An urban street lamp control system based on internet of things

Abstract

The present application relates to the technical field of intelligent lighting scheduling, in particular to a city street lamp control system based on Internet of Things, which comprises: an operating condition acquisition module, a thermoelectric loss evaluation module, a light flux maintenance calculation module, a shielding discrimination module and a power regulation module.In the present application, the voltage and current characteristics of the city street lamp driving circuit are collected in real time, and the environmental reflection echo is demodulated, thereby constructing multi-dimensional perception data containing the internal thermoelectric stress of the device and the external space shielding state, quantifying the light source aging rate under high temperature and high current conditions according to the physical failure mechanism, accurately calculating the light flux attenuation degree in the whole life cycle and generating a dynamic power compensation strategy, intelligently identifying and isolating the invalid lighting direction shielded by the tree crown by using the spatial reflection intensity distribution, converting the power consumed by the invalid area into transferable redundant power and adding it to the key lighting area not shielded, and realizing directional compensation of light decay loss under the premise of strictly controlling the upper limit of the total power consumption of the whole lamp.

Description

Technical Field

[0001] This invention relates to the field of intelligent lighting scheduling technology, and in particular to an Internet of Things-based urban street light control system. Background Technology

[0002] The field of intelligent lighting dispatching technology typically involves the centralized management and automatic control of public lighting facilities such as urban roads, parks, and industrial parks. This includes street light operation status acquisition, lighting strategy formulation, remote command issuance, group and time period control, brightness level switching, fault alarms, and maintenance records. The system generally consists of on-site lighting controllers, communication links, management platforms, and dispatching rules. By uniformly configuring the street light switching sequence, dimming parameters, and control range, it achieves collaborative dispatching and continuous management of lighting tasks in different areas.

[0003] Among them, the urban street light control system refers to the control and management solution for urban street lights. The technical issues it addresses are the unified switching and brightness control of a large number of street lights and the monitoring of their operating status. It usually adopts the method of setting up relay switches and dimming drive circuits at each street light or each group of street lights, and using light sensors or clock controllers to generate switching times and dimming basis. Then, the street light number, switching status, current and voltage parameters and fault information are uploaded to the central terminal through wired or wireless communication. The central terminal sends the light-on, light-off and dimming commands to the designated street lights according to the preset zoning table, time period table and brightness table, and completes the status confirmation and control execution of each street light through address mapping and polling.

[0004] Existing urban street light control technologies typically rely solely on preset schedules or ambient light thresholds to perform mechanical switching and brightness adjustment. This open-loop control mode struggles to detect changes in junction temperature within the light source and the cumulative damage caused by current stress to the device's lifespan. It ignores the nonlinear decay of luminous flux caused by thermoelectric effects during long-term operation of street light LEDs and cannot perform adaptive power compensation based on the actual brightness decline trend. This results in road surface illuminance failing to meet safety standards in the later stages of the lamp's service life. Furthermore, it lacks the ability to instantly identify the shading caused by the growth of trees and foliage around the lamp, leading to continuous ineffective dissipation of lighting energy in the shaded areas. This fails to effectively transfer the redundant power to the areas requiring enhanced lighting, resulting in low energy utilization and an imbalance in lighting performance. Summary of the Invention

[0005] The purpose of this invention is to address the shortcomings of existing technologies by proposing an Internet of Things-based urban street light control system.

[0006] To achieve the above objectives, the present invention adopts the following technical solution: an Internet of Things-based urban street light control system includes:

[0007] The operating condition acquisition module collects the voltage and current feedback signals of the streetlights during operation and analyzes them into the junction temperature of the streetlight source. At the same time, it collects the reflected echo signals in the four quadrants of the streetlight head, demodulates them into the reflected echo signal intensity, and constructs a multi-dimensional sensing dataset.

[0008] The thermoelectric loss assessment module calculates the street light source aging acceleration factor and the corresponding street light source aging time increment based on the junction temperature and current feedback signal of the street light source in the multi-dimensional sensing dataset.

[0009] The luminous flux maintenance calculation module updates the cumulative operating cycle of the street light according to the street light source aging time increment, retrieves the corresponding preset luminous flux maintenance rate, and calculates the street light flux compensation coefficient.

[0010] The occlusion discrimination module classifies the canopy occlusion area status position or unoccluded area status position of the street lamp according to the intensity of the reflected echo signal in the multidimensional sensing dataset, and counts the total number of unoccluded lighting areas.

[0011] The power regulation module calculates the transferable redundant power and aging compensation power of the streetlights based on the streetlight luminous flux compensation coefficient and the preset minimum safe maintenance current value, and adjusts the current of each streetlight in conjunction with the total number of unobstructed lighting areas to obtain the streetlight control result.

[0012] As a further aspect of the present invention, the multidimensional sensing dataset includes street light source junction temperature, current feedback signal, and reflected echo signal intensity; the street light source aging time increment is specifically the product of the street light source aging acceleration factor and the sampling period interval; the street light flux compensation coefficient is specifically the ratio of the difference between the initial luminous output rate and the preset luminous flux maintenance rate to the preset luminous flux maintenance rate; the total number of unobstructed lighting areas is specifically the number of lighting areas that are statistically marked as unobstructed and fall within the critical lighting area; and the street light control result includes the target driving current value allocated to the unobstructed lighting area and the minimum safe maintenance current value allocated to the canopy-obstructed lighting area.

[0013] As a further aspect of the present invention, the operating condition acquisition module includes:

[0014] The electrothermal signal analysis submodule periodically triggers the voltage sensor and Hall current sensor of the street light IoT edge computing node to collect the voltage signal and current feedback signal of the multi-drive circuit under the working state of the street light. It analyzes the voltage signal to obtain the junction temperature of the street light source and integrates the junction temperature of the street light source with the current feedback signal to generate the electrothermal status data of the light source.

[0015] The environmental echo demodulation submodule triggers the infrared reflection sensors installed in the four quadrants of the street light head to emit detection signals, acquires the reflected echo signals relative to the detection signals, demodulates the reflected echo signals to obtain the reflected echo signal intensity, constructs a numerical distribution characterizing the degree of canopy shading based on the reflected echo signal intensity, and generates environmental reflection intensity distribution values.

[0016] The multidimensional perception fusion submodule collects the current timestamp, associates it with the electrothermal state data of the light source and the environmental reflection intensity distribution value, and encapsulates it in a structured manner to generate a multidimensional perception dataset.

[0017] As a further aspect of the present invention, the thermoelectric loss assessment module includes:

[0018] The deviation value calculation submodule acquires the street light source junction temperature and current feedback signal from the multi-dimensional sensing dataset, calculates the difference between the street light source junction temperature and the preset street light reference test temperature value to obtain the street light temperature difference, analyzes the current feedback signal to obtain the real-time drive current value, and calculates the ratio of the real-time drive current value to the preset rated drive current value to obtain the drive current ratio.

[0019] The aging factor mapping submodule calculates the thermal aging acceleration index of the street light source based on the street light temperature difference and the preset thermal aging conversion coefficient, calculates the electrical aging acceleration index of the street light source based on the driving current ratio and the preset electrical aging conversion coefficient, and calculates the product of the thermal aging acceleration index and the electrical aging acceleration index to generate the street light source aging acceleration factor.

[0020] The time increment calculation submodule obtains the current sampling period interval duration, calculates the product of the street light source aging acceleration factor and the sampling period interval duration, and uses the product result as the life loss amplitude within a single sampling period to obtain the street light source aging time increment.

[0021] As a further aspect of the present invention, the optical flux maintenance calculation module includes:

[0022] The periodic cumulative update submodule reads the initial cumulative operating cycle of the streetlights recorded in the local non-volatile memory. If there is no record, the initial cumulative operating cycle is regarded as zero. The module calculates the sum of the initial cumulative operating cycle and the streetlight light source aging time increment to generate the updated cumulative operating cycle.

[0023] The maintenance rate retrieval submodule uses the updated cumulative operating period as the index key to traverse the preset luminous flux maintenance rate standard curve database and retrieve the luminous flux maintenance rate corresponding to the updated cumulative operating period.

[0024] The compensation coefficient calculation submodule calls the preset initial light output rate, calculates the difference between the initial light output rate and the luminous flux maintenance rate, and the ratio of the difference to the luminous flux maintenance rate, to generate the street light flux compensation coefficient.

[0025] As a further aspect of the present invention, the occlusion discrimination module includes:

[0026] The state bit threshold determination submodule compares the environmental reflection intensity distribution value in the multidimensional sensing dataset with a preset free space background noise threshold. When the environmental reflection intensity distribution value exceeds the free space background noise threshold, it generates a state bit for the area marked as canopy occlusion; otherwise, it generates a state bit for the area marked as unoccluded.

[0027] The key area mapping and filtering submodule spatially matches the unobstructed area status bits with the key lighting area boundaries defined by the preset street light key lighting area mapping table, filters areas located within the key lighting area and corresponding unobstructed area status bits, and generates a set of unobstructed areas.

[0028] The unobstructed area statistics submodule counts the number of key lighting areas in the set of unobstructed areas to obtain the number of target areas that need dynamic power allocation, which is taken as the total number of unobstructed lighting areas.

[0029] As a further aspect of the present invention, the power regulation module includes:

[0030] The redundant power calculation submodule identifies the corresponding lighting area based on the status bit of the area blocked by the tree canopy, calls the preset minimum safe maintenance current value and the preset original rated power of the lighting area, sets the driving current of the corresponding lighting area to the minimum safe maintenance current value, calculates the difference between the original rated power of the lighting area and the power corresponding to the minimum safe maintenance current value, and generates transferable redundant power.

[0031] The target power allocation submodule obtains the preset total rated power of the whole lamp, calculates the product of the total rated power of the whole lamp and the street light flux compensation coefficient to obtain the power required for aging compensation, accumulates the transferable redundant power and the power required for aging compensation to obtain the total available power, and distributes the total available power equally and superimposes it to the original rated power of the lighting area corresponding to the total number of unobstructed lighting areas to obtain the target driving power of the lighting area.

[0032] The current control generation submodule converts the target driving power of the lighting area into the corresponding target driving current value based on the voltage characteristics of the driving circuit, constructs a current adjustment command for each unobstructed lighting area, and uses an incremental PID control algorithm combined with the minimum safe holding current value to adjust the output of the IoT LED smart driver power supply to generate street light control results.

[0033] Compared with the prior art, the advantages and positive effects of the present invention are as follows:

[0034] In this invention, by real-time acquisition of voltage and current characteristics of urban street light drive circuits and demodulation of environmental reflected echoes, multi-dimensional sensing data including internal thermoelectric stress of devices and external spatial shading status is constructed. Based on physical failure mechanisms, the aging rate of light sources under high temperature and high current conditions is quantified, the degree of luminous flux attenuation throughout the entire life cycle is accurately calculated, and a dynamic power compensation strategy is generated. The spatial reflection intensity distribution is used to intelligently identify and isolate ineffective lighting locations blocked by tree canopies. The electrical energy originally consumed in the ineffective areas is converted into transferable redundant power and superimposed on the unblocked key lighting areas. Under the premise of strictly controlling the upper limit of the total power consumption of the entire lamp, targeted compensation for light decay loss is achieved, ensuring that the road surface brightness of urban street lights remains constant during the long service life, solving the problem of declining lighting quality caused by light decay and shading, and realizing efficient energy utilization and on-demand allocation. Attached Figure Description

[0035] Figure 1 This is a system flowchart of the present invention;

[0036] Figure 2 This is a flowchart of the operating condition acquisition module of the present invention;

[0037] Figure 3 This is a flowchart of the thermoelectric loss assessment module of the present invention;

[0038] Figure 4 This is a flowchart of the optical flux maintenance calculation module of the present invention;

[0039] Figure 5 This is a flowchart of the occlusion detection module of the present invention;

[0040] Figure 6 This is a flowchart of the power regulation module of the present invention. Detailed Implementation

[0041] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0042] Please see Figure 1 An Internet of Things (IoT)-based urban street light control system includes:

[0043] The operating condition acquisition module collects the voltage and current feedback signals of the streetlights during operation and analyzes them into the junction temperature of the streetlight source. At the same time, it collects the reflected echo signals in the four quadrants of the streetlight head, demodulates them into the reflected echo signal intensity, and constructs a multi-dimensional sensing dataset.

[0044] The thermoelectric loss assessment module calculates the street light source aging acceleration factor and the corresponding street light source aging time increment based on the junction temperature and current feedback signals of the street light source in the multi-dimensional sensing dataset.

[0045] The luminous flux maintenance calculation module updates the cumulative operating cycle of the streetlights based on the incremental aging time of the streetlight source, retrieves the corresponding preset luminous flux maintenance rate, and calculates the luminous flux compensation coefficient of the streetlights.

[0046] The occlusion discrimination module classifies the status bits of the streetlights' canopy occlusion area or unocclusion area based on the intensity of the reflected echo signal in the multidimensional sensing dataset, and counts the total number of unoccluded lighting areas.

[0047] The power control module calculates the transferable redundant power and aging compensation power of the streetlights based on the streetlight luminous flux compensation coefficient and the preset minimum safe holding current value. Combined with the total number of unobstructed lighting areas, it adjusts the current of each streetlight to obtain the streetlight control result.

[0048] The multidimensional sensing dataset includes street light source junction temperature, current feedback signal, and reflected echo signal intensity. The street light source aging time increment is specifically the product of the street light source aging acceleration factor and the sampling period interval. The street light flux compensation coefficient is specifically the ratio of the difference between the initial luminous output rate and the preset luminous flux maintenance rate to the preset luminous flux maintenance rate. The total number of unobstructed lighting areas is specifically the number of lighting areas that are statistically marked as unobstructed and fall within the critical lighting area. The street light control results include the target driving current value allocated to the unobstructed lighting area and the minimum safe maintenance current value allocated to the canopy-obstructed lighting area.

[0049] Please see Figure 2 The operating condition acquisition module includes:

[0050] The electrothermal signal analysis submodule periodically triggers the voltage sensor and Hall current sensor of the street light IoT edge computing node to collect the voltage signal and current feedback signal of the multi-drive circuit under the working state of the street light. It analyzes the voltage signal to obtain the junction temperature of the street light source and integrates the junction temperature of the street light source with the current feedback signal to generate the electrothermal status data of the light source.

[0051] A synchronous trigger command is sent to the voltage sensor and Hall effect current sensor integrated within the connected street light IoT edge computing node. This command is generated by the internal timing controller based on the oscillation frequency of the high-frequency crystal oscillator, processed by a frequency divider circuit, for example, set to trigger a sampling action every 100 milliseconds. The voltage sensor captures the instantaneous voltage waveform at the input of the street light driver in real time, and uses the built-in true RMS converter chip and root mean square algorithm to convert the analog waveform into a digital voltage value, for example, acquiring a digital voltage value of 48 volts. Simultaneously, the Hall effect current sensor senses the magnetic field changes in the multi-channel drive circuit based on the Hall effect principle, outputting the analog current signal of the corresponding branch, which is then converted into a digital current feedback signal by an analog-to-digital converter, for example, acquiring a current value of 1.02 amperes. The data is constructed by calling pre-stored LED voltage-temperature characteristic curve data. The process involves selecting a batch of streetlight light source devices of the same model as a statistical sample, placing them in a precision constant-temperature oil bath with a temperature control accuracy of 0.1 degrees Celsius, and gradually increasing the temperature in 5-degree Celsius increments within a temperature range from -40 degrees Celsius to 125 degrees Celsius. The temperature is stabilized for a certain period at each temperature point to eliminate thermal inertia. The voltage drop during constant-current drive is then recorded. A fifth-order polynomial regression is performed on the large amount of collected voltage and temperature data using the least squares method to establish a high-precision mathematical relationship between voltage and junction temperature. Based on the collected digital voltage values, the corresponding junction temperature value of the streetlight light source is mapped by performing a substitution operation on the characteristic curve data. For example, a voltage value of 48 volts corresponds to a junction temperature of 65 degrees Celsius in the characteristic curve. Subsequently, the junction temperature of the street light source and the current feedback signal of the multi-drive circuit are aligned and correlated in the time dimension. The junction temperature data and current data at the same sampling time are combined to generate the electrothermal state data of the light source, which includes thermal and electrical properties.

[0052] The environmental echo demodulation submodule triggers the infrared reflection sensors installed in the four quadrants of the street light head to emit detection signals, acquires the reflected echo signals relative to the detection signals, demodulates the reflected echo signals to obtain the reflected echo signal intensity, constructs a numerical distribution characterizing the degree of canopy shading based on the reflected echo signal intensity, and generates environmental reflection intensity distribution values.

[0053] Infrared reflection sensors installed in the four quadrants of the streetlight head are triggered, driving infrared emitting diodes to emit modulated pulse detection signals at a frequency of 38 kHz into the surrounding space. Within a preset time window after signal transmission, the infrared receiving tube is activated to capture reflected echo signals from tree branches or other obstacles. Low-frequency interference in the ambient light is filtered out by a bandpass filter, and the amplitude envelope of the reflected echo signal is reconstructed using an envelope detector. This amplitude envelope is then integrated to calculate the intensity of the reflected echo signal. For example, the intensity value obtained after integration of the echo signal received by the north-facing sensor is 2500, while the intensity value obtained by the south-facing sensor is 60. Based on the reflected echo signal intensities obtained by the four quadrant sensors, an inverse distance weighted interpolation algorithm is used to construct a numerical distribution characterizing the degree of canopy occlusion. The specific execution process of the algorithm is as follows: First, the sensor position is defined as the interpolation reference point. The Euclidean distance between the spatial point to be determined and each reference point is calculated. The reciprocal square of this distance is used as the weighting coefficient. The weighting coefficient is set based on the physical characteristics of signal attenuation. The closer the distance, the greater the influence on the interpolation point. For example, when the distance is 2 meters, the formula for calculating the weighting coefficient is: The sum of the products of the intensity values ​​at each reference point and their corresponding weight coefficients is calculated and then divided by the sum of the weight coefficients to generate a continuous numerical matrix, i.e., the distribution values ​​of environmental reflection intensity.

[0054] The multidimensional perception fusion submodule collects the current timestamp-related light source electrothermal state data and environmental reflection intensity distribution values, and encapsulates them in a structured manner to generate a multidimensional perception dataset.

[0055] The system reads the timestamp of a high-precision clock source integrated within the current streetlight control terminal, calibrated via network timing, for example, June 15th, 22:00:00. It then aligns the light source's electrothermal state data with the environmental reflection intensity distribution values ​​at the same timestamp using bit width alignment and encapsulates them in a structured manner according to a preset data frame protocol. This protocol specifies that the data frame header contains a synchronization word, the middle section contains the timestamp, voltage value, current value, junction temperature value, and reflection intensity matrix, and the tail contains a cyclic redundancy check (CRC) code. For example, the previously collected data such as a voltage of 48 volts, a current of 1.02 amperes, a junction temperature of 65 degrees Celsius, a northward reflection intensity of 2500, and a southward reflection intensity of 60 are packaged to generate the final multi-dimensional sensing dataset.

[0056] Please see Figure 3 The thermoelectric loss assessment module includes:

[0057] The deviation value calculation submodule acquires the junction temperature and current feedback signals of the street light source in the multi-dimensional sensing dataset, calculates the difference between the junction temperature of the street light source and the preset street light reference test temperature value to obtain the street light temperature difference, analyzes the current feedback signal to obtain the real-time drive current value, and calculates the ratio of the real-time drive current value to the preset rated drive current value to obtain the drive current ratio.

[0058] The system receives a multi-dimensional sensing dataset and extracts the streetlight lamp source junction temperature and current feedback signals. It acquires a preset streetlight reference test temperature value, set according to the International Commission on Illumination (CIE) standard. Under standard atmospheric pressure and no convective wind conditions, a batch of streetlight samples from the same batch are selected for rated luminous flux testing. The arithmetic mean of the ambient temperature data during the test is calculated and used as the reference value, for example, set to 25 degrees Celsius (298.15 Kelvin). A subtraction operation is performed to calculate the difference between the streetlight lamp source junction temperature and the streetlight reference test temperature value, obtaining the streetlight temperature difference. For example, subtracting the real-time junction temperature of 65 degrees Celsius (338.15 Kelvin) from the reference temperature of 25 degrees Celsius yields the formula: 65 - 25 = 40, resulting in a streetlight temperature difference of 40 degrees Celsius. Simultaneously, the current feedback signal is analyzed to obtain the real-time drive current value, for example, 1020 mA. Obtain the preset rated drive current value, which is defined by the street light driver power supply specifications, for example, 1000 mA. Perform a division operation to calculate the ratio of the real-time drive current value to the rated drive current value, obtaining the drive current ratio. For example, dividing 1020 mA by 1000 mA results in the formula: 1020 / 1000 = 1.02, yielding a drive current ratio of 1.02.

[0059] The aging factor mapping submodule calculates the thermal aging acceleration index of the street light source based on the street light temperature difference and the preset thermal aging conversion coefficient, calculates the electrical aging acceleration index of the street light source based on the driving current ratio and the preset electrical aging conversion coefficient, and calculates the product of the thermal aging acceleration index and the electrical aging acceleration index to generate the street light source aging acceleration factor.

[0060] The total acceleration factor is decomposed into a product of three dimensionless independent factors: the first part is the thermal acceleration factor, which uses the Arrhenius model to describe the exponential effect of temperature on the chemical reaction rate; the second part is the electrical acceleration factor, which uses the inverse power law model to describe the damage to electron migration caused by high current; and the third part is the synergistic stress coupling factor, which, by introducing a normalized ratio, describes the nonlinear synergistic enhancement effect on material aging when high and low temperature differences and current overload occur simultaneously, ensuring that all calculated terms are dimensionless ratios. The calculation formula is as follows: , in the formula: The calculated aging acceleration factor for streetlights is dimensionless, representing the aging rate under current operating conditions as a multiple of the rated operating conditions. The activation energy is measured in electron volts (eV). This parameter is derived from the reliability test datasheet of LED chip materials and is obtained by analyzing the minimum energy barrier required for the material's band structure and failure modes. For example, a value of 0.45 eV is used. Boltzmann's constant has a value of eV / K is derived from the standard physical constants table published by the Committee on Data for Science and Technology (CODATA) of the International Council for Science. The absolute temperature scale value of the preset benchmark test temperature is derived from the statistical average value of the ambient temperature during the aforementioned benchmark setting process, such as 298.15K (25 degrees Celsius). The absolute temperature scale value of the junction temperature of the street light source is obtained from the mapping calculation of real-time voltage data by the electrothermal signal analysis submodule, for example, 338.15K (65 degrees Celsius). The real-time drive current value is derived from real-time data collected by the on-site Hall sensor and converted from analog to digital, for example, 1.02 amperes; The rated drive current value is derived from the nominal operating current parameter in the street light equipment manufacturer's specifications, such as 1.00 amperes; The current acceleration index is derived from the slope of the High Accelerated Life Test (HALT) data. The larger the slope, the more significant the impact of current on lifespan. For example, a value of 2.0 is used. The co-stress coupling coefficient is dimensionless. This parameter is used to quantify the additional influence weight of the product of temperature drift rate and current drift rate on lifetime. It is calculated by comparing the failure time difference ratio between independent stress experiments and combined stress experiments, for example, a value of 20.0.

[0061] The calculation process is as follows: First, calculate the thermal acceleration factor (Arrhenius part), the formula is: Next, the electric acceleration factor term (inverse power-law part) is calculated, and the formula is: Then, the synergistic stress coupling factor term is calculated, and the formula is: Finally, multiply the three results together; the formula is: The calculated aging acceleration factor of the street light source is 8.704. This value indicates that the aging rate under current operating conditions is 8.704 times that under standard operating conditions.

[0062] The time increment calculation submodule obtains the current sampling period interval duration, calculates the product of the street light source aging acceleration factor and the sampling period interval duration, and uses the product result as the life loss amplitude within a single sampling period to obtain the street light source aging time increment.

[0063] The current sampling period interval is obtained. This interval is determined by the trigger frequency of the system timer interrupt and is obtained by reading the setting value of the microprocessor's timer register. For example, if it is set to 10 minutes, it is 0.1667 hours. A multiplication operation is performed to calculate the product of the streetlight source aging acceleration factor and the sampling period interval. This product result is used as the lifespan loss amplitude within a single sampling period, yielding the streetlight source aging time increment. The calculation formula is: The aging time increment was found to be 1.451 hours. This value indicates that under the current high junction temperature and overcurrent conditions, 10 minutes of actual operation is equivalent to consuming 1.451 hours of the standard rated life.

[0064] Please see Figure 4 The optical transmission sustaining calculation module includes:

[0065] The periodic cumulative update submodule reads the initial cumulative operating cycle of the streetlights recorded in the local non-volatile memory. If there is no record, the initial cumulative operating cycle is regarded as zero. The module calculates the sum of the initial cumulative operating cycle and the streetlight light source aging time increment to generate the updated cumulative operating cycle.

[0066] The system reads the initial cumulative operating cycle of the streetlight recorded in the local non-volatile memory. This initial cumulative operating cycle is the sum of all historical aging time increments since the streetlight was first powered on, derived from the write-back records after each operating cycle, for example, a value of 5000 hours. If there is no record in the memory, it is initialized to zero. An addition operation is performed to calculate the sum of the initial cumulative operating cycle and the streetlight light source aging time increments obtained in the previous steps, generating the updated cumulative operating cycle. The calculation formula is: 5000 + 1.451 = 5001.451, resulting in an updated cumulative operating cycle of 5001.451 hours. This updated value is then written back to the non-volatile memory, completing the aging data tracking for the entire lifecycle.

[0067] The maintenance rate retrieval submodule uses the updated cumulative operating period as the index key to traverse the preset luminous flux maintenance rate standard curve database and retrieve the luminous flux maintenance rate corresponding to the updated cumulative operating period.

[0068] The database of standard curves for luminous flux maintenance includes:

[0069] Brightness monitoring data is extracted from street light source devices of a specified model that undergo continuous aging tests in a preset reference test temperature environment. The brightness monitoring data consists of discrete sampling points that record the specified cumulative running time points and the measured light output power values ​​corresponding to the time points.

[0070] Based on the numerical distribution pattern of discrete sampling points in the time dimension, the numerical gap between two adjacent discrete sampling points is filled to construct a benchmark aging trend line that continuously reflects the monotonically decreasing optical output power with operating time.

[0071] The rated initial luminous efficiency recorded in the specifications of the street light source device is used to calculate the ratio of the real-time light output power value on the baseline aging trend line to the rated initial luminous efficiency, and the baseline aging trend line is converted into a luminous flux maintenance rate evolution curve expressed as a percentage.

[0072] Determine the sampling interval step size on the time axis, and extract the corresponding instantaneous maintenance rate value along the time axis of the luminous flux maintenance rate evolution curve according to the sampling interval step size;

[0073] The cumulative time of the intercepted moment is defined as the index key of the database, and the corresponding instantaneous maintenance rate value is defined as the associated data. The corresponding mapping relationship between the index key and the associated data is established and stored to generate a standard curve database of light flux maintenance rate.

[0074] Using the updated cumulative operating cycle as the index key, the preset luminous flux maintenance rate standard curve database is traversed. The database construction process is as follows: A 6000-hour aging test is conducted on specified streetlight light source devices under a preset benchmark test temperature environment, such as a constant temperature environment of 25 degrees Celsius. During the test, at predetermined time intervals, such as every 100 hours, the optical output power is measured using an integrating sphere photometer, generating discrete sampling points consisting of records of the specified cumulative operating time points and the corresponding measured optical output power values. Based on the numerical distribution of the discrete sampling points in the time dimension, the least squares method is used to perform exponential decay fitting on the data, filling the numerical gaps between adjacent discrete sampling points, and constructing a benchmark aging trend line that continuously reflects the monotonically decreasing optical output power with operating time. The rated initial luminous efficacy recorded in the streetlight light source device specifications, such as 10000 lumens, is called, and a division operation is performed to calculate the ratio of the real-time optical output power value on the benchmark aging trend line to the rated initial luminous efficacy, converting the benchmark aging trend line into a luminous flux maintenance rate evolution curve expressed as a percentage. Determine the sampling interval step size on the time axis, for example, 1 hour. Extract the corresponding instantaneous maintenance rate values ​​along the time axis of the luminous flux maintenance rate evolution curve according to the sampling interval step size. Define the cumulative time of the extracted moment as the index key of the database, and define the corresponding instantaneous maintenance rate value as the associated data. Establish and store the mapping relationship between the index key and the associated data. During the retrieval process, if the updated cumulative running period of 5001.451 hours falls between the index key 5001 hours and 5002 hours, calculate the corresponding luminous flux maintenance rate through linear interpolation. For example, 5000 hours corresponds to a maintenance rate of 0.92, and 5001 hours corresponds to a maintenance rate of 0.91998. The calculation formula is: The current luminous flux maintenance rate, calculated by interpolation, is approximately 0.92.

[0075] The compensation coefficient calculation submodule calls the preset initial light output rate, calculates the difference between the initial light output rate and the luminous flux maintenance rate, and the ratio of the difference to the luminous flux maintenance rate, and generates the street light flux compensation coefficient.

[0076] The system calls the preset initial luminous efficiency, where a normalized value of 1.0 represents 100% luminous efficiency. A subtraction operation is performed to calculate the difference between the initial luminous efficiency and the luminous flux maintenance rate. The calculation formula is: 1.0 - 0.92 = 0.08, resulting in a difference of 0.08. A division operation is then performed to calculate the ratio of this difference to the luminous flux maintenance rate, generating the streetlight flux compensation coefficient. The calculation formula is: The street light luminous flux compensation coefficient is approximately 0.087. The physical meaning of this coefficient is that, to offset the 8% light decay, approximately 8.7% more power output is needed than the current output.

[0077] Please see Figure 5 The occlusion detection module includes:

[0078] The state bit threshold determination submodule compares the environmental reflection intensity distribution value in the multidimensional perception dataset with the preset free space background noise threshold. When the environmental reflection intensity distribution value exceeds the free space background noise threshold, it generates a state bit of the area marked as canopy occlusion; otherwise, it generates a state bit of the area marked as unoccluded.

[0079] The preset free-space background noise threshold is retrieved. This threshold is determined as follows: In an open test area without any obstructions, environmental reflection signals are continuously collected for 24 hours using the same type of infrared sensor. After removing outliers, the arithmetic mean and standard deviation of the signal intensity are calculated. The sum of the arithmetic mean and three times the standard deviation is set as the threshold. This setting method is based on normal distribution theory and covers 99.7% of the background noise fluctuation range, ensuring an extremely low false alarm rate. For example, if the measured average background noise is 50 and the standard deviation is 10, the calculation formula is: 50 + 3 × 10 = 80, thus the free-space background noise threshold is determined to be 80. The environmental reflection intensity distribution values ​​in the multidimensional sensing dataset are compared one by one with this threshold. This scenario involves two possible categories: Category A, where there is significant obstruction, is determined by a reflection intensity greater than the threshold, representing the detection of obstacles such as leaves; Category B, where there is no obstruction, is determined by a reflection intensity less than or equal to the threshold, representing an open space. For example, if the northward reflection intensity is 2500, which belongs to category A, it is determined that there is significant shading in that direction, and a status bit is generated to mark the area as canopy shading, usually represented by a logic high level; if the southward reflection intensity is 60, which belongs to category B, it is determined that there is no shading in that direction, and a status bit is generated to mark the area as unshaded, usually represented by a logic low level.

[0080] The key area mapping and filtering submodule spatially matches the status bits of unobstructed areas with the boundaries of key lighting areas defined by the preset key lighting area mapping table for streetlights, filters areas that are located within the key lighting area and have corresponding unobstructed status bits, and generates a set of unobstructed areas.

[0081] The key lighting areas defined by the preset street light key lighting area mapping table include:

[0082] Acquire spatial grid division data within the detection field of view of the street light sensor, and establish a positional mapping relationship between the grid index position in the spatial grid division data and the covered physical road surface area;

[0083] Based on the pre-set road planning attribute data, select target physical areas marked as motor vehicle driving lanes and sidewalks from the physical road surface area;

[0084] Based on the location mapping relationship, extract the set of two-dimensional matrix coordinate indices corresponding to the target physical region in the spatial grid division data, and define the set of two-dimensional matrix coordinate indices as the key lighting region;

[0085] The edge coordinate values ​​of the two-dimensional matrix coordinate index set are parsed and solidified into the key lighting area mapping table of street lights to form the boundary of the key lighting area with spatial matching.

[0086] The process involves retrieving a pre-defined key lighting area mapping table for streetlights. This mapping table definition process includes: acquiring spatial grid division data within the detection field of view of the streetlight sensors, for example, dividing the road surface into a 10x10 grid matrix. Establishing a positional mapping relationship between the grid index positions in the spatial grid division data and the covered physical road surface areas; this relationship is obtained through high-precision GPS positioning and on-site mapping calibration. Based on pre-defined road planning attribute data, target physical areas marked as motor vehicle lanes and sidewalks are selected from the physical road surface areas, excluding non-illuminated areas such as green belts and wastelands. Based on the positional mapping relationship, the set of two-dimensional matrix coordinate indices corresponding to the target physical areas in the spatial grid division data is extracted, and this set is defined as the key lighting area. The edge coordinate values ​​of the two-dimensional matrix coordinate index set are parsed and fixed into the key lighting area mapping table for streetlights. During the selection process, the status bits of unobstructed areas are spatially matched with this mapping table to select areas that are both within the key lighting area and correspond to the status bits of unobstructed areas, generating a set of unobstructed areas. For example, if the three areas facing south, east, and west are all motor vehicle lanes and are not obstructed, then these three areas are included in the set.

[0087] The unobstructed area statistics submodule counts the number of key lighting areas in the set of unobstructed areas to obtain the number of target areas that need dynamic power allocation, which is taken as the total number of unobstructed lighting areas.

[0088] The number of key lighting areas in the set of unobstructed areas is counted. This counting process is not a simple numerical reading, but rather a traversal algorithm that verifies and accumulates all elements in the set one by one. The counter is initialized to zero, and a logical judgment loop is executed for each selected directional area: first, the south-facing area is checked, confirming its presence in the unobstructed set, and the counter is incremented (current value 1); then the east-facing area is checked, confirming its presence in the set, and the counter is incremented (current value 2); subsequently, the west-facing area is checked, confirming its presence in the set, and the counter is incremented (current value 3); finally, the north-facing area is checked, but since it is determined to be obstructed and does not exist in the set, the counter remains unchanged. After the traversal is complete, the number of target areas requiring dynamic power allocation is obtained. For example, if the south, east, and west directions meet the criteria according to the above statistics, the count result is 3. This value is taken as the total number of unobstructed lighting areas.

[0089] Please see Figure 6 The power regulation module includes:

[0090] The redundant power calculation submodule identifies the corresponding lighting area based on the status bit of the area blocked by the tree canopy, calls the preset minimum safe maintenance current value and the preset original rated power of the lighting area, sets the driving current of the corresponding lighting area to the minimum safe maintenance current value, calculates the difference between the original rated power of the lighting area and the power corresponding to the minimum safe maintenance current value, and generates transferable redundant power.

[0091] The system identifies the corresponding lighting area based on the status of the area obstructed by tree canopy shading, for example, identifying a north-facing area that is obstructed. It then calls a preset minimum safe sustaining current value, determined based on the minimum start-up current of the LED driver and the brightness requirements for basic nighttime position indication, for example, set to 100 mA. Next, it calls a preset original rated power for the lighting area, the design power of a single LED module under standard operating conditions, for example, 40 watts. Based on the driving voltage characteristics, the minimum safe sustaining current value is converted into sustaining power, for example, 100 mA corresponds to approximately 4 watts. A subtraction operation is performed to calculate the difference between the original rated power of the lighting area and the power corresponding to the minimum safe sustaining current value, generating transferable redundant power. The calculation formula is: 40 - 4 = 36, resulting in a transferable redundant power of 36 watts.

[0092] The target power allocation submodule obtains the preset total rated power of the whole lamp, calculates the product of the total rated power of the whole lamp and the street light flux compensation coefficient to obtain the power required for aging compensation, accumulates the transferable redundant power and the power required for aging compensation to obtain the total available power, and distributes the total available power equally and superimposes it to the original rated power of the lighting area corresponding to the total number of unobstructed lighting areas to obtain the target driving power of the lighting area.

[0093] Obtain the preset total rated power of the entire lamp. For example, if the street light contains 4 modules with a total power of 160 watts, perform a multiplication operation to calculate the product of the total rated power of the entire lamp and the street light flux compensation coefficient generated in the previous step, to obtain the power required for aging compensation. The calculation formula is: 160 × 0.087 = 13.92, resulting in a power required for aging compensation of 13.92 watts. Perform an addition operation to accumulate the transferable redundant power and the power required for aging compensation, to obtain the total available power. The calculation formula is: 36 + 13.92 = 49.92, resulting in a total available power of 49.92 watts. Perform a division operation to evenly distribute and superimpose the total available power onto the original rated power of the lighting area corresponding to the total number of unobstructed lighting areas. The calculation formula is: 49.92 / 3=16.64, which gives an incremental power of 16.64 watts for each area. Then, an addition operation is performed to add this incremental power of 16.64 watts to the original rated power of 40 watts. The calculation formula is: 40+16.64=56.64, which gives a target driving power of 56.64 watts for the lighting area.

[0094] The current control generation submodule converts the target driving power of the lighting area into the corresponding target driving current value based on the voltage characteristics of the driving circuit, constructs the current adjustment command for each unobstructed lighting area, and uses an incremental PID control algorithm combined with the minimum safe holding current value to adjust the output of the IoT LED smart driver power supply to generate street light control results.

[0095] Based on the voltage characteristics of the drive circuit, such as a 48-volt constant voltage source, a division operation is performed to convert the target drive power of the lighting area into the corresponding target drive current value. The calculation formula is: 56.64 / 48 = 1.18, resulting in a target drive current value of approximately 1.18 amperes. A current regulation command is constructed for each unobstructed lighting area, using an incremental PID algorithm that incorporates an environmental steady-state response factor to generate the control command. The calculation formula for this algorithm is: , in the formula: The calculated PWM duty cycle adjustment increment is dimensionless and ranges from -1 to 1. It is derived from the incremental PID formula mentioned above and represents the change in the control signal. The environmental steady-state response factor is dimensionless. This parameter is used to smooth the output and prevent light flicker when high-frequency fluctuations of the environmental reflection signal are detected. It is obtained by calculating the variance of the environmental reflection intensity samples of the most recent 10 times and taking its normalized reciprocal. The larger the value (the closer to 1), the more stable the environment is and the greater the weight. For example, if the current environment is stable, the value is 0.95. The proportional gain is derived from the Ziegler-Nichols tuning method and is calculated by experimentally finding the critical oscillation gain of the system. It is used to quickly respond to the deviation, for example, it is set to 0.5. The integral coefficient is derived from the reciprocal of the system response time constant and is obtained by analyzing the system's step response curve. It is used to eliminate steady-state errors, for example, and is set to 0.1. The differential coefficient is derived from the system's damping characteristics. It is adjusted to achieve optimal overshoot suppression and is used to predict deviation trends. For example, it can be set to 0.05. The sampling period at the current moment The current deviation, which is the difference between the target drive current value and the actual output current value, comes from the subtraction operation between the real-time sampled value and the target value. For example, the calculation formula is: 1.18-1.175=0.005, and the deviation is 0.005 amperes. The sampling period of the previous moment The current deviation comes from historical data cached by the system, for example, a stored value of 0.003 amperes; The sampling period of the previous time step The current deviation originates from historical data cached by the system, for example, a stored value of 0.001 amperes. The calculation process is as follows: substitute the above parameters into the formula, the calculation formula is: The calculated PWM duty cycle adjustment increment is 0.001425. Subsequently, the system executes a process of adjusting the output of the IoT LED smart driver power supply using this adjustment increment in conjunction with the minimum safe holding current value. This process specifically includes: First, calling the PWM duty cycle value from the previous control cycle, for example, 0.85, performing an addition operation, and adding the calculated adjustment increment to this value. The calculation formula is: 0.85 + 0.001425 = 0.851425, obtaining the updated target duty cycle for the unshaded area; simultaneously, based on the preset minimum safe holding current value (100 mA), retrieving its corresponding holding duty cycle, for example, a mapped value of 0.05; next, constructing a multi-channel control command frame, assigning the updated target duty cycle 0.851425 to the corresponding unshaded lighting area (south, east, west) channels, and assigning the holding duty cycle 0.05 to the shaded north area channel; finally, sending the command frame to the LED driver power supply through a digital lighting interface (such as DALI), and the driver power supply adjusts the output current of each channel accordingly. Experimental results show that, in scenarios where the streetlight aging cycle reaches 5000 hours and there is unilateral shading, this control strategy, by transferring 36 watts of power from the ineffective area and combining it with aging compensation, increases the driving current of the critical lighting area from 1.0 amps to 1.18 amps, effectively compensating for light decay and improving road illuminance. Compared with the uncompensated scheme, the average road illuminance is increased by about 25%.

[0096] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention in any other way. Any person skilled in the art may make changes or modifications to the above-disclosed technical content to create equivalent embodiments that can be applied to other fields. However, any simple modifications, equivalent changes, and modifications made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the protection scope of the present invention.

Claims

1. A city street light control system based on the Internet of Things, characterized in that, The system includes: The operating condition acquisition module collects the voltage and current feedback signals of the streetlights during operation and analyzes them into the junction temperature of the streetlight source. At the same time, it collects the reflected echo signals in the four quadrants of the streetlight head, demodulates them into the reflected echo signal intensity, and constructs a multi-dimensional sensing dataset. The thermoelectric loss assessment module calculates the street light source aging acceleration factor and the corresponding street light source aging time increment based on the junction temperature and current feedback signal of the street light source in the multi-dimensional sensing dataset. The luminous flux maintenance calculation module updates the cumulative operating cycle of the street light according to the street light source aging time increment, retrieves the corresponding preset luminous flux maintenance rate, and calculates the street light flux compensation coefficient. The occlusion discrimination module classifies the canopy occlusion area status position or unoccluded area status position of the street lamp according to the intensity of the reflected echo signal in the multidimensional sensing dataset, and counts the total number of unoccluded lighting areas. The power regulation module calculates the transferable redundant power and aging compensation power of the street lamps based on the street lamp luminous flux compensation coefficient and the preset minimum safe maintenance current value, and adjusts the current of each street lamp in combination with the total number of unobstructed lighting areas to obtain the street lamp control result. The power regulation module includes: The redundant power calculation submodule identifies the corresponding lighting area based on the status bit of the area blocked by the tree canopy, calls the preset minimum safe maintenance current value and the preset original rated power of the lighting area, sets the driving current of the corresponding lighting area to the minimum safe maintenance current value, calculates the difference between the original rated power of the lighting area and the power corresponding to the minimum safe maintenance current value, and generates transferable redundant power. The target power allocation submodule obtains the preset total rated power of the whole lamp, calculates the product of the total rated power of the whole lamp and the street light flux compensation coefficient to obtain the power required for aging compensation, accumulates the transferable redundant power and the power required for aging compensation to obtain the total available power, and distributes the total available power equally and superimposes it to the original rated power of the lighting area corresponding to the total number of unobstructed lighting areas to obtain the target driving power of the lighting area. The current control generation submodule converts the target driving power of the lighting area into the corresponding target driving current value based on the voltage characteristics of the driving circuit, constructs a current adjustment command for each unobstructed lighting area, and uses an incremental PID control algorithm combined with the minimum safe holding current value to adjust the output of the IoT LED smart driver power supply to generate street light control results.

2. The urban street light control system based on the Internet of Things according to claim 1, characterized in that, The multidimensional sensing dataset includes street light source junction temperature, current feedback signal, and reflected echo signal intensity. The street light source aging time increment is specifically the product of the street light source aging acceleration factor and the sampling period interval. The street light flux compensation coefficient is specifically the ratio of the difference between the initial luminous output rate and the preset luminous flux maintenance rate to the preset luminous flux maintenance rate. The total number of unobstructed lighting areas is specifically the number of lighting areas that are statistically marked as unobstructed and fall within the critical lighting area. The street light control results include the target driving current value allocated to the unobstructed lighting area and the minimum safe maintenance current value allocated to the canopy-obstructed lighting area.

3. The urban street light control system based on the Internet of Things according to claim 1, characterized in that, The operating condition acquisition module includes: The electrothermal signal analysis submodule periodically triggers the voltage sensor and Hall current sensor of the street light IoT edge computing node to collect the voltage signal and current feedback signal of the multi-drive circuit under the working state of the street light. It analyzes the voltage signal to obtain the junction temperature of the street light source and integrates the junction temperature of the street light source with the current feedback signal to generate the electrothermal status data of the light source. The environmental echo demodulation submodule triggers the infrared reflection sensors installed in the four quadrants of the street light head to emit detection signals, acquires the reflected echo signals relative to the detection signals, demodulates the reflected echo signals to obtain the reflected echo signal intensity, constructs a numerical distribution characterizing the degree of canopy shading based on the reflected echo signal intensity, and generates environmental reflection intensity distribution values. The multidimensional perception fusion submodule collects the current timestamp, associates it with the electrothermal state data of the light source and the environmental reflection intensity distribution value, and encapsulates it in a structured manner to generate a multidimensional perception dataset.

4. The urban street light control system based on the Internet of Things according to claim 3, characterized in that, The thermoelectric loss assessment module includes: The deviation value calculation submodule acquires the street light source junction temperature and current feedback signal from the multi-dimensional sensing dataset, calculates the difference between the street light source junction temperature and the preset street light reference test temperature value to obtain the street light temperature difference, analyzes the current feedback signal to obtain the real-time drive current value, and calculates the ratio of the real-time drive current value to the preset rated drive current value to obtain the drive current ratio. The aging factor mapping submodule calculates the thermal aging acceleration index of the street light source based on the street light temperature difference and the preset thermal aging conversion coefficient, calculates the electrical aging acceleration index of the street light source based on the driving current ratio and the preset electrical aging conversion coefficient, and calculates the product of the thermal aging acceleration index and the electrical aging acceleration index to generate the street light source aging acceleration factor. The time increment calculation submodule obtains the current sampling period interval duration, calculates the product of the street light source aging acceleration factor and the sampling period interval duration, and uses the product result as the life loss amplitude within a single sampling period to obtain the street light source aging time increment.

5. The urban street light control system based on the Internet of Things according to claim 4, characterized in that, The optical flux maintenance calculation module includes: The periodic cumulative update submodule reads the initial cumulative operating cycle of the streetlights recorded in the local non-volatile memory. If there is no record, the initial cumulative operating cycle is regarded as zero. The module calculates the sum of the initial cumulative operating cycle and the streetlight light source aging time increment to generate the updated cumulative operating cycle. The maintenance rate retrieval submodule uses the updated cumulative operating period as the index key to traverse the preset luminous flux maintenance rate standard curve database and retrieve the luminous flux maintenance rate corresponding to the updated cumulative operating period. The compensation coefficient calculation submodule calls the preset initial light output rate, calculates the difference between the initial light output rate and the luminous flux maintenance rate, and the ratio of the difference to the luminous flux maintenance rate, to generate the street light flux compensation coefficient.

6. The urban street light control system based on the Internet of Things according to claim 5, characterized in that, The occlusion detection module includes: The state bit threshold determination submodule compares the environmental reflection intensity distribution value in the multidimensional sensing dataset with a preset free space background noise threshold. When the environmental reflection intensity distribution value exceeds the free space background noise threshold, it generates a state bit for the area marked as canopy occlusion; otherwise, it generates a state bit for the area marked as unoccluded. The key area mapping and filtering submodule spatially matches the unobstructed area status bits with the key lighting area boundaries defined by the preset street light key lighting area mapping table, filters areas located within the key lighting area and corresponding unobstructed area status bits, and generates a set of unobstructed areas. The unobstructed area statistics submodule counts the number of key lighting areas in the set of unobstructed areas to obtain the number of target areas that need dynamic power allocation, which is taken as the total number of unobstructed lighting areas.

7. The urban street light control system based on the Internet of Things according to claim 5, characterized in that, The luminous flux maintenance standard curve database includes: Brightness monitoring data is extracted from street light source devices of a specified model that undergo continuous aging tests in a preset reference test temperature environment. The brightness monitoring data consists of discrete sampling points that record the specified cumulative running time points and the measured light output power values ​​corresponding to the time points. Based on the numerical distribution pattern of discrete sampling points in the time dimension, the numerical gap between two adjacent discrete sampling points is filled to construct a benchmark aging trend line that continuously reflects the monotonically decreasing optical output power with operating time. The rated initial luminous efficiency recorded in the specifications of the street light source device is used to calculate the ratio of the real-time light output power value on the baseline aging trend line to the rated initial luminous efficiency, and the baseline aging trend line is converted into a luminous flux maintenance rate evolution curve expressed as a percentage. Determine the sampling interval step size on the time axis, and extract the corresponding instantaneous maintenance rate value along the time axis of the luminous flux maintenance rate evolution curve according to the sampling interval step size; The cumulative time of the intercepted moment is defined as the index key of the database, and the corresponding instantaneous maintenance rate value is defined as the associated data. The corresponding mapping relationship between the index key and the associated data is established and stored to generate a standard curve database of luminous flux maintenance rate.

8. The urban street light control system based on the Internet of Things according to claim 6, characterized in that, The key lighting areas defined by the preset street light key lighting area mapping table include: Acquire spatial grid division data within the detection field of view of the street light sensor, and establish a positional mapping relationship between the grid index position in the spatial grid division data and the covered physical road surface area; Based on the pre-set road planning attribute data, select target physical areas marked as motor vehicle driving lanes and sidewalks from the physical road surface area; Based on the location mapping relationship, extract the set of two-dimensional matrix coordinate indices corresponding to the target physical region in the spatial grid division data, and define the set of two-dimensional matrix coordinate indices as the key lighting region; The edge coordinate values ​​of the two-dimensional matrix coordinate index set are parsed and then fixed into the key lighting area mapping table of streetlights to form the boundary of the key lighting area with spatial matching.

9. The urban street light control system based on the Internet of Things according to claim 1, characterized in that, To adjust the output of the IoT LED smart driver power supply, the formula is as follows: ； Calculate the PWM duty cycle adjustment increment Adjust the increment according to the PWM duty cycle Adjust the output of the IoT LED smart driver power supply; in, As an environmental steady-state response factor, This is the proportionality coefficient. The integral coefficient is... The differential coefficients are... The sampling period at the current moment Current deviation, The sampling period of the previous moment Current deviation, The sampling period of the previous time step Current deviation.

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