Event-driven three-dimensional deduction scene visual angle real-time conversion method and device
Through the real-time conversion method of three-dimensional deduced scenario perspective based on event-driven three-dimensional deduction scene perspective, the system task reliability is dynamically evaluated, and the problem of insufficient accuracy and real-time prediction of system task reliability in the prior art is solved, and the accuracy and effectiveness dynamic prediction of system task reliability is achieved.
Patent Information
- Application Number
- CN202411470299.7
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-10-21
- Publication Date
- 2025-02-28
AI Technical Summary
The prior art is difficult to achieve accurate and real-time dynamic prediction of system task reliability, especially when the system functions are complex and the organizational form is free.
The real-time conversion method of three-dimensional deduction scene perspective based on event-driven is adopted. By establishing an activity diagram and a timing diagram, the cumulative working time of the system components is calculated, and the reliability of the system task is dynamically evaluated by combining failure efficiency and functional occurrence probability.
It realizes dynamic and real-time prediction of system task reliability, improves the accuracy and coverage of prediction, and adapts to the actual state of complex systems.
Smart Images
Figure CN119537062A_ABST
Abstract
Description
Technical Field
[0001] The present application relates to the technical field of scene conversion, and in particular to a method and device for real-time conversion of perspectives of a three-dimensional deduction scene based on event-driven. Background Art
[0002] Mission reliability is an important evaluation indicator of the probability of system operation stability, which directly affects the system's maintenance operations and maintenance resource preparation.
[0003] At present, the reliability prediction of system tasks mainly adopts the reliability block diagram method in GJB 813-1990. This method uses the form of series-parallel block diagram to describe the functional relationship of the internal components of the system, which is a static modeling method. As the system functions become more and more complex and the system organization form becomes more and more free, the traditional reliability block diagram method can no longer meet the requirements of accurate and real-time prediction of system task reliability.
[0004] In view of this, how to provide a method for dynamic prediction of system mission reliability has become a technical problem that needs to be solved urgently. Summary of the invention
[0005] Embodiments of the present application provide a method for real-time conversion of perspectives of a three-dimensional deduction scene based on event-driven, an apparatus for real-time conversion of perspectives of a three-dimensional deduction scene based on event-driven, a computing device, and a computer storage medium, which are used to solve the problem of unreliable task prediction in the current system.
[0006] In a first aspect of an embodiment of the present application, a method for real-time conversion of perspectives of a three-dimensional deduction scene based on event-driven is provided, characterized in that it includes:
[0007] According to the functions involved in the tasks, the system task types corresponding to the functions involved in the tasks, and the system component units, an activity diagram and a timing diagram corresponding to the target system are established, and based on the activity diagram and the timing diagram, the accumulated working time corresponding to each function of each system component unit is calculated;
[0008] According to the design information of system components and combined with the reliability prediction standard, determine the failure rate corresponding to each system component;
[0009] Determine the probability of occurrence of a function corresponding to each of the tasks involved in the function based on the pre-collected operation data of the target system within a preset time period, wherein the operation data carries the number of times the function is called;
[0010] The system task reliability is determined based on the accumulated working time corresponding to each function of the system component unit, the failure rate, and the function occurrence probability corresponding to each task-related function.
[0011] Optionally, the method further includes:
[0012] According to the system task type and the functions involved in the task, a use case diagram corresponding to the target system is established, wherein the collaborative task type carries the system operation scenario.
[0013] Optionally, the determining, based on the pre-collected operation data of the target system within a preset time period, the function occurrence probability corresponding to the function involved in each task includes:
[0014] According to a first calculation formula, based on the pre-collected operation data of the target system within a preset time period, the function occurrence probability corresponding to each of the tasks involved is determined, wherein the first calculation formula includes:
[0015]
[0016] Among them, a i,j is the probability of the jth function appearing in the i-th use case per unit time; T is the cumulative running time of the system; N i,j is the number of times the jth function is called in the i-th use case during the system running time T.
[0017] Optionally, determining the system task reliability based on the accumulated working time corresponding to each function of the system component unit, the failure rate, and the function occurrence probability corresponding to each function involved in the task includes:
[0018]
[0019] Among them, R is the system task reliability; t is the total system operation time; n i,j is the total number of system components involved in the jth function under the i-th use case; i,j,k is the failure rate of the kth system component involved in the jth function under the i-th use case.
[0020] In a second aspect of an embodiment of the present application, a device for real-time conversion of perspectives of a three-dimensional deduction scene based on event-driven is provided, comprising:
[0021] A calculation module is configured to establish an activity diagram and a timing diagram corresponding to the target system according to the functions involved in the tasks, the system task types corresponding to the functions involved in the tasks, and the system component units, and calculate the accumulated working time corresponding to each function of each system component unit based on the activity diagram and the timing diagram;
[0022] The first determination module is configured to determine the failure rate corresponding to each system component unit according to the system component unit design information and the reliability prediction standard;
[0023] A second determination module is configured to determine the function occurrence probability corresponding to each of the tasks involved in the function based on the pre-collected operation data of the target system within a preset time period, wherein the operation data carries the number of times the function is called;
[0024] The third determination module is configured to determine the system task reliability based on the accumulated working time corresponding to each function of the system component unit, the failure rate, and the function occurrence probability corresponding to each task-related function.
[0025] According to a third aspect of an embodiment of the present application, a computing device is provided, including:
[0026] Memory and processor;
[0027] The memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions. When the computer-executable instructions are executed by the processor, the steps of the above-mentioned event-driven three-dimensional deduction scene perspective real-time conversion method are implemented.
[0028] According to a fourth aspect of an embodiment of the present application, a computer-readable storage medium is provided, which stores computer-executable instructions, and when the instructions are executed by a processor, the steps of the above-mentioned event-driven three-dimensional deduction scene real-time perspective conversion method are implemented.
[0029] The present application provides a method for real-time conversion of perspectives of three-dimensional deduction scenes based on event-driven, including: establishing activity diagrams and timing diagrams corresponding to target systems according to functions involved in tasks, system task types corresponding to functions involved in each task, and system component units, and calculating the accumulated working time corresponding to each function of each system component unit based on the activity diagram and the timing diagram; determining the failure rate corresponding to each system component unit according to the design information of the system component unit and in combination with the reliability prediction standard; determining the probability of occurrence of functions corresponding to functions involved in each task based on pre-collected operation data of the target system within a preset time period, wherein the operation data carries the number of times the function is called; determining the reliability of the system task based on the accumulated working time corresponding to each function of the system component unit, the failure rate, and the probability of occurrence of functions corresponding to functions involved in each task.
[0030] The event-driven real-time conversion method of three-dimensional deduction scene perspective provided by the embodiment of the present application can dynamically evaluate the reliability of system tasks. Different from the series-parallel model of the reliability block diagram method, the UML model can more finely and comprehensively characterize the logical relationship between the system components, and is more in line with the actual state of the system. At the same time, the system task reliability modeling based on the UML model can be combined with the system operation data to dynamically predict the future reliability level of the system, ensuring the accuracy and effectiveness of the task reliability prediction.
[0031] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, which can be implemented in accordance with the contents of the specification, and in order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below. BRIEF DESCRIPTION OF THE DRAWINGS
[0032] Various other advantages and benefits will become apparent to those of ordinary skill in the art by reading the detailed description of the preferred embodiments below. The accompanying drawings are only for the purpose of illustrating the preferred embodiments and are not to be considered as limiting the present application. Also, the same reference symbols are used throughout the accompanying drawings to represent the same components. In the accompanying drawings:
[0033] Figure 1 A flowchart of a method for dynamically predicting system task reliability based on a UML model provided in an embodiment of the present application;
[0034] Figure 2 An activity diagram of a method for dynamically predicting system task reliability based on a UML model provided in an embodiment of the present application;
[0035] Figure 3 A schematic diagram of a timing diagram of a method for dynamically predicting system task reliability based on a UML model provided in an embodiment of the present application;
[0036] Figure 4 A schematic diagram of a use case diagram of a method for dynamically predicting system task reliability based on a UML model provided in an embodiment of the present application;
[0037] Figure 5 A use case diagram of aviation system A for a method for dynamic prediction of system mission reliability based on a UML model provided in an embodiment of the present application;
[0038] Figure 6 An activity diagram of the photo-taking and recording function of aviation system A of a method for dynamically predicting system task reliability based on a UML model provided in an embodiment of the present application;
[0039] Figure 7 A timing diagram of the photo-taking and recording function of aviation system A of a method for dynamically predicting system task reliability based on a UML model provided in an embodiment of the present application;
[0040] Figure 8 A schematic diagram of the structure of a device for real-time conversion of perspectives of a three-dimensional deduction scene based on event-driven according to an embodiment of the present application;
[0041] Fig. 9 A structural block diagram of a computing device provided in an embodiment of the present application. DETAILED DESCRIPTION
[0042] The exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although the exemplary embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided to enable a more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art.
[0043] The current system task reliability prediction methods mainly have the following problems: on the one hand, the reliability block diagram method can only model all system components under different tasks, and cannot model system components involved in different functions under different tasks; on the other hand, the reliability block diagram method can only use simple logical combinations such as series and parallel, and cannot effectively express the logical relationship structure of complex systems; on the other hand, the reliability block diagram method can only be used for task reliability prediction before system operation, and cannot use system operation data for dynamic task reliability prediction.
[0044] Therefore, the present application provides a method for dynamically predicting system task reliability based on a UML model, a device for dynamically predicting system task reliability based on a UML model, an electronic device and a computer storage medium, which are used to improve the coverage and accuracy of the task reliability prediction method.
[0045] See also Figure 1 , Figure 1 A flowchart of a method for dynamically predicting system task reliability based on a UML model is provided in an embodiment of the present application. Figure 1 As shown, the specific steps include the following steps.
[0046] Step S102: According to the functions involved in the task, the system task types corresponding to the functions involved in each task, and the system component units, establish the activity diagram and timing diagram corresponding to the target system, and based on the activity diagram and the timing diagram, calculate the cumulative working time corresponding to each function of each system component unit.
[0047] In the embodiment of the present application, the activity diagram is represented by ACT, which is used to describe the system component units of each function call of the system. Figure 2 , Figure 2 An activity diagram of a method for dynamically predicting system task reliability based on a UML model provided in an embodiment of the present application.
[0048] The timing diagram is represented by SD, which is used to describe the duration of each function call component unit of the system, see Figure 3 , Figure 3 A schematic diagram of a timing diagram of a method for dynamically predicting system task reliability based on a UML model provided in an embodiment of the present application.
[0049] Step S104: Determine the failure rate corresponding to each system component unit according to the system component unit design information and in combination with the reliability prediction standard.
[0050] In the embodiment of the present application, the system component unit design information is used to describe the component list and mathematical model of the system component unit. Mechanical structure products generally refer to the parts list and CAD model, and electronic products generally refer to the component list.
[0051] Reliability prediction standards refer to the calculation method specifications used to determine the reliability values of component units. Taking electronic products as an example, the component stress analysis method in GJB 299 can be used for calculation.
[0052] In actual applications, the jth function under the i-th use case involves the kth system component unit, and the cumulative working time in this function is t i,j,k It can be obtained by accumulating the time of each component unit lane.
[0053] Step S106: Based on the pre-collected operation data of the target system within a preset time period, the function occurrence probability corresponding to each of the tasks involved is determined, wherein the operation data carries the number of times the function is called.
[0054] In the embodiments of the present application, the running time is used to refer to the duration of each use case or acquisition during the operation of the system.
[0055] Specifically, the determining of the function occurrence probability corresponding to the function involved in each task based on the pre-collected operation data of the target system within a preset time period includes:
[0056] According to a first calculation formula, based on the pre-collected operation data of the target system within a preset time period, the function occurrence probability corresponding to each of the tasks involved is determined, wherein the first calculation formula includes:
[0057]
[0058] Among them, a i,j is the probability of the jth function appearing in the i-th use case per unit time; T is the cumulative running time of the system; N i,j is the number of times the jth function is called in the i-th use case during the system running time T.
[0059] Step S108: Determine the system task reliability based on the accumulated working time corresponding to each function of the system component unit, the failure rate, and the function occurrence probability corresponding to each task-related function.
[0060] Specifically, determining the system task reliability based on the accumulated working time corresponding to each function of the system component unit, the failure rate, and the function occurrence probability corresponding to each function involved in the task includes:
[0061]
[0062] Among them, R is the system task reliability; t is the total system operation time; n i,j is the total number of system components involved in the jth function under the i-th use case; i,j,k is the failure rate of the kth system component involved in the jth function under the i-th use case.
[0063] In one embodiment of the present application, a use case diagram corresponding to the target system is established according to the system task type and the functions involved in the task, wherein the collaborative task type carries the system operation scenario.
[0064] In the embodiment of the present application, the task type refers to a possible operating scenario of the system, which corresponds to the task profile in the reliability block diagram method.
[0065] Use case diagrams are represented by UCD, which are used to describe the task operation scenarios and the main functions called in each scenario. The general performance of the use case diagram is as follows: Figure 4 As shown, Figure 4 A use case diagram of a method for dynamically predicting system task reliability based on a UML model is provided in an embodiment of the present application.
[0066] Taking aviation system A as an example, this aviation system is mainly used for shooting remote target objects and maintaining regional network signal relay.
[0067] Step 1: Determine that aviation system A contains three types of tasks, including patrol, short-distance relay and long-distance relay. The functions involved in each task are shown in Table 1. Draw a use case diagram for aviation system A, such as Figure 5 As shown, Figure 5 A use case diagram of aviation system A of a method for dynamically predicting system mission reliability based on a UML model is provided in an embodiment of the present application.
[0068] Table 1 Aviation System A use cases and related functions
[0069]
[0070] Step 2: Draw the activity diagram and timing diagram of all functions involved in aviation system A. The system components and cumulative working time involved in each function are shown in Table 2. Taking the photo recording function as an example, its activity diagram and timing diagram are as follows: Figure 6 Shown and Figure 7 As shown, Figure 6An activity diagram of the photo-taking and recording function of aviation system A of a method for dynamically predicting system task reliability based on a UML model provided in an embodiment of the present application; Figure 7 A timing diagram of the photo-taking and recording function of aviation system A, which provides a method for dynamically predicting system task reliability based on a UML model in an embodiment of the present application.
[0071] Table 2: Cumulative working time of aviation system A component unit
[0072]
[0073]
[0074] Step 3: According to the component list of the system components, the component stress analysis method in GJB 299 is used to determine the failure rate of all components of the system. The specific values are shown in Table 3.
[0075] Table 3 Failure rate of component units of aviation system A
[0076]
[0077]
[0078] Step 4: Collect the operating data of aviation system A within 100 hours and determine the probability of occurrence of various functions. The calculation results are shown in Table 4.
[0079] Table 4 Occurrence probability of each function of aviation system A
[0080]
[0081]
[0082] Step 5: Using the total probability formula, the mission reliability of aviation system A with a flight delay of 500 h is calculated to be 0.45704. The calculation results are shown in Table 5.
[0083] Table 5 Reliability of aviation system A mission
[0084]
[0085]
[0086] The event-driven real-time conversion method of three-dimensional deduction scene perspective provided by the embodiment of the present application can dynamically evaluate the reliability of system tasks. Different from the series-parallel model of the reliability block diagram method, the UML model can more finely and comprehensively characterize the logical relationship between the system components, and is more in line with the actual state of the system. At the same time, the system task reliability modeling based on the UML model can be combined with the system operation data to dynamically predict the future reliability level of the system, ensuring the accuracy and effectiveness of the task reliability prediction.
[0087] Corresponding to the above method embodiment, this specification also provides an event-driven three-dimensional deduction scene perspective real-time conversion device embodiment, Figure 8 The present invention provides a schematic diagram of a device for real-time conversion of a three-dimensional scene perspective driven by an event. Figure 8 As shown, the device comprises:
[0088] The calculation module 802 is configured to establish an activity diagram and a timing diagram corresponding to the target system according to the functions involved in the tasks, the system task types corresponding to the functions involved in the tasks, and the system component units, and calculate the accumulated working time corresponding to each function of each system component unit based on the activity diagram and the timing diagram;
[0089] The first determination module 804 is configured to determine the failure rate corresponding to each system component unit according to the system component unit design information and the reliability prediction standard;
[0090] The second determination module 806 is configured to determine the function occurrence probability corresponding to each of the tasks involved in the function based on the pre-collected operation data of the target system within a preset time period, wherein the operation data carries the number of times the function is called;
[0091] The third determination module 808 is configured to determine the system task reliability based on the accumulated working time corresponding to each function of the system component unit, the failure rate, and the function occurrence probability corresponding to each task-related function.
[0092] In an optional embodiment, the device further includes:
[0093] The establishment module is configured to establish a use case diagram corresponding to the target system according to the system task type and the functions involved in the task, wherein the collaborative task type carries the system operation scenario.
[0094] In an optional embodiment, the second determining module 806 is further configured to:
[0095] According to a first calculation formula, based on the pre-collected operation data of the target system within a preset time period, the function occurrence probability corresponding to each of the tasks involved is determined, wherein the first calculation formula includes:
[0096]
[0097] Among them, a i,j is the probability of the jth function appearing in the i-th use case per unit time; T is the cumulative running time of the system; N i,j is the number of times the jth function is called in the i-th use case during the system running time T.
[0098] In an optional embodiment, the third determining module 808 is further configured to:
[0099]
[0100] Among them, R is the system task reliability; t is the total system operation time; n i,j is the total number of system components involved in the jth function under the i-th use case; i,j,k is the failure rate of the kth system component involved in the jth function under the i-th use case.
[0101] The event-driven real-time conversion device for three-dimensional deduction scene perspectives provided by the embodiment of the present application can dynamically evaluate the reliability of system tasks. Different from the series-parallel model of the reliability block diagram method, the UML model can more finely and comprehensively characterize the logical relationship between the system components, and is more in line with the actual state of the system. At the same time, the system task reliability modeling based on the UML model can be combined with the system operation data to dynamically predict the future reliability level of the system, ensuring the accuracy and effectiveness of the task reliability prediction.
[0102] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the real-time conversion device of the perspective of a three-dimensional deduction scene based on event-driven, since it is basically similar to the real-time conversion method embodiment of the perspective of a three-dimensional deduction scene based on event-driven, the description is relatively simple, and the relevant parts can be referred to the partial description of the real-time conversion method embodiment of the perspective of a three-dimensional deduction scene based on event-driven.
[0103] Fig. 9 This is a block diagram of a computing device provided in an embodiment of the present application. The components of the computing device 900 include but are not limited to a memory 910 and a processor 920. The processor 920 is connected to the memory 910 via a bus 930, and a database 950 is used to store data.
[0104] The computing device 900 also includes an access device 940, which enables the computing device 900 to communicate via one or more networks 960. Examples of these networks include a public switched telephone network (PSTN), a local area network (LAN), a wide area network (WAN), a personal area network (PAN), or a combination of communication networks such as the Internet. The access device 940 may include one or more of any type of network interface (e.g., a network interface card (NIC)) of wired or wireless, such as an IEEE 802.11 wireless local area network (WLAN) wireless interface, a world-wide interoperability for microwave access (Wi-MAX) interface, an Ethernet interface, a universal serial bus (USB) interface, a cellular network interface, a Bluetooth interface, and a near field communication (NFC).
[0105] In one embodiment of the present specification, the above components of the computing device 900 and Fig. 9 Other components not shown in the figure may also be connected to each other, for example, via a bus. It should be understood that Fig. 9 The computing device structure block diagram shown is only for the purpose of illustration, and is not intended to limit the scope of this specification. Those skilled in the art can add or replace other components as needed.
[0106] The computing device 900 may be any type of stationary or mobile computing device, including a mobile computer or mobile computing device (e.g., a tablet computer, a personal digital assistant, a laptop computer, a notebook computer, a netbook, etc.), a mobile phone (e.g., a smart phone), a wearable computing device (e.g., a smart watch, smart glasses, etc.), or other types of mobile devices, or a stationary computing device such as a desktop computer or a personal computer (PC). The computing device 900 may also be a mobile or stationary server.
[0107] Among them, the processor 920 is used to execute the following computer executable instructions, which, when executed by the processor, implement the steps of the above-mentioned event-driven three-dimensional deduction scene perspective real-time conversion method.
[0108] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the computing device embodiment, since it is basically similar to the event-driven three-dimensional deduction scene perspective real-time conversion method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the event-driven three-dimensional deduction scene perspective real-time conversion method embodiment.
[0109] An embodiment of the present specification also provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the steps of the above-mentioned event-driven three-dimensional deduction scene perspective real-time conversion method.
[0110] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the computer-readable storage medium embodiment, since it is basically similar to the event-driven three-dimensional deduction scene perspective real-time conversion method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the event-driven three-dimensional deduction scene perspective real-time conversion method embodiment.
[0111] An embodiment of the present specification also provides a computer program, wherein when the computer program is executed in a computer, the computer is caused to execute the steps of the above-mentioned event-driven three-dimensional deduction scene perspective real-time conversion method.
[0112] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the computer program embodiment, since it is basically similar to the event-driven three-dimensional deduction scene perspective real-time conversion method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the event-driven three-dimensional deduction scene perspective real-time conversion method embodiment.
[0113] The above is a description of a specific embodiment of the present specification. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recorded in the claims can be performed in an order different from that in the embodiments and still achieve the desired results. In addition, the processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.
[0114] The computer instructions include computer program codes, which may be in source code form, object code form, executable files or some intermediate forms, etc. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electric carrier signal, telecommunication signal and software distribution medium, etc. It should be noted that the content contained in the computer-readable medium may be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electric carrier signals and telecommunication signals.
[0115] It should be noted that the above is a description of a specific embodiment of the present specification. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recorded in the claims can be performed in an order different from that in the embodiments and still achieve the desired results. In addition, the processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required by the embodiments of the present specification.
[0116] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.
[0117] The preferred embodiments of this specification disclosed above are only used to help explain this specification. The optional embodiments do not describe all the details in detail, nor do they limit the invention to only the specific implementation methods described. Obviously, many modifications and changes can be made according to the content of the embodiments of this specification. This specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the embodiments of this specification, so that technicians in the relevant technical field can well understand and use this specification. This specification is only limited by the claims and their full scope and equivalents.
Claims
1. A method for real-time conversion of three-dimensional deduction scene perspective based on event-driven, characterized in that: include: According to the functions involved in the tasks, the system task types corresponding to the functions involved in the tasks, and the system component units, an activity diagram and a timing diagram corresponding to the target system are established, and based on the activity diagram and the timing diagram, the accumulated working time corresponding to each function of each system component unit is calculated; According to the design information of system components and combined with the reliability prediction standard, determine the failure rate corresponding to each system component; Determine the probability of occurrence of a function corresponding to each of the tasks involved in the function based on the pre-collected operation data of the target system within a preset time period, wherein the operation data carries the number of times the function is called; The system task reliability is determined based on the accumulated working time corresponding to each function of the system component unit, the failure rate, and the function occurrence probability corresponding to each task-related function.
2. The method according to claim 1, characterized in that The method further comprises: According to the system task type and the functions involved in the task, a use case diagram corresponding to the target system is established, wherein the collaborative task type carries the system operation scenario.
3. The method according to claim 1, characterized in that The determining, based on the pre-collected operation data of the target system within a preset time period, the function occurrence probability corresponding to the function involved in each task includes: According to a first calculation formula, based on the pre-collected operation data of the target system within a preset time period, the function occurrence probability corresponding to each of the tasks involved is determined, wherein the first calculation formula includes: Among them, a i,j is the probability of the jth function appearing in the i-th use case per unit time; T is the cumulative running time of the system; N i,j is the number of times the jth function is called in the i-th use case during the system running time T.
4. The method according to claim 1, characterized in that The determining of the system task reliability based on the accumulated working time corresponding to each function of the system component unit, the failure rate, and the function occurrence probability corresponding to each function involved in the task includes: Among them, R is the system task reliability; t is the total system operation time; n i,j is the total number of system components involved in the jth function under the i-th use case; i,j,k is the failure rate of the kth system component involved in the jth function under the i-th use case.
5. A real-time conversion device for three-dimensional deduction scene perspective based on event-driven, characterized in that: include: A calculation module is configured to establish an activity diagram and a timing diagram corresponding to the target system according to the functions involved in the tasks, the system task types corresponding to the functions involved in the tasks, and the system component units, and calculate the accumulated working time corresponding to each function of each system component unit based on the activity diagram and the timing diagram; The first determination module is configured to determine the failure rate corresponding to each system component unit according to the system component unit design information and the reliability prediction standard; A second determination module is configured to determine the function occurrence probability corresponding to each of the tasks involved in the function based on the pre-collected operation data of the target system within a preset time period, wherein the operation data carries the number of times the function is called; The third determination module is configured to determine the system task reliability based on the accumulated working time corresponding to each function of the system component unit, the failure rate, and the function occurrence probability corresponding to each task-related function.