Looking for breakthrough ideas for innovation challenges? Try Patsnap Eureka!

Logistics risk assessment model method and device based on dynamic Bayesian network, equipment and medium

A dynamic Bayesian and Bayesian network technology, applied in the field of logistics intelligence, can solve problems such as the inability to realize timely discovery and correct evaluation of logistics, and achieve the effect of improving the evaluation effect and realizing the effect of risk management and control.

Pending Publication Date: 2021-08-06
北京国信云服科技有限公司
View PDF0 Cites 0 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Timely discovery and correct evaluation of logistics cannot be realized only through static analysis

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Logistics risk assessment model method and device based on dynamic Bayesian network, equipment and medium
  • Logistics risk assessment model method and device based on dynamic Bayesian network, equipment and medium
  • Logistics risk assessment model method and device based on dynamic Bayesian network, equipment and medium

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0040] The embodiment of the present application provides a logistics risk assessment model method based on a dynamic Bayesian network, such as figure 1 shown, including:

[0041] Step S100, determine a number of logistics links, and determine candidate risk factors associated with each logistics link to Form a set of candidate risk influencing factors.

[0042] Optionally, according to the possible sources of risk, the logistics process is divided into seven links, including product packaging, distribution processing, logistics warehousing, loading and unloading, product transportation, product distribution and logistics information. Except for logistics information accidents, the other six links occur sequentially over time.

[0043] The above seven logistics links all include certain risk factors associated with them. For example, the product packaging link includes packaging specifications, packaging environment, packaging operations, etc., and logistics warehousing ...

Embodiment 2

[0093] Figure 4 A schematic structural diagram of the logistics risk assessment module 10 based on the dynamic Bayesian network provided in this embodiment.

[0094] The flow risk assessment module 10 includes:

[0095] The candidate risk influencing factor determining module 11 is configured to determine several logistics links, and determine candidate risk influencing factors associated with each logistics link to form a set of candidate risk influencing factors.

[0096] The formal risk influencing factor selection module 12 is configured to select a number of candidate risk influencing factors from the set of candidate risk influencing factors as formal risk influencing factors.

[0097] A priori probability determination module 13, configured to determine the initial state category of each of the formal risk influencing factors and the prior probability value corresponding to each of the initial state categories;

[0098] The static Bayesian network construction module...

Embodiment 3

[0104] Figure 5 A schematic structural diagram of the electronic device 20 provided in the embodiment of the present application, such as Figure 5 As shown, the electronic device 20 includes a processor 21 and a memory 23 , and the processor 21 and the memory 23 are connected, such as through a bus 22 .

[0105] The processor 21 may be a CPU, a general purpose processor, DSP, ASIC, FPGA or other programmable devices, transistor logic devices, hardware components or any other combination. It can implement or execute the various illustrative logical blocks, modules and circuits described in connection with the present disclosure. The processor 21 may also be a combination of computing functions, for example, a combination of one or more microprocessors, a combination of a DSP and a microprocessor, and the like.

[0106] Bus 22 may include a path for communicating information between the components described above. The bus 22 may be a PCI bus or an EISA bus or the like. The...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

PUM

No PUM Login to View More

Abstract

The invention provides a logistics risk assessment model method and device based on a dynamic Bayesian network, equipment and a medium. The method comprises the steps of determining a candidate risk influence factor set associated with each logistics link; selecting official risk influence factors from the candidate risk influence factor set; determining an initial state category and a prior probability value of each formal risk influence factor; constructing a logistics risk assessment static Bayesian network; constructing a state transition relation between the logistics links and a transition probability changing along with time; adding a transition probability into the static Bayesian network to construct a logistics risk assessment dynamic Bayesian network; and based on the logistics risk assessment dynamic Bayesian network, completing assessment of the risk of each logistics link and the total risk in the logistics process. According to the method and device, the dynamic assessment of the risks involved in the logistics links is realized by constructing the dynamic Bayesian network, and the dependency relationship among the logistics links is fully considered, so that the assessment effect of the logistics risks is improved.

Description

technical field [0001] The invention relates to the field of logistics intelligence, in particular to a logistics risk assessment model method, device, equipment and medium based on a dynamic Bayesian network. Background technique [0002] The logistics process involves many participants, the process is complex, and the risk categories are diverse. The realization of risk identification and control in the logistics process has become an urgent problem in the industry. [0003] At this stage, static analysis strategies such as literature analysis and hierarchical analysis are generally used to identify and evaluate logistics risks. However, the logistics process is a dynamic process that changes with time, and there is a certain degree of dependence between logistics links. Timely discovery and correct evaluation of logistics cannot be realized only through static analysis. [0004] The dynamic Bayesian network incorporates the influence of time factors on the target state, ...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

Application Information

Patent Timeline
no application Login to View More
IPC IPC(8): G06Q10/06G06Q10/08G06N7/00
CPCG06Q10/0635G06Q10/08G06N7/01
Inventor 司华友吴振豪高健博吴琛邵童孙圣力
Owner 北京国信云服科技有限公司
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Patsnap Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Patsnap Eureka Blog
Learn More
PatSnap group products

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic, Popular Technical Reports.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap|About US| Contact US: help@patsnap.com