Meteorological prediction-based solar intelligent street lamp regulation and control system and regulation and control method thereof

Abstract

The invention provides a solar intelligent street lamp regulation and control system based on meteorological prediction and a regulation and control method thereof. The solar intelligent street lamp regulation and control system comprises an artificial intelligence management platform, a main control unit, a solar photovoltaic panel, an energy storage battery pack and a multi-mode LED light source, a dynamic dimming module for dynamically adjusting the illumination brightness of each group of LED light sources is also arranged between the multi-mode LED light sources and the main control unit; the main control unit is further connected with an environment sensor module, a meteorological data acquisition module, a wireless communication module and an energy storage prediction module. The environment sensor module is used for monitoring the illuminance, the temperature and the humidity of the environment where the multi-mode LED light source is located; and the main control unit automatically starts a hierarchical energy-saving strategy of the multi-mode LED light source and automatically switches the working mode of the multi-mode LED light source according to the residual electric quantity of the energy storage battery pack and the prediction demand of the energy storage prediction module, and realizes smooth brightness transition of the multi-mode LED light source by adopting fuzzy control.

Description

Technical field

[0001] The present invention relates to smart lighting technology, and in particular to a solar-powered smart streetlight control system and control method based on weather forecasting. [Background Technology]

[0002] Solar energy is widely used because it is easily accessible and free, requiring no additional energy for transport. It is clean, pollution-free, and environmentally friendly, making it a popular choice. Solar streetlights, which use crystalline silicon solar cells for power and energy storage and are controlled by intelligent charge and discharge controllers, are used to replace traditional public electric streetlights.

[0003] Although solar streetlights have become widely used in urban lighting, most rely on timing control or light sensor control, which fails to achieve intelligent segmented control and effectively manages individual lights. In some cases, timing or light control modes cannot timely monitor the operating status of the streetlight system. Failures in the streetlight system cannot be promptly addressed, and streetlight operating conditions cannot be effectively recorded. This reduces the actual operating efficiency of the entire streetlight system, and the safety and lifespan of the energy storage modules.

[0004] Therefore, current solar street lights simply integrate a set of photoelectric conversion devices into ordinary street lights, with a low level of intelligence. This leads to the following problems: a. The brightness is fixed or relies solely on real-time light sensors for adjustment, which cannot predict the problem of insufficient energy storage caused by continuous rainy days; b. The lack of active acquisition of meteorological data makes it easy for lighting to be interrupted after the energy storage is exhausted; c. The traditional PWM dimming mode has high energy consumption and lacks multi-dimensional energy-saving strategies; d. Over-discharge of energy storage batteries shortens their lifespan; e. Isolated node control leads to inconsistent regional lighting brightness. [Summary of the invention]

[0005] The embodiments of the present invention provide a solar-powered intelligent street light control system and control method based on weather forecasting. By combining wireless communication modes with weather forecasting, dynamic power storage management evaluation, and group collaborative control, the system can realize the sharing of power status between street light nodes, effectively respond to instantaneous changes in weather conditions and effectively extend battery life. At the same time, it monitors and predicts the failure cycle of energy storage battery packs, provides early warning for replacement, and extends battery life on rainy days.

[0006] The technical solution adopted by at least one embodiment of the present invention is as follows:

[0007] In a first aspect, the present invention provides a solar intelligent street light control system based on weather forecasting, comprising an artificial intelligence management platform, a main control unit, a solar photovoltaic panel, an energy storage battery pack, and a multi-mode LED light source, wherein the solar photovoltaic panel, the energy storage battery pack, and the multi-mode LED light source are respectively connected to the main control unit, and a dynamic dimming module is further provided between the multi-mode LED light source and the main control unit for dynamically adjusting the lighting brightness of each group of LED light sources;

[0008] The main control unit is also connected to an environmental sensor module, a meteorological data acquisition module, a wireless communication module and an energy storage prediction module respectively;

[0009] The environmental sensor module is used to monitor the illuminance, temperature and humidity of the environment in which the multi-mode LED light source is located;

[0010] The meteorological data acquisition module is used to obtain cloud cover, precipitation probability, temperature, wind speed and sunshine duration data in the refined weather forecast for the next 7 days;

[0011] The energy storage prediction module outputs the daily allocable power based on the weather data for the next 7 days provided by the meteorological data acquisition module and the battery health data of the energy storage battery pack;

[0012] The wireless communication module is used for wireless group control management between the multi-mode LED light source and the main control unit, and for communication connection and data interaction between the main control unit and the artificial intelligence management platform;

[0013] The main control unit automatically starts the hierarchical energy-saving strategy of the multi-mode LED light source and automatically switches the working mode of the multi-mode LED light source according to the remaining power of the energy storage battery pack and the predicted demand of the energy storage prediction module, and adopts fuzzy control to achieve a smooth transition of the brightness of the multi-mode LED light source.

[0014] Preferably, the meteorological data acquisition module, wireless communication module, energy storage prediction module and dynamic dimming module are integrated and installed in a control box corresponding to the main control unit, and the main control unit is integrally assembled and connected with the energy storage battery pack; this makes the overall product more concise and beautiful, and avoids the clutter of wiring harnesses for power supply and connection of each module.

[0015] Preferably, the dynamic dimming module lights corresponding to each group of LED light sources on the multi-mode LED light source perform dynamic 0-100% stepless dimming.

[0016] Preferably, the hierarchical energy-saving strategy of the multi-mode LED light source is to maintain the brightness of the LED light source at 60% on the first day, reduce the brightness of the LED light source to 30% on the second day, and control the LED light source to 10% emergency lighting on the third day.

[0017] Preferably, the working modes of the multi-mode LED light source are divided into a full power mode with 100% brightness, an adaptive mode with dynamic brightness setting from 50% to 100%, an energy-saving mode with 30% brightness, and an emergency mode with 10% brightness to maintain only safety lighting.

[0018] Preferably, the multi-mode LED light source is also integrated with an AI camera that can automatically increase the lighting brightness as needed; for example, the light source brightness is increased when a pedestrian is detected.

[0019] Preferably, an emergency charging interface is installed on the lower side of the street light pole corresponding to the multi-mode LED light source, which opens a USB fast charging interface to the outside when the power is sufficient.

[0020] In a second aspect, the present invention provides a control method for the above-mentioned solar intelligent street light control system based on weather forecast, comprising the following steps:

[0021] S1. After the system is powered on, it will be initialized first.

[0022] S2. After initialization is completed, establish the corresponding tasks;

[0023] S3. Then, start each task through the main control unit and wireless communication module;

[0024] S4. The solar photovoltaic panel charges the energy storage battery pack under the control of the main control unit;

[0025] S5. The main control unit receives meteorological information sensed by the environmental sensor module and the meteorological data acquisition module obtains meteorological information released by the meteorological publishing platform, and controls the multi-mode LED light source and the dynamic dimming module according to the meteorological information. The dynamic dimming module is controlled by the main control unit and the energy storage prediction module, and the energy storage battery pack discharges to provide energy to the multi-mode LED light source;

[0026] The dynamic dimming module dynamically controls each multi-mode LED light source, including the following steps:

[0027] a. After startup, the main control unit determines the time mode;

[0028] b. When in day mode, turn off all multi-mode LED light sources;

[0029] c. When in night mode, receive meteorological data obtained by the meteorological data acquisition module;

[0030] d. Then, determine the severity of the weather;

[0031] e. When the weather level is 1-2, obtain the remaining battery capacity percentage SOC and battery health status SOH of the energy storage battery pack:

[0032] f. Determine the current remaining battery capacity percentage SOC status of the energy storage battery pack;

[0033] g. When the current remaining battery capacity percentage SOC of the energy storage battery pack is not less than 80%, the dynamic dimming module controls the multi-mode LED light source to operate in full power mode at 100% brightness;

[0034] h. When the current remaining battery capacity percentage SOC of the energy storage battery pack is less than 50%, the energy storage prediction module starts the energy-saving algorithm;

[0035] i. Then, enable temperature compensation and generate a brightness decay curve;

[0036] j. Finally, the dynamic dimming module controls the multi-mode LED light source to reduce its brightness to 10% according to the brightness attenuation curve to maintain emergency mode operation for safety lighting;

[0037] k. When the current remaining battery capacity percentage SOC of the energy storage battery pack is not less than 50% and less than 80%, the multi-mode LED light source enters the dynamic adjustment mode;

[0038] 1. The main control unit calculates the safe discharge depth based on the status of the energy storage battery pack;

[0039] m. Then, determine whether the remaining battery life of the energy storage battery pack is greater than or equal to the predicted demand of the energy storage prediction module?

[0040] n. If the remaining battery life ≥ the predicted demand of the energy storage prediction module, maintain the current brightness;

[0041] o. If the remaining battery life is less than the predicted demand of the energy storage prediction module, the brightness of the multi-mode LED light source is proportionally reduced through the dynamic dimming module;

[0042] p. Then, temperature compensation is enabled and a brightness attenuation curve is generated. The dynamic dimming module controls the multi-mode LED light source to reduce the brightness to 10% according to the brightness attenuation curve to maintain emergency mode for safety lighting.

[0043] Furthermore, in step S5, the specific meteorological data fusion steps of the meteorological information sensed by the environmental sensor module and the meteorological information acquired by the meteorological data acquisition module are as follows:

[0044] S5.1. After startup, the meteorological data acquisition module obtains meteorological information published by the meteorological publishing platform through the meteorological API interface;

[0045] S5.2. Then, perform data validity check;

[0046] S5.3. When the data is invalid, switch to the local environmental sensor module;

[0047] S5.4. Then, the environmental sensor module imports the sensed local weather information into the weather API interface or \ and imports it into the subsequent steps for data normalization;

[0048] S5.5. Align multi-source data when available.

[0049] S5.6. The local weather information sensed by the environmental sensor module and the weather information acquired by the weather data acquisition module are unified for data normalization;

[0050] S5.7. Extract characteristic parameters from the data;

[0051] S5.8. The main control unit establishes a prediction model;

[0052] S5.9. Generate a weather index matrix;

[0053] S5.10. Integrate the weather index matrix with the remaining battery capacity percentage (SOC) and battery health status (SOH) of the energy storage battery pack;

[0054] S5.11. Generate a dynamic brightness decision tree for a multi-mode LED light source;

[0055] S5.12. The main control unit issues wireless commands via the wireless communication module;

[0056] S5.13. Dimming each multi-mode LED light source separately through its own dynamic dimming module;

[0057] S5.14. Then, the dimming data of each multi-mode LED light source is transmitted back for storage via the wireless communication module;

[0058] S5.15. Finally, the model is parameterized and the integration of all meteorological data is completed.

[0059] Advantages of the present invention:

[0060] In the present invention, the meteorological data acquisition module, wireless communication module, energy storage prediction module and dynamic dimming module are integrated and installed in the control box corresponding to the main control unit, and the main control unit is assembled and connected with the energy storage battery pack as a whole, which makes the overall product more simple and beautiful, and avoids the mess of the power supply and connection harnesses of each module.

[0061] In the entire system, the meteorological data fusion decision-making system obtains a detailed weather forecast for the next seven days through the meteorological API interface, which includes cloud cover, precipitation probability, and sunshine duration; at the same time, it establishes an energy storage demand prediction model, and the predicted discharge amount = ∑(daily lighting duration × brightness coefficient × weather attenuation factor); its control method adopts a dynamic brightness adjustment algorithm to automatically switch the working mode according to the remaining power and predicted demand, for example, normal mode (100% brightness or customized according to product configuration), energy-saving mode (30% brightness or customized according to product configuration), emergency mode (10% brightness only to maintain safety lighting or customized according to product configuration), and uses fuzzy control to achieve smooth brightness transition to avoid sudden changes that can be noticed by the naked eye; moreover, the wireless communication module supports wireless group control management and Mesh networking, so that meteorological data and power status can be shared between street light nodes.

[0062] During operation, the main control unit automatically starts the hierarchical energy-saving strategy of the multi-mode LED light source and automatically switches the working mode of the multi-mode LED light source according to the remaining power of the energy storage battery pack and the predicted demand of the energy storage prediction module, and adopts fuzzy control to achieve a smooth transition of the brightness of the multi-mode LED light source to avoid sudden changes that can be noticed by the naked eye; moreover, the wireless communication module reports the control log to the artificial intelligence management platform through the wireless network. The wireless communication module also supports the sharing of power status between street light nodes through the LoRaWAN protocol. The energy storage battery pack also adds a supercapacitor module to cope with instantaneous changes in weather, so as to extend the battery life; the energy storage prediction module predicts the failure cycle of the energy storage battery pack through battery internal resistance monitoring, and warns of replacement in advance, effectively extending the battery life on rainy days. [Specific implementation method]

[0063] Figure 1 This is a system working logic diagram of multiple light sources in the present invention;

[0064] Figure 2 Schematic diagram of the structure of a single light source system in the present invention;

[0065] Figure 3 It is the dynamic brightness decision tree logic diagram of the present invention;

[0066] Figure 4 This is the meteorological data fusion flow chart of the present invention. [Specific implementation method]

[0068] In order to make the technical solution of the present invention more clearly understood by those skilled in the art, the following examples are given for illustration. It should be noted that the following examples do not limit the scope of protection claimed by the present invention.

[0069] The present invention provides a solar intelligent street light control system based on weather forecast, such as Figure 1 and 2As shown, it includes an artificial intelligence management platform 1, a main control unit 2, a solar photovoltaic panel 3, an energy storage battery pack 4 and a multi-mode LED light source 5. The solar photovoltaic panel 3, the energy storage battery pack 4 and the multi-mode LED light source 5 are respectively connected to the main control unit 2. A dynamic dimming module 6 for dynamically adjusting the lighting brightness of each group of LED light sources is also provided between the multi-mode LED light source 5 and the main control unit 2; the main control unit 2 is also respectively connected to an environmental sensor module 7, a meteorological data acquisition module 8, a wireless communication module 9 and an energy storage prediction module 10; the environmental sensor module 7 is used to monitor the illumination, temperature and humidity of the environment in which the multi-mode LED light source 5 is located; the meteorological data acquisition module 8 is used to obtain the cloud cover, Precipitation probability, temperature, wind speed and sunshine duration data; the energy storage prediction module 10 outputs the daily allocable power based on the weather data of the next 7 days provided by the meteorological data acquisition module 8 and the battery health data of the energy storage battery pack 4; the wireless communication module 9 is used for wireless group control management between the multi-mode LED light source 5 and the main control unit 2, as well as the communication connection and data interaction between the main control unit 2 and the artificial intelligence management platform 1; the main control unit 2 automatically starts the hierarchical energy-saving strategy of the multi-mode LED light source 5 and automatically switches the working mode of the multi-mode LED light source 5 according to the remaining power of the energy storage battery pack 4 and the predicted demand of the energy storage prediction module 10, and uses fuzzy control to achieve a smooth transition of the brightness of the multi-mode LED light source 5 to avoid sudden changes that can be perceived by the naked eye.

[0070] The solar photovoltaic panel 3 is capable of maximum power point tracking (MPPT). The energy storage battery pack 4 utilizes a lithium iron phosphate (LiFePO4) battery pack with a state-of-charge (SOC) status monitoring module. The multi-mode LED light source 5 supports 0-100% stepless dimming. The main control unit 2 utilizes an MCU controller with integrated meteorological data analysis algorithms, such as the STM32 series. The wireless communication module 9 is a 4G / Wifi / NB-IoT LoRa wireless transmission unit and supports interconnection with a cloud platform. During operation, the main control unit 2 calculates the difference between the current total energy storage (80% SOC) and the predicted energy consumption (120% demand) and automatically initiates a tiered energy-saving strategy. The multi-mode LED light source 5 maintains 60% brightness on the first day, reduces it to 30% on the second day, and controls it to 10% emergency lighting on the third day. The multi-mode LED light source 5 operates in three modes: full power mode (100% brightness), adaptive mode (dynamically adjusting brightness from 50% to 100%), energy-saving mode (30% brightness), and emergency mode (10% brightness for safety purposes only).

[0071] Continue as Figure 2As shown, the meteorological data acquisition module 8, wireless communication module 9, energy storage prediction module 10 and dynamic dimming module 6 are integrated and installed in the control box corresponding to the main control unit 2. The main control unit 2 is assembled and connected with the energy storage battery pack 4 as a whole, which makes the overall product more simple and beautiful, and avoids the messy wiring harnesses of the power supply and connection of each module; the dynamic dimming module 6 corresponding to each group of LED light sources on the multi-mode LED light source 5 performs dynamic 0-100% stepless dimming.

[0072] In addition, the multi-mode LED light source is also integrated with an AI camera that can automatically increase the lighting brightness on demand. For example, it automatically increases the light source brightness when a pedestrian is detected, and automatically restores the original energy-saving working mode after the pedestrian passes. Moreover, the lower side of the street light pole corresponding to the multi-mode LED light source is also installed with an emergency charging port that opens a USB fast charging port to the outside when there is sufficient power.

[0073] In the entire system, the meteorological data fusion decision-making system obtains a detailed weather forecast for the next seven days through the meteorological API interface, which includes cloud cover, precipitation probability, and sunshine duration. At the same time, it establishes an energy storage demand prediction model, and the predicted discharge amount = ∑ (daily lighting duration × brightness coefficient × weather attenuation factor). Its control method uses a dynamic brightness adjustment algorithm to automatically switch working modes according to the remaining power and predicted demand. For example, normal mode (100% brightness or customized according to product configuration), energy-saving mode (30% brightness or customized according to product configuration), emergency mode (10% brightness only to maintain safety lighting or customized according to product configuration), and fuzzy control is used to achieve smooth brightness transition to avoid sudden changes that can be detected by the naked eye. In addition, the wireless communication module 9 supports wireless group control management and Mesh networking, so that meteorological data and power status can be shared between street light nodes.

[0074] The core algorithm of the 7-day forecast model in the main control unit 2 is as follows:

[0075] energy_Predict(weather_data, battery_SOH):

[0076] #Input: weather data for the next 7 days (cloud cover, precipitation, temperature), battery health

[0077] #Output: Daily allocable electricity

[0078] base_load = 24 * current brightness level

[0079] cloud_adj = 1 - (percentage of cloud cover * 0.7)

[0080] temp_adj=1+abs(25-temperature) / 100#temperature compensation coefficient

[0081] return base_load*cloud_adj*temp_adj*battery_SOH

[0082] The control method of the solar intelligent street light control system based on weather forecast includes the following steps:

[0083] S1. After the system is powered on, it will be initialized first.

[0084] S2. After initialization is completed, establish the corresponding tasks;

[0085] S3. Then, each task is started by the main control unit 2 and the wireless communication module 9;

[0086] S4. The solar photovoltaic panel 3 charges the energy storage battery pack 4 under the control of the main control unit 2;

[0087] S5. The main control unit 2 receives the meteorological information sensed by the environmental sensor module 7 and the meteorological data acquisition module 8 obtains the meteorological information released by the meteorological publishing platform, and controls the multi-mode LED light source 5 and the dynamic dimming module 6 according to the meteorological information. The dynamic dimming module 6 is controlled by the main control unit 2 and the energy storage prediction module 10, and the energy storage battery pack 4 discharges to provide energy to the multi-mode LED light source 5;

[0088] Among them, such as Figure 3 As shown, the dynamic dimming module 6 dynamically controls each multi-mode LED light source 5, including the following steps:

[0089] a. After startup, the main control unit 2 determines the time mode;

[0090] b. When in daytime mode, turn off all multi-mode LED light sources 5;

[0091] c. When in night mode, receiving meteorological data acquired by meteorological data acquisition module 8;

[0092] d. Then, determine the severity of the weather;

[0093] e. When the weather level is 1 to 2, obtain the remaining battery capacity percentage SOC and battery health status SOH of the energy storage battery pack 4;

[0094] f. Determine the current remaining battery charge percentage SOC status of the energy storage battery pack 4;

[0095] g. When the energy storage battery pack 4 has a current remaining battery charge percentage SOC state of not less than 80%, the dynamic dimming module 6 controls the multi-mode LED light source 5 to operate in full power mode at 100% brightness;

[0096] h. When the current remaining battery capacity percentage SOC of the energy storage battery pack 4 is less than 50%, the energy storage prediction module 10 starts the energy-saving algorithm;

[0097] i. Then, enable temperature compensation and generate a brightness decay curve;

[0098] j. Finally, the dynamic dimming module 6 controls the multi-mode LED light source 5 according to the brightness attenuation curve to reduce the brightness to 10% to maintain only the emergency mode of safety lighting;

[0099] k. When the current remaining battery capacity percentage SOC of the energy storage battery pack 4 is not less than 50% and less than 80%, the multi-mode LED light source 5 enters the dynamic adjustment mode;

[0100] 1. The main control unit 2 calculates the safe discharge depth based on the status of the energy storage battery pack 4;

[0101] m. Then, determine whether the remaining battery life of the energy storage battery pack 4 is greater than or equal to the predicted demand of the energy storage prediction module 10?

[0102] n. If the remaining battery life ≥ the predicted demand of the energy storage prediction module 10, the current brightness is maintained;

[0103] o If the remaining battery life is less than the predicted demand of the energy storage prediction module 10, the brightness of the multi-mode LED light source 5 is proportionally reduced by the dynamic dimming module 6;

[0104] p. Then, temperature compensation is enabled and a brightness attenuation curve is generated. The dynamic dimming module 6 controls the multi-mode LED light source 5 according to the brightness attenuation curve to reduce the brightness to 10% to maintain only the emergency mode of safety lighting.

[0105] The dynamic dimming strategy for graded threshold levels is shown in the following table:

[0106] SOC range Dimming strategy ＞80％ Full power mode (100% according to the set lighting mode) 50％-80％ Adaptive mode (50%-100% dynamic setting adjustment) 20％-50％ Energy saving mode (30% setting base brightness) <20％ Emergency mode (10% brightness + turn off non-core functions)

[0107] The corresponding emergency protocol triggering conditions are shown in the following table:

[0108] Meteorological parameters Threshold Corresponding actions Continuous rainfall days ≥ 3 days Activate Level 3 emergency Ambient temperature <Set value or >Set value Activate battery protection function

[0109] Data mapping table of energy storage battery pack 4:

[0110] Input parameters dimension Impact Weight SOC ％ 0.6 Cloud cover ％ 0.25 Battery temperature Spend 0.15

[0111] Brightness coefficient of the multi-mode LED light source 5: base value*(1-α.cloud cover)*(1+β.temperature difference)*Y-Soh

[0112] The symbols in the formula are expressed as follows:

[0113]

[0114] like Figure 4 As shown, in step S5, the specific meteorological data fusion steps of the meteorological information sensed by the environmental sensor module 7 and the meteorological information acquired by the meteorological data acquisition module 8 are as follows:

[0115] S5.1 after startup, the meteorological data acquisition module 8 obtains meteorological information released by the meteorological API interface meteorological publishing platform;

[0116] S5.2. Then, perform data validity check;

[0117] S5.3. When the data is invalid, switch to the local environmental sensor module 7;

[0118] S5.4. Then, the environmental sensor module 7 imports the local weather information sensed into the weather API interface or \ and imports it into subsequent steps for data normalization;

[0119] S5.5. Align multi-source data when available.

[0120] For multi-source data alignment, synchronize meteorological API data, such as cloud cover, precipitation, and wind speed, and calibrate local sensors, such as illumination, temperature, and humidity;

[0121] S5.6. The local weather information sensed by the environmental sensor module 7 and the weather data acquisition module 8 obtains the weather information unified data normalization processing;

[0122] S5.7. Extract characteristic parameters from the data;

[0123] S5.8. Main control unit 2 establishes a prediction model;

[0124] S5.9. Generate a weather index matrix;

[0125] The weather index matrix is shown in the following table:

[0126] date Sunshine duration (h) Cloud cover (%) Probability of precipitation D+1 52 30 0.1 D+2 1.8 90 0.8 ... ... ... ...

[0127] S5.10. Fusion of the weather index matrix with the remaining battery capacity percentage SOC and battery health status SOH of the energy storage battery pack 4;

[0128] S5.11. Generate a dynamic brightness decision tree for the multi-mode LED light source 5;

[0129] S5.12. The main control unit 2 issues wireless commands via the wireless communication module 9;

[0130] S5.13. Each multi-mode LED light source 5 is dimmed by its own dynamic dimming module 6;

[0131] S5.14. Then, the data after dimming of each multi-mode LED light source 5 is transmitted back to the storage through the wireless communication module 9;

[0132] S5.15. Finally, the model is parameterized and the integration of all meteorological data is completed.

[0133] When this embodiment is working, the main control unit 2 automatically starts the hierarchical energy-saving strategy of the multi-mode LED light source 5 and automatically switches the working mode of the multi-mode LED light source 5 according to the remaining power of the energy storage battery pack 4 and the predicted demand of the energy storage prediction module 10, and adopts fuzzy control to achieve a smooth transition of the brightness of the multi-mode LED light source 5 to avoid sudden changes that can be detected by the naked eye; moreover, the wireless communication module 9 reports the control log to the artificial intelligence management platform 1 through the wireless network. The wireless communication module 9 also supports the sharing of power status between street light nodes through the LoRaWAN protocol. The energy storage battery pack 4 also adds a supercapacitor module to cope with instantaneous changes in weather, so as to extend the battery life; the energy storage prediction module 10 predicts the failure cycle of the energy storage battery pack 4 by monitoring the internal resistance of the battery, and warns of replacement in advance, thereby effectively extending the battery life on rainy days.

[0134] The above-described embodiments are merely preferred embodiments of the present invention and are not intended to limit the scope of implementation of the present invention. Except for the cases listed in the specific embodiments, all equivalent changes made according to the methods and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. Solar intelligent street light control system based on weather forecast, characterized by: It includes an artificial intelligence management platform, a main control unit, a solar photovoltaic panel, an energy storage battery pack, and a multi-mode LED light source. The solar photovoltaic panel, the energy storage battery pack, and the multi-mode LED light source are respectively connected to the main control unit. A dynamic dimming module is further provided between the multi-mode LED light source and the main control unit to dynamically adjust the lighting brightness of each group of LED light sources. The main control unit is also connected to an environmental sensor module, a meteorological data acquisition module, a wireless communication module and an energy storage prediction module respectively; The environmental sensor module is used to monitor the illuminance, temperature and humidity of the environment in which the multi-mode LED light source is located; The meteorological data acquisition module is used to obtain cloud cover, precipitation probability, temperature, wind speed and sunshine duration data in the refined weather forecast for the next 7 days; The energy storage prediction module outputs the daily allocable power based on the weather data for the next 7 days provided by the meteorological data acquisition module and the battery health data of the energy storage battery pack; The wireless communication module is used for wireless group control management between the multi-mode LED light source and the main control unit, and for communication connection and data interaction between the main control unit and the artificial intelligence management platform; The main control unit automatically starts the hierarchical energy-saving strategy of the multi-mode LED light source and automatically switches the working mode of the multi-mode LED light source according to the remaining power of the energy storage battery pack and the predicted demand of the energy storage prediction module, and adopts fuzzy control to achieve a smooth transition of the brightness of the multi-mode LED light source.

2. The solar intelligent street light control system based on weather forecast according to claim 1 is characterized in that: The meteorological data acquisition module, wireless communication module, energy storage prediction module and dynamic dimming module are integrated and installed in a control box corresponding to the main control unit, and the main control unit is integrally assembled and connected with the energy storage battery pack.

3. The solar intelligent street light control system based on weather forecast according to claim 1 is characterized in that: The dynamic dimming module lights corresponding to each group of LED light sources on the multi-mode LED light source are dynamically dimmed from 0 to 100%.

4. The solar intelligent street light control system and control method based on weather forecast according to claim 1, characterized in that: The hierarchical energy-saving strategy of the multi-mode LED light source is to maintain the brightness of the LED light source at 60% on the first day, reduce the brightness of the LED light source to 30% on the second day, and control the LED light source to 10% emergency lighting on the third day.

5. The solar intelligent street light control system and control method based on weather forecast according to claim 1 is characterized in that: The working modes of the multi-mode LED light source are divided into a full-power mode with 100% brightness, an adaptive mode with dynamic brightness setting from 50% to 100%, an energy-saving mode with 30% brightness, and an emergency mode with 10% brightness to maintain only safety lighting.

6. The solar intelligent street light control system and control method based on weather forecast according to claim 1, characterized in that: The multi-mode LED light source is also integrated with an AI camera that can automatically increase the lighting brightness as needed.

7. The solar intelligent street light control system and control method based on weather forecast according to claim 1, characterized in that: An emergency charging interface is also installed on the lower side of the street light pole corresponding to the multi-mode LED light source, which opens a USB fast charging interface when the power is sufficient.

8. The control method of the solar intelligent street light control system based on weather forecast according to any one of claims 1 to 7, characterized in that: The following steps are involved: S1. After the system is powered on, it first performs initialization; S2. After initialization is completed, establish the corresponding tasks; S3. Then, start each task through the main control unit and wireless communication module; S4. The solar photovoltaic panel charges the energy storage battery pack under the control of the main control unit; S5. The main control unit receives meteorological information sensed by the environmental sensor module and the meteorological data acquisition module obtains meteorological information released by the meteorological publishing platform, and controls the multi-mode LED light source and the dynamic dimming module according to the meteorological information. The dynamic dimming module is controlled by the main control unit and the energy storage prediction module, and the energy storage battery pack discharges to provide energy to the multi-mode LED light source; The dynamic dimming module dynamically controls each multi-mode LED light source, including the following steps: a. After startup, the main control unit determines the time mode; b. When in day mode, turn off all multi-mode LED light sources; c. When in night mode, receive meteorological data obtained by the meteorological data acquisition module; d. Then, determine the severity of the weather; e. When the weather level is 1-2, obtain the remaining battery capacity percentage SOC and battery health status SOH of the energy storage battery pack; f. Determine the current remaining battery capacity percentage SOC status of the energy storage battery pack; g. When the current remaining battery capacity percentage SOC of the energy storage battery pack is not less than 80%, the dynamic dimming module controls the multi-mode LED light source to operate in full power mode at 100% brightness; h. When the current remaining battery capacity percentage SOC of the energy storage battery pack is less than 50%, the energy storage prediction module starts the energy-saving algorithm; i. Then, enable temperature compensation and generate a brightness decay curve; j. Finally, the dynamic dimming module controls the multi-mode LED light source to reduce its brightness to 10% based on the brightness attenuation curve, maintaining emergency mode operation for safety lighting only. k. When the current remaining battery capacity percentage SOC of the energy storage battery pack is not less than 50% and less than 80%, the multi-mode LED light source enters the dynamic adjustment mode; l. The main control unit calculates the safe discharge depth based on the status of the energy storage battery pack; m. Then, determine whether the remaining battery life of the energy storage battery pack is greater than or equal to the predicted demand of the energy storage prediction module? n. If the remaining battery life ≥ the predicted demand of the energy storage prediction module, maintain the current brightness; o. If the remaining battery life is less than the predicted demand of the energy storage prediction module, the brightness of the multi-mode LED light source is proportionally reduced through the dynamic dimming module; p. Then, temperature compensation is enabled and a brightness attenuation curve is generated. The dynamic dimming module controls the multi-mode LED light source to reduce the brightness to 10% according to the brightness attenuation curve to maintain emergency mode for safety lighting.

9. The control method of the solar intelligent street light control system based on weather forecast according to claim 8, characterized in that: In step S5, the specific meteorological data fusion steps of the meteorological information sensed by the environmental sensor module and the meteorological information acquired by the meteorological data acquisition module are as follows: S5.

1. After startup, the meteorological data acquisition module obtains meteorological information published by the meteorological publishing platform through the meteorological API interface; S5.

2. Then, perform data validity check; S5.

3. When the data is invalid, switch to the local environmental sensor module; S5.

4. Then, the environmental sensor module imports the sensed local weather information into the weather API interface or \ and imports it into the subsequent steps for data normalization; S5.

5. Align multi-source data when available. S5.

6. The local weather information sensed by the environmental sensor module and the weather information acquired by the weather data acquisition module are unified for data normalization; S5.

7. Extract characteristic parameters from the data; S5.

8. The main control unit establishes a prediction model; S5.

9. Generate a weather index matrix; S5.

10. Integrate the weather index matrix with the remaining battery capacity percentage (SOC) and battery health status (SOH) of the energy storage battery pack; S5.

11. Generate a dynamic brightness decision tree for a multi-mode LED light source; S5.

12. The main control unit issues wireless commands via the wireless communication module; S5.

13. Dimming each multi-mode LED light source separately through its own dynamic dimming module; S5.

14. Then, the dimming data of each multi-mode LED light source is transmitted back for storage via the wireless communication module; S5.

15. Finally, the model is parameterized and the integration of all meteorological data is completed.

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