Intelligent street lamp monitoring control method and system

Abstract

The invention relates to the technical field of Internet of Things edge computing, in particular to an intelligent street lamp monitoring control method and system. The method comprises the following steps: an edge computing device obtains environment data and street lamp state data collected by a sensor through a communication interface; the edge computing device executes local control logic based on the environment data and the street lamp state data, generates a control instruction and outputs the control instruction to the street lamp driving device; the edge computing device reports the processed data to a cloud platform through an MQTT protocol; and the edge computing device receives the remote instruction from the cloud platform, analyzes and executes the remote instruction. The system comprises an edge computing device, a cloud platform, a Web management end and a sensor network. According to the invention, a distributed architecture combining edge intelligent decision and cloud collaborative management is realized, and the response speed and reliability of the intelligent street lamp system are improved.

Description

Technical Field

[0001] This invention relates to the field of IoT edge computing technology, specifically to a smart street light monitoring and control method and system. Background Technology

[0002] With the advancement of smart city construction, urban lighting systems are evolving from traditional manual control towards intelligent and networked systems. Existing smart streetlight solutions generally adopt a cloud-based centralized processing architecture, where sensor data is uploaded to a cloud platform via communication modules, and the cloud platform analyzes the data before issuing control commands. This architecture suffers from drawbacks such as high response latency, strong network dependence, and limited functionality, making it difficult to meet the real-time requirements of scenarios such as fault handling and emergency response. Furthermore, existing systems have rigid control modes, typically supporting only timed switching or simple dimming, lacking the intelligent control capability to adaptively adjust to environmental changes. In addition, the system has poor scalability, making it difficult to integrate new business functions such as drone landing pads and citizen co-governance. Summary of the Invention

[0003] This invention provides a smart street light monitoring and control method and system, aiming to solve the technical problems of high response delay, weak local decision-making ability, single function and poor scalability of existing smart street light systems.

[0004] In a first aspect, embodiments of the present invention provide a smart street light monitoring and control method, the method comprising the following steps: S100, the edge computing device acquires environmental data and street light status data collected by sensors through a communication interface. The environmental data includes light intensity values, and the street light status data includes current detection values ​​and emergency trigger signals. S200, the edge computing device executes local control logic based on the environmental data and street light status data, generates control commands and outputs them to the street light driving device. The local control logic includes multi-mode control state machine processing, fault diagnosis processing and light adaptive dimming processing. The control commands include switch commands, dimming commands and fault alarm commands. S300, the edge computing device reports the processed data to the cloud platform via the MQTT protocol. The processed data includes sensor data, street light operating status data, and fault diagnosis result data. S400, the edge computing device receives and parses remote instructions from the cloud platform and executes them. The remote instructions include mode switching instructions, parameter configuration instructions, and direct control instructions.

[0005] Optionally, in S200, the multi-mode control state machine processing includes: S210, the edge computing device receives a mode switching signal, the mode switching signal including a manual mode signal, a timing mode signal and a lighting mode signal; S220, when the mode switching signal is a manual mode signal, the edge computing device converts the received switch operation signal into the switch command and outputs it; S230, when the mode switching signal is a timing mode signal, the edge computing device compares the current system time with a preset time period, generates an all-on command when the current system time enters the preset time period, and generates an all-off command when the current system time exceeds the preset time period. S240, when the mode switching signal is a lighting mode signal, the edge computing device performs the lighting adaptive dimming process.

[0006] Optionally, in S240, the adaptive dimming process includes: S241, the edge computing device extracts the current light intensity value from the environmental data and reads the reference light intensity value from the memory; S242, the edge computing device calculates a first quotient obtained by dividing the current illumination intensity value by the reference illumination intensity value; S243, the edge computing device calculates the difference between 1 and the first quotient to obtain the reverse illumination ratio value; S244, the edge computing device multiplies the reverse illumination ratio value by 100 to obtain a dimming ratio value in percentage form; S245, the edge computing device converts the dimming ratio value into a PWM dimming command and outputs it to the street light driver.

[0007] Optionally, in S200, the fault diagnosis process includes: S251, the edge computing device receives the street light status data and performs data frame format verification on the street light status data; S252, after the data frame format verification is passed, the edge computing device extracts the current detection value and compares the current detection value with a preset current threshold. S253, when the current detection value exceeds the preset current threshold, the edge computing device generates an overcurrent fault signal and converts the overcurrent fault signal into a fault alarm command for output; S254, the edge computing device actively pushes the fault diagnosis result data to the cloud platform through the MQTT protocol. The fault diagnosis result data includes the fault device identifier, fault type code and fault occurrence timestamp.

[0008] Optionally, in S251, the data frame format verification includes: The edge computing device detects whether the start bit of the data frame is a first preset identifier; The edge computing device detects whether the end bit of the data frame is a second preset identifier; The edge computing device extracts the device identifier field from the data frame and verifies whether the device identifier field exists in the known device list; The edge computing device detects whether the data length field in the data frame conforms to the expected format. When all of steps S254 to S257 are satisfied, the data frame format verification is deemed to have passed.

[0009] Optionally, the method further includes: the edge computing device executing drone landing pad control logic, the drone landing pad control logic including: S261, the edge computing device receives the helipad connection command and establishes a communication connection with the helipad main control board; S262, the edge computing device receives the hatch opening command and sends a first control signal to the apron main control board, the first control signal being used to trigger the hatch opening action; S263, the edge computing device receives a first status signal from the helipad photoelectric sensor, and when the first status signal indicates that the UAV has hovered, it sends a second control signal to the helipad main control board, the second control signal being used to trigger the door closing action; S264, the edge computing device receives a second status signal from the helipad grating sensor, and when the second status signal indicates that the UAV has fully landed, updates the helipad status to occupied status and reports it to the cloud platform.

[0010] Optionally, in S300, the edge computing device reports the processed data to the cloud platform via the MQTT protocol, including: S311, the edge computing device stores non-emergency type sensor data in a local cache queue, the non-emergency type including temperature and humidity data and PM2.5 data; S312, when the amount of data in the local cache queue reaches a preset quantity threshold or when the cache time reaches a preset time threshold, the edge computing device packages the data in the local cache queue into batch data packets; S313, the edge computing device reports the batch data packets to the cloud platform via a single MQTT message.

[0011] Optionally, in S400, the edge computing device receives parameter configuration instructions from the cloud platform, including: S411, the edge computing device parses the parameter configuration instruction and extracts the parameter values, which include the illumination threshold parameter, the current threshold parameter, and the timing time period parameter; S412, the edge computing device writes the extracted parameter values ​​into a non-volatile memory and replaces the original parameter values; S413, the edge computing device uses the updated parameter values ​​for calculation and judgment in subsequent control logic execution.

[0012] Secondly, embodiments of the present invention provide a smart street light monitoring and control system, the system comprising: An edge computing device, comprising a main control unit, a communication interface unit, a storage unit, and a multi-threaded processing unit, wherein the main control unit is connected to a sensor and a street light driving device through the communication interface unit, and the main control unit executes the method described in any of the above descriptions; The cloud platform communicates with the edge computing device via the MQTT protocol. The cloud platform receives and stores the reported data from the edge computing device and sends remote commands to the edge computing device. The web management terminal is connected to the cloud platform via a network. The web management terminal obtains data from the cloud platform and displays it visually. The web management terminal receives user operations and generates remote instructions to send to the cloud platform. A sensor network is connected to the edge computing device via an RS485 bus or wirelessly. The sensor network collects environmental data and street light status data and transmits them to the edge computing device.

[0013] Optionally, the communication interface unit of the edge computing device includes: A serial communication interface is used to connect the sensor network and the street light main control board to receive environmental data and street light status data. An MQTT communication interface is used for data interaction with the cloud platform; A UDP communication interface is used for local communication with the gateway device.

[0014] The beneficial effects of this invention are as follows: This invention, by deploying local control logic on an edge computing device, achieves real-time processing and rapid response of sensor data, effectively reducing reliance on cloud networks and solving the problem of high response latency in traditional cloud-based centralized processing architectures. The edge computing device can autonomously execute local control logic, including multi-mode control state machine processing, fault diagnosis, and adaptive dimming based on environmental data (such as light intensity) and streetlight status data (such as current detection values), generating control commands and outputting them to the streetlight driver, thus improving the system's local decision-making capabilities and intelligence level. Furthermore, existing systems typically have limited functionality, usually only supporting timed switching or simple dimming, while the edge computing device of this invention supports switching between multiple control modes, including manual, timed, and light-based modes, and can adaptively dim based on light intensity, achieving more flexible and intelligent control. In addition, existing systems have poor scalability and difficulty in integrating new business functions. The edge computing device of this invention can also execute UAV landing pad control logic, receiving signals from landing pad-related sensors to control the UAV landing pad doors and update and report the landing pad status, making it possible to integrate new business functions such as UAV landing pads and enhancing the system's scalability. Attached Figure Description

[0015] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0016] Figure 1 This is a flowchart illustrating the intelligent street light monitoring and control method in an embodiment of the present invention;

[0017] Figure 2 This is a schematic diagram of a scenario for the intelligent street light monitoring and control method in an embodiment of the present invention;

[0018] Figure 3 This is an interactive schematic diagram of the intelligent street light monitoring and control system in an embodiment of the present invention;

[0019] Figure 4 This is an interactive schematic diagram of the UAV landing pad control logic in an embodiment of the present invention;

[0020] Figure 5 This is a schematic diagram of the architecture of the intelligent street light monitoring and control system in an embodiment of the present invention;

[0021] Figure 6 This is a schematic diagram of a scenario for the intelligent street light monitoring and control system in an embodiment of the present invention;

[0022] Figure 7This is a schematic diagram of a scenario for the intelligent street light monitoring and control system in an embodiment of the present invention. Detailed Implementation

[0023] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0024] The core concept of this invention lies in achieving local intelligent decision-making through edge computing devices, constructing a distributed control architecture that integrates edge and cloud. The edge computing device employs a multi-threaded parallel processing mechanism, separating functional modules such as serial port monitoring, UI response, cloud communication, and scheduled tasks to ensure that each task runs independently without blocking others. The system achieves reliable communication between the edge computing device and the cloud platform via the MQTT protocol, and the web management terminal enables data visualization and remote control by calling the cloud platform's API.

[0025] refer to Figure 1 and Figure 2 This invention provides a smart street light monitoring and control method, the method comprising the following steps:

[0026] S100, the edge computing device acquires environmental data and street light status data collected by sensors through a communication interface.

[0027] Specifically, the edge computing device's serial communication interface continuously listens to the RS485 bus and local UDP port, receiving environmental data from light sensors, temperature and humidity sensors, and PM2.5 sensors, as well as current detection values ​​and emergency call button trigger signals from the street light main control board. It also receives street light lifespan information and geographical location information. The received raw data frames are temporarily stored in the receive buffer.

[0028] S200, the edge computing device executes local control logic based on the environmental data and street light status data, generates control commands and outputs them to the street light driving device.

[0029] Specifically, the main control unit of the edge computing device calls the multi-mode control state machine processing module to execute the corresponding control strategy according to the current working mode (manual / timed / illuminated). At the same time, it runs the fault diagnosis processing module to judge the current detection value for anomalies, and generates an alarm command immediately when an overcurrent fault is detected. In the illumination mode, the main control unit performs illumination adaptive dimming processing, calculates the dimming ratio according to the real-time illumination intensity, and generates a PWM dimming command.

[0030] The S300 edge computing device reports the processed data to the cloud platform via the MQTT protocol.

[0031] Specifically, the MQTT client of the edge computing device encapsulates sensor data, street light operating status data, and fault diagnosis results into JSON format messages and sends them to the MQTT broker server of the cloud platform via a TCP / IP network. For non-urgent data, the edge computing device performs caching, storing periodic data such as temperature, humidity, and PM2.5 in a local cache queue. When the queue length reaches 20 entries or the cache time reaches 30 seconds, the data is packaged into a batch data packet and reported all at once.

[0032] The S400 edge computing device receives and executes remote commands from the cloud platform.

[0033] Specifically, the edge computing device subscribes to instruction topics issued by the cloud platform. When it receives a mode switching instruction, parameter configuration instruction, or direct control instruction, the MQTT client delivers the message to the instruction parsing module of the main control unit. The instruction parsing module extracts the instruction type and parameter content, updates the system status or replaces the configuration parameters in the memory, and the changes take effect immediately.

[0034] The steps S100 to S400 provided in this embodiment of the invention complete the closed-loop processing of the core control logic locally through an edge computing device, realizing real-time response and intelligent control of streetlights. Simultaneously, through data reporting and remote command reception, a complete edge-cloud collaborative management system is constructed. Specifically, step S100 serves as the data acquisition entry point, ensuring comprehensive acquisition of environmental and streetlight status information; step S200's local control logic processing, through the collaborative work of sub-modules such as multi-mode state machines, fault diagnosis, and adaptive dimming, achieves intelligent and automated streetlight control; the data reporting mechanism in step S300 uses the MQTT protocol to ensure lightweight and reliable data transmission, while batch processing of non-urgent data effectively reduces network bandwidth consumption; and step S400 empowers the cloud platform with remote intervention capabilities, enabling system maintenance personnel to flexibly adjust the streetlight operating mode and parameter configuration according to actual needs. These four steps are closely linked, forming a complete business process from data collection, local decision-making, data uploading to remote control. This fully demonstrates the technical advantages of combining edge computing and cloud computing in this invention, which not only meets the requirements of real-time control but also realizes global monitoring and management.

[0035] refer to Figure 3 In some embodiments, the specific process of the multi-mode control state machine processing in S200 includes: S210, the edge computing device receives a mode switching signal, the mode switching signal including a manual mode signal, a timing mode signal and a lighting mode signal; S220, when the mode switching signal is a manual mode signal, the edge computing device converts the received switch operation signal into the switch command and outputs it; S230, when the mode switching signal is a timing mode signal, the edge computing device compares the current system time with a preset time period, generates an all-on command when the current system time enters the preset time period, and generates an all-off command when the current system time exceeds the preset time period. S240, when the mode switching signal is a lighting mode signal, the edge computing device performs the lighting adaptive dimming process.

[0036] Specifically, the mode management module of the edge computing device maintains the current operating status variable. When a user sends a mode switching signal through the web management terminal, the edge computing device receives the signal and updates the status variable to the corresponding mode value. In manual mode, the edge computing device directly converts the switch operation signal generated by the web front-end or local interface into a 550101 (light on) or 550100 (light off) instruction code, and sends it to the street light main control board through the serial interface. In timed mode, the edge computing device compares the current system time with the preset on-time period (e.g., 18:00-06:00) every minute. If the time matches, it generates a 550001 (all on) instruction; if the time exceeds the limit, it generates a 550000 (all off) instruction. When switching to illumination mode, the edge computing device triggers the illumination adaptive dimming processing sub-process.

[0037] In this embodiment, a multi-mode control state machine enables flexible switching and precise execution of operating modes. The mode management module uses state variables to mark the current operating mode, ensuring atomic operation of mode switching and avoiding state conflicts. In manual mode, a direct instruction mapping mechanism is used to ensure real-time response to switching operations; in timed mode, unattended automated control is achieved through periodic comparison of system time with preset time periods; and in illumination mode, dynamic adjustment based on ambient light intensity is achieved by calling an adaptive dimming processing sub-process, demonstrating the system's intelligence and energy-saving characteristics. The design of these three modes covers the needs of different application scenarios, allowing users to select the appropriate control mode according to their actual management requirements, thus improving the system's applicability and flexibility.

[0038] In some embodiments, the detailed steps of the adaptive dimming process in S240 are as follows: S241, the edge computing device extracts the current light intensity value from the environmental data and reads the reference light intensity value from the memory; S242, the edge computing device calculates a first quotient obtained by dividing the current illumination intensity value by the reference illumination intensity value; S243, the edge computing device calculates the difference between 1 and the first quotient to obtain the reverse illumination ratio value; S244, the edge computing device multiplies the reverse illumination ratio value by 100 to obtain a dimming ratio value in percentage form; S245, the edge computing device converts the dimming ratio value into a PWM dimming command and outputs it to the street light driver.

[0039] Specifically, the edge computing device extracts the current illuminance value reported by the light sensor, in lux, from the received environmental data. Simultaneously, it reads the user-preset reference illuminance value from non-volatile memory, representing the illuminance when the environment is at its brightest. The main control unit performs a division operation, calculating the quotient of the current illuminance value divided by the reference illuminance value. Then, it calculates the difference between 1 and the quotient to obtain the reverse illuminance ratio value. This reverse illuminance ratio value is multiplied by 100 to convert it into a percentage dimming ratio value. Finally, the dimming ratio value is mapped to a PWM count value from 0 to 1000, encapsulated into a dimming command frame, and sent to the street light driver. For example, when the current illuminance is 5000 lux and the reference value is 10000 lux, the calculated quotient is 0.5, the difference is 0.5, the dimming ratio is 50%, the PWM count value is 500, and the street light operates at 50% brightness.

[0040] In this embodiment, dynamic adjustment of street light brightness is achieved through adaptive dimming processing, the core of which lies in establishing an inverse mapping relationship between light intensity and dimming ratio. The quotient of the current light intensity value and the reference light intensity value reflects the ratio of the actual ambient brightness to the reference brightness. The difference between 1 and this quotient represents the proportion of light that needs to be supplemented, which is converted into an intuitive percentage dimming value by multiplying by 100. This calculation method ensures that when the ambient brightness is high (e.g., close to the reference value), the dimming ratio is low, and the street light operates at a lower brightness; while when the ambient brightness is low (e.g., at night), the dimming ratio is close to 100%, and the street light illuminates at a higher brightness, thereby achieving on-demand lighting and energy saving. In the process of mapping the dimming ratio value to the PWM count value, a linear conversion method is used to ensure the accuracy and continuity of the dimming command, making the street light brightness adjustment smooth and avoiding discomfort to pedestrians caused by sudden brightness changes. At the same time, the reference light intensity value can be remotely configured through a cloud platform, and users can make personalized settings according to the actual lighting conditions of different areas, further improving the system's adaptability and energy-saving effect.

[0041] In some embodiments, in S200, the implementation process of the fault diagnosis process is as follows: S251, the edge computing device receives the street light status data and performs data frame format verification on the street light status data; S252, after the data frame format verification is passed, the edge computing device extracts the current detection value and compares the current detection value with a preset current threshold. S253, when the current detection value exceeds the preset current threshold, the edge computing device generates an overcurrent fault signal and converts the overcurrent fault signal into a fault alarm command for output; S254, the edge computing device actively pushes the fault diagnosis result data to the cloud platform through the MQTT protocol. The fault diagnosis result data includes the fault device identifier, fault type code and fault occurrence timestamp.

[0042] Specifically, the edge computing device receives street light status data frames from the street light main control board and first performs data frame format verification. The main control unit checks whether the start bit of the data frame is 0xFAFA and the end bit is 0xFBFB, extracts the device ID field and searches for a matching item in the known device list, and verifies whether the data length meets the expected format. After the verification passes, the current detection value is extracted and compared with the preset current threshold in the memory. When the current detection value exceeds the threshold for 3 seconds, it is determined to be an overcurrent fault, and a fault alarm command with fault code 0x01 is generated, triggering the local audible and visual alarm device. At the same time, the main control unit encapsulates the faulty device ID, fault code, fault timestamp, and AI-generated maintenance suggestion text into fault diagnosis result data. In addition, it may also include the latitude and longitude information of the specific location of the fault; it actively pushes the data to the cloud platform via the MQTT protocol once per second until the fault is resolved.

[0043] In some embodiments, the detailed process of data frame format verification in S250 is as follows: The edge computing device detects whether the start bit of the data frame is a first preset identifier; The edge computing device detects whether the end bit of the data frame is a second preset identifier; The edge computing device extracts the device identifier field from the data frame and verifies whether the device identifier field exists in the known device list; The edge computing device detects whether the data length field in the data frame conforms to the expected format. When all of steps S254 to S257 are satisfied, the data frame format verification is deemed to have passed.

[0044] Specifically, the verification module of the edge computing device scans the receive buffer byte by byte to find the first preset identifier 0xFAFA as the frame start position. After locating the start position, it continues to scan to find the second preset identifier 0xFBFB as the frame end position. The device identifier field between the two identifiers is extracted. This field is a 2-byte hexadecimal code, and the verification module traverses and matches it in the known device list. If a match is successful, the data length field is extracted, and the number of subsequent data bytes is verified to be consistent with the length field. When the four verifications of the start position, end position, device identifier, and data length all pass, the data frame format verification is considered successful; otherwise, the frame is discarded and an error log is recorded.

[0045] refer to Figure 4 In some embodiments, the method further includes: S260, where the edge computing device executes drone landing pad control logic; in S260, the workflow of the drone landing pad control logic is as follows: S261, the edge computing device receives the helipad connection command and establishes a communication connection with the helipad main control board; S262, the edge computing device receives the hatch opening command and sends a first control signal to the apron main control board, the first control signal being used to trigger the hatch opening action; S263, the edge computing device receives a first status signal from the helipad photoelectric sensor, and when the first status signal indicates that the UAV has hovered, it sends a second control signal to the helipad main control board, the second control signal being used to trigger the door closing action; S264, the edge computing device receives a second status signal from the helipad grating sensor, and when the second status signal indicates that the UAV has fully landed, updates the helipad status to occupied status and reports it to the cloud platform.

[0046] Specifically, the edge computing device receives a helipad connection command from the web management terminal and sends a connection request to the helipad main control board via a UDP communication interface to establish a control channel. After the connection is established, the edge computing device receives a door opening command and sends a first control signal to the helipad main control board. This signal drives a relay to open the door motor in the forward direction. During the door opening process, the edge computing device continuously receives a first status signal from the photoelectric sensor. When this signal changes from high to low, indicating that the UAV has hovered above the helipad, the edge computing device sends a second control signal to the helipad main control board, driving the door motor to close in the reverse direction. Simultaneously, the edge computing device receives a second status signal from the grating sensor. When the grating is blocked and generates a pulse signal, it determines that the UAV has fully landed. The edge computing device updates the helipad status to occupied and reports it to the cloud platform via MQTT.

[0047] In some embodiments, in S300, the edge computing device reports the processed data to the cloud platform via the MQTT protocol, including: S311, the edge computing device stores non-emergency type sensor data in a local cache queue, the non-emergency type including temperature and humidity data and PM2.5 data; S312, when the amount of data in the local cache queue reaches a preset quantity threshold or when the cache time reaches a preset time threshold, the edge computing device packages the data in the local cache queue into batch data packets; S313, the edge computing device reports the batch data packets to the cloud platform via a single MQTT message.

[0048] Specifically, the data classification module of the edge computing device categorizes received sensor data according to urgency. Urgent data such as current detection values ​​and distress signals are marked as real-time reporting types, while non-urgent data such as temperature, humidity, PM2.5, and noise are marked as cached reporting types. For cached data, the edge computing device stores it in a local cache queue, which uses a first-in, first-out (FIFO) structure with a maximum capacity of 50 records. The cache management module monitors the queue length and triggers a packaging operation when the data volume reaches a preset threshold of 20 records; or it forcibly triggers a packaging operation when the cache timer reaches a preset time threshold of 30 seconds. The packaging module sorts the data in the queue by timestamp, encapsulates it into batch data packets in JSON array format, and sends them to the cloud platform via a single MQTT message. The queue is cleared after successful transmission.

[0049] In some embodiments, in S400, the edge computing device receives parameter configuration instructions from the cloud platform, including: S411, the edge computing device parses the parameter configuration instruction and extracts the parameter values, which include the illumination threshold parameter, the current threshold parameter, and the timing time period parameter; S412, the edge computing device writes the extracted parameter values ​​into a non-volatile memory and replaces the original parameter values; S413, the edge computing device uses the updated parameter values ​​for calculation and judgment in subsequent control logic execution.

[0050] Specifically, the edge computing device receives parameter configuration instructions from the cloud platform. These instructions are in JSON format and include parameters for illumination threshold, current threshold, and timing period. The MQTT client delivers the message to the parameter parsing module, which extracts the parameter values ​​and performs validity checks to ensure that the illumination threshold is within the range of 0-65535 lux, the current threshold is within the range of 0-10 amperes, and the timing period conforms to the HH:MM-HH:MM format. After successful verification, the main control unit writes the parameter values ​​to a designated address in non-volatile memory, replacing the original parameter values. After writing, the main control unit returns a configuration success response message to the cloud platform and uses the updated parameter values ​​in real time for calculations and judgments in subsequent control logic execution, taking effect without requiring a device restart.

[0051] refer to Figure 5 This invention also provides a smart street light monitoring and control system, the system comprising:

[0052] The edge computing device, deployed in the street light monitoring room or light pole control box, uses an ARM architecture processor and runs a Linux operating system. The device includes a main control unit, a communication interface unit, a storage unit, and a multi-threaded processing unit. The main control unit executes all the steps in the above method embodiments. The communication interface unit provides a serial communication interface to connect to the sensor network, an MQTT communication interface to connect to the cloud platform, and a UDP communication interface to connect to the gateway device.

[0053] The cloud platform, deployed on a cloud server cluster, adopts the ThingsBoard open-source IoT platform architecture. The cloud platform maintains a persistent connection with edge computing devices via the MQTT protocol, receives and stores reported data in a time-series database, provides a rules engine for data forwarding and alarm triggering, and issues remote commands to the edge computing devices.

[0054] The web management interface is a web application developed based on the Vue.js framework and deployed on an IIS server. It interacts with the cloud platform via HTTP / RESTful APIs to acquire and visualize data, providing interfaces for map monitoring, data analysis, and parameter configuration. It also receives user input and generates remote commands.

[0055] Sensor networks, including light sensors, temperature and humidity sensors, PM2.5 sensors, current sensors, photoelectric sensors, and grating sensors, are connected to edge computing devices via RS485 bus or LoRa wireless connection.

[0056] refer to Figure 6 and Figure 7In this embodiment, a complete smart street light monitoring and control system, from data acquisition and edge processing to cloud management, is constructed through the collaborative work of edge computing devices, cloud platforms, web management terminals, and sensor networks. The edge computing device, as the local core of the system, leverages the high-efficiency computing power and multi-threaded processing mechanism of its ARM architecture processor to achieve real-time processing of key functions such as adaptive dimming, fault diagnosis, and data caching and reporting, reducing reliance on cloud bandwidth and data transmission latency. The cloud platform, relying on the powerful data processing and storage capabilities of the ThingsBoard architecture, centrally manages and analyzes massive amounts of street light operation data, and uses a rule engine to achieve intelligent alarms and data forwarding, providing data support for global decision-making. The web management terminal, with its intuitive visual interface and convenient operation, allows users to remotely monitor street light status, configure operating parameters, and view statistical reports in real time, greatly improving management efficiency. Various sensors, via RS485 bus or LoRa wireless technology, constitute the nerve endings of the sensing layer, accurately collecting key data such as light intensity, temperature and humidity, and equipment current, providing the original basis for the system's intelligent control and fault diagnosis. This layered architecture design ensures both the real-time performance and reliability of local processing, while also enabling the flexibility and scalability of cloud management. This allows the entire system to operate stably, efficiently, and energy-savingly, meeting the diverse needs of modern urban smart lighting.

[0057] In some embodiments, the communication interface unit of the edge computing device includes:

[0058] The serial communication interface adopts a UART to RS485 circuit design, supports the ModBus-RTU protocol, and the baud rate can be configured to 9600-115200bps. It is used to connect sensor networks and street light main control boards.

[0059] The MQTT communication interface, implemented based on the Paho MQTT client library, supports QoS levels 0 / 1 / 2, maintains a heartbeat interval of 60 seconds, and is used for data interaction with the cloud platform.

[0060] The UDP communication interface is implemented using Socket programming and listens on local port 5001 for high-speed local communication with the gateway device.

[0061] In this embodiment, the collaborative design of multiple communication interfaces ensures efficient data interaction between the edge computing device and different types of devices. The serial communication interface, via a UART-to-RS485 circuit, reliably connects to the sensor network and streetlight control board distributed across various light poles. Support for the ModBus-RTU protocol ensures standardized data transmission, while flexible baud rate configuration adapts to communication needs under different distances and interference environments. The MQTT communication interface, as the core channel for interaction with the cloud platform, achieves stable long-term connections using the Paho MQTT client library. Optional QoS levels ensure reliable message transmission, especially for critical information such as fault alarms, which can be delivered via QoS 1 or 2. A 60-second heartbeat interval maintains connection activity while reducing unnecessary network overhead. The UDP communication interface supports high-speed local communication. Through socket programming and listening on a fixed port, it enables low-latency data exchange with gateway devices, suitable for scenarios with high real-time requirements, such as drone landing pad control. This multi-interface integration design enables the edge computing device to serve as the communication hub of the entire system, efficiently integrating local sensing data with cloud commands, and providing a solid communication guarantee for the stable operation of the smart street light system.

[0062] The embodiments of the present invention achieve the following beneficial effects through the above technical solutions:

[0063] By executing local control logic through edge computing devices, system response time is reduced from seconds to milliseconds, significantly improving the real-time performance of fault handling and emergency response. The multi-mode control state machine supports manual, timed, and illumination-based operating modes, which can be remotely switched via the cloud platform, enabling flexible configuration of control strategies. Illumination-adaptive dimming dynamically adjusts streetlight brightness based on real-time light intensity, achieving energy conservation and consumption reduction while ensuring lighting quality. Fault diagnosis is performed at the edge, performing data verification and fault judgment, generating maintenance suggestions, and proactively reporting them, achieving predictive maintenance. The UAV landing pad control logic expands the streetlight system's business functions, supporting the development of the low-altitude economy. Cache processing and batch reporting mechanisms reduce network bandwidth consumption and cloud platform processing pressure. Configurable parameter design allows the system to adapt to different application scenarios, adjusting control strategies without code modification. The overall system adopts an edge-cloud collaborative architecture, ensuring both the real-time performance and reliability of local control while leveraging the data storage and analysis capabilities of the cloud platform, providing an efficient and scalable infrastructure solution for smart city construction.

[0064] Those skilled in the art will understand that all or some of the steps and systems disclosed above can be implemented as software, firmware, hardware, or suitable combinations thereof. Some or all of the physical components can be implemented as software executed by a processor, such as a central processing unit, digital signal processor, or microprocessor, or as hardware, or as an integrated circuit, such as an application-specific integrated circuit. Such software can be distributed on a computer-readable medium, which may include computer storage media and communication media.

[0065] This invention is not limited to the above-described embodiments. Any structural changes or method improvements made by anyone under the guidance of this invention, and any technical solutions that are the same as or similar to this invention, fall within the protection scope of this invention.

Claims

1. A smart street light monitoring and control method, characterized in that, The method includes the following steps: S100, the edge computing device acquires environmental data and street light status data collected by sensors through a communication interface. The environmental data includes light intensity values, and the street light status data includes current detection values ​​and emergency trigger signals. S200, the edge computing device executes local control logic based on the environmental data and street light status data, generates control commands and outputs them to the street light driving device. The local control logic includes multi-mode control state machine processing, fault diagnosis processing and light adaptive dimming processing. The control commands include switch commands, dimming commands and fault alarm commands. S300, the edge computing device reports the processed data to the cloud platform via the MQTT protocol. The processed data includes sensor data, street light operating status data, and fault diagnosis result data. S400, the edge computing device receives and parses remote instructions from the cloud platform and executes them. The remote instructions include mode switching instructions, parameter configuration instructions, and direct control instructions.

2. The method according to claim 1, characterized in that, In S200, the multi-mode control state machine processing includes: S210, the edge computing device receives a mode switching signal, the mode switching signal including a manual mode signal, a timing mode signal and a lighting mode signal; S220, when the mode switching signal is a manual mode signal, the edge computing device converts the received switch operation signal into the switch command and outputs it; S230, when the mode switching signal is a timing mode signal, the edge computing device compares the current system time with a preset time period, generates an all-on command when the current system time enters the preset time period, and generates an all-off command when the current system time exceeds the preset time period. S240, when the mode switching signal is a lighting mode signal, the edge computing device performs the lighting adaptive dimming process.

3. The method according to claim 2, characterized in that, In S240, the adaptive dimming process includes: S241, the edge computing device extracts the current light intensity value from the environmental data and reads the reference light intensity value from the memory; S242, the edge computing device calculates a first quotient obtained by dividing the current illumination intensity value by the reference illumination intensity value; S243, the edge computing device calculates the difference between 1 and the first quotient to obtain the reverse illumination ratio value; S244, the edge computing device multiplies the reverse illumination ratio value by 100 to obtain a dimming ratio value in percentage form; S245, the edge computing device converts the dimming ratio value into a PWM dimming command and outputs it to the street light driver.

4. The method according to claim 1, characterized in that, In S200, the fault diagnosis process includes: S251, the edge computing device receives the street light status data and performs data frame format verification on the street light status data; S252, after the data frame format verification is passed, the edge computing device extracts the current detection value and compares the current detection value with a preset current threshold. S253, when the current detection value exceeds the preset current threshold, the edge computing device generates an overcurrent fault signal and converts the overcurrent fault signal into a fault alarm command for output; S254, the edge computing device actively pushes the fault diagnosis result data to the cloud platform through the MQTT protocol. The fault diagnosis result data includes the fault device identifier, fault type code and fault occurrence timestamp.

5. The method according to claim 4, characterized in that, In S251, the data frame format verification includes: The edge computing device detects whether the start bit of the data frame is a first preset identifier; The edge computing device detects whether the end bit of the data frame is a second preset identifier; The edge computing device extracts the device identifier field from the data frame and verifies whether the device identifier field exists in the known device list; The edge computing device detects whether the data length field in the data frame conforms to the expected format. When all of steps S254 to S257 are satisfied, the data frame format verification is deemed to have passed.

6. The method according to claim 1, characterized in that, Also includes: The edge computing device executes the drone landing pad control logic, which includes: S261, the edge computing device receives the helipad connection command and establishes a communication connection with the helipad main control board; S262, the edge computing device receives the hatch opening command and sends a first control signal to the apron main control board, the first control signal being used to trigger the hatch opening action; S263, the edge computing device receives a first status signal from the helipad photoelectric sensor, and when the first status signal indicates that the UAV has hovered, it sends a second control signal to the helipad main control board, the second control signal being used to trigger the door closing action; S264, the edge computing device receives a second status signal from the helipad grating sensor, and when the second status signal indicates that the UAV has fully landed, updates the helipad status to occupied status and reports it to the cloud platform.

7. The method according to claim 1, characterized in that, In S300, the edge computing device reports the processed data to the cloud platform via the MQTT protocol, including: S311, the edge computing device stores non-emergency type sensor data in a local cache queue, the non-emergency type including temperature and humidity data and PM2.5 data; S312, when the amount of data in the local cache queue reaches a preset quantity threshold or when the cache time reaches a preset time threshold, the edge computing device packages the data in the local cache queue into batch data packets; S313, the edge computing device reports the batch data packets to the cloud platform via a single MQTT message.

8. The method according to claim 1, characterized in that, In S400, the edge computing device receives parameter configuration instructions from the cloud platform, including: S411, the edge computing device parses the parameter configuration instruction and extracts the parameter values, which include the illumination threshold parameter, the current threshold parameter, and the timing time period parameter; S412, the edge computing device writes the extracted parameter values ​​into a non-volatile memory and replaces the original parameter values; S413, the edge computing device uses the updated parameter values ​​for calculation and judgment in subsequent control logic execution.

9. A smart street light monitoring and control system, characterized in that, The system includes: An edge computing device, comprising a main control unit, a communication interface unit, a storage unit, and a multi-threaded processing unit, wherein the main control unit is connected to a sensor and a street light driving device through the communication interface unit, and the main control unit performs the method as described in any one of claims 1 to 8; The cloud platform communicates with the edge computing device via the MQTT protocol. The cloud platform receives and stores the reported data from the edge computing device and sends remote commands to the edge computing device. The web management terminal is connected to the cloud platform via a network. The web management terminal obtains data from the cloud platform and displays it visually. The web management terminal receives user operations and generates remote instructions to send to the cloud platform. A sensor network is connected to the edge computing device via an RS485 bus or wirelessly. The sensor network collects environmental data and street light status data and transmits them to the edge computing device.

10. The system according to claim 9, characterized in that, The communication interface unit of the edge computing device includes: A serial communication interface is used to connect the sensor network and the street light main control board to receive environmental data and street light status data. An MQTT communication interface is used for data interaction with the cloud platform; A UDP communication interface is used for local communication with the gateway device.

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