Urban lighting regulation and control method and device based on digital twinning, medium and equipment

Abstract

The invention provides an urban lighting regulation and control method and device based on digital twinning, a medium and equipment. The method comprises the steps that initial configuration is conducted on lamps of a target road in a three-dimensional digital twinning model; on the basis of the three-dimensional digital twinborn model, on the basis of meeting the illumination requirement, according to the position, the distance, the direction and the power of lamps, a multi-target energy consumption optimization algorithm is adopted, and an illumination strategy with the lowest energy consumption of the target road is determined; based on the real-time environment information of the target road, lamp illumination adjustment is carried out; obtaining a power consumption prediction value of the target road through ARIMA time sequence analysis; and comparing the power consumption predicted value with the actual power consumption of the target road to optimize the lighting strategy of the target road. According to the technical scheme of the embodiment of the invention, dynamic simulation can be effectively carried out on road illumination, and the flexibility and the energy-saving performance of the illumination system are improved.

Description

Technical Field

[0001] The present disclosure belongs to the field of intelligent control technology, and in particular relates to a method, device, medium and equipment for urban lighting control based on digital twins. Background Art

[0002] With the continuous advancement of smart city construction, urban road lighting systems, as an important part of urban infrastructure, face many challenges. In current technical solutions, road lighting planning is often based on empirical design and static simulation tools. However, the above methods lack dynamic simulation capabilities, that is, they cannot dynamically adjust the lighting plan according to actual conditions, resulting in excessive lighting costs or substandard lighting effects. Therefore, how to effectively simulate road lighting dynamically and improve the flexibility and energy efficiency of the lighting system has become a technical problem that needs to be solved urgently. Summary of the invention

[0003] The embodiments of the present application provide a method, device, medium and equipment for urban lighting control based on digital twins, which can effectively simulate road lighting dynamically to at least a certain extent, thereby improving the flexibility and energy efficiency of the lighting system.

[0004] Other features and advantages of the present application will become apparent from the following detailed description, or may be learned in part by the practice of the present application.

[0005] According to one aspect of an embodiment of the present application, a method for controlling urban lighting based on digital twins is provided, including: Initially configure the lamps on the target road in the 3D digital twin model, including the location, height and illumination direction of the lamps; Based on the three-dimensional digital twin model, on the basis of meeting the lighting needs, according to the position, spacing, direction and power of the lamps, a multi-objective energy consumption optimization algorithm is used to determine the lighting strategy with the lowest energy consumption for the target road, wherein the lighting strategy includes lamp configuration and operation plan; Based on the real-time environmental information of the target road, adjusting the lighting of the lamp, wherein the lighting adjustment of the lamp includes adjusting the brightness of the lamp and / or lighting the lamp in sections; By ARIMA time series analysis, a predicted value of power consumption of the target road is obtained; The power consumption prediction value is compared with the actual power consumption of the target road to optimize the lighting strategy of the target road.

[0006] According to one aspect of an embodiment of the present application, a digital twin-based urban lighting control device is provided, including: The configuration module is used to initially configure the lamps on the target road in the 3D digital twin model, including the position, height and illumination direction of the lamps; An optimization module is used to determine the lighting strategy with the lowest energy consumption for the target road based on the three-dimensional digital twin model and according to the position, spacing, direction and power of the lamps on the basis of meeting the lighting needs, using a multi-objective energy consumption optimization algorithm, wherein the lighting strategy includes lamp configuration and operation plan; An adjustment module, configured to adjust the lighting of the lamp based on the real-time environmental information of the target road, wherein the lighting adjustment of the lamp includes adjusting the brightness of the lamp and / or lighting the lamp in sections; A prediction module, used for obtaining a predicted value of power consumption of the target road through ARIMA time series analysis; A processing module is used to compare the power consumption prediction value with the actual power consumption of the target road to optimize the lighting strategy of the target road.

[0007] According to one aspect of an embodiment of the present application, a computer-readable medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the urban lighting control method based on digital twins as described in the above embodiments is implemented.

[0008] According to one aspect of an embodiment of the present application, an electronic device is provided, comprising: one or more processors; a storage device for storing one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors implement the urban lighting control method based on digital twins as described in the above embodiments.

[0009] According to one aspect of an embodiment of the present application, a computer program product or a computer program is provided, the computer program product or the computer program including computer instructions, the computer instructions being stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the computer device executes the urban lighting control method based on digital twins provided in the above embodiment.

[0010] In the technical solutions provided in some embodiments of the present application, the lamps of the target road are initially configured in a three-dimensional digital twin model, including the position, height and illumination direction of the lamps. Based on the three-dimensional digital twin model, on the basis of meeting the lighting needs, a multi-objective energy consumption optimization algorithm is used according to the position, spacing, direction and power of the lamps to determine the lighting strategy with the lowest energy consumption for the target road. The lighting strategy includes lamp configuration and operation plan. Then, based on the real-time environmental information of the target road, the lamp lighting is adjusted. The lamp lighting adjustment includes lamp brightness adjustment and / or segmented lighting. The power consumption forecast value of the target road is obtained through ARIMA time series analysis. The power consumption forecast value is then compared with the actual power consumption of the target road to optimize the lighting strategy of the target road.

[0011] In this way, the three-dimensional digital twin model can be used to dynamically simulate the lighting effects of lamps based on the actual scenes and working conditions of the target road, thereby improving the effectiveness of the determined lighting strategy. In addition, the energy consumption of the lighting system can be determined based on the three-dimensional digital twin model, which can effectively balance the lighting cost and performance and improve the flexibility and energy saving of the lighting system.

[0012] It should be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] The drawings herein are incorporated into the specification and constitute a part of the specification, showing embodiments consistent with the present application, and together with the specification, are used to explain the principles of the present application. Obviously, the drawings described below are only some embodiments of the present application, and for ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work. In the drawings: Figure 1 A schematic diagram of a process of a digital twin-based urban lighting control method according to an embodiment of the present application is shown; Figure 2 A block diagram of a digital twin-based urban lighting control device according to an embodiment of the present application is shown; Figure 3 A schematic diagram of the structure of a computer system suitable for implementing an electronic device of an embodiment of the present application is shown. DETAILED DESCRIPTION

[0014] Example embodiments will now be described more fully with reference to the accompanying drawings. However, example embodiments can be implemented in a variety of forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that this application will be more comprehensive and complete and fully convey the concept of the example embodiments to those skilled in the art.

[0015] In addition, described feature, structure or characteristic can be combined in one or more embodiments in any suitable manner. In the following description, many specific details are provided to provide a full understanding of the embodiments of the present application. However, those skilled in the art will appreciate that the technical scheme of the present application can be put into practice without one or more of the specific details, or other methods, components, devices, steps, etc. can be adopted. In other cases, known methods, devices, realizations or operations are not shown or described in detail to avoid blurring the various aspects of the application.

[0016] The block diagrams shown in the accompanying drawings are merely functional entities and do not necessarily correspond to physically independent entities. That is, these functional entities may be implemented in software form, or in one or more hardware modules or integrated circuits, or in different networks and / or processor devices and / or microcontroller devices.

[0017] The flowcharts shown in the accompanying drawings are only exemplary and do not necessarily include all the contents and operations / steps, nor must they be executed in the order described. For example, some operations / steps can be decomposed, and some operations / steps can be combined or partially combined, so the actual execution order may change according to actual conditions.

[0018] Figure 1 A schematic flow chart of a method for controlling urban lighting based on digital twins according to an embodiment of the present application is shown.

[0019] It should be noted that the method can be applied to a terminal device or a server, wherein the terminal device may include but is not limited to one or more of a smart phone, a tablet computer, a portable computer, and a desktop computer; the server may be a physical server or a cloud server.

[0020] like Figure 1 As shown, the urban lighting control method based on digital twins includes at least steps S110 to S150, which are described in detail as follows (the method is described below using the application of the method to a terminal device as an example, hereinafter referred to as "terminal").

[0021] In step S110, the lamps of the target road are initially configured in the three-dimensional digital twin model, including the position, height and illumination direction of the lamps.

[0022] In this embodiment, the target road may be one or more roads for which lighting system planning is required, and the terminal may pre-acquire geographic data information of the target road, such as the length, width, curve information, etc. of the target road, and create a virtual model of the target road in the three-dimensional digital twin model based on the geographic data information.

[0023] Then, the terminal can perform initial configuration of the target road lamps in the 3D digital twin model, including determining the position, height and illumination direction of the lamps. represents the three-dimensional coordinates of the i-th lamp, x i , y i represents the horizontal and vertical coordinates of the i-th lamp, h i Indicates the height of the i-th lamp. The irradiation direction of the i-th lamp is determined by the pitch angle θ i and azimuth To express.

[0024] In step S120, based on the three-dimensional digital twin model, on the basis of meeting the lighting needs, according to the position, spacing, direction and power of the lamps, a multi-objective energy consumption optimization algorithm is used to determine the lighting strategy with the lowest energy consumption for the target road, and the lighting strategy includes lamp configuration and operation plan.

[0025] In one embodiment, the method further includes: constructing a ray tracing model based on roads, lamps and traffic flow, and calculating the illumination intensity distribution of the target road according to the following formula: ,in, Indicate point P The light intensity at n is the number of lamps, I i Indicates i The luminous flux of a lamp, θ i It represents the angle between the light and the vertical direction of the ground. d i Indicates i Light fixtures and points P distance; and determining whether the road surface of the target road meets the lighting requirements according to the light intensity distribution.

[0026] In this embodiment, the terminal can construct a ray tracing model based on roads, lamps and traffic flow to calculate the illumination intensity distribution of the target road. It should be noted that ray tracing is a process of simulating illumination distribution by tracing the path of light from a light source (lamp) to a target point (e.g., a point on a road). Focusing on the interaction between each ray and the environment, the illumination intensity of the target point is finally calculated by accumulating the contribution of each ray.

[0027] It should be understood that the propagation of light follows these steps: 1. Emit light: Starting from each lamp, emit light along a specific angle.

[0028] 2. Calculate the path: After the light starts from the lamp, it propagates along the direction until it reaches the ground or encounters an obstacle (such as a building, tree, etc.).

[0029] 3. Light attenuation: As the distance increases, the intensity of light will attenuate.

[0030] 4. Final calculation: By superimposing the lighting contributions of different lamps, the final lighting intensity of the target point is obtained.

[0031] In this way, the ray tracing model can simulate the lighting distribution at different locations on the target road, that is, simulate the lighting effects of different lamp configurations in the three-dimensional digital twin model, thereby helping to determine whether the road surface of the target road meets the lighting requirements under a certain lamp configuration, that is, whether it meets the uniform lighting and minimum brightness standards.

[0032] Specifically, this application builds a ray tracing model for urban lighting optimization based on digital twin technology. The model simulates the propagation of light from lamps to the road surface, taking into account factors such as lamp position, power, illumination angle, road surface characteristics, and traffic flow to calculate the light intensity distribution. The model is described as follows: 1. Model Architecture (1) Input parameters: Lighting configuration: including the location of the lighting fixtures , Luminous flux ( I i ) and irradiation direction (angle θ i ).

[0033] Road geometry data: Obtain the geometric shape and surface characteristics of the road through GIS technology to ensure the accuracy of light propagation calculations.

[0034] Traffic flow: Monitor traffic flow in real time, consider the impact of vehicles and pedestrians on lighting, and adjust lighting intensity.

[0035] Environmental data: such as weather conditions (temperature, humidity, visibility), which affect the propagation characteristics of light.

[0036] (2) Model process: Light Emission: Emit light from each luminaire, propagating in the direction of illumination, simulating the interaction of light with the road surface.

[0037] Light propagation and attenuation: Light attenuation is calculated based on the change in distance and angle during light propagation, using the standard light attenuation model (inverse square law): in, Indicate point PThe light intensity at n is the number of lamps, I i It is i The luminous flux of a lamp, θ i is the angle between the light and the vertical direction of the ground. d i It is lamps and dots P The distance between.

[0038] Light reflection and refraction: Light is reflected or refracted by the road surface and surrounding obstacles (such as vehicles and buildings), affecting the light intensity.

[0039] Light intensity calculation: Based on the above factors, the light intensity of each point is calculated to generate a light intensity distribution map.

[0040] 2. Data processing and training process In order to ensure the high accuracy of the ray tracing model, this application uses the following steps for data processing and model training: Data collection: Collect real-time information such as lamp location, road geometry data, traffic flow and environmental data through Internet of Things (IoT) technology.

[0041] Data preprocessing: Clean and standardize the collected data to ensure the consistency and accuracy of the input data.

[0042] Training and Simulation: Historical lighting data, traffic flow and environmental data are used for training to optimize model parameters such as light attenuation parameters and reflection coefficients.

[0043] The model is verified through simulation software to ensure that the lighting effect matches the energy consumption.

[0044] 3. Calculation of light intensity distribution Through the ray tracing model, the light intensity distribution at different locations on the target road can be calculated. The main goal is to achieve the following optimizations: Light uniformity: Ensure that light on the road is evenly distributed to avoid excessive or uneven light.

[0045] Energy consumption optimization: On the basis of meeting lighting needs, the lowest energy consumption is achieved by optimizing the configuration and power of lamps.

[0046] When it is determined that a certain lamp configuration meets the lighting needs, the terminal can determine the lighting strategy with the lowest energy consumption through a multi-objective energy consumption optimization algorithm. The lighting strategy includes lamp configuration and operation plan (such as when to turn on the lights, when to turn off the lights, how many lights to turn on, etc.).

[0047] In one embodiment, the multi-objective energy consumption optimization algorithm can solve the optimal solution through the following four-dimensional parameter sets.

[0048] (1) Location : The position of the lamp in the three-dimensional coordinate system.

[0049] (2) Spacing D : The horizontal distance between adjacent lamps to ensure uniform lighting.

[0050] (3) Irradiation direction : The illumination angle of the lamp (elevation angle and azimuth angle) to ensure light coverage and reduce light pollution.

[0051] (4) Power configuration P i : That is, the working power of the lamp, which is dynamically adjusted to save energy.

[0052] At the beginning, the terminal can randomly generate several groups of initial lamp lighting solutions, including randomly initializing the position of each lamp and configuring the initial power for each lamp. Then, the lighting effect of each group of initial lamp lighting solutions is simulated in the three-dimensional digital twin model to calculate the total energy consumption E, the light uniformity deviation △L and the cost C.

[0053] The formula of the multi-objective energy consumption optimization algorithm is: ， in, E represents the total energy consumption of the lighting scheme, Δ L Indicates the illumination uniformity deviation (i.e. brightness uniformity) on the target road. C The installation and maintenance costs of the lighting solution, α, β, γ is the corresponding weight factor, which is used to balance the priorities of different optimization objectives.

[0054] Next, the lamp position, spacing, illumination direction and power are updated, and a genetic algorithm and particle swarm optimization algorithm are used to generate a new solution to meet the position, spacing, light coverage and illumination direction, as well as light pollution constraints, to obtain the lighting strategy with the lowest energy consumption for the target road.

[0055] Among them, the genetic algorithm generates new solutions through crossover and mutation to gradually approach the optimal solution. The particle swarm optimization algorithm formula includes: The particle velocity update formula is: , The particle position update formula is: , in, For particles i exist t+1 The speed of time, For particles i exist t+1 The location at the moment, For particles i exist t The speed of time, For particles i exist t The location at the moment, ω is the inertia weight, which controls how well the particle stays in its current direction. c 1 and c 2 They are individual learning factors and social learning factors, which control the particle's response to personal historical experience. p i and group historical experience g The degree of dependence. c 1 and c 2 Take equal values, r 1 and r 2 are two random numbers in the range [0,1], which increase the randomness of the algorithm and enable particles to jump out of the local optimum and conduct a wider search. p i It is a particle i The historical optimal position of the particle is the optimal position found in history. g is the global optimal position, that is, the global position of the optimal solution found by all particles in the group.

[0056] In other words, the particle swarm optimization algorithm simulates the "flight" of particles in the search space, adjusts the position and speed of particles according to the individual historical optimal solution and the global optimal solution, and gradually optimizes the quality of the solution. By adjusting the speed of particles, the particle swarm optimization algorithm can effectively find the global optimal solution in complex optimization problems. In the optimization of urban intelligent lighting, the particle swarm optimization algorithm can achieve the optimal balance between energy consumption, lighting uniformity and cost by adjusting the position, power and layout of lamps.

[0057] Constraints: (1) Location constraints: The luminaire must be installed within the specified available location area .

[0058] (2) Spacing constraint: The distance D between adjacent lamps cannot exceed the maximum allowable spacing Dmax.

[0059] (3) Light coverage constraint: lighting intensity I i The minimum lighting requirements for the road must be met I min.

[0060] (4) Direction and light pollution constraints: through elevation and azimuth Adjust the illumination area to avoid excess light pollution.

[0061] After multiple rounds of iterations, the optimization stops when the optimization target converges or reaches a predetermined number of iterations, thereby obtaining the lighting strategy with the lowest energy consumption corresponding to the target road. The terminal can update the 3D digital twin model based on the optimized data, so that users can visually see the optimized lighting effect through the 3D digital twin model, and continuously improve the lighting plan through system feedback and guide the actual deployment.

[0062] Please continue to refer to Figure 1 In step S130, based on the real-time environmental information of the target road, the lamp lighting adjustment is performed, and the lamp lighting adjustment includes lamp brightness adjustment and / or segmented lighting.

[0063] In this embodiment, the terminal can obtain real-time environmental information of the target road, which may include but is not limited to time, weather conditions, and traffic flow (vehicles / hour) of the target road. In one example, a large number of sensor networks (such as traffic flow sensors, meteorological sensors, light sensors, temperature and humidity sensors, etc.) can be deployed on urban roads to collect environmental data in real time. Then, the real-time environmental data is integrated with the lighting control system through the Internet of Things technology, namely IoT. In this way, different types of equipment (such as traffic sensors, weather monitors, lighting control systems, etc.) can share and collaborate on data through the Internet of Things platform, significantly improving the accuracy, real-time and reliability of the data. In addition, IoT integration can ensure the accurate collection of real-time environmental information and provide rapid feedback and adjustment with the control system.

[0064] Then, based on the real-time environmental information, the lighting of the lamps on the target road can be adjusted to achieve adaptive control, such as increasing the brightness during peak hours to ensure the safety of pedestrians and vehicles, reducing the brightness late at night and during low traffic hours to save energy, and so on.

[0065] It should be noted that the lighting adjustment of the lamps on the target road can be achieved by adjusting the brightness of the lamps and / or lighting them in segments, wherein the "and / or" mentioned in this application means that the terminal can only adjust the brightness of the lamps, or only light them in segments, or can simultaneously adjust the brightness of the lamps and light them in segments to achieve the lighting adjustment of the lamps on the target road, and this application does not make any special limitations on this.

[0066] In one embodiment, the real-time environmental information includes time, weather conditions, and traffic flow; Based on the real-time environmental information of the target road, the lighting is adjusted, including the following.

[0067] According to the following formula, adjust the brightness of the lamps on the target road: ,in, L is the brightness of the current lamp, T is the current time, W is the weather condition (such as sunny, cloudy, rainy, numerically converted to 0~1), V is the traffic volume (vehicles / hour), k 1 , k 2 , k 3 is a coefficient used to weigh the impact of time, weather and traffic flow on the brightness of the lamp; the adjusted lamp brightness is sent to the 3D digital twin model for updating and synchronized to the actual lamps on the target road.

[0068] In another embodiment, the real-time environmental information includes time, weather conditions and traffic flow; lamp lighting is adjusted based on the real-time environmental information of the target road, including: determining whether to turn on a segmented lighting mode according to the time, the weather conditions and / or the traffic flow; in the segmented lighting mode, determining the spacing between adjacent lamps in the target road according to the total number of lamps on the target road and the total length of the road; determining the lighting step length corresponding to the target road based on the spacing and a pre-set spacing threshold; in the target road, turning on a lamp every lighting step length to achieve segmented lighting.

[0069] In this embodiment, segmented lighting means that only a part of adjacent lamps are turned on during a specific period or under certain conditions, rather than lighting up the entire road section. Typical application scenarios include turning on some lamps before it gets dark in the evening to improve visibility; when the weather is bad (such as foggy or rainy days), the segmented lighting of lamps is decided based on environmental data to improve road safety; during low traffic periods: such as early morning or late at night, only the lamps in necessary areas are turned on to reduce overall energy consumption.

[0070] The terminal can obtain the illumination, traffic flow and weather data of the target road in real time through sensors. When the illumination reaches a certain threshold (for example, 300 lux), the segmented lighting mode is automatically activated. When the traffic flow or weather conditions deteriorate, the segmented lighting mode can be automatically transitioned to the full lighting mode.

[0071] When the segmented lighting mode is turned on, the spacing D=S / (N-1) between adjacent lamps in the target road can be determined based on the total number of lamps N and the total length of the road S. The maximum allowable distance between adjacent turned-on lamps is Dmax (i.e., the spacing threshold, such as 30m).

[0072] Next, according to the actual distance D between the lamps and the distance threshold Dmax, the lighting step length is determined according to the following formula: , where if the actual spacing of the luminaires D Less than the spacing threshold Dmax , it is allowed to turn on one lamp every other lamp. Indicates rounding down to ensure that the lighting step length is an integer.

[0073] It should be understood that the lighting step length is k, that is, one lamp is turned on every k lamps. The terminal can and the location of the lamps x i , according to the lighting step length k, the set of lamps turned on in the target road is automatically generated A, then the set A is ,in, j is a sequence of natural numbers starting from 1.

[0074] In one example, in a three-dimensional digital twin model, the system can display the effect of segmented lighting in real time. For example, it can be displayed through color coding, with green representing turned-off lamps, yellow representing lamps in segmented lighting mode, and red representing lamps in full lighting mode.

[0075] Users can choose to simulate different time periods or weather conditions in the three-dimensional digital twin model to see the effect of adjacent lamps lighting up alternately. They can also demonstrate how the segmented lighting mode automatically transitions to the full lighting mode over time. In this way, the rationality of different strategies can be verified with the help of the three-dimensional digital twin model, and the optimal lamp activation combination can be found through the dynamic programming algorithm, achieving a balance between safety and energy saving.

[0076] In one embodiment, the target road may be divided into multiple sections, and each section of the road may set and execute different segmented lighting strategies according to its own road length, traffic flow, and lighting requirements to improve the precision of lighting planning.

[0077] Please continue to refer to Figure 1 In step S140, the power consumption forecast value of the target road is obtained through ARIMA time series analysis.

[0078] In this embodiment, ARIMA time series analysis is used to predict future electricity consumption in advance, thereby supporting urban energy management. It should be understood that ARIMA time series analysis is mainly used to process non-stationary time series data, convert non-stationary series into stationary series through difference operation, and then combine autoregression (AR) and moving average (MA) models for modeling and prediction.

[0079] In one example, the power consumption prediction value of the target road is determined according to the following formula: ,in, Y t Indicates time t The predicted value of electricity consumption is Y t-1 , Y t-2 Represents the energy consumption data of the previous moment and the previous two moments. The autoregressive term captures the change pattern of energy consumption over time through these historical values. is the autoregressive coefficient, which reflects the influence of previous energy consumption data on current energy consumption. θ 1 is the moving average coefficient, indicating the influence of the error of the previous moment on the current prediction. ε t-1 is the residual (forecast error) of the previous moment, reflecting the error information of the model prediction. ε t is the error term.

[0080] In this embodiment, ARIMA time series analysis can obtain a forecast curve of electricity consumption for a period of time in the future based on historical energy consumption data, thereby providing decision support for urban energy management departments. In addition, the forecast results can also be used to optimize lighting strategies and adjust the brightness of lamps in advance during peak electricity consumption periods.

[0081] Furthermore, after obtaining the power consumption forecast value of the target road through ARIMA time series analysis, the power consumption forecast value can be compared with the actual power consumption forecast value to obtain the corresponding residual. Then, the residual and the environmental parameters corresponding to the target road are used as inputs of the pre-trained LSTM network so that the LSTM network outputs the optimized power consumption forecast value.

[0082] In this way, we first use ARIMA time series analysis to perform preliminary modeling and forecasting on time series data to capture linear trends. Then, the residuals corresponding to the forecast results of ARIMA time series analysis are combined with other feature data (such as environmental parameters such as temperature, humidity, and flow) and input into the LSTM network, allowing LSTM to capture long-term nonlinear dependencies to optimize the forecast results and ensure the accuracy of the power consumption forecast value.

[0083] In step S150, the power consumption prediction value is compared with the actual power consumption of the target road to optimize the lighting strategy of the target road.

[0084] In this embodiment, the actual power consumption may be power consumption data actually measured on the target road within the predicted time period, which may be obtained in real time through a smart meter or other monitoring equipment, for example.

[0085] According to the comparison between the predicted power consumption and the actual power consumption, the terminal can adjust the parameters of the lighting system, such as the brightness of the lamps, the start and stop time, and the segmented lighting strategy. For example, if the actual power consumption is higher than the predicted value, the brightness of the lamps may need to be reduced or the lighting time may need to be adjusted; if the actual power consumption is lower than the predicted value, the brightness may be appropriately increased or the lighting time may be extended to ensure a balance between lighting effect and energy saving.

[0086] Furthermore, if there is a large gap between the predicted power consumption and the actual power consumption, the parameters of the ARIMA time series analysis can be re-evaluated and adjusted to improve the prediction accuracy.

[0087] In some embodiments of the present application, it should be understood that when optimizing the lighting strategy, the comparison between the power consumption prediction value and the actual power consumption will provide a strong basis for adjusting the operating mode of the lamp. Through this comparison, the system can achieve dynamic adjustment and further optimize energy use. The specific optimization strategy may include one or more of the following strategies: 1. Dynamic brightness adjustment Based on the comparison between the predicted power consumption and the actual power consumption, the system can adopt a dynamic brightness adjustment strategy. That is: (1) If the predicted value is lower than the actual consumption, it means that the energy consumption during that period is higher than expected, which may be caused by factors such as heavy traffic flow and poor weather conditions. At this time, the system needs to: a. Increase lighting brightness: that is, increase the brightness of the lamps or increase the number of lamps turned on to ensure that the set lighting requirements are met.

[0088] b. Increase the operating time of lamps: If the actual traffic volume on the road section is large, the operating time of some lamps can be appropriately extended to ensure the integrity of the lighting.

[0089] (2) If the predicted power consumption is higher than the actual power consumption, it means that the energy consumption is relatively low during this period, which may be caused by factors such as low traffic flow and good weather. At this time, the system should: a. Reduce lighting brightness: Reduce the brightness of lamps or reduce the number of lamps to save energy.

[0090] b. Shorten the working time of lamps: You can appropriately reduce the working time of lamps during periods of low traffic, or reduce the number of lamps turned on in certain sections of the road.

[0091] 2. Energy efficiency optimization (i.e. energy saving mode) When the power consumption forecast is low (e.g., less than a certain threshold) and close to the actual power consumption, the system can enter energy-saving mode to reduce unnecessary energy consumption by: a. Selective lighting: Based on forecast and real-time traffic data, energy consumption can be reduced by lighting up lights in sections or selectively turning on lights. For example, if there is less traffic in certain sections of the road, the number of lights in that area can be reduced or the brightness can be lowered.

[0092] b. Segmented lighting: According to the consumption differences predicted by traffic flow and time period, segmented lighting is implemented on different road sections, and the number and brightness of lamps in each area are dynamically adjusted according to real-time traffic and lighting requirements.

[0093] 3. Global illumination strategy adjustment Early warning mechanism: By continuously monitoring the difference between actual power consumption and power consumption forecast, the system can establish an early warning mechanism and make adjustments in advance. For example, if it is found that the actual power consumption in a certain area is a large percentage higher than the power consumption forecast, the system can automatically adjust the configuration of lamps in the nearby road sections to avoid energy waste.

[0094] Periodic optimization: The lighting system can be optimized regularly, such as adjusting the system configuration according to seasonal changes (such as longer or shorter days), or adjusting the operating time and brightness of lamps during special time periods such as holidays to improve the flexibility and adaptability of the system.

[0095] 4. Adaptive regulation and feedback control Combining ARIMA time series analysis and real-time data feedback, the system can make real-time adjustments by comparing the power consumption forecast with the actual power consumption: a. Adaptive adjustment: If the actual power consumption deviates significantly from the predicted power consumption (for example, the difference is greater than a certain threshold), the system will adaptively adjust the lighting configuration based on the error between the two. For example, if the system predicts a high consumption period, but the actual consumption is lower, the system can automatically adjust the lighting configuration to avoid waste.

[0096] b. Closed-loop feedback: The system forms a closed loop by continuously comparing the predicted power consumption with the actual power consumption. The lighting strategy for the next time can be adjusted based on the comparison results of each prediction and actual value, ensuring the accuracy and real-time nature of lighting adjustment.

[0097] 5. Intelligent priority management If the actual power consumption of a lighting area is higher than the predicted value, control can be optimized by: Priority management: Adjust the lamps in different areas according to the priority of urban lighting. For example, some important main roads or traffic nodes can maintain a higher brightness, while some less busy sections can appropriately lower the brightness or reduce the use of lamps.

[0098] 6. Combining Optimization and Prediction During each optimization process, ARIMA time series analysis can continuously predict future energy consumption and make optimization adjustments based on actual energy consumption data: a. Rolling Forecast: Based on the difference between the predicted power consumption curve and the actual consumption, the system will automatically update the future lighting strategy and fine-tune the lighting plan for each road section according to the actual situation.

[0099] b. Predicting energy consumption peaks and valleys: By comparing the predicted and actual values, the system can effectively identify peak and valley periods of energy consumption and make adjustments in advance. For example, if it is predicted that the power consumption in a certain period is high, the system will adjust the lighting strategy for that period in advance.

[0100] The following describes an apparatus embodiment of the present application, which can be used to execute the urban lighting control method based on digital twins in the above-mentioned embodiments of the present application. For details not disclosed in the apparatus embodiment of the present application, please refer to the above-mentioned embodiment of the urban lighting control method based on digital twins in the present application.

[0101] Figure 2 A block diagram of a digital twin-based urban lighting control device according to an embodiment of the present application is shown.

[0102] Reference Figure 2 As shown, according to an embodiment of the present application, a digital twin-based urban lighting control device includes: A configuration module is used to perform initial configuration of lamps on the target road in the three-dimensional digital twin model, including the position, height and illumination direction of the lamps; an optimization module is used to determine, based on the three-dimensional digital twin model, the lighting strategy with the lowest energy consumption for the target road, using a multi-objective energy consumption optimization algorithm according to the position, spacing, direction and power of the lamps on the basis of meeting the lighting needs, and the lighting strategy includes lamp configuration and operation plan; an adjustment module is used to adjust the lamp lighting based on the real-time environmental information of the target road, and the lamp lighting adjustment includes lamp brightness adjustment and / or segmented lighting; a prediction module is used to obtain the power consumption forecast value of the target road through ARIMA time series analysis; a processing module is used to compare the power consumption forecast value with the actual power consumption of the target road to optimize the lighting strategy of the target road.

[0103] In one embodiment, the optimization module is further used to: construct a ray tracing model based on roads, lamps and traffic flow, and calculate the illumination intensity distribution of the target road according to the following formula: ,in, Indicate point P The light intensity at n is the number of lamps, I i Indicatesi The luminous flux of a lamp, θ i It represents the angle between the light and the vertical direction of the ground. d i Indicates i Light fixtures and points P distance; and determining whether the road surface of the target road meets the lighting requirements according to the light intensity distribution.

[0104] In one embodiment, the adjustment module is further used to: divide the target road into several sections, and execute a corresponding segmented lighting strategy for each section.

[0105] Figure 3 A schematic diagram of the structure of a computer system suitable for implementing an electronic device of an embodiment of the present application is shown.

[0106] It should be noted that Figure 3 The computer system of the electronic device shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0107] like Figure 3 As shown, the computer system includes a central processing unit (CPU) 301, which can perform various appropriate actions and processes according to the program stored in the read-only memory (ROM) 302 or the program loaded from the storage part 308 to the random access memory (RAM) 303, such as executing the method described in the above embodiment. In RAM 303, various programs and data required for system operation are also stored. CPU 301, ROM 302 and RAM 303 are connected to each other through bus 304. Input / output (I / O) interface 305 is also connected to bus 304.

[0108] The following components are connected to the I / O interface 305: an input section 306 including a keyboard, a mouse, etc.; an output section 307 including a cathode ray tube (CRT), a liquid crystal display (LCD), etc., and a speaker, etc.; a storage section 308 including a hard disk, etc.; and a communication section 309 including a network interface card such as a LAN (Local Area Network) card, a modem, etc. The communication section 309 performs communication processing via a network such as the Internet. A drive 310 is also connected to the I / O interface 305 as needed. A removable medium 311, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 310 as needed so that a computer program read therefrom is installed into the storage section 308 as needed.

[0109] In particular, according to an embodiment of the present application, the process described above with reference to the flowchart can be implemented as a computer software program. For example, an embodiment of the present application includes a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a computer program for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network through a communication part 309, and / or installed from a removable medium 311. When the computer program is executed by a central processing unit (CPU) 301, various functions defined in the system of the present application are executed.

[0110] It should be noted that the computer-readable medium shown in the embodiment of the present application may be a computer-readable signal medium or a computer-readable storage medium or any combination of the above two. The computer-readable storage medium may be, for example, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM), a flash memory, an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present application, a computer-readable storage medium may be any tangible medium containing or storing a program, which may be used by or in combination with an instruction execution system, device or device. In the present application, a computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, which carries a computer-readable computer program. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. Computer-readable signal media may also be any computer-readable medium other than computer-readable storage media, which may send, propagate, or transmit programs for use by or in conjunction with an instruction execution system, apparatus, or device. The computer program contained on the computer-readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wired, etc., or any suitable combination of the above.

[0111] The flowchart and block diagram in the accompanying drawings illustrate the possible architecture, functions and operations of the system, method and computer program product according to various embodiments of the present application. Wherein, each box in the flowchart or block diagram can represent a module, a program segment, or a part of the code, and the above-mentioned module, program segment, or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram or flowchart, and the combination of boxes in the block diagram or flowchart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0112] The units involved in the embodiments described in this application may be implemented by software or hardware, and the units described may also be set in a processor. The names of these units do not, in some cases, constitute limitations on the units themselves.

[0113] As another aspect, the present application also provides a computer-readable medium, which may be included in the electronic device described in the above embodiment; or may exist independently without being assembled into the electronic device. The above computer-readable medium carries one or more programs, and when the above one or more programs are executed by an electronic device, the electronic device implements the method described in the above embodiment.

[0114] It should be noted that, although several modules or units of the equipment for action execution are mentioned in the above detailed description, this division is not mandatory. In fact, according to the embodiments of the present application, the features and functions of two or more modules or units described above can be embodied in one module or unit. On the contrary, the features and functions of one module or unit described above can be further divided into being embodied by multiple modules or units.

[0115] Through the description of the above implementation methods, it is easy for those skilled in the art to understand that the example implementation methods described here can be implemented by software, or by combining software with necessary hardware. Therefore, the technical solution according to the implementation method of the present application can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.) or on a network, including several instructions to enable a computing device (which can be a personal computer, a server, a touch terminal, or a network device, etc.) to execute the method according to the implementation method of the present application.

[0116] Those skilled in the art will readily appreciate other embodiments of the present application after considering the specification and practicing the embodiments disclosed herein. The present application is intended to cover any variations, uses or adaptations of the present application, which follow the general principles of the present application and include common knowledge or customary technical means in the art that are not disclosed in the present application.

[0117] It should be understood that the present application is not limited to the precise structures that have been described above and shown in the drawings, and that various modifications and changes may be made without departing from the scope thereof. The scope of the present application is limited only by the appended claims.

Claims

1. A method for urban lighting control based on digital twins, characterized in that: include: Initially configure the lamps on the target road in the 3D digital twin model, including the location, height and illumination direction of the lamps; Based on the three-dimensional digital twin model, on the basis of meeting the lighting needs, according to the position, spacing, direction and power of the lamps, a multi-objective energy consumption optimization algorithm is used to determine the lighting strategy with the lowest energy consumption for the target road, wherein the lighting strategy includes lamp configuration and operation plan; Based on the real-time environmental information of the target road, adjusting the lighting of the lamp, wherein the lighting adjustment of the lamp includes adjusting the brightness of the lamp and / or lighting the lamp in sections; By ARIMA time series analysis, a predicted value of power consumption of the target road is obtained; The power consumption prediction value is compared with the actual power consumption of the target road to optimize the lighting strategy of the target road.

2. The method according to claim 1, characterized in that According to the location, spacing, direction and power of the lamps, a multi-objective energy consumption optimization algorithm is used to determine the lighting strategy with the lowest energy consumption for the target road, including: Generate a plurality of groups of initial lamp lighting solutions, wherein the lamp lighting solutions include lamp layout and power; In the three-dimensional digital twin model, the lighting effect of each group of initial lamp lighting solutions is simulated to calculate the total energy consumption, illumination uniformity deviation and cost of each initial lamp lighting solution; The position, spacing, direction and power of lamps are updated, and new solutions are generated using genetic algorithms and particle swarm optimization algorithms to meet the position, spacing, light coverage, direction and light pollution constraints, and obtain the lighting strategy with the lowest energy consumption for the target road.

3. The method according to claim 1 or 2, characterized in that: The method further comprises: A ray tracing model based on roads, lamps and traffic flow is constructed, and the light intensity distribution of the target road is calculated according to the following formula: in, Indicate point P The light intensity at n is the number of lamps, I i Indicates i The luminous flux of a lamp, θ i It represents the angle between the light and the vertical direction of the ground. d i Indicates i Light fixtures and points P distance; According to the light intensity distribution, it is determined whether the road surface of the target road meets the lighting requirements.

4. The method according to claim 1, characterized in that: The real-time environmental information includes time, weather conditions and traffic flow; Based on the real-time environmental information of the target road, the lighting of the lamps is adjusted, including: The brightness of the lamps on the target road is adjusted according to the following formula: in, L is the brightness of the current lamp, T is the current time, W For weather conditions, V For traffic flow, k 1 , k 2 , k 3 is the coefficient; The adjusted lamp brightness is sent to the three-dimensional digital twin model for updating and synchronized to the actual lamps on the target road.

5. The method according to claim 1, characterized in that The real-time environmental information includes time, weather conditions and traffic flow; Based on the real-time environmental information of the target road, the lighting of the lamps is adjusted, including: Determining whether to turn on the segmented lighting mode according to the time, the weather conditions and / or the traffic flow; In the segmented lighting mode, the spacing between adjacent lamps in the target road is determined according to the total number of lamps in the target road and the total length of the road; Determining a lighting step length corresponding to the target road based on the distance and a preset distance threshold; On the target road, a lamp is turned on every lighting step length to achieve segmented lighting.

6. The method according to claim 5, characterized in that The method further comprises: The target road is divided into several sections, and a corresponding segmented lighting strategy is executed for each section.

7. The method according to claim 1, characterized in that Through ARIMA time series analysis, the power consumption forecast value of the target road is obtained, including: The power consumption prediction value of the target road is determined according to the following formula: in, Y t Indicates time t The predicted value of electricity consumption is Y t-1 , Y t-2 Indicates the energy consumption data of the previous moment and the previous two moments, φ 1 , φ 2 is the autoregression coefficient, θ 1 is the moving average coefficient, ε t-1 is the residual at the previous moment, ε t is the error term; The residual corresponding to the initial power consumption prediction value and the environmental parameters corresponding to the target environmental road are used as inputs of a pre-trained LSTM network so that the LSTM network outputs an optimized power consumption prediction value, wherein the environmental parameters include at least one of temperature, humidity and traffic flow.

8. An urban lighting control device based on digital twins, characterized in that: include: The configuration module is used to initially configure the lamps on the target road in the 3D digital twin model, including the position, height and illumination direction of the lamps; An optimization module is used to determine the lighting strategy with the lowest energy consumption for the target road based on the three-dimensional digital twin model and according to the position, spacing, direction and power of the lamps on the basis of meeting the lighting needs, using a multi-objective energy consumption optimization algorithm, wherein the lighting strategy includes lamp configuration and operation plan; An adjustment module, configured to adjust the lighting of the lamp based on the real-time environmental information of the target road, wherein the lighting adjustment of the lamp includes adjusting the brightness of the lamp and / or lighting the lamp in sections; A prediction module, used for obtaining a predicted value of power consumption of the target road through ARIMA time series analysis; A processing module is used to compare the power consumption prediction value with the actual power consumption of the target road to optimize the lighting strategy of the target road.

9. A computer readable medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the urban lighting control method based on digital twins as described in any one of claims 1 to 7 is implemented.

10. An electronic device, characterized in that: include: one or more processors; A storage device for storing one or more programs, which, when executed by the one or more processors, enables the one or more processors to implement the urban lighting control method based on digital twins as described in any one of claims 1 to 7.

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