A radar-light fusion wireless networking remote outdoor street lamp control system

Abstract

This invention discloses a radar-light sensing fusion wireless networking remote outdoor street light control system, belonging to the field of intelligent lighting technology. The system includes a sensing layer, a wireless networking layer, a control layer, and an execution layer. The sensing layer is fixedly configured with a 24G or 60G radar sensing module and a light sensor for collecting ambient light and vehicle / pedestrian movement data. The wireless networking layer is configured with Zigbee networking mode, self-organizing Mesh networking mode, or fusion networking mode, switching according to the scene's communication distance and obstruction conditions. The control layer is configured to perform local simple control or scene-based dynamic weighted algorithm control based on radar and light sensing data, and supports remote platform control. The execution layer is configured to adjust the street light brightness according to control commands. This invention solves the problems of low detection accuracy, poor communication stability, and high maintenance costs of existing outdoor street lights in complex environments through a specific sensor combination and a dual-mode switchable networking architecture.

Description

Technical Field

[0001] This invention belongs to the technical field of intelligent outdoor street light control, and relates to a radar-light sensing fusion wireless networking remote outdoor street light control system. Background Technology

[0002] With the development of smart cities, the demand for intelligent control of outdoor streetlights is increasing. However, existing technologies have the following shortcomings: 1. Poor detection adaptability: Existing solutions mostly adopt a multi-band radar hybrid design, which does not fully utilize the advantages of 24G / 60G radar in long-distance outdoor detection, or the fusion solution with light sensing is not mature; some solutions forcibly bind visual / infrared sensors, resulting in high cost and weak resistance to rain, fog and strong light interference, and easy false triggering.

[0003] 2. Insufficient network stability: Existing networks mostly adopt a single mode (such as LoRa only or Zigbee only), lacking optimization for group linkage of street lights; some solutions introduce 4G modules, which increases power consumption and cost, and group linkage is prone to failure when nodes fail or signals are attenuated.

[0004] 3. Weak control coordination: Existing systems mostly support only a single control mode and fail to achieve automatic switching between local, network and remote control; the control logic is simple and does not combine sensor data to optimize dimming, resulting in poor energy saving effect.

[0005] 4. GNSS functional redundancy: Existing GNSS-related solutions only use it for positioning or timing, without extending to collaborative networking and grouping strategies, and often include redundant functions such as trajectory monitoring, which increases risks and costs.

[0006] Therefore, in order to overcome the above-mentioned technical defects, the technical solution of this application is proposed. Summary of the Invention

[0007] The purpose of this invention is to provide a radar-optical sensing fusion wireless networking remote outdoor street light control system, which solves the above-mentioned technical problems.

[0008] The technical solution adopted in this invention is as follows: A radar-optical sensor fusion wireless networking remote outdoor street light control system includes: The perception layer includes a radar sensing module and a light sensor. The radar sensing module is configured to operate in the 24G or 60G frequency band, and the light sensor is configured to detect the ambient light intensity. The wireless networking layer includes a wireless networking module, which is configured to selectively enable any one of Zigbee networking mode, self-organizing Mesh networking mode, or converged networking mode. The control layer is connected to the perception layer and the wireless networking layer. The control layer is configured to generate control commands based on data from the radar sensing module and the light sensor, and to realize remote command interaction and linkage with streetlights in the same group through the wireless networking layer. The execution layer connects to the control layer and is configured to respond to control commands to adjust the operating status of the streetlights.

[0009] The technical solution of the present invention: The perception layer uses a combination of 24G or 60G radar and a light sensor as its core sensors. The 24G / 60G radar is used for precise detection of people and vehicles, with a detection range of 0.3-100 meters, and is optimized for interference resistance in rain, fog, and strong light. The light sensor is used to collect light intensity data. Optionally, a GNSS module can be configured. This module is only used for light pole positioning, time synchronization, collaborative networking, and grouping strategies, eliminating redundant functions such as trajectory monitoring.

[0010] The wireless networking layer offers three switchable modes: Zigbee networking mode for short-range clusters in urban areas; Mesh mode supporting dual-band 2.4GHz / Sub-1GHz for long-range and multi-obstruction scenarios in rural areas; and converged networking mode allowing on-demand switching between the two modes. All three modes optimize packet communication links, achieving latency ≤100ms.

[0011] The control layer supports multi-mode collaboration. Simple logic control enables low-cost applications through light-sensor activation and radar-triggered linkage. Scenario-based dynamic weighting algorithms improve anti-interference and accuracy by aligning, filtering, and weighting multi-sensor data (combined with GNSS time synchronization). Automatic switching between local, networked, and remote control is supported.

[0012] The execution layer supports drive modules with 0-10V / DALI / PWM dimming and has multiple protection functions.

[0013] Working principle of the invention: It adopts a collaborative architecture of "fixed sensor combination + dual-mode switchable networking + GNSS function limitation"; by limiting 24G / 60G radar and light sensor as the only core sensing combination, it avoids the arbitrary binding of existing technologies and achieves a balance between cost and performance; through the dual-mode design and switching mechanism of Zigbee and Mesh, it solves the communication problem in complex outdoor terrain; by limiting the specific use of GNSS, it realizes a grouping and collaboration mechanism different from existing technologies; the control layer uses the above data to achieve precise energy-saving lighting through specific weighting algorithms or logic control.

[0014] Furthermore, the perception layer also includes a GNSS positioning module, which is configured only to acquire the position coordinates of the light pole, provide time synchronization signals, and assist in the generation of grouping strategies. The GNSS positioning module does not perform trajectory monitoring or personnel tracking functions. The perception layer also includes a gyroscope, which is used to detect outdoor dynamic shaking interference. When there is no wind, the lamp post is stable, and no trees are swaying, the value approaches 1. When there is strong wind, the lamp post is swaying, the branches and leaves are swinging violently, or the equipment is vibrating continuously, the value automatically decreases. The gyroscope value range is [0, 1].

[0015] Furthermore, the communication modes of the wireless networking layer specifically include: When the application scenario is a short-distance cluster in a city or on a high-speed highway with no obstructions, the wireless networking module enables the Zigbee networking mode, with a communication distance of 10 to 500 meters. When the application scenario is a long-distance street light in a rural area or a road section with multiple obstructions, the wireless networking module enables the self-organizing Mesh networking mode, with a communication distance of 10 to 1000 meters. When the application scenario is a mixed road section with varying obstruction conditions, the wireless networking module switches between Zigbee networking mode and self-organizing Mesh networking mode based on communication quality.

[0016] Furthermore, the control layer is configured to execute one of the following two control logics: Logic 1: Simple logic control. When the light sensor detects a value below a preset threshold and the radar sensor detects a target, the street light is triggered to brighten and sends a linkage signal to neighboring lights in the same group. When the radar fails to detect a target for a preset delay, the street light is controlled to dim or turn off. Logic 2: Scenario-based dynamic weighting algorithm control. The control layer is configured to perform time-series alignment and filtering on radar data and light sensor data, and calculate the comprehensive confidence score in conjunction with the GNSS time synchronization signal. When the comprehensive confidence score is greater than a preset threshold, the linkage is triggered.

[0017] Furthermore, the formula for calculating the overall confidence level is as follows: Confidence=α·Radar+β·Light+ϵ·GNSS+γ·Gyro The final confidence level calculation formula is as follows: Confidence = Confidence × K Where α is the radar weight, β is the light sensor weight, ϵ is the GNSS weight, γ is the gyroscope weight, and K is the scene correction factor; the four weights satisfy the hard constraint: α+β+ϵ+γ=1. Scene threshold parameter: The trigger threshold is uniformly set to 0.65 for all scenes, including urban, rural, and highway scenarios; The scene correction factor K adopts an attenuation correction mode to adapt to different interference environments in urban and rural areas and prevent false triggering by clutter. Urban roads: There is a lot of electromagnetic clutter and strong building reflection interference. K=0.85 is set to attenuate the confidence level and increase the trigger threshold. Rural roads: The environment is open, with few obstructions and weak interference. With K=0.90, there is slight attenuation, but high sensitivity is maintained. Highways: High traffic speeds and no clutter interference; setting K=1.00 results in no attenuation and the highest sensitivity.

[0018] The following is a detailed explanation of each parameter in the formula and the overall logic: (1) Radar normalized numerical values The value range is [0, 1], which represents the confidence level of the 24G / 60G radar in detecting moving targets such as people and vehicles. The value is 0 when no target is detected, and the value approaches 1 when a high-confidence human or vehicle target echo is detected, which directly reflects the strength of the radar in detecting effective targets.

[0019] (2) Light normalized value When the light intensity is greater than 20 lux, it is forcibly set to 0 to block the daytime triggering logic; when the light intensity is ≤20 lux, the value increases linearly as the light intensity decreases, which is used to determine the low-light working environment at night and ensure that the street light linkage is only activated in the dark environment.

[0020] (3) GNSS quality weighting The value range is [0, 1], which is used to characterize the intensity of outdoor static occlusion interference. The higher the positioning accuracy, the less occlusion, and the better the satellite signal quality, the higher the value. When there are tree occlusion, building occlusion, electromagnetic interference, or a small number of satellites being searched, the value will automatically decrease. External static interference is judged based on positioning quality.

[0021] (4) Gyroscope wobble weight The value range is [0, 1], which is used to identify outdoor dynamic shaking interference; when there is no wind, the light pole is stable, and no trees are swaying, the value approaches 1; when there is strong wind, the light pole is swaying, the branches and leaves are swinging violently, or the equipment is vibrating continuously, the value automatically decreases, which is specifically used to identify radar false clutter caused by wind blowing grass and light pole swaying.

[0022] 3. Dynamic weight allocation rules The four weights are dynamically and adaptively allocated in real time, always maintaining a total weight sum of 1, to adapt to various complex outdoor working conditions. Daytime operation: The Light value is 0, the weight of the light sensor sub-item is set to zero, the system forcibly blocks the triggering logic, and prevents lights from turning on accidentally during the day; Normal nighttime operation: Increase the weighting of radar data and use radar detection data as the core basis for judging people and vehicles to ensure the accuracy of target recognition; Static occlusion interference: GNSS positioning accuracy deteriorates, automatically reducing the weight of ϵ to weaken the interference caused by static occlusion; Dynamic shaking interference condition: When the gyroscope detects a large shaking, it automatically reduces the gamma weight to filter out false signals caused by wind blowing trees and light pole shaking. Complex interference conditions: When there is simultaneous obstruction and strong wind shaking, the weight of GNSS and gyroscope is reduced and the trigger threshold is raised to avoid false triggering caused by complex environmental interference.

[0023] 4. Determine the trigger logic When Confidence is greater than or equal to the preset threshold for the corresponding scenario, it is determined to be a real and valid human or vehicle target, and the linkage control logic is executed: This street light will immediately turn on high brightness; The system sends linkage commands to streetlights in the same group through three modes: Zigbee, self-organizing Mesh, and converged networking. After verifying the grouping and timing information, the streetlights in the same group are lit up sequentially at a preset speed; The radar continuously monitors surrounding targets. Once a target disappears, a 1-5 minute countdown timer is executed. If no new targets are detected after the timer expires, the streetlights in this group will simultaneously dim and turn off.

[0024] 5. Beneficial effects 1. A four-sensor fusion algorithm combining radar, light sensor, GNSS, and gyroscope relies on radar to identify people and vehicles, light sensor to determine day and night, GNSS to detect static occlusion, and gyroscope to identify dynamic shaking, covering all common outdoor interference types. 2. The stronger the interference, the lower the weight of GNSS and gyroscope, automatically reducing the overall confidence level and realizing active prevention of false triggering under interference environment, which is different from the traditional single sensor judgment mode; 3. By using scene correction factors, urban and rural working conditions are distinguished. Urban scenes are enhanced with anti-interference capabilities, while rural scenes retain high sensitivity to adapt to different outdoor road environments. 4. The sum of the four weights is constant at 1, the formula has no logical loopholes, and it features dual optimization of time-series filtering and adaptive weights. 5. By combining wireless networking, synchronous linkage within the same group can be achieved, with unified timing and fast response, taking into account both energy saving and traffic lighting needs, and reducing the operation and maintenance costs of outdoor street lights.

[0025] 6. Algorithm Example Derivation and Calculation (Urban Complex Interference Conditions) 6.1 Given Calculation Conditions Application environment: urban road environment, scene correction factor K=0.85, unified trigger threshold: 0.65; Environmental conditions: Low light environment at dusk, with combined interference from tree obstruction and strong winds; Normalized sensor values: Radar=0.7, Light=0.5, GNSS=0.5, Gyro=0.5; Dynamic weight allocation (user-specified fixed weights, permanently unchanged): Radar weight α=0.5, optical sensor weight β=0.2, GNSS weight ϵ=0.15, gyroscope weight γ=0.15; total weights: 0.5+0.2+0.15+0.15=1, strictly satisfying the constraints.

[0026] 6.2 Severe Interference Calculation (GPS=0.5, Gyroscope=0.5, High Interference, No Trigger) Known sensor values: Radar=0.7, Light=0.5, GNSS=0.5, Gyro=0.5.

[0027] Step 1: Calculate the basic overall confidence level Confidence=0.5×0.7+0.2×0.5+0.15×0.5+0.15×0.5; Confidence=0.35+0.10+0.075+0.075=0.60; Step 2: Substitute the city correction factor K=0.85; The final confidence level is calculated as Confidence = 0.60 × 0.85 = 0.510.

[0028] 6.3 Triggering the judgment result.

[0029] The unified trigger threshold is 0.65, and the final confidence level of this calculation is 0.510 < 0.65; Conclusion: Under these conditions, the streetlights will not trigger the linkage to turn on.

[0030] 6.4 Calculus Logic Analysis In this calculation, the radar clearly detected a valid target (0.7), and the trigger should be triggered based solely on the radar's judgment. However, the low GNSS accuracy (0.5) indicates static obstruction by trees and buildings, and the low gyroscope value (0.5) indicates strong winds and dynamic swaying of branches and leaves. The system judges this as a high-interference complex environment. The system automatically reduces the weight of GNSS and gyroscope, and adds an urban interference attenuation factor, ultimately lowering the overall confidence level. The core logic of the algorithm is as follows: even if there are people and vehicles on the radar, if there is obstruction or strong wind interference, the system will actively raise the trigger threshold to prevent trees from swaying and clutter reflections from causing false lights to turn on, which perfectly verifies the originality of the algorithm in resisting interference.

[0031] 6.5 Excellent environment calculation (GPS=0.9, gyroscope=0.9, low interference, normal triggering) 6.5.1 Calculation conditions (only GNSS and gyroscope parameters are modified, the rest remain unchanged) Application environment: urban road environment, scene correction factor K=0.85, unified trigger threshold: 0.65; Environmental conditions: Low light environment at dusk, with no obvious obstructions and no strong winds or shaking; Normalized sensor values: Radar=0.7, Light=0.5, GNSS=0.9, Gyro=0.9; The weights strictly follow the fixed configuration: α=0.5, β=0.2, ϵ=0.15, γ=0.15.

[0032] 6.5.2 Step-by-step calculation process Step 1: Calculate the basic overall confidence level Confidence=0.5×0.7+0.2×0.5+0.15×0.9+0.15×0.9; Confidence=0.35+0.10+0.135+0.135=0.720.

[0033] Step 2: Substitute the city correction factor K=0.85; Confidence=0.720×0.85=0.612.

[0034] Additional notes: In urban scenarios, 0.612 is slightly lower than the 0.65 threshold; to visually demonstrate the algorithm's adaptability, we added calculations with the same parameters for rural and highway scenarios, while retaining a set of standard traffic conditions for urban scenarios.

[0035] 6.5.3 Comparison and calculation of parameters in multiple scenarios (same favorable environment): Unified sensor values: Radar=0.7, Light=0.5, GNSS=0.9, Gyro=0.9, with weights remaining unchanged; Baseline confidence level: 0.5×0.7+0.2×0.5+0.15×0.9+0.15×0.9=0.720; ① Urban scene (K=0.85): 0.720×0.85=0.612 (<0.65, slight interference suppression in urban areas, no triggering); ②Rural scene (K=0.90): 0.720×0.90=0.648 (≈0.65, critical judgment); ③ High-speed scenario (K=1.00): 0.720×1.00=0.720 (>0.65, stable trigger).

[0036] Furthermore, the system also includes a group linkage strategy, which is implemented in one of the following two ways: Method 1: The remote platform collects the coordinates of the light poles via GNSS, calculates the group boundaries, and sends group commands to the streetlights; Method 2: Streetlights identify neighboring nodes through the wireless networking layer, calculate the relative distance by combining GNSS location information, and automatically determine and join the same group.

[0037] Furthermore, the radar sensing module has a detection range of 0.3 to 100 meters, a detection angle of ±60°, and a response time of no more than 100 milliseconds; the optical sensor has a detection range of 0 to 600 lux and an accuracy of ±0.1 lux.

[0038] Furthermore, the execution layer includes a street light driver module, which supports 0 to 10V, DALI or PWM dimming interfaces and has overload, overvoltage and overtemperature protection functions.

[0039] Furthermore, the control layer is also configured to automatically switch to a local simple control unit or a networked collaborative control unit when the remote platform control unit fails, thereby enabling multi-control mode collaboration.

[0040] Furthermore, the self-organizing Mesh networking mode supports the 2.4GHz or Sub-1GHz communication frequency band, while the Zigbee networking mode supports the 2.4GHz communication frequency band.

[0041] In summary, due to the adoption of the above technical solution, the beneficial effects of the present invention are: 1. A radar-optical sensor fusion wireless networking remote outdoor street light control system, which solves the problem of false triggering under rain, fog and strong light by specific fusion of 24G / 60G radar and optical sensor, combined with outdoor anti-interference optimization; 2. In this invention, the dual-mode switchable networking architecture overcomes the limitations of single networking in terms of distance and obstruction adaptability, ensuring communication stability and reducing costs; 3. In this invention, the collaboration of multiple control methods and the selectability of dual logic meet the operation and maintenance needs of different scenarios in urban and rural areas; 4. In this invention, GNSS is limited to positioning, timing, networking, and grouping, thus eliminating the risks and costs associated with redundant functions. Attached Figure Description

[0042] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the embodiments, experimental examples, and comparative examples will be briefly described below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort, wherein: Figure 1 This is an architecture diagram of the system of the present invention; Figure 2 This is a flowchart of the system of the present invention; Figure 3 and Figure 4 These are screenshots of the system page of this invention; Figure 5 These are the actual product and test diagrams of the present invention; Figure 6 This is a field installation diagram of the present invention. Detailed Implementation

[0043] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings, embodiments, experimental examples, and comparative examples. It should be understood that the specific embodiments described herein are only for explaining the invention and are not intended to limit the invention; that is, the described embodiments are merely some embodiments of the invention, and not all embodiments. The components of the embodiments of the invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations.

[0044] Therefore, the following detailed description of the embodiments of the invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the invention without inventive effort are within the scope of protection of the invention.

[0045] It should be noted that relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0046] The features and performance of the present invention will be further described in detail below with reference to embodiments.

[0047] I. Implementation Examples Example 1: Urban or highway main road lighting scenario This invention provides a radar-optical sensor fusion wireless networking remote outdoor street light control system, such as... Figure 1 , Figure 2 , Figure 3 , Figure 4 , Figure 5 , Figure 6 As shown, the specific implementation method of this embodiment is as follows: The perception layer adopts 24G radar, light sensor, GNSS module and gyroscope; the networking layer adopts Zigbee mode; the control layer adopts scene-based dynamic weight algorithm, the light sensor activation threshold is set to 20 lux, the linkage range is 50-200 meters, the lighting speed is 0.5 seconds / light, the high brightness duration is 1 minute, and the confidence threshold for urban scenes is 0.65.

[0048] After the system powers on, the GNSS module completes positioning and timing. The platform issues grouped commands based on GNSS coordinates for each road segment, and the streetlights connect to the Zigbee network. The system activates when the illumination is ≤20 lux. The 24G radar detects a target and calculates the overall confidence level using data from the light sensor and gyroscope. If the confidence level is ≥0.7, the streetlights brighten and send commands to neighboring lights in the same group via Zigbee, causing neighboring lights to turn on every 0.5 seconds. If the radar does not detect a target for one minute, the streetlights simultaneously reduce their brightness to 20%. Administrators can remotely monitor and adjust parameters through a cloud platform.

[0049] Example 2: Rural or ordinary road street light scenario This invention provides a radar-optical sensor fusion wireless networking remote outdoor street light control system, such as... Figure 1 , Figure 2 , Figure 3 , Figure 4 , Figure 5 , Figure 6 As shown, the specific implementation method of this embodiment is as follows: The perception layer adopts 24G radar and light sensor (GNSS optional); the networking layer adopts self-organizing Mesh mode (Sub-1GHz band); the control layer adopts simple logic control, the light sensor activation threshold is 20 lux, the linkage range is 30-100 meters, the lighting speed is 0.8 seconds / lamp, and the high brightness duration is 15 seconds.

[0050] After the system powers on, if GNSS is configured, the streetlights identify neighbors through the mesh network and automatically group them based on GNSS location; if GNSS is not available, grouping relies solely on mesh neighbor identification. The system activates when illumination is ≤20 lux. When the radar detects pedestrians or vehicles, it triggers its own high brightness and sends commands to neighbors in the same group, causing neighboring lights to illuminate every 0.8 seconds. If the radar does not detect any targets for 15 consecutive seconds, the streetlights simultaneously turn off. Local button control and remote monitoring via mobile app are supported.

[0051] Example 3: Urban and Rural Mixed Road Segment Scenario (Integrated Network) This invention provides a radar-optical sensor fusion wireless networking remote outdoor street light control system, such as... Figure 1 , Figure 2 , Figure 3 , Figure 4 , Figure 5 , Figure 6 As shown, the specific implementation method of this embodiment is as follows: the networking layer adopts a converged networking mode. In open road sections, the system automatically switches to Zigbee mode to reduce power consumption; when entering road sections with severe tree obstruction or longer distances, the system automatically switches to Sub-1GHz self-organizing Mesh mode to maintain connectivity.

[0052] Based on the location information provided by GNSS, the control layer automatically matches the weight algorithm threshold for urban or rural scenes to achieve seamless switching and adaptive control.

[0053] Example 4: Internal road scene of the park This invention provides a radar-optical sensor fusion wireless networking remote outdoor street light control system, such as... Figure 1 , Figure 2 , Figure 3 , Figure 4 , Figure 5 , Figure 6 As shown, the specific implementation method of this embodiment is as follows: it is applied to the internal outdoor roads of industrial parks, university campuses, etc. The road characteristics are short distance (street light spacing 20-30 meters), many bends, and partial obstruction by buildings / greenery, but the street light density is high, and the operation and maintenance requirements are biased towards low cost and easy operation.

[0054] Configuration loading: The perception layer uses 24G radar + light sensor (no GNSS required, group identification via Mesh neighbor); the networking layer adopts a converged networking mode (automatic switching to Zigbee in areas with few obstructions, automatic switching to Mesh in areas with many obstructions); the control layer uses simple logic control (adapting to low-complexity requirements of the park); parameter settings: light sensor activation threshold 20 lux, linkage range 30-80 meters, lighting speed 0.6 seconds / light, high brightness duration 30 seconds.

[0055] System initialization: After power-on, the street light identifies neighboring nodes through the self-organizing network Mesh, determines the neighbor relationship based on the signal strength (RSSI), and automatically completes the binding of the same group (without GNSS positioning).

[0056] Detection and control: The system is activated when the light sensor detects that the illumination is ≤20 lux; when the 24G radar detects a pedestrian or vehicle, it triggers its own street light to turn on and sends a linkage command to the neighboring lights in the same group through the fusion network. The neighboring lights turn on sequentially at a rate of 0.6 seconds per light; if the radar does not detect a target for 30 consecutive seconds, the street light itself and the neighboring lights in the same group turn off synchronously.

[0057] Operation and maintenance collaboration: The park property management can temporarily turn the street lights on / off via local buttons, or adjust the linkage range and high brightness duration in batches via mobile APP to adapt to temporary needs such as nighttime overtime work and events in the park.

[0058] Example 5: Mountain road scene This invention provides a radar-optical sensor fusion wireless networking remote outdoor street light control system, such as... Figure 1 , Figure 2 , Figure 3 , Figure 4 , Figure 5 , Figure 6 As shown, the specific implementation method of this embodiment is as follows: it is applied to outdoor scenarios such as mountain roads and scenic mountain roads. The road characteristics are long distance (streetlight spacing 50-100 meters), many mountains / trees blocking the way, high vehicle speed, and high maintenance difficulty.

[0059] Configuration loading: The perception layer uses a 60G radar (high resolution adapted for long-distance vehicle and pedestrian detection) + light sensor + GNSS positioning module (required, used for precise grouping); the networking layer uses a self-organizing mesh mode (Sub-1GHz band, strong obstacle penetration capability, long communication distance); the control layer uses a scenario-based dynamic weight algorithm (adapted to complex environment anti-interference requirements); parameter settings: light sensor activation threshold 20 lux, linkage range 100-300 meters, lighting speed 0.3 seconds / light (matching vehicle speed), high brightness duration 2 minutes (long vehicle passage time), rural scene confidence threshold 0.65.

[0060] System initialization: After the system is powered on, the GNSS module completes positioning (accuracy ≤ 2 meters) and time synchronization; the remote platform divides the GNSS coordinates into groups every 1.5 kilometers and sends group commands through Mesh; the streetlights are equipped with anti-interference configurations (such as filtering mountain reflection clutter) and complete network calibration.

[0061] Detection and Control: The system is activated when the light sensor detects an illumination of ≤20 lux; the 60G radar detects a vehicle, collects data and filters interference, and calculates the comprehensive confidence level by combining the light sensor data and GNSS time synchronization signal (formula in claim 5). When the confidence level is ≥0.65, the system triggers its own street light to be bright, and sends a linkage command to the neighboring lights in the same group through the Mesh. The neighboring lights light up sequentially at a rate of 0.3 seconds / light; if the radar does not detect a vehicle for 2 consecutive minutes, the system and the neighboring lights in the same group simultaneously reduce their brightness to low (30%).

[0062] Operation and maintenance collaboration: Administrators can remotely monitor the status of streetlights (such as battery power and communication quality) and receive fault alarms (such as radar failure and Mesh network outage) through the cloud platform, eliminating the need for on-site inspections and reducing operation and maintenance costs in mountainous areas.

[0063] Example 6: Urban Pedestrian and Vehicle Monitoring (Radar=0.75) This invention provides a radar-optical sensor fusion wireless networking remote outdoor street light control system, such as... Figure 1 , Figure 2 , Figure 3 , Figure 4 , Figure 5 , Figure 6 As shown, the specific implementation method of this embodiment is as follows: Baseline confidence level: 0.5×0.75 + 0.2×0.5 + 0.15×0.9 + 0.15×0.9 = 0.745 Final confidence level for urban scenes: 0.745 × 0.85 = 0.633; when people and vehicles are clearer, the threshold can be stably exceeded to trigger the lighting.

[0064] Judgment conclusion: Strictly non-triggered in harsh interference environments; high-speed and stable triggering in excellent environments; critical judgment in rural areas; conservative judgment in urban areas with high interference, perfectly conforming to the hierarchical anti-interference design logic.

[0065] Excellent clean environment (GNSS=0.9, Gyro=0.9): Confidence level of 0.720 > 0.65 in high-speed scenarios, stable triggering; critical judgment in rural areas, conservative prevention of false triggering in urban areas, and graded adaptation to different road interference levels; Excellent and clean environment (GNSS=0.9, Gyro=0.9, clear visibility of people and vehicles): Confidence level of 0.795 > 0.65 in high-speed scenarios, stable triggering; critical judgment in urban scenarios, meeting the strict threshold of high interference in cities.

[0066] II. Experimental Examples The following experiments were conducted based on the GB / T 34923-2017 "Street Light Control and Management System" standard. The test environment covered typical outdoor scenarios such as cities, villages, industrial parks, and mountainous areas. All data are the average values ​​of three repeated experiments.

[0067] Experiment Example 1: Detection Anti-interference Performance Test Test subject: This invention (24G / 60G radar + optical sensing fusion scheme) vs. existing technology (PIR + microwave radar dual-detection scheme, such as CN120659202A).

[0068] Test scenarios: ① Rainy day (rainfall 50mm / h); ② Strong light (direct sunlight at noon, illuminance 100000 lux); ③ Foggy day (visibility 50 meters).

[0069] Test metrics: false trigger rate (number of false alarms / total number of tests), missed trigger rate (number of missed alarms / total number of tests).

[0070] Table 1 shows the test results of Experiment Example 1. Conclusion: This invention achieves a significantly lower false trigger / missed trigger rate than existing dual-detection solutions through the exclusive fusion of 24G / 60G radar and light sensing, combined with outdoor anti-interference optimizations (such as rain and fog clutter filtering and strong light suppression), making it suitable for complex outdoor environments.

[0071] Experiment Example 2: Network Stability Test Test subject: This invention (integrated networking: Zigbee+Mesh switchable) vs. existing technology (single Zigbee networking, such as CN118870554B).

[0072] Test scenarios: ① Urban road sections with obstructions (trees and buildings obstructing the view, streetlights spaced 30 meters apart); ② Long rural road sections (unobstructed, streetlights spaced 100 meters apart).

[0073] Test metrics: Communication success rate (number of successful transmissions / total number of transmissions), network latency (ms).

[0074] Table 2 shows the test results of Experiment Example 2. Conclusion: The dual-mode switchable networking architecture of this invention uses Zigbee to ensure low latency in urban obstructed scenarios and Mesh (Sub-1GHz) to ensure communication distance in rural long-distance scenarios, with significantly better stability than a single network.

[0075] Experiment Example 3: Control Precision and Energy Saving Effect Test Test subject: This invention (scenario-based dynamic weight algorithm) vs. existing technology (fixed threshold control, such as CN120659202A).

[0076] Test scenarios: ① Urban scenario (mixed pedestrian and vehicle, triggered an average of 120 times per day); ② Rural scenario (mainly vehicles, triggered an average of 40 times per day).

[0077] Test metrics: Linkage accuracy (number of correct linkages / number of triggers), energy saving rate (compared to always-on mode).

[0078] Table 3 shows the test results of Experiment Example 3. Conclusion: This invention uses a dynamic weighting algorithm (combined with radar, light sensing, and GNSS time synchronization) to filter out invalid interference (such as wind rustling through grass or small animals), resulting in higher linkage accuracy and better energy-saving effect.

[0079] Experiment Example 4: GNSS Functionality Simplification Test Test subject: This invention (GNSS is only used for positioning, timing and grouping) vs. existing technology (GNSS includes trajectory monitoring redundancy function).

[0080] Test metrics: grouping error (meters), time synchronization error (ms), hardware cost increment (yuan / node).

[0081] Table 4 shows the test results of Experiment Example 4. Conclusion: This invention limits GNSS to core functions only, removes redundant designs such as trajectory monitoring, and significantly reduces hardware costs and data security risks without affecting grouping and timing performance.

[0082] III. Comparative Example Comparative Example 1: Core Sensor Assembly Missing Test Test subject: Remove the 24G / 60G radar and keep only the light sensor.

[0083] Test scenario: Rural road at night (vehicles traveling in one direction).

[0084] Results: The light sensor can only detect changes in ambient light and cannot identify vehicle movement. The missed trigger rate is 100% (the ambient light does not change when the vehicle passes by, so the linkage is not triggered), and the energy saving rate is 0% (it can only be kept on or switched on at fixed times).

[0085] Conclusion: The core fusion of 24G / 60G radar and optical sensing is a necessary technical feature for the present invention to achieve accurate human and vehicle detection; without it, the system cannot function properly.

[0086] Comparative Example 2: Dual-mode networking missing test Test subject: A single Zigbee network with Mesh switching disabled.

[0087] Test scenario: Long rural road section (streetlight spacing 100 meters, no obstructions).

[0088] Results: Communication success rate was 60%, network latency was 300ms, group linkage frequently failed (more than 40% of linkage commands were lost), and the coordinated operation of streetlights could not be guaranteed.

[0089] Conclusion: The dual-mode switchable architecture of Zigbee and Mesh is an essential technical feature of this invention to adapt to different outdoor scenarios. A single network cannot meet the communication needs of long-distance and multi-obstruction scenarios.

[0090] Comparative Example 3: GNSS Packet Strategy Missing Test (Packet Failure Based on Signal Strength) Test subjects: This invention (grouping strategy based on GNSS coordinates) vs. comparative scheme (grouping strategy based on radio signal strength RSSI, i.e., determining neighbor relationships and grouping solely based on Zigbee / Mesh signal strength).

[0091] Test scenario: A section of urban road with many curves (streetlights spaced 30 meters apart, with buildings obstructing the signal and causing severe signal reflection).

[0092] Test metrics: Grouping error rate (number of incorrectly grouped nodes / total number of nodes), Linkage out-of-bounds rate (number of cross-group erroneous linkages / total number of linkages).

[0093] Table 5 shows the test results of Comparative Example 3. Conclusion: In complex urban environments, wireless signals are easily affected by obstruction and reflection. Relying solely on RSSI for grouping can lead to numerous logical errors (e.g., mistaking a streetlight across the street for a neighboring street). This invention utilizes the absolute position coordinates of GNSS for grouping, completely avoiding grouping errors caused by signal propagation characteristics and ensuring the accuracy of linkage logic. This feature is a necessary technical means to solve the grouping problem in complex outdoor terrain.

[0094] Comparative Example 4: Radar band replacement test (limitations of traditional 5.8G radar solutions) Test subject: This invention (60G radar) vs. comparative scheme (traditional 5.8G microwave radar, commonly used in indoor sensor lights).

[0095] Test scenario: A typical rural road (streetlights spaced 40 meters apart, with trees swaying and small animals present).

[0096] Test metrics: Detection distance compliance rate (the percentage of vehicles that can be stably detected at a distance of 40 meters), false alarm rate (number of false triggers per hour caused by swaying leaves / small animals).

[0097] Table 6 shows the test results of Comparative Example 4. Conclusion: Traditional 5.8GHz radar has a long wavelength, large divergence angle, and low resolution, making it difficult to distinguish between people, vehicles, and interfering objects at long distances. Furthermore, its detection range is typically limited to within 15 meters, failing to meet the spacing requirements of outdoor streetlights. The 24GHz / 60GHz radar used in this invention possesses higher resolution and a more concentrated beam, forming the basis for achieving accurate detection within 0.3-100 meters. Replacing it with conventional low-frequency radar would prevent the system from achieving the expected long-range detection and anti-interference effects, demonstrating the superiority of this specific frequency band selection.

[0098] The above description is only a preferred embodiment, experimental example, and comparative example of the present invention, and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, and improvements made by those skilled in the art within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A radar-optical sensor fusion wireless networking remote outdoor street light control system, characterized in that, include: The perception layer includes a radar sensing module and a light sensor. The radar sensing module is configured to operate in the 24G or 60G frequency band, and the light sensor is configured to detect ambient light intensity. The wireless networking layer includes a wireless networking module, which is configured to selectively enable any one of Zigbee networking mode, self-organizing Mesh networking mode, or converged networking mode. The control layer is connected to the sensing layer and the wireless networking layer respectively. The control layer is configured to generate control commands based on the data of the radar sensing module and the light sensor, and realize remote command interaction and linkage of street lights in the same group through the wireless networking layer. An execution layer, which is connected to the control layer, is configured to respond to the control commands to adjust the operating state of the streetlights.

2. The radar-optical sensing fusion wireless networking remote outdoor street light control system according to claim 1, characterized in that, The perception layer also includes a GNSS positioning module, which is configured to be used only to acquire the position coordinates of the light pole, provide time synchronization signals and assist in the generation of grouping strategies. The GNSS positioning module does not perform trajectory monitoring and personnel tracking functions. The perception layer also includes a gyroscope, which is used to detect outdoor dynamic shaking interference. When there is no wind, the lamp post is stable, and no trees are swaying, the value approaches 1. When there is strong wind, the lamp post is swaying, the branches and leaves are swinging violently, or the equipment is vibrating continuously, the value automatically decreases. The gyroscope value range is [0, 1].

3. The radar-optical sensing fusion wireless networking remote outdoor street light control system according to claim 1, characterized in that, The communication modes of the wireless networking layer specifically include: When the application scenario is a short-distance cluster in an urban or high-speed environment with no obstructions, the wireless networking module enables Zigbee networking mode, with a communication distance of 10 to 500 meters. When the application scenario is a long-distance street light in a rural area or a road section with multiple obstructions, the wireless networking module enables the self-organizing Mesh networking mode, and the communication distance is 10 to 1000 meters. When the application scenario is a mixed road section with varying occlusion conditions, the wireless networking module switches between Zigbee networking mode and self-organizing Mesh networking mode based on communication quality.

4. The radar-optical sensing fusion wireless networking remote outdoor street light control system according to claim 1, characterized in that, The control layer is configured to execute one of the following two control logics: Logic 1: Simple logic control. When the light sensor detects a value below a preset threshold and the radar sensor detects a target, the street light is triggered to brighten and sends a linkage signal to neighboring lights in the same group. When the radar fails to detect a target for a preset delay, the street light is controlled to dim or turn off. Logic 2: Scenario-based dynamic weighting algorithm control. The control layer is configured to perform time-series alignment and filtering on radar data and light-sensing data, and calculate the comprehensive confidence score in conjunction with the GNSS time synchronization signal. When the comprehensive confidence score is greater than a preset threshold, the linkage is triggered.

5. A radar-optical sensing fusion wireless networking remote outdoor street light control system according to claim 4, characterized in that, The formula for calculating the overall confidence level is as follows: Confidence=α·Radar+β·Light+ϵ·GNSS+γ·Gyro The final confidence level calculation formula is as follows: Confidence = Confidence × K Where α is the radar weight, β is the light sensor weight, ϵ is the GNSS weight, γ is the gyroscope weight, and K is the scene correction factor; the four weights satisfy the hard constraint: α+β+ϵ+γ=1. Scene threshold parameter: The trigger threshold is uniformly set to 0.65 for all scenes, including urban, rural, and highway scenarios; The scene correction factor K adopts an attenuation correction mode to adapt to different interference environments in urban and rural areas and prevent false triggering by clutter. Urban roads: There is a lot of electromagnetic clutter and strong building reflection interference. K=0.85 is set to attenuate the confidence level and increase the trigger threshold. Rural roads: The environment is open, with few obstructions and weak interference. With K=0.90, there is slight attenuation, but high sensitivity is maintained. Highways: High traffic speeds and no clutter interference; setting K=1.00 results in no attenuation and the highest sensitivity.

6. The radar-optical sensing fusion wireless networking remote outdoor street light control system according to claim 1, characterized in that, The system also includes a group linkage strategy, which is implemented in one of the following two ways: Method 1: The remote platform collects the coordinates of the light poles via GNSS, calculates the group boundaries, and sends group commands to the streetlights; Method 2: Streetlights identify neighboring nodes through the wireless networking layer, calculate the relative distance by combining GNSS location information, and automatically determine and join the same group.

7. A radar-optical sensing fusion wireless networking remote outdoor street light control system according to claim 1, characterized in that, The radar sensing module has a detection range of 0.3 to 100 meters, a detection angle of ±60°, and a response time of no more than 100 milliseconds; the optical sensor has a detection range of 0 to 600 lux and an accuracy of ±0.1 lux.

8. A radar-optical sensing fusion wireless networking remote outdoor street light control system according to claim 1, characterized in that, The execution layer includes a street light driver module, which supports 0 to 10V, DALI or PWM dimming interfaces and has overload, overvoltage and overtemperature protection functions.

9. A radar-optical sensing fusion wireless networking remote outdoor street light control system according to claim 1, characterized in that, The control layer is also configured to automatically switch to a local simple control unit or a networked collaborative control unit when the remote platform control unit fails, thereby enabling multi-control mode collaboration.

10. A radar-optical sensing fusion wireless networking remote outdoor street light control system according to claim 3, characterized in that, The self-organizing Mesh networking mode supports 2.4GHz or Sub-1GHz communication frequency bands, and the Zigbee networking mode supports 2.4GHz communication frequency bands.

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