A smart city planning and design implementation plan management and control system

Abstract

The present invention discloses a smart city planning and design implementation plan management and control system, relating to the field of smart city technology. This system, through a lighting demand extraction module, can collect, in real time, ambient light intensity Is(x, t), crowd density Np(x, t), traffic flow Tf(x, t), and event data V(x, t) for each area x at time t. A data processing module then preprocesses and integrates this data to obtain a standard lighting demand dataset. This standard lighting demand dataset is then input into a lighting demand prediction formula to dynamically calculate the lighting demand indicator Ld(x, t) for area x and perform a preliminary assessment based on the lighting threshold Td. Compared to traditional lighting systems, this system achieves dynamic lighting control through precise data analysis, dynamically turning lighting equipment on and off according to the needs of different areas, and avoiding energy waste caused by fixed-time switches or unreasonable settings.

Description

Technical Field

[0001] The present invention relates to the field of smart city technology, and in particular to a smart city planning and design implementation plan management and control system. Background Art

[0002] Smart cities are a key development direction in modern information society. 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 a crucial component of smart cities, smart city lighting management and control systems fall under the umbrella of intelligent management of urban infrastructure within smart cities. Specifically, through intelligent control of lighting resources, this system enables automated adjustment and optimization of urban lighting equipment, improving energy efficiency while ensuring nighttime safety. In practical applications, smart city lighting management and control systems must integrate multi-dimensional data such as ambient light, traffic flow, and crowd density to provide adaptive lighting for each urban area, meeting safety, energy conservation, and comfort requirements.

[0003] Urban lighting management currently faces numerous challenges. Traditional lighting systems rely on a single control method, typically employing a fixed brightness and preset on / off time strategy. These systems are unable to dynamically adjust based on real-time data, leading to significant energy waste. Furthermore, while some intelligent lighting systems possess a certain degree of automated adjustment capabilities, they lack comprehensive, multi-dimensional data analysis and management capabilities, and are unable to fully account for the combined impact of ambient light, crowd density, and event data. Furthermore, these systems are often unable to differentiate adjustments based on regional security needs. For example, high-crime areas or areas experiencing emergencies may not receive adequate lighting support, posing a safety hazard. Overall, current lighting management systems are insufficient in terms of energy conservation, flexibility, and security, and cannot fully meet the needs of smart city development. Summary of the Invention

[0004] In response to the shortcomings of the existing technology, the present invention provides a smart city planning and design implementation plan management and control system, which solves the problems mentioned in the background technology.

[0005] To achieve the above objectives, the present invention is implemented 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 assurance analysis module and a comprehensive lighting control module;

[0006] 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, then builds a smart city lighting control system, transmits the collected lighting demand data to the lighting control system via the local area network, and sets up 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, 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;

[0008] 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 for each area x, sets a lighting threshold Td and performs preliminary comparative evaluation with the lighting demand index Ld for each area x, and triggers the energy efficiency optimization mechanism based on the preliminary comparative evaluation results;

[0009] The energy efficiency optimization and safety assurance analysis module triggers the energy efficiency optimization mechanism through preliminary comparative evaluation. Based on the lighting demand index Ld of each area x, it calculates and outputs the energy efficiency value E of each area x. 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.

[0010] The integrated lighting control module constructs an integrated lighting control formula, extracts the lighting demand index Ld, energy efficiency value E and safety assurance coefficient 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 simultaneously 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 results of the secondary comparative evaluation, the brightness value of the lighting equipment in each area x is controlled 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 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;

[0013] The detection sensor group includes a photosensitive sensor, a crowd counting device and a vehicle 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 constructs a smart city lighting control system and integrates 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, thereby transmitting the collected lighting demand data to the smart lighting city control system;

[0016] At the same time, an API application program interface is set up to access the smart city's urban management platform to extract event data;

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

[0018] The number of events V(x, t) in the region x at time t is obtained by counting the number of traffic accidents, public events and emergencies in the history of each region x.

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

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

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

[0022] 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;

[0023] The timestamp alignment is used to uniformly align the data extraction time points in all data collections of the same region x;

[0024] The normalization process is to convert the actual values ​​of the data set of the same region x after the timestamps are aligned 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 region x and obtaining a standard lighting demand data set;

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

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

[0027] The standard lighting requirement datasets 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.

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

[0029] The lighting demand analysis unit constructs a lighting demand prediction formula, writes the port extraction of the ambient light intensity Is(x, t) of the 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;

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

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

[0032] 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.

[0033] Preferably, the lighting demand assessment unit allows the user to set a lighting threshold Td based on historical lighting demand conditions, and performs a preliminary comparative assessment and analysis of the lighting demand conditions of each region x by comparing the set lighting threshold Td with the obtained lighting demand index Ld(x, t) of the region x at time t. The specific assessment content is as follows;

[0034] When the lighting demand indicator 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 smart city lighting control system automatically turns on the lighting equipment in the current area x and implements the energy efficiency optimization mechanism;

[0035] 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.

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

[0037] 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 based on the power consumption of the lighting equipment;

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

[0039]

[0040] 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.

[0041] Preferably, 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 to output the safety assurance coefficient S of the current area x, and quantifies the safety requirements of the current area x;

[0042] The safety assurance coefficient S is calculated and outputted by the following algorithm formula:

[0043]

[0044] Where S(x, t) represents the safety assurance factor of region x at time t, n represents the total number of events, and V v (x, t) represents the number of events of the vth event in region x at time t, β1 represents the weight 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.

[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 a comprehensive lighting control formula and 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 factor 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;

[0047] The comprehensive lighting brightness control value Lc(x, t) of the region x at time t is calculated and outputted by the following comprehensive lighting control formula:

[0048]

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

[0050] The first lighting control threshold Tc1 and the second lighting control threshold Tc2 are calculated and outputted by the following algorithm formula:

[0051]

[0052] 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;

[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 of the safety assurance factor S(x, t) of region x at time t;

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

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

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

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

[0059] Preferably, the lighting evaluation control unit extracts the output first lighting control threshold Tc1 and 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 results to optimize energy efficiency. The specific evaluation content is as follows;

[0060] When the integrated lighting brightness control value Lc(x, t) of area x at time t is greater than or equal to the first lighting control threshold Tc1, the lighting equipment in the current area x is controlled to switch to full brightness mode;

[0061] 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 in the current area x is controlled to switch to energy-saving mode;

[0062] When the integrated lighting brightness control value Lc(x, t) of the area x at time t is less than or equal to the second lighting control threshold Tc2, the lighting equipment of the current area x is controlled to switch to the dim mode.

[0063] The present invention provides a smart city planning and design implementation plan management and control system. 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), crowd 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 a standard lighting demand data set. The standard lighting demand data set is then input into the lighting demand prediction formula to dynamically calculate the lighting demand index Ld(x,t) of area x at time t, and perform a preliminary evaluation based on the lighting threshold Td. Compared with traditional lighting systems, this system realizes dynamic lighting control through precise data analysis, dynamically turning on and off lighting equipment according to the needs of different areas, and avoiding energy waste caused by fixed-time switches or unreasonable settings.

[0065] (2) Through the energy efficiency optimization and safety assurance analysis modules, the system effectively combines 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 to construct a quantitative algorithm for the safety assurance coefficient S(x, t) of area x at time t. By comprehensively evaluating the safety needs 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-scale public event or other emergency, the system automatically increases the brightness to full brightness mode to ensure visibility and safety in the area. At the same time, the integrated lighting control module is used to dynamically adjust the brightness according to the safety coefficient S(x, t) to ensure that high-risk areas receive timely and sufficient lighting protection. Compared with traditional lighting methods, this system can actively respond to safety needs and greatly enhance the role of urban lighting in public safety.

[0066] (3) Through the integrated lighting control module, the system inputs 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 into the integrated control formula, and calculates the integrated lighting brightness control value Lc(x, t) of area x at time t. Combined with the first lighting control threshold Tc1 and the second lighting control threshold Tc2, the system can perform multi-level dynamic regulation of lighting intensity. Through multi-threshold control, the system achieves precise dynamic adjustment, avoids the problem of excessive or insufficient lighting, and provides the city with more flexible and efficient lighting management capabilities. BRIEF DESCRIPTION OF THE DRAWINGS

[0067] Figure 1 This is a flow chart of the management and control system for an implementation plan of smart city planning and design according to the present invention. DETAILED DESCRIPTION

[0068] The following will provide a clear and complete description of the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0069] Example 1

[0070] See also Figure 1 The present invention provides a smart city planning and design implementation plan management and control system. To achieve the above purpose, the present invention is implemented 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 assurance analysis module, and a comprehensive lighting control module;

[0071] The lighting demand extraction module collects lighting demand data in real time by installing detection sensor groups in each area x of the smart city. It then builds a smart city lighting control system and transmits the collected lighting demand data to the lighting control system via the local area network. It also sets up an API application program interface to access the city management platform and extract event data.

[0072] 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 builds 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 a standard lighting demand data set and inputs it into the lighting demand prediction formula. It then calculates and outputs the lighting demand index Ld for each area x. It then sets a lighting threshold Td and performs a preliminary comparative evaluation with the lighting demand index Ld for each area x. The energy efficiency optimization mechanism is triggered based on the preliminary comparative evaluation results.

[0074] The energy efficiency optimization and safety assurance analysis module triggers the energy efficiency optimization mechanism through preliminary comparative evaluation. Based on the lighting demand index Ld of each area x, it calculates and outputs the energy efficiency value E of each area x. 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.

[0075] The integrated lighting control module constructs an integrated lighting control formula, extracts the lighting demand index Ld, energy efficiency value E and safety assurance coefficient 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 results of the secondary comparative evaluation, the brightness value of the lighting equipment in each area x is controlled to optimize energy efficiency.

[0076] In this embodiment, the system collects real-time lighting demand data through the installation of a sensor array. Combined with event data from the city management platform, the data processing module integrates and pre-processes multidimensional data to generate a standardized lighting demand dataset, ensuring efficient and accurate subsequent analysis. The lighting demand prediction module constructs a prediction formula, outputs a lighting demand indicator (Ld), and performs a preliminary comparison with the lighting threshold (Td), providing a trigger mechanism for energy efficiency optimization. Furthermore, the energy efficiency optimization and safety assurance analysis module calculates regional energy efficiency values ​​(E) and safety assurance coefficients (S), achieving a comprehensive assessment of energy efficiency and safety. Finally, the integrated lighting control module implements a multi-threshold control strategy to output a comprehensive lighting brightness control value (Lc), enabling dynamic regulation of lighting equipment in each area. Compared to traditional fixed lighting or single automated control systems, this solution not only improves energy efficiency and avoids waste caused by improper lighting, but also enhances safety assurance capabilities in emergencies and high-density areas. The precise control and real-time optimization mechanism driven by multi-dimensional data enables the system to dynamically adapt to different scenario needs, significantly improving energy efficiency, public safety, and the living experience of residents. This comprehensive smart lighting system has achieved a transformation from extensive management to intelligent and refined management, providing important support for the further development of smart city infrastructure.

[0077] Example 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 collects the lighting demand data of each area x in real time by installing a detection sensor group in each area x of the smart city;

[0080] The detection sensor group includes a light sensor, a crowd counting device and a vehicle counting device;

[0081] The lighting demand data includes 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.

[0082] The data transmission unit builds a smart city lighting control system and integrates 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;

[0083] At the same time, an API application program interface is set up to access the smart city's urban management platform to extract event data;

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

[0085] The number of events V(x, t) in region x at time t is obtained by counting the number of traffic accidents, public events, and emergencies in the history of each region x.

[0086] In this embodiment, the system constructs a comprehensive smart city lighting data collection system through the collaborative work of a brightness demand extraction unit, a data transmission unit, and an event extraction unit. The brightness demand extraction unit uses photosensitive sensors, crowd counters, and vehicle flow counters installed in various areas 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 needs of urban lighting. The data transmission unit utilizes a built-in communication module to connect to the local area network, ensuring efficient and secure data transmission to the smart lighting control system. Simultaneously, the event extraction unit uses an API interface to extract the number of events V(x, t) in area x at time t from the city management platform in real time. This includes statistical information on traffic accidents, public events, and emergencies, providing critical support for subsequent lighting control decisions. Compared to traditional lighting management systems that rely solely on fixed schedules or single parameter adjustments, this module achieves comprehensive integration and dynamic acquisition of multiple 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 emergencies or high-density scenarios, the system can respond and adjust the lighting intensity in a timely manner.

[0087] Example 3

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

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

[0090] Preprocessing includes region x alignment, timestamp alignment and normalization;

[0091] Region x alignment is used to divide the lighting demand data and event data of different regions x, and to divide the lighting demand data and event data of the same region x into the data collection of the same region x;

[0092] Timestamp alignment is used to uniformly align the data extraction time points in all data collections of the same region x;

[0093] Normalization processing uses the Zscore method to convert the 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, eliminating the dimensional influence of all parameters in the data set of the same area x and obtaining a standard lighting demand data set;

[0094] The data storage unit constructs a time series database and sets a write port and a write 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 regions x in the smart city, set the storage table ID corresponding to region x, and match each storage table with a different region x;

[0096] The standard lighting requirement datasets 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.

[0097] In this embodiment, the system uses the data integration preprocessing unit to perform regional alignment, timestamp alignment and normalization on the collected lighting demand data and event data, successfully eliminating the influence of regional differences and data dimensions, and constructing a standardized lighting demand data set, providing accurate and consistent data support for subsequent analysis. At the same time, the data storage unit efficiently stores the standard lighting demand data set by region and time series by constructing a time series database, and adopts a method of matching storage table ID with region 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 regional alignment and timestamp alignment, the problem of inconsistent data across regions and time dimensions is solved; the use of the Zscore method for normalization eliminates the calculation deviation caused by different dimensions between multiple parameters; the introduction of the time series database further improves the storage and retrieval performance of the data, providing solid support for real-time lighting control and dynamic analysis.

[0098] Example 4

[0099] Specifically: the lighting demand prediction module includes a lighting demand analysis unit and a lighting demand assessment unit;

[0100] The lighting demand analysis unit constructs a lighting demand prediction formula, writes the port extraction port, extracts 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, 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;

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

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

[0103] 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, which is used to deal with possible nonlinear effects. a1, a2, a3, and a4 represent the impact of different factors on lighting demand, which are learned and adjusted based on historical data.

[0104] The lighting demand assessment unit performs a preliminary comparative evaluation and analysis of the lighting demand of each region x by setting a lighting threshold Td by the user based on historical lighting demand conditions and comparing the set lighting threshold Td with the obtained lighting demand index Ld(x, t) of region x at time t. The specific evaluation contents are as follows:

[0105] When the lighting demand indicator 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 smart city lighting control system automatically turns on the lighting equipment in the current area x and implements the energy efficiency optimization mechanism;

[0106] 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.

[0107] In this embodiment, the system achieves accurate prediction and dynamic response to urban lighting demand through a lighting demand analysis unit and a lighting demand assessment unit. The lighting demand analysis unit constructs a lighting demand prediction formula, integrating multi-dimensional data such as the ambient light intensity Is(x, t) in zone x at time t, the crowd density Np(x, t) in zone x at time t, and the traffic flow Tf(x, t) in zone x at time t. It dynamically calculates and outputs the lighting demand indicator Ld(x, t) for zone x at time t, accurately predicting the lighting demand of each zone at different times. The lighting demand assessment unit performs a preliminary comparative assessment of the lighting demand indicator Ld(x, t) for zone x at time t, based on the user-set lighting threshold Td. When demand is abnormal, it triggers the lighting device to turn on and optimize energy efficiency. When demand is normal, it automatically shuts down the device, thereby achieving efficient dynamic control. Compared with traditional lighting systems that use single-parameter prediction or fixed control strategies, this module significantly improves the accuracy and real-time performance of lighting predictions. By introducing regression coefficients a1, a2, a3 and nonlinear coefficient a4, the system can fully consider the impact of various factors on lighting needs and handle complex nonlinear relationships; by setting the lighting threshold Td, differentiated management and response to different areas and scenarios are achieved.

[0108] Example 5

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

[0110] The energy efficiency optimization unit extracts the lighting demand index Ld(x, t) of area x at time t after the lighting equipment in the current area x is turned on, and calculates the energy efficiency value E of each area x based on the power consumption of the lighting equipment.

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

[0112]

[0113] 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. These values ​​are dimensionless.

[0114] 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, combines them to calculate and output the safety assurance coefficient S of the current area x, and quantifies the safety requirements of the current area x;

[0115] The safety assurance factor S is calculated and output by the following algorithm formula;

[0116]

[0117] Where S(x, t) represents the safety assurance factor of region x at time t, n represents the total number of events, and V v (x, t) represents the number of events of the vth event in region x at time t, β1 represents the weight 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.

[0118] In this embodiment, the system's energy efficiency optimization unit calculates the lighting demand indicator Ld(x,t) for region x at time t and the power consumption P(x,i,t) of the i-th lighting device in region x at time t. It outputs the energy efficiency value E(x,t) for region x at time t, quantifying the lighting intensity provided per watt of power and providing a precise basis for optimizing energy efficiency. The safety assurance analysis unit extracts regional event data V(x,t) and crowd density Np(x,t) and calculates the safety assurance factor S(x,t) for region x at time t using an algorithm constructed using weighting coefficients and nonlinear influence coefficients. This dynamically assesses the safety needs of each region, particularly in areas with high event frequency or high crowd density, ensuring adequate lighting support to maintain public safety. Compared to traditional lighting systems that rely solely on empirical values ​​or fixed settings, this module achieves breakthrough improvements in energy conservation and safety assurance. Through the energy efficiency optimization unit, the system can accurately identify areas with low energy efficiency and optimize energy allocation in real time to avoid unnecessary energy waste; through the safety assurance analysis unit, the system can provide dynamic responses to emergencies and high-risk areas, effectively reducing safety hazards caused by insufficient lighting.

[0119] Example 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 a comprehensive lighting control formula and 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 factor S(x, t) of area x at time t. These are input 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 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 by 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, performs summary calculation and outputs 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 outputted by the following algorithm formula;

[0126]

[0127] 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;

[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 of the safety assurance factor S(x, t) of region x at time t;

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

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

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

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

[0134] The lighting evaluation control unit extracts the output first lighting control threshold value Tc1 and the second lighting control threshold value Tc2 and performs a secondary comparative evaluation with the comprehensive lighting brightness control value Lc(x, t) of area x at time t. Based on the evaluation results, the brightness value of the lighting equipment in each area x is controlled to optimize energy efficiency. The specific evaluation content is as follows;

[0135] When the integrated lighting brightness control value Lc(x, t) of area x at time t is greater than or equal to the first lighting control threshold Tc1, the lighting equipment in the current area x is controlled to switch to full brightness mode;

[0136] 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 in the current area x is controlled to switch to energy-saving mode;

[0137] When the integrated lighting brightness control value Lc(x, t) of area x at time t ≤ the second lighting control threshold Tc2, the lighting equipment in the current area x is controlled to switch to the dim mode, that is, the lowest brightness of the lighting equipment.

[0138] In this embodiment, the system uses a lighting control parameter output unit to integrate the lighting demand index Ld(x, t) of region x at time t, the energy efficiency value E(x, t) of region x at time t, and the safety assurance factor S(x, t) of region x at time t. It then calculates the comprehensive lighting brightness control value Lc(x, t) for region x at time t using a comprehensive control formula, providing a basis for adaptive brightness adjustment for each region. The lighting threshold setting unit calculates a first lighting control threshold Tc1 and a second lighting control threshold Tc2 based on historical data, reflecting safety requirements and energy conservation requirements, respectively. The lighting evaluation control unit dynamically adjusts the lighting equipment mode for each region, including full brightness mode, energy-saving mode, and dim mode, by performing a secondary comparative evaluation of the comprehensive lighting brightness control value Lc(x, t) of region x at time t with the first and second lighting control thresholds Tc1 and Tc2, to achieve the dual goals of safety assurance and energy efficiency optimization. Compared to traditional lighting control methods, this module achieves significant improvements in control accuracy, adaptability, and intelligence. By introducing a multi-parameter control formula and a dual-threshold evaluation mechanism, the system can provide differentiated responses to the lighting needs of different areas and scenarios. Compared to 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 integrated lighting control module significantly optimizes lighting control efficiency, achieving simultaneous improvements in safety and energy efficiency, and providing advanced technical support for the sustainable development of smart cities.

[0139] While the embodiments of the present invention have been shown and described, it will be apparent to those skilled in the art that various changes, modifications, substitutions, and alterations can be made to the embodiments without departing from the principles and spirit of the 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, then builds a smart city lighting control system, transmits the collected lighting demand data to the lighting control system via the local area network, and sets up 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 for each area x, sets a lighting threshold Td and performs preliminary comparative evaluation with the lighting demand index Ld for each area x, and triggers the energy efficiency optimization mechanism based on the preliminary comparative evaluation results; The energy efficiency optimization and safety assurance analysis module triggers the energy efficiency optimization mechanism through preliminary comparative evaluation. Based on the lighting demand index Ld of each area x, it calculates and outputs the energy efficiency value E of each area x. 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 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 based on 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 region x at time t, N represents the total number of lighting devices in region x, and P(x, i, t) represents the power consumption of the i-th lighting device in region x at time t. 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, combines them to calculate and output the safety assurance coefficient S of the current area x, and quantifies the safety requirements of the current area x; The safety assurance coefficient S is calculated and outputted by the following algorithm formula: Where S(x, t) represents the safety assurance factor of region x at time t, n represents the total number of events, and V v (x, t) represents the number of events of event v in region x at time t, β1 represents the weight of the impact of the event on safety, γ1 represents the baseline impact coefficient of crowd density on safety demand, and γ2 represents the nonlinear impact coefficient of crowd density on safety demand; 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 simultaneously sets a first lighting control threshold Tc1 and a 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. 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 and 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 factor 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 region x at time t is calculated and outputted by the following comprehensive lighting control formula:

2. The smart city planning and design implementation plan management and control system according to claim 1 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 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 and integrates 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, thereby transmitting 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 smart city's urban management platform 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) in the region x at time t is obtained by counting the number of traffic accidents, public events 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 and 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 is to convert the actual values ​​of the data set of the same region x after the timestamps are aligned 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 region 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 port, and writes the acquired standard lighting requirement data set into the time series database through the write port; Set the number of storage tables according to the total number of regions x in the smart city, set the storage table ID corresponding to region x, and match each storage table with a different region x; The standard lighting requirement datasets 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 port extraction of the ambient light intensity Is(x, t) of the 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 assessment unit performs a preliminary comparative evaluation and analysis of the lighting demand of each region x by setting a lighting threshold Td by the user based on historical lighting demand conditions and comparing the set lighting threshold Td with the obtained lighting demand index Ld(x, t) of the region x at time t. The specific evaluation content is as follows: When the lighting demand indicator 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 smart city lighting control system automatically turns on the lighting equipment in the current area x and implements the energy efficiency optimization mechanism; 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. The smart city planning and design implementation plan management and control system according to claim 1 is characterized by: 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 assurance 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.

7. The smart city planning and design implementation plan management and control system according to claim 6, characterized in that: The lighting evaluation control unit extracts the output first lighting control threshold value Tc1 and the second lighting control threshold value Tc2 and performs a 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 results to optimize energy efficiency. The specific evaluation content is as follows; When the integrated lighting brightness control value Lc(x, t) of area x at time t is greater than or equal to the first lighting control threshold Tc1, the lighting equipment in 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 in 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 second lighting control threshold Tc2, the lighting equipment of the current area x is controlled to switch to the dim mode.

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