Energy efficiency management method and system for smart street lamps

Abstract

The present invention provides an energy efficiency management method and system for a smart street lamp, the method comprising: collecting the operating environment data, historical operating data and real-time battery status data of the smart street lamp, and obtaining the lighting demand prediction results and energy consumption prediction results based on predictive analysis. According to these prediction results and battery status data, the battery charging and discharging parameters and deep discharge limits are calculated to generate a battery management strategy. Combined with the virtual energy pool data of the street lamp network where the smart street lamp is located, priority energy allocation is performed according to the battery management strategy and the lighting demand prediction results, and a dynamic energy allocation plan is generated. On this basis, energy management is performed and the energy consumption data of the smart street lamp is monitored in real time, and the battery management strategy and energy allocation plan are dynamically adjusted according to the monitoring results. The present invention can efficiently allocate energy in a complex environment, realize global optimization of the street lamp network, extend battery life and improve the energy management efficiency of smart street lamps.

Description

Technical Field

[0001] The present invention relates to the field of energy efficiency management technology, and in particular to an energy efficiency management method and system for a smart street lamp. Background Art

[0002] As an important part of smart city construction, smart street lights not only have basic lighting functions, but also integrate multiple intelligent functions such as monitoring, communication, and environmental monitoring, providing comprehensive services for urban operations. In practical applications, smart street lights usually rely on battery power and work in conjunction with the virtual energy pool in the street light network to achieve efficient energy management. However, due to the wide distribution range of street lights and the complex use environment, their energy utilization and management have always been the key to the optimization of smart street light systems. Especially in extreme weather or peak lighting demand scenarios, the energy management efficiency of street lights directly affects the stable operation of the street light network and the life of the equipment.

[0003] At present, traditional smart street lamp energy efficiency management methods usually adopt fixed lighting strategies and a single battery management method. This method lacks in-depth analysis of operating environment data, historical operating data, and real-time battery status, and cannot accurately predict lighting needs and energy consumption changes. Summary of the invention

[0004] The main purpose of the present invention is to solve the technical problems in the smart street lamp energy efficiency management method, which lacks in-depth analysis of the operating environment and historical data and cannot be dynamically adjusted according to lighting needs, energy consumption changes and battery status; A first aspect of the present invention provides an energy efficiency management method for a smart street lamp, the energy efficiency management method for a smart street lamp comprising: Collecting the operating environment data, historical operating data and real-time status data of the battery corresponding to the smart street lamp, and performing prediction analysis based on the operating environment data and historical operating data to obtain lighting demand prediction results and energy consumption prediction results respectively; Calculating battery charge and discharge parameters and a depth of discharge limit of the battery based on the lighting demand prediction result, the energy consumption prediction result and the real-time status data of the battery, and generating a battery management strategy based on the battery charge and discharge parameters and the depth of discharge limit; Acquire virtual energy pool data of a virtual energy pool preset in a street light network where the smart street light is located, and perform priority energy allocation based on the virtual energy pool data, battery management strategy, and lighting demand forecast results to obtain a dynamic energy allocation plan within the street light network; Based on the dynamic energy allocation scheme, energy management of smart street lights is performed, energy consumption data of smart street lights is monitored in real time, and the battery management strategy and dynamic energy allocation scheme are dynamically adjusted according to the energy consumption data to optimize the energy utilization efficiency of smart street lights.

[0005] Optionally, in a first implementation of the first aspect of the present invention, the operating environment data includes a light intensity data set, an activity detection data set, and a weather condition data set; The collecting of operating environment data and historical operating data of the smart street lamp, and performing prediction analysis based on the operating environment data and historical operating data, respectively obtaining lighting demand prediction results and energy consumption prediction results include: Performing time series decomposition on the historical operation data to obtain a lighting demand trend component, a lighting demand seasonal component, and a lighting demand random component, and inputting the light intensity data set and the activity detection data set into a preset lighting demand multi-factor regression model to obtain a preliminary lighting demand forecast value; Performing a weighted fusion operation on the preliminary lighting demand forecast value, the lighting demand trend component and the lighting demand seasonal component to obtain a revised lighting demand forecast value, and inputting the revised lighting demand forecast value and the lighting demand random component into a preset combined forecasting algorithm to obtain a lighting demand forecast result; Input the lighting demand forecast result and the weather condition data set into a preset energy consumption estimation neural network to obtain a preliminary energy consumption forecast value, and perform time series decomposition on the energy consumption data in the historical operation data to obtain an energy consumption trend component, an energy consumption seasonal component, and an energy consumption random component; The preliminary energy consumption forecast value, the energy consumption trend component and the energy consumption seasonal component are weighted and fused to obtain a revised energy consumption forecast value, and the revised energy consumption forecast value and the energy consumption random component are input into a preset combined prediction algorithm to obtain an energy consumption forecast result.

[0006] Optionally, in a second implementation manner of the first aspect of the present invention, the calculating the battery charge and discharge parameters and the deep discharge limit of the battery based on the lighting demand prediction result, the energy consumption prediction result and the real-time status data of the battery, and generating a battery management strategy based on the battery charge and discharge parameters and the deep discharge limit includes: Dividing the lighting demand forecast results and the energy consumption forecast results into time periods to obtain peak demand periods and valley demand periods; Establishing a battery health status assessment model based on the real-time status data of the battery to obtain an estimated value of the remaining battery life; Based on the peak demand period, the valley demand period and the estimated value of the remaining battery life, the optimal charge and discharge rate and the charge and discharge time window are calculated to obtain the battery charge and discharge parameters; According to the battery charge and discharge parameters and the estimated value of the remaining battery life, a dynamic deep discharge limit threshold is set to obtain a deep discharge limit; The battery charge and discharge parameters and the depth of discharge limit are input into a preset battery management algorithm to generate a battery management strategy including a charge and discharge strategy, a balanced charging strategy and a temperature control strategy.

[0007] Optionally, in a third implementation of the first aspect of the present invention, setting a dynamic deep discharge limit threshold according to the battery charge and discharge parameters and the estimated value of the remaining battery life to obtain the deep discharge limit includes: Normalizing the estimated value of the remaining life of the battery to obtain a life influence factor, and calculating the battery stress coefficient according to the charge and discharge rate in the battery charge and discharge parameters to obtain a stress influence factor; Inputting the life influence factor and the stress influence factor into a preset fuzzy logic controller to obtain an initial depth discharge limit value; Applying an adaptive adjustment algorithm to correct the initial depth discharge limit value according to historical depth discharge data to obtain a corrected depth discharge limit value; The modified deep discharge limit value is compared with a preset safety threshold, and a smaller value between the modified deep discharge limit value and the preset safety threshold is selected as the deep discharge limit.

[0008] Optionally, in a fourth implementation of the first aspect of the present invention, before obtaining virtual energy pool data of a virtual energy pool preset in a street light network where the smart street light is located, and performing priority energy allocation based on the virtual energy pool data, a battery management strategy, and a lighting demand forecast result, and obtaining a dynamic energy allocation plan within the street light network, it also includes: Collect the power generation capacity data, energy storage capacity data and real-time energy status data of each smart street lamp in the street lamp network, and normalize the power generation capacity data and energy storage capacity data to obtain a standardized energy resource assessment result; Based on the standardized energy resource assessment result and the real-time energy status data, the relative energy level of each smart street lamp is calculated to obtain a network energy distribution map, and according to the network energy distribution map, the street lamp network is divided into surplus and shortage areas to obtain a regional division result; Based on the regional division results, an energy allocation strategy within the street lamp network and an energy exchange strategy with the smart grid are generated, and the energy allocation strategy and the energy exchange strategy are integrated to obtain a virtual energy pool.

[0009] Optionally, in a fifth implementation of the first aspect of the present invention, the virtual energy pool data of the virtual energy pool preset in the street light network where the smart street light is located is obtained, and priority energy allocation is performed based on the virtual energy pool data, battery management strategy and lighting demand prediction results to obtain a dynamic energy allocation plan in the street light network, including: Acquire virtual energy pool data of a virtual energy pool preset in a street lamp network where the smart street lamp is located, and input the virtual energy pool data, battery management strategy and lighting demand forecast results into a multi-objective optimization algorithm to obtain a preliminary energy allocation plan; According to the preliminary energy allocation plan, executing a network load balancing algorithm to obtain a load-balanced energy allocation plan; Applying an emergency response strategy to the energy allocation plan after load balancing, setting an energy reserve threshold, and obtaining an energy allocation plan with emergency response capabilities; The energy allocation plan with emergency response capability is combined with the real-time data of the smart grid, and a dynamic adjustment algorithm is executed to obtain a dynamic energy allocation plan.

[0010] Optionally, in a sixth implementation of the first aspect of the present invention, the energy management of the smart street lamp is performed based on the dynamic energy allocation scheme, the energy consumption data of the smart street lamp is monitored in real time, and the battery management strategy and the dynamic energy allocation scheme are dynamically adjusted according to the energy consumption data to optimize the energy efficiency of the smart street lamp, including: According to the dynamic energy allocation scheme, individual energy control instructions are executed on each smart street lamp, and energy consumption data is collected in real time; Comparing the energy consumption data with a preset energy efficiency benchmark, calculating an energy efficiency deviation value, and executing an adaptive PID control algorithm according to the energy efficiency deviation value to obtain adjustment parameters of a battery management strategy; Applying the adjustment parameter to the battery management strategy to generate an updated battery management strategy; Based on the updated battery management strategy and energy consumption data, the virtual energy pool optimization algorithm is re-executed to obtain an optimized dynamic energy allocation solution.

[0011] A second aspect of the present invention provides an energy efficiency management system for a smart street lamp, the energy efficiency management system for a smart street lamp comprising: A prediction module, which is used to collect operating environment data, historical operating data and real-time status data of the battery corresponding to the smart street lamp, and perform prediction analysis based on the operating environment data and historical operating data to obtain lighting demand prediction results and energy consumption prediction results respectively; a strategy generation module, configured to calculate battery charge and discharge parameters and a depth discharge limit of the battery based on the lighting demand prediction result, the energy consumption prediction result and the real-time status data of the battery, and generate a battery management strategy based on the battery charge and discharge parameters and the depth discharge limit; A deployment module, used to obtain virtual energy pool data of a virtual energy pool preset in a street light network where the smart street light is located, and to perform priority energy allocation based on the virtual energy pool data, battery management strategy and lighting demand forecast results to obtain a dynamic energy deployment plan within the street light network; The optimization module is used to perform energy management of the smart street lamp based on the dynamic energy allocation plan, monitor the energy consumption data of the smart street lamp in real time, and dynamically adjust the battery management strategy and the dynamic energy allocation plan according to the energy consumption data to optimize the energy utilization efficiency of the smart street lamp.

[0012] The energy efficiency management method and system of the above-mentioned smart street lamps obtain lighting demand prediction results and energy consumption prediction results based on predictive analysis by collecting the operating environment data, historical operating data and real-time battery status data of the smart street lamps. According to these prediction results and battery status data, the battery charging and discharging parameters and deep discharge limits are calculated to generate a battery management strategy. Combined with the virtual energy pool data of the street lamp network where the smart street lamp is located, priority energy allocation is performed according to the battery management strategy and lighting demand prediction results, and a dynamic energy allocation plan is generated. On this basis, energy management is performed and the energy consumption data of the smart street lamp is monitored in real time, and the battery management strategy and energy allocation plan are dynamically adjusted according to the monitoring results. The present invention can efficiently allocate energy in a complex environment, realize global optimization of the street lamp network, extend battery life and improve the energy management efficiency of smart street lamps.

[0013] Other features and advantages of the present invention will be described in the following description, and partly become apparent from the description, or understood by practicing the present invention. The purpose and other advantages of the present invention are realized and obtained by the structures particularly pointed out in the description, claims and drawings.

[0014] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, preferred embodiments are given below and described in detail with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 This is a schematic diagram of a first embodiment of an energy efficiency management method for a smart street lamp in an embodiment of the present invention; Figure 2 This is a schematic diagram of an embodiment of an energy efficiency management system for a smart street lamp in an embodiment of the present invention. DETAILED DESCRIPTION

[0016] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution of the present invention will be clearly and completely described below in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0017] The terms "including" and "having" and any variations thereof mentioned in the embodiments of the present invention are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device end including a series of steps or units is not limited to the listed steps or units, but may optionally include other steps or units that are not listed, or may optionally include other steps or units that are inherent to these processes, methods, products or device ends.

[0018] To facilitate understanding of this embodiment, a method for managing energy efficiency of a smart street lamp disclosed in an embodiment of the present invention is first described in detail. Figure 1 As shown, the method comprises the following steps: 101. Collect operating environment data, historical operating data and real-time status data of batteries corresponding to smart street lamps, and perform forecast analysis based on the operating environment data and historical operating data to obtain lighting demand forecast results and energy consumption forecast results respectively; In one embodiment of the present invention, the operating environment data includes a light intensity data set, an activity detection data set and a weather condition data set; the collecting of the operating environment data and historical operating data of the smart street lamp, and performing predictive analysis based on the operating environment data and the historical operating data to obtain lighting demand prediction results and energy consumption prediction results respectively include: performing time series decomposition on the historical operating data to obtain a lighting demand trend component, a lighting demand seasonal component and a lighting demand random component, and inputting the light intensity data set and the activity detection data set into a preset lighting demand multi-factor regression model to obtain a preliminary lighting demand prediction value; performing weighted fusion on the preliminary lighting demand prediction value, the lighting demand trend component and the lighting demand seasonal component Operation is performed to obtain a revised lighting demand forecast value, and the revised lighting demand forecast value and the lighting demand random component are input into a preset combined prediction algorithm to obtain a lighting demand forecast result; the lighting demand forecast result and the weather condition data set are input into a preset energy consumption estimation neural network to obtain a preliminary energy consumption forecast value, and the energy consumption data in the historical operation data are decomposed into a time series to obtain an energy consumption trend component, an energy consumption seasonal component and an energy consumption random component; a weighted fusion operation is performed on the preliminary energy consumption forecast value, the energy consumption trend component and the energy consumption seasonal component to obtain a revised energy consumption forecast value, and the revised energy consumption forecast value and the energy consumption random component are input into a preset combined prediction algorithm to obtain an energy consumption forecast result.

[0019] Specifically, the smart street light system collects light intensity data, activity detection data, and weather condition data through built-in light sensors, motion detectors, and meteorological sensors. These data are recorded in real time and stored in the system's database to form a historical operation data set. Light intensity data reflects the natural light level of the environment, which directly affects the opening time and lighting intensity requirements of street lights. Activity detection data contains the frequency and time distribution of pedestrians and vehicles passing by, reflecting the usage of the road section. Weather condition data includes information such as temperature, humidity, and precipitation, which affect the energy consumption and lighting requirements of street lights. The system preprocesses these raw data, including denoising, standardization, and outlier processing to ensure data quality. Next, the system performs time series decomposition on the historical operation data. This step uses algorithms such as STL (Seasonal and Trend decomposition using Loess) to decompose time series data into trend components, seasonal components, and random components. The trend component reflects the long-term trend of lighting demand, such as the basic lighting demand that may increase year by year as the city develops. The seasonal component captures periodic change patterns, such as the difference in lighting demand on weekdays and weekends, or the change in lighting time length in summer and winter. The random component contains irregular fluctuations that may be caused by special events or unknown factors. This decomposition enables the system to have a deeper understanding of the composition of lighting demand, providing a basis for subsequent predictions. The system then inputs the preprocessed light intensity dataset and activity detection dataset into a preset multi-factor regression model for lighting demand. This model can be a complex machine learning model, such as a random forest or gradient boosting tree, which learns the nonlinear relationship between light intensity and activity level and lighting demand. The inputs to the model include the light intensity at the current time point, activity detection data in the recent period, and other potentially relevant features. The model outputs a preliminary lighting demand forecast value, which reflects the instantaneous lighting demand estimate based on the current environmental conditions. The system then corrects the preliminary lighting demand forecast value. It weightedly fuses the preliminary forecast value with the trend component and seasonal component obtained from the previous decomposition. The fusion process uses a predefined weight allocation scheme that determines the importance of each component based on the analysis results of historical data. For example, during the seasonal transition period when the lighting demand changes dramatically, the seasonal component may be given a higher weight. The fusion operation usually adopts weighted average or more complex nonlinear combination methods. The purpose of this step is to combine the model's immediate prediction with long-term trends and periodic patterns to improve the accuracy and stability of the prediction. Next, the system inputs the revised lighting demand forecast value together with the random component into the preset combined forecast algorithm. This algorithm can be an integrated learning model, such as Bagging or Boosting algorithm, which takes into account both deterministic predictions (revised forecast values) and uncertainty factors (random components).The purpose of the combined prediction algorithm is to further improve the robustness of the prediction and reduce the possible deviations caused by a single model. The algorithm outputs the final lighting demand forecast result, which not only includes the point prediction value, but also may include the prediction interval to indicate the uncertainty range of the prediction. After obtaining the lighting demand forecast result, the system inputs it into the preset energy consumption estimation neural network together with the weather condition data set. This neural network is a deep learning model that can use architectures such as LSTM (Long Short-Term Memory Network) or Transformer to process time series data and capture complex nonlinear relationships. The model learns the complex mapping relationship between lighting demand, weather conditions and energy consumption. The output of the neural network is a preliminary energy consumption forecast value, which reflects the energy consumption estimate based on the predicted lighting demand and weather conditions. At the same time, the system performs time series decomposition on the energy consumption data in the historical operation data, similar to the previous processing of lighting demand data. This step also uses STL or similar algorithms to decompose the energy consumption data into trend components, seasonal components and random components. This decomposition helps to understand the long-term trends, periodic changes and random fluctuations of energy consumption patterns, and provides richer information for subsequent energy consumption forecasts. Finally, the system corrects and combines the initial energy consumption forecast values. The correction process is similar to the correction of the lighting demand forecast, and the initial forecast value is weighted and fused with the energy consumption trend component and the seasonal component. The weights used in the fusion can be optimized based on the characteristics of the energy consumption data. Then, the corrected energy consumption forecast value is input into the combined prediction algorithm together with the random component of energy consumption. This algorithm can be another integrated learning model that is optimized specifically for energy consumption forecasting. The final output energy consumption forecast result includes the point forecast value and the possible forecast interval, providing comprehensive forecast information for the energy management of the smart street lighting system.

[0020] 102. Calculate battery charge and discharge parameters and deep discharge limit of the battery based on the lighting demand prediction result, the energy consumption prediction result and the real-time status data of the battery, and generate a battery management strategy based on the battery charge and discharge parameters and the deep discharge limit; In one embodiment of the present invention, the battery charging and discharging parameters and deep discharge limit of the battery are calculated based on the lighting demand prediction result, the energy consumption prediction result and the real-time status data of the battery, and the battery management strategy is generated based on the battery charging and discharging parameters and the deep discharge limit, including: dividing the lighting demand prediction result and the energy consumption prediction result into time periods to obtain peak demand periods and valley demand periods; establishing a battery health status assessment model based on the real-time status data of the battery to obtain a battery remaining life estimate; calculating the optimal charging and discharging rate and charging and discharging time window based on the peak demand period, valley demand period and the battery remaining life estimate to obtain battery charging and discharging parameters; setting a dynamic deep discharge limit threshold based on the battery charging and discharging parameters and the battery remaining life estimate to obtain a deep discharge limit; inputting the battery charging and discharging parameters and the deep discharge limit into a preset battery management algorithm to generate a battery management strategy including a charging and discharging strategy, a balanced charging strategy and a temperature control strategy.

[0021] Specifically, the lighting demand forecast results and energy consumption forecast results are first divided into time periods. The system uses time series clustering algorithms, such as K-means or hierarchical clustering, to analyze the forecast data. The algorithm considers the absolute value and rate of change of lighting demand and energy consumption, and divides the 24 hours of a day into several time periods. By setting thresholds, peak demand periods and trough demand periods are identified. Peak demand periods usually correspond to peak hours in the morning and evening, when lighting demand and energy consumption are high; while trough demand periods may be late at night or early in the morning, when the system load is low. This time period division provides an important basis for the subsequent formulation of battery management strategies, enabling the system to take differentiated energy management measures at different time periods.

[0022] Specifically, the system builds a battery health status assessment model based on the real-time status data of the battery. This model uses machine learning algorithms such as support vector regression (SVR) or random forests, and the input includes the number of charge and discharge times, deep discharge times, temperature history, voltage and current characteristics of the battery. The model outputs an estimate of the remaining battery life by analyzing the relationship between these parameters and the battery life. This estimate is expressed in cycles or time units, reflecting the current health status of the battery and the expected usable time. Accurate remaining life estimation is crucial to optimizing battery usage strategies, extending battery life, and planning maintenance.

[0023] Specifically, the system calculates the optimal charge and discharge rate and charge and discharge time window based on peak demand periods, valley demand periods, and estimated remaining battery life. This step uses a dynamic programming algorithm that takes into account multiple constraints: peak and valley electricity price differences, predicted energy demand, battery health, etc. The goal of the algorithm is to maximize economic benefits while ensuring that the battery life does not decay too quickly. During peak periods, the system tends to use stored energy for power supply, while charging is prioritized during valley periods. The algorithm outputs a detailed charge and discharge plan, including the charge and discharge rate and duration for each time period, which constitute the battery charge and discharge parameter set.

[0024] Specifically, based on the calculated battery charge and discharge parameters and the estimated value of the remaining battery life, the system further sets a dynamic deep discharge limit threshold. This process uses a fuzzy logic controller, and the input variables include the remaining battery life, current charge and discharge parameters, and historical usage patterns. The controller dynamically adjusts the deep discharge limit based on a predefined set of rules. For example, when the remaining battery life is longer, deeper discharge can be allowed to improve energy utilization; when the battery is close to the end of its life, a more conservative strategy will be adopted to delay attenuation. The output deep discharge limit is a percentage value that represents the maximum depth of discharge allowed. This limit ensures that the battery does not accelerate aging due to excessive discharge, while flexibly adjusting in different situations to balance performance and life requirements.

[0025] Specifically, the system inputs the battery charging and discharging parameters and deep discharge limits into the preset battery management algorithm. This algorithm is a comprehensive decision-making system that can be based on reinforcement learning or model predictive control (MPC) framework. The algorithm comprehensively considers the outputs of the previous steps and also incorporates real-time system status and external environment information. Through optimization calculations, the algorithm generates a comprehensive battery management strategy, which includes three main parts: charging and discharging strategy, balanced charging strategy and temperature control strategy. The charging and discharging strategy specifies the charging and discharging operations for each time period in detail, including power size and duration. The balanced charging strategy aims to extend the battery life by controlling the charging process of each battery cell to ensure that all cells maintain a similar state of charge. The temperature control strategy adjusts the working mode of the cooling system according to the ambient temperature and battery working status to maintain the optimal operating temperature range. This comprehensive battery management strategy ensures that the smart street light system can operate efficiently, safely and for a long time, maximizing the efficiency and life of the battery.

[0026] Furthermore, the dynamic deep discharge limit threshold is set according to the battery charge and discharge parameters and the estimated value of the remaining battery life to obtain the deep discharge limit, which includes: normalizing the estimated value of the remaining battery life to obtain a life influence factor, and calculating the battery stress coefficient according to the charge and discharge rate in the battery charge and discharge parameters to obtain a stress influence factor; inputting the life influence factor and the stress influence factor into a preset fuzzy logic controller to obtain an initial deep discharge limit value; applying an adaptive adjustment algorithm according to historical deep discharge data to correct the initial deep discharge limit value to obtain a corrected deep discharge limit value; comparing the corrected deep discharge limit value with a preset safety threshold, and selecting the smaller value between the corrected deep discharge limit value and the preset safety threshold as the deep discharge limit.

[0027] Specifically, the estimated value of the remaining life of the battery is first normalized to obtain the life impact factor. This step uses the Min-Max normalization method to map the estimated value of the remaining life to a range of 0 to 1. Specifically, the system sets a maximum life threshold (such as 80% of the design life) and a minimum life threshold (such as 20% of the design life), and then calculates the normalized value based on the position of the current estimated value of the remaining life in this range. This life impact factor reflects the degree of influence of the battery health status on the deep discharge limit. The closer the value is to 1, the newer the battery is and can withstand deeper discharge. At the same time, the system calculates the battery stress coefficient based on the charge and discharge rate in the battery charge and discharge parameters to obtain the stress impact factor. This calculation process takes into account the ratio of the charge and discharge rate to the rated power of the battery, as well as the charge and discharge duration. For example, an exponential function is used to map the rate ratio to a range of 0 to 1. The higher the rate, the closer the stress impact factor is to 1, indicating that the pressure on the battery is greater. The introduction of these two factors enables the system to fully consider the health status of the battery and the current usage intensity, providing a quantitative basis for the subsequent deep discharge limit setting.

[0028] Specifically, the system inputs the life impact factor and stress impact factor into the preset fuzzy logic controller to obtain the initial deep discharge limit value. The fuzzy logic controller contains a series of predefined IF-THEN rules, which are formulated based on expert knowledge and historical data analysis results. The controller first fuzzifies the two input impact factors and maps them to language variables (such as "low", "medium", and "high"). Then, the language description of the deep discharge limit is derived based on the fuzzy rule reasoning. For example, a rule may be "IF life impact factor high AND stress impact factor low THEN deep discharge limit high". Finally, through the defuzzification process, the language description is converted into a specific numerical value, that is, the initial deep discharge limit value. This value is usually expressed as a percentage of the battery capacity, such as 80% means that discharge to the remaining 20% ​​capacity is allowed. The use of fuzzy logic controller enables the system to simulate the decision-making process of human experts, handle imprecise and uncertain information, and provide flexibility and robustness for the setting of deep discharge limits.

[0029] Specifically, the system applies an adaptive adjustment algorithm based on historical deep discharge data to correct the initial deep discharge limit value and obtain the corrected deep discharge limit value. This step uses an online learning algorithm, such as recursive least squares (RLS) or online gradient descent, to continuously update the relationship model between the deep discharge limit and battery performance. The algorithm input includes the deep discharge limit setting value, the actual discharge depth, and the corresponding battery performance indicators (such as capacity decay rate, internal resistance change, etc.) in the past period of time. By analyzing these historical data, the algorithm learns the optimal deep discharge limit setting strategy and adjusts the current initial limit value accordingly. For example, if the historical data shows that the current limit value causes the battery to decay too quickly, the algorithm will appropriately reduce the limit value; conversely, if the battery performs well, the limit can be moderately relaxed to improve energy utilization. This adaptive adjustment mechanism ensures that the deep discharge limit can be dynamically optimized as the battery usage and environmental changes, and strikes a balance between extending battery life and improving energy utilization efficiency.

[0030] Specifically, the system compares the corrected deep discharge limit value with the preset safety threshold and selects the smaller value of the two as the final deep discharge limit. The preset safety threshold is a hard limit set based on battery technical specifications and safety considerations, usually provided by the battery manufacturer or determined according to industry standards. The purpose of this step is to establish a dual mechanism between dynamic optimization and safety assurance. Even in extreme cases, such as abnormal algorithms or inaccurate input data, the system can ensure that the safety threshold is not exceeded, thereby preventing irreversible damage caused by excessive discharge of the battery. The comparison process uses a simple conditional judgment. If the corrected limit value is less than the safety threshold, the corrected value is directly adopted; otherwise, the safety threshold is adopted. The final deep discharge limit will be used to guide the actual battery management operation to ensure that the battery works in a safe and efficient state. This multi-level limit setting method takes into account the dynamic characteristics and usage history of the battery, and ensures the safety of the system, providing a reliable guarantee for the long-term stable operation of the smart street light system.

[0031] Furthermore, before obtaining the virtual energy pool data of the virtual energy pool preset in the street light network where the smart street light is located, and performing priority energy allocation based on the virtual energy pool data, battery management strategy and lighting demand forecast results to obtain a dynamic energy allocation plan within the street light network, it also includes: collecting the power generation capacity data, energy storage capacity data and real-time energy status data of each smart street light in the street light network, and normalizing the power generation capacity data and energy storage capacity data to obtain a standardized energy resource assessment result; calculating the relative energy level of each smart street light based on the standardized energy resource assessment result and the real-time energy status data to obtain a network energy distribution map, and dividing the street light network into surplus and shortage areas according to the network energy distribution map to obtain a regional division result; based on the regional division result, generating an energy allocation strategy within the street light network and an energy exchange strategy with the smart grid, and integrating the energy allocation strategy and the energy exchange strategy to obtain a virtual energy pool.

[0032] Specifically, we start with data collection. The system collects the power generation capacity data, energy storage capacity data and real-time energy status data of each smart street lamp in the street lamp network through the intelligent sensor network. The power generation capacity data mainly comes from the photovoltaic panels installed on the street lamps, including the rated power, actual power generation efficiency and historical power generation of the panels. The energy storage capacity data reflects the capacity, charging and discharging efficiency and current health status of the battery system equipped with each street lamp. The real-time energy status data includes the current battery charging level, real-time power generation and power consumption. After preliminary data cleaning and preprocessing, these raw data enter the normalization processing stage. The normalization processing adopts the Min-Max normalization method to map data of different dimensions to a unified interval from 0 to 1. The purpose of this step is to eliminate the data incomparability caused by hardware configuration differences between different street lamps, so that the subsequent analysis and decision-making process can be carried out under a unified standard. The standardized energy resource assessment results obtained after normalization give each street lamp a comparable energy resource indicator, which comprehensively reflects the power generation potential and energy storage capacity of the street lamp.

[0033] Specifically, next, the system calculates the relative energy level of each smart street lamp based on the standardized energy resource assessment results and real-time energy status data, thereby obtaining the energy distribution map of the entire network. The calculation process adopts a weighted average method, in which the energy resource assessment results are used as long-term potential indicators, and the real-time energy status data are used as short-term status indicators, and the two are integrated according to preset weights. The relative energy level is a dynamically changing indicator that reflects the energy abundance of each street lamp in the entire network. The network energy distribution map is presented in the form of a heat map, which intuitively shows the spatial characteristics of energy distribution in the network. Based on this distribution map, the system uses a clustering algorithm (such as K-means or DBSCAN) to divide the street lamp network into surplus and shortage areas. The algorithm groups street lamps with similar relative energy levels and geographical proximity into a group, and finally divides the entire network into several areas with similar energy characteristics. This regional division result provides an important basis for the subsequent formulation of energy allocation strategies, enabling the system to balance and optimize energy at the macro level.

[0034] Specifically, based on the regional division results, the system generates the energy allocation strategy within the street light network and the energy exchange strategy with the smart grid. The internal energy allocation strategy uses the network flow algorithm to redistribute the excess power in the energy surplus area to the energy shortage area through virtual channels. The algorithm takes into account the virtual cost of energy transmission (such as transmission loss) and the demand priority of each region to minimize the overall allocation cost. The energy exchange strategy with the smart grid is formulated based on the predicted overall energy surplus or shortage of the network. When there is energy surplus, the system calculates the amount of electricity that can be fed back to the smart grid and determines the best feedback time based on the current electricity price; when there is energy shortage, the system calculates the amount of electricity that needs to be purchased from the smart grid and formulates the most economical power purchase plan based on the time-of-use electricity price. These two strategies are integrated through a unified optimization model, which aims to minimize the overall operating cost and maximize energy utilization efficiency, while taking into account constraints such as grid stability and user demand satisfaction. The integrated strategy constitutes the core operating mechanism of the virtual energy pool, enabling the entire street light network to interact intelligently with the external grid as a unified energy entity, while achieving internal energy optimization allocation.

[0035] 103. Obtain virtual energy pool data of a virtual energy pool preset in a street light network where the smart street light is located, and perform priority energy allocation based on the virtual energy pool data, battery management strategy, and lighting demand forecast results to obtain a dynamic energy allocation plan within the street light network; In one embodiment of the present invention, the virtual energy pool data of the virtual energy pool preset in the street light network where the smart street light is located is obtained, and priority energy allocation is performed based on the virtual energy pool data, battery management strategy and lighting demand forecast results to obtain a dynamic energy allocation plan in the street light network, including: obtaining the virtual energy pool data of the virtual energy pool preset in the street light network where the smart street light is located, and inputting the virtual energy pool data, battery management strategy and lighting demand forecast results into a multi-objective optimization algorithm to obtain a preliminary energy allocation plan; according to the preliminary energy allocation plan, executing a network load balancing algorithm to obtain a load-balanced energy allocation plan; applying an emergency response strategy to the energy allocation plan after load balancing, setting an energy reserve threshold, and obtaining an energy allocation plan with emergency capabilities; combining the energy allocation plan with emergency capabilities with real-time data of the smart grid, executing a dynamic adjustment algorithm, and obtaining a dynamic energy allocation plan.

[0036] Specifically, we start by obtaining the virtual energy pool data. The system obtains the status information of the virtual energy pool in real time through the preset data interface, including the current energy level, energy storage capacity, power generation capacity, etc. of each street light node. These data reflect the energy distribution and available resources of the entire street light network. Then, the system inputs these virtual energy pool data into the multi-objective optimization algorithm together with the previously generated battery management strategy and lighting demand forecast results. The algorithm uses evolutionary computing methods such as genetic algorithms or particle swarm optimization, with the objective function of minimizing energy consumption, maximizing lighting effects and extending battery life. Through iterative optimization, the algorithm generates a preliminary energy allocation plan under the premise of meeting various constraints (such as minimum lighting requirements, battery safe operating range, etc.). This plan allocates specific energy usage quotas and working modes to each street light node, but does not yet consider the overall balance of the network.

[0037] Specifically, the system then executes a network load balancing algorithm based on the preliminary energy allocation plan. The algorithm uses the minimum spanning tree or network flow algorithm in graph theory to treat the entire street light network as an interconnected energy system. The algorithm first identifies energy-surplus nodes and energy-deficient nodes in the network, and then calculates the optimal flow path of energy between nodes. In this process, the algorithm takes into account the loss of virtual energy transmission, the network topology, and the energy demand priority of each node. Through repeated iterations and adjustments, the algorithm finally obtains a load-balanced energy allocation plan. This plan not only meets the energy needs of each node, but also ensures balanced energy use in the entire network, avoiding local overload or resource waste.

[0038] Specifically, next, the system applies an emergency response strategy to the load-balanced energy allocation scheme. This step is intended to improve the emergency response capability and reliability of the street light network. The system first sets an energy reserve threshold based on historical data and expert knowledge. This threshold is usually expressed as a percentage of the minimum energy level that each node should maintain. Then, the system uses the Monte Carlo simulation method to simulate various possible emergency situations (such as local power outages, sudden large-scale lighting needs, etc.) to evaluate the emergency response capability of the current solution. Based on the simulation results, the system adjusts the energy allocation to ensure that each node retains sufficient energy reserves to cope with emergencies. This process may involve reallocating the energy usage quota of some nodes or adjusting the working mode of some nodes. The resulting energy allocation scheme with emergency capabilities provides additional security for the network while ensuring daily operations.

[0039] Specifically, in the end, the system combines the energy allocation scheme with emergency response capabilities with the real-time data of the smart grid and executes a dynamic adjustment algorithm. The purpose of this step is to enable the street light network to flexibly adjust its operation strategy according to the real-time status of the external power grid. The system first obtains real-time information from the smart grid through a preset data interface, including power supply and demand, real-time electricity prices, grid stability indicators, etc. Then, a dynamic decision model is constructed using a reinforcement learning algorithm (such as Q-learning or deep Q network). The model takes the current state of the street light network, the energy allocation scheme, and the real-time data of the power grid as input and outputs a series of fine-tuning instructions. These instructions may include adjusting the power consumption time of certain nodes, changing the charging and discharging strategy of energy storage equipment, or real-time energy exchange with the power grid. Through continuous learning and optimization, the system can maximize the use of favorable opportunities of the power grid (such as low-peak electricity price periods) while ensuring the normal operation of the street light network, and provide auxiliary services to the power grid (such as demand response) when necessary. The final dynamic energy allocation scheme not only optimizes the energy use of the street light network itself, but also realizes the coordinated operation with a larger range of smart grids, reflecting the overall optimization concept of the smart city energy system.

[0040] 104. Perform energy management of smart street lights based on dynamic energy allocation plans, monitor the energy consumption data of smart street lights in real time, and dynamically adjust the battery management strategy and dynamic energy allocation plan according to the energy consumption data to optimize the energy efficiency of smart street lights.

[0041] In one embodiment of the present invention, the energy management of smart street lights is performed based on the dynamic energy allocation scheme, the energy consumption data of smart street lights is monitored in real time, and the battery management strategy and the dynamic energy allocation scheme are dynamically adjusted according to the energy consumption data to optimize the energy utilization efficiency of smart street lights, including: executing individual energy control instructions for each smart street light according to the dynamic energy allocation scheme, and collecting energy consumption data in real time; comparing the energy consumption data with a preset energy efficiency benchmark, calculating an energy efficiency deviation value, and executing an adaptive PID control algorithm according to the energy efficiency deviation value to obtain adjustment parameters of the battery management strategy; applying the adjustment parameters to the battery management strategy to generate an updated battery management strategy; based on the updated battery management strategy and energy consumption data, re-executing the virtual energy pool optimization algorithm to obtain an optimized dynamic energy allocation scheme.

[0042] Specifically, first, according to the dynamic energy allocation plan, individual energy control instructions are executed for each smart street lamp. The system sends specific control instructions to each smart street lamp through the central control unit. These instructions include the brightness adjustment of the lamp, the battery charging and discharging strategy, and the energy interaction method with peripheral devices. At the same time, the system starts the real-time monitoring module to continuously collect energy consumption data for each street lamp. These data include real-time power consumption, cumulative energy consumption, battery charging and discharging status, etc. Data collection uses high-precision energy metering equipment, and the sampling frequency can reach multiple times per second to ensure that subtle trends in energy consumption changes are captured. The collected raw data undergoes preliminary filtering and outlier detection processing to form a standardized energy consumption data set. This data set not only reflects the real-time energy usage of each street lamp, but also provides a basis for subsequent energy efficiency analysis and strategy adjustment.

[0043] Specifically, the system compares the collected energy consumption data with the pre-set energy efficiency benchmark. The energy efficiency benchmark is an ideal energy consumption standard based on historical data, equipment specifications, and operating environment. The comparison process uses a sliding time window method to calculate the difference between the actual energy consumption and the benchmark value to obtain the energy efficiency deviation value. This deviation value is a dynamically changing indicator that reflects the gap between the current operating state and the ideal state. Subsequently, the system inputs the energy efficiency deviation value into the adaptive PID (proportional-integral-differential) control algorithm. The three parameters of the PID controller (proportional coefficient, integral time, and differential time) are adjusted in real time through an adaptive mechanism to adapt to different operating conditions and external environmental changes. The algorithm calculates a set of optimal control parameters by minimizing the cumulative error of the energy efficiency deviation. These parameters constitute the adjustment instructions for the battery management strategy, including fine-tuning of the charge and discharge rate, dynamic adjustment of the deep discharge limit, etc. The use of adaptive PID control ensures that the system can respond quickly to energy efficiency changes while maintaining stability.

[0044] Specifically, the system applies the obtained adjustment parameters to the current battery management strategy to generate an updated battery management strategy. This process involves dynamic updating and verification of strategy parameters. The system first checks whether the new parameters are within the safe operating range to prevent the risks that may be caused by over-adjustment. After passing the safety check, the new parameters are gradually applied to the battery management system. The update process adopts a smooth transition mechanism to avoid system instability caused by sudden changes. The updated battery management strategy may include new charge and discharge curves, adjusted battery life cycles, optimized temperature control parameters, etc. This dynamic adjustment mechanism enables the battery management strategy to continuously adapt to the actual operating conditions and strike a balance between extending battery life and improving energy efficiency.

[0045] Specifically, the system re-executes the virtual energy pool optimization algorithm based on the updated battery management strategy and the latest energy consumption data. This algorithm regards the entire street light network as a virtual energy pool, with the goal of optimizing the overall energy distribution and use. The algorithm adopts distributed optimization methods, such as distributed model predictive control (DMPC) or consensus-based distributed optimization. Each street light node acts as an independent optimization unit, exchanging information with neighboring nodes and performing local optimization. The central controller is responsible for coordinating the global optimization goal. The optimization process takes into account multiple factors, including the new battery management strategy of each node, real-time energy consumption status, predicted lighting needs, and possible environmental changes (such as weather forecasts). After multiple rounds of iterative calculations, the algorithm finally generates an optimized dynamic energy allocation plan. This new plan not only reflects the performance improvement of a single street light, but also reflects the collaborative optimization effect of the entire network. It allocates updated energy usage quotas to each street light, adjusts the energy exchange strategy between nodes, and may re-plan the interaction with the external power grid. This continuous optimization cycle ensures that the smart street light system can always operate in the most efficient way and adapt to changing environments and needs.

[0046] In this embodiment, by collecting the operating environment data, historical operating data and real-time battery status data of the smart street lamp, the lighting demand prediction results and energy consumption prediction results are obtained based on predictive analysis. According to these prediction results and battery status data, the battery charging and discharging parameters and deep discharge limits are calculated to generate a battery management strategy. Combined with the virtual energy pool data of the street lamp network where the smart street lamp is located, priority energy allocation is performed according to the battery management strategy and lighting demand prediction results, and a dynamic energy allocation plan is generated. On this basis, energy management is performed and the energy consumption data of the smart street lamp is monitored in real time, and the battery management strategy and energy allocation plan are dynamically adjusted according to the monitoring results. The present invention can efficiently allocate energy in a complex environment, realize global optimization of the street lamp network, extend battery life and improve the energy management efficiency of smart street lamps.

[0047] The energy efficiency management method of the smart street lamp in the embodiment of the present invention is described above. The energy efficiency management system of the smart street lamp in the embodiment of the present invention is described below. Figure 2 In one embodiment of the present invention, an energy efficiency management system for a smart street lamp includes: The prediction module 201 is used to collect the operating environment data, historical operating data and real-time status data of the battery corresponding to the smart street lamp, and perform prediction analysis based on the operating environment data and historical operating data to obtain lighting demand prediction results and energy consumption prediction results respectively; A strategy generation module 202, configured to calculate the battery charge and discharge parameters and the deep discharge limit of the battery based on the lighting demand prediction result, the energy consumption prediction result and the real-time status data of the battery, and generate a battery management strategy based on the battery charge and discharge parameters and the deep discharge limit; The allocation module 203 is used to obtain virtual energy pool data of a virtual energy pool preset in the street light network where the smart street light is located, and to perform priority energy allocation based on the virtual energy pool data, battery management strategy and lighting demand prediction results to obtain a dynamic energy allocation plan within the street light network; The optimization module 204 is used to perform energy management of the smart street lamp based on the dynamic energy allocation scheme, monitor the energy consumption data of the smart street lamp in real time, and dynamically adjust the battery management strategy and the dynamic energy allocation scheme according to the energy consumption data to optimize the energy efficiency of the smart street lamp. In an embodiment of the present invention, the energy efficiency management system of the smart street lamp runs the energy efficiency management method of the smart street lamp. The energy efficiency management system of the smart street lamp collects the operating environment data, historical operating data and real-time battery status data of the smart street lamp, and obtains the lighting demand prediction results and energy consumption prediction results based on predictive analysis. According to these prediction results and battery status data, the charging and discharging parameters and deep discharge limits of the battery are calculated to generate a battery management strategy. Combined with the virtual energy pool data of the street lamp network where the smart street lamp is located, priority energy allocation is performed according to the battery management strategy and the lighting demand prediction results, and a dynamic energy allocation plan is generated. On this basis, energy management is performed and the energy consumption data of the smart street lamp is monitored in real time, and the battery management strategy and energy allocation plan are dynamically adjusted according to the monitoring results. The present invention can efficiently allocate energy in a complex environment, realize global optimization of the street lamp network, extend battery life and improve the energy management efficiency of smart street lamps.

[0048] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working process of the above-described system, device, or unit can refer to the corresponding process in the aforementioned method embodiment and will not be repeated here.

[0049] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art or the whole or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions to enable a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), disk or optical disk and other media that can store program code.

[0050] As described above, the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features thereof may be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for energy efficiency management of smart street lamps, characterized in that: The energy efficiency management method of the smart street lamp includes: Collecting the operating environment data, historical operating data and real-time status data of the battery corresponding to the smart street lamp, and performing prediction analysis based on the operating environment data and historical operating data to obtain lighting demand prediction results and energy consumption prediction results respectively; Calculating battery charge and discharge parameters and a depth of discharge limit of the battery based on the lighting demand prediction result, the energy consumption prediction result and the real-time status data of the battery, and generating a battery management strategy based on the battery charge and discharge parameters and the depth of discharge limit; Acquire virtual energy pool data of a virtual energy pool preset in a street light network where the smart street light is located, and perform priority energy allocation based on the virtual energy pool data, battery management strategy, and lighting demand forecast results to obtain a dynamic energy allocation plan within the street light network; Based on the dynamic energy allocation scheme, energy management of smart street lights is performed, energy consumption data of smart street lights is monitored in real time, and the battery management strategy and dynamic energy allocation scheme are dynamically adjusted according to the energy consumption data to optimize the energy utilization efficiency of smart street lights.

2. The energy efficiency management method of the smart street lamp according to claim 1 is characterized in that: The operating environment data includes a light intensity data set, an activity detection data set, and a weather condition data set; The collecting of operating environment data and historical operating data of the smart street lamp, and performing prediction analysis based on the operating environment data and historical operating data, respectively obtaining lighting demand prediction results and energy consumption prediction results include: Performing time series decomposition on the historical operation data to obtain a lighting demand trend component, a lighting demand seasonal component, and a lighting demand random component, and inputting the light intensity data set and the activity detection data set into a preset lighting demand multi-factor regression model to obtain a preliminary lighting demand forecast value; Performing a weighted fusion operation on the preliminary lighting demand forecast value, the lighting demand trend component and the lighting demand seasonal component to obtain a revised lighting demand forecast value, and inputting the revised lighting demand forecast value and the lighting demand random component into a preset combined forecasting algorithm to obtain a lighting demand forecast result; Input the lighting demand forecast result and the weather condition data set into a preset energy consumption estimation neural network to obtain a preliminary energy consumption forecast value, and perform time series decomposition on the energy consumption data in the historical operation data to obtain an energy consumption trend component, an energy consumption seasonal component, and an energy consumption random component; The preliminary energy consumption forecast value, the energy consumption trend component and the energy consumption seasonal component are weighted and fused to obtain a revised energy consumption forecast value, and the revised energy consumption forecast value and the energy consumption random component are input into a preset combined prediction algorithm to obtain an energy consumption forecast result.

3. The energy efficiency management method of the smart street lamp according to claim 1, characterized in that: The calculating the battery charge and discharge parameters and the deep discharge limit of the battery based on the lighting demand prediction result, the energy consumption prediction result and the real-time status data of the battery, and generating a battery management strategy based on the battery charge and discharge parameters and the deep discharge limit includes: Dividing the lighting demand forecast results and the energy consumption forecast results into time periods to obtain peak demand periods and valley demand periods; Establishing a battery health status assessment model based on the real-time status data of the battery to obtain an estimated value of the remaining battery life; Based on the peak demand period, the valley demand period and the estimated value of the remaining battery life, the optimal charge and discharge rate and the charge and discharge time window are calculated to obtain the battery charge and discharge parameters; According to the battery charge and discharge parameters and the estimated value of the remaining battery life, a dynamic deep discharge limit threshold is set to obtain a deep discharge limit; The battery charge and discharge parameters and the depth of discharge limit are input into a preset battery management algorithm to generate a battery management strategy including a charge and discharge strategy, a balanced charging strategy and a temperature control strategy.

4. The energy efficiency management method of the smart street lamp according to claim 3 is characterized in that: The step of setting a dynamic deep discharge limit threshold according to the battery charge and discharge parameters and the estimated value of the remaining battery life to obtain a deep discharge limit comprises: Normalizing the estimated value of the remaining life of the battery to obtain a life influence factor, and calculating the battery stress coefficient according to the charge and discharge rate in the battery charge and discharge parameters to obtain a stress influence factor; Inputting the life influence factor and the stress influence factor into a preset fuzzy logic controller to obtain an initial depth discharge limit value; Applying an adaptive adjustment algorithm to correct the initial depth discharge limit value according to historical depth discharge data to obtain a corrected depth discharge limit value; The modified deep discharge limit value is compared with a preset safety threshold, and a smaller value between the modified deep discharge limit value and the preset safety threshold is selected as the deep discharge limit.

5. The energy efficiency management method of the smart street lamp according to claim 1, characterized in that: Before obtaining virtual energy pool data of a virtual energy pool preset in a street light network where the smart street light is located, and performing priority energy allocation based on the virtual energy pool data, a battery management strategy, and a lighting demand prediction result to obtain a dynamic energy allocation plan in the street light network, the method further includes: Collect the power generation capacity data, energy storage capacity data and real-time energy status data of each smart street lamp in the street lamp network, and normalize the power generation capacity data and energy storage capacity data to obtain a standardized energy resource assessment result; Based on the standardized energy resource assessment result and the real-time energy status data, the relative energy level of each smart street lamp is calculated to obtain a network energy distribution map, and according to the network energy distribution map, the street lamp network is divided into surplus and shortage areas to obtain a regional division result; Based on the regional division results, an energy allocation strategy within the street lamp network and an energy exchange strategy with the smart grid are generated, and the energy allocation strategy and the energy exchange strategy are integrated to obtain a virtual energy pool.

6. The energy efficiency management method of a smart street lamp according to claim 1, characterized in that: The step of obtaining virtual energy pool data of a virtual energy pool preset in a street light network where the smart street light is located, and performing priority energy allocation based on the virtual energy pool data, a battery management strategy, and a lighting demand prediction result to obtain a dynamic energy allocation plan within the street light network includes: Acquire virtual energy pool data of a virtual energy pool preset in a street lamp network where the smart street lamp is located, and input the virtual energy pool data, battery management strategy and lighting demand forecast results into a multi-objective optimization algorithm to obtain a preliminary energy allocation plan; According to the preliminary energy allocation plan, executing a network load balancing algorithm to obtain a load-balanced energy allocation plan; Applying an emergency response strategy to the energy allocation plan after load balancing, setting an energy reserve threshold, and obtaining an energy allocation plan with emergency response capabilities; The energy allocation plan with emergency response capability is combined with the real-time data of the smart grid, and a dynamic adjustment algorithm is executed to obtain a dynamic energy allocation plan.

7. The energy efficiency management method of a smart street lamp according to claim 1, characterized in that: The energy management of the smart street lamp is performed based on the dynamic energy allocation scheme, the energy consumption data of the smart street lamp is monitored in real time, and the battery management strategy and the dynamic energy allocation scheme are dynamically adjusted according to the energy consumption data to optimize the energy efficiency of the smart street lamp, including: According to the dynamic energy allocation scheme, individual energy control instructions are executed on each smart street lamp, and energy consumption data is collected in real time; Comparing the energy consumption data with a preset energy efficiency benchmark, calculating an energy efficiency deviation value, and executing an adaptive PID control algorithm according to the energy efficiency deviation value to obtain adjustment parameters of a battery management strategy; Applying the adjustment parameter to the battery management strategy to generate an updated battery management strategy; Based on the updated battery management strategy and energy consumption data, the virtual energy pool optimization algorithm is re-executed to obtain an optimized dynamic energy allocation solution.

8. An energy efficiency management system for smart street lamps, characterized in that: The energy efficiency management system of the smart street lamp includes: A prediction module, which is used to collect operating environment data, historical operating data and real-time status data of the battery corresponding to the smart street lamp, and perform prediction analysis based on the operating environment data and historical operating data to obtain lighting demand prediction results and energy consumption prediction results respectively; a strategy generation module, configured to calculate battery charge and discharge parameters and a depth discharge limit of the battery based on the lighting demand prediction result, the energy consumption prediction result and the real-time status data of the battery, and generate a battery management strategy based on the battery charge and discharge parameters and the depth discharge limit; A deployment module, used to obtain virtual energy pool data of a virtual energy pool preset in a street light network where the smart street light is located, and to perform priority energy allocation based on the virtual energy pool data, battery management strategy and lighting demand forecast results to obtain a dynamic energy deployment plan within the street light network; The optimization module is used to perform energy management of the smart street lamp based on the dynamic energy allocation plan, monitor the energy consumption data of the smart street lamp in real time, and dynamically adjust the battery management strategy and the dynamic energy allocation plan according to the energy consumption data to optimize the energy utilization efficiency of the smart street lamp.

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