CN103634184B - A kind of network scheduling algorithm of CAN master-slave response mode protocol - Google Patents
A kind of network scheduling algorithm of CAN master-slave response mode protocol Download PDFInfo
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Abstract
The present invention discloses the network scheduling algorithm of a kind of CAN master-slave response mode protocol, is for using master-slave response model application agreement to carry out the scheduling message of network communication in CAN network.This dispatching algorithm is when build time triggers dispatch list, and by using request and response message interval to place, dispatch list adjacent column of going together do not places the request message of identical purpose ID and carry out compartment bin packing algorithm constructive scheduling table as constraints.Propose improved adaptive GA-IAGA Optimized Operation table and monopolize time window, to improve bus utilization.Periodic statistics nodes message, dynamically updates dispatch list and realizes resource rational utilization.Utilize the present invention can improve the utilization rate of network-bus, reduce message waiting delay, rational management message, the deficiency that when solving to use master-slave response pattern, existing network dispatching algorithm exists.
Description
Technical field
The present invention relates to CAN distributing network techniques control field, more specifically, especially relate to CAN
Bus network uses the network scheduling algorithm of master-slave response mode protocol.
Background technology
Controller area network (Controller Area Network, CAN) is due to its high-performance, high reliability, real-time
Industrial automation, various control equipment, the vehicles, Medical Instruments and building, environment control have been widely used in it etc. advantage
The various fields such as system, solar recharging system.
Tradition CAN substantially belongs to event trigger mechanism, and cut-off accesses the mode using CSMA, when bus in network
When clashing, CAN utilizes non-destructive arbitration mechanism to arbitrate message priority, and the message that priority is the highest obtains
Obtain bus and be transmitted message.For guaranteeing predictable message communicating and reducing the time delay of message transmission, Time Triggered agreement
(Time Triggered CAN, TTCAN) has carried out the extension of session layer to CAN protocol.TTCAN uses the mode of TDMA to realize
Scheduling to periodic message, TTCAN has been accepted as ISO11898-4 standard agreement.In dispatch list constructed by TTCAN
Comprising synchronous window, exclusive window and arbitration window, synchronous window is for realizing the synchronization of network clocking；Exclusive window is for the dispatch network cycle
Property message；Arbitration window transmits for arbitration event message.
Owing to CAN define only physical layer and link layer, application layer then leaves user's self-defining for.Adjust at TTCAN
In degree, not for network using the application protocol of master-slave response pattern illustrate.Therefore principal and subordinate is used to answer in reality
When answering the agreement of pattern as CAN procotol, there are two deficiencies:
(1) send request message to after node when main website, be likely to be due to that some processes and time delay returns main from node
The response message stood, owing to when statistics network information, request and response message may be incorporated as an information and carry out by user
Scheduling, but in this case, main website has to wait for slave station, now there is the biggest time window waste.
(2) dispatch list that tradition bin packing algorithm generates is used, owing to bin packing algorithm is a NDP(nondeterministic polynomial)
Cannot accurately solve in problem, i.e. polynomial time, the dispatch list therefore generated is not optimum, can cause many places time window
Distribution exist time quantum waste problem, now bus be Idle state be not used to other information transmission.For this
Situation, has used genetic algorithm to be optimized, but genetic algorithm exists some drawback.
Summary of the invention
For overcoming deficiency of the prior art, it is an object of the invention to propose a kind of CAN master-slave response pattern association
The network scheduling algorithm of view, it is intended to improve the utilization rate of network-bus, reduces response and waits time delay, it is achieved the reasonable tune of message
Degree.
The present invention solves its technical problem proposed scheme:
The present invention, in the step building dispatch list mentioned by TTCAN, uses traditional bin packing algorithm raw for message
During becoming dispatch list, propose a kind of " compartment bin packing algorithm " and replace traditional bin packing algorithm structure master-slave response pattern to assist
The network scheduling table of view.The foundation that this algorithm realizes is: request message MQiAfter transmission, due to response, there is certain cut-off
Time Di(generally equivalent to its cycle Ti), as long as at DiReturn response message from node before.Therefore, at constructive scheduling table
Time, ask message MQiWith response message MRiCan separately process.The principle realized is: in view of MQiAfter transmission, due to from node
Needs unpack etc. and to process some task and can not return response message in time, arrange MR the most at onceiTransmission, but
When generating dispatch list, distribute MQiThe adjacent next column time window of position distributes to remaining message, is then followed by arranging MR againi
Transmission, so can make full use of bus, it is to avoid cause bus unnecessary wait waste.
Technical scheme, comprises the steps:
Step one: all node broadcasts competition host node in network, the node of identifier minimum (priority is minimum) will become
For network host node；
Step 2: network host node broadcast statistical information, all nodes return request and the response message of this node；
Step 3: network host node, according to all message of statistics, uses " compartment bin packing algorithm " constructive scheduling table；
Step 4: network host node uses the dispatch list that genetic algorithm optimization has constructed；
Step 5: the schedule information after network host node will optimize sends to each node in network, then broadcasts
Reference News starts internet message scheduling；
Step 6: network host node is after starting to dispatch a period of time, and repetition step 2 is to step 5 again, according to network
Interior joint ruuning situation upgrades in time dispatch list.
In technical solution of the present invention step 2, the message of statistics includes the solicited message of network node and corresponding response node
Response message, including message attributes have: the transmission time span of message, the cycle of message, the numbering of message.Step 3
In, during constructive scheduling table, need the constraints met: request and corresponding response message must be spaced apart.
The process of compartment bin packing algorithm constructive scheduling table is as follows:
(1) host node is according to all message collected, and calculates the cycle of dispatch list, basic cycle, dispatch list line number；
(2) from the message of storage, message MQ is taken outiAnd MRi, travel through dispatch list, it is judged that certain row in dispatch list whether can
Put down message MQ simultaneouslyiAnd MRi, it is impossible to then continuing traversal dispatch list, searching can put down message MQ simultaneouslyiAnd MRiPosition；
(3) repetitive process (2), until all of information is all put in dispatch list.
Technical solution of the present invention step 5 kind, schedule information includes: scheduling basic cycle, dispatch list cycle, dispatch list
The message identifier that the distribution of line number, dispatch list columns, dispatch list each column time window width, dispatch list each row and column sends.
Genetic algorithm of the present invention is optimized mainly for the time window that bus utilization in exclusive window is non-optimal, adopts to carry
High bus utilization, it is achieved the optimization of dispatch list.Compare traditional genetic algorithm, the improvements major embodiment of genetic algorithm
:
(1) being used by the dispatch list that step 3 constructs as initial individuals after real coding, residue individuality randomly generates；
(2) structure fitness function, fitness function by the bus utilization represented after each individual decoding and punishment because of
Son composition；
(3) carrying out selecting every time, intersect, before three operations that make a variation, be both needed to first calculate the fitness letter of all individualities
Number, and retain optimum individual be not involved in select, intersect and three operations that make a variation；
(4) select to use roulette algorithm to select, use single-point to intersect and the mixing crossover algorithm of two-point crossover, with
The mode of machine number (0,1) selects crossover algorithm, and random number is 0 employing single-point crossover algorithm, and random number is that 1 employing two-point crossover is calculated
Method；Variation then uses the mode of random chance to carry out the variation of random order.
In the genetic algorithm improvements (3) that the present invention proposes, the selection gist of optimum individual is the adaptation of all individualities
Angle value, fitness value the highest for optimum individual, be not involved in selecting, intersect, mutation operation；.
In the genetic algorithm improvements (4) that the present invention proposes, the single-point of mixing crossover algorithm intersects and two-point crossover is
The mode using random number (0,1) selects crossover algorithm, and random number is 0 employing single-point crossover algorithm, and random number is 1 employing two point
Crossover algorithm.If two chromogenes are expressed as a1a2...an-1anAnd b1b2...bn-1bn, then new of two intersected to form
Body may be:
St1:a1a2...ak-1bk...bn-1bnAnd b1b2...bk-1ak...an-1an
St2:b1b2...bk'ak'+1...ak''-1bk''...bn-1bnAnd a1a2...ak'bk'+1...bk''-1ak''...an-1an
St1 is two new individualities that single-point crossover algorithm is formed, and st2 is two new individualities that two-point crossover is formed.Wherein k
The random number produced in the range of mrna length for single-point crossover algorithm；K' and k'' represents what two-point crossover algorithm randomly generated
Two positions in the range of mrna length, and k' < k''.Mixing crossover algorithm combines single-point crossover algorithm and two-point crossover algorithm
Advantage, enhance the search capability to optimum individual.
In the genetic algorithm improvements (2) that the present invention proposes, individual r fitness function frIt is expressed as:Wherein by the message in dispatch list to be optimized again relative numbering be 1,2 ..., k, then PiExcellent for treating
Change the transmission cycle of the message that relative numbering is i, triFor the message transmission time length that relative numbering is i, T is dispatch list
Dispatching cycle, wherein prFor the penalty factor of individual r, decode mainly for some produced during genetic algorithm optimization is individual
Rear columns may exceed the restriction of former dispatch list columns sum, if exceeding, individuality is unreasonable, is otherwise reasonable.And introduce
Penalty factor the most rationally affects this individual inheritance to follow-on probability according to these individual decoded dispatch lists exactly；
If after individual r decoding, corresponding dispatch list is rational, then prBeing 0, be otherwise a constant α the biggest, α is chosen for
More than 1000 times；triFor the message transmission time length that relative numbering is i, T is the dispatching cycle of dispatch list, and Q is exclusive window
The time window number that middle bus utilization is non-optimal, LjThe time window length arranged in jth for distribution, njDistribute the message at j row
Number, i is to distribute the message numbering optimizing exclusive window, and k is to distribute the message number optimizing exclusive window；frRepresent the excellent of individuality
More degree.
The genetic algorithm that the present invention proposes can realize being effectively improved bus utilization, it is to avoid the partial bus of dispatch list
Unserviceable defect, improves the dispatching efficiency of system, is also that the arbitration time window increase in dispatch list processes the time, reduces thing
The wait time delay of part time message.
The present invention is to use CAN2.0B to extend Frame Protocol, by the competition of network node priority, the joint that priority is the highest
Point serves as network host node, adds up all message and scheduling message, uses bus type topological structure to realize the group of network multinode
Net.The application layer protocol related to is the master-slave response formula application protocol used in CAN.
The invention has the beneficial effects as follows: the CAN network scheduling algorithm solving under master-slave response pattern exists
Defect, solves the response that in network, node that may be present is unnecessary and waits latency issue, simultaneously genetic Optimization Algorithm optimization
Dispatch list, it is achieved the peak use rate of network-bus, the scheduling of whole network system realizes optimization.Utilize this technical method,
The scheduling problem of application protocol under the master-slave response pattern used in CAN network can be solved, it is achieved whole CAN
Network system reliablely and stablely carries out the scheduled transmission of message according to dispatch list.
Accompanying drawing explanation
Fig. 1 is the network scheduling algorithm flow chart of CAN master-slave response pattern.
Fig. 2 is compartment bin packing algorithm flow chart.
Fig. 3 is genetic algorithm flowchart.
Fig. 4 is initial schedule table.
Fig. 5 is dispatch list to be optimized.
Fig. 6 is the optimum individual fitness function change of every generation genetic algorithm optimization.
Fig. 7 is the dispatch list through genetic algorithm optimization decoding.
Fig. 8 is final network scheduling table.
Detailed description of the invention
The network scheduling algorithm of the CAN master-slave response mode protocol of the present invention, preferably detailed description of the invention is,
Comprising multiple node in CAN network, each node has the node identifier differed, each node in network it
Between communication modes be the application protocol by master-slave response pattern in CAN application layer (or broadcast), the scheduling of employing
Algorithm is CAN dispatching algorithm based on Time Triggered.
The host node adding up nodal information in network is competed by all nodes of network and is produced.Network power is initial
During change, each node broadcasts local address, the local identifier of each node contrast and the identifier received, and certain node is deposited
Being minimum at local identifier, now this node priority in a network is the highest, and such network statistics host node is i.e.
It it is the node that priority is the highest.The request counted on and response message are stored by this node, and use the present invention to propose
Compartment bin packing algorithm and genetic algorithm, calculate dispatch list and optimize, and it is every that the dispatch list after optimizing sends to network
Individual node.Each node is transmitted the message of this node under the synchronised clock of host node according to the dispatch list received.
It is further detailed enforcement step below in conjunction with the accompanying drawings.
Illustrate that master-slave response mode network dispatching algorithm is embodied as step in conjunction with accompanying drawing 1:
The first step: all node broadcasts local address in CAN network, competition network host node, identifier is minimum (excellent
First level is the highest) node will become network host node.
Second step: in CAN network, all node broadcasts collect schedule information order, and each node is according to the life received
Make the solicited message sent required for this node and need the information of response to be back to the node that priority is the highest from node, excellent
The node that first level is the highest becomes the host node in network, the message of collection is stored to host node internal memory.
3rd step: according to all message collected, determine basic line number, basic cycle and the dispatching cycle of dispatch list；
4th step: read message from the internal memory of storage message, use compartment bin packing algorithm, message is loaded dispatch list
In；
5th step: after all message are fitted into dispatch list, uses genetic algorithm to be optimized dispatch list；
6th step: the dispatch list optimized is broadcasted all nodes to network, and all nodes store the dispatch list received,
And according to the Reference News of host node transmission, carry out transmission and the reception of message；
7th step: host node all of nodal information in carrying out again statistics network at set intervals, generates tune again
Degree table also optimizes, and broadcasts the dispatch list being newly formed, with the wasting of resources preventing node time-out from causing.
The step that is embodied as of compartment bin packing algorithm is described in conjunction with accompanying drawing 2:
The first step: read requests message MQ from the internal memory of storage messageiWith response MRi；
Second step: traversal dispatch list row and column, it is judged that whether certain row can put down request message MQiWith response MRi；
3rd step: place message MQi, and it is spaced string placement message MRi；
4th step: circulation the first to three step, until all message has all been placed.
The step that is embodied as of genetic algorithm is described in conjunction with accompanying drawing 3:
The first step: determine the size of population, mrna length, crossover probability, mutation probability, genetic algebra；
Second step: using current dispatch list as initial individuals, and randomly generate residue individuality, and carry out real coding；
3rd step: calculate the fitness of each individuality in colony；
4th step: judge whether to meet termination condition according to fitness value, satisfied then terminate, it is unsatisfactory for, continues；
5th step: retain the individuality that fitness is the highest, remaining individual participation selects operation；
6th step: calculate and retain the individuality that fitness is the highest, remaining individuality carries out mixing intersection operation；
7th step: calculate and retain the individuality that fitness is the highest, remaining individuality carries out mutation operation；
8th step: double counting the 3rd step is to the 8th step.
The specific implementation process of above-mentioned steps is illustrated in conjunction with instantiation.
In one network by 6 node networkings, node identifier numbering be respectively ID1, ID2, ID3, ID4, ID5 and
ID6.When powering on netinit, 6 nodes by broadcast self identification symbol ID competition bus, due to ID1 priority
Height, therefore identifier is that the node of ID1 is referred to as network node.
Network node ID 1 broadcasts all of communication information in collection network, and the message of collection is deposited to node ID 1 internal memory,
All message are as shown in table 1.In table 1, the numbering of message uses Hexadecimal form, and bit0-bit7 represents actual message number,
Bit8-bit11 represents that message sends the identifier to certain node, and bit12-bit15 represents the node identifier sending message.With
Illustrating as a example by request message MQ of numbered 1201 in table 1, this label represents that the node that identifier is 1 is 2 to identifier
Node send message, message numbering is 1；Corresponding response message MR numbered 2101, represents the node response that identifier is 2
Identifier is the node messages of 1, and message numbering corresponds to 1.
By table 1, the greatest common divisor of all message cycles is 10ms, therefore can calculate network scheduling table basic cycle TBC
=1；The least common multiple of all message is 80ms, therefore can calculate dispatch list cycle T is 80；Therefore dispatch list line number R=T/TBC=
8。
Use interval bin packing algorithm, trigger dispatching algorithm according to be embodied as step the binding time of above-mentioned accompanying drawing 2
Characteristic, can obtain initial schedule table as shown in Figure 4.In figure, F window represents idle window, for the extension of future scheduling table；f
Represent that dispatch list is ultimately present request message and response message cannot be spaced apart, place f window, the length of distribution the most herein
For 10us to reach the purpose at interval, it is simple to carrying out resolution response from equipment, main equipment need not wait always, following f window can
The extension of message it is used for same F window.It is found that the window of Fig. 4 dispatch list bright gray parts is (except Arb window from dispatch list
Outside mouthful) there is no unnecessary blank, this partial bus utilization rate reaches 100%；Arb window represents arbitration window.Initial schedule table is dark-grey
Color part represents that this part exists to be needed many places blank (time window length of Dark grey part row is long by message time in these row
Spend the longest decision), this partial blank cannot be applied to transmit other message and cause the waste of bus, and therefore, dispatch list is deep
This part of Lycoperdon polymorphum Vitt can further optimize to improve bus utilization.
Portion intercepts to be optimized in initial schedule table goes out and is combined as a table, forms tune to be optimized as shown in Figure 5
Degree table.Taking effective time window length in Fig. 5 is the time span needed for the transmission of each message of relative numbering again, i.e. message
6101,2102,3102,4102,5102,2103,3201 and 4202 to renumber respectively be 1,2 ..., 8, can calculate a total of
The time span sum of effect isTotal holding time window is a length of
Bus utilization can be calculated
Genetic algorithm is used to be optimized Fig. 5:
(1) initial individuals: real coding is 00111222, represent message 6101,2102,3102,4102,5102,2103,
3201 and 4202 be respectively distributed in Fig. 5 the 0th of dispatch list the, 0,1,1,1,2,2,2 row.
(2) Population Size is 100, and mrna length is 1, and crossover probability is 0.5, and mutation probability is 0.01, and genetic algebra is
200, fitness functionThe wherein p when individual r is reasonablerEqual to 0, time unreasonable etc.
It is chosen in α, α1000 times.
(3) according to realizing step shown in Fig. 3, each individuality is selected, intersects and mutation operation, and obtain each
The optimum individual fitness function in generation and final optimum individual.The selection gist of optimum individual is the fitness of all individualities
Value, fitness value the highest for optimum individual, be not involved in selecting, intersect, mutation operation.
Wherein selecting operation to use roulette algorithms selection, its process includes following step:
A () obtains the fitness summation of all individualities；
B () calculates the relative adaptability degrees of each individuality, i.e. represent the probability of each individual inheritance colony the most of future generation；
C () selects, according to the result of (b), the individuality that genetic probability is maximum, directly preserve to individuality of future generation；And count
Calculate fitness summation and the heredity extremely follow-on colony probability remaining all individualities；
D each probit is formed a continuous print regional extent by (), whole probit sums are 1；
E () produces the random number between 0 to 1, according to scold at random probability interval select corresponding individuality；Depend on
This mode selects remaining individuality remaining, and the individuality selected the most at last also combines optimum individual as new Next group
Body.
The operation that intersects intersects certain two individual chromosome dyad with some probability exactly, the present invention use by with
Machine number produces 0 and 1 to carry out randomly choosing single-point or two point crossover algorithm, mainly comprises the steps that
A () obtains the fitness summation of all individualities, calculate individual relative fitness, therefrom selects relative adaptability degrees
High individuality directly preserves to individuality of future generation as optimum individual；
A remaining individuality is carried out random pair two-by-two by ()；
B () produces one 0 or the random number of 1, select single-point or two point to intersect according to 0 or 1；
If c () single-point intersects, then randomly generate an integer in the range of mrna length as cross point, general's pairing
Two individualities replace mutually a fragment gene according to cross-point locations, thus produce two new individualities；If two point intersects, then
Randomly generate two integers in the range of mrna length as gene cross point, by two individualities of pairing according to two intersections
Point exchange two fragment genes, thus also produce two new individualities；
D () repetitive operation step (b) (c), until all of pairing individuality all completes to intersect.
Variation is that the genic value to certain individuality is changed by a certain probability, thus produces new individual.Concrete steps bag
Include:
A () obtains the fitness summation of all individualities, calculate individual relative fitness, therefrom selects relative adaptability degrees
High individuality directly preserves to individuality of future generation as optimum individual；
B () randomly chooses the individual object group as variation of the part in residue population；
C () randomly generates one group of gene location needing variation；
(d) according to produce gene location correspondence variation object group gene location, by the genic value of this position from effectively
Residue genic value in randomly choose genic value as an alternative, thus define new individuality.
Fig. 6 is the optimum individual fitness function change of every generation genetic algorithm optimization.It is found that pass through and lose from figure
The optimization of propagation algorithm, optimum individual occurred in the 6th generation, and corresponding fitness is 93.29%, represented that bus utilization is 93.29%,
And more excellent individuality occurs the most again.Through genetic algorithm optimization, the individuality that fitness is the highest is
01021122, it is decoded as dispatch list, as shown in Figure 7.By calculating the dispatch list shown in Fig. 7, effective time window can be taken
Length may be calculatedTotal holding time window is a length of Permissible
Calculate bus utilizationComparison diagram 5 and Fig. 7, it is possible to find bus utilization is excellent in genetic algorithm
93.29% is risen to by 86.79% after change.Therefore, genetic algorithm optimization dispatch list is feasible and effective.Fig. 7 is reduced to
In integrated scheduling table, and the position of the request message according to the dispatch list adjustment correspondence after optimizing, as shown in Fig. 81, formed
Whole network scheduling table is as shown in Figure 8.
Host node will optimize after dispatch list broadcast to each equipment in network, and send Reference News and start network
Scheduling.Host node all of nodal information in carrying out again statistics network at set intervals, again generates dispatch list and optimizes,
And broadcast the dispatch list being newly formed, with the wasting of resources preventing node time-out from causing.
Table 1
Claims (5)
1. the network scheduling algorithm of a CAN master-slave response mode protocol, it is characterised in that comprise the following steps:
Step one: all node broadcasts competition host node in network, the node that identifier is minimum will become network host node；
Step 2: network host node broadcast statistical information, all nodes return request and the response message of this node, deposit to main joint
In some internal memory；
Step 3: network host node, according to all message of statistics collection, uses " compartment bin packing algorithm " constructive scheduling table；
Step 4: network host node uses the dispatch list that genetic algorithm optimization has constructed；
Step 5: the schedule information after network host node will optimize sends to each node in network, then broadcasts reference
Message starts internet message scheduling；
Step 6: network host node is after starting to dispatch a period of time, and repetition step 2 to step 5, according to nodes fortune
Market condition upgrades in time dispatch list；
In described step 3, compartment bin packing algorithm is when constructive scheduling table, places request message MQiRear needs should by corresponding
Answer message MRiCarrying out being spaced string to place, the idle row being spaced are then for the distribution of next message；Compartment bin packing algorithm
The process of constructive scheduling table is as follows:
(1) host node is according to all message collected, and calculates the cycle of dispatch list, basic cycle, dispatch list line number；
(2) from the message of storage, message MQ is taken outiAnd MRi, travel through dispatch list, it is judged that certain line length in dispatch list whether can
Hold message MQ simultaneouslyiAnd MRi, can then place message MQiAnd MRi；Can not then continue to travel through dispatch list, searching can be put down simultaneously
Message MQiAnd MRiPosition；
(3) repetitive process (2), until all of message is all put in dispatch list.
2. the method for claim 1, it is characterised in that statistical information includes asking of each node in described step 2
Asking message and corresponding response message, the attribute of these message includes message number, message transmission time length and message transmission
Cycle.
3. the method for claim 1, it is characterised in that the genetic algorithm in described step 4, step is as follows:
(1) being used by the dispatch list that step 3 constructs as initial individuals after real coding, residue individuality randomly generates；
(2) structure fitness function, fitness function is by the bus utilization represented after each individual decoding and penalty factor group
Become；
(3) carrying out selecting every time, intersect, before three operations that make a variation, be both needed to first calculate the fitness function of all individualities, and
Retain optimum individual to be not involved in selecting, intersecting and three operations that make a variation；
(4) select to use roulette algorithm to select, use single-point to intersect and the mixing crossover algorithm of two-point crossover, random number
The mode of (0,1) selects crossover algorithm, and random number is 0 employing single-point crossover algorithm, and random number is 1 employing two-point crossover algorithm；
Variation then uses the mode of random chance to carry out the variation of random order.
4. method as claimed in claim 3, it is characterised in that the fitness function f of individual rrBy bus utilization and punishment because of
Son composition, is expressed as:Wherein prFor the penalty factor of individual r, produce for during genetic algorithm optimization
After the individual decoding of raw some, columns may exceed the restriction of former dispatch list columns sum, if exceeding, individuality is unreasonable, no
It is then rationally, and the penalty factor introduced the most rationally affects this individuality according to these individual decoded dispatch lists exactly and loses
Reach follow-on probability；If after individual r decoding, corresponding dispatch list is rational, then prBe 0, be otherwise one the biggest normal
Number α, α are chosen forMore than 1000 times；triFor the message transmission time length that relative numbering is i, T is dispatch list
Dispatching cycle, Q is the time window number that in exclusive window, bus utilization is non-optimal, LjThe time window length arranged in jth for distribution,
njDistributing the message number at j row, i is to distribute the message numbering optimizing exclusive window, and k is to distribute the message optimizing exclusive window
Number；frRepresent the superior degree of individuality.
5. the method for claim 1, it is characterised in that send schedule information after optimizing described in described step 5,
The schedule information sent includes dispatching basic cycle, dispatch list cycle, dispatch list line number, dispatch list columns, dispatch list each column
The message identifier that time window width and the distribution of dispatch list each row and column send.
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| CN105245423B (en) * | 2015-08-28 | 2018-06-26 | 中国航天科工集团第三研究院第八三五七研究所 | 1553B bus message transmission optimization methods based on maximum allocated distance algorithm |
| CN105205536B (en) * | 2015-11-06 | 2017-11-10 | 天津津航计算技术研究所 | 1553B bus message transmission optimization methods based on genetic algorithm |
| CN106790130B (en) * | 2016-12-27 | 2020-05-22 | 中国建设银行股份有限公司 | Message matching method and device |
| CN107342833B (en) * | 2017-06-15 | 2019-03-15 | 西安微电子技术研究所 | A kind of time trigger Ethernet exchanging control method |
| CN107645455A (en) * | 2017-09-12 | 2018-01-30 | 天津津航计算技术研究所 | A kind of message transmission dispatching method of CAN |
| CN108848146B (en) * | 2018-05-25 | 2020-02-18 | 西安云维智联科技有限公司 | Scheduling optimization method based on time-triggered communication service |
| CN108920206B (en) * | 2018-06-13 | 2022-01-18 | 北京交通大学 | Plug-in scheduling method and device |
| CN109818876B (en) * | 2019-03-11 | 2021-04-27 | 西安电子科技大学 | Method for generating global optimization time-triggered service schedule |
| EP3790232A1 (en) * | 2019-09-09 | 2021-03-10 | TTTech Computertechnik Aktiengesellschaft | Method for generating a schedule for mixed critical computer networks |
| CN110853376B (en) * | 2019-09-30 | 2021-12-03 | 重庆中信科信息技术有限公司 | Traffic signal lamp of intelligent network |
| CN114884852B (en) * | 2022-05-07 | 2024-04-23 | 武汉思普崚技术有限公司 | Node interaction and protocol identification method, device, equipment and computer medium |
| CN117278639B (en) * | 2023-11-21 | 2024-01-30 | 浙江国利信安科技有限公司 | Method, apparatus and storage medium for deterministic network-based communication time scheduling |
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| CN102739488A (en) * | 2012-06-15 | 2012-10-17 | 烽火通信科技股份有限公司 | CAN (controller area network) bus-based communication method in intelligent ODN (optical distribution network) system |
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