Enterprise-level big data collection method and system integrated with ICT supply chain

Abstract

The application relates to the technical field of data processing, in particular to an enterprise-level big data collection method and system fusing an ICT supply chain. The method comprises the following steps: acquiring an importance degree index of supply chain data of raw materials in a supply chain component; acquiring a repetition index of the raw materials in the supply chain component; acquiring an influence degree index of the supply chain data of the raw materials in the supply chain component according to the importance degree index and the repetition index; acquiring an importance index of the supply chain data for the supply chain component according to the influence degree index; and determining a collection strategy of the supply chain data according to the importance index. According to the application, different data collection strategies are determined according to the importance of data, important data can be effectively transmitted when network bandwidth is insufficient, repeated data collection and storage are reduced, and therefore the data collection efficiency and the real-time performance of important data collection can be improved.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of data processing, and particularly relates to an enterprise-level big data collection method and system fusing an ICT supply chain. BACKGROUND

[0002] With the development of information technology, major enterprises gradually transform to digitalization, and big data collection gradually becomes one of the indispensable capabilities of major enterprises and developers. Especially in the background of information and communication technology and its upstream and downstream ICT supply chain, the operation mode of enterprises is more and more complex, involving many links such as hardware equipment procurement, software service deployment, network infrastructure construction and system operation and maintenance. The data generated in these links is of various types and large volume. The ICT supply chain is composed of raw material procurement, hardware manufacturing, software development, network deployment, system integration and operation and maintenance, which makes it difficult for traditional data collection methods to completely collect the data on the ICT supply chain. As the starting point of the big data processing process, the collection of data has a direct impact on the subsequent data analysis and decision-making of the supply chain.

[0003] However, the existing big data collection method basically disperses massive data to multiple nodes through a distributed system, integrates and transmits and saves the multi-source heterogeneous data. However, for the ICT supply chain, the current product supply chain may involve different countries and regions, and different products involve different links, which leads to different processing speeds of the data on the nodes, that is, there is a multi-node data synchronization problem for the cross-border supply chain of ICT. Moreover, under the influence of the globalization of the supply chain, there is a problem that the same product component suppliers exist, which further leads to the existence of a lot of repeated data on different nodes, which makes the data synchronization more difficult and further causes bandwidth congestion when collecting data. SUMMARY

[0004] In order to solve the above problems, the application embodiment provides an enterprise-level big data collection method and system fusing an ICT supply chain.

[0005] According to a first aspect of the application embodiment, an enterprise-level big data collection method fusing an ICT supply chain is provided, and the method comprises:

[0006] obtaining an importance degree index of supply chain data of raw materials in a supply chain component;

[0007] obtaining a repetitiveness index of the raw materials in the supply chain component;

[0008] obtaining an influence degree index of the supply chain data of the raw materials in the supply chain component on an enterprise according to the importance degree index and the repetitiveness index;

[0009] According to the influence degree index, an importance index of the supply chain data for the supply chain component is acquired;

[0010] According to the importance index, a collection strategy of the supply chain data is determined.

[0011] In an embodiment, the acquiring of the importance degree index of the supply chain data of the raw material in the supply chain component comprises:

[0012] Acquiring a data dimension similarity of the supply chain data of the raw material in the supply chain component and the supply chain data of any supply chain component containing the raw material;

[0013] Acquiring a data length of the supply chain data of the raw material in the supply chain component;

[0014] Acquiring a mean value of the data length of the supply chain data in the supply chain component containing the raw material;

[0015] According to the data dimension similarity, the data length, and the mean value, the importance degree index of the supply chain data of the raw material in the supply chain component is acquired.

[0016] In an embodiment, the acquiring of the data dimension similarity of the supply chain data of the raw material in the supply chain component and the supply chain data of any supply chain component containing the raw material comprises:

[0017] Constructing a supply chain data matrix of the supply chain component, the supply chain data matrix comprising supply chain data vectors of different raw materials of the supply chain component, the supply chain data vector comprising a relevant production data sequence of any raw material of the supply chain component;

[0018] According to the supply chain data matrix of the supply chain component and the supply chain data matrix of any supply chain component containing the raw material, the data dimension similarity of the supply chain data of the raw material in the supply chain component and the supply chain data of any supply chain component containing the raw material is acquired.

[0019] In an embodiment, the acquiring of the repetitiveness index of the raw material in the supply chain component comprises:

[0020] Acquiring a basic similarity of the raw material;

[0021] Acquiring a stability index of the supply chain data of the raw material;

[0022] According to the basic similarity and the stability index, the repetitiveness index of the raw material in the supply chain component is acquired.

[0023] In an embodiment, the obtaining the base similarity of the raw material comprises:

[0024] obtaining a data dimension similarity of supply chain data of any two supply chain components containing the raw material;

[0025] obtaining a total number of data dimensions of the supply chain data of the supply chain component;

[0026] obtaining the base similarity of the raw material according to the data dimension similarity and the total number.

[0027] In an embodiment, the obtaining the stability index of the supply chain data of the raw material comprises:

[0028] obtaining a time length from a last change to any one change of the supply chain data within a set time period;

[0029] obtaining a total number of changes of the supply chain data within the set time period;

[0030] obtaining the stability index of the supply chain data of the raw material according to the time length and the total number of changes.

[0031] In an embodiment, the obtaining the influence degree index of the supply chain data of the raw material in the supply chain component on the enterprise according to the importance index and the repeatability index comprises:

[0032] obtaining an importance index of the supply chain data of the raw material in the supply chain component at any reference time within a set time period;

[0033] obtaining a total number of times of the reference time within the set time period;

[0034] obtaining the influence degree index of the supply chain data of the raw material in the supply chain component on the enterprise according to the importance index, the repeatability index, and the total number of times.

[0035] In an embodiment, the obtaining the importance index of the supply chain data on the supply chain component according to the influence degree index comprises:

[0036] obtaining a maximum value of the influence degree index of the supply chain data of the raw material in the supply chain component on the enterprise;

[0037] obtaining a minimum value of the influence degree index of the supply chain data of the raw material in the supply chain component on the enterprise;

[0038] According to the maximum value and the minimum value, an importance index of the supply chain data to the supply chain component is obtained.

[0039] In an embodiment, the determining of the collection strategy of the supply chain data according to the importance index comprises:

[0040] For the supply chain data with the importance index greater than or equal to a first set threshold, uploading to a server directly;

[0041] For the supply chain data with the importance index less than the first set threshold, uploading to the server after compression and smoothing;

[0042] In a case where network bandwidth utilization is less than or equal to a bandwidth utilization threshold, allocating network bandwidth to all supply chain data according to a proportion of the importance index;

[0043] In a case where the network bandwidth utilization is greater than the bandwidth utilization threshold, temporarily uploading to the server for the supply chain data with the importance index less than a second set threshold, wherein the first set threshold is greater than the second set threshold.

[0044] According to a second aspect of the embodiments of the present application, an enterprise-level big data collection system for an ICT supply chain is provided, and the system comprises a server, and the server comprises:

[0045] a memory having a computer program stored thereon;

[0046] a processor configured to execute the computer program in the memory to implement the steps of the method according to any one of the first aspect.

[0047] The embodiments of the present application have the following beneficial effects: The embodiments of the present application provide an enterprise-level big data collection method for an ICT supply chain, which comprises: obtaining an importance index of supply chain data of raw materials in a supply chain component; obtaining a repetition index of the raw materials in the supply chain component; obtaining an influence degree index of the supply chain data of the raw materials in the supply chain component according to the importance index and the repetition index; obtaining an importance index of the supply chain data to the supply chain component according to the influence degree index; and determining a collection strategy of the supply chain data according to the importance index. The embodiments of the present application can effectively transmit important data and reduce the collection and storage of repeated data when the network bandwidth is insufficient by determining different strategies of data collection according to the importance of data, thereby improving the data collection efficiency and the real-time performance of important data collection. BRIEF DESCRIPTION OF DRAWINGS

[0048] In order to more clearly illustrate the embodiments of the present application, the drawings required to be used in the embodiments will be briefly introduced below. It should be understood that the drawings only show some of the embodiments of the present application, and therefore should not be considered as limiting the scope. For those skilled in the art, other related drawings can also be obtained without creative labor.

[0049] Figure 1 is a flow chart of an enterprise-level big data collection method of a converged ICT supply chain according to an exemplary embodiment.

[0050] Figure 2 is a flow chart of a method of obtaining a supply chain data importance index of a raw material in a supply chain component according to an exemplary embodiment.

[0051] Figure 3 is a flow chart of a method of obtaining a data dimension similarity of supply chain data of a raw material in a supply chain component and supply chain data of any supply chain component containing the raw material according to an exemplary embodiment.

[0052] Figure 4 is a flow chart of a method of obtaining a raw material repetition index in a supply chain component according to an exemplary embodiment.

[0053] Figure 5 is a flow chart of a method of obtaining a raw material base similarity according to an exemplary embodiment.

[0054] Figure 6 is a flow chart of a method of obtaining a supply chain data stability index of a raw material according to an exemplary embodiment.

[0055] Figure 7 is a flow chart of a method of obtaining a supply chain data impact index of a raw material in a supply chain component on an enterprise according to an importance index and a repetition index according to an exemplary embodiment.

[0056] Figure 8 is a flow chart of a method of obtaining a supply chain data importance index of a supply chain component according to an impact index according to an exemplary embodiment.

[0057] Figure 9 is a flow chart of a method of determining a supply chain data collection strategy according to an importance index according to an exemplary embodiment.

[0058] Figure 10 is a block diagram of an enterprise-level big data collection system of a converged ICT supply chain according to an exemplary embodiment.

[0059] Figure 11 is a block diagram of a server according to an example embodiment. DETAILED DESCRIPTION

[0060] In order to clearly illustrate the technical features of the scheme, the following will describe the present application in detail through specific embodiments and in conjunction with the drawings.

[0061] Embodiments of the present application will be described below in greater detail with reference to the accompanying drawings. While certain embodiments of the present application are shown in the drawings, it is understood that the present application can be embodied in various forms and should not be interpreted as being limited to the embodiments set forth herein. Rather, these embodiments are provided so that the present application can be more thoroughly and completely understood. It should be understood that the drawings and embodiments of the present application are for exemplary purposes only and are not intended to limit the scope of protection of the present application.

[0062] It should be understood that each of the steps recited in the method embodiments of the present application can be executed in different orders and / or in parallel. In addition, the method embodiments can include additional steps and / or omit the execution of the steps shown. The scope of the present application is not limited in this respect.

[0063] The term "comprising" and variations thereof as used herein are open-ended, and mean "including but not limited to". The term "based on" means "based, at least in part, on". The term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one additional embodiment"; the term "some embodiments" means "at least some embodiments". Related terms are defined in the description that follows.

[0064] It should be noted that the terms "first", "second", and the like in the present application are merely used to distinguish different devices, modules or units, and do not imply the order or interdependence of the functions performed by these devices, modules or units.

[0065] It should be noted that the modification of "one", "multiple" mentioned in the present application is illustrative but not restrictive, and those skilled in the art should understand that unless otherwise explicitly indicated in the context, it should be understood as "one or more". In the description of the present application, unless otherwise specified, "multiple" refers to two or more than two, and other quantifiers are similar; "at least one", "one or more" or similar expressions refer to any combination of these items, including any combination of single or multiple items. For example, at least one a can represent any number of a; for example, one or more of a, b and c can represent: a, b, c, a-b, a-c, b-c, or a-b-c, where a, b, c can be single or multiple; "and / or" is a description of the relationship between the associated objects, which means that there can be three relationships, for example, A and / or B can represent: A alone, A and B exist at the same time, and B alone, where A and B can be singular or plural.

[0066] Although the operations or steps are described in a specific order in the accompanying drawings in the embodiments of the present application, it should not be understood as requiring the specific order or serial order to perform the operations or steps, or requiring all the shown operations or steps to obtain the desired results. In the embodiments of the present application, the operations or steps can be performed in series; the operations or steps can also be performed in parallel; or a part of the operations or steps can be performed.

[0067] At the same time, it can be understood that the data involved in the present technical solution (including but not limited to data itself, data acquisition or use) should comply with the requirements of relevant laws and regulations and relevant provisions.

[0068] Firstly, the application scenario of the present application is described. A large number of IoT devices, such as RFID, bar code scanners, etc., can be set at different links of the ICT supply chain to collect product-related information of the suppliers at each link, and meanwhile, the relevant data of orders, inventory, and finance of the enterprise can be retrieved through the enterprise system of the cooperating enterprises. In addition, the data flow of each product and device can be processed in real time with the help of the data platform of external partners. Taking the bidding and procurement data of an enterprise as an example, the bidding and procurement data in the past period of time (for example, one year) can be retrieved through the system website of the cooperating enterprises, and the production level and other related information of the IoT devices in the factory in this period of time can be collected. In the context of the ICT supply chain, although the operation of an enterprise needs to involve the information of many supply chains upstream and downstream, the production and operation plan of the enterprise is mostly only influenced by the production plan of some related suppliers, and the influence of these suppliers on the enterprise is also different, among which the production information of the core suppliers is obviously more important than that of other suppliers for the enterprise. Moreover, for an enterprise, if the core suppliers and core distributors can well meet the needs of the enterprise, the enterprise can usher in better development. Therefore, according to the influence of the information of many links on different enterprises, the demand level of different enterprises for data of different links can be constructed, the importance of the data to the enterprise is obtained, and different data collection strategies are determined according to the importance of the data, so that important data can be effectively transmitted when the network bandwidth is insufficient, the collection and storage of repeated data are reduced, and the real-time performance of data collection and important data collection is improved. The present application will be described below in conjunction with specific embodiments.

[0069] Figure 1 is a flowchart of an enterprise-level big data collection method integrating an ICT supply chain according to an exemplary embodiment. As shown in Figure 1 the embodiment of the present application provides an enterprise-level big data collection method integrating an ICT supply chain, which can include the following steps:

[0070] In step S10, an importance index of supply chain data of a raw material in a supply chain component is obtained.

[0071] In this step, the importance index of the supply chain data of the raw material in the supply chain component is obtained. Exemplarily, the data dimension similarity of the supply chain data of the raw material in the supply chain component and the supply chain data of any supply chain component containing the raw material can be obtained first, then the data length of the supply chain data of the raw material in the supply chain component is obtained, and then the average of the data length of the supply chain data of the supply chain component containing the raw material is obtained, and finally the importance index of the supply chain data of the raw material in the supply chain component is obtained according to the data dimension similarity, the data length, and the average.

[0072] In step S20, a repetitiveness index of the raw material in the supply chain component is acquired.

[0073] In this step, the repetitiveness index of the raw material in the supply chain component is acquired. Exemplarily, the base similarity of the raw material can be acquired first, then the stability index of the supply chain data of the raw material is acquired, and then the repetitiveness index of the raw material in the supply chain component is acquired according to the base similarity and the stability index.

[0074] In step S30, an influence degree index of the supply chain data of the raw material in the supply chain component on the enterprise is acquired according to the importance index and the repetitiveness index.

[0075] In this step, the influence degree index of the supply chain data of the raw material in the supply chain component on the enterprise is acquired according to the importance index and the repetitiveness index. Exemplarily, the importance index of the supply chain data of the raw material in the supply chain component at any reference time within a set time period can be acquired first, then the total number of times of the reference time within the set time period is acquired, and then the influence degree index of the supply chain data of the raw material in the supply chain component on the enterprise is acquired according to the importance index, the repetitiveness index, and the total number of times.

[0076] In step S40, an importance index of the supply chain data on the supply chain component is acquired according to the influence degree index.

[0077] In this step, the importance index of the supply chain data on the supply chain component is acquired according to the influence degree index. Exemplarily, the maximum value of the influence degree index of the supply chain data of the raw material in the supply chain component on the enterprise can be acquired first, then the minimum value of the influence degree index of the supply chain data of the raw material in the supply chain component on the enterprise is acquired, and then the importance index of the supply chain data on the supply chain component is acquired according to the maximum value and the minimum value.

[0078] In step S50, an acquisition strategy of the supply chain data is determined according to the importance index.

[0079] In this step, according to the importance index, the collection strategy of the supply chain data is determined. Exemplarily, first, for the supply chain data with the importance index greater than or equal to a first set threshold, the server is directly uploaded, and for the supply chain data with the importance index less than the first set threshold, the server is uploaded after compression and smoothing, then in the case that the network bandwidth utilization rate is less than or equal to a bandwidth utilization rate threshold, the network bandwidth is allocated to all supply chain data according to the proportion of the importance index, and in the case that the network bandwidth utilization rate is greater than the bandwidth utilization rate threshold, the supply chain data with the importance index less than a second set threshold is temporarily uploaded to the server, wherein the first set threshold is greater than the second set threshold.

[0080] The embodiment of the present application has the following beneficial effects: the embodiment of the present application provides an enterprise-level big data collection method fusing an ICT supply chain, which comprises: acquiring an importance degree index of supply chain data of raw materials in a supply chain component; acquiring a repeatability index of the raw materials in the supply chain component; acquiring an influence degree index of the supply chain data of the raw materials in the supply chain component according to the importance degree index and the repeatability index; acquiring an importance index of the supply chain data for the supply chain component according to the influence degree index; and determining a collection strategy of the supply chain data according to the importance index. The embodiment of the present application can effectively transmit important data and reduce the collection and storage of repeated data when the network bandwidth is insufficient by determining different data collection strategies according to the importance of data, thereby improving the data collection efficiency and the real-time performance of important data collection.

[0081] Figure 2 is a flow chart of a method for acquiring an importance degree index of supply chain data of raw materials in a supply chain component according to an exemplary embodiment. As shown in Figure 2 the acquisition of the importance degree index of the supply chain data of the raw materials in the supply chain component can comprise the following steps:

[0082] In step S101, the data dimension similarity of the supply chain data of the raw materials in the supply chain component and the supply chain data of any supply chain component containing the raw materials is acquired.

[0083] In this step, the data dimension similarity of the supply chain data of the raw material in the supply chain component and the supply chain data of any supply chain component containing the raw material is obtained. For example, a supply chain data matrix of the supply chain component can be constructed first, which includes supply chain data vectors of different raw materials of the supply chain component, and the supply chain data vector includes a relevant production data sequence of any raw material of the supply chain component. Then, the data dimension similarity of the supply chain data of the raw material in the supply chain component and the supply chain data of any supply chain component containing the raw material is obtained according to the supply chain data matrix of the supply chain component and the supply chain data matrix of any supply chain component containing the raw material.

[0084] In step S102, the data length of the supply chain data of the raw material in the supply chain component is obtained.

[0085] In this step, the data length of the supply chain data of the raw material d in the supply chain component is obtained. For example, the data length can be understood as the number of bytes of the data.

[0086] In step S103, the average of the data length of the supply chain data in any supply chain component containing the raw material is obtained.

[0087] In this step, the average of the data length of the supply chain data of the raw material d in any supply chain component containing the raw material d is obtained.

[0088] In step S104, the importance index of the supply chain data of the raw material in the supply chain component is obtained according to the data dimension similarity, the data length, and the average.

[0089] In this step, the importance index of the supply chain data of the raw material d in the supply chain component in the data dimension i is obtained according to the data dimension similarity, the data length, and the average of the data length. For example, the importance index of the supply chain data of the raw material d in the supply chain component in the data dimension i can be obtained by the following formula:

[0090] Formula 1

[0091] wherein, is the supply chain component ​​​​​​​​​The supply chain data of raw material d in dimension i, and other supply chain components containing raw material d. Data dimension similarity of supply chain data in aspect i The mean, Not zero.

[0092] Taking the production server as an example, since each raw material may be involved in the application of different components, these components will be located in different places on the server and play different roles. This makes the performance of raw materials that need to be focused on at different stages also different. This makes the relevant data of materials recorded at different stages different in detail in different aspects. That is, for aspects that are more important, more detailed records will be made than for other aspects. Based on this, the importance that each component of the supply chain attaches to the data of various aspects of raw materials can be constructed.

[0093] Figure 3 This is a flowchart illustrating, according to an exemplary embodiment, a method for obtaining the data dimension similarity between supply chain data of raw materials in a supply chain component and supply chain data of any other supply chain component containing that raw material. Figure 3 As shown, obtaining the similarity of the data dimensions of the supply chain data of the raw material in the supply chain component with the supply chain data of any other supply chain component containing the raw material may include the following steps:

[0094] In step S1011, a supply chain data matrix of the supply chain component is constructed. The supply chain data matrix includes supply chain data vectors of different raw materials of the supply chain component, and the supply chain data vectors include relevant production data sequences of any raw material of the supply chain component.

[0095] In this step, supply chain components are built. Supply chain data matrix Supply chain data matrix Including supply chain components Supply chain data vector of any raw material d Supply chain data vector Including supply chain components The relevant production data sequence of any raw material d .in, This represents the supply chain data of data dimension i for raw material d, and j represents the data sequence. The number of data points. For example, data dimension i can be understood as data such as the production time, storage time, logistics time, cost, price, or quality grade of raw materials.

[0096] In step S1012, a data dimension similarity of the supply chain data of the raw material d in the supply chain component and the supply chain data of the raw material d in any other supply chain component containing the raw material d is obtained according to the supply chain data matrix of the supply chain component and the supply chain data matrix of any other supply chain component containing the raw material d.

[0097] In this step, a data dimension similarity of the supply chain data of the raw material d in the supply chain component and the supply chain data of the raw material d in any other supply chain component containing the raw material d is obtained according to the supply chain data matrix of the supply chain component and the supply chain data matrix of any other supply chain component containing the raw material d. For example, the reciprocal of the distance between the data sequence of the same data dimension of the same raw material in the supply chain data matrix and the supply chain data matrix may be taken as the data dimension similarity of the supply chain data of the raw material d in the supply chain component and the supply chain data of the raw material d in any other supply chain component containing the raw material d.

[0098] Figure 4 is a flow chart of a method for obtaining a repeatability index of a raw material in a supply chain component according to an exemplary embodiment. As shown in Figure 4 , the method for obtaining the repeatability index of the raw material in the supply chain component can include the following steps:

[0099] In step S201, a basic similarity of the raw material is obtained.

[0100] In this step, the basic similarity of the raw material is obtained. For example, the data dimension similarity of the supply chain data of any two supply chain components containing the raw material is obtained first, then the total number of data dimensions of the supply chain data of the supply chain component is obtained, and then the basic similarity of the raw material is obtained according to the data dimension similarity and the total number.

[0101] In step S202, a stability index of the supply chain data of the raw material is obtained.

[0102] ​​​​​​In this step, stability indicators for the raw material supply chain data are obtained. For example, one can first obtain the time elapsed between any change in the supply chain data within a set time period and the last change, then obtain the total number of changes in the supply chain data within the set time period, and finally obtain the stability indicators for the raw material supply chain data based on the time elapsed and the total number of changes.

[0103] In step S203, the repeatability index of the raw materials in the supply chain component is obtained based on the basic similarity and the stability index.

[0104] In this step, based on the basic similarity and stability indicators Obtaining supply chain components Repeatability index of raw material d For example, supply chain components Repeatability index of raw material d It can be obtained from the following formula:

[0105] Formula 2

[0106] in, The basic similarity of raw material d, For supply chain components The stability index of the data dimension i of raw material d. This indicates that other supply chain nodes contain supply chain components containing raw material d. Total quantity Not zero, Normalization for positive correlation.

[0107] Taking server manufacturing as an example, it requires a variety of top-tier raw materials and components. The processing equipment used to produce these raw materials must also be highly sophisticated. This leads to some overlap in the descriptions and origins of these raw materials. Therefore, this data from different supply chain components can be merged to calculate the redundancy of data across different supply chain components.

[0108] Figure 5 This is a flowchart illustrating a method for obtaining basic similarity of raw materials according to an exemplary embodiment. Figure 5 As shown, obtaining the basic similarity of the raw materials may include the following steps:

[0109] In step S2011, the data dimension similarity of supply chain data for any two supply chain components containing the raw materials is obtained.

[0110] In this step, any two supply chain components containing raw materials are acquired. and data dimension similarity of data dimensions of supply chain data of the material d . Wherein, The acquisition method of the material d can refer to the embodiment of step S1012, which will not be repeated here.

[0111] In step S2012, the total number of data dimensions of supply chain data of the supply chain component is obtained.

[0112] In this step, the total number of data dimensions of supply chain data of the material d of the supply chain component is obtained.

[0113] In step S2013, the base similarity of the material is obtained according to the data dimension similarity and the total number of data dimensions.

[0114] In this step, the base similarity of the material d is obtained according to the data dimension similarity and the total number of data dimensions . For example, the base similarity of the material d can be obtained by the following formula:

[0115] Formula 3

[0116] Wherein, i represents the i-th data dimension of the material supply chain data, is not zero.

[0117] According to the average level of the similarity of the same material of two components in different data dimensions , the base similarity of the material as a whole is comprehensively evaluated.

[0118] Figure 6 is a flow chart of a method for obtaining a stability index of supply chain data of a material according to an exemplary embodiment. As Figure 6 shown, the method for obtaining the stability index of the supply chain data of the material can include the following steps:

[0119] In step S2021, the time length from the last change to any change of the supply chain data within a set time period is obtained.

[0120] In this step, the time length from the last change to the c-th change of the supply chain data of the material d in the data dimension i within a set time period ​​The set time period can be one month, for example.

[0121] In step S2022, the total number of changes of the supply chain data in the set time period is obtained.

[0122] In this step, the total number of changes of the supply chain data of the raw material d in the data dimension i in the set time period is obtained The set time period can be one month, for example.

[0123] In step S2023, the stability index of the supply chain data of the raw material is obtained according to the time length and the total number of changes.

[0124] In this step, the stability index of the supply chain data of the raw material d in the data dimension i is obtained according to the time length and the total number of changes The stability index of the supply chain data of the raw material d in the data dimension i can be obtained by the following formula:

[0125] Formula 4

[0126] wherein, is not zero, is a positive correlation normalization.

[0127] In the supply chain, most of the production materials of an enterprise basically have fixed suppliers, and the production processes and production machines of these suppliers are also basically fixed, but the batches of materials they use are different, which leads to the fact that most of the data of an enterprise's supply chain are basically unchanged or have a low change frequency. Therefore, the stability is evaluated according to the change frequency of a large number of data.

[0128] Figure 7 FIG. 1 is a flowchart of a method for obtaining an influence degree index of supply chain data of a raw material in a supply chain component on an enterprise according to a degree of importance index and a repeatability index according to an example embodiment. As shown in FIG. 1, obtaining the influence degree index of the supply chain data of the raw material in the supply chain component on the enterprise according to the degree of importance index and the repeatability index can include the following steps: Figure 7

[0129] In step S301, a degree of importance index of supply chain data of a raw material in a supply chain component at any reference time in a set time period is obtained.

[0130] ​​​In this step, the supply chain component at any reference time t within the set time period is obtained. The importance of raw materials (d) in supply chain data across data dimension (i) For example, The method for obtaining the information can be referred to in the embodiment of step S104, and will not be repeated here.

[0131] In step S302, the total number of reference times within the set time period is obtained.

[0132] In this step, the total number of reference times within the set time period is obtained. .

[0133] In step S303, the impact index of the supply chain data of the raw materials in the supply chain component on the enterprise is obtained based on the importance index, the repeatability index, and the total number of moments.

[0134] In this step, based on the importance index Repeatability indicators and the total number of times Obtaining supply chain components The impact of supply chain data on enterprises in the data dimension i, specifically the raw material d. For example, supply chain components The impact of supply chain data on enterprises in the data dimension i, specifically the raw material d. It can be obtained from the following formula:

[0135] Formula 5

[0136] in, Not zero, Normalization for positive correlation.

[0137] For example, in the supply chain of server components, the lower the repetition of the raw material data for a component in various data records, and the more detailed the record of a particular component, the more important that dimension of data is in that link. Therefore, this data in that link is more important to the company building the server. Based on this, an indicator of the impact of each supply chain data on the company can be constructed.

[0138] Figure 8 This is a flowchart illustrating a method for obtaining an importance index of supply chain data for supply chain components based on an impact index, according to an exemplary embodiment. Figure 8 As shown, obtaining the importance index of the supply chain data for the supply chain component based on the impact index may include the following steps:

[0139] In step S401, a maximum value of an influence degree index of supply chain data of the raw material in the supply chain component on the enterprise is obtained.

[0140] In this step, a maximum value of an influence degree index of supply chain data of raw material d in data dimension i on the enterprise in supply chain component is obtained. .

[0141] In step S402, a minimum value of an influence degree index of supply chain data of the raw material in the supply chain component on the enterprise is obtained.

[0142] In this step, a minimum value of an influence degree index of supply chain data of raw material d in data dimension i on the enterprise in supply chain component is obtained. .

[0143] In step S403, an importance index of the supply chain data on the supply chain component is obtained according to the maximum value and the minimum value.

[0144] In this step, an importance index of supply chain data on supply chain component is obtained according to maximum value and minimum value . For example, an importance index of supply chain data on supply chain component may be obtained by the following formula:

[0145] Formula 6

[0146] wherein, the influence degree index of supply chain data on the enterprise in supply chain component is not zero. The importance index of any supply chain data on the supply chain component is represented by the relative ranking (proportion) of the influence degree index of the supply chain data on the enterprise in the influence degree index of the supply chain data on the enterprise.

[0147]

[0148] Figure 9 is a flow chart of a method for determining a collection strategy of supply chain data according to an importance index according to an exemplary embodiment. As shown in Figure 9 , the method for determining the collection strategy of the supply chain data according to the importance index can include the following steps:​​​​

[0149] In step S501, for the supply chain data whose importance index is greater than or equal to a first set threshold, directly upload the server.

[0150] In this step, for the supply chain data whose importance index is greater than or equal to a first set threshold, directly upload the server. The first set threshold can be 0.6, for example. This ensures the real-time collection of important supply chain data.

[0151] In step S502, for the supply chain data whose importance index is less than the first set threshold, upload the server after compression and smoothing.

[0152] In this step, for the supply chain data whose importance index is less than the first set threshold, upload the server after compression and smoothing. The first set threshold can be 0.6, for example. This saves network bandwidth without affecting the collection of supply chain data.

[0153] In step S503, when the network bandwidth utilization is less than or equal to a bandwidth utilization threshold, allocate network bandwidth to all supply chain data according to the proportion of the importance index.

[0154] In this step, when the network bandwidth utilization is less than or equal to a bandwidth utilization threshold, allocate network bandwidth to all supply chain data according to the proportion of the importance index. The bandwidth utilization threshold can be 40%, for example. This ensures that important supply chain data is collected first.

[0155] In step S504, when the network bandwidth utilization is greater than the bandwidth utilization threshold, for the supply chain data whose importance index is less than a second set threshold, temporarily upload the server, wherein the first set threshold is greater than the second set threshold.

[0156] In this step, when the network bandwidth utilization is greater than the bandwidth utilization threshold, for the supply chain data whose importance index is less than a second set threshold, temporarily upload the server, wherein the first set threshold is greater than the second set threshold. The second set threshold can be 0.3, for example. This ensures that important supply chain data is collected first in the case of network congestion, ensuring the real-time collection of important supply chain data.

[0157] ​​​The application further provides a computer readable storage medium, which stores computer program instructions, and the program instructions are executed by a processor to implement steps of the enterprise-level big data collection method for the converged ICT supply chain.

[0158] Figure 10 is a block diagram of an enterprise-level big data collection system for the converged ICT supply chain according to an exemplary embodiment. As shown in Figure 10 , the embodiment of the application provides an enterprise-level big data collection system 1000 for the converged ICT supply chain, which comprises a server 1100.

[0159] Figure 11 is a block diagram of a server according to an exemplary embodiment. Referring to Figure 11 , the server 1100 can comprise a processor 1122, which further comprises one or more processors, and a memory resource represented by a memory 1132, for storing instructions executable by the processor 1122, such as an application program. The application program stored in the memory 1132 can comprise one or more than one module each corresponding to a set of instructions. In addition, the processor 1122 is configured to execute the instructions to perform the enterprise-level big data collection method for the converged ICT supply chain described above.

[0160] The server 1100 can further comprise a power supply component 1126 configured to perform power management of the server 1100, a communication component 1150 configured to connect the server 1100 to a network, and an input / output interface 1158. The server 1100 can operate based on an operating system stored in the memory 1132.

[0161] In another exemplary embodiment, a computer program product is also provided, which comprises a computer program executable by a programmable electronic device, the computer program having code portions for performing the enterprise-level big data collection method for the converged ICT supply chain described above when executed by the programmable electronic device.

[0162] The above-described embodiments only express several implementation manners of the application, which are described in a more specific and detailed manner, but should not be understood as limitations on the scope of the application. It should be noted that, for those skilled in the art, without departing from the concept of the application, several modifications and improvements can be made, which are all within the protection scope of the application.

Claims

1. An enterprise level big data collection method for fusing ICT supply chain, characterized in that, The method comprises: obtaining an importance index of supply chain data of raw materials in a supply chain component, comprising: obtaining a data dimension similarity of the supply chain data of the raw materials in the supply chain component and the supply chain data in any supply chain component containing the raw materials; obtaining a data length of the supply chain data of the raw materials in the supply chain component; obtaining a mean value of the data length of the supply chain data in the supply chain component containing the raw materials; and obtaining the importance index of the supply chain data of raw materials in the supply chain component according to the data dimension similarity, the data length, and the mean value; obtaining a repeatability index of the raw materials in the supply chain component, comprising: obtaining a basic similarity of the raw materials; obtaining a stability index of the supply chain data of the raw materials; and obtaining the repeatability index of the raw materials in the supply chain component according to the basic similarity and the stability index; obtaining an influence degree index of the supply chain data of the raw materials in the supply chain component on an enterprise according to the importance index and the repeatability index, comprising: obtaining the importance index of the supply chain data of raw materials in the supply chain component at any reference time within a set time period; obtaining a total number of time points of the reference time within the set time period; and obtaining the influence degree index of the supply chain data of the raw materials in the supply chain component on the enterprise according to the importance index, the repeatability index, and the total number of time points; obtaining an importance index of the supply chain data for the supply chain component according to the influence degree index, comprising: obtaining a maximum value of the influence degree index of the supply chain data of the raw materials in the supply chain component on the enterprise; obtaining a minimum value of the influence degree index of the supply chain data of the raw materials in the supply chain component on the enterprise; and obtaining the importance index of the supply chain data for the supply chain component according to the maximum value and the minimum value; determining a collection strategy of the supply chain data according to the importance index, comprising: directly uploading the server for the supply chain data with the importance index greater than or equal to a first set threshold; uploading the server after compression and smoothing for the supply chain data with the importance index less than the first set threshold; allocating network bandwidth to all supply chain data according to the proportion of the importance index when the network bandwidth utilization rate is less than or equal to a bandwidth utilization rate threshold; and temporarily delaying uploading the server for the supply chain data with the importance index less than a second set threshold when the network bandwidth utilization rate is greater than the bandwidth utilization rate threshold, wherein the first set threshold is greater than the second set threshold.

2. The enterprise level big data collection method for converged ICT supply chain according to claim 1, wherein, The method comprises: constructing a supply chain data matrix of the supply chain component, the supply chain data matrix comprising supply chain data vectors of different raw materials of the supply chain component, the supply chain data vector comprising a relevant production data sequence of any raw material of the supply chain component; obtaining a data dimension similarity of the supply chain data of the raw material in the supply chain component and the supply chain data of any supply chain component containing the raw material according to the supply chain data matrix of the supply chain component and the supply chain data matrix of any supply chain component containing the raw material.

3. The enterprise level big data collection method for fusing ICT supply chain according to claim 1, characterized in that, The obtaining of the base similarity of the raw material comprises: obtaining a data dimension similarity of the supply chain data of any two supply chain components containing the raw material; obtaining a total number of data dimensions of the supply chain data of the supply chain component; obtaining a base similarity of the raw material according to the data dimension similarity and the total number.

4. The enterprise level big data collection method for fusing ICT supply chain according to claim 1, characterized in that, The obtaining of the stability index of the supply chain data of the raw material comprises: obtaining a time length from the last change to any change of the supply chain data within a set time period; obtaining a total number of changes of the supply chain data within the set time period; obtaining a stability index of the supply chain data of the raw material according to the time length and the total number of changes.

5. An enterprise level big data collection system that fuses ICT supply chain, characterized in that, The system comprises a server, and the server comprises: a memory having a computer program stored thereon; a processor configured to execute the computer program in the memory to implement the steps of the method of any one of claims 1-4.

Images