A smart street lamp lighting control method and system based on Internet of Things technology

Abstract

The present invention discloses a smart street lamp lighting control method and system based on the Internet of Things technology, the method comprising: for each street lamp in the smart street lamp system, obtaining the current lighting parameter setting scheme of the street lamp, collecting the street lamp lighting data corresponding to the scheme through the Internet of Things technology; calculating the energy consumption corresponding to the scheme according to the street lamp lighting data; when the energy consumption exceeds the preset energy consumption threshold range, encoding each lighting parameter in the scheme into a gene string structure; iteratively optimizing the gene string according to the preset objective function to obtain the optimized lighting parameter setting scheme. By using the embodiments of the present invention, real-time, dynamic and accurate lighting control can be achieved, ensuring the energy saving and intelligent level of the lighting system, and meeting the lighting quality requirements under different environmental conditions and user needs.

Description

Technical Field

[0001] The present invention belongs to the technical field of Internet of Things, and in particular to a smart street lamp lighting control method and system based on Internet of Things technology. Background Art

[0002] With the acceleration of modern urbanization, urban street lighting systems, as an important part of urban infrastructure, play an important role in ensuring people's safety when traveling at night, improving urban environment lighting, saving energy consumption, etc. However, traditional street lighting systems mostly use time control or light control methods, which often cannot adapt well to changes in factors such as different weather conditions, traffic flow and citizen activity patterns, making it difficult to achieve efficient use of energy and optimization of lighting quality.

[0003] With the development of Internet of Things technology, collecting environmental data and controlling street lights in real time through sensors and communication technologies has become a research hotspot in the current field of lighting control. Internet of Things technology enables street lights to perceive environmental conditions in real time and intelligently adjust parameters such as brightness, color temperature, and lighting duration according to actual conditions, which can not only achieve energy saving and consumption reduction, but also provide a safer and more comfortable lighting environment. The smart street light system developed from this can achieve more intelligent and automated lighting control and improve the level of intelligent urban management.

[0004] In the existing technology, the control method of smart street lights is mainly based on a central control system, which connects to the sensor network on the street light nodes, collects relevant data, and then performs centralized processing and decision-making. However, this method has certain limitations. For example, the computing and decision-making capabilities of the central control system are limited, and it may not be able to process a large amount of sensor data and make the most optimized lighting adjustment strategy in a short period of time; in addition, the uniformity of the central control strategy may ignore the unique lighting needs of different areas, times and occasions in the city. Summary of the invention

[0005] The purpose of the present invention is to provide a smart street lamp lighting control method and system based on Internet of Things technology to solve the deficiencies in the prior art, to achieve real-time, dynamic and precise lighting control, to ensure the energy saving and intelligence level of the lighting system, and to meet the lighting quality requirements under different environmental conditions and user needs.

[0006] An embodiment of the present application provides a smart street lamp lighting control method based on Internet of Things technology, the method comprising:

[0007] For each street lamp in the smart street lamp system, obtain the current lighting parameter setting scheme of the street lamp, and collect the street lamp lighting data corresponding to the scheme through the Internet of Things technology;

[0008] Calculating the energy consumption corresponding to the scheme according to the street lamp lighting data;

[0009] When the energy consumption exceeds a preset energy consumption threshold range, encoding each lighting parameter in the scheme into a gene string structure, wherein each lighting parameter serves as a gene fragment;

[0010] The gene string is iteratively optimized according to a preset objective function to obtain an optimized lighting parameter setting scheme.

[0011] Optionally, the method further includes:

[0012] The street lamp lighting data corresponding to the optimized lighting parameter setting scheme is collected through the Internet of Things technology, and based on the optimized street lamp lighting data, it is calculated and determined whether the energy consumption corresponding to the scheme falls within the preset energy consumption threshold range.

[0013] Optionally, the method further includes:

[0014] If it does not fall within the preset energy consumption threshold range, continue to execute the step of encoding each lighting parameter in the scheme into a gene string structure when the energy consumption exceeds the preset energy consumption threshold range until the energy consumption corresponding to the optimized lighting parameter setting scheme falls within the preset energy consumption threshold range.

[0015] Optionally, calculating the energy consumption corresponding to the solution according to the street lamp lighting data includes:

[0016] E(x)=∑(P i ·T i )+α·(P s ·(N-∑T i ))+β·ΔT+γ·F(L,V)

[0017] Among them, the P i represents the power consumption of the street lamp at the i-th brightness level, Ti represents the operating time of the street lamp at the i-th brightness level, and P s represents the power consumption of the street lamp in standby or minimum brightness mode, N represents the total time in the evaluation cycle, α represents the coefficient for adjusting the relationship between standby energy consumption and active energy consumption, β represents the temperature adjustment factor, ΔT is the difference between the ambient temperature and the preset optimal operating temperature, γ represents the flow adjustment factor, and F(L, V) is not a function related to the ambient brightness L and the vehicle and pedestrian flow V.

[0018] Optionally, encoding each lighting parameter in the scheme into a gene string structure includes:

[0019] A gene string structure is defined to represent the lighting parameters of a smart street lamp, wherein the gene string is composed of a plurality of gene fragments connected in sequence;

[0020] Mapping each lighting parameter to a specific gene segment, wherein each gene segment consists of a series of bit sequences of a specific length;

[0021] For each lighting parameter, a preset base encoding is used to discretize the range of the parameter attribute into a range of preset base numbers, and each preset base number corresponds to an attribute value;

[0022] The gene fragments of each lighting parameter are connected in a predefined order to form a complete gene string.

[0023] Another embodiment of the present application provides a smart street lamp lighting control system based on Internet of Things technology, the system comprising:

[0024] A collection module, used to obtain the current lighting parameter setting scheme of each street lamp in the smart street lamp system, and collect street lamp lighting data corresponding to the scheme through the Internet of Things technology;

[0025] A calculation module, used for calculating the energy consumption corresponding to the scheme according to the street lamp lighting data;

[0026] An encoding module, used for encoding each lighting parameter in the scheme into a gene string structure when the energy consumption exceeds a preset energy consumption threshold range, wherein each lighting parameter is used as a gene fragment;

[0027] The optimization module is used to iteratively optimize the gene string according to a preset objective function to obtain an optimized lighting parameter setting solution.

[0028] 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.

[0029] 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.

[0030] Compared with the prior art, the present invention provides a smart street lamp lighting control method based on the Internet of Things technology, which obtains the current lighting parameter setting scheme of each street lamp in the smart street lamp system, collects the street lamp lighting data corresponding to the scheme through the Internet of Things technology; calculates the energy consumption corresponding to the scheme according to the street lamp lighting data; when the energy consumption exceeds a preset energy consumption threshold range, encodes each lighting parameter in the scheme into a gene string structure; iteratively optimizes the gene string according to a preset objective function to obtain an optimized lighting parameter setting scheme, thereby realizing real-time, dynamic and precise lighting control, ensuring the energy saving and intelligence level of the lighting system, and meeting the lighting quality requirements under different environmental conditions and user needs. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] Figure 1 A hardware structure block diagram of a computer terminal for a smart street lamp lighting control method based on Internet of Things technology provided by an embodiment of the present invention;

[0032] Figure 2 A schematic diagram of a flow chart of a smart street lamp lighting control method based on Internet of Things technology provided by an embodiment of the present invention;

[0033] Figure 3 A schematic diagram of the structure of a smart street lamp lighting control system based on Internet of Things technology provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0034] 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.

[0035] The embodiment of the present invention first provides a smart street lamp lighting control method based on the Internet of Things technology. The method can be applied to electronic devices such as computer terminals, specifically ordinary computers, etc.

[0036] 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 distributed energy storage management method provided by an embodiment of the present invention is shown in FIG. 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.

[0037] The memory 104 can be used to store software programs and modules of application software, such as the program instructions / modules corresponding to the distributed energy storage management method 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, implementing 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 instances, 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.

[0038] 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.

[0039] See also Figure 2 The embodiment of the present invention provides a smart street lamp lighting control method based on Internet of Things technology, which may include the following steps:

[0040] S201, for each street lamp in the smart street lamp system, obtaining a current lighting parameter setting scheme of the street lamp, and collecting street lamp lighting data corresponding to the scheme through the Internet of Things technology;

[0041] Specifically, you can configure the smart street light system equipment:

[0042] Install a module with a multi-function sensor for each smart street light node, which can monitor light intensity, current, voltage and power consumption;

[0043] A microprocessor is configured in each smart street light node to control the sensor module and process the collected data;

[0044] Configure wireless communication modules to enable each smart street light node to exchange data with the central control system or cloud platform;

[0045] Lighting parameters and data collection methods:

[0046] Design and implement a data recording system based on distributed blockchain technology, where each smart street light node acts as a node in a blockchain network;

[0047] When the smart street light is operating, the microprocessor of each street light node generates a set of data locally including the current lighting parameters (such as brightness, color temperature, lighting duration, etc.) and the corresponding real-time energy consumption data;

[0048] The above data is recorded on the distributed blockchain in an unalterable manner, and each data record is associated with a timestamp to ensure the authenticity and traceability of the data;

[0049] Using the wireless communication module of the smart street light node, the lighting parameter data and energy consumption data stored locally are regularly sent to the central control system or cloud platform;

[0050] Central control system or cloud platform analysis and processing:

[0051] The central control system or cloud platform collects data from each smart street light node, verifies the integrity and consistency of the data, and uses the smart contract function of blockchain technology to automatically filter valid data;

[0052] Through advanced data analysis methods, such as machine learning or artificial intelligence algorithms, the collected data is deeply analyzed to calculate the energy consumption corresponding to each set of current lighting parameters;

[0053] Using the collected historical and real-time data, an energy consumption prediction model is established to determine whether subsequent optimization steps need to be performed.

[0054] This implementation introduces blockchain technology and provides a new data collection and verification process. With the decentralized, tamper-proof and data consistency characteristics of blockchain, it not only enhances data security and transparency, but also maintains data integration in a decentralized environment, providing reliable data to support the execution of smart street lighting control methods.

[0055] S202, calculating the energy consumption corresponding to the solution according to the street lamp lighting data;

[0056] Specifically, in one implementation, it includes:

[0057] 1. Integrated environmental perception technology:

[0058] a. In addition to standard energy consumption measurement sensors (such as current and voltage sensors), add environmental perception sensors such as light sensors, temperature sensors, and motion detectors to each smart street light node;

[0059] b. Collect environmental data related to energy consumption through environmental perception sensors, such as sunlight intensity, climate conditions (temperature, humidity), and activities around street lights (vehicle and pedestrian flow) data.

[0060] 2. Using wireless energy transmission monitoring:

[0061] a. Implement wireless energy transfer systems (such as induction coils) around street light nodes to provide a supplementary means of energy measurement;

[0062] b. Use wireless energy transmission monitoring tools to monitor the energy consumption of street light nodes in real time without the need for physical connection, reducing maintenance costs and the complexity of data collection.

[0063] 3. Establish a high-precision energy consumption model:

[0064] a. Using the collected street lamp operation data and environmental data, apply physical models or data-driven models (such as neural networks) to establish an accurate energy consumption model;

[0065] b. Determine the complex relationship between various parameters and energy consumption, such as the nonlinear relationship between light output and power input, the impact of environmental conditions on energy consumption, etc.

[0066] 4. Implement data fusion and analysis:

[0067] a. Use data fusion techniques, such as Kalman filters, to integrate data from sensors and wireless energy transfer monitoring tools to improve the accuracy of energy consumption estimates;

[0068] b. Apply advanced algorithms in the microprocessor of the street light node or the central control system to process the fused data and calculate the actual energy consumption of the current lighting solution.

[0069] 5. Application energy consumption prediction and correction:

[0070] a. Based on historical energy consumption data, use prediction algorithms (such as time series analysis and regression analysis) to predict future energy consumption and provide a reference for immediate energy consumption calculation;

[0071] b. Adopt a real-time energy consumption correction mechanism to adjust the energy consumption model according to real-time environmental changes and prediction errors to improve the accuracy of energy consumption calculation.

[0072] 6. Asynchronous verification of energy consumption:

[0073] a. Design an asynchronous verification mechanism to independently verify the calculated energy consumption value, such as using distributed ledger technology to record and compare energy consumption data between street light nodes;

[0074] b. Perform verification through a central control system or cloud platform to ensure that the calculated energy consumption data is not affected by single point failures and improve the robustness of the system.

[0075] Through the above steps, an accurate calculation method for the energy consumption corresponding to the smart street lighting parameter scheme can be realized. This method combines multiple technologies, including environmental perception, wireless energy transmission monitoring, data fusion and analysis, energy consumption prediction and correction, and asynchronous verification, providing a comprehensive and sophisticated energy consumption management solution for the smart street lighting system.

[0076] In another implementation, an energy consumption calculation formula may be as follows:

[0077] E(x)=∑(P i ·T i )+α·(P s ·(N-∑T i ))+β·ΔT+γ·F(L,V)

[0078] Among them, the P i represents the power consumption of the street lamp at the i-th brightness level;

[0079] The T i Indicates the time that the street light operates at the i-th brightness level;

[0080] The P s Indicates the power consumption of the street light in standby or minimum brightness mode;

[0081] N represents the total time in the evaluation period, usually the total running time in a day;

[0082] The α represents a coefficient for adjusting the relationship between standby energy consumption and active energy consumption;

[0083] The β represents the temperature adjustment factor, which is used to reflect the impact of temperature on energy consumption;

[0084] The ΔT is the difference between the ambient temperature and the preset optimal operating temperature;

[0085] The γ represents the flow adjustment factor, which reflects the energy impact of pedestrian and vehicle flow on the dimming of smart street lights;

[0086] The F(L, V) is not a function related to the ambient brightness L and the vehicle and pedestrian flow V, and is used to adjust the energy consumption calculation to reflect the impact of dynamic lighting adjustment on the total energy consumption.

[0087] F(L, V) can take the following forms:

[0088] F(L,V)=δ·L^-1+η·g(V)

[0089] In this formula, δ: ambient brightness adjustment coefficient; L^-1 reflects the potential energy savings of reducing artificial lighting under natural light conditions; η: traffic flow adjustment coefficient, used to adjust the impact of traffic flow on energy consumption; g(V): a function related to traffic flow V, used to adjust energy consumption estimates based on real-time data of pedestrian and vehicle traffic. Among them, α, β, δ and η can all be obtained through historical data analysis to ensure the accuracy and practicality of the calculation results. The following is a specific formula for an exemplary quality g(V), assuming that energy consumption increases nonlinearly with traffic flow, but there is an upper limit:

[0090] g(V)=a·(1-e^{-b·V})

[0091] In this formula, a is the upper limit coefficient of traffic energy consumption, which represents the maximum additional energy consumption that the smart street light system may reach under extremely high traffic conditions; e is the base of the natural logarithm, which is approximately equal to 2.71828; b is the traffic energy consumption sensitivity coefficient, which indicates the rate at which the system energy consumption increases as the traffic flow increases; and V is the real-time traffic flow. This function starts at 0 when V is 0 (representing no additional traffic-related energy consumption). As V increases, the additional energy consumption increases rapidly, but due to the influence of the e^{-b·V} term, the rate of increase will gradually slow down and eventually approach a, indicating that the impact of traffic flow on energy consumption has reached saturation.

[0092] S203, when the energy consumption exceeds a preset energy consumption threshold range, encoding each lighting parameter in the scheme into a gene string structure, wherein each lighting parameter serves as a gene fragment;

[0093] Specifically, a gene string structure for representing the lighting parameters of a smart street lamp may be defined, wherein the gene string is composed of a plurality of gene fragments connected in sequence.

[0094] The adjustable lighting parameters (such as brightness, color temperature, lighting duration, etc.) in the street light system can be determined; a gene segment is assigned to each lighting parameter. The length of the gene segment depends on the diversity of the attribute values ​​of the parameter and the expected encoding accuracy; a gene string structure is set, which is composed of the gene segments of all lighting parameters connected in sequence to form a string that can represent the complete lighting parameter setting.

[0095] Each lighting parameter is mapped to a specific gene segment, wherein each gene segment consists of a series of bit sequences of a specific length; each bit can be binary, decimal or other appropriate coding schemes.

[0096] For each lighting parameter, a preset base encoding is adopted to discretize the range of parameter attributes into a range of preset base numbers, and each preset base number corresponds to an attribute value.

[0097] For example, for the brightness parameter, binary encoding is used to discretize the actual range of the brightness level (e.g., 0% to 100%) into a range of binary numbers (e.g., using 8-bit binary numbers to represent 256 discrete brightness levels), and each binary number corresponds to a brightness setting;

[0098] For lighting time parameters, a similar encoding method is used, such as discretizing the time expressed in hours and minutes into a binary number that can fit within the specified bit width. For example, a 12-bit binary number can represent 1440 minutes in a day.

[0099] For the color temperature parameter, a binary code of appropriate length is used to represent the range of color temperature. For example, a 10-bit binary number is used to represent the color temperature range of 1000K to 10000K.

[0100] The gene fragments of each lighting parameter are connected in a predefined order to form a complete gene string. The gene string represents a specific set of lighting control parameter settings, and each combination of gene strings represents a control solution that may be adopted by the street light system.

[0101] In addition, the length of the gene string can be set to adapt to different coding requirements to ensure that various possible lighting parameter setting schemes can be accurately and efficiently expressed and transformed during the iteration process of the optimization algorithm; the bit width of each gene fragment can be adjusted according to the actual control accuracy requirements to obtain the optimal balance between coding efficiency and control resolution; the structure of the gene string can include additional control parameters, such as dynamic response mode, energy-saving mode, etc., and each additional parameter is also encoded with a bit sequence of preset length; it can adapt to various types of smart street lighting equipment, and can realize remote parameter reading and setting through Internet of Things technology.

[0102] S204, iteratively optimizing the gene string according to a preset objective function to obtain an optimized lighting parameter setting solution.

[0103] Specifically, the preset objective function for evaluating the optimization degree of the lighting parameter setting scheme can be designed as follows:

[0104] H(x)=w1·E(x)^(-1)+w2·Q(x)

[0105] Wherein, x represents a lighting parameter setting scheme, E(x) is the expected energy consumption of the scheme, Q(x) is an indicator for meeting lighting quality requirements, and w1 and w2 are weight coefficients; the weight coefficients w1 and w2 of the function are dynamically adjusted and can be updated in real time based on factors such as real-time energy prices, user feedback or environmental changes.

[0106] Generate a set of new initial lighting parameter setting schemes, and incorporate the initial lighting parameter setting schemes into the current lighting parameter setting schemes; initialize the gene population, randomly generate a set of new initial lighting parameter setting schemes, and each lighting parameter setting scheme is a population individual.

[0107] Calculate the objective function value of the current lighting parameter setting scheme;

[0108] The street lamp simulation system can be set up according to the control parameters corresponding to the genetic code x to calculate the expected energy consumption E(x); the lighting quality index Q(x) can be evaluated through simulation or actual scene testing. This index may include metrics related to user experience such as lighting uniformity and light comfort.

[0109] Several lighting parameter setting schemes with objective function values ​​greater than a preset threshold are determined as parent schemes; a selection operation can be applied to select individuals with higher objective function values, such as using strategies such as roulette wheel selection or elite selection.

[0110] Performing genetic operations on the parent solution to obtain a next generation solution, and returning to execute the step of calculating the objective function value of the current lighting parameter setting solution until a preset iteration termination condition is reached;

[0111] For example, crossover operations can be applied to produce offspring. Crossover operations can adopt strategies such as single-point crossover, multi-point crossover or uniform crossover to simulate the recombination process in biological genetics. Mutation operations can be applied to improve population diversity. Mutation operations can be achieved by randomly changing one or more bits in the individual gene code.

[0112] After reaching the iteration termination condition, the solution with the highest objective function value is selected as the optimized lighting parameter setting solution for the control of the smart street light system. The iteration termination condition includes the preset number of iterations or the convergence of the objective function. The population evolution process can be executed in parallel in multiple independent street light control areas, and the optimal solution generated in each area can be shared with other areas to promote overall optimization.

[0113] Through the above iterative optimization implementation method, it is designed specifically for the needs of smart street lighting control systems. It not only improves energy efficiency and lighting quality, but also increases the adaptability and flexibility of the system in dealing with changing environmental conditions. The automation and intelligent implementation of this process helps to achieve energy saving and continuous improvement of smart city lighting systems.

[0114] Furthermore, the Internet of Things technology can be used to collect street lamp lighting data corresponding to the optimized lighting parameter setting scheme, and based on the optimized street lamp lighting data, calculate and determine whether the energy consumption corresponding to the scheme falls within the preset energy consumption threshold range.

[0115] This step emphasizes that after obtaining a lighting parameter setting scheme optimized by the algorithm, the system needs to re-collect the actual street lighting data corresponding to this new scheme. The collected data will be used to calculate the actual energy consumption of the optimized scheme and compare it with the preset energy consumption threshold. The purpose of this step is to verify whether the optimization process has actually produced a lighting parameter setting scheme with lower energy consumption and better lighting quality requirements. The role and significance of this step are:

[0116] 1. Verify the optimization effect: ensure the effectiveness of the optimization process, that is, the optimized solution can meet the preset energy-saving goals.

[0117] 2. Feedback control: This step provides a feedback control mechanism for the smart street lighting control system, allowing the system to adjust and optimize again based on the optimized results to ensure that the final result is robust.

[0118] 3. Continuous iterative improvement: If the energy consumption of the new solution does not fall within the preset threshold range, the system will continue to perform the optimization process. This means that the system can continue to iterate and continuously improve the lighting parameters until a solution that meets the energy consumption requirements is found.

[0119] 4. Reduce the gap between actual and expected: In actual applications, there may be many factors that cause the difference between actual energy consumption and predicted energy consumption. This gap can be reduced through actual data verification, making the lighting control strategy more accurate and practical.

[0120] 5. Improve system reliability: By conducting actual energy consumption tests on the optimized lighting parameters, it can be ensured that the system will not ignore possible problems in actual operation due to theoretical optimization, thereby improving the reliability of the entire smart lighting system.

[0121] 6. Energy saving and cost-effectiveness: Only by testing and confirming that the optimization plan can effectively reduce energy consumption, can the smart lighting system achieve its energy-saving purpose, which can not only reduce energy consumption, but also reduce maintenance costs and operating costs in the long run.

[0122] 7. User experience and public interest: By ensuring that the optimization plan is actually implemented, energy can be saved while maintaining lighting quality, which will enhance the user experience and meet the public interest in sustainable urban development.

[0123] Through these steps, the overall performance and effectiveness of the intelligent lighting system can be improved, providing an energy-saving and efficient solution for urban lighting management.

[0124] Furthermore, if it is determined that the energy consumption corresponding to the scheme does not fall within the preset energy consumption threshold range, the step of encoding each lighting parameter in the scheme into a gene string structure when the energy consumption exceeds the preset energy consumption threshold range can be continued until the energy consumption corresponding to the optimized lighting parameter setting scheme falls within the preset energy consumption threshold range.

[0125] Specifically, the following steps can be included:

[0126] 1. Verify the energy consumption of the optimized solution: Calculate and verify the energy consumption of the optimized lighting parameter setting solution to confirm whether it meets the preset energy consumption threshold range.

[0127] 2. Determine whether the energy consumption meets the conditions: Based on the calculation results, determine whether the energy consumption of the optimized solution falls within the preset energy consumption threshold range. If the energy consumption meets expectations, the optimization process is completed; if the energy consumption does not meet expectations, further iterative optimization is required.

[0128] 3. Continue iterative optimization: When the calculated energy consumption does not reach the expected range, the system will repeat the above optimization process. This process includes re-encoding the lighting parameters into gene strings and iteratively optimizing the gene strings again through the preset objective function. The significance of this is:

[0129] 1. Ensure the effectiveness of optimization: Through multiple iterations, we ensure that a lighting parameter setting solution that meets energy consumption requirements and is practical is obtained, thus achieving the effectiveness of the optimization process.

[0130] 2. Improve lighting efficiency: Through continuous iterative optimization, more energy-saving lighting solutions can be discovered, thereby reducing energy consumption and improving the energy efficiency of the street lighting system.

[0131] 3. Strong adaptability: The iterative optimization method allows the smart street lighting system to self-adjust according to the actual measurement data, which reflects a high degree of adaptability and can adapt to various possible environmental changes and lighting needs.

[0132] 4. Cost savings: With the implementation of the optimal lighting parameter solution, in the long run, the operating cost of the street lighting system will be significantly reduced due to energy-saving effects.

[0133] 5. Improve system stability: The continuous iterative optimization process enhances the smart street light system's ability to respond to external changes and improves the stability of system operation.

[0134] 6. Sustainable development: By ensuring that the lighting system meets the energy consumption threshold, it promotes the practice of environmental protection and sustainable development and complies with the policy requirements of energy conservation and emission reduction.

[0135] 7. Improved user satisfaction: The finely optimized lighting system can save energy while meeting lighting needs, and improve the satisfaction of end users and city managers with the smart street lighting system.

[0136] The iterative optimization method proposed above is a key link in the design of smart street lighting control systems. It ensures that the system is not only optimal in theory, but also achieves the best energy-saving effect in actual operation, which is crucial to improving the overall performance and sustainability of smart city infrastructure.

[0137] It can be seen that, by obtaining the current lighting parameter setting scheme of each street lamp in the smart street lamp system, the street lamp lighting data corresponding to the scheme is collected through the Internet of Things technology; the energy consumption corresponding to the scheme is calculated according to the street lamp lighting data; when the energy consumption exceeds the preset energy consumption threshold range, the various lighting parameters in the scheme are encoded into a gene string structure; the gene string is iteratively optimized according to the preset objective function to obtain the optimized lighting parameter setting scheme, thereby realizing real-time, dynamic and precise lighting control, ensuring the energy saving and intelligence level of the lighting system, and meeting the lighting quality requirements under different environmental conditions and user needs.

[0138] Another embodiment of the present invention provides a smart street lighting control system based on Internet of Things technology, see Figure 3 , the system may include:

[0139] The collection module 301 is used to obtain the current lighting parameter setting scheme of each street lamp in the smart street lamp system, and collect the street lamp lighting data corresponding to the scheme through the Internet of Things technology;

[0140] A calculation module 302, configured to calculate the energy consumption corresponding to the solution according to the street lighting data;

[0141] The encoding module 303 is used to encode each lighting parameter in the scheme into a gene string structure when the energy consumption exceeds a preset energy consumption threshold range, wherein each lighting parameter is used as a gene segment;

[0142] The optimization module 304 is used to iteratively optimize the gene string according to a preset objective function to obtain an optimized lighting parameter setting solution.

[0143] It can be seen that, by obtaining the current lighting parameter setting scheme of each street lamp in the smart street lamp system, the street lamp lighting data corresponding to the scheme is collected through the Internet of Things technology; the energy consumption corresponding to the scheme is calculated according to the street lamp lighting data; when the energy consumption exceeds the preset energy consumption threshold range, the various lighting parameters in the scheme are encoded into a gene string structure; the gene string is iteratively optimized according to the preset objective function to obtain the optimized lighting parameter setting scheme, thereby realizing real-time, dynamic and precise lighting control, ensuring the energy saving and intelligence level of the lighting system, and meeting the lighting quality requirements under different environmental conditions and user needs.

[0144] 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.

[0145] Specifically, in this embodiment, the above storage medium may be configured to store a computer program for performing the following steps:

[0146] S201, for each street lamp in the smart street lamp system, obtaining a current lighting parameter setting scheme of the street lamp, and collecting street lamp lighting data corresponding to the scheme through the Internet of Things technology;

[0147] S202, calculating the energy consumption corresponding to the solution according to the street lamp lighting data;

[0148] S203, when the energy consumption exceeds a preset energy consumption threshold range, encoding each lighting parameter in the scheme into a gene string structure, wherein each lighting parameter serves as a gene fragment;

[0149] S204, iteratively optimizing the gene string according to a preset objective function to obtain an optimized lighting parameter setting solution.

[0150] It can be seen that, by obtaining the current lighting parameter setting scheme of each street lamp in the smart street lamp system, the street lamp lighting data corresponding to the scheme is collected through the Internet of Things technology; the energy consumption corresponding to the scheme is calculated according to the street lamp lighting data; when the energy consumption exceeds the preset energy consumption threshold range, the various lighting parameters in the scheme are encoded into a gene string structure; the gene string is iteratively optimized according to the preset objective function to obtain the optimized lighting parameter setting scheme, thereby realizing real-time, dynamic and precise lighting control, ensuring the energy saving and intelligence level of the lighting system, and meeting the lighting quality requirements under different environmental conditions and user needs.

[0151] 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.

[0152] 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.

[0153] Specifically, in this embodiment, the processor may be configured to perform the following steps through a computer program:

[0154] S201, for each street lamp in the smart street lamp system, obtaining a current lighting parameter setting scheme of the street lamp, and collecting street lamp lighting data corresponding to the scheme through the Internet of Things technology;

[0155] S202, calculating the energy consumption corresponding to the solution according to the street lamp lighting data;

[0156] S203, when the energy consumption exceeds a preset energy consumption threshold range, encoding each lighting parameter in the scheme into a gene string structure, wherein each lighting parameter serves as a gene fragment;

[0157] S204, iteratively optimizing the gene string according to a preset objective function to obtain an optimized lighting parameter setting solution.

[0158] Specifically, the specific examples in this embodiment can refer to the examples described in the above embodiments and optional implementation modes, and this embodiment will not be described in detail here.

[0159] It can be seen that, by obtaining the current lighting parameter setting scheme of each street lamp in the smart street lamp system, the street lamp lighting data corresponding to the scheme is collected through the Internet of Things technology; the energy consumption corresponding to the scheme is calculated according to the street lamp lighting data; when the energy consumption exceeds the preset energy consumption threshold range, the various lighting parameters in the scheme are encoded into a gene string structure; the gene string is iteratively optimized according to the preset objective function to obtain the optimized lighting parameter setting scheme, thereby realizing real-time, dynamic and precise lighting control, ensuring the energy saving and intelligence level of the lighting system, and meeting the lighting quality requirements under different environmental conditions and user needs.

[0160] 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 smart street lamp lighting control method based on Internet of Things technology, characterized in that: The method comprises: For each street lamp in the smart street lamp system, obtain the current lighting parameter setting scheme of the street lamp, and collect the street lamp lighting data corresponding to the scheme through the Internet of Things technology; Calculating the energy consumption corresponding to the scheme according to the street lamp lighting data; the calculating the energy consumption corresponding to the scheme according to the street lamp lighting data comprises: E(x)=∑(P i ·T i )+α·(P s ·(N -∑T i ))+β·∆T+γ·F(L, V) Among them, the P i represents the power consumption of the street lamp at the i-th brightness level, Ti represents the operating time of the street lamp at the i-th brightness level, and P s represents the power consumption of the street lamp in standby or minimum brightness mode, N represents the total time in the evaluation cycle, α represents the coefficient for adjusting the relationship between standby energy consumption and active energy consumption, β represents the temperature adjustment factor, ∆T is the difference between the ambient temperature and the preset optimal operating temperature, γ represents the flow adjustment factor, and F(L, V) is a function related to the ambient brightness L and the vehicle and pedestrian flow V; F(L, V) is in the following form: F(L,V)=δ·L^-1+η·g(V) In this formula, δ is the ambient brightness adjustment coefficient; L^-1 reflects the potential energy savings of reducing artificial lighting under natural light conditions; η is the traffic flow adjustment coefficient, which is used to adjust the impact of traffic flow on energy consumption; g(V) is a function related to traffic flow V, which is used to adjust the energy consumption estimate based on real-time data of pedestrian and vehicle flow: g(V) = a·(1-e^{-b·V}) a is the upper limit coefficient of traffic energy consumption, which represents the maximum additional energy consumption that the smart street light system can achieve under extremely high traffic conditions; e is the base of the natural logarithm; b is the traffic energy consumption sensitivity coefficient, which indicates the rate at which the system energy consumption increases as the traffic flow increases; V is the real-time traffic flow; When the energy consumption exceeds a preset energy consumption threshold range, encoding each lighting parameter in the scheme into a gene string structure, wherein each lighting parameter serves as a gene fragment; The gene string is iteratively optimized according to a preset objective function to obtain an optimized lighting parameter setting scheme, wherein the preset objective function is: H(x) =w1·E(x)^(-1)+w2·Q(x) Wherein, x represents a lighting parameter setting scheme, E(x) is the expected energy consumption of the scheme, Q(x) is an indicator for meeting lighting quality requirements, and w1 and w2 are weight coefficients.

2. The method according to claim 1, characterized in that The method further comprises: The street lamp lighting data corresponding to the optimized lighting parameter setting scheme is collected through the Internet of Things technology, and based on the optimized street lamp lighting data, it is calculated and determined whether the energy consumption corresponding to the scheme falls within the preset energy consumption threshold range.

3. The method according to claim 2, characterized in that The method further comprises: If it does not fall within the preset energy consumption threshold range, continue to execute the step of encoding each lighting parameter in the scheme into a gene string structure when the energy consumption exceeds the preset energy consumption threshold range until the energy consumption corresponding to the optimized lighting parameter setting scheme falls within the preset energy consumption threshold range.

4. The method according to claim 3, characterized in that The encoding of each lighting parameter in the scheme into a gene string structure comprises: A gene string structure is defined to represent the lighting parameters of a smart street lamp, wherein the gene string is composed of a plurality of gene fragments connected in sequence; Mapping each lighting parameter to a specific gene segment, wherein each gene segment consists of a series of bit sequences of a specific length; For each lighting parameter, a preset base encoding is used to discretize the range of the parameter attribute into a range of preset base numbers, and each preset base number corresponds to an attribute value; The gene fragments of each lighting parameter are connected in a predefined order to form a complete gene string.

5. A smart street lighting control system based on Internet of Things technology, characterized in that: The system comprises: A collection module, used to obtain the current lighting parameter setting scheme of each street lamp in the smart street lamp system, and collect street lamp lighting data corresponding to the scheme through the Internet of Things technology; A calculation module, used for calculating the energy consumption corresponding to the scheme according to the street lamp lighting data; the calculation of the energy consumption corresponding to the scheme according to the street lamp lighting data comprises: E(x)=∑(P i ·T i )+α·(P s ·(N -∑T i ))+β·∆T+γ·F(L, V) Among them, the P i represents the power consumption of the street lamp at the i-th brightness level, Ti represents the operating time of the street lamp at the i-th brightness level, and P s represents the power consumption of the street lamp in standby or minimum brightness mode, N represents the total time in the evaluation cycle, α represents the coefficient for adjusting the relationship between standby energy consumption and active energy consumption, β represents the temperature adjustment factor, ∆T is the difference between the ambient temperature and the preset optimal operating temperature, γ represents the flow adjustment factor, and F(L, V) is a function related to the ambient brightness L and the vehicle and pedestrian flow V; F(L, V) is in the following form: F(L,V)=δ·L^-1+η·g(V) In this formula, δ is the ambient brightness adjustment coefficient; L^-1 reflects the potential energy savings of reducing artificial lighting under natural light conditions; η is the traffic flow adjustment coefficient, which is used to adjust the impact of traffic flow on energy consumption; g(V) is a function related to traffic flow V, which is used to adjust the energy consumption estimate based on real-time data of pedestrian and vehicle flow: g(V) = a·(1-e^{-b·V}) a is the upper limit coefficient of traffic energy consumption, which represents the maximum additional energy consumption that the smart street light system can achieve under extremely high traffic conditions; e is the base of the natural logarithm; b is the traffic energy consumption sensitivity coefficient, which indicates the rate at which the system energy consumption increases as the traffic flow increases; V is the real-time traffic flow; An encoding module, used for encoding each lighting parameter in the scheme into a gene string structure when the energy consumption exceeds a preset energy consumption threshold range, wherein each lighting parameter serves as a gene segment; The optimization module is used to iteratively optimize the gene string according to a preset objective function to obtain an optimized lighting parameter setting scheme, wherein the preset objective function is: H(x) =w1·E(x)^(-1)+w2·Q(x) Wherein, x represents a lighting parameter setting scheme, E(x) is the expected energy consumption of the scheme, Q(x) is an indicator for meeting lighting quality requirements, and w1 and w2 are weight coefficients.

6. The system according to claim 5, characterized in that The system further comprises: The judgment module is used to collect the street lamp lighting data corresponding to the optimized lighting parameter setting scheme through the Internet of Things technology, and calculate and judge whether the energy consumption corresponding to the scheme falls within the preset energy consumption threshold range based on the optimized street lamp lighting data.

7. The system according to claim 6, characterized in that The system further comprises: The execution module is used to continue to execute the step of encoding each lighting parameter in the scheme into a gene string structure when the energy consumption exceeds the preset energy consumption threshold range if it does not fall within the preset energy consumption threshold range, until the energy consumption corresponding to the optimized lighting parameter setting scheme falls within the preset energy consumption threshold range.

8. 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 4 when executed.

9. 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 4.

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