A control method and system of a solar street lamp and a storage medium

Abstract

The present application relates to the technical field of solar street lamps, in particular to a control method and system for solar street lamps and a storage medium. The method comprises the following steps: first, a three-dimensional environment model is established with multiple solar street lamps as the minimum control unit; the overlapping nature of the lighting rays between the solar street lamps is considered, and initial lighting adjustment parameters are generated in combination with the characteristics of the lighting area and the basic parameters of the solar street lamps; second, a first-order lighting parameter adjustment method is proposed to analyze real-time monitoring video data and real-time monitoring meteorological data by using a solar street lamp parameter adjustment model, thereby achieving dynamic adjustment of the initial lighting adjustment parameters; finally, a second-order lighting parameter adjustment method is proposed to evaluate the actual lighting effect of each region after the first-order adjustment, and to adjust the first-order lighting parameters according to the error between the actual lighting effect and the lighting expectation; the above methods are combined to achieve accurate control of multiple solar street lamps working together.

Description

Technical Field

[0001] This invention relates to the field of solar street light technology, specifically to a control method, system, and storage medium for a solar street light. Background Technology

[0002] Streetlights are primarily used for nighttime illumination to ensure road visibility and driving safety, help reduce nighttime accidents, enhance a sense of security, and also improve the aesthetics of the community and the convenience of travel.

[0003] Solar streetlights, as a sustainable new type of lighting fixture, use solar panels to collect and store energy, solving the problem that traditional streetlights cannot provide illumination without grid connection. Solar streetlights not only reduce energy costs but also reduce carbon emissions, making them suitable for parks, trails, and remote areas.

[0004] With the rapid development of internet technology, technical personnel are attempting to combine it with solar streetlights to achieve precise control, optimize energy efficiency, and extend equipment lifespan. Through an intelligent control system, solar streetlights can automatically adjust their brightness based on ambient light, temperature, and other factors, ensuring sufficient illumination under varying weather conditions while minimizing energy waste.

[0005] In existing technologies, most methods achieve intelligent control of individual solar streetlights based on surrounding environmental factors. However, in real-world scenarios, solar streetlights are distributed in groups, meaning multiple solar streetlights typically exist in an area and operate simultaneously. For example, on roads, solar streetlights are arranged at equal intervals; this design not only ensures uniform lighting but also improves road safety. Therefore, intelligent control of individual streetlights has limitations in practical applications and cannot achieve precise control.

[0006] Therefore, this invention proposes a control method, system, and storage medium for solar streetlights. Summary of the Invention

[0007] The purpose of this invention is to provide a control method, system, and storage medium for solar streetlights. The main points are as follows: First, a three-dimensional environment model is established, using the illumination range of multiple solar streetlights as the minimum control unit. Initial lighting adjustment parameters are generated for the solar streetlights in the three-dimensional environment model. These initial lighting adjustment parameters meet the minimum lighting requirements in different areas of the three-dimensional environment model. Second, this invention proposes a solar streetlight parameter adjustment model to perform first-order adjustment of the initial lighting adjustment parameters. This model obtains the corresponding lighting adjustment coefficients by collecting and analyzing real-time monitoring videos and real-time meteorological data from the minimum control unit. Finally, this invention proposes a second-order lighting parameter adjustment method that evaluates the actual lighting effect of each area after the first-order adjustment and adjusts the first-order lighting parameters based on the error between the actual lighting effect and the lighting expectation.

[0008] To achieve the above objectives, the present invention provides the following technical solution:

[0009] In a first aspect, the present invention proposes a control method for a solar street light, comprising:

[0010] The minimum control unit is the illumination range of multiple solar streetlights;

[0011] Furthermore, a relevant three-dimensional environment model is established based on the minimum control unit; wherein, the three-dimensional environment model runs on a remote server and is networked with the solar street light to control the dynamic adjustment of lighting parameters;

[0012] Furthermore, the three-dimensional environment model is marked according to the physical coordinates of the solar streetlights in the real scene;

[0013] Further, the parameters of each solar street light in the three-dimensional model are obtained; wherein, the solar street light parameters include: height, power, luminous flux, illuminance, battery capacity, and fault status; the fault status includes: illuminateable and non-illuminable, represented by 1 and 0 respectively;

[0014] Further, the natural light data of the minimum control unit is obtained;

[0015] Furthermore, a property analysis is performed on each region in the three-dimensional environment model to obtain ordinary regions and special regions;

[0016] Furthermore, based on the results of the property analysis, minimum lighting requirements are set for each area;

[0017] Furthermore, different lighting expectations are set for the ordinary area and the special area;

[0018] Furthermore, the contribution of solar streetlights to illumination in each area is calculated based on the obstructions present in the three-dimensional environment model;

[0019] Furthermore, based on the natural illumination data, the physical coordinates, and the solar street light parameters, and combining the illumination expectation of each area, the solar street light illumination contribution, and the normalized relative overlapping illumination contribution, the initial illumination adjustment parameters for each solar street light are obtained; wherein, the initial illumination adjustment parameters are used to adjust the current and voltage values ​​of the solar street light and ensure that the minimum illumination requirements are met.

[0020] The initial lighting adjustment parameters further include: obtaining the geometric illumination range of the solar street light in the three-dimensional environment model based on each solar street light parameter; calculating the overlapping area of ​​light illumination in the geometric illumination range using a geometric algorithm; calculating the relative overlapping illumination contribution of each overlapping area of ​​light illumination; normalizing the relative overlapping illumination contribution to obtain a normalized relative overlapping illumination contribution; and substituting the normalized relative overlapping illumination contribution into the generation process of the initial lighting adjustment parameters.

[0021] Furthermore, the initial lighting adjustment parameters are adjusted using a solar street light parameter adjustment model to obtain first-order lighting adjustment parameters;

[0022] The solar street light parameter adjustment model includes:

[0023] The real-time information acquisition module is used to monitor the smallest control unit in real time and acquire real-time monitoring video and real-time monitoring meteorological data respectively.

[0024] The data processing module is used to preprocess the real-time monitoring video and the real-time monitoring meteorological data to obtain standard real-time video data and standard real-time meteorological data, respectively; wherein, the data processing module includes: a video data processing unit and a meteorological data processing unit;

[0025] The video data processing unit includes: extracting video frames from the real-time monitoring video to obtain a first real-time monitoring video; performing noise reduction processing on the first real-time monitoring video to obtain a second real-time monitoring video; performing video enhancement on the second real-time monitoring video to obtain a third real-time monitoring video; and performing target detection and segmentation on the third real-time monitoring video to obtain the standard real-time video data.

[0026] The meteorological data processing unit includes: performing outlier processing on the real-time monitored meteorological data to obtain first real-time monitored meteorological data; standardizing the first real-time monitored meteorological data to obtain second real-time monitored meteorological data; and extracting features from the second real-time monitored meteorological data to obtain standard real-time meteorological data; wherein the standard real-time meteorological data includes any one of cloud cover, precipitation, humidity, wind speed, temperature, and air quality.

[0027] The first lighting parameter influence analysis module is used to perform solar street light lighting analysis on the standard real-time video data;

[0028] The first lighting parameter influence analysis module includes: extracting key features of the target from the standard real-time data, including: target type, size features, motion features, shape features, location features, and behavior features;

[0029] Furthermore, the lighting requirements for the current scene are calculated based on the aforementioned key features;

[0030] Furthermore, machine learning methods are used to solve the adjustment relationship between the initial lighting adjustment parameters and the lighting demand to obtain the first lighting parameter adjustment coefficient;

[0031] The second lighting parameter impact analysis module is used to perform solar street light lighting analysis on the standard real-time meteorological data.

[0032] The second lighting parameter impact analysis module includes: calculating the current meteorological lighting demand based on the standard real-time meteorological data;

[0033] Furthermore, a nonlinear relationship is established between the standard real-time meteorological data and the current meteorological lighting demand;

[0034] Furthermore, the initial lighting adjustment parameters are solved based on the nonlinear relationship to obtain the second lighting parameter adjustment coefficient;

[0035] The lighting adjustment coefficient calculation module is used to calculate the lighting adjustment coefficient based on the first lighting parameter adjustment coefficient and the second lighting parameter adjustment coefficient.

[0036] The lighting adjustment coefficient calculation module is used to calculate the lighting adjustment coefficient based on the output results of the first lighting parameter influence analysis module and the second lighting parameter influence analysis module.

[0037] The lighting parameter adjustment module is used to adjust the initial lighting adjustment parameters using the lighting adjustment coefficient to obtain the first-order lighting adjustment parameters.

[0038] Furthermore, the lighting effect of the first-order lighting adjustment parameters is evaluated, and the first-order lighting adjustment parameters are adjusted according to the evaluation results to obtain the second-order lighting adjustment parameters;

[0039] The generation process of the second-order lighting adjustment parameters includes: collecting actual lighting effect data of each area after adjustment by the first-order lighting adjustment parameters, including: light intensity and distribution;

[0040] Furthermore, the lighting effect of each area is evaluated based on the actual lighting effect data to obtain the lighting effect evaluation results;

[0041] Further, the expected error between the lighting effect evaluation result and the lighting expectation is calculated;

[0042] Further, the adjustment coefficient for the second-order lighting parameters is calculated based on the expected error;

[0043] Furthermore, the first-order lighting adjustment parameters are adjusted according to the second-order lighting parameter adjustment coefficient to obtain the second-order lighting adjustment parameters.

[0044] Furthermore, the solar streetlights in the minimum control unit are controlled according to the second-order lighting adjustment parameters.

[0045] Secondly, the present invention proposes a control system for a solar street light, the system comprising: a control range generation unit, an environment model generation unit, a networking unit, a solar street light management unit, a data monitoring unit, a lighting parameter generation unit, a lighting parameter adjustment unit, a control unit, an evaluation unit, and a feedback unit;

[0046] The control range generation unit is used to generate a minimum control unit based on the lighting range of multiple solar streetlights.

[0047] The environment model generation unit is used to generate a three-dimensional environment model based on the real scene of the minimum control unit;

[0048] The networking unit is used to connect the physical solar street light in the minimum control unit to the three-dimensional environment model running on a remote server;

[0049] The solar street light management unit is used to manage the solar street lights, including recording the physical coordinates, height, power, luminous flux, illuminance, battery capacity, and fault status of the solar street lights.

[0050] The data monitoring unit uses multiple monitoring devices to monitor the smallest control unit in real time and acquires meteorological data in real time through meteorological equipment;

[0051] The lighting parameter generation unit is used to generate the initial lighting parameters of the solar street light; specifically, it includes: acquiring the natural light data of the minimum control unit;

[0052] Furthermore, a property analysis is performed on each region in the three-dimensional environment model to obtain ordinary regions and special regions;

[0053] Furthermore, based on the results of the property analysis, minimum lighting requirements are set for each area;

[0054] Furthermore, different lighting expectations are set for the ordinary area and the special area;

[0055] Furthermore, the contribution of solar streetlights to illumination in each area is calculated based on the obstructions present in the three-dimensional environment model;

[0056] Furthermore, based on the natural illumination data, the physical coordinates, and the solar street light parameters, and combining the illumination expectation of each area, the solar street light illumination contribution, and the normalized relative overlapping illumination contribution, the initial illumination adjustment parameters for each solar street light are obtained; wherein, the initial illumination adjustment parameters are used to adjust the current and voltage values ​​of the solar street light and ensure that the minimum illumination requirements are met.

[0057] The lighting parameter adjustment unit is used to adjust the lighting parameters of the solar street light, including: first-order lighting adjustment parameters and second-order lighting adjustment parameters.

[0058] The first-order lighting adjustment parameters are adjusted using a solar street light parameter adjustment model. The specific process includes:

[0059] The real-time information acquisition module is used to monitor the smallest control unit in real time and acquire real-time monitoring video and real-time monitoring meteorological data respectively.

[0060] The data processing module is used to preprocess the real-time monitoring video and the real-time monitoring meteorological data to obtain standard real-time video data and standard real-time meteorological data, respectively.

[0061] The first lighting parameter influence analysis module is used to perform solar street light lighting analysis on the standard real-time video data;

[0062] The first lighting parameter influence analysis module includes:

[0063] Extract key features of the target from the standard real-time data, including: target type, size features, motion features, shape features, location features, and behavior features;

[0064] Calculate the target lighting requirements for the current scene based on the key features;

[0065] The relationship between the initial lighting adjustment parameters and the lighting demand is solved using machine learning methods to obtain the first lighting parameter adjustment coefficient;

[0066] The second lighting parameter impact analysis module is used to perform solar street light lighting analysis on the standard real-time meteorological data.

[0067] The second lighting parameter influence analysis module includes:

[0068] Calculate the current meteorological lighting requirements based on the aforementioned standard real-time meteorological data;

[0069] Establish a nonlinear relationship between the standard real-time meteorological data and the current meteorological lighting demand;

[0070] The initial lighting adjustment parameters are solved based on the nonlinear relationship to obtain the second lighting parameter adjustment coefficient;

[0071] The lighting adjustment coefficient calculation module is used to calculate the lighting adjustment coefficient based on the first lighting parameter adjustment coefficient and the second lighting parameter adjustment coefficient.

[0072] The lighting parameter adjustment module is used to adjust the initial lighting adjustment parameters using the lighting adjustment coefficient to obtain the first-order lighting adjustment parameters.

[0073] The generation process of the second-order illumination adjustment parameters includes:

[0074] Collect actual lighting effect data for each area after adjustment with the first-order lighting adjustment parameters, including: light intensity and distribution;

[0075] Based on the actual lighting effect data, the evaluation unit evaluates the lighting effect of each area to obtain the lighting effect evaluation result.

[0076] Calculate the expected error between the lighting effect evaluation result and the lighting expectation;

[0077] Calculate the second-order lighting parameter adjustment coefficient based on the expected error;

[0078] The first-order lighting adjustment parameters are adjusted according to the second-order lighting parameter adjustment coefficient to obtain the second-order lighting adjustment parameters.

[0079] The control unit is used to adjust the voltage and current according to the output result of the lighting parameter adjustment unit;

[0080] The evaluation unit is used to evaluate the lighting effect of the solar street light after the lighting parameters are adjusted;

[0081] The feedback unit is used to provide feedback on the lighting parameters and lighting effects of the solar street light each time they are adjusted.

[0082] Thirdly, the present invention proposes a control storage medium for a solar street light, wherein the control storage medium stores a solar street light intelligent control program, and when the solar street light intelligent control program is executed by a processor, it implements the control process of the solar street light as described in either the first or second aspect.

[0083] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0084] 1. This invention proposes a control method for solar streetlights based on a minimum control unit. This method uses the lighting range of multiple solar streetlights as the control unit, solving the problem that the interaction between the lights of solar streetlights was not considered in the prior art; it realizes synchronous control of multiple solar streetlights; this method not only reduces the energy consumption of solar streetlights, but also plays a role in the precise control of multiple solar streetlights.

[0085] 2. This invention proposes a method for generating initial lighting adjustment parameters for solar streetlights; this method obtains the parameters of the solar streetlights in the minimum control unit to initialize the initial streetlight lighting for different types of areas; and further improves the control accuracy of solar streetlights by taking into account the possible overlap between adjacent solar streetlights.

[0086] 3. This invention proposes a solar street light parameter adjustment model for first-order adjustment of initial lighting adjustment parameters. This model analyzes real-time monitoring data and real-time meteorological data, considering the adjustment of street lights from both environmental and weather dimensions. This not only enables real-time dynamic control of solar street lights, but also improves the control accuracy of solar street lights according to actual conditions.

[0087] 4. This invention proposes a second-order lighting parameter adjustment method for solar streetlights, used to correct the output results of the solar streetlight parameter adjustment model; this method evaluates the lighting effect of the solar streetlight after first-order adjustment, obtaining the effectiveness of lighting from multiple perspectives; and by calculating the error between the lighting effect and the lighting expectation of each area, the lighting parameters after first-order adjustment are adjusted using a lighting expectation optimization algorithm; thus, not only is the lighting quality improved, but the control accuracy of the solar streetlight is further enhanced. Attached Figure Description

[0088] Figure 1 A flowchart of a control method for a solar street light provided in an embodiment of the present invention;

[0089] Figure 2This is a structural diagram of a control system for a solar street light provided in an embodiment of the present invention. Detailed Implementation

[0090] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0091] Solar streetlights are a new type of lighting tool that collects and stores energy through solar panels for illumination. Compared to traditional streetlights, solar streetlights offer advantages such as utilizing renewable energy, being environmentally friendly and energy-efficient, offering flexible installation, and having low maintenance costs, making them an ideal lighting solution.

[0092] Today, solar streetlights are being integrated with internet technology to form intelligent streetlight systems. These systems feature data monitoring, remote control, intelligent adjustment, and data analysis, improving lighting efficiency and urban management. Through intelligent control systems, solar streetlights can automatically adjust their brightness based on ambient light, temperature, and other factors, ensuring sufficient illumination under different weather conditions while reducing energy waste.

[0093] In existing technologies, most methods achieve intelligent control of individual solar streetlights based on surrounding environmental factors. However, in real-world scenarios, solar streetlights are distributed in groups, typically with multiple streetlights operating simultaneously within an area. Therefore, intelligent control of individual streetlights has limitations in practical applications and cannot achieve precise control.

[0094] To further improve the accuracy of solar street light control and avoid energy waste, this invention proposes a control method, system, and storage medium for solar street lights. This invention employs a two-stage adjustment scheme to achieve optimal control of the solar street lights. The implementation process of this invention will be described in detail through the following embodiments.

[0095] Example 1:

[0096] In this application embodiment, the lighting control of solar streetlights is achieved by combining the method and system proposed in this invention; see reference. Figure 1 and Figure 2 content, Figure 1The content regarding the implementation steps of this invention includes: S10. Planning the minimum control unit; S20. Establishing a three-dimensional environment model; S30. Marking the location of the solar street light in the three-dimensional environment model; S40. Obtaining the solar street light parameters; S50. Generating initial lighting adjustment parameters; S60. Generating first-order lighting adjustment parameters; S70. Generating second-order lighting adjustment parameters; S80. Executing solar street light illumination. Figure 2 The system structure diagram of this invention includes: a control range generation unit, an environment model generation unit, a networking unit, a solar street light management unit, a data monitoring unit, a lighting parameter generation unit, a lighting parameter adjustment unit, a control unit, an evaluation unit, and a feedback unit. The method steps and flowcharts correspond to the various units of the system, and will be discussed below. Figure 1 and Figure 2 Please provide an explanation.

[0097] Based on the control range of the system, the minimum control unit of the solar street light in this application embodiment is generated, corresponding to... Figure 1 Step S10; wherein, the minimum control unit refers to the maximum lighting range that can be achieved by clustering multiple solar streetlights;

[0098] In this embodiment of the application, area A is taken as the target, and the lighting range of 5 adjacent solar street lights is selected as the minimum control unit;

[0099] Further, the environment model generation unit of the system performs three-dimensional modeling based on the minimum control unit to obtain a three-dimensional environment model, corresponding to step S20;

[0100] The environment model generation unit creates a virtual interface to connect with the 3D modeling software; it collects data on solar streetlights, terrain, landforms, and buildings in the actual environment from the minimum control unit and imports it into the 3D modeling software to achieve modeling; the 3D environment model is run on a remote server by the network unit of the system, and the virtual solar streetlights in the model are connected to the real solar streetlights through a network connection to achieve synchronous control.

[0101] Further, according to step S30, the physical coordinates of the solar streetlights in the minimum control unit are mapped in the three-dimensional environment model, and the virtual coordinates are marked;

[0102] Referring to Table 1, the physical coordinates of the solar street light in the minimum control unit of this application embodiment and the corresponding virtual coordinates in the three-dimensional environment model are given in Table 1;

[0103] Table 1 shows the physical and virtual coordinates of the solar streetlights in the minimum control unit of Zone A.

[0104] Solar street light number Physical coordinates (X, Y, Z) Virtual coordinates (X', Y', Z') L1 (10.5,20.3,0) (100.5,200.3,0) L2 (12.1,20.7,0) (102.1,200.7,0) L3 (13.8,20.5,0) (103.8,200.5,0) L4 (11.0,22.1,0) (101.0,202.1,0) L5 (12.6,21.8,0) (102.6,201.8,0)

[0105] Further, the solar street light parameters of L1, L2, L3, L4 and L5 are obtained from the solar street light management unit of the system, corresponding to step S40; wherein, the solar street light parameters include: height, power, luminous flux, illuminance, battery capacity and fault status; refer to Table 2, which gives the parameters of the solar street lights in the embodiments of this application;

[0106] Table 2 Explanation of Solar Street Light Parameters

[0107]

[0108] In Table 2, regarding the fault status of solar streetlights, "1" indicates that the lights are illuminating and "0" indicates that the lights are not illuminating.

[0109] Further, the lighting parameter generation unit of the system generates the initial lighting parameters for each solar street light in the minimum control unit, corresponding to step S50; specific process:

[0110] Natural light data of the minimum control unit is acquired as an auxiliary light source; wherein, the natural light data is collected using a light sensor.

[0111] Furthermore, the properties of the lighting areas contained in the three-dimensional environment model are analyzed and divided into ordinary areas and special areas; wherein, in the embodiments of this application, ordinary areas include places where regular pedestrians pass, such as streets and parks; special areas include important intersections, schools and hospitals.

[0112] Furthermore, the property analysis results set minimum lighting requirements for each area, as shown in Table 3.

[0113] Table 3. Minimum Lighting Requirements for the Minimum Control Unit in Zone A

[0114] Region Type Minimum lighting requirements (lx) Remark ordinary area 100 Ensure basic safety lighting Special Area 150 To improve security, especially at night, additional measures are needed.

[0115] Table 3 provides an explanation of the minimum lighting requirements for ordinary and special areas, which is only for illustrative purposes in this application embodiment. The minimum lighting requirements are not uniformly set; the value can be set specifically by collecting feedback from citizens and can be set according to the actual situation of different areas, such as hospitals, schools, and parks.

[0116] Furthermore, different lighting expectations are set for general areas and special areas; wherein the process of setting the lighting expectations includes:

[0117] (1) Area Description

[0118] General areas: including streets, parks and other regular pedestrian areas; pedestrian traffic is relatively stable and demand is moderate.

[0119] Special areas: including important intersections, schools, hospitals, etc.; high pedestrian traffic, requiring higher safety and visibility.

[0120] (2) Requirements Analysis

[0121] Research and data collection: Collect user feedback and historical lighting data to understand the usage and lighting needs of each area; conduct on-site surveys to record pedestrian traffic, activity types, and time periods.

[0122] (3) Lighting expectation setting

[0123] Based on the demand analysis, the basic lighting expectations for each area are obtained.

[0124] Furthermore, the contribution of solar streetlights to illumination in each area is calculated based on the obstructions in the three-dimensional environment model; wherein the formula for calculating the contribution of solar streetlights to illumination is expressed as: Among them, SRLC i,j I represents the contribution of solar street light i to the solar street light illumination of area j; i η represents the luminous flux of solar street light i; i The efficiency of solar street light i is expressed as A; j Let represent the effective illumination area of ​​region j; d represents the distance; and O represents the shading factor.

[0125] Furthermore, the initial lighting adjustment parameters for each solar street light in the three-dimensional environment model are calculated; wherein, the initial lighting adjustment parameters involve the overlapping parts of the lights between adjacent solar street lights, so the influence of light overlap should be considered in the process of generating the parameter lighting parameters;

[0126] The geometric illumination range of each solar street light is obtained in the three-dimensional environment model based on the parameters of each solar street light. The process of obtaining the geometric illumination range is as follows: the position and direction of the light source are defined using the three-dimensional environment model; the actual luminous characteristics of the solar street light, including light attenuation, distribution, and beam angle, are considered; a light analysis tool is used to simulate and calculate the propagation and distribution of light in the three-dimensional environment model; and the geometric illumination range is visualized on the three-dimensional environment model. The geometric illumination range is represented by an elliptical shape.

[0127] Furthermore, a geometric algorithm is used to calculate the overlapping area of ​​light illumination within the geometric illumination range;

[0128] Specifically, the overlapping areas of the light illumination overlap regions are checked using an intersection algorithm to examine the geometric intersection of each geometric illumination range. Specifically, an ellipse method is used to detect the intersection, with the equation being: Where x represents the horizontal coordinate of the center of the ellipse; y represents the vertical coordinate of the center of the ellipse; (h x ,h y ) represents the center position of the ellipse; a and b represent the lengths of the horizontal and vertical semi-axis of the ellipse, respectively;

[0129] For two ellipses, first check the distance between their centers. If the distance is less than the sum of the semi-major axes of the two ellipses, then perform an intersection calculation.

[0130] The polygon intersection algorithm is used to find the intersection region of the ellipse;

[0131] For intersecting elliptical regions, the area of ​​the overlapping region is calculated using numerical integration.

[0132] Furthermore, the relative overlap illumination contribution of each light-illuminated overlapping area is calculated;

[0133] The calculation process for the relative overlapping illumination contribution is as follows:

[0134] Set the illumination intensity of the solar streetlights as follows: Among them, LS(h x ,h y () represents the light intensity to the center point of the ellipse; cd represents the distance from the light source to the center point of the ellipse; P represents the power of the solar street light;

[0135] The total illumination intensity of the overlapping region is calculated as: LS i,j (h x ,h y ) = LS i (h x ,h y )+LS j (h x ,h y ); where LS i,j (h x ,h y ) represents the total illuminance of solar streetlights i and j in the overlapping area; LS i (h x ,h y ) represents the illuminance of solar street light i up to the center of the ellipse; LS j (h x ,h y () represents the illuminance of solar street light j up to the center of the ellipse;

[0136] The contribution of illumination is calculated as: C i =∫ R LS i (h x ,h y )dA; where C i This represents the light contribution of solar street light i within the overlapping region R; R represents the overlapping region; dAb represents a small area element of the overlapping region R.

[0137] The relative overlapping illumination contribution of each solar street light is calculated as follows: Among them, R i C represents the relative overlapping illumination contribution of solar street light i in the overlapping region R; total It is represented as the sum of the illumination contributions in the overlapping region R;

[0138] Furthermore, the relative overlapping illumination contribution is normalized to obtain the normalized relative overlapping illumination contribution.

[0139] In this application embodiment, the overlapping of lighting rays between solar streetlights in real-world scenarios is used to generate initial lighting adjustment parameters. The overlapping of lighting rays is mainly considered in terms of the light contribution of the solar streetlights in the overlapping area. This precise control scheme can not only improve lighting quality but also achieve more efficient energy management.

[0140] Furthermore, based on the natural illumination data, the physical coordinates, and the solar street light parameters, combined with the illumination expectation of each area, the solar street light illumination contribution, and the normalized relative overlapping illumination contribution, the initial illumination adjustment parameters for each solar street light are derived.

[0141] Refer to Table 4, which provides the initial lighting adjustment parameters for the solar streetlights in the embodiments of this application;

[0142] Table 4 Initial lighting adjustment parameters for solar streetlights in the minimum control unit of Zone A.

[0143]

[0144] In Table 4, solar street light L3 is malfunctioning and cannot be adjusted, therefore all data for solar street light L3 in Table 4 are 0;

[0145] Further, the initial lighting adjustment parameters generated are adjusted by the lighting parameter adjustment unit to obtain first-order lighting adjustment parameters, corresponding to step S60 above;

[0146] The first-order lighting adjustment parameters are generated based on real-time monitoring of the minimum control unit using the solar street light parameter adjustment model, and according to the actual scenario; the specific process is as follows:

[0147] The system utilizes a real-time information acquisition module, namely the data monitoring unit, to acquire real-time monitoring video and real-time meteorological data; wherein, the data monitoring unit mainly consists of multiple night vision high-definition cameras and meteorological equipment;

[0148] Furthermore, the data processing module preprocesses the real-time data acquired by the data monitoring unit;

[0149] The data processing module includes: a video data processing unit and a meteorological data processing unit;

[0150] The video data processing unit includes: extracting video frames from the real-time monitoring video to obtain a first real-time monitoring video; performing noise reduction processing on the first real-time monitoring video to obtain a second real-time monitoring video; performing video enhancement on the second real-time monitoring video to obtain a third real-time monitoring video; and performing target detection and segmentation on the third real-time monitoring video to obtain the standard real-time video data.

[0151] The meteorological data processing unit includes: performing outlier processing on the real-time monitored meteorological data to obtain first real-time monitored meteorological data; standardizing the first real-time monitored meteorological data to obtain second real-time monitored meteorological data; and extracting features from the second real-time monitored meteorological data to obtain standard real-time meteorological data; wherein the standard real-time meteorological data includes any one of cloud cover, precipitation, humidity, wind speed, temperature, and air quality.

[0152] In this embodiment, by preprocessing the real-time monitoring video data and real-time monitoring meteorological data within the minimum control unit, not only is the quality and availability of the data improved, but it also provides strong support for real-time lighting management.

[0153] Furthermore, the solar street light analysis module performs an analysis on standard real-time video data to obtain the adjustment coefficient of the first lighting parameter. The analysis process is as follows:

[0154] Extract key features of the target from the standard real-time data, including: target type, size features, motion features, shape features, location features, and behavior features;

[0155] Among them, target type: classifying targets into different types such as people, vehicles and animals; size characteristics: recording the target's height, width and area; motion characteristics: analyzing the target's speed, acceleration and direction of motion; shape characteristics: identifying the target's shape (such as rectangle, circle, complex shape) and its outline features; and behavioral characteristics: observing and analyzing the target's behavioral patterns (such as stillness, movement, gathering and dispersal).

[0156] The target lighting requirements for the current scene are calculated based on the key features; wherein, the target lighting requirements are based on the standard of meeting the minimum lighting requirements of most target objects within the minimum control unit.

[0157] The relationship between the initial lighting adjustment parameters and the lighting demand is solved using machine learning methods to obtain the first lighting parameter adjustment coefficient;

[0158] Among them, the machine learning method for solving the adjustment coefficient of the first lighting parameter adopts a pre-trained decision tree model; the decision tree model trains the model parameters by using historical lighting data, including initial lighting parameters, target features and actual lighting requirements as training data;

[0159] Furthermore, the second lighting parameter impact analysis module performs solar street light analysis under standard real-time meteorological data conditions to obtain the second lighting parameter adjustment coefficient. The analysis process is as follows:

[0160] Calculate the current meteorological lighting requirements based on the aforementioned standard real-time meteorological data;

[0161] Establish a nonlinear relationship between the standard real-time meteorological data and the current meteorological lighting demand; wherein, this nonlinear relationship can be obtained by a nonlinear function, i.e. in, This represents the nonlinear mapping between the standard real-time meteorological data and the current meteorological lighting demand; f() represents the nonlinear function; MLD represents the current meteorological lighting demand; SRTMD represents the standard real-time meteorological data;

[0162] The initial lighting adjustment parameters are solved based on the nonlinear relationship to obtain the second lighting parameter adjustment coefficient;

[0163] Further, the lighting adjustment coefficient calculation module calculates the lighting adjustment coefficient based on the first lighting parameter adjustment coefficient and the second lighting parameter adjustment coefficient; wherein, the lighting adjustment coefficient calculation module is expressed by the formula: Wherein, LC represents the lighting adjustment coefficient; LC1 represents the first lighting parameter adjustment coefficient; and LC2 represents the second lighting parameter adjustment coefficient. and Represented as weighting coefficients;

[0164] Furthermore, the initial lighting adjustment parameters are adjusted by the lighting parameter adjustment module to obtain the first-order lighting adjustment parameters;

[0165] In this embodiment, a solar street light parameter adjustment model is used for the first-order adjustment of the initial lighting adjustment parameters. In this model, real-time monitoring video and real-time meteorological data within the minimum control unit are analyzed. Real-time monitoring video analysis can identify the movement of pedestrians and vehicles, thereby determining changes in lighting demand. Real-time meteorological data analysis can adapt to different environmental requirements and adjust lighting accordingly. Combining these two aspects significantly enhances the intelligence, flexibility, and effectiveness of the solar street light control system.

[0166] Furthermore, the system's evaluation unit evaluates the lighting effect of the solar streetlights after adjusting the first-order lighting parameters, and based on the evaluation results, the lighting parameter adjustment unit performs second-order adjustments accordingly. Figure 1 The S70 steps; the specific process includes:

[0167] Collect actual lighting effect data for each area after adjustment with the first-order lighting adjustment parameters, including: light intensity and distribution; refer to Table 5, which shows the lighting effect of the solar streetlights after the first-order adjustment;

[0168] Table 5 shows the lighting effect of the solar street light in Zone A after first-order adjustment of the minimum control unit.

[0169]

[0170] Furthermore, the lighting effect of each area is evaluated based on the actual lighting effect data to obtain the lighting effect evaluation results;

[0171] The evaluation criteria for lighting effects in each area include setting relevant standards; see Table 6 for a series of evaluation criteria.

[0172] Table 6. Explanation of Evaluation Criteria

[0173] Evaluation criteria describe Lighting intensity Set minimum and ideal lighting intensities according to area type. Illumination uniformity Scores assess the uniformity of lighting Blind spot situation Are there areas with insufficient lighting? Satisfaction User evaluations of lighting effects are typically collected through questionnaires or feedback.

[0174] Further, the expected error between the lighting effect evaluation result and the lighting expectation is calculated;

[0175] Further, the second-order lighting parameter adjustment coefficient is calculated based on the expected error; wherein the calculation process of the second-order lighting parameter adjustment coefficient is as follows: Where k represents the second-order lighting parameter adjustment coefficient; ELRV represents the desired lighting demand; ATLV represents the actual lighting quantity after first-order adjustment; and APIV represents the influence value of the adjustment parameter.

[0176] Furthermore, the first-order lighting adjustment parameters are adjusted according to the second-order lighting parameter adjustment coefficient to obtain the second-order lighting adjustment parameters.

[0177] In this application example, the lighting parameters of the solar street light after the first-order adjustment are adjusted in a second-order manner. This second-order adjustment process evaluates the lighting effect after the first-order adjustment from multiple perspectives. Furthermore, by calculating the error between the lighting effect and the lighting expectation of each area, the lighting parameters after the first-order adjustment are adjusted using a lighting expectation optimization algorithm. This not only improves the quality of lighting but also further enhances the control precision of the solar street light.

[0178] In the system by Figure 2 The control unit outputs voltage and current after each lighting parameter adjustment, realizing lighting with the minimum control unit, corresponding to step S80;

[0179] Furthermore, after each solar street light illumination, the corresponding lighting parameters and lighting effects are fed back by the feedback system.

[0180] In this embodiment, the control of solar streetlights in area A is achieved by using five solar streetlights as the minimum control unit. The main steps include: First, establishing a three-dimensional environmental model using the five solar streetlights as the minimum control unit. This model realistically simulates the actual scene. Considering the overlap of light between the solar streetlights, initial lighting adjustment parameters are generated based on the basic parameters of the solar streetlights and the properties of the illuminated area. Second, to further improve the control accuracy of the solar streetlights, this invention also proposes a first-order lighting parameter adjustment method and a second-order lighting parameter adjustment method. The first-order lighting parameter adjustment method uses the solar streetlight parameter adjustment model to analyze real-time monitoring video data and real-time monitoring meteorological data of the minimum control unit, achieving dynamic first-order adjustment of the initial lighting adjustment parameters. The second-order lighting parameter adjustment method evaluates the actual lighting effect of each area after the first-order adjustment and adjusts the first-order lighting parameters based on the error between the actual lighting effect and the lighting expectation. Finally, the lighting task is executed based on the repeatedly adjusted lighting parameters. The combined methods achieve precise control of multiple solar streetlights operating simultaneously.

[0181] Example 2:

[0182] In Example 1, the method and system proposed in this invention were used to control the solar streetlights in the minimum control unit of area A. To further illustrate the effectiveness of this invention, in Example 2, the number of solar streetlights in the minimum control unit will be increased to expand the lighting range. The specific control is as follows:

[0183] The minimum control unit is defined by the illumination range of eight solar streetlights.

[0184] Furthermore, a relevant three-dimensional environment model is established based on the minimum control unit;

[0185] Furthermore, the three-dimensional environment model is marked according to the physical coordinates of the solar streetlights in the real scene;

[0186] Further, the parameters of each solar street light in the three-dimensional model are obtained; wherein, the solar street light parameters include: height, power, luminous flux, illuminance, battery capacity, and fault status; the fault status includes: illuminateable and non-illuminable, represented by 1 and 0 respectively;

[0187] Refer to Table 7, which shows the parameters of the solar street light that are extended based on Example 1;

[0188] Table 7 Explanation of Parameters for Expanded Solar Streetlights

[0189]

[0190] Table 7 expands upon Table 2 by adding three solar street light parameter descriptions, namely L6, L7 and L8; among them, the solar street light numbered L8 is a faulty light.

[0191] Further, the natural light data of the minimum control unit is obtained;

[0192] Furthermore, a property analysis is performed on each region in the three-dimensional environment model to obtain ordinary regions and special regions;

[0193] Furthermore, based on the results of the property analysis, minimum lighting requirements are set for each area;

[0194] Furthermore, different lighting expectations are set for the ordinary area and the special area;

[0195] Furthermore, the contribution of solar streetlights to illumination in each area is calculated based on the obstructions present in the three-dimensional environment model;

[0196] Furthermore, based on the natural illumination data, the physical coordinates, and the solar street light parameters, and combining the illumination expectation of each area, the solar street light illumination contribution, and the normalized relative overlapping illumination contribution, the initial illumination adjustment parameters for each solar street light are obtained; wherein, the initial illumination adjustment parameters are used to adjust the current and voltage values ​​of the solar street light and ensure that the minimum illumination requirements are met.

[0197] Refer to Table 8, which shows the initial lighting adjustment parameters in the embodiments of this application;

[0198] Table 8 Initial Lighting Adjustment Parameters for Expanding the Number of Solar Streetlights

[0199]

[0200] Table 8 shows the changes in initial lighting parameters after the expansion of solar streetlights. For example, compared with the data in Table 4, the solar streetlight numbered L1 showed changes in output power, illumination range (long half-axis), illumination range (short half-axis), and initial lighting intensity, which were 35W, 9m, 6m, and 160lx, respectively. The solar streetlights numbered L3 and L8 malfunctioned and could not be controlled.

[0201] The initial lighting adjustment parameters further include: obtaining the geometric illumination range of the solar street light in the three-dimensional environment model based on each solar street light parameter; calculating the overlapping area of ​​light illumination in the geometric illumination range using a geometric algorithm; calculating the relative overlapping illumination contribution of each overlapping area of ​​light illumination; normalizing the relative overlapping illumination contribution to obtain a normalized relative overlapping illumination contribution; and substituting the normalized relative overlapping illumination contribution into the generation process of the initial lighting adjustment parameters.

[0202] Furthermore, the initial lighting adjustment parameters are adjusted using a solar street light parameter adjustment model to obtain first-order lighting adjustment parameters;

[0203] The solar street light parameter adjustment model includes:

[0204] The real-time information acquisition module is used to monitor the smallest control unit in real time and acquire real-time monitoring video and real-time monitoring meteorological data respectively.

[0205] The data processing module is used to preprocess the real-time monitoring video and the real-time monitoring meteorological data to obtain standard real-time video data and standard real-time meteorological data, respectively.

[0206] The first lighting parameter influence analysis module is used to perform solar street light lighting analysis on the standard real-time video data;

[0207] The first lighting parameter influence analysis module includes:

[0208] Extract key features of the target from the standard real-time data, including: target type, size features, motion features, shape features, location features, and behavior features;

[0209] Furthermore, the lighting requirements for the current scene are calculated based on the aforementioned key features;

[0210] Furthermore, machine learning methods are used to solve the adjustment relationship between the initial lighting adjustment parameters and the lighting demand to obtain the first lighting parameter adjustment coefficient;

[0211] The second lighting parameter impact analysis module is used to perform solar street light lighting analysis on the standard real-time meteorological data.

[0212] The second lighting parameter influence analysis module includes:

[0213] Calculate the current meteorological lighting requirements based on the aforementioned standard real-time meteorological data;

[0214] Furthermore, a nonlinear relationship is established between the standard real-time meteorological data and the current meteorological lighting demand;

[0215] Furthermore, the initial lighting adjustment parameters are solved based on the nonlinear relationship to obtain the second lighting parameter adjustment coefficient;

[0216] The lighting adjustment coefficient calculation module is used to calculate the lighting adjustment coefficient based on the first lighting parameter adjustment coefficient and the second lighting parameter adjustment coefficient.

[0217] The lighting adjustment coefficient calculation module is used to calculate the lighting adjustment coefficient based on the output results of the first lighting parameter influence analysis module and the second lighting parameter influence analysis module.

[0218] The lighting parameter adjustment module is used to adjust the initial lighting adjustment parameters using the lighting adjustment coefficient to obtain the first-order lighting adjustment parameters.

[0219] Furthermore, the lighting effect of the first-order lighting adjustment parameters is evaluated, and the first-order lighting adjustment parameters are adjusted according to the evaluation results to obtain the second-order lighting adjustment parameters;

[0220] The generation process of the second-order illumination adjustment parameters includes:

[0221] Collect actual lighting effect data for each area after adjustment with the first-order lighting adjustment parameters, including: light intensity and distribution;

[0222] Furthermore, the lighting effect of each area is evaluated based on the actual lighting effect data to obtain the lighting effect evaluation results;

[0223] Further, the expected error between the lighting effect evaluation result and the lighting expectation is calculated;

[0224] Further, the adjustment coefficient for the second-order lighting parameters is calculated based on the expected error;

[0225] Furthermore, the first-order lighting adjustment parameters are adjusted according to the second-order lighting parameter adjustment coefficient to obtain the second-order lighting adjustment parameters.

[0226] Furthermore, the solar streetlights in the minimum control unit are controlled according to the second-order lighting adjustment parameters.

[0227] Example 3:

[0228] A control storage medium for a solar street light, wherein the control storage medium stores a solar street light intelligent control program, and when the solar street light intelligent control program is executed by a processor, it implements all the control processes of the solar street light disclosed in Embodiment 1.

[0229] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A control method for a solar street light, characterized in that, include: The minimum control unit is defined by the illumination range of multiple solar streetlights. A three-dimensional environment model is built based on the minimum control unit, incorporating data on solar streetlights, terrain, landforms, and buildings from the real-world scene. The three-dimensional environment model is then marked according to the physical coordinates of the solar streetlights in the real-world scene. The process involves: acquiring solar street light parameters, including height, power, luminous flux, illuminance, battery capacity, and fault status; acquiring natural light data for the minimum control unit; performing property analysis on each region in the three-dimensional environment model to identify ordinary and special regions; setting minimum lighting requirements for each region based on the property analysis results; setting different lighting expectations for the ordinary and special regions; calculating the solar street light illuminance contribution of each region based on obstructions in the three-dimensional environment model; and obtaining initial lighting adjustment parameters for each solar street light based on the natural light data, physical coordinates, and solar street light parameters, combined with the lighting expectations, solar street light illuminance contribution, and normalized relative overlap illuminance contribution of each region. The initial lighting adjustment parameters are adjusted using a solar street light parameter adjustment model to obtain first-order lighting adjustment parameters. The solar street light parameter adjustment model includes using machine learning methods to solve the adjustment relationship between the initial lighting adjustment parameters and lighting demand. The lighting effect of the first-order lighting adjustment parameters is evaluated, and the first-order lighting adjustment parameters are adjusted according to the evaluation results to obtain second-order lighting adjustment parameters. The adjustment includes calculating the expected error between the lighting effect evaluation result and the lighting expectation, and calculating the second-order lighting parameter adjustment coefficient according to the expected error. The solar streetlights in the minimum control unit are controlled according to the second-order lighting adjustment parameters. The geometric illumination range of the solar street light in the three-dimensional environment model is obtained based on each of the solar street light parameters; The overlapping area of ​​light illumination within the geometric illumination range is calculated using a geometric algorithm; Calculate the relative overlap illumination contribution of each light-illuminated overlapping area; The relative overlapping illumination contribution is normalized to obtain the normalized relative overlapping illumination contribution. The intersection condition is detected using an ellipse method, with the equation: Where x represents the horizontal coordinate of the center of the ellipse; y represents the vertical coordinate of the center of the ellipse. (h x ,h y ) represents the center position of the ellipse; a and b represent the lengths of the horizontal and vertical semi-axis of the ellipse, respectively.

2. The control method for a solar street light according to claim 1, characterized in that, The solar street light parameter adjustment model includes: The real-time information acquisition module is used to monitor the smallest control unit in real time and acquire real-time monitoring video and real-time monitoring meteorological data respectively. The data processing module is used to preprocess the real-time monitoring video and the real-time monitoring meteorological data to obtain standard real-time video data and standard real-time meteorological data, respectively. The first lighting parameter influence analysis module is used to perform solar street light lighting analysis on the standard real-time video data; The first lighting parameter influence analysis module includes: Extract key features of the target from the standard real-time data, including: target type, size features, motion features, shape features, location features, and behavior features; Calculate the target lighting requirements for the current scene based on the key features; The relationship between the initial lighting adjustment parameters and the lighting demand is solved using machine learning methods to obtain the first lighting parameter adjustment coefficient; The second lighting parameter impact analysis module is used to perform solar street light lighting analysis on the standard real-time meteorological data. The second lighting parameter influence analysis module includes: Calculate the current meteorological lighting requirements based on the aforementioned standard real-time meteorological data; Establish a nonlinear relationship between the standard real-time meteorological data and the current meteorological lighting demand; The initial lighting adjustment parameters are solved based on the nonlinear relationship to obtain the second lighting parameter adjustment coefficient; The lighting adjustment coefficient calculation module calculates the lighting adjustment coefficient based on the first lighting parameter adjustment coefficient and the second lighting parameter adjustment coefficient. The lighting parameter adjustment module adjusts the initial lighting adjustment parameters according to the lighting adjustment coefficient to obtain the first-order lighting adjustment parameters.

3. The control method for a solar street light according to claim 2, characterized in that, The data processing module includes: a video data processing unit and a meteorological data processing unit; The video data processing unit includes: extracting video frames from the real-time monitoring video to obtain a first real-time monitoring video; performing noise reduction processing on the first real-time monitoring video to obtain a second real-time monitoring video; performing video enhancement on the second real-time monitoring video to obtain a third real-time monitoring video; and performing target detection and segmentation on the third real-time monitoring video to obtain the standard real-time video data. The meteorological data processing unit includes: performing outlier processing on the real-time monitored meteorological data to obtain first real-time monitored meteorological data; standardizing the first real-time monitored meteorological data to obtain second real-time monitored meteorological data; and extracting features from the second real-time monitored meteorological data to obtain standard real-time meteorological data; wherein the standard real-time meteorological data includes any one of cloud cover, precipitation, humidity, wind speed, temperature, and air quality.

4. The control method for a solar street light according to claim 1, characterized in that, The process of generating the second-order illumination adjustment parameters includes: Collect actual lighting effect data for each area after adjustment with the first-order lighting adjustment parameters, including: light intensity and distribution; The lighting effect of each area is evaluated based on the actual lighting effect data to obtain the lighting effect evaluation results. Calculate the expected error between the lighting effect evaluation result and the lighting expectation; Calculate the second-order lighting parameter adjustment coefficient based on the expected error; The first-order lighting adjustment parameters are adjusted according to the second-order lighting parameter adjustment coefficient to obtain the second-order lighting adjustment parameters.

5. A control system for a solar street light, comprising performing the method as described in any one of claims 1-4, characterized in that, The system includes: a control range generation unit for generating a minimum control unit based on the lighting range of multiple solar streetlights; an environment model generation unit for generating a three-dimensional environment model based on the real-world scene of the minimum control unit; a networking unit for connecting the physical solar streetlight of the minimum control unit, the three-dimensional environment model, and a remote server via a wireless network; a solar streetlight management unit for managing the solar streetlights, including recording the physical coordinates, height, power, luminous flux, illuminance, battery capacity, and fault status of the solar streetlights; a data monitoring unit for real-time monitoring of the minimum control unit using multiple monitoring devices and real-time acquisition of meteorological data through meteorological equipment; a lighting parameter generation unit for generating initial lighting parameters for the solar streetlights; a lighting parameter adjustment unit for adjusting the lighting parameters of the solar streetlights, including first-order lighting adjustment parameters and second-order lighting adjustment parameters; a control unit for controlling voltage and current based on the output of the lighting parameter adjustment unit; an evaluation unit for evaluating the lighting effect after adjusting the lighting parameters of the solar streetlights; and a feedback unit for providing feedback on the lighting parameters and lighting effect of each adjustment of the solar streetlights.

6. The control system for a solar street light according to claim 5, characterized in that, The lighting parameter generation unit includes: Obtain the natural light data of the minimum control unit; The properties of each region in the three-dimensional environment model are analyzed to obtain ordinary regions and special regions; Minimum lighting requirements are set for each area based on the property analysis results; Different lighting expectations are set for the general area and the special area; The contribution of solar streetlights to illumination in each area is calculated based on the obstructions present in the three-dimensional environment model. Based on the natural illumination data, the physical coordinates, and the solar street light parameters, initial illumination adjustment parameters for each solar street light are obtained by combining the illumination expectation for each area, the solar street light illumination contribution, and the normalized relative overlap illumination contribution. These initial illumination adjustment parameters are used to adjust the current and voltage values ​​of the solar street lights, ensuring that the minimum illumination requirements are met.

7. The control system for a solar street light according to claim 5, characterized in that, The first-order lighting adjustment parameters are adjusted using a solar street light parameter adjustment model. The specific process includes: The real-time information acquisition module is used to monitor the smallest control unit in real time and acquire real-time monitoring video and real-time monitoring meteorological data respectively. The data processing module is used to preprocess the real-time monitoring video and the real-time monitoring meteorological data to obtain standard real-time video data and standard real-time meteorological data, respectively. The first lighting parameter influence analysis module is used to perform solar street light lighting analysis on the standard real-time video data; The first lighting parameter influence analysis module includes: Extract key features of the target from the standard real-time data, including: target type, size features, motion features, shape features, location features, and behavior features; Calculate the target lighting requirements for the current scene based on the key features; The relationship between the initial lighting adjustment parameters and the lighting demand is solved using machine learning methods to obtain the first lighting parameter adjustment coefficient; The second lighting parameter impact analysis module is used to perform solar street light lighting analysis on the standard real-time meteorological data. The second lighting parameter influence analysis module includes: Calculate the current meteorological lighting requirements based on the aforementioned standard real-time meteorological data; Establish a nonlinear relationship between the standard real-time meteorological data and the current meteorological lighting demand; The initial lighting adjustment parameters are solved based on the nonlinear relationship to obtain the second lighting parameter adjustment coefficient; The lighting adjustment coefficient calculation module is used to calculate the lighting adjustment coefficient based on the first lighting parameter adjustment coefficient and the second lighting parameter adjustment coefficient. The lighting parameter adjustment module is used to adjust the initial lighting adjustment parameters using the lighting adjustment coefficient to obtain the first-order lighting adjustment parameters.

8. The control system for a solar street light according to claim 5, characterized in that, The process of generating the second-order illumination adjustment parameters includes: Collect actual lighting effect data for each area after adjustment with the first-order lighting adjustment parameters, including: light intensity and distribution; Based on the actual lighting effect data, the evaluation unit evaluates the lighting effect of each area to obtain the lighting effect evaluation result. Calculate the expected error between the lighting effect evaluation result and the lighting expectation; Calculate the second-order lighting parameter adjustment coefficient based on the expected error; The first-order lighting adjustment parameters are adjusted according to the second-order lighting parameter adjustment coefficient to obtain the second-order lighting adjustment parameters.

9. A control storage medium for a solar street light, characterized in that, The control storage medium stores a solar street light intelligent control program, which, when executed by a processor, implements the solar street light control method as described in any one of claims 1 to 4.

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