Tunnel illumination energy-saving control method and related equipment

Abstract

The invention provides a tunnel lighting energy-saving control method and related equipment, and the method comprises the steps: analyzing the spatial correlation characteristics of a lighting environment between adjacent subareas through the lighting environment data of each subarea, and calculating a power adaptation interval of the corresponding lighting power of an adjustable lighting lamp of each subarea according to all spatial correlation characteristics; determining coordinated regulation and control time delays during brightness adjustment between each group of adjacent partitions, and determining illumination space overlapping degrees between light coverage ranges of the adjustable illumination lamps according to all the coordinated regulation and control time delays and all the power adaptation intervals; further determining the brightness deviation gradient of the target tunnel in the process of adjusting the adjustable illuminating lamp; and based on the brightness deviation gradient, performing optimization adjustment on the power adaptation interval of the illumination power corresponding to the adjustable illumination lamp in each subarea in the target tunnel so as to control all the illumination lamps in the target tunnel. By adopting the scheme of the invention, energy-saving adjustment can be performed on the illumination power of the illumination lamp in the tunnel based on the real-time brightness deviation of the tunnel.

Description

Technical Field

[0001] This application relates to the field of energy-saving control technology, and more specifically, to a method and related equipment for energy-saving control of tunnel lighting. Background Technology

[0002] Energy-saving control refers to control methods that reduce energy consumption and improve energy utilization efficiency while ensuring the normal operation and performance requirements of equipment or systems through scientific strategies, technical means, or intelligent management. The core objective is to optimize energy consumption without affecting the core functions of the system (such as safety, comfort, and production efficiency).

[0003] As a special type of transportation infrastructure, tunnels require lighting systems that simultaneously meet the dual demands of driving safety and energy conservation. The lighting environment inside tunnels is complex, especially in the entrance and exit areas where natural light irradiance varies drastically. For example, during the day, the entrance may experience a "black hole effect," while the exit may experience a "white hole effect." Lighting adjustments are necessary to ensure drivers' visual adaptation and prevent traffic accidents. Traditional methods often employ fixed brightness adjustment modes, which involve preset brightness for a specific time period or simple dynamic brightness adjustment based on a single traffic flow threshold parameter. However, these methods fail to consider the coupled effects of multiple factors such as natural light, vehicle position, and spatial relationships between zones. This results in either redundant brightness (excessive energy consumption) or insufficient brightness (potential safety hazards). Furthermore, the adaptation range of traditional lighting power is mostly fixed and cannot be dynamically updated based on real-time brightness deviations and vehicle traffic conditions. Therefore, how to adjust the lighting power of tunnel lights for energy conservation based on the real-time brightness deviations of the tunnel has become a challenge for the industry. Summary of the Invention

[0004] This application provides a method and related equipment for energy-saving control of tunnel lighting, which can adjust the lighting power of the lighting lamps in the tunnel based on the real-time brightness deviation of the tunnel.

[0005] In a first aspect, this application provides a method for energy-saving control of tunnel lighting, wherein the target tunnel includes multiple adjustable lighting lamps, and the target tunnel is divided into multiple zones along its length. The method includes the following steps: Collect lighting environment data for each zone within the target tunnel; By analyzing the lighting environment data of each zone, the spatial correlation characteristics of the lighting environment between adjacent zones are analyzed. Then, by combining all the spatial correlation characteristics with the traffic flow and natural light irradiance of the target tunnel, the power adaptation range of the adjustable lighting power of each zone is calculated. Determine the coordination delay when adjusting brightness between adjacent zones in each group. Based on all coordination delays and all power adaptation intervals, combined with the real-time traffic flow parameters of the target tunnel, dynamically adjust the brightness of all lights in each zone of the target tunnel in a graded manner, and determine the degree of overlap of lighting space between the light coverage areas of each adjustable light during the dynamic graded adjustment process. Based on the overlap of all lighting spaces, a brightness difference analysis was performed on the brightness of the target tunnel during the adjustment of the adjustable lighting, and the brightness deviation gradient of the target tunnel during the adjustment of the adjustable lighting was obtained. Based on the brightness deviation gradient, the power adaptation range of the adjustable lighting power corresponding to each zone of the target tunnel is optimized and adjusted in order to control all lighting in the target tunnel.

[0006] In some embodiments, analyzing the spatial correlation characteristics of the lighting environment between adjacent groups of zones using lighting environment data from each zone specifically includes: Select a set of neighboring partitions as the selected neighboring partitions, and determine the lighting environment data vector of the selected neighboring partitions based on the lighting environment data corresponding to the selected neighboring partitions; The spatial correlation characteristics of the lighting environment between selected neighboring zones are determined using the lighting environment data vector; Continue to determine the spatial association characteristics of the environment between the remaining neighboring partitions.

[0007] In some embodiments, the power adaptation range for the adjustable lighting power of each zone's lighting lamps is calculated by combining all spatial correlation features with the traffic flow and natural light irradiance of the target tunnel. Specifically, this includes: Obtain the traffic flow and natural light irradiance of the target tunnel; The traffic flow and natural light irradiance of the target tunnel are fused with various spatial correlation features to obtain multiple fused features; Obtain the baseline power curve of the lighting fixtures in the target tunnel; Using the various fusion features and the aforementioned basic power curve as constraints, the power adaptation range of the adjustable lighting lamps in each zone is calculated.

[0008] In some embodiments, determining the collaborative control delay when adjusting brightness between adjacent groups of partitions specifically includes: Acquire historical communication transmission delay data for adjustable lighting in the target tunnel and historical physical response delay data of adjustable lighting when brightness is stable; The collaborative control delay for brightness adjustment between adjacent partitions is determined based on the historical communication transmission delay data and the historical physical response delay data.

[0009] In some embodiments, the brightness of all lights in each section of the target tunnel is dynamically and hierarchically adjusted based on all coordinated control delays and all power adaptation intervals combined with the real-time traffic flow parameters of the target tunnel, and the determination of the lighting spatial overlap between the light coverage areas of each adjustable light during the dynamic hierarchical adjustment process specifically includes: Collect real-time traffic flow parameters for the target tunnel; The adjustable power range of each zone's lighting under the current operating conditions is determined by matching the real-time traffic flow parameters with the power adaptation range. The time difference for brightness adjustment of adjacent zones is determined based on the aforementioned collaborative control delay. Based on the time difference, the target brightness value of each zone lighting lamp is adjusted within the power adjustable range to correspond to the brightness level. The lighting coverage of each lamp at its corresponding brightness level is simulated using parameter data and real-time location information of adjustable lighting lamps in the target tunnel. Determine the degree of overlap in lighting space between the lighting coverage areas of each adjustable lighting fixture based on the total lighting coverage area.

[0010] In some embodiments, a brightness difference analysis is performed on the brightness of the target tunnel during the adjustment of the adjustable lighting lamps based on the overlap of all lighting spaces, and the brightness deviation gradient of the target tunnel during the adjustment of the adjustable lighting lamps specifically includes: Based on the overlap of all lighting spaces, high overlap and low overlap areas of each zone in the target tunnel during the adjustment of adjustable lighting are selected. Calculate the brightness difference between high-overlap and low-overlap regions in each partition; The brightness deviation gradient of the target tunnel during the adjustment of the adjustable lighting is determined based on all brightness differences.

[0011] In some embodiments, optimizing the power adaptation range of the adjustable lighting power corresponding to each zone of the target tunnel based on the brightness deviation gradient to control all lighting in the target tunnel specifically includes: The brightness abrupt transition zone of the adjustable lighting in the target tunnel during adjustment is determined based on the brightness deviation gradient. Based on the brightness abrupt transition zone, the power adaptation range of the adjustable lighting power corresponding to each zone of the target tunnel is adjusted to obtain the adjusted power adaptation range. Based on the adjusted power adaptation ranges, all lights in each zone are adjusted.

[0012] Secondly, this application provides a tunnel lighting energy-saving control system, wherein the target tunnel includes multiple adjustable lighting lamps, and the target tunnel is divided into multiple zones along its length. The system includes: The acquisition module is used to collect lighting environment data for each zone in the target tunnel; The processing module is used to analyze the spatial correlation characteristics of the lighting environment between adjacent groups of lighting environments through the lighting environment data of each zone, and to calculate the power adaptation range of the adjustable lighting power of each zone by combining all the spatial correlation characteristics with the traffic flow and natural light irradiance of the target tunnel. The processing module is also used to determine the collaborative control delay when adjusting the brightness between adjacent partitions, and to dynamically adjust the brightness of all lights in each partition of the target tunnel according to all collaborative control delays and all power adaptation intervals combined with the real-time traffic flow parameters of the target tunnel, and to determine the degree of overlap of the lighting space between the light coverage of each adjustable light during the dynamic adjustment process. The processing module is also used to perform brightness difference analysis on the brightness of the target tunnel during the adjustment of the adjustable lighting lamp based on the overlap of all lighting spaces, and to obtain the brightness deviation gradient of the target tunnel during the adjustment of the adjustable lighting lamp. The execution module is used to optimize and adjust the power adaptation range of the adjustable lighting power of each zone in the target tunnel based on the brightness deviation gradient, so as to control all the lighting in the target tunnel.

[0013] Thirdly, this application provides a computer device, the computer device including a memory and a processor, the memory storing code, and the processor being configured to acquire the code and execute the above-described tunnel lighting energy-saving control method.

[0014] Fourthly, this application provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described tunnel lighting energy-saving control method.

[0015] The technical solutions provided by the embodiments disclosed in this application have the following beneficial effects: The tunnel lighting energy-saving control method and related equipment provided in this application first collect lighting environment data of each zone in the target tunnel; analyze the spatial correlation characteristics of the lighting environment between adjacent zones through the lighting environment data of each zone, and calculate the power adaptation range of the adjustable lighting power corresponding to each zone by combining all spatial correlation characteristics with the traffic flow and natural light irradiance of the target tunnel; determine the collaborative control delay when adjusting the brightness between adjacent zones, and dynamically adjust the brightness of all lights in each zone of the target tunnel according to all collaborative control delays and all power adaptation ranges combined with the real-time traffic flow parameters of the target tunnel, and determine the lighting spatial overlap degree between the light coverage areas of each adjustable light during the dynamic graded adjustment process; analyze the brightness difference of the target tunnel during the adjustment of adjustable lights according to all lighting spatial overlap degrees, and obtain the brightness deviation gradient of the target tunnel during the adjustment of adjustable lights; optimize the power adaptation range of the adjustable lighting power corresponding to each zone of the target tunnel based on the brightness deviation gradient, so as to control all lights in the target tunnel.

[0016] Therefore, this application, in the process of energy-saving control of tunnel lighting, collects lighting environment data from each zone, analyzes the spatial correlation characteristics of lighting in adjacent zones, and calculates the power adaptation range based on traffic flow and natural light irradiance. This fully considers the spatial correlation of tunnel lighting and actual environmental influences, ensuring that lighting power adjustment is based on a reasonable range to avoid blind adjustment. Simultaneously, it determines the collaborative control delay and dynamically adjusts brightness in stages based on real-time traffic flow parameters, while also paying attention to the spatial overlap of lighting to achieve dynamic and collaborative lighting adjustment. This allows for flexible adaptation to traffic changes and reduces waste of lighting resources. Furthermore, it analyzes brightness differences based on spatial overlap to obtain deviation gradients, promptly identifying brightness imbalances during adjustment. Then, based on the deviation gradient power adaptation range, it controls the lighting, forming a closed loop of data acquisition, analysis and calculation, dynamic adjustment, deviation correction, and optimized control. This effectively improves the accuracy of lighting power adjustment and ultimately achieves the goal of energy-saving tunnel lighting adjustment based on real-time brightness deviation. Using the above scheme, energy-saving adjustment of the lighting power of tunnel lights can be performed based on the real-time brightness deviation of the tunnel. Attached Figure Description

[0017] Figure 1 This is an exemplary flowchart of a tunnel lighting energy-saving control method according to some embodiments of this application; Figure 2 This is an exemplary flowchart illustrating the determination of the power adaptation range according to some embodiments of this application; Figure 3 This is an exemplary flowchart illustrating the determination of a brightness deviation gradient according to some embodiments of this application; Figure 4 This is a schematic diagram of the structure of a tunnel lighting energy-saving control system according to some embodiments of this application; Figure 5 This is a schematic diagram of the structure of a computer device for implementing a tunnel lighting energy-saving control method according to some embodiments of this application. Detailed Implementation

[0018] To better understand the technical solution of this application, the technical solution of this application will be described in detail below with reference to the accompanying drawings and specific embodiments.

[0019] refer to Figure 1 The figure is an exemplary flowchart of a tunnel lighting energy-saving control method according to some embodiments of this application. The tunnel lighting energy-saving control method mainly includes the following steps: It should be noted that the target tunnel includes multiple adjustable lighting lamps. These adjustable lighting lamps are LED tunnel lights with a 0-10V pulse width modulation dimming interface, with a light decay rate of less than 3% / year, a color rendering index Ra≥80, and an IP65 protection rating. They are evenly arranged along the tunnel's central axis at 8-meter intervals and adopt a distributed control architecture. Each set of 20 lights is equipped with an intelligent dimming controller (supporting Modbus remote terminal unit protocol). The controllers are connected in series via an RS485 bus to form a subnet, and data communication between the subnets is achieved using industrial Ethernet switches.

[0020] In some embodiments, dividing the target tunnel into multiple zones along its length can be achieved in the following manner: based on the differences in the tunnel's functional attributes and lighting requirements, the tunnel is divided into unequal-distance zones along its length. The entrance section has three gradient zones with lengths of 30 meters, 50 meters, and 80 meters, respectively, to accommodate the driver's visual transition from strong light to weak light. The middle section is divided into several basic lighting zones at 100-meter intervals. The exit section has two enhanced zones, each 50 meters long, to ensure a smooth visual transition for the driver. Dual-beam infrared beam sensors are installed at the zone boundaries, with a detection accuracy of ±5cm, for vehicle zone positioning.

[0021] In step 101, lighting environment data for each zone in the target tunnel are collected.

[0022] In specific implementation, a multi-type sensor network is uniformly deployed in each partition. This sensor network includes a high-precision light sensor (measurement range 0-100,000 lux, accuracy ±1%), a spectrum analyzer (covering the 380-780nm band, resolution 1nm), and an infrared thermal imager (temperature measurement range -20℃ to 150℃, accuracy ±0.5℃). The lighting environment data of each partition in the target tunnel is collected through this multi-type sensor network. The lighting environment data represents a quantitative set of information on the lighting conditions and related environmental characteristics in the target tunnel, which can be used to reflect the operating status of the lighting system, the light quality, and the impact on the surrounding environment, including the light intensity, color temperature, color rendering index, and lamp temperature in the partition. Other methods can also be used for data collection in other embodiments, which are not limited here.

[0023] In step 102, the spatial correlation characteristics of the lighting environment between adjacent groups of each zone are analyzed by analyzing the lighting environment data of each zone, and the power matching range of the adjustable lighting power of each zone is calculated by combining all the spatial correlation characteristics with the traffic flow and natural light irradiance of the target tunnel.

[0024] In some embodiments, analyzing the spatial correlation characteristics of the lighting environment between adjacent groups of zones using lighting environment data from each zone can be achieved through the following steps: Select a set of neighboring partitions as the selected neighboring partitions, and determine the lighting environment data vector of the selected neighboring partitions based on the lighting environment data corresponding to the selected neighboring partitions; The spatial correlation characteristics of the lighting environment between selected neighboring zones are determined using the lighting environment data vector; Continue to determine the spatial association characteristics of the environment between the remaining neighboring partitions.

[0025] In specific implementation, firstly, the lighting environment data of each selected neighboring partition is arranged in order from entrance to exit to obtain the lighting environment sequence of the corresponding partition. The lighting environment sequence of each selected neighboring partition is then combined to form a lighting environment data vector, i.e., lighting environment data vector = (lighting environment sequence 1, lighting environment sequence 2). Secondly, vector similarity calculation (such as cosine similarity, Pearson correlation coefficient) is used to analyze the overall fit of the lighting environment data vector. Alternatively, a spatial autocorrelation model can be used to determine whether there are spatial clustering characteristics within the lighting environment data vector, such as high-value lighting data adjacent to high-value data (high-high clustering) and low-value data adjacent to low-value data (low-low clustering). Local spatial correlation indicators can also be used to locate significantly correlated partition pairs within the lighting environment data vector, thereby clarifying the degree of correlation and the consistency (same direction or opposite direction) of the numerical change trends of the neighboring partitions in the lighting environment. Other methods can also be used in other embodiments, which are not limited here.

[0026] It should be noted that the spatial correlation feature in this application describes the characteristics of the spatial distribution of the lighting environment in different areas of the target tunnel, which are interconnected and mutually influential. It can be used to reveal that the attribute values ​​of each area in the target tunnel are not randomly distributed, but rather have a certain regular spatial dependence or spatial interaction.

[0027] In some embodiments, reference Figure 2 As shown in the figure, this is an exemplary flowchart of determining the power adaptation range in some embodiments of this application. In this embodiment, the power adaptation range of the adjustable lighting lamps in each zone can be calculated by combining all spatial correlation features with the traffic flow and natural light irradiance of the target tunnel using the following steps: First, in step 1021, the traffic flow and natural light irradiance of the target tunnel are obtained; Secondly, in step 1022, the traffic flow and natural light irradiance of the target tunnel are fused with various spatial correlation features to obtain multiple fused features; Then, in step 1023, the basic power curve of the lighting in the target tunnel is obtained; Finally, in step 1024, the power adaptation range of the adjustable lighting power corresponding to each zone is calculated using the various fusion features and the basic power curve as constraints.

[0028] It should be noted that spatial correlation characteristics (such as the synergistic relationship between adjacent lighting zones and the brightness influence law of high / low overlap areas) limit the spatial constraints of power adjustment for each zone, ensuring coordinated lighting changes in adjacent areas to avoid abrupt brightness changes. Traffic flow reflects the traffic load within the tunnel and determines the basic intensity of lighting demand; for example, higher brightness is required to ensure clear visibility when traffic flow is high. Natural light irradiance dynamically affects the supplementary lighting needs of areas such as tunnel entrances and exits; for example, artificial lighting power can be reduced when natural light intensity is high. When these three factors are combined, the basic power range for each zone is first determined by traffic flow and natural light irradiance. Then, the boundaries of the basic range are corrected based on spatial correlation characteristics (such as the need to maintain a certain proportion of synergy in high overlap areas). At the same time, the physical parameters of the luminaires (such as maximum power and adjustable step size) are incorporated as constraints. Finally, a power adaptation range that meets both real-time lighting needs and spatial coordination requirements is formed. Essentially, this is a modeling of a feasible lighting power range under the fusion of multi-dimensional scene parameters and spatial constraints.

[0029] The system uses traffic flow monitoring equipment (such as loop detectors and video analysis devices) installed inside the target tunnel to collect real-time traffic flow data at different times, including information on the number of vehicles and the distribution of vehicle types. At the same time, light sensors are set up at the tunnel entrance, exit, and surrounding area to monitor changes in natural light irradiance and record irradiance data at different times.

[0030] In specific implementation, the traffic flow and natural light irradiance of the target tunnel are fused with various spatial correlation features to obtain multiple fused features. This can be achieved in the following way: First, the traffic flow and natural light irradiance of the target tunnel are standardized with various spatial correlation features, for example, the values ​​are mapped to the same order of magnitude or normalized to the [0,1] interval to eliminate dimensional differences. Then, feature splicing (combining the time series data of traffic flow, the time series data of natural light irradiance, and the quantitative indicators of spatial correlation features, such as spatial autocorrelation coefficients and similarity matrices, by dimension) or weighted fusion (based on the influence weight of each feature on the lighting power, such as determining the weight through machine learning feature importance assessment and performing linear weighted summation) is used to generate a fused feature vector that simultaneously contains traffic dynamics, natural light changes, and spatial correlation patterns. The fused feature vector represents key features in the target tunnel with different physical meanings, dimensions, or sources, such as traffic flow, natural light irradiance, and spatial correlation features of each zone. Other methods can also be used to determine these features in other embodiments, which are not limited here.

[0031] The acquisition of the basic power curves of the lighting fixtures in the target tunnel can be achieved in the following ways: First, based on tunnel lighting design specifications (such as the basic lighting power requirements for the tunnel entrance, transition, intermediate, and exit sections in national or industry standards), combined with the length of each tunnel section, design speed, and luminaire type parameters, the baseline power values ​​for different sections under standard operating conditions can be determined. Second, by analyzing historical tunnel operation data, statistically analyzing the power variation trends of lighting fixtures in each section under typical operating conditions, and fitting a basic power curve that varies with time or environmental parameters, the basic power curve is a curve model describing the changes in lighting power of adjustable lighting fixtures in each section of the target tunnel under standard or baseline operating conditions with time, environment, and traffic conditions, and can be used as a benchmark reference for power optimization.

[0032] In addition, in specific implementation, the power adaptation range of the adjustable lighting power corresponding to each zone can be calculated using the following method, with each fusion feature and the basic power curve as constraints: the fusion feature is used as an input variable to reflect the comprehensive needs of real-time traffic, natural light and spatial correlation; the baseline range of power adjustment is defined by the basic power curve (e.g., ±30% of the basic power as the initial boundary), while combining the tunnel lighting safety threshold (e.g., minimum illuminance requirement, illuminance uniformity standard) and the lamp adjustment capability (e.g., upper and lower limits of adjustable power) constraints; the feasible power range that meets the safety and energy-saving goals of each zone under the current fusion feature is obtained by solving through linear programming, genetic algorithm or machine learning regression model, i.e., the power adaptation range; other methods can also be used for calculation in other embodiments, which are not limited here.

[0033] It should be noted that the power adaptation range in this application represents the allowable range of lighting power adjustment during the optimization of the lighting power of adjustable lights in each zone of the target tunnel. This range changes dynamically with traffic flow and light radiation to ensure that the lighting power is adapted to the real-time environment and spatial requirements. It can be used to provide a clear optimization boundary for the power adjustment of lights in each zone, ensuring that the lighting power can adapt to the real-time scene requirements and is within a reasonable and efficient operating range.

[0034] In step 103, the collaborative control delay between adjacent zones is determined. When a vehicle is detected passing through the target tunnel entrance, the brightness of all lights in each zone of the target tunnel is dynamically graded and adjusted based on all collaborative control delays and all power adaptation intervals combined with the real-time traffic flow parameters of the target tunnel. The degree of overlap of lighting space between the light coverage areas of each adjustable light is determined during the dynamic graded adjustment process.

[0035] In some embodiments, determining the collaborative control delay when adjusting brightness between adjacent groups of partitions can be achieved using the following steps: Acquire historical communication transmission delay data for adjustable lighting in the target tunnel and historical physical response delay data of adjustable lighting when brightness is stable; The collaborative control delay for brightness adjustment between adjacent partitions is determined based on the historical communication transmission delay data and the historical physical response delay data.

[0036] In specific implementation, when acquiring historical communication transmission delay data of adjustable lights in the target tunnel, the transmission time of control commands in the tunnel lighting control system between adjacent zone lighting controllers can be recorded, including the signal sending time, receiving time, and relay time in the transmission path. Historical communication transmission delay data under different time periods and data volumes can be collected. The historical communication transmission delay in the historical communication transmission delay data represents the time consumed during the transmission of control commands in the target tunnel lighting control system. For historical physical response delay data of adjustable lights when brightness is stable, different brightness adjustment ranges can be simulated in a laboratory or tunnel site, such as adjusting from the current brightness to the target brightness. The time interval from receiving the brightness adjustment command to the brightness reaching a stable state is recorded, and the set of all time intervals is used as historical physical response delay data. The historical physical response delay in the historical physical response delay data represents the time interval experienced by the adjustable lights after receiving the brightness adjustment command, from the start of the adjustment action to the complete stabilization of their brightness. Other methods can also be used in other embodiments, which are not limited here.

[0037] In addition, in specific implementation, when determining the coordinated control delay of brightness adjustment for each group of adjacent partitions based on historical communication transmission delay data and historical physical response delay data, the two types of data need to be preprocessed first, such as removing outliers and calculating statistical characteristics (e.g., mean, variance). Subsequently, for each group of adjacent partitions, the historical communication transmission delay statistics (e.g., average transmission delay) and historical physical response delay statistics (e.g., average response delay) of that group are superimposed, or a weighted sum is obtained (based on the influence weights of the two on coordinated control, such as communication delay accounting for 60% and physical response delay accounting for 40%) to obtain the coordinated control delay of the group of adjacent partitions during brightness adjustment. Thus, the coordinated control delay of brightness adjustment between each group of adjacent partitions is obtained. At the same time, historical data of different brightness adjustment scenarios can be combined to establish a scenario-based delay mapping relationship to ensure that the results are more in line with actual control requirements. Other methods can also be used to determine the delay in other embodiments, which are not limited here.

[0038] It should be noted that the coordinated control delay in this application refers to the total time from the issuance of the coordinated control command by the control terminal to the completion of brightness adjustment and the attainment of a stable brightness state by the lights in the adjacent zones when the brightness of the adjustable lights in each group of adjacent zones is adjusted in the target tunnel. This can provide a time-dimensional reference for achieving precise and synchronous control of the current of the lights in each group of adjacent zones.

[0039] In some embodiments, the brightness of all lights in each zone of the target tunnel is dynamically and hierarchically adjusted based on all coordinated control delays and all power adaptation intervals, combined with the real-time traffic flow parameters of the target tunnel. The determination of the lighting spatial overlap between the light coverage areas of each adjustable light during the dynamic hierarchical adjustment process can be achieved through the following steps: Collect real-time traffic flow parameters for the target tunnel; The adjustable power range of each zone's lighting under the current operating conditions is determined by matching the real-time traffic flow parameters with the power adaptation range. The time difference for brightness adjustment of adjacent zones is determined based on the aforementioned collaborative control delay. Based on the time difference, the target brightness value of each zone lighting lamp is adjusted within the power adjustable range to correspond to the brightness level. The lighting coverage of each lamp at its corresponding brightness level is simulated using parameter data and real-time location information of adjustable lighting lamps in the target tunnel. Determine the degree of overlap in lighting space between the lighting coverage areas of each adjustable lighting fixture based on the total lighting coverage area.

[0040] It should be noted that the coordinated control delay determines the temporal coordination relationship of brightness adjustments in adjacent zones, ensuring that the rhythm of lighting changes in each zone is consistent to avoid visual abrupt changes; the power adaptation range provides the adjustable boundary of the lighting power of each zone, satisfying both the lower limit of power for safe lighting and the upper limit of power for energy saving and equipment limitations; real-time traffic flow parameters (such as traffic volume and natural light) dynamically trigger different brightness demand levels, making the adjustment fit the current operating conditions. When the three are combined, the power adaptation range is first determined by matching the real-time data with the power adaptation range, and then the adjustment sequence of each zone is planned according to the coordinated control delay to achieve dynamic adaptation of graded brightness. The determination of the lighting spatial overlap is based on the real-time parameters of the lamps (such as the illumination range corresponding to the power) and location information during the adjustment process. By calculating the overlap ratio of the illumination range of different lamps, the spatial correlation of light coverage is quantified, providing a basis for subsequent analysis of brightness uniformity and optimization of control strategies. Essentially, it is the dynamic matching and spatial correlation quantification of the lighting system and the real-time scene under multiple constraints.

[0041] Specifically, real-time traffic flow parameters of the target tunnel can be collected through a sensor network deployed inside the tunnel: traffic flow monitoring equipment, such as radar and video analysis devices, are installed at the tunnel entrance, exit, and each section of the road to obtain the number of vehicles, driving speed, and traffic density per unit time in real time; natural light irradiance sensors are set up outside the tunnel to record the intensity of natural light at different times; at the same time, brightness sensors are installed near the lighting in each section to collect the actual brightness value of the current area in real time. The collected data are used as the real-time traffic flow parameters of the target tunnel, which reflect the dynamic data of the target tunnel in its current state.

[0042] In specific implementation, when determining the adjustable power range of each zone's lighting under the current operating conditions based on real-time traffic flow parameters and power adaptation range matching, it is necessary to first call the pre-stored power adaptation range model for each zone (this model is associated with the power range corresponding to different traffic volumes and natural light irradiance; the construction of the power adaptation range model needs to be combined with the historical data of the target tunnel and multi-dimensional constraints, and obtained through data modeling and iterative optimization). The real-time collected traffic flow data and natural light irradiance values ​​are input into the model, and the corresponding power range is located through a data matching algorithm (e.g., similar operating condition matching based on K nearest neighbors). At the same time, combined with the deviation between the real-time brightness value and the target brightness, for example, if the current brightness is lower than the target value, the power upper limit is appropriately relaxed, and the initially matched range is fine-tuned. Finally, the adjustable power range of each zone under the current operating conditions that can meet both lighting needs and energy-saving requirements is determined. The adjustable power range represents the dynamic range of the lighting power output of the target tunnel zone under the current operating conditions that is allowed to be adjusted. In other embodiments, other methods can also be used to determine this, which are not limited here.

[0043] In addition, in specific implementation, when determining the time difference for brightness adjustment of adjacent partitions based on the collaborative control delay, it is necessary to first extract the collaborative control delay corresponding to each group of adjacent partitions (e.g., the collaborative control delay between partition A and partition B is 0.5 seconds), and construct a connection table of adjacent partitions according to the tunnel spatial layout; then, taking the brightness adjustment command sending time of one of the partitions as the benchmark (e.g., setting the command sending time of partition A as t0), the command sending time of the adjacent partition (e.g., partition B) (t0 + 0.5 seconds) is calculated based on the collaborative control delay. The difference between the two (0.5 seconds) is the time difference for brightness adjustment of the group of adjacent partitions. Here, the time difference refers to the time interval between the two receiving the brightness adjustment command when the lighting lights of adjacent partitions in the target tunnel are adjusting their brightness, ensuring that the brightness adjustment actions of adjacent partitions are coordinated in time and avoiding sudden brightness changes or delays.

[0044] In addition, in specific implementation, when adjusting the target brightness value of each zone's lighting according to the time difference within the power adjustable range, it is necessary to first set multiple brightness level standards based on real-time traffic flow, natural light irradiance, and road environment. For example, when traffic flow is high and natural light is weak, it is set to level one high brightness, and when traffic flow is low and natural light is strong, it is set to level three low brightness. Then, according to the time difference between adjacent zones, brightness adjustment instructions are sent to each zone controller in the order from entrance to exit. The instructions contain the target brightness value of the corresponding brightness level (this value must fall within the previously determined power adjustable range). At the same time, the brightness adjustment progress of each zone is received in real time through a feedback mechanism to ensure a smooth transition from the current brightness to the brightness level within the set time difference.

[0045] In specific implementation, when simulating the light coverage of each adjustable light at its corresponding brightness level using parameter data and real-time location information of the adjustable lights in the target tunnel, it is necessary to first retrieve the parameters (such as beam angle, illumination distance, light intensity distribution curve, and illumination radius at different powers) and real-time installation location information (such as coordinates, installation height, and projection angle) of each adjustable light from the equipment database corresponding to the target tunnel. Then, combined with the brightness level of each light (which is associated with a specific power), a three-dimensional model is constructed using lighting simulation software (such as Dianelux) to simulate the light coverage area of ​​a single light at the current power (presented in polygon or grid form, including light intensity distribution). The light coverage area represents the range of illumination coverage of the adjustable lights in the target tunnel. Other methods can also be used to determine this in other embodiments, which are not limited here.

[0046] Finally, in specific implementation, when determining the degree of overlap between the lighting coverage areas of each adjustable light fixture based on the coverage areas of all lights, the simulated lighting coverage areas of each light fixture must first be converted into vector graphics (such as polygons). Then, a spatial overlap analysis algorithm (such as the intersection analysis tool in Dienerex) is used to calculate the area of ​​the overlapping region between any two light fixtures. Subsequently, the overlapping area is compared with the coverage area of ​​each of the two lights to obtain the overlap ratio (e.g., the overlapping area of ​​light A and light B accounts for 30% of the coverage area of ​​light A and 25% of the coverage area of ​​light B). The average or weighted value of the two is taken as the degree of overlap between the lighting space of these two lights. Finally, pairwise calculations are performed on all adjustable lights to obtain the degree of overlap between the lighting coverage areas of each adjustable light fixture. In other embodiments, other methods can also be used to determine this, which are not limited here.

[0047] It should be noted that the lighting spatial overlap degree in this application represents the parameter value of the degree of overlap between the light coverage areas of different adjustable lighting lamps in the target tunnel, and can be used to reflect the degree of overlap of the coverage areas of each lamp.

[0048] In step 104, a brightness difference analysis is performed on the brightness of the target tunnel during the adjustment of the adjustable lighting lamps based on the overlap of all lighting spaces, and the brightness deviation gradient of the target tunnel during the adjustment of the adjustable lighting lamps is obtained.

[0049] In some embodiments, reference Figure 3 As shown, this figure is an exemplary flowchart for determining the brightness deviation gradient in some embodiments of this application. In this embodiment, the brightness difference of the target tunnel during the adjustment of the adjustable lighting lamp is analyzed based on the overlap of all lighting spaces. The brightness deviation gradient of the target tunnel during the adjustment of the adjustable lighting lamp can be obtained by the following steps: First, in step 1041, based on the overlap of all lighting spaces, the high overlap and low overlap areas of each zone of the target tunnel during the adjustable lighting adjustment process are screened out from the target tunnel. Secondly, in step 1042, the brightness difference between the high-overlapping region and the low-overlapping region in each partition is calculated; Finally, in step 1043, the brightness deviation gradient of the target tunnel during the adjustment of the adjustable lighting is determined based on all the brightness differences.

[0050] It should be noted that the degree of overlap of lighting space directly reflects the degree of overlap of different areas covered by multiple lighting lamps. Areas with high overlap are usually brighter due to the superposition of multiple lamps, while areas with low overlap are less bright due to the coverage of fewer lamps. The inherent difference between the two constitutes the difference in brightness.

[0051] In practice, the high and low overlap regions of each zone in the target tunnel during the adjustable lighting adjustment process can be identified based on the overlap of all lighting spaces. This can be achieved by setting an overlap threshold, for example, determining the top 30% as the high overlap threshold and the bottom 30% as the low overlap threshold through statistical analysis of historical data. Subsequently, for each zone, sub-regions with lighting space overlap exceeding the high overlap threshold are classified as high overlap regions. A high overlap region indicates that a sub-region within the target tunnel is illuminated by multiple zone lights (such as its own lighting). The overlapping areas (where the illumination range of a zone overlaps with that of adjacent zone lights) form areas with high overlap. Sub-regions below the low overlap threshold are classified as low overlap areas. A low overlap area indicates that a sub-region within the target tunnel has a low overlap due to illumination from multiple zone lights (such as overlapping illumination ranges between a zone and adjacent zone lights). This allows for the screening of high and low overlap areas in each zone of the target tunnel during the adjustable lighting adjustment process, and the spatial location and extent of each area are marked. Other screening methods can be used in other embodiments, which are not limited here.

[0052] In addition, in specific implementation, when calculating the brightness difference between high-overlapping and low-overlapping areas in each partition, it is necessary to collect the brightness values ​​of high-overlapping and low-overlapping areas in real time through brightness sensors distributed in each area during the adjustable lighting adjustment process, such as at different stages of brightness adjustment. For example, the data is recorded once every 10 seconds, and the brightness data of the two types of areas in the same partition at the same time are paired. Then, the brightness difference between the two types of areas in a single partition is calculated using the absolute difference (the absolute value of the brightness value of the high-overlapping area minus the absolute value of the brightness value of the low-overlapping area) or the relative difference (the ratio of the absolute difference to the brightness value of the low-overlapping area), thereby obtaining the brightness difference between high-overlapping and low-overlapping areas in each partition. The brightness difference represents the degree of difference in brightness between the high-overlapping and low-overlapping areas. Other calculation methods can also be used in other embodiments, which are not limited here.

[0053] In addition, in specific implementation, when determining the brightness deviation gradient of the target tunnel during the adjustment of adjustable lighting based on all brightness differences, it is necessary to first perform spatiotemporal alignment of the brightness difference data of each zone (e.g., sorting by adjustment time sequence and spatial location of the zones); then, by calculating the rate of change of brightness difference between adjacent zones in the same adjustment stage (e.g., dividing the difference in brightness difference between the next zone and the previous zone by the spatial distance), or by using spatial interpolation methods (e.g., Kriging interpolation), a spatial distribution surface of brightness difference across the entire tunnel is generated, and then the slope and aspect features of the surface are extracted. The slope reflects the degree of change in brightness deviation, and the aspect features reflect the spatial direction of change of deviation. The slope and aspect features together constitute the brightness deviation gradient of the target tunnel during the adjustment process; other methods can also be used to determine this in other embodiments, which are not limited here.

[0054] It should be noted that the brightness deviation gradient in this application represents the gradient of the brightness difference between different areas of the target tunnel along the spatial direction during the adjustment of the adjustable lighting, reflecting the degree and direction of the brightness difference change, and can intuitively present the variation law of brightness difference along the space.

[0055] In step 105, the power adaptation range of the adjustable lighting power of each zone in the target tunnel is optimized and adjusted based on the brightness deviation gradient, so as to control all the lighting in the target tunnel.

[0056] In some embodiments, optimizing the power adaptation range of the adjustable lighting power corresponding to each zone of the target tunnel based on the brightness deviation gradient to control all lighting in the target tunnel can be achieved through the following steps: The brightness abrupt transition zone of the adjustable lighting in the target tunnel during adjustment is determined based on the brightness deviation gradient. Based on the brightness abrupt transition zone, the power adaptation range of the adjustable lighting power corresponding to each zone of the target tunnel is adjusted to obtain the adjusted power adaptation range. Based on the adjusted power adaptation ranges, all lights in each zone are adjusted.

[0057] In specific implementation, when determining the brightness abrupt transition zone of adjustable lighting in the target tunnel based on the brightness deviation gradient, it is necessary to first extract the slope value data from the brightness deviation gradient, set a slope threshold (e.g., slope value exceeding 20%), and mark the area with a slope value exceeding the threshold as a potential transition zone with drastic brightness changes. Then, combine the slope aspect information to analyze the spatial continuity of these areas. If the slope values ​​of adjacent areas all exceed the threshold and the slope aspect is consistent (e.g., both change along the tunnel axis), they are merged into a continuous brightness abrupt transition zone. The brightness abrupt transition zone represents the area in the target tunnel where the brightness changes abruptly when adjusting the adjustable lighting, and the partition range, start and end positions, and maximum slope value of the transition zone are recorded to identify the areas that need to be optimized in lighting adjustment. In addition, in specific implementation, based on the brightness abrupt transition zone, the power adaptation range of the adjustable lighting power corresponding to each zone of the target tunnel is adjusted. When obtaining the adjusted power adaptation range, the adjustment intensity needs to be determined for the zone covered by the transition zone according to the maximum slope of the transition zone (the greater the slope, the stronger the adjustment intensity; for example, when the maximum value exceeds the threshold by 30%, the lower limit of the power adaptation range of that zone is increased by 5% and the upper limit is decreased by 3%). At the same time, considering the spatial relationship between the zones on both sides of the transition zone (such as the need to maintain power coordination in highly overlapping areas), the adaptation domains of adjacent zones are adjusted in a coordinated manner (for example, when the upper limit of the power of the zone on the left side of the transition zone is reduced, the adjacent zone on the right side is reduced by 2%). This ensures that the adjusted adaptation domain can reduce the brightness difference of the transition zone without exceeding the physical limitations of the equipment and safety lighting standards, and finally forms the adjusted power adaptation range for each zone.

[0058] In addition, in specific implementation, when adjusting all lights in each zone based on the adjusted power adaptation ranges, real-time traffic flow is collected using LiDAR, millimeter-wave radar, or video recognition technology, and natural light irradiance is collected using photosensitive sensors. This data is transmitted to edge computing nodes via long-distance radio protocols or industrial gigabit passive optical networks. Intelligent algorithms using fuzzy control, neural networks, or genetic algorithms determine the target power of each zone's lights based on the adjusted power adaptation ranges of each zone, real-time traffic flow, and natural light irradiance (this power must fall within the adjusted adaptation range). Subsequently, the adjustment order and time difference are set according to the collaborative control delay of adjacent zones using the CAN bus communication protocol. Power adjustment commands are sent to each zone's lights sequentially before the vehicle arrives. Pulse width modulation or 0-10V analog dimming technology is used to achieve smooth power adjustment, ensuring that the lights are stable at the target power when the vehicle passes through each zone, and that the brightness difference in the brightness transition zone is controlled within a safe range.

[0059] In another aspect, in some embodiments, this application provides a tunnel lighting energy-saving control system, referencing... Figure 4The figure is a schematic diagram of the structure of a tunnel lighting energy-saving control system 400 according to some embodiments of this application. The tunnel lighting energy-saving control system 400 includes: a data acquisition module 401, a processing module 402, and an execution module 403, which are described below: Acquisition module 401, in this application, is mainly used to acquire lighting environment data of each zone in the target tunnel; Processing module 402, in this application, is used to analyze the spatial correlation characteristics of the lighting environment between adjacent groups of lighting environments through the lighting environment data of each zone, and calculate the power adaptation range of the adjustable lighting power of each zone by combining all the spatial correlation characteristics with the traffic flow and natural light irradiance of the target tunnel. It should be noted that the processing module 402 in this application is also used to determine the collaborative control delay when adjusting the brightness between adjacent partitions of each group, and to dynamically adjust the brightness of all lights in each partition of the target tunnel according to all collaborative control delays and all power adaptation intervals combined with the real-time traffic flow parameters of the target tunnel, and to determine the degree of overlap of the lighting space between the light coverage range of each adjustable light during the dynamic adjustment process. Additionally, it should be noted that the processing module 402 in this application is also used to perform a brightness difference analysis on the brightness of the target tunnel during the adjustment of the adjustable lighting lamp based on the superposition degree of all lighting spaces, and to obtain the brightness deviation gradient of the target tunnel during the adjustment of the adjustable lighting lamp. The execution module 403 in this application is mainly used to optimize and adjust the power adaptation range of the adjustable lighting power of each zone in the target tunnel based on the brightness deviation gradient, so as to control all the lighting in the target tunnel.

[0060] In addition, this application also provides a computer device, the computer device including a memory and a processor, the memory storing code, and the processor being configured to acquire the code and execute the above-described tunnel lighting energy-saving control method.

[0061] In some embodiments, reference Figure 5 The figure is a schematic diagram of the structure of a computer device for implementing a tunnel lighting energy-saving control method according to some embodiments of this application. The tunnel lighting energy-saving control method in the above embodiments can... Figure 5 The computer device shown is used to implement this, and the computer device 500 includes at least one processor 501, a communication bus 502, a memory 503, and at least one communication interface 504.

[0062] Processor 501 can be a general-purpose central processing unit (CPU) or an application-specific integrated circuit (ASIC).

[0063] The communication bus 502 can be used to transmit information between the aforementioned components.

[0064] Memory 503 may be a read-only memory (ROM) or other type of static storage device capable of storing static information and instructions, random access memory (RAM) or other type of dynamic storage device capable of storing information and instructions, or electrically erasable programmable read-only memory (EEPROM), compact disc read-only memory (CD-ROM) or other optical disc storage, optical disc storage (including compressed optical discs, laser discs, optical discs, digital versatile optical discs, Blu-ray discs, etc.), magnetic disks or other magnetic storage devices, or any other medium capable of carrying or storing desired program code in the form of instructions or data structures and accessible by a computer, but not limited thereto. Memory 503 may exist independently and be connected to processor 501 via communication bus 502. Memory 503 may also be integrated with processor 501.

[0065] The memory 503 stores program code for executing the scheme of this application, and its execution is controlled by the processor 501. The processor 501 executes the program code stored in the memory 503. The program code may include one or more software modules. The method used in the above embodiments can be implemented by the processor 501 and one or more software modules in the program code in the memory 503.

[0066] Communication interface 504 uses any transceiver-like device to communicate with other devices or communication networks, such as Ethernet, radio access network (RAN), wireless local area networks (WLAN), etc.

[0067] In a specific implementation, as one example, a computer device may include multiple processors, each of which may be a single-core (single-CPU) processor or a multi-core (multi-CPU) processor. Here, a processor may refer to one or more devices, circuits, and / or processing cores used to process data (e.g., computer program instructions).

[0068] The aforementioned computer device can be a general-purpose computer device or a special-purpose computer device. In specific implementations, the computer device can be a desktop computer, a portable computer, a network server, a handheld digital assistant (PDA), a mobile phone, a tablet computer, a wireless terminal device, a communication device, or an embedded device. This application does not limit the type of computer device.

[0069] In addition, this application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described tunnel lighting energy-saving control method.

[0070] Although preferred embodiments of this application have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of this application.

[0071] Obviously, those skilled in the art can make various modifications and variations to this application without departing from the spirit and scope of this application. Therefore, if such modifications and variations fall within the scope of the claims of this application and their equivalents, this application also intends to include such modifications and variations.

Claims

1. A method for energy-saving control of tunnel lighting, wherein, The target tunnel includes multiple adjustable lighting fixtures, and the target tunnel is divided into multiple sections along its length. The method is characterized by the following steps: Collect lighting environment data for each zone within the target tunnel; By analyzing the lighting environment data of each zone, the spatial correlation characteristics of the lighting environment between adjacent zones are analyzed. Then, by combining all the spatial correlation characteristics with the traffic flow and natural light irradiance of the target tunnel, the power adaptation range of the adjustable lighting power of each zone is calculated. Determine the coordination delay when adjusting brightness between adjacent zones in each group. Based on all coordination delays and all power adaptation intervals, combined with the real-time traffic flow parameters of the target tunnel, dynamically adjust the brightness of all lights in each zone of the target tunnel in a graded manner, and determine the degree of overlap of lighting space between the light coverage areas of each adjustable light during the dynamic graded adjustment process. Based on the overlap of all lighting spaces, a brightness difference analysis was performed on the brightness of the target tunnel during the adjustment of the adjustable lighting, and the brightness deviation gradient of the target tunnel during the adjustment of the adjustable lighting was obtained. Based on the brightness deviation gradient, the power adaptation range of the adjustable lighting power corresponding to each zone of the target tunnel is optimized and adjusted in order to control all lighting in the target tunnel.

2. The method as described in claim 1, characterized in that, The spatial correlation characteristics of the lighting environment between adjacent zones are analyzed through lighting environment data analysis of each zone, specifically including: Select a set of neighboring partitions as the selected neighboring partitions, and determine the lighting environment data vector of the selected neighboring partitions based on the lighting environment data corresponding to the selected neighboring partitions; The spatial correlation characteristics of the lighting environment between selected neighboring zones are determined using the lighting environment data vector; Continue to determine the spatial association characteristics of the environment between the remaining neighboring partitions.

3. The method as described in claim 1, characterized in that, The power adaptation range for adjustable lighting in each zone is calculated by combining all spatial correlation features with the traffic flow and natural light irradiance of the target tunnel. Specifically, this includes: Obtain the traffic flow and natural light irradiance of the target tunnel; The traffic flow and natural light irradiance of the target tunnel are fused with various spatial correlation features to obtain multiple fused features; Obtain the baseline power curve of the lighting fixtures in the target tunnel; Using the various fusion features and the aforementioned basic power curve as constraints, the power adaptation range of the adjustable lighting lamps in each zone is calculated.

4. The method as described in claim 1, characterized in that, The specific details of determining the coordinated control delay when adjusting brightness between adjacent partitions in each group include: Acquire historical communication transmission delay data for adjustable lighting in the target tunnel and historical physical response delay data of adjustable lighting when brightness is stable; The collaborative control delay for brightness adjustment between adjacent partitions is determined based on the historical communication transmission delay data and the historical physical response delay data.

5. The method as described in claim 1, characterized in that, Based on all coordinated control delays and all power adaptation intervals, combined with the real-time traffic flow parameters of the target tunnel, the brightness of all lights in each zone of the target tunnel is dynamically adjusted in stages. The degree of overlap in lighting space between the light coverage areas of each adjustable light fixture during the dynamic adjustment process is specifically determined, including: Collect real-time traffic flow parameters for the target tunnel; The adjustable power range of each zone's lighting under the current operating conditions is determined by matching the real-time traffic flow parameters with the power adaptation range. The time difference for brightness adjustment of adjacent zones is determined based on the aforementioned collaborative control delay. Based on the time difference, the target brightness value of each zone lighting lamp is adjusted within the power adjustable range to correspond to the brightness level. The lighting coverage of each lamp at its corresponding brightness level is simulated using parameter data and real-time location information of adjustable lighting lamps in the target tunnel. Determine the degree of overlap in lighting space between the lighting coverage areas of each adjustable lighting fixture based on the total lighting coverage area.

6. The method as described in claim 1, characterized in that, Based on the overlap of all lighting spaces, a brightness difference analysis was performed on the target tunnel's brightness during the adjustment of adjustable lighting. The resulting brightness deviation gradient of the target tunnel during the adjustment of adjustable lighting specifically includes: Based on the overlap of all lighting spaces, high overlap and low overlap areas of each zone in the target tunnel during the adjustment of adjustable lighting are selected. Calculate the brightness difference between high-overlap and low-overlap regions in each partition; The brightness deviation gradient of the target tunnel during the adjustment of the adjustable lighting is determined based on all brightness differences.

7. The method as described in claim 1, characterized in that, Based on the aforementioned brightness deviation gradient, the power adaptation range of the adjustable lighting power corresponding to each zone of the target tunnel is optimized and adjusted to control all lighting in the target tunnel. Specifically, this includes: The brightness abrupt transition zone of the adjustable lighting in the target tunnel during adjustment is determined based on the brightness deviation gradient. Based on the brightness abrupt transition zone, the power adaptation range of the adjustable lighting power corresponding to each zone of the target tunnel is adjusted to obtain the adjusted power adaptation range. Based on the adjusted power adaptation ranges, all lights in each zone are adjusted.

8. A tunnel lighting energy-saving control system, wherein, The target tunnel includes multiple adjustable lighting fixtures, dividing the target tunnel into multiple zones along its length. The system is characterized by comprising: The acquisition module is used to collect lighting environment data for each zone in the target tunnel; The processing module is used to analyze the spatial correlation characteristics of the lighting environment between adjacent groups of lighting environments through the lighting environment data of each zone, and to calculate the power adaptation range of the adjustable lighting power of each zone by combining all the spatial correlation characteristics with the traffic flow and natural light irradiance of the target tunnel. The processing module is also used to determine the collaborative control delay when adjusting the brightness between adjacent partitions, and to dynamically adjust the brightness of all lights in each partition of the target tunnel according to all collaborative control delays and all power adaptation intervals combined with the real-time traffic flow parameters of the target tunnel, and to determine the degree of overlap of the lighting space between the light coverage of each adjustable light during the dynamic adjustment process. The processing module is also used to perform brightness difference analysis on the brightness of the target tunnel during the adjustment of the adjustable lighting lamp based on the overlap of all lighting spaces, and to obtain the brightness deviation gradient of the target tunnel during the adjustment of the adjustable lighting lamp. The execution module is used to optimize and adjust the power adaptation range of the adjustable lighting power of each zone in the target tunnel based on the brightness deviation gradient, so as to control all the lighting in the target tunnel.

9. A computer device, characterized in that, The computer device includes a memory and a processor, the memory storing code, and the processor being configured to retrieve the code and execute the tunnel lighting energy-saving control method as described in any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the tunnel lighting energy-saving control method as described in any one of claims 1 to 7.

Images