Smart city energy management system based on energy conservation and emission reduction

Abstract

The invention discloses a smart city energy management system based on energy conservation and emission reduction, and relates to the technical field of smart city energy management. By using an intelligent sensor group, illumination related data of illumination equipment is obtained, an illumination related data set R is constructed, and a light attenuation factor Gs, a convective heat transfer index DL and a use intensity factor Qd are extracted; the method comprises the following steps: dynamically evaluating the thermal load intensity of an urban area, scientifically dividing thermal risk grades, combining performance brightness prediction and control strategy matching to realize subarea and lamp-divided refined dimming control, using a feedback self-evaluation and optimization regulation and control mechanism, carrying out power reestimation and strategy alternation on an area with a substandard control effect, and carrying out power control on the area with the substandard control effect. Redundant energy consumption is effectively reduced, heat accumulation is restrained, and the lighting efficiency and the environment comfort degree are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of smart city energy management, and specifically to a smart city energy management system based on energy conservation and emission reduction. Background Art

[0002] In smart city energy management, the intelligent lighting system is an important part of energy management. It not only affects the overall energy consumption of the city, but also directly relates to the operating costs of public facilities and the quality of life of residents. In recent years, with the wide application of LED lighting technology, the intelligent lighting system has gradually developed towards a more refined and data-driven direction. One of the key issues is how to make the lighting system adapt to the dynamic changes of the urban environment, especially the impact of the thermal environment on the performance and energy consumption of lighting equipment. The current intelligent lighting system mainly relies on factors such as time control, ambient light intensity sensors, and pedestrian flow detection to adjust the lighting brightness, but rarely considers the impact of the thermal environment on lighting equipment. In practical applications, the luminous efficacy of LED lighting equipment is closely related to temperature. Excessive ambient temperature may cause light brightness attenuation, increased energy consumption, and even affect the equipment life. However, the existing system lacks a real-time monitoring and feedback control mechanism for local thermal environment changes, so that in high-temperature or extreme environments, the lighting equipment still operates according to the preset strategy, resulting in unnecessary energy waste. At the same time, the heat dissipation efficiency of LED lighting has not been incorporated into the urban energy management system for systematic optimization, affecting the overall energy conservation and emission reduction effect.

[0003] In the Chinese invention patent with the publication number CN118278028B, a smart city energy management system and method are disclosed, belonging to the field of urban energy management, including a data acquisition module, a signal communication module, a data encryption module, a data management and analysis module, a management and control center module, and a management feedback module. The data acquisition module is deployed at the end of the energy consumption node to collect and record the energy consumption of the energy consumption node; the signal communication module is used to send the collected energy consumption data, and the signal communication module performs encryption key hashing and data encryption through the data encryption module; the data management and analysis module is used to decrypt the encrypted data and analyze the decrypted data; the energy data is managed, allocated, and scheduled through the management and control center module, and energy-saving suggestions and optimization measures are provided.

[0004] The above system can achieve the timely collection of data, use the data management and analysis module for decryption and management analysis, further analyze the energy data, and manage, allocate, and schedule the energy data through the management and control center module, and provide energy-saving suggestions and optimization measures. However, in addition to this, in the existing smart city energy management system, the energy consumption nodes are usually directly detected, and the energy consumption data is collected for further analysis.

[0005] However, this smart city energy management system does not take into account the impact of the thermal environment, resulting in energy waste. In high-temperature areas, if lighting equipment operates for a long time, the chip temperature will be too high, which will accelerate the light decay, reduce the lighting quality, and may shorten the equipment life and increase the maintenance cost. In addition, since the luminous efficacy of lighting equipment decreases with the increase of temperature, in the same input power, the high-temperature environment may lead to insufficient light output, resulting in a decrease in visibility in places such as road lighting, pedestrian areas, and parking lots, affecting traffic safety and the night-time activity experience of pedestrians. Therefore, the lack of an intelligent lighting system based on adaptive regulation of the thermal environment not only reduces the operating efficiency of lighting equipment, but also increases the overall energy consumption of the city and weakens the energy conservation and emission reduction goals of the smart city energy management system.

[0006] To this end, the present invention provides a smart city energy management system based on energy conservation and emission reduction. Summary of the Invention

[0007] Aiming at the deficiencies of the prior art, the present invention provides a smart city energy management system based on energy conservation and emission reduction, which solves the problems in the above-mentioned background technology.

[0008] To achieve the above objectives, the present invention is realized through the following technical solutions: A smart city energy management system based on energy conservation and emission reduction includes a data acquisition module, a feature extraction module, a thermal environment analysis module, a lighting performance calculation and preliminary regulation module, a feedback evaluation module, and a deep optimization module;

[0009] The data acquisition module is used to obtain lighting-related data in real time based on the urban sensor network and the lighting system, and construct a lighting-related data set R;

[0010] The feature extraction module is used to obtain the light decay factor Gs, the convective heat transfer index DL, and the usage intensity factor Qd based on the lighting-related data set R;

[0011] The thermal environment analysis module is used to calculate the thermal load intensity score of the lighting equipment area for area division;

[0012] The lighting performance calculation and preliminary regulation module is used to extract lighting-related data, perform summary calculations, obtain the performance brightness of the lighting equipment and calculate the performance brightness of the lighting equipment to obtain the regulation strategy of the lighting equipment;

[0013] The feedback evaluation module is used to collect lighting-related data during the intelligent regulation of lighting equipment, screen grid areas with unqualified optimization effects, and construct a set S of grid areas to be optimized;

[0014] The deep optimization module is used to optimize and adjust the grid areas in the set S of grid areas to be optimized.

[0015] Preferably, the data acquisition module is used to install an intelligent sensor group in the urban lighting area, set the sampling frequency of the intelligent sensor group to 1 minute, and perform timing alignment using the unified UTC timestamp to collect lighting-related data of the city in real time. Among them, the lighting-related data includes environmental data, equipment operation data, and urban usage data;

[0016] The environmental data includes the surface temperature of the lighting equipment housing , air temperature , wind speed v, and relative humidity RH;

[0017] The equipment operation data includes the calibrated luminous intensity of the lighting equipment and the actual luminous intensity ;

[0018] The urban usage data includes the regional pedestrian flow , regional vehicle flow , the coverage area of the lighting equipment and the GPS coordinates of the lighting equipment;

[0019] Preprocess the obtained lighting-related data, construct a lighting-related data set R based on the preprocessed lighting-related data, and store the lighting-related data set R in the urban temperature control database. Among them, the preprocessing includes outlier processing and missing value imputation.

[0020] Preferably, the feature extraction module is used to perform summary calculations based on the lighting-related data set R to obtain the light decay factor Gs, the convective heat transfer index DL, and the usage intensity factor Qd. Among them, the way to obtain the light decay factor Gs is:

[0021] ;

[0022] In the formula, represents the calibrated luminous intensity, represents the actual luminous intensity, represents the surface temperature of the lighting equipment housing, represents the reference surface temperature of the lighting equipment housing;

[0023] The way to obtain the convective heat transfer index DL is:

[0024] ;

[0025] In the formula, represents the wind speed, RH represents the relative humidity, represents the air density, represents the specific heat capacity at constant pressure;

[0026] The way to obtain the usage intensity factor Qd is:

[0027] ;

[0028] In the formula, represents the pedestrian flow in the area, represents the vehicle flow in the area, represents the coverage area of lighting equipment, and respectively represent the pedestrian flow in the area and the vehicle flow in the area weight coefficients, represents the weight factor of the regional function sensitivity, represents the area tolerance factor.

[0029] Preferably, the thermal environment analysis module includes a heat load analysis unit and a regional evaluation unit;

[0030] The heat load analysis unit is used to obtain the urban lighting distribution map based on the urban lighting system, and divide the urban lighting distribution map into several grid areas. Each grid area includes several lighting equipment and temperature points. For several grid areas, taking one of the grid areas i as an example, extract the lighting-related data of the grid area i and calculate and obtain the local spatial temperature gradient , where the local spatial temperature gradient The acquisition method is:

[0031] ;

[0032] In the formula, i represents the grid area number, represents the temperature value of temperature sensing point 1, represents the temperature value of temperature sensing point 2, represents the Euclidean distance between temperature points;

[0033] Based on the local spatial temperature gradient and the convective heat transfer index DL, perform summary calculations to obtain the heat load intensity score of the grid area i, where the heat load intensity score of the grid area i is obtained as follows:

[0034] ;

[0035] In the formula, represents the average surface temperature of the lighting equipment housing in the grid area i mean value, represents the local spatial temperature gradient of the grid area i, represents the regional characteristic length of the grid area i, represents the thermal conductivity, Represents the convective heat transfer index of grid area i.

[0036] Preferably, the area evaluation unit is used to obtain the heat load intensity score Fh of each grid area, and preset the first heat load intensity threshold and the second heat load intensity threshold , and compare and analyze the first heat load intensity threshold and the second heat load intensity threshold with the heat load intensity score Fh to evaluate the thermal environment state of each grid area and perform area classification. The specific process is as follows:

[0037] If Fh ≤ , it means that the heat dissipation of the grid area is normal, and it is in the first risk area, and the first risk area is marked as a green area;

[0038] If <Fh< , it means that there is an obvious thermal disturbance phenomenon in the grid area, and it is in the second risk area, and the second risk area is marked as a yellow area;

[0039] If Fh ≥ , it means that the heat accumulation in the grid area is serious, the degradation risk is high, and it is in the third risk area, and the third risk area is marked as a red area;

[0040] According to the evaluation result of the thermal environment state, a structured data table is generated. Among them, the structured data table includes the grid area number, the heat load intensity score Fh, the grade label, the spatial coordinate range, and the recommended regulation strategy.

[0041] Preferably, the lighting performance calculation and preliminary regulation module includes an LED lighting performance calculation unit and a regulation strategy generation unit;

[0042] The LED lighting performance calculation unit is used to collect the lighting-related data of each lighting device in the grid areas marked as yellow and red to obtain the performance brightness of the lighting device , and the specific acquisition process is as follows:

[0043] Set the thermal response function :

[0044] ;

[0045] In the formula, represents the LED thermal sensitivity coefficient, represents the surface temperature of the lighting device housing, represents that the function structure is an exponential function, represents the reference temperature of the surface of the lighting device housing;

[0046] Substitute the lighting - related data of each lighting device in the grid area into the thermal response function to obtain the specific value of the thermal response function and calculate the performance brightness of the lighting device . Taking lighting device j as an example, the specific calculation method is as follows:

[0047] ;

[0048] In the formula, represents the calibrated luminous intensity of lighting node j, represents the light decay factor, represents the surface temperature of the lighting device housing;

[0049] Based on the performance brightness of the lighting device , the usage intensity factor Qd, and the heat load intensity score Fh, perform a summary calculation to obtain the lighting control factor . Among them, the specific way to obtain the lighting control factor is as follows:

[0050] ;

[0051] In the formula, represents the lighting brightness compensation value.

[0052] The regulation strategy generation unit is used to preset the first regulation threshold and the first regulation threshold , and analyze the regulation strategy of each lighting device in the grid areas marked yellow and red. The specific analysis process is as follows:

[0053] If ≤ , it is determined that the regulation strategy of the lighting device is load - limited operation. At this time, adjust the current power supply to 65% of the rated power, and execute the intermittent lighting strategy, generate a lighting monitoring report, and send the lighting monitoring report to the maintenance personnel to remind the maintenance personnel to perform lighting device maintenance until the maintenance personnel respond;

[0054] If < < , it is determined that the regulation strategy of the lighting device is normal operation. At this time, keep the current power supply unchanged and continuously monitor the performance indicators of the lighting device and the heat load data;

[0055] If ≥ , it is determined that the regulation strategy of the lighting device is supplementary lighting operation. At this time, adjust the current power supply to 115% of the rated power, and at the same time monitor the temperature change and collect the temperature change data.

[0056] Preferably, the feedback evaluation module is used to perform intelligent regulation of lighting equipment according to the regulation strategy, and continuously collect lighting-related data during the intelligent regulation of lighting equipment to obtain the energy efficiency health index Nx of each grid area. Taking grid area i as an example, the energy efficiency health index of grid area i The specific acquisition method is as follows:

[0057] ;

[0058] In the formula, represents the average unit energy efficiency of grid area i, represents the thermal load intensity score of grid area i, represents the total number of lighting devices in grid area i, represents the actual input power of the jth lighting device, represents the target input power of the jth lighting device, and represent the non-linear compression adjustment coefficient;

[0059] Preset the energy efficiency health threshold Nxyz, and compare and analyze the energy efficiency health index of each grid area with the energy efficiency health threshold Nxyz. If ≥Nxyz, it indicates that the optimization effect of the intelligent regulation strategy of lighting equipment is qualified. At this time, the current control strategy is maintained. If <Nxyz, it indicates that the optimization effect of the intelligent regulation strategy of lighting equipment is unqualified, and the grid areas with unqualified optimization effects of the intelligent regulation strategy of lighting equipment are summarized to construct a set S of grid areas to be optimized.

[0060] Preferably, the deep optimization module includes a regional power re-evaluation unit and an intelligent feedback unit;

[0061] The regional power re-evaluation unit is used to recalculate the control power for each grid area in the set S of grid areas to be optimized , and the specific calculation method is as follows:

[0062] ;

[0063] In the formula, represents the average output power of the current grid area, represents the thermal load regulation coefficient, represents the light efficiency adjustment compensation coefficient, represents the thermal load intensity score of grid area i, represents the average unit energy efficiency of grid area i;

[0064] According to the control power , generate a control timing scheme, and the specific content is as follows:

[0065] Taking grid area i as an example, the N lighting devices in grid area i are divided into three control subgroups, one of which is a low-power group and the other two are regular groups. A fixed rotation cycle is set to alternately work the low-power group and the regular group, and for the low-power group, the output power is set to :

[0066] ;

[0067] In the formula, Indicates the minimum power safety threshold for the operation of lighting equipment. Indicates the adjustment step size.

[0068] Preferably, the intelligent feedback unit is used to optimize the lighting equipment control according to the control timing scheme, and to collect lighting-related data in real time during the lighting equipment control optimization process, and to feed back the lighting-related data to the thermal environment analysis module to continuously optimize the working process of the lighting equipment.

[0069] The present invention provides a smart city energy management system based on energy conservation and emission reduction, which has the following beneficial effects:

[0070] (1) By deploying intelligent sensor groups in urban lighting areas, we can realize high-frequency acquisition of multi-source environmental parameters such as lighting equipment surface temperature, air temperature, wind speed, and humidity in minutes, accurately evaluate the heat load status and lighting performance of each grid area, and rely on the closed-loop collaborative control mechanism of modules four and six to actively implement "load limit operation" and "intermittent lighting" in high heat risk areas, and moderate "supplementary lighting operation" in low light efficiency areas. Under the premise of ensuring the quality of light, we can effectively reduce the overall energy consumption of the urban lighting system by more than 10%, and achieve the green lighting goal of zoned dimming and on-demand energy supply.

[0071] (2) With the thermal environment analysis module as the core, a multi-level thermal environment perception system was constructed from sensor data → grid division → heat load intensity score → regional level division. With the help of the dual-factor linkage of heat load score and energy efficiency health index, the system can dynamically identify red high-risk grids and trigger deep optimization control, including regional power reassessment, rotating low-power grouping strategy and feedback correction mechanism, effectively breaking the chain of heat backlog formation. In actual operation, the system can reduce the LED lighting power in hot spots by 15% to 20%, suppress heat source accumulation from the source, and achieve smoothing of the local thermal environment in the city and mitigation of the heat island effect.

[0072] (3) By constructing a complete closed-loop link of data acquisition - feature extraction - analysis and decision-making - intelligent regulation - feedback evaluation, the lighting control behavior is based on structured indicators such as the light attenuation factor Gs, the convective heat transfer index DL, and the usage intensity factor Qd, etc., forming a standardized report and spatial positioning mark. Combining with the time-series record of the urban temperature control database and the control strategy log management, the system can achieve dynamic perception, precise decision-making, and tracking optimization of urban-level lighting resources, improving the response speed and management transparency of urban energy management, and providing core support technologies that can be popularized, evaluated, and reused for the smart city energy management system. Description of the Drawings

[0073] Figure 1 It is a block diagram of a smart city energy management system based on energy conservation and emission reduction of the present invention;

[0074] Figure 2 It is a block diagram of the regional evaluation unit of the present invention;

[0075] Figure 3 It is a block diagram of the heat load analysis unit of the present invention;

[0076] Figure 4 It is a bar chart of the control strategy of the control strategy generation unit of the present invention. Specific Embodiments

[0077] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0078] Embodiment 1

[0079] Please refer to Figure 1 , the present invention provides a smart city energy management system based on energy conservation and emission reduction, including a data acquisition module, a feature extraction module, a thermal environment analysis module, a lighting performance calculation and preliminary regulation module, a feedback evaluation module, and a deep optimization module;

[0080] The data acquisition module is used to obtain lighting-related data in real time based on the urban sensor network and the lighting system, and construct a lighting-related data set R;

[0081] The feature extraction module is used to obtain the light attenuation factor Gs, the convective heat transfer index DL, and the usage intensity factor Qd based on the lighting-related data set R;

[0082] The thermal environment analysis module is used to calculate the heat load intensity score of the lighting equipment area , for regional division;

[0083] The lighting performance calculation and preliminary regulation module is used to extract lighting-related data, perform summary calculations, and obtain the performance brightness of lighting devices. And calculate the performance brightness of lighting devices to obtain the regulation strategy of lighting devices;

[0084] The feedback evaluation module is used to collect lighting-related data during the intelligent regulation process of lighting devices, screen grid areas with unqualified optimization effects, and construct a set S of grid areas to be optimized.

[0085] The deep optimization module is used to optimize and adjust the grid areas in the set S of grid areas to be optimized.

[0086] In the embodiment, by using a multi-source sensor network to obtain the environmental, operating, and usage data of lighting devices, constructing a lighting-related data set R, and extracting the light decay factor Gs, the convective heat transfer index DL, and the usage intensity factor Qd, the non-linear coupling relationship between the thermal environment and lighting performance is deeply explored, the regional thermal load intensity is dynamically evaluated, the thermal risk level is scientifically divided, combined with performance brightness prediction and control strategy matching, refined dimming control for different areas and different luminaires is realized, and a feedback self-evaluation and optimization regulation mechanism is used to re-evaluate the power, rotate strategies, and correct single lamps for areas where the control effect does not meet the standard, effectively reducing redundant energy consumption, suppressing heat accumulation, improving lighting efficiency and environmental comfort, and comprehensively supporting the realization of the energy conservation and emission reduction goals of smart cities.

[0087] Embodiment 2

[0088] Please refer to Figure 1 Specifically: The data acquisition module is used to install an intelligent sensor group in the urban lighting area, set the sampling frequency of the intelligent sensor group to 1 minute, and perform time series alignment using a unified UTC timestamp to collect the lighting-related data of the city in real time. Among them, the lighting-related data includes environmental data, equipment operation data, and urban usage data;

[0089] The intelligent sensor group includes a thermocouple sensor, a digital temperature and humidity integrated sensor, an ultrasonic wind speed sensor, and a light brightness detector;

[0090] The environmental data includes the surface temperature of the lighting device housing , air temperature , wind speed v, and relative humidity RH;

[0091] The surface temperature of the lighting device housing is obtained by using a thermocouple sensor;

[0092] The air temperature and relative humidity RH are obtained by using a digital temperature and humidity integrated sensor;

[0093] The wind speed v is obtained by using an ultrasonic wind speed sensor;

[0094] The device operation data includes the calibrated light brightness of the lighting device and the actual light brightness ;

[0095] The calibrated light brightness is obtained through the lighting device manufacturing parameter database;

[0096] The actual light brightness is obtained by using a light brightness detector;

[0097] The urban usage data includes the regional pedestrian flow , the regional vehicle flow , the coverage area of the lighting device and the GPS coordinates of the lighting device, which are obtained through the urban traffic video image analysis system;

[0098] The obtained lighting-related data is preprocessed, and based on the preprocessed lighting-related data, a lighting-related data set R is constructed and stored in the urban temperature control database, where the preprocessing includes outlier processing and missing value imputation.

[0099] Example 3

[0100] Please refer to Figure 1 , specifically: The feature extraction module is used to perform summary calculations based on the lighting-related data set R to obtain the light decay factor Gs, the convective heat transfer index DL, and the usage intensity factor Qd. Among them, the way to obtain the light decay factor Gs is:

[0101] ;

[0102] In the formula, represents the calibrated light brightness, represents the actual light brightness, represents the surface temperature of the lighting device housing, represents the reference surface temperature of the lighting device housing;

[0103] The reference surface temperature of the lighting device housing is obtained through the lighting fixture standard database;

[0104] With the use time and heat accumulation of LED lighting fixtures, temperature rise-induced light brightness attenuation will occur. The light decay factor Gs is used to represent the brightness decline rate caused by unit temperature rise and is a direct quantitative indicator of the degradation of thermal-optical performance. The existing lighting systems lack a perception and response mechanism for the non-linear relationship between the thermal environment and lighting performance. Using the light decay factor Gs as a characteristic index can guide subsequent dimming and supplementary lighting strategies.

[0105] The convection heat transfer index DL is obtained as follows:

[0106] ;

[0107] In the formula, represents wind speed, RH represents relative humidity, represents the air density, represents the specific heat capacity at constant pressure;

[0108] Specific heat at constant pressure It is the heat absorbed by a certain substance per unit mass when the temperature rises by 1K under constant pressure. Under standard atmospheric pressure and normal temperature, the constant pressure specific heat capacity of dry air is Can be approximated to a constant value.

[0109] The heat dissipation of LED lamps depends on the convective heat transfer capacity of the ambient air. The convective heat transfer index DL is used to estimate the ability of external air to remove heat from the surface of the lamp. It is an evaluation indicator of the passive thermal environment control ability. By using the convective heat transfer index DL, it is possible to accurately identify which areas have serious local heat retention due to poor ventilation or high humidity, so as to implement strategies such as power reduction or intermittent lighting in a timely manner.

[0110] The intensity factor Qd is obtained as follows:

[0111] ;

[0112] In the formula, Indicates the regional flow of people. Indicates the regional traffic volume, Indicates the area covered by the lighting equipment. and Represents the regional flow of people and regional traffic volume The weight coefficient of represents the regional function sensitivity weight factor, Represents the area tolerance factor, which is used to prevent the denominator of the formula from being zero;

[0113] The usage intensity factor Qd comprehensively measures the regional lighting usage density and lighting pressure, reflecting the degree of match between the "regional required brightness" and the "existing lighting coverage intensity". The existing systems mostly perform power scheduling according to the "unified load standard", fail to consider the differences in regional usage intensity, ignore regional functional differences and dynamic changes in pedestrian and vehicle flows, resulting in insufficient illumination in high-usage areas and excessive energy consumption in low-usage areas. Combined with the usage intensity factor Qd, it can respond to the usage intensity of urban space, thereby building a more intelligent, efficient and service-oriented energy-saving control mechanism.

[0114] The following are some examples:

[0115] Assume that in the urban lighting coverage area, lighting-related data of lighting equipment is collected, as shown in Table 1 below:

[0116] Table 1

[0117] According to Table 1 above, obtain the light decay factor Gs, the convective heat transfer index DL, and the usage intensity factor Qd. Among them, the light decay factor Gs is 0.005, the convective heat transfer index DL is 1085.4, and the usage intensity factor Qd is 1.021.

[0118] In the embodiment, through in-depth feature extraction of the lighting-related data set R, the system accurately constructs three key performance indicators: the light decay factor Gs, the convective heat transfer index DL, and the usage intensity factor Qd. The light decay factor Gs combines the calibrated light brightness and the actual brightness, and considering the temperature rise amplitude, quantifies the sensitivity of temperature to the degradation of LED light brightness, providing a physical basis for subsequent performance brightness prediction. The convective heat transfer index DL synthesizes environmental parameters such as wind speed, humidity, and air physical properties, reflecting the external coupling efficiency of the heat dissipation capacity of lighting equipment and supporting the accuracy of the heat load assessment model. The usage intensity factor Qd integrates the regional pedestrian and vehicle flow, lighting coverage area, and regional sensitivity level to realize the quantitative expression of the urban space usage density. This feature extraction mechanism not only enhances the model's response ability to the non-linear relationship between the thermal environment and lighting performance, but also improves the intelligence and adaptability of subsequent modules in energy-saving regulation, regional classification, strategy generation, etc., and is an important basis for realizing the refined operation of the energy management system.

[0119] Example 4

[0120] Please refer to Figures 1 to 3 , specifically: The thermal environment analysis module includes a heat load analysis unit and a regional assessment unit;

[0121] The heat load analysis unit is used to obtain the urban lighting distribution map according to the urban lighting system, and divide the urban lighting distribution map into several grid areas. Each grid area includes several lighting equipment and temperature points. For several grid areas, taking one of the grid areas i as an example, extract the lighting-related data of the grid area i, and calculate and obtain the local spatial temperature gradient , where the local spatial temperature gradient The acquisition method is:

[0122] ;

[0123] In the formula, i represents the grid area number, represents the temperature value of temperature sensing point 1, represents the temperature value of temperature sensing point 2, represents the Euclidean distance between temperature points;

[0124] According to the local space temperature gradient and the convective heat transfer index DL, perform a summary calculation to obtain the heat load intensity score of grid area i , where the heat load intensity score of grid area i The acquisition method is as follows:

[0125] ;

[0126] In the formula, represents the average surface temperature of the lighting equipment housing in grid area i average value, represents the local space temperature gradient of grid area i, represents the regional characteristic length of grid area i, represents the thermal conductivity, represents the convective heat transfer index of grid area i, represents the reciprocal of the regional thermal conductivity. The worse the thermal conductivity, the larger the value of , indicating that the region is less likely to dissipate heat, resulting in an increase in heat load.

[0127] Thermal conductivity is obtained through a thermal conductivity experiment on the lighting equipment;

[0128] The heat load intensity score Fh is a regional-level thermal environment risk quantification index, which is used to determine whether there are problems such as heat retention, high heat accumulation, lighting degradation risk, and energy efficiency imbalance in the grid area. Through the comprehensive modeling of heat sources, heat transfer channels, thermal conductivity, and convective cooling factors, the quantification assessment of the thermal environment state is realized, enabling the system to accurately identify potential heat accumulation risk areas and providing a key decision-making basis for partition energy-saving regulation and subsequent closed-loop optimization. It is the technical hub for achieving lighting energy conservation, equipment protection, and urban heat island mitigation.

[0129] The regional evaluation unit is used to obtain the heat load intensity score Fh of each grid area and preset the first heat load intensity threshold and the second heat load intensity threshold , and compare the first heat load intensity threshold and the second heat load intensity threshold with the heat load intensity score Fh for comparative analysis, evaluate the thermal environment state of each grid area, and perform regional classification. The specific process is as follows:

[0130] If Fh ≤ , it indicates that the heat dissipation of the grid area is normal, in the first risk area, marks the first risk area as the green area, and sets the recommended control strategy as "operating at normal brightness";

[0131] If <Fh< , it indicates that there is an obvious heat disturbance phenomenon in the grid area, in the second risk area, marks the second risk area as the yellow area, and sets the recommended control strategy as "recommended conservative dimming";

[0132] If Fh≥ , it indicates that the heat accumulation in the grid area is serious, the degradation risk is high, in the third risk area, marks the third risk area as the red area, and sets the recommended control strategy as "recommended brightness limit, peak-shifting lighting and optimized temperature control";

[0133] According to the evaluation results of the thermal environment state, a structured data table is generated. Among them, the structured data table includes the grid area number, the heat load intensity score Fh, the grade label, the spatial coordinate range and the recommended control strategy.

[0134] In the embodiment, through the collaborative work of the heat load analysis unit and the area evaluation unit, the fine recognition of the thermal environment state and the output of the hierarchical control recommendation in the urban lighting system are realized. The system divides the city into multiple grid areas, calculates the local spatial temperature change by using the Euclidean gradient between temperature sensing points, and combines the convective heat transfer index DL to quantitatively evaluate the heat load intensity score Fh of each grid area. Subsequently, the area evaluation unit performs intelligent classification on the heat load intensity score Fh based on the preset threshold, outputs three types of grade labels: green, yellow and red, and generates matching recommended control strategies. This mechanism not only realizes the transformation of the urban lighting thermal environment from "non-quantifiable" to "structured evaluation", but also provides accurate input for the strategy formulation of subsequent modules, improves the thermal perception response ability and energy consumption governance decision-making accuracy of the system, and effectively supports the lighting heat pressure control, urban heat island mitigation and energy efficiency operation stability goals.

[0135] Embodiment 5

[0136] Please refer to Figure 1 and Figure 4 , specifically: The lighting performance calculation and preliminary control module includes an LED lighting performance calculation unit and a control strategy generation unit;

[0137] The LED lighting performance calculation unit is used to collect the lighting-related data of each lighting device in the grid areas marked as yellow and red to obtain the performance brightness of the lighting device , and the specific acquisition process is as follows:

[0138] Set the heat response function :

[0139] ;

[0140] Wherein, represents the LED thermal sensitivity coefficient, which is used to represent the amplitude of the light decay index caused by each unit increase in temperature, represents the surface temperature of the lighting device housing, represents that the function structure is an exponential function, reflecting that the light decay does not increase linearly, but the higher the temperature, the faster the light performance decreases, represents the reference temperature of the surface of the lighting device housing;

[0141] The performance brightness of the lighting device refers to the light brightness that the lighting device should have under the current thermal environment and aging conditions, which is the "expected performance" of the lighting device, and the calibrated light brightness is the nominal light brightness when the lighting device leaves the factory under the standard experimental environment, and the actual light brightness is the real-time light brightness measured by the sensor.

[0142] Substitute the lighting-related data of each lighting device in the grid area into the thermal response function , and obtain the thermal response function specific value, and calculate the performance brightness of the lighting device . Taking the lighting device j as an example, the specific calculation method is:

[0143] ;

[0144] Wherein, represents the calibrated light brightness of the lighting node j, represents the light decay factor, represents the surface temperature of the lighting device housing;

[0145] According to the performance brightness of the lighting device , the usage intensity factor Qd and the heat load intensity score Fh, perform a summary calculation to obtain the lighting control factor , wherein, the lighting control factor is specifically obtained as follows:

[0146] ;

[0147] Wherein, represents the lighting brightness compensation value.

[0148] The regulation strategy generation unit is used to preset the first regulation threshold and the first regulation threshold , and analyze the regulation strategy of each lighting device in the grid areas marked yellow and red. The specific analysis process is as follows:

[0149] If ≤ , it is determined that the control strategy of the lighting device is load-limited operation. At this time, the current power supply is adjusted to 65% of the rated power, and the intermittent lighting strategy is executed, that is, it runs for 3 minutes and is turned off for 2 minutes. A lighting monitoring report is generated and sent to the maintenance personnel to remind the maintenance personnel to perform lighting device maintenance until the maintenance personnel respond;

[0150] If < < , it is determined that the control strategy of the lighting device is normal operation. At this time, the current power supply is maintained unchanged, and the performance indicators and heat load data of the lighting device are continuously monitored;

[0151] If ≥ , it is determined that the control strategy of the lighting device is supplementary lighting operation. At this time, the current power supply is adjusted to 115% of the rated power, and the temperature change is monitored at the same time, and the temperature change data is collected.

[0152] By constructing a two-level linkage mechanism of "LED lighting performance calculation unit" and "control strategy generation unit", combined with the thermal environment and light decay characteristics, the performance brightness of the lighting device under actual working conditions is dynamically predicted, and then a personalized control strategy is generated. The performance brightness calculation uses an exponential thermal response function to accurately reflect the heat attenuation characteristics of LEDs. On this basis, the system further introduces the usage intensity factor Qd and the heat load intensity score Fh to construct the lighting control factor , comprehensively quantify the current lighting state that each lamp should have. The control strategy generation unit automatically determines the operation level of the lighting device according to this factor, realizes three types of precise adjustments of "load-limited operation", "normal operation" and "supplementary lighting operation", and introduces intermittent lighting and maintenance warning in high heat load areas to improve the operation safety and energy efficiency response sensitivity of the equipment. This mechanism improves the stability and energy saving of the lighting system in non-ideal thermal environments, realizes the integration of thermal-light-energy perception and control, and provides strong support for the city-level lighting system to achieve sub-node control, dynamic response and energy consumption optimization.

[0153] Example 6

[0154] Please refer to Figure 1 , specifically: The feedback evaluation module is used to perform intelligent control of the lighting device according to the control strategy, and continuously collect lighting-related data during the intelligent control process of the lighting device to obtain the energy efficiency health index Nx of each grid area. Taking grid area i as an example, the energy efficiency health index of grid area i The specific acquisition method is:

[0155] ;

[0156] In the formula, represents the average unit energy efficiency of grid area i, represents the heat load intensity score of grid area i, represents the total number of lighting devices in grid area i, represents the actual input power of the j-th lighting device, represents the target input power of the j-th lighting device, and represents the non-linear compression adjustment coefficient. " " makes the formula positive and the formula conforms to mathematical logic. represents the relative deviation between the actual power input of all lighting devices and the target power set for them by the system. The greater the relative deviation, the worse the regulation execution effect and the lower the energy efficiency health index.

[0157] The average unit energy efficiency of grid area i is used to measure the "energy usage efficiency" of the lighting devices in this area and is obtained through monitoring by the lighting system.

[0158] The non-linear compression adjustment coefficient and are obtained by fitting using historical data.

[0159] The preset energy efficiency health threshold Nxyz is used to compare and analyze the energy efficiency health index of each grid area with the energy efficiency health threshold Nxyz. If ≥Nxyz, it indicates that the optimization effect of the intelligent regulation strategy for lighting devices is qualified, and the current control strategy is maintained at this time. If <Nxyz, it indicates that the optimization effect of the intelligent regulation strategy for lighting devices is unqualified, and the grid areas with unqualified optimization effects of the intelligent regulation strategy for lighting devices are summarized to construct a set S of grid areas to be optimized.

[0160] In the embodiment, a quantitative evaluation mechanism for the regulation effect of lighting devices is constructed through the "feedback evaluation module", and an energy efficiency health index based on the integration of three factors of "unit energy efficiency - heat load - execution deviation" is proposed to comprehensively measure the lighting regulation performance of each grid area. After the intelligent regulation strategy is executed, device operation data is continuously collected, and the effectiveness of the regulation strategy is comprehensively evaluated by combining the difference between the predicted power and the actual power, the heat load intensity score, and the unit energy efficiency. When When it exceeds the set threshold Nxyz, the system determines that the regional regulation strategy is effective and maintains the existing control mode. Otherwise, it automatically incorporates this region into the "set of grid regions to be optimized S", triggers the in-depth optimization process, and realizes the feedback closed-loop. This mechanism has real-time, self-adaptive, and differential identification capabilities, avoiding the drawbacks of the traditional strategy of "adjusting to the end" and "difficulty in perceiving the optimization effect", improving the self-monitoring and iterative optimization capabilities of the urban lighting system, and providing strong data support and execution guarantee for dynamic energy management in smart cities.

[0161] Embodiment 7

[0162] Please refer to Figure 1 , specifically: The in-depth optimization module includes a regional power re-estimation unit and an intelligent feedback unit;

[0163] The regional power re-estimation unit is used to recalculate the control power for each grid region in the set of grid regions to be optimized S , and the specific calculation method is:

[0164] ;

[0165] In the formula, represents the average output power of the current grid region, represents the heat load regulation coefficient, represents the light efficiency adjustment compensation coefficient, represents the heat load intensity score of grid region i, represents the average unit energy efficiency of grid region i, and the heat load regulation coefficient and the light efficiency adjustment compensation coefficient are set by the customer according to the actual situation;

[0166] Based on the control power , a control timing scheme is generated, and the specific content is as follows:

[0167] Taking grid region i as an example, the N lighting devices in grid region i are divided into 3 control subgroups. Among them, one group is the low-power group, and the other two groups are the regular groups. A fixed rotation period is set, and the low-power group and the regular groups work alternately. For the low-power group, the output power is set to :

[0168] ;

[0169] In the formula, represents the minimum power safety threshold for the operation of the lighting device, represents the adjustment step size.

[0170] The intelligent feedback unit is used to optimize the control of lighting equipment according to the control timing scheme, collect lighting-related data in real time during the optimization process of lighting equipment control, and feed back the lighting-related data to the thermal environment analysis module to continuously optimize the working process of lighting equipment.

[0171] In the embodiment, through the coordinated operation of the "regional power re-evaluation unit" and the "intelligent feedback unit", closed-loop refined control of the areas with unqualified lighting strategy optimization effects is achieved. In the regional power re-evaluation link, the system dynamically adjusts the target control power based on the average output power, thermal load intensity score, and unit energy efficiency level of the current grid area, reasonably compresses or compensates for energy consumption, divides the lighting equipment into a low-power group and a conventional group, and implements an alternating control strategy, which can effectively reduce the peak energy consumption per unit time, suppress the rapid rise of local thermal load, improve the control flexibility and thermal stability. At the same time, during the execution of the control instruction, the intelligent feedback unit collects the operation data of the lighting equipment in real time and feeds it back to the thermal environment analysis module to realize the continuous evaluation and self-correction of the control effect, and constructs a "perception - decision - control - feedback" closed-loop chain. This mechanism significantly improves the adaptive adjustment ability of the urban lighting system and effectively promotes the evolution of the urban energy system towards low-carbon, efficient, and self-optimizing directions.

[0172] Although the embodiments of the present invention have been shown and described, those of ordinary skill in the art can understand that various changes, modifications, substitutions, and variations can be made to these embodiments without departing from the principles and spirit of the present invention. The scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A smart city energy management system based on energy conservation and emission reduction, characterized by: It includes data acquisition module, feature extraction module, thermal environment analysis module, lighting performance calculation and preliminary control module, feedback evaluation module and deep optimization module; The data acquisition module is used to obtain lighting-related data in real time based on the urban sensor network and lighting system, and to construct a lighting-related data set R; The feature extraction module is used to obtain the light decay factor Gs, the convection heat transfer index DL and the use intensity factor Qd according to the lighting related data set R; Thermal environment analysis module is used to calculate the heat load intensity score of the lighting equipment area , for regional division; The lighting performance calculation and preliminary control module is used to extract lighting-related data, perform summary calculations, and obtain the performance brightness of lighting equipment. , and calculate the lighting equipment performance brightness , obtain the control strategy of lighting equipment; The feedback evaluation module is used to collect lighting-related data during the intelligent control of lighting equipment, screen out grid areas with unqualified optimization effects, and construct a set S of grid areas to be optimized; The deep optimization module is used to optimize and adjust the grid areas in the set S of grid areas to be optimized.

2. According to claim 1, a smart city energy management system based on energy conservation and emission reduction is characterized in that: The data acquisition module is used to install smart sensor groups in the urban lighting area, set the sampling frequency of the smart sensor groups to 1 minute, and use a unified UTC timestamp for time alignment to collect the city's lighting-related data in real time, where the lighting-related data includes environmental data, equipment operation data, and urban usage data; Environmental data including lighting equipment housing surface temperature , air temperature , wind speed v and relative humidity RH; Equipment operation data including the calibrated brightness of lighting equipment and actual brightness ; Urban usage data including regional foot traffic , Regional traffic volume , Lighting equipment coverage area and lighting equipment GPS coordinates; The acquired lighting-related data are preprocessed, and a lighting-related data set R is constructed based on the preprocessed lighting-related data, and the lighting-related data set R is stored in the urban temperature control database, wherein the preprocessing includes outlier processing and missing value interpolation.

3. According to claim 2, a smart city energy management system based on energy conservation and emission reduction is characterized in that: The feature extraction module is used to perform summary calculations based on the lighting-related data set R to obtain the light decay factor Gs, the convection heat transfer index DL, and the use intensity factor Qd. The light decay factor Gs is obtained as follows: ； In the formula, Indicates the calibrated light brightness, Indicates the actual brightness. Indicates the surface temperature of the lighting equipment casing. Indicates the reference temperature of the lighting equipment housing surface; The convection heat transfer index DL is obtained as follows: ； In the formula, represents wind speed, RH represents relative humidity, represents the air density, represents the specific heat capacity at constant pressure; The intensity factor Qd is obtained as follows: ； In the formula, Indicates the regional flow of people. Indicates the regional traffic volume, Indicates the area covered by the lighting equipment. and Represents the regional flow of people and regional traffic volume The weight coefficient of represents the regional function sensitivity weight factor, Represents the area tolerance factor.

4. According to claim 3, a smart city energy management system based on energy conservation and emission reduction is characterized in that: The thermal environment analysis module includes a heat load analysis unit and a regional assessment unit; The heat load analysis unit is used to obtain the urban lighting distribution map according to the urban lighting system, and divide the urban lighting distribution map into several grid areas, each of which includes several lighting devices and temperature points. For several grid areas, take one grid area i as an example, extract the lighting related data of grid area i, and calculate and obtain the local space temperature gradient , where the local spatial temperature gradient The method of obtaining is: ； Where i represents the grid area number, Indicates the temperature value of temperature sensing point 1, Indicates the temperature value of temperature sensing point 2, Represents the Euclidean distance between temperature points; Based on the local spatial temperature gradient and the convection heat transfer index DL, and perform summary calculations to obtain the heat load intensity score of grid area i , where the heat load intensity score of grid area i is The method of obtaining is: ； In the formula, Represents the surface temperature of the lighting device housing in grid area i Mean, represents the local spatial temperature gradient of grid area i, represents the regional characteristic length of the grid region i, represents the thermal conductivity, Represents the convective heat transfer index of grid area i.

5. A smart city energy management system based on energy conservation and emission reduction according to claim 4, characterized in that: The regional evaluation unit is used to obtain the heat load intensity score Fh of each grid area and preset the first heat load intensity threshold and the second heat load intensity threshold , the first heat load intensity threshold and the second heat load intensity threshold Compare and analyze with the heat load intensity score Fh, evaluate the thermal environment status of each grid area, and classify the area. The specific process is as follows: If Fh≤ , it means that the heat dissipation in the grid area is normal and it is in the first risk area, and the first risk area is marked as a green area; like <Fh< , it means that there is obvious thermal disturbance in the grid area, which is in the second risk area, and the second risk area is marked as a yellow area; If Fh≥ , it means that the grid area has serious heat backlog and high degradation risk, and is in the third risk area, and the third risk area is marked as a red area; According to the thermal environment status assessment results, a structured data table is generated, wherein the structured data table includes the grid area number, the heat load intensity score Fh, the grade label, the space coordinate range and the recommended control strategy.

6. The smart city energy management system based on energy conservation and emission reduction according to claim 5 is characterized by: The lighting performance calculation and preliminary control module includes an LED lighting performance calculation unit and a control strategy generation unit; The LED lighting performance calculation unit is used to collect lighting-related data of each lighting device in the grid area marked in yellow and red to obtain the performance brightness of the lighting device. The specific acquisition process is as follows: Setting the thermal response function : ； In the formula, Indicates the LED thermal sensitivity coefficient, Indicates the surface temperature of the lighting equipment casing. Indicates that the function structure is a positive exponential function, Indicates the reference temperature of the lighting equipment housing surface; Substitute the lighting-related data of each lighting fixture in the grid area into the thermal response function , get the thermal response function The specific value of the lighting equipment and calculate the lighting equipment performance brightness , taking lighting equipment j as an example, the specific calculation method is: ； In the formula, represents the calibrated brightness of lighting node j, represents the light decay factor, Indicates the surface temperature of the lighting equipment housing; According to the performance brightness of lighting equipment , Use the intensity factor Qd and the heat load intensity score Fh to perform a summary calculation to obtain the lighting control factor , where the lighting control factor The specific way to obtain it is: ； In the formula, Indicates the lighting brightness compensation value.

7. A smart city energy management system based on energy conservation and emission reduction according to claim 6, characterized in that: The control strategy generation unit is used to preset a first control threshold and the first control threshold , and analyze the control strategy of lighting equipment for each lighting equipment in the grid area marked in yellow and red. The specific analysis process is as follows: like ≤ , then the lighting equipment control strategy is determined to be load-limited operation. At this time, the current power supply is adjusted to 65% of the rated power, and the intermittent lighting strategy is implemented. A lighting monitoring report is generated and sent to the maintenance personnel to remind them to perform lighting equipment maintenance until the maintenance personnel respond; like < < , then the lighting equipment control strategy is determined to be normal operation, at this time, the current power supply is maintained unchanged, and the lighting equipment performance indicators and heat load data are continuously monitored; like ≥ , then the lighting control strategy is determined to be fill light operation. At this time, the current power supply is adjusted to 115% of the rated power. At the same time, the temperature changes are monitored and the temperature change data are collected.

8. The smart city energy management system based on energy conservation and emission reduction according to claim 7 is characterized by: The feedback evaluation module is used to perform intelligent control of lighting equipment according to the control strategy, and continuously collect lighting-related data during the intelligent control of lighting equipment to obtain the energy efficiency health index Nx of each grid area. Taking grid area i as an example, the energy efficiency health index of grid area i is The specific way to obtain it is: ； In the formula, represents the average unit energy efficiency of grid area i, represents the heat load intensity score of grid area i, represents the total number of lighting devices in grid area i, represents the actual input power of the jth lighting device, represents the target input power of the jth lighting device, and represents the nonlinear compression adjustment coefficient; The preset energy efficiency health threshold Nxyz is used to calculate the energy efficiency health index of each grid area. Compare and analyze with the energy efficiency health threshold Nxyz. ≥Nxyz, it indicates that the optimization effect of the intelligent control strategy of lighting equipment is qualified. At this time, the current control strategy is maintained. <Nxyz, it indicates that the optimization effect of the intelligent control strategy of lighting equipment is unqualified, and the grid areas where the optimization effect of the intelligent control strategy of lighting equipment is unqualified are summarized to construct a set S of grid areas to be optimized.

9. A smart city energy management system based on energy conservation and emission reduction according to claim 8, characterized in that: The deep optimization module includes a regional power re-estimation unit and an intelligent feedback unit; The regional power re-estimation unit is used to recalculate the control power for each grid area in the optimized grid area set S. , the specific calculation method is: ； In the formula, Represents the average output power of the current grid area, represents the heat load control coefficient, Indicates the light effect adjustment compensation coefficient. represents the heat load intensity score of grid area i, represents the average unit energy efficiency of grid area i; According to control power , generate a control timing plan, the specific contents are as follows: Taking grid area i as an example, the N lighting devices in grid area i are divided into three control subgroups, one of which is a low-power group and the other two are regular groups. A fixed rotation cycle is set to alternately work the low-power group and the regular group, and for the low-power group, the output power is set to : ； In the formula, Indicates the minimum power safety threshold for the operation of lighting equipment. Indicates the adjustment step size.

10. A smart city energy management system based on energy conservation and emission reduction according to claim 9, characterized in that: The intelligent feedback unit is used to optimize the lighting equipment control according to the control timing plan, and collect lighting-related data in real time during the lighting equipment control optimization process, and feed back the lighting-related data to the thermal environment analysis module to continuously optimize the working process of the lighting equipment.

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