CN107124294A - A kind of In-vehicle networking evaluation method based on data fusion - Google Patents
A kind of In-vehicle networking evaluation method based on data fusion Download PDFInfo
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- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L12/00—Data switching networks
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Abstract
The invention discloses a kind of In-vehicle networking evaluation method based on data fusion, including：For the In-vehicle networking scheme of Preliminary design, In-vehicle networking topological structure and In-vehicle networking database are extracted respectively；In-vehicle networking topological structure based on extraction calculates each data characteristics path length of In-vehicle networking to analyze, so as to obtain vehicle network topological structure evaluation result；Based on In-vehicle networking database, the scalability and In-vehicle networking dynamic data scalability Analysis result of vehicle network static data are obtained；Obtained In-vehicle networking topological structure evaluation result, In-vehicle networking static data scalability Analysis result and In-vehicle networking dynamic data scalability Analysis result are merged using data fusion method；Based on fusion results, overall merit is carried out to In-vehicle networking.Effective integration network topology characteristic path length of the present invention, total inorganic nitrogen and the whole In-vehicle networking of data delay scalability overall merit, improve the comprehensive and versatility of In-vehicle networking evaluation method.
Description
Technical field
The invention belongs to automotive communication network technical field, more particularly to a kind of In-vehicle networking evaluation based on data fusion
Method.
Background technology
The development of In-Vehicle Networks of safety, energy-saving and environmental protection and vehicle intellectualized demand driving, it is corresponding
The emulation of In-vehicle networking and evaluation system also become the problem of each research department pays close attention to manufacturer.
CAN technology has become one of most ripe In-vehicle networking standard at present, is widely used in Hyundai Motor
In control, but due to the complexity of vehicular applications environment, real-time, fault-tolerance, reliability and the band of CAN In-vehicle networking
Wide resource utilization capacity still fails to meet actual requirement and development need so far.Therefore, except solving the problems, such as automobile strong electromagnetic
Outside network electric fault itself, the efficient integrated of network how is effectively improved, how effectively to assess network performance to ensure net
Network schedulability and reliability, the problems such as improving bandwidth resources utilization rate are all the passes that In-vehicle networking is needed to be studied and solved
Key theoretical question.
Non-patent literature《Some key theory researchs of CAN protocol In-vehicle networking》(《Northeastern University》- 2008) in mention one
Plant typical In-vehicle networking evaluation method and analyze (Rate Monotonic Algorithm, RMA) method using rate monotonic, should
Method is a kind of classical period task scheduling analysis method, real-time by the analysis mode analysis information for introducing real-time system
Property.The ratio produced according to event distributes priority, and the smaller i.e. ratio of the time interval that event is produced is bigger, then allocated
Priority is higher, on the contrary then lower.When the information priorities distribution applied to CAN, the double application of information is sent
Between minimum interval it is smaller, then the ratio of information is bigger, and the priority of information is higher, information distribution mark it is smaller.
RMA algorithms are simple and easy to apply, and the network tunable degree based on RMA algorithms analyzes also comparative maturity.
Non-patent literature《In-vehicle networking emulates the realization with evaluating system》(《Harbin Institute of Technology》- 2009) in carry
Go out a kind of method of the resource requirement evaluation and test of vehicle netbios, discussed in terms of static analysis and dynamic simulation analysis two
The method of vehicle netbios network performance evaluation and test, more complete realizes the evaluation to vehicle netbios network performance.
But, there is problems with the A+E research of above In-vehicle networking：
(1) do not distinguish and analyze for different network topologies, handled as same informational capacity, ignore network
The characteristic of topological structure.And network topology structure is to influence the key factor of data transfer path, and directly affect In-vehicle networking
Data delay and gateway route etc. network performance.
(2) most researchs only from mono signal binding and layout or combine the flexibility that the angle analysis network of binding and layout extends, and
Commercial car is designed in accordance with SAE J1939 standards mostly, and data binding and layout mode is fixed, so binding and layout mode can not embody all-network
The flexibility of extension, lacks the analysis and research suitable for all In-vehicle networking scalabilities, and scalability is each manufacturer's progress
Platform network design needs the key factor considered.
(3) traditional In-vehicle networking evaluation method is generally only compared to a performance, or by multiple performance indications
It is compared one by one, does not consider that the overall merit of multinomial network performance index and the form as how quantified are represented.
The content of the invention
For above-mentioned technical problem, the present invention provides a kind of In-vehicle networking evaluation method based on data fusion, this method
Effective assay In-vehicle networking, is opened up by analyzing In-vehicle networking in terms of analysis of networks topology and network data analysis two
Structure Calculation data characteristics path length is flutterred, In vehicle network bus load factor and data are calculated by analyzing In-vehicle networking database
Postpone scalability, and by the above-mentioned Performance Analysis result of Dempster-Shafer evidence theory effective integrations, so that shape
The overall merit of paired In-vehicle networking.
The technical solution adopted by the present invention is：
The present invention provides a kind of In-vehicle networking evaluation method based on data fusion, including：
S100：For the In-vehicle networking scheme of Preliminary design, In-vehicle networking topological structure G and In-vehicle networking number are extracted respectively
According to storehouse M；
S200：In-vehicle networking topological structure G based on extraction calculates each data characteristics path length of In-vehicle networking to analyze,
So as to obtain In-vehicle networking topological structure evaluation result；
S300：Based on In-vehicle networking database M, the network data to In-vehicle networking carries out statistical data analysis and dynamic number
According to analysis, wherein, statistical data analysis is used for the scalability for analyzing the total inorganic nitrogen of each subnet in In-vehicle networking, with
To In-vehicle networking static data scalability Analysis result, the data delay that dynamic data analyzes for analyzing network data can expand
Malleability, to obtain In-vehicle networking dynamic data scalability Analysis result；
S400：Using data fusion method to the In-vehicle networking topological characteristic path length evaluation knot that is obtained in step S200
Really, the In-vehicle networking static data scalability Analysis result and In-vehicle networking dynamic data scalability obtained in step S300
Analysis result is merged, and the fusion results of In-vehicle networking are obtained in the form of quantization；
S500：Based on step S400 fusion results, overall merit is carried out to In-vehicle networking.
Alternatively, the In-vehicle networking topological structure evaluation structure is based on following formula (1) and (2) are determined：
Wherein, f1(G, M) be In-vehicle networking topological structure evaluation result, value for (0,1]；L (G, M) opens up for In-vehicle networking
The average shortest path length flutterred, value is i for the sending node represented more than or equal to 1, mij in In-vehicle networking and receiving node is j
Message, d (mij) represents that sending node is i and the shortest path of message that receiving node is j, | M | statement In-vehicle networking data
Message amount in the M of storehouse.
Alternatively, the In-vehicle networking static data scalability Analysis result is determined by following formula (3) to (5)：
Wherein, Busload (Mi) represent i-th of subnet total inorganic nitrogen, Size (mij) represent message mijMessage it is long
Degree, TmijRepresent message mijCycle time, τ represents bus baud rate, and Max (G, M) represents that total inorganic nitrogen is most in each subnet
Big value, f2(G, M) represents In-vehicle networking static data scalability Analysis result, k1For each subnet total inorganic nitrogen it is upper
Limit value.
Alternatively, the In-vehicle networking dynamic data scalability Analysis result is determined by following formula (6) and (7)：
Wherein, DmijIt is message mijSend most high delay time, LmijIt is than message mijIn the low all messages of priority
Most long message bus holding time, CjIt is than message mijThe high all data frame hp (m of priorityij) bus holding time,
EmijIt is the time delay for the erroneous frame being likely to occur in bus, TmijRepresent message mijCycle time；k2It is each message
Data delay scalability coefficient, span is (0,1), f3(G, M) represents In-vehicle networking dynamic data scalability Analysis
As a result.
Alternatively, In-vehicle networking topological structure evaluation result is merged based on Dempster-Shafer evidence theories, it is vehicle-mounted
The expansible analysis result of network static data and In-vehicle networking dynamic data scalability Analysis result, are drawn in the form of quantization
The overall merit of In-vehicle networking；
If final appraisal results are identification framework Θ={ A, B, C }, A represents that final appraisal results are " outstanding ", and B is represented most
Whole evaluation result is " good ", and C represents that final appraisal results are " failing ", for three mass functions on identification framework Θ
Shown in m1, m2 and m3 Dempster-Shafer composition rules such as following formula (8) and (9)：
If wherein K=0, then it is assumed that m1, m2And m3Contradiction, it is impossible to which they are combined；If K ≠ 0, m1, m2And m3Group
Basic Probability As-signment after conjunction is DS (S)；m1Represent In-vehicle networking topological structure evaluation result f1(G, M), m2Represent In-vehicle networking
Static data scalability Analysis result f2(G, M), m3Represent In-vehicle networking dynamic data scalability Analysis result f3(G, M),
DS (S) represents m1, m2And m3To S synthesis degree of support.
Alternatively, as DS (S)=Max { DS (A), DS (B), DS (C) }=DS (A), represent that the synthesis of In-vehicle networking is commented
Valency result is outstanding；As DS (S)=Max { DS (A), DS (B), DS (C) }=DS (B), the overall merit of In-vehicle networking is represented
As a result it is good；As DS (S)=Max { DS (A), DS (B), DS (C) }=DS (C), the overall merit knot of In-vehicle networking is represented
Fruit is to fail.
Compared with prior art, the invention has the advantages that：
(1) present invention according to the characteristic path lengths of In-vehicle networking Analysis of Topological Structure data due to that can compensate for mesh
The deficiency that preceding In-vehicle networking evaluation method is not distinguished and analyzed for heterogeneous networks topology, improves In-vehicle networking evaluation side
The accuracy of method.
(2) scalability of the invention due to total inorganic nitrogen can be analyzed according to In vehicle network bus load factor, according to
Data delay calculates the scalability of analyze data delay, in the case where ensureing bus high usage and low data delay, has
Effect evaluates the scalability of In-vehicle networking, improves the scalability of In-vehicle networking evaluation method.
(3) present invention is due to can be by Dempster-Shafer evidence theory Decision fusion technologies, and effective integration is vehicle-mounted
Network topology characteristic path length, total inorganic nitrogen and the whole In-vehicle networking of data delay scalability overall merit, are improved
The comprehensive and versatility of In-vehicle networking evaluation method.Thus the synthesis of the invention that can be widely applied to various In-vehicle networkings is commented
Valency.The present invention effectively can instruct In-vehicle networking design work in the early stage design phase, be prevented effectively from during subsequent development and run into
Problem need to overthrow redesign, or can not thoroughly evaluating optimization In-vehicle networking framework the problems such as, effectively reduce design cost, improve
The reliability and scalability of In-vehicle networking design, effective evaluation method and theoretical foundation are provided for In-vehicle networking design.
Brief description of the drawings
Fig. 1 is a kind of In-vehicle networking evaluation method schematic diagram based on data fusion of the present invention.
Fig. 2 is the schematic diagram of the In-vehicle networking design of one embodiment of the invention.
Embodiment
To make the technical problem to be solved in the present invention, technical scheme and advantage clearer, below in conjunction with accompanying drawing and tool
Body embodiment is described in detail.
The embodiment of the present invention provides a kind of In-vehicle networking evaluation method based on data fusion, and this method is divided from topological structure
Analysis and the aspect of network data analysis two are effectively analyzed the multiple In-vehicle networking schemes preliminarily formed, by analyzing vehicle-mounted net
Network topological structure calculate data characteristics path length, by analyze In-vehicle networking database calculate In vehicle network bus load factor and
Data delay scalability, and by the above-mentioned Performance Analysis result of Dempster-Shafer evidence theory effective integrations, from
And the overall merit to In-vehicle networking is formed, to judge which kind of scheme more preferably, scalability is higher.Specific evaluation procedure is as schemed
Shown in 1, comprise the following steps：
S100：Extract In-vehicle networking topological structure G and In-vehicle networking database M
For the In-vehicle networking scheme of input, In-vehicle networking topological structure G and In-vehicle networking database M is extracted respectively；
S200：In-vehicle networking Analysis of Topological Structure
In-vehicle networking topological structure G based on extraction calculates each data characteristics path length of In-vehicle networking to analyze, so that
Obtain In-vehicle networking topological structure evaluation result；
S300：In-vehicle networking data analysis
Based on In-vehicle networking database M, the network data to In-vehicle networking carries out statistical data analysis and dynamic data point
Analysis, wherein, statistical data analysis is used for the scalability for analyzing the total inorganic nitrogen of each subnet in In-vehicle networking, to obtain car
Contained network network static data scalability Analysis result, dynamic data is analyzed expansible for the data delay of analyzing network data
Property, to obtain In-vehicle networking dynamic data scalability Analysis result；
S400：Data fusion is analyzed
Using data fusion method to the In-vehicle networking topological characteristic path length evaluation result that is obtained in step S200 and
The In-vehicle networking static data scalability Analysis result and In-vehicle networking dynamic data scalability point obtained in step S300
Analysis result is merged, and the fusion results of In-vehicle networking are obtained in the form of quantization；
S500：In-vehicle networking overall merit
Based on step S400 fusion results, overall merit is carried out to In-vehicle networking.
Hereinafter, above-mentioned steps are described in detail.
Step S100：Extract In-vehicle networking topological structure G and In-vehicle networking database M
For the In-vehicle networking scheme of input, In-vehicle networking topological structure G and database information M is extracted respectively.Any car
Carrying network plan includes In-vehicle networking topology diagram G and In-vehicle networking database M.Wherein In-vehicle networking topological diagram is expressed as G
={ V, E } digraph, wherein V represents the node set V={ v of all-network node composition1,v2,……,vn, | V | represent
Network node quantity；E represents the set E={ e of all side compositions1,e2,……,e2n}；
The set that In-vehicle networking database M is made up of all message datas of network, is expressed as：
M={ i, j ∈ mij}
Wherein, mijRepresent the message that sending node is i and receiving node is j.| M | represent database message amount.If
When one message there are multiple receiving nodes simultaneously, then node farthest in all receiving nodes is taken as receiving node, now most
The packet route of remote receiving node has covered the packet route of other receiving nodes.
Step S200：In-vehicle networking Analysis of Topological Structure
Analysis of Topological Structure calculates each data characteristics path length of In-vehicle networking, so as to draw structure evaluation result.In the past
Evaluation system research do not distinguish and analyze for different network topologies, as same informational capacity handle, ignore
The characteristic of network topology structure.And network topology structure is to influence the key factor of data transfer path, and directly affect vehicle-mounted
The network performance of network.The present invention proposes data characteristics path length performance indications, and one complete data of reflection are saved from transmission
Point needs to undergo the shortest length in path to receiving node.Packet route length is long, then illustrates time of the message needs by route
Number is more, and data delay time is long；Otherwise, then illustrate that number of times of the message needs by route is few, data delay time is few.Feature road
Electrical path length is to weigh the important indicator of In-vehicle networking performance.If defining d (mij) represent that sending node is i and receiving node is j
Message shortest path, then the characteristic path length L (G, M) based on In-vehicle networking topology G and database M passes through following formula
(1) obtain：
That is, characteristic path length L (G, M) is all messagesShortest path average value.Ideally,
The sending node and receiving node of all data are respectively positioned on consolidated network, when data are sent on network, and receiving node can be direct
Obtain, the characteristic path length of all data is 1, data routing times are minimum, and now data delay time is most short, i.e. L (G,
M)≥1。
In-vehicle networking topological structure evaluation result is In-vehicle networking average characteristics path length percentage reciprocal, is expressed as
f1(G, M), shown in computational methods such as following formula (2)：
Wherein, f1(G,M)∈(0,1].If characteristic path length value is bigger, i.e., all messagesShortest path
The average value in footpath is bigger, then the gateway node that message passes through is more, and the data delay caused is bigger, and it is poorer that structural behaviour is evaluated；
Conversely, the gateway node that message passes through is fewer, the data delay caused is smaller, and structural behaviour is evaluated higher.
Step S300：In-vehicle networking data analysis
Network data analysis is divided into statistical data analysis and dynamic data analysis.Statistical data analysis is in static analysis module
It is middle to carry out, for analyzing the total inorganic nitrogen of each subnet, and consider scalability factor, draw static data evaluation result.It is dynamic
State data analysis is carried out in dynamic analysis module, draws the scalability of In-vehicle networking data delay.Specific method is as follows：
(a) total inorganic nitrogen of each subnet of statistical data analysis module analysis, and consider the scalability of network, propose total
The scalability coefficient k 1 of linear load rate, draws static data evaluation result.If In-vehicle networking { G, M } has n subnet, Mi
The message set of i-th of subnet is represented, | Mi| message amount in i-th of subnet is represented, Max (G, M) represents bus in each subnet
The maximum of load factor, static data evaluation result f2(G, M) computational methods such as following formula (3), shown in (4) and (5)：
Wherein, Busload (Mi) represent i-th of subnet total inorganic nitrogen, Size (mij) represent message mijMessage it is long
Degree, TmijRepresent message mijCycle time.τ represents bus baud rate, in passenger car CAN network, and τ takes fixed value 500kb/
s；In commercial car CAN network, τ takes fixed value 250kb/s；In LIN networks, τ takes fixed value 19.2kb/s.Total inorganic nitrogen
Scalability coefficient k1∈ (0,1), k1The knowledge and warp that can be provided by expert system according to the one or more experts in the field
Test, make inferences and judge, the decision process of human expert is simulated, to obtain k1Value is obtained by experience assignment.This hair
Bright use experience assignment method, network reliability factor is considered during general In-vehicle networking design, it is desirable to each subnet bus load
The higher limit of rate is 50%, therefore makes k1=50%.
(b) dynamic data analysis module is prolonged by the data of classical RMA rate monotonic analytical In-vehicle networkings
Late, the scalability of In-vehicle networking data delay is further evaluated.Analysis for network data real-time is usually introduced in real time
The analysis mode RMA rate monotonic analysis methods of system.
RMA data delay analysis methods are：Message mijAll data frames higher than its priority are all when to be sent, in network
In competition bus right to occupation, and it is that bus holding time is most long in priority ratio its low all data frame that network sends at present
One, shown in calculation formula such as following formula (6)：
DmijIt is message mijSend most high delay time, LmijIt is than message mijIt is most long in the low all messages of priority
Message bus holding time, CjIt is than message mijThe high all data frame hp (m of priorityij) bus holding time, EmijIt is
The time delay for the erroneous frame being likely to occur in bus.Because lean design and product reliability require can not occur on network
Erroneous frame, therefore EmijValue levels off to 0.
Data delay scalability Analysis module analysis data delay DmijScalability, obtain dynamic data evaluation knot
Fruit f3(G, M), calculating side is represented by following formula (7)：
Wherein k2It is data delay scalability coefficient, span is (0,1)；DmijValue should be less than being equal to k2Tmij, it is no
Then f3(G, M) value is 0, and scalability is worst；And work as DmijValue is less than k2Tmij, and DmijValue is smaller, then f3(G, M) value is got over
Greatly, scalability is higher；k2The knowledge and experience that can be provided by expert system according to the one or more experts in the field, is carried out
Reasoning and judgement, simulate the decision process of human expert, to obtain k2Value is obtained by experience assignment method.The present invention is adopted
Experience assignment method is used, the data delay of network is considered during general In-vehicle networking design, it is desirable to which the data delay of each message is not
The 10% of the message cycle should be exceeded, therefore make k2=10%.
Step S400：Data fusion is analyzed
The present invention calculates feature road using the fusion of Dempster-Shafer evidence theories decision-making technic in terms of network structure
The topological structure that electrical path length is obtained evaluates f1(G, M), the static data that analysis total inorganic nitrogen is obtained in terms of static data are commented
Valency result f2(G, M) and the dynamic data evaluation result f that analyze data delay scalability is obtained in terms of dynamic data3(G, M)
Three performance indications, draw the overall merit of In-vehicle networking in the form of quantization.Specific evaluation procedure is as follows：
If final appraisal results are identification framework Θ={ A, B, C }, A represents that final appraisal results are " outstanding ", and B is represented most
Whole evaluation result is " good ", and C represents that final appraisal results are " failing ", and A, B and C are incompatible.
If function m:2Θ→ [0,1] is met：
M (φ)=0 (8)
Then m (S) is called S Basic Probability As-signment.If m (S)>0, then S is called function m Jiao's member.
ForThree basic probability assignment function m on identification framework Θ1, m2And m3, and corresponding burnt member point
Wei not S1, S2And S3, then Dempster-Shafer composition rules calculate DS (S), calculation pass through following formula (10) represent：
If wherein K=0, then it is assumed that m1, m2And m3Contradiction, it is impossible to which they are combined；If K ≠ 0, m1, m2And m3Group
Basic Probability As-signment after conjunction is DS (S)；m1Represent In-vehicle networking topological structure evaluation result f1(G, M), m2Represent In-vehicle networking
Static data scalability Analysis result f2(G, M), m3Represent In-vehicle networking dynamic data scalability Analysis result f3(G, M),
DS (S) represents m1, m2And m3To S synthesis degree of support.
S500：In-vehicle networking overall merit
The Dempster-Shafer evidence theory fusion results obtained based on step S400, overall merit In-vehicle networking.When
During DS (S)=Max { DS (A), DS (B), DS (C) }=DS (A), the comprehensive evaluation result for representing In-vehicle networking is outstanding；Work as DS
(S) during=Max { DS (A), DS (B), DS (C) }=DS (B), represent that the comprehensive evaluation result of In-vehicle networking is good；As DS (S)
During=Max { DS (A), DS (B), DS (C) }=DS (C), represent the comprehensive evaluation result of In-vehicle networking to fail.
【Embodiment】
Fig. 2 is the In-vehicle networking scenario-frame schematic diagram of one embodiment of the invention.Hereinafter, this is described in detail with reference to Fig. 2
Invention specific implementation step.It is a kind of based on data fusion using the present invention for the In-vehicle networking Solution Embodiments shown in Fig. 2
In-vehicle networking evaluation method, effectively instructs In-vehicle networking design work, for In-vehicle networking design provide effective evaluation method and
Theoretical foundation.
In-vehicle networking scheme shown in Fig. 2 is made up of three tunnel CANs, including post processing CAN subnets, power CAN subnets
With chassis CAN subnets, the traffic rate of each subnet is 250kb/s.CAN subnets are post-processed to control including Nox nitrogen oxide sensors
Unit and EMS control unit of engine；Power CAN subnets include EMS control unit of engine, TCU transmission control units and
VCU full-vehicle control units；Chassis CAN subnets include VCU full-vehicle controls unit, ABS anti-lock braking system, ACC and adaptively patrolled
Navigate control unit, RCU retarders control unit and BCM vehicle body control units.
The specific steps of the evaluation carried out using a kind of In-vehicle networking evaluation method based on data fusion of the present invention are such as
Shown in lower：
1.Extract In-vehicle networking topological structure G and In-vehicle networking database M
For the In-vehicle networking Solution Embodiments shown in Fig. 2, In-vehicle networking topological structure G={ V, E }, wherein V tables are extracted
Show the node set V={ v of all-network node composition1,v2,……,v8, node implication is as shown in table 1；E represents all side groups
Into set E={ e1,e2,……,e16, concrete meaning is as shown in table 2；
The In-vehicle networking Solution Embodiments nodename of table 1
| Node identification | Node is abridged | Nodename |
| v1 | EMS | Control unit of engine |
| v2 | Nox | Nitrogen oxide sensor |
| v3 | TCU | Transmission control unit |
| v4 | VCU | Full-vehicle control unit |
| v5 | BCM | Vehicle body control unit |
| v6 | ABS | Anti-lock braking system |
| v7 | ACC | Self-adaptive controller |
| v8 | RCU | Retarder control unit |
The In-vehicle networking Solution Embodiments side title of table 2
| Side is identified | While representing | Side implication |
| E1, e9 | {v1,v2},{v2,v1} | {EMS,Nox},{Nox,EMS} |
| e2,e10 | {v1,v3},{v3,v1} | {EMS,TCU},{TCU,EMS} |
| e3,e11 | {v1,v4},{v4,v1} | {EMS,VCU},{VCU,EMS} |
| e4,e12 | {v3,v4},{v4,v3} | {TCU,VCU},{VCU,TCU} |
| e5,e13 | {v4,v5},{v5,v4} | {VCU,BCM},{BCM,VCU} |
| e6,e14 | {v4,v6},{v6,v4} | {VCU,ABS},{ABS,VCU} |
| e7,e15 | {v4,v7},{v7,v4} | {VCU,ACC},{ACC,VCU} |
| e8,e16 | {v4,v8},{v8,v4} | {VCU,RCU},{RCU,VCU} |
The database of In-vehicle networking in the present embodiment is extracted, is respectively：
It is M to post-process CAN subnet databasesPost-process CAN={ AT1IG1, AT1OG1, EEC3 }, power CAN subnet databases
MPower CAN={ AMB, AMT_1, DM1_AMT ... ..., VW }, chassis CAN subnet databases MChassis CAN=ACC1, ACC2, CCSS,
CCVS,……,TC1}.Duplicate message is deleted from three subnets and finally obtains whole In-vehicle networking database M such as table 3 below institutes
Show：
The In-vehicle networking database of table 3
2.In-vehicle networking Analysis of Topological Structure
Analysis of Topological Structure calculates each data characteristics path length of In-vehicle networking, so as to draw topological structure evaluation result.
According to the In-vehicle networking topological structure G and In-vehicle networking database M extracted in step 1, the shortest path d (m of message are obtainedij) such as
Shown in table 4：
The In-vehicle networking database shortest path of table 4
The average shortest path length that In-vehicle networking topology is can obtain according to above-mentioned formula (1) is L (G, M)=1.5, so as to obtain
In-vehicle networking topological structure evaluation result f1(G, M)=1/1.5=66.67%.
3.In-vehicle networking data analysis
Network data analysis is divided into statistical data analysis and dynamic data analysis.
(a) total inorganic nitrogen of each subnet of statistical data analysis module analysis, and consider the scalability of network, draw quiet
State data evaluation result.In-vehicle networking { G, M } has 3 subnets in Fig. 2, is calculated according to above-mentioned formula (3) and obtains Busload
(MPost-process CAN)=2.71%, Busload (MPower CAN)=37.04%, Busload (MChassis CAN)=39.50%, and further basis
Formula (4) and formula (5) obtain static data evaluation result f2(G, M)=39.50%.
(b) dynamic data analysis module is prolonged by the data of classical RMA rate monotonic analytical In-vehicle networkings
Late, the scalability of In-vehicle networking data delay is further evaluated.For convenience of calculating, it is assumed that each message data priority such as table 5
It is shown, using RMA data delay analysis methods, calculated according to formula (6) and obtain message mijSend most high delay time Dmij, such as
Shown in table 5.Wherein Bus Speed follows SAEJ1939 standards for 250kb/s, and data frame is extension frame.
The In-vehicle networking dynamic data of table 5 is analyzed
Finally dynamic data evaluation result f is obtained according to above-mentioned formula (7)3(G, M)=(1-0.9744) × 100%=
2.56%.
4.Data fusion is analyzed
Characteristic path length is calculated in terms of network structure using the fusion of Dempster-Shafer evidence theories decision-making technic
Obtained topological structure evaluates f1(G, M), the static data evaluation result f that total inorganic nitrogen is obtained is analyzed in terms of static data2
(G, M) and the dynamic data evaluation result f that analyze data delay scalability is obtained in terms of dynamic data3(G, M) three property
Energy index, the overall merit of In-vehicle networking is drawn according to formula (10) in the form of quantization.Specific evaluation procedure is as follows：
If final appraisal results are identification framework Θ={ A, B, C }, A represents that final appraisal results are " outstanding ", and B is represented most
Whole evaluation result is " good ", and C represents that final appraisal results are " failing ", and A, B and C are incompatible.m1Represent vehicle-mounted net
Network topological characteristic path length evaluation result f1(G, M), m2Represent In-vehicle networking static data scalability Analysis result f2(G,
M), m3Represent In-vehicle networking dynamic data scalability Analysis result f3(G, M), DS (S) represents m1, m2And m3To S comprehensive branch
Hold degree.
M can obtain according to step 1-3 combination expert analysis modes1、m2、m3Evaluation result, as shown in table 6：
The In-vehicle networking data fusion of table 6 is analyzed
| m1 | m2 | m3 | |
| A | 0.6667 | 0.605 | 0.9743 |
| B | 0.2333 | 0.198 | 0.0128 |
| C | 0.09 | 0.188 | 0.188 |
| Θ={ A, B, C } | 0.01 | 0.01 | 0.01 |
It is available according to above-mentioned formula (10)：
In summary, DS (A)=0.454, DS (B)=0.0011, DS (C)=0.0044, DS (Θ)=0.00011.
5. In-vehicle networking overall merit
The Dempster-Shafer evidence theory fusion results obtained based on step 4, overall merit In-vehicle networking.Due to
DS (S)=Max { DS (A), DS (B), DS (C) }=DS (A), therefore the comprehensive evaluation result of the In-vehicle networking embodiment is outstanding.
Described above is the preferred embodiment of the present invention, it is noted that for those skilled in the art
For, on the premise of principle of the present invention is not departed from, some improvements and modifications can also be made, these improvements and modifications
It should be regarded as protection scope of the present invention.
Claims (6)
1. a kind of In-vehicle networking evaluation method based on data fusion, it is characterised in that including：
S100：For the In-vehicle networking scheme of Preliminary design, In-vehicle networking topological structure G and In-vehicle networking database are extracted respectively
M；
S200：In-vehicle networking topological structure G based on extraction calculates each data characteristics path length of In-vehicle networking to analyze, so that
Obtain In-vehicle networking topological structure evaluation result；
S300：Based on In-vehicle networking database M, the network data to In-vehicle networking carries out statistical data analysis and dynamic data point
Analysis, wherein, statistical data analysis is used for the scalability for analyzing the total inorganic nitrogen of each subnet in In-vehicle networking, to obtain car
Contained network network static data scalability Analysis result, dynamic data is analyzed expansible for the data delay of analyzing network data
Property, to obtain In-vehicle networking dynamic data scalability Analysis result；
S400：Using data fusion method to obtained in step S200 In-vehicle networking topological characteristic path length evaluation result,
The In-vehicle networking static data scalability Analysis result and In-vehicle networking dynamic data scalability point obtained in step S300
Analysis result is merged, and the fusion results of In-vehicle networking are obtained in the form of quantization；
S500：Based on step S400 fusion results, overall merit is carried out to In-vehicle networking.
2. the In-vehicle networking evaluation method according to claim 1 based on data fusion, it is characterised in that the vehicle-mounted net
Network topological structure evaluation result is based on following formula (1) and (2) are determined：
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Wherein, f1(G, M) be In-vehicle networking topological structure evaluation result, value for (0,1]；L (G, M) is In-vehicle networking topology
Average shortest path length, the report that value is i for the sending node represented more than or equal to 1, mij in In-vehicle networking and receiving node is j
Text, d (mij) represents the shortest path for the message that sending node is i and receiving node is j, | M | statement In-vehicle networking database M
In message amount.
3. the In-vehicle networking evaluation method according to claim 1 based on data fusion, it is characterised in that the vehicle-mounted net
Network static data scalability Analysis result is determined by following formula (3) to (5)：
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Wherein, Busload (Mi) represent i-th of subnet total inorganic nitrogen, Size (mij) represent message mijMessage length,
TmijRepresent message mijCycle time, τ represents bus baud rate, and Max (G, M) represents the maximum of total inorganic nitrogen in each subnet
Value, f2(G, M) represents In-vehicle networking static data scalability Analysis result, k1For the upper limit of the total inorganic nitrogen of each subnet
Value.
4. the In-vehicle networking evaluation method according to claim 1 based on data fusion, it is characterised in that the vehicle-mounted net
Network dynamic data scalability Analysis result is determined by following formula (6) and (7)：
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Wherein, DmijIt is message mijSend most high delay time, LmijIt is than message mijIt is most long in the low all messages of priority
Message bus holding time, CjIt is than message mijThe high all data frame hp (m of priorityij) bus holding time, EmijIt is
The time delay for the erroneous frame being likely to occur in bus, TmijRepresent message mijCycle time；k2It is that the data of each message are prolonged
Slow scalability coefficient, span is (0,1), f3(G, M) represents In-vehicle networking dynamic data scalability Analysis result.
5. the In-vehicle networking evaluation method according to claim 1 based on data fusion, it is characterised in that be based on
Dempster-Shafer evidence theories are expansible to merge In-vehicle networking topological structure evaluation result, In-vehicle networking static data
Analysis result and In-vehicle networking dynamic data scalability Analysis result, show that the synthesis of In-vehicle networking is commented in the form of quantization
Valency；
If final appraisal results are identification framework Θ={ A, B, C }, A represents that final appraisal results are " outstanding ", and B represents most final review
Valency result is " good ", and C represents that final appraisal results are " failing ", for three mass function m1 on identification framework Θ,
Shown in m2 and m3 Dempster-Shafer composition rules such as following formula (8) and (9)：
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If wherein K=0, then it is assumed that m1, m2And m3Contradiction, it is impossible to which they are combined；If K ≠ 0, m1, m2And m3After combination
Basic Probability As-signment be DS (S)；m1Represent In-vehicle networking topological structure evaluation result f1(G, M), m2Represent that In-vehicle networking is static
Data scalability Analysis result f2(G, M), m3Represent In-vehicle networking dynamic data scalability Analysis result f3(G, M), DS
(S) m is represented1, m2And m3To S synthesis degree of support.
6. the In-vehicle networking evaluation method according to claim 5 based on data fusion, it is characterised in that when DS (S)=
During Max { DS (A), DS (B), DS (C) }=DS (A), the comprehensive evaluation result for representing In-vehicle networking is outstanding；As DS (S)=Max
During { DS (A), DS (B), DS (C) }=DS (B), represent that the comprehensive evaluation result of In-vehicle networking is good；As DS (S)=Max { DS
(A), DS (B), DS (C) }=DS (C) when, represent the comprehensive evaluation result of In-vehicle networking to fail.
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|---|---|---|---|---|
| CN109582562A (en) * | 2018-10-19 | 2019-04-05 | 北京航空航天大学 | Based on the intelligent software test and cloud platform construction method for generating confrontation network |
| CN114095903A (en) * | 2021-11-11 | 2022-02-25 | 盐城市华悦汽车部件有限公司 | Construction method of automobile electrical appliance network |
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