An expandable and upgradeable aviation obstruction light system

Abstract

The present invention discloses an expandable and upgradeable aviation obstruction light system in the field of aviation safety lighting technology. The system includes a dynamic simulation module, a performance monitoring module, a fault prediction module, a parameter optimization module, an energy efficiency analysis module, a modular design module, a data management module, and a control strategy module. In the present invention, the introduction of the dynamic simulation module greatly improves the adaptability of the system to complex and extreme meteorological conditions. With the help of differential equation modeling and Runge‑Kutta numerical solution, the system can accurately simulate the dynamic changes under different environmental conditions, thereby effectively improving the responsiveness and flight safety in a changing environment. The application of the performance monitoring module enhances the system's continuous performance monitoring and early fault identification capabilities. By analyzing historical and real-time data through a dynamic time bending algorithm, the system can promptly identify the patterns and trends of performance changes, effectively reducing operational risks.

Description

Technical Field

[0001] The present invention relates to the technical field of aviation safety lighting, and in particular to an expandable and upgradeable aviation obstruction light system. Background Art

[0002] The field of aviation safety lighting technology focuses on the use of lighting systems to improve aviation safety. Aviation safety lighting is used to mark air obstacles, such as tall buildings, TV towers, wind turbines, etc., to prevent aircraft from colliding with these obstacles. These lighting devices must meet specific standards and specifications to ensure that they can be effectively identified by pilots under different weather conditions and line of sight distances.

[0003] The aviation obstruction light system is an important part of the aviation safety lighting field. Its main purpose is to identify the location of buildings or other obstacles to prevent aircraft in flight from colliding with them. This system consists of bright lights installed on buildings such as high-rise buildings, towers, tall chimneys, etc. that pose a threat to flight safety. By emitting continuous or flashing light, aviation obstruction lights remind pilots to avoid obstacles at night or in conditions of poor visibility, thereby achieving the effect of protecting aviation safety. The aviation obstruction light system uses high-brightness, low-energy LED lamps. Combined with control technology, the system can automatically adjust the brightness to adapt to different ambient light conditions. In addition, it will be equipped with backup power supplies and fault detection tools to ensure that it can still work normally in an emergency. The system also integrates remote monitoring and control functions, allowing operation and maintenance personnel to remotely detect the status of the lights through the network and perform maintenance and troubleshooting in a timely manner.

[0004] Although existing technologies have achieved remarkable results in the identification of aviation obstacles and flight safety, there are still challenges in dynamically adapting to complex and extreme climate conditions. This leads to insufficient system response in a changing environment, affecting flight safety. Although existing technologies have effectively used high-brightness, low-energy LED lamps to improve aviation safety, they still face certain limitations in continuous performance monitoring and early fault identification. This makes it difficult for the system to respond in a timely manner when faced with continuous performance changes or potential failures, increasing operational risks. Although existing technologies have performed well in ensuring that aviation safety lighting meets standards and specifications, there is still room for improvement in deeply analyzing the causes of system failures and predicting potential risks. This limits the system's comprehensive understanding and effective response to different risk factors. Although existing technologies have made progress in ensuring a safe distance between aircraft and obstacles, there is still room for improvement in optimizing system parameters and improving energy efficiency. It is easy to lead to insufficient energy utilization, increase operating costs and affect the environment. Although existing technologies have achieved some results in ensuring the normal operation of aviation obstruction light systems in emergency situations, there are still deficiencies in conducting detailed energy efficiency analysis and optimization. It is easy to lead to energy waste and environmental impact, reducing the overall economy and sustainability of the system. Although existing technologies have realized remote monitoring and control functions, they still face challenges in terms of system scalability and upgradeability, which limits the adaptability and long-term maintainability of the system in a rapidly evolving technological environment, leading to increased maintenance costs.

[0005] Based on this, the present invention designs an expandable and upgradeable aviation obstruction light system to solve the above problems. Summary of the invention

[0006] The purpose of the present invention is to provide an expandable and upgradeable aviation obstruction light system to solve the problems mentioned in the above background technology. Although the prior art has achieved remarkable results in the identification of aviation obstacles and flight safety, it still faces challenges in dynamically adapting to complex and extreme climatic conditions. As a result, the system response is insufficient in a changing environment, affecting flight safety. Although the prior art effectively uses high-brightness, low-energy LED lamps to improve aviation safety, it still faces certain limitations in continuous performance monitoring and early fault identification. This makes it difficult for the system to respond in time when facing continuous performance changes or potential failures, increasing operational risks. Although the prior art performs well in ensuring that aviation safety lighting meets standards and specifications, there is still room for improvement in deeply analyzing the causes of system failures and predicting potential risks. The system is limited in its comprehensive understanding and effective response to different risk factors. Although the prior art has made progress in ensuring a safe distance between aircraft and obstacles, there is still room for improvement in optimizing system parameters and improving energy efficiency. It is easy to lead to insufficient energy utilization, increase operating costs and affect the environment. Although the prior art has achieved some results in ensuring the normal operation of the aviation obstruction light system in an emergency, it is still insufficient in conducting detailed energy efficiency analysis and optimization. It is easy to cause energy waste and environmental impact, reducing the overall economy and sustainability of the system. Although existing technologies have realized remote monitoring and control functions, they still face challenges in terms of system scalability and upgradeability. This limits the adaptability and long-term maintainability of the system in a rapidly developing technological environment, leading to the problem of increased maintenance costs.

[0007] To achieve the above-mentioned purpose, the present invention provides the following technical solutions: an expandable and upgradeable aviation obstruction light system, the system comprising a dynamic simulation module, a performance monitoring module, a fault prediction module, a parameter optimization module, an energy efficiency analysis module, a modular design module, a data management module, and a control strategy module;

[0008] The dynamic simulation module is based on the physical and engineering principles of aviation obstruction lights and adopts ordinary differential equation modeling to construct a mathematical model of the behavior of the aviation obstruction light system. The aviation obstruction light system includes aviation obstruction lights and associated control and monitoring equipment. The Runge-Kutta numerical solution is used to solve the model, simulate the dynamic changes of the system under multiple conditions, and generate the system dynamic response analysis results;

[0009] The performance monitoring module uses a dynamic time warping algorithm based on the system dynamic response analysis results to analyze the time series similarity between historical and real-time data, identify abnormal patterns and trend changes in performance data, and generate performance change pattern recognition results;

[0010] The fault prediction module uses Spearman rank correlation analysis based on the performance change pattern recognition results to analyze multiple fault factors, including the relationship between temperature, humidity and current, and combines the linear regression model to establish the association between the fault and the influencing factors to generate a fault risk assessment result;

[0011] The parameter optimization module uses a grid search algorithm based on the fault risk assessment results to traverse a variety of parameter combinations in the set parameter space, evaluates the impact of each combination on system performance through multivariate performance analysis, optimizes the parameter combination, and generates the selected parameter configuration;

[0012] The energy efficiency analysis module uses kernel density estimation based on the selected parameter configuration to perform statistical analysis on the energy consumption data of the system, combines cluster analysis to analyze the potential patterns and features in the data, and proposes energy efficiency optimization measures to generate energy efficiency optimization solutions;

[0013] The modular design module is based on the energy efficiency optimization solution and adopts the modular architecture design principle to build a modular structure of multiple components of the system. Through the standardized universal interface protocol, new components are integrated and the system is expanded to generate a modular expansion solution.

[0014] The data management module is based on a modular expansion solution and adopts a relational database architecture to centrally store and manage all generated data, and uses online analytical processing and data mining algorithms to process and analyze the data to generate integrated data analysis results;

[0015] The control strategy module uses a decision tree analysis method based on the integrated data analysis results to automatically adjust the system status and performance data, applies adaptive feedback control technology, adjusts the control process according to the real-time feedback of the system, and generates an intelligent control solution.

[0016] Preferably, the system dynamic response analysis results include response speed indicators, environmental adaptability analysis and system stability data, the performance change pattern recognition results include abnormal frequency change recognition, brightness fluctuation analysis and performance degradation trend indicators, the fault risk assessment results include temperature abnormality alarms, humidity sensitivity analysis and current instability indicators, the selected parameter configurations include brightness adjustment range, strobe adjustment mode and current usage efficiency indicators, the energy efficiency optimization scheme includes energy consumption optimization measures, energy utilization improvement strategies and energy-saving targets, the modular expansion scheme includes lamp body module design, controller module upgrade and sensor module integration scheme, the integrated data analysis results include comprehensive performance sets, fault mode statistics and parameter adjustment history analysis, and the intelligent control scheme includes automatic brightness adjustment strategy, strobe response optimization scheme and real-time fault response measures.

[0017] Preferably, the dynamic simulation module includes an equation construction submodule, a numerical approximate solution submodule, and a dynamic behavior analysis submodule;

[0018] The equation building submodule is based on the physical and engineering principles of aviation obstruction lights and adopts partial differential equation modeling. It establishes mathematical equations to express the relationship between light brightness, strobe frequency and environmental factors, and mathematically describes the factors and light behaviors to generate a mathematical model of system behavior.

[0019] The numerical approximate solution submodule is based on the system behavior mathematical model, adopts the fourth-order Runge-Kutta numerical solution method, approximates the solution of the differential equation through iterative calculation, obtains the predicted performance of the system under multiple working conditions, and generates dynamic change numerical analysis results;

[0020] The dynamic behavior analysis submodule adopts dynamic response mode analysis based on the dynamic change numerical analysis results, simulates the influence of various environmental variables on system behavior, analyzes the performance of the system under various working conditions, and generates system dynamic response analysis results.

[0021] Preferably, the performance monitoring module includes a historical data comparison submodule, a pattern recognition submodule, and a performance trend analysis submodule;

[0022] The historical data comparison submodule uses time series analysis based on the system dynamic response analysis results to analyze the changing trend of system performance by comparing past and immediate system performance data, and generates historical and real-time data comparison analysis results;

[0023] The pattern recognition submodule uses a dynamic time warping algorithm based on the comparative analysis results of historical and real-time data to identify irregular changes and abnormal patterns in performance data by quantifying the similarity between time series and generate abnormal pattern recognition results;

[0024] The performance trend analysis submodule uses trend analysis technology based on abnormal pattern recognition results to track and continuously analyze data changes, mine early signs of performance degradation or potential failures, and generate performance change pattern recognition results.

[0025] Preferably, the fault prediction module includes a variable selection submodule, a correlation analysis submodule, and a fault prediction modeling submodule;

[0026] The variable selection submodule uses a factor analysis method to perform multivariate analysis based on the performance change pattern recognition results, selects key factors affecting system performance by screening key variables associated with system failures, including temperature, humidity, and current, and generates fault prediction factor recognition results;

[0027] The correlation analysis submodule uses Spearman rank correlation analysis based on the fault prediction factor identification results to evaluate the degree of their influence on system performance by statistically analyzing the correlation strength between variables and generate fault impact relationship evaluation results;

[0028] The fault prediction modeling submodule uses logistic regression modeling technology based on the fault impact relationship assessment results to establish a mathematical model between variables and system faults, predict the fault types and frequencies that may occur in the system under various operating environments, and generate fault risk assessment results.

[0029] Preferably, the parameter optimization module includes a parameter definition submodule, a parameter combination search submodule, and a performance evaluation submodule;

[0030] The parameter definition submodule uses variance analysis based on the fault risk assessment results to distinguish key factors by evaluating the influence of multiple parameters, including brightness, strobe frequency and current intensity on system performance, select the target parameter range for system performance optimization, and generate key parameter definition analysis results;

[0031] The parameter combination search submodule is based on the key parameter definition analysis results, adopts the design experiment method and grid search algorithm, sets up multiple parameter combination experiments, observes the performance of the system under each combination, and optimizes the parameters in the parameter space to generate parameter combination mining results;

[0032] The performance evaluation submodule uses multivariate regression analysis based on the parameter combination mining results to compare the impact of each parameter combination on system performance, evaluate the comprehensive performance of the combination, identify and match parameter configurations under multiple working conditions, and generate selected parameter configurations.

[0033] Preferably, the energy efficiency analysis module includes an energy data collection submodule, a non-parametric statistical analysis submodule, and an optimization measure formulation submodule;

[0034] The energy data collection submodule collects the energy consumption data of the system, including the power usage and working hours, and generates an energy consumption data set by using data collection technology based on the selected parameter configuration and by real-time monitoring and recording the operating parameters of the system;

[0035] The non-parametric statistical analysis submodule uses a kernel density estimation method based on the energy consumption data set to analyze the distribution characteristics and patterns of energy usage, identify key influencing factors and abnormal consumption points of energy consumption, and generate energy efficiency statistical analysis results;

[0036] The optimization measures formulation submodule uses cluster analysis technology based on the energy efficiency statistical analysis results to explore the hidden patterns and features in the data, propose energy efficiency improvement measures and energy-saving strategies, and generate energy efficiency optimization plans.

[0037] Preferably, the modular design module includes a design concept planning submodule, an interface standardization submodule, and a system expansion strategy submodule;

[0038] The design concept planning submodule is based on the energy efficiency optimization scheme and adopts the modular design principle to plan the modular structure of the aviation obstruction light system, including the light body, controller and sensor, select the overall framework and goals of the modular design, and generate a preliminary modular structure scheme;

[0039] The interface standardization submodule is based on a modular preliminary composition scheme, adopts a communication protocol integration method, implements a standardized interface protocol, enables new components and modules to be integrated and communicated, optimizes the interoperability and scalability of the system, and generates an interface standardization scheme;

[0040] The system expansion strategy submodule is based on the interface standardization solution and adopts a modular strategy planning method to formulate a system expansion strategy, including the integration of new functional components and performance optimization of existing components, to match and continuously update the system, and to generate a modular expansion solution.

[0041] Preferably, the data management module includes a data storage submodule, a data processing submodule, and a data analysis submodule;

[0042] The data storage submodule is based on a modular expansion solution and adopts an SQL database management system. By designing a database architecture and establishing data indexes, it performs storage and retrieval of various data types such as performance monitoring, fault records and operation logs, and generates a centralized data warehouse;

[0043] The data processing submodule is based on a centralized data warehouse and uses data fusion technology to integrate data from multiple sources to manage the data in a unified manner and generate a unified view data set;

[0044] The data analysis submodule is based on a unified view data set, adopts OLAP and association rule mining algorithms, builds a multidimensional data model and mines potential association rules in the data, analyzes the inherent connections and hidden trends in the data, and generates integrated data analysis results.

[0045] Preferably, the control strategy module includes a strategy formulation submodule, a parameter automatic adjustment submodule, and a response optimization submodule;

[0046] The strategy formulation submodule adopts decision analysis technology based on the integrated data analysis results, analyzes and parses the current state and performance data of the system, formulates a control strategy that matches the current operating status, and generates a preliminary control strategy;

[0047] The automatic parameter adjustment submodule is based on the preliminary control strategy and adopts the PID control algorithm to respond to the performance changes of the system in real time and generate an automatic parameter adjustment strategy through continuous monitoring and automatic adjustment of key system parameters, including brightness and strobe frequency;

[0048] The response optimization submodule is based on an automated parameter adjustment strategy and adopts adaptive control technology. By analyzing the real-time feedback of the system and changes in the external environment, the control strategy is dynamically adjusted to optimize the response capability and stability of the system and generate an intelligent control solution.

[0049] Compared with the prior art, the beneficial effects of the present invention are as follows: the introduction of the dynamic simulation module greatly improves the adaptability of the system to complex and extreme meteorological conditions. With the help of differential equation modeling and Runge-Kutta numerical solution, the system can accurately simulate the dynamic changes under different environmental conditions, thereby effectively improving the responsiveness and flight safety in a changing environment. The application of the performance monitoring module enhances the system's continuous performance monitoring and early fault identification capabilities. By analyzing historical and real-time data through the dynamic time bending algorithm, the system can timely identify the patterns and trends of performance changes, effectively reducing operational risks. The addition of the fault prediction module enables the system to deeply analyze and predict fault risks. Using Spearman rank correlation analysis and linear regression models, the system can more comprehensively understand different risk factors, prevent potential faults in advance, and improve the reliability and safety of the system. The use of the parameter optimization module optimizes the system parameter configuration and improves energy efficiency. The grid search algorithm helps find the optimal parameter combination, ensuring efficient use of energy and reduced operating costs. The introduction of the energy efficiency analysis module also brings significant energy-saving effects to the system. Through kernel density estimation and cluster analysis, the system can analyze energy consumption more accurately, formulate effective energy-saving measures, reduce energy waste, and enhance the economy and sustainability of the system. Finally, the application of modular design and data management modules improves the scalability and upgradeability of the system. The modular structure and standardized interface protocol make the system easy to maintain and upgrade, enhancing its adaptability and long-term maintainability in technological development. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for describing the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0051] Figure 1 A module diagram of an expandable and upgradeable aviation obstruction light system is provided for the present invention;

[0052] Figure 2A system framework diagram of an expandable and upgradeable aviation obstruction light system is proposed for the present invention;

[0053] Figure 3 A schematic diagram of a dynamic simulation module in an expandable and upgradeable aviation obstruction light system is provided for the present invention;

[0054] Figure 4 A schematic diagram of a performance monitoring module in an expandable and upgradeable aviation obstruction light system is provided for the present invention;

[0055] Figure 5 A schematic diagram of a fault prediction module in an expandable and upgradeable aviation obstruction light system is provided for the present invention;

[0056] Figure 6 A schematic diagram of a parameter optimization module in an expandable and upgradeable aviation obstruction light system is provided for the present invention;

[0057] Figure 7 A schematic diagram of an energy efficiency analysis module in an expandable and upgradeable aviation obstruction light system is provided for the present invention;

[0058] Figure 8 A schematic diagram of a modular design module in an expandable and upgradeable aviation obstruction light system is provided for the present invention;

[0059] Fig. 9 A schematic diagram of a data management module in an expandable and upgradeable aviation obstruction light system is provided for the present invention;

[0060] Fig.10 The present invention provides a schematic diagram of a control strategy module in an expandable and upgradeable aviation obstruction light system. DETAILED DESCRIPTION

[0061] 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.

[0062] See also Figure 1 ,The present invention provides a technical solution: an expandable and upgradeable aviation obstruction light system, the system includes a dynamic simulation module, a performance monitoring module, a fault prediction module, a parameter optimization module, an energy efficiency analysis module, a modular design module, a data management module, and a control strategy module;

[0063] The dynamic simulation module is based on the physical and engineering principles of aviation obstruction lights and uses ordinary differential equations to build a mathematical model of the behavior of the aviation obstruction light system. The aviation obstruction light system includes aviation obstruction lights and associated control and monitoring equipment. The Runge-Kutta numerical solution is used to solve the model, simulate the dynamic changes of the system under multiple conditions, and generate the system dynamic response analysis results;

[0064] The performance monitoring module uses a dynamic time warping algorithm based on the system dynamic response analysis results to analyze the time series similarity between historical and real-time data, identify abnormal patterns and trend changes in performance data, and generate performance change pattern recognition results;

[0065] The fault prediction module uses Spearman rank correlation analysis based on the performance change pattern recognition results to analyze multiple fault factors, including the relationship between temperature, humidity and current. It also uses a linear regression model to establish the relationship between faults and influencing factors and generate fault risk assessment results.

[0066] Based on the fault risk assessment results, the parameter optimization module uses a grid search algorithm to traverse a variety of parameter combinations in the set parameter space, evaluate the impact of each combination on system performance through multivariate performance analysis, optimize the parameter combination, and generate the selected parameter configuration;

[0067] The energy efficiency analysis module uses kernel density estimation based on the selected parameter configuration to perform statistical analysis on the system's energy consumption data. It combines cluster analysis to analyze the potential patterns and features in the data, and proposes energy efficiency optimization measures to generate energy efficiency optimization solutions.

[0068] The modular design module is based on the energy efficiency optimization solution and adopts the modular architecture design principle to build a modular structure of multiple components of the system. Through the standardized universal interface protocol, new components are integrated and the system is expanded to generate a modular expansion solution.

[0069] The data management module is based on a modular expansion solution and adopts a relational database architecture to centrally store and manage all generated data. It uses online analytical processing and data mining algorithms to process and analyze data and generate integrated data analysis results.

[0070] Based on the integrated data analysis results, the control strategy module adopts the decision tree analysis method to automatically adjust the system status and performance data, applies adaptive feedback control technology, adjusts the control process according to the real-time feedback of the system, and generates an intelligent control solution.

[0071] The results of the system dynamic response analysis include response speed indicators, environmental adaptability analysis and system stability data. The results of performance change pattern recognition include abnormal frequency change recognition, brightness fluctuation analysis and performance degradation trend indicators. The results of fault risk assessment include temperature abnormality alarms, humidity sensitivity analysis and current instability indicators. The selected parameter configurations include brightness adjustment range, strobe adjustment mode and current utilization efficiency indicators. The energy efficiency optimization plan includes energy consumption optimization measures, energy utilization improvement strategies and energy-saving targets. The modular expansion plan includes lamp body module design, controller module upgrade and sensor module integration plan. The integrated data analysis results include comprehensive performance sets, fault mode statistics and parameter adjustment history analysis. The intelligent control plan includes automatic brightness adjustment strategy, strobe response optimization plan and real-time fault response measures.

[0072] In the dynamic simulation module, the system uses ordinary differential equation modeling methods to construct mathematical models of the behavior of aviation obstruction light systems. These models are based on the physical and engineering principles of aviation obstruction lights and describe the relationship between light brightness, strobe frequency and environmental factors. Using the Runge-Kutta method to solve these differential equations, the system can simulate dynamic changes under various conditions. Specifically, the Runge-Kutta method provides numerical solutions through iterative calculations, allowing the model to accurately predict the performance of the system under different operating conditions. This process generates system dynamic response analysis results, including response speed indicators, environmental adaptability analysis, and system stability data. These results are critical for evaluating the adaptability and stability of the system.

[0073] In the performance monitoring module, the system uses a dynamic time warping algorithm to analyze the time series similarity between historical and real-time data. By quantifying the similarity between time series, the algorithm is able to identify abnormal patterns and trend changes in performance data. This includes abnormal frequency change identification, brightness fluctuation analysis, and performance degradation trend indicators. Through this meticulous performance monitoring, the system can promptly detect any deviation from the normal pattern, improving early warning capabilities and maintenance efficiency.

[0074] In the fault prediction module, the system combines Spearman rank correlation analysis and linear regression model to establish the association between faults and influencing factors. Spearman rank correlation analysis is used to evaluate the relationship between various fault factors (such as temperature, humidity and current), while the linear regression model predicts the fault risk on this basis. This process produces fault risk assessment results, including temperature anomaly alarm, humidity sensitivity analysis and current instability indicators. These detailed analysis results enable the system to identify potential faults in advance and reduce the risk of sudden failures.

[0075] In the parameter optimization module, the system uses a grid search algorithm to traverse various parameter combinations within the set parameter space. This process evaluates the impact of each combination on system performance through multivariate performance analysis and selects the most optimized parameter configuration. For example, the system adjusts parameters such as brightness adjustment range, strobe adjustment mode, and current usage efficiency to achieve the best performance and energy efficiency ratio. This process produces the selected parameter configuration to improve the overall performance and efficiency of the system.

[0076] In the energy efficiency analysis module, the system uses kernel density estimation and cluster analysis methods to perform statistical analysis on energy consumption data. Kernel density estimation is used to analyze the distribution characteristics and patterns of energy usage, while cluster analysis is used to identify potential patterns and characteristics in the data. These analyses help the system identify key factors affecting energy consumption and abnormal consumption points, and develop energy efficiency optimization plans, including energy consumption optimization measures, energy utilization improvement strategies, and energy saving targets.

[0077] In the modular design module, the system adopts the modular architecture design principle based on the energy efficiency optimization solution to build a modular structure of multiple components such as the lamp body module, controller module and sensor module. Through the standardized universal interface protocol, new components can be easily integrated into the system to improve the interoperability and scalability of the system. This process generates a modular expansion solution, including lamp body module design, controller module upgrade and sensor module integration solution.

[0078] In the data management module, the system uses a relational database architecture to centrally store and manage all data. Using online analytical processing and data mining algorithms, the system can effectively process and analyze data and generate integrated data analysis results. This includes comprehensive performance sets, failure mode statistics, and parameter adjustment history analysis, providing valuable information for further optimization and maintenance of the system.

[0079] In the control strategy module, the system uses decision tree analysis to automatically adjust system status and performance data based on the results of integrated data analysis. Using adaptive feedback control technology, the system can adjust the control process based on real-time feedback. This process generates intelligent control solutions, including automatic brightness adjustment strategies, strobe response optimization solutions, and real-time fault response measures to ensure efficient and stable operation of the system under various working conditions.

[0080] See also Figure 2 and Figure 3 ,The dynamic simulation module includes the equation building submodule, the numerical approximate solution submodule, and the dynamic behavior analysis submodule;

[0081] The equation building submodule is based on the physical and engineering principles of aviation obstruction lights and adopts partial differential equation modeling. It establishes mathematical equations to express the relationship between light brightness, strobe frequency and environmental factors, and mathematically describes the factors and light behavior to generate a mathematical model of system behavior.

[0082] The numerical approximate solution submodule is based on the mathematical model of system behavior and adopts the fourth-order Runge-Kutta numerical solution method to approximate the differential equation through iterative calculation, obtain the predicted performance of the system under multiple working conditions, and generate dynamic change numerical analysis results;

[0083] The dynamic behavior analysis submodule is based on the dynamic change numerical analysis results and adopts dynamic response mode analysis. By simulating the impact of various environmental variables on system behavior, it analyzes the performance of the system under various working conditions and generates system dynamic response analysis results.

[0084] In the equation building submodule, the system analyzes the physical and engineering principles of aviation obstruction lights and uses partial differential equation modeling methods to express the relationship between light brightness, strobe frequency and environmental factors. This process first involves collecting and analyzing the operating parameters of aviation obstruction lights, such as light intensity, frequency and environmental conditions (temperature, humidity, wind speed, etc.). Then, based on these parameters, mathematical equations are constructed to describe the dynamic relationship between these variables. For example, the equation will express how the light brightness changes with ambient temperature or how the strobe frequency responds to different visibility conditions. After completing the construction of these equations, they are integrated into a complete mathematical model of system behavior, which can comprehensively reflect the behavior of aviation obstruction lights under various working conditions. The result of this process is the generation of a mathematical model that can describe the overall dynamic behavior of the system, which is the basis for subsequent numerical analysis and dynamic behavior analysis.

[0085] In the numerical approximate solution submodule, the system uses the fourth-order Runge-Kutta method to numerically solve the mathematical model generated in the equation construction submodule. This process first involves setting initial conditions, which are based on the actual working conditions and environmental settings of the aviation obstruction light system. Then, through iterative calculations, the fourth-order Runge-Kutta method provides an accurate and efficient way to approximate these differential equations. At each iteration, the algorithm calculates the approximate solution for the next time point, thereby gradually building the behavior of the system over the entire predetermined time range. This numerical analysis process ultimately generates dynamically changing numerical analysis results that can predict the performance of the system under different working conditions, such as the change in the brightness of the light under different temperature or humidity conditions.

[0086] In the dynamic behavior analysis submodule, the system uses the previously obtained dynamic change numerical analysis results to perform dynamic response mode analysis. This process includes simulating and analyzing the impact of different environmental variables (such as temperature, humidity, wind speed, etc.) on system performance. The system simulates the changes in these environmental variables and observes their impact on key performance indicators such as aviation obstruction light brightness and strobe frequency. In this way, the submodule can evaluate the performance of the system under various working conditions, such as the stability of the light under low temperature or high humidity conditions. The result of this analysis process is the generation of dynamic response analysis results of the system, which not only provides insights into the performance of the system under different environmental conditions, but also is an important basis for optimizing system design and adjusting operating strategies.

[0087] See also Figure 2 and Figure 4 ,The performance monitoring module includes a historical data comparison submodule, a pattern recognition submodule, and a performance trend analysis submodule;

[0088] The historical data comparison submodule uses time series analysis based on the system dynamic response analysis results to analyze the changing trend of system performance by comparing past and immediate system performance data, and generates historical and real-time data comparison analysis results;

[0089] The pattern recognition submodule uses a dynamic time warping algorithm based on the comparative analysis results of historical and real-time data to quantify the similarity between time series, identify irregular changes and abnormal patterns in performance data, and generate abnormal pattern recognition results;

[0090] The performance trend analysis submodule uses trend analysis technology based on abnormal pattern recognition results to track and continuously analyze data changes, mine early signs of performance degradation or potential failures, and generate performance change pattern recognition results.

[0091] In the historical data comparison submodule, the system uses time series analysis methods to compare historical and current system performance data. This process first involves collecting historical performance data, such as past light brightness, strobe frequency, and related environmental data (temperature, humidity, etc.). Then, these historical data are compared with the current real-time data to analyze the differences and changing trends between the two. For example, if the current light brightness is significantly lower than the historical average, this indicates that the performance of the lamp has degraded. Through this comparative analysis, the submodule is able to generate historical and real-time data comparative analysis results, which help identify performance fluctuations or downward trends and are the basis for subsequent pattern recognition and trend analysis.

[0092] In the pattern recognition submodule, the system uses the dynamic time warping algorithm to analyze the time series data provided by the historical data comparison submodule. The DTW algorithm quantifies the similarity between different time series by calculating the best match between them, and can identify irregular changes and abnormal patterns in performance data. This process involves establishing a standard pattern for the time series (based on historical performance data) and comparing it with the current data to identify any significant deviations. For example, if the current data shows that the light flicker frequency is significantly different from the standard pattern, this indicates that the lamp is faulty. Through this meticulous pattern recognition, the submodule is able to generate abnormal pattern recognition results, which are critical for early diagnosis of performance problems and prevention of potential failures.

[0093] In the performance trend analysis submodule, the system uses trend analysis technology to further explore early signs of performance degradation or potential failures based on the abnormal pattern recognition results provided by the pattern recognition submodule. This process involves continuously tracking and analyzing the changing trends of time series data to determine whether performance is developing in an unfavorable direction. For example, if the data for several consecutive days shows that the light brightness is gradually decreasing, this indicates that the lamp is about to fail. Through this trend analysis, the submodule is able to generate performance change pattern recognition results, which not only help to detect and repair faults in a timely manner, but also provide valuable guidance for system maintenance and optimization. Through the close collaboration of these three submodules, the performance monitoring module becomes a powerful tool that can monitor system performance in real time and identify and prevent potential problems in a timely manner.

[0094] See also Figure 2 and Figure 5 ,The fault prediction module includes a variable selection submodule, a correlation analysis submodule, and a fault prediction modeling submodule;

[0095] The variable selection submodule uses factor analysis method to perform multivariate analysis based on the performance change pattern recognition results. By screening key variables associated with system failures, including temperature, humidity, and current, the key factors affecting system performance are selected to generate fault prediction factor identification results.

[0096] The correlation analysis submodule uses Spearman rank correlation analysis based on the fault prediction factor identification results to evaluate the degree of their impact on system performance by statistically analyzing the correlation strength between variables and generate fault impact relationship evaluation results;

[0097] The fault prediction modeling submodule is based on the fault impact relationship assessment results and uses logistic regression modeling technology to establish a mathematical model between variables and system faults to predict the type and frequency of faults that may occur in the system under various operating environments and generate fault risk assessment results.

[0098] In the variable selection submodule, the system first uses factor analysis to perform multivariate analysis based on the performance change pattern recognition results. This process selects key factors that are closely related to system failures, such as temperature, humidity, and current, from a large number of variables that affect system performance. Specifically, the factor analysis method determines which variables are most critical to fault prediction by evaluating the statistical significance and relative contribution of each variable to system performance. For example, if the analysis shows that temperature changes are highly correlated with system failures, temperature will be selected as a key predictor. The result of this process is the generation of fault prediction factor identification results, which lists the variables that are most important for system failure prediction. These identified key factors provide an accurate basis for subsequent fault prediction, ensuring the accuracy and effectiveness of the prediction model.

[0099] In the correlation analysis submodule, the system uses Spearman rank correlation analysis to evaluate the strength of correlation between these key variables based on the results of the variable selection submodule. Spearman rank correlation analysis is a non-parametric statistical method that ranks the correlations between variables to determine the degree of influence between them. In this process, the system calculates the Spearman rank correlation coefficient between each pair of variables (such as temperature and current) to identify which variables have the most significant impact on system performance. This process generates fault impact relationship assessment results that describe the interactions between the various variables in detail, providing an important basis for accurately predicting system failures.

[0100] In the fault prediction modeling submodule, the system uses logistic regression modeling technology to establish a mathematical model between variables and system faults based on the fault impact relationship assessment results. Logistic regression is a statistical model widely used for classification and prediction, and is suitable for processing the relationship between prediction variables (such as temperature and humidity) and binary results (such as normal / faulty). In this process, the system trains a logistic regression model based on known variable values ​​(input) and past fault records (output) to identify the probability of failure under different variable combinations. After the model training is completed, it can be used to predict the type and frequency of system failures under specific operating environments (such as high temperature and high humidity conditions). The result of this process is the generation of fault risk assessment results, which provide key risk warnings for system operation and maintenance, allowing the maintenance team to take measures in advance to prevent potential failures, thereby greatly improving the reliability and safety of the system.

[0101] See also Figure 2 and Figure 6 ,The parameter optimization module includes the parameter definition submodule, the parameter combination search submodule, and the ,performance evaluation submodule;

[0102] Based on the fault risk assessment results, the parameter definition submodule uses variance analysis to evaluate the impact of multiple parameters, including brightness, strobe frequency, and current intensity on system performance, to distinguish key factors, select the target parameter range for system performance optimization, and generate key parameter definition analysis results;

[0103] The parameter combination search submodule is based on the key parameter definition analysis results, adopts the design experiment method and grid search algorithm, sets up multiple parameter combination experiments, observes the performance of the system under each combination, and optimizes the parameters in the parameter space to generate parameter combination mining results;

[0104] The performance evaluation submodule uses multivariate regression analysis based on the parameter combination mining results to compare the impact of each parameter combination on system performance, evaluate the comprehensive performance of the combination, identify and match the parameter configuration under multiple working conditions, and generate the selected parameter configuration.

[0105] In the parameter definition submodule, the system uses variance analysis to evaluate the impact of multiple parameters, including brightness, strobe frequency, and current intensity on system performance based on the fault risk assessment results. First, the system collects historical and current data including parameters such as brightness, strobe frequency, and current, and performs variance analysis on these data to determine the impact of each parameter on system performance. For example, by comparing system performance data at different brightness levels, variance analysis can reveal the impact of brightness changes on system performance. Based on these analysis results, the submodule selects the most critical parameters, which are the targets of system performance optimization. The result of this process is the generation of key parameter definition analysis results, which not only point out the parameters that need to be optimized most, but also provide a basis for subsequent parameter combination searches to ensure the pertinence and effectiveness of the optimization work.

[0106] In the parameter combination search submodule, the system uses the design of experiments and grid search algorithms to optimize parameters based on the results of the parameter definition submodule. First, based on the key parameter definition analysis results, the system sets a series of parameter combinations. Then, using the grid search algorithm, the system systematically tests these parameter combinations to observe their specific impact on system performance. For example, the system tests performance under different combinations of brightness and strobe frequency. In this way, the submodule can find the most optimized parameter combinations that can improve energy efficiency while ensuring system performance. The result of this process is the generation of parameter combination mining results, which provide a scientific basis for the system's parameter settings and ensure that the system can achieve optimal performance under various working conditions.

[0107] In the performance evaluation submodule, the system uses multivariate regression analysis to evaluate the comprehensive performance of various parameter combinations based on the parameter combination mining results. This process involves comparing the impact of different parameter combinations on system performance to evaluate the effect of each combination. For example, by comparing performance data under different brightness and strobe frequency combinations, multivariate regression analysis can help identify the best parameter configuration. This comprehensive performance evaluation takes into account the joint impact of various parameters on system performance and ensures the comprehensiveness of parameter optimization. The result of this process is the generation of selected parameter configurations that can provide the best performance for the system under different working conditions. Through this scientific and rigorous approach, the performance evaluation submodule ensures the efficiency and accuracy of system parameter optimization, which helps to improve the overall performance and reliability of the system.

[0108] See also Figure 2 and Figure 7 ,The energy efficiency analysis module includes the energy data collection submodule, the ,nonparametric statistical analysis submodule, and the optimization measures formulation submodule;

[0109] The energy data collection submodule uses data acquisition technology based on the selected parameter configuration to collect the system's energy consumption data, including power usage and working hours, by real-time monitoring and recording the system's operating parameters, and generates an energy consumption data set;

[0110] The non-parametric statistical analysis submodule uses the kernel density estimation method based on the energy consumption data set to analyze the distribution characteristics and patterns of energy usage, identify the key influencing factors and abnormal consumption points of energy consumption, and generate energy efficiency statistical analysis results;

[0111] The optimization measures formulation submodule is based on the energy efficiency statistical analysis results and uses cluster analysis technology to explore the hidden patterns and characteristics in the data, propose energy efficiency improvement measures and energy-saving strategies, and generate energy efficiency optimization plans.

[0112] In the energy data collection submodule, the system uses data acquisition technology to monitor and record the operating parameters of the system in real time according to the selected parameter configuration. This includes collecting data related to energy consumption such as power usage and working hours. In the specific process, the system will configure sensors and data recording devices to capture and record the operating parameters of the aviation obstruction lights in real time, such as light brightness, strobe frequency, and current consumed. This data is then formatted and stored to form a detailed energy consumption data set. In this way, the submodule can capture the full picture of the system operation and provide basic data for further energy efficiency analysis. The generated energy consumption data set is crucial to understanding the energy usage pattern of the system and identifying energy saving potential.

[0113] In the non-parametric statistical analysis submodule, the system uses the kernel density estimation method to analyze the distribution characteristics and patterns of energy usage based on the collected energy consumption data set. This process involves using statistical techniques to evaluate the distribution of energy usage data and identify the regularity and anomalies of consumption. For example, kernel density estimation can reveal the concentrated trend of energy consumption or abnormally high usage in a specific period of time. Through these analyses, the submodule is able to identify key factors affecting energy efficiency, such as high consumption rates in a specific time period or energy waste under specific conditions. The generated energy efficiency statistical analysis results provide an important basis for the energy efficiency optimization of the system and reveal the potential direction and optimization points for energy saving.

[0114] In the optimization measures formulation submodule, the system uses cluster analysis techniques to explore the hidden patterns and features in the data based on the results of energy efficiency statistical analysis. This process involves classifying energy usage data to identify similar consumption patterns or behaviors. For example, cluster analysis finds patterns of low energy efficiency under specific environmental conditions (such as high or low temperatures). Based on these findings, the submodule will formulate targeted energy efficiency improvement measures and energy-saving strategies, such as adjusting light brightness settings or optimizing strobe frequency to reduce energy consumption. These optimization measures are aimed at improving the energy efficiency of the system and reducing unnecessary energy waste. The generated energy efficiency optimization plan not only helps to reduce operating costs, but also has positive significance for environmental protection. Through the collaborative work of these three submodules, the energy efficiency analysis module has become a key component for improving system energy efficiency and achieving sustainable operations.

[0115] See also Figure 2 and Figure 8 ,The modular design module includes the design concept planning submodule, the ,interface standardization submodule, and the system expansion strategy submodule;

[0116] The design concept planning submodule is based on the energy efficiency optimization scheme and adopts the modular design principle to plan the modular composition of the aviation obstruction light system, including the light body, controller and sensor, select the overall framework and goals of the modular design, and generate a preliminary modular composition scheme;

[0117] The interface standardization submodule is based on the modular preliminary composition scheme, adopts the communication protocol integration method, implements the standardized interface protocol, enables the integration and communication of new components and modules, optimizes the interoperability and scalability of the system, and generates the interface standardization scheme;

[0118] The system expansion strategy submodule is based on the interface standardization solution and adopts a modular strategy planning method to formulate the system expansion strategy, including the integration of new functional components and performance optimization of existing components, matching and continuous updating of the system, and generating a modular expansion solution.

[0119] In the design concept planning submodule, the system uses structured data formats, such as XML or JSON, to store and describe the modular composition of the aviation obstruction light system. By using the modular design principle, the system first analyzes the key requirements and indicators in the energy efficiency optimization solution, and then uses the system analysis method to determine the functions and interfaces of the lamp body module, controller module, and sensor module based on these requirements. In this process, data flow diagrams and UML (Unified Modeling Language) diagrams are used to visualize the relationship between modules. Next, system simulation tools, such as MATLAB or Simulink, are used to simulate the interaction and integration effects of each module to ensure the rationality of the design. Finally, the overall framework and goals of the modular design are encoded into a structured data file to form a preliminary modular composition plan, which describes in detail the functions, interfaces, and interactions of each module, providing a clear blueprint for subsequent development and integration.

[0120] In the interface standardization submodule, the system uses communication protocols such as TCP / IP or MQTT, combined with API (application programming interface) description files, such as the OpenAPI specification, to standardize the interfaces between modules. Based on the preliminary modular composition scheme, the input and output requirements and data formats of each module are first analyzed, and then the common data exchange format and communication protocol are defined to ensure effective communication and collaboration between modules. The interface is described in detail using the interface definition language (IDL) and converted into an API document to facilitate developers' understanding and use. At the same time, simulation test tools such as Postman or Swagger are used to verify the functionality and performance of the interface to ensure that it meets the design requirements. Finally, an interface standardization scheme is generated, which includes standardized interface definitions, communication protocols, and API documents, providing a solid foundation for the interoperability and scalability of the system.

[0121] In the system expansion strategy submodule, the system uses decision support systems and project management tools, such as Microsoft Project or JIRA, to plan and implement the system's expansion strategy. According to the interface standardization plan, the existing modules of the system are first evaluated to identify opportunities for performance upgrades and functional expansion. By using decision trees and cost-benefit analysis, the integration costs and expected benefits of the new functional modules are evaluated to ensure the rationality of the expansion decision. Next, project management tools are used to plan and track the progress of the expansion project, including task allocation, timeline planning, and resource management, to ensure that the project proceeds as planned. Finally, a modular expansion plan report is prepared, which describes in detail the integration plan for the new functional modules, the performance upgrade strategy for the existing modules, and the matching and update process of the overall system. This plan provides a clear guide and implementation plan for the continuous updating and optimization of the system.

[0122] See also Figure 2 and Fig. 9,The data management module includes a data storage submodule, a data processing submodule, and a data analysis submodule;

[0123] The data storage submodule is based on a modular expansion solution and uses an SQL database management system. By designing the database architecture and establishing data indexes, it stores and retrieves various data types such as performance monitoring, fault records, and operation logs, and generates a centralized data warehouse.

[0124] The data processing submodule is based on a centralized data warehouse and uses data fusion technology to integrate data from multiple sources, manage the data in a unified manner, and generate a unified view data set;

[0125] The data analysis submodule is based on a unified view data set and uses OLAP and association rule mining algorithms to build a multidimensional data model and mine potential association rules in the data to analyze the inherent connections and hidden trends in the data and generate integrated data analysis results.

[0126] In the data storage submodule, the system uses a structured SQL database management system, such as MySQL or PostgreSQL, to store and retrieve various data types such as performance monitoring, fault records, and operation logs. The database design follows the three-paradigm principle to ensure data consistency and maintainability. First, data modeling is performed to define table structures and relationships. For example, special tables are designed for performance data, fault data, and log data, and necessary associations are established. Then, the table structure is created using the SQL language, and field types and constraints are set to ensure correct data storage. Next, the database is indexed and query efficiency is improved by creating appropriate indexes, such as establishing B-tree indexes for frequently queried fields. In addition, regular database maintenance operations, such as data backup and defragmentation, are performed to keep the database healthy. Finally, a centralized data warehouse is generated, which stores all key data for system operation and provides reliable data support for data analysis and decision-making.

[0127] In the data processing submodule, the system uses data fusion technology to integrate data from different sources and generate a unified view data set. Using the ETL process, data is first extracted from the centralized data warehouse, and then the data is cleaned and transformed according to predefined rules, such as standardizing the date format of different data sources, merging duplicate records, and filling missing values. After that, the data is aggregated and reshaped, such as resampling time series data to generate a data format that is more suitable for analysis. Through this process, data of different formats and structures are unified to form a unified view data set. This data set provides a comprehensive and integrated data view that facilitates in-depth data analysis and mining.

[0128] In the data analysis submodule, the system uses OLAP and association rule mining algorithms, such as the Apriori algorithm, to analyze the unified view data set. First, a multidimensional data model is constructed to define fact tables and dimension tables, such as using performance data as fact tables and time, location, equipment type, etc. as dimension tables. This model enables users to slice, dice, and drill data from different angles and levels. Then, OLAP tools are used to perform multidimensional analysis of the data, such as calculating average performance indicators for different time periods and analyzing the trend of failure frequency. Next, association rule mining algorithms are applied to analyze potential associations in the data, such as mining the relationship between specific failure modes and environmental factors. Through these analyses, the inherent connections and hidden trends in the data are revealed, and integrated data analysis results are generated. This result provides important insights for system optimization and decision-making, and helps improve the overall performance and reliability of the system.

[0129] See also Figure 2 and Fig.10 ,The control strategy module includes a strategy formulation submodule, a parameter automatic adjustment submodule, and a response optimization submodule;

[0130] The strategy formulation submodule uses decision analysis technology based on the integrated data analysis results to formulate a control strategy that matches the current operating status and generate a preliminary control strategy by analyzing and parsing the current status and performance data of the system;

[0131] The automatic parameter adjustment submodule is based on the preliminary control strategy and adopts the PID control algorithm. It responds to the performance changes of the system in real time and generates an automatic parameter adjustment strategy by continuously monitoring and automatically adjusting the key system parameters, including brightness and strobe frequency.

[0132] The response optimization submodule is based on the automatic parameter adjustment strategy and adopts adaptive control technology. By analyzing the real-time feedback of the system and the changes in the external environment, it dynamically adjusts the control strategy, optimizes the system's responsiveness and stability, and generates an intelligent control solution.

[0133] In the strategy formulation submodule, the system uses decision analysis techniques, such as decision trees and expert systems, to process the results of integrated data analysis and generate control strategies that match the current operating conditions. First, the system identifies key performance indicators and system status parameters, such as system failure rate, response time, and energy efficiency, based on the results of integrated data analysis. Next, a decision tree algorithm is used to classify and analyze these parameters, and a decision tree model is constructed to predict system performance under different parameter configurations. In addition, the expert system is combined to integrate the experience and knowledge of industry experts to improve the accuracy and reliability of decision-making. Through these analyses and models, the system can automatically formulate the optimal control strategy based on the current operating conditions, such as adjusting the operating mode and changing the maintenance plan. The preliminary control strategy generated by this process is stored as a text or structured data file to guide the real-time operation and management of the system, thereby improving the performance and reliability of the system.

[0134] In the parameter automatic adjustment submodule, the system uses the PID (proportional-integral-differential) control algorithm to continuously monitor and automatically adjust the key parameters of the system. First, according to the preliminary control strategy, the target value of the PID controller is set, for example, the ideal value of brightness and strobe frequency is set. Next, the current values ​​of these parameters are monitored in real time and compared with the target values ​​to calculate the deviation. Then, according to the size of the deviation, the PID controller adjusts its control quantity, in which the proportional term responds to the deviation quickly, the integral term eliminates long-term deviations, and the differential term predicts future deviation changes. In this way, the PID controller can accurately adjust the parameters so that the system can reach the target state quickly and smoothly. The generated automated parameter adjustment strategy is stored as an algorithm parameter configuration file and is used to adjust the operating parameters of the system in real time to ensure the efficiency and stability of the system.

[0135] In the response optimization submodule, the system applies adaptive control technology to dynamically adjust the control strategy based on the real-time feedback of the system and changes in the external environment. First, the system collects real-time feedback data, such as performance indicators, environmental conditions, and adjustment results from the automated parameter adjustment strategy. Next, adaptive algorithms, such as model reference adaptive control or neural network control, are used to analyze this data and predict the performance of the system under different conditions. Then, based on the prediction results, the control strategy is dynamically adjusted, such as changing control parameters or switching control modes. The intelligent control scheme generated by this process is stored as a configuration file or algorithm model to optimize the responsiveness and stability of the system in real time. Through this adaptive control, the system can flexibly respond to various operating conditions and environmental changes, improving the overall performance and reliability of the system.

[0136] In the description of this specification, the description with reference to the terms "one embodiment", "example", "specific example", etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

[0137] The preferred embodiments of the present invention disclosed above are only used to help illustrate the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to the specific implementation methods described. Obviously, many modifications and changes can be made according to the content of this specification. This specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the present invention, so that those skilled in the art can understand and use the present invention well. The present invention is limited only by the claims and their full scope and equivalents.

Claims

1. An expandable and upgradeable aviation obstruction light system, characterized in that: The system includes a dynamic simulation module, a performance monitoring module, a fault prediction module, a parameter optimization module, an energy efficiency analysis module, a modular design module, a data management module, and a control strategy module; The dynamic simulation module is based on the physical and engineering principles of aviation obstruction lights and adopts ordinary differential equation modeling to construct a mathematical model of the behavior of the aviation obstruction light system. The aviation obstruction light system includes aviation obstruction lights and associated control and monitoring equipment. The Runge-Kutta numerical solution is used to solve the model, simulate the dynamic changes of the system under multiple conditions, and generate the system dynamic response analysis results; The performance monitoring module uses a dynamic time warping algorithm based on the system dynamic response analysis results to analyze the time series similarity between historical and real-time data, identify abnormal patterns and trend changes in performance data, and generate performance change pattern recognition results; The fault prediction module uses Spearman rank correlation analysis based on the performance change pattern recognition results to analyze multiple fault factors, including the relationship between temperature, humidity and current, and combines the linear regression model to establish the association between the fault and the influencing factors to generate a fault risk assessment result; The parameter optimization module uses a grid search algorithm based on the fault risk assessment results to traverse a variety of parameter combinations in the set parameter space, evaluates the impact of each combination on system performance through multivariate performance analysis, optimizes the parameter combination, and generates the selected parameter configuration; The energy efficiency analysis module uses kernel density estimation based on the selected parameter configuration to perform statistical analysis on the energy consumption data of the system, combines cluster analysis to analyze the potential patterns and features in the data, and proposes energy efficiency optimization measures to generate energy efficiency optimization solutions; The modular design module is based on the energy efficiency optimization solution and adopts the modular architecture design principle to build a modular structure of multiple components of the system. Through the standardized universal interface protocol, new components are integrated and the system is expanded to generate a modular expansion solution. The data management module is based on a modular expansion solution and adopts a relational database architecture to centrally store and manage all generated data, and uses online analytical processing and data mining algorithms to process and analyze the data to generate integrated data analysis results; The control strategy module uses a decision tree analysis method based on the integrated data analysis results to automatically adjust the system status and performance data, applies adaptive feedback control technology, adjusts the control process according to the real-time feedback of the system, and generates an intelligent control solution.

2. The expandable and upgradeable aviation obstruction light system according to claim 1, characterized in that: The system dynamic response analysis results include response speed indicators, environmental adaptability analysis and system stability data, the performance change pattern recognition results include abnormal frequency change recognition, brightness fluctuation analysis and performance degradation trend indicators, the fault risk assessment results include temperature abnormality alarms, humidity sensitivity analysis and current instability indicators, the selected parameter configurations include brightness adjustment range, strobe adjustment mode and current utilization efficiency indicators, the energy efficiency optimization plan includes energy consumption optimization measures, energy utilization improvement strategies and energy-saving targets, the modular expansion plan includes lamp body module design, controller module upgrade and sensor module integration plan, the integrated data analysis results include comprehensive performance sets, fault mode statistics and parameter adjustment history analysis, the intelligent control plan includes automatic brightness adjustment strategy, strobe response optimization plan and real-time fault response measures.

3. The expandable and upgradeable aviation obstruction light system according to claim 1, characterized in that: The dynamic simulation module includes an equation construction submodule, a numerical approximate solution submodule, and a dynamic behavior analysis submodule; The equation building submodule is based on the physical and engineering principles of aviation obstruction lights and adopts partial differential equation modeling. It establishes mathematical equations to express the relationship between light brightness, strobe frequency and environmental factors, and mathematically describes the factors and light behaviors to generate a mathematical model of system behavior. The numerical approximate solution submodule is based on the system behavior mathematical model, adopts the fourth-order Runge-Kutta numerical solution method, approximates the solution of the differential equation through iterative calculation, obtains the predicted performance of the system under multiple working conditions, and generates dynamic change numerical analysis results; The dynamic behavior analysis submodule adopts dynamic response mode analysis based on the dynamic change numerical analysis results, simulates the influence of various environmental variables on system behavior, analyzes the performance of the system under various working conditions, and generates system dynamic response analysis results.

4. The expandable and upgradeable aviation obstruction light system according to claim 1, characterized in that: The performance monitoring module includes a historical data comparison submodule, a pattern recognition submodule, and a performance trend analysis submodule; The historical data comparison submodule uses time series analysis based on the system dynamic response analysis results to analyze the changing trend of system performance by comparing past and immediate system performance data, and generates historical and real-time data comparison analysis results; The pattern recognition submodule uses a dynamic time warping algorithm based on the comparative analysis results of historical and real-time data to identify irregular changes and abnormal patterns in performance data by quantifying the similarity between time series and generate abnormal pattern recognition results; The performance trend analysis submodule uses trend analysis technology based on abnormal pattern recognition results to track and continuously analyze data changes, mine early signs of performance degradation or potential failures, and generate performance change pattern recognition results.

5. The expandable and upgradeable aviation obstruction light system according to claim 1, characterized in that: The fault prediction module includes a variable selection submodule, a correlation analysis submodule, and a fault prediction modeling submodule; The variable selection submodule uses a factor analysis method to perform multivariate analysis based on the performance change pattern recognition results, selects key factors affecting system performance by screening key variables associated with system failures, including temperature, humidity, and current, and generates fault prediction factor recognition results; The correlation analysis submodule uses Spearman rank correlation analysis based on the fault prediction factor identification results to evaluate the degree of their influence on system performance by statistically analyzing the correlation strength between variables and generate fault impact relationship evaluation results; The fault prediction modeling submodule uses logistic regression modeling technology based on the fault impact relationship assessment results to predict the fault types and frequencies that may occur in the system under various operating environments by establishing a mathematical model between variables and system faults, and generates fault risk assessment results.

6. The expandable and upgradeable aviation obstruction light system according to claim 1, characterized in that: The parameter optimization module includes a parameter definition submodule, a parameter combination search submodule, and a performance evaluation submodule; The parameter definition submodule uses variance analysis based on the fault risk assessment results to distinguish key factors by evaluating the influence of multiple parameters, including brightness, strobe frequency and current intensity on system performance, select the target parameter range for system performance optimization, and generate key parameter definition analysis results; The parameter combination search submodule is based on the key parameter definition analysis results, adopts the design experiment method and grid search algorithm, sets up multiple parameter combination experiments, observes the performance of the system under each combination, and optimizes the parameters in the parameter space to generate parameter combination mining results; The performance evaluation submodule uses multivariate regression analysis based on the parameter combination mining results to compare the impact of each parameter combination on system performance, evaluate the comprehensive performance of the combination, identify and match parameter configurations under multiple working conditions, and generate selected parameter configurations.

7. The expandable and upgradeable aviation obstruction light system according to claim 1, characterized in that: The energy efficiency analysis module includes an energy data collection submodule, a non-parametric statistical analysis submodule, and an optimization measure formulation submodule; The energy data collection submodule collects the energy consumption data of the system, including the power usage and working hours, by real-time monitoring and recording the operating parameters of the system based on the selected parameter configuration and using data collection technology to generate an energy consumption data set; The non-parametric statistical analysis submodule is based on the energy consumption data set and adopts the kernel density estimation method to analyze the distribution characteristics and patterns of energy usage, identify the key influencing factors and abnormal consumption points of energy consumption, and generate energy efficiency statistical analysis results; The optimization measures formulation submodule uses cluster analysis technology based on the energy efficiency statistical analysis results to explore the hidden patterns and features in the data, propose energy efficiency improvement measures and energy-saving strategies, and generate energy efficiency optimization plans.

8. The expandable and upgradeable aviation obstruction light system according to claim 1, characterized in that: The modular design module includes a design concept planning submodule, an interface standardization submodule, and a system expansion strategy submodule; The design concept planning submodule is based on the energy efficiency optimization scheme and adopts the modular design principle to plan the modular structure of the aviation obstruction light system, including the light body, controller and sensor, select the overall framework and goal of the modular design, and generate a preliminary modular structure scheme; The interface standardization submodule is based on a modular preliminary composition scheme, adopts a communication protocol integration method, implements a standardized interface protocol, enables new components and modules to be integrated and communicated, optimizes the interoperability and scalability of the system, and generates an interface standardization scheme; The system expansion strategy submodule is based on the interface standardization solution and adopts a modular strategy planning method to formulate a system expansion strategy, including the integration of new functional components and performance optimization of existing components, to match and continuously update the system, and to generate a modular expansion solution.

9. The expandable and upgradeable aviation obstruction light system according to claim 1, characterized in that: The data management module includes a data storage submodule, a data processing submodule, and a data analysis submodule; The data storage submodule is based on a modular expansion solution and adopts an SQL database management system. By designing a database architecture and establishing data indexes, it performs storage and retrieval of various data types such as performance monitoring, fault records and operation logs, and generates a centralized data warehouse; The data processing submodule is based on a centralized data warehouse and uses data fusion technology to integrate data from multiple sources, manage the data in a unified manner, and generate a unified view data set; The data analysis submodule is based on a unified view data set, adopts OLAP and association rule mining algorithms, builds a multidimensional data model and mines potential association rules in the data, analyzes the inherent connections and hidden trends in the data, and generates integrated data analysis results.

10. The expandable and upgradeable aviation obstruction light system according to claim 1, characterized in that: The control strategy module includes a strategy formulation submodule, a parameter automatic adjustment submodule, and a response optimization submodule; The strategy formulation submodule adopts decision analysis technology based on the integrated data analysis results, analyzes and parses the current state and performance data of the system, formulates a control strategy that matches the current operating status, and generates a preliminary control strategy; The automatic parameter adjustment submodule is based on the preliminary control strategy and adopts the PID control algorithm to respond to the performance changes of the system in real time and generate an automatic parameter adjustment strategy through continuous monitoring and automatic adjustment of key system parameters, including brightness and strobe frequency; The response optimization submodule is based on an automated parameter adjustment strategy and adopts adaptive control technology. By analyzing the real-time feedback of the system and changes in the external environment, the control strategy is dynamically adjusted to optimize the response capability and stability of the system and generate an intelligent control solution.

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