CN-122028274-A - Intelligent control method and system for street lamp of Internet of things

CN122028274ACN 122028274 ACN122028274 ACN 122028274ACN-122028274-A

Abstract

The invention belongs to the technical field of intelligent illumination of the Internet of things, and particularly discloses an intelligent control method and system of a street lamp of the Internet of things, wherein the method comprises the steps of collecting traffic flow, ambient light, weather conditions, date type and specific time data, and obtaining total power allowance of a system, rated power of the street lamp and accumulated running time; the traffic state is identified based on traffic flow, an illuminance reference value is determined, multi-source data is fused through a dynamic weight distribution model, a dynamic target illuminance requirement value is calculated, a power requirement value is determined based on accumulated running time calculation light efficiency parameters and combined with the dynamic target illuminance requirement value and a preset irradiation area, the total power limit change trend of the system is predicted, the dynamic target illuminance requirement value, the power requirement value and a basic safety illuminance value are used as input, a power instruction set is generated through calculation under system constraint, and the power instruction set is executed in a time-staggered or smooth gradual change mode, so that energy saving, safety and self-adaptive regulation and control of road illumination are realized.

Inventors

Shao Yazhen
Qiao Youhao
SHAO WEN

Assignees

江苏明思维交通照明科技有限公司

Dates

Publication Date: 20260512
Application Date: 20260204

Claims (10)

1. The intelligent control method for the street lamp of the Internet of things is characterized by comprising the following steps of: S1, acquiring external environment sensing data and dynamic situation factor data of a target road, and acquiring operation state parameters of the street lamps, wherein the external environment sensing data comprise traffic flow data and environment illumination data, the dynamic situation factor data comprise real-time weather condition data, date type data and specific time data, and the operation state parameters comprise total power limit of a system, rated power of each street lamp and accumulated operation time; S2, dynamically fusing and demand assessment, namely identifying traffic states of roads based on the traffic flow data, determining corresponding illuminance reference values, inputting the dynamic situation factor data and the external environment perception data into a dynamic weight distribution model, dynamically adjusting the illuminance reference values based on the date type data, the specific time data and the real-time weather condition data, and calculating a dynamic target illuminance demand value through weighting fusion by inhibiting instantaneous fluctuation of the input data; s3, illumination capability assessment and prediction, namely calculating a current light efficiency parameter based on the accumulated running time, and determining a power requirement value of the street lamp based on the dynamic target illumination requirement value, a preset illumination area and the current light efficiency parameter; S4, generating a constraint power instruction set, namely taking the dynamic target illumination requirement value, the power requirement value and the basic safety illumination value as inputs, and carrying out optimization calculation under system constraint through an illumination-power mapping relation to generate a power instruction set aiming at each street lamp in an area in a future set period; and S5, executing the power instruction set, and adjusting the output power of the adjacent street lamp in a staggered time or smooth gradual change mode.
2. The intelligent control method of an internet of things street lamp according to claim 1, wherein in S2, identifying a traffic state of a road based on the traffic flow data and determining a corresponding illuminance reference value includes: Setting a first vehicle flow threshold and a second vehicle flow threshold, the first vehicle flow threshold being less than the second vehicle flow threshold; When the traffic flow is lower than the first traffic flow threshold, identifying an idle state, and setting a basic safety illuminance value for the idle state; When the traffic flow is between the first traffic flow threshold and the second traffic flow threshold, identifying a sparse flow state, and setting a road segment enhancement illuminance value for the sparse flow state, wherein the road segment enhancement illuminance value is higher than the basic safety illuminance value; When the vehicle flow is higher than the second vehicle flow threshold, recognizing a continuous flow state, and setting a full road section uniform illuminance value for the continuous flow state, wherein the full road section uniform illuminance value is higher than the road section enhanced illuminance value; the illuminance reference value is used as an input reference of the dynamic weight distribution model.
3. The intelligent control method of the street lamp of the internet of things according to claim 1, wherein in S2, dynamically adjusting the illuminance reference value based on the date type data, the specific time data and the real-time weather condition data comprises: Dividing the date type data into weekdays, weekends and holidays, identifying traffic peak periods based on the date type data and specific time data, and assigning relatively higher weights to the traffic flow data during the traffic peak periods; And distinguishing the weather condition data into normal weather and bad weather, wherein the illuminance reference value is adaptively adjusted upwards under the bad weather condition.
4. The intelligent control method of the street lamp of the internet of things according to claim 1, wherein in the step S3, the power requirement value of the street lamp is calculated by the following method: Power requirement= (dynamic target illuminance requirement x preset irradiation area)/current light efficiency parameter; the preset irradiation area is determined based on the installation height, the light distribution type and the arrangement interval of the street lamps, and the current light efficiency parameter is calculated based on the preset initial light efficiency parameter and through the accumulated running time of the street lamps and the preset light attenuation curve model.
5. The intelligent control method of the internet of things street lamp according to claim 1, wherein in S3, the time sequence model is an autoregressive integral moving average model (ARIMA), and the time sequence model is input as historical system total power limit data and output as a predicted value of the system total power limit within several hours in the future.
6. The intelligent control method of the street lamp of the internet of things according to claim 1, wherein in S4, the optimization calculation under the system constraint comprises: And taking the dynamic target illumination requirement value as an optimization target, taking a basic safety illumination value for guaranteeing road safety as an illumination lower limit, taking rated power of each street lamp and the total power limit predicted value of the system as power constraint, and carrying out constraint optimization calculation by referring to the power requirement value and through an illumination-power mapping relation.
7. The intelligent control method of the street lamp of the internet of things according to claim 1, wherein in S4, outputting the power instruction set for each street lamp in the area within the future set period of time comprises: Outputting a power instruction set corresponding to the dynamic target illuminance requirement value when there are solutions satisfying all constraint conditions; and when solutions meeting all constraint conditions do not exist, re-solving the basic safety illumination value as an optimization target, and outputting a degraded power instruction set.
8. The intelligent control method of the street lamp of the internet of things according to claim 7, wherein the street lamp comprises a conventional street lamp powered by a municipal power grid and a solar street lamp powered by solar energy; The conventional street lamp is mainly operated in a state of outputting a power instruction set corresponding to the dynamic target illumination requirement value because of stable total system power allowance; the solar street lamp fluctuates due to the fact that the total power quota of the system is affected by weather, and the solar street lamp mainly operates in a state of outputting the degraded power instruction set.
9. The intelligent control method of the street lamp of the internet of things according to claim 1, wherein in S5, adjusting the output power of the adjacent street lamp by means of time-staggered or smooth gradual change comprises: When the sparse flow state is identified, a time-staggered adjustment mode is adopted, and the street lamps on the road section in front of the vehicle are controlled to sequentially adjust the brightness at safe time intervals according to a preset sequence; When the continuous flow state or the idle state is identified, a smooth gradual change mode is adopted to control all the street lamps to uniformly transition to the target brightness at a safe change rate.
10. An intelligent control system for an internet of things street lamp according to any one of claims 1-9, comprising: The multi-dimensional data acquisition module is used for acquiring external environment perception data and dynamic situation factor data of a target road and acquiring running state parameters of the street lamp; the illuminance requirement fusion module is used for identifying the traffic state of the road based on the traffic flow data, determining a corresponding illuminance reference value, dynamically adjusting the illuminance reference value through a dynamic weight distribution model, and calculating a dynamic target illuminance requirement value through weighting fusion by inhibiting the instantaneous fluctuation of the input data; The power demand assessment module is used for calculating the current light efficiency parameter based on the accumulated running time, and determining the power demand value of the street lamp based on the dynamic target illumination demand value, a preset illumination area and the current light efficiency parameter; The power limit prediction module is used for predicting the change trend of the total power limit of the system in a future set period through a time sequence model based on the total power limit of the system and historical data thereof; the optimization instruction generation module is used for taking the dynamic target illumination requirement value, the power requirement value and the basic safety illumination value as inputs, and generating a power instruction set aiming at each street lamp in the area in a future set period through optimization calculation under system constraint; And the region cooperative execution module is used for executing the power instruction set and adjusting the output power of the adjacent street lamp in a time-staggered or smooth gradual manner.

Description

Intelligent control method and system for street lamp of Internet of things Technical Field The invention relates to the technical field of intelligent illumination of the Internet of things, in particular to an intelligent control method and system for street lamps of the Internet of things. Background With the development of the internet of things technology, a road lighting control system has gradually evolved from traditional timing control to an intelligent direction. In the prior art, an automatic dimming system based on ambient light detection is arranged, the brightness of a street lamp can be adjusted according to the natural light intensity, and meanwhile, a traffic flow statistical technology is adopted in the system to provide differentiated illumination in different traffic flow time periods. The system realizes the energy-saving effect to a certain extent through single parameter perception and fixed threshold control, has simple system structure and lower deployment cost, and lays a foundation for the intelligent illumination field. But in actual use, it still has some drawbacks: 1. The existing system usually only considers single or few environmental parameters to make decisions, lacks a collaborative analysis and dynamic weight distribution mechanism for multi-dimensional situation factors such as date type, specific time and real-time weather conditions, and the like, and the static or single-dimensional control mode cannot accurately identify complex scenes such as severe weather superimposed traffic peaks, so that an illumination strategy deviates from actual safety requirements, traffic safety hidden danger exists, and energy waste is caused; 2. The system based on ambient light acquisition generally lacks an effective instantaneous fluctuation suppression mechanism of input data, directly responds to instantaneous measurement values containing noise, and generates unnecessary brightness adjustment instructions when the system encounters transient disturbances such as cloud cover drift, flying bird sweep or vehicle lamplight, so that the lamp output frequently fluctuates, thereby affecting the driving visual comfort and road safety, accelerating equipment aging and increasing the system maintenance cost; 3. The existing scheme fails to establish an optimization model integrating the prediction of the total power limit of the system and multi-objective constraint, lacks the prediction capability of future change trend of the total power limit of the system, and cannot generate an optimal power distribution instruction set under the multiple constraints of single-lamp rated power, basic safety illumination, predicted value of the total power limit of the system, overall power demand and the like when the power supply fluctuation of a power grid or the shortage of solar energy supply is faced, so that smooth degradation and reliable operation cannot be realized when the energy is tensed, and the overall stability of the system is affected. Disclosure of Invention In order to overcome the defects in the prior art, the invention provides an intelligent control method and system for an Internet of things street lamp, which solve the problems in the background art. In order to achieve the above purpose, the invention provides an intelligent control method for street lamps of the internet of things, comprising the following steps: S1, acquiring external environment sensing data and dynamic situation factor data of a target road, and acquiring operation state parameters of the street lamps, wherein the external environment sensing data comprise traffic flow data and environment illumination data, the dynamic situation factor data comprise real-time weather condition data, date type data and specific time data, and the operation state parameters comprise total power limit of a system, rated power of each street lamp and accumulated operation time; S2, dynamically fusing and demand assessment, namely identifying traffic states of roads based on the traffic flow data, determining corresponding illuminance reference values, inputting the dynamic situation factor data and the external environment perception data into a dynamic weight distribution model, dynamically adjusting the illuminance reference values based on the date type data, the specific time data and the real-time weather condition data, and calculating a dynamic target illuminance demand value through weighting fusion by inhibiting instantaneous fluctuation of the input data; s3, illumination capability assessment and prediction, namely calculating a current light efficiency parameter based on the accumulated running time, and determining a power requirement value of the street lamp based on the dynamic target illumination requirement value, a preset illumination area and the current light efficiency parameter; S4, generating a constraint power instruction set, namely taking the dynamic target illumination requirement value, the power r