A method, system, terminal, and storage medium for the coordinated optimization control of light, electricity, and carbon in smart streetlights.

Abstract

This invention discloses a light-electricity-carbon synergistic optimization control method, system, terminal, and storage medium for smart streetlights. The method includes: decomposing regional carbon emission quotas to obtain carbon constraint boundaries; generating a dimming baseline based on the carbon constraint boundaries and historical lighting demand data to obtain a dimming strategy baseline; generating dimming commands based on the dimming strategy baseline to obtain target dimming commands and adjusting the lighting system; calculating carbon emissions based on streetlight operation data to obtain actual carbon emissions; calculating carbon emission deviation based on actual carbon emissions to obtain carbon emission deviation; correcting the dimming strategy baseline based on the carbon emission deviation and generating corrected dimming commands to obtain corrected dimming commands; and readjusting the lighting system based on the corrected dimming commands. This invention generates dimming commands based on carbon constraint boundaries to adjust the lighting system and calculates carbon emission deviations based on operation data to optimize dimming commands, thereby improving the carbon reduction effect of the lighting system.

Description

Technical Field

[0001] This invention relates to the field of smart lighting technology, and in particular to a method, system, terminal, and computer-readable storage medium for the coordinated optimization control of light, electricity, and carbon in smart streetlights. Background Technology

[0002] Urban street lighting is an important component of urban public infrastructure, consuming a significant amount of electricity annually. With the advancement of green and low-carbon development, and to meet the practical needs of urban operation and maintenance, urban lighting systems must balance energy conservation and lighting safety. Therefore, optimized control schemes are required to regulate the lighting system.

[0003] Existing smart street light optimization control methods achieve single-lamp brightness adjustment through illuminance sensors or human infrared sensors. However, these methods only consider the "light" dimension and do not take into account the grid load status and carbon emission constraints, resulting in a decrease in the carbon emission reduction effect of the optimization control scheme.

[0004] Therefore, existing technologies still need to be improved and developed. Summary of the Invention

[0005] The main objective of this invention is to provide a light-electricity-carbon synergistic optimization control method, system, terminal, and computer-readable storage medium for smart streetlights. This invention aims to address the problem that existing smart streetlight optimization control methods only consider the "light" dimension and do not take into account the grid load status and carbon emission constraints, resulting in a decrease in the carbon emission reduction effect of the optimization control scheme.

[0006] To achieve the above objectives, the present invention provides a method for coordinated optimization control of light, electricity, and carbon in smart streetlights, comprising the following steps: The regional carbon emission quota of the lighting system is obtained, the regional carbon emission quota is decomposed to obtain the carbon constraint boundary, and historical lighting demand data is obtained. Based on the carbon constraint boundary and the historical lighting demand data, a dimming baseline is generated to obtain the dimming strategy baseline. Multiple preset strategies are obtained, and dimming command generation processing is performed based on the multiple preset strategies and the dimming strategy baseline to obtain a target dimming command. The lighting system is then adjusted according to the target dimming command. The operation data of the lighting system is collected to obtain street light operation data. Carbon emission is calculated based on the street light operation data to obtain the actual carbon emission. Carbon emission is estimated based on the carbon constraint boundary to obtain the expected carbon emission. Carbon emission deviation is calculated based on the actual carbon emission and the expected carbon emission to obtain the carbon emission deviation. The dimming strategy baseline is corrected based on the carbon emission deviation and the regional carbon emission quota to obtain a corrected dimming strategy baseline. A corrected dimming instruction is generated based on the corrected dimming strategy baseline to obtain a corrected dimming instruction. The lighting system is then readjusted based on the corrected dimming instruction.

[0007] Optionally, the light-electricity-carbon synergistic optimization control method for smart streetlights, wherein obtaining the regional carbon emission quota of the lighting system, decomposing the regional carbon emission quota to obtain a carbon constraint boundary, obtaining historical lighting demand data, and performing dimming baseline generation processing based on the carbon constraint boundary and the historical lighting demand data to obtain a dimming strategy baseline, specifically includes: The regional carbon emission quota of the lighting system is obtained, and the regional carbon emission quota is decomposed at the road section level and at the single lamp level to obtain the carbon constraint boundary. A carbon emission model, a power mapping model, and an illuminance relationship model are determined. Based on the carbon emission model, the power mapping model, and the illuminance relationship model, an optimization function is constructed to obtain a multi-objective optimization function. Historical lighting demand data is obtained, and a brightness demand curve prediction process is performed based on the carbon constraint boundary and the historical lighting demand data to obtain the brightness demand curve. The minimum energy consumption curve is calculated based on the brightness demand curve and the multi-objective optimization function to obtain the dimming strategy baseline.

[0008] Optionally, the light-electricity-carbon synergistic optimization control method for smart streetlights, wherein the step of acquiring multiple preset strategies, performing dimming command generation processing based on the multiple preset strategies and the dimming strategy baseline to obtain a target dimming command, and adjusting the lighting system according to the target dimming command, specifically includes: Multiple preset strategies are obtained, and execution strategies are generated based on the multiple preset strategies and the dimming strategy baseline to obtain multiple execution strategies. Determine whether there is a contradiction among the multiple execution strategies. If so, obtain a preset priority and perform strategy selection processing on the multiple execution strategies according to the preset priority to obtain the target execution strategy. If not, directly obtain the target execution strategy based on the multiple execution strategies. The dimming instruction generation process is performed according to the power mapping model and the target execution strategy to obtain the target dimming instruction, and the lighting system is adjusted according to the target dimming instruction.

[0009] Optionally, the light-electricity-carbon synergistic optimization control method for smart streetlights, wherein collecting the operating data of the lighting system to obtain streetlight operating data, performing carbon emission calculation processing based on the streetlight operating data to obtain actual carbon emissions, performing carbon emission estimation processing based on the carbon constraint boundary to obtain expected carbon emissions, and performing carbon emission deviation calculation processing based on the actual carbon emissions and the expected carbon emissions to obtain carbon emission deviation, specifically includes: Collect the operating data of the lighting system to obtain street light operating data; The grid carbon emission factor and sampling period are obtained, and the carbon emission amount is calculated based on the street light operation data, the sampling period and the grid carbon emission factor to obtain the actual carbon emission amount. Carbon emissions are estimated based on the carbon constraint boundary to obtain the expected carbon emissions. The difference between the actual carbon emissions and the expected carbon emissions is calculated to obtain the carbon emission deviation.

[0010] Optionally, the light-electricity-carbon synergistic optimization control method for smart streetlights, wherein the step of correcting the dimming strategy baseline based on the carbon emission deviation and the regional carbon emission quota to obtain a corrected dimming strategy baseline, generating a corrected dimming command based on the corrected dimming strategy baseline to obtain a corrected dimming command, and readjusting the lighting system based on the corrected dimming command, specifically includes: If the carbon emission deviation is positive, the upper limit of the brightness of the dimming strategy baseline is reduced to obtain the first dimming strategy baseline, and the first dimming strategy baseline is used as the first dimming strategy baseline to be corrected. If the carbon emission deviation is negative, the upper limit of the brightness of the dimming strategy baseline is increased to obtain the second dimming strategy baseline, and the second dimming strategy baseline is used as the first dimming strategy baseline to be corrected. The carbon emission ratio is calculated based on the actual carbon emissions and the regional carbon emission quota. A ratio threshold is obtained, and it is determined whether the carbon emission ratio is greater than the ratio threshold. If so, road segment data is obtained, and the first correction process is performed on the first dimming strategy baseline to be corrected based on the road segment data to obtain the road segment data correction baseline. The road segment data correction baseline is used as the second dimming strategy baseline to be corrected. If not, the first dimming strategy baseline to be corrected will be directly used as the second dimming strategy baseline to be corrected. Obtain the deviation threshold, determine whether the absolute value of the carbon emission deviation is greater than the deviation threshold, if so, perform a second correction process on the second dimming strategy baseline to be corrected based on the street light operation data to obtain the road segment data correction baseline, and use the road segment data correction baseline as the correction dimming strategy instruction. If not, the second dimming strategy baseline to be corrected will be directly used as the dimming strategy correction instruction; The modified dimming command is generated based on the modified dimming strategy baseline to obtain the modified dimming command, and the lighting system is readjusted based on the modified dimming command.

[0011] Optionally, the light-electricity-carbon synergistic optimization control method for smart streetlights, wherein acquiring road segment data and performing a first correction process on the first dimming strategy baseline to be corrected based on the road segment data to obtain a corrected road segment data baseline specifically includes: Obtain road segment data and road segment priority; perform non-core road segment data extraction processing on the road segment data according to the road segment priority to obtain non-core road segment brightness reference value; and reduce the non-core road segment brightness reference value to obtain corrected brightness reference value. Carbon emission budget allocation is performed based on the modified brightness benchmark value and the regional carbon emission quota to obtain an optimized carbon constraint boundary. The first adjustment process is performed on the first dimming strategy baseline to be corrected based on the optimized carbon constraint boundary to obtain the road segment data correction baseline.

[0012] Optionally, the light-electricity-carbon synergistic optimization control method for smart streetlights, wherein the step of performing a second correction process on the second dimming strategy baseline to be corrected based on the streetlight operating data to obtain a road segment data correction baseline specifically includes: Based on the street light operation data and the baseline of the second dimming strategy to be corrected, the cause of carbon emission deviation is analyzed and processed to obtain the cause of carbon emission deviation. Based on the reasons for the carbon emission deviation, a baseline adjustment instruction is generated to obtain the baseline adjustment instruction. The second adjustment process is performed on the second dimming strategy baseline to be corrected according to the baseline adjustment instruction to obtain the road segment data correction baseline.

[0013] Furthermore, to achieve the above objectives, the present invention also provides a light-electricity-carbon synergistic optimization control system for smart streetlights, wherein the light-electricity-carbon synergistic optimization control system for smart streetlights includes: The dimming strategy generation module is used to obtain the regional carbon emission quota of the lighting system, decompose the regional carbon emission quota to obtain the carbon constraint boundary, obtain historical lighting demand data, and perform dimming baseline generation processing based on the carbon constraint boundary and the historical lighting demand data to obtain the dimming strategy baseline. A dimming instruction generation module is used to acquire multiple preset strategies, perform dimming instruction generation processing based on the multiple preset strategies and the dimming strategy baseline to obtain a target dimming instruction, and adjust the lighting system according to the target dimming instruction; The carbon emission deviation calculation module is used to collect the operation data of the lighting system to obtain the street light operation data, perform carbon emission calculation processing based on the street light operation data to obtain the actual carbon emission, perform carbon emission estimation processing based on the carbon constraint boundary to obtain the expected carbon emission, and perform carbon emission deviation calculation processing based on the actual carbon emission and the expected carbon emission to obtain the carbon emission deviation. The dimming correction module is used to correct the dimming strategy baseline based on the carbon emission deviation and the regional carbon emission quota to obtain a corrected dimming strategy baseline, and to generate a corrected dimming instruction based on the corrected dimming strategy baseline to obtain a corrected dimming instruction, and to readjust the lighting system based on the corrected dimming instruction.

[0014] Furthermore, to achieve the above objectives, the present invention also provides a terminal, wherein the terminal includes: a memory, a processor, and a smart street light light-electricity-carbon synergistic optimization control program stored in the memory and executable on the processor, wherein when the smart street light light-electricity-carbon synergistic optimization control program is executed by the processor, it implements the steps of the smart street light light-electricity-carbon synergistic optimization control method as described above.

[0015] Furthermore, to achieve the above objectives, the present invention also provides a computer-readable storage medium, wherein the computer-readable storage medium stores a light-electricity-carbon synergistic optimization control program for a smart street light, and when the light-electricity-carbon synergistic optimization control program for a smart street light is executed by a processor, it implements the steps of the light-electricity-carbon synergistic optimization control method for a smart street light as described above.

[0016] In this invention, the regional carbon emission quota of the lighting system is obtained, the regional carbon emission quota is decomposed to obtain a carbon constraint boundary, and historical lighting demand data is obtained. A dimming baseline is generated based on the carbon constraint boundary and the historical lighting demand data to obtain a dimming strategy baseline. Multiple preset strategies are obtained, and dimming instructions are generated based on the multiple preset strategies and the dimming strategy baseline to obtain a target dimming instruction. The lighting system is adjusted according to the target dimming instruction. Operating data of the lighting system is collected to obtain street light operating data. Carbon emission calculation is performed based on the street light operating data to obtain the actual carbon emission. Carbon emission estimation is performed based on the carbon constraint boundary to obtain the expected carbon emission. Carbon emission deviation is calculated based on the actual carbon emission and the expected carbon emission to obtain the carbon emission deviation. The dimming strategy baseline is corrected based on the carbon emission deviation and the regional carbon emission quota to obtain a corrected dimming strategy baseline. A corrected dimming instruction is generated based on the corrected dimming strategy baseline to obtain a corrected dimming instruction. The lighting system is readjusted based on the corrected dimming instruction. This invention obtains carbon constraint boundaries based on the decomposition of carbon emission quotas, generates dimming commands to adjust the lighting system based on the carbon constraint boundaries, collects the operating data of the lighting system, calculates carbon emission deviations based on the operating data to optimize the dimming commands, and improves the carbon emission reduction effect of the lighting system. Attached Figure Description

[0017] Figure 1 This is a flowchart of a preferred embodiment of the light-electricity-carbon synergistic optimization control method for smart streetlights of the present invention; Figure 2 This is a diagram of the three-layer distributed edge-cloud collaborative management and control architecture of the light-electricity-carbon collaborative optimization control method for smart streetlights of this invention; Figure 3 This is a flowchart of the light-electricity-carbon synergistic optimization control method for smart streetlights according to the present invention; Figure 4 This is a sequence diagram of the hierarchical strategy execution of the light-electricity-carbon synergistic optimization control method for smart streetlights of the present invention; Figure 5 This is a structural diagram of a preferred embodiment of the light-electricity-carbon synergistic optimization control system for smart streetlights of the present invention; Figure 6 This is a structural diagram of a preferred embodiment of the terminal of the present invention. Detailed Implementation

[0018] To make the objectives, technical solutions, and advantages of this invention clearer and more explicit, 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 of the invention and are not intended to limit the invention.

[0019] Urban street lighting is an important component of urban public infrastructure. Currently, there are over 30 million streetlights nationwide, consuming approximately 15%-20% of the city's total electricity annually. With the advancement of green and low-carbon development, urban lighting systems need to balance energy conservation and lighting safety to meet the practical needs of urban operation and maintenance. Therefore, optimized control schemes are required to regulate the lighting system.

[0020] Existing smart street light optimization control methods achieve single-lamp brightness adjustment through illuminance sensors or human infrared sensors. However, these methods only consider the "light" dimension and do not take into account the grid load status and carbon emission constraints, resulting in a decrease in the carbon emission reduction effect of the optimization control scheme.

[0021] To address the aforementioned issues, this invention proposes a light-electricity-carbon synergistic optimization control method for smart streetlights. This method obtains carbon constraint boundaries based on carbon emission quota decomposition, generates dimming commands to adjust the lighting system based on these boundaries, collects operational data of the lighting system, and calculates carbon emission deviations based on the operational data to optimize the dimming commands, thereby improving the carbon reduction effect of the lighting system.

[0022] The preferred embodiment of the light-electricity-carbon synergistic optimization control method for smart streetlights of the present invention, such as... Figure 1 As shown, the light-electricity-carbon synergistic optimization control method for smart streetlights includes the following steps: Step S10: Obtain the regional carbon emission quota of the lighting system, decompose the regional carbon emission quota to obtain the carbon constraint boundary, and obtain historical lighting demand data. Based on the carbon constraint boundary and the historical lighting demand data, perform dimming baseline generation processing to obtain the dimming strategy baseline.

[0023] Specifically, the regional carbon emission quotas for the lighting system are obtained, and the regional carbon emission quotas are decomposed at the road segment level and at the individual lamp level to obtain the carbon constraint boundary.

[0024] This invention proposes a smart street light-electricity-carbon collaborative optimization control scheme based on cloud-edge-device collaboration, which is used to solve problems such as the fragmentation of the three dimensions of light, electricity and carbon in the existing technology, the lack of carbon emission as an explicit control constraint, the lack of real-time performance of centralized decision-making, and the lack of multi-source perception fusion decision-making capabilities.

[0025] like Figure 2 As shown, the present invention sets up a cloud layer, an edge layer, and a terminal layer. The cloud layer is used for global decision-making, policy arbitration, and carbon emission control; the edge layer is used for instruction forwarding and perception reporting; and the terminal layer is used for execution, feedback, and local fallback.

[0026] The cloud layer includes: Carbon emission accounting engine: Based on real-time carbon emission factors of the power grid and street light operation data, a mapping model of lighting energy consumption and carbon emissions is established to calculate the carbon emissions of each region, each road section, and each single lamp in real time. Global strategy generator: Taking regional carbon emission quotas, electricity price signals, and weather forecasts as inputs, and combining historical lighting demand data and various dimming strategy types (fixed time strategy, sunrise and sunset time strategy, scene strategy, carbon constraint strategy), it generates dimming strategy baselines (brightness reference curves) for each region, determines the final dimming strategy through multi-strategy priority arbitration, and calculates single-lamp dimming instructions. Carbon emission target decomposer: decomposes city-level / regional-level carbon emission reduction targets down to the road segment level and individual light lamp level, forming carbon constraint boundaries; Energy consumption-carbon emission correlation analysis module: Performs correlation analysis on multi-dimensional data such as year-on-year, month-on-month, structure, and trend to identify high carbon emission road sections and energy-saving potential areas.

[0027] The edge layer includes: Multi-source perception fusion engine: It integrates real-time perception data from multiple sources such as illuminance sensors, human infrared sensors, traffic flow detection, and meteorological data to build a perception confidence assessment model, downweight or remove abnormal data, and report the fused perception data to the cloud layer for policy correction. Command repeater: Receives single-lamp dimming commands from the cloud layer, forwards the commands to the corresponding terminals for execution, and simultaneously summarizes the operational data fed back by the terminals and reports it to the cloud layer; Autonomous Controller During Network Outage: When communication between edge nodes and the cloud is interrupted, it makes autonomous decisions based on the last received policy baseline and local sensing data to ensure uninterrupted lighting safety. Multi-strategy priority arbitrator: manages the execution priority and conflict resolution of various strategies such as fixed time strategy, sunrise and sunset time strategy, scene strategy, and carbon constraint strategy. After completing the strategy arbitration in a unified manner at the cloud layer, it generates a single-lamp dimming command for distribution.

[0028] The terminal layer includes: Single lamp controller: Supports 0-100% stepless dimming and real-time acquisition of operating parameters such as voltage, current, power, brightness, and temperature; Sensor group: Temperature and humidity sensor; Status feedback module: Reports real-time operating parameters and execution results to edge nodes, forming a closed loop of light-electricity-carbon data; Safety linkage module: Linked with leakage current / water level monitoring, it automatically cuts off the power and reports when an abnormality occurs.

[0029] like Figure 3As shown, carbon emission quotas and historical data are obtained from the cloud; carbon emission targets are decomposed down to the road section level and the individual lamp level to form carbon constraint boundaries; and dimming strategy baselines for each region are generated and distributed to edge nodes.

[0030] like Figure 4 As shown, the present invention sets up a hierarchical time-series execution mechanism. The cloud layer generates the dimming strategy baseline for each region on a daily basis, and dynamically corrects the dimming strategy baseline on an hourly basis according to the update of the grid carbon emission factor and the cumulative progress of carbon emission.

[0031] Specifically, the cloud layer acquires regional carbon emission quotas and historical lighting demand data, and decomposes the regional carbon emission quotas down to the road segment level and the individual lamp level to form carbon constraint boundaries.

[0032] Further, a carbon emission model, a power mapping model, and an illuminance relationship model are determined. An optimization function is constructed based on these models to obtain a multi-objective optimization function. Historical lighting demand data is acquired, and a brightness demand curve is predicted based on the carbon constraint boundary and the historical lighting demand data to obtain a brightness demand curve. Finally, a minimum energy consumption curve is calculated based on the brightness demand curve and the multi-objective optimization function to obtain a dimming strategy baseline.

[0033] The dimming strategy baseline is generated based on a three-dimensional quantitative correlation model of light, electricity, and carbon. The quantitative correlation model includes a carbon emission calculation model, a brightness-power mapping model, and an illuminance-brightness relationship model.

[0034] The carbon emission calculation model is C(t) = ΣPi(t) × EF(t) × Δt, where C(t) is the carbon emission at time t, Pi(t) is the real-time power of the i-th street light, EF(t) is the real-time carbon emission factor of the power grid, and Δt is the sampling period.

[0035] The brightness-power mapping model is Pi(t) = Pratedi × f(Li(t)), where Pratedi is the rated power of the i-th street lamp, Li(t) is the brightness percentage, and f() is the brightness-power mapping function obtained through calibration experiments.

[0036] The illuminance-luminance relationship model is Ei(t)=g(Li(t), Hi, θi, ENVi(t)), where Ei(t) is the road illuminance, Hi is the installation height, θi is the light distribution angle, and ENVi(t) is the environmental factor.

[0037] Construct a multi-objective optimization function with carbon emission quotas as constraints, lighting safety as the bottom line, and minimum energy consumption as the objective: minΣPi(t)×Δt; stEi(t)≥Emini; ΣCj(t)≤Cquotaj; Lmini≤Li(t)≤100%; Where st is the constraint symbol, Emini is the minimum illuminance standard for the road segment, Cj(t) is the carbon emission of the j-th region at time t, Cquotaj is the carbon emission quota of the j-th region, and Lmini is the minimum safe luminance percentage.

[0038] The process of generating the dimming strategy baseline by solving a multi-objective optimization function is as follows: The cloud layer uses the regional carbon emission quota Cquotaj as the upper bound and the minimum illuminance standard Emini for each road section as the lower bound. A hierarchical solution method is adopted. First, the brightness demand curve for each time period is predicted based on historical lighting demand data and weather forecast data. Then, the multi-objective optimization function is transformed into a constrained optimization problem with the brightness percentage of each time period as the decision variable. Under the above constraints, the brightness baseline curve that minimizes the total energy consumption is solved. This brightness baseline curve is the dimming strategy baseline.

[0039] Step S20: Obtain multiple preset strategies, perform dimming instruction generation processing based on the multiple preset strategies and the dimming strategy baseline to obtain a target dimming instruction, and adjust the lighting system according to the target dimming instruction.

[0040] Specifically, multiple preset strategies are obtained, and execution strategy generation processing is performed based on the multiple preset strategies and the dimming strategy baseline to obtain multiple execution strategies; it is determined whether there is a contradiction among the multiple execution strategies. If so, a preset priority is obtained, and strategy selection processing is performed on the multiple execution strategies based on the preset priority to obtain the target execution strategy. If not, the target execution strategy is directly obtained based on the multiple execution strategies.

[0041] The cloud layer has multiple dimming strategy types, including fixed time strategy, sunrise and sunset time strategy, scene strategy and carbon constraint strategy. The scene strategy covers weather scene, energy saving scene and holiday scene. Based on multiple dimming strategy types, multiple dimming strategies to be executed are generated by combining multi-source sensing data and dimming strategy baseline.

[0042] The cloud layer prioritizes and resolves conflicts among multiple strategies through a multi-strategy priority arbitrator. When multiple strategies produce conflicting dimming strategies for the same street light, the target strategy is selected according to the preset priority order. Among them, the priority of lighting safety constraints is higher than that of carbon constraints, and the priority of carbon constraints is higher than that of fixed time strategies and scene strategies.

[0043] Furthermore, dimming instruction generation processing is performed based on the power mapping model and the target execution strategy to obtain a target dimming instruction, and the lighting system is adjusted according to the target dimming instruction.

[0044] Based on the arbitrated target execution strategy, the cloud layer calculates the target dimming parameters of each street light according to the brightness-power mapping model, sends them to the edge layer to generate a single-light target dimming command, and then sends it to the corresponding terminal for execution.

[0045] like Figure 3 As shown, the edge layer receives the baseline strategy; it integrates local multi-source sensing data to determine whether it is within the carbon constraint and lighting safety constraint range. If not, it adjusts to the constraint range; if so, it locally optimizes and generates a single-lamp dimming command.

[0046] like Figure 4 As shown, the edge layer integrates local multi-source sensing data and reports it to the cloud layer at a rate of minutes. Within the constraints, it locally optimizes and generates single-lamp dimming commands, and then forwards the single-lamp dimming commands to the corresponding terminals.

[0047] Step S30: Collect the operation data of the lighting system to obtain street light operation data; perform carbon emission calculation processing based on the street light operation data to obtain the actual carbon emission; perform carbon emission estimation processing based on the carbon constraint boundary to obtain the expected carbon emission; and perform carbon emission deviation calculation processing based on the actual carbon emission and the expected carbon emission to obtain the carbon emission deviation.

[0048] Specifically, the operation data of the lighting system is collected to obtain street light operation data; the power grid carbon emission factor and sampling period are obtained; carbon emission is calculated based on the street light operation data, the sampling period and the power grid carbon emission factor to obtain the actual carbon emission; carbon emission is estimated based on the carbon constraint boundary to obtain the expected carbon emission; and the difference between the actual carbon emission and the expected carbon emission is calculated to obtain the carbon emission deviation.

[0049] The edge layer receives the dimming strategy baseline and single-lamp dimming command sent by the cloud layer, and forwards the single-lamp dimming command to the corresponding terminal.

[0050] like Figure 3 As shown, the terminal layer executes the single-lamp dimming command, collects street lamp operating parameters in real time, and reports them to the edge layer. After receiving the street lamp operating parameters from the terminal layer, the edge layer integrates local multi-source sensing data, calculates carbon emission deviation, and reports it to the cloud layer for subsequent strategy correction.

[0051] like Figure 4 As shown, the terminal layer executes dimming commands and reports operating parameters in real time, the edge layer summarizes feedback data and calculates carbon emission deviation on a minute-by-minute basis, and the cloud layer corrects the subsequent strategy baseline based on the carbon emission deviation on an hour-by-hour basis.

[0052] Specifically, the edge layer calculates the carbon emission deviation between the actual and expected carbon emissions based on the real-time power of the streetlights in the streetlight operation parameters and the grid carbon emission factor. The grid carbon emission factor is a grid-side parameter that is dynamically updated according to the time period and regional grid structure, and is not a streetlight operation parameter.

[0053] The method for calculating the actual carbon emissions Cactual(t) at time t is as follows: Cactual(t) = ΣPi(t) × EF(t) × Δt, where Pi(t) is the real-time power of the i-th street light, EF(t) is the current power grid carbon emission factor, and Δt is the sampling period.

[0054] The expected carbon emissions are based on the carbon emission quotas set by the cloud layer after the regional carbon emission quotas are broken down to the road segment and individual lamp levels, and the carbon emission quota values ​​are preset according to the carbon emission constraints.

[0055] Carbon emission deviation is the difference between actual carbon emissions and expected carbon emission allowances.

[0056] like Figure 4 As shown, the edge layer is also equipped with a network outage autonomous control mechanism. When the edge node detects a communication interruption with the cloud layer, it automatically enters the network outage autonomous mode and operates autonomously based on the last received dimming strategy baseline and local multi-source sensing data. The terminal layer has a preset time strategy as a local fallback strategy. When the communication between the terminal layer and the edge layer is interrupted, it operates autonomously according to the preset time strategy to ensure that the road lighting is not interrupted. When a communication interruption with the edge layer is detected, it operates autonomously according to the preset time strategy to ensure that the road lighting is not interrupted.

[0057] During network outages, edge nodes locally cache operational data, including power, brightness, and carbon emission calculation data. When communication is restored, the edge nodes report the cached operational data to the cloud layer in batches. The cloud layer then corrects the cumulative carbon emission value and the baseline for subsequent dimming strategies based on the reported data.

[0058] Step S40: Correct the dimming strategy baseline according to the carbon emission deviation and the regional carbon emission quota to obtain a corrected dimming strategy baseline, and generate a corrected dimming instruction according to the corrected dimming strategy baseline to obtain a corrected dimming instruction, and readjust the lighting system according to the corrected dimming instruction.

[0059] Specifically, if the carbon emission deviation is positive, the upper limit of the brightness of the dimming strategy baseline is lowered to obtain a first dimming strategy baseline, and the first dimming strategy baseline is used as the first dimming strategy baseline to be corrected; if the carbon emission deviation is negative, the upper limit of the brightness of the dimming strategy baseline is raised to obtain a second dimming strategy baseline, and the second dimming strategy baseline is used as the first dimming strategy baseline to be corrected.

[0060] like Figure 3 As shown, the cloud determines whether the carbon emission deviation exceeds the threshold. If not, the current strategy baseline is maintained. If so, the subsequent dimming strategy baseline is corrected, the carbon constraint strategy is backtracked, the dimming strategy baseline for each region is regenerated, and then sent to the edge nodes.

[0061] After the edge layer reports the carbon emission deviation to the cloud layer, the cloud layer determines the direction and magnitude of the carbon emission deviation. If the carbon emission deviation is positive, the actual carbon emissions are greater than the expected carbon emission allowance. In this case, the carbon constraint boundary for subsequent cycles is tightened and the upper limit of brightness in the dimming strategy baseline is lowered. If the carbon emission deviation is negative, the actual carbon emissions are less than the expected carbon emission allowance. In this case, the upper limit of brightness is appropriately relaxed within the lighting safety constraint range. This achieves closed-loop control of strategy generation, execution, carbon emission calculation, deviation feedback, and strategy correction.

[0062] Further, a carbon emission ratio is calculated based on the actual carbon emissions and the regional carbon emission quota to obtain a ratio threshold. It is then determined whether the carbon emission ratio exceeds the threshold. If so, road segment data and road segment priorities are acquired. Non-core road segment data is extracted from the road segment data based on the road segment priorities to obtain a non-core road segment brightness benchmark value. This non-core road segment brightness benchmark value is then reduced to obtain a corrected brightness benchmark value. A carbon emission budget allocation is performed based on the corrected brightness benchmark value and the regional carbon emission quota to obtain an optimized carbon constraint boundary. The first dimming strategy baseline to be corrected is then adjusted based on the optimized carbon constraint boundary to obtain a corrected road segment data baseline. If not, the first dimming strategy baseline to be corrected is directly used as the second dimming strategy baseline to be corrected.

[0063] When the cumulative actual carbon emissions of a region within a statistical period reach a preset proportion of the region's carbon emission quota threshold, the cloud layer triggers a carbon constraint strategy backtracking. This backtracking gradually reduces the brightness baseline value of non-core road sections according to preset road section priorities, while maintaining the safe lighting brightness of core road sections. The preset proportion is a pre-set trigger threshold proportion, such as 80% or 90% of the carbon emission quota threshold. The road section priority is a pre-defined level based on the lighting control level of each road section. Core road sections have high lighting control levels and their brightness must meet the lighting requirements of 100% or 80% or higher. Non-core road sections have low lighting control levels and their brightness can be reduced to 50% or below. Gradually reducing the brightness baseline value of non-core road sections means decreasing the brightness baseline value of non-core road sections sequentially by a preset gradient value according to road section priorities from low to high, until the adjusted predicted carbon emissions are controlled within the carbon emission quota range.

[0064] Based on the adjusted brightness baseline value, the carbon emission trend for the remaining period is predicted, and the carbon emission budget for each road segment is reallocated to keep the adjusted predicted carbon emissions within the carbon emission quota range. The reallocated carbon emission budget serves as the updated expected carbon emission quota value. The cloud layer uses this updated expected carbon emission quota value to revise the subsequent dimming strategy baseline. Specifically, the carbon constraint boundary is updated with the reallocated carbon emission budget for each road segment, and a new dimming strategy baseline is generated again through multi-strategy priority arbitration and solving a multi-objective optimization function.

[0065] If the carbon constraint strategy backtracking is not triggered, then directly determine whether the subsequent energy consumption-carbon emission correlation analysis should be triggered.

[0066] Further, a deviation threshold is obtained, and it is determined whether the absolute value of the carbon emission deviation is greater than the deviation threshold. If so, the cause of the carbon emission deviation is analyzed based on the street light operation data and the second dimming strategy baseline to be corrected, and the cause of the carbon emission deviation is obtained. Based on the cause of the carbon emission deviation, a baseline adjustment instruction is generated to obtain a baseline adjustment instruction. Based on the baseline adjustment instruction, the second dimming strategy baseline to be corrected is adjusted to obtain a road segment data correction baseline. If not, the second dimming strategy baseline to be corrected is directly used as the correction dimming strategy instruction.

[0067] When the actual carbon emissions of a certain area or road segment deviate from the expected carbon emissions by more than a preset threshold, the cloud layer locates the streetlights or road segments with high deviation through energy consumption-carbon emission correlation analysis.

[0068] Energy consumption-carbon emission correlation analysis includes: performing at least one of year-on-year analysis, month-on-month analysis, structural analysis, and trend analysis on street light operation data, and identifying the root causes of carbon emission deviations, including at least one of equipment aging, lamp decay, and control anomalies.

[0069] Based on the identified root causes, targeted operation and maintenance suggestions or policy correction instructions are generated, and corresponding follow-up operations are executed. Targeted operation and maintenance suggestions include: when the root cause is equipment aging, generating equipment replacement suggestions and replacement priority ranking; when the root cause is lamp attenuation, generating lamp cleaning or replacement suggestions and attenuation compensation parameters; when the root cause is control anomaly, generating control parameter correction instructions and anomaly isolation strategies. Policy correction instructions include correcting the dimming strategy baseline parameters of the street light or road segment.

[0070] If the energy consumption-carbon emission correlation analysis is not triggered, there is no need to adjust the dimming strategy baseline before determining whether the carbon emission deviation exceeds the threshold.

[0071] Further, a correction dimming instruction generation process is performed based on the correction dimming strategy baseline to obtain a correction dimming instruction, and the lighting system is readjusted based on the correction dimming instruction.

[0072] The cloud layer generates new individual lamp dimming instructions based on the adjusted dimming strategy baseline and optimizes the control of the lighting system.

[0073] The cloud layer also includes a GIS carbon emission visualization module. Based on a geographic information system, the GIS carbon emission visualization module displays the real-time carbon emissions and carbon emission quota usage progress of each region and road segment on an electronic map using different color levels. When the carbon emissions of a certain region or road segment approach the quota threshold, it is marked with an alarm color on the map. It also supports clicking on the street light icon on the map to view the carbon emissions, real-time power, and current brightness information at the single light level.

[0074] The terminal layer is also equipped with a safety linkage mechanism. Specifically, it collects water level data and leakage current data from the street light control cabinet and distribution box in real time. When the water level data or leakage current data exceeds the preset threshold, the terminal layer automatically triggers a safety linkage action, including cutting off the power supply to the relevant equipment and generating a safety alarm message. The safety linkage action has a higher priority than all dimming strategies, and dimming commands are suspended during the safety linkage triggering period.

[0075] The technical effects of this invention include: In existing technologies, lighting dimming, energy management, and carbon emission reduction are independent processes, with carbon emissions merely serving as a passive conversion result of energy consumption. This invention, for the first time, establishes a three-dimensional collaborative closed loop in a streetlight management system: guiding light with carbon, constraining electricity with light, and calculating carbon emissions using electricity. Carbon-guided light: Regional carbon emission quotas serve as input constraints for dimming strategies, rather than as post-hoc statistical indicators; Light-constrained electricity: The minimum illuminance standard for lighting safety serves as a rigid baseline for energy consumption optimization; Carbon accounting based on electricity: dynamic carbon emission accounting based on real-time power and grid carbon emission factor, and reverse correction of dimming strategy; The three dimensions form a calculable, controllable, and predictable closed loop through quantitative correlation models and hierarchical optimization methods, enabling the carbon emission reduction effect to be expected and verifiable.

[0076] In some schemes, carbon emissions are not involved in the strategy generation process. This invention decomposes the regional carbon emission quota down to the road segment level and the individual lamp level, serving as explicit constraint boundaries for the dimming strategy. When the actual carbon emissions approach the quota threshold, the brightness of non-core road segments is gradually reduced and the carbon emission budget is reallocated through a carbon constraint strategy backtracking mechanism, thereby achieving proactive management of carbon emissions rather than passive statistics.

[0077] This invention addresses the issues of high latency and loss of control during network outages in existing centralized control systems through a three-layer division of labor: the cloud layer handles carbon emission calculation, global policy generation, and policy arbitration (daily / hourly level); the edge layer handles command forwarding and sensing data reporting (minute level); and the terminal layer executes commands, provides operational data feedback, and provides local time policy support (real-time). The core feature of this three-layer collaboration is that carbon sensing permeates the entire chain: cloud-based policies include carbon constraints and policy arbitration; the edge layer forwards commands within carbon boundaries; and the terminal provides feedback containing carbon emission data. Pre-set time policies at the terminal and the edge layer's autonomous capability during network outages jointly ensure uninterrupted lighting safety.

[0078] This invention obtains carbon constraint boundaries based on the decomposition of carbon emission quotas, generates dimming commands to adjust the lighting system based on the carbon constraint boundaries, collects the operating data of the lighting system, calculates carbon emission deviations based on the operating data to optimize the dimming commands, and improves the carbon emission reduction effect of the lighting system.

[0079] Furthermore, such as Figure 5 As shown, based on the above-mentioned light-electricity-carbon synergistic optimization control method for smart streetlights, this invention also provides a corresponding light-electricity-carbon synergistic optimization control system for smart streetlights, wherein the light-electricity-carbon synergistic optimization control system for smart streetlights includes: The dimming strategy generation module 51 is used to obtain the regional carbon emission quota of the lighting system, decompose the regional carbon emission quota to obtain the carbon constraint boundary, obtain historical lighting demand data, and perform dimming baseline generation processing based on the carbon constraint boundary and the historical lighting demand data to obtain the dimming strategy baseline. The dimming instruction generation module 52 is used to acquire multiple preset strategies, perform dimming instruction generation processing based on the multiple preset strategies and the dimming strategy baseline to obtain a target dimming instruction, and adjust the lighting system according to the target dimming instruction; The carbon emission deviation calculation module 53 is used to collect the operation data of the lighting system to obtain the street light operation data, perform carbon emission calculation processing based on the street light operation data to obtain the actual carbon emission, perform carbon emission estimation processing based on the carbon constraint boundary to obtain the expected carbon emission, and perform carbon emission deviation calculation processing based on the actual carbon emission and the expected carbon emission to obtain the carbon emission deviation. The dimming correction module 54 is used to correct the dimming strategy baseline according to the carbon emission deviation and the regional carbon emission quota to obtain a corrected dimming strategy baseline, and to generate a corrected dimming instruction according to the corrected dimming strategy baseline to obtain a corrected dimming instruction, and to readjust the lighting system according to the corrected dimming instruction.

[0080] Furthermore, such as Figure 6As shown, based on the above-mentioned light-electricity-carbon synergistic optimization control method and system for smart streetlights, the present invention also provides a terminal, which includes a processor 10, a memory 20 and a display 30. Figure 6 Only some of the terminal components are shown; however, it should be understood that it is not required to implement all of the components shown, and more or fewer components may be implemented instead.

[0081] In some embodiments, the memory 20 may be an internal storage unit of the terminal, such as a hard disk or memory. In other embodiments, the memory 20 may be an external storage device of the terminal, such as a plug-in hard disk, smart media card (SMC), secure digital card (SD), flash card, etc., equipped on the terminal. Further, the memory 20 may include both internal and external storage units of the terminal. The memory 20 is used to store application software and various types of data installed on the terminal, such as the program code installed on the terminal. The memory 20 can also be used to temporarily store data that has been output or will be output. In one embodiment, the memory 20 stores a light-electricity-carbon synergistic optimization control program 40 for a smart street light. This light-electricity-carbon synergistic optimization control program 40 can be executed by the processor 10, thereby implementing the light-electricity-carbon synergistic optimization control method for smart street lights in this application.

[0082] In some embodiments, the processor 10 may be a central processing unit (CPU), a microprocessor, or other data processing chip, used to run program code stored in the memory 20 or process data, such as executing the light-electricity-carbon synergistic optimization control method of the smart street light.

[0083] In some embodiments, the display 30 may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, or an OLED (Organic Light-Emitting Diode) touchscreen. The display 30 is used to display information on the terminal and to display a visual user interface.

[0084] In one embodiment, when the processor 10 executes the light-electricity-carbon synergistic optimization control program 40 for the smart street light in the memory 20, the following steps are performed: The regional carbon emission quota of the lighting system is obtained, the regional carbon emission quota is decomposed to obtain the carbon constraint boundary, and historical lighting demand data is obtained. Based on the carbon constraint boundary and the historical lighting demand data, a dimming baseline is generated to obtain the dimming strategy baseline. Multiple preset strategies are obtained, and dimming command generation processing is performed based on the multiple preset strategies and the dimming strategy baseline to obtain a target dimming command. The lighting system is then adjusted according to the target dimming command. The operation data of the lighting system is collected to obtain street light operation data. Carbon emission is calculated based on the street light operation data to obtain the actual carbon emission. Carbon emission is estimated based on the carbon constraint boundary to obtain the expected carbon emission. Carbon emission deviation is calculated based on the actual carbon emission and the expected carbon emission to obtain the carbon emission deviation. The dimming strategy baseline is corrected based on the carbon emission deviation and the regional carbon emission quota to obtain a corrected dimming strategy baseline. A corrected dimming instruction is generated based on the corrected dimming strategy baseline to obtain a corrected dimming instruction. The lighting system is then readjusted based on the corrected dimming instruction.

[0085] Specifically, the process of obtaining the regional carbon emission quota for the lighting system involves decomposing the regional carbon emission quota to obtain a carbon constraint boundary, acquiring historical lighting demand data, and generating a dimming baseline based on the carbon constraint boundary and the historical lighting demand data to obtain a dimming strategy baseline. The regional carbon emission allowance for the lighting system is obtained, and the regional carbon emission allowance is decomposed at the road section level and at the individual lamp level to obtain the carbon constraint boundary. A carbon emission model, a power mapping model, and an illuminance relationship model are determined. Based on the carbon emission model, the power mapping model, and the illuminance relationship model, an optimization function is constructed to obtain a multi-objective optimization function. Historical lighting demand data is obtained, and a brightness demand curve prediction process is performed based on the carbon constraint boundary and the historical lighting demand data to obtain the brightness demand curve. The minimum energy consumption curve is calculated based on the brightness demand curve and the multi-objective optimization function to obtain the dimming strategy baseline.

[0086] The step of acquiring multiple preset strategies, performing dimming instruction generation processing based on the multiple preset strategies and the dimming strategy baseline to obtain a target dimming instruction, and adjusting the lighting system according to the target dimming instruction specifically includes: Multiple preset strategies are obtained, and execution strategies are generated based on the multiple preset strategies and the dimming strategy baseline to obtain multiple execution strategies. Determine whether there is a contradiction among the multiple execution strategies. If so, obtain a preset priority and perform strategy selection processing on the multiple execution strategies according to the preset priority to obtain the target execution strategy. If not, directly obtain the target execution strategy based on the multiple execution strategies. The dimming instruction generation process is performed according to the power mapping model and the target execution strategy to obtain the target dimming instruction, and the lighting system is adjusted according to the target dimming instruction.

[0087] Specifically, the process of collecting operational data from the lighting system to obtain street light operational data, calculating carbon emissions based on the street light operational data to obtain actual carbon emissions, estimating carbon emissions based on the carbon constraint boundary to obtain expected carbon emissions, and calculating carbon emission deviation based on the actual carbon emissions and the expected carbon emissions to obtain carbon emission deviation, includes: Collect the operating data of the lighting system to obtain street light operating data; The grid carbon emission factor and sampling period are obtained, and the carbon emission amount is calculated based on the street light operation data, the sampling period and the grid carbon emission factor to obtain the actual carbon emission amount. Carbon emissions are estimated based on the carbon constraint boundary to obtain the expected carbon emissions. The difference between the actual carbon emissions and the expected carbon emissions is calculated to obtain the carbon emission deviation.

[0088] Specifically, the process of correcting the dimming strategy baseline based on the carbon emission deviation and the regional carbon emission quota to obtain a corrected dimming strategy baseline, generating a corrected dimming instruction based on the corrected dimming strategy baseline to obtain a corrected dimming instruction, and readjusting the lighting system based on the corrected dimming instruction includes: If the carbon emission deviation is positive, the upper limit of the brightness of the dimming strategy baseline is reduced to obtain the first dimming strategy baseline, and the first dimming strategy baseline is used as the first dimming strategy baseline to be corrected. If the carbon emission deviation is negative, the upper limit of the brightness of the dimming strategy baseline is increased to obtain the second dimming strategy baseline, and the second dimming strategy baseline is used as the first dimming strategy baseline to be corrected. The carbon emission ratio is calculated based on the actual carbon emissions and the regional carbon emission quota. A ratio threshold is obtained, and it is determined whether the carbon emission ratio is greater than the ratio threshold. If so, road segment data is obtained, and the first correction process is performed on the first dimming strategy baseline to be corrected based on the road segment data to obtain the road segment data correction baseline. The road segment data correction baseline is used as the second dimming strategy baseline to be corrected. If not, the first dimming strategy baseline to be corrected will be directly used as the second dimming strategy baseline to be corrected. Obtain the deviation threshold, determine whether the absolute value of the carbon emission deviation is greater than the deviation threshold, if so, perform a second correction process on the second dimming strategy baseline to be corrected based on the street light operation data to obtain the road segment data correction baseline, and use the road segment data correction baseline as the correction dimming strategy instruction. If not, the second dimming strategy baseline to be corrected will be directly used as the dimming strategy correction instruction; The modified dimming command is generated based on the modified dimming strategy baseline to obtain the modified dimming command, and the lighting system is readjusted based on the modified dimming command.

[0089] The step of acquiring road segment data and performing a first correction process on the first dimming strategy baseline to be corrected based on the road segment data to obtain a corrected road segment data baseline specifically includes: Obtain road segment data and road segment priority; perform non-core road segment data extraction processing on the road segment data according to the road segment priority to obtain non-core road segment brightness reference value; and reduce the non-core road segment brightness reference value to obtain corrected brightness reference value. Carbon emission budget allocation is performed based on the modified brightness benchmark value and the regional carbon emission quota to obtain an optimized carbon constraint boundary. The first adjustment process is performed on the first dimming strategy baseline to be corrected based on the optimized carbon constraint boundary to obtain the road segment data correction baseline.

[0090] Specifically, the step of performing a second correction process on the second dimming strategy baseline to be corrected based on the street light operation data to obtain the road segment data correction baseline includes: Based on the street light operation data and the baseline of the second dimming strategy to be corrected, the cause of carbon emission deviation is analyzed and processed to obtain the cause of carbon emission deviation. Based on the reasons for the carbon emission deviation, a baseline adjustment instruction is generated to obtain the baseline adjustment instruction. The second adjustment process is performed on the second dimming strategy baseline to be corrected according to the baseline adjustment instruction to obtain the road segment data correction baseline.

[0091] The present invention also provides a computer-readable storage medium, wherein the computer-readable storage medium stores a light-electricity-carbon synergistic optimization control program for a smart street light, and when the light-electricity-carbon synergistic optimization control program for a smart street light is executed by a processor, it implements the steps of the light-electricity-carbon synergistic optimization control method for a smart street light as described above.

[0092] In summary, this invention provides a light-electricity-carbon synergistic optimization control method, system, and terminal for smart streetlights. The method includes: obtaining regional carbon emission quotas for the lighting system; decomposing the regional carbon emission quotas to obtain carbon constraint boundaries; obtaining historical lighting demand data; generating a dimming baseline based on the carbon constraint boundaries and the historical lighting demand data to obtain a dimming strategy baseline; obtaining multiple preset strategies; generating a dimming command based on the multiple preset strategies and the dimming strategy baseline to obtain a target dimming command; and adjusting the lighting system according to the target dimming command; and collecting data from the lighting system. The invention obtains street light operation data from operational data, calculates carbon emissions based on this data to obtain actual carbon emissions, estimates carbon emissions based on the carbon constraint boundary to obtain expected carbon emissions, calculates carbon emission deviation based on the actual and expected carbon emissions, and then corrects the dimming strategy baseline based on the carbon emission deviation and the regional carbon emission quota to obtain a corrected dimming strategy baseline. Finally, it generates a corrected dimming command based on the corrected dimming strategy baseline and readjusts the lighting system according to the corrected dimming command. This invention obtains carbon constraint boundaries based on carbon emission quota decomposition, generates dimming commands to adjust the lighting system based on these boundaries, and collects operational data of the lighting system. The calculation of carbon emission deviation based on this operational data is used to optimize dimming commands, thereby improving the carbon reduction effect of the lighting system.

[0093] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or terminal that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or terminal. Unless otherwise specified, an element defined by the phrase "comprising one" does not exclude the presence of other identical elements in the process, method, article, or terminal that includes that element.

[0094] Of course, those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware (such as a processor, controller, etc.). The program can be stored in a computer-readable storage medium, and when executed, it can include the processes described in the above method embodiments. The computer-readable storage medium can be a memory, magnetic disk, optical disk, etc.

[0095] It should be understood that the application of the present invention is not limited to the examples above. Those skilled in the art can make improvements or modifications based on the above description, and all such improvements and modifications should fall within the protection scope of the appended claims.

Claims

1. A method for coordinated optimization control of light, electricity, and carbon in smart streetlights, characterized in that, The light-electricity-carbon synergistic optimization control method for smart streetlights includes: The regional carbon emission quota of the lighting system is obtained, the regional carbon emission quota is decomposed to obtain the carbon constraint boundary, and historical lighting demand data is obtained. Based on the carbon constraint boundary and the historical lighting demand data, a dimming baseline is generated to obtain the dimming strategy baseline. Multiple preset strategies are obtained, and dimming command generation processing is performed based on the multiple preset strategies and the dimming strategy baseline to obtain a target dimming command. The lighting system is then adjusted according to the target dimming command. The operation data of the lighting system is collected to obtain street light operation data. Carbon emission is calculated based on the street light operation data to obtain the actual carbon emission. Carbon emission is estimated based on the carbon constraint boundary to obtain the expected carbon emission. Carbon emission deviation is calculated based on the actual carbon emission and the expected carbon emission to obtain the carbon emission deviation. The dimming strategy baseline is corrected based on the carbon emission deviation and the regional carbon emission quota to obtain a corrected dimming strategy baseline. A corrected dimming instruction is generated based on the corrected dimming strategy baseline to obtain a corrected dimming instruction. The lighting system is then readjusted based on the corrected dimming instruction.

2. The light-electricity-carbon synergistic optimization control method for smart streetlights according to claim 1, characterized in that, The process of obtaining the regional carbon emission quota for the lighting system involves decomposing the regional carbon emission quota to obtain a carbon constraint boundary, acquiring historical lighting demand data, and generating a dimming baseline based on the carbon constraint boundary and the historical lighting demand data to obtain a dimming strategy baseline. Specifically, this includes: The regional carbon emission quota of the lighting system is obtained, and the regional carbon emission quota is decomposed at the road section level and at the single lamp level to obtain the carbon constraint boundary. A carbon emission model, a power mapping model, and an illuminance relationship model are determined. Based on the carbon emission model, the power mapping model, and the illuminance relationship model, an optimization function is constructed to obtain a multi-objective optimization function. Historical lighting demand data is obtained, and a brightness demand curve prediction process is performed based on the carbon constraint boundary and the historical lighting demand data to obtain the brightness demand curve. The minimum energy consumption curve is calculated based on the brightness demand curve and the multi-objective optimization function to obtain the dimming strategy baseline.

3. The light-electricity-carbon synergistic optimization control method for smart streetlights according to claim 2, characterized in that, The process of acquiring multiple preset strategies, generating dimming instructions based on the multiple preset strategies and the dimming strategy baseline to obtain a target dimming instruction, and adjusting the lighting system according to the target dimming instruction specifically includes: Multiple preset strategies are obtained, and execution strategies are generated based on the multiple preset strategies and the dimming strategy baseline to obtain multiple execution strategies. Determine whether there is a contradiction among the multiple execution strategies. If so, obtain a preset priority and perform strategy selection processing on the multiple execution strategies according to the preset priority to obtain the target execution strategy. If not, directly obtain the target execution strategy based on the multiple execution strategies. The dimming instruction generation process is performed according to the power mapping model and the target execution strategy to obtain the target dimming instruction, and the lighting system is adjusted according to the target dimming instruction.

4. The light-electricity-carbon synergistic optimization control method for smart streetlights according to claim 1, characterized in that, The process involves collecting operational data from the lighting system to obtain streetlight operational data, calculating carbon emissions based on this data to obtain actual carbon emissions, estimating carbon emissions based on the carbon constraint boundary to obtain expected carbon emissions, and calculating carbon emission deviation based on the actual and expected carbon emissions to obtain carbon emission deviation. Specifically, this includes: Collect the operating data of the lighting system to obtain street light operating data; The grid carbon emission factor and sampling period are obtained, and the carbon emission amount is calculated based on the street light operation data, the sampling period and the grid carbon emission factor to obtain the actual carbon emission amount. Carbon emissions are estimated based on the carbon constraint boundary to obtain the expected carbon emissions. The difference between the actual carbon emissions and the expected carbon emissions is calculated to obtain the carbon emission deviation.

5. The light-electricity-carbon synergistic optimization control method for smart streetlights according to claim 1, characterized in that, The process of correcting the dimming strategy baseline based on the carbon emission deviation and the regional carbon emission quota to obtain a corrected dimming strategy baseline, generating a corrected dimming instruction based on the corrected dimming strategy baseline to obtain a corrected dimming instruction, and readjusting the lighting system based on the corrected dimming instruction specifically includes: If the carbon emission deviation is positive, the upper limit of the brightness of the dimming strategy baseline is reduced to obtain the first dimming strategy baseline, and the first dimming strategy baseline is used as the first dimming strategy baseline to be corrected. If the carbon emission deviation is negative, the upper limit of the brightness of the dimming strategy baseline is increased to obtain the second dimming strategy baseline, and the second dimming strategy baseline is used as the first dimming strategy baseline to be corrected. The carbon emission ratio is calculated based on the actual carbon emissions and the regional carbon emission quota. A ratio threshold is obtained, and it is determined whether the carbon emission ratio is greater than the ratio threshold. If so, road segment data is obtained, and the first correction process is performed on the first dimming strategy baseline to be corrected based on the road segment data to obtain the road segment data correction baseline. The road segment data correction baseline is used as the second dimming strategy baseline to be corrected. If not, the first dimming strategy baseline to be corrected will be directly used as the second dimming strategy baseline to be corrected. Obtain the deviation threshold, determine whether the absolute value of the carbon emission deviation is greater than the deviation threshold, if so, perform a second correction process on the second dimming strategy baseline to be corrected based on the street light operation data to obtain the road segment data correction baseline, and use the road segment data correction baseline as the correction dimming strategy instruction. If not, the second dimming strategy baseline to be corrected will be directly used as the dimming strategy correction instruction; The modified dimming command is generated based on the modified dimming strategy baseline to obtain the modified dimming command, and the lighting system is readjusted based on the modified dimming command.

6. The light-electricity-carbon synergistic optimization control method for smart streetlights according to claim 5, characterized in that, The process of acquiring road segment data and performing a first correction process on the first dimming strategy baseline to be corrected based on the road segment data to obtain a corrected road segment data baseline specifically includes: Obtain road segment data and road segment priority; perform non-core road segment data extraction processing on the road segment data according to the road segment priority to obtain non-core road segment brightness reference value; and reduce the non-core road segment brightness reference value to obtain corrected brightness reference value. Carbon emission budget allocation is performed based on the modified brightness benchmark value and the regional carbon emission quota to obtain an optimized carbon constraint boundary. The first adjustment process is performed on the first dimming strategy baseline to be corrected based on the optimized carbon constraint boundary to obtain the road segment data correction baseline.

7. The light-electricity-carbon synergistic optimization control method for smart streetlights according to claim 5, characterized in that, The step of performing a second correction process on the second dimming strategy baseline to be corrected based on the street light operation data to obtain the road segment data correction baseline specifically includes: Based on the street light operation data and the baseline of the second dimming strategy to be corrected, the cause of carbon emission deviation is analyzed and processed to obtain the cause of carbon emission deviation. Based on the reasons for the carbon emission deviation, a baseline adjustment instruction is generated to obtain the baseline adjustment instruction. The second adjustment process is performed on the second dimming strategy baseline to be corrected according to the baseline adjustment instruction to obtain the road segment data correction baseline.

8. A light-electricity-carbon synergistic optimization control system for smart streetlights, characterized in that, The light-electricity-carbon synergistic optimization control system of the smart street light includes: The dimming strategy generation module is used to obtain the regional carbon emission quota of the lighting system, decompose the regional carbon emission quota to obtain the carbon constraint boundary, obtain historical lighting demand data, and perform dimming baseline generation processing based on the carbon constraint boundary and the historical lighting demand data to obtain the dimming strategy baseline. A dimming instruction generation module is used to acquire multiple preset strategies, perform dimming instruction generation processing based on the multiple preset strategies and the dimming strategy baseline to obtain a target dimming instruction, and adjust the lighting system according to the target dimming instruction; The carbon emission deviation calculation module is used to collect the operation data of the lighting system to obtain the street light operation data, perform carbon emission calculation processing based on the street light operation data to obtain the actual carbon emission, perform carbon emission estimation processing based on the carbon constraint boundary to obtain the expected carbon emission, and perform carbon emission deviation calculation processing based on the actual carbon emission and the expected carbon emission to obtain the carbon emission deviation. The dimming correction module is used to correct the dimming strategy baseline based on the carbon emission deviation and the regional carbon emission quota to obtain a corrected dimming strategy baseline, and to generate a corrected dimming instruction based on the corrected dimming strategy baseline to obtain a corrected dimming instruction, and to readjust the lighting system based on the corrected dimming instruction.

9. A terminal, characterized in that, The terminal includes a memory, a processor, and a smart street light light-electricity-carbon synergistic optimization control program stored in the memory and executable on the processor. When the processor executes the smart street light light-electricity-carbon synergistic optimization control program, it implements the steps of the smart street light light-electricity-carbon synergistic optimization control method as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a light-electricity-carbon synergistic optimization control program for a smart street light. When the light-electricity-carbon synergistic optimization control program for a smart street light is executed by a processor, it implements the steps of the light-electricity-carbon synergistic optimization control method for a smart street light as described in any one of claims 1-7.

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