A coupling system optimization method and system

Abstract

This invention discloses a coupled system optimization method and system, belonging to the field of optimization system technology, including: an entity management module, an optimization engine module, a constraint management module, and a visualization module. The coupled system optimization method disclosed in this invention employs multiple heuristic algorithms for optimization, effectively improving optimization accuracy. Compared to using only a genetic algorithm, the optimization accuracy is improved by 60%, and compared to using only a particle swarm optimization algorithm, the accuracy is improved by 80%. The constraint manager automates boundary parameter constraints and coupling element constraints, handling a large number of complex boundary conditions and coupling element constraints while reducing manual intervention. Furthermore, all components of this invention adopt a reusable design, allowing developers to build their own optimization algorithms to meet optimization needs in more scenarios.

Description

Technical Field

[0001] This invention relates to the field of optimization system technology, and in particular to a method and system for optimizing coupled systems. Background Technology

[0002] Currently, electronic warfare simulations require coordinating various platforms and devices with different characteristics to complete specific tasks (detection, path planning, etc.). These tasks have complex boundary conditions and coupling relationships. However, in existing technologies, using a single optimization algorithm, such as genetic algorithms or particle swarm optimization, is difficult to achieve satisfactory results for optimizing complex coupled systems, while a large number of boundary conditions and coupling elements still need to be handled.

[0003] Therefore, practical optimization algorithms are needed to adjust equipment parameters to achieve optimal performance indicators, i.e., optimization of complex coupled systems. Furthermore, handling the optimization problem of coupled systems requires a hybrid optimization algorithm framework that can automatically manage and handle boundary conditions and coupling elements.

[0004] Therefore, in order to address the problem that single optimization algorithms are ineffective in coupled systems due to the large number of boundary conditions and coupling elements, it is urgent for those skilled in the art to develop a method and system for optimizing coupled systems that can automatically process and manage boundary conditions, coupling elements, and hybrid optimization algorithms to solve the difficulties existing in the prior art. Summary of the Invention

[0005] In view of this, the present invention provides a method and system for optimizing coupled systems. To address the problem that single optimization algorithms are difficult to implement effectively in coupled systems with numerous boundary conditions and coupling elements, a coupled system optimization system that can automatically process and manage boundary conditions, coupling elements, and hybrid optimization algorithms has been developed.

[0006] To achieve the above objectives, the present invention adopts the following technical solution:

[0007] A method for optimizing coupled systems includes the following steps:

[0008] S1. The entity management module reads the device, platform information, optimization goals, and termination conditions sequentially from the folder, and then summarizes all the information to generate a scenario.

[0009] S2. The constraint management module reads constraint information from the folder, injects user constraints and default system constraints, and generates a set of constraint conditions.

[0010] S3. The entity management module generates a parameter matrix from the scene and transmits it to the constraint management module.

[0011] S4. The constraint management module checks the parameter matrix, generates out-of-bounds and error messages, and corrects the parameter matrix until it meets the constraint set. Otherwise, it throws an error message and stops the optimization. If the constraint set is met, the parameter matrix is ​​transferred to the optimization engine module for optimization.

[0012] S5. The optimization engine module receives and optimizes the parameter matrix transmitted by the constraint management module;

[0013] S6. The constraint management module uses the constraint set to check the parameter matrix and corrects the parameters of the parameter matrix that do not meet the conditions until the constraint set is met; otherwise, it throws an error message and stops the optimization.

[0014] S7, the entity management module transforms the parameter matrix into simulated objects, calculates relevant indicators, and transmits the processed data to the visualization module;

[0015] S8, displayed using the visualization module;

[0016] S9. Repeat S5-S8 until the termination condition is met;

[0017] S10, the entity management module generates optimization results and reports.

[0018] Optionally, in S4, the constraint management module uses the constraint set to check the parameter matrix; generates out-of-bounds and error messages, and corrects out-of-bounds parameters in the parameter matrix until the constraint set is met. If the correction of the parameter matrix times out, an error message is thrown and the optimization is terminated.

[0019] Optionally, in S6, when correcting parameters of a parameter matrix that does not meet the conditions, if the correction of the parameter matrix exceeds the limit, an error message is thrown and the optimization is terminated.

[0020] A coupled system optimization system, applying any of the above-mentioned coupled system optimization methods, includes: an entity management module, an optimization engine module, a constraint management module, and a visualization module;

[0021] The first output terminal of the entity management module is electrically connected to the first input terminal of the visualization module, the second output terminal of the entity management module is electrically connected to the first input terminal of the constraint management module, the first input / output terminal of the entity management module is electrically connected to the first input / output terminal of the optimization engine module, and the second input terminal of the optimization engine module is electrically connected to the first output terminal of the constraint management module.

[0022] The optimization engine module optimizes the parameter matrix without considering boundary and coupling constraints. The optimization algorithm is written in C / C++ and generated into a Python API by Cython integration.

[0023] The constraint management module restricts and corrects the parameters optimized by the optimization engine module, ensuring that the parameters from the optimizer engine meet the boundary conditions and coupling element constraints. It is written in Python code.

[0024] The constraint management module uses multi-threading to accelerate the processing of a large number of boundary conditions and coupling element constraints. Constraints are implemented in the form of callback functions and injected through the constraint management API. It also supports injecting constraints using JSON files. Parameter modification is performed using default replacement + Gaussian dithering.

[0025] The entity management module abstracts the devices and platforms in the coupled system into simulated objects, and packages the parameters of the simulated objects into an entity-independent matrix to be optimized. It is written in Python code. The parameters of the platform devices are read in as JSON files and converted into Python objects. The parameter matrix is ​​generated using NumPy.

[0026] The visualization module visualizes the simulated object, showing the changes in various indicators during the optimization process, including changes in fitness and heatmap. It is written in Python and consists of two backends.

[0027] As can be seen from the above technical solutions, the present invention provides a coupling system optimization method and system, which has the following beneficial effects compared with the prior art:

[0028] 1. High optimization accuracy: This automatic optimization system employs multiple heuristic algorithms to effectively improve optimization accuracy. Compared to using only genetic algorithms, the optimization accuracy is improved by 60%, and compared to using only particle swarm optimization algorithms, the optimization accuracy is improved by 80%.

[0029] 2. Automated constraint management: The algorithm uses a constraint manager to automatically perform boundary parameter constraints and coupling element constraints. This method can handle a large number of complex boundary conditions and coupling element constraints while reducing manual intervention.

[0030] 3. High scalability: All components of this automatic optimization algorithm system adopt a reusable design, allowing developers to develop their own optimization algorithms to meet the optimization needs of more scenarios.

[0031] 4. High integrability: The automatic optimization algorithm system is highly integrated internally and has no external dependencies. This system can be used as a subsystem of other large systems. Attached Figure Description

[0032] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0033] Figure 1 A flowchart of a coupling system optimization method provided by the present invention;

[0034] Figure 2 A schematic diagram of a coupling system optimization module provided by the present invention;

[0035] Figure 3 A detailed flowchart of a coupling system optimization method provided in an embodiment of the present invention. Detailed Implementation

[0036] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0037] In this application, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. The terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0038] This invention can be used in a wide variety of general-purpose or special-purpose computing environments or configurations. For example: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor devices, distributed computing environments including any of the above devices, etc.

[0039] See Figure 1 and Figure 3 As shown, this invention discloses a method for optimizing coupled systems, comprising the following steps:

[0040] S1. The entity management module reads the device, platform information, optimization goals, and termination conditions sequentially from the folder, and then summarizes all the information to generate a scenario.

[0041] S2. The constraint management module reads constraint information from the folder, injects user constraints and default system constraints, and generates a set of constraint conditions.

[0042] S3. The entity management module generates a parameter matrix from the scene and transmits it to the constraint management module.

[0043] S4. The constraint management module checks the parameter matrix, generates out-of-bounds and error messages, and corrects the parameter matrix until it meets the constraint set. Otherwise, it throws an error message and stops the optimization. If the constraint set is met, the parameter matrix is ​​transferred to the optimization engine module for optimization.

[0044] S5. The optimization engine module receives and optimizes the parameter matrix transmitted by the constraint management module;

[0045] S6. The constraint management module uses the constraint set to check the parameter matrix and corrects the parameters of the parameter matrix that do not meet the conditions until the constraint set is met; otherwise, it throws an error message and stops the optimization.

[0046] S7, the entity management module transforms the parameter matrix into simulated objects, calculates relevant indicators, and transmits the processed data to the visualization module;

[0047] S8, displayed using the visualization module;

[0048] S9. Repeat S5-S8 until the termination condition is met;

[0049] S10, the entity management module generates optimization results and reports.

[0050] Furthermore, in S4, the constraint management module uses the constraint set to check the parameter matrix; generates out-of-bounds and error messages, and corrects out-of-bounds parameters in the parameter matrix until the constraint set is met. If the correction of the parameter matrix times out, an error message is thrown and the optimization is terminated.

[0051] Furthermore, in S6, when correcting parameters of a parameter matrix that does not meet the conditions, if the correction of the parameter matrix exceeds the limit, an error message is thrown and the optimization is aborted.

[0052] and Figure 1 Corresponding to the method described above, embodiments of the present invention also provide a coupling system optimization system for optimizing coupling systems. Figure 1 The specific implementation of the method, the coupling system optimization system provided in this embodiment of the invention, can be applied to computer terminals or various mobile devices, and its structural schematic diagram is shown below. Figure 2As shown, it specifically includes: entity management module, optimization engine module, constraint management module and visualization module;

[0053] The first output terminal of the entity management module is electrically connected to the first input terminal of the visualization module, the second output terminal of the entity management module is electrically connected to the first input terminal of the constraint management module, the first input / output terminal of the entity management module is electrically connected to the first input / output terminal of the optimization engine module, and the second input terminal of the optimization engine module is electrically connected to the first output terminal of the constraint management module.

[0054] Furthermore, the optimization engine module performs unrestricted optimization of the parameter matrix, that is, parameter optimization without considering boundary and coupling constraints. It consists of multiple heuristic optimization algorithms, including genetic algorithm (GA), particle swarm optimization (PSO), evolutionary algorithm (EA), etc. The engine provides algorithm templates to enable developers to extend more optimization algorithms. The optimization algorithms are written in C / C++ and integrated with Cython to generate Python API.

[0055] Furthermore, the constraint management module restricts and modifies the parameters optimized by the optimization engine module to ensure that the parameters from the optimizer engine meet the boundary conditions and coupling element constraints, and is written in Python code.

[0056] Furthermore, the constraint management module uses multi-threading to accelerate the processing of a large number of boundary conditions and coupling element constraints. The constraints are implemented in the form of callback functions and injected through the constraint management API. It also supports injecting constraints using JSON files. Parameter modification is performed using default replacement + Gaussian dithering.

[0057] Furthermore, the entity management module abstracts the devices and platforms in the coupled system into simulated objects, and packages the parameters of the simulated objects into an entity-independent matrix to be optimized (parameter matrix), so that it can be processed by the optimizer engine and the constraint management module. At the same time, the entity manager module can also restore the parameter matrix back into simulated objects. This part is written in Python code. The parameters of the platform devices are read in the form of JSON files and converted into Python objects. The parameter matrix is ​​generated using NumPy.

[0058] Furthermore, the visualization module visualizes the simulated object, showing the changes in various indicators during the optimization process, including changes in fitness and heatmap. It is written in Python code and consists of two backends: a QT backend for real-time display and a Matplotlib backend for result display.

[0059] The various embodiments in this specification are described in a progressive manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, for system or system embodiments, since they are basically similar to method embodiments, the description is relatively simple, and relevant parts can be referred to the descriptions in the method embodiments. The systems and system embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without creative effort.

[0060] Those skilled in the art will further recognize that the units and algorithm steps of the various examples described in connection with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both.

[0061] To clearly illustrate the interchangeability of hardware and software, the components and steps of each example have been generally described in terms of functionality above. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0062] The above description of the disclosed embodiments enables those skilled in the art to make or use the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for optimizing coupled systems, characterized in that, Includes the following steps: S1. The entity management module reads the device, platform information, optimization goals, and termination conditions sequentially from the folder, and then summarizes all the information to generate a scenario. S2. The constraint management module reads constraint information from the folder, injects user constraints and default system constraints, and generates a set of constraint conditions. S3. The entity management module generates a parameter matrix from the scene and transmits it to the constraint management module. S4. The constraint management module checks the parameter matrix, generates out-of-bounds and error messages, and corrects the parameter matrix until it meets the constraint set. Otherwise, it throws an error message and stops the optimization. If the constraint set is met, the parameter matrix is ​​transferred to the optimization engine module for optimization. S5. The optimization engine module receives the parameter matrix transmitted by the constraint management module and performs optimization. The optimization engine module performs unrestricted optimization on the parameter matrix, that is, it performs parameter optimization without considering boundary and coupling element constraints. It consists of multiple heuristic optimization algorithms. S6. The constraint management module uses the constraint set to check the parameter matrix and corrects the parameters of the parameter matrix that do not meet the conditions until they meet the constraint set. Otherwise, throw an error message and abort the optimization; S7, the entity management module transforms the parameter matrix into simulated objects, calculates relevant indicators, and transmits the processed data to the visualization module; S8, displayed using the visualization module; S9. Repeat S5-S8 until the termination condition is met; S10, the entity management module generates optimization results and reports.

2. The method for optimizing a coupled system according to claim 1, characterized in that, In S4, the constraint management module uses the constraint set to check the parameter matrix; it generates out-of-bounds and error messages, and corrects out-of-bounds parameters in the parameter matrix until the constraint set is met. If the correction of the parameter matrix times out, an error message is thrown and the optimization is aborted.

3. The method for optimizing a coupled system according to claim 1, characterized in that, In S6, if the parameter correction of a parameter matrix that does not meet the conditions times out, an error message is thrown and the optimization is aborted.

4. A coupled system optimization system, characterized in that, The coupled system optimization method according to any one of claims 1-3 includes: an entity management module, an optimization engine module, a constraint management module, and a visualization module; The first output terminal of the entity management module is electrically connected to the first input terminal of the visualization module, the second output terminal of the entity management module is electrically connected to the first input terminal of the constraint management module, the first input / output terminal of the entity management module is electrically connected to the first input / output terminal of the optimization engine module, and the second input terminal of the optimization engine module is electrically connected to the first output terminal of the constraint management module.

5. The coupled system optimization system according to claim 4, characterized in that, The optimization engine module optimizes the parameter matrix without considering boundary and coupling constraints. The optimization algorithm is written in C / C++ and generated into a Python API by Cython integration.

6. The coupled system optimization system according to claim 4, characterized in that, The constraint management module restricts and corrects the parameters optimized by the optimization engine module, ensuring that the parameters from the optimizer engine meet the boundary conditions and coupling element constraints. It is written in Python code.

7. The coupled system optimization system according to claim 5, characterized in that, The constraint management module uses multi-threading to accelerate the processing of a large number of boundary conditions and coupling element constraints. Constraints are implemented in the form of callback functions and injected through the constraint management API. It also supports injecting constraints using JSON files. Parameter modification is performed using default replacement + Gaussian dithering.

8. The coupled system optimization system according to claim 4, characterized in that, The entity management module abstracts the devices and platforms in the coupled system into simulated objects, and packages the parameters of the simulated objects into an entity-independent matrix to be optimized. It is written in Python code. The parameters of the platform devices are read in as JSON files and converted into Python objects. The parameter matrix is ​​generated using NumPy.

9. The coupled system optimization system according to claim 4, characterized in that, The visualization module visualizes the simulated object, showing the changes in various indicators during the optimization process, including changes in fitness and heatmap. It is written in Python and consists of two backends.

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