPU), and the processor may also be other general-purpose processors, digital signal processors (DSP), application specific integrated circuits (ASIC), field programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0078] It should also be understood that the memory in the embodiments of the present application may be a volatile memory or a non-volatile memory, or may include both volatile and non-volatile memories. Among them, the non-volatile memory may be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), or a flash memory. The volatile memory may be a random access memory (RAM), which is used as an external cache. By way of example and not limitation, many forms of random access memory (RAM) are available, such as static RAM (SRAM), dynamic random access memory (DRAM), synchronous DRAM (SDRAM), double data rate synchronous dynamic random access memory (DDR SDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous link DRAM (SLDRAM), and direct rambus RAM (DR RAM).

[0079] The above embodiments can be implemented in whole or in part by software, hardware (such as circuits), firmware or any other combination. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When a computer instruction or computer program is loaded or executed on a computer, a process or function according to an embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. Computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, computer instructions can be transmitted from one website, computer, server or data center to another website, computer, server or data center by wired (such as infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or data center that contains one or more available media sets. The available medium can be a magnetic medium (for example, a floppy disk, a hard disk, a tape), an optical medium (for example, a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state hard disk.

[0080] It should be understood that the term "and / or" in this article is only a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. A and B can be singular or plural. In addition, the character " / " in this article generally indicates that the associated objects before and after are in an "or" relationship, but it may also indicate an "and / or" relationship. Please refer to the context for specific understanding.

[0081] In this application, "at least one" means one or more, and "plurality" means two or more. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single items or plural items. For example, at least one of a, b, or c can mean: a, b, c, ab, ac, bc, or abc, where a, b, and c can be single or multiple.

[0082] It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0083] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0084] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0085] In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of units is only a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some feature fields can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0086] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0087] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0088] If the function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including a number of instructions to enable a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the various embodiments of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), disk or optical disk, and other media that can store program codes.

[0089] The above are only specific implementations of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A supply chain collaborative management method based on artificial intelligence, characterized in that: The method is applied to manage a device, and the method comprises: The management device obtains supply data of accessories provided by N supply chains in different time periods, and obtains sales data of the product in different time periods, where N is an integer greater than 1, the product includes accessories provided by the N supply chains, and the time period in which the N supply chains provide accessories is related to the time when the product is sold; The management device merges the supply data with the sales data to obtain merged data; The management device analyzes the fused data through a neural network model to obtain supply data of accessories that each of the N supply chains needs to provide in a future time period.

2. The method according to claim 1, characterized in that N=1, the N supply chains are the first supply chains; the supply data of accessories provided by the first supply chain in different time periods include: the first supply data of accessories provided by the first supply chain in the first time period and the second supply data of accessories provided by the first supply chain in the second time period; the sales data of the product in different time periods: the sales data of the product sold at different times in the third time period; the first time period, the second time period, and the third time period are time periods that are adjacent in chronological order; Correspondingly, the management device merges the supply data with the sales data to obtain merged data, including: The management device merges the first supply data and the second supply data with the sales data to obtain the merged data.

3. The method according to claim 2, characterized in that The first time period includes K1 unit time, K1 is an integer greater than 1, the first supply data includes the number of accessories provided by the first supply chain at each unit time in the K1 unit time, and a total of K1 number of accessories; the second time period includes K2 unit time, K2 is an integer greater than 1, the second supply data includes the number of accessories provided by the first supply chain at each unit time in the K2 unit time, and a total of K2 number of accessories; the third time period includes K3 unit time, K3 is an integer greater than 1, and the sales data includes the sales number value of the product at each unit time in the K3 unit time, and a total of K3 sales number values; Correspondingly, the management device merges the first supply data and the second supply data with the sales data to obtain the merged data, including: The management device determines that the total number of the K1 accessory number values ​​is M1, and the total number of the K2 accessory number values ​​is M2, and both M1 and M2 are integers greater than 2; The management device randomly selects [X1 / (M1+M2)]*K3 sales number values ​​from the K3 sales number values ​​and randomly inserts them into the K1 accessory number values ​​to obtain a first fused data set. At this time, K3-[X / (M1+M2)]*K3 sales number values ​​remain in the K3 sales number values, [] is rounded down, if M1 is less than M2, then X1=M1, if M1 is greater than M2, then X1=M2; The management device randomly inserts the K3-[X1 / (M1+M2)]*K3 sales quantity values ​​into the K2 accessory quantity values ​​to obtain a second fused data set, and the fused data includes the first fused data set and the second fused data set.

4. The method according to claim 3, characterized in that The management device analyzes the fused data through a neural network model to obtain supply data of the parts that each of the N supply chains needs to provide in a future time period, including: The management device inputs the first fused data set and the second fused data set into the neural network model analysis to obtain the supply data of accessories that the first supply chain needs to provide within a fourth time period output by the neural network model analysis.

5. The method according to claim 1, characterized in that N=2, the N supply chains are the first supply chain and the second supply chain, the supply data of accessories provided by the first supply chain in different time periods include: the first supply data of accessories provided by the first supply chain in the first time period, the second supply data of accessories provided by the first supply chain in the second time period, the supply data of accessories provided by the second supply chain in different time periods include: the third supply data of accessories provided by the second supply chain in the first time period, the fourth supply data of accessories provided by the second supply chain in the second time period; the sales data of the product sold at different times in the third time period; the first time period, the second time period, and the third time period are time periods that are adjacent in chronological order; Correspondingly, the management device merges the supply data with the sales data to obtain merged data, including: The management device merges the first supply data with the third supply data to obtain first merged supply data, and merges the second supply data with the fourth supply data to obtain second merged supply data; The management device fuses the first fused supply data and the second fused supply data with the sales data to obtain the fused data.

6. The method according to claim 5, characterized in that The first time period includes K1 unit time, K1 is an integer greater than 1, the first supply data includes the number value of first accessories provided by the first supply chain at each unit time in the K1 unit time, and a total of K1 first accessories number values; the second time period includes K2 unit time, K2 is an integer greater than 1, the second supply data includes the number value of first accessories provided by the first supply chain at each unit time in the K2 unit time, and a total of K2 first accessories number values; the third supply data includes the number value of second accessories provided by the second supply chain at each unit time in the K1 unit time, and a total of K1 second accessories number values; the fourth supply data includes the number value of second accessories provided by the second supply chain at each unit time in the K2 unit time, and a total of K2 second accessories number values; the third time period includes K3 unit time, K3 is an integer greater than 1, and the sales data includes the sales number value of the product at each unit time in the K3 unit time, and a total of K3 sales number values; The management device merges the first supply data with the third supply data to obtain first merged supply data, and merges the second supply data with the fourth supply data to obtain second merged supply data, including: The management device maps the K1 first accessory number values ​​into K1 first vectors in a one-to-one correspondence, and maps the K2 first accessory number values ​​into K2 first vectors in a one-to-one correspondence; and the management device maps the K1 second accessory number values ​​into K1 second vectors in a one-to-one correspondence, and maps the K2 second accessory number values ​​into K2 second vectors in a one-to-one correspondence; The management device fuses the i-th first vector among the K1 first vectors with the i-th second vector among the K1 second vectors to obtain the i-th fused vector, and when i traverses from 1 to K1, K1 fused vectors are obtained; The management device fuses the jth first vector among the K2 first vectors with the jth second vector among the K2 second vectors to obtain the jth fused vector, and when j traverses from 1 to K2, K2 fused vectors are obtained; The first fused supply includes the K1 fused vectors, and the second fused supply data includes the K2 fused vectors.

7. The method according to claim 6, characterized in that The management device fuses the first fused supply data and the second fused supply data with the sales data to obtain the fused data, including: The management device maps the K3 sales quantity values ​​into K3 vectors in a one-to-one correspondence; The management device determines that the total number of the K1 first accessory number values ​​is M1, the total number of the K2 first accessory number values ​​is M2, the total number of the K1 second accessory number values ​​is M3, and the total number of the K2 second accessory number values ​​is M4, and M1, M2, M3 and M4 are all integers greater than 2; The management device randomly selects [X2 / (M1+M2+M3+M4)]*K3 vectors from the K3 vectors and randomly inserts them into the K1 fused vectors to obtain a first fused vector set. At this time, K3-[X2 / (M1+M2+M3+M4)]*K3 vectors remain in the K3 vectors, [] is rounded down, if M1+M3 is less than M2+M4, then X2=M1+M3, if M1+M3 is greater than M2+M4, then X2=M2+M4; The management device randomly inserts the K3-[X2 / (M1+M2+M3+M4)]*K3 vectors into the K2 fused vectors to obtain a second fused vector set, and the fused data includes the first fused vector set and the second fused vector set.

8. The method according to claim 7, characterized in that The management device analyzes the fused data through a neural network model to obtain supply data of the parts that each of the N supply chains needs to provide in a future time period, including: The management device inputs the first fusion vector set and the second fusion vector set into the neural network model analysis to obtain supply data of accessories that the first supply chain and the second supply chain need to provide respectively within a fourth time period output by the neural network model analysis.

9. The method according to any one of claims 1 to 8, characterized in that The neural network model is a deep neural network model.

10. A supply chain collaborative management system based on artificial intelligence, characterized in that: The system is applied to manage equipment; The management device obtains supply data of accessories provided by K supply chains in different time periods, and obtains sales data of the product in different time periods, where K is an integer greater than 1, the product includes accessories provided by the K supply chains, and the time period in which the K supply chains provide accessories is related to the time period in which the product is sold; The management device merges the supply data with the sales data to obtain merged data; The management device analyzes the fused data through a neural network model to obtain supply data of accessories that each of the K supply chains needs to provide in a future time period.

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