Roadway lighting system based on Internet of Things

Abstract

The invention discloses a roadway lighting system based on the Internet of Things, and relates to the technical field of roadway lighting, the roadway lighting system comprises an Internet of Things platform, the Internet of Things platform is in communication connection with a sensing module, a data processing module, a lighting regulation and control module, a fault monitoring alarm module and a user interaction module, and the modules are in electric signal connection. The road lighting system can intelligently adjust the brightness and the on-off state of the street lamp according to the real-time environment condition and the traffic flow through the Internet of Things technology, the lighting efficiency is improved, the energy consumption is remarkably reduced, the illumination intensity is monitored in real time through the illumination sensor, the indexes are evaluated in combination with the weather conditions, the brightness of the street lamp is intelligently adjusted, and the lighting efficiency is improved. Proper illumination is provided under different weather and illumination conditions, traffic peak and low peak periods can be identified through the traffic flow identification index, the brightness is dynamically adjusted, and energy waste in the period with low traffic flow is avoided.

Description

Technical Field

[0001] The present invention relates to the technical field of road lighting, and in particular to a road lighting system based on the Internet of Things. Background Art

[0002] With the acceleration of urbanization, the scale of cities continues to expand, and the construction and management of urban infrastructure has become increasingly complex. The Internet of Things technology realizes information exchange and intelligent control between objects and objects, and between objects and people through various sensors, communication technologies and network connections. In recent years, the Internet of Things technology has developed rapidly, and its application scope has continued to expand, including smart homes, smart transportation, smart security and other fields. In order to meet the needs of urban lighting management and improve lighting efficiency and energy-saving effects, road lighting systems based on the Internet of Things have come into being.

[0003] For example, a road lighting system based on the Internet of Things (IoT) disclosed in Chinese patent publication number CN113163559A includes: a road lighting device, which is used to be arranged on at least one side of a road to realize a lighting function; a server, which is used to realize data interaction with the road lighting device and control the road lighting device; and a mobile terminal device, which is used to at least interact with the server to obtain lighting data of the road where at least one road lighting device is located.

[0004] In the prior art, users are guided according to the actual street lighting conditions to achieve intelligent lighting. However, road lighting is still limited by traffic flow and environmental factors. Only relying on the actual street lighting conditions to guide lighting adjustment has poor overall environmental adaptability. Therefore, how to perform intelligent lighting adjustment according to traffic flow and environmental conditions to dynamically adjust road strategies and adapt to different road lighting needs is a problem we need to solve. To this end, a road lighting system based on the Internet of Things is proposed. Summary of the invention

[0005] The purpose of the present invention is to provide a road lighting system based on the Internet of Things to solve the problems raised in the above background technology.

[0006] In order to solve the above technical problems, the technical solution adopted by the present invention is: A road lighting system based on the Internet of Things, comprising an Internet of Things platform, wherein the Internet of Things platform is communicatively connected with a perception module, a data processing module, a lighting control module, a fault monitoring and alarm module, and a user interaction module, wherein electrical signals are connected between the modules; The perception module deploys various sensors at street light nodes, uses the sensors to monitor the light intensity, traffic flow and environmental conditions of the road, wherein the sensors include light sensors, radar detectors, temperature and humidity sensors, and realizes data transmission and command control between street light nodes and the Internet of Things platform, establishes a network between street lights, and transmits the status information and control instructions of street lights to the Internet of Things platform; The data processing module is used to pre-process, analyze and mine the data collected by each sensor, extract lighting adjustment features and analyze the changing trends of light intensity, traffic flow and environmental conditions; The lighting control module uses a machine learning algorithm to dynamically adjust the lighting strategy of the street lamps based on the output of the data processing module and according to traffic flow and environmental conditions, and intelligently adjusts the brightness and switching time parameters of the street lamps; The fault monitoring and alarm module monitors the working status of the street lamp in real time, promptly discovers and reports fault information, and reduces the frequency of manual inspections and maintenance costs through remote monitoring and maintenance functions; The user interaction module provides a user interaction interface and display platform to view the real-time status, historical data, and alarm information of street lamps, which helps managers better understand the operation of street lamps and make corresponding adjustments and optimizations.

[0007] A further improvement of the technical solution of the present invention is that in the perception module, the process of monitoring the light intensity, traffic flow and environmental conditions of the road is: Install and deploy a variety of sensors on street light nodes, including light sensors, radar detectors, and temperature and humidity sensors; Sensors are used to monitor the light intensity, traffic flow and environmental conditions of the road to obtain road and environmental information. The light sensor is used to monitor the current light intensity, the radar detector is used to detect the vehicle flow on the road, and the presence and speed of the vehicle are determined by the intensity and time difference of the reflected wave. The temperature and humidity sensor is used to collect environmental temperature and humidity data; The road and environment information collected by the sensor is transmitted to the IoT platform through the wireless communication network. The wireless communication network includes low-power wide area network technologies such as Ethernet, LoRa, and ZigBee. A network is established between street lamps through a mesh network to form a distributed perception system. Each street lamp node in the network has communication capabilities and can transmit data and conduct command control with other street lamp nodes or IoT platforms. Based on the network connection established between street lamps, the current status information of the street lamps is collected and uploaded to the Internet of Things platform. The current status information of the street lamps includes brightness, switch status, and energy consumption data.

[0008] A further improvement of the technical solution of the present invention is that in the data processing module, the process of analyzing the change trend of light intensity, traffic flow and environmental conditions is as follows: Aggregate data from light sensors, radar detectors, temperature and humidity sensors from the IoT platform and preprocess the sensor data. The preprocessing includes data cleaning, data smoothing, data format conversion, and data fusion. Data cleaning removes invalid or abnormal data. Data smoothing is used to process slightly fluctuating data caused by sensor noise to improve data accuracy and stability. Data format conversion converts raw sensor data into digital signals and standardizes them for subsequent analysis. Data fusion fuses data from different sensors to form more comprehensive road and environmental information. Extract features related to lighting regulation from the preprocessed data, where the light intensity features are the average, maximum, and minimum values ​​of the light intensity, the traffic flow features are the peak, average, and fluctuation of the traffic flow, and the environmental conditions features are the changing trends of the ambient temperature and humidity; Combined with the characteristics of light intensity, traffic flow and environmental conditions related to lighting regulation, the current brightness, switch status and energy consumption status information of street lamps are integrated, and the time series analysis method is used to analyze the changing trends of light intensity, traffic flow and environmental conditions; Combined with the average, maximum and minimum characteristics of light intensity, the lighting evaluation index is calculated, the changing trend of light intensity is analyzed, and it is determined whether the brightness of street lamps needs to be adjusted according to the changes in natural light. Combined with the peak, average and fluctuation characteristics of traffic flow, the traffic flow identification index is calculated, and traffic flow data is analyzed to identify peak and low-flow periods. Combined with the changing trend characteristics of ambient temperature and humidity, the environmental condition evaluation index is calculated, the changing trend of environmental conditions is analyzed, and the impact of environmental changes on lighting needs is identified.

[0009] A further improvement of the technical solution of the present invention is that the expression of the illumination evaluation index is: ; in, It is the lighting evaluation index, which is used to evaluate the deviation between the current lighting conditions and the reference lighting intensity. It is the average value of light intensity, which means the average value of light intensity over a period of time and reflects the overall light level. is the maximum value of light intensity, indicating the maximum value of light intensity during the observation period, reflecting the peak condition of light. is the minimum value of light intensity, indicating the minimum value of light intensity during the observation period, reflecting the minimum conditions of light. The reference light intensity is a preset value used to compare with the actual light intensity to assess whether the actual light level needs to be adjusted. The value range is ; The expression of the traffic flow identification index is: ; ; ; in, It is a traffic flow identification index, which is used to evaluate the degree of deviation between the current traffic flow and the benchmark traffic flow. is the peak value of traffic flow during the observation period, is the average value of traffic flow, which means the average value of traffic flow over a period of time and reflects the overall traffic flow level. is the standard deviation of traffic flow, reflecting the fluctuation of traffic flow. The baseline traffic flow is a preset value used to compare with the actual traffic flow to assess whether the actual traffic flow is at a peak or low peak. is the total number of observation periods, For the Traffic flow during the observation period, The value range is ; The expression of the environmental condition evaluation index is: ; in, It is an environmental condition assessment index, which is used to assess the degree of deviation between the current environmental conditions and the benchmark environmental conditions. is the current ambient temperature, is the current ambient humidity, The reference temperature is a preset value used for comparison with the actual ambient temperature. The reference humidity is a preset value used for comparison with the actual ambient humidity. and represents the maximum and minimum values ​​of the ambient temperature during the observation period, and Indicates the maximum and minimum values ​​of ambient humidity during the observation period, The value range is .

[0010] A further improvement of the technical solution of the present invention is that in the lighting control module, the process of dynamically adjusting the street lighting strategy is: Receive the output from the data processing module, including the pre-processed and feature-extracted data of traffic flow, environmental conditions, and light intensity, and extract the lighting adjustment history data of the street light nodes from the IoT platform and database, including the past street light brightness adjustment records and switching time, and divide the lighting adjustment history data of the street light nodes into a training set and a test set; Use the training set data combined with the neural network model to build a lighting control prediction model to predict the lighting demand of street lamps, and use the test set data to test and evaluate the trained lighting control prediction model; The lighting evaluation index, traffic flow identification index and weather condition evaluation index are used as input parameters, and the lighting control coefficient is calculated in combination with the lighting control prediction model to analyze the impact of different factors on lighting demand. According to the lighting control coefficient, the lighting demand is divided into different lighting control modes, namely energy-saving lighting control mode, conventional lighting control mode and high-brightness lighting control mode, and a corresponding control threshold is matched for each lighting control mode; According to the divided lighting control modes, set the corresponding lighting strategy for each lighting control mode, and generate specific control commands according to the lighting control mode, including brightness level and switching time, and send the control commands to each street light node through the Internet of Things platform; The street light node receives the control command and performs corresponding brightness adjustment and switching operations to dynamically adjust the lighting status of the street light.

[0011] A further improvement of the technical solution of the present invention is that the expression of the lighting control coefficient is: ; in, is the lighting control coefficient, which is used to determine the final lighting strategy. is the light evaluation index, is the reference value of the lighting evaluation index. is the traffic flow identification index, is the benchmark value of the traffic flow identification index, and Indicates the maximum and minimum values ​​of the traffic flow identification index, is the weather condition assessment index, is the benchmark value of the weather condition assessment index, , , is the weight coefficient.

[0012] A further improvement of the technical solution of the present invention is that: the plurality of lighting control modes correspond to the plurality of control thresholds, wherein the control thresholds include an upper threshold and a lower threshold; The plurality of lighting control modes and the plurality of control thresholds satisfy the following relationship: Energy-saving lighting control mode ; Reduce brightness, possibly setting it to the lowest level and only turning on street lights when necessary; Conventional lighting control mode ; Maintain medium brightness and adjust brightness according to traffic flow and ambient light; High brightness lighting control mode ; Increase brightness to the highest level to ensure road safety and visibility; in, is the lighting control coefficient, is the lower threshold corresponding to the conventional lighting control mode and the upper threshold corresponding to the energy-saving lighting control mode, It is the lower threshold corresponding to the high-brightness lighting control mode and the upper threshold corresponding to the conventional lighting control mode.

[0013] A further improvement of the technical solution of the present invention is that: in the fault monitoring and alarm module, the process of discovering and reporting fault information is: Continuously monitor the operating status of street lights, collect the current, voltage, and power data of street lights, analyze the brightness, switch status, and energy consumption of street lights based on the current status information of street lights, and detect the usage status of street lights; Comprehensively analyze the street lamp current, voltage, power, brightness, switch status and energy consumption, calculate the abnormality detection coefficient, determine whether the street lamp is in normal working condition, and identify the fault type when the data is abnormal; Analyze the severity of street light faults based on the identified fault types and set high-level maintenance priorities for faults with high severity; The identified fault-related data is marked and an alarm message is generated to locate the specific location of the faulty street lamp. The fault type, maintenance priority, fault data node and fault time are output and sent to relevant personnel in real time via SMS, email and APP push.

[0014] A further improvement of the technical solution of the present invention is that the expression of the abnormality detection coefficient is: ; ; in, is the abnormality detection coefficient, which is used to determine whether the street light is in normal working condition. is the measurement deviation function, is the current, is the reference value of the current, is the standard deviation of the current, is the current voltage, is the reference value of voltage, is the standard deviation of the voltage, is the current power, is the base value of power, is the standard deviation of power, is the current brightness, is the base value of brightness, is the standard deviation of brightness, is the switch state (on is 1, off is 0), is the expected switching state reference value, is the current energy consumption, is the baseline value of energy consumption, is the standard deviation of energy consumption, The value range is .

[0015] Due to the adoption of the above technical solution, the present invention has the following technical advances compared with the prior art: 1. The present invention provides a road lighting system based on the Internet of Things. The Internet of Things technology enables the road lighting system to intelligently adjust the brightness and switch status of street lamps according to real-time environmental conditions and traffic flow, which not only improves the lighting efficiency but also significantly reduces energy consumption. The light intensity is monitored in real time by a light sensor, and the brightness of the street lamps is intelligently adjusted in combination with a weather condition evaluation index to ensure that appropriate lighting is provided under different weather and lighting conditions. The traffic flow recognition index can be used to identify peak and off-peak traffic periods, dynamically adjust the brightness, and avoid wasting energy during periods of low traffic flow.

[0016] 2. The present invention provides a road lighting system based on the Internet of Things. Through a fault monitoring and alarm module, the working status of street lamps is monitored in real time. Once an abnormality is found, an alarm message is automatically generated and sent to maintenance personnel. The active fault detection and alarm mechanism greatly reduces the need for manual inspections and reduces maintenance costs, allowing managers to quickly locate and repair problems, avoiding traffic safety hazards caused by street lamp failures. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0018] Figure 1 It is a module structure diagram of the present invention; Figure 2 A flow chart for analyzing the trend of light intensity, traffic flow and environmental conditions for the present invention; Figure 3 A flow chart of the present invention for dynamically adjusting the street lighting strategy; Figure 4 A flow chart of discovering and reporting fault information according to the present invention. DETAILED DESCRIPTION

[0019] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution 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 creative work are within the scope of protection of the present invention. Embodiment 1, as Figure 1 , Figure 2 As shown, the present invention provides a road lighting system based on the Internet of Things, including an Internet of Things platform, the Internet of Things platform is communicatively connected with a perception module, a data processing module, a lighting control module, a fault monitoring and alarm module, and a user interaction module, wherein electrical signals are connected between the modules; Perception module, deploy various sensors at street light nodes, use sensors to monitor the light intensity, traffic flow and environmental conditions of the road, including light sensors, radar detectors, temperature and humidity sensors, and realize data transmission and command control between street light nodes and the Internet of Things platform, establish a network between street lights, transmit street light status information and control instructions to the Internet of Things platform, install and deploy various sensors at street light nodes, including light sensors, radar detectors and temperature and humidity sensors, use sensors to monitor the light intensity, traffic flow and environmental conditions of the road, and obtain road and environmental information, including light sensors are used to monitor the current light intensity, radar detectors are used to detect vehicle flow on the road, and through feedback The intensity and time difference of the radio wave determine the presence and speed of the vehicle. The temperature and humidity sensor is used to collect ambient temperature and humidity data. The road and environmental information collected by the sensor is transmitted to the Internet of Things platform through the wireless communication network. The wireless communication network includes low-power wide area network technologies such as Ethernet, LoRa, and ZigBee. A network is established between street lamps through a mesh network to form a distributed perception system. Each street lamp node in the network has communication capabilities and can transmit data and command control to other street lamp nodes or the Internet of Things platform. Based on the network connection established between street lamps, the current status information of the street lamp is collected and uploaded to the Internet of Things platform. The current status information of the street lamp includes brightness, switch status, and energy consumption data. The data processing module is used to preprocess, analyze and mine the data collected by each sensor, extract lighting adjustment features to analyze the changing trends of light intensity, traffic flow and environmental conditions, aggregate data from light sensors, radar detectors, temperature and humidity sensors from the Internet of Things platform, and preprocess the sensor data. The preprocessing includes data cleaning, data smoothing, data format conversion and data fusion steps. Among them, data cleaning removes invalid or abnormal data, data smoothing is used to process slightly fluctuating data caused by sensor noise to improve data accuracy and stability, data format conversion converts sensor raw data into digital signals and standardizes them for subsequent analysis, and data fusion fuses data from different sensors to form more comprehensive road and environmental information. Features related to lighting adjustment are extracted from the preprocessed data, among which the light intensity feature is the average light intensity. , maximum and minimum values. The characteristics of traffic flow are the peak value, average value and fluctuation of traffic flow. The characteristics of environmental conditions are the changing trend of ambient temperature and humidity. Combined with the characteristics of light intensity, traffic flow and environmental conditions related to lighting adjustment, the current brightness, switch status and energy consumption status information of street lamps are integrated. The time series analysis method is used to analyze the changing trend of light intensity, traffic flow and environmental conditions. Combined with the average value, maximum value and minimum value characteristics of light intensity, the lighting evaluation index is calculated. The changing trend of light intensity is analyzed to determine whether the brightness of street lamps needs to be adjusted according to the changes in natural light. Combined with the peak value, average value and fluctuation characteristics of traffic flow, the traffic flow identification index is calculated. The traffic flow data is analyzed to identify peak hours and low flow hours. Combined with the changing trend characteristics of ambient temperature and humidity, the environmental condition evaluation index is calculated to analyze the changing trend of environmental conditions and identify the impact of environmental changes on lighting needs. Furthermore, the expression of the lighting evaluation index is: ; in, It is the lighting evaluation index, which is used to evaluate the deviation between the current lighting conditions and the reference lighting intensity. It is the average value of light intensity, which means the average value of light intensity over a period of time and reflects the overall light level. is the maximum value of light intensity, indicating the maximum value of light intensity during the observation period, reflecting the peak condition of light. is the minimum value of light intensity, indicating the minimum value of light intensity during the observation period, reflecting the minimum conditions of light. The reference light intensity is a preset value used to compare with the actual light intensity to assess whether the actual light level needs to be adjusted. The value range is ,when near hour, When it approaches 0, it means that the current lighting condition is close to the reference lighting intensity and there is no need to adjust the street light brightness. Much greater than hour, The value increases, indicating that the current light intensity is much higher than the reference value, and the street light brightness needs to be lowered. Less than and and When approaching, The value also increases, indicating that the fluctuation of light intensity is small but lower than the benchmark value, and the brightness of the street light needs to be increased; The expression of traffic flow identification index is: ; ; ; in, It is a traffic flow identification index, which is used to evaluate the degree of deviation between the current traffic flow and the benchmark traffic flow. is the peak value of traffic flow during the observation period, is the average value of traffic flow, which means the average value of traffic flow over a period of time and reflects the overall traffic flow level. is the standard deviation of traffic flow, reflecting the fluctuation of traffic flow. The baseline traffic flow is a preset value used to compare with the actual traffic flow to assess whether the actual traffic flow is at a peak or low peak. is the total number of observation periods, For the Traffic flow during the observation period, The value range is ,when near hour, A smaller value indicates that the current traffic flow is close to the benchmark traffic flow and the traffic situation is relatively stable. Much greater than and When smaller, The value increases, indicating that although the traffic flow does not fluctuate much, there are obvious peak periods. Greater than When will increase rapidly, making The value increases significantly, indicating that the current traffic flow is higher than the baseline value and is in the peak period; The expression of environmental condition assessment index is: ; in, It is an environmental condition assessment index, which is used to assess the degree of deviation between the current environmental conditions and the benchmark environmental conditions. is the current ambient temperature, is the current ambient humidity, The reference temperature is a preset value used for comparison with the actual ambient temperature. The reference humidity is a preset value used for comparison with the actual ambient humidity. and represents the maximum and minimum values ​​of the ambient temperature during the observation period, and Indicates the maximum and minimum values ​​of ambient humidity during the observation period, The value range is ,when and near and hour, A smaller value indicates that the current environment condition is close to the baseline environment condition and the lighting demand does not need to be adjusted. or When the difference from the benchmark value is large, the square term of the exponent will increase, indicating that the current environmental conditions deviate greatly from the benchmark conditions and the lighting strategy needs to be adjusted to adapt to the environmental changes. It will penalize large deviations, making The values ​​decrease as the deviation increases, indicating that more attention needs to be paid to the impact of environmental conditions on lighting requirements; The lighting control module uses machine learning algorithms to dynamically adjust the lighting strategy of street lamps based on the output of the data processing module and according to traffic flow and environmental conditions, and intelligently adjusts the brightness and switching time parameters of street lamps; Fault monitoring and alarm module monitors the working status of street lamps in real time, discovers and reports fault information in time, and reduces the frequency of manual inspections and maintenance costs through remote monitoring and maintenance functions; The user interaction module provides a user interaction interface and display platform to view the real-time status, historical data, and alarm information of street lights, which helps managers better understand the operation of street lights and make corresponding adjustments and optimizations.

[0020] Embodiment 2, as Figure 3 , Figure 4 As shown, based on Example 1, the present invention provides a technical solution: Preferably, in the lighting control module, the process of dynamically adjusting the street lamp lighting strategy is: Receive the output from the data processing module, including the pre-processed and feature-extracted data of traffic flow, environmental conditions, and light intensity, and extract the lighting adjustment history data of the street light nodes from the IoT platform and database, including the past street light brightness adjustment records and switching time. Divide the lighting adjustment history data of the street light nodes into a training set and a test set. Use the training set data in combination with the neural network model to build a lighting control prediction model to predict the lighting needs of street lights. Use the test set data to test and evaluate the trained lighting control prediction model. Use the lighting evaluation index, traffic flow recognition index, and weather condition evaluation index as input parameters, and combine the lighting control prediction model to calculate the lighting demand. Control coefficient, to analyze the influence of different factors on lighting demand, divide lighting demand into different lighting control modes according to lighting control coefficient, namely energy-saving lighting control mode, conventional lighting control mode and high-brightness lighting control mode, and match corresponding control threshold for each lighting control mode, set corresponding lighting strategy for each lighting control mode according to the divided lighting control mode, and generate specific control commands according to the lighting control mode, including brightness level and switching time, and send the control commands to each street light node through the Internet of Things platform, the street light node receives the control command, and performs corresponding brightness adjustment and switching operation, and dynamically adjusts the lighting status of the street light; Furthermore, the expression of the lighting control coefficient is: ; in, is the lighting control coefficient, which is used to determine the final lighting strategy. is the light evaluation index, is the reference value of the lighting evaluation index. is the traffic flow identification index, is the benchmark value of the traffic flow identification index, and Indicates the maximum and minimum values ​​of the traffic flow identification index, is the weather condition assessment index, is the benchmark value of the weather condition assessment index, , , is the weight coefficient, when near hour, When it approaches 0, it means that the lighting condition is close to the ideal state and has little impact on the lighting demand. near When , the cubic term approaches 0, indicating that the traffic flow is close to the baseline value and has little impact on lighting demand. keep away When It will approach 0, indicating that the weather conditions are significantly different from the benchmark and have a significant impact on lighting demand; Furthermore, the plurality of lighting control modes correspond to a plurality of control thresholds, wherein the control thresholds include an upper threshold and a lower threshold; Multiple lighting control modes and multiple control thresholds satisfy the following relationship: Energy-saving lighting control mode ; Reduce brightness, possibly setting it to the lowest level and only turning on street lights when necessary; Conventional lighting control mode ; Maintain medium brightness and adjust brightness according to traffic flow and ambient light; High brightness lighting control mode ; Increase brightness to the highest level to ensure road safety and visibility; in, is the lighting control coefficient, is the lower threshold corresponding to the conventional lighting control mode and the upper threshold corresponding to the energy-saving lighting control mode, The lower threshold corresponding to the high-brightness lighting control mode and the upper threshold corresponding to the conventional lighting control mode; In the fault monitoring and alarm module, the process of discovering and reporting fault information is as follows: Continuously monitor the operating status of street lamps, collect the current, voltage and power data of street lamps, analyze the brightness, switch status and energy consumption of street lamps in combination with the current status information of street lamps, detect the usage status of street lamps, conduct comprehensive analysis of the current, voltage, power and brightness, switch status and energy consumption of street lamps, calculate the abnormal detection coefficient, determine whether the street lamps are in normal working condition, and identify the fault type when the data is abnormal. According to the determined fault type, analyze the severity of the street lamp fault, set a high level of maintenance priority for faults with high severity, mark the identified fault-related data, generate alarm information, locate the specific location of the faulty street lamp, output the fault type, maintenance priority, fault data node and fault time, and send them to relevant personnel in real time via SMS, email, and APP push; Furthermore, the expression of the anomaly detection coefficient is: ; ; in, is the abnormality detection coefficient, which is used to determine whether the street light is in normal working condition. is the measurement deviation function, is the current, is the reference value of the current, is the standard deviation of the current, is the current voltage, is the reference value of voltage, is the standard deviation of the voltage, is the current power, is the base value of power, is the standard deviation of power, is the current brightness, is the base value of brightness, is the standard deviation of brightness, is the switch state (on is 1, off is 0), is the expected switching state reference value, is the current energy consumption, is the baseline value of energy consumption, is the standard deviation of energy consumption, The value range is , when all measurements are close to their baseline values, The value will be close to 0, indicating that the street light is in normal working condition. When any measured value deviates significantly from its baseline value, The value will increase, indicating that there is an abnormality in the street light. The item will increase significantly when the switch state is not as expected, even if other values ​​are normal. The item will be checked when the energy consumption data is abnormal. The value has a greater impact, if If the value exceeds the preset threshold, the system will trigger an alarm and perform fault diagnosis. The value can be used to determine the severity of the fault and decide the maintenance priority.

[0021] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A road lighting system based on the Internet of Things, including an Internet of Things platform, characterized in that: The Internet of Things platform is communicatively connected with a perception module, a data processing module, a lighting control module, a fault monitoring and alarm module, and a user interaction module, wherein electrical signals are connected between the modules; The perception module deploys various sensors at street light nodes and uses the sensors to monitor the light intensity, traffic flow and environmental conditions of the road. The sensors include light sensors, radar detectors, temperature and humidity sensors, and establish a network between street lights to transmit the status information and control instructions of the street lights to the Internet of Things platform. The data processing module is used to pre-process, analyze and mine the data collected by each sensor, extract lighting adjustment features and analyze the changing trends of light intensity, traffic flow and environmental conditions; The lighting control module uses a machine learning algorithm to dynamically adjust the lighting strategy of the street lamp based on the output of the data processing module and according to factors such as traffic flow and environmental conditions; The fault monitoring and alarm module monitors the working status of the street lamp in real time, and promptly detects and reports fault information; The user interaction module provides a user interaction interface and display platform to view the real-time status, historical data, and alarm information of the street lamp.

2. The road lighting system based on the Internet of Things according to claim 1, characterized in that: In the perception module, the process of monitoring the road's light intensity, traffic flow, and environmental conditions is as follows: Install and deploy a variety of sensors on street light nodes, including light sensors, radar detectors, and temperature and humidity sensors; Use sensors to monitor the light intensity, traffic flow and environmental conditions of the road to obtain road and environmental information. The light sensor is used to monitor the current light intensity, the radar detector is used to detect the vehicle flow on the road, and the temperature and humidity sensor is used to collect environmental temperature and humidity data. The road and environment information collected by sensors is transmitted to the IoT platform through the wireless communication network, and a network is established between street lights through the mesh network to form a distributed perception system; Based on the network connection established between street lamps, the current status information of the street lamps is collected and uploaded to the Internet of Things platform. The current status information of the street lamps includes brightness, switch status, and energy consumption data.

3. The road lighting system based on the Internet of Things according to claim 2 is characterized in that: In the data processing module, the process of analyzing the changing trends of light intensity, traffic flow and environmental conditions is as follows: Aggregate data from light sensors, radar detectors, and temperature and humidity sensors from the IoT platform and preprocess the sensor data, including data cleaning, data smoothing, data format conversion, and data fusion steps; Extract features related to lighting regulation from the preprocessed data, where the light intensity features are the average, maximum, and minimum values ​​of the light intensity, the traffic flow features are the peak, average, and fluctuation of the traffic flow, and the environmental condition features are the changing trends of the ambient temperature and humidity; Combined with the characteristics of light intensity, traffic flow and environmental conditions related to lighting regulation, the current brightness, switch status and energy consumption status information of street lamps are integrated, and the time series analysis method is used to analyze the changing trends of light intensity, traffic flow and environmental conditions; Combined with the average, maximum and minimum characteristics of light intensity, the lighting evaluation index is calculated, the changing trend of light intensity is analyzed, and it is determined whether the brightness of street lamps needs to be adjusted according to the changes in natural light. Combined with the peak, average and fluctuation characteristics of traffic flow, the traffic flow identification index is calculated, and traffic flow data is analyzed to identify peak and low-flow periods. Combined with the changing trend characteristics of ambient temperature and humidity, the environmental condition evaluation index is calculated, the changing trend of environmental conditions is analyzed, and the impact of environmental changes on lighting needs is identified.

4. The road lighting system based on the Internet of Things according to claim 3 is characterized in that: The expression of the illumination evaluation index is: ； in, It is the lighting evaluation index, which is used to evaluate the deviation between the current lighting conditions and the reference lighting intensity. is the average light intensity, is the maximum value of light intensity, is the minimum value of light intensity, is the baseline light intensity; The expression of the traffic flow identification index is: ； ； ； in, It is a traffic flow identification index, which is used to evaluate the degree of deviation between the current traffic flow and the benchmark traffic flow. is the peak value of traffic flow during the observation period, is the average value of traffic flow, is the standard deviation of traffic flow, is the base traffic flow, is the total number of observation periods, For the Traffic flow during each observation period; The expression of the environmental condition evaluation index is: ； in, It is an environmental condition assessment index, which is used to assess the degree of deviation between the current environmental conditions and the benchmark environmental conditions. is the current ambient temperature, is the current ambient humidity, The reference temperature is a preset value used for comparison with the actual ambient temperature. is the base humidity, and represents the maximum and minimum values ​​of the ambient temperature during the observation period, and Indicates the maximum and minimum values ​​of ambient humidity during the observation period.

5. The road lighting system based on the Internet of Things according to claim 4 is characterized in that: In the lighting control module, the process of dynamically adjusting the street lighting strategy is as follows: Receive the output from the data processing module, including the pre-processed and feature-extracted data of traffic flow, environmental conditions, and light intensity, and extract the lighting adjustment history data of the street light nodes from the IoT platform and database, including the past street light brightness adjustment records and switching time, and divide the lighting adjustment history data of the street light nodes into a training set and a test set; Use the training set data combined with the neural network model to build a lighting control prediction model to predict the lighting demand of street lamps, and use the test set data to test and evaluate the trained lighting control prediction model; The lighting evaluation index, traffic flow identification index and weather condition evaluation index are used as input parameters, and the lighting control coefficient is calculated in combination with the lighting control prediction model to analyze the impact of different factors on lighting demand. According to the lighting control coefficient, the lighting demand is divided into different lighting control modes, namely energy-saving lighting control mode, conventional lighting control mode and high-brightness lighting control mode, and a corresponding control threshold is matched for each lighting control mode; According to the divided lighting control modes, set the corresponding lighting strategy for each lighting control mode, and generate specific control commands according to the lighting control mode, including brightness level and switching time, and send the control commands to each street light node through the Internet of Things platform; The street light node receives the control command and performs corresponding brightness adjustment and switching operations to dynamically adjust the lighting status of the street light.

6. The road lighting system based on the Internet of Things according to claim 5, characterized in that: The expression of the lighting control coefficient is: ； in, is the lighting control coefficient, is the light evaluation index, is the reference value of the lighting evaluation index. is the traffic flow identification index, is the benchmark value of the traffic flow identification index, and Indicates the maximum and minimum values ​​of the traffic flow identification index, is the weather condition assessment index, is the benchmark value of the weather condition assessment index. , , is the weight coefficient.

7. The road lighting system based on the Internet of Things according to claim 6, characterized in that: The plurality of lighting control modes correspond to the plurality of control thresholds, wherein the control thresholds include an upper threshold and a lower threshold; The plurality of lighting control modes and the plurality of control thresholds satisfy the following relationship: Energy-saving lighting control mode ; Conventional lighting control mode ; High brightness lighting control mode ; in, is the lighting control coefficient, is the lower threshold corresponding to the conventional lighting control mode and the upper threshold corresponding to the energy-saving lighting control mode, It is the lower threshold corresponding to the high-brightness lighting control mode and the upper threshold corresponding to the conventional lighting control mode.

8. The road lighting system based on the Internet of Things according to claim 7, characterized in that: In the fault monitoring and alarm module, the process of discovering and reporting fault information is as follows: Continuously monitor the operating status of street lights, collect the current, voltage, and power data of street lights, analyze the brightness, switch status, and energy consumption of street lights based on the current status information of street lights, and detect the usage status of street lights; Comprehensively analyze the street lamp current, voltage, power, brightness, switch status and energy consumption, calculate the abnormality detection coefficient, determine whether the street lamp is in normal working condition, and identify the fault type when the data is abnormal; Analyze the severity of street light faults based on the identified fault types and set high-level maintenance priorities for faults with high severity; The identified fault-related data is marked and an alarm message is generated to locate the specific location of the faulty street lamp. The fault type, maintenance priority, fault data node and fault time are output and sent to relevant personnel in real time via SMS, email and APP push.

9. The road lighting system based on the Internet of Things according to claim 8, characterized in that: The expression of the anomaly detection coefficient is: ； ； in, is the abnormality detection coefficient, which is used to determine whether the street light is in normal working condition. is the measurement deviation function, is the current, is the reference value of the current, is the standard deviation of the current, is the current voltage, is the reference value of voltage, is the standard deviation of the voltage, is the current power, is the base value of power, is the standard deviation of power, is the current brightness, is the base value of brightness, is the standard deviation of brightness, is the switch state, is the expected switching state reference value, is the current energy consumption, is the baseline value of energy consumption, is the standard deviation of energy consumption.

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