Intelligent street lamp and road emergency communication system

Abstract

The invention discloses an intelligent street lamp and a road emergency communication system, which integrates two light sources with information transmission and illumination functions into the same street lamp system by combining wireless optical communication with widely distributed city street lamps, adopting a distributed architecture and utilizing a dual-mode fusion light source driving technology. And abnormal information in the road surface data is monitored in real time by using the multi-mode road surface intelligent detection system, so that the accurate sensing of the road state is realized. In a road emergency communication system, a multi-node emergency communication network for information interaction is constructed by combining smart street lamps with light receivers, and efficient and stable information transmission and processing capacity is provided for smart city road emergency communication; the intelligent street lamps are used for deeply mining and analyzing city data, intelligent and personalized decision support is provided for city management, convenience is brought to the life of city residents, and the city management level is greatly improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of smart city transportation, and particularly relates to a smart street lamp and a road emergency communication system. Background Art

[0002] With the continuous expansion of urban functions, urban management also faces many challenges. In addition to traditional urban management, emerging fields such as smart transportation and smart environment also need to be included in the scope of urban management, increasing the complexity and difficulty of management. Therefore, how to effectively utilize digital technology and intelligent means to improve the efficiency and quality of urban management has also become one of the problems to be solved in the current urban development. Traditional urban street lamps have problems such as extensive management methods, high energy consumption, low operation and maintenance efficiency, and inflexible lighting control, which greatly increase the difficulty and cost of urban management. In recent years, the concept of smart street lamps has been proposed, and the traditional street lamp system has been technologically upgraded, initially solving the above problems. However, the current application of smart street lamps is mainly the upgrade and management of lighting functions. With the rapid development of the Internet of Things technology, street lamps can be upgraded into nodes for the interconnection of all things in the city, and a large amount of urban data can be collected by using their wide distribution characteristics, covering fields such as traffic management, environmental detection, and urban planning, so as to explore the intelligent application of smart street lamps in different life scenarios of smart cities.

[0003] With the acceleration of the urbanization process, the road traffic flow continues to grow, and sudden road safety incidents (such as road surface collapse, bridge fracture, traffic accidents, etc.) occur frequently, putting forward higher requirements for the emergency response ability of urban traffic. However, the existing road monitoring and information release systems usually rely on manual inspections or data uploads from discrete monitoring points, which not only have problems such as incomplete information collection and lagging response, but also are prone to being unable to transmit emergency information in a timely manner due to the limitations of communication methods (such as radio frequency band interference or insufficient coverage), resulting in the further expansion of potential hazards. Especially in urban areas with dense traffic flow, traditional wireless communication methods are vulnerable to interference and cannot meet the requirements of efficient and real-time information transmission.

[0004] The prior art mainly relies on means such as manual inspections, the layout of monitoring points, and real-time traffic condition reports from navigation users to obtain road hazard information and publish it. Some studies have improved the prior art on this basis. Zeng Song et al. proposed a solution for spreading emergency information through various methods such as broadcasting, APP push, electronic screen display, and text messages; Gao Yonghui used rich media technology to report and spread road emergency information, enhancing the diversity and timeliness of information transmission. In addition, Xi Daozhen studied the emergency information broadcast strategy in the vehicle-to-everything (V2X) network and designed a hybrid emergency message broadcast protocol based on roadside unit assistance, which is specifically applied to the transmission of emergency information on urban roads. Huang Zitao et al. proposed a method for road collapse warning by inputting monitoring data and the type of road to be evaluated into a preset road collapse probability prediction model to predict possible collapse risks. These improvement solutions have, to a certain extent, improved the efficiency and accuracy of road hazard information release, but there are still problems such as information transmission delays and limited coverage.

[0005] Although manual inspections can, to a certain extent, detect abnormal situations on the road, their efficiency is low and limited by the inspection frequency, making it difficult to achieve comprehensive and real-time monitoring. The solution designed by Zeng Song for spreading emergency information through various methods requires multi-channel distribution and cross-platform collaboration. The response time differences of different media may cause driver decision-making errors, and the APP push relying on the cellular network fails in remote areas or when the network is congested; rich media is prone to channel congestion in high-traffic areas due to its high-bandwidth content requirements; the V2X broadcast strategy relies on roadside units for relaying, increasing additional construction costs. Therefore, the limitations of the prior art result in a certain time lag and blind area in the discovery and information release of road hazards, being unable to effectively respond to emergencies and ensure traffic safety. Additionally, the current technical feature is that the lighting system and the communication system are two independent systems and cannot transmit information while lighting. Summary of the Invention

[0006] To solve the above problems existing in the prior art, the present invention provides a smart street lamp and a road emergency communication system. The technical problems to be solved by the present invention are achieved through the following technical solutions:

[0007] In a first aspect, the present invention provides a smart street lamp, comprising:

[0008] an environmental perception module, an LED light board, a road safety perception module, a light source driving module, and a core control module; adjacent smart street lamps are connected through power lines, wherein,

[0009] the environmental perception module is used to obtain environmental information;

[0010] The road safety perception module is used to collect road surface data, detect and analyze the collected road surface data by using a multi-modal intelligent road surface detection system, and monitor in real time whether there is abnormal information in the road surface data. When abnormal information appears, warning information of the corresponding level is generated according to the level of the abnormal information and sent to the core control module;

[0011] The core control module is used to output a lighting control signal to the light source driving module according to the environmental information; output an emergency control signal of the corresponding level to the light source driving module according to the level of the warning information;

[0012] The light source driving module is used to automatically adjust the brightness and switch of the lighting lamps in the LED light board according to the lighting control signal by using a dual-mode fusion light source driving technology, control the communication lamps in the LED light board to give corresponding early warnings according to the level of the emergency control signal, and upload the abnormal information to the server for the reference of the management department.

[0013] In an embodiment of the present invention, the environmental information includes:

[0014] External light intensity and longitude and latitude information.

[0015] In an embodiment of the present invention, the multi-modal intelligent road surface detection system is used in the road safety perception module to detect and analyze the collected road surface data, and monitor in real time whether there is abnormal information in the road surface data, including:

[0016] The road safety perception module uses a deep learning object detection module based on improved YOLOV8 to detect the road surface data, and judges whether there is abnormal aggregation of vehicle flow, abnormal aggregation of pedestrian flow, temporary obstacles or road surface cracks in the road surface data. If so, a first abnormal result is output;

[0017] Use a marking integrity detection module based on edge analysis to detect the road markings in the road surface data, and judge whether there is an abnormal state in the road markings. If there is an abnormal state, a second abnormal result is output;

[0018] Use a road surface deformation monitoring module based on strain sensors to detect the road structure in the road surface data, and judge whether there is collapse or settlement in the road structure. If there is collapse or settlement, a third abnormal result is output;

[0019] The first abnormal result, the second abnormal result and the third abnormal result constitute the abnormal information.

[0020] In an embodiment of the present invention, the levels of the abnormal information include:

[0021] Low-level early warning, medium-level early warning and high-level early warning; among them,

[0022] The low-level warning for abnormal information includes only one of the first abnormal result, the second abnormal result, or the third abnormal result;

[0023] The medium-level warning for abnormal information includes two of the first abnormal result, the second abnormal result, or the third abnormal result;

[0024] The high-level warning for abnormal information includes the first abnormal result, the second abnormal result, and the third abnormal result.

[0025] In one embodiment of the present invention, the abnormal states include:

[0026] The road markings are blurred, missing, or offset.

[0027] In one embodiment of the present invention, in the light source driving module, the communication lights in the LED light board are controlled for corresponding warnings according to the level of the emergency control signal, and the abnormal information is uploaded for the reference of the management department, including:

[0028] When the level of the emergency control signal is low, control the communication lights in the LED light board to give warnings to the following vehicles;

[0029] When the level of the emergency control signal is medium, control the communication lights in the LED light board to give warnings to the following vehicles, and use the power line to transmit warning information to adjacent smart street lights through power line carrier technology, so that all the smart street lights on the whole road give warnings;

[0030] When the level of the emergency control signal is high, control the communication lights in the LED light board to give warnings to the following vehicles, and use the power line to transmit warning information to adjacent smart street lights through power line carrier technology, so that all the smart street lights on the whole road give warnings, and upload the abnormal information to the server for the reference of the management department.

[0031] In one embodiment of the present invention, the light source driving module includes:

[0032] An adjustable MOSFET array, a dual-channel control mechanism, and an impedance detection circuit; wherein,

[0033] The input end of the adjustable MOSFET array is connected to the output end of the core control module and is powered on, and the output end is connected to the input end of the dual-channel control mechanism;

[0034] The first output end of the dual-channel control mechanism is connected to the lighting lamp in the LED light board, and the second output end is connected to the input end of the impedance detection circuit;

[0035] The first output end of the impedance detection circuit is connected to the communication light in the LED light board, and the second output end is connected to the input end of the core control module.

[0036] In one embodiment of the present invention, the core control module is further configured to, under the remote control of a preset APP, adjust the brightness and switch of the lighting lamps in the LED light board by controlling the light source driving module.

[0037] In one embodiment of the present invention, the core control module is further configured to, when a warning instruction is issued by the management department, make the communication lights in the LED light board give warnings by controlling the light source driving module.

[0038] In a second aspect, a road emergency communication system includes:

[0039] The intelligent street lamp, optical receiver and management platform as described in the first aspect; wherein,

[0040] The intelligent street lamp is used for real-time monitoring of road surface data, and when abnormal information appears, it gives warnings to the optical receiver and the management platform according to the level of the abnormal information;

[0041] The optical receiver is arranged in a traveling vehicle and is used for reminding the driver of the traveling vehicle according to the warning received from the intelligent street lamp;

[0042] The management platform is used for timely processing of the warnings received from the intelligent street lamp; in case of an emergency, under the operation of the management department, it controls the intelligent street lamp to send a warning prompt to the nearby optical receiver to remind the driver of the traveling vehicle.

[0043] Advantages of the present invention:

[0044] In the solution provided by the present invention, by combining wireless optical communication with widely distributed urban street lamps, adopting a distributed architecture, and using a dual-mode fusion light source driving technology, two light sources for information transmission and lighting functions are integrated in the same street lamp system; among them, the first light source is a traditional lighting light source for providing the light intensity required by the environment, and the second light source is a communication light source, which will be modulated by optical signals of a specific frequency to transmit necessary data information. The multi-modal road surface intelligent detection system is used for real-time monitoring of abnormal information in road surface data to achieve accurate perception of the road state. In the road emergency communication system, an intelligent street lamp is combined with an optical receiver to construct a multi-node emergency communication network for information interaction, providing efficient and stable information transmission and processing capabilities for smart city road emergency communication; the intelligent street lamp is used for in-depth mining and analysis of urban data, providing intelligent and personalized decision-making support for urban management, thus bringing convenience to the lives of urban residents and greatly improving the level of urban governance. Description of the Drawings

[0045] Figure 1Schematic diagram of the structure of a smart street lamp provided by an embodiment of the present invention;

[0046] Figure 2 Schematic diagram of the topological structure of a light source driving module provided by an embodiment of the present invention;

[0047] Figure 3 Communication timing diagram of a light source driving module provided by an embodiment of the present invention;

[0048] Figure 4 Schematic diagram of the structure of a deep learning object detection module based on improved YOLOV8 in a road safety perception module provided by an embodiment of the present invention;

[0049] Figure 5 Schematic diagram of the structure of a road emergency communication system provided by an embodiment of the present invention;

[0050] Figure 6 Schematic diagram of the structure of a wide - field - of - view optical receiving system in a road emergency communication system provided by an embodiment of the present invention;

[0051] Figure 7 Schematic diagram of the topological structure of an optical receiver in a road emergency communication system provided by an embodiment of the present invention;

[0052] Figure 8 Illumination control test diagram of a smart street lamp provided by an embodiment of the present invention;

[0053] Figure 9 Road safety warning test diagram provided by an embodiment of the present invention;

[0054] Figure 10 Transmission system bandwidth test diagram in a road emergency communication system provided by an embodiment of the present invention;

[0055] Figure 11 Receiving system bandwidth test diagram in a road emergency communication system provided by an embodiment of the present invention. Detailed implementation manners

[0056] The following further describes the present invention in detail with specific embodiments, but the implementation manners of the present invention are not limited thereto.

[0057] An embodiment of the present invention provides a smart street lamp and a road emergency communication system.

[0058] Next, a smart street lamp provided by an embodiment of the present invention will be introduced first.

[0059] A smart street lamp provided by an embodiment of the present invention, as Figure 1 shown, may include:

[0060] An environmental perception module, an LED light board, a road safety perception module, a light source driving module, and a core control module; adjacent intelligent street lights are connected through power lines, where,

[0061] The environmental perception module is used to obtain environmental information;

[0062] The road safety perception module is used to collect road surface data, detect and analyze the collected road surface data using a multimodal road surface intelligent detection system, and monitor in real time whether there is abnormal information in the road surface data. When abnormal information appears, a warning message of the corresponding level is generated according to the level of the abnormal information and sent to the core control module;

[0063] The core control module is used to output a lighting control signal to the light source driving module according to the environmental information; output an emergency control signal of the corresponding level to the light source driving module according to the level of the warning message;

[0064] The light source driving module is used to utilize the dual-mode fusion light source driving technology to automatically adjust the brightness and switch of the lighting lamps in the LED light board according to the lighting control signal, control the communication lamps in the LED light board to give corresponding early warnings according to the level of the emergency control signal, and upload the abnormal information to the server for the reference of the management department.

[0065] The intelligent street light proposed in the embodiment of the present invention combines wireless optical communication with widely distributed urban street lights, adopts a distributed architecture, and utilizes the dual-mode fusion light source driving technology to integrate two light sources for information transmission and lighting functions in the same street light system; among them, the first light source is a traditional lighting light source, which is used to provide the light intensity required by the environment, and the second light source is a communication light source, and this communication light source will be modulated by optical signals of a specific frequency to transmit necessary data information. The multimodal road surface intelligent detection system is used to monitor the abnormal information in the road surface data in real time to achieve precise perception of the road state.

[0066] The intelligent street light proposed in the embodiment of the present invention uses the environmental perception module to obtain environmental information, and the environmental information can include: external light intensity and longitude and latitude information. The core control module outputs a lighting control signal to the light source driving module according to the environmental information to complete the automatic adjustment of the brightness and switch of the lighting lamps in the LED light board.

[0067] In traditional smart streetlights, the lighting function mainly relies on LED light sources, while the communication function usually adopts radio waves or other wireless communication technologies. However, radio waves are often interfered by factors such as buildings, vehicles, and weather in urban environments, resulting in a reduction in signal quality and communication reliability. In the smart streetlight proposed in the embodiments of the present invention, the environmental perception module uses its own illuminance sensor to obtain the external light intensity, and the core control module dynamically adjusts the brightness and switch of the lighting lamps in the LED light board according to the external light intensity. The core control module can also adapt to the seasonal changes by combining the longitude and latitude information to optimize the switch time of the lighting lamps. In addition, the core control module can also, under the remote control of a preset APP (such as a streetlight remote controller), adjust the brightness and switch of the lighting lamps in the LED light board by controlling the light source driving module.

[0068] The light source driving module provided by the embodiments of the present invention, as Figure 2 shown, may include:

[0069] an adjustable MOSFET array, a dual-channel control mechanism, and an impedance detection circuit; wherein,

[0070] the input end of the adjustable MOSFET array is connected to the output end of the core control module and is powered on, and the output end is connected to the input end of the dual-channel control mechanism;

[0071] the first output end of the dual-channel control mechanism is connected to the lighting lamp in the LED light board, and the second output end is connected to the input end of the impedance detection circuit;

[0072] the first output end of the impedance detection circuit is connected to the communication lamp in the LED light board, and the second output end is connected to the input end of the core control module.

[0073] Specifically, the embodiments of the present invention propose a dual-mode fusion light source driving technology based on the collaborative innovation of the hardware architecture and intelligent control algorithm, which can achieve the efficient integration of lighting dimming and optical communication modulation on a single driving platform. This dual-mode fusion light source driving technology optimizes the circuit redundancy problem existing in the dual-light source driving system, adopts a low-position driving topology design with the positive pole directly connected to the power supply, and combines the impedance detection circuit and the dual-channel control mechanism at the negative pole to construct an integrated driving scheme with wide-range dimming and high-speed communication capabilities. The core principle of this dual-mode fusion light source driving technology is to perform time-division multiplexing of the PWM dimming cycle and the OOK communication pulse through a physical layer isolation strategy, that is, inserting OOK modulation pulses to transmit data during the PWM low-level interval, which not only ensures the stability of the lighting system but also provides a low-interference transmission environment for optical communication. At the hardware architecture level, by using a multiplexed parallel MOSFET array to replace the independently set communication impedance matching circuit in the traditional scheme, the number of components and the circuit complexity are significantly reduced. The circuit topology is as Figure 2As shown, in traditional driving schemes, it is usually necessary to configure multiple discrete inductors and capacitor components to construct a communication impedance matching circuit. However, in the embodiments of the present invention, by utilizing the dynamically adjustable characteristics of MOSFETs, the dual functions of constant current driving and impedance matching are achieved on a single chip. When the system is in the lighting mode, the MOSFET array acts as a constant current source load to maintain the stable operation of the white LED. When switched to the communication mode, the on-resistance of the MOSFET is changed by dynamically adjusting the gate voltage, and the line characteristic impedance matching is completed in cooperation with the impedance detection circuit based on the principle of vector network analysis.

[0074] Specifically, by sampling the voltage V(t) and current I(t) of the line, the instantaneous impedance Z(t) = V(t) / I(t) is calculated, and then the theoretical matching impedance Z is calculated according to the first formula. match The expression of the first formula is as follows:

[0075]

[0076] Where L trace represents the line inductance, C par represents the parasitic capacitance, and C load represents the load capacitance.

[0077] By comparing the instantaneous impedance Z(t) with the theoretical matching impedance Z match , an error signal ΔZ = Z match -Z(t) is generated. The gate voltage of the MOSFET is adjusted by the core control module to change the on-resistance, so that ΔZ → 0. The application of the dynamic impedance matching technology effectively solves the problem of interference of high-frequency signal reflection on communication quality. By real-time collecting the voltage and current fluctuation data of the driving line and combining the error feedback mechanism to dynamically adjust the MOSFET, the impedance matching accuracy in the communication frequency band is ensured. Compared with the impedance network with fixed parameters in the traditional scheme, the dynamically adjustable method proposed in the embodiments of the present invention can adapt to the line characteristic changes under different working conditions, especially showing stronger robustness in the scenarios of temperature fluctuation or load change, greatly reducing the energy loss in the signal transmission process, significantly improving the communication signal integrity, and laying a hardware foundation for high-speed data transmission.

[0078] The innovation of the software control algorithm is reflected in the establishment of a dual-channel cooperative scheduling mechanism. This software control algorithm divides the driving cycle into multiple time slices and coordinates the resource allocation between lighting and communication through a priority dynamic adjustment strategy. In the default operating mode, the lighting channel dominates, achieving wide-range brightness adjustment through precise PWM dimming while retaining the fast response ability to communication requests. When it is detected that communication data arrives, the algorithm adds OOK modulation pulses in the low-level interval of the PWM dimming waveform, which not only avoids the impact of sudden light intensity changes on visual perception but also ensures the accuracy of communication timing. Its communication timing diagram is asFigure 3 As shown, for the emergency communication scenario, the system also designs a resource preemption mechanism, enabling it to prioritize ensuring the real-time nature of data transmission under specific conditions. This flexibility allows the technical solution to adapt to the diverse application requirements in scenarios such as smart streetlights. The core advantage of this dual-mode fusion light source driving technology lies in the deep combination of hardware reuse and algorithm optimization, achieving functions that could only be completed by traditional dual systems on a single driving platform. At the hardware level, the MOSFET array reuse technology is adopted, replacing the independent circuit modules composed of a large number of discrete components in the traditional solution, significantly reducing the overall number of components. At the software level, through time-sharing reuse and dynamic scheduling strategies, the resource conflict problem between lighting and communication is effectively solved, expanding the communication bandwidth while ensuring the dimming accuracy. This software and hardware collaborative design not only improves the system integration but also enhances the collaborative efficiency between different working modes, providing high-reliability driving support for lighting-communication dual-mode fusion devices.

[0079] In practical applications, this dual-mode fusion light source driving technology demonstrates a high degree of adaptability to complex environments. By establishing the system state vector model S = [I light , V com , T cycle , where I light represents current, V com represents voltage, and the optimization objective function where ΔI light represents current fluctuation, ΔV com represents voltage fluctuation, T cycle represents communication delay, and α, β, and γ respectively represent the first weight factor, the second weight factor, and the third weight factor. It can be understood that the first weight factor, the second weight factor, and the third weight factor can be flexibly set by users according to their own needs. The software control algorithm can monitor key parameters such as current fluctuation and communication delay in real time and dynamically adjust the pulse timing and duty cycle allocation. This closed-loop control mechanism enables the system to maintain stable dimming characteristics and communication quality in the face of external interference or load changes. Especially in the wide temperature range scenario, the system effectively suppresses the performance fluctuations caused by component parameter drift through a dynamic compensation mechanism, ensuring the stability of long-term operation. These characteristics enable this technical solution to meet the multiple requirements of smart city infrastructure for device reliability, real-time nature, and energy efficiency optimization.

[0080] From an engineering implementation perspective, the modular design of this drive architecture reduces the complexity of deployment and maintenance. By integrating dimming control, impedance matching, and communication modulation functions into a single-chip solution, not only is the hardware volume and the number of wiring layers reduced, but also the interface losses between different functional modules are minimized. This highly integrated design concept facilitates large-scale deployment and reduces the material costs in the production and manufacturing process. In addition, the standardized communication protocol and configurable parameter system adopted by the system enable terminal devices to quickly access existing Internet of Things platforms, accelerating the application of the lighting-communication fusion system in fields such as smart streetlights and industrial lighting. Generally speaking, through innovative architecture design and control strategies, the dual-mode fusion light source drive technology not only optimizes the number of components and energy consumption, but more importantly, it constructs a technical paradigm of deep collaboration between lighting and communication, providing an extensible underlying architecture for the development of future optical Internet of Things devices.

[0081] The road safety perception module uses its own camera to collect road surface data. After obtaining the road surface data, the road safety perception module uses a multi-modal road surface intelligent detection system to detect and analyze the collected road surface data, and real-time monitors whether there is abnormal information in the road surface data, which may include:

[0082] The road safety perception module uses a deep learning object detection module based on improved YOLOV8 to detect the road surface data, and judges whether there is abnormal aggregation of vehicle flow, abnormal aggregation of pedestrian flow, temporary obstacles, or road surface cracks in the road surface data. If any exists, it outputs the first abnormal result;

[0083] Use a lane line integrity detection module based on edge analysis to detect the lane lines in the road surface data, and judge whether there is an abnormal state in the lane lines. If there is an abnormal state, it outputs the second abnormal result;

[0084] Use a road surface deformation monitoring module based on strain sensors to detect the road structure in the road surface data, and judge whether there is collapse or settlement in the road structure. If there is collapse or settlement, it outputs the third abnormal result;

[0085] The first abnormal result, the second abnormal result, and the third abnormal result constitute abnormal information.

[0086] Specifically, in the emergency communication system, the goal of road surface detection is to comprehensively understand the road conditions and promptly identify potential risks, thereby improving traffic safety and management efficiency. The multi-modal intelligent road surface detection system adopted in the embodiments of the present invention constructs a three-dimensional monitoring system covering the surface state, structural safety, and marking integrity of the road surface through multi-dimensional perception and algorithm collaboration. This technology breaks through the limitations of single monitoring means, integrates cross-domain methods such as computer vision, mechanical sensors, and deep learning, and forms an intelligent analysis system with environmental adaptability and data complementarity. Its implementation path mainly includes three core technology modules: a deep learning object detection module based on improved YOLOV8, a marking integrity detection module based on edge analysis, and a road surface deformation monitoring module based on strain sensors. The three jointly support the accurate perception of road conditions through a data fusion and decision-making collaboration mechanism.

[0087] In the deep learning object detection module based on improved YOLOV8, the traditional YOLOv8 algorithm faces two challenges when dealing with complex road scenarios: on the one hand, complex lighting, road surface stains, and dynamic interference lead to low feature extraction efficiency; on the other hand, the detection of multi-scale objects (such as the coexistence of small cracks and large obstacles) suffers from accuracy loss caused by insufficient inter-layer feature fusion. Based on the above problems, the "Sparse-FuseNet" method proposed by the embodiments of the present invention based on the traditional YOLOv8 model achieves breakthrough improvements through architecture reconstruction and algorithm innovation. For example Figure 4As shown, the model first introduces a sparse coding Transformer mechanism in the backbone network, divides the input image into local regions for sparse sampling, establishes a non-dense connection topology through a dynamic path selection algorithm, only retains the associations between key feature channels, and uses compressive sensing technology to perform high-dimensional encoding on local texture features. This design enables the Transformer mechanism to accurately capture the microscopic deformations at the crack edges and the contour details of obstacles with low computational resource consumption. To address the multi-scale detection problem, Sparse-FuseNet constructs a dynamic feature fusion network and embeds a feature correlation evaluation unit between the original convolutional layers of YOLOv8. This feature correlation evaluation unit calculates the correlation weights between low-level detailed features (such as crack patterns) and high-level semantic features (such as the overall contour of a vehicle) in real time through spatial alignment of cross-layer feature maps and channel attention mechanisms, and adopts an adaptive weighting strategy to achieve feature fusion. This mechanism improves the information attenuation problem of traditional pyramid structures in small target detection, and simultaneously processes three types of tasks through a single-stage detection framework: parallel decoding of the sparse-coded feature map to generate a pixel-level segmentation mask for the crack area, the bounding box coordinates of traffic participants, and the classification confidence of obstacles, and finally eliminates the logical conflicts between detection results through a spatial constraint algorithm. This integrated processing flow avoids the delay accumulation caused by cascading multiple models and realizes the intensive utilization of computational resources through sharing the feature extraction layer.

[0088] In practical applications, when the road surface data collected by the camera is input, Sparse-FuseNet first performs multi-scale sparse sampling, focuses on extracting local features of suspected target areas, and then gradually aggregates global context information through cascaded sparse coding Transformer blocks. For the traffic participant detection task, the model combines a temporal analysis module to associate the target movement trajectories in consecutive frames, effectively distinguishing stationary obstacles from moving vehicles; the obstacle classification branch incorporates the reflection characteristics of obstacles with different materials into the classification decision through a material texture analysis network. The entire processing process achieves real-time detection on an embedded edge computing device, taking into account the requirements of the intelligent street lamp system for real-time performance and accuracy.

[0089] The deep learning object detection module based on the improved YOLOV8 can achieve road surface crack detection, traffic participant detection, and road blockage and obstacle detection.

[0090] The road marking integrity detection module based on edge analysis constructs a two-dimensional analysis system based on computer vision technology. In the spatial dimension, an improved Canny edge detection algorithm is used to extract the road marking contour, and the clarity index of the road marking is quantified specifically through gray distribution statistics and contrast analysis. In response to the interference caused by light changes and road surface stains, an adaptive threshold adjustment mechanism is introduced on the basis of the traditional Canny edge detection algorithm to dynamically optimize the edge extraction parameters. In the semantic dimension, a lightweight semantic segmentation network is applied to divide the road marking area and the background area, and morphological operations are combined to eliminate discrete noise points in the detection results. Through the cross-verification of spatial features and semantic information, this module can accurately identify various abnormal states such as blurred, missing, and offset lane lines. Its detection results form an effective complement to the deep learning module, jointly improving the digital description of the road surface state.

[0091] The abnormal states may include:

[0092] Blurred road markings, missing road markings, or offset road markings.

[0093] The road marking integrity detection module based on edge analysis introduces an adaptive threshold adjustment mechanism to dynamically optimize the edge extraction parameters in response to the interference caused by light changes and road surface stains, so as to realize the detection and recognition of road markings in road surface data.

[0094] The road surface deformation monitoring module based on strain sensors constructs a physical state perception layer of the road structure relying on a highly sensitive strain sensor network. The sensor array is based on the strain effect principle. By capturing the resistance change caused by the micro-deformation of the road surface, the mechanical signal is converted into an electrical signal for quantitative analysis. The system adopts a distributed deployment strategy, sets sensor nodes in key areas to form a monitoring grid, and eliminates single-point data errors through a spatio-temporal correlation algorithm. The data processing process includes three stages: signal denoising, baseline calibration, and pattern recognition. First, wavelet transform is used to filter out environmental vibration noise. Second, a deformation reference model is established according to historical data. Finally, a support vector machine is used to classify the deformation patterns, effectively distinguishing different types such as temperature deformation, vehicle load deformation, and structural damage deformation. This module provides irreplaceable mechanical parameters for road safety assessment, and has unique advantages especially in the early identification of hidden risks such as road surface collapse and subgrade settlement.

[0095] The road surface deformation monitoring module based on strain sensors classifies the deformation patterns through a support vector machine, effectively distinguishing different types such as temperature deformation, vehicle load deformation, and structural damage deformation.

[0096] Through the organic combination of the above three detection modules, the core competitiveness of the multi-modal pavement intelligent detection system is constituted. At the data acquisition layer, the vision sensor and the mechanical sensor form a joint perception network with spatio-temporal alignment to ensure the synchrony and correlation of image data and physical deformation data. The data processing layer adopts a parallel computing architecture, and the three detection modules operate independently on dedicated computing units, and the intermediate results are interacted in real time through a memory sharing mechanism. For example, when the deep learning module detects a local crack, it can immediately retrieve the deformation monitoring data of the corresponding area for correlation analysis, and verify the expansion risk of the crack through mechanical parameters. The decision-making output layer establishes a multi-dimensional evaluation matrix, weights and fuses the object detection confidence, marking clarity index and deformation deviation value, and generates a comprehensive evaluation report covering the road surface condition, marking compliance and structural safety. The multi-modal pavement intelligent detection system mines potential risk patterns that cannot be captured by a single sensor through multi-dimensional data association to achieve complementary enhancement of multi-source data.

[0097] When abnormal information appears, the road safety perception module generates warning information of the corresponding level according to the level of the abnormal information and sends it to the core control module.

[0098] The levels of abnormal information may include:

[0099] Low-level warning, medium-level warning and high-level warning; among them,

[0100] The low-level warning means that the abnormal information only includes one of the first abnormal result, the second abnormal result or the third abnormal result;

[0101] The medium-level warning means that the abnormal information includes two of the first abnormal result, the second abnormal result or the third abnormal result;

[0102] The high-level warning means that the abnormal information includes the first abnormal result, the second abnormal result and the third abnormal result.

[0103] The algorithm cooperation mechanism shows significant advantages in the disposal of abnormal events. The system presets a three-level warning response strategy: the primary warning is for isolated abnormalities detected by a single module (such as temporary obstacles), and a prompt signal is sent to the following vehicles through optical communication; the medium-level warning is triggered when the dual-module detects associated risks (such as cracks accompanied by deformation), and the surrounding street lights are linked to enhance the warning light intensity; the high-level warning is started when the triple-module simultaneously detects major hidden dangers (such as obstacles piled up in a collapsed area and missing markings), and an emergency response request is sent to the traffic management department. This hierarchical response mechanism not only ensures the disposal efficiency of routine events, but also reserves sufficient response time for major risks.

[0104] The advantages of the multi-modal intelligent road detection system stem from the complementary enhancement effect of multi-source data. The deep learning object detection module is good at capturing visible features on the road surface, but lacks the ability to perceive hidden dangers inside the structure; the road deformation monitoring module is proficient in detecting hidden structural changes, but it is difficult to locate specific damage positions; the marking integrity detection module specializes in road sign compliance and requires other modules to provide environmental context information. The three form a complete closed-loop cognitive system of road conditions through data fusion: the deep learning detection results can provide spatial location references for deformation analysis, the edge detection data can assist in calibrating the crack detection area, and the mechanical parameters can be used as the basis for verifying the reliability of visual detection results. This cross-verification mechanism reduces the overall false alarm rate of the system to a practical level, and at the same time, potential risk patterns that cannot be captured by a single sensor are mined through multi-dimensional data association.

[0105] The core control module outputs lighting control signals to the light source driving module according to environmental information; and outputs corresponding-level emergency control signals to the light source driving module according to the level of warning information.

[0106] Specifically, if the level of warning information is a low-level warning, a low-level emergency control signal is output to control the light source driving module to perform corresponding operations; if the level of warning information is medium, a medium-level emergency control signal is output to control the light source driving module to perform corresponding operations; if the level of warning information is high, a high-level emergency control signal is output to control the light source driving module to perform corresponding operations. For the detailed operations performed by the light source driving module according to the level of the emergency control signal, please refer to the following description.

[0107] The light source driving module uses dual-mode fusion light source driving technology and automatically adjusts the brightness and switch of the lighting lamps in the LED light board according to the lighting control signal; controls the communication lamps in the LED light board to give corresponding warnings according to the level of the emergency control signal, and uploads abnormal information for the reference of the management department, which may include:

[0108] When the level of the emergency control signal is low, control the communication lamp in the LED light board to give a warning to the following vehicle;

[0109] When the level of the emergency control signal is medium, control the communication lamp in the LED light board to give a warning to the following vehicle, and use the power line to transmit warning information to adjacent smart street lights through power line carrier technology, so that all smart street lights on the whole road give warnings;

[0110] When the level of the emergency control signal is high, control the communication lamp in the LED light board to give a warning to the following vehicle, use the power line to transmit warning information to adjacent smart street lights through power line carrier technology, so that all smart street lights on the whole road give warnings, and upload abnormal information to the server for the reference of the management department.

[0111] The intelligent street lamp proposed in the embodiment of the present invention combines wireless optical communication with widely distributed urban street lamps, adopts a distributed architecture, and utilizes a dual-mode fusion light source driving technology to integrate two light sources for information transmission and lighting functions in the same street lamp system; among them, the first light source is a traditional lighting light source for providing the light intensity required by the environment, and the second light source is a communication light source, which will modulate the light signal through a specific frequency to transmit necessary data information. The multi-modal road surface intelligent detection system is used to monitor the abnormal information in the road surface data in real time to achieve accurate perception of the road conditions.

[0112] In a second aspect, corresponding to the above-mentioned intelligent street lamp embodiment, the embodiment of the present invention also provides a road emergency communication system, as Figure 5 shown, which may include:

[0113] The intelligent street lamp, optical receiver and management platform as in the first aspect; among them,

[0114] The intelligent street lamp is used to monitor the road surface data in real time. When abnormal information appears, it will give early warnings to the optical receiver and management platform according to the level of the abnormal information.

[0115] The optical receiver is arranged in the driving vehicle and is used to remind the driver of the driving vehicle according to the early warning received from the intelligent street lamp.

[0116] The management platform is used to process the early warning received from the intelligent street lamp in a timely manner; in case of an emergency, under the operation of the management department, it controls the intelligent street lamp to send a warning prompt to the nearby optical receiver to remind the driver of the driving vehicle.

[0117] For details of the intelligent street lamp, please refer to the description in the first aspect.

[0118] For the optical receiver, through the deep integration of optical communication with the in-vehicle system, a vehicle-road collaborative control system with high reliability and low latency is constructed. Its core technology is reflected in the trinity collaborative innovation of the innovative design of the wide-field-of-view optical receiver, the native compatibility architecture of vehicle regulations protocol and the miniaturized hardware integration, which solves the problems of limited coverage, signal interference and protocol fragmentation of traditional emergency communication in dynamic traffic scenarios, and provides a directional and safe emergency communication solution for the intelligent transportation system.

[0119] Schematic diagram of the structure of the wide-field optical receiving system in the road emergency communication system, as Figure 6As shown in the figure, an aspherical doublet lens group and a Fresnel diffraction compensation technology are used in concert to construct a wide-field optical architecture. The front lens group realizes the preliminary focusing of large-angle incident light and spherical aberration correction through the difference in the radius of curvature (the front lens is 8.5 mm and the rear lens is 12 mm). The Fresnel compensation module further eliminates off-axis astigmatism based on wavefront phase shaping, expanding the effective field of view angle to 150°, covering the dynamic reception range of ±7.5 m laterally for the vehicle. A narrowband filter is embedded in the optical link, selectively transmitting the target wavelength of 850 nm through a multilayer dielectric film, attenuating the environmental stray light to less than one ten-thousandth, and significantly improving the signal purity in a strong light interference scenario. The photoelectric conversion layer is realized by a silicon photomultiplier tube array composed of 18,980 independent micro-units. Its pixelated layout (the size of a single micro-unit is 35×35) matches the optical field of view mapping. The total effective photosensitive area of the array is 6×6, and the fill factor is 64%. Each micro-unit corresponds to a spatial resolution of 1.09°, providing stable communication quality for the vehicle during driving. The bias voltage adaptive adjustment module dynamically optimizes the operating point by real-time monitoring the output voltage signal of the SIPM (Silicon photomultiplier): The control unit adjusts the equivalent resistance value of the switching power supply feedback network according to the change trend of the signal intensity, making the bias voltage float reversely with the ambient light intensity - reducing the voltage to suppress avalanche noise in a strong light environment and increasing the voltage to enhance the single-photon response sensitivity in a low light condition, forming a closed-loop gain control loop. The topological structure is as shown in Figure 7 As shown in the figure, through the triple technology integration of optical structure expansion, spectral screening, and electronic gain adaption, the entire design can accurately extract the modulation signal from a complex optical environment. While ensuring a 150° wide-field coverage, it also takes into account miniaturized integration (module size 85 mm×50 mm×40 mm). The in-vehicle installation takes power through a standard OBD-II interface. The module can be embedded in the rearview mirror housing or the roof shark fin antenna, and can be quickly deployed without professional tool debugging.

[0120] To achieve plug-and-play compatibility with the in-vehicle control network, the optical receiver adopts an OOK-CAN fusion communication protocol. The physical layer strictly follows the ISO 11898-2 standard. Through OOK modulation, the CAN protocol bits are encoded into 2 μs (logic 1) / 1 μs (logic 0) pulse width signals. The preamble consists of 5 alternating 1 ms pulses, which are used for clock synchronization and frame start identification. The data link layer directly multiplexes the CAN2.0B extended frame format. In the 29-bit identifier, the first 16 bits are fixed with the street lamp geocoding (section grid identifier based on the Geohash algorithm), and the last 13 bits define the event priority (0x0000 - 0x1FFF corresponds to 8 levels of urgency). The 8-byte data segment contains 4 bytes of concise event information (type + coordinate offset + timestamp) and a CRC-15 check code. This design ensures that the in-vehicle ECU can parse the optical communication data without protocol conversion.

[0121] Through the trinity design of wide - field optical reception, native compatibility with vehicle protocols, and miniaturized hardware, the optical receiver constructs an efficient and reliable intelligent street - lamp emergency communication reception system and fully re - uses the existing CAN network architecture of vehicles. This technical path provides a road - test information fusion solution for vehicles with low retrofit costs and high timeliness, improving the collaborative response ability to sudden traffic events and abnormal road conditions.

[0122] The road emergency communication system proposed in the embodiments of the present invention integrates wireless optical communication and road - state perception functions on the basis of intelligent street lamps and designs an optical receiver that can be carried on a mobile terminal.

[0123] Specific tests on intelligent street lamps and road emergency communication systems are carried out below.

[0124] The lighting control test diagram of the intelligent street lamp provided by the embodiments of the present invention is as Figure 8 shown. Based on the connection of the mobile phone terminal to WiFi, intelligent control such as the management of multiple intelligent street - lamp placement points, street - lamp switching, brightness adjustment, device - information prompting, etc. and the monitoring of environmental information are realized. The corresponding APP needs to be installed on the mobile phone.

[0125] In order to verify the functional effectiveness and performance stability of the road safety perception module in actual applications, ensure that it can accurately and quickly detect and identify road conditions (including road surface cracks, blurred road markings, detection of traffic targets, and aggregation - degree statistics) in the pre - processed video - stream data, and automatically save images and upload warning information to the server when abnormal aggregation is detected, the module is tested.

[0126] After the camera captures the video, it is transmitted to the host computer using the real - time streaming protocol. At this time, the running deep - learning network can quickly and accurately detect road surface cracks and blurred road markings, and detect the aggregation of vehicles and pedestrians. The warning threshold for road surface cracks in this module is set as 1 or more large cracks, 3 or more wheel - damage marks, and relatively large - area blurred markings within the detection range; in the traffic state, the warning thresholds for vehicles and pedestrians are 10 and 20 respectively.

[0127] This module can accurately give early warnings for abnormal situations, automatically save relevant images and mark time stamps, and at the same time can upload warning information to the server and display it on the management - end interface of the host computer, as Figure 9 shown.

[0128] When testing the optical illumination communication of the road emergency communication system, in order to ensure the quality and reliability of the optical signals of the wireless optical communication system and further improve the stability and availability of the system, it is necessary to test the optical power of the light source of the transmitting system. When the transmitting light source is working normally and stably, the optical power of the transmitting system can be obtained as 6.4W. The bandwidth of the transmitting system in the road emergency communication system directly affects the communication performance of the optical communication system. By testing the bandwidth of the transmitting system, the transmission capacity and performance indicators of the communication system can be evaluated. The test chart of the transmitting system bandwidth is as shown in Figure 10 shown. It can be seen from Figure 10 that the communication bandwidth of the road emergency communication system can be 800KHz.

[0129] Test the optical receiving system in the road emergency communication system, test the sensitivity of the receiving system, and at the detector end, by superimposing optical attenuation sheets, test the minimum optical signal intensity that the optical receiver can accept, and evaluate the performance of the optical receiving module. The optical power of the optical transmitting module is tested to be 17.85mW. After passing through the combined optical attenuation sheets of 0.01%, 1%, 1%, 40%, 25%, and 50% respectively, there is still a weak signal that can respond. From this, the sensitivity of the receiving module is calculated to be 89.25nW.

[0130] By testing the bandwidth of the receiving system, the receiving ability of the system for signals in different frequency ranges can be understood, so as to ensure that the system can effectively receive and demodulate signals during the transmission process, and ensure the quality and reliability of signal transmission. The test chart of the receiving system bandwidth is as shown in Figure 11 shown. After testing, the signal flatness of the receiving system is good within the frequency range of 1MHz. After the optical receiving system is debugged, it is packaged. The small size of this module can be flexibly matched with various mobile terminals, and warning information can be obtained within the optical communication coverage range.

[0131] In the embodiment of the present invention, on the basis of realizing street lamp control, a road safety perception module is added, and the road emergency information release based on wireless optical communication is successfully realized. The road safety perception module realizes the collection, identification and judgment of road information, generates alarm information after a dangerous situation occurs, and transmits it to the intelligent street lamps at each node through power line carrier technology to realize the release of emergency information. The optical receivers that can be flexibly mounted on vehicles can receive emergency information to achieve emergency avoidance.

[0132] In the embodiments of the present invention, by combining wireless optical communication with widely distributed urban street lamps, adopting a distributed architecture, and using a dual-mode fusion light source driving technology, two light sources for information transmission and lighting functions are integrated in the same street lamp system; wherein, the first light source is a traditional lighting light source for providing the required illumination intensity for the environment, and the second light source is a communication light source, which will modulate the light signal through a specific frequency to transmit necessary data information. The multi-modal road surface intelligent detection system is used to monitor the abnormal information in the road surface data in real time to achieve accurate perception of the road conditions. In the road emergency communication system, intelligent street lamps are combined with optical receivers to construct a multi-node emergency communication network for information interaction, providing efficient and stable information transmission and processing capabilities for the emergency communication of smart city roads; intelligent street lamps are used to deeply mine and analyze urban data to provide intelligent and personalized decision-making support for urban management, thus bringing convenience to the lives of urban residents and greatly improving the level of urban governance.

[0133] It should be noted that in the description of the present invention, it should be understood that the terms "first" and "second" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance or implicitly specifying the quantity of the indicated technical features. Thus, the features defined with "first" and "second" may explicitly or implicitly include one or more of such features. In the description of the present invention, "a plurality" means two or more, unless otherwise specifically defined.

[0134] The above are only the preferred embodiments of the present invention and are not intended to limit the protection scope of the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention are included in the protection scope of the present invention.

Claims

1. A smart street lamp, characterized in that, It includes: An environmental perception module, an LED light board, a road safety perception module, a light source driving module, and a core control module; adjacent intelligent street lights are connected by power lines, where The environmental perception module is used to obtain environmental information; The road safety perception module is used to collect road surface data, detect and analyze the collected road surface data using a multi-modal road surface intelligent detection system, and real-time monitor whether there is abnormal information in the road surface data. When abnormal information appears, a warning information of the corresponding level is generated according to the level of the abnormal information and sent to the core control module; The core control module is used to output a lighting control signal to the light source driving module according to the environmental information; output an emergency control signal of the corresponding level to the light source driving module according to the level of the warning information; The light source driving module is used to use a dual-mode fusion light source driving technology to automatically adjust the brightness and switch of the lighting lamps in the LED light board according to the lighting control signal, control the communication lamps in the LED light board to give corresponding early warnings according to the level of the emergency control signal, and upload the abnormal information to the server for the reference of the management department.

2. The intelligent street lamp according to claim 1, characterized in that, The environmental information includes: External light intensity and longitude and latitude information.

3. A smart street lamp according to claim 1, characterized in that, The multi-modal road surface intelligent detection system in the road safety perception module is used to detect and analyze the collected road surface data, and real-time monitor whether there is abnormal information in the road surface data, including: The road safety perception module uses a deep learning object detection module based on improved YOLOV8 to detect the road surface data, and judges whether there is abnormal aggregation of vehicle flow, abnormal aggregation of pedestrian flow, temporary obstacles, or road surface cracks in the road surface data. If any, a first abnormal result is output; Use a marking integrity detection module based on edge analysis to detect the road markings in the road surface data, and judge whether there is an abnormal state in the road markings. If there is an abnormal state, a second abnormal result is output; Use a road surface deformation monitoring module based on strain sensors to detect the road structure in the road surface data, and judge whether there is collapse or settlement in the road structure. If there is collapse or settlement, a third abnormal result is output; The first abnormal result, the second abnormal result, and the third abnormal result constitute abnormal information.

4. The intelligent street lamp according to claim 3, characterized in that, The levels of the abnormal information include: Low-level warning, medium-level warning, and high-level warning; where The low-level warning means that the abnormal information only includes one of the first abnormal result, the second abnormal result, or the third abnormal result; The medium-level warning means that the abnormal information includes two of the first abnormal result, the second abnormal result, or the third abnormal result; The high-level warning means that the abnormal information includes the first abnormal result, the second abnormal result, and the third abnormal result.

5. The intelligent street lamp according to claim 3, characterized in that, The abnormal state includes: Road markings are blurred, road markings are missing, or road markings are offset.

6. The intelligent street lamp according to claim 4, characterized in that, In the light source driving module, the communication lamps in the LED light board are controlled to give corresponding early warnings according to the level of the emergency control signal, and the abnormal information is uploaded for the reference of the management department, including: When the level of the emergency control signal is low, control the communication lamps in the LED light board to give a warning to the following vehicles; When the level of the emergency control signal is medium, control the communication lights in the LED light board to give early warnings to the vehicles behind, and use the power line to transmit warning messages to adjacent smart street lights through power line carrier technology, so that all the smart street lights on the whole road give early warnings; When the level of the emergency control signal is high, control the communication lights in the LED light board to give early warnings to the vehicles behind, and use the power line to transmit warning messages to adjacent smart street lights through power line carrier technology, so that all the smart street lights on the whole road give early warnings, and upload the abnormal information to the server for the reference of the management department.

7. A smart street lamp according to claim 1, characterized in that, The light source driving module includes: An adjustable MOSFET array, a dual-channel control mechanism and an impedance detection circuit; where The input end of the adjustable MOSFET array is connected to the output end of the core control module and is powered on, and the output end is connected to the input end of the dual-channel control mechanism; The first output end of the dual-channel control mechanism is connected to the lighting lamp in the LED light board, and the second output end is connected to the input end of the impedance detection circuit; The first output end of the impedance detection circuit is connected to the communication lamp in the LED light board, and the second output end is connected to the input end of the core control module.

8. A smart street lamp according to claim 1, characterized in that, The core control module is also used to, under the remote control of a preset APP, adjust the brightness and switch of the lighting lamp in the LED light board by controlling the light source driving module.

9. A smart street lamp according to claim 1, characterized in that, The core control module is also used to, when the management department issues a warning instruction, make the communication lamp in the LED light board give a warning by controlling the light source driving module.

10. An emergency road communication system, characterized in that, Including: The smart street light, optical receiver and management platform according to any one of claims 1-9; where The smart street light is used to monitor the road surface data in real time. When abnormal information appears, it gives early warnings to the optical receiver and the management platform according to the level of the abnormal information; The optical receiver is arranged in a driving vehicle and is used to remind the driver of the driving vehicle according to the early warning received from the smart street light; The management platform is used to timely process the early warning received from the smart street light; in case of an emergency, under the operation of the management department, control the smart street light to send a warning prompt to the nearby optical receiver to remind the driver of the driving vehicle.

Images