CN115964959A - Domain-centralized electronic and electrical architecture modeling and multi-objective optimization method and system - Google Patents

Domain-centralized electronic and electrical architecture modeling and multi-objective optimization method and system Download PDF

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CN115964959A
CN115964959A CN202310231043.XA CN202310231043A CN115964959A CN 115964959 A CN115964959 A CN 115964959A CN 202310231043 A CN202310231043 A CN 202310231043A CN 115964959 A CN115964959 A CN 115964959A
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objective optimization
ecu
swc
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hardware
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CN115964959B (en
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田子昂
孔国杰
于会龙
席军强
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Beijing Institute of Technology BIT
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Abstract

The invention discloses a domain-centralized electronic and electrical architecture modeling and multi-objective optimization method and system, relates to the technical field of electronic and electrical architecture modeling and optimization, and respectively establishes a communication relation adjacent matrix of a software layer, a hardware structure adjacent matrix of a hardware layer and an SWC-ECU distribution relation matrix so as to model an electronic and electrical architecture. And then establishing a multi-objective optimization model, solving the multi-objective optimization model by using a multi-objective optimization algorithm to minimize a cost value, a load rate and a time delay value to obtain an optimal solution of a hardware structure adjacency matrix and an SWC-ECU distribution relation matrix, and performing multi-objective optimization based on cost, load and delay on the hardware structure by using the multi-objective optimization algorithm.

Description

Domain-centralized electronic and electrical architecture modeling and multi-objective optimization method and system
Technical Field
The invention relates to the technical field of electronic and electrical architecture modeling and optimization, in particular to a domain centralized electronic and electrical architecture modeling and multi-objective optimization method and system for an unmanned special vehicle.
Background
At present, the key research and development are carried out on unmanned special vehicles at home and abroad. With the continuous development of unmanned vehicles, the number of sensors and actuators mounted on unmanned special vehicles is increasing day by day, and different from common civil vehicles, the unmanned special vehicles are mounted with more sensors and actuators than the common civil vehicles, so that more and more complex tasks except automatic driving can be completed in more complex environments. Under such background, the increase of function can only be dealt with through the quantity that constantly increases ECU to traditional distributed electron electric framework, and this will lead to the interior wiring harness quantity of car to sharply increase, and the wiring harness arranges complicacy, has increased the maintenance degree of difficulty of vehicle when reducing vehicle performance, is unfavorable for unmanned special vehicle's development.
According to an evolution route diagram of an automobile EEA (Electronic Architecture) proposed by BOSCH in 2018 and 4 months, the calculation power of automobiles in the future is more and more centralized, and it is a current development trend to integrate a plurality of functions in the automobile into various domains. The unmanned special vehicle has multiple functions, a large number of sensors and actuators are mounted, and the necessary trend of future development is to reduce the length of a wiring harness, simplify the hardware connection mode and enhance the vehicle performance by adopting a domain centralized electronic and electrical architecture. However, the existing domain centralized electronic and electrical architecture is often designed and optimized in a manual development mode, and in the design aspect, the manual design is affected by subjective factors, so that the design period of the whole vehicle is long, and the universality of the architecture is poor; in the aspect of optimization, various indexes of the domain centralized electronic and electrical architecture influence each other, so that the optimization efficiency is low and the optimization effect is poor.
Based on this, there is a need for an automated domain-centralized electrical and electronic architecture modeling and multi-objective optimization technique.
Disclosure of Invention
The invention aims to provide a domain centralized electronic electrical architecture modeling and multi-objective optimization method and system, which are used for carrying out mathematical modeling on a hardware structure and a software architecture of a vehicle by utilizing an adjacency matrix and carrying out multi-objective optimization on the hardware structure based on cost, load and delay by adopting a multi-objective optimization algorithm.
In order to achieve the purpose, the invention provides the following scheme:
a domain centralized electronic and electrical architecture modeling and multi-objective optimization method comprises the following steps:
establishing a communication relation adjacency matrix of a software layer according to a signal transmission relation among the sensor, the SWC and the actuator; the communication relation is adjacent to the element of the ith row and the jth column of the matrix and is used for characterizing the signal transmission relation of the ith first assembly and the jth first assembly, and the first assembly comprises a sensor, an SWC and an actuator;
establishing a hardware structure adjacency matrix of a hardware layer according to the hardware connection relation among the sensor, the common CAN node, the ECU and the actuator; establishing an SWC-ECU distribution relation matrix according to the connection relation between the SWC and the ECU; the elements of the ith row and the jth column of the hardware structure adjacent matrix are used for representing the hardware connection relationship between the ith second assembly and the jth second assembly, and the second assembly comprises a sensor, a common CAN node, an ECU and an actuator;
establishing a multi-objective optimization model for optimizing the hardware structure adjacency matrix and the SWC-ECU distribution relation matrix; the multi-objective optimization model aims to minimize a cost value, a load rate and a time delay value, and constraint conditions comprise time delay value constraint, hardware requirement constraint and co-location constraint;
and solving the multi-objective optimization model by using a multi-objective optimization algorithm to obtain the optimal solution of the hardware structure adjacency matrix and the SWC-ECU distribution relation matrix.
A domain-centric electronic-electrical architecture modeling and multi-objective optimization system, comprising:
the software layer modeling module is used for establishing a communication relation adjacency matrix of a software layer according to the signal transmission relation among the sensor, the SWC and the actuator; the communication relation is adjacent to the element of the ith row and the jth column of the matrix and is used for characterizing the signal transmission relation of the ith first assembly and the jth first assembly, and the first assembly comprises a sensor, an SWC and an actuator;
the hardware layer modeling module is used for establishing a hardware structure adjacency matrix of a hardware layer according to the hardware connection relation among the sensor, the public CAN node, the ECU and the actuator; establishing an SWC-ECU distribution relation matrix according to the connection relation between the SWC and the ECU; elements of an ith row and a jth column of the hardware structure adjacency matrix are used for representing the hardware connection relation of an ith second assembly and a jth second assembly, and the second assembly comprises a sensor, a common CAN node, an ECU and an actuator;
the optimization model establishing module is used for establishing a multi-objective optimization model for optimizing the hardware structure adjacency matrix and the SWC-ECU distribution relation matrix; the objective of the multi-objective optimization model is to minimize a cost value, a load rate and a time delay value, and the constraint conditions comprise a time delay value constraint, a hardware requirement constraint and a co-location constraint;
and the optimization solving module is used for solving the multi-objective optimization model by using a multi-objective optimization algorithm to obtain the optimal solution of the hardware structure adjacency matrix and the SWC-ECU distribution relation matrix.
According to the specific embodiment provided by the invention, the invention discloses the following technical effects:
the invention provides a domain centralized electronic and electrical architecture modeling and multi-objective optimization method and system, which are used for establishing a communication relation adjacent matrix of a software layer according to a signal transmission relation among a sensor, an SWC and an actuator, establishing a hardware structure adjacent matrix of a hardware layer according to a hardware connection relation among the sensor, a common CAN node, an ECU and the actuator, and establishing an SWC-ECU distribution relation matrix according to a connection relation among the SWC and the ECU, thereby modeling a hardware structure and a software architecture of the electronic and electrical architecture. And then establishing a multi-objective optimization model, solving the multi-objective optimization model by using a multi-objective optimization algorithm to minimize a cost value, a load rate and a time delay value to obtain an optimal solution of a hardware structure adjacency matrix and an SWC-ECU distribution relation matrix, and performing multi-objective optimization based on cost, load and delay on the hardware structure by using the multi-objective optimization algorithm.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings needed to be used in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings without inventive exercise.
FIG. 1 is a process flow diagram of the method provided in example 1 of the present invention;
FIG. 2 is a schematic block diagram of a method provided in embodiment 1 of the present invention;
FIG. 3 is a schematic representation of a chromosome provided in example 1 of the present invention;
fig. 4 is a plan view of a mounting board harness provided in embodiment 1 of the present invention;
FIG. 5 is a bottom view of a mounting plate bottom harness provided in embodiment 1 of the present invention;
fig. 6 is a sectional view of an inner side surface wiring harness provided in embodiment 1 of the present invention;
fig. 7 is a system block diagram of the system provided in embodiment 2 of the present invention.
Description of the symbols:
1-a first lidar; 2-a second lidar; 3-a first vehicle-mounted camera; 4-a second vehicle-mounted camera; 5-a third vehicle-mounted camera; 6-a fourth vehicle-mounted camera; 7-GPS; 8-mechanical arm camera; 9-a mechanical arm; 10-mounting plate front wiring hole; 11-mounting plate rear wiring hole; 12-a first onboard ECU; 13-a second on-board ECU.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be obtained by a person skilled in the art without making any creative effort based on the embodiments in the present invention, belong to the protection scope of the present invention.
The invention aims to provide a domain centralized electronic electrical architecture modeling and multi-objective optimization method and system, which are used for carrying out mathematical modeling on a hardware structure and a software architecture of a vehicle by utilizing an adjacency matrix and carrying out multi-objective optimization on the hardware structure based on cost, load and delay by adopting a multi-objective optimization algorithm.
In order to make the aforementioned objects, features and advantages of the present invention comprehensible, embodiments accompanied with figures are described in further detail below.
Example 1:
because the design and optimization of the domain centralized electronic and electrical architecture by adopting the artificial development mode have a plurality of problems, the design and optimization of the domain centralized electronic and electrical architecture by adopting the multi-objective optimization algorithm are scientific and efficient means, not only can a design scheme for reference be provided, the design period of a vehicle is shortened, but also various indexes can be comprehensively optimized, or a certain index is subjected to key optimization by setting a weight coefficient. Under the background, the embodiment provides a centralized electronic electrical architecture modeling and multi-objective optimization method for an unmanned special vehicle domain, which comprises a method for performing mathematical modeling on a software architecture and a hardware structure of a vehicle EEA and a vehicle EEA multi-objective optimization algorithm, and can well solve the modeling problem and the optimization problem of the current vehicle EEA.
Based on this, the present embodiment is configured to provide a domain centralized electronic and electrical architecture modeling and multi-objective optimization method, as shown in fig. 1 and fig. 2, including:
s1: establishing a communication relation adjacency matrix of a software layer according to a signal transmission relation among the sensor, the SWC and the actuator; the communication relation is adjacent to the element of the ith row and the jth column of the matrix and is used for characterizing the signal transmission relation of the ith first assembly and the jth first assembly, and the first assembly comprises a sensor, an SWC and an actuator;
the electronic and electrical architecture of the whole vehicle is abstracted into a demand layer, a task layer, a software layer and a hardware layer for mathematical modeling. Before mathematical modeling is carried out on a software layer and a hardware layer, electronic and electrical architecture modeling preprocessing is carried out, and the process of the modeling preprocessing is the mathematical modeling process of a demand layer and a task layer.
(1) Mathematical modeling of the requirement layer:
the electronic and electrical architecture modeling of the whole vehicle starts from a requirement layer, at this level, the types and the number of hardware components (ECU, sensors and actuators) and software components (SWC) contained in the whole vehicle are required to be determined, namely, the mathematical modeling process of the requirement layer comprises the following steps: the type and number of various third components included in the vehicle are determined, the third components including sensors, SWCs, actuators, and ECUs. In this embodiment, the total number of ECUs is set to | E |, the total number of sensors is set to | K |, the total number of actuators is set to | a |, and the total number of SWCs is set to | C |.
(2) Mathematical modeling of the task layer:
after the mathematical modeling of the requirement layer is completed, the third components need to be divided into task groups in the mathematical modeling of the task layer, that is, the third components which jointly complete a certain vehicle function need to be divided into the same set. The task layer firstly counts the total functions of the whole vehicle, namely, determines the number F of task groups, then uniquely numbers each third component to distinguish each third component, and finally divides the third components according to the actual function condition of the whole vehicle to obtain the task groups
Figure SMS_1
. The mathematical modeling of the task layer includes: and dividing all third components which finish the functions of a certain vehicle together into the same task group to obtain a plurality of task groups with the same number as the total functions of the vehicle.
And performing mathematical modeling on the demand layer and the task layer to complete the modeling pretreatment of the electronic and electric framework of the vehicle.
After the modeling preprocessing is completed, an overall communication relation adjacency matrix needs to be established in a software layer, and a whole vehicle network topological relation is generated and serves as a basis for analyzing the performance of the vehicle electronic and electrical architecture. Because the connection relationship between the software layer and the hardware layer of the electronic and electrical architecture of the whole vehicle is complex, in order to describe the architecture of the whole vehicle, the adoption of the adjacency matrix as the representation of the EEA of the whole vehicle is a feasible scheme. A adjacency matrix is a method of representing a graph as a boolean (0 or 1) matrix, and a directed graph or an undirected graph is represented on a computer in the form of a square matrix, where the boolean value of the matrix indicates whether or not a direct path exists between two vertices.
S1 may include:
(1) The signal transfer relationships of the sensors are input into the adjacency matrix.
And inputting the signal transmission relation related to the sensor into the adjacency matrix column by column according to the sequence of the serial number of the sensor determined in the task layer.
(2) The signaling relationships of the SWC are input into the adjacency matrix.
And inputting the signal transmission relations related to the SWCs into the adjacency matrix row by row and column by column according to the sequence of the SWCs determined in the task layer.
(3) The signal transfer relationships of the actuators are input into the adjacency matrix.
And inputting the signal transmission relation related to the actuators into the adjacency matrix column by column according to the sequence of the actuator serial numbers determined in the task layer.
Through the steps, an overall communication relation adjacency matrix S can be obtained, the overall communication relation adjacency matrix S is used for representing the specific transmission mode of the signals in the vehicle, and the calculation expression is as follows:
Figure SMS_2
；
wherein S is a communication relation adjacency matrix of a software layer;
Figure SMS_3
the element values of the ith row and the jth column of the adjacent matrix for communication are used for representing the signal transmission relationship between the ith first component and the jth first component, and if the ith first component is connected with the jth first component by signals, the->
Figure SMS_4
Is 1, otherwise->
Figure SMS_5
Is 0; />
Figure SMS_6
(ii) a V is a first component comprising a sensor, an actuator and an SWC; e is an edgeAnd (4) collecting.
In this embodiment, the signal transmission relationship between the sensor and the first component is before the correspondence in the communication relationship adjacency matrix
Figure SMS_7
The position of the row, the signal transfer relationship of the SWC and the first element corresponds to an intermediate ^ in the communication relationship adjacency matrix>
Figure SMS_8
The signal transmission relationship of the row position, the actuator and the first component corresponds to the last->
Figure SMS_9
The positions of the rows and the finally obtained communication relation adjacency matrix of the software layer are as follows: />
Figure SMS_10
Through the steps, the mathematical modeling of the software layer of the electronic and electric framework of the vehicle can be completed.
S2: establishing a hardware structure adjacency matrix of a hardware layer according to the hardware connection relation among the sensor, the common CAN node, the ECU and the actuator; establishing an SWC-ECU distribution relation matrix according to the connection relation between the SWC and the ECU; the elements of the ith row and the jth column of the hardware structure adjacent matrix are used for representing the hardware connection relationship between the ith second assembly and the jth second assembly, and the second assembly comprises a sensor, a common CAN node, an ECU and an actuator;
the hardware component connection is completed in a hardware layer, the SWC is distributed to the ECU, the bus when the hardware component is connected CAN be selected from CAN or Ethernet, meanwhile, in order to ensure the diversity, the component CAN be selected to be directly connected to the ECU or connected to a public CAN node (a vehicle public CAN bus node), and the number of the public CAN nodes is set to be | N |. When the component is connected, the bus and the ECU are connected firstly, and then the component is connected to the ECU or a common CAN node.
The process of establishing the hardware structure adjacency matrix can comprise the following steps:
(1) The hardware connection relationships of the sensors are input into the adjacency matrix.
And inputting the hardware connection relation related to the sensors into the adjacency matrix column by column according to the sequence number sequence of the sensors determined in the task layer.
(2) And inputting the hardware connection relation of the common CAN node into the adjacency matrix.
And inputting the hardware connection relation related to the public CAN nodes into the adjacency matrix row by row and column by column according to the serial number sequence of the public CAN nodes currently designed by the vehicle.
(3) The hardware connection relationship of the ECU is input into the adjacency matrix.
And inputting hardware connection relations related to the ECU into the adjacency matrix column by column according to the sequence of the ECU serial number of the current design of the vehicle.
(4) And inputting the hardware connection relation of the actuator into the adjacency matrix.
And inputting the hardware connection relation related to the actuator into the adjacency matrix column by column according to the sequence of the actuator serial number determined in the task layer.
Through the steps, the hardware structure adjacency matrix H of the whole vehicle can be obtained, and the calculation expression is as follows:
Figure SMS_11
；/>
h is a hardware structure adjacency matrix of a hardware layer;
Figure SMS_12
the element values of the ith row and the jth column of the adjacent matrix of the hardware structure are used for representing the hardware connection relationship between the ith second component and the jth second component, and if the ith second component is connected with the jth second component in a hardware mode, the value of the element in the ith row and the jth column of the adjacent matrix of the hardware structure is->
Figure SMS_13
Is w, otherwise->
Figure SMS_14
Is 0; />
Figure SMS_15
(ii) a w represents a bus class, w =1, being a CAN bus; w =2, ethernet bus; v is a second component which comprises a sensor, a common CAN node, an ECU and an actuator; e is the set of edges.
The hardware connection condition of the sensor and the second component corresponds to the front in the hardware structure adjacency matrix
Figure SMS_16
The position of the row, the hardware connection of the common CAN node to the second component corresponds in the hardware-structural adjacency matrix to the middle after the sensor->
Figure SMS_17
The position of the row, the hardware connection of the ECU to the second component in the hardware-structural adjacency matrix corresponds to the middle after the common CAN node->
Figure SMS_18
The position of the row, the hardware connection of the actuator to the second component corresponds to the last->
Figure SMS_19
The position of the row, and the resulting hardware structure adjacency matrix H representing the hardware layer of the in-vehicle hardware structure connection scheme are as follows:
Figure SMS_20
the establishment process of the SWC-ECU distribution relation matrix comprises the following steps:
and distributing the SWCs to the ECUs line by line according to the sequence of the SWCs in the task layer to obtain an SWC-ECU distribution relation matrix. The calculation expression of the SWC-ECU distribution relation matrix is as follows:
Figure SMS_21
；
wherein U is an SWC-ECU distribution relation matrix;
Figure SMS_22
assigning element values of the ith row and jth column of the relation matrix to the SWC-ECU for representing whether the jth SWC is assigned to the ith ECU, and if the jth SWC is assigned to the ith ECU, then ^ and/or ^ based on the assigned element values>
Figure SMS_23
Is n, otherwise->
Figure SMS_24
Is 0; />
Figure SMS_25
(ii) a n is the number of SWC, i.e., n = j; />
Figure SMS_26
Is jth SWC; />
Figure SMS_27
Is the ith ECU. />
Based on the above steps, the obtained SWC-ECU assignment relationship matrix for characterizing the SWC and ECU assignment relationship in the vehicle is as follows:
Figure SMS_28
the mathematical modeling of the hardware layer of the electronic and electric framework of the vehicle can be completed by establishing the hardware structure adjacency matrix and the SWC-ECU distribution relation matrix of the whole vehicle.
It should be noted that the mathematical modeling process converts a known or artificially determined vehicle electrical and electronic architecture into a mathematical model, so that the signal transmission relationship, the hardware connection relationship, and the distribution relationship between the SWC and the ECU are known.
S3: establishing a multi-objective optimization model for optimizing the hardware structure adjacency matrix and the SWC-ECU distribution relation matrix; the objective of the multi-objective optimization model is to minimize a cost value, a load rate and a time delay value, and the constraint conditions comprise a time delay value constraint, a hardware requirement constraint and a co-location constraint;
specifically, the harness cost of the entire vehicle is composed of a CAN line and an Ethernet line, and the first objective function of the multi-objective optimization model is as follows:
Figure SMS_29
；
wherein ,
Figure SMS_30
is an individual in the starting population->
Figure SMS_31
The cost of the entire vehicle wiring harness; />
Figure SMS_32
A cost value for the CAN bus; />
Figure SMS_33
Is a cost value of the Ethernet bus.
The second objective function of the multi-objective optimization model is:
Figure SMS_34
；
wherein ,
Figure SMS_35
is the bus load rate; b is a bus set, namely a bus set of the whole vehicle; />
Figure SMS_36
Is the weighted bus load of bus b.
The third objective function of the multi-objective optimization model is:
Figure SMS_37
；
wherein ,
Figure SMS_38
a bus signal propagation delay; />
Figure SMS_39
Is the transfer delay of the CAN bus; />
Figure SMS_40
Is the propagation delay of the Ethernet bus.
The constraint conditions include:
(1) And (3) time delay value constraint:
for population individuals
Figure SMS_41
And (3) carrying out time delay value constraint, wherein the constraint formula is as follows:
Figure SMS_42
；
wherein ,
Figure SMS_43
is a delay value constraint; s is a signal set in a software architecture; the | S | is the number of signals in the signal set S; />
Figure SMS_44
Is the worst case propagation delay for signal s; />
Figure SMS_45
Is the end-to-end time requirement of signal s; 1{P represents a function that returns a 1 if P is true and a 0 otherwise.
(2) And (3) hardware requirement constraint:
hardware demand constraints are carried out on the population individuals, and certain SWCs have specific distribution requirements on the ECU, and the constraint formula is as follows:
Figure SMS_46
；
wherein ,
Figure SMS_47
is a hardware requirement constraint; c is the whole SWC set, namely the set consisting of all SWCs; | C | is the number of SWCs in the set C; />
Figure SMS_48
ECU assigned for SWC; />
Figure SMS_49
A specific assigned ECU is required for SWC;
(3) Co-location constraint:
and (3) carrying out co-location demand constraint on the population individuals, wherein some SWCs have demands distributed in the same ECU, and the constraint formula is as follows:
Figure SMS_50
；
wherein ,
Figure SMS_51
is a co-location constraint; c is a set consisting of all SWCs; | C | is the number of SWCs in the set C; />
Figure SMS_52
Is the set of SWCs assigned to the ECU; />
Figure SMS_53
Is the set of SWCs required to be assigned to the same ECU.
S4: and solving the multi-objective optimization model by using a multi-objective optimization algorithm to obtain the optimal solution of the hardware structure adjacency matrix and the SWC-ECU distribution relation matrix.
The multi-objective optimization algorithm of this embodiment is an NSGA-ii algorithm, that is, the NSGA-ii algorithm is adopted to optimize a hardware structure adjacency matrix and an SWC-ECU allocation relationship matrix (that is, an SWC allocation scheme) of a hardware layer of the electronic and electrical architecture of the entire vehicle, and then S4 may include:
(1) Encoding a hardware structure adjacency matrix and an SWC-ECU distribution relation matrix into a chromosome; and carrying out random initialization operation on the chromosome to obtain an initial population.
In the embodiment, the hardware structure adjacency matrix and the SWC-ECU allocation relation matrix of the hardware layer are encoded into a chromosome, and the obtained chromosome is shown in fig. 3. In FIG. 3, ek, ea, and ESWC are shown asRespectively showing the connection relation between the sensors and the actuators and the ECU and the distribution relation of the SWC to the ECU; hw represents the harness category of the bus; kz and Az represent the sensor and actuator wiring harness distribution on the mounting board, respectively. In the chromosome
Figure SMS_54
In (1), each index i corresponds to the number of a component (sensor, actuator, SWC) or wire harness, and the variable->
Figure SMS_55
The value of (d) corresponds to the connection relationship, assignment relationship, or category of the wiring harness of the component i. />
Figure SMS_56
The value of (a) can only be an integer and uniquely represents an allocation. Wherein the variation range of Ek, ESWC and Ea is
Figure SMS_57
Hw, kz, az vary within a range ^ h>
Figure SMS_58
. To reduce the search space, the SWC set with co-location requirements is encoded by a single gene in the chromosome, and each variable ≧ is>
Figure SMS_59
Are all represented by one gene in the chromosome.
The acquisition process of the initial population comprises the following steps: and (3) carrying out random initialization operation on the chromosome, and endowing the gene values in the chromosome with random integer values in a variation range to generate an initial population.
Through the steps, the adjacent matrix of the hardware structure to be optimized and the SWC-ECU distribution relation matrix are coded to generate the chromosome of the NSGA-II multi-target optimization algorithm, and further, the initial population is randomly generated according to the chromosome and is input into the NSGA-II algorithm.
(2) Calculating a target value of each individual in the initial population under the constraint of a constraint condition by using a multi-objective optimization model; the target values include cost values, load ratios, and delay values.
The specific process of calculating the cost value, the load rate and the time delay value of each individual in the initial population is as follows:
(2.1) solving the cost value of the initial population:
in the embodiment, the cost value can be calculated by using the first objective function of the multi-objective optimization model provided in S3, and in calculating the cost value, the hardware layout of the special vehicle is as shown in fig. 4, 5 and 6, the vehicle is composed of a chassis and a top mount, the top mount comprises all sensors and actuators, and the sensors and the actuators are mounted on a mounting plate fixed on the chassis by fasteners. The ECU is arranged in the chassis, and the wire harnesses between the ECU and the chassis are connected together through a wiring hole on the mounting plate. In the drawing, 1 is a first laser radar, 2 is a second laser radar, 3 is a first vehicle-mounted camera, 4 is a second vehicle-mounted camera, 5 is a third vehicle-mounted camera, 6 is a fourth vehicle-mounted camera, 7 is a GPS,8 is a robot arm camera, 9 is a robot arm, 10 is a mounting plate front wiring hole, 11 is a mounting plate rear wiring hole, 12 is a first vehicle-mounted ECU,13 is a second vehicle-mounted ECU, a broken line represents a CAN line, and a thin solid line represents an Ethernet line.
1) Calculating the harness length of a common CAN bus
Figure SMS_60
。
The components may be connected to a common CAN bus, in which case the total harness length under the board to which the components correspond is
Figure SMS_61
The calculation formula is as follows:
Figure SMS_62
；
wherein ,
Figure SMS_63
is the vertical height of the mounting plate; />
Figure SMS_64
Is the Z coordinate of the ECU; />
Figure SMS_65
Is the Y coordinate of the CAN bus; />
Figure SMS_66
Is the Y coordinate of the wiring hole.
2) Calculating the harness length of the CAN bus b
Figure SMS_67
。
During calculation, the wire harness is divided into an upper part and a lower part and a vehicle edge, wherein the wire harness length of the CAN bus b
Figure SMS_68
The calculation formula is as follows:
Figure SMS_69
；
wherein ,
Figure SMS_70
is the length on the plate; />
Figure SMS_71
The length of the lower plate and the edge of the vehicle.
The calculation formula for the length on the plate is as follows:
Figure SMS_72
；
wherein ,
Figure SMS_73
、/>
Figure SMS_74
is a component X, Y coordinate, based on a coordinate system>
Figure SMS_75
、/>
Figure SMS_76
Is the X, Y coordinate of the wire hole.
The calculation formula of the length of the lower plate and the edge is as follows:
Figure SMS_77
；
wherein ,
Figure SMS_78
is the Y coordinate of the common CAN bus; />
Figure SMS_79
Is the vertical height of the mounting plate; />
Figure SMS_80
、/>
Figure SMS_81
Is the ECU's X, Z coordinates.
3) Calculating the length of the Ethernet bus b
Figure SMS_82
。
Length of Ethernet bus b
Figure SMS_83
Based on the calculation mode and the harness length of the CAN bus b>
Figure SMS_84
The same way of calculation is used.
4) Calculating the Total cost value of the CAN bus
Figure SMS_85
。/>
Calculated by 1)
Figure SMS_86
And 2) the calculated->
Figure SMS_87
CAN calculate the cost value of the CAN bus
Figure SMS_88
As follows:
Figure SMS_89
；
wherein ,
Figure SMS_90
the price per unit length (unit: element/m) of the CAN bus.
5) Calculating cost values for Ethernet buses
Figure SMS_91
。
Calculated by 3)
Figure SMS_92
The cost value for the Ethernet bus may be calculated>
Figure SMS_93
As follows:
Figure SMS_94
；
wherein ,
Figure SMS_95
is the price per unit length of the Ethernet bus.
6) Calculating the cost of the wire harness of the whole vehicle of the individual x in the population
Figure SMS_96
。
Calculated from 4)
Figure SMS_97
And 5) calculated >>
Figure SMS_98
The vehicle harness cost ^ of the individual x in the calculated population can be calculated>
Figure SMS_99
As follows:
Figure SMS_100
。
and (2.2) solving the load rate of the initial population.
1) Calculating the load factor of the bus b
Figure SMS_101
。
Figure SMS_102
For b, the calculation formula of the unbiased measurement of the bus bandwidth occupied by the bus is as follows:
Figure SMS_103
；
wherein ,
Figure SMS_104
is a signal set transmitted through a CAN bus b; />
Figure SMS_105
Is an overhead factor, i.e. the ratio between the size of the entire frame and the payload size; />
Figure SMS_106
Is the bit size of the signal s; />
Figure SMS_107
Is the bit rate of the bus; />
Figure SMS_108
Is the cycle time of the signal on bus b.
2) Calculating the weighted load rate of the bus b
Figure SMS_109
。
Calculated from 1)
Figure SMS_110
Can calculate the sum of the bus bWeighted bus load->
Figure SMS_111
The calculation formula is as follows:
Figure SMS_112
；
wherein ,
Figure SMS_113
are weights.
3) Calculating the bus load rate of an individual x in a population
Figure SMS_114
。/>
Weighted bus load calculated by 2)
Figure SMS_115
The bus load rate of the individual x in the population can be calculated
Figure SMS_116
As follows:
Figure SMS_117
。
and (2.3) solving the time delay value of the initial population.
1) Calculating the transmission time of the signal s on the CAN bus b
Figure SMS_118
。
Transmission time of signal s on CAN bus b
Figure SMS_119
The calculation formula of (a) is as follows:
Figure SMS_120
；
wherein ,
Figure SMS_121
for protocol overhead, by no-data frame length>
Figure SMS_122
And the number of bits->
Figure SMS_123
Composition is carried out; />
Figure SMS_124
Is the signal length; />
Figure SMS_125
CAN line transmission rate.
2) Calculating the waiting time of the signal s on the CAN bus b
Figure SMS_126
。
Waiting time of signal s on CAN bus b
Figure SMS_127
The calculation formula of (a) is as follows:
Figure SMS_128
；
3) Calculating the transfer delay of a CAN bus
Figure SMS_129
。
Obtained by 1)
Figure SMS_130
And 2) the determined->
Figure SMS_131
CAN calculate the transfer delay of the CAN bus>
Figure SMS_132
As follows:
Figure SMS_133
；
wherein ,
Figure SMS_134
is a signal set transmitted through a CAN bus B, and B is a bus set of the whole vehicle.
4) Calculating the transmission time of the signal s on the Ethernt bus b
Figure SMS_135
。
For Ethernet bus, the embodiment will wait time because of its high transmission efficiency
Figure SMS_136
Transmission time->
Figure SMS_137
The calculation formula of (a) is as follows:
Figure SMS_138
；
wherein ,
Figure SMS_139
indicating the length of the data block; />
Figure SMS_140
The Ethernet line transmission rate.
5) Calculating the propagation delay of Ethernet bus
Figure SMS_141
。
Calculated from 4)
Figure SMS_142
Can determine the transfer delay of the Ethernet bus>
Figure SMS_143
As follows:
Figure SMS_144
；/>
6) Calculating bus signal transmission delay of individual x in population
Figure SMS_145
。
Calculated by 3)
Figure SMS_146
And 5) calculated >>
Figure SMS_147
The bus signal propagation delay of an individual x in the population can be calculated->
Figure SMS_148
As follows:
Figure SMS_149
。
in the above formula, the known quantities include: vertical height of mounting board, wiring hole coordinates, position coordinates of each hardware component, signal length (bit size), unit length price of each bus, bit rate of each bus, cycle time of signal on bus, overhead factor, and power factor,
Figure SMS_150
、S、Ds、C、/>
Figure SMS_151
、/>
Figure SMS_152
。
In this embodiment, the target value of each individual in the initial population can be calculated by using the above formula, and when optimizing the cost value, the load rate, and the delay value, it is necessary to set an optimized constraint condition to calculate a constraint violation condition according to the three constraint conditions.
(3) Screening the initial population by using a championship selection method based on the target value of each individual in the initial population to obtain a screened population; carrying out two-point type cross variation, exchange mutation and point mutation on the screened population to obtain a progeny population; combining the initial population and the offspring population to obtain a combined population; and performing non-dominant sorting on the merged population according to the target value to obtain an updated population.
In the embodiment, the cost value, the load rate and the time delay value are used as optimization targets of the population, and the set time delay value, the hardware requirement and the common position are used as optimization constraint conditions for iterative optimization. Screening the initial population by using a tournament selection method, carrying out two-point type cross variation, exchange mutation and point mutation on chromosomes of winning individuals in the obtained screened population to obtain a progeny population, combining the initial population and the progeny population, carrying out non-dominated sorting on the individuals in the combined population according to a target value, calculating the crowding degree of the obtained population, and selecting the individuals according to the crowding degree to form a new parent so as to obtain an updated population.
(4) Judging whether an iteration termination condition is reached;
if the iteration termination condition of this embodiment may be the maximum iteration number, determining whether the iteration termination condition is reached is equivalent to determining whether the current iteration number reaches the maximum iteration number.
(5) And if so, outputting the optimal individual of the updated population, wherein the optimal individual is the optimal solution of the hardware structure adjacency matrix and the SWC-ECU distribution relation matrix.
And selecting chromosomes of suitable individuals according to the updated target values of the individuals in the population, decoding the chromosomes into an optimal solution of a hardware structure of the vehicle, and outputting the optimal solution.
(6) And if not, taking the updated population as the initial population of the next iteration, and returning to the step of calculating the target value of each individual in the initial population under the constraint of the constraint condition by using the multi-objective optimization model.
The embodiment provides a domain centralized electronic and electrical architecture modeling and multi-objective optimization method for an unmanned special vehicle, which is characterized in that an adjacency matrix is utilized to model a hardware layer and a software layer of an electronic and electrical architecture of the vehicle, and a multi-objective optimization algorithm based on NSGA-II is adopted to perform multi-objective optimization aiming at cost, load rate and time delay value on the domain centralized electronic and electrical architecture of the unmanned special vehicle, so that various performance indexes of the electronic and electrical architecture are comprehensively improved, and a reference architecture scheme can be output, so that the design and optimization of the architecture are more reasonable and efficient.
Example 2:
the embodiment is configured to provide a domain centralized electronic and electrical architecture modeling and multi-objective optimization system, as shown in fig. 7, including:
the software layer modeling module M1 is used for establishing a communication relation adjacency matrix of a software layer according to the signal transmission relation among the sensor, the SWC and the actuator; the communication relation is adjacent to the element of the ith row and the jth column of the matrix and is used for characterizing the signal transmission relation of the ith first assembly and the jth first assembly, and the first assembly comprises a sensor, an SWC and an actuator;
the hardware layer modeling module M2 is used for establishing a hardware structure adjacency matrix of a hardware layer according to the hardware connection relation among the sensor, the common CAN node, the ECU and the actuator; establishing an SWC-ECU distribution relation matrix according to the connection relation between the SWC and the ECU; elements of an ith row and a jth column of the hardware structure adjacency matrix are used for representing the hardware connection relation of an ith second assembly and a jth second assembly, and the second assembly comprises a sensor, a common CAN node, an ECU and an actuator;
the optimization model establishing module M3 is used for establishing a multi-objective optimization model for optimizing the hardware structure adjacency matrix and the SWC-ECU distribution relation matrix; the multi-objective optimization model aims to minimize a cost value, a load rate and a time delay value, and constraint conditions comprise time delay value constraint, hardware requirement constraint and co-location constraint;
and the optimization solving module M4 is used for solving the multi-objective optimization model by using a multi-objective optimization algorithm to obtain the optimal solution of the hardware structure adjacency matrix and the SWC-ECU distribution relation matrix.
The same and similar parts in the various embodiments of the present specification may be referred to each other. For the system disclosed by the embodiment, the description is relatively simple because the system corresponds to the method disclosed by the embodiment, and the relevant points can be referred to the description of the method part.
The principles and embodiments of the present invention have been described herein using specific examples, which are provided only to help understand the method and the core concept of the present invention; meanwhile, for a person skilled in the art, according to the idea of the present invention, the specific embodiments and the application range may be changed. In view of the foregoing, the description is not to be taken in a limiting sense.

Claims (10)

1. A domain-centralized electronic and electrical architecture modeling and multi-objective optimization method is characterized by comprising the following steps:
establishing a communication relation adjacency matrix of a software layer according to a signal transmission relation among the sensor, the SWC and the actuator; the communication relation is adjacent to the element of the ith row and the jth column of the matrix and is used for characterizing the signal transmission relation of the ith first assembly and the jth first assembly, and the first assembly comprises a sensor, an SWC and an actuator;
establishing a hardware structure adjacency matrix of a hardware layer according to the hardware connection relation among the sensor, the common CAN node, the ECU and the actuator; establishing an SWC-ECU distribution relation matrix according to the connection relation between the SWC and the ECU; elements of an ith row and a jth column of the hardware structure adjacency matrix are used for representing the hardware connection relation of an ith second assembly and a jth second assembly, and the second assembly comprises a sensor, a common CAN node, an ECU and an actuator;
establishing a multi-objective optimization model for optimizing the hardware structure adjacency matrix and the SWC-ECU distribution relation matrix; the multi-objective optimization model aims to minimize a cost value, a load rate and a time delay value, and constraint conditions comprise time delay value constraint, hardware requirement constraint and co-location constraint;
and solving the multi-objective optimization model by using a multi-objective optimization algorithm to obtain the optimal solution of the hardware structure adjacency matrix and the SWC-ECU distribution relation matrix.
2. The domain centralized electronic-electrical architecture modeling and multi-objective optimization method of claim 1, further comprising, prior to establishing a software-layer communication relationship adjacency matrix based on signal-passing relationships between sensors, SWCs, and actuators:
determining the type and the number of each third component contained in the vehicle; the third component includes a sensor, a SWC, an actuator, and an ECU.
3. The domain centralized electronic-electrical architecture modeling and multi-objective optimization method of claim 2, further comprising, after determining the type and number of each third component included in the vehicle:
and dividing all the third components which finish the functions of a certain vehicle together into the same task group to obtain a plurality of task groups with the same number as the total functions of the vehicle.
4. The domain centralized electronic-electrical architecture modeling and multi-objective optimization method of claim 1, wherein the first objective function of the multi-objective optimization model is:
Figure QLYQS_1
；
wherein ,
Figure QLYQS_2
the cost of the finished vehicle wiring harness; />
Figure QLYQS_3
A cost value for the CAN bus; />
Figure QLYQS_4
Is a cost value of the Ethernet bus.
5. The domain centralized electronic-electrical architecture modeling and multi-objective optimization method of claim 1, wherein the second objective function of the multi-objective optimization model is:
Figure QLYQS_5
；
wherein ,
Figure QLYQS_6
is the bus load rate; b is a bus set; />
Figure QLYQS_7
Is the weighted bus load of bus b.
6. The domain centralized electronic-electrical architecture modeling and multi-objective optimization method of claim 1, wherein the third objective function of the multi-objective optimization model is:
Figure QLYQS_8
；
wherein ,
Figure QLYQS_9
a bus signal propagation delay; />
Figure QLYQS_10
Is the transfer delay of the CAN bus; />
Figure QLYQS_11
Is the propagation delay of the Ethernet bus. />
7. The domain centralized electronic-electrical architecture modeling and multi-objective optimization method of claim 1,
the delay value constraint is:
Figure QLYQS_12
；
wherein ,
Figure QLYQS_13
is a delay value constraint; s is a signal set in a software architecture; the | S | is the number of signals in the signal set S; />
Figure QLYQS_14
Is the worst-case propagation delay of signal s; />
Figure QLYQS_15
An end-to-end time requirement for signal s;
the hardware requirement constraints are:
Figure QLYQS_16
；
wherein ,
Figure QLYQS_17
is a hardware requirement constraint; c is a set consisting of all SWCs; | C | is the number of SWCs in the set C; />
Figure QLYQS_18
ECU assigned for SWC; />
Figure QLYQS_19
A specific assigned ECU is required for SWC;
the co-location constraint is:
Figure QLYQS_20
；
wherein ,
Figure QLYQS_21
is a co-location constraint; c is a set consisting of all SWCs; | C | is SWC in set CThe number of (2); />
Figure QLYQS_22
Is the SWC set allocated to the ECU; />
Figure QLYQS_23
Is the set of SWCs required to be assigned to the same ECU.
8. The domain centralized electronic and electrical architecture modeling and multi-objective optimization method of claim 1, wherein the multi-objective optimization algorithm is an NSGA-II algorithm.
9. The domain-centralized electronic-electrical architecture modeling and multi-objective optimization method of claim 8, wherein the solving the multi-objective optimization model using a multi-objective optimization algorithm to obtain the optimal solution of the hardware structure adjacency matrix and the SWC-ECU allocation relationship matrix specifically comprises:
encoding the hardware structure adjacency matrix and the SWC-ECU distribution relation matrix into chromosomes; carrying out random initialization operation on the chromosome to obtain an initial population;
calculating a target value of each individual in the initial population under the constraint of the constraint condition by using the multi-objective optimization model; the target values comprise cost values, load rates and time delay values;
screening the initial population by using a championship selection method based on the target value of each individual in the initial population to obtain a screened population; carrying out two-point type cross variation, exchange mutation and point mutation on the screened population to obtain a progeny population; merging the initial population and the offspring population to obtain a merged population; performing non-dominant sorting on the merged population according to the target value to obtain an updated population;
judging whether an iteration termination condition is reached;
if so, outputting the optimal individual of the updated population, wherein the optimal individual is the optimal solution of the hardware structure adjacency matrix and the SWC-ECU distribution relation matrix;
if not, taking the updated population as an initial population of the next iteration, and returning to the step of calculating the target value of each individual in the initial population under the constraint of the constraint condition by using the multi-objective optimization model.
10. A domain-centric electronic-electrical architecture modeling and multi-objective optimization system, comprising:
the software layer modeling module is used for establishing a communication relation adjacency matrix of a software layer according to the signal transmission relation among the sensor, the SWC and the actuator; the communication relation is adjacent to the element of the ith row and the jth column of the matrix and is used for characterizing the signal transmission relation of the ith first assembly and the jth first assembly, and the first assembly comprises a sensor, an SWC and an actuator;
the hardware layer modeling module is used for establishing a hardware structure adjacency matrix of a hardware layer according to the hardware connection relation among the sensor, the public CAN node, the ECU and the actuator; establishing an SWC-ECU distribution relation matrix according to the connection relation between the SWC and the ECU; the elements of the ith row and the jth column of the hardware structure adjacent matrix are used for representing the hardware connection relationship between the ith second assembly and the jth second assembly, and the second assembly comprises a sensor, a common CAN node, an ECU and an actuator;
the optimization model establishing module is used for establishing a multi-objective optimization model for optimizing the hardware structure adjacency matrix and the SWC-ECU distribution relation matrix; the multi-objective optimization model aims to minimize a cost value, a load rate and a time delay value, and constraint conditions comprise time delay value constraint, hardware requirement constraint and co-location constraint;
and the optimization solving module is used for solving the multi-objective optimization model by using a multi-objective optimization algorithm to obtain the optimal solution of the hardware structure adjacency matrix and the SWC-ECU distribution relation matrix.
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