A garden light control method, device and medium

Abstract

The application discloses a garden light control method and device and a medium, and relates to the technical field of light control. The method comprises the following steps: collecting the environmental brightness in the garden area in real time; collecting the vegetation canopy layer data and three-dimensional space data in the garden area to construct a digital twin model containing dynamic occlusion relationship; dividing the garden area into a plurality of space units according to the positions of the lighting devices in the garden area, simulating the light projection path of the lighting devices in the space units based on the digital twin model, and determining the corresponding occlusion level of the space units under the vegetation occlusion according to the corresponding simulation lighting data of the space units; correcting the environmental brightness according to the occlusion level, so as to determine the required illumination level of each space unit through the corrected environmental brightness; generating the corresponding lighting strategy of the space units based on the illumination level and the functional partition to which the space unit belongs, and controlling the lighting devices to perform lighting according to the lighting strategy.

Description

Technical Field

[0001] This application relates to the field of lighting control technology, specifically to a method, device and medium for controlling garden lighting. Background Technology

[0002] Landscape lighting is a crucial infrastructure for creating urban nighttime landscapes and ensuring public safety. With the advancement of smart city construction, landscape lighting systems are gradually evolving from traditional timed switching modes to intelligent systems. However, most existing landscapes use timed switching or time-segmented dimming control, uniformly turning on and off lighting devices and setting fixed brightness levels. However, the vegetation distribution in landscape areas exhibits significant spatial heterogeneity. Differences in canopy density, height, and leaf area index among different spatial units cause light emitted by various lighting devices to attenuate to varying degrees after passing through different vegetation canopies. This results in extremely uneven distribution of actual ground illuminance. Lighting devices with uniform power configurations are difficult to adjust differently, leading to insufficient illuminance in some areas and excessive lighting in others. This affects lighting quality and traffic safety, and also results in significant energy waste. Summary of the Invention

[0003] To address the aforementioned problems, this application proposes a method for controlling garden lighting, comprising: According to a preset collection cycle, the ambient brightness within the garden area is collected in real time using light sensors deployed within the garden area. Collect vegetation canopy data and three-dimensional spatial data within the garden area to construct a digital twin model that includes dynamic shading relationships; Based on the location of the lighting devices in the garden area, the garden area is divided into several spatial units. Based on the digital twin model, the light projection path of the lighting devices in the spatial units is simulated, so as to determine the shading level of the spatial unit under vegetation shading according to the simulated lighting data corresponding to the spatial unit. Based on the occlusion level, the ambient brightness is corrected so as to determine the required illumination level for each spatial unit using the corrected ambient brightness. Based on the illumination level and the functional zone to which the spatial unit belongs, a lighting strategy corresponding to the spatial unit is generated, and the lighting device is controlled to provide illumination according to the lighting strategy.

[0004] In one implementation of this application, a lighting strategy corresponding to the spatial unit is generated based on the illumination level and the functional zone to which the spatial unit belongs, specifically including: Determine the functional zones to which the spatial unit belongs; wherein, the functional zones include passage areas, landscape viewing areas, and ecological restoration areas; When the functional zones are the passage area and the landscape viewing area, the lighting parameter constraint information corresponding to the spatial unit is obtained according to the lighting level; wherein, the lighting parameter constraint information includes the lower limit of illuminance, the lower limit of color rendering index, and the target chromaticity constraint. With minimizing lighting power as the optimization objective and the lighting parameters as constraints, the driving parameters corresponding to the lighting device are solved, and the lighting command corresponding to the spatial unit is generated through the driving parameters. When the functional zone is the ecological restoration zone, the lighting device is switched to the basic supplemental lighting mode to provide light restoration for the vegetation by outputting the minimum light required by the vegetation through the basic supplemental lighting mode.

[0005] In one implementation of this application, before real-time acquisition of ambient brightness within the garden area, the method further includes: Obtain the current date and time of the geographical location of the garden area, and determine the critical lighting time corresponding to the geographical location based on the current date; Calculate the time difference between the current moment and the critical moment of illumination, and determine the current illumination stage of the garden area based on the time difference; wherein the illumination stage includes a period of dramatic brightness change and a period of stable brightness. If it is the period of dramatic brightness change, the acquisition period is shortened to a first preset frequency; otherwise, the acquisition period is extended to a second preset frequency. The period of dramatic brightness change includes at least a time interval from a first preset duration before the critical lighting moment to a second preset duration after the critical lighting moment.

[0006] In one implementation of this application, the shading level of the spatial unit under vegetation shading is determined based on the simulated lighting data corresponding to the spatial unit, specifically including: Based on the digital twin model, the first simulated illuminance value of the lighting device projected onto a preset reference point in the spatial unit under unobstructed conditions, and the second simulated illuminance value projected onto the preset reference point under vegetation obstruction are obtained. Calculate the illuminance attenuation rate corresponding to the spatial unit based on the first simulated illuminance value and the second simulated illuminance value; Based on the light projection path between the spatial unit and the lighting device in the digital twin model, the amount of light rays that are intercepted by the vegetation canopy in the light rays emitted from the lighting device and reaching the spatial unit is collected to calculate the light interception rate; Based on the illuminance attenuation rate and the light interception rate, the corresponding occlusion factor is calculated, and the occlusion factor is compared with several preset occlusion level thresholds to determine the occlusion level corresponding to the spatial unit; wherein, the occlusion level includes at least no occlusion level, mild occlusion level, moderate occlusion level and severe occlusion level, and different occlusion levels correspond to different illuminance correction coefficients.

[0007] In one implementation of this application, after constructing a digital twin model containing dynamic occlusion relationships, the method further includes: Determine whether the spatial unit meets the preset occlusion state update conditions: If so, the current vegetation canopy data and three-dimensional spatial data corresponding to the spatial unit are collected to iteratively update the vegetation canopy structure and optical properties within the spatial unit in the digital twin model; After the digital twin model is updated, the occlusion level of the spatial unit is recalculated based on the updated digital twin model.

[0008] In one implementation of this application, determining whether the spatial unit meets the preset occlusion state update conditions specifically includes: Collect the current vegetation canopy data corresponding to each spatial unit within the garden area according to the preset collection cycle; Calculate the difference between the current vegetation canopy data and the historical vegetation canopy data stored at the time of the last update; When the difference exceeds a preset threshold, or when a maintenance signal indicating that a pruning operation has been performed is received, or when the growth cycle stage of the vegetation undergoes a seasonal change, the spatial unit is determined to meet the preset shading status update conditions.

[0009] In one implementation of this application, minimizing lighting power is the optimization objective, and the illumination parameters are used as constraints to solve for the driving parameters corresponding to the lighting device. Specifically, this includes: Obtain the adjustable luminous flux output range of each primary color channel in the lighting device within a preset driving current range and its corresponding single-channel power consumption curve; Based on the lower limit of illuminance in the illumination parameters, determine the lower limit of total luminous flux required by the lighting device, and determine the luminous flux ratio constraint range between each primary color channel required to satisfy the lower limit of color rendering index and the target chromaticity constraint; Based on the adjustable luminous flux output range, the driving current of each primary color channel is discretized and sampled with a preset step size to generate several candidate driving current combinations. Based on the lower limit of total luminous flux and the luminous flux ratio constraint range, from the candidate drive current combinations, combinations whose actual total luminous flux is lower than the lower limit of total luminous flux and whose actual luminous flux ratio does not meet the luminous flux ratio constraint range are eliminated, to obtain feasible drive current combinations. Based on the single-channel power consumption curve, the total power consumption corresponding to each feasible drive current combination is calculated, and the feasible drive parameter combination with the minimum total power consumption is selected as the drive parameter of the lighting device.

[0010] In one implementation of this application, the garden area is divided into several spatial units based on the location of the lighting devices within the garden area, specifically including: Based on the digital twin model, the actual illuminance values ​​projected by each lighting device at various points on the ground are simulated to generate a ground illuminance distribution map corresponding to each lighting device. According to the preset effective illuminance threshold, the continuous areas in the ground illuminance distribution map corresponding to each lighting device where the illuminance value is greater than or equal to the effective illuminance threshold are marked as the effective coverage area of ​​the lighting device; The garden area is divided into several spatial units based on the effective coverage area of ​​each lighting device.

[0011] This application embodiment provides a garden lighting control device, the device comprising: At least one processor; And, a memory communicatively connected to the at least one processor; The memory stores instructions that can be executed by the at least one processor, which, when executed by the at least one processor, enables the at least one processor to perform a garden lighting control method as described above.

[0012] This application provides a non-volatile computer storage medium storing computer-executable instructions, wherein the computer-executable instructions are configured to execute a garden lighting control method as described in any of the preceding claims.

[0013] The garden lighting control method proposed in this application can bring the following beneficial effects: By constructing a digital twin model incorporating dynamic shading relationships to spatially divide the garden area, and calculating the shading level of each spatial unit based on vegetation shading, the collected ambient brightness can be differentiated according to the degree of vegetation shading attenuation in different spatial units. This ensures that the corrected ambient brightness accurately reflects the actual natural light intensity on the ground surface of each spatial unit, thereby accurately determining the required illumination level for each unit. This effectively avoids the problem of underestimating lighting needs in severely shaded areas due to excessive reduction of natural light. Furthermore, by minimizing lighting power as the optimization objective to solve the driving parameters, ineffective energy consumption can be minimized while meeting illumination requirements. This solves the problems of uneven illuminance and energy waste caused by traditional uniform power configurations, ensuring both lighting quality and traffic safety in the garden area, while also adapting to the lighting needs of different functional zones, achieving intelligent energy-saving lighting control. Attached Figure Description

[0014] The accompanying drawings, which are included to provide a further understanding of this application and form part of this application, illustrate exemplary embodiments of this application and are used to explain this application, but do not constitute an undue limitation of this application. In the drawings: Figure 1 A flowchart illustrating a garden lighting control method provided in an embodiment of this application; Figure 2 This is a structural schematic diagram of a garden lighting control device provided in an embodiment of this application. Detailed Implementation

[0015] To make the objectives, technical solutions, and advantages of this application clearer, the technical solutions of this application will be clearly and completely described below in conjunction with specific embodiments and corresponding drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0016] The technical solutions provided by the various embodiments of this application are described in detail below with reference to the accompanying drawings.

[0017] like Figure 1 As shown in the embodiment of this application, a garden lighting control method includes: S101: According to the preset collection cycle, the ambient brightness in the garden area is collected in real time by light sensors deployed in the garden area.

[0018] Light sensors are deployed at reference locations within the garden area. These reference locations can be determined based on the center of the main activity areas for people within the garden. There can be one or more reference locations, the number of which is set according to the area of ​​the garden. Generally, the light sensors need to be installed at a preset height above the ground in an open, unobstructed location to ensure they can receive natural light, thus accurately reflecting the overall ambient brightness level of the garden area and avoiding acquisition errors caused by localized obstructions. Following a preset acquisition cycle, the light sensors collect ambient brightness data in real time within the garden area. By capturing the current ambient brightness, the overall lighting conditions within the garden area are obtained, providing a brightness reference benchmark for subsequent localized lighting control in various spatial units.

[0019] In one embodiment, the ambient brightness acquisition cycle is not fixed but dynamically adjusted according to the current season and time. This is because the sunset and sunrise times differ at the same geographical location in different seasons. For example, the sunset time varies little throughout the year in areas near the equator, while in high-latitude areas, the difference in sunset time between summer and winter can be several hours. Therefore, it is necessary to obtain the current date and time corresponding to the current geographical location of the garden area. Based on the current date, the critical lighting time for the garden area at that location can be further determined. The critical lighting time refers to the critical time point when the intensity of natural light changes significantly, i.e., the sunset and sunrise times. The critical lighting time can be determined by calculating the solar declination angle based on the latitude of the geographical location, combining it with the longitude to calculate the hour angle, and then obtaining the conversion relationship between the local true solar time and standard time. Finally, the critical lighting time for that geographical location on that day can be determined. The above data can be obtained daily by a controller set up on the edge side, or the critical lighting time of the day can be obtained from a meteorological service. Alternatively, the two methods can be combined. After the controller calculates the critical lighting time, it can be verified and corrected by data obtained through the network to ensure the accuracy of the critical lighting time. It should be noted that, considering actual weather conditions, such as rain, fog, etc., the ambient brightness may have already decreased to below the lighting requirement threshold before sunset. In this embodiment, a global ambient light sensor can also be deployed in the garden area to compare the real-time collected ambient brightness value with the preset brightness threshold. When the ambient brightness decreases to below the threshold and the time is close to the lighting critical moment, the actual trigger time is used as the correction value of the lighting critical moment to compensate for the deviation caused by abnormal weather.

[0020] After obtaining the critical lighting time for the day, the time difference between the current time and the critical lighting time is calculated, and the current lighting stage of the garden area is determined based on this time difference. Specifically, the lighting stage includes at least a period of dramatic brightness change and a period of stable brightness. The period of dramatic brightness change refers to the time when the rate of change of natural light intensity over time exceeds a preset threshold, typically occurring around sunset and sunrise, during which ambient brightness rapidly decreases or increases over time. The period of stable brightness refers to other times far from the critical lighting time, during which the rate of change of ambient brightness is gradual, and the amount of brightness change per unit time is small. The period of dramatic brightness change includes at least the time interval from a first preset duration before the critical lighting time to a second preset duration after the critical lighting time. For example, the first preset duration can be set to 60 minutes, and the second preset duration can be set to 30 minutes, meaning the period of dramatic brightness change is from 60 minutes before sunset to 30 minutes after sunset, covering the period around sunset when ambient brightness changes most drastically. The corresponding period of dramatic brightness change at sunrise is similar, lasting from 30 minutes before sunrise to 60 minutes after sunrise. The specific duration can be adjusted according to the actual light variation patterns of the geographical location. By linking the start and end times of the dramatic brightness change period with the critical moment of illumination, the adjustment basis of the data collection cycle can automatically adapt to seasonal changes. This avoids the problems caused by using a fixed time period, such as winter lighting demand appearing earlier without timely adjustment of the data collection cycle, or the data collection cycle entering high-frequency sampling too early in summer, resulting in unnecessary energy consumption.

[0021] If the time difference indicates that the current moment is during a period of rapid brightness change, the sampling period of the light sensor is shortened to a first preset frequency. The first preset frequency is a sampling frequency higher than the default sampling frequency; for example, it can be set to once every ten minutes. The specific interval can be set according to the flow of people in the park. In park areas with higher pedestrian traffic, the sampling frequency can be appropriately increased to ensure a good lighting environment. Using a higher sampling frequency during periods of rapid brightness change can promptly capture rapid changes in ambient brightness, thereby accurately triggering the lighting device to turn on or gradually control brightness near the critical moment of lighting. This avoids delays in lighting activation or abrupt brightness changes caused by excessively large sampling intervals, improving traffic safety and visual comfort.

[0022] Conversely, if the current moment is determined to be within a period of stable brightness, the sampling period is extended to a second preset frequency. The second preset frequency is a sampling frequency lower than the default sampling frequency, for example, it can be set to once every half hour. During periods of stable brightness, ambient brightness changes slowly, and a lower sampling frequency can accurately reflect the current ambient brightness level. This also significantly reduces the power consumption of the light sensor and its associated wireless transmission module. Especially for battery-powered wireless sensor nodes, extending the sampling period can significantly improve the device's lifespan and reduce maintenance costs.

[0023] It should be noted that when it is determined that the current illumination phase has entered a period of stable brightness for some time, or when the current ambient brightness does not require illumination, the current acquisition of ambient brightness can be stopped. Acquisition and judgment can be restarted only when the next time supplemental lighting is needed. This further reduces the power consumption of the sensing nodes, achieving low-power acquisition without affecting the accurate capture of the illumination status. The acquisition cycle is adjusted by the controller sending frequency adjustment commands to each light sensor. After receiving the adjustment command, the light sensor in each spatial unit updates the interrupt trigger interval of its internal timer and performs ambient brightness sampling according to the new acquisition cycle. When the illumination phase changes again, such as from a period of rapid brightness change to a period of stable brightness, or vice versa, the controller reissues the adjustment command, so that the acquisition cycle dynamically switches with the change of illumination phase.

[0024] The aforementioned dynamic acquisition cycle adjustment mechanism matches the sampling frequency of the light sensor with the actual rate of change in ambient brightness. During critical periods of rapid ambient brightness change, the sampling density is increased to ensure control accuracy, while during periods of relatively stable ambient brightness, the sampling density is decreased to reduce system energy consumption, thus achieving a balance between lighting control quality and system energy saving. Furthermore, this mechanism can automatically adapt to the solar radiation patterns of different regions with seasonal changes without manual intervention, demonstrating strong universality and robustness.

[0025] S102: Collect vegetation canopy data and three-dimensional spatial data within the garden area to construct a digital twin model that includes dynamic shading relationships.

[0026] Multi-source sensing devices deployed within the park area collect vegetation canopy data and 3D spatial data. The vegetation canopy data specifically includes canopy geometric parameters and leaf area index (LAI). Geometric parameters characterize the 3D morphological structure of the canopy, including canopy height, canopy diameter, canopy volume, and spatial density of branches and leaves. LAI refers to the total area of ​​individual leaves per unit surface area; a higher LAI indicates a greater number of leaves per unit space, resulting in more significant scattering and absorption effects of light passing through the canopy. 3D spatial data characterizes the topographic relief, spatial location information of surface structures, and vegetation within the park area. This data can be acquired using UAV-borne LiDAR, ground-based LiDAR, or a combination of both, or supplemented by 3D laser scanning to ensure complete and accurate data coverage.

[0027] After data collection, the aforementioned multi-source heterogeneous data are fused to construct a digital twin model of the garden area. A digital twin model is a three-dimensional digital mirror image constructed in virtual space and mapped in real time to the garden area. It not only contains the geometric structure information of the garden scene but also associates the optical attribute parameters of the vegetation canopy. First, the vegetation canopy data is filtered, classified, and a canopy height model is reconstructed to extract the canopy width, tree height, canopy volume, and branch topology parameters of each vegetation type, generating a vegetation canopy geometric model. Simultaneously, ground point cloud data is filtered and a digital elevation model is constructed to generate a terrain three-dimensional model. Then, the vegetation canopy geometric model and the terrain three-dimensional model are registered and fused in a unified spatial coordinate system to form a basic three-dimensional spatial model. Based on this, the leaf area index and canopy radiation attenuation coefficient of each vegetation type within the garden area are obtained, and these optical attribute parameters are associated with the corresponding vegetation canopy geometric models in the basic three-dimensional spatial model, ensuring that each vegetation unit carries its corresponding optical attribute information, thereby generating a digital twin model. Among them, the leaf area index is used to quantify the total area of ​​leaves per unit ground area, characterizing the canopy's ability to intercept light; the canopy radiation attenuation coefficient is used to characterize the degree of physical attenuation of light as it passes through the vegetation canopy and is absorbed and scattered.

[0028] The digital twin model constructed using the above method can, in subsequent simulations of light projection paths, determine whether light is physically blocked based on the geometric structure of the vegetation canopy, and calculate the radiation attenuation of light passing through the canopy based on associated optical property parameters, thus achieving a dynamic representation of vegetation shading relationships. When the vegetation canopy undergoes structural changes due to natural growth or human pruning, only iterative updates to the corresponding vegetation canopy geometric model and its optical property parameters are needed to keep the digital twin model synchronized with the physical garden scene, providing a data foundation for accurately determining the actual shading level of each spatial unit.

[0029] S103: Based on the location of the lighting devices in the garden area, the garden area is divided into several spatial units. Based on the digital twin model, the light projection path of the lighting devices in the spatial units is simulated. Based on the simulated lighting data of the spatial units, the shading level of the spatial units under vegetation shading is determined.

[0030] After constructing the digital twin model, the first step is to divide the entire garden area into several spatial units based on the distribution of lighting installations. A spatial unit is the smallest spatial partition used for independent assessment of lighting conditions and control; its granularity determines the refinement of subsequent light simulation and control strategies. After dividing the spatial units, based on the constructed digital twin model, simulated light projection is performed on each unit. Virtual light rays are emitted from the lighting installation locations to the ground surface within the unit, and the projection path of each ray in three-dimensional space is traced. Based on the simulation results of the optical projection paths, simulated lighting data for each spatial unit can be obtained. This simulated lighting data includes the amount of light intercepted by the vegetation canopy and the energy change information of the light from its origin to its arrival at the spatial unit. Based on this simulated lighting data, the influence of the vegetation canopy on the lighting effect in both geometric interception and radiative attenuation dimensions can be quantified, thereby determining the shading level of each spatial unit under vegetation cover. The shading level is used to assess the overall degree of shading of lighting light by vegetation within the current spatial unit, and includes at least an unshading level, a slight shading level, a moderate shading level, and a severe shading level.

[0031] In one embodiment, based on the constructed digital twin model, two sets of simulated illuminance values ​​are obtained for each spatial unit. The first set is the first simulated illuminance value under unobstructed conditions. This value refers to the illuminance value projected by the lighting device onto a preset reference point within the spatial unit after temporarily removing all vegetation canopy geometry and optical properties in the digital twin model. It represents the theoretical light intensity that the spatial unit can receive when there is no vegetation obstruction. The second set is the second simulated illuminance value under vegetation obstruction conditions. This value refers to the illuminance value actually projected by the lighting device onto the same preset reference point after the light is intercepted and attenuated by the vegetation canopy, while retaining all current vegetation canopy geometry and optical property parameters in the digital twin model. The preset reference point can be pre-set according to the actual use of the spatial unit, such as the center point of the surface within the spatial unit, an observation point at a preset height above the ground, or the location with the lowest illuminance within the spatial unit. By obtaining the above two sets of simulated illuminance values, illuminance comparison data of the same spatial unit under both unobstructed and obstructed conditions can be obtained.

[0032] Based on this, the illuminance attenuation rate corresponding to the spatial unit is calculated according to the difference between the first and second simulated illuminance values, as well as the first simulated illuminance value itself. The illuminance attenuation rate quantifies the degree of light intensity loss caused by the vegetation canopy; the larger the value, the more severe the impact of vegetation shading on the spatial unit. Simultaneously, based on the light projection path between the spatial unit and the lighting device in the digital twin model, the number of simulated rays emitted from the lighting device and successfully reaching the spatial unit that are intercepted by the vegetation canopy geometry is counted, and the light interception rate is calculated. During the light projection simulation, a large number of virtual rays are emitted from the lighting device location towards the target area within the spatial unit, each ray carrying its propagation direction information. The vegetation canopy geometry in the digital twin model exists in the form of three-dimensional patches. When the propagation path of a virtual ray intersects with any vegetation geometric patch, the ray is considered intercepted. The ratio of the number of intercepted rays to the total number of emitted rays is the light interception rate. The light interception rate reflects the degree of shading of the lighting light by the vegetation canopy from a geometric perspective.

[0033] After obtaining the illuminance attenuation rate and light interception rate, the two are fused and calculated to generate the shading factor corresponding to the spatial unit. The shading factor characterizes the comprehensive attenuation of illumination light by the vegetation canopy in two dimensions: geometric interception and radiative attenuation. The light interception rate reflects the physical blocking effect of the canopy geometry on light, while the illuminance attenuation rate further reflects the energy attenuation caused by absorption and scattering of light by leaves as it passes through the canopy. By comparing the calculated shading factor with several preset shading level thresholds one by one, the shading level corresponding to the spatial unit can be determined. The shading levels include at least no shading, slight shading, moderate shading, and severe shading. The no-shading level corresponds to a shading factor lower than the first threshold, the slight shading level corresponds to a shading factor between the first and second thresholds, the moderate shading level corresponds to a shading factor between the second and third thresholds, and the severe shading level corresponds to a shading factor higher than the third threshold. Different shading levels are pre-associated with different illuminance correction coefficients. The higher the shading level, the larger the illuminance correction coefficient, which enables subsequent steps to adaptively correct the target light parameters based on the severity of vegetation shading.

[0034] It should be noted that the canopy structure of vegetation within a garden area continuously changes over time. For example, the canopy width and leaf area index increase during the growth cycle, seasonal leaf fall reduces canopy density, or artificial pruning causes abrupt changes in branch structure. If the digital twin model is not updated for a long period, the shading relationships it represents will deviate from the physical garden scene, leading to errors in the determination of shading levels. Therefore, this application implements a digital twin model that adaptively adjusts to follow the actual changes in vegetation shading relationships.

[0035] Specifically, the process first determines whether each spatial unit meets the preset occlusion status update conditions. These conditions trigger updates to the digital twin model, avoiding unnecessary computational overhead from overly frequent updates and ensuring timely model synchronization when occlusion relationships change significantly. The determination of occlusion status update conditions follows a preset data collection cycle, with each spatial unit assessed independently. The collection cycle is pre-set based on the vegetation growth rate; for example, a monthly cycle for fast-growing tree species and a quarterly cycle for slow-growing species balances model accuracy and computational cost.

[0036] In each data collection cycle, the current vegetation canopy data for the spatial unit is re-collected using methods such as UAV-borne LiDAR or ground-based LiDAR. This includes geometric parameters such as crown width, tree height, canopy volume, and branch topology of each individual tree. Then, the collected current vegetation canopy data is compared with the historical vegetation canopy data stored during the last digital twin model update of the spatial unit, and the difference between the two is calculated. The difference can be specifically expressed as changes in crown width, tree height, leaf area index, or a combination of these indicators. If the difference exceeds a preset update trigger threshold, meaning the change in canopy structure is sufficient to significantly affect the propagation and attenuation characteristics of illumination light within the spatial unit, then the spatial unit is determined to meet the shading state update conditions. In addition to the periodic judgments triggered by the difference threshold mentioned above, when the system receives an externally input maintenance signal indicating that the vegetation within the spatial unit has undergone pruning, or when the system detects a seasonal transition in the growth cycle stage of the vegetation within the spatial unit—for example, from dormancy to budding, or from vigorous growth to leaf fall—it directly determines that the spatial unit meets the shading status update conditions without waiting for the difference threshold judgment to be triggered. In these two triggering methods, the difference threshold is used to capture gradual shading changes caused by the natural, slow growth of vegetation, while the maintenance signal and seasonal transition are used to capture abrupt shading changes caused by pruning or phenological changes.

[0037] Once a spatial unit meets the occlusion update conditions, current vegetation canopy data and 3D spatial data for that unit are collected using methods such as LiDAR or oblique photography. This updates the vegetation canopy geometry within that spatial unit's range in the digital twin model. Specifically, the previous vegetation canopy geometry is replaced with a new geometry reconstructed based on the currently collected data. Furthermore, the currently measured leaf area index and canopy radiation attenuation coefficient, among other optical properties, of each plant within the spatial unit are re-associated, ensuring that the vegetation geometry and optical properties of that spatial unit are consistent with the current state of the physical scene. It should be noted that this update process only performs a local update on spatial units that meet the update conditions; other spatial units that do not meet the update conditions retain their original model data, thus effectively reducing the computational cost of model updates.

[0038] After the digital twin model is updated, the light projection path is simulated again for the spatial unit based on the updated model. This yields the first simulated illuminance value under unobstructed conditions and the second simulated illuminance value under current shading conditions. The illuminance attenuation rate and light interception rate are then calculated to determine the updated shading level of the spatial unit. This dynamic update mechanism ensures that the digital twin model keeps pace with the growth, pruning, and seasonal changes of the vegetation canopy, guaranteeing that the shading level of each subsequent spatial unit is always evaluated based on the current actual shading relationship.

[0039] S104: Based on the occlusion level, the ambient brightness is corrected to determine the required illumination level for each spatial unit using the corrected ambient brightness.

[0040] The light sensors are not deployed inside each spatial unit, but rather in open, unobstructed locations within the garden area, such as open lawns, the center of a plaza, or above water features—locations with unobstructed views and no vegetation canopy. The ambient brightness collected by the light sensors in these open areas represents the baseline level of natural light in the garden area, i.e., the original ambient brightness unaffected by any local vegetation obstruction. Because the light sensors are located in open areas, their collected values ​​are not affected by local vegetation canopy obstruction, and therefore can be used as a unified natural light baseline value for the entire garden area. However, the degree of vegetation obstruction varies among the spatial units within the garden area. The higher the degree of obstruction, the less natural light the ground surface actually receives; that is, the actual usable natural light in that spatial unit is lower than the baseline value collected by the sensors in open areas. For severely obstructed spatial units, although the ambient brightness in open areas has reached a certain level, the amount of natural light received by the ground surface of that spatial unit is far lower than the baseline value due to the interception and attenuation by the vegetation canopy. Therefore, the ambient brightness collected by the light sensor in an open area cannot be directly used as the basis for lighting control of each spatial unit. Instead, the baseline ambient brightness must be adjusted differently according to the occlusion level of each spatial unit in order to restore the actual natural light level that the ground surface of each spatial unit can receive, and then accurately determine the amount of artificial light that each spatial unit needs to supplement.

[0041] For each spatial unit, its corresponding shading level and the ambient brightness collected in real time by light sensors deployed in open areas of the garden are obtained. Different shading levels are pre-associated with different ambient brightness attenuation coefficients. The ambient brightness attenuation coefficient is used to characterize the degree of attenuation of natural light reaching the surface of the spatial unit by the vegetation canopy. Its value is negatively correlated with the shading level, that is, the higher the shading level, the smaller the ambient brightness attenuation coefficient, indicating that the proportion of natural light that the surface of the spatial unit can actually receive relative to the baseline value of the open area is lower.

[0042] The specific value of the ambient brightness attenuation coefficient can be predetermined as follows: In the digital twin model, simulate the theoretical value of natural light in each spatial unit under no vegetation obstruction conditions, and the simulated value of natural light received by the ground surface under the current vegetation obstruction conditions. The ratio of the latter to the former is used as the ambient brightness attenuation coefficient corresponding to that obstruction level. Alternatively, by deploying reference light sensors at a reference point in an open area and in each spatial unit with typical obstruction levels for long-term synchronous measurement, the attenuation ratio of natural light received by the ground surface under different obstruction levels is statistically analyzed, and the attenuation coefficient corresponding to each obstruction level is calibrated by actual measurement. After the above calibration, the mapping relationship between each obstruction level and the ambient brightness attenuation coefficient can be pre-stored in the configuration parameter table of the controller. For example, the attenuation coefficient corresponding to no obstruction level is 1.0, the attenuation coefficient corresponding to slight obstruction level is approximately 0.7~0.9, the attenuation coefficient corresponding to moderate obstruction level is approximately 0.4~0.7, and the attenuation coefficient corresponding to severe obstruction level is approximately 0.1~0.4. The smaller the coefficient, the more severe the attenuation of natural light by vegetation.

[0043] Based on this, the ambient brightness baseline value collected by the light sensor in the open area is multiplied by the ambient brightness attenuation coefficient corresponding to the spatial unit's shading level to obtain the corrected ambient brightness of the spatial unit. The corrected ambient brightness characterizes the actual level of natural light that the surface of the spatial unit can receive under the current vegetation shading conditions; that is, it is the estimated ambient brightness value after eliminating the attenuation effect of the vegetation canopy on the baseline natural light. Through the above correction process, the amount of natural light lost by the spatial unit due to vegetation shading is deducted, so that the corrected ambient brightness can truly reflect the actual amount of natural light available on the surface of the spatial unit, thus providing an accurate basis for determining subsequent lighting needs.

[0044] After obtaining the corrected ambient brightness for each spatial unit, the required illumination level for that unit is determined based on the corrected ambient brightness. The illumination level characterizes the target illuminance level of artificial lighting required for that spatial unit under the current available natural light conditions. Multiple mapping relationships between ambient brightness ranges and illumination levels are pre-defined, with these ranges set according to lighting design standards and the actual usage needs of the garden area. After determining the mapping relationships, the corrected ambient brightness of the spatial unit is compared with multiple preset illuminance thresholds to determine the ambient brightness range to which the corrected value belongs, thereby determining the corresponding illumination level for that spatial unit. The illumination level and the corrected ambient brightness are negatively correlated; the lower the corrected ambient brightness, the higher the corresponding illumination level, and the higher the target illuminance value included in the subsequently generated lighting strategy, to compensate for the loss of natural light caused by vegetation shading.

[0045] It should be noted that the illumination level includes a case where no supplemental lighting is required. When the ambient brightness of a space unit after correction has reached or exceeded the preset sufficient illuminance threshold, it indicates that the natural light currently available to the space unit is sufficient to meet the lighting needs of the corresponding functional area, and no additional artificial lighting is required. At this time, the illumination level corresponding to the space unit is determined to be a level where no supplemental lighting is required, and there is no need to turn on the lighting device.

[0046] S105: Based on the illumination level and the functional zone to which the space unit belongs, generate the lighting strategy corresponding to the space unit, and control the lighting device to provide illumination according to the lighting strategy.

[0047] After determining the illumination levels for each spatial unit, the functional zones to which each spatial unit belongs are first determined. Functional zones are pre-defined areas with different lighting objectives, based on the spatial use and lighting requirements of the garden area during the garden planning stage. Functional zones include at least access areas, landscape viewing areas, and ecological restoration areas. Access areas refer to roads, walkways, plaza passages, etc., within the garden for pedestrian traffic; their core lighting objective is to ensure pedestrian safety, with clear requirements for horizontal illuminance uniformity and minimum illuminance. Landscape viewing areas refer to areas within the garden containing vegetation, water features, sculptures, and other viewing nodes with landscape display value; their core lighting objective is to highlight the aesthetic appeal and artistic expression of the landscape, requiring high-quality lighting. Ecological restoration areas refer to areas within the garden where vegetation communities are in a state of ecological restoration or protection; their lighting objective is to meet the light conditions required for normal vegetation growth and development. The functional zone information for each spatial unit is pre-stored in the controller's configuration database and linked using the spatial unit identifier as an index.

[0048] After determining the functional zone to which a spatial unit belongs, differentiated lighting strategy generation logic is executed based on the different functional zones. If the functional zone is a passageway or a landscape viewing area, the required lighting parameter constraints for that spatial unit are obtained based on the corresponding illuminance level. The illuminance level characterizes the degree to which the spatial unit requires supplemental artificial lighting under the current ambient brightness conditions; a higher illuminance level indicates less available natural light and a greater amount of supplemental artificial lighting required. The lighting parameter constraints include the lower limit of illuminance, the lower limit of color rendering index, and target chromaticity constraints.

[0049] When obtaining illumination parameter constraint information based on illumination level, it is necessary to process the information according to whether the illumination level indicates the need for supplemental lighting. If the illumination level indicates that supplemental lighting is not required, there is no need to obtain illumination parameter constraint information or perform subsequent drive parameter solving steps. Instead, a shutdown command or standby command for the lighting device is directly generated to control the lighting device to remain off or enter a low-power standby state, thereby achieving maximum energy saving. If the illumination level indicates a specific level that requires supplemental lighting, the mapping relationship between the preset illumination level and illumination parameter constraint information is queried based on the illumination level to obtain the lower limit of illuminance, lower limit of color rendering index, and target chromaticity constraint corresponding to the spatial unit.

[0050] Specifically, the lower limit of illuminance refers to the minimum illuminance value that a lighting device must achieve on the surface or reference surface of a spatial unit to meet its visual or safety requirements under a corresponding light level. The lower limit of illuminance is negatively correlated with the light level; the higher the light level, the higher the lower limit of illuminance; conversely, the lower the light level, the lower the lower limit of illuminance. The lower limit of color rendering index (CRI) is a quantitative indicator of a light source's ability to reproduce the colors of objects. Its value is determined by functional zoning. Traffic areas generally require a CRI of no less than 60-70 to ensure color recognition of roads and obstacles, while landscape viewing areas require a CRI of no less than 80-90 to accurately reproduce the color details and texture of vegetation and landscapes. Within the same functional zoning, this value does not change with the light level. Target chromaticity constraints limit the range of color coordinates or correlated color temperature of the light output from the lighting device; its value is also determined by functional zoning.

[0051] After obtaining the illumination parameter constraints, the driving parameters of the lighting device are solved with the goal of minimizing illumination power and the illumination parameters as constraints. For lighting devices using multi-channel LED arrays, the driving current of each primary color channel directly determines the luminous flux output and power consumption of that channel. The larger the driving current, the higher the luminous flux output, but also the greater the power consumption. Furthermore, the luminous flux ratio of different channels determines the color rendering index and chromaticity coordinates of the mixed light. Therefore, solving for the driving parameters is essentially finding the optimal combination of driving parameters under multiple constraints. After obtaining the driving parameters, an illumination command corresponding to the spatial unit is generated based on these parameters and sent to the lighting device corresponding to that spatial unit. The illumination command includes at least the specific values ​​of the driving parameters and execution time information. Upon receiving the illumination command, the lighting device adjusts its internal controllable variables according to the driving parameters, outputting light that satisfies the triple constraints of illuminance, color rendering index, and chromaticity, thus achieving precise supplemental lighting for the current spatial unit.

[0052] In the specific solution, firstly, obtain the adjustable luminous flux output range of each primary color channel in the lighting device within the preset drive current range and its corresponding single-channel power consumption curve. The adjustable luminous flux output range characterizes the range of luminous flux that each primary color channel can output within the range from the minimum drive current to the maximum drive current, which is determined by the hardware specifications of the lighting device. The single-channel power consumption curve describes the mapping relationship between the drive current of each primary color channel and the corresponding power consumption. This curve can be obtained through factory calibration or actual measurement.

[0053] Furthermore, the lower limit of the total luminous flux required by the lighting device is determined based on the lower limit of illuminance in the target illumination parameters. Illuminance and luminous flux can be converted using the area of ​​the spatial unit and the light energy utilization coefficient. Given the area of ​​the spatial unit, the installation height of the lighting device, the beam angle, etc., the lower limit of the total luminous flux required to reach the target illuminance lower limit can be uniquely determined. Simultaneously, based on the lower limit of the color rendering index and the target chromaticity constraint in the target illumination parameters, the luminous flux ratio constraint range between the primary color channels required to meet the above two requirements is determined. The color rendering index and chromaticity coordinates of the mixed light are determined by the proportional mixing of the luminous flux of each primary color channel. Through the principle of color mixing and the color rendering index evaluation method, the required ratio range of luminous flux of each primary color channel to reach the target color rendering index lower limit and target chromaticity coordinates can be calculated in reverse.

[0054] After obtaining the adjustable luminous flux output range, the lower limit of total luminous flux, and the luminous flux ratio constraint range, since the driving current is continuously adjustable, there are theoretically infinitely many combinations of driving currents, making it impossible to calculate them one by one. Therefore, the driving current of each primary color channel is discretized and sampled with a preset step size. That is, points are taken at fixed intervals within the adjustable current range of each channel, generating a finite number of candidate driving current combinations. For example, if the adjustable current range of a certain channel is 100mA to 500mA, and the step size is set to 10mA, then 40 sampling points are generated for that channel. The sampling points from multiple channels are then combined pairwise to form a set of candidate driving current combinations.

[0055] Based on the lower limit of total luminous flux and the luminous flux ratio constraint range, feasible combinations are selected from the above candidate drive current combinations. For each candidate drive current combination, the actual total luminous flux of the combination and the actual luminous flux ratio between the channels are calculated based on the luminous flux output value of each channel under the corresponding drive current. Combinations with actual total luminous flux lower than the lower limit of total luminous flux and combinations with actual luminous flux ratio that do not meet the luminous flux ratio constraint range are eliminated. The remaining candidate combinations that simultaneously meet the total luminous flux requirement and the ratio requirement are the feasible drive current combinations.

[0056] Finally, based on the single-channel power consumption curve, the total power consumption corresponding to each feasible drive current combination is calculated. This involves adding the power consumption of each channel under the corresponding drive current for that combination to obtain the total power of the combination. The combination with the minimum total power consumption is selected from all feasible combinations as the drive parameters for the lighting device. Through this method, while ensuring that the actual output illumination meets the triple constraints of illuminance, color rendering index, and chromaticity, the lighting power is minimized, thus reducing the energy consumption of the lighting system to the greatest extent while ensuring lighting quality.

[0057] If the functional zone to which the spatial unit belongs is an ecological restoration zone, a different lighting strategy will be implemented compared to the passageway and landscape viewing areas. The lighting objective of the ecological restoration zone is not to serve human visual needs, but rather to serve the normal growth and development of vegetation. When the functional zone is an ecological restoration zone, the lighting devices will be switched to basic supplemental lighting mode. The illuminance value output by the basic supplemental lighting mode is the minimum light requirement for vegetation to maintain basic physiological activities. In the basic supplemental lighting mode, the lighting devices continuously output the minimum light requirement at a constant power to provide the necessary light supply for vegetation to maintain photosynthesis and normal growth, while avoiding excessive supplemental lighting that would cause unnecessary energy waste and potential disturbance to the vegetation.

[0058] It should be noted that the delineation of ecological restoration zones needs to be dynamically adjusted based on the vegetation recovery status. As ecological restoration work progresses, once the vegetation within the ecological restoration area reaches a certain recovery standard, it can be converted into a regular management area, namely a passageway and a landscape viewing area. The lighting strategy for this area will then switch from the basic supplemental lighting mode to the same supplemental lighting mode as the passageway or landscape viewing area.

[0059] The above are embodiments of the methods proposed in this application. Based on the same idea, some embodiments of this application also provide devices and non-volatile computer storage media corresponding to the above methods.

[0060] Figure 2 This is a structural schematic diagram of a garden lighting control device provided in an embodiment of this application. Figure 2 As shown, it includes: At least one processor; and, At least one processor-communication-connected memory; wherein, The memory stores instructions that can be executed by at least one processor, such that the at least one processor is able to perform a garden lighting control method as described in any of the preceding claims.

[0061] This application provides a non-volatile computer storage medium storing computer-executable instructions, which are configured as follows: A garden lighting control method as described in any of the preceding items.

[0062] The various embodiments in this application are described in a progressive manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, the device and medium embodiments are basically similar to the method embodiments, so the description is relatively simple; relevant parts can be referred to the description of the method embodiments.

[0063] The above description is merely an embodiment of this application and is not intended to limit this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principle of this application should be included within the scope of the claims of this application.

Claims

1. A method for controlling garden lighting, characterized in that, The method includes: According to a preset collection cycle, the ambient brightness within the garden area is collected in real time using light sensors deployed within the garden area. Collect vegetation canopy data and three-dimensional spatial data within the garden area to construct a digital twin model that includes dynamic shading relationships; Based on the location of the lighting devices in the garden area, the garden area is divided into several spatial units. Based on the digital twin model, the light projection path of the lighting devices in the spatial units is simulated, so as to determine the shading level of the spatial unit under vegetation shading according to the simulated lighting data corresponding to the spatial unit. Based on the occlusion level, the ambient brightness is corrected so as to determine the required illumination level for each spatial unit using the corrected ambient brightness. Based on the illumination level and the functional zone to which the spatial unit belongs, a lighting strategy corresponding to the spatial unit is generated, and the lighting device is controlled to provide illumination according to the lighting strategy. Based on the simulated lighting data corresponding to the spatial unit, the shading level of the spatial unit under vegetation cover is determined, specifically including: Based on the digital twin model, the first simulated illuminance value of the lighting device projected onto a preset reference point in the spatial unit under unobstructed conditions, and the second simulated illuminance value projected onto the preset reference point under vegetation obstruction are obtained. Calculate the illuminance attenuation rate corresponding to the spatial unit based on the first simulated illuminance value and the second simulated illuminance value; Based on the light projection path between the spatial unit and the lighting device in the digital twin model, the amount of light rays that are intercepted by the vegetation canopy in the light rays emitted from the lighting device and reaching the spatial unit is collected to calculate the light interception rate; Based on the illuminance attenuation rate and the light interception rate, the corresponding occlusion factor is calculated, and the occlusion factor is compared with several preset occlusion level thresholds to determine the occlusion level corresponding to the spatial unit; wherein, the occlusion level includes at least no occlusion level, mild occlusion level, moderate occlusion level and severe occlusion level, and different occlusion levels correspond to different illuminance correction coefficients. After constructing a digital twin model that includes dynamic occlusion relationships, the method further includes: Determine whether the spatial unit meets the preset occlusion state update conditions: If so, the current vegetation canopy data and three-dimensional spatial data corresponding to the spatial unit are collected to iteratively update the vegetation canopy structure and optical properties within the spatial unit in the digital twin model; After the digital twin model is updated, the occlusion level of the spatial unit is recalculated based on the updated digital twin model.

2. The garden lighting control method according to claim 1, characterized in that, Based on the illumination level and the functional zone to which the spatial unit belongs, a lighting strategy corresponding to the spatial unit is generated, specifically including: Determine the functional zones to which the spatial unit belongs; wherein, the functional zones include passage areas, landscape viewing areas, and ecological restoration areas; When the functional zones are the passage area and the landscape viewing area, the lighting parameter constraint information corresponding to the spatial unit is obtained according to the lighting level; wherein, the lighting parameter constraint information includes the lower limit of illuminance, the lower limit of color rendering index, and the target chromaticity constraint. With minimizing lighting power as the optimization objective and the lighting parameters as constraints, the driving parameters corresponding to the lighting device are solved, and the lighting command corresponding to the spatial unit is generated through the driving parameters. When the functional zone is the ecological restoration zone, the lighting device is switched to the basic supplemental lighting mode to provide light restoration for the vegetation by outputting the minimum light required by the vegetation through the basic supplemental lighting mode.

3. The garden lighting control method according to claim 1, characterized in that, Before real-time acquisition of ambient brightness within the garden area, the method further includes: Obtain the current date and time of the geographical location of the garden area, and determine the critical lighting time corresponding to the geographical location based on the current date; Calculate the time difference between the current moment and the critical moment of illumination, and determine the current illumination stage of the garden area based on the time difference; wherein the illumination stage includes a period of dramatic brightness change and a period of stable brightness. If it is the period of dramatic brightness change, the acquisition period is shortened to a first preset frequency; otherwise, the acquisition period is extended to a second preset frequency. The period of dramatic brightness change includes at least a time interval from a first preset duration before the critical lighting moment to a second preset duration after the critical lighting moment.

4. The garden lighting control method according to claim 1, characterized in that, Determining whether the spatial unit meets the preset occlusion state update conditions specifically includes: Collect the current vegetation canopy data corresponding to each spatial unit within the garden area according to the preset collection cycle; Calculate the difference between the current vegetation canopy data and the historical vegetation canopy data stored at the time of the last update; When the difference exceeds a preset threshold, or when a maintenance signal indicating that a pruning operation has been performed is received, or when the growth cycle stage of the vegetation undergoes a seasonal change, the spatial unit is determined to meet the preset shading status update conditions.

5. A garden lighting control method according to claim 2, characterized in that, With minimizing lighting power as the optimization objective and the lighting parameters as constraints, the driving parameters corresponding to the lighting device are solved, specifically including: Obtain the adjustable luminous flux output range of each primary color channel in the lighting device within a preset driving current range and its corresponding single-channel power consumption curve; Based on the lower limit of illuminance in the illumination parameters, determine the lower limit of total luminous flux required by the lighting device, and determine the luminous flux ratio constraint range between each primary color channel required to satisfy the lower limit of color rendering index and the target chromaticity constraint; Based on the adjustable luminous flux output range, the driving current of each primary color channel is discretized and sampled with a preset step size to generate several candidate driving current combinations. Based on the lower limit of total luminous flux and the luminous flux ratio constraint range, from the candidate drive current combinations, combinations whose actual total luminous flux is lower than the lower limit of total luminous flux and whose actual luminous flux ratio does not meet the luminous flux ratio constraint range are eliminated, to obtain feasible drive current combinations. Based on the single-channel power consumption curve, the total power consumption corresponding to each feasible drive current combination is calculated, and the feasible drive parameter combination with the minimum total power consumption is selected as the drive parameter of the lighting device.

6. A garden lighting control method according to claim 1, characterized in that, Based on the location of the lighting devices within the garden area, the garden area is divided into several spatial units, specifically including: Based on the digital twin model, the actual illuminance values ​​projected by each lighting device at various points on the ground are simulated to generate a ground illuminance distribution map corresponding to each lighting device. According to the preset effective illuminance threshold, the continuous areas in the ground illuminance distribution map corresponding to each lighting device where the illuminance value is greater than or equal to the effective illuminance threshold are marked as the effective coverage area of ​​the lighting device; The garden area is divided into several spatial units based on the effective coverage area of ​​each lighting device.

7. A garden lighting control device, characterized in that, The device includes: At least one processor; And, a memory communicatively connected to the at least one processor; The memory stores instructions that can be executed by the at least one processor, which are executed by the at least one processor to enable the at least one processor to perform a garden lighting control method as described in any one of claims 1-6.

8. A non-volatile computer storage medium storing computer-executable instructions, characterized in that, The computer-executable instructions are set as follows: Perform a garden lighting control method as described in any one of claims 1-6.

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