CN106228499A - A kind of cargo security evaluation model based on people's bus or train route goods multi-risk System source - Google Patents

A kind of cargo security evaluation model based on people's bus or train route goods multi-risk System source Download PDF

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CN106228499A
CN106228499A CN201610529822.8A CN201610529822A CN106228499A CN 106228499 A CN106228499 A CN 106228499A CN 201610529822 A CN201610529822 A CN 201610529822A CN 106228499 A CN106228499 A CN 106228499A
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road
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赵池航
张秋各
林盛梅
赵敏慧
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Southeast University
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Abstract

The invention discloses a kind of cargo security evaluation model based on people's bus or train route goods multi-risk System source, comprise the following steps: build cargo security sensory perceptual system, obtain driver, vehicle, road (environment) and goods information；Use study of Accident-Causing Theories to analyze goods stock Accident-causing factor, definition people, car, road and the property set of goods four class risk source, and use methods of fault tree property set is analyzed and screens；According to the property set after screening, use analytic hierarchy process (AHP) to build cargo security risk indicator system, and determine the weight that driver, vehicle, road (environment) and each factor of goods information occur for accident；According to the shipping Risk Comprehensive Evaluation index system set up and each evaluation criterion weight, fuzzy evaluation theory is used to build cargo security risk evaluation model.The present invention can be the technical support that highway goods transportation trouble free service provides intelligent early warning.

Description

A kind of cargo security evaluation model based on people-Che-road-goods multi-risk System source
Technical field
Patent of the present invention relates to road cargo security risk research field, the especially risk source at highway goods transportation and knows Not and cargo security evaluation aspect.
Background technology
In recent years, along with national economy and the fast development of society, road freight status day in the comprehensive system of transport Benefit strengthens.But meanwhile, also bring huge challenge for the safe operation of freight traffic and supervision.Therefore, it is necessary to borrow Help the technological means such as intellectuality, informationization and digitized, around road freight driver, goods stock, road conditions and car Loading thing four class security risk source, research safety risk assessment key technology, it is achieved the dynamic risk identification of road Freightage. This key technology research meets macroscopical requirement of safety traffic, and the inherence being an up road freight level of security and conevying efficiency needs Want.
Cargo security Risk Monitoring and assessment technique are as one of the focus in traffic safety risk investigation field, mesh Before, for the assessment of traffic safety risk and the theoretical research of evaluation mainly for static risk, do not account for risk System dynamics, causes its safety emergent and early warning response is the most delayed, and application is subject to certain restrictions.This patent is around driving Each risk sources such as people, vehicle, road and vehicle-mounted cargo, it is proposed that a kind of cargo security based on people-Che-road-goods multi-risk System source Evaluation model, to realize the abnormality dynamic monitoring to road freight safety, safely provides technical support for road freight, enters And promote road freight transportation service level of security.
Summary of the invention
It is an object of the invention to provide a kind of cargo security evaluation model based on people-Che-road-goods multi-risk System source, solve Problem certainly is the Risk Source Factor in monitoring goods transportation, and cargo security is carried out Real-Time Evaluation, for road Traffic safety risk investigation aspect, is particularly highway in terms of the risk source identification and cargo security evaluation of highway goods transportation Goods transport provides the technical support of intelligent early warning.
The technical solution used in the present invention is: a kind of cargo security evaluation model based on people-Che-road-goods multi-risk System source, Comprise the following steps:
1) build cargo security sensory perceptual system, obtain driver, vehicle, road (environment) and goods information；
2) study of Accident-Causing Theories is used to analyze goods stock Accident-causing factor, definition people, car, road and goods four class risk source Property set, and use methods of fault tree property set is analyzed and screens；
3) according to the property set after screening, use analytic hierarchy process (AHP) to build cargo security risk indicator system, and determine and drive Sail the weight that people, vehicle, road (environment) and each factor of goods information occur for accident；
4) according to the shipping Risk Comprehensive Evaluation index system set up and each evaluation criterion weight, fuzzy evaluation theory is used Build cargo security risk evaluation model.
As preferably, described step 1) in, by building sensory perceptual system framework, carry out cargo security information identification to obtain Driver, vehicle, road (environment) and goods information.
The complication system that goods transport systems is made up of people, car, road and goods, this patent is by monitoring driver, car, road With the dangerous matter sources of four aspects of goods, and carry out danger sources information Real time identification.
First, respectively two starlight level video sensors are arranged in driver's cabin directly over driver position and copilot Upper right side, room, faces and side elevation image with Real-time Collection driver, and builds driver's detection model, uses sparse representation theoretical Carry out driver's gesture recognition, obtain drivers information matrix B1′；Secondly, in terms of vehicle, satellite positioning-terminal is integrated in Car body top is to obtain the information such as vehicle spot speed, longitude, latitude and elevation in real time, and uses Wavelet wavelet transformation pair Satellite positioning-terminal information carry out pretreatment, thus obtain car status information matrix B2′；Then, obtain at road information Aspect, uses video sensor that freight traffic environment carries out information gathering, and use Curvelet wavelet transformation and support to Amount machine identification road surface breakage, fall the road scene such as article and road barrier, obtain road environment information matrix B3′；Finally, In cargo state context of detection, inside and outside vehicle, front top is fixing integrated by accelerometer, air velocity transducer and temperature sensor An assembly of elements, to obtain in real time the status information of goods, use neutral net temperature, acceleration and wind speed to goods Merge etc. information, and use kernel probability density estimation theory to carry out cargo state identification, obtain cargo state matrix B4′。
As preferably, described step 2) in, use study of Accident-Causing Theories to analyze goods stock Accident-causing factor, definition People, car, road and the property set of goods four class risk source, utilize methods of fault tree be analyzed property set and screen.
In shipping risk causation analysis, set up the Fault Tree Model T that human factors causes accident to occur respectively1, vehicle Factor causes the Fault Tree Model T that accident occurs2, Road Factor cause the Fault Tree Model T that accident occurs3Lead with goods factor The Fault Tree Model T that cause accident occurs4, Fault Tree Model is made up of top event and elementary event.If four class Fault Tree Model T= [T1,T2,T3,T4] respectively by n elementary event 1,2 ... i ... n forms, and the most each elementary event is the change of desirable two kinds of numerical value Amount xi, i.e.
Similarly, top event also has a two states:
Depend entirely on the state of elementary event in the state of top event, then top event state is these elementary event states Function, i.e.
φ=φ (x) (3)
After setting up Fault Tree Model T, carry out minimal cut set solve and probabilistic compct sequence, and remove driver, Factor little on freight traffic accident impact in vehicle, road (environment) and goods information, thus obtain and freight traffic accident is affected big driving Sail people factor matrix [B11,B12,B13,B14,B15,B16,B17], vehicle factor matrix [B21,B22,B23,B24,B25,B26,B27], road (environment) factor matrix [B31,B32,B33] and goods factor matrix [B41,B42,B43,B44,B45]。
As preferably, described step 3) in, according to the property set after screening, use analytic hierarchy process (AHP) to build cargo security wind Danger index system, and determine the weight that driver, vehicle, road (environment) and each factor of goods information occur for accident:
First, the goods risk in transit analysis level using analytic hierarchy process (AHP) (AHP) to build analyzes model destination layer, standard Then layer and indicator layer three layers, destination layer is cargo transportation security Risk Comprehensive Evaluation, rule layer include driver's factor, vehicle because of Element, Road Factor and goods factor four part, indicator layer is the specific targets relevant to each factor to affect.
Secondly, use expert assessment method to construct judgment matrixs at different levels, determine destination layer-rule layer A-Bi(i=1,2,3, 4), rule layer-factor layer Bi-Bij(i=1,2,3,4；J=1,2 ..., 7) judgment matrix, and according to obtain judgment matrix ask Obtain the corresponding characteristic vector W of matrix=(w1,w2,…,wn)T, by characteristic vector W normalization, indicator layer weighted value bi(i =1,2,3,4) the weight vectors B=(B of and four class dangerous matter sources1,B2,B3,B4)。
Finally, the consistency ration coefficient CR of judgment matrix is calculated, as CR ＜ 0.10, it is believed that judgment matrix meets consistent Property condition, otherwise make judgment matrix suitably to revise.
As preferably, described step 4) in, according to the shipping Risk Comprehensive Evaluation index system set up and each evaluation index Weight, employing fuzzy evaluation theory structure cargo security risk evaluation model:
This patent uses fuzzy overall evaluation according to people, car, road and the goods four class danger sources information obtained and weight matrix The Theory Construction cargo security risk evaluation model.
First, degree of danger and risk level according to highway goods transportation security risk will be many based on people-Che-road-goods The cargo security evaluation of risk source is divided into I level (the most serious), II grade (seriously), III grade (typically), IV level (safer) and V level (safety) five grades, and construct cargo security risk fuzzy evaluation membership function respectively for five kinds of opinion ratings (p=1,2,3,4,5)；
Secondly, use confidence interval method to determine factor evaluation grade thresholding, and refer to according to risk index assessment method and detection Scale value determines real-time value-at-risk R of four class dangerous matter sourcesi={ R1,R2,R3,R4And real-time value-at-risk R of each sub-dangerous matter sourcesij(i= 1,2,3,4；J=1,2 ..., 7).Determine, according to membership function, the degree of membership that each evaluation index is the most corresponding, and calculate fuzzy closing It is matrix, the degree of membership of corresponding five the fuzzy evaluation grades of the most each real-time value-at-risk of sub-dangerous matter sources(i=1,2,3,4；J=1, 2…,7；P=1,2,3,4,5)；
Then, respectively people, car, road and the sub-dangerous matter sources of goods four class are carried out one-level fuzzy overall evaluation, i.e.
C i ‾ = b i × μ ijv p , ( i = 1 , 2 , 3 , 4 ; j = 1 , 2 ... , 7 ; p = 1 , 2 , 3 , 4 , 5 ) - - - ( 4 )
Obtain one-level fuzzy overall evaluation matrix of consequence
Finally, with one-level fuzzy overall evaluation matrix of consequence μ as the Evaluations matrix of two grades of fuzzy evaluations, four class danger are used The weight vectors B in source, danger carries out Secondary Fuzzy Comprehensive Evaluation, i.e. to people, car, road and goods four class dangerous matter sources
C=B × μ (5)
According to the Secondary Fuzzy Comprehensive Evaluation matrix of consequence C obtained, build shipping based on people-Che-road-goods multi-risk System source Model for Safety Evaluation, i.e.
F p = C P Σ P = 1 5 C p , ( p = 1 , 2 , 3 , 4 , 5 ) - - - ( 6 )
By this model can obtain the real-time risk of cargo security be cited as I level (the most serious), II grade (seriously), III grade (one As), IV level (safer) and the probability of V level (safety) five grades, what probability matrix was maximum be cargo security evaluates Whole grade.
Beneficial effect: the present invention is with a kind of cargo security evaluation model based on people-Che-road-goods multi-risk System source, Quan Mianguan Note people, car, road (environment) and goods four class risk source, use methods of fault tree to sieve the risk source property set of definition Choosing, uses analytic hierarchy process (AHP) to set up cargo security risk indicator system after screening, and based on fuzzy synthetic appraisement method to shipping Safety is carried out in real time, overall merit, can be the technical support of the intelligent early warning of highway goods transportation trouble free service offer.
Accompanying drawing explanation
Fig. 1 is the present inventor, car, road and goods information Perception frame system figure；
Fig. 2 is initial risks index system figure of the present invention.
Detailed description of the invention
With specific embodiments, the technical program is further illustrated below in conjunction with the accompanying drawings:
A kind of cargo security evaluation model based on people-Che-road-goods multi-risk System source, comprises the following steps:
The first step: build cargo security information Perception system based on people-Che-road (environment)-goods, and carry out driver, Vehicle, road (environment) and goods information identification.Constructed information Perception system framework is as shown in Figure 1.
First, respectively two starlight level video sensors are arranged in driver's cabin directly over driver position and copilot Upper right side, room, faces and side elevation image with Real-time Collection driver, and builds driver's detection model, uses sparse representation theoretical Carry out driver's gesture recognition, obtain drivers information matrix B1′；Secondly, in terms of vehicle, satellite positioning-terminal is integrated in Car body top is to obtain the information such as vehicle spot speed, longitude, latitude and elevation in real time, and uses Wavelet wavelet transformation pair Satellite positioning-terminal information carry out pretreatment, thus obtain car status information matrix B2′；Then, obtain at road information Aspect, uses video sensor that freight traffic environment carries out information gathering, and use Curvelet wavelet transformation and support to Amount machine identification road surface breakage, fall the road scene such as article and road barrier, obtain road environment information matrix B3′；Finally, In cargo state context of detection, inside and outside vehicle, front top is fixing integrated by accelerometer, air velocity transducer and temperature sensor An assembly of elements, to obtain in real time the status information of goods, use neutral net temperature, acceleration and wind speed to goods Merge etc. information, and use kernel probability density estimation theory to carry out cargo state identification, obtain cargo state matrix B4′。
Second step: use study of Accident-Causing Theories to analyze goods stock Accident-causing factor, definition people, car, road and goods four class The property set of risk source, utilizes methods of fault tree be analyzed property set and screen.
First, in shipping risk causation analysis, set up the Fault Tree Model T that human factors causes accident to occur respectively1、 Vehicle factor causes the Fault Tree Model T that accident occurs2, Road Factor cause the Fault Tree Model T that accident occurs3With goods because of Element causes the Fault Tree Model T that accident occurs4, Fault Tree Model is made up of top event and elementary event；Secondly, accident is being set up After tree-model T, carry out minimal cut set and solve and probabilistic compct sequence；Finally, remove driver, vehicle, road (environment) and Factor little on freight traffic accident impact in goods information, thus obtain the driver factor matrix [B big on freight traffic accident impact11, B12,B13,B14,B15,B16,B17], vehicle factor matrix [B21,B22,B23,B24,B25,B26,B27], road (environment) factor matrix [B31,B32,B33] and goods factor matrix [B41,B42,B43,B44,B45]。
3rd step: according to the property set after screening, uses analytic hierarchy process (AHP) to build cargo security risk indicator system, and really Determine the weight that driver, vehicle, road (environment) and each factor of goods information occur for accident.
First, the goods risk in transit analysis level that this patent uses analytic hierarchy process (AHP) (AHP) to build analyzes model mesh Mark layer, rule layer and indicator layer three layers, concrete as in figure 2 it is shown, destination layer is cargo transportation security Risk Comprehensive Evaluation, rule layer Including driver's factor, vehicle factor, Road Factor and goods factor four part, indicator layer is relevant to each factor to affect Specific targets；Secondly, use expert assessment method to construct judgment matrixs at different levels, determine destination layer-rule layer A-Bi(i=1,2,3, 4), rule layer-factor layer Bi-Bij(i=1,2,3,4；J=1,2 ..., 7) judgment matrix, and according to obtain judgment matrix ask Obtain the corresponding characteristic vector W of matrix=(w1,w2,…,wn)T, by characteristic vector W normalization, indicator layer weighted value bi(i =1,2,3,4) the weight vectors B=(B of and four class dangerous matter sources1,B2,B3,B4)；Finally, the consistency ration of judgment matrix is calculated Coefficient CR, as CR ＜ 0.10, it is believed that judgment matrix meets consistency condition, otherwise makees judgment matrix suitably to revise.
4th step: according to the shipping Risk Comprehensive Evaluation index system set up and each evaluation criterion weight, uses fuzzy commenting Valence theory builds cargo security risk evaluation model.
First, degree of danger and risk level according to highway goods transportation security risk will be many based on people-Che-road-goods The cargo security evaluation of risk source is divided into I level (the most serious), II grade (seriously), III grade (typically), IV level (safer) and V level (safety) five grades, and construct cargo security risk fuzzy evaluation membership function respectively for five kinds of opinion ratings(R) (p=1,2,3,4,5)；Secondly, use confidence interval method to determine factor evaluation grade thresholding, and according to risk index assessment method and Testing index value determines real-time value-at-risk R of four class dangerous matter sourcesi={ R1,R2,R3,R4And the real-time value-at-risk of each sub-dangerous matter sources Rij(i=1,2,3,4；J=1,2 ..., 7).Determine, according to membership function, the degree of membership that each evaluation index is the most corresponding, obtain mould Stick with paste relational matrix, the subordinated-degree matrix of corresponding five the fuzzy evaluation grades of the most each real-time value-at-risk of sub-dangerous matter sources(i=1,2, 3,4；J=1,2 ..., 7；P=1,2,3,4,5)；Then, people, car, road and the sub-dangerous matter sources of goods four class carry out one-level respectively obscure Overall merit, obtains one-level fuzzy overall evaluation matrix of consequenceFinally, tie with one-level fuzzy overall evaluation Really matrix μ is as the Evaluations matrix of two grades of fuzzy evaluations, uses the weight vectors B of four class dangerous matter sources to people, car, road and goods four class Dangerous matter sources carries out Secondary Fuzzy Comprehensive Evaluation and obtains Secondary Fuzzy Comprehensive Evaluation matrix of consequence C, and builds based on people-Che-road-goods The cargo security evaluation model in multi-risk System source, this model can calculate the real-time risk of cargo security be cited as I level (the most serious), II Level (seriously), III grade (typically), IV level (safer) and the probability of V level (safety) five grades.
It should be pointed out that, for those skilled in the art, under the premise without departing from the principles of the invention, Can also make some improvements and modifications, these improvements and modifications also should be regarded as protection scope of the present invention.In the present embodiment not Clear and definite each ingredient all can use prior art to be realized.

Claims (5)

1. a cargo security evaluation model based on people-Che-road-goods multi-risk System source, it is characterised in that: comprise the following steps:
1) build cargo security sensory perceptual system, obtain driver, vehicle, road and goods information；
2) study of Accident-Causing Theories is used to analyze goods stock Accident-causing factor, definition people, car, road and the genus of goods four class risk source Property collection, and use methods of fault tree property set is analyzed and screens；
3) according to screening after property set, use analytic hierarchy process (AHP) build cargo security risk indicator system, and determine driver, The weight that each factor of vehicle, road and goods information occurs for accident；
4) according to the shipping Risk Comprehensive Evaluation index system set up and each evaluation criterion weight, fuzzy evaluation theory is used to build Cargo security risk evaluation model.
A kind of cargo security evaluation model based on people-Che-road-goods multi-risk System source the most according to claim 1, its feature Be: described step 1) in, by build sensory perceptual system framework, carry out cargo security information identification with obtain driver, vehicle, Road and goods information；
First, respectively two starlight level video sensors are arranged on directly over driver position and copilot room in driver's cabin right Top, faces and side elevation image with Real-time Collection driver, and builds driver's detection model, uses sparse representation theory to carry out Driver's gesture recognition, obtains drivers information matrix B '1；Secondly, in terms of vehicle, satellite positioning-terminal is integrated in car body Top is to obtain the information such as vehicle spot speed, longitude, latitude and elevation in real time, and uses Wavelet wavelet transformation to satellite Position end message carries out pretreatment, thus obtains car status information matrix B '2；Then, in terms of road information acquisition, Use video sensor that freight traffic environment carries out information gathering, and use Curvelet wavelet transformation and support vector machine to know Other road surface breakage, fall the road scene such as article and road barrier, obtain road environment information matrix B '3；Finally, at goods State-detection aspect, inside and outside vehicle, front top is fixing by integrated one of accelerometer, air velocity transducer and temperature sensor Assembly of elements, to obtain in real time the status information of goods, uses neutral net to information such as temperature, acceleration and the wind speed of goods Merge, and use kernel probability density estimation theory to carry out cargo state identification, obtain cargo state matrix B '4。
A kind of cargo security evaluation model based on people-Che-road-goods multi-risk System source the most according to claim 1, its feature It is: described step 2) in, use study of Accident-Causing Theories to analyze goods stock Accident-causing factor, definition people, car, road and goods four The property set of class risk source, utilizes methods of fault tree be analyzed property set and screen；
In shipping risk causation analysis, set up the Fault Tree Model T that human factors causes accident to occur respectively1, vehicle factor leads The Fault Tree Model T that cause accident occurs2, Road Factor cause the Fault Tree Model T that accident occurs3Accident is caused with goods factor The Fault Tree Model T occurred4, Fault Tree Model is made up of top event and elementary event.If four class Fault Tree Model T=[T1,T2, T3,T4] respectively by n elementary event 1,2 ... i ... n forms, and the most each elementary event is the variable x of desirable two kinds of numerical valuei, i.e.
Similarly, top event also has a two states:
Depend entirely on the state of elementary event in the state of top event, then top event state is the letter of these elementary event states Number, i.e.
φ=φ (x) (3)
After setting up Fault Tree Model T, carry out minimal cut set solve and probabilistic compct sequence, and remove driver, vehicle, Factor little on freight traffic accident impact in road and goods information, thus obtain the driver factor matrix big on freight traffic accident impact [B11,B12,B13,B14,B15,B16,B17], vehicle factor matrix [B21,B22,B23,B24,B25,B26,B27], road factor matrix [B31, B32,B33] and goods factor matrix [B41,B42,B43,B44,B45]。
A kind of cargo security evaluation model based on people-Che-road-goods multi-risk System source the most according to claim 1, its feature It is: described step 3) in, according to the property set after screening, use analytic hierarchy process (AHP) to build cargo security risk indicator system, And determine the weight that each factor of driver, vehicle, road and goods information occurs for accident:
First, the goods risk in transit analysis level using analytic hierarchy process (AHP) to build analyzes model to be had destination layer, rule layer and refers to Three layers of layer of mark, destination layer is cargo transportation security Risk Comprehensive Evaluation, and rule layer includes driver's factor, vehicle factor, road Factor and goods factor four part, indicator layer is the specific targets relevant to each factor to affect；
Secondly, use expert assessment method to construct judgment matrixs at different levels, determine destination layer-rule layer A-Bi(i=1,2,3,4), criterion Layer-factor layer Bi-Bij(i=1,2,3,4；J=1,2 ..., 7) judgment matrix, and according to obtain judgment matrix try to achieve matrix Corresponding characteristic vector W=(w1,w2,…,wn)T, by characteristic vector W normalization, indicator layer weighted value bi(i=1,2, 3,4) the weight vectors B=(B of and four class dangerous matter sources1,B2,B3,B4)；
Finally, the consistency ration coefficient CR of judgment matrix is calculated, as CR ＜ 0.10, it is believed that judgment matrix meets concordance bar Part, otherwise make judgment matrix suitably to revise.
A kind of cargo security evaluation model based on people-Che-road-goods multi-risk System source the most according to claim 1, its feature It is: described step 4) in, according to the shipping Risk Comprehensive Evaluation index system set up and each evaluation criterion weight, use fuzzy Evaluation theory structure cargo security risk evaluation model:
First, degree of danger and risk level according to highway goods transportation security risk will be based on people-Che-road-goods multi-risk Systems The cargo security evaluation in source is divided into that I level-very is serious, II grade-serious, III grade-general, IV grade-relatively safety and V grade-safety five Individual grade, and construct cargo security risk fuzzy evaluation membership function respectively for five kinds of opinion ratings
Secondly, use confidence interval method to determine factor evaluation grade thresholding, and true according to risk index assessment method and Testing index value Real-time value-at-risk R of fixed four class dangerous matter sourcesi={ R1,R2,R3,R4And real-time value-at-risk R of each sub-dangerous matter sourcesij(i=1,2,3,4；J= 1,2…,7)；Determine, according to membership function, the degree of membership that each evaluation index is the most corresponding, and calculate fuzzy relation matrix, the most each son The degree of membership of corresponding five the fuzzy evaluation grades of the real-time value-at-risk of dangerous matter sources
Then, respectively people, car, road and the sub-dangerous matter sources of goods four class are carried out one-level fuzzy overall evaluation, i.e.
C i ‾ = b i × μ ijv p , ( i = 1 , 2 , 3 , 4 ; j = 1 , 2 ... , 7 ; p = 1 , 2 , 3 , 4 , 5 ) - - - ( 4 )
Obtain one-level fuzzy overall evaluation matrix of consequence
Finally, with one-level fuzzy overall evaluation matrix of consequence μ as the Evaluations matrix of two grades of fuzzy evaluations, four class dangerous matter sources are used Weight vectors B people, car, road and goods four class dangerous matter sources are carried out Secondary Fuzzy Comprehensive Evaluation, i.e.
C=B × μ (5)
According to the Secondary Fuzzy Comprehensive Evaluation matrix of consequence C obtained, build cargo security based on people-Che-road-goods multi-risk System source Evaluation model, i.e.
F p = C P Σ P = 1 5 C p , ( p = 1 , 2 , 3 , 4 , 5 ) - - - ( 6 )
By this model can obtain the real-time risk of cargo security be cited as I level-very serious, II grade-serious, III grade-general, IV grade- Relatively safety and the probability of five grades of V grade-safety, what probability matrix was maximum is the final grade that cargo security is evaluated.
CN201610529822.8A 2016-07-06 2016-07-06 A kind of cargo security evaluation model based on people's bus or train route goods multi-risk System source Pending CN106228499A (en)

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