Control method for self-adjusting brightness of street lamp

Abstract

The invention provides a street lamp self-adjusting brightness control method, and belongs to the field of PID control optimization, and the method comprises the specific steps: 1, carrying out the intelligent initialization of the brightness adjustment of a street lamp according to the use scene and time period of an area where the street lamp is located, and determining a basic brightness parameter; 2, associating street lamp brightness adjustment with external environment change in combination with real-time environment dynamic conditions, and triggering street lamp brightness adjustment logic; 3, optimizing and iterating the parameters of the street lamp brightness controller through a self-adjusting algorithm, so that the street lamp brightness controller can adapt to the change conditions of different environments and dynamic targets; the self-adjusting algorithm is an improved eagle algorithm, and the specific improvement is that a self-adaptive step length mechanism is introduced in the development stage of the eagle algorithm, and the step length is dynamically adjusted according to the distance between the current solution and the optimal solution; and step 4, on the basis of parameter optimization of the street lamp brightness controller, the street lamp brightness control system further accurately adjusts the brightness through a feedback closed loop, and self-adjustment accurate control of the street lamp brightness is realized.

Description

Technical Field

[0001] The invention belongs to the field of PID control optimization, and in particular relates to a control method for self-adjusting brightness of a street lamp. Background Art

[0002] As an important part of the dam infrastructure, dam street lights undertake the key tasks of nighttime lighting and improving the safety of the dam. However, traditional street light systems usually adopt a fixed brightness lighting method, which lacks flexibility and leads to particularly prominent energy waste problems. For example, in the late night when no one is around or in the early morning when the ambient light intensity is high, the street lights still remain in full brightness mode, which not only increases electricity consumption but also affects the goal of green and low-carbon development. In addition, traditional street light systems lack the ability to respond to dynamic environmental changes and cannot make intelligent adjustments based on real-time ambient light brightness and pedestrian and vehicle activity, which may cause insufficient or excessive lighting effects. Therefore, designing an intelligent control method that can adaptively adjust brightness in combination with real-time environmental information has become the key to solving the above problems. By integrating light sensors and dynamic target detection equipment, and using an intelligent street light control system that combines optimization algorithms with feedback closed-loop control technology, it can not only achieve rapid response to environmental changes and accurately adjust brightness, but also significantly reduce energy consumption and improve lighting effects, meeting the needs of modern cities for efficient energy saving and intelligent management.

[0003] PID control algorithm is a classic closed-loop control method. It adjusts the system error in real time through the three parts of proportion (P), integration (I) and differential (D) to achieve accurate matching of target value and actual value. Proportional control responds quickly according to the current error. Integral control accumulates historical errors to eliminate steady-state deviations. Differential control predicts the error change trend to improve system stability. The PID algorithm has a simple structure and strong robustness. It is suitable for linear and nonlinear systems and is widely used in industrial automation and process control to achieve fast response, stable and accurate control effects.

[0004] The Osprey Optimization Algorithm (OOA) is a swarm intelligence optimization algorithm based on the osprey's predation behavior. It simulates the osprey's circling and diving to hunt fish schools to complete global search and local development. The algorithm is divided into an exploration phase and an development phase. The exploration phase simulates the osprey's circling and searching behavior; the development phase simulates the osprey's precise diving attack on the target fish school. The Osprey Optimization Algorithm is simple and efficient, has strong global search capabilities and flexible parameter adjustment, but is prone to falling into local optimal solutions and reaching convergence early to a certain extent. Summary of the invention

[0005] The purpose of the present invention is to achieve adaptive and stable control of street lamp brightness adjustment through a control method for self-adjusting brightness of street lamps; to improve the Osprey algorithm by introducing an adaptive step size mechanism, to improve the global search capability and local development capability of the algorithm, to enhance the algorithm optimization performance, and to avoid falling into the local optimum; to optimize and iterate the parameters of the street lamp brightness controller by using the improved Osprey algorithm, so that the street lamp brightness controller can quickly adapt to different environmental dynamic changes; on the basis of the optimization of the street lamp brightness controller, in combination with a feedback closed-loop control mechanism, to achieve accurate correction of the brightness error, and to further improve the response speed and control accuracy of the street lamp brightness adjustment system.

[0006] In order to achieve the above-mentioned purpose, the present invention adopts a control method for self-adjusting brightness of street lamps, and the specific steps are as follows.

[0007] Step 1: Intelligently initialize the brightness adjustment of the street lamps according to the usage scenarios and time periods of the area where the street lamps are located, and determine the basic brightness parameters.

[0008] Step 2: Combine the real-time environmental dynamics to associate the street light brightness adjustment with external environmental changes to trigger the street light brightness adjustment logic.

[0009] Step three: Optimize and iterate the parameters of the street light brightness controller through a self-adjusting algorithm so that the street light brightness controller can adapt to changes in different environments and dynamic targets; the self-adjusting algorithm is an improved Osprey algorithm, and the specific improvements are: an adaptive step size mechanism is introduced in the development stage of the Osprey algorithm, and the step size is dynamically adjusted according to the distance between the current solution and the optimal solution.

[0010] Step 4: Based on the optimization of the street lamp brightness controller parameters, the street lamp brightness control system further accurately adjusts the brightness through a feedback closed loop to achieve precise self-regulation and control of the street lamp brightness.

[0011] Preferably, in step one, the brightness adjustment of the street lamp is intelligently initialized according to the usage scenarios and time periods of the area where the street lamp is located. Intelligent initialization means that before the street lamp system is operated, the initial parameters of the street lamp brightness adjustment are set according to the functional characteristics of the area where the street lamp is located, the usage scenarios and the lighting needs of different time periods. This initialization method can provide targeted adjustment logic for the street lamp system, reduce energy consumption and meet diverse usage needs.

[0012] Preferably, the basic brightness parameters in step 1 include a basic brightness value, a low brightness value, a high brightness value, a current ambient light intensity, and a maximum ambient light intensity reference value.

[0013] Preferably, the street lamp brightness adjustment logic model in step 2 is:

[0014] L target =W target ·Lhigh +(1-W target )·[P env ·L low +(1-P env )·L base ](1);

[0015]

[0016] Where, L target is the target brightness value, indicating the brightness that the street light needs to achieve, W target is the dynamic target weight, L high is a high brightness value, used to provide sufficient lighting, L low is a low brightness value, used in energy saving mode, L base Is the basic brightness value, used to maintain basic lighting needs, P env is the normalization coefficient of ambient light intensity, indicating the relative proportion of ambient light intensity, L env is the current ambient light intensity, L max It is the maximum ambient light intensity reference value.

[0017] Preferably, in the street light brightness adjustment logic model, when a dynamic target (pedestrian or vehicle) is detected, the model directly sets the target brightness value to a high brightness value L high , to meet the actual lighting needs; when there is no dynamic target, the model adjusts the brightness value according to the ambient light intensity. The higher the ambient light intensity, the lower the brightness value L low As you get closer, the lower the ambient light intensity, the brightness moves toward the base brightness value L base near.

[0018] Preferably, the specific improvement of the Osprey algorithm in step 3 is: introducing an adaptive step size mechanism in the development stage of the Osprey algorithm, and dynamically adjusting the step size according to the distance between the current solution and the optimal solution. The specific implementation formula is:

[0019]

[0020] In the formula, For the new position of Osprey in the development phase, x i，j is the current position of the Osprey, lb j Search for the lower bound of the algorithm, ub j The algorithm searches for an upper bound, r i，j is a random value between 0 and 1, t is the current iteration number, ΔX is the adaptive step size, X best It is the global optimal solution.

[0021] Preferably, the advantage of introducing an adaptive step-size mechanism in the development stage of the Osprey algorithm is reflected in improving the balance between global search capability and local development capability; the adaptive step-size mechanism dynamically adjusts the search step-size according to the distance between the current solution and the optimal solution, and can use a larger step-size to quickly explore the global solution space in the early stage of the search to avoid the algorithm from falling into the local optimum; and the step-size gradually decreases in the later stage of the search, which is conducive to the refined development of the solution and improves the convergence accuracy of the algorithm; in addition, the adaptive step-size mechanism enables the algorithm to dynamically adjust the search intensity according to the characteristics of different problems, enhance its adaptability, and be suitable for complex and diverse optimization problems; compared with the fixed step-size strategy, the adaptive step-size can effectively reduce invalid search steps, improve algorithm efficiency, while maintaining population diversity, and avoid premature convergence or stagnation; overall, the introduction of the adaptive step-size mechanism significantly enhances the global exploration capability, local optimization accuracy, and robustness and adaptability of the Osprey algorithm to complex problems.

[0022] Preferably, the specific steps of optimizing and iterating the parameters of the street lamp brightness controller by the self-adjusting algorithm, i.e., the improved Osprey algorithm in step three are:

[0023] Step 1: Encode the parameters Kp, Ki, and Kd to be optimized of the street lamp brightness controller as optimization variables of the improved Osprey algorithm. During the iteration process of the algorithm, the position of each individual in the population represents a set of candidate solutions for the controller parameters.

[0024] Step 2: Initialize the parameters of the improved Osprey algorithm, including the population size N of the algorithm, the problem dimension d, the maximum number of iterations T, and the algorithm search upper bound ub j , the algorithm searches for the lower bound lb j ;

[0025] Step 3, simulate the optimization process of each stage of the improved Osprey algorithm and update the individual positions of the population;

[0026] Step 4, calculate the objective function value of the new position, and update the global optimal solution and the individual optimal solution;

[0027] Step 5. If the maximum number of iterations is reached, the final optimal parameters Kp, Ki, and Kd are output for use by the street light brightness controller, otherwise the iteration continues.

[0028] Preferably, step 1 encodes the parameters Kp, Ki, and Kd to be optimized of the street lamp brightness controller as optimization variables of the improved Osprey algorithm. In the iterative process of the algorithm, the position of each population individual represents a set of candidate solutions for controller parameters. As the positions of the population individuals are updated, the optimization process dynamically adjusts the controller parameters so that the parameters gradually approach the optimal values. The encoding vector is:

[0029] x = [Kp, Ki, Kd];

[0030] Where x is the encoding vector, Kp, Ki, and Kd are the proportional parameter, integral parameter, and differential parameter of the street light brightness controller, respectively.

[0031] Preferably, the step 3 simulates the optimization process of each stage of the improved Osprey algorithm, and the specific steps are:

[0032] S1, simulation algorithm exploration phase, executing formula (6);

[0033]

[0034] In the formula, To explore the new position of the Osprey, x i，j is the current position of the osprey, r i，j is a random value between 0 and 1, SF i，j Fish selected for Osprey, I i，j is a random number in the set {1, 2};

[0035] S2: In the simulation algorithm development phase, an adaptive step size mechanism is introduced to execute formulas (3)-(4).

[0036] Preferably, the step 4 calculates the objective function value of the new position, and the objective function formula is:

[0037]

[0038] Where J is the objective function value, e(a) is the brightness error, u(a) is the control signal output by the street light brightness controller, w1 and w2 are weight factors, which respectively reflect the priority of brightness adjustment accuracy and the importance of energy-saving optimization, a is the current time, and b is the total system operation time.

[0039] Preferably, in step 4, based on the optimization of the parameters of the street lamp brightness controller, the street lamp brightness control system further accurately adjusts the brightness through a feedback closed loop, and the brightness adjustment formula is:

[0040] L current (t) = α·u(t) + L base (5);

[0041] Where, L current (t) is the actual brightness value of the street lamp at the current moment, u(t) is the brightness control signal output by the controller, α is the brightness gain coefficient, which is used to map the brightness control signal u(t) to the actual brightness change, L base Base brightness value.

[0042] By adopting the above technical scheme, the beneficial effects of the present invention are as follows: by introducing an adaptive step size mechanism, the Osprey algorithm is improved, which significantly improves the global search capability and local development capability of the algorithm, and avoids the algorithm from falling into the local optimum; the parameters of the street light brightness controller are optimized and iterated using the improved Osprey algorithm, so that the street light brightness controller can quickly adapt to different usage scenarios, time periods and real-time environmental dynamic changes, thereby improving the response speed and adaptability of the system; on the basis of the optimization of the parameters of the street light brightness controller, the brightness error is accurately corrected through feedback closed-loop control to ensure that the actual brightness quickly approaches the target brightness, thereby reducing the steady-state error and dynamic overshoot of the system, improving the stability and accuracy of the system, and realizing the precise control of the street light brightness self-regulation. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 The present invention is a flowchart of the steps of a method for controlling the self-adjusting brightness of a street lamp.

[0044] Figure 2 Flowchart of the specific steps for iterative optimization of controller parameters to improve the Osprey algorithm.

[0045] Figure 3 The fitness value comparison curve of the basic Osprey algorithm and the improved Osprey algorithm.

[0046] Figure 4 Diagram of the process of iteratively optimizing the parameter values ​​of street light brightness controller for improving the Osprey algorithm.

[0047] Figure 5 A comparison chart of the optimization effects of the basic Osprey algorithm and the improved Osprey algorithm on street light brightness controller. DETAILED DESCRIPTION

[0048] The following examples further illustrate the content of the present invention, but should not be construed as limiting the present invention. Without departing from the spirit and substance of the present invention, modifications or substitutions made to the methods, steps or conditions of the present invention all fall within the scope of the present invention.

[0049] The present invention provides a technical solution: a method for controlling the self-adjusting brightness of a street lamp, which specifically comprises the following steps: Figure 1 shown.

[0050] Step 1: Intelligently initialize the brightness adjustment of the street lamps according to the usage scenarios and time periods of the area where the street lamps are located, and determine the basic brightness parameters.

[0051] Furthermore, the basic brightness parameters in step one include a basic brightness value, a low brightness value, a high brightness value, a current ambient light intensity, and a maximum ambient light intensity reference value.

[0052] Step 2: Combine the real-time environmental dynamics to associate the street light brightness adjustment with external environmental changes to trigger the street light brightness adjustment logic.

[0053] Furthermore, the street light brightness adjustment logic model in step 2 is:

[0054] L target =W target ·L high +(1-W target )·[P env ·L low +(1-P env )·L base ] (1);

[0055]

[0056] Where, L target is the target brightness value, indicating the brightness that the street light needs to achieve, W target is the dynamic target weight, L high is a high brightness value, used to provide sufficient lighting, L low is a low brightness value, used in energy saving mode, L base Is the basic brightness value, used to maintain basic lighting needs, P env is the normalization coefficient of ambient light intensity, indicating the relative proportion of ambient light intensity, L env is the current ambient light intensity, L max It is the maximum ambient light intensity reference value.

[0057] Step three: Optimize and iterate the parameters of the street light brightness controller through a self-adjusting algorithm so that the street light brightness controller can adapt to changes in different environments and dynamic targets; the self-adjusting algorithm is an improved Osprey algorithm, and the specific improvements are: an adaptive step size mechanism is introduced in the development stage of the Osprey algorithm, and the step size is dynamically adjusted according to the distance between the current solution and the optimal solution.

[0058] Furthermore, the specific improvement of the Osprey algorithm in step 3 is: introducing an adaptive step size mechanism in the development stage of the Osprey algorithm, and dynamically adjusting the step size according to the distance between the current solution and the optimal solution. The specific implementation formula is:

[0059]

[0060] In the formula, For the new position of Osprey in the development phase, x i，j is the current position of the Osprey, lb j Search for the lower bound of the algorithm, ub j The algorithm searches for an upper bound, r i，j is a random value between 0 and 1, t is the current iteration number, ΔX is the adaptive step size, Xbest It is the global optimal solution.

[0061] Furthermore, in step three, the street light brightness controller parameters are optimized and iterated by using a self-adjusting algorithm, i.e., an improved Osprey algorithm, such as Figure 2 As shown, the specific steps are:

[0062] Step 1: Encode the parameters Kp, Ki, and Kd to be optimized of the street light brightness controller as optimization variables of the improved Osprey algorithm. During the iteration of the algorithm, the position of each individual in the population represents a set of candidate solutions for the controller parameters.

[0063] Step 2: Initialize the parameters of the improved Osprey algorithm, including the population size N of the algorithm, the problem dimension d, the maximum number of iterations T, and the algorithm search upper bound ub j , the algorithm searches for the lower bound lb j ;

[0064] Step 3, simulate the optimization process of each stage of the improved Osprey algorithm and update the individual positions of the population;

[0065] Step 4, calculate the objective function value of the new position, and update the global optimal solution and the individual optimal solution;

[0066] Step 5. If the maximum number of iterations is reached, the final optimal parameters Kp, Ki, and Kd are output for use by the street light brightness controller, otherwise the iteration continues.

[0067] Furthermore, the step 1 encodes the parameters Kp, Ki, and Kd to be optimized of the street lamp brightness controller as optimization variables of the improved Osprey algorithm. In the iterative process of the algorithm, the position of each population individual represents a set of candidate solutions for controller parameters. As the position of the population individual is updated, the optimization process dynamically adjusts the controller parameters so that the parameters gradually approach the optimal value. The encoding vector is:

[0068] x = [Kp, Ki, Kd];

[0069] Where x is the encoding vector, Kp, Ki, and Kd are the proportional parameter, integral parameter, and differential parameter of the street light brightness controller, respectively.

[0070] Furthermore, the step 3 simulates the optimization process of each stage of the improved Osprey algorithm, and the specific steps are:

[0071] S1, simulation algorithm exploration phase, executing formula (6);

[0072]

[0073] In the formula, To explore the new position of the Osprey, x i,jis the current position of the osprey, r i，j is a random value between 0 and 1, SF i，j Fish selected for Osprey, I i，j is a random number in the set {1, 2};

[0074] S2: In the simulation algorithm development phase, an adaptive step size mechanism is introduced to execute formulas (3)-(4).

[0075] Furthermore, the step 4 calculates the objective function value of the new position, and the objective function formula is:

[0076]

[0077] Where J is the objective function value, e(a) is the brightness error, u(a) is the control signal output by the street lamp brightness controller, W1 and W2 are weight factors, which respectively reflect the priority of brightness adjustment accuracy and the importance of energy-saving optimization, a is the current time, and b is the total system operation time.

[0078] Step 4: Based on the optimization of the street lamp brightness controller parameters, the street lamp brightness control system further accurately adjusts the brightness through a feedback closed loop to achieve precise self-regulation and control of the street lamp brightness.

[0079] Furthermore, in step 4, based on the optimization of the street lamp brightness controller parameters, the street lamp brightness control system further accurately adjusts the brightness through a feedback closed loop, and the brightness adjustment formula is:

[0080] L current (t) = α·u(t) + L base (5);

[0081] Where, L current (t) is the actual brightness value of the street lamp at the current moment, u(t) is the brightness control signal output by the controller, α is the brightness gain coefficient, which is used to map the brightness control signal u(t) to the actual brightness change, and Lbase is the base brightness value.

[0082] In order to verify the control method of street lamp self-adjusting brightness proposed in the present invention, simulation experiments are carried out through Matlab and Simulink. First, the mathematical model of the basic Osprey algorithm is improved through Matlab, and the street lamp brightness control system model is established through Simulink; the parameters of the improved Osprey algorithm are initialized, including the algorithm population size N = 30, the problem dimension d = 3, the maximum number of iterations T = 30, and the algorithm search upper bound ub j =1, the algorithm searches for the lower bound lb j = 0, run the Matlab program, and get the fitness value comparison curve of the basic Osprey algorithm and the improved Osprey algorithm, as shown in Figure 3As shown in the figure, it can be seen that the improved Osprey algorithm has reached the optimal fitness value of 15.14 in the 20th iteration, which shows that the improved Osprey algorithm has significantly improved the algorithm performance by introducing an adaptive step size mechanism. The advantages are reflected in four aspects: global search capability, local development capability, convergence speed and stability. In the global search stage, the improved algorithm can dynamically adjust the step size according to the distance between the current solution and the optimal solution. The initial step size is large, which effectively expands the search range and avoids falling into the local optimum, significantly improving the global exploration capability. In the local development stage, as the iteration proceeds, the step size gradually decreases, enabling the algorithm to The solution can be optimized more finely, thereby improving the accuracy of local development and the quality of the final solution; in addition, the convergence speed of the improved algorithm is significantly faster than that of the basic algorithm, and a stable state can be reached in fewer iterations, which not only improves the optimization efficiency but also reduces the occupation of computing resources; in terms of stability, the fitness value of the improved algorithm changes more smoothly during the optimization process, without obvious oscillation, showing good robustness; through the dynamic balance between the global and the local, the improved Osprey algorithm performs well in both expanding the exploration range and refining the solution, making it more adaptable and capable of solving complex optimization problems.

[0083] Furthermore, Figure 4 To improve the Osprey algorithm, the optimal control parameters of the street light brightness controller are Kp = 0.224, Ki = 0.221, Kd = 0.401. The optimal control parameters are input into the street light brightness controller to obtain the optimal control effect of the street light brightness. Figure 5 The comparison chart of the optimization effect of the basic Osprey algorithm and the improved Osprey algorithm on the street light brightness controller is shown in the figure. The target value is set to 1 unit. It can be seen from the figure that the street light brightness controller optimized by the improved Osprey algorithm shows significant advantages over the basic algorithm, which is mainly reflected in four aspects: response speed, overshoot, steady-state accuracy and system stability. First, in terms of response speed, the controller optimized by the improved algorithm can reach the target brightness value faster, and the system rise time is significantly shortened, which reflects the ability of the improved algorithm to quickly adjust the control parameters in the initial stage. Secondly, in terms of overshoot, the basic algorithm has a large overshoot phenomenon, while the improved algorithm uses an adaptive step length machine to adjust the control parameters quickly. The introduction of the control optimizes the dynamic response of the controller, significantly reduces the overshoot, and avoids unnecessary brightness fluctuations; thirdly, in terms of steady-state accuracy, the improved algorithm can make the actual brightness closer to the target value, the brightness error is smaller, and the system control accuracy is further improved; at the same time, in terms of system stability, the controller optimized by the improved algorithm has no obvious oscillation phenomenon after reaching the steady state, showing higher stability and robustness; overall, the improved Osprey algorithm enhances the ability of global search and local development through the dynamic adjustment of the step size mechanism, making the controller parameter optimization more efficient, and significantly improving the performance and reliability of the street lamp brightness control system in complex scenarios.

Claims

1. A method for controlling the self-adjusting brightness of a street lamp, characterized in that: The specific steps include: Step 1: Intelligently initialize the brightness adjustment of the street lamps according to the usage scenarios and time periods of the area where the street lamps are located, and determine the basic brightness parameters; Step 2: Combine the real-time environment dynamics to associate the brightness adjustment of the street lamp with the changes in the external environment, and trigger the street lamp brightness adjustment logic; Step 3: Optimize and iterate the parameters of the street light brightness controller through a self-adjusting algorithm, so that the street light brightness controller can adapt to the changes of different environments and dynamic targets; the self-adjusting algorithm is an improved Osprey algorithm, and the specific improvements are: introducing an adaptive step size mechanism in the development stage of the Osprey algorithm, and dynamically adjusting the step size according to the distance between the current solution and the optimal solution; Step 4: Based on the optimization of the street lamp brightness controller parameters, the street lamp brightness control system further accurately adjusts the brightness through a feedback closed loop to achieve precise self-regulation and control of the street lamp brightness.

2. A method for controlling the self-adjusting brightness of a street lamp according to claim 1, characterized in that: The basic brightness parameters in step 1 include a basic brightness value, a low brightness value, a high brightness value, a current ambient light intensity, and a maximum ambient light intensity reference value.

3. A method for controlling the self-adjusting brightness of a street lamp according to claim 2, characterized in that: The street lamp brightness adjustment logic model in step 2 is: L target =W targe ·L high +(1+W target )·[P env ·L low +(1-P env )·L base ] (1); Where, L target is the target brightness value, indicating the brightness that the street light needs to achieve, W target is the dynamic target weight, L high is a high brightness value, used to provide sufficient lighting, L low is a low brightness value, used in energy saving mode, L base Is the basic brightness value, used to maintain basic lighting needs, P env is the normalization coefficient of ambient light intensity, indicating the relative proportion of ambient light intensity, L env is the current ambient light intensity, L max It is the maximum ambient light intensity reference value.

4. A method for controlling the self-adjusting brightness of a street lamp according to claim 3, characterized in that: The specific improvement of the Osprey algorithm in step 3 is: introducing an adaptive step size mechanism in the development stage of the Osprey algorithm, and dynamically adjusting the step size according to the distance between the current solution and the optimal solution. The specific implementation formula is: In the formula, For the new position of Osprey in the development phase, x i，j is the current position of the Osprey, lb j Search for the lower bound of the algorithm, ub j The algorithm searches for an upper bound, r i，j is a random value between 0 and 1, t is the current iteration number, ΔX is the adaptive step size, X best It is the global optimal solution.

5. A method for controlling the self-adjusting brightness of a street lamp according to claim 4, characterized in that: The specific steps of optimizing and iterating the parameters of the street light brightness controller by the self-adjusting algorithm, i.e., the improved Osprey algorithm, in step 3 are as follows: Step 1: Encode the parameters Kp, Ki, and Kd to be optimized of the street light brightness controller as optimization variables of the improved Osprey algorithm. During the iteration of the algorithm, the position of each individual in the population represents a set of candidate solutions for the controller parameters. Step 2: Initialize the parameters of the improved Osprey algorithm, including the population size N of the algorithm, the problem dimension d, the maximum number of iterations T, and the algorithm search upper bound ub j , the algorithm searches for the lower bound lb j ; Step 3, simulate the optimization process of each stage of the improved Osprey algorithm and update the individual positions of the population; Step 4, calculate the objective function value of the new position, and update the global optimal solution and the individual optimal solution; Step 5. If the maximum number of iterations is reached, the final optimal parameters Kp, Ki, and Kd are output for use by the street light brightness controller, otherwise the iteration continues.

6. A method for controlling the self-adjusting brightness of a street lamp according to claim 5, characterized in that: In step 4, based on the optimization of the street lamp brightness controller parameters, the street lamp brightness control system further accurately adjusts the brightness through a feedback closed loop. The brightness adjustment formula is: L current (t)=α·u(t)+L base (5); Where, L current (t) is the actual brightness value of the street lamp at the current moment, u(t) is the brightness control signal output by the street lamp brightness controller, α is the brightness gain coefficient, which is used to map the brightness control signal u(t) to the actual brightness change, L base Base brightness value.

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