Landscape Design Recognition Method and System Based on Neural Network Model

Abstract

The present application provides a landscape design recognition method and system based on a neural network model. Among them, the method includes: by acquiring landscape design data, lighting device data, and real-time load data of the energy supply network within a target area, establishing an optical model to analyze the spatial attenuation effect of vegetation occlusion and the reflectivity of artificial structures on the lighting beam, and calculating the energy consumption mapping relationship between the lamp power distribution and the energy network load based on the energy transfer model; generating a dynamic dimming strategy by combining the spatial attenuation effect and the energy consumption mapping relationship, coupling it with the real-time collected tourist distribution heat map and environmental light intensity data, inputting it into the neural network model for iterative optimization, dynamically adjusting the lamp power distribution, beam angle coverage range, and light source color temperature parameters, realizing the dynamic optimization of the landscape lighting effect and the minimization of energy consumption, while improving the visual experience and comfort of tourists. The present application provides an intelligent dynamic optimization technical solution for the landscape lighting effect.

Description

Technical Field

[0001] This application relates to the technical field of data processing, and in particular, to a landscape design recognition method and system based on a neural network model. Background Art

[0002] In modern urban landscape lighting projects, there are technical challenges in how to efficiently manage energy consumption and optimize lighting effects. With the expansion of the urban scale and the increasing demand for environmentally friendly solutions, a technical solution that can intelligently adjust the parameters of lighting devices according to real-time environmental data and dynamically changing pedestrian flow density is needed. Such a solution not only needs to support the basic control functions of lighting devices, but also must be able to process complex input data sets to achieve precise lighting control, ensure the best lighting experience in different usage scenarios, and minimize unnecessary energy consumption as much as possible.

[0003] The existing solution provides an intelligent lighting system based on a sensor network. This system deploys a series of environmental sensors to monitor the surrounding environmental light level and pedestrian flow in real time. According to this data, the system can automatically adjust the working state of the lamps, such as reducing the brightness or changing the light source color temperature during low pedestrian flow periods to save electricity.

[0004] However, the intelligent lighting system based on a sensor network relies on fixed algorithms or preset rules for decision-making, lacks sufficient adaptability to cope with complex and changeable actual application environments, and affects their overall performance and user satisfaction in different application scenarios. Summary of the Invention

[0005] The embodiments of this application provide a landscape design recognition method and system based on a neural network model to solve the problem in the prior art that in a complex and changeable actual application environment, the accuracy of landscape lighting effects and energy consumption management is poor, resulting in high energy consumption and poor landscape lighting effects.

[0006] In a first aspect, the embodiments of this application provide a landscape design recognition method based on a neural network model, including:

[0007] Obtain landscape design data, lighting device data, and real-time load data of the energy supply network in the target area. The landscape design data includes vegetation occlusion contours and surface reflectivity of artificial structures. The lighting device data includes lamp power distribution, beam angle coverage range, and light source color temperature parameters;

[0008] Establish an optical model based on the landscape design data and the lighting device data, and analyze the spatial attenuation effect of the vegetation occlusion contours and the surface reflectivity of the artificial structures on the light beams emitted by the lighting devices through the optical model;

[0009] An energy transfer model is established based on the lighting device data and the real-time load data of the energy supply network, and the energy consumption mapping relationship between the lamp power distribution and the real-time load data of the energy supply network is calculated through the energy transfer model;

[0010] A dynamic dimming strategy is generated by combining the spatial attenuation effect and the energy consumption mapping relationship, and the dynamic dimming strategy is coupled with the real-time collected tourist distribution heat map and environmental light intensity data to obtain a coupling result. The coupling result is input into a neural network model to iteratively optimize the lamp power distribution, the beam angle coverage range, and the light source color temperature parameters through the neural network model, so as to achieve dynamic optimization of the landscape lighting effect and minimization of energy consumption, while improving the visual experience and comfort of tourists.

[0011] Optionally, the generating a dynamic dimming strategy by combining the spatial attenuation effect and the energy consumption mapping relationship, coupling the dynamic dimming strategy with the real-time collected tourist distribution heat map and environmental light intensity data to obtain a coupling result, and inputting the coupling result into a neural network model to iteratively optimize the lamp power distribution, the beam angle coverage range, and the light source color temperature parameters through the neural network model includes:

[0012] Construct an objective function based on the spatial attenuation effect and the energy consumption mapping relationship, and generate a dynamic dimming strategy through an optimization algorithm based on the objective function;

[0013] Couple the dynamic dimming strategy with the real-time collected tourist distribution heat map and environmental light intensity data to obtain a coupling result, and the coupling result is a multi-dimensional feature vector;

[0014] Input the multi-dimensional feature vector into a neural network model, calculate the lighting effect prediction value through the forward propagation algorithm of the neural network model, and iteratively optimize the lamp power distribution, the beam angle coverage range, and the light source color temperature parameters based on a preset loss function and the backpropagation algorithm of the neural network model;

[0015] Output the optimized lamp power distribution, beam angle coverage range, and light source color temperature parameters to the lighting control system to adjust the lighting device data in the target area in real time according to the optimized lamp power distribution, beam angle coverage range, and light source color temperature parameters.

[0016] Optionally, the inputting the multi-dimensional feature vector into a neural network model, calculating the lighting effect prediction value through the forward propagation algorithm of the neural network model, and iteratively optimizing the lamp power distribution, the beam angle coverage range, and the light source color temperature parameters based on a preset loss function and the backpropagation algorithm of the neural network model includes:

[0017] Normalize the multi-dimensional feature vector to obtain a preprocessed multi-dimensional feature vector. The multi-dimensional features include the coordinates and dynamic change trends of the high-density areas in the tourist distribution heat map, the time series features of the environmental light intensity data, and the spatial distribution features of the vegetation occlusion contour and the surface reflectivity of artificial structures.

[0018] Input the preprocessed multi-dimensional feature vector into a neural network model. Combine the forward propagation algorithm in the neural network model to perform non-linear transformation through the hidden layer of the neural network model, and generate a lighting effect prediction value at the output layer.

[0019] Based on the difference between the lighting effect prediction value and the preset target value, calculate the function value of the preset loss function. Based on the function value, use the backpropagation algorithm to iteratively optimize and adjust the lamp power distribution, beam angle coverage range, and light source color temperature parameters until the function value of the loss function converges to a preset threshold.

[0020] Optionally, the step of inputting the preprocessed multi-dimensional feature vector into a neural network model, combining the forward propagation algorithm in the neural network model, performing non-linear transformation through the hidden layer of the neural network model, and generating a lighting effect prediction value at the output layer includes:

[0021] Design the structure of the neural network model. The structure of the neural network model includes an input layer, a hidden layer, and an output layer. The number of nodes in the input layer is the same as the dimension of the multi-dimensional feature vector. The hidden layer adopts a multi-layer structure. The number of nodes in the output layer is the same as the dimension of the lighting effect prediction value.

[0022] Input the preprocessed multi-dimensional feature vector into the input layer. Combine the forward propagation algorithm in the neural network model to perform weighted summation on the multi-dimensional features corresponding to the preprocessed multi-dimensional feature vector in the hidden layer, and perform non-linear transformation on the weighted summation result through an activation function to calculate the multi-dimensional feature mapping result layer by layer.

[0023] In the output layer, perform weighted summation on the multi-dimensional feature mapping result output by the last hidden layer, and generate a lighting effect prediction value through the activation function of the output layer. The lighting effect prediction value includes the light intensity at each position in the target area, the total energy consumption, and the visual comfort score.

[0024] Optionally, the step of establishing an optical model based on the landscape design data and the lighting device data, and analyzing the spatial attenuation effect of the vegetation occlusion contour and the surface reflectivity of the artificial structure on the light beam emitted by the lighting device includes:

[0025] Digitize the vegetation occlusion contour to obtain the spatial distribution characteristics of the vegetation and the occlusion intensity parameters, and divide the surface reflectivity of the artificial structure into regions to generate a reflectivity distribution map;

[0026] Based on the spatial distribution characteristics and occlusion intensity parameters of the vegetation, and the reflectivity distribution map, combine the lamp power distribution, the beam angle coverage range, and the light source color temperature parameters to generate an optical model;

[0027] Based on the optical model, simulate the propagation process of the beam emitted by the lighting device in the target area, calculate the influence of the beam by the vegetation occlusion contour and the surface reflectivity of the artificial structure, and generate the attenuation parameters of the beam;

[0028] Based on the attenuation parameters of the beam, generate a spatial attenuation effect distribution map, which is used to characterize the spatial attenuation effect of the vegetation occlusion contour and the surface reflectivity of the artificial structure on the beam emitted by the lighting device.

[0029] Optionally, the step of based on the optical model, simulating the propagation process of the beam emitted by the lighting device in the target area, calculating the influence of the beam by the vegetation occlusion contour and the surface reflectivity of the artificial structure, and generating the attenuation parameters of the beam includes:

[0030] According to the optical model, the lamp power distribution, the beam angle coverage range, and the light source color temperature parameters, construct a beam propagation path model, and divide the target area into multiple grid cells based on the beam propagation path model;

[0031] In the beam propagation path model, calculate the attenuation effect of the vegetation occlusion contour on the beam for each grid cell to generate a vegetation occlusion attenuation coefficient, and update the light intensity value of each grid cell according to the vegetation occlusion attenuation coefficient;

[0032] Identify the grid cells that intersect the surface of the artificial structure in the beam propagation path model, calculate the reflection path and reflection intensity of the beam on the surface of the artificial structure, and update the light intensity value of the grid cells covered by the reflection path according to the reflection intensity;

[0033] According to the light intensity value of each grid cell, generate the beam attenuation parameter of each grid cell.

[0034] Optionally, the step of in the beam propagation path model, calculating the attenuation effect of the vegetation occlusion contour on the beam for each grid cell to generate a vegetation occlusion attenuation coefficient, and updating the light intensity value of each grid cell according to the vegetation occlusion attenuation coefficient includes:

[0035] Extract the height, density, and distribution range parameters of the vegetation from the vegetation occlusion contour, and map the height, density, and distribution range parameters of the vegetation to the grid cells of the target area;

[0036] In the beam propagation path model, for each grid cell, calculate the attenuation coefficient of the beam after penetrating the vegetation according to the vegetation occlusion intensity value and the mapping data corresponding to the height, density, and distribution range parameters of the vegetation, and generate the vegetation occlusion attenuation coefficient of each grid cell;

[0037] Combined with the scattering effect of the vegetation on the beam, simulate the scattering path of the beam after penetrating the vegetation, calculate the propagation direction and intensity distribution of the scattered beam, and update the illumination intensity value of the grid cells covered by the scattering path according to the propagation direction and the intensity distribution;

[0038] Apply the vegetation occlusion attenuation coefficient of each grid cell and the illumination intensity value of the grid cells covered by the scattering path to the beam propagation path model to update the illumination intensity value of each grid cell through the beam propagation path model.

[0039] In a second aspect, an embodiment of the present application provides a landscape design recognition system based on a neural network model, including:

[0040] An acquisition module, configured to acquire landscape design data, lighting device data, and real-time load data of the energy supply network in the target area, where the landscape design data includes a vegetation occlusion contour and the surface reflectivity of artificial structures, and the lighting device data includes the lamp power distribution, the beam angle coverage range, and the light source color temperature parameter;

[0041] A model establishment and analysis module, configured to establish an optical model based on the landscape design data and the lighting device data, and analyze the spatial attenuation effect of the vegetation occlusion contour and the surface reflectivity of the artificial structures on the beam emitted by the lighting device through the optical model;

[0042] A model establishment and calculation module, configured to establish an energy transfer model based on the lighting device data and the real-time load data of the energy supply network, and calculate the energy consumption mapping relationship between the lamp power distribution and the real-time load data of the energy supply network through the energy transfer model;

[0043] A generation coupling module is used to generate a dynamic dimming strategy by combining the spatial attenuation effect and the energy consumption mapping relationship, couple the dynamic dimming strategy with the real-time collected tourist distribution heat map and environmental light intensity data to obtain a coupling result, and input the coupling result into a neural network model to iteratively optimize the lamp power distribution, the beam angle coverage range, and the light source color temperature parameters through the neural network model, so as to realize the dynamic optimization of the landscape lighting effect and the minimization of energy consumption, while improving the visual experience and comfort of tourists.

[0044] In a third aspect, an embodiment of the present application provides a computing device, including a processing component and a storage component; the storage component stores one or more computer instructions; the one or more computer instructions are used to be called and executed by the processing component to implement a landscape design recognition method based on a neural network model as described in any item of the first aspect.

[0045] In a fourth aspect, an embodiment of the present application provides a computer storage medium storing a computer program, and when the computer program is executed by a computer, it implements a landscape design recognition method based on a neural network model as described in any item of the first aspect.

[0046] In an embodiment of the present application, a landscape design recognition method based on a neural network model is provided. The method includes: obtaining landscape design data, lighting device data, and real-time load data of the energy supply network in a target area, where the landscape design data includes vegetation occlusion contours and surface reflectivity of artificial structures, and the lighting device data includes lamp power distribution, beam angle coverage range, and light source color temperature parameters; establishing an optical model based on the landscape design data and the lighting device data, and analyzing the spatial attenuation effect of the vegetation occlusion contours and the surface reflectivity of artificial structures on the light beams emitted by the lighting devices through the optical model; establishing an energy transfer model based on the lighting device data and the real-time load data of the energy supply network, and calculating the energy consumption mapping relationship between the lamp power distribution and the real-time load data of the energy supply network through the energy transfer model; generating a dynamic dimming strategy by combining the spatial attenuation effect and the energy consumption mapping relationship, coupling the dynamic dimming strategy with the real-time collected tourist distribution heat map and environmental light intensity data to obtain a coupling result, and inputting the coupling result into a neural network model to iteratively optimize the lamp power distribution, the beam angle coverage range, and the light source color temperature parameters through the neural network model, so as to realize the dynamic optimization of the landscape lighting effect and the minimization of energy consumption, while improving the visual experience and comfort of tourists.

[0047] In the embodiments of the present application, by obtaining real-time load data of vegetation occlusion contours, surface reflectivity of artificial structures, lamp power distribution, beam angle coverage, light source color temperature parameters, and energy supply networks, a comprehensive data basis is provided for subsequent modeling and optimization, ensuring that the regulation of the lighting system can accurately adapt to environmental characteristics and energy requirements. By analyzing the spatial attenuation effect of vegetation occlusion contours and surface reflectivity of artificial structures on lighting beams through an optical model, the influence of environmental factors on lighting effects can be quantified, providing a basis for the generation of subsequent dimming strategies. By calculating the energy consumption mapping relationship between lamp power distribution and real-time load data of the energy supply network through an energy transfer model, accurate prediction and optimization of lighting system energy consumption can be achieved, providing support for efficient energy utilization. By comprehensively considering the spatial attenuation effect and energy consumption mapping relationship to generate a dynamic dimming strategy, a preliminary balance between lighting effects and energy consumption can be achieved, providing a basis for subsequent refined optimization. By coupling the tourist distribution heat map and environmental light intensity data, the lighting strategy can be dynamically adjusted to better meet the actual scenario requirements and improve the visual experience and comfort of tourists. Through the iterative optimization of the neural network model, refined adjustment of lighting parameters can be achieved, dynamically optimizing lighting effects and minimizing energy consumption while improving the intelligent level of the system. Further, through the construction of an objective function, generation of multi-dimensional feature vectors, and iterative optimization of the neural network model, dynamic and refined adjustment of lighting parameters is achieved, achieving an optimal balance between lighting effects and energy consumption while improving the visual experience and comfort of tourists, demonstrating the efficiency and adaptability of intelligent lighting control.

[0048] These aspects or other aspects of the present application will be more clearly understood in the following description of the embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] In order to more clearly illustrate the technical solutions in the embodiments of the present application or in the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are some embodiments of the present application. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0050] Figure 1 It is a flowchart of a landscape design recognition method based on a neural network model provided by an embodiment of the present application;

[0051] Figure 2 It is a schematic structural diagram of a landscape design recognition system based on a neural network model provided by an embodiment of the present application;

[0052] Figure 3 It is a schematic structural diagram of a computing device provided by an embodiment of the present application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0053] To enable those skilled in the art to better understand the solution of this application, the technical solutions in the embodiments of this application will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of this application.

[0054] In some processes described in the specification, claims and above-mentioned drawings of this application, a plurality of operations appear in a specific order. However, it should be clearly understood that these operations may not be executed in the order in which they appear in this text or may be executed in parallel. The serial numbers of the operations, such as 11, 12, etc., are only used to distinguish each different operation, and the serial numbers themselves do not represent any execution order. In addition, these processes may include more or fewer operations, and these operations may be executed in sequence or in parallel. It should be noted that the descriptions such as "first", "second", etc. in this text are used to distinguish different messages, devices, modules, etc., do not represent a sequence, nor do they limit that "first" and "second" are of different types.

[0055] The technical solutions in the embodiments of this application will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of this application. Obviously, the described embodiments are only a part of the embodiments of this application, rather than all the embodiments. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative efforts belong to the scope of protection of this application.

[0056] In the prior art, there are problems in the landscape lighting effect and the accuracy of energy consumption management in complex and changeable actual application environments, resulting in large energy consumption and poor landscape lighting effects. Through multi-source data fusion and intelligent modeling, the embodiments of this application realize the dynamic optimization of the landscape lighting system and the efficient utilization of energy. Specifically, it includes: by obtaining landscape design data, lighting equipment data and real-time load data of the energy supply network, establishing an optical model to analyze the spatial attenuation effect of the light beam, and calculating the energy consumption mapping relationship through an energy transfer model. Combining dynamic dimming strategies with real-time environmental data, using a neural network model to iteratively optimize the lamp parameters, realizing the dynamic optimization of the landscape lighting effect and the minimization of energy consumption, while improving the visual experience and comfort of tourists.

[0057] Figure 1 The flowchart of a landscape design recognition method based on a neural network model provided for the embodiments of this application is as Figure 1 shown, and this method includes:

[0058] S11. Obtain landscape design data, lighting equipment data and real-time load data of the energy supply network within the target area. The landscape design data includes the vegetation occlusion contour and the surface reflectivity of artificial structures. The lighting equipment data includes the lamp power distribution, the beam angle coverage range and the light source color temperature parameters.

[0059] Among them, the target area can be urban parks, urban squares, greenways, commercial blocks, tourist attractions, industrial parks, etc. The real-time load data of the energy supply network includes electrical parameters such as voltage, current, and power factor, as well as the real-time load situation of the overall network.

[0060] The vegetation occlusion contour refers to the distribution of vegetation (such as trees, shrubs, etc.) in space and its light-blocking effect. Specifically, the vegetation occlusion contour includes parameters such as the height, density, and distribution range of the vegetation, which together determine the attenuation degree of the light beam after penetrating the vegetation. By digitally processing the vegetation occlusion contour, the spatial distribution characteristics and occlusion intensity parameters of the vegetation can be obtained, providing data support for quantifying the impact of vegetation on the intensity attenuation of the light beam after penetration. For example, in a night lighting scenario, vegetation may cause insufficient lighting in some areas. Understanding its occlusion contour can help adjust the position or power of the lighting fixtures to compensate for this impact. Also, the surface reflectivity of artificial structures involves the light reflection ability of the surface materials of non-natural constructed objects (such as buildings, sculptures, roads, etc.). Different materials have different reflection characteristics. Therefore, when the same intensity of light shines on different surfaces, the intensity of the reflected light will also be different. The lighting fixture power distribution refers to the power configuration plan of each lighting device in different spatial positions within the target area, specifically including the power value of a single lighting fixture, the power adjustment range, and its collaborative distribution relationship in the overall lighting network.

[0061] S12. Establish an optical model based on the landscape design data and lighting device data, and analyze the spatial attenuation effect of the vegetation occlusion contour and the surface reflectivity of artificial structures on the light beam emitted by the lighting device through the optical model.

[0062] Among them, the optical model is used to simulate the process of the light beam propagating in the target area starting from the lighting fixture, taking into account the effects of factors such as vegetation occlusion and artificial structure reflection. The spatial attenuation effect refers to the phenomenon that the intensity of the light beam emitted by the lighting device gradually weakens with the increase of the spatial distance during the propagation process due to the influence of environmental factors. This spatial attenuation effect can be displayed through a spatial attenuation effect distribution map.

[0063] S13. Establish an energy transfer model based on the lighting device data and the real-time load data of the energy supply network, and calculate the energy consumption mapping relationship between the lighting fixture power distribution and the real-time load data of the energy supply network through the energy transfer model.

[0064] Among them, the energy transfer model may include an electric power calculation formula, an active power calculation formula, and an energy consumption calculation formula. The energy transfer model can determine the power requirements of each lamp in the target area according to lighting design requirements and environmental conditions (such as vegetation occlusion profile, reflectivity of artificial structures, etc.), such as the power value of a single lamp and its adjustment range. Using the above-mentioned electric power calculation formula, combined with the working voltage and working current of each lamp, the actual power consumption of each lamp is calculated. If the power factor is considered, the active power formula is used for correction. According to the wire resistance and other electrical characteristics on the power transmission path, the energy loss from the power supply to the lamp is calculated. The power consumption of all lamps is summed up, and the loss during power transmission is added to obtain the total energy consumption of the entire lighting system. The total energy consumption calculated above is combined with the real-time load data of the energy supply network to establish an energy consumption mapping relationship between the lamp power distribution and the real-time load data of the energy supply network.

[0065] Exemplarily, the energy transfer model can be expressed by the following formula:

[0066] ;

[0067] Among them, is the total energy consumption, N is the number of lamps in the target area; is the actual power of the i-th lamp, which is determined by the working current of the lamp, is the working voltage of the lamp, is the internal resistance of the lamp, t is the working time length, M is the number of line segments on the power transmission path, is the resistance of the j-th line segment on the power transmission path, I j is the current of the j-th line segment on the power transmission path.

[0068] S14. Generate a dynamic dimming strategy by combining the spatial attenuation effect and the energy consumption mapping relationship, couple the dynamic dimming strategy with the real-time collected tourist distribution heat map and environmental light intensity data to obtain a coupling result, and input the coupling result into the neural network model to iteratively optimize the lamp power distribution, beam angle coverage range, and light source color temperature parameters through the neural network model, so as to realize the dynamic optimization of the landscape lighting effect and the minimization of energy consumption, while enhancing the visual experience and comfort of tourists.

[0069] Among them, the tourist distribution heat map is used to reflect the density and distribution of people in a specific area. Such charts usually use different colors to represent the pedestrian flow density in different areas. The darker the color (such as red or warm tones), the greater the pedestrian flow in that area, while the lighter the color (such as blue or cold tones) indicates less pedestrian flow. Coupling can be achieved through data fusion technology. The environmental light intensity data is used to reflect the brightness level of natural or artificial light sources at a specific location or area at a specific time point. The dynamic dimming strategy refers to an intelligent control scheme that dynamically adjusts the lamp power distribution, beam angle coverage range, and light source color temperature parameters according to real-time environmental data (such as tourist distribution, environmental light intensity, vegetation occlusion contour, artificial structure reflectivity, etc.) and preset optimization goals (such as minimizing energy consumption and enhancing visual experience).

[0070] The following is a specific example: In the landscape lighting system of an urban park, first, the vegetation occlusion contour and the surface reflectivity of artificial structures are obtained through LiDAR (Light Detection and Ranging) scanning and spectral analysis. At the same time, lighting equipment data and energy load data are obtained from lamp manufacturers and smart meters. Based on these data, an optical model and an energy transfer model are established to analyze the spatial attenuation effect of the beam and the energy consumption mapping relationship respectively. Subsequently, combined with the real-time collected tourist distribution heat map (through infrared sensors and cameras) and environmental light intensity data, a dynamic dimming strategy is generated and input into the neural network model for optimization. Finally, the system dynamically adjusts the lamp power, beam angle, and color temperature according to the optimization results, achieving energy-saving lighting at night while providing a comfortable visual experience for tourists.

[0071] By executing S11 - S14, the embodiments of the present application provide a comprehensive data basis for subsequent modeling and optimization by obtaining real - time load data of vegetation occlusion profiles, artificial structure surface reflectivity, lamp power distribution, beam angle coverage, light source color temperature parameters, and energy supply networks, ensuring that the regulation of the lighting system can accurately adapt to environmental characteristics and energy demands. By analyzing the spatial attenuation effect of vegetation occlusion profiles and artificial structure surface reflectivity on lighting beams through an optical model, the impact of environmental factors on lighting effects can be quantified, providing a basis for generating subsequent dimming strategies. By calculating the energy consumption mapping relationship between lamp power distribution and real - time load data of the energy supply network through an energy transfer model, accurate prediction and optimization of lighting system energy consumption can be achieved, providing support for efficient energy utilization. By comprehensively considering the spatial attenuation effect and energy consumption mapping relationship to generate a dynamic dimming strategy, a preliminary balance between lighting effects and energy consumption can be achieved, providing a basis for subsequent refined optimization. By coupling the tourist distribution heat map and environmental light intensity data, the lighting strategy can be dynamically adjusted to better meet the actual scenario requirements, enhancing the visual experience and comfort of tourists. Through iterative optimization of the neural network model, refined adjustment of lighting parameters can be achieved, dynamically optimizing lighting effects and minimizing energy consumption while enhancing the intelligence level of the system.

[0072] In a possible embodiment, S14, generating a dynamic dimming strategy by combining the spatial attenuation effect and the energy consumption mapping relationship, coupling the dynamic dimming strategy with the real - time collected tourist distribution heat map and environmental light intensity data to obtain a coupling result, and inputting the coupling result into a neural network model to iteratively optimize the lamp power distribution, the beam angle coverage, and the light source color temperature parameters through the neural network model, so as to achieve dynamic optimization of the landscape lighting effect and minimization of energy consumption, while enhancing the visual experience and comfort of tourists, includes:

[0073] Step 141, constructing an objective function based on the spatial attenuation effect and the energy consumption mapping relationship, and generating a dynamic dimming strategy through an optimization algorithm based on the objective function.

[0074] Among them, the objective function usually contains multiple variables and constraints, and is used to minimize or maximize certain key metrics (such as energy consumption, visual comfort score, etc.). For example, in the landscape lighting scenario, the objective function may be designed to minimize the total energy consumption while maximizing the visual experience of tourists. Or, in order to minimize energy consumption as much as possible while maintaining a certain lighting level, the objective function may comprehensively consider factors such as lamp power distribution, beam angle coverage, light source color temperature parameters, and ambient light intensity, and calculate the overall score through a weighted formula. The optimization algorithm can be a genetic algorithm, ant colony algorithm, gradient descent method, simulated annealing algorithm, etc. The dynamic dimming strategy customizes exclusive dimming strategies according to the characteristics of different areas (such as the trail area, sculpture area, etc.) to ensure that each area can obtain an appropriate lighting effect.

[0075] Exemplarily, the optimization algorithm can be multi-objective particle swarm optimization, and the objective function is: Etotal−Scomfort; where Etotal is the energy consumption, Scomfort is the visual comfort score, the energy consumption refers to the total energy consumption, which can be calculated through the energy transfer model, and the visual comfort score is ∑||I pred (x,y)−I target (x,y)|| 2 , I pred (x,y) is the predicted light intensity, and I target (x,y) is the target light intensity.

[0076] Step 142: Couple the dynamic dimming strategy with the real-time collected tourist distribution heat map and ambient light intensity data to obtain a coupling result, and the coupling result is a multi-dimensional feature vector.

[0077] Among them, the multi-dimensional feature vector refers to integrating various different types of data (such as dynamic dimming strategy, tourist distribution heat map, ambient light intensity data, vegetation occlusion contour, artificial structure surface reflectivity, etc.) into a high-dimensional mathematical representation form through data fusion technology.

[0078] Step 143: Input the multi-dimensional feature vector into the neural network model, calculate the lighting effect prediction value through the forward propagation algorithm of the neural network model, and iteratively optimize the lamp power distribution, beam angle coverage, and light source color temperature parameters based on the preset loss function and the backpropagation algorithm of the neural network model.

[0079] Among them, the preset loss function is a mathematical expression used to quantify the difference between the model prediction value and the real target value during the neural network training process.

[0080] Step 144: Output the optimized lamp power distribution, beam angle coverage range, and light source color temperature parameters to the lighting control system to adjust the lighting device data in the target area in real time according to the optimized lamp power distribution, beam angle coverage range, and light source color temperature parameters.

[0081] Among them, the beam angle coverage range is the horizontal / vertical divergence angle range formed by the light emitted by the lighting device in space, usually expressed in the form of full angle or half angle.

[0082] The following is a specific example:

[0083] In the landscape lighting system of a city square, first, a target function is constructed based on the space attenuation effect and energy consumption mapping relationship to generate a dynamic dimming strategy (such as 60W power in the sidewalk area and 120W power in the sculpture area). Subsequently, the dimming strategy is coupled with the real-time collected heat map of tourist distribution (crowded in the sidewalk area) and environmental light intensity data (nighttime light intensity is 0 lux) to generate a multi-dimensional feature vector. Then, the multi-dimensional feature vector is input into the neural network model, and the lamp parameters are iteratively optimized through the forward propagation and backpropagation algorithms of the neural network model (such as the power in the sidewalk area is increased to 70W, and the power in the sculpture area is reduced to 100W). Finally, the optimized parameters are output to the lighting control system to adjust the lamp power, beam angle, and light source color temperature parameters in real time, achieving a balance between energy-saving lighting and the visual experience of tourists.

[0084] By executing Steps 141 to 144, the embodiment of the present application realizes the intelligent dynamic dimming of the landscape lighting system through target function construction, data coupling, neural network optimization, and real-time control. Combining the space attenuation effect, energy consumption mapping relationship, and real-time environmental data, it improves the dynamic adaptability of the lighting effect and energy utilization efficiency, while optimizing the visual experience and comfort of tourists, providing an efficient and intelligent solution for landscape lighting management in smart cities.

[0085] In a possible embodiment, Step 143: Input the multi-dimensional feature vector into the neural network model, calculate the lighting effect prediction value through the forward propagation algorithm of the neural network model, and iteratively optimize the lamp power distribution, beam angle coverage range, and light source color temperature parameters based on the preset loss function and the backpropagation algorithm of the neural network model, including:

[0086] Step a1: Standardize the multi-dimensional feature vector to obtain the preprocessed multi-dimensional feature vector. The multi-dimensional features include the high-density area coordinates and dynamic change trends of the tourist distribution heat map, the time series features of the environmental light intensity data, and the spatial distribution features of the vegetation occlusion contour and the surface reflectivity of artificial structures.

[0087] Among them, the time - series features can refer to the statistical laws, periodic patterns, and dynamic trends presented by the environmental light intensity data over time.

[0088] Step a2: Input the pre - processed multi - dimensional feature vector into the neural network model. Combining with the forward propagation algorithm in the neural network model, perform non - linear transformation through the hidden layer of the neural network model, and generate a predicted value of the lighting effect at the output layer.

[0089] Among them, non - linear transformation refers to the process of converting the linear combination of input data into a non - linear output through a mathematical function. In the embodiments of the present application, this process occurs in the hidden layer of the neural network.

[0090] Exemplarily, the formula adopted by the forward propagation algorithm in the neural network model is as follows:

[0091] Z (l) =W (l) a (l−1) +b (l) ,a (l) =σ(z (l) );

[0092] Among them, Z (l) is the predicted value of the lighting effect output by the neural network model, a (l−1) is the multi - dimensional feature vector extracted from the (l - 1) - th layer, a (0) is the pre - processed multi - dimensional feature vector, l is the l - th layer in the neural network model, W (l) is the weight of the l - th layer, b (l) is the bias of the l - th layer. σ is an activation function (such as ReLU).

[0093] Step a3: Based on the difference between the predicted value of the lighting effect and the preset target value, calculate the function value of the preset loss function. Based on the function value, through the back - propagation algorithm, iteratively optimize and adjust the lamp power distribution, beam angle coverage range, and light source color temperature parameters until the function value of the loss function converges to a preset threshold.

[0094] Among them, the function value converging to a preset threshold can mean that during the neural network training process, the model parameters are continuously adjusted through the back - propagation algorithm, so that the value of the loss function gradually approaches the pre - set target value or target range, and finally stabilizes within this target range, indicating that the model training is completed.

[0095] Exemplarily, the preset loss function can adopt the following formula:

[0096] ;

[0097] Among them, is the function value of the preset loss function, is the energy consumption predicted by the neural network, is the light intensity predicted by the neural network, is the comfort level predicted by the neural network, is the actual energy consumption, is the actual light intensity, is the target comfort score, and λ1, λ2, λ3 are the weights corresponding to each loss term.

[0098] The following is a specific example:

[0099] In the landscape lighting system of a city park, the processing and optimization process of the multi-dimensional feature vector. Heat map of tourist distribution: The pedestrian path area has a dense crowd (density 0.9), and the sculpture area has a sparse crowd (density 0.3). Ambient light intensity: The light intensity at night is 0 lux. Vegetation occlusion contour: The occlusion degree in the pedestrian path area is 50%. Reflectivity data: The reflectivity of the pedestrian path area is 0.6, and the reflectivity of the sculpture area is 0.8. Standardization process: Normalize all data to the interval [0,1] to generate a feature vector. Input the feature vector into the neural network model, and generate the predicted value of the lighting effect through forward propagation. Calculate the value of the loss function, and optimize the lamp parameters through the backpropagation algorithm. Output the optimized parameters to the lighting control system to adjust the lamp power and beam angle in real time.

[0100] By performing steps a1~a3, the embodiment of the present application realizes the intelligent dynamic dimming of the landscape lighting system through the standardization process of the multi-dimensional feature vector, the forward propagation and backpropagation optimization of the neural network model. Combining real-time environmental data with preset optimization goals, it improves the dynamic adaptability of the lighting effect and energy utilization efficiency, and at the same time optimizes the visual experience and comfort of tourists, providing an efficient and intelligent solution for the landscape lighting management in the smart city.

[0101] In a possible embodiment, step a2 inputs the preprocessed multi-dimensional feature vector into the neural network model. Combining with the forward propagation algorithm in the neural network model, it performs non-linear transformation through the hidden layer of the neural network model and generates the predicted value of the lighting effect at the output layer, including:

[0102] Step b1: Design the structure of the neural network model. The structure of the neural network model includes an input layer, a hidden layer, and an output layer. The number of nodes in the input layer is the same as the dimension of the multi-dimensional feature vector. The hidden layer adopts a multi-layer structure, and the number of nodes in the output layer is the same as the dimension of the predicted value of the lighting effect.

[0103] Among them, the hidden layer is a parameterizable layer located between the input layer and the output layer in the neural network. Its core function is to extract high-order abstract representations from the input features through non-linear transformation to capture the complex mapping relationship between the input data and the output target.

[0104] Step b2: Input the preprocessed multi-dimensional feature vector into the input layer. Combine with the forward propagation algorithm in the neural network model to perform weighted summation on the multi-dimensional features corresponding to the preprocessed multi-dimensional feature vector in the hidden layer, and perform non-linear transformation on the weighted summation result through the activation function, and calculate the multi-dimensional feature mapping result layer by layer.

[0105] Among them, the neural network model is a machine learning model based on the multi-dimensional feature input - non-linear transformation - output prediction architecture. Its core function is to learn complex mapping relationships from input data through hierarchical abstraction and output optimized lighting device parameters.

[0106] Step b3: In the output layer, perform weighted summation on the multi-dimensional feature mapping result output by the last hidden layer, and generate a lighting effect prediction value through the activation function of the output layer. The lighting effect prediction value includes the light intensity at each position in the target area, the total energy consumption, and the visual comfort score.

[0107] Among them, the total energy consumption refers to the total electrical energy consumed by all lighting devices in the target area within a specific time period.

[0108] The following is a specific example: In the landscape lighting system of a city square, the design and prediction process of the neural network model. Input layer: 8 nodes (corresponding to an 8-dimensional feature vector). Hidden layer: 2 layers, with 16 nodes and 8 nodes respectively. Output layer: 3 nodes (corresponding to light intensity, total energy consumption, visual comfort score). Input the feature vector into the input layer, perform non-linear transformation through the hidden layer to generate a multi-dimensional feature mapping result. Perform weighted summation on the output of the hidden layer to generate a lighting effect prediction value. Light intensity: [0.8, 0.6, 0.9]; Total energy consumption: 0.7; Visual comfort score: 0.85. Output the prediction value to the lighting control system to adjust the lamp parameters in real time.

[0109] By executing steps b1 - b3, the embodiment of the present application realizes the intelligent prediction and optimization of the landscape lighting effect through the structural design of the neural network model, forward propagation calculation, and output layer prediction. Combining the multi-dimensional feature vector with real-time environmental data improves the dynamic adaptability and energy utilization efficiency of the lighting system, while optimizing the visual experience and comfort of tourists, providing an efficient and intelligent solution for landscape lighting management in smart cities.

[0110] In a possible embodiment, S12: Establish an optical model based on the landscape design data and lighting device data, and analyze the spatial attenuation effect of the vegetation occlusion contour and the surface reflectivity of artificial structures on the light beam emitted by the lighting device through the optical model, including:

[0111] Step 121: Digitally process the vegetation occlusion contour to obtain the spatial distribution characteristics of the vegetation and the occlusion intensity parameters, and divide the surface reflectivity of artificial structures into regions to generate a reflectivity distribution map.

[0112] Among them, the reflectivity distribution map is a two-dimensional visualization chart generated through regional division technology, which is used to characterize the spatial distribution characteristics of the surface reflectivity of artificial structures in the target area. In the reflectivity distribution map, a higher reflectivity means that more light is reflected back into the environment, which is very important for increasing the brightness of certain areas or creating specific visual effects.

[0113] Step 122: Based on the spatial distribution characteristics of the vegetation, the occlusion intensity parameters, and the reflectivity distribution map, combined with the lamp power distribution, beam angle coverage range, and light source color temperature parameters, generate an optical model.

[0114] Among them, the optical model is a light propagation simulation framework constructed through numerical simulation methods based on the landscape design data and lighting equipment parameters of the target area.

[0115] Step 123: Based on the optical model, simulate the propagation process of the light beam emitted by the lighting equipment in the target area, calculate the influence of the light beam by the vegetation occlusion contour and the surface reflectivity of artificial structures, and generate the attenuation parameters of the light beam.

[0116] Among them, the attenuation parameter of the light beam is a physical quantity that quantifies the intensity attenuation of the light beam during propagation in the target area due to vegetation occlusion, artificial structure reflection, and scattering effects. Its core purpose is to provide precise constraints on the spatial light intensity change for the dynamic dimming strategy to ensure the balance between lighting effects and energy consumption.

[0117] Step 124: Based on the attenuation parameters of the light beam, generate a spatial attenuation effect distribution map, which is used to characterize the spatial attenuation effect of the vegetation occlusion contour and the surface reflectivity of artificial structures on the light beam emitted by the lighting equipment.

[0118] Among them, the spatial attenuation effect refers to the phenomenon that the light intensity of the light beam emitted by the lighting equipment decays non-linearly with the spatial distance or environmental characteristics during propagation due to physical effects such as vegetation occlusion, artificial structure reflection, and scattering.

[0119] The following is a specific example:

[0120] In a landscape lighting system of an urban park, the process of constructing and applying an optical model obtains the vegetation occlusion contour through LiDAR scanning and calculates the occlusion intensity parameters (such as the occlusion rate of the footpath area is 50%). The reflectivity of artificial structures is measured by a spectral analyzer to generate a reflectivity distribution map (such as the reflectivity of the footpath area is 0.6 and the reflectivity of the sculpture area is 0.8). Combining the vegetation occlusion contour, the reflectivity distribution map and the lamp parameters (such as power 60W, beam angle 90°), an optical model is constructed. Simulate the beam propagation path, calculate that the light intensity attenuation in the footpath area is 50% and the reflected light intensity in the sculpture area increases by 20%. Generate a distribution map showing the light intensity attenuation in the footpath area and the enhanced reflected light intensity in the sculpture area. Output the distribution map to the lighting control system and adjust the lamp power in the footpath area to 80W to compensate for the light intensity attenuation.

[0121] By performing steps 121 to 124, the embodiments of the present application generate a spatial attenuation effect distribution map through digitalization of vegetation occlusion contours, division of reflectivity regions, construction of an optical model, and beam propagation simulation, providing a basis for the dynamic dimming strategy of the landscape lighting system. Combining real-time environmental data with the optical model improves the dynamic adaptability of lighting effects and energy utilization efficiency, while optimizing the visual experience and comfort of tourists, providing an efficient and intelligent solution for landscape lighting management in smart cities.

[0122] In a possible embodiment, step 123: Based on the optical model, simulate the propagation process of the light beam emitted by the lighting device in the target area, calculate the influence of the vegetation occlusion contour and the surface reflectivity of artificial structures on the light beam, and generate the attenuation parameters of the light beam.

[0123] Step 1231: According to the optical model, lamp power distribution, beam angle coverage range, and light source color temperature parameters, construct a light beam propagation path model, and divide the target area into multiple grid cells based on the light beam propagation path model.

[0124] Among them, the light beam propagation path model is a numerical calculation model that simulates the propagation process of the light beam in the target area based on the grid method. Its core function is to generate the light beam attenuation parameters and the spatial attenuation effect distribution map by analyzing the vegetation occlusion, surface reflection, and scattering effects of each grid cell, providing physical constraints for the dynamic dimming strategy.

[0125] Step 1232: In the light beam propagation path model, calculate the attenuation effect of the vegetation occlusion contour on the light beam for each grid cell, generate the vegetation occlusion attenuation coefficient, and update the light intensity value of each grid cell according to the vegetation occlusion attenuation coefficient.

[0126] Among them, the vegetation occlusion attenuation coefficient is a physical quantity that quantifies the intensity attenuation of the light beam after passing through the vegetation, reflecting the absorption, scattering, and blocking effects of the vegetation density, height, and other characteristics on the light energy.

[0127] Step 1233: Identify the grid cells that intersect with the surface of the artificial structure in the beam propagation path model, calculate the reflection path and reflection intensity of the beam on the surface of the artificial structure, and update the light intensity value of the grid cells covered by the reflection path according to the reflection intensity.

[0128] Among them, the light intensity value of the grid cells covered by the reflection path refers to the light intensity of the grid cells in the target area after the beam is reflected on the surface of the artificial structure and propagates through the reflection path. Its calculation needs to combine the initial intensity of the beam, surface reflectivity, incident angle, effective area of the reflection surface, propagation distance of the reflection path, etc. This reflection intensity is used as the light intensity value of the grid cells covered by the reflection path to correct the influence of the secondary light path on the light distribution in the beam propagation model.

[0129] Exemplarily, the formula used to calculate the reflection intensity of the beam on the surface of the artificial structure is as follows:

[0130] I(x,y)=I 0 ×γ(x,y)×cosθ(x,y)×[Ar(x,y) / r2(x,y)];

[0131] Among them, I(x,y) is the reflection intensity of the beam on the surface of the artificial structure, I 0 is the initial intensity of the incident beam, γ(x,y) is the surface reflectivity, θ(x,y) is the incident angle, Ar(x,y) is the effective area of the reflection surface in square meters, and r(x,y) is the propagation distance of the reflection path in meters.

[0132] Step 1234: Generate the beam attenuation parameter for each grid cell according to the light intensity value of each grid cell.

[0133] Among them, the beam attenuation parameter is a quantitative index that characterizes the intensity attenuation of the beam during propagation in the target area after considering multiple factors such as vegetation occlusion, reflection of artificial structures, and scattering effects.

[0134] The following is a specific example:

[0135] In the landscape lighting system of an urban square, in the process of constructing and applying the beam propagation path model, a beam propagation path model is constructed according to the lamp parameters (power 60W, beam angle 90°), and the target area is divided into grid cells of 1m×1m. Calculate the vegetation occlusion attenuation coefficient (such as 0.5) of the grid cells in the sidewalk area, and update the illuminance value (decrease from 100 lux to 50 lux). Identify the grid cells intersecting the surface of the sculpture, calculate the reflection path and reflection intensity (such as reflectivity 0.8, reflection intensity 40 lux), and update the illuminance value of the grid cells covered by the reflection path (increase from 50 lux to 90 lux). Calculate the beam attenuation parameter of each grid cell (such as the attenuation rate of the sidewalk area is 50%, and the attenuation rate of the sculpture area is 10%).

[0136] By performing steps 1231 to 1234, the embodiments of the present application realize the accurate modeling of the beam propagation path and the quantification of the attenuation effect in the landscape lighting system through the construction of the beam propagation path model, the calculation of the vegetation occlusion attenuation effect, the calculation of the reflection effect, and the generation of the beam attenuation parameter. Combining real-time environmental data with the optical model improves the dynamic adaptability of the lighting effect and the energy utilization efficiency, while optimizing the visual experience and comfort of tourists, providing an efficient and intelligent solution for the landscape lighting management in the smart city.

[0137] In a possible embodiment, step 1232, in the beam propagation path model, calculate the attenuation effect of the vegetation occlusion profile on the beam for each grid cell, generate the vegetation occlusion attenuation coefficient, and update the illuminance value of each grid cell according to the vegetation occlusion attenuation coefficient, including:

[0138] Step c1: Extract the height, density, and distribution range parameters of the vegetation from the vegetation occlusion profile, and map the height, density, and distribution range parameters of the vegetation to the grid cells of the target area.

[0139] Step c2: In the beam propagation path model, for each grid cell, calculate the attenuation coefficient after the beam penetrates the vegetation according to the vegetation occlusion intensity value and the mapped data corresponding to the height, density, and distribution range parameters of the vegetation, and generate the vegetation occlusion attenuation coefficient of each grid cell.

[0140] Exemplarily, the calculation formula of the attenuation coefficient after the beam penetrates the vegetation is as follows:

[0141] α(x,y)=1−exp(−β×h(x,y)×ρ(x,y)×d(x,y));

[0142] Among them, α(x, y) is the vegetation occlusion attenuation coefficient of the grid cell (x, y), β is the vegetation occlusion intensity value, which can be understood as the vegetation type coefficient. Exemplarily, for trees, β = 0.05, and for shrubs, β = 0.03. h(x, y) is the mapped data corresponding to the vegetation height of the grid cell (x, y) in meters, ρ(x, y) is the mapped data corresponding to the density, and d(x, y) is the mapped data corresponding to the distribution range parameter, for example, the proportion of the vegetation distribution range.

[0143] Among them, the distribution range parameter refers to a set of parameters describing the spatial distribution characteristics of vegetation in the target area, including the horizontal distribution range and the vertical distribution range.

[0144] Step c3: Apply the vegetation occlusion attenuation coefficient of each grid cell and the light intensity value of the grid cells covered by the scattering path to the beam propagation path model to update the light intensity value of each grid cell through the beam propagation path model.

[0145] Among them, the attenuation coefficient after the beam penetrates the vegetation is a parameter quantifying the intensity attenuation after the beam penetrates the vegetation, reflecting the absorption and scattering effects of vegetation density, height, and scattering characteristics on light energy.

[0146] The following is a specific example:

[0147] In a landscape lighting system of an urban park, in the process of calculating and applying the vegetation occlusion attenuation effect, the vegetation height (2m), density (0.6), and distribution range (50%) are obtained through LiDAR scanning and mapped into 1m×1m grid cells. Calculate the occlusion intensity value (such as 0.6) of the grid cells in the sidewalk area to generate the attenuation coefficient (such as 0.45). Update the light intensity value of the grid cells in the sidewalk area according to the attenuation coefficient (from 100 lux to 55 lux). Calculate the scattering path of the beam in the vegetation and update the light intensity value of the grid cells covered by the scattering path (from 55 lux to 70 lux).

[0148] By performing steps c1~c3, the embodiments of the present application achieve precise modeling and quantification of the vegetation occlusion effect in the landscape lighting system through vegetation parameter extraction and mapping, vegetation occlusion attenuation coefficient calculation, and light intensity update. Combining the beam propagation path model with real-time environmental data improves the dynamic adaptability and energy utilization efficiency of the lighting effect, while optimizing the visual experience and comfort of tourists, providing an efficient and intelligent solution for landscape lighting management in smart cities.

[0149] Figure 2 The structural schematic diagram of a landscape design recognition system based on a neural network model provided by the embodiments of the present application is as Figure 2 shown, and the system includes:

[0150] An acquisition module 21, configured to acquire landscape design data, lighting device data, and real-time load data of the energy supply network within a target area. The landscape design data includes vegetation occlusion profiles and surface reflectivity of artificial structures. The lighting device data includes lamp power distribution, beam angle coverage, and light source color temperature parameters.

[0151] A model establishment and analysis module 22, configured to establish an optical model based on the landscape design data and the lighting device data, and analyze the spatial attenuation effect of the vegetation occlusion profiles and the surface reflectivity of artificial structures on the light beams emitted by the lighting devices through the optical model.

[0152] A calculation module 23, configured to establish an energy transfer model based on the lighting device data and the real-time load data of the energy supply network, and calculate the energy consumption mapping relationship between the lamp power distribution and the real-time load data of the energy supply network through the energy transfer model.

[0153] A generation and coupling module 24, configured to generate a dynamic dimming strategy by combining the spatial attenuation effect and the energy consumption mapping relationship, couple the dynamic dimming strategy with a heat map of tourist distribution and environmental light intensity data collected in real time to obtain a coupling result, and input the coupling result into a neural network model to iteratively optimize the lamp power distribution, beam angle coverage, and light source color temperature parameters through the neural network model, so as to achieve dynamic optimization of the landscape lighting effect and minimization of energy consumption, and at the same time improve the visual experience and comfort of tourists.

[0154] Figure 2 The above-mentioned landscape design recognition system based on a neural network model can execute Figure 1 The landscape design recognition method based on a neural network model described in the embodiments shown. The implementation principles and technical effects will not be elaborated here. For the landscape design recognition system based on a neural network model in the above embodiments, the specific manners in which each module and unit perform operations have been described in detail in the embodiments related to the method, and will not be elaborated here.

[0155] In a possible design, Figure 2 The landscape design recognition system based on a neural network model in the embodiments shown can be implemented as a computing device, such as Figure 3 shown, and the computing device may include a storage component 31 and a processing component 32.

[0156] The storage component 31 stores one or more computer instructions, and the one or more computer instructions are called and executed by the processing component 32.

[0157] The processing component 32 is used for: obtaining landscape design data, lighting device data, and real-time load data of the energy supply network within the target area, where the landscape design data includes vegetation occlusion profiles and surface reflectivity of artificial structures, and the lighting device data includes lamp power distribution, beam angle coverage, and light source color temperature parameters; establishing an optical model based on the landscape design data and the lighting device data, and analyzing the spatial attenuation effect of the vegetation occlusion profiles and the surface reflectivity of artificial structures on the light beams emitted by the lighting devices through the optical model; establishing an energy transfer model based on the lighting device data and the real-time load data of the energy supply network, and calculating the energy consumption mapping relationship between the lamp power distribution and the real-time load data of the energy supply network through the energy transfer model; generating a dynamic dimming strategy by combining the spatial attenuation effect and the energy consumption mapping relationship, coupling the dynamic dimming strategy with the heat map of tourist distribution and the environmental light intensity data collected in real time to obtain a coupling result, and inputting the coupling result into a neural network model to iteratively optimize the lamp power distribution, beam angle coverage, and light source color temperature parameters through the neural network model, so as to achieve dynamic optimization of the landscape lighting effect and minimization of energy consumption, while enhancing the visual experience and comfort of tourists.

[0158] Among them, the processing component 32 may include one or more processors to execute computer instructions to complete all or part of the steps in the above method. Of course, the processing component may also be implemented by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components for executing the above method.

[0159] The storage component 31 is configured to store various types of data to support the operation of the terminal. The storage component can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as random access memory (RAM), static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk or optical disk.

[0160] Of course, the computing device may necessarily further include other components, such as an input / output interface, a display component, a communication component, etc.

[0161] The input / output interface provides an interface between the processing component and the peripheral interface module, and the peripheral interface module may be an output device, an input device, etc.

[0162] The communication component is configured to facilitate communication between the computing device and other devices in a wired or wireless manner, etc.

[0163] Among them, the computing device may be a physical device or an elastic computing host provided by a cloud computing platform, etc. At this time, the computing device may refer to a cloud server, and the above-mentioned processing component, storage component, etc. may be basic server resources leased or purchased from a cloud computing platform.

[0164] The embodiment of the present application also provides a computer storage medium storing a computer program, and when the computer program is executed by a computer, it can implement the above-mentioned Figure 1 landscape design recognition method based on a neural network model shown in the embodiment.

[0165] Those skilled in the art can clearly understand that for the convenience and brevity of description, the specific working processes of the above-described systems, devices, and units can refer to the corresponding processes in the foregoing method embodiments, and will not be described in detail herein.

[0166] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed to multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment. Those of ordinary skill in the art can understand and implement it without creative work.

[0167] Through the description of the above embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus a necessary general hardware platform, and of course, it can also be implemented by hardware. Based on this understanding, the essence of the above technical solution, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to enable a computer device (which can be a personal computer, server, or network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0168] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements for some of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of each embodiment of the present application.

Claims

1. A landscape design recognition method based on a neural network model, characterized in that: include: Acquire landscape design data, lighting equipment data and real-time load data of the energy supply network in the target area, wherein the landscape design data includes vegetation shading outline and artificial structure surface reflectivity, and the lighting equipment data includes lamp power distribution, beam angle coverage and light source color temperature parameters; An optical model is established based on the landscape design data and the lighting equipment data, and the spatial attenuation effect of the vegetation shielding contour and the artificial structure surface reflectivity on the light beam emitted by the lighting equipment is analyzed by the optical model; Establishing an energy transfer model based on the lighting equipment data and the real-time load data of the energy supply network, and calculating the energy consumption mapping relationship between the power distribution of the lamps and the real-time load data of the energy supply network through the energy transfer model; Generate a dynamic dimming strategy by combining the spatial attenuation effect with the energy consumption mapping relationship, couple the dynamic dimming strategy with the real-time collected tourist distribution heat map and ambient light intensity data to obtain a coupling result, and input the coupling result into a neural network model to iteratively optimize the lamp power distribution, the beam angle coverage range and the light source color temperature parameters through the neural network model, so as to achieve dynamic optimization of landscape lighting effects and minimization of energy consumption, while improving the visual experience and comfort of tourists; The optical model is established based on the landscape design data and the lighting equipment data, and the spatial attenuation effect of the vegetation shielding contour and the artificial structure surface reflectivity on the light beam emitted by the lighting equipment is analyzed by the optical model, including: The vegetation shielding contour is digitally processed to obtain the spatial distribution characteristics and shielding intensity parameters of the vegetation, and the surface reflectivity of the artificial structure is divided into regions to generate a reflectivity distribution map; Based on the spatial distribution characteristics and shading intensity parameters of the vegetation, and the reflectivity distribution diagram, combined with the lamp power distribution, the beam angle coverage range and the light source color temperature parameters, an optical model is generated; Based on the optical model, the propagation process of the light beam emitted by the lighting device in the target area is simulated, the influence of the vegetation blocking contour and the reflectivity of the surface of the artificial structure on the light beam is calculated, and the attenuation parameter of the light beam is generated; Based on the attenuation parameters of the light beam, a spatial attenuation effect distribution diagram is generated, and the spatial attenuation effect distribution diagram is used to characterize the spatial attenuation effect of the vegetation shielding contour and the artificial structure surface reflectivity on the light beam emitted by the lighting device.

2. The method according to claim 1, characterized in that The dynamic dimming strategy is generated by combining the spatial attenuation effect with the energy consumption mapping relationship, the dynamic dimming strategy is coupled with the real-time collected tourist distribution heat map and ambient light intensity data to obtain a coupling result, and the coupling result is input into a neural network model to iteratively optimize the lamp power distribution, the beam angle coverage range and the light source color temperature parameters through the neural network model, including: Building an objective function based on the mapping relationship between the spatial attenuation effect and the energy consumption, and generating a dynamic dimming strategy through an optimization algorithm based on the objective function; The dynamic dimming strategy is coupled with the real-time collected tourist distribution heat map and ambient light intensity data to obtain a coupling result, wherein the coupling result is a multi-dimensional feature vector; The multidimensional feature vector is input into a neural network model, a lighting effect prediction value is calculated by a forward propagation algorithm of the neural network model, and the lamp power distribution, beam angle coverage and light source color temperature parameters are iteratively optimized based on a preset loss function and a back propagation algorithm of the neural network model; The optimized lamp power distribution, beam angle coverage and light source color temperature parameters are output to the lighting control system to adjust the lighting equipment data in the target area in real time according to the optimized lamp power distribution, beam angle coverage and light source color temperature parameters.

3. The method according to claim 2, characterized in that The multidimensional feature vector is input into the neural network model, the lighting effect prediction value is calculated by the forward propagation algorithm of the neural network model, and the lamp power distribution, beam angle coverage and light source color temperature parameters are iteratively optimized based on a preset loss function and the back propagation algorithm of the neural network model, including: The multidimensional feature vector is standardized to obtain a preprocessed multidimensional feature vector, wherein the multidimensional features include the high-density area coordinates and dynamic change trends of the tourist distribution heat map, the time series characteristics of the ambient light intensity data, and the spatial distribution characteristics of the vegetation shading contour and the surface reflectivity of the artificial structure; Inputting the preprocessed multidimensional feature vector into a neural network model, combining the forward propagation algorithm in the neural network model, performing nonlinear transformation through the hidden layer of the neural network model, and generating a lighting effect prediction value at the output layer; Based on the difference between the lighting effect prediction value and the preset target value, the function value of the preset loss function is calculated. Based on the function value, the lamp power distribution, beam angle coverage and light source color temperature parameters are iteratively optimized and adjusted through the back propagation algorithm until the function value of the loss function converges to a preset threshold.

4. The method according to claim 3, characterized in that The preprocessed multidimensional feature vector is input into the neural network model, combined with the forward propagation algorithm in the neural network model, a nonlinear transformation is performed through the hidden layer of the neural network model, and a lighting effect prediction value is generated at the output layer, including: Designing a structure of a neural network model, wherein the structure of the neural network model includes an input layer, a hidden layer and an output layer, the number of nodes in the input layer is consistent with the dimension of the multidimensional feature vector, the hidden layer adopts a multi-layer structure, and the number of nodes in the output layer is consistent with the dimension of the lighting effect prediction value; The preprocessed multidimensional feature vector is input into the input layer, and in combination with the forward propagation algorithm in the neural network model, the multidimensional features corresponding to the preprocessed multidimensional feature vector are weighted summed in the hidden layer, and the weighted summation result is nonlinearly transformed by an activation function, and the multidimensional feature mapping result is calculated layer by layer; In the output layer, the multidimensional feature mapping results output by the last hidden layer are weighted and summed, and the lighting effect prediction value is generated through the activation function of the output layer. The lighting effect prediction value includes the light intensity, total energy consumption and visual comfort score of each location in the target area.

5. The method according to claim 1, characterized in that Based on the optical model, the propagation process of the light beam emitted by the lighting device in the target area is simulated, the influence of the vegetation blocking outline and the surface reflectivity of the artificial structure on the light beam is calculated, and the attenuation parameters of the light beam are generated, including: Constructing a beam propagation path model according to the optical model, the lamp power distribution, the beam angle coverage range and the light source color temperature parameter, and dividing the target area into a plurality of grid units based on the beam propagation path model; In the light beam propagation path model, the attenuation effect of the vegetation shading contour on the light beam is calculated grid unit by grid unit, a vegetation shading attenuation coefficient is generated, and the light intensity value of each grid unit is updated according to the vegetation shading attenuation coefficient; Identify the grid cells that intersect with the surface of the artificial structure in the light beam propagation path model, calculate the reflection path and reflection intensity of the light beam on the surface of the artificial structure, and update the light intensity value of the grid cells covered by the reflection path according to the reflection intensity; According to the light intensity value of each grid cell, a beam attenuation parameter of each grid cell is generated.

6. The method according to claim 5, characterized in that In the light beam propagation path model, the attenuation effect of the vegetation occlusion contour on the light beam is calculated grid by grid unit, a vegetation occlusion attenuation coefficient is generated, and the light intensity value of each grid unit is updated according to the vegetation occlusion attenuation coefficient, including: Extracting the height, density and distribution range parameters of vegetation from the vegetation occlusion contour, and mapping the height, density and distribution range parameters of vegetation to the grid cells of the target area; In the beam propagation path model, for each grid unit, according to the vegetation shielding intensity value, the mapping data corresponding to the vegetation height, density and distribution range parameters, the attenuation coefficient of the beam after penetrating the vegetation is calculated to generate the vegetation shielding attenuation coefficient of each grid unit; Combined with the scattering effect of vegetation on the light beam, the scattering path of the light beam after penetrating the vegetation is simulated, the propagation direction and intensity distribution of the scattered light beam are calculated, and the light intensity value of the grid unit covered by the scattering path is updated according to the propagation direction and the intensity distribution; The vegetation shading attenuation coefficient of each grid unit and the light intensity value of the grid unit covered by the scattering path are applied to the light beam propagation path model to update the light intensity value of each grid unit through the light beam propagation path model.

7. A landscape design recognition system based on a neural network model, characterized in that: include: An acquisition module is used to acquire landscape design data, lighting equipment data and real-time load data of the energy supply network in the target area, wherein the landscape design data includes vegetation shading contours and artificial structure surface reflectivity, and the lighting equipment data includes lamp power distribution, beam angle coverage and light source color temperature parameters; Establishing an analysis module for establishing an optical model based on the landscape design data and the lighting equipment data, and analyzing the spatial attenuation effect of the vegetation shielding contour and the artificial structure surface reflectivity on the light beam emitted by the lighting equipment through the optical model; Establishing a calculation module, used to establish an energy transfer model based on the lighting equipment data and the real-time load data of the energy supply network, and calculating the energy consumption mapping relationship between the power distribution of the lamps and the real-time load data of the energy supply network through the energy transfer model; Generate a coupling module for combining the spatial attenuation effect with the energy consumption mapping relationship to generate a dynamic dimming strategy, couple the dynamic dimming strategy with the real-time collected tourist distribution heat map and ambient light intensity data to obtain a coupling result, and input the coupling result into a neural network model to iteratively optimize the lamp power distribution, the beam angle coverage range and the light source color temperature parameters through the neural network model to achieve dynamic optimization of landscape lighting effects and minimization of energy consumption, while improving the visual experience and comfort of tourists; Wherein, the establishment of the parsing module is also used for: The vegetation shielding contour is digitally processed to obtain the spatial distribution characteristics and shielding intensity parameters of the vegetation, and the surface reflectivity of the artificial structure is divided into regions to generate a reflectivity distribution map; Based on the spatial distribution characteristics and shading intensity parameters of the vegetation, and the reflectivity distribution diagram, combined with the lamp power distribution, the beam angle coverage range and the light source color temperature parameters, an optical model is generated; Based on the optical model, the propagation process of the light beam emitted by the lighting device in the target area is simulated, the influence of the vegetation blocking contour and the reflectivity of the surface of the artificial structure on the light beam is calculated, and the attenuation parameter of the light beam is generated; Based on the attenuation parameters of the light beam, a spatial attenuation effect distribution diagram is generated, and the spatial attenuation effect distribution diagram is used to characterize the spatial attenuation effect of the vegetation shielding contour and the artificial structure surface reflectivity on the light beam emitted by the lighting device.

8. A computing device, characterized in that It comprises a processing component and a storage component; the storage component stores one or more computer instructions; the one or more computer instructions are used to be called and executed by the processing component to implement a landscape design recognition method based on a neural network model as described in any one of claims 1 to 6.

9. A computer storage medium, characterized in that A computer program is stored, and when the computer program is executed by a computer, a landscape design recognition method based on a neural network model as described in any one of claims 1 to 6 is implemented.

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