Reliability design method and system of intelligent perception system based on mbse

Abstract

The application provides a reliability design method and system of an intelligent perception system based on MBSE, comprising: establishing an entity domain of the intelligent perception system based on the structure of the intelligent perception system; establishing a logical model of the intelligent perception system in a domain based on an entity domain and a business demand driven modeling method; constructing a physical simulation model of the intelligent perception system based on the logical model and a model driven method; calculating the response reliability of the intelligent perception system based on the physical simulation model; and optimizing the reliability design of the intelligent perception system based on the response reliability. The application alleviates the technical problems of low efficiency and iteration difficulty in the prior art.

Description

Technical Field

[0001] This invention relates to the field of sensing system design technology, and in particular to a reliability design method and system for an intelligent sensing system based on MBSE. Background Technology

[0002] In recent years, with the continuous development and application of technologies such as cloud computing and the Internet of Things, it has become possible to achieve comprehensive perception, reliable transmission, and intelligent processing. Research on perception systems composed of numerous sensors and terminals has increasingly become a research focus, such as smart city perception systems and aerospace equipment perception systems. However, intelligent perception systems are complex and open mega-systems, containing a rich variety of multi-source, complex, and heterogeneous service scenarios, as well as users, targets, and perception terminals. On the one hand, the complex and multi-source, intricate structure and relationships of the perception system make it difficult to improve its reliability, requiring consideration of the coupling issues of multiple scenarios and modules during its construction, making the reliability design of intelligent perception systems paramount. On the other hand, the iteration speed of intelligent perception terminal design is accelerating, the change cycle is shortening, and the product development process is becoming increasingly complex with highly overlapping related work. Therefore, the understanding and assimilation of dynamic development requirements often extends throughout the entire product design and development phase. This means that the system must have a flexible logical framework and interface relationships to respond quickly to changes in requirements. Therefore, in order to further explore the reliability design and simulation verification methods of intelligent sensing systems, it is particularly important to conduct standardized and systematic design of smart city sensing systems and aviation equipment sensing systems.

[0003] With the continuous development and application of technologies such as cloud computing and the Internet of Things, designing an intelligent sensing system that encompasses various service scenarios, users, and sensing terminals, capable of comprehensive perception, reliable transmission, and intelligent processing, has become an urgent need and a key research focus. Traditional document-based systems engineering design methods rely on natural language and text-based documents to form system architectures. However, as time progresses, document-based systems engineering inevitably generates a large number of different versions of documents. This not only makes document management, information retrieval, and modification difficult, but also makes it hard to ensure the consistency of relevant information across different documents. Therefore, traditional document-based systems engineering suffers from low efficiency and difficulty in iteration. Summary of the Invention

[0004] In view of this, the purpose of this invention is to provide a reliability design method and system for an intelligent sensing system based on MBSE, so as to alleviate the technical problems of low efficiency and difficulty in iteration in the prior art.

[0005] In a first aspect, embodiments of the present invention provide a reliability design method for an intelligent sensing system based on MBSE, comprising: establishing entity domains of the intelligent sensing system based on the structure of the intelligent sensing system; establishing logical models of the intelligent sensing system by domain based on entity domains and business requirement-driven modeling methods; constructing physical simulation models of the intelligent sensing system based on logical models and model-driven methods; calculating the response reliability of the intelligent sensing system based on the physical simulation models; and optimizing the reliability design of the intelligent sensing system based on the response reliability.

[0006] Furthermore, the entity domain includes: the user role domain, information service domain, perception control domain, target object domain, and the mapping relationship between entities in the intelligent perception system.

[0007] Furthermore, the logical model includes: a requirement model, a use case model, an activity model, a timing model, a structural model, a normal fault state transition model, and a parameter model. Based on an entity domain and business requirement-driven modeling approach, the logical models of the intelligent sensing system are established by domain, including: establishing a requirement model for the intelligent sensing system based on the information service domain; constructing a use case model for the intelligent sensing system based on the requirement model and the mapping relationship between the information service domain and the user role domain; deepening the functional use cases in the use case model based on the business function logic relationship of the intelligent sensing system, and establishing the activity model and timing model of the intelligent sensing system; establishing a structural model for the intelligent sensing system based on the functional allocation relationship between the information service domain and the sensing control domain; capturing the state of each module of the intelligent sensing system and establishing a normal fault state transition model based on reliability design requirements; and establishing a parameter model based on the target object domain and the requirement model.

[0008] Furthermore, based on the logical model and model-driven method, a physical simulation model of the intelligent sensing system is constructed, including: building the architecture of the multi-agent simulation of the intelligent sensing system based on the structural model; constructing the action graph of each agent in the multi-agent simulation based on the activity model; constructing the state graph of each agent in the multi-agent simulation based on the normal fault state transition model; constructing the protocol graph of each agent in the multi-agent simulation based on the timing model; and designing the input and output parameters of the multi-agent simulation based on the parameter model.

[0009] Furthermore, based on the physical simulation model, the response reliability of the intelligent sensing system is calculated, including: calculating the response reliability of the intelligent sensing system using the following formula: Among them, R response Let n be the total number of unit signals, r(t) be the number of unit information that can be responded to in a timely and correct manner at time t, l(T) be the indicator function for unit information in the timeout state, T be the unit information delay, l(Δ) be the indicator function for unit information in the offset state, and Δ be the unit information accuracy.

[0010] Secondly, embodiments of the present invention also provide a reliability design system for an intelligent sensing system based on MBSE, comprising: a first establishment module, a second establishment module, a third establishment module, a calculation module, and an optimization module; wherein, the first establishment module is used to establish the entity domains of the intelligent sensing system based on the structure of the intelligent sensing system; the second establishment module is used to establish logical models of the intelligent sensing system by domain based on the entity domains and a business requirement-driven modeling method; the third establishment module is used to construct a physical simulation model of the intelligent sensing system based on the logical model and a model-driven method; the calculation module is used to calculate the response reliability of the intelligent sensing system based on the physical simulation model; and the optimization module is used to optimize the reliability design of the intelligent sensing system based on the response reliability.

[0011] Furthermore, the entity domain includes: the user role domain, information service domain, perception control domain, target object domain, and the mapping relationship between entities in the intelligent perception system.

[0012] Furthermore, the calculation module is also used to: calculate the response reliability of the intelligent sensing system using the following formula: Among them, R response Let n be the total number of unit signals, r(t) be the number of unit information that can be responded to in a timely and correct manner at time t, l(T) be the indicator function for unit information in the timeout state, T be the unit information delay, l(Δ) be the indicator function for unit information in the offset state, and Δ be the unit information accuracy.

[0013] Thirdly, embodiments of the present invention also provide an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the method described in the first aspect.

[0014] Fourthly, embodiments of the present invention also provide a computer-readable medium having processor-executable non-volatile program code that causes the processor to perform the method described in the first aspect.

[0015] This invention provides a reliability design method and system for an intelligent sensing system based on MBSE. Through a standardized design approach and methodology, it establishes the entity domain of the intelligent sensing system, creates a graphical logical model of the intelligent sensing system based on the entity domain, and establishes a simulation model based on the logical model to verify the reliability design of the intelligent sensing system. This forms an iterative and continuously updated methodology for the reliability design and verification of the intelligent sensing system, providing a general solution to the reliability design problem of the intelligent sensing system and alleviating the technical problems of low efficiency and difficulty in iteration in the prior art. Attached Figure Description

[0016] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0017] Figure 1 A flowchart illustrating a reliability design method for an MBSE-based intelligent sensing system provided in an embodiment of the present invention;

[0018] Figure 2 A flowchart illustrating a method for establishing a logical model of an intelligent sensing system by domain division, as provided in an embodiment of the present invention;

[0019] Figure 3 This is a schematic diagram of a modeling framework for an intelligent sensing system provided in an embodiment of the present invention;

[0020] Figure 4 A flowchart illustrating a method for constructing a physical simulation model of an intelligent sensing system, provided in an embodiment of the present invention;

[0021] Figure 5 A schematic diagram of a model-driven mechanism provided in an embodiment of the present invention;

[0022] Figure 6 This is a schematic diagram of a reliability design system for an intelligent sensing system based on MBSE, provided as an embodiment of the present invention. Detailed Implementation

[0023] The technical solution of the present invention will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0024] Example 1:

[0025] To address the complexity and iterative optimization issues in the reliability design of intelligent sensing terminals, as well as the inefficiency and difficulty in iteration of traditional document-based systems engineering, this invention provides a reliability design method for intelligent sensing systems based on MBSE (Mean Interface Sequence). Specifically, Figure 1 This is a flowchart illustrating a reliability design method for an MBSE-based intelligent sensing system according to an embodiment of the present invention. Figure 1 As shown, the method specifically includes the following steps:

[0026] Step S102: Based on the structure of the intelligent sensing system, establish the entity domain of the intelligent sensing system.

[0027] Optionally, in this embodiment of the invention, the entity domain includes: the user role domain, information service domain, perception control domain, target object domain, and mapping relationship between entities in the intelligent perception system.

[0028] Step S104: Based on the modeling method driven by entity domain and business needs, establish the logical model of the intelligent perception system by domain.

[0029] Step S106: Based on the logical model and model-driven method, construct the physical simulation model of the intelligent sensing system.

[0030] Step S108: Calculate the response reliability of the intelligent sensing system based on the physical simulation model.

[0031] Step S110: Optimize the reliability design of the intelligent sensing system based on response reliability.

[0032] This invention provides a reliability design and verification method for a sensing system based on Model-Based Systems Engineering (MBSE). Through a standardized design approach and methodology, it establishes the entity domain of the intelligent sensing system, creates a graphical logical model of the intelligent sensing system based on the entity domain, and establishes a simulation model based on the logical model to verify the reliability design of the intelligent sensing system. This forms an iterative and continuously updated methodology for the reliability design and verification of intelligent sensing systems, providing a general solution to the reliability design problem of intelligent sensing systems and alleviating the technical problems of low efficiency and difficulty in iteration in existing technologies.

[0033] First, we establish the entity domain of the intelligent sensing system. The following section will take the analysis of the reliability design and verification problem of an intelligent sensing system as an example.

[0034] Because intelligent sensing systems are complex and open mega-systems, encompassing a rich variety of diverse and heterogeneous service scenarios, as well as users, targets, and sensing terminals, they exhibit complex and intricate sensing system structures and relationships. Therefore, the first step is to clarify the structure and relationships of the intelligent sensing system, namely, to establish the entity domains of the intelligent sensing system, including constructing the user role domain, information service domain, sensing control domain, target object domain, and the mapping relationships between entities in these four entity domains.

[0035] Specifically, the user role domain is a collection of roles such as users and managers in the intelligent sensing system, and is the object of information service provision; the information service domain is a collection of business services and functional requirements provided by the intelligent sensing system to users, which can realize data processing and information provision, and provide interfaces for sensing and control services for users of the intelligent sensing system; the sensing and control domain is a collection of various devices that acquire information from the target object domain, and provides hardware support for entities in the information service domain; the target object domain is a collection of object entities in the physical world that users hope to obtain through business services, and is also the object perceived and controlled by entities in the sensing and control domain.

[0036] Taking the smart city sensing system as an example, the user role domain includes residents, managers, and maintenance personnel; the information service domain includes environmental monitoring, smart fire protection, smart energy use, and smart transportation; the sensing and control domain includes sensor networks, terminal networks, power supply equipment, communication modules, and storage devices; and the target object domain includes air quality, voltage, current, and fire conditions. The specific entities in the entity domain of the smart city sensing system are shown in Table 1.

[0037] Table 1 Entity Domain of Smart City Sensing System

[0038] Entity domain content User Role Domain Residents, managers, maintenance personnel, etc. Information service domain Environmental monitoring, smart fire safety, smart energy use, smart transportation, etc. Perceptual control domain Sensor networks, terminal networks, power supply equipment, communication modules, storage devices, etc. Target object domain Air quality, voltage, current, fire situation, etc.

[0039] After obtaining the entity domains of the intelligent sensing system to be analyzed, the logical model of the intelligent sensing system can be modeled based on the elements of the entity domains and the relationships between them.

[0040] Optionally, in this embodiment of the invention, the logical model includes: a requirement model, a use case model, an activity model, a timing model, a structural model, a normal fault state transition model, and a parameter model.

[0041] Figure 2 This is a flowchart illustrating a method for establishing a logical model of an intelligent sensing system by domain division, according to an embodiment of the present invention. Figure 2 As shown, step S104 specifically includes the following steps:

[0042] Step S201: Based on the information service domain, establish a demand model for the intelligent sensing system.

[0043] Step S202: Based on the demand model and the mapping relationship between the information service domain and the user role domain, construct the use case model of the intelligent perception system.

[0044] Step S203: Based on the business function logic relationship of the intelligent perception system, deepen the functional use cases in the use case model, and establish the activity model and timing model of the intelligent perception system.

[0045] Step S204: Based on the functional allocation relationship between the information service domain and the perception control domain, establish a structural model of the intelligent perception system.

[0046] Step S205: Based on reliability design requirements, capture the status of each module of the intelligent sensing system and establish a normal fault state transition model.

[0047] Step S206: Establish a parameter model based on the target object domain and the requirement model.

[0048] The following section will use the logical model of establishing a specific intelligent sensing system as an example for introduction.

[0049] In this embodiment of the invention, the logical model of the intelligent sensing system uses SysML as the modeling language, based on... Figure 3 The modeling framework for the intelligent sensing system is presented. Specifically, a demand model for the intelligent sensing system is established based on the information service entity domain, refining the requirements of the information service entities. Taking the air monitoring entity in the smart city sensing system entity domain as an example, the main requirements include functional requirements (pollutant monitoring, multi-source data fusion, data storage, data transmission, data display, etc.) and non-functional requirements (response reliability, number of sensors connected, sensor connection method, coverage, volume, weight, data processing frequency, data processing accuracy, storage space, storage frequency, etc.).

[0050] Based on the obtained air quality monitoring requirement model, and combined with the user roles in the user role entity domain, the functional requirement use cases are associated with the roles to form a use case model for the smart city perception system. For example, managers use the backend platform for macro-level data analysis, system operation management, and decision-making, while maintenance personnel are responsible for the daily maintenance of the system's hardware and software to ensure the normal operation of the system. Residents obtain air quality information provided by the system through client apps and other means.

[0051] Based on the use case model, the functional use cases in the use case model are captured through the activity model, and the use cases are refined into functional steps, or the use cases are linked together by the functional flow, so that they have clear and detailed process steps, forming an activity model of the intelligent perception system. For example, the main functional flow of air quality monitoring is: (1) pollutant monitoring (2) multi-source data fusion to obtain air quality index (3) data result storage (4) data display; In addition, the information flow of functional use cases is captured through the time sequence model to describe the information interaction relationship between various modules or entities of the system. For example, the main information flow of air quality monitoring is: the concentration information of each pollutant is first obtained by the sensor network, transmitted to the terminal for multi-source fusion to obtain air quality index, and then stored.

[0052] Based on the entity elements of the perception and control domain and the functional allocation relationship between the information service domain and the perception and control domain, an intelligent perception architecture model is established, including an internal block model and a block definition model. The internal block model mainly describes the internal structure of the perception system modules, while the block definition model mainly describes the top-level structure of the perception system. For example, the intelligent perception architecture includes a sensor network and terminals. The block definition model describes the overall top-level structure of the sensor network and terminal network, while the internal block models describe the internal structures of the sensor network and terminal network respectively.

[0053] Based on the reliability design requirements of entity elements and intelligent sensing systems in the perception and control domain, the states of each module of the intelligent sensing system are captured, including normal and fault states, and transition conditions are set to establish a normal-fault state transition model. For example, in the smart city perception system, sensors and terminals mainly have two states: normal operation and fault. The transition condition is that the system functions of both fail and cannot perform normal tasks.

[0054] Based on the target object domain and requirement model, elements within the target object domain are captured, and non-functional requirements are refined to establish a parameter index system for the intelligent sensing system, thereby building a parameter model. The parameter model includes parameter graphs and block graph attributes. The parameter graphs describe the constraints between parameters in the intelligent sensing system, while the block graph attributes describe the specific parameter indicators of the intelligent sensing system. For example, in air quality monitoring, the parameter model includes functional parameters such as the Air Quality Index (AQI), PM2.5 concentration, and SO2 concentration, as well as performance parameters such as coverage, data processing frequency, and response reliability, and the constraints between them.

[0055] After establishing the logical model of the intelligent sensing system based on the entity domain and the modeling method driven by business needs, the physical simulation model of the intelligent sensing system can be constructed based on the logical model and the model-driven method.

[0056] Optionally, Figure 4 This is a flowchart illustrating a method for constructing a physical simulation model of an intelligent sensing system according to an embodiment of the present invention. Figure 4 As shown, step S106 specifically includes the following steps:

[0057] Step S401: Based on the structural model, build the architecture of the multi-agent simulation of the intelligent perception system.

[0058] Step S402: Based on the activity model, construct the action graph of each agent in the multi-agent simulation.

[0059] Step S403: Based on the normal fault state transition model, construct the state diagram of each agent in the multi-agent simulation.

[0060] Step S404: Based on the time series model, construct the protocol diagram of each agent in the multi-agent simulation.

[0061] Step S405: Based on the parametric model, design the input and output parameters for multi-agent simulation.

[0062] The following section will use the establishment of a physical simulation model of a specific intelligent sensing system for air quality monitoring as an example.

[0063] The simulation physical model of the intelligent sensing system is based on the principle of multi-agent simulation, and through... Figure 5 The model is modeled using the model-driven mechanism shown.

[0064] Specifically, the structural model of the intelligent sensing system's logical model outputs the hierarchical structure of the intelligent sensing system to the multi-agent simulation, forming a basic multi-agent simulation architecture. For an intelligent sensing system for air quality monitoring, the structural model includes the overall top-level structure and internal structure of the sensor network and terminal network. The sensors and terminal entities in the structure are implemented as intelligent agents, and the hierarchical organizational relationships of each intelligent agent are assigned according to the structural model.

[0065] The activity model of the intelligent sensing system logic model outputs the behavioral activity logic of the intelligent sensing system to the multi-agent simulation, forming the action diagram of the agent, describing the functional activity flow of each agent, and establishing the functional flow transmission relationship between the simulation architecture of the intelligent sensing system, thereby driving the simulation operation from the inside. For an intelligent sensing system for air quality monitoring, the activity model describes the functional flow of air quality monitoring, mainly including (1) pollutant monitoring, (2) multi-source data fusion to obtain the air quality index, (3) data result storage, and (4) data display. In the simulation, multiple process modules can be used to represent the above functional flow.

[0066] The state model of the intelligent sensing system's logical model outputs the module states and state transition conditions to the multi-agent simulation. The state diagram represents the attributes of each module in the system. Therefore, a state diagram is established in each module agent, assigning normal, fault, and other states to the module and setting transition conditions.

[0067] The timing model of the logical model of the intelligent sensing system outputs information data interaction relationships to the multi-agent simulation. In the multi-agent simulation of the intelligent sensing system, a protocol diagram is formed, which is used to describe the information, data interaction content and relationships between the modules.

[0068] The parameter model outputs parameters and constraints from the logical model of the intelligent sensing system to the multi-agent simulation. These parameters serve as the experimental and verification objects for the intelligent sensing system simulation. By setting the multi-agent simulation parameters of the intelligent sensing system and using the simulation to obtain parameter index results, the design quality of the sensing system is evaluated. Parameter settings depend on the simulation architecture, activity graph, and state graph. Parameters can be divided into: agent-specific performance parameters, directly assigned to agents such as sensors and terminals, such as data processing frequency, number of access points, and coverage area; and functional parameters, assigned to the activity graph, such as Air Quality Index (AQI), PM2.5 concentration, and SO2 concentration.

[0069] After establishing a physical simulation model based on the logical model of the intelligent sensing system, the response reliability of the intelligent sensing system can be analyzed based on the simulation physical model.

[0070] Optionally, in this embodiment of the invention, response reliability is defined as the ability of the sensing result to affect the actuator to make a correct response under the condition of given information offset and information delay of each unit of the system within a specified time.

[0071] Information latency refers to the time it takes for a unit of information to be collected and recognized. It mainly comes from the time spent on information collection, interaction (transmission), and recognition. Let the unit information latency be T. When the unit information latency exceeds a threshold δ... T If the unit information is in a timeout state, meaning the unit information sensing system cannot enable the actuator to respond in a timely manner, as shown in the following formula:

[0072]

[0073] Where 1(T) is an indicator function for the unit information being in a timeout state.

[0074] Information accuracy refers to the degree of deviation between the observed value of a unit of information after acquisition and recognition and the true value. If the deviation exceeds a threshold, the information is considered erroneous. Let the accuracy of the unit information be Δ, and the accuracy of the acquisition and recognition processes be Δ' and Δ'' respectively. c , △ s When the unit information offset is greater than the threshold δ c δ s When this happens, the unit information is considered to be in an offset state, meaning the unit information sensing system cannot make the actuator respond correctly, as shown in the following formula:

[0075]

[0076] Wherein, 1(△) is an indicator function for the unit information being the offset state.

[0077] The method provided in this embodiment of the invention calculates the response reliability of the intelligent sensing system using the following formula:

[0078]

[0079]

[0080] Among them, R response For response reliability, n is the total number of unit signals, and r(t) is the number of unit information that can be responded to in a timely and correct manner at time t.

[0081] After analyzing the response reliability of the intelligent sensing system based on the simulation physical model, the reliability design of the intelligent sensing system can be optimized based on the response reliability. Step S110 is described in detail below.

[0082] Step S110 mainly involves comparing the response reliability of the intelligent sensing system obtained from the simulation with the designed response reliability requirements to verify whether the requirements are met. If the target requirements are not met, step S104 needs to be repeated to redesign and optimize the logical model of the intelligent sensing system.

[0083] The following section will take the response reliability analysis of a specific intelligent sensing system for air quality monitoring as an example.

[0084] Given a parameter scheme (offsets and delays for each unit), discrete event simulations are performed on the model to calculate the execution reliability, response accuracy, and response time of the smart city architecture's environmental monitoring function. This allows for the optimization of the parameter configuration scheme and further refinement of the architecture and unit designs. The threshold for converting a signal to an offset state is 0.1, and the threshold for converting it to a timeout state is also 0.1 (unit: ms). The experimental parameter configuration scheme is shown in Table 2. In the discrete simulation experiment configuration interface, the simulation parameters are set sequentially as shown in Table 2. The simulation system is run for 3600s (1h), and the system reliability fluctuates around 0.968, which is consistent with the reliability requirements of the smart city architecture.

[0085] The experimental parameter configuration scheme two is shown in Table 3. The results show that the system response reliability fluctuates around 0.755. Compared to parameter configuration scheme two, scheme one better meets the response reliability requirements of a smart city architecture.

[0086] Based on the difference in reliability between parameter configuration scheme 1 and scheme 2, design guidance for controlling the response delay and offset of sensors and terminals can be derived. By improving the response accuracy and response time of sensors and terminals, the reliability of the system can be improved. The design can be optimized using simulation results, and the optimal design scheme can be selected so that the design achieves a balance between reliability and design budget.

[0087] Table 1 Parameter Configuration Scheme 1

[0088] event offset Delay SO2 sensor normal(0.01,0.01) normal(0.01,0.04) NO2 sensor normal(0.01,0.01) normal(0.01,0.04) CO sensor normal(0.01,0.01) normal(0.01,0.04) O3 sensor normal(0.01,0.01) normal(0.01,0.01) PM10 sensor normal(0.01,0.01) normal(0.01,0.01) PM2.5 sensor normal(0.01,0.01) normal(0.01,0.01) SO2 terminal normal(0.01,0.01) normal(0.01,0.01) NO2 terminal normal(0.01,0.01) normal(0.01,0.01) CO terminal normal(0.01,0.01) normal(0.01,0.01) O3 terminal normal(0.01,0.01) normal(0.01,0.01) PM10 terminal normal(0.01,0.01) normal(0.01,0.01) PM2.5 terminal normal(0.01,0.01) normal(0.01,0.01)

[0089] Table 2 Parameter Configuration Scheme Two

[0090]

[0091]

[0092] As described above, this invention provides a reliability design method for an intelligent sensing system based on MBSE. Through a standardized design approach and methodology, it establishes the entity domain of the sensing system, and creates a graphical logical model of the sensing system based on the entity domain using the standardized and flexible SysML language. Based on the logical model, it establishes a simulation model to verify the reliability design of the sensing system, forming an iterative and continuously updated methodology for the reliability design and verification of the sensing system, providing a general solution to the reliability design problem of the sensing system.

[0093] Example 2:

[0094] Figure 6 This is a schematic diagram of a reliability design system for an MBSE-based intelligent sensing architecture according to an embodiment of the present invention. Figure 6 As shown, the system includes: a first establishment module 10, a second establishment module 20, a third establishment module 30, a calculation module 40, and an optimization module 50.

[0095] Specifically, the first module 10 is used to establish the entity domain of the intelligent sensing system based on the structure of the intelligent sensing system.

[0096] Optionally, in this embodiment of the invention, the entity domain includes: the user role domain, information service domain, perception control domain, target object domain, and mapping relationship between entities in the intelligent perception system.

[0097] The second module 20 is used to establish the logical model of the intelligent perception system based on the modeling method driven by entity domains and business needs.

[0098] The third module 30 is used to construct a physical simulation model of the intelligent sensing system based on the logical model and the model-driven method.

[0099] The calculation module 40 is used to calculate the response reliability of the intelligent sensing system based on the physical simulation model.

[0100] Specifically, the calculation module 40 is also used to: calculate the response reliability of the intelligent sensing system using the following formula:

[0101]

[0102]

[0103] Among them, R response For the response reliability, n is the total number of unit signals, r(t) is the number of unit messages that can be responded to in a timely and correct manner at time t, l(T) is the indicator function for unit messages in the timeout state, T is the unit message delay, l(Δ) is the indicator function for unit messages in the offset state, and Δ is the unit message accuracy.

[0104] Optimization module 50 is used to optimize the reliability design of the intelligent sensing system based on response reliability.

[0105] This invention provides a reliability design system for an intelligent sensing system based on MBSE. Through a standardized design approach and methodology, it establishes the entity domain of the intelligent sensing system, creates a graphical logical model of the intelligent sensing system based on the entity domain, and establishes a simulation model based on the logical model to verify the reliability design of the intelligent sensing system. This forms a continuously iterative and updated methodology for the reliability design and verification of the intelligent sensing system, providing a general solution to the reliability design problem of the intelligent sensing system and alleviating the technical problems of low efficiency and difficulty in iteration in the prior art.

[0106] Optionally, in this embodiment of the invention, the logical model includes: a requirement model, a use case model, an activity model, a timing model, a structural model, a normal fault state transition model, and a parameter model; the second establishment module 20 is further used for:

[0107] Based on the information service domain, a requirement model for the intelligent sensing system is established; based on the requirement model and the mapping relationship between the information service domain and the user role domain, a use case model for the intelligent sensing system is constructed; based on the business function logic relationship of the intelligent sensing system, the functional use cases in the use case model are deepened, and an activity model and a timing model for the intelligent sensing system are established; based on the functional allocation relationship between the information service domain and the sensing control domain, a structural model for the intelligent sensing system is established; based on reliability design requirements, the state of each module of the intelligent sensing system is captured, and a normal fault state transition model is established; based on the target object domain and the requirement model, a parameter model is established.

[0108] Optionally, in this embodiment of the invention, the third establishment module 30 is further configured to: build the architecture of the multi-agent simulation of the intelligent sensing system based on the structural model; construct the action diagram of each agent in the multi-agent simulation based on the activity model; construct the state diagram of each agent in the multi-agent simulation based on the normal fault state transition model; construct the protocol diagram of each agent in the multi-agent simulation based on the timing model; and design the input and output parameters of the multi-agent simulation based on the parameter model.

[0109] This invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the steps of the method in Embodiment 1 described above.

[0110] This invention also provides a computer-readable medium having processor-executable non-volatile program code that causes the processor to perform the method described in Embodiment 1 above.

[0111] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features therein. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A reliability design method for an intelligent sensing system based on MBSE, characterized in that, include: Based on the structure of the intelligent sensing system, the entity domain of the intelligent sensing system is established; Based on the modeling method driven by the entity domain and business needs, the logical model of the intelligent perception system is established by domain. Based on the aforementioned logical model and model-driven method, a physical simulation model of the intelligent sensing system is constructed. Based on the physical simulation model, the response reliability of the intelligent sensing system is calculated; Based on the aforementioned response reliability, the reliability design of the intelligent sensing system is optimized; Based on the physical simulation model, the response reliability of the intelligent sensing system is calculated, including: The response reliability of the intelligent sensing system is calculated using the following formula: in, Let n be the total number of unit signals, and r( ) be the response reliability. t )for It can always respond promptly and accurately to the quantity of unit information. It is an indicator function for when the unit information is in a timeout state. For unit information delay, It is an indicator function where the unit information is the offset state. For unit information precision; The logical model includes: requirement model, use case model, activity model, sequence model, structural model, normal fault state transition model, and parameter model; Based on the entity domain and business requirement-driven modeling method, a logical model of the intelligent sensing system is established by domain, including: Based on the information service domain, a requirement model for the intelligent sensing system is established. Based on the demand model and the mapping relationship between the information service domain and the user role domain, a use case model for the intelligent perception system is constructed. Based on the business function logic relationship of the intelligent sensing system, the functional use cases in the use case model are deepened, and the activity model and timing model of the intelligent sensing system are established. Based on the functional allocation relationship between the information service domain and the perception control domain, a structural model of the intelligent perception system is established. Based on reliability design requirements, the state of each module of the intelligent sensing system is captured and a normal fault state transition model is established. Based on the target object domain and the aforementioned requirement model, a parameter model is established; Based on the aforementioned logical model and model-driven method, a physical simulation model of the intelligent sensing system is constructed, including: Based on the aforementioned structural model, a multi-agent simulation architecture for the intelligent sensing system is constructed. Based on the activity model, construct the action graph of each agent in the multi-agent simulation; Based on the normal fault state transition model, the state diagram of each agent in the multi-agent simulation is constructed. Based on the aforementioned timing model, a protocol diagram for each agent in the multi-agent simulation is constructed. Based on the parameter model, the input and output parameters of the multi-agent simulation are designed.

2. The method according to claim 1, characterized in that, The entity domain includes the mapping relationship between the user role domain, information service domain, perception control domain, target object domain, and entities in the intelligent perception system.

3. A reliability design system for an intelligent sensing architecture based on MBSE, characterized in that, include: The system comprises a first establishment module, a second establishment module, a third establishment module, a calculation module, and an optimization module; wherein, the first establishment module is used to establish the entity domain of the intelligent sensing system based on the structure of the intelligent sensing system; The second establishment module is used to establish the logical model of the intelligent perception system by domain based on the entity domain and the business demand-driven modeling method; The third establishment module is used to construct a physical simulation model of the intelligent sensing system based on the logical model and the model-driven method. The computing module is used to calculate the response reliability of the intelligent sensing system based on the physical simulation model. The optimization module is used to optimize the reliability design of the intelligent sensing system based on the response reliability. The computing module is also used for: The response reliability of the intelligent sensing system is calculated using the following formula: in, Let n be the total number of unit signals, and r( ) be the response reliability. t )for It can always respond promptly and accurately to the quantity of unit information. It is an indicator function for when the unit information is in a timeout state. For unit information delay, It is an indicator function where the unit information is the offset state. For unit information precision; The logical model includes: requirement model, use case model, activity model, sequence model, structural model, normal fault state transition model, and parameter model; The second establishment module is further configured to: establish a requirement model for the intelligent sensing system based on the information service domain; construct a use case model for the intelligent sensing system based on the requirement model and the mapping relationship between the information service domain and the user role domain; refine the functional use cases in the use case model based on the business function logic relationship of the intelligent sensing system, and establish an activity model and a timing model for the intelligent sensing system; establish a structural model for the intelligent sensing system based on the functional allocation relationship between the information service domain and the sensing control domain; capture the state of each module of the intelligent sensing system and establish a normal fault state transition model based on reliability design requirements; and establish a parameter model based on the target object domain and the requirement model. The third establishment module is further configured to: build the architecture of the multi-agent simulation of the intelligent perception system based on the structural model; construct the action diagram of each agent in the multi-agent simulation based on the activity model; construct the state diagram of each agent in the multi-agent simulation based on the normal fault state transition model; construct the protocol diagram of each agent in the multi-agent simulation based on the timing model; and design the input and output parameters of the multi-agent simulation based on the parameter model.

4. The system according to claim 3, characterized in that, The entity domain includes the mapping relationship between the user role domain, information service domain, perception control domain, target object domain, and entities in the intelligent perception system.

5. An electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the method described in any one of claims 1 to 2.

6. A computer-readable medium having processor-executable non-volatile program code, characterized in that, The program code causes the processor to execute the method according to any one of claims 1-2.

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