Urban lighting energy-saving monitoring method and system based on environmental parameters

Abstract

The invention discloses an urban lighting energy-saving monitoring method and system based on environmental parameters. The method comprises the following steps: collecting parameter data of environmental parameters and traffic parameters of each lighting sub-region of a city; according to the parameter data, utilizing a neural network to predict an illumination demand level of each illumination sub-region, and calculating a sub-illumination demand index of each illumination sub-region; according to the lighting demand level, configuring a corresponding initial lighting energy-saving scheme for each lighting sub-region; and optimizing the initial illumination energy-saving scheme according to the sub-illumination demand index so as to realize urban illumination energy-saving monitoring. According to the embodiment of the invention, monitoring of various parameters can be combined with neural network prediction, the intelligent level of urban lighting can be improved, energy consumption can be effectively reduced, the life quality of citizens can be improved, and the method has important social and economic values.

Description

Technical Field

[0001] The present invention belongs to the technical field of urban lighting, and in particular to an urban lighting energy-saving monitoring method and system based on environmental parameters. Background Art

[0002] With the acceleration of urbanization and the continuous expansion of urban scale, the demand for urban lighting is increasing day by day. Nighttime urban lighting not only affects the beauty of the city, but also directly affects the safety of citizens, traffic efficiency and energy consumption. Therefore, how to optimize urban lighting and reduce energy consumption has become a social problem that needs to be solved urgently. Traditional urban lighting control systems mostly use fixed lighting solutions, which fail to effectively consider changes in environmental and traffic conditions. This approach not only leads to energy waste, but also fails to fully meet actual lighting needs. For example, during periods of low pedestrian and vehicle traffic, lighting equipment still runs at full capacity, resulting in unnecessary energy consumption. In addition, due to differences in environmental parameters (such as weather, brightness, noise, etc.) and traffic parameters (such as vehicle volume, number of pedestrians, etc.) in different areas of the city, their lighting needs also vary significantly. Therefore, it is particularly important to dynamically adjust the lighting needs of different areas and time periods. Summary of the invention

[0003] The purpose of the present invention is to provide an urban lighting energy-saving monitoring method and system based on environmental parameters to address the deficiencies in the prior art. The method can combine the monitoring of multiple parameters with neural network prediction, which can not only improve the intelligence level of urban lighting, but also effectively reduce energy consumption and improve the quality of life of citizens, and has important social and economic value.

[0004] An embodiment of the present application provides a method for monitoring energy saving of urban lighting based on environmental parameters, the method comprising: Collect parameter data of environmental parameters and traffic parameters of each lighting sub-area in the city; According to the parameter data, a neural network is used to predict the lighting demand level of each lighting sub-area, and a sub-lighting demand index of each lighting sub-area is calculated; According to the lighting demand level, a corresponding initial lighting energy-saving plan is configured for each lighting sub-area; According to the sub-lighting demand index, the initial lighting energy-saving plan is optimized to achieve urban lighting energy-saving monitoring.

[0005] Optionally, the calculation formula of the sub-lighting demand index is:

[0006] Among them, the is the sub-lighting demand index of the i-th lighting sub-area, is the ambient light intensity of the i-th lighting sub-area, is the weight coefficient of the environmental factor related to the i-th sub-region, is the traffic and pedestrian interaction function of the i-th lighting sub-area, is the traffic flow of the i-th lighting sub-area, is the crowd density of the i-th lighting sub-area, is the environment and traffic function of the i-th lighting sub-area, is the traffic speed of the i-th lighting sub-area, is the length of the road section of the i-th lighting sub-area, is the safety and event function of the i-th lighting sub-area, is the safety factor of the i-th lighting sub-area, is the specific event coefficient of the i-th illumination sub-area, is the total number of sub-regions involved in the calculation, used for normalization.

[0007] Optionally, the traffic and pedestrian flow interaction function of the i-th lighting sub-area is:

[0008] Among them, the is the intensity impact index.

[0009] Optionally, the calculation formula of the environment and traffic function of the i-th lighting sub-area is:

[0010] Among them, the is the traffic speed influence coefficient.

[0011] Optionally, the calculation formula of the safety and event function of the i-th lighting sub-area is:

[0012] Among them, the is the adjustment factor.

[0013] Another embodiment of the present application provides an urban lighting energy-saving monitoring system based on environmental parameters, the system comprising: A collection module is used to collect parameter data of environmental parameters and traffic parameters of each lighting sub-area in the city; A prediction module, used to predict the lighting demand level of each lighting sub-area using a neural network according to the parameter data, and calculate the sub-lighting demand index of each lighting sub-area; A configuration module, configured to configure a corresponding initial lighting energy-saving plan for each lighting sub-area according to the lighting demand level; The monitoring module is used to optimize the initial lighting energy-saving plan according to the sub-lighting demand index to achieve urban lighting energy-saving monitoring.

[0014] Yet another embodiment of the present application provides a storage medium, wherein the storage medium stores a computer program, wherein the computer program is configured to execute any of the above methods when running.

[0015] Yet another embodiment of the present application provides an electronic device, including a memory and a processor, wherein the memory stores a computer program, and the processor is configured to run the computer program to execute any of the methods described above.

[0016] Compared with the prior art, the present invention provides an environmental parameter-based urban lighting energy-saving monitoring method, which collects parameter data of environmental parameters and traffic parameters of each lighting sub-area in the city; based on the parameter data, a neural network is used to predict the lighting demand level of each lighting sub-area, and a sub-lighting demand index of each lighting sub-area is calculated; based on the lighting demand level, a corresponding initial lighting energy-saving plan is configured for each lighting sub-area; based on the sub-lighting demand index, the initial lighting energy-saving plan is optimized to achieve urban lighting energy-saving monitoring, thereby combining the monitoring of multiple parameters with neural network prediction, which can not only improve the intelligence level of urban lighting, but also effectively reduce energy consumption and improve the quality of life of citizens, and has important social and economic value. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 A hardware structure block diagram of a computer terminal for a method for monitoring energy saving of urban lighting based on environmental parameters provided by an embodiment of the present invention; Figure 2 A schematic diagram of a flow chart of a method for monitoring energy saving of urban lighting based on environmental parameters provided by an embodiment of the present invention; Figure 3 A schematic diagram of the structure of an urban lighting energy-saving monitoring system based on environmental parameters provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0018] The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, but should not be construed as limiting the present invention.

[0019] The embodiment of the present invention firstly provides a method for monitoring energy saving of urban lighting based on environmental parameters. The method can be applied to electronic devices such as computer terminals, specifically ordinary computers, etc.

[0020] The following describes it in detail by taking running on a computer terminal as an example. Figure 1 The hardware structure block diagram of a computer terminal of a method for monitoring energy saving of urban lighting based on environmental parameters provided by an embodiment of the present invention. Figure 1 As shown, the computer terminal may include one or more ( Figure 1 Only one is shown in the figure) a processor 102 (the processor 102 may include but is not limited to a processing device such as a microprocessor MCU or a programmable logic device FPGA) and a memory 104 for storing data. Optionally, the computer terminal may also include a transmission device 106 for communication functions and an input and output device 108. It can be understood by those skilled in the art that Figure 1 The structure shown is only for illustration and does not limit the structure of the above-mentioned computer terminal. Figure 1 More or fewer components as shown, or with Figure 1 Different configurations are shown.

[0021] The memory 104 can be used to store software programs and modules of application software, such as the program instructions / modules corresponding to the urban lighting energy-saving monitoring method based on environmental parameters in the embodiment of the present application. The processor 102 executes various functional applications and data processing by running the software programs and modules stored in the memory 104, that is, to implement the above method. The memory 104 may include a high-speed random access memory, and may also include a non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, the memory 104 may further include a memory remotely arranged relative to the processor 102, and these remote memories may be connected to the computer terminal via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0022] The transmission device 106 is used to receive or send data via a network. The specific example of the above network may include a wireless network provided by a communication provider of a computer terminal. In one example, the transmission device 106 includes a network adapter (Network Interface Controller, NIC), which can be connected to other network devices through a base station so as to communicate with the Internet. In one example, the transmission device 106 can be a radio frequency (RF) module, which is used to communicate with the Internet wirelessly.

[0023] See also Figure 2 The embodiment of the present invention provides a method for monitoring energy saving of urban lighting based on environmental parameters, which may include the following steps: S201, collecting parameter data of environmental parameters and traffic parameters of each lighting sub-area in the city; In the first step of the urban lighting energy-saving monitoring method, it is necessary to systematically collect environmental parameters and traffic parameters of each lighting sub-area in the city. This process is the basis for realizing intelligent lighting management. Only accurate and comprehensive data can provide a reliable basis for subsequent analysis and decision-making. Environmental parameters include but are not limited to: - Ambient light intensity: The natural light intensity of each lighting sub-area is monitored in real time through light sensors. - Temperature and humidity: Use environmental monitoring stations to collect meteorological data to understand how climate conditions affect lighting needs. - Air quality: Use air quality monitoring equipment to record PM2.5, PM10 and other indicators to analyze the impact of air pollution on human flow and traffic. Traffic parameters include but are not limited to: - Traffic flow: Counting the number of vehicles and people through traffic monitoring cameras or sensors installed on the road. - Crowd density: At important intersections or commercial areas, use crowd counters or traffic monitoring cameras to obtain real-time crowd data. - Traffic speed: Analyze vehicle speeds through radar speed guns or video surveillance to obtain information on traffic flow. The core of this step is to comprehensively and accurately obtain the real-time environment and traffic information of each lighting sub-area. These data will directly affect the subsequent prediction of lighting demand and the formulation of energy-saving plans. By collecting these parameters, we can better understand the actual needs of urban lighting in different time periods, weather conditions, and traffic conditions, thereby achieving more flexible and intelligent lighting control, improving the city's energy efficiency, and reducing unnecessary energy consumption. One implementation method may include: Step 1: Design a multi-level sensor network. A multi-level sensor network is deployed in key lighting sub-areas of the city to ensure that the environmental parameters and traffic parameters of each area are fully covered. These sensors include light sensors, temperature and humidity sensors, air quality sensors, and traffic monitoring sensors, which are combined into an integrated monitoring system. Step 2: Data fusion and preprocessing The data obtained from each sensor are fused, and noise is removed through data cleaning and preprocessing techniques to avoid data deviation caused by sensor failure or misreading. Combined with the time series analysis algorithm, a time series data set of environmental and traffic parameters of each lighting sub-area is formed for subsequent analysis. Step 3: Dynamically adjust the collection frequency Based on the monitoring results of real-time traffic flow and crowd density, dynamically adjust the collection frequency of parameter data in different areas. For example, during peak hours, increase the collection frequency of traffic flow monitoring, and appropriately reduce it during off-peak hours to optimize resource allocation and ensure the real-time and accuracy of data collection. Step 4: Machine learning model training In the analysis phase after data collection, machine learning models, such as time series analysis and clustering algorithms, are used to analyze the patterns of specific environmental and traffic conditions affecting lighting demand. These models are trained using historical data to improve the ability to predict real-time data and predict changes in lighting demand in different sub-areas. Step 5: Establish a feedback mechanism. While achieving real-time monitoring, establish a closed-loop feedback mechanism to compare the monitoring results with the actual lighting effects, regularly optimize the sensor configuration and data collection strategy, and ensure that the most accurate and comprehensive environmental and traffic parameters can always be obtained. Through the above complex and unique implementation steps, it can be ensured that in the process of collecting environmental parameters and traffic parameters of each lighting sub-area in the city, the data obtained is not only comprehensive, accurate and real-time, but also lays a solid foundation for subsequent lighting demand forecasting and energy-saving plan formulation.

[0024] S202, predicting the lighting demand level of each lighting sub-area using a neural network according to the parameter data, and calculating a sub-lighting demand index of each lighting sub-area; In this step, a neural network model is used to analyze and predict the collected environmental and traffic parameter data. Neural networks are algorithms designed to mimic biological nervous systems and are particularly suitable for processing complex and nonlinear pattern recognition tasks. By inputting the environmental and traffic parameters of each lighting sub-area, the model can learn and extract the potential relationship between the parameters, thereby predicting the lighting demand level and sub-lighting demand index of each area. Specifically, the model first receives multi-dimensional input data, including parameters such as ambient light intensity, traffic flow, and crowd density. Through layer-by-layer calculation and activation functions, the neural network can perform in-depth feature extraction on the data and ultimately output the lighting demand level corresponding to each lighting sub-area. This process not only relies on current parameter data, but also takes into account the impact of historical data, making the prediction more accurate. After predicting the lighting demand level, the relevant parameters are then used to calculate the sub-lighting demand index of each lighting sub-area to form a quantitative indicator for subsequent lighting solution optimization. The core of this step is to achieve dynamic adjustment of urban lighting through intelligent prediction methods. Accurately predicting the lighting demand level in different areas can effectively avoid energy waste caused by a one-size-fits-all lighting solution. At the same time, the quantification of the sub-lighting demand index provides a practical reference for the subsequent formulation and optimization of lighting energy-saving solutions, so that urban lighting can achieve a better balance between energy saving and safety. One implementation method may include: Step 1: Data standardization and preprocessing Before neural network modeling, the collected environmental and traffic parameters are standardized to eliminate the influence of different dimensions. Z-score standardization or Min-Max standardization method is used to convert all input data into values ​​between 0 and 1 to ensure that the influence of each parameter on lighting demand prediction in the network is relatively balanced. Step 2: Build a multi-layer neural network Design a deep neural network architecture, including an input layer, multiple hidden layers, and an output layer. The number of nodes in the input layer is the same as the dimension of the input parameters, and the hidden layer selects a nonlinear activation function (such as ReLU or Leaky ReLU) to improve the model's fitting ability. Select the optimal network structure and adjust the hyperparameters to ensure that the network can capture the complex relationship between environmental parameters and lighting requirements. Step 3: Training and Validation Model Use the historical data set to train the constructed neural network. The data set is randomly divided into a training set and a validation set, and the performance of the model is evaluated using the cross-validation method. The loss function can be the mean square error (MSE) or the cross entropy loss function, and optimized according to the type of prediction task. Through the back-propagation algorithm, the network weights are continuously adjusted to reduce the prediction error. Step 4: Real-time prediction and demand level output After the model training is completed, the real-time environment and traffic parameters are input into the trained neural network for prediction calculation. The output layer converts the output of the neural network into the corresponding lighting demand level through an appropriate activation function (such as the Softmax function), forming a grading system (such as high, medium, and low). Step 5: Calculation of sub-lighting demand index The sub-lighting demand index is calculated based on the predicted lighting demand level, combined with factors such as ambient light intensity and traffic flow.

[0025] Specifically, a calculation formula for the sub-lighting demand index is:

[0026] This formula is used to comprehensively evaluate the lighting demand index of the i-th lighting sub-area, integrating multiple environmental and traffic factors and reflecting the multi-dimensional characteristics of comprehensive lighting demand.

[0027] Among them, the is the sub-lighting demand index of the i-th lighting sub-area, indicating the lighting demand intensity of the area. The higher the value, the greater the demand. is the ambient light intensity of the i-th lighting sub-area, which directly affects the basic value of the demand. The lower the light intensity, the higher the lighting demand. is the weight coefficient of the environmental factor related to the ith sub-area, reflecting the importance of different environmental conditions in lighting demand, which needs to be determined based on historical data and expert experience. is the traffic and pedestrian interaction function of the i-th lighting sub-area, is the traffic flow of the i-th lighting sub-area, is the crowd density of the i-th lighting sub-area, is the environment and traffic function of the i-th lighting sub-area, combining the ambient light, traffic speed and road section length to analyze the comprehensive demand. is the traffic speed of the i-th lighting sub-area, is the length of the road section of the i-th lighting sub-area, is the safety and event function of the i-th lighting sub-area, taking into account the safety factor and the impact of specific events to improve the safety of urban lighting. is the safety factor of the i-th lighting sub-area, reflecting the safety of the area. A higher value indicates that the corresponding lighting demand can be reduced accordingly. is the specific event coefficient of the i-th lighting sub-area, indicating the frequency or severity of a specific event. The higher the frequency, the higher the demand. is the total number of sub-regions involved in the calculation, used for normalization.

[0028] Specifically, a traffic and pedestrian flow interaction function of the i-th lighting sub-area is:

[0029] This formula is used to describe the combined impact of traffic flow and crowd density on lighting demand. By introducing logarithmic and power functions, the nonlinear relationship is effectively captured, so that the impact of traffic flow and density is not exaggerated at small values, while also being amplified for high traffic.

[0030] Among them, the It is the intensity impact index, which reflects the sensitivity of crowd density to demand. It reflects the interactive impact between traffic flow and crowd density by combining their logarithms and powers. When traffic flow increases, the impact of crowd density will increase.

[0031] Specifically, a calculation formula for the environment and traffic function of the i-th lighting sub-area is:

[0032] This formula combines ambient light intensity, traffic speed and road segment length to comprehensively evaluate the impact on lighting demand under different traffic conditions. A power function is used to reflect the impact of road segment length, ensuring that longer road segments have a more significant impact in the lighting demand assessment.

[0033] Among them, the is the traffic speed influence coefficient. The ambient light is combined with the traffic speed, and the nonlinear relationship is adjusted through the inverse of the road section length, reflecting the impact of the road section length on the overall lighting demand.

[0034] Specifically, a calculation formula for the safety and event function of the i-th lighting sub-area is:

[0035] This formula evaluates the dual impact of safety factor and specific events on lighting demand. It introduces an exponential form to reflect the nonlinear impact of important events on safety demand, so that lighting demand can be reasonably enhanced in areas where events are frequent or safety is low.

[0036] Among them, the The combination of the safety factor and the index of a specific event takes into account the impact of high-risk areas on lighting needs when a specific event occurs, ensuring the responsiveness of lighting at critical moments.

[0037] Step 6: Adaptive learning and periodic updates In order to improve the long-term adaptability of the model, the neural network is retrained regularly, and the network weights are updated with newly acquired data to maintain the accuracy of the model. This process can be combined with transfer learning technology to combine historical data with new data to improve the model's ability to respond to emergencies or pattern changes. Through this complex and effective implementation method, it is possible to ensure that high-precision results are obtained in the process of using neural networks to predict lighting demand levels and calculate sub-lighting demand indexes, providing a solid foundation for subsequent urban lighting energy-saving control.

[0038] S203, configuring a corresponding initial lighting energy-saving plan for each lighting sub-area according to the lighting demand level; In this step, according to the lighting demand level obtained in the previous stage, the corresponding initial lighting energy-saving plan is formulated for different lighting sub-areas. The lighting demand level reflects the lighting intensity and equipment operation strategy required for each sub-area in different time periods and different environmental conditions. By configuring the initial energy-saving plan on this basis, it can be ensured that energy consumption is minimized and lighting efficiency is improved while meeting safety and visual requirements. The core of this step is to use the lighting demand level hierarchical management and configure specific lighting equipment and operation modes according to the lighting strategy corresponding to each level. For example, for areas with higher demand levels, sufficient lighting may be required to ensure safety, while in areas with lower demand levels, low-power lighting equipment can be used, or intelligent dimming can be implemented in different time periods to save energy resources. The implementation of this step helps to achieve accurate urban lighting management and avoid the problems of over-lighting and under-lighting. By rationally configuring the lighting plan, it can reduce power waste and reduce operation and maintenance costs, while also improving the city's environmental quality and residents' living comfort. In addition, the configuration of the initial lighting energy-saving plan provides a basis for subsequent optimization and dynamic adjustment, thereby supporting the goal of intelligent urban management. One implementation method may include: Step 1: Establish a regional demand model First, classify the urban lighting sub-areas according to the lighting demand level and establish a regional demand model. Divide the sub-areas into three lighting demand levels: high, medium, and low, and set corresponding lighting standards and strategies for each level. This process can be achieved through data clustering algorithms (such as K-Means clustering), analyzing the regions based on historical data and real-time data to identify demand patterns. Step 2: Define lighting equipment configuration standards. Define a set of lighting equipment configuration standards for different lighting demand levels. Specifically, select different power, light source types and lighting layouts to ensure that the lighting configuration of each sub-area meets its needs. For example: - High demand level areas: Configure high lumen LED lights and adjust the brightness in real time to ensure that safety lighting requirements are met during peak hours at night. - Medium demand level area: Use medium-power LED lights and introduce an intelligent dimming system to dynamically adjust based on real-time pedestrian and vehicle flow conditions. - Low demand level areas: Use low-power energy-saving lamps, set timer switches or light sensor controls to reduce unnecessary energy consumption. Step 3: Develop a comprehensive evaluation tool. Develop a comprehensive evaluation tool that integrates multiple evaluation indicators (such as energy consumption, light uniformity, safety factor, etc.) to evaluate the equipment configuration and operating effect of each lighting sub-area. The tool combines multiple algorithms (such as hierarchical analysis method, fuzzy comprehensive evaluation, etc.) to ensure the rationality, adaptability and sustainability of equipment configuration. Step 4: Implement dynamic monitoring and feedback system After the lighting plan is implemented, set up a dynamic monitoring system to monitor the operating status of lighting equipment and changes in environmental parameters in each sub-area in real time. Once it is found that the real-time lighting demand of a certain area does not match the plan, adjust the lighting plan in time through data analysis and feedback mechanism. For example, when the density of people suddenly increases in a low-demand area, the intelligent control system can automatically increase the brightness to meet the sudden demand. Step 5: Regular optimization and evaluation mechanism Establish a regular evaluation and optimization mechanism, review the lighting plan every quarter or year, and continuously adjust the equipment configuration and lighting strategy based on new demand data and technological changes. Use historical data and new lighting technologies on the market to form a closed-loop system of continuous improvement to ensure that the trend of intelligent and energy-saving urban lighting continues. Through the above implementation methods, not only can the areas with different lighting demand levels obtain the lighting configuration they need, but they can also dynamically adapt to the changing environment and flow of people, thus providing long-term guarantee for the high efficiency and energy saving of urban lighting. This method makes lighting management not limited to simple configuration, but also more intelligent and adaptable, improving the management level of the entire urban lighting system.

[0039] S204: Optimize the initial lighting energy-saving plan according to the sub-lighting demand index to achieve urban lighting energy-saving monitoring.

[0040] In this step, the preliminary lighting energy-saving plan configured for each lighting sub-area is optimized using the sub-lighting demand index calculated previously. This optimization process aims to ensure optimal energy efficiency and resource utilization while meeting the lighting demand level. The sub-lighting demand index not only reflects the lighting demand intensity of each sub-area, but also provides specific guidance for the lighting plan based on different environments and traffic conditions. The key to the optimization is to automatically adjust the power, lighting time and working mode of the lighting equipment by analyzing the match between the sub-lighting demand index and the initial energy-saving plan to achieve higher energy-saving effects. For example, areas with lower demand can reduce the lighting intensity or turn off the lighting equipment in advance, while areas with higher demand may need to increase the lighting intensity or extend the lighting time. Role and significance: This optimization step is important for several reasons: 1. Improve energy efficiency: By optimizing configuration, reducing unnecessary energy consumption, and implementing more refined management, we can achieve energy-saving goals. 2. Enhanced safety: Ensure that each area maintains the necessary lighting intensity under different demand levels to ensure the safety of pedestrians and vehicles. 3. Respond to dynamic changes: effectively respond to changes in environmental or traffic conditions, always maintain the suitability and economy of lighting, and provide data support for urban management. One implementation method may include: Step 1: Build a multi-objective optimization model - First, build a multi-objective optimization model with the sub-lighting demand index as the core and define the optimization goals, including minimizing energy consumption, maximizing lighting uniformity, and improving safety. Each goal is quantified in the model. For example, energy consumption can be calculated by lamp power and working hours, while uniformity and safety can be evaluated by light distribution diagrams. Step 2: Apply genetic algorithm to solve the optimization model - Genetic algorithm (GA) is used to search for the best solution and initialize a population, where each individual represents a lighting configuration. The individual contains information about the power, quantity, layout and working hours of different lamps. By evaluating the fitness function (including energy consumption, uniformity and safety), excellent individuals are selected in each generation for crossover and mutation to generate a new generation of individuals, thus gradually moving towards the optimal solution. Step 3: Define the fitness evaluation mechanism In the genetic algorithm, a fitness evaluation mechanism needs to be designed. The actual energy consumption, lighting uniformity and safety index of each lighting configuration scheme are calculated. The weight factor can be derived by combining quantitative data and expert opinions. Different goals are weighted according to their importance, so that the optimization scheme takes into account both energy saving and safety and comfort. Step 4: Integrate real-time data feedback mechanism During the optimization process, integrate the real-time feedback mechanism to monitor the environmental parameters and traffic flow conditions of each area in real time. When the sub-lighting demand index of a certain area changes significantly, timely adjust the genetic operations in the genetic algorithm, such as dynamically adjusting the parameter range of individuals in the population, allowing the model to adapt to new environmental changes more flexibly and improve the response speed and real-time performance of the optimization. Step 5: Conduct multiple rounds of iterations and evaluate solutions. Through multiple rounds of iterations, evaluate the optimal configuration solution generated in each generation, record its energy consumption and safety performance, and ensure that the final solution is a robust solution output after multiple iterations. Finally, select the configuration solution with the best fitness for implementation. Step 6: Implementation plan and subsequent effect tracking After the optimization is completed, the best lighting energy-saving plan will be implemented in each lighting sub-area. During the implementation process, subsequent effect tracking is required to evaluate the energy consumption and safety of each area to form a closed-loop feedback so as to better adjust the model parameters and optimization goals in future lighting optimization. This implementation not only effectively optimizes the initial configuration plan, but also dynamically adapts to changes in the environment and the flow of people, thereby achieving more efficient urban lighting energy-saving monitoring and management goals. This method makes full use of advanced optimization technology and real-time feedback mechanisms, bringing a higher level of intelligence to urban lighting and improving the flexibility and sustainability of lighting management.

[0041] It can be seen that parameter data of environmental parameters and traffic parameters of each lighting sub-area in the city are collected; based on the parameter data, a neural network is used to predict the lighting demand level of each lighting sub-area, and the sub-lighting demand index of each lighting sub-area is calculated; based on the lighting demand level, a corresponding initial lighting energy-saving plan is configured for each lighting sub-area; based on the sub-lighting demand index, the initial lighting energy-saving plan is optimized to realize urban lighting energy-saving monitoring, so that the monitoring of multiple parameters can be combined with neural network prediction, which can not only improve the intelligence level of urban lighting, but also effectively reduce energy consumption and improve the quality of life of citizens, which has important social and economic value.

[0042] Another embodiment of the present invention provides an urban lighting energy-saving monitoring system based on environmental parameters, see Figure 3 , the system may include: The collection module 301 is used to collect parameter data of environmental parameters and traffic parameters of each lighting sub-area in the city; A prediction module 302, configured to predict the lighting demand level of each lighting sub-area using a neural network according to the parameter data, and calculate a sub-lighting demand index of each lighting sub-area; A configuration module 303, configured to configure a corresponding initial lighting energy-saving plan for each lighting sub-area according to the lighting demand level; The monitoring module 304 is used to optimize the initial lighting energy-saving plan according to the sub-lighting demand index to achieve urban lighting energy-saving monitoring.

[0043] It can be seen that parameter data of environmental parameters and traffic parameters of each lighting sub-area in the city are collected; based on the parameter data, a neural network is used to predict the lighting demand level of each lighting sub-area, and the sub-lighting demand index of each lighting sub-area is calculated; based on the lighting demand level, a corresponding initial lighting energy-saving plan is configured for each lighting sub-area; based on the sub-lighting demand index, the initial lighting energy-saving plan is optimized to realize urban lighting energy-saving monitoring, so that the monitoring of multiple parameters can be combined with neural network prediction, which can not only improve the intelligence level of urban lighting, but also effectively reduce energy consumption and improve the quality of life of citizens, which has important social and economic value.

[0044] An embodiment of the present invention further provides a storage medium, in which a computer program is stored, wherein the computer program is configured to execute the steps of any of the above method embodiments when running.

[0045] Specifically, in this embodiment, the above storage medium may be configured to store a computer program for performing the following steps: S201, collecting parameter data of environmental parameters and traffic parameters of each lighting sub-area in the city; S202, predicting the lighting demand level of each lighting sub-area using a neural network according to the parameter data, and calculating a sub-lighting demand index of each lighting sub-area; S203, configuring a corresponding initial lighting energy-saving plan for each lighting sub-area according to the lighting demand level; S204: Optimize the initial lighting energy-saving plan according to the sub-lighting demand index to achieve urban lighting energy-saving monitoring.

[0046] It can be seen that parameter data of environmental parameters and traffic parameters of each lighting sub-area in the city are collected; based on the parameter data, a neural network is used to predict the lighting demand level of each lighting sub-area, and the sub-lighting demand index of each lighting sub-area is calculated; based on the lighting demand level, a corresponding initial lighting energy-saving plan is configured for each lighting sub-area; based on the sub-lighting demand index, the initial lighting energy-saving plan is optimized to realize urban lighting energy-saving monitoring, so that the monitoring of multiple parameters can be combined with neural network prediction, which can not only improve the intelligence level of urban lighting, but also effectively reduce energy consumption and improve the quality of life of citizens, which has important social and economic value.

[0047] An embodiment of the present invention further provides an electronic device, including a memory and a processor, wherein the memory stores a computer program, and the processor is configured to run the computer program to execute the steps in any one of the above method embodiments.

[0048] Specifically, the electronic device may further include a transmission device and an input / output device, wherein the transmission device is connected to the processor, and the input / output device is connected to the processor.

[0049] Specifically, in this embodiment, the processor may be configured to perform the following steps through a computer program: S201, collecting parameter data of environmental parameters and traffic parameters of each lighting sub-area in the city; S202, predicting the lighting demand level of each lighting sub-area using a neural network according to the parameter data, and calculating a sub-lighting demand index of each lighting sub-area; S203, configuring a corresponding initial lighting energy-saving plan for each lighting sub-area according to the lighting demand level; S204: Optimize the initial lighting energy-saving plan according to the sub-lighting demand index to achieve urban lighting energy-saving monitoring.

[0050] It can be seen that parameter data of environmental parameters and traffic parameters of each lighting sub-area in the city are collected; based on the parameter data, a neural network is used to predict the lighting demand level of each lighting sub-area, and the sub-lighting demand index of each lighting sub-area is calculated; based on the lighting demand level, a corresponding initial lighting energy-saving plan is configured for each lighting sub-area; based on the sub-lighting demand index, the initial lighting energy-saving plan is optimized to realize urban lighting energy-saving monitoring, so that the monitoring of multiple parameters can be combined with neural network prediction, which can not only improve the intelligence level of urban lighting, but also effectively reduce energy consumption and improve the quality of life of citizens, which has important social and economic value.

[0051] The above describes in detail the structure, features and effects of the present invention based on the embodiments shown in the drawings. The above is only a preferred embodiment of the present invention, but the present invention is not limited to the scope of implementation shown in the drawings. Any changes made according to the concept of the present invention, or modifications to equivalent embodiments with equivalent changes, which still do not exceed the spirit covered by the description and drawings, should be within the protection scope of the present invention.

Claims

1. A method for monitoring energy saving of urban lighting based on environmental parameters, characterized in that: The method comprises: Collect parameter data of environmental parameters and traffic parameters of each lighting sub-area in the city; According to the parameter data, a neural network is used to predict the lighting demand level of each lighting sub-area, and a sub-lighting demand index of each lighting sub-area is calculated; According to the lighting demand level, a corresponding initial lighting energy-saving plan is configured for each lighting sub-area; According to the sub-lighting demand index, the initial lighting energy-saving plan is optimized to achieve urban lighting energy-saving monitoring.

2. The method according to claim 1, characterized in that The calculation formula of the sub-lighting demand index is: Among them, the is the sub-lighting demand index of the i-th lighting sub-area, is the ambient light intensity of the i-th lighting sub-area, is the weight coefficient of the environmental factor related to the i-th sub-region, is the traffic and pedestrian interaction function of the i-th lighting sub-area, is the traffic flow of the i-th lighting sub-area, is the crowd density of the i-th lighting sub-area, is the environment and traffic function of the i-th lighting sub-area, is the traffic speed of the i-th lighting sub-area, is the length of the road section of the i-th lighting sub-area, is the safety and event function of the i-th lighting sub-area, is the safety factor of the i-th lighting sub-area, is the specific event coefficient of the i-th illumination sub-area, is the total number of sub-regions involved in the calculation, used for normalization.

3. The method according to claim 2, characterized in that The traffic and pedestrian interaction function of the i-th lighting sub-area is: Among them, the is the intensity impact index.

4. The method according to claim 3, characterized in that The calculation formula of the environment and traffic function of the i-th lighting sub-area is: Among them, the is the traffic speed influence coefficient.

5. The method according to claim 4, characterized in that The calculation formula of the safety and event function of the i-th lighting sub-area is: Among them, the is the adjustment factor.

6. An urban lighting energy-saving monitoring system based on environmental parameters, characterized in that: The system comprises: A collection module is used to collect parameter data of environmental parameters and traffic parameters of each lighting sub-area in the city; A prediction module, used to predict the lighting demand level of each lighting sub-area using a neural network according to the parameter data, and calculate the sub-lighting demand index of each lighting sub-area; A configuration module, configured to configure a corresponding initial lighting energy-saving plan for each lighting sub-area according to the lighting demand level; The monitoring module is used to optimize the initial lighting energy-saving plan according to the sub-lighting demand index to achieve urban lighting energy-saving monitoring.

7. The system according to claim 6, characterized in that The calculation formula of the sub-lighting demand index is: Among them, the is the sub-lighting demand index of the i-th lighting sub-area, is the ambient light intensity of the i-th lighting sub-area, is the weight coefficient of the environmental factor related to the i-th sub-region, is the traffic and pedestrian interaction function of the i-th lighting sub-area, is the traffic flow of the i-th lighting sub-area, is the crowd density of the i-th lighting sub-area, is the environment and traffic function of the i-th lighting sub-area, is the traffic speed of the i-th lighting sub-area, is the length of the road section of the i-th lighting sub-area, is the safety and event function of the i-th lighting sub-area, is the safety factor of the i-th lighting sub-area, is the specific event coefficient of the i-th illumination sub-area, is the total number of sub-regions involved in the calculation, used for normalization.

8. The system according to claim 7, characterized in that The traffic and pedestrian interaction function of the i-th lighting sub-area is: Among them, the is the intensity impact index.

9. A storage medium, characterized in that: The storage medium stores a computer program, wherein the computer program is configured to execute the method according to any one of claims 1 to 5 when executed.

10. An electronic device comprising a memory and a processor, characterized in that: A computer program is stored in the memory, and the processor is configured to run the computer program to perform the method according to any one of claims 1 to 5.

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