CN113505823A - Supply chain security analysis method and computer-readable storage medium - Google Patents
Supply chain security analysis method and computer-readable storage medium Download PDFInfo
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Abstract
The invention discloses a supply chain security analysis method and a computer-readable storage medium. The supply chain safety analysis method comprises the following steps: respectively coding and quantizing parameters of a plurality of components into key value pairs, wherein the plurality of components are components on supply chains of a plurality of industries, and the parameters at least comprise the industries of the components in the supply chains and the component names of the components; performing primary clustering on the key value pairs of the plurality of components to generate a plurality of clusters; performing secondary clustering on each cluster to generate a plurality of subclass clusters; and determining the industry proportion of the components with the same component names in the subclass clusters according to each subclass cluster, and determining the supply chain risk prevention and control level of the components according to the industry proportion. On one hand, the method solves the problems that resources in industries are respectively closed, high-quality components are not circulated, the industry has poor cooperative capability, and the risk prevention and control granularity of a supply chain is coarse; on the other hand, decision reference is provided for the possibility of cross-industry component replacement.
Description
Technical Field
The invention relates to the field of supply chains, in particular to the field of safety risk monitoring and analysis of supply chains in key industries.
Background
The importance of supply chain safety has been a key direction of concern in all countries, especially the key industry supply chain of national civilians. The safety of the supply chain relates to the aspects of the industry, a complete supply chain relates to multi-party responsibility, manufacturers, suppliers, integrators and the like, and products (such as components) on the supply chain are more complex and various and are all five-fold. With the development of the 5G network, the supply chain develops towards more complex and fine-grained direction, and the related security problem is more complex. Once the supply chain is attacked or abnormal, the consequences are not enough to be imagined, especially the supply chain security of the key industry is more important. Therefore, the safety of the industry supply chain, especially the key industry supply chain, has become an important basic guarantee for the development of the country and the industry.
The current research on supply chain safety risks presents the following problems: the granularity of risk research is coarse, and smaller constituent units such as components on a supply chain are less touched; most of the components stay in the industry or safety assessment on the industry chain, cross-industry cooperative capability analysis of the components on the supply chain is lacked, namely, aiming at the condition that the same type of components are applied to different industries, resources among the industries are respectively closed, the cooperative capability is weak, and the importance of the overall safety of the components is not well reflected; in the existing mode of component replacement, component replacement in the industry is mostly considered preferentially, and component replacement with similar functions and performances or better quality in other industries is ignored, so that high-quality components are difficult to circulate.
Disclosure of Invention
The present invention provides a method for supply chain security analysis and a computer-readable storage medium to solve at least one of the above technical problems.
According to a first aspect of the present invention, there is provided a supply chain security analysis method, the method comprising:
respectively coding and quantizing parameters of a plurality of components into key value pairs, wherein the plurality of components are components on supply chains of a plurality of industries, and the parameters at least comprise the industries of the components in the supply chains and the component names of the components;
performing primary clustering on the key value pairs of the plurality of components to generate a plurality of clusters;
performing secondary clustering on each of the plurality of clusters, and generating a plurality of sub-clusters from each cluster; and
and determining the industry proportion of the components with the same component names in each subclass cluster, and determining the supply chain risk prevention and control level of the components with the same component names according to the industry proportion.
Optionally, the parameters further include at least one of a production area, a specification model, a supplier, supplier uniqueness, and performance of the component.
Optionally, the primary clustering is clustering based on a K-Means algorithm, and the secondary clustering is clustering based on a maximum-minimum distance algorithm.
Optionally, the secondary clustering comprises: and obtaining a plurality of clustering centers in each cluster generated by the primary clustering according to the maximum-minimum distance algorithm principle, and then generating a plurality of sub-cluster according to the K-Means algorithm.
Optionally, the determining an industry proportion of components having the same component name in the subclass cluster includes:
obtaining the number of industries in the subclass cluster and the number of industries corresponding to the components with the same component name in the subclass cluster based on the key value pair in the subclass cluster, and
and calculating the ratio of the number of industries corresponding to the components with the same component names in the subclass cluster to the number of industries in the subclass cluster, and determining the supply chain risk prevention and control level of the components according to the ratio.
Optionally, the supply chain security analysis method further includes: and determining the possibility of cross-industry replacement of the component according to the supply chain risk prevention and control grade of the component.
Optionally, the plurality of industries are predetermined key industries; and before the step of coding and quantizing the parameters of the plurality of components into key value pairs respectively, the supply chain safety analysis method further comprises the following steps:
and combing the key industries to obtain the parameters of the components on the supply chain of each key industry.
According to a second aspect of the present invention, there is provided a computer readable storage medium having stored thereon a computer program which, when executed by a processor, performs the aforementioned supply chain security analysis method.
Compared with the prior art, the supply chain safety analysis method solves the problems that resources are respectively closed among industries, high-quality components are not circulated, the industry coordination capability is poor, and the supply chain risk prevention and control granularity is coarse; on the other hand, decision reference is provided for the possibility of cross-industry component replacement.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the principles of the invention and not to limit the invention, in which:
FIG. 1 is a schematic diagram of a supply chain security analysis method according to an embodiment of the invention;
FIG. 2 is a flow diagram of key industry clustering partitioning based on the K-Means algorithm according to one example of the present invention;
fig. 3 is a flow chart of Maximum Minimum Distance (MMD) based quadratic clustering performed subsequent to the flow of fig. 2.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the embodiments and specific examples of the present invention are described in detail below with reference to the accompanying drawings. It should be understood that the embodiments and specific examples described herein are intended only to illustrate and explain the present invention and are not intended to limit the present invention. In addition, the embodiments and features of the embodiments of the present invention may be arbitrarily combined with each other without conflict.
Fig. 1 is a flowchart of a supply chain security analysis method according to an embodiment of the present invention, as shown in fig. 1, the method includes the following steps:
s202: and respectively coding and quantizing the parameters of the plurality of components into key value pairs. The plurality of components are components on supply chains of a plurality of industries, and the parameters at least comprise the industry of the components in the supply chains and the component names of the components.
S204: and clustering the key value pairs of the components once to generate a plurality of clusters.
S206: performing secondary clustering on each of the plurality of class clusters, and generating a plurality of subclass clusters from each class cluster.
S208: and determining the industry proportion of the components with the same component names in each subclass cluster, and determining the supply chain risk prevention and control level of the components with the same component names according to the industry proportion.
In the method, the accuracy of risk assessment of the components on the supply chain can be improved and the value of replaceability can be enlarged by implementing two-step clustering, and cross-industry risk prevention and control of the components on the supply chain can be realized by determining the supply chain risk prevention and control level according to the industry proportion of the components in the subclass cluster. Therefore, the problems that resources are respectively closed among industries, high-quality components are not circulated, the industry coordination capacity is poor, and the risk prevention and control granularity of a supply chain is thick are solved; on the other hand, decision reference is provided for the possibility of cross-industry component replacement.
In one embodiment, the parameters further include, but are not limited to: at least one of a place of origin, a model, a vendor uniqueness, a performance of the component. Therefore, the similarity of the component data in the same subclass cluster can be improved, and the accuracy of component risk assessment can be further improved.
In one embodiment, the primary clustering is a K-Means algorithm based clustering, also known as K-Means clustering; the quadratic clustering is a clustering based on the maximum-minimum distance algorithm, also called MMD clustering. By carrying out K-Means clustering and then carrying out MMD clustering, the risk prevention and control level of the supply chain can be obtained most accurately, and the value of replaceability is enlarged.
In one embodiment, the quadratic clustering, i.e. the clustering based on the maximum-minimum distance algorithm, includes: in each cluster generated by the primary clustering, a plurality of clustering centers are obtained according to the maximum-minimum distance algorithm principle, and then a plurality of sub-cluster are generated according to the K-Means algorithm
In an embodiment, the determining the industry proportion of the components having the same component name in the sub-cluster in step S208 includes the following sub-steps:
s2081: obtaining the number of industries corresponding to the components with the same component name in the subclass cluster and the number of industries in the subclass cluster based on the key value pair in the subclass cluster; and
s2082: and calculating the ratio of the number of industries corresponding to the components with the same component names in the subclass cluster to the number of industries in the subclass cluster, and determining the supply chain risk prevention and control level of the components according to the ratio.
For example, it is assumed that 5 industries (e.g., communication, power, space, transportation, and medical treatment) are involved in a certain sub-cluster after secondary clustering, and 4 industries (e.g., communication, power, space, and transportation) are involved in a component having, for example, the same component name "CPU (central processing unit)"; components having the same component name "ONU (optical network unit)" relate to 1 industry (for example, communication, where industries including a plurality of ONU components in the whole sub-cluster are all communication and only one ONU component is related in the whole sub-group). In this case, in step S2082, the calculated ratio is 4/5-80% for the supply chain risk prevention and control level of the CPU, and 1/5-20% for the supply chain risk level of the ONU.
In one embodiment, the supply chain security analysis method further comprises: and determining the possibility of cross-industry replacement of the component according to the supply chain risk prevention and control grade of the component. Therefore, decision reference is provided for cross-industry component replacement, so that the same products can realize cross-industry same-function same-performance replacement, and the method is particularly beneficial to promoting the domestic replacement of core components in key industries.
In one embodiment, the plurality of industries are predetermined key industries, and before step S202, the supply chain safety analysis method further comprises: and combing the key industries to obtain the parameters of the components on the supply chain of each key industry. In practice, the user can set important industries of interest as needed.
In order to enable a person skilled in the art to better understand the principle and practical application of the present invention, a specific example according to the first aspect of the present invention is described below with reference to fig. 2 and 3.
In the supply chain safety analysis method according to the present example, the important industries include, for example, communications, power, aerospace, transportation, medical, petroleum. Referring to fig. 2, in step S21, key industries are first sorted to obtain parameters of components on each key industry and its supply chain, and then the obtained parameters of the components are encoded and quantized and represented by key value pairs for subsequent use of a clustering algorithm. The quantified parameters include the industry to which the component belongs (the same component in different supply chains may belong to different industries), the name of the component, the brand, the place of origin, the model number, and the uniqueness of the supplier (i.e., whether the supplier is unique). For example, as in the communications industry, set the code to 11; a component CPU on a supply chain is provided with a code of 111, a brand is Inter, and a quantization value is 1.1; the country of origin, U.S. quantitative 10, model i7-10700KF, quantitative 107, vendor unique, quantitative 1, and the resulting key-value pair expressed as <11,111(1.1,10,107,1) >. The advantage of forming key-value pairs is that the parameter values of the components in the supply chain can be opened layer by layer according to subsequent needs, and if not needed, only the codes corresponding to the industry and the component names, namely <11,111>, are displayed in a hidden state, so as to enhance the privacy protection of data. In addition, when the data volume is huge, the Hadoop can be used for rapid analysis by using a large data analysis frame, and the data processing efficiency is improved.
After the quantization in the previous step, a plurality of key industries are quantized into data, and then, in step S22, a K value and an initial clustering center are input according to the principle of a K-Means clustering algorithm for a quantized data source, so as to perform clustering calculation. In step S23, it is determined whether the calculation result converges, and if not, the process returns to step S22, and if so, the process proceeds to step S24. In step S24, the calculated K cluster classes are obtained and output. The K-Means clustering has the characteristics of data similarity maximization in the clusters and data similarity minimization between the clusters. At the moment, a plurality of key industries and a plurality of component data of the industries exist in the same cluster, so that the method is beneficial to reducing the range of risk assessment of the supply chain and the optional range of component substitute products, and the data in the same cluster have greater similarity. Taking a CPU as an example, in the output K1 cluster, there are data of a plurality of CPUs from different industries, brands, production places and suppliers. For example, the industry includes communication industry, power industry, aerospace industry, etc., the brands of CPUs include Inter, AMD and kylin, the production places include usa and china, etc., one or more suppliers may exist, and corresponding data parameters are different. Therefore, the data distribution pattern in K1 exists in the form of <11,111(1.1,10,107,1) >, <12,112(1.2,12,107,1) >, <13,132(1.5,46,100,0) >, and the like. Therefore, the K clusters have respective in-cluster data characteristics, and a foundation is laid for next fine-grained component grading and replacement feasibility analysis.
And forming a class cluster with K characteristics through the primary clustering of the last step, wherein the same class cluster has larger similarity, and in order to improve the accuracy of risk evaluation of components on a supply chain and expand the value of replaceability, secondary clustering based on a maximum-minimum distance algorithm needs to be performed in the same class cluster. As shown in fig. 3, in step S25, the K cluster classes are input. Then, in steps S26-S28, according to the principle of the Maximum Minimum Distance (MMD) algorithm, inputting a K' value, and randomly selecting a data source in the cluster as a first initial cluster center, such as selecting<11,111(1.1,10,107,1)>(Note: key-value pair symbols may be removed at the time of computation<>) Then calculating the data source farthest from the first initial cluster center as the second initial clusterCalculating the distances between the data sources left after the two initial clustering centers are removed and the first clustering center and the second clustering center respectively, selecting the minimum value of the distances to form a minimum value cluster, selecting the data source corresponding to the data with the maximum value in the cluster as the third initial clustering center, repeating the steps to find K ' clustering centers, and outputting K ' clusters according to the principle of K-Means clustering, wherein the cluster at the moment is defined as a small cluster for distinguishing from the primary clustering and is recorded as K 'nkWherein n is in the range of [1, K ]]K is [1, K']. At this time, K 'small cluster 1 to K' are formed in 1 to K of the K cluster respectively, so that the similarity between the data sources is more prominent.
The purpose of secondary clustering is to find more similar data among data with greater similarity to form new class clusters, namely to form K ' subclass clusters K ' in each of the class clusters 1-K '11To K'1K’The resulting data is equivalent to forming K x K' clusters, such that the similarity between data sources is maximized, with the same small cluster, e.g., K11The data in the method are higher in similarity degree, and the substitution feasibility of like products is higher.
Then, a fine-grained assessment of supply chain security risk prevention and control rankings may be performed. In this example, the supply chain risk prevention and control level is defined by calculating the industry proportion of a certain component in a subclass cluster, the supply chain risk prevention and control level can be classified into A, B, C levels from high to low, the component with the high risk prevention and control level is required to be highly emphasized in the supply chain safety protection subsequently, the corresponding safety protection strategy is required to be higher than the protection measures of other levels, and the corresponding protection measures of other levels are correspondingly adjusted from high to low. The grading has the advantage of providing decision value for the supply chain safety risk protection strategy, so that the emergency capacity of the industry for dealing with sudden or abnormal situations is improved. If 5 industries (communication, electric power, aerospace, transportation and medical treatment) exist in a subclass cluster, wherein the industry proportion of the components M in the communication, electric power, aerospace and transportation industries is 4/5, namely 80%, the component risk prevention and control level A with the proportion of 80% or more is defined, and high safety measures are adopted in corresponding protection strategies; similarly, assuming that the component N accounts for 3/5, and the rating accounts for 50-80%, the risk prevention and control grade is rated as grade B; and if the S proportion of the component is 1/5 and is lower than 50%, the risk prevention and control grade is defined as C grade, and the protection strategy is adjusted correspondingly.
In addition, the feasibility reference of cross-industry substitution of products with the same functional performance can be carried out, namely under the condition that components are of the same type and have the same functions and performances, the components can be subjected to cross-industry substitution, such as a component CPU, the components are arranged on various supply chain equipment of communication, electric power and spaceflight, manufacturers related to the three industries have Inter, AMD and kylin, under the condition of the same functions and performances, the industry without using the kylin CPU can use the kylin CPU as an alternative for CPU substitution, domestic substitution is carried out, the safety risk of the supply chain is reduced, the high-quality component substitution reference of the cross-industry is realized, the collaborative development of the industry is promoted together, the safety and the toughness are enhanced, and the reference is provided for the comprehensive promotion of the autonomous controllable technology.
The 5G network growth promotion industry is subjected to digital transformation, the key industry occupies the position of the traffic column, and as can be seen from the embodiments and the specific examples, the key industry supply chain safety analysis method based on K-Means and MMD provided by the invention solves the problems that resources are respectively closed, high-quality components and parts are not circulated, the resources are not fully utilized to the maximum extent, the industry coordination capacity is poor, and the supply chain risk prevention and control granularity is coarse; on the other hand, decision reference is provided for cross-industry component replacement through algorithm analysis, particularly under the condition that the existing international relationship is complex and changeable, the localization replacement of core components in key industries is promoted, and the significance of realizing independent controllability of core technologies is great. Therefore, the invention has important value significance.
Based on the same technical concept, the present invention accordingly also provides a computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, the processor performs the supply chain safety analysis method as described above.
One of ordinary skill in the art will appreciate that all or some of the steps of the methods disclosed above may be implemented as software, firmware, hardware, and suitable combinations thereof. Computer-readable media disclosed above may include computer storage media (or non-transitory media) and communication media (or transitory media). The term computer storage media includes volatile and nonvolatile, removable and non-removable media implemented in any method or technology for storage of information such as computer readable instructions, data structures, program modules or other data, as is well known to those of ordinary skill in the art. Computer storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, Digital Versatile Disks (DVD) or other optical disk storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to store the desired information and which can accessed by a computer. In addition, communication media typically embodies computer readable instructions, data structures, program modules or other data in a modulated data signal such as a carrier wave or other transport mechanism and includes any information delivery media as known to those skilled in the art.
It is to be understood that the above embodiments and specific examples are merely illustrative of exemplary embodiments/examples that may be employed to illustrate the principles of the present invention, and that the present invention is not limited thereto. It will be apparent to those skilled in the art that various modifications and improvements can be made without departing from the spirit and substance of the invention, and these modifications and improvements are also considered to be within the scope of the invention.
Claims (8)
1. A supply chain security analysis method, characterized in that the supply chain security analysis method comprises:
respectively coding and quantizing parameters of a plurality of components into key value pairs, wherein the plurality of components are components on supply chains of a plurality of industries, and the parameters at least comprise the industries of the components in the supply chains and the component names of the components;
performing primary clustering on the key value pairs of the plurality of components to generate a plurality of clusters;
performing secondary clustering on each of the plurality of clusters, and generating a plurality of sub-clusters from each cluster; and
and determining the industry proportion of the components with the same component names in each subclass cluster, and determining the supply chain risk prevention and control level of the components with the same component names according to the industry proportion.
2. The supply chain security analysis method of claim 1, wherein the parameters further include at least one of a production location, a specification model, a supplier uniqueness, and a performance of the component.
3. The supply chain safety analysis method according to claim 1, wherein the first-order clustering is based on a K-Means algorithm, and the second-order clustering is based on a maximum-minimum distance algorithm.
4. The supply chain safety analysis method according to claim 3, wherein the quadratic clustering comprises: and obtaining a plurality of clustering centers in each cluster generated by the primary clustering according to the maximum-minimum distance algorithm principle, and then generating a plurality of sub-cluster according to the K-Means algorithm.
5. The supply chain safety analysis method according to any one of claims 1 to 4, wherein the determining the industry proportion of the components having the same component name in the sub-cluster comprises:
obtaining the number of industries in the subclass cluster and the number of industries corresponding to the components with the same component name in the subclass cluster based on the key value pair in the subclass cluster, and
and calculating the ratio of the number of industries corresponding to the components with the same component names in the subclass cluster to the number of industries in the subclass cluster, and determining the supply chain risk prevention and control level of the components according to the ratio.
6. The supply chain security analysis method according to any one of claims 1 to 4, further comprising:
and determining the possibility of cross-industry replacement of the component according to the supply chain risk prevention and control grade of the component.
7. The supply chain safety analysis method according to any one of claims 1 to 4, wherein the plurality of industries are predetermined key industries; and is
Before the step of coding and quantizing the parameters of the plurality of components into key value pairs respectively, the supply chain safety analysis method further includes:
and combing the key industries to obtain the parameters of the components on the supply chain of each key industry.
8. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out a supply chain security analysis method according to any one of claims 1 to 7.
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