Internet of Things intelligent lighting management system and method

Abstract

The invention discloses an Internet of Things intelligent lighting management system and method, and relates to the field of urban lighting management. An illumination state acquisition module is used for acquiring real-time state data of an illumination device; the cable health state acquisition module is used for acquiring cable data of the lighting device; the electricity acquisition module is used for acquiring electricity consumption of the lighting device; the Internet of Things intelligent power supply edge server is used for receiving the real-time state data of the lighting device and the cable data and monitoring the lighting state and the cable health state in real time; the power of the lighting device is predicted. According to the invention, real-time, rapid and accurate monitoring and management can be carried out on the lighting system in the lighting area, and the energy-saving efficiency is improved.

Description

Technical Field

[0001] The present invention relates to the field of urban lighting management, and in particular to an Internet of Things smart lighting management system and method. Background Art

[0002] With the development of the times, the pace of urban modernization construction is accelerating, and the demand for urban road lighting and urban lighting projects is also greater. The contradiction between energy supply and demand is becoming more and more prominent, and the requirements for energy saving and green lighting are becoming more and more urgent and higher. The traditional methods of manual control and clock control of urban lighting systems can no longer meet the requirements. How to make full use of high-tech means to solve the above contradictions has become a new and urgent topic in the current lighting control field.

[0003] Urban street lighting is an indispensable public facility in people's daily life. All parts of the country are facing various problems caused by power shortages. Faced with the power supply shortage, street lamp inspection is a manpower-intensive task for municipal departments. It is difficult to manage, has a low safety factor, and a large workload. It cannot detect the working conditions of the entire line street lamps. There is no street lamp condition monitoring. The existing lighting facility management work mainly adopts the manual inspection mode, which not only has a large workload, but also wastes manpower, material resources, and financial resources. The basis for failure mainly comes from the reports of inspection personnel and citizen complaints, which lack initiative, timeliness and reliability. It cannot monitor the operation status of street lamps in the whole city in real time, accurately and comprehensively, and lacks an effective fault warning mechanism. Summary of the invention

[0004] In view of the above-mentioned defects in the prior art, the technical problem to be solved by the present invention is how to perform real-time, accurate and comprehensive monitoring of the lighting system and timely fault warning.

[0005] To achieve the above objectives, the first aspect of the present invention provides an IoT smart lighting management system, comprising:

[0006] A lighting status acquisition module is used to collect real-time status data of lighting devices;

[0007] A cable health status collection module is used to collect cable data of lighting devices;

[0008] A power collection module, used to collect the power consumption of the lighting device;

[0009] The IoT intelligent power supply edge server is used to receive real-time status data and cable data of lighting devices, monitor the lighting status and cable health status in real time, and predict the power consumption of lighting devices.

[0010] Furthermore, the management system includes a cloud platform for storing historical status data of the lighting devices.

[0011] The second aspect of the present invention provides an IoT smart lighting management method, which is implemented based on the IoT smart lighting management system described in the first aspect of the present invention, and includes:

[0012] Collect real-time status data of lighting devices;

[0013] Collect cable data for lighting installations;

[0014] To collect the electricity consumption of lighting devices;

[0015] Receive real-time status data and cable data of lighting devices, monitor the lighting status and cable health status in real time; and predict and calculate the power consumption of lighting devices.

[0016] Furthermore, the method for monitoring the lighting status includes:

[0017] The online status of the lighting devices in the lighting area is obtained. When multiple lighting devices in the lighting area are offline and the offline duration exceeds a threshold time, an alarm signal is issued.

[0018] Furthermore, the method for monitoring the health status of the cable includes:

[0019] Compare the collected real-time cable data with the initial cable data, record the changes in the cable data, and determine the current health status of the cable based on the changes.

[0020] Furthermore, the management method also includes an environmental monitoring process, in which the acquired temperature and humidity data are used to calculate an environmental change curve through a differential algorithm and a prediction algorithm, and when the environmental change curve is abnormal, an abnormal weather alarm is issued.

[0021] Compared with the prior art, the present invention has the following technical effects:

[0022] The present invention is based on Internet of Things technology, with the control center as the core, the terminal controller as the focus, communication equipment and network as the link, integrating wireless communication networks, computer control systems, Internet of Things technology, geographic information system technology, and a powerful computing algorithm as the guarantee for a programmed, modular, networked, and intelligent intelligent centralized management and control system, providing users with a second-level early warning and automatic control function for edge space faults.

[0023] The concept, specific structure and technical effects of the present invention will be further described below in conjunction with the accompanying drawings to fully understand the purpose, characteristics and effects of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 It is a structural principle diagram of an Internet of Things smart lighting management system in a specific embodiment of the present invention;

[0025] Figure 2 It is a schematic diagram of the connection structure of an IoT smart power supply edge server in a specific embodiment of the present invention;

[0026] Figure 3 It is a circuit structure schematic diagram of an IoT smart power supply edge server in a specific embodiment of the present invention;

[0027] Figure 4 It is a functional interface diagram of an IoT smart lighting management system in a specific embodiment of the present invention. DETAILED DESCRIPTION

[0028] The following describes the embodiments of the present invention by specific examples, and those skilled in the art can easily understand other advantages and effects of the present invention from the contents disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed in various ways based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that the following embodiments and features in the embodiments can be combined with each other without conflict.

[0029] It should be noted that the illustrations provided in the following embodiments are only used to illustrate the basic concept of the present invention in a schematic manner, and thus the illustrations only show components related to the present invention rather than being drawn according to the number, shape and size of components in actual implementation. In actual implementation, the type, quantity and proportion of each component may be changed arbitrarily, and the component layout may also be more complicated.

[0030] While some exemplary embodiments of the present invention have been described for the purpose of illustration, it should be understood that the present invention may be implemented in other ways not specifically shown in the drawings.

[0031] like Figure 1 As shown, in a specific embodiment, a smart lighting management system based on the Internet of Things is provided, including a lighting status acquisition module, a cable health status acquisition module, a power acquisition module, an Internet of Things intelligent power supply edge server and a cloud platform, wherein the lighting status acquisition module is used to collect real-time status data of the lighting device; the cable health status acquisition module is used to collect cable data of the lighting device; the power acquisition module is used to collect power consumption of the lighting device; the Internet of Things intelligent power supply edge server is used to receive real-time status data and cable data of the lighting device, monitor the lighting status and cable health status in real time, and predict the power consumption of the lighting device; the cloud platform is used to store historical status data of the lighting device; in this embodiment, the Internet of Things intelligent power supply edge server adopts an MCU chip, and its model is STMH743.

[0032] like Figure 2-4As shown, the power parameters include voltage, current, power, etc.; the switch input detection is mainly used to detect the switch state, such as door opening / closing, which can realize door opening alarm, high / low water level, which can realize water level detection alarm, etc. The IoT smart power supply edge server can output 6 loops, and other devices can be powered on and off through the control loop. Analog quantity acquisition refers to the process of converting the continuously changing signal of physical quantity into digital signal so that the IoT smart power supply edge server can process and record these signals.

[0033] Based on the IoT-based smart lighting management system described in the above embodiment, in another embodiment, an IoT-based smart lighting management method is provided, comprising the following steps:

[0034] Data collection step, collecting real-time status data of lighting devices, cable data and power consumption;

[0035] Monitor the lighting status and install a single lamp controller in the lamp pole to collect the lamp pole line status including voltage, current, power and other power parameters. The single lamp controller communicates with the IoT smart power supply edge server through the 4G / 5G network to obtain the online status of the lighting devices in the lighting area. When 3 or more single lamp controllers are offline in the lighting area and the continuous offline time exceeds the threshold time, an alarm signal is issued.

[0036] Monitor the health status of the cable, compare the collected real-time cable data with the initial cable data, and record the changes in the cable data when the collected data changes. According to the cable laying time and life, after multiple comparisons, judge the current health status of the cable. If the changes continue to increase, it is judged that the health status of the cable has declined and needs to be paid special attention to, and notify manual maintenance and troubleshooting. The cable data described in this embodiment includes load current and voltage, and the monitoring process of the cable health status is realized by load current monitoring and voltage monitoring.

[0037] The load current monitoring method comprises:

[0038] Abnormal current change monitoring: Real-time recording of current values ​​under normal cable operation. When the load current continues to be out of the normal current and voltage range, for example, the current is higher than the normal value and the current value continues to increase, that is, the load current is abnormal, indicating that some parts of the cable are overheating, short-circuiting or having poor contact, which may affect the health of the cable.

[0039] Unbalanced current monitoring: Real-time monitoring of three-phase current. When the three-phase current is unbalanced, it indicates that there are problems in the cable or circuit, such as insulation fault, grounding fault or uneven load distribution.

[0040] The voltage monitoring method comprises:

[0041] Voltage drop monitoring: Real-time monitoring of cable voltage. When the cable voltage drops too much, it indicates that the cable line may have high impedance contact problems, or the conductors and joints of the cable may be aging or corroded.

[0042] Voltage fluctuation monitoring is a real-time monitoring of cable voltage fluctuations. When the voltage waveform fluctuates abnormally, it indicates that there are local defects or electrical interference in the cable, which may cause further damage to the cable.

[0043] Predict power consumption, obtain lighting power and power consumption time, and calculate power consumption, the power consumption is:

[0044] Electricity consumption = power consumption × electricity consumption time;

[0045] The collected data is stored in an accessible database, and the electricity consumption curve is obtained through the least squares method. The law of data change is obtained based on historical data, and a linear model is established to predict future electricity consumption. Then the energy saving rate can be calculated. By improving the energy saving rate, the green, energy-saving, and low-carbon development of the city can be promoted, and support can be provided for achieving the carbon peak and carbon neutrality goals in the field of urban lighting. The energy saving rate is:

[0046] Energy saving rate (%) = [(predicted power consumption - actual power consumption) / predicted power consumption] * 100%;

[0047] In this embodiment, the least square method (linear regression) includes:

[0048] A linear model is built based on historical data to predict future electricity consumption. Assume that there is a set of historical data, where x represents time (such as year, month, date), and y represents the corresponding electricity consumption. For example, time (x) = [1, 2, 3, 4, 5], and electricity consumption (y) = [100, 150, 200, 250, 300].

[0049] The linear model between electricity consumption y and time x is constructed as: y = a + bx;

[0050] Where a and b are coefficients that can minimize the sum of squares of the error between the predicted value y' and the actual value y, that is, satisfy the following formula:

[0051]

[0052] The coefficients a and b are calculated by differentiating the sum of squared errors and setting it to zero, and the optimal calculation formulas for a and b are obtained as follows:

[0053]

[0054]

[0055] Monitor the environmental status and calculate the environmental change curve using differential and prediction algorithms based on the acquired temperature and humidity data. When the environmental change curve is abnormal, an abnormal weather alert will be issued.

[0056] In this embodiment, the differential algorithm is used to calculate the amount of change in data in order to identify trends or anomalies, and the process includes:

[0057] Data collection steps: Get temperature and humidity data from sensors (such as DHT series, BME280) in real time;

[0058] Calculate the difference step: Calculate the difference of temperature and humidity data at consecutive time points to obtain the temperature change ΔT t and humidity change ΔH t for:

[0059] ΔT t =T t -T t-1 ;

[0060] ΔH t =H t -H t-1 ;

[0061] Among them, T t and H t are the temperature data and humidity data at time t, T t-1 and H t-1 It is the temperature data and humidity data at time t-1.

[0062] Analyze differential data steps: Observe the changing trends of temperature and humidity to determine whether there are abnormal fluctuations.

[0063] The prediction algorithm is used to predict future temperature and humidity changes based on historical data (differential data). In this embodiment, the least squares method is used to fit the temperature change curve and the humidity change curve. The horizontal axis (x-axis) of the curve represents time, which can be a specific timestamp (such as date and time) or an index in the time series (such as the first data point, the second data point, etc.), and the vertical axis (y-axis) represents temperature and humidity.

[0064] Through the temperature and humidity change curve, the change trends of temperature and humidity can be obtained, including:

[0065] Rapid temperature drop: If the temperature change curve shows an obvious rapid downward trend, this may indicate the arrival of rain and snow.

[0066] Sub-zero conditions: During the winter months, if the temperature drops dramatically and falls below freezing, this trend is especially likely to indicate snowfall.

[0067] Rapid increase in humidity: Before rain or snow falls, the humidity in the air usually rises rapidly. If the humidity curve shows a sudden and significant increase in humidity values, and it approaches 100%, this may indicate that there is sufficient water vapor in the air and precipitation is about to occur.

[0068] Stable high humidity conditions: In rainy and snowy weather, humidity usually remains at a high level. If the humidity curve quickly reaches and remains at a high level in a short period of time, it indicates that precipitation may be occurring or about to occur.

[0069] If the temperature drops and the humidity rises significantly at the same time, this synchronous change is usually a typical feature of rain and snow. When cold air arrives, the temperature drops, the water vapor in the air condenses into rain or snow, causing the humidity to rise rapidly.

[0070] Humidity lags temperature: Sometimes the increase in humidity will slightly lag the decrease in temperature because the cold air will first lower the temperature, and then the humidity will increase significantly as the cold air moves further in.

[0071] The temperature curve is V-shaped: the temperature curve drops sharply and then rises quickly, which may reflect a short-term precipitation process, such as rain or snow showers.

[0072] The temperature curve is U-shaped: If the temperature curve drops and remains at a low level for a long time, it may indicate continuous snowfall.

[0073] Steep rise of the humidity curve: If the humidity curve shows a steep rise when the temperature drops and reaches a high value quickly, it indicates that rain or snow is about to fall.

[0074] In the fault warning step, the IoT smart power supply edge server collects the voltage, current, power and other parameters of the single lamp controller in the lamp pole in real time, and filters the collected data through the first-order low-pass filtering process and the mean filtering process to remove noise and improve the accuracy of the data. The single controller data processed by the filter is compared with the original data in real time, and the comparison results are stored. If the data is abnormal after 3 or more comparisons, a fault warning is issued.

[0075] The low-pass filtering process is:

[0076] y[n]=b o x[n]+b1x[n-1]+b2x[n-2]-a1y[n-1]-a2;

[0077] y[n-2];

[0078] Where x[n] is the input signal, y[n] is the output signal, and b o , b1, b2 are forward coefficients, a1, a2 are feedback coefficients. The input signal can be filtered through this differential equation, and the obtained output signal is the low-frequency component of the input signal.

[0079] The mean filtering process is a weighted mean filtering process, which is:

[0080] Each sampling value is added with different weight coefficients (the fresher the sampling value, the greater the weight), that is:

[0081] Y=(C1-y1+C2-y2+C3-y3+…+Cm-ym);

[0082] Wherein, the current time is T, then y1 represents the output signal at time T-1, y2 represents the output signal at time T-2, and y3 represents the output signal at time T-3. m is generally taken as 4. In this embodiment, when m=4, the weight coefficient is: C1=e 3t / R,C2=e -2t / R,C3=e -t / R, C4 = 1 / R, where R = e -3t +e -2t +e -t +1.

[0083] The weighted mean filtering process can coordinate the contradiction between system smoothness and sensitivity, improve sensitivity without reducing smoothness, and is more suitable for objects with large pure lag.

[0084] In the power monitoring step, the MCU collects the voltage, current, power and other data of the power box in real time, and filters the collected data through the first-order low-pass filter and mean filter algorithm to remove noise and improve the accuracy of the data. The filtered power box data is compared with the original data in real time, and the comparison results are stored. If the data is abnormal for many times, it is considered that the power box has a device or circuit breaker tripped.

[0085] In terms of edge collection: MCU integrates the data of the single controller of the IoT smart power supply edge server, and compares and analyzes the data with the data collected by the IoT smart power supply edge server. If the conditions are met, the MCU uploads the data to the database. If the data difference is too large, and the data difference occurs multiple times, an alarm is issued. The power parameters of the single lamp controller in the lamp pole can be directly collected through the IoT smart power supply edge server to realize edge collection.

[0086] The above embodiments are merely illustrative of the principles and effects of the present invention, and are not intended to limit the present invention. Anyone familiar with the art may modify or alter the above embodiments without departing from the spirit and scope of the present invention. Therefore, all equivalent modifications or alterations made by a person of ordinary skill in the art without departing from the spirit and technical concept disclosed by the present invention shall still be covered by the claims of the present invention.

Claims

1. The IoT smart lighting management system is characterized by: include: A lighting status acquisition module is used to collect real-time status data of lighting devices; A cable health status collection module is used to collect cable data of lighting devices; A power collection module, used to collect the power consumption of the lighting device; The IoT intelligent power supply edge server is used to receive real-time status data and cable data of lighting devices, monitor the lighting status and cable health status in real time, and predict the power consumption of lighting devices.

2. According to claim 1, the smart lighting management system based on the Internet of Things is characterized in that: The management system includes a cloud platform for storing historical status data of the lighting device.

3. The IoT smart lighting management method is characterized by: The implementation of the smart lighting management system based on the Internet of Things according to any one of claims 1-2 includes: Collect real-time status data of lighting devices; Collect cable data for lighting installations; To collect the electricity consumption of lighting devices; Receive real-time status data and cable data of lighting devices, monitor the lighting status and cable health status in real time; and predict and calculate the power consumption of lighting devices.

4. According to the method for intelligent lighting management based on the Internet of Things in claim 3, it is characterized in that: The method for monitoring the lighting status comprises: The online status of the lighting devices in the lighting area is obtained. When multiple lighting devices in the lighting area are offline and the offline duration exceeds a threshold time, an alarm signal is issued.

5. According to the method for intelligent lighting management based on the Internet of Things in claim 3, it is characterized in that: The method for monitoring the health status of the cable comprises: Compare the collected real-time cable data with the initial cable data, record the changes in the cable data, and determine the current health status of the cable based on the changes.

6. According to claim 3, the method for intelligent lighting management based on the Internet of Things is characterized in that: The management method also includes an environmental monitoring process, in which the acquired temperature and humidity data are used to calculate an environmental change curve through a differential algorithm and a prediction algorithm. When the environmental change curve is abnormal, an abnormal weather alarm is issued.

Images