Intelligent transportation system simulation method and system based on combined PRES network

Abstract

The present disclosure provides a kind of intelligent transportation system simulation method based on combination PRES net, comprising: the function and module division of intelligent transportation system;Based on the function and module division, respectively, the object-oriented PRES net model of road monitoring subsystem and the object-oriented PRES net model of Internet of Vehicles subsystem are constructed;The subnet of the object-oriented PRES net model of road monitoring subsystem and the object-oriented PRES net model of Internet of Vehicles subsystem meets the combination condition of two object-oriented PRES net models is combined, and the shared object subnet combination net is obtained;The object-oriented PRES net model of road monitoring subsystem and the object-oriented PRES net model of Internet of Vehicles subsystem share the shared object subnet combination net, and the object-oriented PRES net combination model is obtained;Based on the obtained object-oriented PRES net combination model, the simulation of the intelligent transportation system is realized.

Description

Technical Field

[0001] This disclosure belongs to the field of intelligent transportation system simulation and analysis technology, and in particular relates to an intelligent transportation system simulation method and system based on a combined PRES network. Background Technology

[0002] The statements in this section are merely background information relating to this disclosure and do not necessarily constitute prior art.

[0003] Embedded systems are widely used in intelligent transportation, smart cities, smart homes, automotive electronics, 5G chips, and other fields. The inventors discovered that existing PRES (Petri net based Representation for Embedded Systems) networks have a good hierarchical structure and can capture real-time information, enabling them to model local embedded systems. However, they face difficulties in dividing the functional modules of large-scale, complex embedded systems. The significant advantage of object-oriented Petri nets (OOPN) lies in effectively dividing the entire large-scale complex system into multiple simple subsystems and modeling them accordingly. However, OOPN is not suitable for describing embedded systems. Furthermore, using OOPN or PRES alone to model large-scale, complex intelligent transportation systems with embedded system technology at their core results in low simulation accuracy and inefficiency. Summary of the Invention

[0004] To address the aforementioned problems, this disclosure provides a simulation method and system for intelligent transportation systems based on a combined PRES network. The scheme combines object-oriented technology with the Petri net representation model of embedded systems to propose an object-oriented PRES network. The intelligent transportation system is then simulated and modeled based on the object-oriented PRES network, effectively improving its simulation accuracy and efficiency.

[0005] According to a first aspect of the embodiments of this disclosure, a simulation method for an intelligent transportation system based on a combined PRES network is provided, comprising:

[0006] The intelligent transportation system is divided into functions and modules;

[0007] Based on the divided functions and modules, object-oriented PRES network models of the road monitoring subsystem and the vehicle networking subsystem are constructed respectively.

[0008] The subnets that satisfy the combination conditions of the two object-oriented PRES network models in the road monitoring subsystem and the vehicle network subsystem are combined to obtain a shared object subnet combined network.

[0009] The object-oriented PRES network model of the road monitoring subsystem and the object-oriented PRES network model of the vehicle network subsystem share the shared object subnetwork combined network to obtain the object-oriented PRES network combined model.

[0010] Based on the obtained object-oriented PRES network combination model, the simulation of the intelligent transportation system is realized.

[0011] Furthermore, the object-oriented PRES network model is specifically defined as a quintuple O = {P, T, F, Q, W}, where P is the set of places in the subnet, T is the set of transitions in the subnet, F is the flow relation, Q is the set of message places, message places that receive messages from the subnet and transmit them to the gateway are called output places, and message places that receive messages from the gateway and transmit them to the subnet are called input places; W is a weight function, which defines the weights on the flow relation.

[0012] Furthermore, the combination condition of the two object-oriented PRES net models is specifically as follows: the first subnet SN1 = {P1, T1, I1, O1, W1, Q1} and the second subnet SN2 = {P2, T2, I2, O2, W2, Q2}, which belong to the two object-oriented PRES net models respectively, must satisfy the following combination condition:

[0013] (1) and and W0 = W1 + W2; where P is the set of places, T is the set of transitions, I is the set of input arcs, O is the set of output arcs, W is the weight function, and Q is the set of message places;

[0014] (2) For the transition t in T0 i , (1≤i≤k), its transition function f i Satisfy: f i =f 1i ; and its change time delay (1≤i≤k) satisfies:

[0015] Furthermore, the acquisition of the shared object subnet combination network specifically refers to:

[0016] Based on the combination conditions of the two object-oriented PRES network models, as well as the subnets in the object-oriented PRES network models of the road monitoring subsystem and the vehicle networking subsystem, the subnets that can be combined are determined, and the combined subnets are obtained to obtain a shared object subnet combination network.

[0017] Furthermore, the intelligent transportation system includes a road monitoring subsystem and a vehicle-to-everything (V2X) subsystem. The road monitoring subsystem is divided into a road traffic information data acquisition unit, a data upload unit, and a road traffic control unit. The V2X subsystem is divided into a vehicle information data acquisition unit, a data upload unit, and a vehicle control unit.

[0018] Furthermore, the object-oriented PRES network model specifically includes a quadruple, which includes a set of subnets, a flow relationship between subnets and gateways, a set of gateways, and a system identifier. The set of subnets corresponds to several units in which the intelligent transportation system is divided, the set of gateways corresponds to the interfaces between subnets, the flow relationship corresponds to the functional relationship of information transmission between subnets, and the system identifier corresponds to the control information and data information in the intelligent transportation system.

[0019] According to a second aspect of the present disclosure, a simulation system for an intelligent transportation system based on a combined PRES network is provided, comprising:

[0020] System partitioning unit, which is used to divide the intelligent transportation system into functions and modules;

[0021] The model building unit is used to construct object-oriented PRES network models for the road monitoring subsystem and the vehicle networking subsystem, respectively, based on the partitioned functions and modules.

[0022] The object subnet combination unit is used to combine the subnets in the object-oriented PRES network model of the road monitoring subsystem and the object-oriented PRES network model of the vehicle network subsystem that meet the combination conditions of the two object-oriented PRES network models to obtain a shared object subnet combination network.

[0023] An object-oriented PRES network model combination unit is used to share the shared object subnet combination network between the object-oriented PRES network model of the road monitoring subsystem and the object-oriented PRES network model of the vehicle network subsystem, thereby obtaining an object-oriented PRES network combination model.

[0024] The simulation analysis unit is used to simulate the intelligent transportation system based on the obtained object-oriented PRES network combination model.

[0025] According to a third aspect of the present invention, a computer-readable storage medium is provided having a program stored thereon that, when executed by a processor, implements a simulation method for an intelligent transportation system based on a combined PRES network as described above.

[0026] According to a fourth aspect of the present invention, an electronic device is provided, including a memory, a processor, and a program stored in the memory and executable on the processor, wherein the processor executes the program to implement a simulation method for an intelligent transportation system based on a combined PRES network as described above.

[0027] Compared with the prior art, the beneficial effects of this disclosure are:

[0028] (1) The present disclosure proposes a simulation method and system for intelligent transportation systems based on combined PRES networks. By combining object-oriented technology with the Petri net representation model of embedded systems (PRES network), an object-oriented PRES network (OOPRES network) is proposed. The intelligent transportation system is simulated and modeled based on the OOPRES network, which effectively improves its simulation accuracy and simulation efficiency.

[0029] (2) The scheme described in this disclosure provides a method for combining shared object subnets of OOPRES network, and studies the problem of maintaining liveness and boundedness of the combination operation, obtains relevant preservation conditions, and effectively solves the "state space explosion" problem of OOPRES network through the obtained preservation conditions.

[0030] Advantages of this disclosure in additional aspects will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of this disclosure. Attached Figure Description

[0031] The accompanying drawings, which form part of this disclosure, are used to provide a further understanding of this disclosure. The illustrative embodiments of this disclosure and their descriptions are used to explain this disclosure and do not constitute an undue limitation of this disclosure.

[0032] Figure 1 This is an example of the PRES network described in Embodiment 1 of this disclosure;

[0033] Figure 2 Example of an OOPN subnet as described in Embodiment 1 of this disclosure.

[0034] Figure 3 This is an example of the OOPRES subnet described in Embodiment 1 of this disclosure;

[0035] Figure 4 This is a schematic diagram of the OOPRES network system assembly described in Embodiment 1 of this disclosure.

[0036] Figure 5 This is a schematic diagram of the road monitoring system model described in Embodiment 1 of this disclosure;

[0037] Figure 6 This is a schematic diagram of the vehicle networking system model described in Embodiment 1 of this disclosure;

[0038] Figure 7 This is a schematic diagram of the combined system model of the intelligent transportation system described in Embodiment 1 of this disclosure. Detailed Implementation

[0039] The present disclosure will be further described below with reference to the accompanying drawings and embodiments.

[0040] It should be noted that the following detailed descriptions are exemplary and intended to provide further illustration of this disclosure. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this disclosure pertains.

[0041] It should be noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the exemplary embodiments according to this disclosure. As used herein, the singular form is intended to include the plural form as well, unless the context clearly indicates otherwise. Furthermore, it should be understood that when the terms “comprising” and / or “including” are used in this specification, they indicate the presence of features, steps, operations, devices, components, and / or combinations thereof.

[0042] Where there is no conflict, the embodiments and features described herein can be combined with each other.

[0043] Example 1:

[0044] The purpose of this embodiment is to provide a simulation method for intelligent transportation systems based on combined PRES networks.

[0045] A simulation method for intelligent transportation systems based on combined PRES networks includes:

[0046] The intelligent transportation system is divided into functions and modules;

[0047] Based on the divided functions and modules, object-oriented PRES network models of the road monitoring subsystem and the vehicle networking subsystem are constructed respectively.

[0048] The subnets that satisfy the combination conditions of the two object-oriented PRES network models in the road monitoring subsystem and the vehicle network subsystem are combined to obtain a shared object subnet combined network.

[0049] The object-oriented PRES network model of the road monitoring subsystem and the object-oriented PRES network model of the vehicle network subsystem share the shared object subnetwork combined network to obtain the object-oriented PRES network combined model.

[0050] Based on the obtained object-oriented PRES network combination model, the simulation of the intelligent transportation system is realized.

[0051] Furthermore, the object-oriented PRES network model is specifically defined as a quintuple O = {P, T, F, Q, W}, where P is the set of places in the subnet, T is the set of transitions in the subnet, F is the flow relation, Q is the set of message places, message places that receive messages from the subnet and transmit them to the gateway are called output places, and message places that receive messages from the gateway and transmit them to the subnet are called input places; W is a weight function, which defines the weights on the flow relation.

[0052] Furthermore, the combination condition of the two object-oriented PRES net models is specifically as follows: the first subnet SN1 = {P1, T1, I1, O1, W1, Q1} and the second subnet SN2 = {P2, T2, I2, O2, W2, Q2}, which belong to the two object-oriented PRES net models respectively, must satisfy the following combination condition:

[0053] (1) and and W0 = W1 + W2; where P is the set of places, T is the set of transitions, I is the set of input arcs, O is the set of output arcs, W is the weight function, and Q is the set of message places;

[0054] (2) For T o Changes in t i , (1≤i≤k), its transition function f i Satisfy: f i =f 1i ; and its change time delay (1≤i≤k) satisfies:

[0055] Furthermore, the acquisition of the shared object subnet combination network specifically refers to:

[0056] Based on the combination conditions of the two object-oriented PRES network models, as well as the subnets in the object-oriented PRES network models of the road monitoring subsystem and the vehicle networking subsystem, the subnets that can be combined are determined, and the combined subnets are obtained to obtain a shared object subnet combination network.

[0057] Furthermore, the intelligent transportation system includes a road monitoring subsystem and a vehicle-to-everything (V2X) subsystem. The road monitoring subsystem is divided into a road traffic information data acquisition unit, a data upload unit, and a road traffic control unit. The V2X subsystem is divided into a vehicle information data acquisition unit, a data upload unit, and a vehicle control unit.

[0058] Furthermore, the object-oriented PRES network model specifically includes a quadruple, which includes a set of subnets, a flow relationship between subnets and gateways, a set of gateways, and a system identifier. The set of subnets corresponds to several units in which the intelligent transportation system is divided, the set of gateways corresponds to the interfaces between subnets, the flow relationship corresponds to the functional relationship of information transmission between subnets, and the system identifier corresponds to the control information and data information in the intelligent transportation system.

[0059] Specifically, for ease of understanding, the solution described in this disclosure will be explained in detail below with reference to the accompanying drawings and specific examples:

[0060] (I) Basic Theory

[0061] Definition 1: A PRES network is a quintuple N = (P, T, I, O, M), where P = {p1, p2, ..., p...} m Let} be a non-empty finite set of places, T = {t1, t2, ..., t} n} is a non-empty finite set of transitions. It is a non-empty finite set of input arcs. It is a non-empty finite set of output arcs. M is an identifier representing the distribution of tokens in the place. k=<v, r> is a token, where v represents the value of the token and r represents the time of the token.

[0062] like Figure 1 As shown, a PRES net example is given, where P = {p1, p2, p3, p4, p5}, T = {t1, t2, t3}, I = {(p1, t1), (p2, t3), (p3, t2), (p4, t2), (p5, t3)}, O = {(t1, p2), (t1, p3), (t1, p4), (t2, p5), (t3, p1)}. M0 is the initial identifier, M0(p1) = {(3, 0)}.

[0063] Definition 2: For every transition t∈T, there exists a transition function f associated with it, where f: τ(p1)×τ(p2)×…×τ(p) a )→τ(q), where τ is the type function associated with each library, p1, p2, ... p aLet be all places in the set before transition t, and q be a place in the set after transition t. For each transition t∈T, there exists a minimum transition delay d. - And a maximum transition delay d + d - and d + This represents the lower and upper bounds of the execution time of the transition function associated with transition t.

[0064] exist Figure 1 In the equation, the transition functions associated with transitions t1, t2, and t3 are f1, f2, and f3, respectively, and the transition delays are [a1, b1], [a2, b2], and [a3, b3], respectively.

[0065] Definition 3: An object subnet of an object-oriented Petri net (OOPN) is defined as a quintuple O = {P, T, F, Q, W}. Where P = {p1, p2, ..., p...} m Let T = {t1, t2, ..., t} be the set of places in the subnet. n} represents the set of transitions in a subnet, and F defines the flow relationships in the network, which consists of two parts: F I and F O , of which F I = (P×T)∪(Q×T), F O =(T×P)∪(T×Q). Q={q1, q2,…,q m} is a collection of message repositories. The message repository that receives messages from within the subnet and forwards them to the gateway is called the output repository Q. o The message library that receives messages from the gateway and transmits them to the subnet is called the input library, Q. I W is a weight function that defines the weights on the flow relation.

[0066] like Figure 2 As shown, an example of an OOPN subnet is given, where P = {p1}, T = {t1, t2, t3}, F I ={(q1, t1), (q1, t2), (p1, t3)}, F O ={(t1, p1), (t2, p1), (t3, q2)}, Q I ={q1}, Q O ={q2}. The weight W defaults to 1.

[0067] Definition 4: An OOPN network system is defined as a quadruple N = {O, M, G, F}. Where O = {o1, o2, ..., o3} m} is the object subnet set, M: p→{0, 1, 2…n} is an identifier of ∑, initially identified as M0. G={g1, g2, …g s} is the set of gateways between object subnets. F defines the flow relationships between object subnets and gateways, including F I With F O Two parts, of which F I = (Q×G), F O = (G × Q).

[0068] Definition 5: An OOPRES (Object-Oriented PRES) subnet consists of a six-tuple SN = {P, T, I, O, W, Q}. Where P = {p1, p2, ..., p...} m} is a collection set, denoted by SN(P); T = {t1, t2, ..., t} n} is a transition set, denoted by SN(T). It is the input arc set; It is the output arc set; W is the weight function; Q = {q1, q2, ..., q} s} is a set of message libraries, denoted by SN(Q), which is divided into output libraries Q. O and input library Q I .

[0069] For each transition t, there also exists a transition function and a transition delay.

[0070] like Figure 3 As shown, an example of an OOPRES subnet is given. Figure 3 In the given diagram, P = {p1}, T = {t1, t2}, I = {(q1, t1), (p1, t2)}, O = {(t1, p1), (t2, q2)}, initial identifier M0 = [M(p), M(q)] = [1, 0, 0], Q = {q1, q2}, where q1 is the input place and q2 is the output place. f1 is the transition function for transition t1, and [a1, b1] is the transition delay for t1. f2 is the transition function for transition t2, and [a2, b2] is the transition delay for t2.

[0071] Definition 6: An OOPRES network system consists of a quadruple ∑={N, F, G, M}, where N={SN1, SN2…SN… m} is the OOPRES subnet set; F defines the flow relationship between the subnet and the gateway, including F I With F O Two parts, of which F I = (Q×G), F O = (G×Q); G = {g1, g2…g} n} is a set of gateways; M is the system identifier, initially identified as M0.

[0072] Note: N(P) represents the set of places in the network system, N(T) represents the set of transitions, and N(Q) represents the set of message places.

[0073] Definition 7: Let ∑={N,F,G,M} be an OOPRES network system, M∈R(M0), and M0 be the initial identifier.

[0074] 1) For transition t∈T, if Then t is said to be enabled under M (M[t>) if and only if the following condition is satisfied:

[0075]

[0076]

[0077] 2) For transition t∈T, if Then t is said to be enabled under M if and only if it satisfies formula (1).

[0078] 3) After transition t is enabled, the system identifier changes: M→M′, where,

[0079]

[0080]

[0081] Where ·t is the preceding set and t· is the following set.

[0082] Definition 8: Let an OOPRES network system be ∑={N,F,G,M0}, where M0 is the initial identifier, t∈N(T), then:

[0083] 1) If for any M∈R(M0), there exists M′∈R(M) such that M′[t>, then transition t is said to be live; where M′[t> indicates that transition t can be triggered in state M′; M′[t> is the fixed notation of Petri nets.

[0084] 2) If for the system ∑, If t is alive, then the OOPRES network system ∑ is said to be alive.

[0085] Definition 9: Let an OOPRES system be ∑={N,F,G,M0}, where M0 is the initial identifier, p∈N(P), q∈N(Q), then:

[0086] 1) If there exists a positive integer K such that Then the place p is said to be bounded. If there exists a positive integer B such that... Then the message library q is said to be bounded.

[0087] 2) If for the system ∑, If both p and q are bounded, then the OOPRES network system ∑ is said to be bounded.

[0088] (II) Analysis of OOPRES Network Combinations and Properties

[0089] This section proposes the shared object subnet combination operation of the OOPRES network system, and the conditions for the combined OOPRES system to maintain its liveness and boundedness.

[0090] Definition 10: Let SN1 = {P1, T1, I1, O1, W1, Q1} and SN2 = {P2, T2, I2, O2, W2, Q2} be two OOPRES subnets. SN0 = {P0, T0, I0, O0, W0, Q0} is called a shared object subnet of SN1 and SN2 if and only if SN0 satisfies:

[0091] 1) and

[0092] 2) and

[0093] 3) W0 = W1 + W2.

[0094] 4) For the transition t in T0 i , (1≤i≤k), its transition function f i Satisfy: f i =f 1i , (where f 1i =f 2i f 1i and f 2i (These are the transition functions for the strain transition in T1 and T2, respectively).

[0095] 5) For the transition t in T0 i , (i≤i≤k), its transition delay (1≤i≤k) satisfies: (in, and (These are the transition delays of the corresponding strain transitions in T1 and T2, respectively).

[0096] Definition 11: Let ∑1={N1,F1,G1,M1} and ∑2={N2,F2,G2,M2} be two OOPRES network systems. Then ∑={N,F,G,M} is called a combined network system of shared subnets of ∑1 and ∑2 if and only if:

[0097] 1) N=N1∪N2, G=G1∪G2.

[0098] 2) F in F I =FI1 ∪F I2 F o =F O1 ∪F O2 .

[0099]

[0100] 4) ∑1 and ∑2 share a single object subnet.

[0101] like Figure 4 The diagram shown is a combination of two OOPRES systems.

[0102] The following analysis addresses the issue of maintaining the activity and boundedness of combined network systems.

[0103] Theorem 1: Suppose that the OOPRES network system ∑={N,F,G,M} is obtained by combining two OOPRES network systems ∑1={N1,F1,G1,M1} and ∑2={N2,F2,G2,M2}, and ∑1 and ∑2 share an object subnet SN0. Then ∑ is live if and only if ∑1 and ∑2 are live.

[0104] Proof of sufficiency: Since N1 and N2 share an OOPRES subnet, and based on the characteristics of the OOPRES subnet, it can be known that the systems ∑1 and ∑2 have the same sequence of induced transitions with respect to the transition set T0 in the combined subnet SN0. Assume t1, t2…t k This can trigger a sequence. Since ∑1 and ∑2 are both live, by Definition 8, there exists M. 10 M 10 [t1>, there exists M] 20 M 20 [t1>. By definition 11, in the combined network system ∑={N, F, G, M0}, M0[t1>, in ∑1 M 10 [t1>M 11 M 11 [t2>,in ∑2 M 20 [t1>M 21 M 21 [t2>。 That is, in ∑, after t1 occurs, t2 can always be enabled. Therefore, in ∑, M0[σ>M1, M2[t2>。 Similarly, M0[σ′>M p , M q [t k >, that is t is alive. Because ∑1 and ∑2 are both alive, therefore in ∑, t is alive. In short, ∑ is alive.

[0105] Necessity: Using proof by contradiction without loss of generality, we assume that ∑1 is inactive, then... From Definition 11, we know that so That is, ∑ is inactive, which is a contradiction. Therefore, ∑1 and ∑2 are both active.

[0106] Theorem 2: Suppose that the OOPRES network system ∑={N,F,G,M} is obtained by combining two OOPRES network systems ∑1={N1,F1,G1,M1} and ∑2={N2,F2,G2,M2}, and ∑1 and ∑2 share an object subnet SN0. Then ∑ is bounded if and only if ∑1 and ∑2 are bounded.

[0107] Prove sufficiency: Since ∑1 and ∑2 are bounded, by Definition 9, there exist positive integers K1 and B1 such that...

[0108] There exist positive integers K2 and B2 such that For ∑, Then p∈N1(p)-SN0(p), or p∈N2(p)-SN0(p), or p∈SN0(p). If p∈N1(p)-SN0(p), then in ∑, Make M1(p) = M 11 ′(p)≤K1, M1(q)=M 11 ′(q)≤B1. Similarly, if p∈N2(p)-SN0(p), then in ∑, Make M1(p) = M 21 ′(p)≤K2,M1(q)=M 21 '(q)≤B2. If p∈SN0(p), By Definition 11, M1(p)≤K1, M1(q)≤B1. Let K = max{K1, K2}, B = max{B1, B2}, then... Therefore, there is a boundary.

[0109] Necessity: Using proof by contradiction, if ∑1 is unbounded, then M i (p) > K1, or M i (p) > B1. From Definition 11, so M i (p) > K1, or M i Since (p) > B1, ∑ is unbounded, which is a contradiction. Therefore, ∑1 and ∑2 are both bounded.

[0110] Note: For the shared object subnet set SN = {SN s0 SN s1 …SN sk In the case of (k∈N), as long as no two subnets in SN have a common front set and a back set, that is: (where i, j = 0, 1, ..., k, i ≠ j), the two systems can still be combined using the above method while maintaining the activity and boundedness of the original system. Therefore, we can obtain:

[0111] Corollary 1: Suppose that the system ∑={N,F,G,M} is a shared object subnet SN={SN={N1,F1,G1,M1} and ∑2={N2,F2,G2,M2}. s0 SN s1 ..SN sk}, (k∈N) combined network system, where, (where i, j = 0, 1, ..., k, i ≠ j), then the system ∑ is live if and only if the systems ∑1 and ∑2 are live.

[0112] Corollary 2: Suppose that the system ∑={N,F,G,M} is a shared object subnet SN={SN={N1,F1,G1,M1} and ∑2={N2,F2,G2,M2}. s0 SN s1 …SN sk}, (k∈N) combined network system, where, (where i, j = 0, 1, ..., k, i ≠ j), then the system ∑ is bounded if and only if the systems ∑1 and ∑2 are bounded.

[0113] (III) Simulation of Intelligent Transportation Systems Based on Combined PRES Networks

[0114] In the operation of intelligent transportation systems, road monitoring systems and vehicle-to-everything (V2X) systems operate independently. In order to achieve the integrated construction of intelligent transportation systems and improve system operating efficiency, the solution described in this disclosure applies the OOPRES network system shared subnet combination operation method proposed above to the modeling and analysis of road monitoring systems and V2X systems in intelligent transportation systems, so as to characterize the system operation process and verify the reliability of the combination method.

[0115] The road monitoring system model mainly consists of three parts: road traffic information data collection, data uploading, and road traffic control. After collecting data, the hardware devices distributed along the road upload the data to the cloud platform. The data is then analyzed to further control information such as the duration of red lights at intersections, before starting the next data collection cycle. The vehicle-to-everything (V2X) system model mainly consists of three parts: vehicle information data collection, data uploading, and vehicle control. Sensors distributed on vehicles and roads collect data and upload it to the cloud platform. After data analysis, intelligent vehicle driving control is achieved.

[0116] The following sections will model and analyze the road monitoring system model and the vehicle network system model respectively.

[0117] like Figure 5 The figure shows the OOPRES network system model of the road monitoring system ∑1={N1, F1, G1, M1}.

[0118] exist Figure 5 In this context, t1: Request data collection; t2: System data collection; t3: Data transmission; t4: Data upload; t5: Data upload completed; t6: Request data upload; t7: Data analysis; these data refer to information such as the number and speed of vehicles passing through the location collected by road sensors; t8: Information dissemination; here, it refers to the dissemination of control signal information; t9: Traffic light control; this refers to the control of traffic light color changes; t 10 : Complete road traffic control; SN1, SN2, SN3: Object subnets; g1, g2, g3: Gateways; f i : Transition function in response to strain transition; [a i b i ]: The time delay of the change in response to strain.

[0119] like Figure 6 The figure shows the OOPRES network system model of the vehicle-to-everything (V2) system ∑2={N2, F2, G2, M2}.

[0120] exist Figure 6 In the middle, t 11 Requesting data access; refers to requesting vehicle sensor data and road sensor data. 12 t13: Data acquired by vehicle sensors; refers to the speed of the vehicle itself and the speed information of surrounding vehicles. t14: Data acquired by road sensors; data collected by road sensors regarding the number and speed of vehicles passing through the area. 14 Data transmission refers to the transfer of information acquired by vehicle sensors and road sensors. 18 : To publish data; t 19: Distributing data to vehicle owners; refers to distributing information such as parking, passage, or speed limits to vehicle owners. 20 : Complete vehicle control; SN4, SN5, SN6: Object subnets; g4, g5, g6: Gateways; f i : The corresponding transition function; [a i b i ]: The time delay of the change in response to strain.

[0121] Figure 6 SN5 and Figure 5 SN2 in the definition refers to subnets of the same type. From Definition 11, we can conclude that... Figure 5 The object subnet SN2 and Figure 6 The object subnet SN5 satisfies the combination condition of two OOPRES object subnets. Therefore, according to the OOPRES network system combination method proposed above, object subnet SN2 and object subnet SN5 are combined into object subnet SN0. In SN0, transitions are represented by t. si This indicates that the library uses p si This indicates that the message database uses q. si This indicates that the transition function and transition delay have also been changed accordingly, and their meanings are the same as those in SN0.

[0122] The combined system model is as follows Figure 7 As shown.

[0123] Property Analysis: According to Definitions 8 and 9, the systems ∑1={N1,F1,G1,M1} and ∑2={N2,F2,G2,M2} are both live and bounded. According to Theorem 1 and Theorem 2, the combined network system ∑={M,F,G,M} is also live and bounded.

[0124] The verification of the system's boundedness can be directly derived from the model diagram. The verification of the system's activity is as follows:

[0125] For the system ∑1={N1,F1,G1,M1}, its initial identifier is:

[0126] M 10 =[M 10 (p1), M 10 (p2), M 10 (p3), M 10 (p4), M 10 (p5), M 10 (p6), M 10 (p7), M 10 (p8), M 10 (q1), M 10 (q2), M 10 (q3), M 10 (q4), M10 (q5), M 10 [(q6)] = [0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0], so it can be seen that ∑1 is alive.

[0127] For the system ∑2={N2,F2,G2,M2}, its initial identifier is:

[0128] M 20 =[M 20 (p9), M 20 (p 10 M 20 (p 11 M 20 (p 12 M 20 (p 13 M 20 (p 14 M 20 (p 15 M 20 (p 16 M 20 (p 17 M 20 (q7), M 20 (q8), M 20 (q9), M 20 (q 10 M 20 (q 11 M 20 (q 12 )]==[0,0,1,0,1,0,0,0,0,0,0,0,0,0,0],∑2 is also alive.

[0129] For a combined network system ∑={N,F,G,M}, its initial identifier is:

[0130] M0=[M0(p1), M0(p2), M0(p3), M0(p4), M0(p5), M0(p6), M0(p7), M0(p8), M0(p9), M0(p 10 ), M0(p 11 ), M0(p s1 ), M0(p s2 ), M0(p s3 ), M0(q1), M0(q2), M0(q3), M0(q4), M0(q5), M0(q6), M0(q7), M0(q8), M0(q s1 M0(q) s2)]=[0,0,1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0], Obviously, ∑ is alive.

[0131] This disclosure combines object-oriented technology with the Petri net representation model (PRES net) for embedded systems, proposing the concept of an object-oriented PRES net (OOPRES net). A method for combining shared object subnets in the OOPRES net is presented, and the preservation of liveness and boundedness by this combination operation is investigated, obtaining relevant preservation conditions. This combination operation is then applied to the modeling and analysis of intelligent transportation systems. This scheme provides a new approach for the modeling and analysis of large-scale complex embedded systems.

[0132] Example 2:

[0133] The purpose of this embodiment is to provide an intelligent transportation system simulation system based on a combined PRES network.

[0134] A simulation system for intelligent transportation systems based on a combined PRES network includes:

[0135] System partitioning unit, which is used to divide the intelligent transportation system into functions and modules;

[0136] The model building unit is used to construct object-oriented PRES network models for the road monitoring subsystem and the vehicle networking subsystem, respectively, based on the partitioned functions and modules.

[0137] The object subnet combination unit is used to combine the subnets in the object-oriented PRES network model of the road monitoring subsystem and the object-oriented PRES network model of the vehicle network subsystem that meet the combination conditions of the two object-oriented PRES network models to obtain a shared object subnet combination network.

[0138] An object-oriented PRES network model combination unit is used to share the shared object subnet combination network between the object-oriented PRES network model of the road monitoring subsystem and the object-oriented PRES network model of the vehicle network subsystem, thereby obtaining an object-oriented PRES network combination model.

[0139] The simulation analysis unit is used to simulate the intelligent transportation system based on the obtained object-oriented PRES network combination model.

[0140] In further embodiments, the following is also provided:

[0141] An electronic device includes a memory and a processor, as well as computer instructions stored in the memory and running on the processor. When executed by the processor, the computer instructions perform the method described in Embodiment 1. For brevity, further details are omitted here.

[0142] It should be understood that in this embodiment, the processor can be a central processing unit (CPU), or it can be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or any conventional processor, etc.

[0143] Memory may include read-only memory and random access memory, and provides instructions and data to the processor. A portion of memory may also include non-volatile random access memory. For example, memory may also store information about the device type.

[0144] A computer-readable storage medium for storing computer instructions, which, when executed by a processor, perform the method described in Embodiment 1.

[0145] The method in Embodiment 1 can be directly implemented by a hardware processor, or implemented by a combination of hardware and software modules within the processor. The software modules can reside in readily available storage media in the art, such as random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, or registers. This storage medium is located in memory; the processor reads information from the memory and, in conjunction with its hardware, completes the steps of the above method. To avoid repetition, a detailed description is not provided here.

[0146] Those skilled in the art will recognize that the units, i.e., algorithm steps, of the various examples described in connection with this embodiment can be implemented in electronic hardware or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0147] The above embodiments provide a simulation method and system for intelligent transportation systems based on combined PRES networks, which can be implemented and has broad application prospects.

[0148] The above description is merely a preferred embodiment of this disclosure and is not intended to limit this disclosure. Various modifications and variations can be made to this disclosure by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this disclosure should be included within the scope of protection of this disclosure.

Claims

1. A simulation method for intelligent transportation systems based on combined PRES networks, characterized in that, include: The intelligent transportation system is divided into functions and modules; Based on the divided functions and modules, object-oriented PRES network models of the road monitoring subsystem and the vehicle networking subsystem are constructed respectively. An OOPRES network system consists of a quadruple ∑={N, F, G, M}, where N={SN1, SN2… SN… m } is the OOPRES subnet set; F defines the flow relationship between the subnet and the gateway, including F I With F O Two parts, of which F I = (Q×G), F O = (G×Q); G = {g1, g2…g} n } is the set of gateways; Q is the set of message databases; M is the system identifier, initially identified as M0; The subnets that satisfy the combination conditions of the two object-oriented PRES network models in the road monitoring subsystem and the vehicle network subsystem are combined to obtain a shared object subnet combined network. The object-oriented PRES network model of the road monitoring subsystem and the object-oriented PRES network model of the vehicle network subsystem share the shared object subnetwork combined network to obtain the object-oriented PRES network combined model. Based on the obtained object-oriented PRES network combination model, the simulation of the intelligent transportation system is realized.

2. The intelligent transportation system simulation method based on a combined PRES network as described in claim 1, characterized in that, The combination condition for the two object-oriented PRES net models is specifically: the first subnet belonging to each of the two object-oriented PRES net models. Second Subnet The combination conditions for the two subnets must satisfy: (1) , , ,and ; ,and ; ; in, P For collection of warehouses, T For the set of changes, I Given the set of input arcs, O For the output arc set, W For the weight function, Q ; is a collection of message databases; (2) For Changes in Its transition function satisfy: And its change time delay satisfy:[ , ] [ , ].

3. The intelligent transportation system simulation method based on a combined PRES network as described in claim 1, characterized in that, The specific details of obtaining the shared object subnet combination network are as follows: Based on the combination conditions of the two object-oriented PRES network models, as well as the subnets in the object-oriented PRES network models of the road monitoring subsystem and the vehicle networking subsystem, the subnets that can be combined are determined, and the combined subnets are obtained to obtain a shared object subnet combination network.

4. The intelligent transportation system simulation method based on a combined PRES network as described in claim 1, characterized in that, The intelligent transportation system includes a road monitoring subsystem and a vehicle networking subsystem. The road monitoring subsystem is divided into a road traffic information data acquisition unit, a data upload unit, and a road traffic control unit. The vehicle networking subsystem is divided into a vehicle information data acquisition unit, a data upload unit, and a vehicle control unit.

5. The intelligent transportation system simulation method based on a combined PRES network as described in claim 1, characterized in that, The object-oriented PRES network model specifically includes a quadruple, which includes a set of subnets, flow relationships between subnets and gateways, a set of gateways, and a system identifier. The set of subnets corresponds to several units in which the intelligent transportation system is divided, the set of gateways corresponds to the interfaces between subnets, the flow relationships correspond to the functional relationships of information transmission between subnets, and the system identifier corresponds to the control information and data information in the intelligent transportation system.

6. A simulation system for an intelligent transportation system based on a combined PRES network, characterized in that, include: System partitioning unit, which is used to divide the intelligent transportation system into functions and modules; The model building unit is used to construct object-oriented PRES network models for the road monitoring subsystem and the vehicle networking subsystem, respectively, based on the partitioned functions and modules. An OOPRES network system consists of a quadruple ∑={N, F, G, M}, where N={SN1, SN2… SN… m } is the OOPRES subnet set; F defines the flow relationship between the subnet and the gateway, including F I With F O Two parts, of which F I = (Q×G), F O = (G×Q); G = {g1, g2…g} n } is the set of gateways; Q is the set of message databases; M is the system identifier, initially identified as M0; The object subnet combination unit is used to combine the subnets in the object-oriented PRES network model of the road monitoring subsystem and the object-oriented PRES network model of the vehicle network subsystem that meet the combination conditions of the two object-oriented PRES network models to obtain a shared object subnet combination network. An object-oriented PRES network model combination unit is used to share the shared object subnet combination network between the object-oriented PRES network model of the road monitoring subsystem and the object-oriented PRES network model of the vehicle network subsystem, thereby obtaining an object-oriented PRES network combination model. The simulation analysis unit is used to simulate the intelligent transportation system based on the obtained object-oriented PRES network combination model.

7. A computer-readable storage medium having a program stored thereon that, when executed by a processor, implements a simulation method for an intelligent transportation system based on a combined PRES network as described in any one of claims 1-5.

8. An electronic device, comprising a memory, a processor, and a program stored in the memory and executable on the processor, wherein the processor executes the program to implement a simulation method for an intelligent transportation system based on a combined PRES network as described in any one of claims 1-5.

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