Quick construction method for weapon platform-oriented electronic system digital model

Abstract

The invention discloses a weapon platform-oriented electronic system digital model rapid construction method, and belongs to the technical field of digital model construction. In order to solve the problems of high repeated development cost, long system development period and poor adaptive capacity to complex environments, developers can directly call standardized modules for configuration and integration by constructing a modular component library, development work starting from zero is avoided, the flexibility, adaptability and development efficiency of the system are improved, and the development efficiency of the system is improved. An integrated modeling framework is adopted to automatically generate models and configure modules, proper modules are automatically selected and combined based on demand parameters, it is ensured that the system design can flexibly cope with different technical demands and environment changes, comprehensive testing and verification of the system are achieved by configuring various simulation environments and working scenes, and the system design efficiency is improved. Design defects and performance bottlenecks can be identified in a short time, rapid adjustment and optimization are carried out based on results, and it is ensured that the system can stably operate in various complex environments.

Description

Technical Field

[0001] The invention relates to the technical field of digital model construction, and in particular to a method for quickly constructing a digital model for an electronic system of a weapon platform. Background Art

[0002] In the design and development process of modern weapon platforms, electronic systems, as their core components, undertake key functions such as signal processing, data transmission, and execution control. With the advancement of technology, the functions of weapon platforms are becoming increasingly complex, and the performance, reliability, and flexibility required by the system are also constantly improving. Therefore, how to efficiently and accurately design, verify, and optimize the electronic systems of weapon platforms has become an important challenge in system development.

[0003] However, there are still the following problems in actual operation: In traditional system development, developers usually need to write code and design system architecture from scratch, especially since each new project may involve different weapon platforms or electronic systems, resulting in a lot of repetitive work and high development costs. At the same time, the system development cycle is often long, especially when faced with complex weapon platform electronic systems. Designers need to invest a lot of time in manual adjustments, combinations and configurations, and it is difficult to fully consider the complex and changeable operating environment. Especially in a variety of working environments, the behavior and response of weapon platform electronic systems are often affected by environmental changes. Summary of the invention

[0004] The purpose of the present invention is to provide a method for quickly constructing a digital model of an electronic system of a weapon platform to solve the problems raised in the above-mentioned background technology.

[0005] To achieve the above object, the present invention provides the following technical solution: a method for rapidly constructing a digital model of an electronic system of a weapon platform, comprising the following steps: Demand module division: Analyze the requirements of the weapon platform electronic system, determine the key functions and performance indicators that the weapon platform electronic system needs to achieve, and divide the system functions into multiple independent and functionally clear modules based on the results of the demand analysis; Standard interface design: design standardized data interfaces and interaction protocols between modules to achieve seamless integration between different modules; Build a reusable component library, build an accumulation component library, and pre-model the functions in the electronic system of the weapon platform. The component library includes standardized hardware models and control algorithm modules; An integrated modeling framework is used to build an automated modeling framework based on system requirements, module design and component library. The automated modeling framework is used to quickly generate, configure and adjust the digital model of the electronic system of the weapon platform. The automated modeling framework automatically creates models, combines modules and configures connections between modules according to the input requirement parameters, and generates a preliminary digital model that meets the system requirements. Dynamic simulation adjustment: Real-time testing and verification of the constructed digital model based on dynamic simulation. According to different working scenarios and parameter settings of the electronic system of the weapon platform, the system operation process is simulated to identify potential design defects and performance bottlenecks, and automatically feed back to the digital model for adjustment; Model verification and optimization: multi-level and multi-dimensional model verification is performed on the constructed digital model. The model verification includes function verification, performance verification and stability verification. The verification results are automatically fed back to the digital model, and parameter optimization is performed based on the test data. Version iterative updates record the change history of each version of the digital model, and iteratively update the digital model based on new technical requirements or system upgrades.

[0006] Furthermore, the construction of the reusable component library further includes the following steps: Abstract and standardize functions in weapon platform electronic systems, including signal processing, data storage, actuator control, sensor data acquisition, and communication protocols; Each function is refined into an independent module and modularized design is performed, wherein each module has standardized input and output interfaces.

[0007] Furthermore, the construction of the reusable component library further includes the following steps: Build and organize component libraries based on standardized modules. Each module is stored in the form of a unit component, with function descriptions, interface instructions, usage methods, and configuration parameter documents attached. The component library stores the properties of each unit component in a standardized data format, establishes a version control mechanism, and records changes to the unit components in the component library; Each unit component is subjected to unit testing, which includes functional testing, boundary condition testing and performance testing. After the unit testing is completed, the qualified unit components are added to the component library.

[0008] Furthermore, the process of performing performance testing on each unit component includes the following steps: Real-time monitoring of the test running parameters generated during the performance test of each unit component, wherein the test running parameters include task execution response time, CPU occupancy, memory usage, task execution success rate and number of concurrently executed tasks; The task execution operation evaluation coefficient corresponding to each unit component is obtained by using the concurrent execution amount of each unit component and the task execution response time corresponding to each task execution; The task execution evaluation coefficient corresponding to each unit component is obtained by the following formula: Among them, R represents the task execution evaluation coefficient corresponding to each unit component; n represents the number of tasks executed by each unit component; X i represents the task execution response time corresponding to the i-th task execution; X c P represents the preset task execution response time reference coefficient; cmax and P nmax Indicates the CPU occupancy and memory usage corresponding to the maximum task execution response time among n tasks; X pmax Indicates the task execution response time corresponding to the maximum CPU usage of n tasks; X pmin Indicates the task execution response time corresponding to the minimum CPU usage value in the execution of n tasks; P min Indicates the minimum CPU usage of n tasks; P max Indicates the maximum CPU usage during the execution of n tasks; Comparing the task execution operation evaluation coefficient corresponding to each unit component with a preset operation evaluation coefficient threshold; When the task execution operation evaluation coefficient corresponding to each unit component is lower than a preset operation evaluation coefficient threshold, it is determined that the unit component has an operation abnormality risk, and the unit component with the operation abnormality risk is used as a target unit component; The CPU occupancy rate, memory usage rate, task execution success rate and number of concurrently executed tasks of the target unit component are used to determine performance anomalies.

[0009] Furthermore, the CPU occupancy rate, memory usage rate, task execution success rate and number of concurrently executed tasks of the target unit component are used to determine performance anomalies, including: The CPU occupancy rate and memory usage rate corresponding to each task executed by the target unit component are used to obtain a first abnormality determination coefficient corresponding to the execution of each task; The first abnormality determination coefficient corresponding to executing each task is obtained by the following formula: Among them, A 01 Indicates the first abnormality determination coefficient corresponding to each task; R m Represents the task execution evaluation coefficient corresponding to the target unit component; R yIndicates the preset operation evaluation coefficient threshold; P c and P n Indicates the CPU usage and memory usage corresponding to each task; Obtain a second abnormality determination coefficient corresponding to each task by using the task execution success rate and the number of concurrently executed tasks corresponding to each task executed by the target unit component; The second abnormality determination coefficient corresponding to each task is obtained by the following formula: Among them, A 02 Indicates the second abnormality determination coefficient corresponding to each task; R m Represents the task execution evaluation coefficient corresponding to the target unit component; R y represents the preset operation evaluation coefficient threshold; S represents the task execution success rate of the corresponding target unit component when each task is completed; N represents the number of concurrent tasks executed when each task is executed; T s Indicates the task execution response time corresponding to each task execution; T z Indicates the median response time of all tasks completed corresponding to each task execution completion moment; Compare the first abnormality determination coefficient corresponding to the execution of each task with a preset first abnormality determination coefficient threshold, and obtain the number of tasks whose first abnormality determination coefficient exceeds the first abnormality determination coefficient threshold as the first task number; Compare the second abnormality determination coefficient corresponding to the execution of each task with a preset second abnormality determination coefficient threshold, and obtain the number of tasks whose second abnormality determination coefficient exceeds the second abnormality determination coefficient threshold as the second task number; Comparing the first task quantity with a preset first quantity threshold; Comparing the second task quantity with a preset second quantity threshold; When the number of the first tasks exceeds a preset first number threshold, or the number of the second tasks exceeds a preset second number threshold, it is determined that the target unit component has a performance abnormality, and an abnormality alarm is issued; When the number of the first tasks does not exceed the preset first quantity threshold, and the number of the second tasks does not exceed the preset second quantity threshold, the first abnormality determination coefficient and the second abnormality determination coefficient corresponding to each task are used to perform performance abnormality determination on each target unit component.

[0010] Further, when the number of the first tasks does not exceed a preset first number threshold, and the number of the second tasks does not exceed a preset second number threshold, a performance abnormality determination is performed on each target unit component using a first abnormality determination coefficient and a second abnormality determination coefficient corresponding to each task, including: When the number of the first tasks does not exceed a preset first number threshold, and the number of the second tasks does not exceed a preset second number threshold, calling a first abnormality determination coefficient and a second abnormality determination coefficient corresponding to each task; comparing the first abnormality determination coefficient and the second abnormality determination coefficient; Retrieve the task corresponding to the first abnormality determination coefficient being greater than the second abnormality determination coefficient as the target task; Extracting a first abnormality determination coefficient and a second abnormality determination coefficient corresponding to the target task; Obtaining a comprehensive determination coefficient of a target unit component by using a first abnormality determination coefficient and a second abnormality determination coefficient corresponding to the target task; The comprehensive determination coefficient of the target unit component is obtained by the following formula: Where E represents the comprehensive determination coefficient of the target unit component; m represents the total number of target tasks contained in the target unit component; A 01i A represents the first abnormality determination coefficient corresponding to the i-th target task; 02i represents the second abnormality determination coefficient corresponding to the i-th target task; S mi represents the task execution success rate corresponding to the completion of the i-th target task; P ci and P ni represents the CPU occupancy and memory usage corresponding to the i-th target task; N i Indicates the number of concurrently executed tasks corresponding to the i-th target task; N z Indicates the total execution task volume of the target unit component; comparing the comprehensive determination coefficient of the target unit component with a preset comprehensive coefficient threshold; When the comprehensive determination coefficient of the target unit component exceeds a preset comprehensive coefficient threshold, the target unit component has a performance abnormality and an abnormality alarm is issued.

[0011] Furthermore, the integrated modeling framework further includes the following steps: Design and define the functions of the integrated automated modeling framework, including automatic generation of models, module combination, module connection configuration, and parameter setting; The automated modeling framework can automatically select appropriate modules based on the input demand parameters and automatically generate a preliminary system model according to preset rules; The automated modeling framework seamlessly integrates modules through a unified interface protocol.

[0012] Furthermore, the integrated modeling framework further includes the following steps: Specific design requirement parameters are input, including processing power, data rate, and response time. The automated modeling framework automatically selects and configures appropriate modules according to the requirement parameters and generates a preliminary digital model.

[0013] Furthermore, the dynamic simulation adjustment further includes the following steps: Configuring a simulation environment and a work scenario, wherein the work scenario is set based on the operating environment of the weapon platform, and configuring simulation parameters according to different mission requirements, wherein the simulation parameters include sensor input, target dynamics, and operator input; The simulation environment is capable of switching between different working states, including standby, launch and failure, to simulate the performance of the electronic system of the weapon platform under various working conditions.

[0014] Furthermore, the dynamic simulation adjustment further includes the following steps: After configuring the simulation environment, the digital model is dynamically tested and verified through real-time simulation; Simulate the system's response under different working conditions based on real-time simulation, record and verify the interaction between modules, data transmission and actuator delays; The simulation results are fed back to the automated modeling framework in real time to evaluate system performance, identify design defects and potential risks based on the simulation results.

[0015] Furthermore, the dynamic simulation adjustment further includes the following steps: According to the design defects and performance bottlenecks found during the simulation process, the feedback results are automatically transmitted to the digital model to adjust and optimize the model parameters; The optimization measures include adjusting the control algorithm, optimizing the data transmission rate, adding redundant modules, and performing simulation verification again after the optimization measures are modified.

[0016] Compared with the prior art, the present invention has the following beneficial effects: 1. The present invention can comprehensively and accurately grasp the actual performance of the unit components during operation by real-time monitoring of key performance indicators such as the task execution response time, CPU occupancy, memory usage, task execution success rate, and number of concurrently executed tasks of each unit component, which helps to timely discover potential performance problems. By calculating the task execution operation evaluation coefficient (R) of each unit component, the overall efficiency of the unit component during the task execution process can be quantitatively evaluated. This coefficient comprehensively considers factors such as the task execution response time, CPU and memory usage, and the success rate of task execution, so it can more accurately reflect the actual operating status of the unit component. By comparing the calculated task execution operation evaluation coefficient with the preset operation evaluation coefficient threshold, those unit components whose operating efficiency is lower than the expected standard can be discovered in time, which helps to reduce the system downtime caused by unit component failure, improve the overall stability and reliability of the system, improve the system's operating efficiency, and reduce the system's operating cost and maintenance cost.

[0017] 2. The present invention calculates the first abnormality determination coefficient and the second abnormality determination coefficient corresponding to each task. This technical solution can more finely evaluate the performance of the target unit component when executing each task. This refined judgment helps to more accurately identify which tasks or scenarios may have performance problems with the unit component. The multi-dimensional performance analysis can more comprehensively reflect the performance status of the unit component and help to find potential performance bottlenecks or abnormal points. The dynamic threshold comparison method can more flexibly adapt to the performance requirements in different scenarios and improve the accuracy and reliability of the judgment. The alarm and positioning functions help to quickly respond to and handle performance problems and reduce system downtime caused by unit component failures. By continuously monitoring and determining the performance anomalies of the unit components, the technical solution can promptly discover and handle potential performance problems, thereby improving the overall stability and reliability of the system. This helps to ensure that the system can still maintain good operating performance in complex scenarios such as high concurrency and high load.

[0018] 3. The present invention constructs a highly modular component library by abstracting and standardizing the functions in the weapon platform electronic system. Developers can directly call and combine these standardized modules when designing the system. Developers do not need to write code and design architecture from scratch. They only need to use existing standard unit components and configure and integrate them according to requirements. The same unit component can be repeatedly used in different weapon platforms or different electronic systems without the need to redesign and implement each system. In the future upgrade or expansion of the system, the function can be expanded or replaced with minimal modification cost, which greatly improves the flexibility and adaptability of the system. Developers can reduce repetitive work for each project, greatly shorten the system development cycle, reduce the workload of developers, and thus reduce development costs.

[0019] 4. The integrated automated modeling framework of the present invention significantly shortens the system design cycle by automatically generating models and configuring modules. According to the input demand parameters, the framework can automatically select and combine appropriate modules, reducing manual intervention, allowing designers to quickly complete the initial construction of complex systems, thereby greatly improving development efficiency. The architecture and functions of the model can be flexibly adjusted according to different performance indicators to ensure that the final system model meets the expected functions and performance requirements, and can quickly respond to changes in technical requirements and environmental conditions. By automatically selecting and configuring modules, the system design can be quickly adjusted to adapt to new requirements. The framework avoids the tedious process of redesign and a large number of manual adjustments, ensuring that the system can maintain a high degree of adaptability and flexibility in the event of changes in technology and the working environment, and meet the needs of different stages and tasks.

[0020] 5. By configuring a variety of simulation environments and work scenarios, the present invention can fully test and verify the electronic system of the weapon platform during the design phase to ensure that it can cope with various complex operating conditions. The real-time simulation function enables designers to dynamically test the digital model in a short period of time, simulate the system's response under different working conditions, quickly identify potential design defects, performance bottlenecks and system failure points, and significantly speed up the system verification process. The simulation results are transmitted to the modeling framework in real time, the system performance and potential risks are evaluated based on the results, and the model is adjusted and optimized. The dynamic simulation technology provides flexible adjustment capabilities, and the simulation parameters can be modified in real time according to the requirements of different work tasks or operating environments, so that the system can quickly adapt to different environments and task requirements. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 The figure is a flow chart of the method for rapidly constructing a digital model of an electronic system for a weapon platform according to the present invention. DETAILED DESCRIPTION

[0022] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0023] In order to solve the problem in traditional system development, developers usually need to write code and design system architecture from scratch, especially each new project may involve different weapon platforms or electronic systems, resulting in a lot of repetitive work and high development costs. At the same time, the system development cycle is often long, especially when facing complex weapon platform electronic systems. Designers need to invest a lot of time in manual adjustment, combination and configuration, and it is difficult to fully consider the complex and changeable operating environment. In particular, in a variety of working environments, the behavior and response of weapon platform electronic systems are often affected by environmental changes. Technical issues, please refer to Figure 1 , the present invention provides the following technical solutions: The method for rapidly constructing a digital model of an electronic system for a weapon platform includes the following steps: Demand module division: Analyze the requirements of the weapon platform electronic system, determine the key functions and performance indicators that the weapon platform electronic system needs to achieve, and divide the system functions into multiple independent and functionally clear modules based on the results of the demand analysis; Standard interface design: design standardized data interfaces and interaction protocols between modules to achieve seamless integration between different modules; Build a reusable component library, build an accumulation component library, and pre-model the functions in the electronic system of the weapon platform. The component library includes standardized hardware models and control algorithm modules; An integrated modeling framework is used to build an automated modeling framework based on system requirements, module design and component library. The automated modeling framework is used to quickly generate, configure and adjust the digital model of the electronic system of the weapon platform. The automated modeling framework automatically creates models, combines modules and configures connections between modules according to the input requirement parameters, and generates a preliminary digital model that meets the system requirements. Dynamic simulation adjustment: Real-time testing and verification of the constructed digital model based on dynamic simulation. According to different working scenarios and parameter settings of the electronic system of the weapon platform, the system operation process is simulated to identify potential design defects and performance bottlenecks, and automatically feed back to the digital model for adjustment; Model verification and optimization: multi-level and multi-dimensional model verification is performed on the constructed digital model. The model verification includes function verification, performance verification and stability verification. The verification results are automatically fed back to the digital model, and parameter optimization is performed based on the test data. Version iterative updates record the change history of each version of the digital model, and iteratively update the digital model based on new technical requirements or system upgrades.

[0024] In the above embodiment, the complex weapon platform electronic system is effectively disassembled into multiple modules with clear functions and relatively independent functions through the division of required modules and the design of standard interfaces, which simplifies the system design process. By building a reusable component library, a variety of standardized hardware models and control algorithm modules are accumulated, avoiding repeated design and modeling, thereby reducing the development cycle and cost. The dynamic simulation adjustment link simulates different working scenarios and parameter settings to comprehensively test the system operation process, effectively identify potential design defects and performance bottlenecks, and can discover problems at an early stage and make timely adjustments. It can also verify the performance and stability of the system through simulation before deployment. Through the automatic feedback mechanism, the system can optimize the model according to real-time results during the simulation test, which speeds up the design verification and ensures that the model is more accurate and stable after each iteration.

[0025] Building a reusable component library also includes the following steps: Abstract and standardize functions in weapon platform electronic systems, including signal processing, data storage, actuator control, sensor data acquisition, and communication protocols; Each function is refined into an independent module and modularized design is performed, wherein each module has standardized input and output interfaces.

[0026] Build and organize component libraries based on standardized modules. Each module is stored in the form of a unit component, with function descriptions, interface instructions, usage methods, and configuration parameter documents attached. The component library stores the properties of each unit component in a standardized data format, establishes a version control mechanism, and records changes to the unit components in the component library; Each unit component is subjected to unit testing, which includes functional testing, boundary condition testing and performance testing. After the unit testing is completed, the qualified unit components are added to the component library.

[0027] In the above embodiment, by abstracting and standardizing the functions in the weapon platform electronic system and building a highly modular component library, developers can directly call and combine these standardized modules when designing the system. Developers do not need to write code and design architecture from scratch, but only need to use existing standard unit components and configure and integrate them according to requirements.

[0028] In the above embodiment, the design principles of the component library are universality and standardization. The same unit component can be repeatedly used in different weapon platforms or different electronic systems without the need to be redesigned and implemented for each system. This allows the system to expand or replace functions with minimal modification costs during future upgrades or expansions, greatly improving the flexibility and adaptability of the system. Developers can reduce repetitive work for each project, significantly shortening the system development cycle, reducing the workload of developers, and thus reducing development costs.

[0029] Specifically, the process of performing performance testing on each unit component includes the following steps: Real-time monitoring of the test running parameters generated during the performance test of each unit component, wherein the test running parameters include task execution response time, CPU occupancy, memory usage, task execution success rate and number of concurrently executed tasks; The task execution operation evaluation coefficient corresponding to each unit component is obtained by using the concurrent execution amount of each unit component and the task execution response time corresponding to each task execution; The task execution evaluation coefficient corresponding to each unit component is obtained by the following formula: Among them, R represents the task execution evaluation coefficient corresponding to each unit component; n represents the number of tasks executed by each unit component; X i represents the task execution response time corresponding to the i-th task execution; X c P represents the preset task execution response time reference coefficient; cmax and P nmax Indicates the CPU occupancy and memory usage corresponding to the maximum task execution response time among n tasks; X pmax Indicates the task execution response time corresponding to the maximum CPU usage of n tasks; X pmin Indicates the task execution response time corresponding to the minimum CPU usage value in the execution of n tasks; P min Indicates the minimum CPU usage of n tasks; P max Indicates the maximum CPU usage during the execution of n tasks; Comparing the task execution operation evaluation coefficient corresponding to each unit component with a preset operation evaluation coefficient threshold; When the task execution operation evaluation coefficient corresponding to each unit component is lower than a preset operation evaluation coefficient threshold, it is determined that the unit component has an operation abnormality risk, and the unit component with the operation abnormality risk is used as a target unit component; The CPU occupancy rate, memory usage rate, task execution success rate and number of concurrently executed tasks of the target unit component are used to determine performance anomalies.

[0030] The technical effect of the above technical solution is: by real-time monitoring of key performance indicators such as task execution response time, CPU occupancy, memory usage, task execution success rate and number of concurrent task executions of each unit component, the actual performance of the unit component during operation can be fully and accurately grasped. This comprehensive monitoring helps to discover potential performance problems in a timely manner. By calculating the task execution operation evaluation coefficient (R) of each unit component, the overall efficiency of the unit component during task execution can be quantitatively evaluated. This coefficient comprehensively considers factors such as task execution response time, CPU and memory usage, and task execution success rate, so it can more accurately reflect the actual operating status of the unit component.

[0031] By comparing the calculated task execution evaluation coefficient corresponding to each unit component with the preset operation evaluation coefficient threshold, those unit components whose operation efficiency is lower than the expected standard can be discovered in time. This timely anomaly detection helps to reduce system downtime caused by unit component failure and improve the overall stability and reliability of the system. When it is found that there is a risk of abnormal operation of a unit component, the technical solution can further use parameters such as the CPU occupancy rate, memory usage rate, task execution success rate and number of concurrent task executions of the target unit component to determine performance anomalies. This refined analysis helps to quickly locate performance bottlenecks and provide strong support for subsequent optimization and improvement. By continuously monitoring and evaluating the performance indicators of unit components and combining the results of performance anomaly determination, the system can be continuously optimized and adjusted. This can not only improve the operating efficiency of the system, but also reduce the operating and maintenance costs of the system.

[0032] In summary, the technical effects of this technical solution in terms of performance indicators are mainly reflected in comprehensive monitoring, accurate evaluation, timely detection of anomalies, accurate positioning of bottlenecks, and optimization of system performance. These technical effects together constitute the powerful advantages of this technical solution in improving system stability and reliability.

[0033] Specifically, the CPU occupancy rate, memory usage rate, task execution success rate and number of concurrently executed tasks of the target unit component are used to determine performance anomalies, including: The CPU occupancy rate and memory usage rate corresponding to each task executed by the target unit component are used to obtain a first abnormality determination coefficient corresponding to the execution of each task; The first abnormality determination coefficient corresponding to executing each task is obtained by the following formula: Among them, A01 Indicates the first abnormality determination coefficient corresponding to each task; R m Represents the task execution evaluation coefficient corresponding to the target unit component; R y Indicates the preset operation evaluation coefficient threshold; P c and P n Indicates the CPU usage and memory usage corresponding to each task; Obtain a second abnormality determination coefficient corresponding to each task by using the task execution success rate and the number of concurrently executed tasks corresponding to each task executed by the target unit component; The second abnormality determination coefficient corresponding to each task is obtained by the following formula: Among them, A 02 Indicates the second abnormality determination coefficient corresponding to each task; R m Represents the task execution evaluation coefficient corresponding to the target unit component; R y represents the preset operation evaluation coefficient threshold; S represents the task execution success rate of the corresponding target unit component when each task is completed; N represents the number of concurrent tasks executed when each task is executed; T s Indicates the task execution response time corresponding to each task execution; T z Indicates the median response time of all tasks completed corresponding to each task execution completion moment; Compare the first abnormality determination coefficient corresponding to the execution of each task with a preset first abnormality determination coefficient threshold, and obtain the number of tasks whose first abnormality determination coefficient exceeds the first abnormality determination coefficient threshold as the first task number; Compare the second abnormality determination coefficient corresponding to the execution of each task with a preset second abnormality determination coefficient threshold, and obtain the number of tasks whose second abnormality determination coefficient exceeds the second abnormality determination coefficient threshold as the second task number; Comparing the first task quantity with a preset first quantity threshold; Comparing the second task quantity with a preset second quantity threshold; When the number of the first tasks exceeds a preset first number threshold, or the number of the second tasks exceeds a preset second number threshold, it is determined that the target unit component has a performance abnormality, and an abnormality alarm is issued; When the number of the first tasks does not exceed the preset first quantity threshold, and the number of the second tasks does not exceed the preset second quantity threshold, the first abnormality determination coefficient and the second abnormality determination coefficient corresponding to each task are used to perform performance abnormality determination on each target unit component.

[0034] The technical effect of the above technical solution is: by calculating the first abnormality determination coefficient and the second abnormality determination coefficient corresponding to each task, the technical solution can more finely evaluate the performance of the target unit component when executing each task. This refined determination helps to more accurately identify which tasks or scenarios may have performance problems with the unit component. The technical solution combines multiple performance indicators such as CPU occupancy, memory usage, task execution success rate, and number of concurrent task executions for abnormality determination. This multi-dimensional performance analysis can more comprehensively reflect the performance status of the unit component and help to discover potential performance bottlenecks or abnormal points. By comparing the calculated abnormality determination coefficient with the preset threshold, the technical solution can dynamically determine whether the performance of the unit component is abnormal. This dynamic threshold comparison method can more flexibly adapt to the performance requirements in different scenarios and improve the accuracy and reliability of the determination. When it is determined that the target unit component has performance abnormalities, the technical solution can issue an abnormality alarm and indicate the number of tasks or scenarios where the abnormality occurs. This alarm and positioning function helps to quickly respond to and handle performance problems and reduce system downtime caused by unit component failures. After determining that the target unit component has performance anomalies, the technical solution can also use the first anomaly determination coefficient and the second anomaly determination coefficient corresponding to each task to further determine the performance anomalies of each target unit component. This determination result can provide guidance for subsequent performance optimization and help developers or operation and maintenance personnel find the direction and focus of performance optimization. By continuously monitoring and determining the performance anomalies of unit components, the technical solution can promptly discover and handle potential performance problems, thereby improving the overall stability and reliability of the system. This helps ensure that the system can still maintain good operating performance in complex scenarios such as high concurrency and high load.

[0035] In summary, the technical effects of this technical solution in terms of performance indicators are mainly reflected in refined anomaly determination, multi-dimensional performance analysis, dynamic threshold comparison, anomaly alarm and positioning, performance optimization guidance, and improved system stability. These technical effects together constitute the powerful advantages of this technical solution in improving system performance and reliability.

[0036] Specifically, when the number of the first tasks does not exceed a preset first number threshold, and the number of the second tasks does not exceed a preset second number threshold, a performance abnormality determination is performed on each target unit component using a first abnormality determination coefficient and a second abnormality determination coefficient corresponding to each task, including: When the number of the first tasks does not exceed a preset first number threshold, and the number of the second tasks does not exceed a preset second number threshold, calling a first abnormality determination coefficient and a second abnormality determination coefficient corresponding to each task; comparing the first abnormality determination coefficient and the second abnormality determination coefficient; Retrieve the task corresponding to the first abnormality determination coefficient being greater than the second abnormality determination coefficient as the target task; Extracting a first abnormality determination coefficient and a second abnormality determination coefficient corresponding to the target task; Obtaining a comprehensive determination coefficient of a target unit component by using a first abnormality determination coefficient and a second abnormality determination coefficient corresponding to the target task; The comprehensive determination coefficient of the target unit component is obtained by the following formula: Where E represents the comprehensive determination coefficient of the target unit component; m represents the total number of target tasks contained in the target unit component; A 01i A represents the first abnormality determination coefficient corresponding to the i-th target task; 02i represents the second abnormality determination coefficient corresponding to the i-th target task; S mi represents the task execution success rate corresponding to the completion of the i-th target task; P ci and P ni represents the CPU occupancy and memory usage corresponding to the i-th target task; N i Indicates the number of concurrently executed tasks corresponding to the i-th target task; N z Indicates the total execution task volume of the target unit component; comparing the comprehensive determination coefficient of the target unit component with a preset comprehensive coefficient threshold; When the comprehensive determination coefficient of the target unit component exceeds a preset comprehensive coefficient threshold, the target unit component has a performance abnormality and an abnormality alarm is issued.

[0037] The technical effect of the above technical solution is: by comparing the first abnormality determination coefficient and the second abnormality determination coefficient of each task, and calling the task with the first abnormality determination coefficient greater than the second abnormality determination coefficient as the target task, the technical solution can more accurately identify the task that has a greater impact on the performance of the target unit component. This comprehensive performance evaluation method helps to more comprehensively reflect the performance status of the target unit component. The comprehensive determination coefficient of the target unit component is calculated by combining the first abnormality determination coefficient and the second abnormality determination coefficient corresponding to the target task, and combining multiple performance indicators such as the task execution success rate, CPU occupancy rate, memory usage rate, and the number of concurrent executions of tasks. This refined abnormality determination method can more accurately determine whether the target unit component has performance abnormalities, thereby improving the accuracy of the determination. By comparing the calculated comprehensive determination coefficient of the target unit component with the preset comprehensive coefficient threshold, the technical solution can dynamically determine whether the performance of the target unit component is abnormal. This dynamic threshold comparison method can more flexibly adapt to the performance requirements in different scenarios, thereby improving the flexibility and reliability of the determination. When it is determined that the target unit component has performance abnormalities, the technical solution can issue an abnormality alarm and point out the specific target task where the abnormality occurs. This alarm and positioning function helps to quickly respond to and handle performance issues, reducing system downtime caused by unit component failures. The comprehensive determination coefficient of the target unit component not only reflects the overall performance status of the target unit component, but also provides guidance for subsequent performance optimization. By analyzing the composition and changes of the comprehensive determination coefficient of the target unit component, developers or operation and maintenance personnel can find the direction and focus of performance optimization and improve system performance. By continuously monitoring and determining the performance anomalies of the target unit components, this technical solution can promptly discover and handle potential performance problems, thereby improving the overall stability and reliability of the system. This helps ensure that the system can still maintain good operating performance in complex scenarios such as high concurrency and high load.

[0038] In summary, the technical effects of this technical solution in terms of performance indicators are mainly reflected in comprehensive performance evaluation, refined abnormality determination, dynamic threshold comparison, abnormality alarm and positioning, performance optimization guidance, and improved system stability. These technical effects together constitute the powerful advantages of this technical solution in improving system performance and reliability, and help ensure the stable operation and efficient performance of the system.

[0039] The integrated modeling framework further includes the following steps: Design and define the functions of the integrated automated modeling framework, including automatic generation of models, module combination, module connection configuration, and parameter setting; The automated modeling framework can automatically select appropriate modules based on the input demand parameters and automatically generate a preliminary system model according to preset rules; The automated modeling framework seamlessly integrates modules through a unified interface protocol.

[0040] Specific design requirement parameters are input, including processing power, data rate, and response time. The automated modeling framework automatically selects and configures appropriate modules according to the requirement parameters and generates a preliminary digital model.

[0041] In the above embodiment, the integrated automated modeling framework greatly shortens the system design cycle by automatically generating models and configuring modules, automatically selecting and combining appropriate modules according to the input design requirement parameters, reducing manual intervention, and enabling designers to complete the initial construction of complex systems in a very short time, thereby greatly improving the efficiency of the entire development process. Especially in the design of high-precision and high-reliability systems such as weapon platforms, the rapid generation and verification of preliminary digital models enables the team to quickly enter the subsequent optimization and verification stages.

[0042] In the above embodiment, the integrated automated modeling framework can automatically select and configure appropriate modules according to the demand parameters. The automated modeling framework can flexibly adjust the architecture and functions of the model according to different performance indicators, so that the final model can meet the expected standards in terms of function and performance. After inputting the design demand parameters, the automated modeling framework can automatically select and configure appropriate modules and generate a preliminary digital model, so that the modeling framework can flexibly adapt to changes in different requirements and quickly adjust the model design to meet new requirements. For weapon platform electronic systems, technical requirements and environmental conditions often change. The integrated automated modeling framework can respond quickly to these changes and adjust the system model according to the new demand parameters, avoiding the tedious process of redesign and a large number of manual adjustments. Therefore, it can maintain high adaptability and flexibility in a changing technical and working environment to meet the needs of different stages and tasks.

[0043] Dynamic simulation adjustment specifically includes the following steps: Configuring a simulation environment and a work scenario, wherein the work scenario is set based on the operating environment of the weapon platform, and configuring simulation parameters according to different mission requirements, wherein the simulation parameters include sensor input, target dynamics, and operator input; The simulation environment is capable of switching between different working states, including standby, launch and failure, to simulate the performance of the electronic system of the weapon platform under various working conditions.

[0044] After configuring the simulation environment, the digital model is dynamically tested and verified through real-time simulation; Simulate the system's response under different working conditions based on real-time simulation, record and verify the interaction between modules, data transmission and actuator delays; The simulation results are fed back to the automated modeling framework in real time to evaluate system performance, identify design defects and potential risks based on the simulation results.

[0045] According to the design defects and performance bottlenecks found during the simulation process, the feedback results are automatically transmitted to the digital model to adjust and optimize the model parameters; The optimization measures include adjusting the control algorithm, optimizing the data transmission rate, adding redundant modules, and performing simulation verification again after the optimization measures are modified.

[0046] In the above embodiment, by configuring the simulation environment and working scenarios, especially the simulation of different operating environments, the electronic system of the weapon platform can be comprehensively tested and verified, so that the system can be fully verified in the face of various complex and changing operating conditions during the design phase. The real-time simulation function enables designers to conduct comprehensive dynamic testing of digital models in a short period of time, simulate the response of the system under different working conditions in real time, and record key parameters such as interaction between modules, data transmission, and actuator delays, thereby speeding up system verification and quickly discovering potential design defects, performance bottlenecks, and system failure points.

[0047] In the above embodiment, during the dynamic simulation process, the system's feedback mechanism can transmit the simulation results to the automated modeling framework in real time, and evaluate the system's performance and potential risks based on these results. This closed-loop feedback system allows the model design to be adjusted and optimized in a timely manner after each simulation test. For example, when the system performs poorly under a certain condition, the simulation results can reveal performance bottlenecks or design defects. The automated modeling framework can automatically adjust the control algorithm, optimize the data transmission rate, and even add redundant modules to ensure system stability based on the feedback results. This automatic feedback and optimization mechanism supports the continuous improvement of the system through multiple simulations and iterations, ensuring that the final design meets the high requirements in actual use.

[0048] In the above embodiment, through the real-time feedback of simulation results, potential risks and defects can be identified and resolved before the system is put into use, greatly reducing the operational risk of the system in actual deployment. Weapon platform electronic systems usually need to have extremely high reliability and response speed, and dynamic simulation can simulate the performance of the system under different mission requirements, ensuring that the various functions and performance indicators of the system can operate stably under various conditions. In addition, by optimizing control algorithms and adding redundant modules, the performance of the system can be effectively improved to ensure that it can respond quickly and effectively perform tasks in complex environments.

[0049] In the above embodiments, dynamic simulation adjustment can not only perform instant optimization based on actual simulation results, but also adjust simulation parameters according to changing needs. For example, the requirements of the electronic system of a weapon platform may be different under different work tasks or operating environments. Dynamic simulation provides a flexible way to adjust the simulation environment and parameter configuration in real time according to new task requirements and quickly evaluate the performance of the system in the new environment. This flexibility enables the system to quickly adapt to different work environments and task requirements and achieve optimal performance in different operating modes.

[0050] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical scheme and inventive concept of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. A method for rapidly constructing a digital model of an electronic system for a weapon platform, characterized in that: The following steps are involved: Demand module division: Analyze the requirements of the weapon platform electronic system, determine the key functions and performance indicators that the weapon platform electronic system needs to achieve, and divide the system functions into multiple independent and functionally clear modules based on the results of the demand analysis; Standard interface design: design standardized data interfaces and interaction protocols between modules to achieve seamless integration between different modules; Build a reusable component library, build an accumulation component library, and pre-model the functions in the electronic system of the weapon platform. The component library includes standardized hardware models and control algorithm modules; An integrated modeling framework is used to build an automated modeling framework based on system requirements, module design and component library. The automated modeling framework is used to quickly generate, configure and adjust the digital model of the electronic system of the weapon platform. The automated modeling framework automatically creates models, combines modules and configures connections between modules according to the input requirement parameters, and generates a preliminary digital model that meets the system requirements. Dynamic simulation adjustment: Real-time testing and verification of the constructed digital model based on dynamic simulation. According to different working scenarios and parameter settings of the electronic system of the weapon platform, the system operation process is simulated to identify potential design defects and performance bottlenecks, and automatically feed back to the digital model for adjustment; Model verification and optimization: multi-level and multi-dimensional model verification is performed on the constructed digital model. The model verification includes function verification, performance verification and stability verification. The verification results are automatically fed back to the digital model, and parameter optimization is performed based on the test data. Version iterative update records the change history of each version of the digital model and performs iterative updates of the digital model.

2. The method for rapidly constructing a digital model of a weapon platform electronic system as claimed in claim 1, characterized in that: The construction of the reusable component library specifically includes the following steps: Abstract and standardize functions in weapon platform electronic systems, including signal processing, data storage, actuator control, sensor data acquisition, and communication protocols; Each function is refined into an independent module and modularized design is performed, wherein each module has standardized input and output interfaces.

3. The method for rapidly constructing a digital model of a weapon platform electronic system as claimed in claim 2, characterized in that: The construction of the reusable component library specifically includes the following steps: Build and organize component libraries based on standardized modules. Each module is stored in the form of a unit component, with function descriptions, interface instructions, usage methods, and configuration parameter documents attached. The component library stores the properties of each unit component in a standardized data format, establishes a version control mechanism, and records changes to the unit components in the component library; Each unit component is subjected to unit testing, which includes functional testing, boundary condition testing and performance testing. After the unit testing is completed, the qualified unit components are added to the component library.

4. The method for rapidly constructing a digital model of a weapon platform electronic system as claimed in claim 3, characterized in that: The process of performance testing each unit component includes the following steps: Real-time monitoring of the test running parameters generated during the performance test of each unit component, wherein the test running parameters include task execution response time, CPU occupancy, memory usage, task execution success rate and number of concurrently executed tasks; The task execution operation evaluation coefficient corresponding to each unit component is obtained by using the concurrent execution amount of each unit component and the task execution response time corresponding to each task execution; The task execution evaluation coefficient corresponding to each unit component is obtained by the following formula: Among them, R represents the task execution evaluation coefficient corresponding to each unit component; n represents the number of tasks executed by each unit component; X i represents the task execution response time corresponding to the i-th task execution; X c P represents the preset task execution response time reference coefficient; cmax and P nmax Indicates the CPU occupancy and memory usage corresponding to the maximum task execution response time among n tasks; X pmax Indicates the task execution response time corresponding to the maximum CPU usage of n tasks; X pmin Indicates the task execution response time corresponding to the minimum CPU usage value in the execution of n tasks; P min Indicates the minimum CPU usage of n tasks; P max Indicates the maximum CPU usage during the execution of n tasks; Comparing the task execution operation evaluation coefficient corresponding to each unit component with a preset operation evaluation coefficient threshold; When the task execution operation evaluation coefficient corresponding to each unit component is lower than a preset operation evaluation coefficient threshold, it is determined that the unit component has an operation abnormality risk, and the unit component with the operation abnormality risk is used as a target unit component; The CPU occupancy rate, memory usage rate, task execution success rate and number of concurrently executed tasks of the target unit component are used to determine performance anomalies.

5. The method for rapidly constructing a digital model of a weapon platform electronic system as claimed in claim 4, characterized in that: The CPU occupancy rate, memory usage rate, task execution success rate and number of concurrently executed tasks of the target unit component are used to determine performance anomalies, including: The CPU occupancy rate and memory usage rate corresponding to each task executed by the target unit component are used to obtain a first abnormality determination coefficient corresponding to the execution of each task; The first abnormality determination coefficient corresponding to executing each task is obtained by the following formula: Among them, A 01 Indicates the first abnormality determination coefficient corresponding to each task; R m Represents the task execution evaluation coefficient corresponding to the target unit component; R y Indicates the preset operation evaluation coefficient threshold; P c and P n Indicates the CPU usage and memory usage corresponding to each task; Obtain a second abnormality determination coefficient corresponding to each task by using the task execution success rate and the number of concurrently executed tasks corresponding to each task executed by the target unit component; The second abnormality determination coefficient corresponding to each task is obtained by the following formula: Among them, A 02 represents the second abnormality determination coefficient corresponding to the execution of each task; S represents the task execution success rate of the corresponding target unit component when each task is completed; N represents the number of concurrent tasks executed when each task is executed; T s Indicates the task execution response time corresponding to each task execution; T z Indicates the median response time of all tasks completed corresponding to each task execution completion moment; Compare the first abnormality determination coefficient with a preset first abnormality determination coefficient threshold, and obtain the number of tasks whose first abnormality determination coefficient exceeds the first abnormality determination coefficient threshold as the first task number; Compare the second abnormality determination coefficient with a preset second abnormality determination coefficient threshold, and obtain the number of tasks whose second abnormality determination coefficient exceeds the second abnormality determination coefficient threshold as the second task number; Comparing the first task quantity with a preset first quantity threshold; Comparing the second task quantity with a preset second quantity threshold; When the number of the first tasks exceeds a preset first number threshold, or the number of the second tasks exceeds a preset second number threshold, it is determined that the target unit component has a performance abnormality, and an abnormality alarm is issued; When the number of the first tasks does not exceed the preset first quantity threshold, and the number of the second tasks does not exceed the preset second quantity threshold, the first abnormality determination coefficient and the second abnormality determination coefficient corresponding to each task are used to perform performance abnormality determination on each target unit component.

6. The method for rapidly constructing a digital model of a weapon platform electronic system as claimed in claim 5, characterized in that: When the number of the first tasks does not exceed a preset first number threshold, and the number of the second tasks does not exceed a preset second number threshold, a performance abnormality determination is performed on each target unit component using a first abnormality determination coefficient and a second abnormality determination coefficient corresponding to each task, including: When the number of the first tasks does not exceed a preset first number threshold, and the number of the second tasks does not exceed a preset second number threshold, calling a first abnormality determination coefficient and a second abnormality determination coefficient corresponding to each task; comparing the first abnormality determination coefficient and the second abnormality determination coefficient; Retrieve the task corresponding to the first abnormality determination coefficient being greater than the second abnormality determination coefficient as the target task; Extracting a first abnormality determination coefficient and a second abnormality determination coefficient corresponding to the target task; Obtaining a comprehensive determination coefficient of a target unit component by using a first abnormality determination coefficient and a second abnormality determination coefficient corresponding to the target task; The comprehensive determination coefficient of the target unit component is obtained by the following formula: Where E represents the comprehensive determination coefficient of the target unit component; m represents the total number of target tasks contained in the target unit component; A 01i A represents the first abnormality determination coefficient corresponding to the i-th target task; 02i represents the second abnormality determination coefficient corresponding to the i-th target task; S mi represents the task execution success rate corresponding to the completion of the i-th target task; P ci and P ni represents the CPU occupancy and memory usage corresponding to the i-th target task; N i Indicates the number of concurrently executed tasks corresponding to the i-th target task; N z Indicates the total execution task volume of the target unit component; comparing the comprehensive determination coefficient of the target unit component with a preset comprehensive coefficient threshold; When the comprehensive determination coefficient of the target unit component exceeds a preset comprehensive coefficient threshold, the target unit component has a performance abnormality and an abnormality alarm is issued.

7. The method for rapidly constructing a digital model of a weapon platform electronic system as claimed in claim 1, characterized in that: The integrated modeling framework further includes the following steps: Design and define the functions of the integrated modeling framework, including automatic generation of models, module combination, module connection configuration, and parameter setting; The automated modeling framework can automatically select appropriate modules based on the input demand parameters and automatically generate a preliminary system model according to preset rules; The automated modeling framework seamlessly integrates modules through a unified interface protocol.

8. The method for rapidly constructing a digital model of a weapon platform electronic system as claimed in claim 7, characterized in that: The integrated modeling framework further includes the following steps: Specific design requirement parameters are input, including processing power, data rate, and response time. The automated modeling framework automatically selects and configures appropriate modules according to the requirement parameters and generates a preliminary digital model.

9. The method for rapidly constructing a digital model of a weapon platform electronic system as claimed in claim 1, characterized in that: The dynamic simulation adjustment specifically further includes the following steps: Configuring a simulation environment and a work scenario, wherein the work scenario is set based on the operating environment of the weapon platform, and configuring simulation parameters according to different mission requirements, wherein the simulation parameters include sensor input, target dynamics, and operator input; The simulation environment is capable of switching between different working states, including standby, launch and failure, to simulate the performance of the electronic system of the weapon platform under various working conditions.

10. The method for rapidly constructing a digital model of a weapon platform electronic system as claimed in claim 9, characterized in that: The dynamic simulation adjustment specifically further includes the following steps: After configuring the simulation environment, the digital model is dynamically tested and verified through real-time simulation; Simulate the system's response under different working conditions based on real-time simulation, record and verify the interaction between modules, data transmission and actuator delays; Feedback simulation results to the automated modeling framework in real time to evaluate system performance, identify design defects and potential risks based on simulation results; According to the design defects and performance bottlenecks found during the simulation process, the feedback results are automatically transmitted to the digital model to adjust and optimize the model parameters; The optimization measures include adjusting the control algorithm, optimizing the data transmission rate, adding redundant modules, and performing simulation verification again after the optimization measures are modified.

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