A method and system for matching a driving circuit with a light source module

Abstract

The present invention relates to a method and system for matching a driving circuit with a light source module, and relates to the field of vehicle control technology. The method comprises: determining the optimal modular component combination for the vehicle's headlight system based on the compatibility scoring function; constructing a dynamic power management function by combining fuzzy logic and an adaptive control algorithm, and determining the output power of the headlight system at the current moment based on the dynamic power management function; constructing an ambient light adaptive function, and adjusting the output brightness of the light source of the headlight system at the current moment in the current environment based on the ambient light adaptive function; predicting the optimal parameter set of the headlight system at the next moment through a deep learning model, and controlling the headlight system at the next moment based on the optimal parameter set; calculating the current comprehensive performance score of the headlight system; and optimizing the driving circuit and light source module of the headlight system according to the comprehensive performance score. The present invention can improve the overall performance of the headlight system.

Description

Technical Field

[0001] The present invention relates to the field of vehicle control technology, and in particular to a method, system, electronic device and non-transitory computer-readable storage medium for matching a driving circuit with a light source module. Background Art

[0002] In the field of modern lighting technology, the design of the driver circuit and the matching of the light source module are the key to improving light efficiency and energy efficiency. Traditional driver circuit design often focuses on power efficiency and stability, but with the rapid development of intelligent lighting systems, the demand for driver circuits is no longer limited to basic functions, and more intelligent and adaptive designs are required to meet the needs of complex application scenarios. For example, in the lighting of modern vehicles, the control of the light source requires not only high efficiency, but also the ability to automatically adjust according to environmental changes to provide a more humane light environment.

[0003] However, traditional driver circuit designs lack the ability to dynamically respond to environmental changes and are usually unable to automatically adjust light source output to match lighting needs at different times and spaces. For example, under fixed parameters, the light source may consume excessive power when full brightness is not required, or provide insufficient light when brighter lighting is required, resulting in energy waste and user dissatisfaction. This lack of flexibility results in inefficient lighting systems in a variety of application scenarios, failing to provide optimal visual experience and energy efficiency. Summary of the invention

[0004] In response to the technical problems existing in the prior art, the present invention provides a method, system, electronic device and non-transitory computer-readable storage medium for matching a driving circuit with a light source module, which can improve the overall performance, reliability and user experience of a vehicle lighting system while achieving energy saving and cost optimization.

[0005] The technical solution of the present invention to solve the above technical problems is as follows:

[0006] The present invention provides a method for matching a driving circuit with a light source module, the method comprising:

[0007] Constructing a compatibility scoring function according to the CSP algorithm, and determining an optimal modular component combination for a vehicle lighting system based on the compatibility scoring function;

[0008] Constructing a dynamic power management function by combining fuzzy logic and adaptive control algorithm, and determining the output power of the vehicle lighting system at the current moment based on the dynamic power management function;

[0009] Constructing an ambient light adaptive function, and adjusting the output brightness of the light source of the vehicle lighting system at a current moment in the current environment based on the ambient light adaptive function;

[0010] Predicting an optimal parameter set of the vehicle lighting system at a next moment through a deep learning model, and controlling the vehicle lighting system at the next moment based on the optimal parameter set;

[0011] Calculating a current comprehensive performance score of the vehicle lighting system;

[0012] The driving circuit and light source module of the vehicle lighting system are optimized according to the comprehensive performance score.

[0013] Optionally, constructing a compatibility scoring function according to a CSP algorithm includes:

[0014] Obtaining a component combination to be evaluated, a performance score of each component in the component combination, and an initial compatibility score of the component combination;

[0015] Calculating the compatibility score between each two adjacent components, and the interaction influence function and interaction influence coefficient between any two components;

[0016] Obtaining a function of the impact of environmental factors on the performance of each of the components;

[0017] The compatibility scoring function is constructed based on the performance score of each component in the component set, the initial compatibility score of the component combination, the compatibility score between every two adjacent components, the interaction influence function between any two components, the interaction influence coefficient and the performance influence function of each component.

[0018] Optionally, the compatibility scoring function is expressed as:

[0019]

[0020] in, is the compatibility scoring function of the set of components to be evaluated, is the set of components to be evaluated, n is the total number of components contained in the component combination, is the performance score of the ith component, is the compatibility score between the i-th component and the i+1-th component, is the interaction coefficient, is the interaction influence function, is the initial compatibility score of the component set, is the environmental factor weight corresponding to each component, is the function of the environmental factors affecting the performance of each component, The fifth, sixth and seventh weights respectively.

[0021] Optionally, dynamic power management functions can be built by combining fuzzy logic and adaptive control algorithms, including:

[0022] Obtaining the basic power, fluctuation coefficient, environmental change frequency, and accumulated energy consumption of the vehicle lighting system;

[0023] constructing a fuzzy logic function and an adaptive factor of the fuzzy logic function according to the voltage, current and temperature of each power management module;

[0024] constructing a cumulative energy consumption function of the vehicle lamp system according to the cumulative energy consumption, and an adjustment coefficient corresponding to the cumulative energy consumption function;

[0025] The dynamic power management function is constructed according to the basic power, fluctuation coefficient, environmental change frequency of the vehicle lighting system, the fuzzy logic function, the adaptive factor, the cumulative energy consumption function, and the adjustment coefficient.

[0026] Optionally, the dynamic power management function is expressed as:

[0027]

[0028] in, is the output power at the current time t, is the base power, is the coefficient of volatility, is the frequency of environmental changes, It is a fuzzy logic function based on voltage V, current I and temperature T. is the adaptive factor, is the weight of each power management module, is the adjustment factor related to historical energy consumption, is the cumulative energy consumption function.

[0029] Optionally, predicting an optimal parameter set of the vehicle lighting system at the next moment by a deep learning model includes:

[0030] Obtaining a parameter set of the vehicle lighting system at the current moment;

[0031] Determining a regularization factor based on historical performance of the vehicle lighting system;

[0032] Constructing the gradient of the loss function of the deep learning model with respect to the parameter set at the current moment;

[0033] An optimal parameter set of the vehicle lighting system at the next moment is determined according to the parameter set at the current moment, the regularization factor and the gradient of the loss function.

[0034] Optionally, the optimal parameter set at the next moment is expressed as:

[0035]

[0036] in, is the optimal parameter set for the next time t+1, is the parameter set at the current time t, z is the learning rate, is the gradient of the loss function, is a regularization factor based on historical performance.

[0037] Optionally, calculating the current comprehensive performance score of the vehicle lighting system includes:

[0038] Obtaining a power consumption stability score, an environmental adaptability score, a system response speed score, and a comprehensive performance stability score of the vehicle lighting system;

[0039] Obtaining a first weight of the power consumption stability score, a second weight of the environmental adaptability score, a third weight of the system response speed score, and a fourth weight of the comprehensive performance stability score;

[0040] Based on the first weight, the second weight, the third weight and the fourth weight, the power consumption stability score, the environmental adaptability score, the system response speed score and the comprehensive performance stability score are weightedly summed to obtain a comprehensive performance score of the vehicle lighting system.

[0041] Optionally, the comprehensive performance score is expressed as:

[0042]

[0043] Among them, Q is the comprehensive performance score, is the power consumption stability score, is the environmental adaptability score, is the system response speed score, is the comprehensive performance stability score, They are the first weight, the second weight, the third weight and the fourth weight respectively.

[0044] The present invention also provides a system for matching a driving circuit with a light source module, the system comprising:

[0045] A component matching module, configured to construct a compatibility scoring function according to a CSP algorithm, and determine an optimal modular component combination for a vehicle lighting system based on the compatibility scoring function;

[0046] A power management module, configured to construct a dynamic power management function by combining fuzzy logic and an adaptive control algorithm, and determine the output power of the vehicle light system at a current moment based on the dynamic power management function;

[0047] A brightness output module, used to construct an ambient light adaptive function, and adjust the output brightness of the light source of the vehicle lighting system at a current moment in a current environment based on the ambient light adaptive function;

[0048] A parameter prediction module, used to predict an optimal parameter set of the headlight system at the next moment through a deep learning model, and control the headlight system at the next moment based on the optimal parameter set;

[0049] A performance evaluation module, used to calculate the current comprehensive performance score of the vehicle lighting system;

[0050] A system optimization module is used to optimize the driving circuit and light source module of the vehicle light system according to the comprehensive performance score.

[0051] In addition, to achieve the above objectives, the present invention also proposes an electronic device, comprising: a memory for storing a computer software program; a processor for reading and executing the computer software program, thereby implementing a method for matching a driving circuit with a light source module as described above.

[0052] In addition, to achieve the above objectives, the present invention also proposes a non-transitory computer-readable storage medium, in which a computer software program is stored. When the computer software program is executed by a processor, a method for matching a driving circuit with a light source module as described above is implemented.

[0053] The beneficial effects of the present invention are:

[0054] (1) By introducing the constraint satisfaction problem (CSP) algorithm, the present invention can automatically select the best combination among many components, which not only improves the compatibility and overall performance of the system, but also effectively reduces the development cost and time. The algorithm takes into account the interaction between components to ensure that the selected combination can achieve the best effect in practical applications.

[0055] (2) The present invention combines fuzzy logic and adaptive control of dynamic power management to dynamically adjust power output according to actual environmental conditions and reduce unnecessary energy consumption. Under different driving conditions, the system can maintain stable power output, while improving the energy efficiency of the headlight system and extending the service life of the system.

[0056] (3) The present invention uses adaptive adjustment of ambient light to enable the vehicle lighting system to respond to changes in external light in real time, automatically adjust brightness, and provide optimal lighting effects. This not only improves driving safety and comfort, but also achieves significant energy savings and adapts to different driving environments and weather conditions.

[0057] (4) The present invention introduces a deep learning optimization model that can continuously update and optimize system parameters based on historical data to ensure the continued high efficiency of the system in long-term operation. Deep learning enables the system to learn and adapt to different environmental changes and maintain the best state, thereby improving the overall response speed and adaptability of the system.

[0058] (5) The comprehensive performance scoring system of the present invention can comprehensively measure the performance of the system in terms of energy efficiency, environmental adaptability, response speed and long-term operating stability, so that the system can be continuously optimized and improved during development and use, ensuring that it can still operate reliably and efficiently under various complex driving conditions.

[0059] (6) The present invention adopts a modular design, which makes the various parts of the system independent and compatible with each other, which not only facilitates future upgrades and maintenance, but also reduces the complexity of system development and enhances the scalability and flexibility of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] Figure 1 A scene diagram of a method for matching a driving circuit with a light source module provided by the present invention;

[0061] Figure 2 A flow chart of a method for matching a driving circuit with a light source module provided by the present invention;

[0062] Figure 3 A schematic diagram of the structure of a system for matching a driving circuit with a light source module provided by the present invention;

[0063] Figure 4 A schematic diagram of the hardware structure of a possible electronic device provided by the present invention;

[0064] Figure 5 A schematic diagram of the hardware structure of a possible computer-readable storage medium provided by the present invention. DETAILED DESCRIPTION

[0065] 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 those skilled in the art without creative work are within the scope of protection of the present invention.

[0066] In the description of the present invention, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Thus, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. In the description of the present invention, the meaning of "plurality" is two or more, unless otherwise clearly and specifically defined.

[0067] In the description of the present invention, the term "for example" is used to mean "used as an example, illustration or explanation". Any embodiment described as "for example" in the present invention is not necessarily to be construed as being more preferred or advantageous than other embodiments. The following description is given to enable any technician in the field to implement and use the present invention. In the following description, details are listed for the purpose of explanation. It should be understood that a person of ordinary skill in the art can recognize that the present invention can be implemented without using these specific details. In other examples, well-known structures and processes will not be elaborated in detail to avoid obscuring the description of the present invention with unnecessary details. Therefore, the present invention is not intended to be limited to the embodiments shown, but is consistent with the widest scope consistent with the principles and features disclosed in the present invention.

[0068] See also Figure 1 , Figure 1 A scene diagram of a method for matching a driving circuit with a light source module provided by the present invention. Figure 1 As shown, the terminal and the server are connected via a network, such as a wired or wireless network connection. The terminal may include but is not limited to portable terminals such as mobile phones and tablets installed with various network platform applications, as well as fixed terminals such as computers, query machines, and advertising machines. The server provides users with various business services, including service push servers, user recommendation servers, etc.

[0069] It should be noted that Figure 1 The scene diagram of a method for matching a driving circuit to a light source module is only an example. The terminal, server and application scenario described in the embodiment of the present invention are intended to more clearly illustrate the technical solution of the embodiment of the present invention, and do not generate limitations on the technical solution provided by the embodiment of the present invention. Ordinary technicians in this field can know that with the evolution of the system and the emergence of new business scenarios, the technical solution provided by the embodiment of the present invention is also applicable to similar technical problems.

[0070] Among them, the terminal can be used for:

[0071] Constructing a compatibility scoring function according to the CSP algorithm, and determining an optimal modular component combination for a vehicle lighting system based on the compatibility scoring function;

[0072] Constructing a dynamic power management function by combining fuzzy logic and adaptive control algorithm, and determining the output power of the vehicle lighting system at the current moment based on the dynamic power management function;

[0073] Constructing an ambient light adaptive function, and adjusting the output brightness of the light source of the vehicle lighting system at a current moment in the current environment based on the ambient light adaptive function;

[0074] Predicting an optimal parameter set of the vehicle lighting system at a next moment through a deep learning model, and controlling the vehicle lighting system at the next moment based on the optimal parameter set;

[0075] Calculating a current comprehensive performance score of the vehicle lighting system;

[0076] The driving circuit and light source module of the vehicle lighting system are optimized according to the comprehensive performance score.

[0077] See also Figure 2 , provides a flow chart of a method for matching a driving circuit with a light source module of the present invention, comprising the following steps:

[0078] Step 201: construct a compatibility scoring function according to the CSP algorithm, and determine an optimal modular component combination for a vehicle lighting system based on the compatibility scoring function.

[0079] In some embodiments, step 201 may include:

[0080] Obtaining a component combination to be evaluated, a performance score of each component in the component combination, and an initial compatibility score of the component combination;

[0081] Calculating the compatibility score between each two adjacent components, and the interaction influence function and interaction influence coefficient between any two components;

[0082] Obtaining a function of the impact of environmental factors on the performance of each of the components;

[0083] The compatibility scoring function is constructed based on the performance score of each component in the component set, the initial compatibility score of the component combination, the compatibility score between every two adjacent components, the interaction influence function between any two components, the interaction influence coefficient and the performance influence function of each component.

[0084] In some embodiments, the compatibility scoring function is expressed as:

[0085]

[0086] in, is the compatibility scoring function of the set of components to be evaluated, is the set of components to be evaluated, n is the total number of components contained in the component combination, is the performance score of the ith component, is the compatibility score between the i-th component and the i+1-th component, is the interaction coefficient, is the interaction influence function, is the initial compatibility score of the component set, is the environmental factor weight corresponding to each component, is the function of the environmental factors affecting the performance of each component, The fifth, sixth and seventh weights respectively.

[0087] In the specific implementation, is the compatibility scoring function of the component set to be evaluated, which represents the compatibility of the component set This score measures the compatibility and overall performance of these components in the system.

[0088] is the set of components to be evaluated, n is the total number of components in the component combination, and is a set of n components that will work together in the system. represents the i-th component.

[0089] is the i-th component The performance score of the system is based on indicators such as power efficiency, durability, operating temperature range, etc. Part of the score provides a description of how each component performs under individual conditions. is the corresponding weight, indicating the importance of the component performance score in the overall score.

[0090] It is the compatibility score between the i-th component and the i+1-th component, involving physical compatibility, electrical compatibility, or signal compatibility, etc. It ensures that adjacent components can work together efficiently to avoid compatibility issues. is the corresponding weight, indicating the importance of compatibility between components in the overall score.

[0091] It is the interaction coefficient, which indicates the strength or weight of the interaction between them. It is used to quantify the degree of mutual influence between different components. The larger the weight, the greater the contribution of the interaction between components to the score.

[0092] It is an interaction function that is used to quantify the complex relationships between them (such as electromagnetic interference, heat transfer, etc.) and is used to evaluate how the mutual influence between two components affects the overall performance of the system.

[0093] It is the initial compatibility score of the component set, which is the initial compatibility assessment of the entire component set x. It can be a comprehensive score based on historical data, experimental results or prior knowledge.

[0094] It is the environmental factor weight corresponding to each component, indicating the environmental factor weight related to each component. The higher the weight, the greater the impact of the environmental factor on the performance of the component. Consider the impact of the external environment (such as temperature, humidity, etc.) on component performance to ensure that the component can adapt to environmental changes in actual applications.

[0095] It is a function of the impact of environmental factors on the performance of each component, measuring how environmental factors affect components performance, such as the impact of high temperature on electronic components, the impact of humidity on mechanical parts, etc. and , the actual impact of external environmental conditions on each component in the system can be evaluated. The fifth, sixth and seventh weights respectively.

[0096] In summary, this formula can comprehensively consider the performance of individual components, the compatibility between adjacent components, the complex interactions between components, and the impact of the external environment on components. By maximizing this scoring function, the system can select the optimal combination of components to ensure the best performance and stability in practical applications. This comprehensive and complex evaluation method can effectively optimize system design, avoid potential compatibility issues, and improve the overall performance of the system.

[0097] Step 202: construct a dynamic power management function by combining fuzzy logic and adaptive control algorithm, and determine the output power of the vehicle lighting system at the current moment based on the dynamic power management function.

[0098] In some embodiments, step 202 may include:

[0099] Obtaining the basic power, fluctuation coefficient, environmental change frequency, and accumulated energy consumption of the vehicle lighting system;

[0100] constructing a fuzzy logic function and an adaptive factor of the fuzzy logic function according to the voltage, current and temperature of each power management module;

[0101] constructing a cumulative energy consumption function of the vehicle lamp system according to the cumulative energy consumption, and an adjustment coefficient corresponding to the cumulative energy consumption function;

[0102] The dynamic power management function is constructed according to the basic power, fluctuation coefficient, environmental change frequency of the vehicle lighting system, the fuzzy logic function, the adaptive factor, the cumulative energy consumption function, and the adjustment coefficient.

[0103] In some embodiments, the dynamic power management function is expressed as:

[0104]

[0105] in, is the output power at the current time t, is the base power, is the coefficient of volatility, is the frequency of environmental changes, It is a fuzzy logic function based on voltage V, current I and temperature T. is the adaptive factor, is the weight of each power management module, is the adjustment factor related to historical energy consumption, is the cumulative energy consumption function.

[0106] In the specific implementation, is the output power at the current time t. The output power is the power actually provided by the system at a specific point in time and is affected by many factors.

[0107] It is the basic power, which represents the basic power of the system under ideal conditions, that is, the standard output power when it is not affected by external factors. The basic power is the standard power level predetermined when the system is designed, and is the starting point and basis for the entire formula calculation.

[0108] It is the fluctuation coefficient, which indicates the fluctuation range of the system output power and reflects the influence of environmental changes on the system power. It is used to control the influence of environmental changes on power output. If the environmental changes are large, the fluctuation coefficient is also large.

[0109] It is the frequency of environmental changes, which indicates the frequency of changes in the external environment, such as the rate of change of external conditions such as sunlight intensity, temperature, driving speed, etc. The frequency of environmental changes affects the fluctuation speed of power output and reflects the response speed of the system to environmental changes.

[0110] It is a fuzzy logic function based on voltage V, current I and temperature T. It is used to evaluate the performance of the power management module under specific voltage, current and temperature conditions, helping the system to adjust the output power under various conditions and ensuring that the system can maintain stable and efficient output under different environmental parameters.

[0111] It is an adaptive factor learned from historical data. It reflects the system's ability to adapt to environmental changes. It adjusts the current output power according to the system's historical performance so that the system can better cope with current environmental conditions and demand changes.

[0112] It is the weight of each power management module, indicating the influence of each module and the relative importance of each module in the overall power output. Different power management modules can have different effects on the overall power output of the system. The weight helps adjust the contribution of each module, allowing the system to more accurately manage and distribute power output.

[0113] It is an adjustment coefficient related to historical energy consumption, which is used to adjust the system's response to long-term energy consumption. This coefficient is used to balance the impact of historical energy consumption on current power output, ensuring that the system can reasonably allocate energy in long-term use and does not affect current power output due to excessive consumption.

[0114] is the cumulative energy consumption function, which means that The total energy consumption of the internal system. The cumulative energy consumption function is obtained by integrating Calculate the total energy consumption from the initial time to the current time t. This value reflects the historical energy consumption of the system and is The coefficient affects the current power output.

[0115] Step 203: construct an ambient light adaptive function, and adjust the output brightness of the light source of the vehicle lighting system at the current moment in the current environment based on the ambient light adaptive function.

[0116] In some embodiments, the ambient light adaptation function may be expressed as:

[0117]

[0118] in, It is the output brightness of the light source at the current time t. This brightness is the actual lighting intensity provided by the system at a specific point in time and is affected by a variety of environmental and dynamic factors.

[0119] It is the maximum brightness, which represents the maximum brightness output that the lighting system can achieve under ideal conditions. The maximum brightness is the highest light source output level predetermined when the system is designed, which serves as the basis for brightness adjustment.

[0120] It is the ambient light intensity, which indicates the ambient light intensity from different directions or wavelengths at a certain time. Different ambient light sources (such as sunlight, street lights, etc.) may have different effects on the light output of the system. The ambient light intensity directly affects the output brightness of the headlights. The stronger the light, the lower the brightness requirement of the headlights, and vice versa.

[0121] k is the response coefficient, which is the response coefficient of the system to changes in ambient light intensity. It indicates the sensitivity of the system brightness to changes in ambient light. The response coefficient k determines the speed and degree of the headlight system's response to changes in ambient light. The larger the coefficient, the faster the system responds to changes in light intensity and the greater the adjustment range.

[0122] It is a seasonal adjustment factor based on sunrise and sunset times. It is a seasonal adjustment factor based on sunrise and sunset times. It takes into account the changes in the light hours in different seasons and is used to adjust the brightness of the headlights to adapt to seasonal changes. For example, in winter when the light is weak, the headlights may need higher brightness, while in summer when the sunlight is long, the brightness requirement may be reduced.

[0123] It is a dynamic adjustment factor based on the vehicle speed v, which indicates the impact of vehicle speed on the brightness of the headlights. As the vehicle speed changes, the brightness of the headlights should also be adjusted accordingly. Usually when driving at high speeds, the brightness of the headlights needs to be higher to ensure sufficient lighting distance and safety.

[0124] is the weight of the ambient light intensity, i.e. The weights at different wavelengths or directions are the weight coefficients of ambient light intensity at different wavelengths or directions. This weight coefficient is used to indicate the degree of influence of different ambient lights on the brightness of the system light source. It is used to adjust the relative influence of each ambient light source on the system to ensure that the influence of different types of light on the brightness of the headlights can be accurately reflected.

[0125] It is a function of the influence of other environmental factors (such as humidity, wind speed, etc.) over time, taking into account the influence of multi-dimensional environmental factors and the influence of other environmental factors on the brightness of the headlights in addition to light intensity. For example, high humidity may cause light scattering, requiring higher brightness to maintain a clear view.

[0126] It is the influence coefficient of other environmental factors on light regulation, which is used to adjust the influence intensity of different environmental factors on the system. The system can adapt to the brightness requirements under different environmental conditions, ensuring that the lights can provide appropriate lighting in various environments.

[0127] Step 204: predict an optimal parameter set of the vehicle lighting system at the next moment through a deep learning model, and control the vehicle lighting system at the next moment based on the optimal parameter set.

[0128] In some embodiments, the parameters of the vehicle lighting system may include driving circuit parameters (e.g., output voltage level, current control parameters, PWM modulation frequency and duty cycle, power factor correction parameters), light source module parameters (e.g., operating current of LED chip, color temperature adjustment parameters, luminous flux control parameters), thermal management system parameters (e.g., radiator fan speed, active cooling control threshold), environmental adaptation parameters (e.g., photosensor sensitivity setting, ambient light response curve parameters), dynamic control parameters (e.g., proportional, integral, differential coefficients of PID controller, membership function parameters of fuzzy logic controller), energy efficiency optimization parameters (e.g., energy allocation strategy parameters, standby mode trigger threshold), life management parameters (e.g., component stress control parameters, recycling strategy parameters), etc.

[0129] In some embodiments, step 204 may include:

[0130] Obtaining a parameter set of the vehicle lighting system at the current moment;

[0131] Determining a regularization factor based on historical performance of the vehicle lighting system;

[0132] Constructing the gradient of the loss function of the deep learning model with respect to the parameter set at the current moment;

[0133] An optimal parameter set of the vehicle lighting system at the next moment is determined according to the parameter set at the current moment, the regularization factor and the gradient of the loss function.

[0134] In some embodiments, the optimal parameter set at the next moment is expressed as:

[0135]

[0136] in, is the optimal parameter set for the next time t+1, is the parameter set at the current time t, z is the learning rate, is the gradient of the loss function, is a regularization factor based on historical performance.

[0137] In the specific implementation, is the optimal parameter set for the next time t+1. It is the parameter set at the current time t, including the control parameters and configuration parameters of the system. It is the basis for this update and reflects the settings and status of the system at the current time t.

[0138] z is the learning rate, which is a constant that controls the step size of parameter updates. The learning rate determines the parameter set Towards Update speed. If the learning rate is too high, it may cause the update to be too fast and miss the optimal solution; if the learning rate is too low, the update will be too slow, affecting the system response speed.

[0139] is the gradient of the loss function, which indicates the error direction and magnitude of the current parameter set of the system. It indicates the direction of parameter update, that is, the direction in which the system needs to adjust the parameter set To reduce the error and improve performance. This gradient is usually calculated based on training data or historical data.

[0140] It is a regularization factor based on historical performance. It adjusts the impact of the current gradient based on the system's historical performance data to control the magnitude of the gradient update, prevent excessive or insufficient parameter updates, and ensure the stability and adaptability of the system update process. By introducing historical performance data, the system can avoid over-reliance on current training data, thereby reducing the risk of overfitting.

[0141] In summary, the present invention achieves continuous optimization of the performance of the vehicle lighting system by continuously adjusting system parameters, gradually approaching the optimal performance state at different times, and ensuring that the vehicle lighting system can always maintain the best performance under changing environmental conditions.

[0142] Step 205: Calculate the current comprehensive performance score of the vehicle lighting system.

[0143] In some embodiments, step 205 may include:

[0144] Obtaining a power consumption stability score, an environmental adaptability score, a system response speed score, and a comprehensive performance stability score of the vehicle lighting system;

[0145] Obtaining a first weight of the power consumption stability score, a second weight of the environmental adaptability score, a third weight of the system response speed score, and a fourth weight of the comprehensive performance stability score;

[0146] Based on the first weight, the second weight, the third weight and the fourth weight, the power consumption stability score, the environmental adaptability score, the system response speed score and the comprehensive performance stability score are weightedly summed to obtain a comprehensive performance score of the vehicle lighting system.

[0147] In some embodiments, the overall performance score is expressed as:

[0148]

[0149] Among them, Q is the comprehensive performance score, is the power consumption stability score, is the environmental adaptability score, is the system response speed score, is the comprehensive performance stability score, They are the first weight, the second weight, the third weight and the fourth weight respectively.

[0150] In specific implementation, Q is a comprehensive performance score, which indicates the overall performance score of the vehicle lighting system. It integrates the performance of the system in multiple aspects and is used to measure the overall performance of the system. It is the basis for system optimization and adjustment. A higher Q value indicates that the system performs well in all aspects and is suitable for practical applications.

[0151] It is a power consumption stability score that evaluates the power consumption stability of the system under different environmental conditions and measures the fluctuation and consistency of the system's power consumption. It reflects the system's performance in energy saving and whether the system can maintain stable power consumption under various environmental conditions. High The Zhiming system performs well in power consumption management, with small power fluctuation and good energy-saving effect.

[0152] It is an environmental adaptability score that evaluates the system's adaptability under different ambient lighting conditions and measures the system's response and adjustment capabilities to changes in the external environment. It reflects whether the system can automatically adjust the brightness to provide the best lighting effect under various lighting conditions (such as daytime, nighttime, inside and outside tunnels, etc.). High The value indicates that the system can flexibly adapt to environmental changes and always maintain appropriate brightness output.

[0153] It is a system response speed score, which measures the system's response speed when facing environmental changes and evaluates the timeliness of the system's adjustment of its brightness, power and other parameters. It reflects the system's ability to quickly adjust the corresponding parameters after detecting external changes (such as light changes, vehicle speed changes). High The value indicates that the system can respond quickly to changes in external conditions, ensuring that the system is always in the best condition.

[0154] It is a comprehensive performance stability score that evaluates the comprehensive performance stability of the system in long-term operation and measures the consistency and reliability of the system in long-term use. It reflects whether the system can maintain stable performance in long-term operation, including sustained performance in power consumption, brightness output, response speed, etc. High The system will not show obvious performance degradation or fluctuation during long-term operation and has high reliability.

[0155] They are the first weight, the second weight, the third weight and the fourth weight. The weight setting can be adjusted according to the system design requirements to highlight the performance indicators that are particularly important in the system design.

[0156] In summary, the present invention comprehensively considers the four key performance indicators of power consumption, environmental adaptability, response speed and long-term stability, and assigns different weights according to system design requirements. The comprehensive performance score can be used to guide system optimization to ensure that the vehicle lighting system can maintain efficient and stable performance under various conditions.

[0157] Step 206: Optimize the driving circuit and light source module of the vehicle lamp system according to the comprehensive performance score.

[0158] In some embodiments, relevant data can be collected through the actual operation of the vehicle lighting system, including power consumption changes, environmental adaptability (such as brightness adjustment effects under different lighting conditions), system response speed (such as reaction time when the environment changes rapidly), and long-term operation stability of the system.

[0159] In some embodiments, the weaker parts of the headlight system can be identified by analyzing the overall performance score and the individual scores. For example, it may be found that the system has a low environmental adaptability score, indicating that the headlights are not timely or accurate enough to adjust the brightness under rapidly changing lighting conditions. At the same time, the distribution of weight coefficients is analyzed to determine whether the current score is consistent with the design goals of the system. For example, if the Q value is low and the third weight is high, the response speed score is low, and the response speed of the system needs to be optimized first.

[0160] In some embodiments, adjustments can be made in the driver circuit design to address the identified bottlenecks. If the score is low, you can optimize the power management strategy and adjust the parameters of the fuzzy logic control function so that the driver circuit can control power consumption more effectively under different conditions and reduce unnecessary energy waste. If the system response speed score is low, you can improve the response speed of the driver circuit by adjusting the learning rate and parameter set update algorithm so that the circuit responds to the control signal of the light source module more quickly.

[0161] In some embodiments, the light source module can be adjusted to better respond to the control of the driving circuit. For example, if the environmental adaptability score is low, the brightness adjustment mechanism of the light source module can be optimized, and the response coefficient and the ambient light intensity weight can be adjusted so that the light source can adjust the brightness more quickly and accurately. If the comprehensive performance stability score is low, it is possible to consider improving the material and structural design of the light source module to enhance its durability and stability and reduce the light attenuation or performance instability that may occur during long-term operation.

[0162] In some embodiments, after preliminary optimization of the driving circuit and light source module, the scores of various items and the comprehensive performance score can be recalculated. If the Q value after optimization is significantly improved, it means that the optimization measures are effective; if the improvement is limited, further analysis is required to find new performance bottlenecks and further optimize.

[0163] Through comprehensive performance scoring, the driving circuit and light source module of the headlight system can be continuously optimized. The specific process includes collecting performance data, identifying system bottlenecks, adjusting the driving circuit and light source module in a targeted manner, and gradually improving the overall performance of the system through feedback and iteration. Ultimately, the headlight system will be able to provide the best lighting effect in different driving environments, ensuring efficient and reliable operation of the system.

[0164] See also Figure 3 , Figure 3 A schematic structural diagram of a system for matching a driving circuit with a light source module provided by the present invention.

[0165] like Figure 3 As shown, a system for matching a driving circuit with a light source module according to an embodiment of the present invention includes:

[0166] A component matching module 301 is used to construct a compatibility scoring function according to the CSP algorithm, and determine an optimal modular component combination for the vehicle lighting system based on the compatibility scoring function;

[0167] A power management module 302, configured to construct a dynamic power management function by combining fuzzy logic and an adaptive control algorithm, and determine the output power of the vehicle light system at a current moment based on the dynamic power management function;

[0168] The brightness output module 303 is used to construct an ambient light adaptive function and adjust the output brightness of the light source of the vehicle lighting system at a current moment in the current environment based on the ambient light adaptive function;

[0169] A parameter prediction module 304, configured to predict an optimal parameter set of the headlight system at a next moment through a deep learning model, and control the headlight system at the next moment based on the optimal parameter set;

[0170] The performance evaluation module 305 is used to calculate the current comprehensive performance score of the vehicle light system;

[0171] The system optimization module 306 is used to optimize the driving circuit and light source module of the vehicle light system according to the comprehensive performance score.

[0172] See also Figure 4 , Figure 4 Schematic diagram of an electronic device provided by an embodiment of the present invention. Figure 4As shown, an embodiment of the present invention provides an electronic device 400, including a memory 410, a processor 420, and a computer program 411 stored in the memory 410 and executable on the processor 420. When the processor 420 executes the computer program 411, the following steps are implemented:

[0173] Constructing a compatibility scoring function according to the CSP algorithm, and determining an optimal modular component combination for a vehicle lighting system based on the compatibility scoring function;

[0174] Constructing a dynamic power management function by combining fuzzy logic and adaptive control algorithm, and determining the output power of the vehicle lighting system at the current moment based on the dynamic power management function;

[0175] Constructing an ambient light adaptive function, and adjusting the output brightness of the light source of the vehicle lighting system at a current moment in the current environment based on the ambient light adaptive function;

[0176] Predicting an optimal parameter set of the vehicle lighting system at a next moment through a deep learning model, and controlling the vehicle lighting system at the next moment based on the optimal parameter set;

[0177] Calculating a current comprehensive performance score of the vehicle lighting system;

[0178] The driving circuit and light source module of the vehicle lighting system are optimized according to the comprehensive performance score.

[0179] See also Figure 5 , Figure 5 A schematic diagram of an embodiment of a computer-readable storage medium provided in an embodiment of the present invention. Figure 5 As shown, this embodiment provides a computer-readable storage medium 500, on which a computer program 411 is stored. When the computer program 411 is executed by a processor, the following steps are implemented:

[0180] Constructing a compatibility scoring function according to the CSP algorithm, and determining an optimal modular component combination for a vehicle lighting system based on the compatibility scoring function;

[0181] Constructing a dynamic power management function by combining fuzzy logic and adaptive control algorithm, and determining the output power of the vehicle lighting system at the current moment based on the dynamic power management function;

[0182] Constructing an ambient light adaptive function, and adjusting the output brightness of the light source of the vehicle lighting system at a current moment in the current environment based on the ambient light adaptive function;

[0183] Predicting an optimal parameter set of the vehicle lighting system at a next moment through a deep learning model, and controlling the vehicle lighting system at the next moment based on the optimal parameter set;

[0184] Calculating a current comprehensive performance score of the vehicle lighting system;

[0185] The driving circuit and light source module of the vehicle lighting system are optimized according to the comprehensive performance score.

[0186] It should be noted that in the above embodiments, the description of each embodiment has its own emphasis, and for parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0187] It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0188] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded computer, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A system that specifies the functions of a box or boxes.

[0189] These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer-readable memory produce a product including an instruction system, which is implemented in the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0190] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1The steps for the functions specified in one or more boxes.

[0191] Although the preferred embodiments of the present invention have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0192] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A method for matching a driving circuit with a light source module, characterized in that: The method comprises: Constructing a compatibility scoring function according to a constraint satisfaction problem (CSP) algorithm, and determining an optimal modular component combination for a vehicle lighting system based on the compatibility scoring function; Constructing a dynamic power management function by combining fuzzy logic and adaptive control algorithm, and determining the output power of the vehicle lighting system at the current moment based on the dynamic power management function; Constructing an ambient light adaptive function, and adjusting the output brightness of the light source of the vehicle lighting system at a current moment in the current environment based on the ambient light adaptive function; Predicting an optimal parameter set of the vehicle lighting system at a next moment through a deep learning model, and controlling the vehicle lighting system at the next moment based on the optimal parameter set; Calculating a current comprehensive performance score of the vehicle lighting system; The driving circuit and light source module of the vehicle lighting system are optimized according to the comprehensive performance score.

2. The method for matching a driving circuit with a light source module according to claim 1, characterized in that: The compatibility scoring function is constructed according to the constraint satisfaction problem CSP algorithm, including: Obtaining a component combination to be evaluated, a performance score of each component in the component combination, and an initial compatibility score of the component combination; Calculating the compatibility score between each two adjacent components, and the interaction influence function and interaction influence coefficient between any two components; Obtaining a function of the impact of environmental factors on the performance of each of the components; The compatibility scoring function is constructed based on the performance score of each component in the component set, the initial compatibility score of the component combination, the compatibility score between every two adjacent components, the interaction influence function between any two components, the interaction influence coefficient and the performance influence function of each component.

3. The method for matching a driving circuit with a light source module according to claim 2, characterized in that: The compatibility scoring function is expressed as: in, is the compatibility scoring function of the set of components to be evaluated, is the set of components to be evaluated, n is the total number of components contained in the component combination, is the performance score of the ith component, is the compatibility score between the i-th component and the i+1-th component, is the interaction coefficient, is the interaction influence function, is the initial compatibility score of the component set, is the environmental factor weight corresponding to each component, is the function of the environmental factors affecting the performance of each component, The fifth, sixth and seventh weights respectively.

4. The method for matching a driving circuit with a light source module according to claim 3, characterized in that: The dynamic power management function is constructed by combining fuzzy logic and adaptive control algorithm, including: Obtaining the basic power, fluctuation coefficient, environmental change frequency, and accumulated energy consumption of the vehicle lighting system; constructing a fuzzy logic function and an adaptive factor of the fuzzy logic function according to the voltage, current and temperature of each power management module; constructing a cumulative energy consumption function of the vehicle lamp system according to the cumulative energy consumption, and an adjustment coefficient corresponding to the cumulative energy consumption function; The dynamic power management function is constructed according to the basic power, fluctuation coefficient, environmental change frequency of the vehicle lighting system, the fuzzy logic function, the adaptive factor, the cumulative energy consumption function, and the adjustment coefficient.

5. The method for matching a driving circuit with a light source module according to claim 4, characterized in that: The dynamic power management function is expressed as: in, is the output power at the current time t, is the base power, is the coefficient of volatility, is the frequency of environmental changes, It is a fuzzy logic function based on voltage V, current I and temperature T. is the adaptive factor, is the weight of each power management module, is the adjustment factor related to historical energy consumption, is the cumulative energy consumption function, Is a time value.

6. The method for matching a driving circuit with a light source module according to claim 5, characterized in that: The predicting the optimal parameter set of the vehicle lighting system at the next moment by using a deep learning model includes: Obtaining a parameter set of the vehicle lighting system at the current moment; Determining a regularization factor based on historical performance of the vehicle lighting system; Constructing the gradient of the loss function of the deep learning model with respect to the parameter set at the current moment; An optimal parameter set of the vehicle lighting system at the next moment is determined according to the parameter set at the current moment, the regularization factor and the gradient of the loss function.

7. The method for matching a driving circuit with a light source module according to claim 6, characterized in that: The optimal parameter set at the next moment is expressed as: in, is the optimal parameter set for the next time t+1, is the parameter set at the current time t, z is the learning rate, is the gradient of the loss function, is a regularization factor based on historical performance.

8. The method for matching a driving circuit with a light source module according to claim 7, characterized in that: Calculate the current comprehensive performance score of the vehicle lighting system, including: Obtaining a power consumption stability score, an environmental adaptability score, a system response speed score, and a comprehensive performance stability score of the vehicle lighting system; Obtaining a first weight of the power consumption stability score, a second weight of the environmental adaptability score, a third weight of the system response speed score, and a fourth weight of the comprehensive performance stability score; Based on the first weight, the second weight, the third weight and the fourth weight, the power consumption stability score, the environmental adaptability score, the system response speed score and the comprehensive performance stability score are weightedly summed to obtain a comprehensive performance score of the vehicle lighting system.

9. The method for matching a driving circuit with a light source module according to claim 8, characterized in that: The comprehensive performance score is expressed as: Among them, Q is the comprehensive performance score, is the power consumption stability score, is the environmental adaptability score, is the system response speed score, is the comprehensive performance stability score, They are the first weight, the second weight, the third weight and the fourth weight respectively.

10. A system for matching a driving circuit with a light source module, characterized in that: The system comprises: A component matching module, configured to construct a compatibility scoring function according to a constraint satisfaction problem (CSP) algorithm, and determine an optimal modular component combination for a vehicle lighting system based on the compatibility scoring function; A power management module, configured to construct a dynamic power management function by combining fuzzy logic and an adaptive control algorithm, and determine the output power of the vehicle light system at a current moment based on the dynamic power management function; A brightness output module, used to construct an ambient light adaptive function, and adjust the output brightness of the light source of the vehicle lighting system at a current moment in a current environment based on the ambient light adaptive function; A parameter prediction module, used to predict an optimal parameter set of the headlight system at the next moment through a deep learning model, and control the headlight system at the next moment based on the optimal parameter set; A performance evaluation module, used to calculate the current comprehensive performance score of the vehicle lighting system; A system optimization module is used to optimize the driving circuit and light source module of the vehicle light system according to the comprehensive performance score.

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