CN116028042A - SysML combined modeling language for random hybrid system and method for converting SysML combined modeling language into probability hybrid automaton - Google Patents
SysML combined modeling language for random hybrid system and method for converting SysML combined modeling language into probability hybrid automaton Download PDFInfo
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Abstract
The invention discloses a random hybrid system-oriented SysML combined modeling language and a method for converting the same into a probability hybrid automaton. And modeling an information system and a physical system of the hybrid system by using the SysML and the Modelica respectively, introducing probability attachments into the SysML model, and establishing communication rules of the SysML and the Modelica based on a meta-model mechanism. Since the combination of SysML and Modelica is modeled as a semi-formal model, the present invention introduces transformation rules and algorithms to transform it into a formalized automaton model. In order to describe the communication function and uncertainty of the hybrid system, a probabilistic hybrid interface automaton is adopted as a formalization model and is used as a basis for post formalization verification. The invention can model a hybrid system with communication functions and uncertainty, and the model can be used as a post model detection and reliability verification work.
Description
Technical Field
The invention discloses a random hybrid system-oriented SysML combined modeling language and a method for converting the random hybrid system-oriented SysML combined modeling language into a probability hybrid automaton, which are mainly used for performing semi-formal modeling on a hybrid system by using the SysML and an extended modeling language thereof and then converting the hybrid system into a formal automaton model through a model conversion rule. The invention relates to a semi-formalized modeling of a hybrid system and a modeling conversion method for converting the semi-formalized modeling into a formalized model.
Background
The hybrid system is a dynamic system with tightly coupled software and hardware subsystems, is widely applied to the fields of avionics, telemedicine, automotive electronics and the like at present, and is generally composed of discrete and continuous two parts. However, the existing modeling languages cannot describe two discrete and continuous properties of the hybrid system at the same time, so that it is generally necessary to use two or more modeling languages for combined modeling.
SysML is a graphical modeling language with wide application and rich ideas, can visualize the structure, behavior, demand, parameters (mathematical model) and the like of an information system, is convenient for the communication and modification of design contents in the later stage, but lacks the capability of describing the continuity of the system. Modelica is a mathematical modeling language based on equations, and can build mathematical models of complex systems in a standardized manner, so that simulation of dynamic systems is realized, and continuous behaviors of information physical systems can be described.
However, the combination of SysML and Modelica is modeled as a semi-formal model, which cannot be directly formalized. The present invention proposes a conversion algorithm from a semi-formalized model to a formalized model. To describe the communication properties and uncertainty of the hybrid system, a probabilistic hybrid interface automaton is chosen as a formalized model. Based on the above, the later-unfolding formal verification work.
Disclosure of Invention
Hybrid systems exist with not only continuous and discrete migration variations, but also generally have communication properties and uncertainties. At the same time, the model needs to be converted into formal description for further formal verification. Based on the above situation, the invention adopts SysML and Modelica to carry out combined modeling and converts the SysML and Modelica into a probability hybrid weighted automaton, and further provides a novel SysML combined modeling language oriented to a random hybrid system and a method for converting the SysML combined modeling language into the probability hybrid automaton.
The invention relates to a random mixing system-oriented SysML combined modeling language and a method for converting the same into a probability mixing automaton, which comprises the following specific implementation methods:
step 1: selecting probability hybrid interface automata as formal model of description system
The probability hybrid interface automaton A is a multi-tuple
The method comprises the steps of respectively representing a state set, an initial state set, a real number set, an initial real number set, an input activity set, an output activity set, an internal activity set, a tag function set, a guard function set, a migration probability matrix and a conversion relation set.
Step 2: and modeling an information part and a physical part of the hybrid system by adopting SysML and Modelica respectively, and establishing a corresponding relation between the information part and the physical part based on a meta model mechanism.
The information part of the hybrid system is described by a block definition diagram and a state machine diagram of SysML. The Block definition map is used to define the characteristics of blocks and the relationships between blocks. The behavior state machine is a directed graph, which consists of a set of nodes and a corresponding set of transfer functions. Wherein, the node is called a state, and the transfer function is called a state transfer condition. The behavior state machine "runs" by responding to a series of events. Modelica is an object-oriented language with general class features. Since it is built on the basis of equations, the continuous nature of the system can be described in a system of mixed differential algebraic equations. In order to better describe the correspondence between SysM [ and Modelica ], the invention defines a new six-tuple containing meta-model, object meta-model, source meta-model type, dependency condition, constraint and priority as the correspondence rule.
Step 3: and (5) providing a model conversion rule and an algorithm according to the grammar semantics of the semi-formalized model and the probability hybrid interface automaton.
And (3) comparing the grammar semantics of the semi-formal model and the grammar semantics of the automaton, and providing a corresponding conversion algorithm according to the corresponding rules between the SysML and the Modelica model.
Step 4: the combination rule of automaton obtained by converting the SysML model and the Modelica model is proposed.
According to the features of PHIA obtained by converting SysML and Modelica models, a new automaton combination method different from the traditional hybrid automaton combination is provided.
The invention expands probability attribute of SysML language according to actual characteristic of the hybrid system, so that the system can sufficiently describe discrete change, continuous change, uncertainty, communication attribute and other properties of the hybrid system, and simultaneously, a model conversion algorithm is used for converting the model into an automaton model, thereby laying foundation for subsequent further formal verification and analysis work.
Drawings
FIG. 1 is a random hybrid system oriented SysML combined modeling language and method for converting the same into a probabilistic hybrid automaton
FIG. 2 is a diagram of conversion of SysML model into PHIA_s pseudocode
FIG. 3 is a schematic representation of Modelica model conversion to PHIA_m pseudocode
FIG. 4 is a combined algorithm pseudocode for PHIA_s and PHIA_m
FIG. 5 is a state machine diagram of a decision portion of the overtaking system
FIG. 6 is a complete PHIA model
Detailed Description
The implementation of the invention provides a SysML combined modeling language oriented to a random mixing system and a method for converting the SysML combined modeling language into a probability mixing automaton, and in order to enable a person skilled in the art to better understand the technical scheme of the invention, the invention is further described in detail below with reference to the accompanying drawings and the specific embodiments. The embodiments described by referring to the drawings are exemplary only and are not to be construed as limiting the invention.
The invention aims at solving the problem that a complex hybrid system cannot be directly formalized and modeled, and comprises the step of performing half-formalized and modeled on the hybrid system with uncertainty and communication properties, and provides a model conversion method for converting preliminary half-formalized and modeled into formalized automaton.
For the semi-formal modeling method, the information part and the physical part of the hybrid system are modeled by adopting the SysML and Modelica modeling languages, the SysML modeling language is expanded to solve the uncertainty and the communication attribute of the hybrid system, the SysML probabilistic behavior accessory is provided, and the corresponding relation between the Modelica and the SysML model is established on the basis of the meta model in the aspect of the physical system. Finally, a model of a probabilistic hybrid interface automaton is employed that can describe the uncertainty of the hybrid system and the nature of component communication.
The flow of the SysML extended modeling language facing the hybrid system and the conversion of the SysML extended modeling language into the automaton of the probability hybrid interface is shown in the figure 1. The specific implementation method of the invention is as follows:
1. selecting probability hybrid interface automata as formal model of description system
The probability hybrid interface automaton not only can describe the discrete and continuous change properties of the information physical fusion system, but also can correspondingly express the uncertainty and communication properties of the system.
Probability hybrid interface automaton (Probability Hybrid Interface Automata, PHIA) the probability hybrid interface automaton a is a multi-tuple
Wherein, the liquid crystal display device comprises a liquid crystal display device,
(1)S A is a state set in automata;
(2)
is the initial state set, and +.>
Comprises at least one element; if->
Then P is referred to as null.
(3)X A Is a finite set of real-valued variables in the automaton;
(4)
is a set of real-valued variables in an initial state;
(5)
respectively input activity, output activity and internal activity set, and are mutually disjoint, and all the activity sets are recorded as R A And->
(6)L：S→2 AP Is a tag function which is set S P Is assigned a set L(s) with atomic propositions.
(7)Φ：S A ×X A The { tank expressions } is a guard function of a location, indicating that when the system is in state s, the corresponding variable x satisfies this expression for state transition.
(8) Beta is one of S A Adding a tag function to the flow condition (differential equation) for each state s, which means that the corresponding variable over time satisfies this condition while the system remains in the relevant state.
(9)P：S A ×S A →[0，1]Is a migration probability matrix for all S εs A Has the following components
(10) T is a set of conversion relationships, and the element T in T comprises
Wherein S, S' is S A Elements of (a) and (b); p and probability on transition; migration guard->
Is an element in phi, is a labeling function for labeling the conversion T in T as a group of constraints, and is in the form of a set formed by Boolean expressions;
2. and modeling an information part and a physical part of the hybrid system by adopting SysML and Modelica respectively, and establishing a corresponding relation between the information part and the physical part based on a meta model mechanism.
2.1 modeling an information system of the hybrid system by using SysML, and providing a SysML probabilistic behavior accessory.
2.1.1 Block definition map
The Block definition map is used to define characteristics of blocks and relationships between blocks, such as Association (Association), generalization (Generation), and Dependency (Dependency). The Block definition map expresses attributes and operations on the Block, and relationships such as a Block-based system hierarchy or a system classification tree. The Association represents the relationship with common Association (Association), composition (Composition) or Aggregation (Aggregation) among blocks, and is represented by a solid diamond and a hollow diamond respectively; generalizing the inheritance relationship (is a) between blocks, wherein the inheritance relationship is between classes, and the inheritance relationship is between interfaces; dependencies represent interdependencies (use a) between blocks, where a change in one Block will affect another Block.
2.1.2 behavior State diagram
The state machine diagram defines a set of concepts that can model discrete event-driven behavior using formalized criteria for a finite state machine in SysML. The behavior state machine adopted by the invention is a directed graph and consists of a group of nodes and a group of corresponding transfer functions. Wherein, the node is called a state, and the transfer function is called a state transfer condition. The behavior state machine "runs" by responding to a series of events. Starting from the initial state, each state has a corresponding state transfer function, when a corresponding event arrives, the state is switched, at least one of all states is a termination state, and the state machine stops when the state machine runs to the termination state.
2.1.3 Probability expansion of SysML model
In order to describe the probability behavior of the system, the invention adds a guard transition with probability on the basis of the original transition of SysML, and each branch of the transition is provided with a number which represents the probability of selecting the branch.
2.2 modeling the physical part of a hybrid System by Modelica
The class, equation structure of Modelica can model both continuous and discrete changes that exist in the physical part of the hybrid system.
Class 2.2.1
Each object in Modelica has a class that defines its data and behavior. The class contains three types of members, variables, equations, and the class itself. The variables may associate classes and their objects; equation representation class behavior, and the interaction of equations between different classes determines the solving process of the model; and the class itself may also be a member of other classes.
2.2.2 equation
The assignment statement in the traditional language is usually expressed by an equation in Modelica, and the modeling method based on the equation does not specify the direction of the data flow and the execution sequence, so that the method is more flexible than assignment, and meanwhile, reusability of Mode class is enhanced.
2.3 correspondence rules of SysML model and Modelica model
Considering the modeling characteristics of SysML and Modelica, the meta-model correspondence rules between the two can be roughly described as modeling the relationship and behavior between the composition and sub-models of the model. In order to more clearly describe the correspondence between SysML and Modelica, the invention abstracts the template of the correspondence rule, formally expressed as: f (S, O, SP, DC, C, P). S represents an instance of a source meta-class and O represents an instance of a target meta-class. SP is an attribute of a source meta-class instance, and if the attribute is an original type, then the instance specifying the target meta-class also has the same type: if the attribute is an enumeration type, then the same enumeration type needs to be constructed in the target language; DC is a dependency condition of source meta-class instance, the model to be converted may be other model attribute, and the current conversion can be performed only when the conversion exists in the model to which the DC belongs. C represents a constraint, which can be implemented only if the condition is satisfied. P is a priority, and a plurality of mapping rules may be corresponding between the source meta-class and the target meta-class, and whether the conditions are satisfied must be sequentially determined according to the order of the priority.
3. The conversion rule is given through the definition of the semantic and SysML-Modelica combined modeling of the probability hybrid interface automaton, and a corresponding model conversion algorithm is provided
The semi-formalized model modeled by the combination of SysML and Modelica can be converted into a probability hybrid interface automaton corresponding to the semi-formalized model
The conversion rule is as follows:
3.1SysML model to probability hybrid interface automaton (PHIA_s) conversion rules
(1) Takes as input a block definition diagram and a state machine diagram in the SysML model
(2) Setting the initial state of the probability hybrid interface automaton as the initial state in a state machine, determining the target state (target_state) of the probability hybrid interface automaton according to the source state and the target state in the state machine, and synchronizing the data of the corresponding state in the state machine with the probability hybrid interface automaton
(3) Internal activities of different states are described in the block definition diagram, states in the block definition diagram and the probability hybrid interface automaton generated in the step (2) are traversed, the internal states and time in the block definition diagram are transferred into corresponding states of the probability hybrid interface automaton, and the internal activities are used
Exists.
The algorithmic pseudocode for SysML model conversion is shown in FIG. 2.
3.2Modelica model to probabilistic hybrid interface automaton (PHIA_m) conversion rules
(1) Different classes of Modelica models are taken as inputs.
(2) Traversing the class in Modelica, converting the id of the class into the state of the probability hybrid interface automaton, and converting the equation relation in different classes into a function (beta) of the flow condition in the probability interface hybrid automaton.
(3) For the connection between different classes, transition to transition between different states of the probability hybrid interface automaton (t) is performed to input activity
And output Activity +.>
And storing in a probability hybrid interface automaton.
The algorithmic pseudocode of the Modelica model transformation is shown in FIG. 3.
4. According to the characteristics of the probability hybrid interface automata obtained after the conversion of the SysML and Modelica models, the combination rule of the probability hybrid interface automata obtained by the conversion of the SysML and Modelica models is put forward, and the complete probability hybrid interface automata is finally formed.
(1) Taking the probability hybrid vehicle interface automata PHIA_s obtained by conversion of the Modelica model and the probability hybrid vehicle interface automata PHIA_m obtained by conversion of the SysML model in the step 3 as inputs, respectively representing as follows:
(2) Traversing states in PHIA_s and PHIA_m, and returning to error if the intersection of the states is an empty set.
(3) If the states in the two states have the same tag function, the two states are combined, and the two states have the same real number set, the same input activity, the same output activity and the same internal activity.
(4) For the same migration, but only the conditions of different source states or target states, firstly, the same states and migration are kept unchanged, the different two states are combined, real numbers in the two states are combined, cartesian products are made for input activities, output activities and internal activities of the real numbers, and finally, the complete probability hybrid interface automaton is obtained.
The combined algorithm pseudocode for phia_s and phia_m is shown in fig. 4.
Description of the preferred embodiments
This embodiment takes an unmanned overtaking system as an example, which is a typical hybrid system. The unmanned vehicle judges whether the overtaking condition is met according to the perceived surrounding environment and the state of the unmanned vehicle, and sends a predicted result of the lane change intention to the track planning module as an instruction. According to the traditional experience, when the unmanned vehicle has a lane change, the probability of the lane change and the follow-up is 0.34 and 0.66. After receiving the instruction, the track planning module calculates to obtain specific instructions about steering angle, accelerator and brake according to the moving target point and the target speed, and then transmits the instructions to the bottom layer control module. The present case demonstrates a partially semi-formalized model of an automatic overtaking system, wherein a state machine diagram of SysML is used to model the change in state of the overtaking system, and Modelica is used to model the speed measurement portion of the overtaking system.
The decision-making part frame of the unmanned overtaking system is divided into a top-layer state machine and a corresponding sub-state machine, wherein the overtaking sub-state machine is arranged under the overtaking top-layer state machine, logic modeling is carried out on the transition of different driving stages in the overtaking process, and the overtaking sub-state comprises left lane change, parallel overtaking and right lane change similar to the overtaking process of a human driver. The parallel overrun state is mainly used for guiding the vehicle to carry out reasonable speed planning in the overtaking stage of the vehicle, and particularly when the vehicle occupies a lane for overtaking, overtaking is completed in time and the vehicle returns to the original lane. The information system was modeled using SysML as shown in FIG. 5.
The speed measuring part of the unmanned overtaking system firstly measures the rotation speed of the rotating shaft and then converts the rotation speed into the running speed of the automobile. A Sample and hold (Sample and hold) speed measurement method is used herein, where a sensor samples the speed at a given point in time and then holds it. In this model, the variable omegal_measured is declared as a discrete variable that makes discrete transitions during the simulation.
The physical system was modeled using Modelica as follows:
finally, according to the step 3 and the step 4, a complete probability hybrid interface automaton is obtained, see figure 6.
Claims (5)
1. The method for converting SysML combined modeling language to probability hybrid automaton facing to random hybrid system is characterized in that: mainly comprises the following steps:
(1) Modeling an information system of the hybrid system by using a state machine diagram and a block definition diagram of SysML; the physical system therein was modeled using Modelica.
(2) And establishing the connection between the SysML and Modelica through a meta-model mechanism, and defining a six-tuple comprising the meta-model, the target meta-model, the source meta-model type, the dependent condition, the constraint and the priority as a corresponding rule of the SysML and Modelic.
(3) The conversion rule is given through the definition of the semantic and SysML-Modelica combined modeling of the probability hybrid interface automaton, and a corresponding model conversion algorithm is provided.
(4) According to the characteristics of the probability hybrid interface automata obtained after the conversion of the SysML and Modelica models, the combination rules of the two probability hybrid interface automata are provided, and the complete probability hybrid interface automata is finally formed.
2. The random blending system-oriented sysML combinatorial modeling language and method of converting into a probabilistic hybrid automaton described in claim 1, wherein: the described step (1) uses a state machine diagram and a block definition diagram of SysML to model the information system of a hybrid system, wherein:
the Block definition map is used to define characteristics of blocks and relationships between blocks, such as Association (Association), generalization (Generation), and Dependency (Dependency). It expresses attributes and operations on Block, and relationships such as a Block-based system hierarchy or a system classification tree. The Association represents the relationship with common Association (Association), composition (Composition) or Aggregation (Aggregation) among blocks, and is represented by a solid diamond and a hollow diamond respectively; generalizing the inheritance relationship (is a) between blocks, wherein the inheritance relationship is between classes, and the inheritance relationship is between interfaces; dependencies represent interdependencies (use a) between blocks, where a change in one Block will affect another Block.
The state machine diagram defines a set of behavior concepts that can be modeled for discrete event driven behavior using formalized criteria for a finite state machine in SysML. The behavior state machine adopted by the invention is a directed graph and consists of a group of nodes and a group of corresponding transfer functions. Wherein, the node is called a state, and the transfer function is called a state transfer condition. The behavior state machine "runs" by responding to a series of events. Starting from the initial state, each state has a corresponding state transfer function, when a corresponding event arrives, the state is switched, at least one of all states is a termination state, and the state machine stops when the state machine runs to the termination state.
3. The random blending system-oriented sysML combinatorial modeling language and method of converting into a probabilistic hybrid automaton described in claim 1, wherein:
the step (2) establishes the connection between the SysML and the Modelica through a meta-model mechanism, wherein synonymous elements are extracted from the SysML and the Modelica to correspond, and the modeling process can be roughly described as modeling the connection and the behavior between the composition and the sub-model of the model in consideration of the modeling characteristics of the SysML and the Modelica. The implementation of each transformation is guided by defining six tuples containing metamodels, target metamodels, source metamodel types, dependency conditions, constraints, and priorities.
4. The random blending system-oriented sysML combinatorial modeling language and method of converting into a probabilistic hybrid automaton described in claim 1, wherein: and (3) providing a conversion rule of the SysML and Modelica model to the probability hybrid interface automaton, providing the conversion rule of the model according to the corresponding relation between the SysML and Modelica model and the semantics of the probability hybrid interface automaton, and realizing the conversion of the model according to the conversion rule.
5. The random blending system-oriented sysML combinatorial modeling language and method of converting into a probabilistic hybrid automaton described in claim 1, wherein: and (4) providing a combination rule of the probability hybrid interface automaton obtained by converting the SysML and Modelica models, and providing a combination rule of the information system part and the physical system part according to the characteristics of the hybrid automaton obtained by converting the information system part and the physical system part. The rule takes a probability hybrid interface automaton obtained by converting a SysML model and a Modelica model as input, and the combination of the SysML model and the Modelica model is completed according to a migration relation to obtain a complete probability hybrid interface automaton.
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