Highway intelligent lighting energy-saving management system

Abstract

The invention discloses a road intelligent lighting energy-saving management system, and relates to the technical field of intelligent traffic and new energy crossing. According to the invention, data such as environment illumination intensity, traffic flow and weather change are collected in real time, so that the intelligent lighting system can automatically adjust the brightness or on-off of the lighting equipment according to actual requirements, and unnecessary energy waste is avoided. For example, in the time period with less traffic flow, the system automatically turns down the brightness of the street lamp, and power consumption is reduced; in the peak period, the brightness is automatically enhanced, and the road safety is ensured; the system can autonomously perform data acquisition, analysis and decision-making without manual intervention. By means of the automatic and intelligent management mode, the management efficiency of the urban lighting system can be greatly improved, the manual operation and maintenance cost is reduced, the response speed is higher, and environment changes can be adapted in time.

Description

Technical Field

[0001] The present invention relates to the cross - technical field of intelligent transportation and new energy, and specifically to an intelligent lighting energy - saving management system for highways. Background Art

[0002] With the acceleration of the urbanization process, the management and maintenance of urban lighting systems are facing increasing challenges.

[0003] Traditional lighting control systems are mostly time - controlled or manually controlled, with problems such as energy waste, high equipment maintenance difficulty, and low intelligence.

[0004] To solve these problems, in recent years, with the development of technologies such as the Internet of Things (IoT), big data, and artificial intelligence (AI), intelligent lighting systems have gradually become an important part of urban management.

[0005] By introducing intelligent technologies and combining real - time collection and analysis of sensor data, urban lighting systems can achieve functions such as on - demand adjustment, energy - saving optimization, and fault detection, greatly improving the operation efficiency and energy - saving effect of the lighting system;

[0006] However, there are still some positions that need to be adjusted and strengthened to achieve better management and adjustment of highway intelligent lighting. Therefore, a new solution to the above problems is needed. Summary of the Invention

[0007] The purpose of the present invention is to provide an intelligent lighting energy - saving management system for highways to solve the technical problems raised in the background art.

[0008] To achieve the above purpose, the present invention provides the following technical solution: An intelligent lighting energy - saving management system for highways, including a perception layer, a control layer, a decision - making layer, an execution layer, and a communication layer;

[0009] The perception layer integrates a variety of sensors and edge - computing devices to collect data in real - time and perform preliminary processing;

[0010] The control layer realizes dynamic adjustment and refined control based on multi - dimensional data decisions;

[0011] The decision - making layer relies on big data, artificial intelligence, and optimization algorithms to execute more accurate and forward - looking decisions;

[0012] The execution layer is used for real - time execution and feedback, and for energy conservation and convenient synchronous settlement according to energy;

[0013] The communication layer adopts an efficient communication protocol and network architecture to ensure the efficient and stable operation of the system.

[0014] Further, the execution layer includes a dynamic adaptive lighting control module, an energy recovery and reuse module, an intelligent fault detection and self-repair module, and a blockchain-based energy trading and payment module.

[0015] Further, the decision-making layer includes a deep data analysis and optimization decision module, and an edge computing and real-time control module.

[0016] Further, the functions of the dynamic adaptive lighting control module at least include intelligent traffic flow prediction and adjustment, context awareness and local dimming, and integration for smart cities;

[0017] The intelligent traffic flow prediction and adjustment combines traffic flow, weather changes, and historical data to predict traffic flow and driving patterns through machine learning algorithms, and dynamically adjusts the road lighting brightness. For example, the lighting intensity is automatically increased during peak traffic periods or in bad weather, and automatically reduced during smooth periods to reduce energy consumption;

[0018] The context awareness and local dimming automatically adjusts the brightness of local sections according to factors such as roads, traffic flow, and weather. For example, in sections without vehicles or areas with few vehicles, the system automatically reduces the lighting to the lowest level, and only turns on the brightness automatically through infrared sensing or video analysis technology when a vehicle passes by;

[0019] The integration for smart cities is to interconnect with other infrastructures, and the infrastructures at least include intelligent transportation systems (ITS) and autonomous driving, and coordinates the control of street lamp brightness and switching according to real-time traffic data and predictions.

[0020] Further, the functions of the energy recovery and reuse module at least include solar / wind energy integration, energy recovery and storage, and wireless power transmission;

[0021] The solar / wind energy integration installs solar panels or small wind power generation equipment in some areas, integrates green energy into lighting equipment according to system requirements, realizes energy self-sufficiency and supply scheduling, and reduces the system's dependence on the traditional power grid;

[0022] The energy recovery and storage integrates a storage battery and an energy recovery device. When the light is strong or the wind is strong, the street lamp system stores the excess energy and releases it when the power demand is high, reducing the dependence on external energy;

[0023] The wireless power transmission pilots the wireless power transmission technology in some areas to provide energy support for street lamps through non-contact power transmission, avoiding laying power lines and improving the flexibility of the system.

[0024] Further, the functions of the intelligent fault detection and self-repair module at least include fault detection and early warning, self-repair function, and predictive maintenance

[0025] The fault detection and early warning automatically detect abnormal conditions of lighting devices through built-in sensors and intelligent analysis. The abnormal conditions at least include current overload, bulb aging, and line disconnection, automatically generate an alarm, and transmit the information to maintenance personnel through a wireless network;

[0026] The self-repair function uses modular design. When a street lamp fails, the intelligent control system will automatically switch to an adjacent backup street lamp or backup system to ensure the uninterrupted lighting function;

[0027] The predictive maintenance combines big data analysis and machine learning to predict the working cycle of street lamp equipment, intervene in and maintain possible faults in advance, and reduce labor costs.

[0028] Furthermore, the functions of the blockchain-based energy trading and payment module at least include distributed energy trading, a transparent payment mechanism, and smart contracts

[0029] The distributed energy trading includes integrating blockchain technology to conduct energy trading and payment among multiple energy nodes. The energy nodes at least include solar panels and wind turbines to achieve autonomous allocation and trading settlement of regional energy;

[0030] The transparent payment mechanism ensures the transparency and security of energy trading through blockchain technology, making the energy flow and payment between different regions and different departments more efficient and fair;

[0031] The smart contract is based on smart contract technology to achieve automatic payment and settlement, further reducing manual intervention and improving efficiency.

[0032] Furthermore, the functions of the deep data analysis and optimization decision-making module at least include real-time big data analysis, an artificial intelligence self-learning system, and multi-dimensional collaborative optimization

[0033] The real-time big data analysis conducts real-time analysis of lighting requirements through multiple data sources. The data sources at least include weather forecasts, traffic flow, and environmental monitoring, and makes optimal decisions on energy conservation and safety based on big data;

[0034] The artificial intelligence self-learning system adaptively adjusts the lighting strategy by continuously learning historical data and feedback, using deep learning algorithms to optimize the energy-saving effect and enhance the intelligent decision-making ability of the lighting system;

[0035] The multi-dimensional collaborative optimization uses multi-objective optimization algorithms to minimize energy consumption to the greatest extent while ensuring traffic safety, and dynamically adjusts the system behavior to cope with emergencies.

[0036] Furthermore, the functions of the deep data analysis and optimization decision-making module include edge computing processing, real-time adjustment and remote command, and distributed control.

[0037] The edge computing processing is to distribute data processing and computing tasks from the central server to the on-site sensor nodes, reducing latency and improving response speed. For example, data such as traffic flow, temperature, and humidity are directly processed at the street lamp controller end, reducing network load and providing real-time response.

[0038] The real-time adjustment and remote command allow administrators to perform remote operations through the cloud platform and mobile applications. At the same time, local devices have sufficient self-processing capabilities to ensure timely response and adjustment in case of emergencies.

[0039] The distributed control enables the street lamp system to make adaptive decisions based on the on-site environment, optimizing the working status of each lighting unit and avoiding reliance on a single control center.

[0040] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0041] 1. By collecting data such as ambient light intensity, traffic flow, and weather changes in real time, the intelligent lighting system can automatically adjust the brightness or switch of lighting devices according to actual needs, avoiding unnecessary energy waste. For example, during periods of low traffic flow, the system automatically reduces the brightness of street lamps to reduce power consumption; during peak hours, it automatically increases the brightness to ensure road safety.

[0042] 2. The system of the present invention can independently collect, analyze, and make decisions without manual intervention. This automated and intelligent management method can greatly improve the management efficiency of the urban lighting system, reduce the costs of manual operation and maintenance, and has a faster response speed, being able to adapt to environmental changes in a timely manner.

[0043] 3. The present invention introduces an intelligent fault detection and self-repair function, which can diagnose faults in a timely manner when equipment fails and automatically switch to the backup system to avoid long-term lighting interruption. At the same time, fault information is real-time fed back to the control layer and decision-making layer to ensure that the system can be maintained and repaired in a timely manner. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for describing the embodiments will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0045] Figure 1 It is a schematic diagram of the overall structure system of the present invention. Detailed implementation mode

[0046] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments.

[0047] Please refer to Figure 1 , a highway intelligent lighting energy-saving management system, including a perception layer, a control layer, a decision-making layer, an execution layer, and a communication layer;

[0048] The perception layer integrates a variety of sensors and edge computing devices to collect data in real time and perform preliminary processing;

[0049] The control layer realizes dynamic adjustment and refined control based on multi-dimensional data decisions;

[0050] The decision-making layer relies on big data, artificial intelligence, and optimization algorithms to execute more accurate and forward-looking decisions;

[0051] The execution layer is used for real-time execution and feedback, and for energy conservation and convenient synchronous settlement according to energy;

[0052] The communication layer adopts an efficient communication protocol and network architecture to ensure the efficient and stable operation of the system.

[0053] The execution layer includes a dynamic adaptive lighting control module, an energy recovery and reuse module, an intelligent fault detection and self-repair module, and a blockchain-based energy trading and payment module.

[0054] The decision-making layer includes a deep data analysis and optimization decision module, and an edge computing and real-time control module.

[0055] The functions of the dynamic adaptive lighting control module at least include intelligent traffic flow prediction and adjustment, context awareness and local dimming, and integration for smart cities;

[0056] Intelligent traffic flow prediction and adjustment is to combine traffic flow, weather changes, and historical data, predict traffic flow and driving patterns through machine learning algorithms, and dynamically adjust the road lighting brightness. For example, the lighting intensity is automatically increased during peak traffic hours or in bad weather, and automatically reduced during smooth periods to reduce energy consumption;

[0057] Context awareness and local dimming is to automatically adjust the brightness of local sections according to factors such as roads, traffic flow, and weather. For example, in sections without vehicles or areas with few vehicles, the system automatically reduces the lighting to the lowest level, and only when a vehicle passes by, the brightness is automatically turned on through infrared sensing or video analysis technology;

[0058] Integration for smart cities is to interconnect with other infrastructures, which at least include intelligent transportation systems (ITS) and autonomous driving, and coordinate the control of street lamp brightness and switching according to real-time traffic data and predictions.

[0059] The functions of the energy recovery and reuse module at least include solar / wind energy integration, energy recovery and storage, and wireless power transmission;

[0060] Solar / wind energy integration is to install solar panels or small wind power generation equipment in some areas, integrate green energy into lighting equipment according to system requirements, achieve energy self-sufficiency and supply scheduling, and reduce the system's dependence on the traditional power grid;

[0061] Energy recovery and storage is to integrate storage batteries and energy recovery devices. When there is strong sunlight or strong wind, the street lamp system stores the excess energy and releases it when the power demand is high, reducing the dependence on external energy;

[0062] Wireless power transmission is to pilot wireless power transmission technology in some areas, provide energy support for street lamps through non-contact power transmission, avoid laying power lines, and improve the flexibility of the system.

[0063] The functions of the intelligent fault detection and self-repair module at least include fault detection and warning, self-repair function, and predictive maintenance

[0064] Fault detection and warning automatically detect abnormal conditions of lighting equipment through built-in sensors and intelligent analysis. The abnormal conditions at least include current overload, bulb aging, and line disconnection, automatically generate alarms, and transmit the information to maintenance personnel through the wireless network;

[0065] The self-repair function uses modular design. When a street lamp fails, the intelligent control system will automatically switch to adjacent backup street lamps or backup systems to ensure uninterrupted lighting functions;

[0066] Predictive maintenance combines big data analysis and machine learning to predict the working cycle of street lamp equipment, intervene and maintain possible faults in advance, and reduce labor costs.

[0067] The functions of the blockchain-based energy trading and payment module at least include distributed energy trading, transparent payment mechanism, and smart contract

[0068] Distributed energy trading includes integrating blockchain technology to conduct energy trading and payment among multiple energy nodes. The energy nodes at least include solar panels and wind turbines, realizing autonomous allocation and trading settlement of regional energy;

[0069] The transparent payment mechanism ensures the transparency and security of energy transactions through blockchain technology, making the energy flow and payment between different regions and departments more efficient and fair;

[0070] The smart contract, based on smart contract technology, realizes automatic payment and settlement, further reducing manual intervention and improving efficiency.

[0071] The functions of the deep data analysis and optimization decision-making module at least include big data real-time analysis, artificial intelligence self-learning system, and multi-dimensional collaborative optimization

[0072] The big data real-time analysis conducts real-time analysis of lighting requirements through multiple data sources. The data sources at least include weather forecasts, traffic flows, and environmental monitoring, and makes optimal decisions on energy conservation and security based on big data;

[0073] The artificial intelligence self-learning system, by continuously learning historical data and feedback, uses deep learning algorithms to adaptively adjust lighting strategies, optimize energy-saving effects, and enhance the intelligent decision-making ability of the lighting system;

[0074] The multi-dimensional collaborative optimization, through multi-objective optimization algorithms, minimizes energy consumption to the greatest extent while ensuring traffic safety, and dynamically adjusts system behavior to cope with emergencies.

[0075] The functions of the deep data analysis and optimization decision-making module include edge computing processing, real-time adjustment and remote command, and distributed control

[0076] The edge computing processing distributes data processing and computing tasks from the central server to on-site sensor nodes, reducing latency and improving response speed. For example, data such as vehicle flow, temperature, and humidity are directly processed at the street lamp controller end, reducing network load and responding in real time;

[0077] The real-time adjustment and remote command enable administrators to perform remote operations through the cloud platform and mobile applications. At the same time, local devices have sufficient self-processing capabilities to ensure timely response and adjustment during emergencies;

[0078] The distributed control enables the street lamp system to make adaptive decisions based on the on-site environment, optimize the working state of each lighting unit, and avoid relying on a single control center.

[0079] For those skilled in the art, it is obvious that the present invention is not limited to the details of the above-described exemplary embodiments, and the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention. Therefore, in any aspect, the embodiments should be regarded as exemplary and non-limiting. The scope of the present invention is defined by the appended claims rather than the above description. Therefore, all changes falling within the meaning and scope of the equivalent elements of the claims are intended to be embraced within the present invention. Any reference signs in the claims should not be construed as limiting the claims involved.

Claims

1. An intelligent lighting energy-saving management system for roads, characterized in that: It includes a perception layer, a control layer, a decision-making layer, an execution layer, and a communication layer; The perception layer integrates multiple sensors and edge computing devices to collect data in real time and perform preliminary processing; The control layer realizes dynamic adjustment and refined control based on multi-dimensional data decision-making; The decision-making layer relies on big data, artificial intelligence, and optimization algorithms to execute more accurate and forward-looking decisions; The execution layer is used for real-time execution and feedback, and to achieve energy conservation and convenient synchronous settlement according to energy; The communication layer adopts an efficient communication protocol and network architecture to ensure the efficient and stable operation of the system.

2. The intelligent lighting energy-saving management system for highways according to claim 1, wherein: The execution layer includes a dynamic adaptive lighting control module, an energy recovery and reuse module, an intelligent fault detection and self-repair module, and a blockchain-based energy trading and payment module.

3. A highway intelligent lighting energy-saving management system according to claim 1, characterized in that: The decision-making layer includes a deep data analysis and optimization decision module, and an edge computing and real-time control module.

4. The intelligent lighting energy-saving management system for highways according to claim 2, wherein: The functions of the dynamic adaptive lighting control module at least include intelligent traffic flow prediction and adjustment, context awareness and local dimming, and integration for smart cities; The intelligent traffic flow prediction and adjustment combines traffic flow, weather changes, and historical data to predict traffic flow and driving patterns through machine learning algorithms, and dynamically adjusts the brightness of road lighting; The context awareness and local dimming automatically adjusts the brightness of local sections according to factors such as roads, traffic flow, and weather; The integration for smart cities is to interconnect with other infrastructures, and the infrastructures at least include an intelligent transportation system and autonomous driving. According to real-time traffic data and predictions, it coordinates and controls the brightness and switching of street lights.

5. The intelligent lighting energy-saving management system for highways according to claim 2, wherein: The functions of the energy recovery and reuse module at least include solar / wind energy integration, energy recovery and storage, and wireless power transmission; The solar / wind energy integration installs solar panels or small wind power generation equipment in some areas, integrates green energy into lighting equipment according to system requirements, realizes energy self-sufficiency and supply scheduling, and reduces the system's dependence on the traditional power grid; The energy recovery and storage integrates a storage battery and an energy recovery device. When the light is strong or the wind is strong, the street light system stores the excess energy and releases it when the power demand is high, reducing the dependence on external energy; The wireless power transmission pilots the wireless power transmission technology in some areas to provide energy support for street lights through non-contact power transmission, avoiding laying power lines and improving the flexibility of the system.

6. The intelligent lighting energy-saving management system for highways according to claim 2, characterized in that: The functions of the intelligent fault detection and self-repair module at least include fault detection and early warning, self-repair function, and predictive maintenance The fault detection and early warning automatically detects abnormal conditions of lighting equipment through built-in sensors and intelligent analysis. The abnormal conditions at least include current overload, bulb aging, and line disconnection, automatically generates an alarm, and transmits the information to maintenance personnel through a wireless network; The self-repair function uses a modular design. When a street light fails, the intelligent control system will automatically switch to an adjacent backup street light or backup system to ensure that the lighting function is uninterrupted; The predictive maintenance combines big data analysis and machine learning to predict the working cycle of street lamp equipment, intervene and maintain possible faults in advance, and reduce labor costs.

7. An intelligent lighting energy-saving management system for highways according to claim 2, characterized in that: The functions of the blockchain-based energy trading and payment module at least include distributed energy trading, a transparent payment mechanism, and smart contracts. The distributed energy trading includes integrating blockchain technology to conduct energy trading and payment among multiple energy nodes. The energy nodes at least include solar panels and wind turbines to achieve autonomous allocation and trading settlement of regional energy. The transparent payment mechanism ensures the transparency and security of energy trading through blockchain technology, making the energy flow and payment between different regions and different departments more efficient and fair. The smart contract is based on smart contract technology to achieve automatic payment and settlement, further reducing manual intervention and improving efficiency.

8. The intelligent lighting energy-saving management system for highways according to claim 3, wherein: The functions of the in-depth data analysis and optimization decision-making module at least include real-time big data analysis, an artificial intelligence self-learning system, and multi-dimensional collaborative optimization. The real-time big data analysis conducts real-time analysis of lighting requirements through multiple data sources. The data sources at least include weather forecasts, traffic flow, and environmental monitoring, and optimize decisions on energy conservation and safety based on big data. The artificial intelligence self-learning system adaptively adjusts the lighting strategy by continuously learning historical data and feedback, using deep learning algorithms to optimize the energy-saving effect and enhance the intelligent decision-making ability of the lighting system. The multi-dimensional collaborative optimization uses multi-objective optimization algorithms to minimize energy consumption to the greatest extent while ensuring traffic safety, and dynamically adjusts the system behavior to cope with emergencies.

9. The intelligent lighting energy-saving management system for highways according to claim 3, characterized in that: The functions of the in-depth data analysis and optimization decision-making module include edge computing processing, real-time adjustment and remote command, and distributed control. The edge computing processing distributes data processing and computing tasks from the central server to on-site sensor nodes, reducing latency and improving response speed. The real-time adjustment and remote command allow administrators to perform remote operations through the cloud platform and mobile applications. However, local devices have sufficient self-processing capabilities to ensure timely response and adjustment during emergencies. The distributed control enables the street lamp system to make adaptive decisions based on the on-site environment, optimize the working state of each lighting unit, and avoid relying on a single control center.

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