Urban Lighting Energy Consumption Simulation and Lifespan Prediction Management Methods and Systems

Abstract

This invention relates to the field of urban lighting management technology, providing a method and system for urban lighting energy consumption simulation and lifespan prediction management. The method includes: collecting basic urban lighting data based on natural light data, and constructing a digital twin model of urban lighting by combining building geometry, materials, and optical properties; using this model as a carrier, inputting ground illuminance and natural light data to calculate the actual light output, real-time energy consumption, and light decay status of the luminaires; establishing a lifespan prediction model using real-time energy consumption, light decay status, and electrical parameters to calculate the lifespan loss increment and obtain the predicted remaining lifespan value; comparing the remaining lifespan with maintenance thresholds, marking luminaires to be maintained and analyzing failure risks, determining maintenance priorities, and generating maintenance work orders; and feeding the lifespan prediction results back to the energy consumption and light decay calculation process to optimize the prediction scheme. This invention achieves accurate energy consumption simulation and dynamic lifespan prediction, optimizes maintenance decisions, and improves the efficiency and reliability of urban lighting management.

Description

Technical Field

[0001] This invention belongs to the field of urban lighting management technology, and in particular relates to a method and system for urban lighting energy consumption simulation and lifespan prediction management. Background Technology

[0002] As a core component of smart city infrastructure, urban lighting systems cover all areas including roads, commercial areas, residential areas, and public green spaces. Their operational stability and energy consumption levels are directly related to urban operating costs, public safety, and residents' quality of life.

[0003] With the acceleration of urbanization, the industry's management needs for urban lighting have shifted from traditional lighting control to refined energy consumption optimization and predictive maintenance. Traditional lighting systems, due to the large number and dispersed distribution of lamps, suffer from problems such as vague energy consumption statistics and high energy consumption operation, and urgently need to achieve accurate energy consumption simulation and control through intelligent means. Summary of the Invention

[0004] The purpose of this invention is to provide a method for simulating urban lighting energy consumption and predicting lifespan, which aims to solve the technical problems existing in the prior art as identified in the background art.

[0005] This invention is implemented as follows: a method for simulating and predicting the lifespan of urban lighting energy consumption, the method comprising:

[0006] Based on natural lighting data, basic urban lighting data is collected, and a digital twin model of urban lighting is constructed based on building geometry, material and optical properties.

[0007] Based on the constructed digital twin model of urban lighting, the actual light output and current working efficiency of each lamp are calculated using the final ground illuminance and natural light data as input. The real-time energy consumption of the lamp is calculated based on the rated power and actual light output, the light decay status is evaluated, and the real-time energy consumption and light decay status of each lamp are obtained.

[0008] By utilizing the real-time energy consumption and light decay status of each lamp, and continuously receiving the real-time operating electrical parameters and natural light data of the lamps, a lifespan prediction model is established. The lifespan loss increment of each lamp is calculated in real time, and all loss increments are aggregated to obtain the remaining lifespan prediction value based on the simulated changes in energy consumption.

[0009] Read the maintenance threshold of the luminaire, combine it with the remaining life prediction value, mark the luminaire when the remaining life prediction value is lower than the maintenance threshold, perform failure mode and impact analysis on the marked luminaire, determine the maintenance priority, and generate a maintenance work order containing luminaire information;

[0010] The lifespan prediction results are fed back into the calculation process of the lamp's real-time energy consumption and light decay status to optimize the lifespan prediction scheme.

[0011] As a further aspect of the present invention, the construction of the digital twin model for urban lighting specifically includes:

[0012] By pre-deploying light intensity sensors and IoT environmental sensors within the city, urban lighting data and natural light data are collected in real time, and the collected urban lighting data and natural light data are pre-processed.

[0013] Using urban lighting data and natural light data as environmental references, the location coordinates and usage parameters of each luminaire are obtained to form structured basic urban lighting data;

[0014] The city's BIM system exports three-dimensional geometric models of buildings within the city's lighting coverage area, and obtains building parameters, including facade materials and optical properties. Based on the three-dimensional geometric model and building parameters, a building reflection model containing light reflection and scattering characteristics is constructed.

[0015] Using standard timestamps as a benchmark, natural lighting data, urban lighting data, and building reflection models are uniformly mapped to a unified coordinate system. The lighting fixture data in the basic urban lighting data is matched with the building reflection models of the corresponding areas to construct a complete digital twin model of urban lighting.

[0016] As a further aspect of the present invention, the acquisition of real-time energy consumption and light decay status of each lamp specifically includes:

[0017] The digital twin model of urban lighting is used as the computing platform, and the final ground illuminance and natural light data are input.

[0018] The reflection angle and intensity of light from the building surface to the lamps are calculated based on the building reflection model. The attenuation process of light from the lamps to the ground is deduced by combining natural lighting data, and then the actual light output of each lamp is obtained.

[0019] The rated luminous flux of luminaires is extracted from the basic data of urban lighting, the ratio of the actual light output of the luminaires to the rated luminous flux of the luminaires is calculated, and the influence curve of the luminous efficiency of the luminaires is calculated in combination with the ambient temperature to obtain the current working efficiency of the luminaires.

[0020] The rated power of each lamp is extracted from the basic data of urban lighting, and the current photoelectric conversion efficiency is calculated in combination with the actual light output. Then, the actual light output is converted to a unit to obtain the real-time energy consumption of each lamp, and the results are stored in the energy consumption database.

[0021] The initial operating efficiency of luminaires is obtained from urban lighting basic data. The current operating efficiency of the luminaires is compared with the initial operating efficiency, and the light decay level is determined according to the ratio range. At the same time, the trend of real-time energy consumption is analyzed. If the increase in real-time energy consumption exceeds the set value three times in a row under the same actual light output, the light decay is determined to be aggravated. The light decay status of each luminaire is determined by combining the ratio of the current operating efficiency of the luminaires to the initial operating efficiency, and the data is stored in association with the real-time energy consumption data.

[0022] As a further aspect of the present invention, obtaining the remaining lifetime prediction value based on energy consumption simulation changes specifically includes:

[0023] Read the lamp's operating parameters, identify the lamp's real-time operating electrical parameters, and associate them with the lamp's real-time energy consumption and light decay status through the lamp's unique identifier to form a timestamped parameter dataset;

[0024] Based on the parameter dataset, and combined with real-time energy consumption and light decay status, a lifespan prediction model is constructed to calculate the lifespan loss of luminaires under different loads.

[0025] The rated service life of each lamp is obtained from the basic data of urban lighting. Combined with the life loss of the lamp under different loads, the life loss increment per unit time is calculated in real time using the life prediction model.

[0026] The cumulative lifespan loss is obtained by summing the incremental lifespan loss within a fixed period. The sum of all cumulative lifespan losses from the time the lamp was put into use to the present is then integrated to obtain the total cumulative lifespan loss. Finally, the remaining lifespan prediction value based on the energy consumption simulation changes is calculated.

[0027] By comparing the total cumulative lifespan loss of the lamps with their rated lifespan, risky lamps can be identified and a pre-alarm mechanism can be triggered.

[0028] As a further aspect of the present invention, the generation of a maintenance work order containing lighting fixture information specifically includes:

[0029] Read the maintenance threshold of the lamps, compare the predicted remaining life of each lamp with the corresponding maintenance threshold, and if the predicted remaining life is lower than the maintenance threshold, mark the lamp in the digital twin model of urban lighting, and record the lamp number, current remaining life and location coordinates to form a list of lamps to be maintained.

[0030] For the lighting fixtures to be maintained, the potential failure modes of the fixtures are determined by analyzing the real-time operating electrical parameters, light decay status, and natural light data. At the same time, the impact of each failure mode is assessed to form a failure risk assessment report.

[0031] Based on the fault risk assessment report, combined with the location of the luminaire and the predicted remaining lifespan, the comprehensive score of each luminaire to be maintained is calculated to determine the maintenance priority.

[0032] Another object of the present invention is to provide an urban lighting energy consumption simulation and lifespan prediction management system, the system comprising:

[0033] The basic data acquisition module is used to collect basic urban lighting data based on natural light data, and at the same time, to build a digital twin model of urban lighting based on building geometry, material and optical properties.

[0034] The twin model estimation module is used to estimate the actual light output and current working efficiency of each lamp based on the constructed urban lighting digital twin model, taking the final ground illuminance and natural light data as input. It also calculates the real-time energy consumption of the lamp based on the rated power and actual light output, evaluates the light decay status, and obtains the real-time energy consumption and light decay status of each lamp.

[0035] The lamp life prediction module is used to continuously receive real-time operating electrical parameters and natural light data of each lamp by utilizing the real-time energy consumption and light decay status of each lamp, to establish a life prediction model, calculate the life loss increment of each lamp in real time, and aggregate all loss increments to obtain the remaining life prediction value based on the simulated changes in energy consumption.

[0036] The priority analysis module is used to read the maintenance threshold of the lamps and combine it with the remaining life prediction value. When the remaining life prediction value of the lamps is lower than the maintenance threshold, the lamps are marked. Failure mode and impact analysis is performed on the marked lamps to determine the maintenance priority and generate a maintenance work order containing lamp information.

[0037] The lifespan prediction and optimization module is used to feed the lifespan prediction results back into the calculation process of the lamp's real-time energy consumption and light decay status, thereby optimizing the lifespan prediction scheme.

[0038] As a further embodiment of the present invention, the basic data acquisition module includes:

[0039] The lighting data acquisition unit is used to collect urban lighting data and natural light data in real time through light intensity sensors and IoT environmental sensors pre-deployed within the city, and to preprocess the collected urban lighting data and natural light data.

[0040] The data structuring unit is used to obtain the location coordinates and usage parameters of each lamp by taking urban lighting data and natural light data as environmental references, thus forming structured basic urban lighting data;

[0041] The model building unit is used to export the three-dimensional geometric model of buildings within the urban lighting coverage area through the urban BIM system, and at the same time obtain building parameters, including facade materials and optical properties, and build a building reflection model containing light reflection and scattering characteristics based on the three-dimensional geometric model and building parameters.

[0042] The digital twin model building unit is used to map natural lighting data, urban lighting data, and building reflection models to a unified coordinate system based on standard timestamps, and to match the lighting fixture data in the urban lighting basic data with the building reflection models of the corresponding areas to build a complete urban lighting digital twin model.

[0043] As a further embodiment of the present invention, the twin model calculation module includes:

[0044] The model input unit is used to input the final ground illuminance and natural light data by using the digital twin model of urban lighting as a computing carrier.

[0045] The building light reflection calculation unit is used to calculate the reflection angle and reflected light intensity of the building surface to the light from the lamps based on the building reflection model. It combines natural lighting data to deduce the attenuation process of light from the lamps to the ground, and thus obtain the actual light output of each lamp.

[0046] The luminaire working efficiency calculation unit is used to extract the rated luminous flux of the luminaire from the basic data of urban lighting, calculate the ratio of the actual light output of the luminaire to the rated luminous flux of the luminaire, and calculate the influence curve of the luminaire luminous efficiency in combination with the ambient temperature to obtain the current working efficiency of the luminaire.

[0047] The real-time energy consumption calculation unit is used to extract the rated power of each lamp from the basic data of urban lighting, calculate the current photoelectric conversion efficiency in combination with the actual light output, and then convert the actual light output to obtain the real-time energy consumption of each lamp, and store the results in the energy consumption database.

[0048] The light decay status determination unit is used to obtain the initial working efficiency of the lamps from the basic data of urban lighting, compare the current working efficiency of the lamps with the initial working efficiency of the lamps, determine the light decay level according to the ratio range, and analyze the change trend of real-time energy consumption. If the increase in real-time energy consumption exceeds the set value three times in a row under the same actual light output, the light decay is determined to be aggravated. The light decay status of each lamp is determined by combining the ratio of the current working efficiency of the lamps to the initial working efficiency of the lamps, and is associated with and stored with the real-time energy consumption data.

[0049] As a further embodiment of the present invention, the lamp life prediction module includes:

[0050] The dataset forming unit is used to read the lamp usage parameters, identify the real-time operating electrical parameters of the lamp, and associate them with the real-time energy consumption and light decay status of the lamp through the lamp's unique identifier to form a timestamped parameter dataset.

[0051] The lifespan prediction unit is used to build a lifespan prediction model based on the parameter dataset and combined with real-time energy consumption and light decay status, and to calculate the lifespan loss of the lamps under different loads.

[0052] The loss increment calculation unit is used to obtain the rated service life of each lamp from the basic data of urban lighting, and combine the life loss of the lamp under different loads to calculate the life loss increment per unit time in real time using the life prediction model.

[0053] The cycle cumulative loss integration unit is used to sum the lifetime loss increment within a fixed cycle to obtain the cycle cumulative loss. It integrates the sum of all cycle cumulative losses from the time the lamp is put into use to the present to obtain the total cumulative lifetime loss, and then calculates the remaining lifetime prediction value based on the energy consumption simulation change.

[0054] The risk lighting fixture identification unit is used to compare the total cumulative lifespan loss of lighting fixtures with their rated lifespan, identify risky lighting fixtures, and trigger a pre-alarm mechanism.

[0055] As a further embodiment of the present invention, the priority analysis module includes:

[0056] The list generation unit is used to read the maintenance threshold of the lamps, compare the predicted remaining life of each lamp with the corresponding maintenance threshold, and if the predicted remaining life is lower than the maintenance threshold, mark the lamp in the digital twin model of urban lighting, and record the lamp number, current remaining life and location coordinates to form a list of lamps to be maintained.

[0057] The risk assessment module uses the luminaire to be maintained and analyzes its real-time operating electrical parameters, light decay status, and natural light data to determine the potential failure modes of the luminaire. At the same time, it assesses the impact of each failure mode and generates a failure risk assessment report.

[0058] The maintenance priority calculation unit is used to calculate the comprehensive score of each lamp to be maintained based on the fault risk assessment report, combined with the location of the lamp and the predicted value of the remaining life, and to determine the maintenance priority.

[0059] The beneficial effects of this invention are:

[0060] This invention constructs a digital twin model of urban lighting by integrating natural lighting data, building geometry and optical properties, achieving a realistic mapping of lighting scenes. By combining building reflection models to deduce the actual light output of the luminaires, the accuracy of energy consumption simulation is greatly improved, enabling real-time energy consumption data to truly reflect the operating status of the luminaires under different environments and loads, providing reliable data support for energy consumption optimization and management.

[0061] A dynamic life prediction model is built based on real-time energy consumption, light decay status and electrical parameters. By calculating and accumulating the life loss increment per unit time, it breaks through the limitations of traditional static linear prediction. It can accurately capture the life loss rate of lamps in different aging stages and environmental conditions, making the remaining life prediction value more realistic and providing a scientific basis for preventive maintenance.

[0062] By determining maintenance thresholds, performing failure mode and impact analysis, and prioritizing in multiple dimensions, the remaining lifespan is combined with failure risk and regional importance to ensure that maintenance resources are prioritized for core areas and high-risk lighting fixtures. This avoids maintenance delays or resource waste and significantly improves maintenance efficiency and the operational reliability of urban lighting systems.

[0063] By feeding the lifetime prediction results back into the energy consumption and light decay calculation process, a data closed loop is formed, enabling the entire management system to continuously iterate based on actual operating data, thereby continuously improving the accuracy of energy consumption simulation and lifetime prediction. In the long run, this can effectively reduce the operating costs of urban lighting and contribute to the green and low-carbon development of smart cities. Attached Figure Description

[0064] Figure 1 A flowchart illustrating the urban lighting energy consumption simulation and lifespan prediction management method provided in an embodiment of the present invention;

[0065] Figure 2 A flowchart for constructing a digital twin model of urban lighting provided in an embodiment of the present invention;

[0066] Figure 3 This is a flowchart for obtaining the real-time energy consumption and light decay status of each lamp provided in an embodiment of the present invention;

[0067] Figure 4 A flowchart for obtaining a predicted remaining lifetime value based on energy consumption simulation changes, provided for an embodiment of the present invention;

[0068] Figure 5 A flowchart for generating a maintenance work order containing lighting information is provided in an embodiment of the present invention;

[0069] Figure 6 This is a structural block diagram of the urban lighting energy consumption simulation and lifespan prediction management system provided in an embodiment of the present invention;

[0070] Figure 7 This is a structural block diagram of the basic data acquisition module provided in an embodiment of the present invention;

[0071] Figure 8 This is a structural block diagram of the twin model estimation module provided in an embodiment of the present invention;

[0072] Figure 9 This is a structural block diagram of the lamp life prediction module provided in an embodiment of the present invention;

[0073] Figure 10 This is a structural block diagram of the priority analysis module provided in an embodiment of the present invention. Detailed Implementation

[0074] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0075] Figure 1 A flowchart of the urban lighting energy consumption simulation and lifespan prediction management method provided in this embodiment of the invention is shown below. Figure 1 As shown, the method includes:

[0076] S100 collects basic urban lighting data based on natural lighting data, and constructs a digital twin model of urban lighting based on building geometry, material and optical properties.

[0077] Throughout the city, based on the functional differences of the lighting coverage areas (such as roads, commercial areas, residential areas, parks, etc.), a systematic deployment of light intensity sensors and IoT environmental sensors is implemented. These sensors form a distributed sensing network that collects real-time data on natural light intensity, as well as lighting data such as the on / off status and initial luminous parameters of the luminaires. The operating status of urban lighting is highly coupled with natural light; changes in natural light at different times and under different weather conditions directly affect the actual workload of the luminaires. Only by acquiring this dynamic data in real time can a realistic environmental benchmark be provided for subsequent energy consumption simulations, avoiding calculation errors caused by the lag of static data.

[0078] Using the processed natural light and illumination data as environmental references, the location coordinates and usage parameters (rated power, rated luminous flux, initial operating efficiency) of each luminaire are integrated to form structured urban lighting basic data. A unique data file is established for each luminaire, so that the calculation of energy consumption and light decay of a single lamp in subsequent steps can be accurately located to the specific individual, rather than a fuzzy average calculation of the entire area.

[0079] While constructing the basic data, three-dimensional geometric models of all buildings within the lighting coverage area are exported, fully considering the impact of buildings on light propagation in the urban environment. Urban buildings are densely packed and diverse in material; the optical properties of different materials, such as glass curtain walls, stone walls, and metal finishes, vary significantly, directly altering the propagation path and intensity of light from luminaires. For example, the high reflectivity of glass curtain walls may increase ground illuminance in some areas due to enhanced reflected light. Ignoring this factor and directly calculating the luminaire output would be out of sync with actual ground lighting needs. Therefore, a building reflection model incorporating light reflection and scattering characteristics is constructed by combining the three-dimensional geometric model of the buildings with their facade materials and optical properties. This model can simulate the effect of different building surfaces on luminaire light.

[0080] Using standard timestamps as a unified benchmark, natural light data, urban lighting data, and building reflection models are mapped to the same urban coordinate system. At the same time, the lighting data in the structured basic data is precisely matched with the building reflection models of the corresponding areas to form a complete digital twin model of urban lighting. This achieves spatiotemporal collaboration and ensures that all data and models are associated within the same spatiotemporal framework. For example, the natural light data of a street at a certain moment and the working data of the street's lighting fixtures can accurately correspond to the reflection models of the buildings on both sides, making the constructed twin model a "virtual mirror" of the actual urban lighting scene.

[0081] like Figure 2 As shown, the construction of the digital twin model for urban lighting specifically includes:

[0082] S110 collects urban lighting data and natural light data in real time through light intensity sensors and IoT environmental sensors pre-deployed within the city, and preprocesses the collected urban lighting data and natural light data.

[0083] S120 uses urban lighting data and natural light data as environmental references to obtain the location coordinates and usage parameters of each luminaire, forming structured basic urban lighting data;

[0084] S130 uses the city BIM system to export the three-dimensional geometric model of buildings within the urban lighting coverage area, and obtains building parameters, including facade materials and optical properties. Based on the three-dimensional geometric model and building parameters, a building reflection model containing light reflection and scattering characteristics is constructed.

[0085] S140 uses standard timestamps as a benchmark to map natural lighting data, urban lighting data, and building reflection models into a unified coordinate system. It also matches the lighting fixture data in the basic urban lighting data with the building reflection models of the corresponding areas to construct a complete digital twin model of urban lighting.

[0086] S200, based on the constructed digital twin model of urban lighting, takes the final ground illuminance and natural light data as input, calculates the actual light output and current working efficiency of each lamp, and calculates the real-time energy consumption of the lamp based on the rated power and actual light output, evaluates the light decay status, and obtains the real-time energy consumption and light decay status of each lamp.

[0087] Using the established digital twin model of urban lighting as the core computing platform, the final ground illuminance and natural light data are input. The final ground illuminance directly reflects the actual service effect of urban lighting, while natural light is the core environmental variable affecting the actual workload of luminaires. Fluctuations in natural light at different times and under different weather conditions directly change the effective light output required by the luminaires. If the influence of natural light is ignored and calculations are based solely on the rated parameters of the luminaires, energy consumption will be severely out of sync with actual operating conditions. For example, in the entire urban area, natural light is sufficient during the daytime, especially at noon, when ground illuminance is mainly contributed by natural light, and luminaires only need to maintain low power operation to meet the demand; however, at dusk or night, natural light drops sharply, and luminaires need to increase output to ensure that ground illuminance meets the standards.

[0088] Based on the building reflection model in the digital twin model, the reflection angle and intensity of light from the building surface to the luminaires are calculated. Then, combined with natural lighting data, the attenuation process of light from the luminaires to the ground is deduced to obtain the actual light output of each luminaire. In urban scenarios, there are many buildings of different materials and shapes. The light emitted by the luminaires does not only reach the ground in a direct manner. Some light is reflected by the building surface before reaching the ground. If only direct light is calculated, it will lead to a misjudgment of the actual light output of the luminaires. The reflected light intensity in glass curtain wall areas may cause the actual light output of the luminaires to be lower than the theoretically calculated value when the actual ground illuminance meets the standard.

[0089] By simulating the reflection process using a building reflection model, the final ground illuminance is first subtracted from the natural light to obtain the total illuminance actually contributed by the luminaires. Then, the direct illuminance and reflected illuminance are further broken down. Finally, the actual light output is derived by reverse-engineering the illuminance formula. This ensures that the calculated light output fully matches the actual light propagation path, guaranteeing the accuracy of the starting data for subsequent energy consumption and efficiency calculations.

[0090] After obtaining the actual light output, the rated luminous flux of the luminaires is extracted from the city's basic lighting data. The ratio of the actual light output to the rated luminous flux is calculated, and then the influence curve of the luminaire's luminous efficiency is calculated in conjunction with the ambient temperature. Finally, the current operating efficiency of the luminaire is obtained. The luminous efficiency of a luminaire is not a constant rated value but fluctuates due to the influence of ambient temperature. For example, in high-temperature environments, the chip temperature of LED luminaires increases, leading to a decrease in luminous efficiency. Even if the ratio of the actual light output to the rated luminous flux is normal, ignoring the temperature effect will underestimate the actual operating losses of the luminaire. In low-temperature environments, the luminous efficiency of some luminaires may be slightly improved. Therefore, correcting the efficiency ratio by incorporating the temperature influence curve aims to ensure that the current operating efficiency truly reflects the performance status of the luminaire in the real-time environment, rather than relying solely on theoretical parameters.

[0091] The rated power of each luminaire is extracted from the basic data, and the current photoelectric conversion efficiency is calculated based on the actual light output. Then, the actual light output is converted to units to obtain the real-time energy consumption of each luminaire, which is stored in the energy consumption database. The real-time energy consumption of a luminaire is directly related to its actual light output and photoelectric conversion efficiency. Photoelectric conversion efficiency reflects the proportion of electrical energy converted into light energy; the lower the efficiency, the more electrical energy is consumed for the same light output. Calculating the conversion efficiency by combining the actual light output and rated power, and then deriving the real-time energy consumption, avoids miscalculations of energy consumption caused by discrepancies between rated power and actual operating power. Storing the real-time energy consumption in the database not only enables real-time monitoring of energy consumption data but also provides energy consumption-time correlation data for the subsequent lifespan prediction module, allowing lifespan calculations to be based on actual energy load rather than theoretical load.

[0092] In urban residential areas, the photoelectric conversion efficiency of some courtyard lights that have been in use for a long time has decreased significantly. Under the condition of maintaining the same ground illuminance (i.e. the same actual light output), the real-time energy consumption will be higher than that of newly installed lights of the same model. These differences will be accurately recorded in the energy consumption database to provide a basis for subsequent judgment on the life wear rate of such lights.

[0093] Finally, the initial operating efficiency of the luminaire is obtained from the basic data. The current operating efficiency is compared with the initial efficiency, and the light decay level is determined based on the ratio range. Simultaneously, the real-time energy consumption trend is analyzed. If, under the same actual light output, the real-time energy consumption increase exceeds the set value multiple times consecutively, the light decay is determined to be severe. Finally, the light decay state is determined by combining the efficiency ratio and energy consumption trend, and this is stored in association with the real-time energy consumption data. Light decay is not only characterized by decreased efficiency but also by increased energy consumption. When a luminaire experiences light decay, more energy is required to maintain the same light output (meeting ground illuminance requirements). Determining the light decay state solely through the efficiency ratio may be lagging (the efficiency ratio has just entered the range of slight light decay, but energy consumption has already begun to rise continuously). Combining the energy consumption trend allows for earlier and more comprehensive identification of the light decay state. Linking and storing light decay and energy consumption data forms a closed loop of state and data, allowing the subsequent lifespan prediction module to simultaneously calculate lifespan loss based on the degree of light decay and energy load, ensuring the accuracy of the prediction results.

[0094] like Figure 3 As shown, the acquisition of real-time energy consumption and light decay status of each lamp specifically includes:

[0095] S210 uses the digital twin model of urban lighting as a computing carrier and inputs the final ground illuminance and natural light data;

[0096] S220, Calculate the reflection angle and reflected light intensity of the building surface to the light from the luminaire based on the building reflection model:

[0097] ;

[0098] in, Reflected light intensity refers to the intensity of light reflected from a building surface in a certain direction. Incident light intensity refers to the light intensity emitted by a luminaire onto a building surface. The reflectivity of the building surface material is obtained from building parameters. The angle between the reflection direction and the surface normal, in this case, refers to the angle between the reflected ray and the observation direction;

[0099] By combining natural lighting data, the attenuation process of light from the lamps to the ground is deduced, thereby obtaining the actual light output of each lamp:

[0100] Input final ground illuminance and natural light intensity .

[0101] Calculate the illuminance contributed by the luminaires: .

[0102] Calculate reflected illuminance using a building reflection model , ;

[0103] in, The angle between the reflected ray and the ground normal. The distance from the building's reflection point to the target point on the ground;

[0104] Total Illuminance Equation: The direct illuminance of the lamp, of which This is the direct component.

[0105] To determine the actual light output of a luminaire, use the illuminance formula: To reverse the process:

[0106] ;

[0107] in:

[0108] This indicates the actual light output of the lamp. The angle of incidence of light. The height of the light fixture above the ground is obtained from the coordinate position.

[0109] The actual light output is determined through iterative optimization:

[0110] .

[0111] S230 extracts the rated luminous flux of luminaires from urban lighting baseline data, calculates the ratio of the actual luminous output of the luminaires to their rated luminous flux, and combines this with the influence curve of ambient temperature to calculate the luminous efficiency of the luminaires, thereby obtaining the current operating efficiency of the luminaires. :

[0112] ;

[0113] in, The rated luminous flux is obtained from basic urban lighting data. The temperature effect function is obtained from the luminous efficiency effect curve of the lamp.

[0114] Current operating efficiency represents the current light output compared to when it was brand new. It is a relative performance indicator that directly reflects the health status of the luminaire, i.e., the degree of light decay.

[0115] S240 extracts the rated power of each luminaire from the city's basic lighting data, calculates the current photoelectric conversion efficiency based on the actual light output, and then performs a unit conversion on the actual light output to obtain the real-time energy consumption of each luminaire. The results are then stored in the energy consumption database.

[0116] ;

[0117] ;

[0118] Among them, is the current photoelectric conversion efficiency, is the rated power of the lamp;

[0119] The current photoelectric conversion efficiency represents how much of the consumed electrical energy is converted into light energy. It is an absolute performance indicator and is directly related to the energy consumption cost.

[0120] S250. Obtain the initial working efficiency of the lamp from the basic data of urban lighting. Compare the current working efficiency of the lamp with the initial working efficiency of the lamp. Determine the light decay level according to the ratio range. At the same time, analyze the change trend of the real-time energy consumption. If the increase amplitude of the real-time energy consumption exceeds the set amplitude value continuously for 3 times under the same actual light output, it is determined that the light decay degree has increased. Combine the ratio of the current working efficiency of the lamp to the initial working efficiency of the lamp to determine the light decay state of each lamp, and store it in association with the real-time energy consumption data.

[0121] Specifically:

[0122] Obtain the initial working efficiency of the lamp from the basic data of urban lighting (efficiency at the time of new lamp).

[0123] Calculate the current working efficiency .

[0124] Calculate the efficiency ratio .

[0125] Determine the light decay level according to the ratio range: r > 0.9 is normal, 0.7 < r ≤ 0.9 is slight light decay, r ≤ 0.7 is severe light decay.

[0126] Analyze the real-time energy consumption of the change trend: If under the same actual light output , the increase amplitude of the real-time energy consumption exceeds the set amplitude value (set to 5% in the plan) continuously for 3 times, it is determined that the light decay degree has increased.

[0127] Combine the efficiency ratio and the energy consumption trend to determine the final light decay state, and store it in association with the real-time energy consumption data.

[0128] Light decay causes the efficiency of the lamp to decrease and the energy consumption to increase under the same light output. Therefore, the efficiency ratio and the energy consumption trend together improve the judgment accuracy. Detecting continuously for 3 times avoids false alarms and ensures reliability. Associative storage is convenient for life prediction and maintenance decision-making.

[0129] S300. Using the obtained real-time energy consumption and light decay state of each lamp, continuously receive the real-time operating electrical parameters and natural light data of the lamp, establish a life prediction model, calculate the life loss increment of each lamp in real time, and aggregate all loss increments to obtain the remaining life prediction value based on the simulated change of energy consumption;

[0130] The system reads the basic operating parameters of the luminaires and simultaneously collects their electrical operating parameters in real time via IoT sensors. These electrical parameters are then deeply correlated with real-time energy consumption and light decay data using the luminaire's unique identifier, ultimately forming a timestamped parameter dataset. Urban lighting systems involve a vast number of luminaires across diverse application scenarios, with significant differences in their operating parameters and environments. Relying solely on single data points cannot accurately reflect the true aging state of the luminaires. By using unique identifiers to correlate multi-dimensional data and combining this with timestamps to record the temporal changes of the data, the system provides comprehensive, multi-dimensional foundational data for subsequent model building, avoiding prediction biases caused by data fragmentation.

[0131] In urban lighting management, the rated power and applicable current of street lights are significantly different from those of park landscape lights. The real-time current fluctuations during operation (street lights may require frequent adjustments to illuminance due to high traffic volume, resulting in large current fluctuations) also differ from the correlation between energy consumption and light decay. By integrating these differentiated data into a time-series dataset through unique identifiers, it can be ensured that the prediction model for each light fixture is built based on its own actual operating trajectory, rather than uniformly applying a standard model.

[0132] Based on the parameter dataset, a lifespan prediction model is constructed by combining real-time energy consumption and light decay status to calculate the lifespan loss of luminaires under different loads. The lifespan loss of luminaires is not determined by a single time factor, but is closely related to the actual operating load (real-time energy consumption directly reflects the load size) and the degree of aging (light decay is the core manifestation of aging): when luminaires are operating under high load, the aging rate of internal components accelerates, exacerbating lifespan loss; simultaneously, the more severe the light decay, the more advanced the aging process within the luminaire, and even with a constant load, the lifespan loss rate will be higher than that of new luminaires. Traditional lifespan prediction models are mostly based on linear calculations of rated lifespan minus usage time, ignoring the dynamic influence of actual load and aging status. The model constructed in this step, by using energy consumption (load) and light decay (aging) as core variables, can more realistically simulate the aging process of luminaires.

[0133] For example, some of the streetlights installed in the same batch are located in commercial areas (which require high illuminance, have high real-time energy consumption, and fast light decay), while others are located in suburban areas (which have low illuminance requirements, low energy consumption, and slow light decay). This model can calculate the lifespan loss rate of the two types of lights under different loads, avoiding the problem of the predicted lifespan of suburban lights being too short or the predicted lifespan of commercial lights being too long due to uniform calculation.

[0134] Subsequently, the rated lifespan of each luminaire was extracted from the city's basic lighting data. Combined with the lifespan loss results under different loads, a lifespan prediction model was used to calculate the lifespan loss increment per unit time in real time. Lifespan loss is a continuous, cumulative process; the loss increment per unit time dynamically reflects the current aging rate of the luminaire, rather than averaging it over the entire lifespan. In the high temperatures of summer, the luminaire's heat dissipation efficiency decreases, requiring more energy to maintain the same illuminance (real-time energy consumption increases). At this time, the lifespan loss increment per unit time will be significantly higher than in spring and autumn. Furthermore, when a luminaire experiences a minor malfunction, real-time electrical parameters will become abnormal, leading to energy consumption fluctuations, and the loss increment per unit time will also change accordingly. By calculating the loss increment in real time, the impact of these dynamic factors on lifespan can be captured promptly, ensuring the timeliness and accuracy of lifespan prediction.

[0135] The incremental lifespan loss within a fixed period is summed to obtain the cumulative loss for that period. This cumulative loss is then combined with the sum of all cumulative losses from the time the light fixture was put into use until the current time, ultimately calculating the predicted remaining lifespan based on energy consumption simulation changes. This incremental accumulation method accurately quantifies the total aging of the light fixture from its initial use to the present, avoiding errors caused by one-time estimations.

[0136] The total cumulative lifespan loss of each luminaire is compared with its rated lifespan. When the cumulative loss approaches or reaches a certain percentage of the rated lifespan, the luminaire is identified as a high-risk luminaire and an alarm mechanism is triggered. This step is designed to identify potential failure risks in advance, allowing sufficient time for maintenance decisions and preventing lighting interruptions due to sudden luminaire failure.

[0137] like Figure 4 As shown, obtaining the remaining lifetime prediction value based on energy consumption simulation changes specifically includes:

[0138] S310 reads the lamp's operating parameters, identifies the lamp's real-time operating electrical parameters, and associates them with the lamp's real-time energy consumption and light decay status through the lamp's unique identifier to form a timestamped parameter dataset;

[0139] Based on the parameter dataset, and combined with real-time energy consumption and light decay status, the S320 constructs a lifespan prediction model to calculate the lifespan loss of the luminaire under different loads.

[0140] ;

[0141] in, This refers to the rate of lifespan degradation. These are the characteristic constants of the luminaire, obtained from life test data.

[0142] S330 obtains the rated lifespan of each luminaire from the city's basic lighting data, and combines this with the lifespan loss of the luminaires under different loads, using a lifespan prediction model to calculate the lifespan per unit time in real time. Increment in lifespan loss :

[0143] ;

[0144] S340: The cumulative lifespan loss within a fixed period is summed to obtain the cumulative lifespan loss. This sum is then integrated with the cumulative lifespan loss from the time the light fixture was put into use until the present, resulting in the total cumulative lifespan loss. Finally, the predicted remaining lifespan based on energy consumption simulation changes is calculated. :

[0145] ;

[0146] in, The rated service life is obtained from basic urban lighting data. For the first The increase in lifetime loss per unit time This refers to the number of unit time intervals from when the luminaire was put into use until the present.

[0147] Total accumulated lifespan loss This indicates the total loss.

[0148] By summing up accumulated losses, the actual aging situation is dynamically reflected, which is more accurate than a fixed lifespan based on real-time energy consumption and light decay status.

[0149] The S350 compares the total cumulative lifespan loss of the luminaires with their rated lifespan to identify risky luminaires and trigger a pre-alarm mechanism.

[0150] S400 reads the maintenance threshold of the luminaire, combines it with the remaining life prediction value, and marks the luminaire when the remaining life prediction value is lower than the maintenance threshold. It then performs failure mode and impact analysis on the marked luminaire, determines the maintenance priority, and generates a maintenance work order containing luminaire information.

[0151] The maintenance threshold for lighting fixtures is not a uniform fixed value, but is comprehensively formulated based on the type of lighting fixture, the importance of the usage scenario, the maintenance resource reserves, and historical maintenance data. This makes the maintenance triggering mechanism more in line with actual management needs, balances the timeliness of maintenance with the economy of resources, and prevents the maintenance of critical areas from being delayed or non-critical areas from being over-maintained due to a uniform threshold.

[0152] Once the maintenance threshold is determined, the predicted remaining lifespan of each luminaire is compared with the corresponding threshold. If the remaining lifespan is lower than the threshold, the luminaire is visually marked in the urban lighting digital twin model. Simultaneously, the luminaire's unique ID (for linking to historical operating data), current remaining lifespan (providing maintenance personnel with a reference for aging levels), and precise location coordinates are recorded, ultimately forming a structured list of luminaires to be maintained. This transforms abstract remaining lifespan data into a concrete list of maintenance objects, solving the problem of "fuzzy maintenance objects" caused by the large number and dispersed distribution of luminaires in urban lighting systems.

[0153] Failure Mode and Effects Analysis (FMEA) is conducted on the luminaires to be maintained. This involves analyzing the luminaire's real-time operating electrical parameters, light decay status, and natural light data to identify potential failure modes. The potential impact of each failure mode is assessed, including its effects on public safety, lighting quality, subsequent energy consumption, and maintenance costs. This results in a failure risk assessment report. The design logic behind this step is that relying solely on remaining lifespan to determine maintenance needs is insufficient; even luminaires with the same remaining lifespan can exhibit significantly different levels of hazard from potential failures.

[0154] Based on the fault risk assessment report, the maintenance priority of each light fixture is determined by combining the importance of its location and the predicted remaining lifespan. A comprehensive scoring method is then used to assign weights to each dimension according to management needs, ultimately calculating a comprehensive score for each light fixture. A higher score indicates a higher maintenance priority. This optimizes the efficiency of maintenance resource allocation. Urban maintenance resources are always limited, and it's impossible to work on all light fixtures simultaneously. Prioritization ensures that lights in core areas and those with high-risk faults receive priority maintenance.

[0155] like Figure 5 As shown, generating a maintenance work order containing lighting fixture information specifically includes:

[0156] S410, Read the lamp maintenance threshold, compare the predicted remaining life of each lamp with the corresponding maintenance threshold, if the predicted remaining life is lower than the maintenance threshold, mark the lamp in the urban lighting digital twin model, and record the lamp number, current remaining life and location coordinates to form a list of lamps to be maintained;

[0157] S420: For the luminaire to be maintained, the potential failure modes of the luminaire are determined by analyzing the luminaire's real-time operating electrical parameters, light decay status, and natural light data. At the same time, the impact of each failure mode is assessed, and a failure risk assessment report is generated.

[0158] S430 calculates the comprehensive score of each lamp to be maintained based on the fault risk assessment report, combined with the location of the lamp and the predicted remaining life value, and determines the maintenance priority.

[0159] The S500 feeds back the lifespan prediction results to the calculation process of the luminaire's real-time energy consumption and light decay status, thereby optimizing the lifespan prediction scheme.

[0160] Figure 6 This is a structural block diagram of the urban lighting energy consumption simulation and lifespan prediction management system provided in an embodiment of the present invention, as shown below. Figure 6 As shown, the system includes:

[0161] The basic data acquisition module 100 is used to collect basic urban lighting data based on natural light data, and at the same time, to construct a digital twin model of urban lighting based on building geometry, material and optical properties.

[0162] The twin model calculation module 200 is used to calculate the actual light output and current working efficiency of each lamp based on the constructed urban lighting digital twin model, taking the final ground illuminance and natural light data as input, and calculate the real-time energy consumption of the lamp based on the rated power and actual light output, evaluate the light decay status, and obtain the real-time energy consumption and light decay status of each lamp.

[0163] The lamp life prediction module 300 is used to continuously receive the real-time operating electrical parameters and natural light data of each lamp by utilizing the real-time energy consumption and light decay status of each lamp, to establish a life prediction model, calculate the life loss increment of each lamp in real time, and aggregate all loss increments to obtain the remaining life prediction value based on the simulated changes in energy consumption.

[0164] The priority analysis module 400 is used to read the maintenance threshold of the lamps and combine it with the remaining life prediction value. When the remaining life prediction value of the lamps is lower than the maintenance threshold, the lamps are marked. Failure mode and impact analysis is performed on the marked lamps to determine the maintenance priority and generate a maintenance work order containing lamp information.

[0165] The lifespan prediction and optimization module 500 is used to feed back the lifespan prediction results into the calculation process of the lamp's real-time energy consumption and light decay status, thereby optimizing the lifespan prediction scheme.

[0166] like Figure 7 As shown, the basic data acquisition module 100 includes:

[0167] The lighting data acquisition unit 110 is used to collect urban lighting data and natural light data in real time through light intensity sensors and IoT environmental sensors pre-deployed within the city, and to preprocess the collected urban lighting data and natural light data.

[0168] The data structuring unit 120 is used to obtain the location coordinates and usage parameters of each lamp using urban lighting data and natural light data as environmental references, forming structured basic urban lighting data.

[0169] Model building unit 130 is used to export the three-dimensional geometric model of buildings within the urban lighting coverage area through the urban BIM system, and at the same time obtain building parameters, including facade materials and optical properties, and construct a building reflection model containing light reflection and scattering characteristics based on the three-dimensional geometric model and building parameters.

[0170] The digital twin model building unit 140 is used to map natural lighting data, urban lighting data and building reflection models to a unified coordinate system based on standard timestamps, and to match the lighting data in the urban lighting basic data with the building reflection models of the corresponding areas to build a complete urban lighting digital twin model.

[0171] like Figure 8 As shown, the twin model inference module 200 includes:

[0172] The model input unit 210 is used to input the final ground illuminance and natural light data into the urban lighting digital twin model as a calculation carrier.

[0173] The building light reflection calculation unit 220 is used to calculate the reflection angle and reflected light intensity of the building surface to the light from the lamps based on the building reflection model, and to deduce the attenuation process of light from the lamps to the ground by combining natural lighting data, so as to obtain the actual light output of each lamp.

[0174] The luminaire working efficiency calculation unit 230 is used to extract the rated luminous flux of the luminaire from the basic data of urban lighting, calculate the ratio of the actual light output of the luminaire to the rated luminous flux of the luminaire, and calculate the influence curve of the luminaire luminous efficiency in combination with the ambient temperature to obtain the current working efficiency of the luminaire.

[0175] The real-time energy consumption calculation unit 240 is used to extract the rated power of each lamp from the basic data of urban lighting, calculate the current photoelectric conversion efficiency in combination with the actual light output, and then convert the actual light output to obtain the real-time energy consumption of each lamp, and store the results in the energy consumption database.

[0176] The light decay status determination unit 250 is used to obtain the initial working efficiency of the lamps from the basic data of urban lighting, compare the current working efficiency of the lamps with the initial working efficiency of the lamps, determine the light decay level according to the ratio range, and analyze the change trend of real-time energy consumption. If the increase in real-time energy consumption exceeds the set value three times consecutively under the same actual light output, the light decay is determined to be aggravated. The light decay status of each lamp is determined by combining the ratio of the current working efficiency of the lamps to the initial working efficiency of the lamps, and is associated with and stored with the real-time energy consumption data.

[0177] like Figure 9 As shown, the lamp life prediction module 300 includes:

[0178] The dataset forming unit 310 is used to read the lamp usage parameters, identify the real-time operating electrical parameters of the lamp, and associate them with the real-time energy consumption and light decay status of the lamp through the lamp's unique identifier to form a parameter dataset with a timestamp.

[0179] The lifespan prediction unit 320 is used to build a lifespan prediction model based on the parameter dataset and combined with real-time energy consumption and light decay status, and to calculate the lifespan loss of the lamp under different loads.

[0180] The loss increment calculation unit 330 is used to obtain the rated service life of each lamp from the basic data of urban lighting, and combine the life loss of the lamp under different loads to calculate the life loss increment per unit time in real time using the life prediction model.

[0181] The cycle cumulative loss integration unit 340 is used to sum the life loss increment within a fixed cycle to obtain the cycle cumulative loss, integrate the sum of all cycle cumulative losses from the time the lamp is put into use to the present, obtain the total cumulative life loss, and then calculate the remaining life prediction value based on the energy consumption simulation change.

[0182] The risk lighting fixture identification unit 350 is used to compare the total cumulative lifespan loss of the lighting fixtures with their rated lifespan, identify risky lighting fixtures, and trigger a pre-alarm mechanism.

[0183] like Figure 10 As shown, the priority analysis module 400 includes:

[0184] The list generation unit 410 is used to read the lamp maintenance threshold, compare the predicted remaining life of each lamp with the corresponding maintenance threshold, and if the predicted remaining life is lower than the maintenance threshold, mark the lamp in the urban lighting digital twin model, and record the lamp number, current remaining life and location coordinates to form a list of lamps to be maintained.

[0185] The risk assessment module 420 uses the luminaire to be maintained to analyze and determine the potential failure modes of the luminaire by combining the luminaire's real-time operating electrical parameters, light decay status, and natural light data. At the same time, it assesses the impact of each failure mode and generates a failure risk assessment report.

[0186] The maintenance priority calculation unit 430 is used to calculate the comprehensive score of each lamp to be maintained based on the fault risk assessment report, combined with the location of the lamp and the predicted value of the remaining life, and to determine the maintenance priority.

[0187] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0188] The embodiments described above are merely illustrative of several implementations of the present invention, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these modifications and improvements all fall within the scope of protection of the present invention. Therefore, the scope of protection of this patent should be determined by the appended claims.

[0189] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for simulating urban lighting energy consumption and predicting its lifespan, characterized in that, The method includes: Based on natural lighting data, basic urban lighting data is collected, and a digital twin model of urban lighting is constructed based on building geometry, material and optical properties. Based on the constructed digital twin model of urban lighting, the actual light output and current working efficiency of each lamp are calculated using the final ground illuminance and natural light data as input. The real-time energy consumption of the lamp is calculated based on the rated power and actual light output, the light decay status is evaluated, and the real-time energy consumption and light decay status of each lamp are obtained. By utilizing the real-time energy consumption and light decay status of each lamp, and continuously receiving the real-time operating electrical parameters and natural light data of the lamps, a lifespan prediction model is established. The lifespan loss increment of each lamp is calculated in real time, and all loss increments are aggregated to obtain the remaining lifespan prediction value based on the simulated changes in energy consumption. Read the maintenance threshold of the luminaire, combine it with the remaining life prediction value, mark the luminaire when the remaining life prediction value is lower than the maintenance threshold, perform failure mode and impact analysis on the marked luminaire, determine the maintenance priority, and generate a maintenance work order containing luminaire information; The lifespan prediction results are fed back into the calculation process of the lamp's real-time energy consumption and light decay status to optimize the lifespan prediction scheme. in: The construction of the digital twin model for urban lighting specifically includes: By pre-deploying light intensity sensors and IoT environmental sensors within the city, urban lighting data and natural light data are collected in real time, and the collected urban lighting data and natural light data are pre-processed. Using urban lighting data and natural light data as environmental references, the location coordinates and usage parameters of each luminaire are obtained to form structured basic urban lighting data; The city's BIM system exports three-dimensional geometric models of buildings within the city's lighting coverage area, and obtains building parameters, including facade materials and optical properties. Based on the three-dimensional geometric model and building parameters, a building reflection model containing light reflection and scattering characteristics is constructed. Based on the standard timestamp, natural light data, urban lighting data and building reflection models are mapped to a unified coordinate system, and the lighting data in the basic urban lighting data are matched with the building reflection models of the corresponding areas to construct a complete digital twin model of urban lighting. The acquisition of real-time energy consumption and light decay status of each lamp specifically includes: The digital twin model of urban lighting is used as the computing platform, and the final ground illuminance and natural light data are input. The reflection angle and intensity of light from the building surface to the lamps are calculated based on the building reflection model. The attenuation process of light from the lamps to the ground is deduced by combining natural lighting data, and then the actual light output of each lamp is obtained. The rated luminous flux of luminaires is extracted from the basic data of urban lighting, the ratio of the actual light output of the luminaires to the rated luminous flux of the luminaires is calculated, and the influence curve of the luminous efficiency of the luminaires is calculated in combination with the ambient temperature to obtain the current working efficiency of the luminaires. The rated power of each lamp is extracted from the basic data of urban lighting, and the current photoelectric conversion efficiency is calculated in combination with the actual light output. Then, the actual light output is converted to a unit to obtain the real-time energy consumption of each lamp, and the results are stored in the energy consumption database. The initial operating efficiency of luminaires is obtained from urban lighting basic data. The current operating efficiency of the luminaires is compared with the initial operating efficiency, and the light decay level is determined according to the ratio range. At the same time, the trend of real-time energy consumption is analyzed. If the increase in real-time energy consumption exceeds the set value three times in a row under the same actual light output, the light decay is determined to be aggravated. The light decay status of each luminaire is determined by combining the ratio of the current operating efficiency of the luminaires to the initial operating efficiency, and the data is stored in association with the real-time energy consumption data.

2. The method according to claim 1, characterized in that, Obtaining the remaining lifetime prediction value based on energy consumption simulation changes specifically includes: Read the lamp's operating parameters, identify the lamp's real-time operating electrical parameters, and associate them with the lamp's real-time energy consumption and light decay status through the lamp's unique identifier to form a timestamped parameter dataset; Based on the parameter dataset, and combined with real-time energy consumption and light decay status, a lifespan prediction model is constructed to calculate the lifespan loss of luminaires under different loads. The rated service life of each lamp is obtained from the basic data of urban lighting. Combined with the life loss of the lamp under different loads, the life loss increment per unit time is calculated in real time using the life prediction model. The cumulative lifespan loss is obtained by summing the incremental lifespan loss within a fixed period. The sum of all cumulative lifespan losses from the time the lamp was put into use to the present is then integrated to obtain the total cumulative lifespan loss. Finally, the remaining lifespan prediction value based on the energy consumption simulation changes is calculated. By comparing the total cumulative lifespan loss of the lamps with their rated lifespan, risky lamps can be identified and a pre-alarm mechanism can be triggered.

3. The method according to claim 2, characterized in that, The generation of the maintenance work order containing lighting information specifically includes: Read the maintenance threshold of the lamps, compare the predicted remaining life of each lamp with the corresponding maintenance threshold, and if the predicted remaining life is lower than the maintenance threshold, mark the lamp in the digital twin model of urban lighting, and record the lamp number, current remaining life and location coordinates to form a list of lamps to be maintained. For the lighting fixtures to be maintained, the potential failure modes of the fixtures are determined by analyzing the real-time operating electrical parameters, light decay status, and natural light data. At the same time, the impact of each failure mode is assessed to form a failure risk assessment report. Based on the fault risk assessment report, combined with the location of the luminaire and the predicted remaining lifespan, the comprehensive score of each luminaire to be maintained is calculated to determine the maintenance priority.

4. A city lighting energy consumption simulation and lifespan prediction management system, characterized in that, The system includes: The basic data acquisition module is used to collect basic urban lighting data based on natural light data, and at the same time, to build a digital twin model of urban lighting based on building geometry, material and optical properties. The twin model estimation module is used to estimate the actual light output and current working efficiency of each lamp based on the constructed urban lighting digital twin model, taking the final ground illuminance and natural light data as input. It also calculates the real-time energy consumption of the lamp based on the rated power and actual light output, evaluates the light decay status, and obtains the real-time energy consumption and light decay status of each lamp. The lamp life prediction module is used to continuously receive real-time operating electrical parameters and natural light data of each lamp by utilizing the real-time energy consumption and light decay status of each lamp, to establish a life prediction model, calculate the life loss increment of each lamp in real time, and aggregate all loss increments to obtain the remaining life prediction value based on the simulated changes in energy consumption. The priority analysis module is used to read the maintenance threshold of the lamps and combine it with the remaining life prediction value. When the remaining life prediction value of the lamps is lower than the maintenance threshold, the lamps are marked. Failure mode and impact analysis is performed on the marked lamps to determine the maintenance priority and generate a maintenance work order containing lamp information. The lifespan prediction and optimization module is used to feed the lifespan prediction results back into the calculation process of the lamp's real-time energy consumption and light decay status, thereby optimizing the lifespan prediction scheme. The basic data acquisition module includes: The lighting data acquisition unit is used to collect urban lighting data and natural light data in real time through light intensity sensors and IoT environmental sensors pre-deployed within the city, and to preprocess the collected urban lighting data and natural light data. The data structuring unit is used to obtain the location coordinates and usage parameters of each lamp by taking urban lighting data and natural light data as environmental references, thus forming structured basic urban lighting data; The model building unit is used to export the three-dimensional geometric model of buildings within the urban lighting coverage area through the urban BIM system, and at the same time obtain building parameters, including facade materials and optical properties, and build a building reflection model containing light reflection and scattering characteristics based on the three-dimensional geometric model and building parameters. The digital twin model building unit is used to map natural lighting data, urban lighting data and building reflection models to a unified coordinate system based on a standard timestamp, and to match the lighting data in the urban lighting basic data with the building reflection models of the corresponding areas to build a complete urban lighting digital twin model. The twin model inference module includes: The model input unit is used to input the final ground illuminance and natural light data by using the digital twin model of urban lighting as a computing carrier. The building light reflection calculation unit is used to calculate the reflection angle and reflected light intensity of the building surface to the light from the lamps based on the building reflection model. It combines natural lighting data to deduce the attenuation process of light from the lamps to the ground, and thus obtain the actual light output of each lamp. The luminaire working efficiency calculation unit is used to extract the rated luminous flux of the luminaire from the basic data of urban lighting, calculate the ratio of the actual light output of the luminaire to the rated luminous flux of the luminaire, and calculate the influence curve of the luminaire luminous efficiency in combination with the ambient temperature to obtain the current working efficiency of the luminaire. The real-time energy consumption calculation unit is used to extract the rated power of each lamp from the basic data of urban lighting, calculate the current photoelectric conversion efficiency in combination with the actual light output, and then convert the actual light output to obtain the real-time energy consumption of each lamp, and store the results in the energy consumption database. The light decay status determination unit is used to obtain the initial working efficiency of the lamps from the basic data of urban lighting, compare the current working efficiency of the lamps with the initial working efficiency of the lamps, determine the light decay level according to the ratio range, and analyze the change trend of real-time energy consumption. If the increase in real-time energy consumption exceeds the set value three times in a row under the same actual light output, the light decay is determined to be aggravated. The light decay status of each lamp is determined by combining the ratio of the current working efficiency of the lamps to the initial working efficiency of the lamps, and the light decay status is associated with and stored with the real-time energy consumption data.

5. The system according to claim 4, characterized in that, The lamp life prediction module includes: The dataset forming unit is used to read the lamp usage parameters, identify the real-time operating electrical parameters of the lamp, and associate them with the real-time energy consumption and light decay status of the lamp through the lamp's unique identifier to form a timestamped parameter dataset. The lifespan prediction unit is used to build a lifespan prediction model based on the parameter dataset and combined with real-time energy consumption and light decay status, and to calculate the lifespan loss of the lamps under different loads. The loss increment calculation unit is used to obtain the rated service life of each lamp from the basic data of urban lighting, and combine the life loss of the lamp under different loads to calculate the life loss increment per unit time in real time using the life prediction model. The cycle cumulative loss integration unit is used to sum the lifetime loss increment within a fixed cycle to obtain the cycle cumulative loss. It integrates the sum of all cycle cumulative losses from the time the lamp is put into use to the present to obtain the total cumulative lifetime loss, and then calculates the remaining lifetime prediction value based on the energy consumption simulation change. The risk lighting fixture identification unit is used to compare the total cumulative lifespan loss of lighting fixtures with their rated lifespan, identify risky lighting fixtures, and trigger a pre-alarm mechanism.

6. The system according to claim 5, characterized in that, The priority analysis module includes: The list generation unit is used to read the maintenance threshold of the lamps, compare the predicted remaining life of each lamp with the corresponding maintenance threshold, and if the predicted remaining life is lower than the maintenance threshold, mark the lamp in the digital twin model of urban lighting, and record the lamp number, current remaining life and location coordinates to form a list of lamps to be maintained. The risk assessment module uses the luminaire to be maintained and analyzes its real-time operating electrical parameters, light decay status, and natural light data to determine the potential failure modes of the luminaire. At the same time, it assesses the impact of each failure mode and generates a failure risk assessment report. The maintenance priority calculation unit is used to calculate the comprehensive score of each lamp to be maintained based on the fault risk assessment report, combined with the location of the lamp and the predicted value of its remaining life, and to determine the maintenance priority.

Images