A highway electromechanical equipment energy efficiency optimization device

Abstract

The application discloses a kind of expressway electromechanical equipment energy efficiency optimization device, it is related to Internet of Things control technical field, including truss, the truss is fixedly installed with installation box, lighting lamp is provided in the installation box, controller electrically connected with lighting lamp is further provided in the installation box, the outside of the installation box is fixedly connected with mobile guide rail assembly, pneumatic spray head is fixedly installed on the mobile guide rail assembly, processing chip is provided in the controller, the processing chip is electrically signal connected with the edge computing gateway deployed in toll station machine room, the application constructs the dynamic demand quantization framework of two dimensions of driving safety and personnel safety, by associating traffic flow, road braking performance, three types of scene parameters such as visibility, the accurate matching of lighting demand and actual operation scene is realized, simultaneously by the bottom setting of personnel existence threshold, avoid the security risk of lighting demand too low in extreme scene, balance safety guarantee and energy-saving target.

Description

Technical Field

[0001] This invention relates to the field of Internet of Things (IoT) control technology, specifically to an energy efficiency optimization device for highway electromechanical equipment. Background Technology

[0002] As a crucial node in the road network, highway toll stations rely heavily on their plaza lighting systems, which are core electromechanical facilities ensuring safe nighttime traffic and orderly toll collection operations. Currently, toll station plaza lighting generally employs time-controlled or light-controlled graded dimming schemes, adjusting lamp output power based on preset time periods or ambient brightness thresholds. Some advanced schemes also dynamically adjust lighting levels based on traffic flow statistics. These systems are equipped with basic operational functions such as remote on / off control of lighting circuits and fault alarms. The overall architecture has achieved basic automated operation, meeting basic lighting needs for both driving and operational purposes.

[0003] However, in actual operation, the existing solution still has many shortcomings in refined control: First, the calculation of lighting demand is based only on macro parameters such as traffic flow and overall ambient brightness, without being related to key scenario parameters that directly affect driving safety, and there is no differentiated lighting guarantee mechanism for personnel work areas, making it difficult to achieve the optimal balance between safety and energy saving; Second, the calculation of lamp output power does not consider the temperature lag of the lamp beads, and only relies on nominal parameters or a single cumulative usage time to estimate luminous efficacy, resulting in a deviation between actual output illuminance and theoretical demand, which can easily lead to excessive brightness causing energy waste or insufficient brightness affecting safety; Third, the generation of control commands does not consider the impact of dynamic scenarios such as personnel movement and instantaneous interference from vehicle lights, which can easily lead to problems such as flickering caused by power jumps and incorrect dimming caused by sensor interference, which not only affect user comfort but also reduce control accuracy. Therefore, the existing energy efficiency optimization device still needs to be improved. Summary of the Invention

[0004] The purpose of this invention is to provide an energy efficiency optimization device for highway electromechanical equipment, which solves the problems mentioned in the background art.

[0005] To achieve the above objectives, the present invention provides the following technical solution: an energy efficiency optimization device for highway electromechanical equipment, comprising a truss, on which a mounting box is fixedly installed, a lighting lamp is installed inside the mounting box, and a controller electrically connected to the lighting lamp is also installed inside the mounting box. A movable guide rail assembly is fixedly connected to the outside of the mounting box, and a pneumatic nozzle is fixedly installed on the movable guide rail assembly. A processing chip is installed inside the controller, and the processing chip is electrically connected to an edge computing gateway deployed in a toll station computer room. The edge computing gateway includes: The data acquisition layer and processing layer are used to acquire comprehensive data related to the lighting of the toll plaza, including data on safety lighting, effective luminous efficacy, and power control. The acquired data is then processed in the processing layer. The processing layer also includes an edge computing layer. The processed data is then input into the edge computing layer, and the brightness of the lights is controlled based on the data calculated by the edge computing layer.

[0006] Optionally, the edge computing layer includes a safety lighting requirement module, an effective luminous efficacy calculation module, and an output power control module.

[0007] Optionally, the processing logic of the safety lighting requirement module is as follows: Based on the basic lighting standards, traffic flow correction coefficient, real-time 60km / h safe braking distance, minimum safe braking distance, benchmark visibility, historical average of similar weather conditions for the same road segment, calibration value of the last time without vehicles, and real-time hourly rainfall data, dynamic safety lighting demand values ​​are output. The minimum safety lighting demand value is quantified in the current scenario.

[0008] Optionally, the processing logic of the effective light effect calculation module is as follows: Based on the nominal luminous efficacy of the new luminaire, the cumulative lighting time of the luminaire, the real-time operating temperature of the LED beads, the dust accumulation and salt spray attenuation coefficient, the individual difference correction coefficient, the historical value of the LED bead temperature 5 minutes ago, the historical value of the LED bead temperature 10 minutes ago, the average wind speed during the calibration period, the dust accumulation alarm threshold, and the forced cleaning reminder interval, the real-time effective luminous efficacy of the luminaire is output. By correcting the luminous efficacy attenuation through the real-time effective luminous efficacy of the luminaire, the actual light production capacity of the luminaire is accurately reflected.

[0009] Optionally, the processing logic of the output power control module is as follows: Based on the data related to power control, such as the square area covered by a single lamp, lamp utilization coefficient, maintenance coefficient, personnel position correction coefficient, rated maximum power of the lamp, average illuminance during the past hour without vehicles, average illuminance for the current minute, personnel correction coefficient for the previous minute, and current target correction coefficient, the output power control value of the lamp is output. The output power control value of the lamp balances energy saving and operational safety.

[0010] Optionally, the edge computing gateway further includes an optimization module, which includes an identification component and a triggering component.

[0011] Optionally, the triggering logic of the triggering component is as follows: the initial state uses the default value, and iterative optimization is started when a user who has been authenticated by the identification component actively triggers the dimming command.

[0012] Optionally, the iteration convergence condition of the triggering component is: stopping when any one of these conditions is met. A1: No new dimming command has been issued for 30 consecutive seconds; A2: The number of iterations has reached the maximum value of 5. A3: The change in coefficients after two consecutive iterations is less than 0.02.

[0013] Compared with the prior art, the beneficial effects of the present invention are as follows: I. This invention uses a pneumatic nozzle to remove dust from lighting lamps. In conjunction with the use of a moving guide rail assembly, it not only expands the working range but also allows the airflow to flow linearly along the surface of the lighting lamp, achieving a "sweeping" effect and optimizing the energy efficiency of the lighting system.

[0014] Second, this invention breaks away from the traditional approach of relying solely on fixed time periods and general ambient brightness to set lighting requirements, and instead constructs a dynamic demand quantification framework that considers both traffic safety and pedestrian safety. By linking three scenario parameters that directly affect traffic safety—traffic flow, road braking performance, and visibility—it achieves precise matching between lighting requirements and actual operating scenarios. Furthermore, by setting a safety net based on pedestrian presence thresholds, it avoids the safety hazards of excessively low lighting requirements in extreme scenarios, thus balancing safety and energy-saving goals from the source of demand.

[0015] Third, this invention changes the traditional approach of estimating light output capacity using only the nominal luminous efficacy of the lamp or a single aging coefficient. It fully covers four core factors of luminous efficacy decay during outdoor operation of LED lamps: aging, temperature deviation, surface dust accumulation, and individual performance differences. Through the calculation logic of multi-factor multiplication and superposition, it accurately restores the actual light production capacity of the lamp in complex outdoor environments, avoiding insufficient or excessively bright waste of actual illuminance caused by luminous efficacy estimation deviation.

[0016] Fourth, this invention derives power requirements from the fundamental optical principles of lighting design, incorporates scenario-based correction factors such as enhanced personnel position and smooth power transition, and sets a hardware rated power limit protection, thus achieving a complete closed loop of demand-capacity-control. It can dynamically adjust the illuminance of the area according to the personnel's position to ensure the visual needs of workers, avoid sudden power fluctuations that could affect lighting comfort, and prevent damage to the luminaires from overpower operation in extreme scenarios. Attached Figure Description

[0017] Figure 1 This is an application scenario diagram of the present invention; Figure 2 This is an assembly diagram of the mounting box of the present invention; Figure 3 This is a schematic diagram of the internal components of the mounting box of the present invention; Figure 4 This is a schematic diagram of the lighting lamp of the present invention; Figure 5 This is a processing logic diagram of the edge computing layer of the present invention; Figure 6 This is a flowchart of the edge computing gateway processing of the present invention.

[0018] In the diagram: 1. Truss; 2. Mounting box; 3. Lighting lamp; 4. Controller; 5. Moving guide rail assembly; 6. Pneumatic nozzle; 7. Processing chip. Detailed Implementation

[0019] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0020] Example: Please refer to Figures 1 to 6 This invention provides an energy efficiency optimization device for highway electromechanical equipment, including a truss 1, a mounting box 2 fixedly installed on the truss 1, a lighting lamp 3 installed inside the mounting box 2, and a controller 4 electrically connected to the lighting lamp 3 also installed inside the mounting box 2. A movable guide rail assembly 5 is fixedly connected to the outside of the mounting box 2, and a pneumatic nozzle 6 is fixedly installed on the movable guide rail assembly 5. Normally, the lighting lamp 3 provides working illumination for the toll station area. With increased usage time, especially in high-traffic areas like toll stations, dust easily accumulates on the surface of the lighting lamp 3. The presence of dust directly affects the luminous effect. In traditional scenarios, staff would increase the brightness of the lighting lamp 3 to meet daily work needs, leading to unnecessary energy waste. In this embodiment, by activating the pneumatic nozzle 6, the nozzle on the pneumatic nozzle 6 can be aimed at the lighting lamp 3, thereby removing dust from the lighting lamp 3.

[0021] Simultaneously, the moving guide rail assembly 5 can be activated. The moving guide rail assembly 5 has its own drive motor, controlling the pneumatic nozzle 6 to move linearly. This not only increases the working range but also allows the airflow to flow linearly along the surface of the lighting lamp 3, achieving a "sweeping" effect. The controller 4 contains a processing chip 7, which is electrically connected to the edge computing gateway deployed in the toll station's computer room. While some lighting lamps 3 already have power self-adjustment functions, they are still limited to situations where there are many vehicles and the lighting is bright, or few vehicles and dim. In this embodiment, energy efficiency management can be implemented in the following manner to intelligently control the lighting lamp 3: The edge computing gateway includes a data acquisition layer and a processing layer. The data acquisition layer acquires full-dimensional data related to the lighting of the toll plaza, including data related to safety lighting, effective luminous efficacy, and power control.

[0022] The data related to safe lighting includes the basic lighting standard L.base The default value for regular traffic sites is 50 lux, while sites with extremely high traffic can be adjusted to 60-80 lux. Traffic flow correction factor K. traffic The real-time safe braking distance S at 60 km / h is calculated by collecting traffic flow data through meteorological auxiliary sensors deployed on the roof of the toll station. The values ​​are: 1.2 for traffic flow ≥ 100 vehicles / hour, 0.8 for ≤ 20 vehicles / hour, and 1.0 for other periods. act The minimum safe braking distance S is obtained directly from road surface friction coefficient sensors deployed at the plaza entrance lane, with a value range of 40-150m. min The value is taken as 40m, a common standard in the transportation industry. The real-time environmental visibility V is directly collected by a visibility sensor deployed at a high point in the square, with a value range of 10-10000. The baseline visibility V is... ref The value is 1000, and the average braking distance S over the past 5 minutes is... act-5min The historical average weather value S for the same road section during the same period act-avg The last calibration value S during a car-free period act-last The real-time hourly rainfall R is directly collected by a meteorological auxiliary sensor deployed on the roof of the toll station.

[0023] Furthermore, effective luminous efficacy data includes the nominal luminous efficacy η0 of the new lamp, read from the lamp's factory parameters; the cumulative lighting time t, directly collected by the built-in timer inside the LED lamp; and the real-time operating temperature T of the LED beads. led The temperature is directly collected by the built-in temperature sensor inside the LED lamp, with a range of -40 to 85℃, and the dust accumulation and salt spray attenuation coefficient η. dust The value is obtained through weekly automatic calibration calculations, ranging from 0.6 to 1.0, with an individual difference correction factor η. corr The value is obtained through self-calibration calculation, ranging from 0.9 to 1.1. The individual difference correction factor is calculated as: actual calibrated illuminance / theoretical calculated illuminance. The historical value T of the LED bead temperature five minutes ago was obtained from the built-in temperature sensor of the luminaire. led-5min Compared with the historical LED bead temperature T from 10 minutes ago led-10min The average wind speed W during the calibration period is calculated by averaging the wind speed data collected during the calibration period by a meteorological auxiliary sensor deployed on the roof of the toll station. The preset dust accumulation alarm threshold η is also included. dust-threshold The default value is 0.7, with 0.75 for coastal salt spray areas and 0.65 for low-pollution suburban sites. The preset mandatory cleaning reminder interval is T. clean-interval The default cleaning period is 90 days, which can be adjusted according to local conditions. The system records the last time the light fixtures were cleaned, T. last-clean It supports updates by maintenance personnel.

[0024] Furthermore, the power control-related data includes the plaza area S covered by a single lamp, obtained by surveying the installation location; the lamp utilization factor U, pre-determined based on the light distribution curve; the maintenance factor M, a general value of 0.7 for outdoor road lighting, adjusted to 0.65 in coastal salt spray areas; and the personnel position correction factor K. worker(x,y) The location was calculated using millimeter-wave radar deployed on the top of the light pole. The initial value was 1.8 when people were within 30m, 1.2 for adjacent areas, and 1.0 for areas without people. The smoothing adjustment coefficient K... smooth The rated maximum power of the lamp P max The average illuminance (L) during the past hour when there were no vehicles was read from the manufacturer's specifications. i-avg-nocar The data was collected by pavement illuminance sensors deployed on the plaza surface, along with the current 1-minute average illuminance L. i-avg The system records the personnel correction factor K for the previous minute. w-last Current target correction coefficient K w-target The dimming direction factor D is calculated based on the current personnel positioning results of the millimeter-wave radar. The preset fixed value of the iterative learning rate α is 0.05 by default. The dimming direction factor D is determined according to the dimming command triggered by the user dimming interaction terminal through the toll booth / square pillar. The brightness is increased by +1 and decreased by -1.

[0025] All the sensor data mentioned above are transmitted to the edge computing gateway deployed in the toll station's computer room via a communication protocol. The data is then cleaned by the processing layer in the edge computing gateway, and the cleaned data is input into the edge computing layer.

[0026] Furthermore, the edge computing layer includes a security lighting requirement module, specifically: Based on the collected safety lighting-related data, the safety lighting requirement module outputs a dynamic safety lighting requirement value L. req This quantifies the minimum safe lighting requirements in the current scenario, directly impacting vehicle braking safety and personnel operational safety. Specifically, the calculation logic is as follows: Based on basic lighting standards L in safety lighting related data base Traffic flow correction factor K traffic Real-time safe braking distance S at 60km / h act Minimum safe braking distance S min Benchmark visibility V ref The average value of similar weather conditions on the same road section during the same period in history (S) act-avg The calibration value S from the last time there was no vehicle was 1. act-last And real-time hourly rainfall R outputs dynamic safety lighting demand value L req , that is:

[0027] Where Lbase As a basic lighting standard, serving as the benchmark for calculating overall lighting needs, and matching minimum safe lighting requirements, different traffic sites can be configured differently to adapt to actual scenarios. traffic This is a traffic flow correction coefficient. Higher traffic flow increases the probability of vehicle intersections and pedestrians getting on and off vehicles, requiring higher illumination to ensure safety. The coefficient is reduced during low-flow periods to save energy, and segmented values ​​avoid frequent dimming caused by small fluctuations in traffic flow; 1 + lg((S act ×K s-cal ) / S min ) is the braking distance correction term, and the ratio (S) act ×K s-cal ) / S min This reflects the gap between the current road braking performance and the ideal state. A larger ratio indicates a slipperier road surface and a longer braking distance, requiring higher illumination. Logarithmic calculations ensure that the increase in lighting demand decreases marginally, aligning with the driver's visual perception patterns. Adding 1 ensures the correction coefficient is at least 1, avoiding unreasonable results where the coefficient is less than 1 when braking performance is better than the ideal state. (V) ref / (V×K v-rain )) 0.7 The visibility correction term has a ratio that reflects the difference between the current visibility and clear weather. The larger the ratio, the lower the visibility and the higher the required illuminance. 0.7 is the weighting coefficient, which is based on the visibility measured in the lighting industry, i.e., the illuminance response law. This ensures that the illuminance is sufficiently increased in low visibility conditions, while avoiding excessively high demand values ​​that would lead to energy waste. Apt is the minimum illuminance safety threshold term, which sets a minimum safety net threshold to prevent the dynamically calculated value from being too low in extreme scenarios. It is set to 20 when there are personnel working to meet the visual comfort requirements of personnel operation, and to 5 when there are no personnel to meet the basic security monitoring requirements. The maximum value is used to ensure that the calculated result will not be lower than the minimum illuminance requirement for personnel safety, regardless of the driving scenario, thus achieving dual protection for both types of safety objectives.

[0028] The safety lighting requirements module breaks away from the traditional approach of relying solely on fixed time periods and general ambient brightness to set lighting needs. Instead, it constructs a dynamic demand quantification framework encompassing both vehicle safety and passenger safety. By linking three scenario parameters directly impacting traffic safety—traffic flow, road braking performance, and visibility—it achieves precise matching between lighting requirements and actual operating scenarios. Furthermore, by setting a safety net based on passenger presence thresholds, it avoids the safety hazards of excessively low lighting requirements in extreme scenarios, balancing safety assurance and energy conservation goals from the source of demand.

[0029] More specifically, the braking distance sensor interference correction coefficient K s-cal The calculation logic is as follows:

[0030] When the fluctuation of continuous 5-minute measurements is <5%, a stable scenario correction logic is adopted. 0.92 is the basic correction coefficient for the inherent accuracy of the sensor, which offsets about 8% of the inherent error of the road friction coefficient sensor. 0.08 is the dynamic adjustment weight to adapt to the slow changes in road conditions. The 5-minute average value is used to smooth the instantaneous vibration interference caused by vehicle rolling on the sensor, reflecting the true friction state of the road surface in a short period of time. When the fluctuation of continuous 5-minute measurements is ≥5%, it indicates that the current sensor data is abnormal due to interference such as mud covering and heavy vehicle rolling. The fluctuation scenario correction logic is adopted. The historical average value and the calibration value without vehicles are each weighted at 0.5. Both are reliable reference values ​​that are not affected by the current interference. The equal weighting takes into account the road surface patterns under historical weather conditions and refers to the most recent accurate calibration result to avoid the deviation of a single reference value. The calibration coefficient is obtained by dividing by the current real-time measurement value, which corrects the abnormal real-time measurement value to a reasonable range.

[0031] The above calculations solve the problem of measurement data distortion caused by long-term vehicle vibration and road mud and oil stains when the road friction coefficient sensor is deployed in the lane area. Correction logic is designed for both stable and fluctuating measurement scenarios. This not only smooths out measurement errors caused by instantaneous vibrations but also calibrates outliers using historical benchmark data when the sensor is severely interfered with. This significantly improves the reliability of braking distance parameters and provides accurate input for calculating driving safety lighting requirements.

[0032] Furthermore, the visibility sensor's precipitation interference correction coefficient K v-rain The calculation logic is as follows:

[0033] Where 1-e -R / 10 For the rainfall-related term, the exponential function e -R / 10 The simulation of precipitation interference growth patterns shows that when rainfall is low, interference increases rapidly with increasing rainfall. After rainfall reaches a certain level, the growth rate of interference slows down, which is consistent with real-world scenarios. Dividing by 10 is the scaling factor, mapping the numerical range of rainfall to the 0-1 interval, which is suitable for the input range of the exponential function. 0.4 is the maximum interference coefficient, corresponding to a maximum underestimation of approximately 40% by the visibility sensor under precipitation weather conditions. This ensures that the correction coefficient is at most 1.4 during heavy rain, preventing over-correction that would lead to excessively high required values. The base coefficient of 1 ensures that the correction coefficient is 1 when there is no rainfall, having no impact on visibility measurements, which is consistent with the normal operating state of the sensor in clear weather.

[0034] The above calculations eliminate the problem of low visibility measurements caused by visibility sensors misidentifying raindrops and snowflakes as obstructions during rain and snowfall. An exponential function is used to simulate the marginal variation of precipitation interference, accurately matching the difference between the driver's actual field of vision and the sensor's measurement. This avoids excessive lighting power and energy waste due to underestimated visibility during precipitation, while ensuring accurate calculation of lighting requirements in non-precipitation low-visibility scenarios such as fog and haze.

[0035] Furthermore, the edge computing layer also includes an effective luminous efficacy calculation module, which outputs the real-time effective luminous efficacy η of the lamp based on the aforementioned collected effective luminous efficacy related data. eff It corrects for luminous efficacy degradation caused by factors such as aging of lamps, temperature, dust / salt spray, etc., and accurately reflects the actual light production capacity of the lamps. Specifically, the calculation logic is as follows: Based on effective luminous efficacy data, the new luminous fixture's nominal luminous efficacy η0, cumulative lighting time t, and real-time operating temperature T of the LED chips are used. led Dust accumulation and salt spray attenuation coefficient η dust Individual difference correction coefficient η corr Historical LED bead temperature T from 5 minutes ago led-5min Historical LED bead temperature T from 10 minutes ago led-10min Average wind speed W during calibration period, dust accumulation alarm threshold η dust-threshold and mandatory cleaning reminder interval T clean-interval Real-time effective luminous efficacy η of output lamps eff , that is:

[0036] Where η0 is the nominal luminous efficacy of the new lamp, serving as the initial benchmark for luminous efficacy calculation and reflecting the ideal light-producing capacity of a brand-new lamp; 1-t×0.00012 is the aging attenuation term, reflecting the linear decay of LED lamp luminous efficacy over time. 0.00012 is the industry-standard annual attenuation rate conversion value. The lamp is lit for approximately 8000 hours per year, with an annual attenuation rate of approximately 10%, which translates to an hourly attenuation rate of 0.1 / 8000≈0.00012. This ensures that after 5 years of use, the luminous efficacy will decrease by approximately 50%, which is consistent with the actual aging pattern of LED lamps.

[0037] 1-|T led-corr -25|×0.0025 represents the temperature decay term. The optimal operating temperature for LED chips is 25℃. When the temperature deviates from 25℃ (whether too high or too low), the luminous efficacy decreases. Therefore, the absolute value of the temperature difference is taken, and 0.0025 is the temperature decay coefficient. Actual measurements show that for every 1℃ deviation of the LED chip temperature from its optimal operating temperature, the luminous efficacy decreases by 0.25%, consistent with industry-tested temperature-luminous efficacy response curves; η dust ×K d-windFor the dust accumulation attenuation term, the basic attenuation ratio is first obtained through weekly calibration. Then, a wind direction correction coefficient is used to eliminate the problem of high calibration values ​​caused by windy weather blowing away dust on the calibration day, thus obtaining the actual daily dust accumulation attenuation ratio of the lamps; η corr For individual differences correction, performance differences in the manufacturing process of different lamps are eliminated. After correction, the deviation between the calculated value and the actual value of the luminous efficacy of each lamp is controlled within ±10%, thereby improving the overall control accuracy.

[0038] The real-time effective luminous efficacy calculation module changes the traditional approach of estimating light output capacity using only the nominal luminous efficacy of the luminaire or a single aging coefficient. It fully covers four core luminous efficacy attenuation factors during the outdoor operation of LED luminaires: aging, temperature deviation, surface dust accumulation, and individual performance differences. Through the calculation logic of multi-factor multiplication and superposition, it accurately restores the actual light production capacity of the luminaire in complex outdoor environments, avoiding insufficient or excessive illuminance waste caused by luminous efficacy estimation errors, and providing a core foundation for the accuracy of subsequent power control.

[0039] More specifically, the equivalent temperature T for dynamic hysteresis correction of LED temperature. led-corr The calculation logic is as follows:

[0040] The weighting coefficients 0.7, 0.2, and 0.1 correspond to the weights of the current temperature, the temperature 5 minutes ago, and the temperature 10 minutes ago, respectively, and the sum is 1. The lag time of the LED bead temperature change is about 5-10 minutes, so the current temperature has the highest weight, reflecting the latest temperature state. The weight of the temperature 5 minutes ago is 0.2, and the weight of the temperature 10 minutes ago is 0.1. The older the temperature, the smaller its influence on the current equivalent temperature, which is consistent with the lag law of heat conduction.

[0041] The above calculations resolve the issue of luminous efficacy calculation errors caused by a 5-10 minute lag in LED temperature changes after luminaire power adjustment. By integrating current and historical temperature data using a time-weighted approach, the actual physical process of LED heat accumulation is simulated, avoiding frequent ineffective dimming due to discrepancies between instantaneous temperature measurements and actual LED temperatures. This improves the accuracy of luminous efficacy calculations and reduces the impact of unnecessary power fluctuations on luminaire lifespan.

[0042] Furthermore, the dust accumulation coefficient and wind direction interference correction factor K d-wind The calculation logic is as follows:

[0043] When the wind speed is less than 3 m / s, the wind force is insufficient to blow away the floating dust on the surface of the lamps, and the calibration value can reflect the true dust accumulation state. Therefore, the correction factor is 1.0 and no correction is needed. When the wind speed is greater than or equal to 3 m / s, 0.95 is the basic correction value, which corresponds to the degree of overestimation of the calibration value caused by the floating dust being blown away at a wind speed of 3 m / s. 0.01 is the wind speed correction factor. For every 1 m / s increase in wind speed, the correction range increases by 1%. This conforms to the rule that the higher the wind speed, the more floating dust is blown away and the more obvious the overestimation of the calibration value. The lower limit of the factor is 0.85. When the wind speed reaches 15 m / s (level 7 wind), the correction factor is 0.85, which corresponds to a maximum correction range of 15%, to avoid over-correction that would lead to an underestimation of the dust accumulation coefficient.

[0044] This corrects the issue of overestimating the dust accumulation coefficient during weekly calibration, where strong winds blow away dust from the lamp surface. A graded correction logic is designed based on wind speed differences during calibration periods, ensuring that the calibrated dust accumulation coefficient accurately reflects the actual dust accumulation status of the lamps during daily operation. This avoids underestimating dust accumulation attenuation due to overestimating the calibration value and prevents the actual illuminance from gradually falling below the required value during long-term operation.

[0045] It is worth noting that when η dust ≤η dust-threshold The current time is a Level 1 alarm, and the current date is T. last-clean ≥T clean-interval A level 2 alarm occurs when the current dust accumulation coefficient is less than or equal to a preset threshold, otherwise there is no alarm. The condition for a level 1 alarm is that the current dust accumulation coefficient is less than or equal to a preset threshold, η. dust-threshold The default value is 0.7, corresponding to a 30% reduction in light efficiency due to dust accumulation. Continuing to use the device under these conditions will significantly increase energy consumption, thus triggering a high-priority alarm and requiring cleaning. The second-level alarm condition is that the number of days between the current date and the last cleaning date is greater than or equal to the preset interval, T. clean-interval The default setting is 90 days as a safety net. Even if the dust sensor fails to detect attenuation, it can still ensure that the lamp is cleaned at least once a quarter to prevent long-term dust accumulation from corroding the lamp surface.

[0046] Both level 1 and level 2 alarms are recorded and immediately sent to the processing chip 7 in the controller 4, thereby controlling the moving guide rail assembly 5 and the pneumatic nozzle 6 to start working through the controller 4.

[0047] Furthermore, the edge computing layer also includes an output power control module, which is based on the aforementioned dynamic safety lighting requirement value L. req Real-time effective luminous efficacy η of lamps eff Output data related to power control: lamp output power control value P out Combining the calculation results from the first two layers with personnel location requirements, precise control commands are generated, balancing energy conservation and operational safety. Specifically, the calculation logic is as follows: Based on power control related data, the square area S covered by a single lamp, the lamp utilization coefficient U, the maintenance coefficient M, and the personnel position correction coefficient K are used. worker(x,y) The rated maximum power P of the lamp max Average illuminance (L) during the car-free period of the past hour i-avg-nocar The current average illuminance (L) over 1 minute i-avg Personnel correction coefficient K for the previous minute w-last Current target correction coefficient K w-target Output power control value P of the lamp out , that is:

[0048] In the numerator, L req ×S is the target illuminance multiplied by the area covered by a single lamp, yielding the total luminous flux required for the area covered by a single lamp. This conforms to the basic principles of lighting design. K worker(x,y) This is a personnel location correction factor. The area where the personnel are located is multiplied by a factor greater than 1 to increase illuminance and ensure operational safety. K w-smooth (t) and K smooth K is a two-level smoothing coefficient, specifically designed to address demand fluctuations caused by personnel movement and changes in the environment. smooth The value ranges from 0.8 to 1.2, and is adaptively adjusted based on the fluctuation range of lighting demand over the past 5 minutes. The greater the fluctuation, the closer the coefficient is to 1, avoiding frequent power adjustments; the denominator term η eff In ×U×M, η eff The effective luminous efficacy of the lamp is the luminous flux generated per watt of electrical energy. U is the lamp utilization factor, which is the proportion of luminous flux projected onto the road surface. M is the maintenance factor, which is the proportion of light output retained after long-term use. Multiplying these three factors gives the effective luminous flux generated on the road surface per watt of input power. Therefore, dividing the numerator by the denominator gives the required input power. The minimum value is used to limit the operating power to not exceed the lamp's rated upper limit. This prevents the calculated power from exceeding the lamp's rated value in extreme scenarios, avoiding damage caused by over-power operation and ensuring hardware safety. The output power control value P is... out Then, it is sent to the processing chip 7, and the controller 4 can change the power of the lighting lamp 3.

[0049] The output power control module derives power requirements from the fundamental optical principles of lighting design, incorporating scenario-based correction factors such as personnel position enhancement and smooth power transition. It also features hardware rated power limit protection, achieving a complete closed loop of demand-capacity-control. This allows for dynamic adjustment of area illuminance based on personnel position to ensure visual needs while preventing sudden power fluctuations that could impact lighting comfort. Furthermore, it prevents damage to luminaires from overpower operation in extreme scenarios, making it the core of the system for achieving precise control.

[0050] It is worth noting that the above-mentioned road illuminance sensor headlight interference correction coefficient K i-car The calculation logic is as follows:

[0051] When the illuminance fluctuation is less than 10% within 1 minute, it indicates that there is no vehicle headlight illumination, and the sensor measurement value is the true road illuminance. The correction coefficient is 1.0 and no correction is needed. When the illuminance fluctuation is ≥10%, it indicates that there is vehicle headlight illumination, and the current measurement value is too high. The average illuminance during the vehicle-free period is the true value without interference. Dividing it by the current inflated measurement value gives the correction coefficient, and the calibrated illuminance value is brought back to the true level.

[0052] This correction is only used in the system calibration process. During the next optimization test, the calculation method of the individual difference correction coefficient will be changed, that is, the individual difference correction coefficient = actual calibrated illuminance / theoretical calculated illuminance × vehicle headlight interference correction coefficient, to avoid incorrectly lowering the lighting power when vehicles pass by.

[0053] The above calculations resolved the issue of inflated measurements caused by the headlights of passing vehicles illuminating the road surface in the system calibration process. By assessing the amplitude of illuminance fluctuations to determine if vehicle headlight interference exists, and using benchmark illuminance from periods without vehicles to correct abnormal measurements, the system avoids calculation errors in individual difference correction coefficients due to distorted calibration data. This ensures the accuracy of the system's self-calibration and improves long-term control precision through closed-loop feedback.

[0054]

[0055] Where min(0.2,|K w-target -K w-last |) represents the step size limit, taking the smaller of the absolute value of the difference between the target coefficient and the current coefficient and 0.2. 0.2 is the maximum step size limit, corresponding to a power adjustment range of approximately 20% per minute, which aligns with the comfortable perception range of human eye for brightness changes. An excessively large step size can easily cause flickering, while an excessively small step size results in a slow response speed that cannot match personnel movement; sign(K w-target -K w-last ) is the direction control term, a sign function. It returns +1 if the target value is greater than the current value, and -1 if it is less than the current value, controlling the adjustment direction to ensure the coefficient moves closer to the target value; K w-last This is the correction coefficient value from the previous minute, ensuring the continuity of the adjustment process and achieving a smooth transition rather than abrupt changes.

[0056] The above calculations eliminate the issues of lamp power jumps and spotlight flicker triggered by millimeter-wave radar detecting rapid personnel movement. By using a step-limiting adjustment method, the coefficient adjustments caused by changes in personnel position are limited to a range comfortable for the human eye. This ensures both the response speed of lighting adjustments and avoids visual discomfort caused by sudden brightness changes, thus improving the user experience for billing and maintenance personnel.

[0057] The edge computing gateway also includes an optimization module, which comprises an identification component and a triggering component. The triggering logic of the triggering component is as follows: the initial state uses default values; when a user authenticated by the identification component actively triggers a dimming command, iterative optimization is initiated. The authentication method of the identification component is password authentication via the operating terminal inside the toll booth. Specifically, the correction logic is as follows:

[0058] Where α is the learning rate, fixed at 0.05, with an adjustment increment of 5% per iteration. This ensures efficient iteration, reaching user preferences within 5 iterations, while avoiding large single adjustments that could cause sudden changes in illuminance and negatively impact user experience. D is the dimming direction factor, incremented by +1 for brightness and decremented by -1 for brightness, converting user dimming needs into a coefficient adjustment direction. The coefficient is increased when the user perceives darkness and decreased when they perceive brightness. The iteration boundary constraint is 1.5 ≤ K. worker The coefficient is ≤2.2. When the coefficient is below 1.5, the downward adjustment stops, and when it is above 2.2, the upward adjustment stops. 1.5 is the lower limit of the coefficient corresponding to the minimum safe illumination for personnel operation. If it is below this value, the operation safety cannot be met. 2.2 is the upper limit of energy saving. If it is above this value, it will cause unnecessary energy waste. The goal is to balance safety and energy saving.

[0059] The iteration convergence condition for triggering the component is: stopping when any one of these conditions is met. A1: No new dimming command has been issued for 30 consecutive seconds; A2: The number of iterations has reached the maximum value of 5. A3: After two consecutive iterations, the coefficient change is less than 0.02 (adjustment range < 2%, reaching a state of user satisfaction).

[0060] By establishing an adaptive learning mechanism for personnel lighting preferences, the system can automatically and iteratively adjust the illuminance enhancement coefficient of personnel areas based on users' active dimming commands without the need for manual parameter configuration. This adapts to the working habits of different toll stations and the differences in visual sensitivity among different personnel. Without compromising safety standards, it achieves user-defined optimization of lighting effects and improves the system's adaptability to different stations.

[0061] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. An energy efficiency optimization device for highway electromechanical equipment, comprising a truss (1), characterized in that: A mounting box (2) is fixedly installed on the truss (1). A lighting lamp (3) is installed inside the mounting box (2). A controller (4) electrically connected to the lighting lamp (3) is also installed inside the mounting box (2). A moving guide rail assembly (5) is fixedly connected to the outside of the mounting box (2). A pneumatic nozzle (6) is fixedly installed on the moving guide rail assembly (5). A processing chip (7) is installed inside the controller (4). The processing chip (7) is electrically connected to an edge computing gateway deployed in the toll station computer room. The edge computing gateway includes: The data acquisition layer and processing layer acquire all-dimensional data related to the lighting of the toll station plaza through the data acquisition layer, including data related to safety lighting, data related to effective light effect and data related to power control. The acquired data is input into the processing layer for cleaning. The processing layer also includes an edge computing layer. The cleaned data is input into the edge computing layer. The brightness of the lighting lamp (3) is controlled by the data calculated by the edge computing layer.

2. The energy efficiency optimization device for highway electromechanical equipment according to claim 1, characterized in that: The edge computing layer includes a safety lighting requirement module, an effective luminous efficacy calculation module, and an output power control module.

3. The energy efficiency optimization device for highway electromechanical equipment according to claim 2, characterized in that: The processing logic of the safety lighting requirement module is as follows: Based on the basic lighting standards, traffic flow correction coefficient, real-time 60km / h safe braking distance, minimum safe braking distance, benchmark visibility, historical average of similar weather conditions for the same road segment, calibration value of the last time without vehicles, and real-time hourly rainfall data, dynamic safety lighting demand values ​​are output. The minimum safety lighting demand value is quantified in the current scenario.

4. The energy efficiency optimization device for highway electromechanical equipment according to claim 3, characterized in that: The processing logic of the effective light effect calculation module is as follows: Based on the nominal luminous efficacy of the new luminaire, the cumulative lighting time of the luminaire, the real-time operating temperature of the LED beads, the dust accumulation and salt spray attenuation coefficient, the individual difference correction coefficient, the historical value of the LED bead temperature 5 minutes ago, the historical value of the LED bead temperature 10 minutes ago, the average wind speed during the calibration period, the dust accumulation alarm threshold, and the forced cleaning reminder interval, the real-time effective luminous efficacy of the luminaire is output. By correcting the luminous efficacy attenuation through the real-time effective luminous efficacy of the luminaire, the actual light production capacity of the luminaire is accurately reflected.

5. The energy efficiency optimization device for highway electromechanical equipment according to claim 4, characterized in that: The processing logic of the output power control module is as follows: Based on the data related to power control, such as the square area covered by a single lamp, lamp utilization coefficient, maintenance coefficient, personnel position correction coefficient, rated maximum power of the lamp, average illuminance during the past hour without vehicles, average illuminance for the current minute, personnel correction coefficient for the previous minute, and current target correction coefficient, the output power control value of the lamp is output. The output power control value of the lamp balances energy saving and operational safety.

6. The energy efficiency optimization device for highway electromechanical equipment according to claim 5, characterized in that: The edge computing gateway also includes an optimization module, which includes an identification component and a triggering component.

7. The energy efficiency optimization device for highway electromechanical equipment according to claim 6, characterized in that: The triggering logic of the triggering component is as follows: the initial state uses the default value, and iterative optimization is started when a user who has been authenticated by the identification component actively triggers the dimming command.

8. The energy efficiency optimization device for highway electromechanical equipment according to claim 7, characterized in that: The iteration convergence condition for the triggering component is: it stops when any one of these conditions is met. A1: No new dimming command has been issued for 30 consecutive seconds; A2: The number of iterations has reached the maximum value of 5. A3: The change in coefficients after two consecutive iterations is less than 0.02.

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