CN113505823B - 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 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; clustering the key value pairs of the components for one time to generate a plurality of class clusters; performing secondary clustering on each class cluster to generate a plurality of sub-class clusters; and determining the industry proportion of the components with the same component name in the sub-class cluster aiming at each sub-class cluster, and determining the risk prevention and control level of the supply chain of the components according to the industry proportion. The method solves the problems that resources among industries are respectively closed, high-quality components are not circulated, industry coordination capacity is poor, and supply chain risk prevention and control granularity is coarse; on the other hand, decision references are 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
Supply chain security has long been the key direction of concern in various countries, especially in the key industry supply chain of national relatives and folk life, of self-evident importance. Supply chain safety relates to aspects of industry, a complete supply chain relates to multiparty responsibility, and products (such as components) on the supply chain are more complex, various and five-flower eight-door. With the development of 5G networks, the supply chain is moving towards more complex and refined, and the security problems involved are more complex. The consequences are not considered to be especially important for the safety of supply chains in the critical industry, once they are attacked or abnormal. Therefore, the safety of industry supply chains, particularly important industry supply chains, has become an important basic guarantee for national and industry development.
Current research on supply chain security risk has the following problems: the granularity of the risk study is thicker, and smaller constituent units on the supply chain, such as components, are less touched; most of the safety evaluation stays in the industry components or the industry chain, and cross-industry collaborative capability analysis of the components on the supply chain is lacking, namely, the resources among industries are respectively closed and collaborative capability is weak aiming at the condition that the same type of components are applied to different industries, so that the importance of the overall safety of the components is not well reflected; in the current mode of component replacement, most of the component replacement in the industry is prioritized, and component replacement with similar functions, performances or better quality in other industries is ignored, so that good quality components are difficult to circulate.
Disclosure of Invention
The invention provides a supply chain security analysis method and a computer readable storage medium, which are used for solving at least one of the 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 components are components on supply chains of a plurality of industries, and the parameters at least comprise the industries of which the components belong in the supply chains and the component names of the components;
clustering the key value pairs of the components for one time to generate a plurality of class clusters;
performing secondary clustering on each of the plurality of class clusters, and generating a plurality of sub-class clusters from each class cluster; and
and determining the industry proportion of the components with the same component names in the sub-class clusters according to each sub-class 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 place of production, a specification model, a vendor uniqueness, and a performance of the component.
Optionally, the primary cluster is a cluster based on a K-Means algorithm, and the secondary cluster is a cluster based on a maximum minimum distance algorithm.
Optionally, the secondary clustering includes: and in each class cluster generated by the primary clustering, a plurality of clustering centers are obtained according to the principle of a maximum and minimum distance algorithm, and then a plurality of sub-class clusters are generated according to a K-Means algorithm.
Optionally, the determining the industry ratio of the components with the same component name in the sub-cluster includes:
based on the key value pairs in the sub-cluster, the number of industries in the sub-cluster and the number of industries corresponding to the components with the same component names in the sub-cluster are obtained, and
and calculating the ratio between the number of industries corresponding to the components with the same component names in the sub-cluster and the number of industries in the sub-cluster, and determining the risk prevention and control level of the supply chain of the components according to the ratio.
Optionally, the supply chain security analysis method further comprises: and determining the cross-industry substitution possibility of the component according to the supply chain risk prevention and control level of the component.
Optionally, the plurality of industries is a predetermined accent industry; and before the step of encoding and quantizing the parameters of the plurality of components into key-value pairs, respectively, the supply-chain security analysis method further includes:
and carding the key industries to obtain parameters of components on a 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 closed respectively, high-quality components are not circulated, industry coordination capacity is poor, and supply chain risk prevention and control granularity is thick; on the other hand, decision references are provided for the possibility of cross-industry component replacement.
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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 and embodiments of the invention and together with the description serve to explain the principles of the invention and are not intended to limit the invention, wherein:
FIG. 1 is a schematic diagram of a supply chain security analysis method according to an embodiment of the present invention;
FIG. 2 is a flow chart of key industry cluster partitioning based on the K-Means algorithm according to one example of the invention;
FIG. 3 is a flow chart of Maximum Minimum Distance (MMD) based secondary clustering performed subsequent to the flow of FIG. 2.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the embodiments and specific examples of the present invention will be described in detail below with reference to the accompanying drawings. It should be understood that the embodiments and specific examples described herein are for the purpose of illustration and explanation only and are not intended to limit the present invention. In addition, the embodiments of the present invention and the features in the embodiments may be arbitrarily combined with each other without collision.
Fig. 1 is a flowchart of a method for analyzing safety of a supply chain according to an embodiment of the present invention, as shown in fig. 1, the method includes the following steps:
s202: and respectively encoding and quantizing parameters of the components into key value pairs. The plurality of components are components on a supply chain of a plurality of industries, and the parameters at least comprise the industries of which the components belong in the supply chain and the component names of the components.
S204: and clustering the key value pairs of the components once to generate a plurality of class clusters.
S206: performing secondary clustering for each of the plurality of class clusters, and generating a plurality of sub-class clusters from each class cluster.
S208: and determining the industry proportion of the components with the same component names in the sub-class clusters according to each sub-class 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 the 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 risk prevention and control level of the supply chain according to the industry proportion of the components in the sub-cluster. Therefore, on one hand, the problems that resources among industries are respectively closed, high-quality components are not circulated, industry coordination capacity is poor, and supply chain risk prevention and control granularity is thicker are solved; on the other hand, decision references are 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 production, a model, a vendor uniqueness, and a performance of the component. Therefore, the similarity degree of the component data in the same subclass cluster can be improved, and the accuracy of component risk assessment is further improved.
In one embodiment, the primary clusters are K-Means algorithm-based clusters, also known as K-Means clusters; the secondary clusters are clusters based on a maximum minimum distance algorithm, also known as MMD clusters. Through carrying out K-Means clustering first and then MMD clustering, the risk prevention and control level of the supply chain can be obtained most accurately, and the value of the replaceability is enlarged.
In one embodiment, the secondary clustering, i.e. clustering based on a maximum minimum distance algorithm, comprises: in each class cluster generated by the primary clustering, a plurality of clustering centers are obtained according to the principle of a maximum-minimum distance algorithm, and then a plurality of sub-class clusters are generated according to a K-Means algorithm
In one embodiment, the determining the industry ratio of the components with the same component name in the sub-cluster in the step S208 includes the following sub-steps:
s2081: based on the key value pairs in the sub-cluster, obtaining the number of industries corresponding to the components with the same component names in the sub-cluster and the number of industries in the sub-cluster; and
s2082: and calculating the ratio between the number of industries corresponding to the components with the same component names in the sub-cluster and the number of industries in the sub-cluster, and determining the risk prevention and control level of the supply chain of the components according to the ratio.
For example, suppose that components in a certain sub-cluster after secondary clustering involve 5 industries (e.g., communication, power, space, traffic, medical), and that components in the sub-cluster having, for example, the same component name "CPU (central processing unit)" involve 4 industries (e.g., communication, power, space, traffic); the industries to which components having the same component name "ONU (optical network unit)" relate are 1 (for example, communication, including a case where industries of a plurality of ONU components in the entire sub-group are all communication and a case where only one ONU component is involved in the entire sub-group). In this case, in step S2082, the calculated ratio is 4/5=80% for the supply chain risk prevention 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 cross-industry substitution possibility of the component according to the supply chain risk prevention and control level of the component. Therefore, decision references are provided for cross-industry component replacement, and similar products can realize cross-industry same-function and same-performance replacement, so that the method is particularly beneficial to promoting the localization replacement of core components in key industries.
In one embodiment, the plurality of industries are predetermined accent industries, and prior to step S202, the supply chain security analysis method further comprises: and carding the key industries to obtain parameters of components on a supply chain of each key industry. In practice, the user can set the key industry of interest as desired.
In order to enable those skilled in the art to better understand the principles and practical applications of the present invention, a specific example according to a first aspect of the present invention is described below in connection with fig. 2 and 3.
In the supply chain security analysis method according to the present example, important industries include, for example, communication, electric power, aerospace, traffic, medical, petroleum. Referring to fig. 2, in step S21, the key industries are first combed, parameters of components on each key industry and its supply chain are obtained, and then the obtained component parameters 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 components belong (the same component in different supply chains may belong to different industries), the component name, brand, place of production, specification model, and vendor uniqueness (i.e., whether the vendor is unique). For example, as in the communications industry, the code is set to 11; the CPU of the component on the supply chain is 111, the brand is Inter, and the quantized value is 1.1; the origin united states, quantized value 10, model i7-10700KF, quantized value 107, vendor unique, quantized value 1, the final key pair formed is denoted <11,111 (1.1,10,107,1) >. The key value pair is formed in such a way that the parameter values of the components on the supply chain can be opened layer by layer according to the subsequent requirement, and if not required, only codes corresponding to industry and component names, namely <11,111>, are displayed in a hidden state so as to enhance the privacy protection of the data. In addition, when the data volume is huge, the Hadoop of the big data analysis framework can be utilized for rapid analysis, and the data processing efficiency is improved.
After the quantization in the previous step, a plurality of key industries are quantized into data, and next, in step S22, for the quantized data source, a K value and an initial clustering center are input according to the principle of a K-Means clustering algorithm, and clustering calculation is performed. In step S23, it is determined whether the calculation result is converged, and if not, the process returns to step S22, and if so, the process returns to step S24. In step S24, the calculated K class clusters are obtained and output. The K-Means cluster has the characteristics of maximizing the similarity of data in the clusters and minimizing the similarity of data among the clusters. At this time, there are multiple key industries and multiple component data of the industries in the same cluster, which is beneficial to narrowing the range of risk assessment of the supply chain and the selectable range of the component replacement products, because the data in the same cluster have larger similarity. Taking a CPU as an example, in the output K1 cluster, there are data of multiple CPUs from different industries, brands, places of production and suppliers. For example, industries include communications, power, aerospace, etc., CPU brands include Inter, AMD, kylin, united states, china, etc., suppliers may have one or more, and corresponding data parameters may be different. The data distribution pattern in K1 has the following forms, <11,111 (1.1,10,107,1) >, <12,112 (1.2,12,107,1) >, <13,132 (1.5,46,100,0) >, and the like. Thus, the K class clusters have the characteristics of data in each cluster, and lay a foundation for the next finer-granularity component grading and substitution feasibility analysis.
After the primary clustering of the previous step, class clusters with K characteristics are formed, and the same class cluster has larger similarityIn order to improve the accuracy of risk assessment of components on a supply chain and expand the value of alternatives, secondary clustering based on a maximum-minimum distance algorithm is required to be performed in the same cluster. As shown in fig. 3, in step S25, the K class clusters are input. Then, in step S26-S28, according to the principle of Maximum Minimum Distance (MMD) algorithm, the K' value is input, and one data source in the cluster is randomly selected as the first initial cluster center, for example, selection<11,111(1.1,10,107,1)>(note: key-value pair symbols can be removed at the time of calculation)<>) Then calculating the furthest data source from the first initial clustering center as a second initial clustering center, calculating the distance between the left data source after the two initial clustering centers are removed and the first and second clustering centers respectively, selecting the minimum value of the distance to form a minimum value set, selecting the data source corresponding to the data with the largest value in the set as a third initial clustering center, analogizing in sequence to find K 'clustering centers, and outputting K' clusters according to the principle of K-Means clustering, wherein the clusters are defined as subclasses for distinguishing with primary clusters and are recorded as K '' nk Wherein n is in the range of [1, K ]]The value of k is [1, K ]']. At this time, K 'sub-class clusters 1 to K' are formed in K class clusters 1 to K, respectively, so that the similarity between the data sources is more remarkable.
The purpose of the secondary clustering is to find more similar data among the data with larger similarity to form new class clusters, namely K ' subclasses K ' are formed in each of the class clusters 1-K ' 11 To K' 1K’ The resulting data corresponds to the formation of K X K' clusters, such that the similarity between the data sources is maximized, with the same subclass cluster, e.g., K 11 The data similarity of the data is higher, and the feasibility of substitution of similar products is higher.
Then, fine-grained assessment of supply chain security risk prevention and control rankings may be performed. In this example, the industry proportion of a certain component in the subclass cluster is calculated to define the risk prevention and control grade of the supply chain, which can be divided into A, B, C three levels from high to low, and the component with the high risk prevention and control grade should be highly valued in the safety protection of the supply chain subsequently, and the corresponding safety protection strategy should 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 method has the advantages that decision value is provided for the safety risk protection strategy of the supply chain, so that emergency capability of industry for dealing with sudden or abnormal situations is improved. If there are 5 industries (communication, electric power, aerospace, traffic, medical) in the subclass cluster, wherein the component M exists in the communication, electric power, aerospace, traffic industries, the industry ratio is 4/5, namely 80%, the component risk prevention and control level A with the ratio of 80% or more is defined, and the corresponding protection strategy adopts high security measures; similarly, assuming that the component N accounts for 3/5, the demarcation accounts for 50-80%, and the demarcation risk prevention and control level is level B; and the S ratio of the components is 1/5 and is lower than 50%, the risk prevention and control level is defined as C level, and the protection strategy is correspondingly adjusted.
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 similar and have the same functions and performance, the components can be subjected to cross-industry substitution, such as a component CPU, the components are arranged on a plurality of supply chain devices of communication, electric power and aerospace, manufacturers involved in three industries have Inter, AMD and kylin, under the condition of the same functions and performance, the industry without using the kylin CPU can take the kylin CPU as a CPU substitution alternative, home-made substitution is carried out, the safety risk of a supply chain is reduced, the high-quality component substitution reference of the cross-industry is realized, the collaborative development of the industry is jointly promoted, the safety toughness is enhanced, and the reference is provided for comprehensively promoting the autonomous controllable technology.
The 5G network induced growth industry digital transformation, the key industry occupies the position of the middle stream column, and the embodiment and the specific example can show that the key industry supply chain safety analysis method based on K-Means and MMD provided by the invention solves the problems that resources among industries are respectively closed, high-quality components are not circulated, the resource maximization is not fully utilized, the industry coordination capability is poor, and the supply chain risk prevention and control granularity is thicker; on the other hand, through algorithm analysis, decision references are provided for cross-industry component replacement, and especially under the condition that the current international relation is complex and changeable, the localization replacement of core components in key industries is promoted, so that the autonomous and controllable significance of the core technology is great. Therefore, the invention has important value significance.
Based on the same technical idea, 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, performs the supply chain security analysis method as described above.
Those 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. The 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 both 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 known to those skilled 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 be accessed by a computer. Furthermore, as is well known to those of ordinary skill in the art, 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.
It is to be understood that the above embodiments and specific examples are merely illustrative of the exemplary embodiments/examples employed to illustrate the principles of the invention, however, the invention is not limited thereto. Various modifications and improvements may be made by those skilled in the art without departing from the spirit and substance of the invention, and are also considered to be within the scope of the invention.
Claims (8)
1. A supply chain security analysis method, the supply chain security analysis method comprising:
respectively coding and quantizing parameters of a plurality of components into key value pairs, wherein the components are components on supply chains of a plurality of industries, and the parameters at least comprise the industries of which the components belong in the supply chains and the component names of the components;
clustering the key value pairs of the components for one time to generate a plurality of class clusters;
performing secondary clustering on each of the plurality of class clusters, and generating a plurality of sub-class clusters from each class cluster; and
and determining the industry proportion of the components with the same component names in the sub-class clusters according to each sub-class 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 comprise at least one of a place of production, a specification model, a vendor uniqueness, a performance of the component.
3. The supply chain security analysis method according to claim 1, wherein the primary cluster is a K-Means algorithm-based cluster and the secondary cluster is a maximum minimum distance algorithm-based cluster.
4. The supply chain security analysis method of claim 3, wherein the secondary clustering comprises: and in each class cluster generated by the primary clustering, a plurality of clustering centers are obtained according to the principle of a maximum and minimum distance algorithm, and then a plurality of sub-class clusters are generated according to a K-Means algorithm.
5. The supply chain security analysis method of any one of claims 1 to 4, wherein the determining an industry ratio of components having the same component name in the sub-cluster comprises:
based on the key value pairs in the sub-cluster, the number of industries in the sub-cluster and the number of industries corresponding to the components with the same component names in the sub-cluster are obtained, and
and calculating the ratio between the number of industries corresponding to the components with the same component names in the sub-cluster and the number of industries in the sub-cluster, and determining the risk prevention and control level of the supply chain of the components according to the ratio.
6. The supply chain safety analysis method according to any one of claims 1 to 4, further comprising:
and determining the cross-industry substitution possibility of the component according to the supply chain risk prevention and control level 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 also provided with
Before the step of encoding and quantizing the parameters of the plurality of components into key-value pairs, the supply-chain security analysis method further includes:
and carding the key industries to obtain parameters of components on a supply chain of each key industry.
8. A computer readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, performs the supply chain safety analysis method according to any one of claims 1 to 7.
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Citations (24)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2012026415A1 (en) * | 2010-08-27 | 2012-03-01 | テクノポリマー株式会社 | Contact component with reduced squeak noise made from thermoplastic resin composition |
| CN104376403A (en) * | 2014-10-30 | 2015-02-25 | 广东电网有限责任公司东莞供电局 | Substation sag sensitivity grading method based on subordinate user industry characteristics |
| CN106570729A (en) * | 2016-11-14 | 2017-04-19 | 南昌航空大学 | Air conditioner reliability influence factor-based regional clustering method |
| CN107819770A (en) * | 2017-11-15 | 2018-03-20 | 中国联合网络通信集团有限公司 | Medical data sharing method for secret protection and device based on block chain |
| CN207683143U (en) * | 2017-12-13 | 2018-08-03 | 珠海纳思达企业管理有限公司 | Ink-feeding device and ink-jet printer |
| CN108846532A (en) * | 2018-03-21 | 2018-11-20 | 宁波工程学院 | Business risk appraisal procedure and device applied to logistics supply platform chain |
| CN109214683A (en) * | 2018-09-06 | 2019-01-15 | 平安科技（深圳）有限公司 | A kind of Application of risk decision method and device |
| CN109375594A (en) * | 2018-10-10 | 2019-02-22 | 杭州润缘信息科技有限公司 | Urban safety wisdom control platform and managing and control system |
| CN110470094A (en) * | 2018-05-09 | 2019-11-19 | 青岛海尔股份有限公司 | The safety device and refrigerator of electric components |
| CN110503295A (en) * | 2019-07-05 | 2019-11-26 | 深圳壹账通智能科技有限公司 | Risk analysis method, device, computing terminal and the storage medium of supply chain finance |
| CN110527835A (en) * | 2019-09-02 | 2019-12-03 | 清华大学 | A kind of method of waste and old ternary lithium battery Soft Roll full constituent recycling |
| CN110619231A (en) * | 2019-08-26 | 2019-12-27 | 北京航空航天大学 | Differential discernability k prototype clustering method based on MapReduce |
| CN111240062A (en) * | 2020-03-20 | 2020-06-05 | Oppo广东移动通信有限公司 | Liquid crystal display screen integrating fingerprint identification function and electronic device |
| CN111539626A (en) * | 2020-04-23 | 2020-08-14 | 中国环境科学研究院 | Ecological risk assessment method based on key industry development of urban area |
| CN111781478A (en) * | 2019-03-18 | 2020-10-16 | 北京北方华创微电子装备有限公司 | Component service life monitoring method and system |
| CN111858544A (en) * | 2019-04-29 | 2020-10-30 | 北京振兴计量测试研究所 | Component information management system |
| CN112334880A (en) * | 2019-11-05 | 2021-02-05 | 深圳市大疆创新科技有限公司 | Obstacle processing method and device for movable platform and computer storage medium |
| CN112613664A (en) * | 2020-12-25 | 2021-04-06 | 武汉理工大学 | Early warning method and system based on water traffic accident risk prediction and evaluation |
| CN112613740A (en) * | 2020-12-23 | 2021-04-06 | 中国科学院城市环境研究所 | Visualization platform and method for risk level evaluation of enterprise emergency environment event |
| CN112749884A (en) * | 2020-12-29 | 2021-05-04 | 中国航空工业集团公司西安飞机设计研究所 | Aircraft electronic component localization substitution risk decision method |
| CN112765660A (en) * | 2021-01-25 | 2021-05-07 | 湖南大学 | Terminal security analysis method and system based on MapReduce parallel clustering technology |
| CN112785461A (en) * | 2021-02-05 | 2021-05-11 | 北京信息科技大学 | Food safety data supervision integration method |
| CN112801529A (en) * | 2021-02-05 | 2021-05-14 | 北京同邦卓益科技有限公司 | Financial data analysis method and device, electronic device and medium |
| CN112886655A (en) * | 2021-01-08 | 2021-06-01 | 中国联合网络通信集团有限公司 | Charging management method, system, mobile terminal and computer readable storage medium |
Family Cites Families (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20130226652A1 (en) * | 2012-02-28 | 2013-08-29 | International Business Machines Corporation | Risk assessment and management |
| US10268844B2 (en) * | 2016-08-08 | 2019-04-23 | Data I/O Corporation | Embedding foundational root of trust using security algorithms |
| US10789550B2 (en) * | 2017-04-13 | 2020-09-29 | Battelle Memorial Institute | System and method for generating test vectors |
| US20200327470A1 (en) * | 2019-04-15 | 2020-10-15 | International Business Machines Corporation | Cognitively-Derived Knowledge Base of Supply Chain Risk Management |
-
2021
- 2021-07-02 CN CN202110748972.9A patent/CN113505823B/en active Active
Patent Citations (25)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2012026415A1 (en) * | 2010-08-27 | 2012-03-01 | テクノポリマー株式会社 | Contact component with reduced squeak noise made from thermoplastic resin composition |
| CN104376403A (en) * | 2014-10-30 | 2015-02-25 | 广东电网有限责任公司东莞供电局 | Substation sag sensitivity grading method based on subordinate user industry characteristics |
| CN106570729A (en) * | 2016-11-14 | 2017-04-19 | 南昌航空大学 | Air conditioner reliability influence factor-based regional clustering method |
| CN107819770A (en) * | 2017-11-15 | 2018-03-20 | 中国联合网络通信集团有限公司 | Medical data sharing method for secret protection and device based on block chain |
| CN207683143U (en) * | 2017-12-13 | 2018-08-03 | 珠海纳思达企业管理有限公司 | Ink-feeding device and ink-jet printer |
| CN108846532A (en) * | 2018-03-21 | 2018-11-20 | 宁波工程学院 | Business risk appraisal procedure and device applied to logistics supply platform chain |
| CN110470094A (en) * | 2018-05-09 | 2019-11-19 | 青岛海尔股份有限公司 | The safety device and refrigerator of electric components |
| CN109214683A (en) * | 2018-09-06 | 2019-01-15 | 平安科技（深圳）有限公司 | A kind of Application of risk decision method and device |
| CN109375594A (en) * | 2018-10-10 | 2019-02-22 | 杭州润缘信息科技有限公司 | Urban safety wisdom control platform and managing and control system |
| CN111683126A (en) * | 2018-10-10 | 2020-09-18 | 杭州润缘信息科技有限公司 | City safety wisdom management and control system |
| CN111781478A (en) * | 2019-03-18 | 2020-10-16 | 北京北方华创微电子装备有限公司 | Component service life monitoring method and system |
| CN111858544A (en) * | 2019-04-29 | 2020-10-30 | 北京振兴计量测试研究所 | Component information management system |
| CN110503295A (en) * | 2019-07-05 | 2019-11-26 | 深圳壹账通智能科技有限公司 | Risk analysis method, device, computing terminal and the storage medium of supply chain finance |
| CN110619231A (en) * | 2019-08-26 | 2019-12-27 | 北京航空航天大学 | Differential discernability k prototype clustering method based on MapReduce |
| CN110527835A (en) * | 2019-09-02 | 2019-12-03 | 清华大学 | A kind of method of waste and old ternary lithium battery Soft Roll full constituent recycling |
| CN112334880A (en) * | 2019-11-05 | 2021-02-05 | 深圳市大疆创新科技有限公司 | Obstacle processing method and device for movable platform and computer storage medium |
| CN111240062A (en) * | 2020-03-20 | 2020-06-05 | Oppo广东移动通信有限公司 | Liquid crystal display screen integrating fingerprint identification function and electronic device |
| CN111539626A (en) * | 2020-04-23 | 2020-08-14 | 中国环境科学研究院 | Ecological risk assessment method based on key industry development of urban area |
| CN112613740A (en) * | 2020-12-23 | 2021-04-06 | 中国科学院城市环境研究所 | Visualization platform and method for risk level evaluation of enterprise emergency environment event |
| CN112613664A (en) * | 2020-12-25 | 2021-04-06 | 武汉理工大学 | Early warning method and system based on water traffic accident risk prediction and evaluation |
| CN112749884A (en) * | 2020-12-29 | 2021-05-04 | 中国航空工业集团公司西安飞机设计研究所 | Aircraft electronic component localization substitution risk decision method |
| CN112886655A (en) * | 2021-01-08 | 2021-06-01 | 中国联合网络通信集团有限公司 | Charging management method, system, mobile terminal and computer readable storage medium |
| CN112765660A (en) * | 2021-01-25 | 2021-05-07 | 湖南大学 | Terminal security analysis method and system based on MapReduce parallel clustering technology |
| CN112785461A (en) * | 2021-02-05 | 2021-05-11 | 北京信息科技大学 | Food safety data supervision integration method |
| CN112801529A (en) * | 2021-02-05 | 2021-05-14 | 北京同邦卓益科技有限公司 | Financial data analysis method and device, electronic device and medium |
Non-Patent Citations (5)
| Title |
|---|
| "5G供应链安全风险与应对策略研究";陆勰等;《信息安全研究》;第7卷(第5期);第423-427页 * |
| "Analysis of electronic component inventory optimization in six stages supply chain management for warehouse with ABC using genetic algorithm and PSO";C. B. Gupta等;《Selforganizology》;第4卷(第4期);第52-64页 * |
| "Automatic Extraction of Component Inspection Regions from Printed Circuit Board by Image Clustering";Tae-Hyoung Park等;《The transactions of The Korean Institute of Electrical Engineers》;第61卷(第3期);第 472-478页 * |
| "军用电子元器件可靠性强化试验的可行性研究";朱朝轩等;《电子产品可靠性与环境试验》;第36卷(第4期);第40-43页 * |
| "国产元器件质量风险指标体系建设探讨与分析";高坤奇等;《质量与可靠性》(第6期);第42-46页 * |
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