Smart city street lamp intelligent control system and method

Abstract

The invention discloses a smart city street lamp intelligent control system and method. The method comprises the following steps: performing multi-aspect inspection on each hardware device through a comprehensive intelligent control module and a communication module; the environment monitoring module is used for collecting multi-source data in real time and integrating and analyzing the multi-source data through the data fusion module; generating a dynamic dimming strategy of the common road section and the high-priority road section according to the collected data and regional requirements; brightness adjustment and on-off control are executed one by one through the single lamp control module, and equipment operation parameters are collected through the real-time feedback and monitoring module. The remote management platform is used for monitoring the running state of the street lamp in real time and dynamically adjusting strategy optimization analysis; different operation scenes are simulated through a system simulation test, actual deployment is carried out in a pilot area, and parameters and operation logic are optimized. Through modular design, intelligent decision making and data driving modes, the management efficiency and the energy-saving level of urban street lamps are remarkably improved, and reliable technical support is provided for smart city construction.

Description

Technical Field

[0001] The present invention relates to the technical field of control systems, and in particular to an intelligent control system and method for smart city street lamps. Background Art

[0002] With the rapid development of smart city construction, urban lighting systems, as an important part of urban infrastructure, are gradually developing towards intelligence and efficiency. In recent years, with the rapid development of Internet of Things technology, artificial intelligence technology and new energy technology, combining these technologies with urban street lighting systems has gradually become an important direction for solving traditional street lighting problems.

[0003] Traditional urban street lighting systems usually use fixed-time control or simple light-operated switches, lacking dynamic adjustment capabilities, which not only causes a lot of energy waste, but also increases maintenance costs and management difficulties. Current technical solutions generally have problems such as low system integration, insufficient coordination among modules, and limited applicable scenarios, making it difficult to meet the needs of smart cities for multifunctional and efficient street lighting control systems.

[0004] The present invention provides an intelligent control system and method for smart city street lamps, aiming to address the shortcomings of traditional systems. It can achieve energy saving and high efficiency through dynamic data collection, intelligent decision-making and precise control, while taking into account multi-scenario applications and operational reliability, providing a more complete solution for smart lighting in smart cities. Summary of the invention

[0005] In view of the above problems, the present invention provides a smart city street lamp intelligent control system and method to solve the problems that the prior art lacks dynamic adjustment capability, has low integration, insufficient coordination among modules, and has limited applicable scenarios.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0007] The present invention provides a smart city street lamp intelligent control method, which is characterized by comprising the following steps:

[0008] Step S1, by configuring the integrated intelligent control module and the communication module, checking the connection status, power supply and communication function of each hardware device;

[0009] Wherein step S1 also includes the following sub-steps:

[0010] S1-1, configure the integrated intelligent control module, which includes: a single lamp control unit, a centralized management unit, and a power management unit; check whether the communication connection between the single lamp control unit and the centralized management unit is normal, simulate the mains power outage to test whether the photovoltaic power supply switching time meets the second-level requirement, and calculate the power supply time that the energy storage battery can support in the event of an emergency power outage through the photovoltaic power supply switching logic, as shown in formula (1):

[0011]

[0012] Among them, P backup is the power supply time of the energy storage battery, E battery is the current energy storage capacity, L consumption is the total power consumption;

[0013] S1-2, the communication module includes: PLC power carrier communication unit and LoRa wireless communication unit, which use analog signals to perform data transmission tests between different modules in the street lamp to verify the anti-interference ability and transmission speed of signal transmission; test the LoRa wireless communication capability, simulate the remote communication scenario, and test the stability and transmission distance of data uploaded to the management platform.

[0014] Step S2, using the environmental monitoring module to collect multi-source data of light intensity, vehicle flow and weather in real time, and integrating and analyzing them through the data fusion module;

[0015] Wherein step S2 also includes the following sub-steps:

[0016] S2-1, collect the ambient light intensity, weather conditions and road traffic volume through the environmental monitoring module. The photosensor in the environmental monitoring module is used to detect the light intensity, and the infrared sensor detects the traffic volume. The data is transmitted to the centralized management unit, and the target brightness is calculated according to the collected light intensity, as shown in formula (2):

[0017] L desired =L ambient ×K factor Formula (2)

[0018] Among them, L desired is the target brightness, L ambient is the ambient light intensity, K factor is the dimming coefficient, which is dynamically adjusted according to the light intensity level;

[0019] S2-2, through the data fusion and transmission module, integrate the multi-segment data to generate a global dimming strategy, use the centralized management unit to collect the sensor data of each area, and use the weight algorithm to integrate the data, as shown in formula (3):

[0020]

[0021] Among them, D fused is the fused data, w i is the data weight of the ith sensor, D i is the data collected by the i-th sensor.

[0022] Step S3, generating dynamic dimming strategies for ordinary sections and high-priority sections based on the collected environmental data and regional requirements, and achieving a balance between energy saving and safety through light intensity adjustment;

[0023] Wherein step S3 also includes the following sub-steps:

[0024] S3-1, the dynamic dimming module for ordinary roads dynamically calculates the output brightness according to the traffic flow and light intensity, and adjusts the brightness of the street lights on ordinary roads. The centralized management unit calculates the traffic flow and light intensity requirements and sends instructions to the single lamp control unit, as shown in formula (4):

[0025]

[0026] Among them, L output is the output brightness, T current is the current traffic volume, T max is the maximum traffic flow, L max is the maximum brightness;

[0027] S3-2, high priority section light intensity adjustment module, high priority section includes tunnel area, through the light intensity graded adjustment to optimize the light transition inside and outside the tunnel, using the light intensity transition formula to calculate the tunnel brightness, using PWM dimming technology to adjust the brightness, as shown in formula (5):

[0028]

[0029] Among them, L tunnel is the brightness of a point in the tunnel, L entry is the entrance brightness, L exit is the exit brightness, x is the distance from the current point to the entrance, and d is the total length of the tunnel.

[0030] Step S4, using the single lamp control module to perform brightness adjustment and switch control for each lamp, and at the same time collecting equipment operating parameters through the real-time feedback and monitoring module to promptly discover and handle abnormalities or faults in operation;

[0031] Wherein step S4 also includes the following sub-steps:

[0032] S4-1, single lamp control module, based on the initial detection of s1-1, receives instructions from the centralized management unit during the operation phase, and performs PWM dimming, lamp-by-lamp brightness adjustment and switching operations;

[0033] S4-2, real-time feedback and monitoring module, detects abnormal conditions through built-in fault diagnosis function, collects current, voltage and brightness data to detect whether there are abnormal conditions, as shown in formula (6):

[0034] F score =α·V deviation +β·I deviation +γ·L deviation Formula (6)

[0035] Among them, F score is the fault score, V deviation , I deviation , L deviation They are voltage, current, and brightness deviation, and α, β, and γ are weight coefficients.

[0036] Step S5, using the remote management platform to monitor the operating status of the street lamp in real time, dynamically adjust the brightness, switch and power supply strategy, and perform optimization analysis based on historical data;

[0037] Wherein, in step S5, the following sub-steps are also included:

[0038] S5-1, remote monitoring and maintenance module, which enables remote adjustment of street light parameters and power supply priority, uses the management platform to view real-time data, and manually adjusts the brightness or power supply priority of a specific area;

[0039] S5-2, data storage and optimization analysis module, stores historical data and optimizes future strategies through regression analysis algorithm, uses energy efficiency optimization formula as objective function, and adjusts dimming strategy based on historical vehicle flow and brightness requirements, as shown in formula (7):

[0040]

[0041] Among them, E optimized is the energy efficiency optimization coefficient, L i is the average brightness of the i-th lamp, T i It's time to light up, E total is the total energy consumption of the system.

[0042] Step S6, simulating different operating scenarios through simulation tests, verifying the response and stability of the dimming strategy, and actually deploying it in the pilot area, and further optimizing the parameters and operating logic based on the feedback.

[0043] Wherein, in step S6, the following sub-steps are also included:

[0044] S6-1, system simulation test module, simulates the operation of street lights in different scenarios on the simulation platform, and tests the response time of the dimming strategy and system stability;

[0045] S6-2, through the pilot deployment and verification module, deploy the system in the pilot area and monitor the operation effect, and adjust the strategy parameters according to the pilot feedback.

[0046] The present invention provides a smart city street lamp intelligent control system, which is characterized by comprising:

[0047] Comprehensive intelligent control module, communication module, environmental monitoring module, data fusion and transmission module, dynamic dimming module for ordinary roads, light intensity adjustment module for high-priority roads, single lamp control module, real-time feedback and monitoring module, remote monitoring and maintenance module, data storage and optimization analysis module, system simulation test module, pilot deployment and verification module;

[0048] The integrated intelligent control module automatically coordinates the operation of the single lamp control unit, the centralized management unit and the power management unit when it is started, checks the hardware status and communication connection of each unit; monitors the power supply priority configuration of the system in real time to ensure seamless connection between the photovoltaic power supply and the mains power switching, and synchronously initializes the control parameters;

[0049] The communication module is responsible for achieving real-time data transmission between modules and stable communication with the remote management platform during operation; its core consists of a PLC power carrier communication unit and a LoRa wireless communication unit, which are used for internal short-distance communication and remote data transmission respectively;

[0050] The environmental monitoring module collects environmental parameters such as ambient light intensity, weather conditions and vehicle flow in real time during operation, dynamically updates environmental change data and uploads it to the centralized management unit to support the generation of intelligent dimming strategies;

[0051] The data fusion and transmission module collects multi-segment sensor data during operation and uses a weight algorithm to perform fusion analysis on the data; monitors the communication status in real time and provides feedback on the accuracy and delay of the fused data;

[0052] The ordinary road section dynamic dimming module calculates the target brightness according to the preset dimming formula after receiving the ambient light intensity and vehicle flow data; adjusts the brightness of the ordinary road section street lights in real time, monitors the execution effect of the dimming strategy, and records the brightness adjustment log and energy consumption change data;

[0053] The high-priority road section light intensity adjustment module collects the light intensity data and traffic information of the tunnel entrance and exit, and calculates the target brightness of each section according to the light intensity transition formula; dynamically adjusts the light intensity inside the tunnel to ensure a smooth visual transition, and monitors the light intensity change and the smoothness of the dimming curve in real time;

[0054] The single lamp control module detects the brightness, current and voltage status of the street lamp in real time when receiving instructions; independently adjusts the brightness and switch status of each lamp, performs the dimming operation of each lamp according to the dynamic dimming strategy, and records the energy consumption data and device response time during operation;

[0055] The real-time feedback and monitoring module collects the operating status of each lamp, such as current, voltage, brightness, etc., during operation, and monitors the risk of failure in real time; analyzes the equipment deviation data through the built-in diagnostic algorithm, generates a fault score and triggers an alarm mechanism, and records abnormal data and maintenance logs;

[0056] The remote monitoring and maintenance module continuously updates the street lamp operation status data during operation, remotely adjusts the street lamp brightness, switch status and power supply priority through the smart city management platform, records user operation logs in real time, and analyzes the adjustment response time and strategy execution status;

[0057] The data storage and optimization analysis module continuously records historical data during system operation, including brightness parameters, vehicle flow, lighting time and energy consumption data; predicts future brightness requirements and optimizes dimming strategies, while evaluating energy efficiency optimization coefficients and adjustment effects;

[0058] The system simulation test module tests the dimming strategy under different environmental conditions and traffic flow in the simulated scene; analyzes the strategy response time and system stability, records abnormal data and performance parameters during the simulation process, and generates a test report for optimizing the actual operation;

[0059] The pilot deployment and verification module deploys street light control systems in target areas, monitors operating data in real time and collects user feedback; optimizes dimming parameters and power supply strategies based on pilot data to ensure stability and energy-saving effects in actual application scenarios.

[0060] Compared with the prior art, the present invention has the following beneficial effects:

[0061] By configuring the comprehensive intelligent control module and communication module, the connection status, power supply and communication function of each hardware device can be checked; the environmental monitoring module is used to collect multi-source data such as light intensity, traffic flow and weather in real time, and integrated and analyzed through the data fusion module; according to the collected environmental data and regional needs, dynamic dimming strategies for ordinary sections and high-priority sections are generated, and energy saving and safety are balanced through light intensity adjustment; the single-lamp control module is used to perform brightness adjustment and switch control on a lamp-by-lamp basis, and the equipment operating parameters are collected through the real-time feedback and monitoring module to promptly discover and handle abnormalities or faults in operation; the remote management platform is used to monitor the operating status of street lights in real time, dynamically adjust the brightness, switch and power supply strategies, and optimize and analyze in combination with historical data; different operating scenarios are simulated through simulation tests to verify the response and stability of the dimming strategy, and it is actually deployed in the pilot area, and the parameters and operating logic are further optimized based on the feedback.

[0062] The present invention combines real-time data collection with a dynamic dimming strategy to accurately adjust the brightness of street lamps according to dynamic data such as ambient light intensity and vehicle flow, thereby avoiding energy waste under the traditional fixed-time control method and significantly improving the energy efficiency of the system.

[0063] The present invention designs differentiated dimming strategies for ordinary sections and high-priority sections, and can adapt to a variety of complex traffic environments and special lighting requirements through light intensity transition formulas and PWM dimming technology.

[0064] The present invention can comprehensively monitor the operating status of each lamp through the real-time feedback and monitoring module, and quickly detect and locate abnormalities in combination with the built-in fault diagnosis function, greatly improving the reliability of system operation and maintenance efficiency.

[0065] The present invention combines the communication module with the remote management platform, supports real-time adjustment of the street lamp operating status, and optimizes future dimming strategies using historical data, so that the system has good scalability and convenience. BRIEF DESCRIPTION OF THE DRAWINGS

[0066] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for use in the embodiments will be briefly introduced below. It is understood that the following drawings only show certain embodiments of the present invention and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without creative work.

[0067] Figure 1 is a flow chart of the method of the present invention;

[0068] Figure 2 It is a system architecture diagram of the present invention. DETAILED DESCRIPTION

[0069] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work belong to the scope of protection of the present invention. Therefore, the following detailed description of the embodiments of the present invention provided in the drawings is not intended to limit the scope of the invention claimed for protection, but is only for selected embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work belong to the scope of protection of the present invention.

[0070] Please refer to Figure 1 This is a flow chart of a smart city street lamp intelligent control method provided by an embodiment of the present invention, comprising the following steps:

[0071] Step S1, by configuring the integrated intelligent control module and the communication module, checking the connection status, power supply and communication function of each hardware device;

[0072] Wherein step S1 also includes the following sub-steps:

[0073] S1-1, configure the integrated intelligent control module, which includes: a single lamp control unit, a centralized management unit, and a power management unit; check whether the communication connection between the single lamp control unit and the centralized management unit is normal, simulate the mains power outage to test whether the photovoltaic power supply switching time meets the second-level requirement, and calculate the power supply time that the energy storage battery can support in the event of an emergency power outage through the photovoltaic power supply switching logic, as shown in formula (1):

[0074]

[0075] Among them, P backup is the power supply time of the energy storage battery, E battery is the current energy storage capacity, L consumption is the total power consumption;

[0076] S1-2, the communication module includes: PLC power carrier communication unit and LoRa wireless communication unit, which use analog signals to perform data transmission tests between different modules in the street lamp to verify the anti-interference ability and transmission speed of signal transmission; test the LoRa wireless communication capability, simulate the remote communication scenario, and test the stability and transmission distance of data uploaded to the management platform.

[0077] It should be noted that the single-lamp control unit is used to realize independent dimming and switch control of each street lamp, the centralized management unit is used to integrate the data of all sub-modules, generate global dimming and power supply strategies, and the power management unit is used to complete the seamless switching of solar storage power supply and AC power.

[0078] After the configuration of the integrated intelligent control module is completed, the single lamp control unit will pass the initialized hardware parameters, such as initial brightness and switch status, to the centralized management unit. The centralized management unit maintains a real-time connection with the single lamp control module mentioned in S4-1 through the communication module, providing basic support for the execution of dimming instructions during the operation phase.

[0079] The data fusion module integrates the collected light intensity, vehicle flow and other data in real time to generate a dimming demand data table, which is then transmitted to the dynamic decision module of S3 through the communication module. A dynamic weight adjustment algorithm is used during the transmission process to ensure data priority in different scenarios, such as vehicle flow data being higher than light intensity data.

[0080] Step S2, using the environmental monitoring module to collect multi-source data of light intensity, vehicle flow and weather in real time, and integrating and analyzing them through the data fusion module;

[0081] Wherein step S2 also includes the following sub-steps:

[0082] S2-1, collect the ambient light intensity, weather conditions and road traffic volume through the environmental monitoring module. The photosensor in the environmental monitoring module is used to detect the light intensity, and the infrared sensor detects the traffic volume. The data is transmitted to the centralized management unit, and the target brightness is calculated according to the collected light intensity, as shown in formula (2):

[0083] L desired =L ambient ×K factor Formula (2)

[0084] Among them, L desired is the target brightness, L ambient is the ambient light intensity, K factor is the dimming coefficient, which is dynamically adjusted according to the light intensity level;

[0085] S2-2, through the data fusion and transmission module, integrate the multi-segment data to generate a global dimming strategy, use the centralized management unit to collect the sensor data of each area, and use the weight algorithm to integrate the data, as shown in formula (3):

[0086]

[0087] Among them, D fused is the fused data, w i is the data weight of the ith sensor, D i is the data collected by the i-th sensor.

[0088] It should be noted that in the S2-2 data fusion and transmission module, multi-section refers to the system's data collection and processing of roads of different types or areas. These sections include: ordinary sections, which refer to sections with relatively stable traffic flow and relatively consistent ambient lighting conditions. The data of these sections are usually used for conventional strategy optimization of dynamic dimming; high-priority sections, such as tunnels, intersections, crowded areas or areas with a high incidence of traffic accidents. These sections have special requirements for lighting, for example, light transition needs to be achieved in tunnels, or higher light intensity needs to be provided in crowded areas; other special areas: parking lots, square areas, the lighting requirements of these sections may be different from those of roads, requiring higher brightness distribution uniformity or specific lighting schedules.

[0089] Step S3, generating dynamic dimming strategies for ordinary sections and high-priority sections based on the collected environmental data and regional requirements, and achieving a balance between energy saving and safety through light intensity adjustment;

[0090] Wherein step S3 also includes the following sub-steps:

[0091] S3-1, the dynamic dimming module for ordinary roads dynamically calculates the output brightness according to the traffic flow and light intensity, and adjusts the brightness of the street lights on ordinary roads. The centralized management unit calculates the traffic flow and light intensity requirements and sends instructions to the single lamp control unit, as shown in formula (4):

[0092]

[0093] Among them, L output is the output brightness, T current is the current traffic volume, T max is the maximum traffic flow, L max is the maximum brightness;

[0094] S3-2, high priority section light intensity adjustment module, high priority section includes tunnel area, through the light intensity graded adjustment to optimize the light transition inside and outside the tunnel, using the light intensity transition formula to calculate the tunnel brightness, using PWM dimming technology to adjust the brightness, as shown in formula (5):

[0095]

[0096] Among them, L tunnel is the brightness of a point in the tunnel, L entry is the entrance brightness, L exit is the exit brightness, x is the distance from the current point to the entrance, and d is the total length of the tunnel.

[0097] It should be noted that the high-priority road section light intensity adjustment module of S3-2 is not only applicable to tunnel scenarios, but can also be extended to other complex areas, such as crowded stations, sections with high traffic accident rates, and large parking lots. In these areas, light intensity adjustment needs to be considered comprehensively: the ratio of pedestrian flow to vehicle flow: give priority to adapting to the visual needs of pedestrians in the area; uniformity of light coverage: ensure that there is no obvious contrast between light and dark in the area to avoid visual discomfort.

[0098] Step S4, using the single lamp control module to perform brightness adjustment and switch control for each lamp, and at the same time collecting equipment operating parameters through the real-time feedback and monitoring module to promptly discover and handle abnormalities or faults in operation;

[0099] Wherein step S4 also includes the following sub-steps:

[0100] S4-1, single lamp control module, based on the initial detection of s1-1, receives instructions from the centralized management unit during the operation phase, and performs PWM dimming, lamp-by-lamp brightness adjustment and switching operations;

[0101] S4-2, real-time feedback and monitoring module, detects abnormal conditions through built-in fault diagnosis function, collects current, voltage and brightness data to detect whether there are abnormal conditions, as shown in formula (6):

[0102] F score =α·V deviation +β·I deviation +γ·L deviation Formula (6)

[0103] Among them, F score is the fault score, V deviation , I deviation , L deviation They are voltage, current, and brightness deviation, and α, β, and γ are weight coefficients.

[0104] It should be noted that PWM dimming is a method of adjusting LED brightness by controlling the duty cycle of the power signal, that is, the ratio of the high level time to the cycle.

[0105] During the dimming process, PWM quickly switches the on / off state of the LED power supply to make the LED flash quickly in a very short period, controls the proportion of the high level time in the entire period, changes the average power output of the LED, and thus adjusts the brightness.

[0106] In the single-lamp control module, PWM dimming is used to achieve brightness adjustment for each lamp. Its application scenarios include: dynamic dimming strategy execution, adjusting the brightness of each lamp according to the strategy of the dynamic intelligent decision-making module. For example, when the traffic volume on ordinary roads is low, the PWM duty cycle is reduced to 30% to achieve energy-saving dimming; when high-priority sections require higher lighting, the PWM duty cycle is increased to 80%-100%.

[0107] Zone brightness control: lights in different sections can be independently controlled by adjusting the PWM duty cycle to avoid light waste.

[0108] Stepless dimming, PWM dimming achieves continuous change of brightness without mechanical switch or potentiometer adjustment, improving dimming accuracy and response speed.

[0109] Step S5, using the remote management platform to monitor the operating status of the street lamp in real time, dynamically adjust the brightness, switch and power supply strategy, and perform optimization analysis based on historical data;

[0110] Wherein, in step S5, the following sub-steps are also included:

[0111] S5-1, remote monitoring and maintenance module, which enables remote adjustment of street light parameters and power supply priority, uses the management platform to view real-time data, and manually adjusts the brightness or power supply priority of a specific area;

[0112] S5-2, data storage and optimization analysis module, stores historical data and optimizes future strategies through regression analysis algorithm, uses energy efficiency optimization formula as objective function, and adjusts dimming strategy based on historical vehicle flow and brightness requirements, as shown in formula (7):

[0113]

[0114] Among them, E optimized is the energy efficiency optimization coefficient, L i is the average brightness of the i-th lamp, T i It's time to light up, E total is the total energy consumption of the system.

[0115] It should be noted that the S5-2 data storage and optimization analysis module uses a variety of AI algorithms to analyze historical data and optimize dimming strategies:

[0116] Time series analysis: used to predict future changes in traffic volume and ambient light intensity, and support the dynamic decision-making module to generate forward-looking strategies; linear regression model: based on historical brightness, lighting time and energy consumption data, fit the energy efficiency optimization formula, calculate the energy saving rate and optimize the dimming time; decision tree optimization: classify the environmental characteristics of the road section, such as ordinary sections and high-priority sections, and provide a zone dimming strategy optimization solution.

[0117] Step S6, simulating different operating scenarios through simulation tests, verifying the response and stability of the dimming strategy, and actually deploying it in the pilot area, and further optimizing the parameters and operating logic based on the feedback.

[0118] Wherein, in step S6, the following sub-steps are also included:

[0119] S6-1, system simulation test module, simulates the operation of street lights in different scenarios on the simulation platform, and tests the response time of the dimming strategy and system stability;

[0120] S6-2, through the pilot deployment and verification module, deploy the system in the pilot area and monitor the operation effect, and adjust the strategy parameters according to the pilot feedback.

[0121] It should be noted that in the simulation test, quantitative indicators are set to evaluate system performance, including: response time, energy saving rate, and stability;

[0122] Response time, the delay time from environmental data collection to execution of dimming instructions; energy saving rate, the ratio of total energy consumption in the simulation scenario to the benchmark energy consumption, calculates the improvement in energy saving effect; stability: packet loss rate and delay of data transmission by the communication module.

[0123] In the pilot deployment, the following key indicators are monitored: brightness adaptation rate, that is, the degree of consistency between the actual value of the street lamp brightness and the target value of the decision-making module; failure rate, that is, the ratio of the number of abnormalities reported by the single lamp control module to the total number of operations.

[0124] Please refer to Figure 2 This is an architecture diagram of a smart city street lamp intelligent control system provided by an embodiment of the present invention, including:

[0125] Comprehensive intelligent control module, communication module, environmental monitoring module, data fusion and transmission module, dynamic dimming module for ordinary roads, light intensity adjustment module for high-priority roads, single lamp control module, real-time feedback and monitoring module, remote monitoring and maintenance module, data storage and optimization analysis module, system simulation test module, pilot deployment and verification module;

[0126] The integrated intelligent control module automatically coordinates the operation of the single lamp control unit, the centralized management unit and the power management unit when it is started, checks the hardware status and communication connection of each unit; monitors the power supply priority configuration of the system in real time to ensure seamless connection between the photovoltaic power supply and the mains power switching, and synchronously initializes the control parameters;

[0127] The communication module is responsible for achieving real-time data transmission between modules and stable communication with the remote management platform during operation; its core is composed of a PLC power carrier communication unit and a LoRa wireless communication unit, which are used for internal short-distance communication and remote data transmission respectively.

[0128] The environmental monitoring module collects environmental parameters such as ambient light intensity, weather conditions and vehicle flow in real time during operation, dynamically updates environmental change data and uploads it to the centralized management unit to support the generation of intelligent dimming strategies;

[0129] The data fusion and transmission module collects multi-segment sensor data during operation and uses a weight algorithm to perform fusion analysis on the data; monitors the communication status in real time and provides feedback on the accuracy and delay of the fused data;

[0130] The ordinary road section dynamic dimming module calculates the target brightness according to the preset dimming formula after receiving the ambient light intensity and vehicle flow data; adjusts the brightness of the ordinary road section street lights in real time, monitors the execution effect of the dimming strategy, and records the brightness adjustment log and energy consumption change data;

[0131] The high-priority road section light intensity adjustment module collects the light intensity data and traffic information of the tunnel entrance and exit, and calculates the target brightness of each section according to the light intensity transition formula; dynamically adjusts the light intensity inside the tunnel to ensure a smooth visual transition, and monitors the light intensity change and the smoothness of the dimming curve in real time;

[0132] The single lamp control module detects the brightness, current and voltage status of the street lamp in real time when receiving instructions; independently adjusts the brightness and switch status of each lamp, performs the dimming operation of each lamp according to the dynamic dimming strategy, and records the energy consumption data and device response time during operation;

[0133] The real-time feedback and monitoring module collects the operating status of each lamp, such as current, voltage, brightness, etc., during operation, and monitors the risk of failure in real time; analyzes the equipment deviation data through the built-in diagnostic algorithm, generates a fault score and triggers an alarm mechanism, and records abnormal data and maintenance logs;

[0134] The remote monitoring and maintenance module continuously updates the street lamp operation status data during operation, remotely adjusts the street lamp brightness, switch status and power supply priority through the smart city management platform, records user operation logs in real time, and analyzes the adjustment response time and strategy execution status;

[0135] The data storage and optimization analysis module continuously records historical data during system operation, including brightness parameters, vehicle flow, lighting time and energy consumption data; predicts future brightness requirements and optimizes dimming strategies, while evaluating energy efficiency optimization coefficients and adjustment effects;

[0136] The system simulation test module tests the dimming strategy under different environmental conditions and traffic flow in the simulated scene; analyzes the strategy response time and system stability, records abnormal data and performance parameters during the simulation process, and generates a test report for optimizing the actual operation;

[0137] The pilot deployment and verification module deploys street light control systems in target areas, monitors operating data in real time and collects user feedback; optimizes dimming parameters and power supply strategies based on pilot data to ensure stability and energy-saving effects in actual application scenarios.

[0138] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention has various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A smart city street lamp intelligent control method, characterized in that: The following steps are involved: Step S1, by configuring the integrated intelligent control module and the communication module, checking the connection status, power supply and communication function of each hardware device; Step S2, using the environmental monitoring module to collect multi-source data of light intensity, vehicle flow and weather in real time, and integrating and analyzing them through the data fusion module; Step S3, generating dynamic dimming strategies for ordinary sections and high-priority sections based on the collected environmental data and regional requirements, and achieving a balance between energy saving and safety through light intensity adjustment; Step S4, using the single lamp control module to perform brightness adjustment and switch control for each lamp, and at the same time collecting equipment operating parameters through the real-time feedback and monitoring module to promptly discover and handle abnormalities or faults in operation; Step S5, using the remote management platform to monitor the operating status of the street lamp in real time, dynamically adjust the brightness, switch and power supply strategy, and perform optimization analysis based on historical data; Step S6, simulating different operating scenarios through simulation tests, verifying the response and stability of the dimming strategy, and actually deploying it in the pilot area, and further optimizing the parameters and operating logic based on the feedback.

2. According to claim 1, a smart city street lamp intelligent control method is characterized by: Wherein step S1 also includes the following sub-steps: S1-1, configure the integrated intelligent control module, which includes: a single lamp control unit, a centralized management unit, and a power management unit; check whether the communication connection between the single lamp control unit and the centralized management unit is normal, simulate the mains power outage to test whether the photovoltaic power supply switching time meets the second-level requirement, and calculate the power supply time that the energy storage battery can support in the event of an emergency power outage through the photovoltaic power supply switching logic, as shown in formula (1): Among them, P backup is the power supply time of the energy storage battery, E battery is the current energy storage capacity, L consumption is the total power consumption; S1-2, the communication module includes: PLC power carrier communication unit and LoRa wireless communication unit, which use analog signals to perform data transmission tests between different modules in the street lamp to verify the anti-interference ability and transmission speed of signal transmission; test the LoRa wireless communication capability, simulate the remote communication scenario, and test the stability and transmission distance of data uploaded to the management platform.

3. According to claim 1, a smart city street lamp intelligent control method is characterized in that: Wherein step S2 also includes the following sub-steps: S2-1, collect the ambient light intensity, weather conditions and road traffic volume through the environmental monitoring module. The photosensor in the environmental monitoring module is used to detect the light intensity, and the infrared sensor detects the traffic volume. The data is transmitted to the centralized management unit, and the target brightness is calculated according to the collected light intensity, as shown in formula (2): L desired =L ambient ×K factor Formula (2) Among them, L desired is the target brightness, L ambient is the ambient light intensity, K factor is the dimming coefficient, which is dynamically adjusted according to the light intensity level; S2-2, through the data fusion and transmission module, integrate the multi-segment data to generate a global dimming strategy, use the centralized management unit to collect the sensor data of each area, and use the weight algorithm to integrate the data, as shown in formula (3): Among them, D fused is the fused data, w i is the data weight of the ith sensor, D i is the data collected by the i-th sensor.

4. The intelligent control method of a smart city street lamp according to claim 1, characterized in that: Wherein step S3 also includes the following sub-steps: S3-1, the dynamic dimming module for ordinary roads dynamically calculates the output brightness according to the traffic flow and light intensity, and adjusts the brightness of the street lights on ordinary roads. The centralized management unit calculates the traffic flow and light intensity requirements and sends instructions to the single lamp control unit, as shown in formula (4): Among them, L output is the output brightness, T current is the current traffic volume, T max is the maximum traffic flow, L max is the maximum brightness; S3-2, high priority section light intensity adjustment module, high priority section includes tunnel area, through the light intensity graded adjustment to optimize the light transition inside and outside the tunnel, using the light intensity transition formula to calculate the tunnel brightness, using PWM dimming technology to adjust the brightness, as shown in formula (5): Among them, L tunnel is the brightness of a point in the tunnel, L entry is the entrance brightness, L exit is the exit brightness, x is the distance from the current point to the entrance, and d is the total length of the tunnel.

5. According to claim 1, a smart city street lamp intelligent control method is characterized in that: Wherein step S4 also includes the following sub-steps: S4-1, single lamp control module, based on the initial detection of s1-1, receives instructions from the centralized management unit during the operation phase, and performs PWM dimming, lamp-by-lamp brightness adjustment and switching operations; S4-2, real-time feedback and monitoring module, detects abnormal conditions through built-in fault diagnosis function, collects current, voltage and brightness data to detect whether there are abnormal conditions, as shown in formula (6): F score =α·V deviation +β·I deviation +γ·L deviation expression(6) Among them, F score is the fault score, V deviation , I deviation , L deviation They are voltage, current, and brightness deviation, and α, β, and γ are weight coefficients.

6. The intelligent control method of a smart city street lamp according to claim 1, characterized in that: Wherein, in step S5, the following sub-steps are also included: S5-1, remote monitoring and maintenance module, which enables remote adjustment of street light parameters and power supply priority, uses the management platform to view real-time data, and manually adjusts the brightness or power supply priority of a specific area; S5-2, data storage and optimization analysis module, stores historical data and optimizes future strategies through regression analysis algorithm, uses energy efficiency optimization formula as objective function, and adjusts dimming strategy based on historical vehicle flow and brightness requirements, as shown in formula (7): Among them, E optimized is the energy efficiency optimization coefficient, L i is the average brightness of the i-th lamp, T i It's time to light up, E total is the total energy consumption of the system.

7. The intelligent control method of a smart city street lamp according to claim 1, characterized in that: Wherein, in step S6, the following sub-steps are also included: S6-1, system simulation test module, simulates the operation of street lights in different scenarios on the simulation platform, and tests the response time of the dimming strategy and system stability; S6-2, through the pilot deployment and verification module, deploy the system in the pilot area and monitor the operation effect, and adjust the strategy parameters according to the pilot feedback.

8. A smart city street lamp intelligent control system, characterized in that: include: Comprehensive intelligent control module, communication module, environmental monitoring module, data fusion and transmission module, dynamic dimming module for ordinary roads, light intensity adjustment module for high-priority roads, single lamp control module, real-time feedback and monitoring module, remote monitoring and maintenance module, data storage and optimization analysis module, system simulation test module, pilot deployment and verification module; The integrated intelligent control module automatically coordinates the operation of the single lamp control unit, the centralized management unit and the power management unit when it is started, checks the hardware status and communication connection of each unit; monitors the power supply priority configuration of the system in real time to ensure seamless connection between the photovoltaic power supply and the mains power switching, and synchronously initializes the control parameters; The communication module is responsible for achieving real-time data transmission between modules and stable communication with the remote management platform during operation; Its core consists of a PLC power carrier communication unit and a LoRa wireless communication unit, which are used for internal short-distance communication and long-distance data transmission respectively; The environmental monitoring module collects environmental parameters such as ambient light intensity, weather conditions and vehicle flow in real time during operation, dynamically updates environmental change data and uploads it to the centralized management unit to support the generation of intelligent dimming strategies; The data fusion and transmission module collects multi-segment sensor data during operation and uses a weight algorithm to perform fusion analysis on the data; monitors the communication status in real time and provides feedback on the accuracy and delay of the fused data; The ordinary road section dynamic dimming module calculates the target brightness according to the preset dimming formula after receiving the ambient light intensity and vehicle flow data; adjusts the brightness of the ordinary road section street lights in real time, monitors the execution effect of the dimming strategy, and records the brightness adjustment log and energy consumption change data; The high-priority road section light intensity adjustment module collects the light intensity data and traffic information of the tunnel entrance and exit, and calculates the target brightness of each section according to the light intensity transition formula; dynamically adjusts the light intensity inside the tunnel to ensure a smooth visual transition, and monitors the light intensity change and the smoothness of the dimming curve in real time; The single lamp control module detects the brightness, current and voltage status of the street lamp in real time when receiving instructions; independently adjusts the brightness and switch status of each lamp, performs the dimming operation of each lamp according to the dynamic dimming strategy, and records the energy consumption data and device response time during operation; The real-time feedback and monitoring module collects the operating status of each lamp, such as current, voltage, brightness, etc., during operation, and monitors the risk of failure in real time; analyzes the equipment deviation data through the built-in diagnostic algorithm, generates a fault score and triggers an alarm mechanism, and records abnormal data and maintenance logs; The remote monitoring and maintenance module continuously updates the street lamp operation status data during operation, remotely adjusts the street lamp brightness, switch status and power supply priority through the smart city management platform, records user operation logs in real time, and analyzes the adjustment response time and strategy execution status; The data storage and optimization analysis module continuously records historical data during system operation, including brightness parameters, vehicle flow, lighting time and energy consumption data; predicts future brightness requirements and optimizes dimming strategies, while evaluating energy efficiency optimization coefficients and adjustment effects; The system simulation test module tests the dimming strategy under different environmental conditions and vehicle flow in a simulated scenario; Analyze strategy response time and system stability, record abnormal data and performance parameters during simulation, and generate test reports for optimizing actual operation; The pilot deployment and verification module deploys street light control systems in target areas, monitors operating data in real time and collects user feedback; optimizes dimming parameters and power supply strategies based on pilot data to ensure stability and energy-saving effects in actual application scenarios.

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