Implementation scheme management and control system for smart city planning and design

Abstract

The invention discloses an implementation scheme management and control system for smart city planning and design, and relates to the technical field of smart cities, and the system can collect the environment illumination intensity Is (x, t), the crowd density Np (x, t), the traffic flow Tf (x, t) and event data V (x, t) of each region x at the t moment in real time through an illumination demand extraction module. And the data processing module is used for preprocessing and integrating the data to obtain a standard illumination demand data set. And inputting the standard illumination demand data set into an illumination demand prediction formula, dynamically calculating an illumination demand index Ld (x, t) of the area x, and carrying out preliminary evaluation according to an illumination threshold Td. Compared with a traditional lighting system, the system achieves dynamic lighting regulation and control through accurate data analysis, lighting equipment is dynamically turned on and turned off according to the requirements of different areas, and energy waste caused by fixed time switching or unreasonable setting is avoided.

Description

Technical Field

[0001] The present invention relates to the technical field of smart cities, and specifically to an implementation plan control system for smart city planning and design. Background Art

[0002] Smart cities are an important development direction of modern information society, and their goal is to achieve intelligent management and optimization of urban resources through technologies such as the Internet of Things, big data, and artificial intelligence. As an important part of smart cities, the intelligent city lighting control system belongs to the category of intelligent management of urban infrastructure in smart cities. Specifically, through intelligent control of lighting resources, the system realizes automatic adjustment and optimization of urban lighting equipment, which can not only improve energy utilization efficiency but also ensure the safety of the city at night. In practical applications, the intelligent city lighting control system needs to combine multi-dimensional data such as ambient light, traffic flow, and population density to provide adaptive lighting for each urban area to meet the requirements of safety, energy conservation, and comfort.

[0003] At present, urban lighting management still faces many problems. On the one hand, the control method of traditional lighting systems is single, usually adopting a switching strategy with fixed brightness and preset time, which cannot be dynamically adjusted according to real-time data, resulting in serious energy waste. On the other hand, although some intelligent lighting systems have certain automatic adjustment capabilities, they lack comprehensive multi-dimensional data analysis and management capabilities and cannot fully consider the comprehensive impact of ambient light, population density, and event data. In addition, these systems usually cannot perform differential adjustment according to the security requirements of the area. For example, areas with high crime rates or emergency areas may not receive sufficient lighting support, thus posing potential safety hazards. Generally speaking, the current lighting management systems are insufficient in terms of energy conservation, flexibility, and safety, and cannot fully meet the needs of smart city development. Summary of the Invention

[0004] In view of the deficiencies of the prior art, the present invention provides an implementation plan control system for smart city planning and design, which solves the problems mentioned in the background art.

[0005] To achieve the above objectives, the present invention is realized through the following technical solutions: including a lighting demand extraction module, a data processing module, a lighting demand prediction module, an energy efficiency optimization and safety guarantee analysis module, and a comprehensive lighting control module;

[0006] The lighting demand extraction module installs a detection sensor group in each area x of the smart city to collect lighting demand data in real time, then constructs a smart city lighting control system, transmits the collected lighting demand data to the lighting control system through a local area network, and at the same time sets an API application program interface to access the city management platform to extract event data;

[0007] The data processing module integrates lighting demand data and event data in the smart city lighting control system, preprocesses the lighting demand data and event data to obtain a standard lighting demand data set, and simultaneously constructs a time series database to store the standard lighting demand data set;

[0008] The lighting demand prediction module constructs a lighting demand prediction formula, extracts the standard lighting demand data set and inputs it into the lighting demand prediction formula for calculation to output the lighting demand index Ld of each area x, sets a lighting threshold Td, and conducts a preliminary comparison and evaluation with the lighting demand index Ld of each area x, and triggers an energy efficiency optimization mechanism based on the preliminary comparison and evaluation results;

[0009] After triggering the energy efficiency optimization mechanism through the preliminary comparison and evaluation, the energy efficiency optimization and safety guarantee analysis module calculates and outputs the energy efficiency value E of each area x based on the obtained lighting demand index Ld of each area x. At the same time, it constructs a safety guarantee algorithm formula, extracts the standard lighting demand data set and inputs it into the safety guarantee algorithm formula for calculation to output the safety guarantee coefficient S;

[0010] The comprehensive lighting control module constructs a comprehensive lighting control formula, extracts the lighting demand index Ld, energy efficiency value E, and safety guarantee coefficient S of each area x and inputs them into the comprehensive lighting control formula for calculation to output the comprehensive lighting brightness control value Lc of each area x. At the same time, it sets a first lighting control threshold Tc1 and a second lighting control threshold Tc2 for a secondary comparison and evaluation with the comprehensive lighting brightness control value Lc, and controls the brightness value of the lighting equipment in each area x based on the secondary comparison and evaluation results to optimize energy efficiency.

[0011] Preferably, the lighting demand extraction module includes a brightness demand extraction unit, a data transmission unit, and an event extraction unit;

[0012] The brightness demand extraction unit installs a detection sensor group in each area x of the smart city to collect the lighting demand data of each area x in real time;

[0013] The detection sensor group includes a photosensitive sensor, a crowd counting device, and a traffic flow counting device;

[0014] The lighting demand data includes the ambient light intensity Is(x, t) of area x at time t, the crowd density Np(x, t) of area x at time t, and the traffic flow Tf(x, t) of area x at time t;

[0015] The data transmission unit integrates and connects the communication module built in the detection sensor group with the smart city lighting control system through the local area network in each area x of the smart city by constructing a smart city lighting control system, and transmits the collected lighting demand data to the smart city lighting control system;

[0016] Meanwhile, an API application program interface is set up to access the urban management platform of the smart city and extract event data;

[0017] The event data includes the number of events V(x, t) in area x at time t;

[0018] The number of events V(x, t) in area x at time t is obtained by counting the historical number of traffic accident events, public activity events and emergency events in each area x.

[0019] Preferably, the data processing module includes a data integration and preprocessing unit and a data storage unit;

[0020] The data integration and preprocessing unit integrates and preprocesses the obtained lighting demand data and event data to obtain a standard lighting demand data set;

[0021] The preprocessing includes area x alignment, timestamp alignment and normalization processing;

[0022] The area x alignment is used to divide the lighting demand data and event data obtained from different areas x, and divide the lighting demand data and event data of the same area x into the data set of the same area x;

[0023] The timestamp alignment is used to extract the time points of the data in the data sets of all the same area x and perform unified alignment;

[0024] The normalization processing uses the Zscore method to convert the actual values of the data set of the same area x after timestamp alignment into standardized values with zero mean and unit standard deviation, eliminates the dimensional influence of all parameters in the data set of the same area x, and obtains a standard lighting demand data set;

[0025] The data storage unit constructs a time series database, sets a write port and a read port, and writes the obtained standard lighting demand data set into the time series database through the write port;

[0026] Set the number of storage tables according to the total number of areas x in the smart city, set the storage table ID corresponding to area x, and match each storage table with a different area x;

[0027] The standard lighting demand data sets for different regions x are stored in the corresponding storage tables according to the storage table IDs, and sorted in the storage tables in the order of time stamps.

[0028] Preferably, the lighting demand prediction module includes a lighting demand analysis unit and a lighting demand evaluation unit;

[0029] The lighting demand analysis unit constructs a lighting demand prediction formula, writes the ambient light intensity Is(x, t) of area x at time t, the population density Np(x, t) of area x at time t, and the traffic flow Tf(x, t) of area x at time t into the lighting demand prediction formula, calculates and outputs the lighting demand index Ld of each area x, and predicts the lighting demand situation of each area x.

[0030] The lighting demand index Ld is calculated and output through the following algorithm formula;

[0031] Ld(x, t) = [a1·Is(x, t) + a2·Np(x, t) + a3·Tf(x, t)] a4 ;

[0032] In the formula, Ld(x, t) represents the lighting demand index of area x at time t, a1, a2, and a3 represent the regression coefficients of the ambient light intensity Is, the population density Np, and the traffic flow Tf, and a4 represents the non-linear coefficient.

[0033] Preferably, the lighting demand evaluation unit, based on the historical lighting demand situation, sets the lighting threshold Td by the user, and makes a preliminary comparison and evaluation of the lighting demand situation of each area x by comparing the set lighting threshold Td with the obtained lighting demand index Ld(x, t) of area x at time t. The specific evaluation content is as follows;

[0034] When the lighting demand index Ld(x, t) of area x at time t ≥ the lighting threshold Td, it indicates that the lighting demand of the current area x is abnormal. At this time, the lighting equipment of the current area x is automatically turned on through the smart city lighting control system, and the energy efficiency optimization mechanism is executed;

[0035] When the lighting demand index Ld(x, t) of area x at time t < the lighting threshold Td, it indicates that the lighting demand of the current area x is normal. At this time, the lighting equipment of the current area x is automatically turned off through the smart city lighting control system.

[0036] Preferably, the energy efficiency optimization and safety guarantee analysis module includes an energy efficiency optimization unit and a safety guarantee analysis unit;

[0037] After the lighting equipment in the current area x is turned on, the energy efficiency optimization unit extracts the lighting demand index Ld(x, t) of area x at time t, and calculates and outputs the energy efficiency value E of each area x by combining the power consumption of the lighting equipment.

[0038] The energy efficiency value E is calculated and output through the following algorithm formula;

[0039]

[0040] In the formula, E(x, t) represents the energy efficiency value of area x at time t, N represents the total number of lighting equipment in area x, and P(x, i, t) represents the power consumption of the i-th lighting equipment in area x at time t.

[0041] Preferably, the safety guarantee analysis unit extracts the number of events V(x, t) in area x at time t and the population density Np(x, t) in area x at time t through the write port, combines and calculates to output the safety guarantee coefficient S of the current area x, and quantifies the safety requirement of the current area x.

[0042] The safety guarantee coefficient S is calculated and output through the following algorithm formula;

[0043]

[0044] In the formula, S(x, t) represents the safety guarantee coefficient of area x at time t, n represents the total number of events, V v (x, t) represents the number of events of the v-th event in area x at time t, β1 represents the influence weight value of the event on safety, γ1 represents the benchmark influence coefficient of population density on safety requirements, and γ2 represents the non-linear influence coefficient of population density on safety requirements.

[0045] Preferably, the integrated lighting control module includes a lighting control parameter output unit, a lighting threshold setting unit, and a lighting evaluation control unit;

[0046] The lighting control parameter output unit constructs an integrated lighting control formula, extracts the lighting demand index Ld(x, t), the energy efficiency value E(x, t) of area x at time t, and the safety guarantee coefficient S(x, t) of area x at time t, inputs them into the lighting control formula, and calculates and outputs the integrated lighting brightness control value Lc(x, t) of area x at time t to analyze the brightness control value of the current area x.

[0047] The integrated lighting brightness control value Lc(x, t) of area x at time t is calculated and output through the following integrated lighting control formula;

[0048]

[0049] Preferably, the lighting threshold setting unit extracts the historical standard lighting demand data set of the current area x, and performs summary calculation to output the first lighting control threshold Tc1 and the second lighting control threshold Tc2;

[0050] The first lighting control threshold Tc1 and the second lighting control threshold Tc2 are calculated and output through the following algorithm formulas;

[0051]

[0052] In the formula, the first lighting control threshold Tc1 reflects the safety requirements and lighting demands of area x, and the second lighting control threshold Tc2 reflects the energy-saving requirements and energy efficiency demands of area x;

[0053] avg(Ld(x, t)) represents the average lighting demand index Ld(x, t) of area x at time t;

[0054] max(Sf(x, t)) represents the upper limit value of the safety guarantee coefficient S(x, t) of area x at time t;

[0055] max(Np(x, t)) represents the upper limit value of the population density Np(x, t) of area x at time t;

[0056] avg(E(x, t)) represents the average energy efficiency value E(x, t) of area x at time t;

[0057] min(Is(x, t)) represents the lower limit value of the ambient light intensity Is(x, t) of area x at time t;

[0058] min(Np(x, t)) represents the lower limit value of the population density Np(x, t) of area x at time t.

[0059] Preferably, the lighting evaluation and control unit extracts the first lighting control threshold Tc1 and the second lighting control threshold Tc2 output and the comprehensive lighting brightness control value Lc(x, t) of area x at time t for secondary comparison and evaluation, and controls the brightness value of the lighting equipment in each area x based on the evaluation result for energy efficiency optimization. The specific evaluation content is as follows;

[0060] When the comprehensive lighting brightness control value Lc(x, t) of area x at time t ≥ the first lighting control threshold Tc1, the lighting equipment of the current area x is controlled to switch to the full-bright mode at this time;

[0061] When the second lighting control threshold Tc2 < the comprehensive lighting brightness control value Lc(x, t) of area x at time t < the first lighting control threshold Tc1, the lighting equipment of the current area x is controlled to switch to the energy-saving mode at this time;

[0062] When the comprehensive lighting brightness control value Lc(x, t) of area x at time t ≤ the first lighting control threshold Tc1, the lighting device in the current area x is controlled to switch to the low-light mode at this time.

[0063] The present invention provides an implementation plan control system for smart city planning and design. It has the following beneficial effects:

[0064] (1) Through the lighting demand extraction module, the system can collect the ambient light intensity Is(x, t), population density Np(x, t), traffic flow Tf(x, t), and event data V(x, t) of each area x at time t in real time, and use the data processing module to preprocess and integrate these data to obtain the standard lighting demand data set. Then, the standard lighting demand data set is input into the lighting demand prediction formula to dynamically calculate the lighting demand index Ld(x, t) of area x at time t, and a preliminary evaluation is carried out according to the lighting threshold Td. Compared with the traditional lighting system, this system realizes dynamic lighting control through accurate data analysis, dynamically turns on and off lighting devices according to the needs of different areas, and avoids energy waste caused by fixed-time switching or unreasonable settings.

[0065] (2) Through the energy efficiency optimization and safety guarantee analysis module, the system effectively combines the number of events V(x, t) in area x at time t and the population density Np(x, t) in area x at time t to construct a quantitative algorithm for the safety guarantee coefficient S(x, t) of area x at time t. By comprehensively evaluating the safety requirements of each area, the system can provide accurate lighting support for high-risk areas or emergency areas. For example, in the event of a traffic accident, large public event or other emergency, the brightness is automatically increased to the full-bright mode to ensure visibility and safety within the area. At the same time, using the comprehensive lighting control module, the brightness is dynamically adjusted according to the safety coefficient S(x, t) to ensure that high-risk areas obtain timely and sufficient lighting guarantee. Compared with the traditional lighting method, this system can actively respond to safety needs and greatly improve the role of urban lighting in public safety.

[0066] (3) Through the comprehensive lighting control module, the system inputs the lighting demand index Ld(x, t), the energy efficiency value E(x, t), and the safety guarantee coefficient S(x, t) of area x at time t into the comprehensive control formula to calculate the comprehensive lighting brightness control value Lc(x, t) of area x at time t. Combining the first lighting control threshold Tc1 and the second lighting control threshold Tc2, the system can perform multi-level dynamic regulation of the lighting intensity. Through multi-threshold control, this system realizes precise dynamic adjustment, avoids the problems of over-illumination or under-illumination, and provides more flexible and efficient lighting management capabilities for the city. Description of the Drawings

[0067] Figure 1 This is a schematic diagram of the implementation plan control system process for a smart city planning and design of the present invention. Detailed implementation manners

[0068] 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.

[0069] Embodiment 1

[0070] Please refer to Figure 1 , the present invention provides an implementation plan control system for smart city planning and design. To achieve the above objectives, the present invention is realized through the following technical solutions: including a lighting demand extraction module, a data processing module, a lighting demand prediction module, an energy efficiency optimization and safety guarantee analysis module, and a comprehensive lighting control module;

[0071] The lighting demand extraction module installs a detection sensor group in each area x of the smart city to collect lighting demand data in real time, then constructs a smart city lighting control system, transmits the collected lighting demand data to the lighting control system through a local area network, and at the same time sets an API application program interface to access the city management platform to extract event data;

[0072] The data processing module integrates lighting demand data and event data in the smart city lighting control system, preprocesses the lighting demand data and event data to obtain a standard lighting demand data set, and at the same time constructs a time series database to store the standard lighting demand data set;

[0073] The lighting demand prediction module constructs a lighting demand prediction formula, extracts the standard lighting demand data set and inputs it into the lighting demand prediction formula for calculation to output the lighting demand index Ld of each area x, and sets a lighting threshold Td to make a preliminary comparison and evaluation with the lighting demand index Ld of each area x, and triggers an energy efficiency optimization mechanism based on the preliminary comparison and evaluation results;

[0074] After triggering the energy efficiency optimization mechanism through the preliminary comparison and evaluation, the energy efficiency optimization and safety guarantee analysis module calculates and outputs the energy efficiency value E of each area x based on the obtained lighting demand index Ld of each area x, and at the same time constructs a safety guarantee algorithm formula, extracts the standard lighting demand data set and inputs it into the safety guarantee algorithm formula for calculation to output a safety guarantee coefficient S;

[0075] The integrated lighting control module extracts the lighting demand index Ld, energy efficiency value E, and safety guarantee coefficient S of each area x by constructing an integrated lighting control formula, inputs them into the integrated lighting control formula for calculation, and outputs the integrated lighting brightness control value Lc of each area x. At the same time, the first lighting control threshold Tc1 and the second lighting control threshold Tc2 are set for secondary comparison and evaluation with the integrated lighting brightness control value Lc, and based on the results of the secondary comparison and evaluation, the brightness values of the lighting devices in each area x are controlled to optimize energy efficiency.

[0076] In this embodiment, the system installs a detection sensor group to collect lighting demand data in real time, combines the event data of the urban management platform, and uses the data processing module to integrate and preprocess multi-dimensional data to generate a standard lighting demand data set to ensure the efficiency and accuracy of subsequent analysis; constructs a prediction formula through the lighting demand prediction module, outputs the lighting demand index Ld and makes a preliminary comparison with the lighting threshold Td to provide a trigger mechanism for energy efficiency optimization; further calculates the regional energy efficiency value E and safety guarantee coefficient S through the energy efficiency optimization and safety guarantee analysis module to achieve a comprehensive evaluation of energy efficiency and safety; finally, the integrated lighting control module outputs the integrated lighting brightness control value Lc through a multi-threshold control strategy to achieve dynamic regulation of the lighting devices in each area. Compared with the traditional fixed lighting or single automated adjustment system, this solution not only improves energy utilization efficiency and avoids waste caused by unreasonable lighting, but also enhances the safety guarantee ability for emergencies and high-density areas. The precise control and real-time optimization mechanism driven by multi-dimensional data enable the system to dynamically adapt to different scenario requirements, and significant improvements have been achieved in terms of energy-saving effect, public safety guarantee, and residents' living experience. This comprehensive intelligent lighting system realizes the transformation from extensive management to intelligent and refined management, providing important support for the further development of smart city infrastructure.

[0077] Embodiment 2

[0078] Specifically: The lighting demand extraction module includes a brightness demand extraction unit, a data transmission unit, and an event extraction unit;

[0079] The brightness demand extraction unit installs a detection sensor group in each area x of the smart city to collect the lighting demand data of each area x in real time;

[0080] The detection sensor group includes a photosensitive sensor, a crowd counting device, and a traffic flow counting device;

[0081] The lighting demand data includes the ambient light intensity Is(x, t) of area x at time t, the crowd density Np(x, t) of area x at time t, and the traffic flow Tf(x, t) of area x at time t;

[0082] The data transmission unit constructs a smart city lighting control system, and through the local area network within each area x of the smart city, integrally connects the communication module built in the detection sensor group with the smart city lighting control system, and transmits the collected lighting demand data to the smart city lighting control system;

[0083] Meanwhile, an API application program interface is set up to access the urban management platform of the smart city and extract event data;

[0084] The event data includes the number of events V(x, t) in area x at time t;

[0085] The number of events V(x, t) in area x at time t is obtained by counting the historical number of traffic accident events, public activity events and emergency events in each area x.

[0086] In this embodiment, through the collaborative work of the brightness demand extraction unit, the data transmission unit and the event extraction unit, a comprehensive smart city lighting data acquisition system is constructed. The brightness demand extraction unit uses photosensitive sensors, crowd counting devices and traffic flow counting devices installed in each area to obtain multi-dimensional data such as the ambient light intensity Is(x, t) in area x at time t, the crowd density Np(x, t) in area x at time t, and the traffic flow Tf(x, t) in area x at time t in real time, comprehensively covering the dynamic demand of urban lighting; the data transmission unit uses the built-in communication module to connect with the local area network to ensure that data can be transmitted to the smart lighting control system efficiently and securely; meanwhile, the event extraction unit extracts the number of events V(x, t) in area x at time t from the urban management platform in real time through the set API interface, including statistical information on traffic accidents, public activities and emergencies, providing key support for subsequent lighting control decisions. Compared with the traditional lighting management system that only relies on fixed schedules or single-parameter adjustment methods, this module realizes the comprehensive integration and dynamic acquisition of various real-time data, significantly improving the timeliness and diversity of data. At the same time, through the introduction of event data, the system can accurately identify the special lighting needs of the area. Especially in the event of an emergency or a high-density scenario, the system can respond in a timely manner and adjust the lighting intensity.

[0087] Embodiment 3

[0088] Specifically: The data processing module includes a data integration and preprocessing unit and a data storage unit;

[0089] The data integration and preprocessing unit integrates and preprocesses the obtained lighting demand data and event data to obtain a standard lighting demand data set;

[0090] The preprocessing includes area x alignment, timestamp alignment and normalization processing;

[0091] The area x alignment is used to divide the lighting demand data and event data obtained from different areas x, and divide the lighting demand data and event data of the same area x into the data collection of the same area x;

[0092] The timestamp alignment is used to extract the time points of the data in all the data collections of the same area x and perform unified alignment;

[0093] The normalization process uses the Zscore method to transform the actual values of the data collection of the same area x after timestamp alignment into standardized values with zero mean and unit standard deviation, eliminating the dimensional influence of all parameters in the data collection of the same area x and obtaining the standard lighting demand data set;

[0094] The data storage unit constructs a time series database, sets a write port and a read port, and writes the obtained standard lighting demand data set into the time series database through the write port;

[0095] Set the number of storage tables according to the total number of areas x in the smart city, and set the storage table IDs corresponding to the areas x, and match each storage table with a different area x;

[0096] The standard lighting demand data sets of different areas x are stored in the corresponding storage tables according to the storage table IDs, and sorted in the storage tables in timestamp order.

[0097] In this embodiment, the system performs area alignment, timestamp alignment and normalization processing on the collected lighting demand data and event data through the data integration and preprocessing unit, successfully eliminating the influence of regional differences and data dimensions, constructing a standardized lighting demand data set, and providing accurate and consistent data support for subsequent analysis. At the same time, the data storage unit constructs a time series database, efficiently stores the standard lighting demand data set according to regions and time series, and adopts the method of matching storage table IDs with regions to ensure the efficiency of data management and the accuracy of retrieval. Compared with traditional data management systems, this module has achieved significant optimization in data integration and storage efficiency. Through area alignment and timestamp alignment, the problem of inconsistent data across regions and time dimensions is solved; the use of the Zscore method for normalization processing eliminates the calculation deviation caused by different dimensions among multiple parameters; the introduction of the time series database further improves the storage and retrieval performance of data, providing solid support for real-time lighting control and dynamic analysis.

[0098] Embodiment 4

[0099] Specifically: The lighting demand prediction module includes a lighting demand analysis unit and a lighting demand evaluation unit;

[0100] The lighting demand analysis unit writes the ambient light intensity Is(x, t) of the port extraction area x at time t, the population density Np(x, t) of area x at time t, and the traffic flow Tf(x, t) of area x at time t into the lighting demand prediction formula by constructing the lighting demand prediction formula, and calculates and outputs the lighting demand index Ld of each area x to predict the lighting demand situation of each area x.

[0101] The lighting demand index Ld is calculated and output through the following algorithm formula;

[0102] Ld(x, t) = [a1·Is(x, t) + a2·Np(x, t) + a3·Tf(x, t)] a4 ;

[0103] In the formula, Ld(x, t) represents the lighting demand index of area x at time t, a1, a2, and a3 represent the regression coefficients of the ambient light intensity Is, population density Np, and traffic flow Tf, a4 represents the non-linear coefficient used to handle possible non-linear effects, and a1, a2, a3, and a4 represent the influence of different factors on lighting demand, which are learned and adjusted according to historical data.

[0104] The lighting demand evaluation unit sets the lighting threshold Td by the user based on the historical lighting demand situation, and makes a preliminary comparison and evaluation of the lighting demand situation of each area x by comparing the set lighting threshold Td with the obtained lighting demand index Ld(x, t) of area x at time t. The specific evaluation content is as follows;

[0105] When the lighting demand index Ld(x, t) of area x at time t ≥ the lighting threshold Td, it indicates that the lighting demand of the current area x is abnormal. At this time, the lighting equipment of the current area x is automatically turned on through the smart city lighting control system, and the energy efficiency optimization mechanism is executed;

[0106] When the lighting demand index Ld(x, t) of area x at time t < the lighting threshold Td, it indicates that the lighting demand of the current area x is normal. At this time, the lighting equipment of the current area x is automatically turned off through the smart city lighting control system.

[0107] In this embodiment, the system realizes the accurate prediction and dynamic response of urban lighting demand through the lighting demand analysis unit and the lighting demand evaluation unit. The lighting demand analysis unit dynamically calculates and outputs the lighting demand index Ld(x,t) of area x at time t by constructing a lighting demand prediction formula and integrating multi-dimensional data such as the ambient light intensity Is(x,t) of area x at time t, the population density Np(x,t) of area x at time t, and the traffic flow Tf(x,t) of area x at time t in the region, so as to accurately predict the lighting demand of each area at different times. The lighting demand evaluation unit combines the lighting threshold Td set by the user to conduct a preliminary comparison and evaluation of the lighting demand index Ld(x,t) of area x at time t. When the demand is abnormal, it triggers the lighting device to turn on and the energy efficiency optimization mechanism. When the demand is normal, it automatically turns off the device, thus realizing efficient dynamic regulation. Compared with the single-parameter prediction or fixed regulation strategy of traditional lighting systems, this module significantly improves the accuracy and real-time performance of lighting prediction. By introducing the regression coefficients a1, a2, a3 and the non-linear coefficient a4, the system can fully consider the influence of various factors on lighting demand and handle complex non-linear relationships; by setting the lighting threshold Td, it realizes differential management and response for different regions and scenarios.

[0108] Embodiment 5

[0109] Specifically: The energy efficiency optimization and safety guarantee analysis module includes an energy efficiency optimization unit and a safety guarantee analysis unit;

[0110] After the lighting device in the current area x is turned on, the energy efficiency optimization unit extracts the lighting demand index Ld(x,t) of area x at time t, and calculates and outputs the energy efficiency value E of each area x in combination with the power consumption of the lighting device;

[0111] The energy efficiency value E is calculated and output through the following algorithm formula;

[0112]

[0113] In the formula, E(x,t) represents the energy efficiency value of area x at time t, N represents the total number of lighting devices in area x, P(x,i,t) represents the power consumption of the i-th lighting device in area x at time t, and the value after dimensionless processing.

[0114] The safety guarantee analysis unit extracts the number of events V(x,t) of area x at time t and the population density Np(x,t) of area x at time t through the write port, combines and calculates to output the safety guarantee coefficient S of the current area x, and quantifies the safety requirement of the current area x;

[0115] The safety guarantee coefficient S is calculated and output through the following algorithm formula;

[0116]

[0117] In the formula, S(x, t) represents the safety factor of area x at time t, n represents the total number of events, V v (x, t) represents the number of occurrences of the v-th event in area x at time t, β1 represents the influence weight value of the event on safety, γ1 represents the baseline influence coefficient of population density on safety requirements, and γ2 represents the non-linear influence coefficient of population density on safety requirements.

[0118] In this embodiment, the system calculates the energy efficiency value E(x, t) of area x at time t through the combination of the lighting demand index Ld(x, t) of area x at time t and the power consumption P(x, i, t) of the i-th lighting device in area x at time t by the energy efficiency optimization unit, quantifies the lighting intensity provided per watt of power, and provides an accurate basis for the optimization of energy utilization efficiency. The safety guarantee analysis unit calculates the safety factor S(x, t) of area x at time t through an algorithm formula constructed using the weight coefficient and non-linear influence coefficient by extracting the area event data V(x, t) and population density Np(x, t), dynamically evaluates the safety requirements of each area, especially in areas with high event occurrence frequencies or high population densities, and ensures that sufficient lighting support is provided to maintain public safety. Compared with the traditional lighting system that simply relies on empirical values or fixed settings, this module has achieved a breakthrough improvement in energy conservation and safety guarantee. Through the energy efficiency optimization unit, the system can accurately identify areas with low energy efficiency and optimize the energy allocation in real time to avoid unnecessary energy waste; through the safety guarantee analysis unit, the system can provide dynamic responses to emergencies and high-risk areas, effectively reducing potential safety hazards caused by insufficient lighting.

[0119] Embodiment 6

[0120] Specifically: The integrated lighting control module includes a lighting control parameter output unit, a lighting threshold setting unit, and a lighting evaluation control unit;

[0121] The lighting control parameter output unit constructs an integrated lighting control formula, extracts the lighting demand index Ld(x, t) of area x at time t, the energy efficiency value E(x, t) of area x at time t, and the safety factor S(x, t) of area x at time t, inputs them into the lighting control formula for calculation, and outputs the integrated lighting brightness control value Lc(x, t) of area x at time t to analyze the brightness control value of the current area x;

[0122] The integrated lighting brightness control value Lc(x, t) of area x at time t is calculated and output through the following integrated lighting control formula;

[0123]

[0124] The lighting threshold setting unit extracts the historical standard lighting demand data set of the current area x, and performs summary calculations to output the first lighting control threshold Tc1 and the second lighting control threshold Tc2;

[0125] The first lighting control threshold Tc1 and the second lighting control threshold Tc2 are calculated and output through the following algorithm formulas;

[0126]

[0127] In the formula, the first lighting control threshold Tc1 reflects the safety requirements and lighting demands of area x, and the second lighting control threshold Tc2 reflects the energy-saving requirements and energy efficiency demands of area x;

[0128] avg(Ld(x, t)) represents the average lighting demand index Ld(x, t) of area x at time t;

[0129] max(Sf(x, t)) represents the upper limit value of the safety guarantee coefficient S(x, t) of area x at time t;

[0130] max(Np(x, t)) represents the upper limit value of the population density Np(x, t) of area x at time t;

[0131] avg(E(x, t)) represents the average energy efficiency value E(x, t) of area x at time t;

[0132] min(Is(x, t)) represents the lower limit value of the ambient light intensity Is(x, t) of area x at time t;

[0133] min(Np(x, t)) represents the lower limit value of the population density Np(x, t) of area x at time t.

[0134] The lighting evaluation control unit extracts the output first lighting control threshold Tc1 and second lighting control threshold Tc2 and the comprehensive lighting brightness control value Lc(x, t) of area x at time t for secondary comparison and evaluation, and controls the brightness value of the lighting equipment in each area x based on the evaluation results for energy efficiency optimization. The specific evaluation content is as follows;

[0135] When the comprehensive lighting brightness control value Lc(x, t) of area x at time t ≥ the first lighting control threshold Tc1, the lighting equipment in the current area x is controlled to switch to the full-bright mode at this time;

[0136] When the second lighting control threshold Tc2 < the comprehensive lighting brightness control value Lc(x, t) of area x at time t < the first lighting control threshold Tc1, the lighting equipment in the current area x is controlled to switch to the energy-saving mode at this time;

[0137] When the comprehensive lighting brightness control value Lc(x, t) of area x at time t ≤ the first lighting control threshold Tc1, the lighting device in the current area x is controlled to switch to the dim light mode, that is, the lowest brightness of the lighting device.

[0138] In this embodiment, the system synthesizes the lighting demand index Ld(x, t) of area x at time t, the energy efficiency value E(x, t) of area x at time t, and the safety factor S(x, t) of area x at time t through the lighting control parameter output unit, and calculates the comprehensive lighting brightness control value Lc(x, t) of area x at time t through the comprehensive control formula, providing a basis for adapting the brightness adjustment for each area; the lighting threshold setting unit calculates the first lighting control threshold Tc1 and the second lighting control threshold Tc2 based on historical data, respectively reflecting the safety requirements and energy-saving requirements; the lighting evaluation control unit then dynamically adjusts the lighting device modes of each area, including the full-brightness mode, the energy-saving mode, and the dim light mode, through the secondary comparison and evaluation of the comprehensive lighting brightness control value Lc(x, t) of area x at time t with the first lighting control threshold Tc1 and the second lighting control threshold Tc2, so as to achieve the dual goals of safety guarantee and energy efficiency optimization. Compared with the traditional lighting control method, this module has made significant improvements in control accuracy, adaptability, and intelligence level. By introducing the multi-parameter control formula and the dual-threshold evaluation mechanism, the system can provide different responses to the lighting requirements of different areas and scenarios. Compared with the previous single brightness control or fixed mode adjustment methods, this module not only improves the adaptability and safety of regional lighting, but also effectively reduces energy waste. Overall, the comprehensive lighting control module significantly optimizes the lighting control efficiency, realizes the simultaneous improvement of safety and energy-saving benefits, and provides advanced technical support for the sustainable development of smart cities.

[0139] Although the embodiments of the present invention have been shown and described, it will be understood by those of ordinary skill in the art that various changes, modifications, substitutions, and variations can be made to these embodiments without departing from the principles and spirit of the present invention.

Claims

1. A smart city planning and design implementation plan management and control system, characterized by: It includes lighting demand extraction module, data processing module, lighting demand prediction module, energy efficiency optimization and safety assurance analysis module and comprehensive lighting control module; The lighting demand extraction module collects lighting demand data in real time by installing a detection sensor group in each area x of the smart city, and then constructs a smart city lighting control system, transmits the collected lighting demand data to the lighting control system through a local area network, and sets an API application program interface to access the city management platform to extract event data; The data processing module integrates lighting demand data and event data in the smart city lighting control system, pre-processes the lighting demand data and event data, obtains a standard lighting demand data set, and constructs a time series database to store data of the standard lighting demand data set; The lighting demand prediction module constructs a lighting demand prediction formula, extracts a standard lighting demand data set and inputs it into the lighting demand prediction formula, calculates and outputs the lighting demand index Ld of each area x, sets the lighting threshold Td and the lighting demand index Ld of each area x for preliminary comparative evaluation, and triggers the energy efficiency optimization mechanism based on the preliminary comparative evaluation result; The energy efficiency optimization and safety assurance analysis module calculates and outputs the energy efficiency value E of each area x based on the lighting demand index Ld of each area x after preliminary comparative evaluation and triggering the energy efficiency optimization mechanism. At the same time, it constructs a safety assurance algorithm formula, extracts the standard lighting demand data set and inputs it into the safety assurance algorithm formula to calculate and output the safety assurance coefficient S. The integrated lighting control module constructs an integrated lighting control formula, extracts the lighting demand index Ld, energy efficiency value E and safety assurance factor S of each area x, inputs them into the integrated lighting control formula, calculates and outputs the integrated lighting brightness control value Lc of each area x, and sets the first lighting control threshold Tc1 and the second lighting control threshold Tc2 to perform a secondary comparative evaluation with the integrated lighting brightness control value Lc. Based on the secondary comparative evaluation results, the brightness value of the lighting equipment in each area x is controlled to optimize energy efficiency.

2. According to claim 1, the implementation scheme management and control system of smart city planning and design is characterized by: The lighting demand extraction module includes a brightness demand extraction unit, a data transmission unit and an event extraction unit; The brightness demand extraction unit collects lighting demand data of each area x in real time by installing a detection sensor group in each area x of the smart city; The detection sensor group includes a photosensitive sensor, a crowd counting device and a vehicle flow counting device; The lighting demand data includes the ambient light intensity Is(x, t) of area x at time t, the crowd density Np(x, t) of area x at time t, and the traffic flow Tf(x, t) of area x at time t; The data transmission unit constructs a smart city lighting control system, integrates and connects the communication module built into the detection sensor group with the smart city lighting control system through the local area network in each area x of the smart city, and transmits the collected lighting demand data to the smart lighting city control system; At the same time, an API application program interface is set up to access the urban management platform of the smart city to extract event data; The event data includes the number of events V(x, t) in region x at time t; The number of events V(x, t) of the region x at time t is obtained by counting the number of traffic accidents, public activities and emergencies in the history of each region x.

3. The smart city planning and design implementation plan management and control system according to claim 2 is characterized by: The data processing module includes a data integration preprocessing unit and a data storage unit; The data integration preprocessing unit integrates and preprocesses the acquired lighting demand data and event data to obtain a standard lighting demand data set; The preprocessing includes region x alignment, timestamp alignment and normalization processing; The region x alignment is used to divide the lighting demand data and event data obtained from different regions x, and to divide the lighting demand data and event data of the same region x into a data collection of the same region x; The timestamp alignment is used to uniformly align the data extraction time points in all data collections of the same region x; The normalization process converts actual values ​​of the data set of the same area x after time stamp alignment into standardized values ​​with zero mean and unit standard deviation by using the Zscore method, thereby eliminating the dimensional influence of all parameters in the data set of the same area x and obtaining a standard lighting demand data set; The data storage unit constructs a time series database and sets a write port and a write-out port, and writes the acquired standard lighting demand data set into the time series database through the write port; The number of storage tables is set according to the total number of regions x in the smart city, and the storage table ID corresponding to the region x is set, and each storage table is matched with a different region x; The standard lighting requirement data sets of different regions x are stored in corresponding storage tables according to the storage table IDs, and are sorted in the storage tables in the order of timestamps.

4. The smart city planning and design implementation plan management and control system according to claim 3 is characterized by: The lighting demand prediction module includes a lighting demand analysis unit and a lighting demand assessment unit; The lighting demand analysis unit constructs a lighting demand prediction formula, writes the ambient light intensity Is(x, t) of the port extraction area x at time t, the crowd density Np(x, t) of the area x at time t, and the traffic flow Tf(x, t) of the area x at time t, inputs them into the lighting demand prediction formula, calculates and outputs the lighting demand index Ld of each area x, and predicts the lighting demand situation of each area x; The lighting demand index Ld is calculated and output by the following algorithm formula; Ld(x,t)=[a1·Is(x,t)+a2·Np(x,t)+a3·Tf(x,t)] a4 ; Where Ld(x, t) represents the lighting demand index of area x at time t, a1, a2 and a3 represent the regression coefficients of ambient light intensity Is, crowd density Np and traffic flow Tf, and a4 represents the nonlinear coefficient.

5. The smart city planning and design implementation plan management and control system according to claim 4 is characterized by: The lighting demand evaluation unit sets the lighting threshold Td by the user based on the historical lighting demand situation, and compares the set lighting threshold Td with the obtained lighting demand index Ld(x, t) of the area x at time t to perform a preliminary evaluation and analysis of the lighting demand situation of each area x. The specific evaluation content is as follows; When the lighting demand index Ld(x, t) of area x at time t ≥ the lighting threshold Td, it means that the lighting demand of the current area x is abnormal. At this time, the lighting equipment of the current area x is automatically turned on through the smart city lighting control system, and the energy efficiency optimization mechanism is executed; When the lighting demand index Ld(x, t) of area x at time t is less than the lighting threshold Td, it means that the lighting demand of the current area x is normal. At this time, the lighting equipment of the current area x is automatically turned off through the smart city lighting control system.

6. A smart city planning and design implementation plan management and control system according to claim 5, characterized in that: The energy efficiency optimization and safety assurance analysis module includes an energy efficiency optimization unit and a safety assurance analysis unit; The energy efficiency optimization unit extracts the lighting demand index Ld(x, t) of the area x at time t after the lighting equipment in the current area x is turned on, and calculates and outputs the energy efficiency value E of each area x in combination with the power consumption of the lighting equipment; The energy efficiency value E is calculated and output by the following algorithm formula; Where E(x, t) represents the energy efficiency value of area x at time t, N represents the total number of lighting devices in area x, and P(x, i, t) represents the power consumption of the i-th lighting device in area x at time t.

7. A smart city planning and design implementation plan management and control system according to claim 6, characterized in that: The safety assurance analysis unit extracts the number of events V(x, t) in area x at time t and the crowd density Np(x, t) in area x at time t through the write port, performs combined calculation and outputs the safety assurance coefficient S of the current area x, and quantifies the safety requirements of the current area x; The safety guarantee coefficient S is calculated and output by the following algorithm formula: In the formula, S(x, t) represents the safety guarantee factor of region x at time t, n represents the total number of events, V v (x, t) represents the number of events of the vth event in area x at time t, β1 represents the weight value of the impact of the event on safety, γ1 represents the baseline impact coefficient of crowd density on safety requirements, and γ2 represents the nonlinear impact coefficient of crowd density on safety requirements.

8. The smart city planning and design implementation plan management and control system according to claim 1 is characterized by: The integrated lighting control module includes a lighting control parameter output unit, a lighting threshold setting unit and a lighting evaluation control unit; The lighting control parameter output unit constructs a comprehensive lighting control formula, extracts the lighting demand index Ld(x, t) of area x at time t, the energy efficiency value E(x, t) of area x at time t, and the safety guarantee coefficient S(x, t) of area x at time t, inputs them into the lighting control formula to calculate and output the comprehensive lighting brightness control value Lc(x, t) of area x at time t, and analyzes the brightness control value of the current area x; The comprehensive lighting brightness control value Lc(x, t) of the area x at time t is calculated and output by the following comprehensive lighting control formula:

9. A smart city planning and design implementation plan management and control system according to claim 8, characterized in that: The lighting threshold setting unit extracts the historical standard lighting demand data set of the current area x, performs summary calculation and outputs the first lighting control threshold Tc1 and the second lighting control threshold Tc2; The first lighting control threshold Tc1 and the second lighting control threshold Tc2 are calculated and outputted by the following algorithm formula; Wherein, the first lighting control threshold Tc1 reflects the safety requirements and lighting requirements of area x, and the second lighting control threshold Tc2 reflects the energy saving requirements and energy efficiency requirements of area x; avg(Ld(x, t)) represents the average lighting demand index Ld(x, t) of area x at time t; max(Sf(x, t)) represents the upper limit of the safety factor S(x, t) of region x at time t; max(Np(x, t)) represents the upper limit of the crowd density Np(x, t) in area x at time t; avg(E(x, t)) represents the average energy efficiency value E(x, t) of region x at time t; min(Is(x, t)) represents the lower limit of the ambient light intensity Is(x, t) of area x at time t; min(Np(x, t)) represents the lower limit of the crowd density Np(x, t) in area x at time t.

10. A smart city planning and design implementation plan management and control system according to claim 9, characterized in that: The lighting evaluation control unit extracts the output first lighting control threshold Tc1 and the second lighting control threshold Tc2 and performs secondary comparative evaluation with the comprehensive lighting brightness control value Lc(x, t) of the area x at time t, and controls the brightness value of the lighting equipment in each area x based on the evaluation result to optimize the energy efficiency. The specific evaluation content is as follows; When the comprehensive lighting brightness control value Lc(x, t) of the area x at time t is ≥ the first lighting control threshold Tc1, the lighting equipment of the current area x is controlled to switch to full brightness mode; When the second lighting control threshold Tc2 is less than the integrated lighting brightness control value Lc(x, t) of the area x at time t and less than the first lighting control threshold Tc1, the lighting equipment of the current area x is controlled to switch to energy-saving mode; When the integrated lighting brightness control value Lc(x, t) of the area x at time t is less than or equal to the first lighting control threshold Tc1, the lighting equipment of the current area x is controlled to switch to the dim light mode.

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