High-pole lamp illumination intelligent control system and method

Abstract

The invention provides a high-pole lamp illumination intelligent control system and method, and the system comprises an intelligent illumination control cloud box and an illumination control management system, and the intelligent illumination control cloud box is in wireless communication connection with the illumination control management system. According to the invention, by operating the illumination management platform, the mobile phone APP and the illumination intelligent control cloud box to send out instructions, independent control, batch control and plan control are carried out on field lamps, time-phased and regional control can also be carried out, and intelligent monitoring control and fine management of illumination of the high-pole lamp are realized. Meanwhile, the functions of map function, equipment inspection, illumination control, electric quantity overreading, gateway inspection, equipment timing, electricity price clock, batch parameter adjustment, energy efficiency management, alarm and the like can be realized, and the functions of traffic flow monitoring, safety monitoring, light illumination and coverage control based on image recognition and neural network technologies can be realized; the method is of great significance in improving the lighting efficiency, reducing the energy consumption and realizing the intelligent management of urban traffic.

Description

Technical Field

[0001] The present invention and the field of intelligent lighting technology specifically relate to an intelligent control system and method for high pole lamp lighting. Background Art

[0002] At present, the global energy problem is becoming increasingly prominent. Traditional lighting control systems can no longer meet the needs of refined management and energy conservation and emission reduction. Lighting energy-saving control is an important direction for energy conservation. The most common way is to use a large number of lamps on high poles for long-term lighting. The lighting brightness and lighting height of this method are often fixed, which has many problems.

[0003] The switch control method of lighting equipment is single, and the equipment control parameters are difficult to modify according to the actual situation. The operating parameters of the lighting equipment such as voltage, current, and electric energy cannot be monitored. When a lighting equipment fails, it cannot be promptly alarmed and handled, and the operating data of the lighting equipment cannot be centrally statistically managed.

[0004] On the one hand, the illumination of the lamps cannot be adjusted, and the illumination and coverage cannot be adjusted according to the number of vehicles and personnel or the working time; on the other hand, the actual lighting conditions of the moving vehicles and pedestrians cannot be realized, that is, the dynamic intelligent lighting control of turning on the lights when people come and turning off the lights when people leave cannot be realized according to the changes in the moving vehicles and pedestrians, let alone the control of the dynamic partition intelligent lighting of the square according to the regional distribution of moving vehicles and pedestrians. These defects not only easily cause power loss and light pollution, but also have a direct impact on the service life of the lamps, increasing various maintenance and maintenance costs.

[0005] High pole lamps are located in industrial operation areas and various traffic roads in cities, and their unique location is not fully utilized to perform other functions besides lighting, such as traffic flow monitoring and safety monitoring.

[0006] In the prior art, this fixed lighting mode with poor flexibility, coupled with the high pole and high lighting power of the high pole lamp, wastes a lot of electricity resources and public resources. In addition, due to the existence of the fixed lighting mode, the light pollution problem is serious, which not only harms people's health, but also has an irreversible impact on the ecological environment. Therefore, it is of great significance to develop an intelligent control system and method for high pole lamps with reasonable design, easy use and simple structure to improve lighting efficiency, reduce energy consumption and realize intelligent lighting management. Summary of the invention

[0007] The technical problem to be solved by the present invention is to provide a high pole lamp lighting intelligent control system and method and control method thereof, so as to solve the deficiencies of the prior art mentioned in the above background technology.

[0008] To solve the above technical problems, the embodiments of the present invention provide the following technical solutions:

[0009] A high pole lamp lighting intelligent control system, comprising a plurality of intelligent lighting cloud boxes, a lighting management cloud platform, a lighting control background, a data monitoring screen, a mobile terminal and a database server, wherein the plurality of intelligent lighting cloud boxes are electrically connected to corresponding circuit loops of a plurality of lamps, the intelligent lighting cloud boxes are wirelessly connected to the lighting management cloud platform, the lighting management cloud platform is wirelessly connected to the lighting control background, the data monitoring screen and the database server, and the lighting management cloud platform is wirelessly connected to the mobile terminal;

[0010] The intelligent lighting cloud box includes a cloud box control mainboard and a liquid crystal display screen electrically connected to the cloud box control mainboard, a GPS and 4G / 5G Internet of Things integrated module, a power module, a high-power relay integrated module, a local on / off light button, a local / remote selection button, a 4-position aviation plug socket and an image acquisition module, wherein the GPS and 4G / 5G Internet of Things integrated module is wirelessly connected to the lighting management cloud platform;

[0011] The power module is electrically connected to the leakage protection switch, the GPS and 4G / 5G Internet of Things integrated module is electrically connected to the GPS positioning antenna interface and the 4G / 5G Internet of Things antenna interface, and the high-power relay integrated module is electrically connected to the input / output terminal block;

[0012] Among them, the lighting management cloud platform and lighting control background are servers or industrial computers.

[0013] The present invention also provides a control method for the above-mentioned high pole lamp lighting intelligent control system, comprising:

[0014] The real-time working parameters of the circuits monitored by the monitoring modules set on the corresponding circuit loops of multiple lamps are sent to the cloud box control mainboard of the intelligent lighting cloud box. The cloud box control mainboard wirelessly sends the real-time working parameter data to the lighting management cloud platform through GPS and 4G / 5G Internet of Things integrated modules. The lighting management cloud platform then forwards the real-time working parameter data to the lighting control background, data monitoring large screen, mobile terminal and database server;

[0015] The lighting management cloud platform is linked with the smart lighting cloud box. The GPS and 4G / 5G IoT integrated modules of the smart lighting cloud box automatically obtain longitude and latitude information. The cloud box control mainboard controls the GPS and 4G / 5G IoT integrated modules to wirelessly report progress data to the lighting management cloud platform. The real-time location of the equipment is retrieved through Amap or Baidu Maps, and the operating status, power consumption data, circuit working status, real-time current, voltage and power information of the circuit lighting status, and historical usage data of the high pole lamp are displayed. The high pole lamp can also be switched on and off and monitored for operation.

[0016] The image acquisition module acquires environmental video images at a predetermined distance, extracts and stores the motion trajectory information of moving vehicles and pedestrians, and establishes a motion trajectory database; at the same time, it detects specific targets based on environmental video images, and performs remote sound and light warning broadcasts for vehicles and pedestrians if dangerous factors are detected; wherein the motion trajectory database includes the image acquisition time, image area, and weather at the time of image acquisition; the specific targets include but are not limited to obstacles, subsidence areas, and accident sites;

[0017] Based on the motion trajectory information of the moving vehicles and pedestrians in the motion trajectory database, the total number of moving vehicles and pedestrians within a predetermined distance range of the target high pole lamp is determined, and a neural network training sample library is formed in combination with the motion trajectory database information; based on the neural network training sample library, a pedestrian and vehicle flow prediction model based on a neural network is constructed;

[0018] Based on the pedestrian and vehicle flow prediction model, the total number of traveling vehicles and pedestrians is predicted, and the brightness value of the target high pole lamp and the light coverage value are determined; at the same time, the pedestrian and vehicle flow information is wirelessly pushed to the GIS map system as the subsequent vehicle road condition broadcast data.

[0019] Optionally, the switching control and operation monitoring of the high pole lamp specifically includes:

[0020] Calculate the time of darkness and daybreak in the current time period, control the automatic turning on of lights at night and automatic turning off of lights at daybreak, adjust the time of turning on and off lights in advance, manually correct the longitude and latitude, correct the synchronization of equipment time, and set different switching times of lighting circuits; and automatically switch the equipment offline to local mode to execute the preset offline longitude and latitude automatic switching operation by judging whether it is communicating normally with the cloud platform server;

[0021] By remotely opening and closing all circuits or a single circuit of the equipment, monitoring the working status of the relay, and monitoring the voltage and current values ​​of the equipment through the circuit monitoring module, it is determined whether the circuit of the lamp is actually opened or closed; by setting the current, voltage, power and normal working range values ​​of the lamp equipment in different circuits, the fault of the lamp and the circuit is determined, and the database server records the fault and notifies the data monitoring screen and the lighting control background, automatically combining the configured trigger alarm conditions to realize real-time alarm and generate a fault work order for the lamp, and automatically dispatching the work order to the preset operation and maintenance management personnel, and notifying the operation and maintenance personnel to handle the fault in time through mobile terminal message reminders and SMS notifications;

[0022] Among them, the control of automatically turning on the lights at night and automatically turning off the lights at dawn includes two control methods: local control and remote control. The local control is through the lighting control cloud box local control button switch control loop relay to turn on and off; the remote control is through the lighting control background or mobile phone terminal, issuing switch instructions, using MQTT communication protocol attributes to issue attributes, and finally the cloud box control mainboard receives the instructions, controls the high-power relay integrated module to turn on or off, and then controls the switch of the light.

[0023] Optionally, it also includes: the cloud box main control board receives the real-time working parameters of the circuit monitored by the monitoring module set on the corresponding circuit loop of the lamp, records the power consumption current, voltage, and power information of the circuit and calculates the power consumption of the corresponding high pole lamp, and wirelessly reports the power consumption to the lighting management cloud platform through GPS and 4G / 5G Internet of Things integration modules, and stores the power consumption data of the device through the database server; through the electricity fee configuration module of the lighting control background, the electricity price information is input to automatically calculate the electricity cost, and support annual, monthly, weekly, and daily electricity consumption statistics query.

[0024] Optionally, after the lighting control background receives the real-time working parameter data, it also processes the data statistically, and collects statistics on the overall power consumption, daily power consumption, year-on-year power consumption, month-on-month power consumption, and time-sharing power consumption of single equipment, single circuit, multiple equipment, projects, and regional equipment, and displays the equipment energy efficiency information in real time through the data monitoring large screen.

[0025] Optionally, it also includes selecting multiple smart lighting cloud boxes that need to be adjusted through the lighting control background when the smart lighting cloud box is in an online communication state, adjusting the switch control instructions or device parameters, and sending the attributes to different cloud box control mainboards through the MQTT protocol, and the cloud box control mainboard executes the corresponding parameter data.

[0026] Optionally, it also includes sending the server time to the cloud box control mainboard of the smart lighting cloud box through the lighting control background, the cloud box control mainboard automatically synchronizes the server time, and manually adjusts the device time when the synchronization fails.

[0027] Optionally, the detecting of a specific target based on an environmental video image is specifically:

[0028] Collect specific target images of the road to build a feature database, select representative pictures in the feature database as training sets and test sets, and annotate the photos with dangerous features in the training set, wherein the dangerous features include obstacles, collapse areas, and accident scenes;

[0029] Perform image preprocessing on the training set sample images; the preprocessing includes image scaling, cropping, brightness adjustment, and data enhancement, including rotation, translation, and flipping;

[0030] The preprocessed training set sample images are processed through the Yolov5 algorithm, and the target obstacles are selected using the labelimg software to start training the specific target image dataset;

[0031] A specific target image dataset is used to train the basic YOLOv5 network model to obtain a trained YOLOv5 network model;

[0032] The trained YOLOv5 network model is used to recognize specific target images, and the output result of the trained YOLOv5 network model is used as the recognition result to complete the detection of specific target images; among them, the basic YOLOv5 network model is the YOLOv5s network model, and the loss function in the training process is the WIoU loss function.

[0033] Optionally, the construction of a pedestrian and vehicle flow prediction model based on a neural network according to the neural network training sample library is specifically as follows:

[0034] Dividing the neural network training sample library into training samples and test samples, using the learning samples as BP neural network input vectors, and using the total number of running vehicles and pedestrians as target output;

[0035] Set the initial weights and initial thresholds of the training samples, set the minimum expected error of the BP neural network, the learning rate and the number of cycles, and calculate the output of the hidden layer and output layer of the BP neural network and the deviation between the target value and the actual output;

[0036] Determine whether the deviation is within the allowable range. If not, optimize the output layer and hidden layer weights by using a swarm particle algorithm until the error is within the allowable range, thereby completing the training of the pedestrian and vehicle flow prediction model based on a neural network.

[0037] Input the test samples into the constructed pedestrian and vehicle flow prediction model based on neural network, and predict the total number of moving vehicles and pedestrians to verify the pedestrian and vehicle flow prediction model;

[0038] The relevant parameters of the total number of moving vehicles and pedestrians to be predicted are input into the verified pedestrian and vehicle flow prediction model to predict the total number of moving vehicles and pedestrians in the future time series.

[0039] Optionally, the total number of vehicles and pedestrians is predicted based on the pedestrian and vehicle flow prediction model, and the target high pole lamp brightness value and the light coverage value are determined, specifically:

[0040] Based on the pedestrian and vehicle flow prediction model, the number x of vehicles and pedestrians within the predetermined distance range of the target high pole lamp is obtained, and the corresponding target high pole lamp required brightness value is used as the required value L(x), and the current brightness value of the target high pole lamp is used as the comparison value L. The target high pole lamp required brightness value L(x) is calculated according to the formula:

[0041]

[0042] Among them, L max represents the maximum brightness of the target high pole lamp, λ represents the brightness attenuation parameter of the target high pole lamp, x represents the total number of moving vehicles and pedestrians, and ξ represents the preset reference value of the total number of moving vehicles and pedestrians;

[0043] Compare the demand value L(x) with the comparison value L: when L(x)>L, the target high pole lamp is operated to increase the brightness; when L(x)<L, the target high pole lamp is operated to reduce the brightness; when L(x)=L, the brightness of the target high pole lamp is not changed;

[0044] Based on the number x of vehicles and pedestrians within the predetermined distance range of the target high pole lamp, calculate the traffic flow density value ρ, where ρ = x / S, where S = πL2, and L is the distance within the predetermined distance range;

[0045] Based on the traffic flow density value ρ, the light coverage value is adjusted, wherein the light coverage radius R and the traffic flow density value ρ satisfy R=K*ρ, K is a constant; wherein, specific methods for adjusting the light coverage value include adjusting the light pitch angle, adjusting the light beam angle, and adjusting the light height from the ground.

[0046] The beneficial effects of the above technical solution of the present invention are as follows:

[0047] The present invention issues instructions by operating the lighting management platform, mobile phone APP, and lighting intelligent control cloud box to control the on-site lamps individually, in batches, and by plan. It can also control them in different time periods and regions, so as to realize intelligent monitoring and control and refined management of high pole lighting. At the same time, it can realize map functions, equipment inspections, lighting control, power over-reading, gateway inspections, equipment time calibration, electricity price clocks, batch parameter adjustment, energy efficiency management, alarms and other functions, as well as traffic flow monitoring, safety monitoring, light illumination and coverage control functions based on image recognition and neural network technology, which is of great significance for improving lighting efficiency, reducing energy consumption, and realizing intelligent management of urban traffic. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 It is a framework diagram of the high pole lamp intelligent lighting control system described in the present invention.

[0049] Figure 2 It is a hierarchical structure diagram of the high pole lamp intelligent lighting control system described in the present invention.

[0050] Figure 3 It is a principle block diagram of the intelligent control cloud box of the present invention. DETAILED DESCRIPTION

[0051] In order to make the technical problems, technical solutions and advantages to be solved by the present invention more clear, a detailed description will be given below with reference to the accompanying drawings and specific embodiments.

[0052] Example 1

[0053] like Figure 1 and Figure 3 As shown, the present invention proposes an intelligent control system for high pole lamp lighting, including multiple intelligent lighting cloud boxes 1, a lighting management cloud platform 2, a lighting control background 3, a data monitoring screen 4, a mobile terminal 5 and a database server 6. The multiple intelligent lighting cloud boxes 1 are electrically connected to the corresponding circuit loops 7 of multiple lamps 8 respectively, the intelligent lighting cloud box 1 is wirelessly connected to the lighting management cloud platform 2, the lighting management cloud platform 2 is communicatively connected to the lighting control background 3, the data monitoring screen 4, and the database server 6, and the lighting management cloud platform 2 and the mobile terminal 5 are wirelessly connected.

[0054] The intelligent lighting cloud box 1 includes a cloud box control mainboard 11 and a liquid crystal display screen 12 electrically connected to the cloud box control mainboard 11, a GPS and 4G / 5G Internet of Things integrated module 13, a power module 14, a high-power relay integrated module 15, a local on / off light button 16, a local / remote selection button 17, a 4-position aviation socket 111 and an image acquisition module 113, wherein the GPS and 4G / 5G Internet of Things integrated module 13 is wirelessly connected to the lighting management cloud platform 2.

[0055] The power supply module 14 is electrically connected to the leakage protection switch 18 , the GPS and 4G / 5G Internet of Things integrated module 13 is electrically connected to the GPS positioning antenna interface 19 and the 4G / 5G Internet of Things antenna interface 110 , and the high-power relay integrated module 15 is electrically connected to the input / output terminal block 112 .

[0056] Among them, the lighting management cloud platform 2 and the lighting control background 3 are servers or industrial computers.

[0057] The lamp circuit is connected to the lamp, the relay controls the circuit switch to be energized, the circuit monitoring module monitors the real-time working parameters of the circuit and sends them to the main control board. The main control board realizes automatic control through real-time communication between the communication module and the control management system composed of the lighting management cloud platform 2, the lighting control background 3, the data monitoring large screen 4, the mobile terminal 5 and the database server 6. The LCD display can view the working status of the circuit locally; the GPS positioning module provides the implementation of geographic location information positioning.

[0058] Through the linkage between the control system and the equipment, the equipment positioning module automatically obtains the longitude and latitude information, calculates the dark and dawn times of the current time period, and realizes automatic light turning on at night and automatic light turning off at dawn. The light switching time can be adjusted in advance, the longitude and latitude can be manually corrected, the equipment time can be corrected synchronously, and different switching times of the lamp circuits can be set separately. At the same time, by judging whether the communication with the cloud platform server is normal, the equipment can be automatically switched to the local mode when it is offline to execute the preset offline longitude and latitude automatic light switching mode.

[0059] By remotely opening and closing all circuits of the equipment or a single circuit, the switch is turned on to monitor the working status of the relay. At the same time, the voltage and current values ​​of the equipment are monitored through the circuit detection device to determine whether the lighting circuit is actually turned on or off.

[0060] By real-time monitoring of the working status of lamps and lanterns and reporting to the management platform, the management platform can set the current, voltage, power and normal working range values ​​of lamps and lanterns in different circuits to judge the faults of lamps and circuits. The background system records the fault notification to the implementation large screen and management background. The background system automatically generates a fault work order for the lamps and lanterns. The system automatically distributes the work order to the preset operation and maintenance management personnel, and notifies the operation and maintenance personnel to deal with the fault in time through mobile phone APP message reminder and SMS. The work order is automatically completed and filed in the work order history record by the fault handling system.

[0061] The GPS positioning module of the lighting control cloud box collects the current equipment latitude and longitude information and sends it to the lighting management platform. At the same time, when the positioning correction fails, the latitude and longitude correction positioning information can be entered. After the positioning is completed, the positioning information is automatically synchronized to the main control board.

[0062] The present invention adopts high pole lamp multi-circuit different mode lighting control, the specific contents are as follows:

[0063] Remote automatic control: The high pole lamp main control board receives control instructions from the remote computer and controls the opening or closing of multiple groups of high pole lamps according to the instructions; the high pole lamp main control board maintains communication connection with the remote computer through wireless means (4G / 5G) or optical cable, controls the opening or closing of the lamps according to the instructions of the remote computer, and uploads the collected data to the remote computer.

[0064] Local automatic control function: When the communication is interrupted or fails, the high pole lamp main control board automatically recognizes that the communication is in an abnormal state. In the case of abnormal communication, the high pole lamp control automatically switches to the local automatic control state. Each high pole lamp main control board can automatically calculate the light on and off time according to the local longitude and latitude, and automatically control the opening or closing of the lamp according to the calculated time.

[0065] Local manual control: The high pole lamp main control board has an input interface for receiving local status feedback. When the local signal is turned on, the lamp is turned on. When the local signal is disconnected, the lamp is turned off. After a delay of 10 seconds, the remote automatic control mode is restored.

[0066] Remote time control: When the high pole lamp main control board is in remote working mode, different lighting times are set for different circuits of the high pole lamp through the background. During the lighting working time, the background automatically issues switch commands to the circuit. Multiple working time periods can be set. Different circuits of the same lamp pole can be set separately without affecting each other. Multiple different lighting control strategies can be set in advance.

[0067] Cluster batch control: The above control mode can be used for cluster batch control of high pole lights.

[0068] In addition, the loop power consumption is monitored and transmitted to the cloud management platform through the loop monitoring module of the control cloud box. The management platform can set the electricity price. The platform can accurately calculate the power consumption and generate statistical results. It can realize annual time-sharing electricity consumption statistics, fixed electricity price statistics, independent statistical queries for different loops, and export result reports. The system consists of the device access module on the device side and the platform automatically obtains the device address. The device identification, server communication address and key are set in advance during the production of the device. When the device is installed, the device identification and key are entered through the platform device access interface to automatically access the management platform. The system automatically connects with the device to obtain the working status and data of the device, which is ready for use.

[0069] Example 2

[0070] The present invention also provides a control method for the above-mentioned high pole lamp lighting intelligent control system, comprising the following steps:

[0071] S1. The real-time working parameters of the circuits monitored by the monitoring modules set on the corresponding circuit loops of multiple lamps are sent to the cloud box control mainboard of the intelligent lighting cloud box. The cloud box control mainboard wirelessly sends the real-time working parameter data to the lighting management cloud platform through GPS and 4G / 5G Internet of Things integrated module. The lighting management cloud platform then forwards the real-time working parameter data to the lighting control background, data monitoring screen, mobile terminal and database server.

[0072] S2. The lighting management cloud platform is linked with the smart lighting cloud box. The GPS and 4G / 5G IoT integrated modules of the smart lighting cloud box automatically obtain longitude and latitude information. The cloud box control mainboard controls the GPS and 4G / 5G IoT integrated modules to wirelessly report progress data to the lighting management cloud platform. The real-time location of the equipment is retrieved through Amap or Baidu Map, and the operating status, power consumption data, circuit working status, circuit lighting status, real-time current, voltage and power information, and historical usage data of the high pole lamp are displayed. The high pole lamp can also be switched on and off and monitored for operation.

[0073] S3, collect environmental video images at a predetermined distance, extract and store the motion trajectory information of traveling vehicles and pedestrians, and establish a motion trajectory database. At the same time, detect specific targets based on environmental video images, and if dangerous factors are detected, remote sound and light warning broadcasts are made to vehicles and pedestrians. For example, if a road collapse occurs 50-100 meters in front of the vehicle, the high pole lamp adjusts the light to a high beam flash, and a sound alarm is sounded to prevent subsequent vehicles from entering the danger zone without knowing the danger. Specifically, the video image at a predetermined distance centered on the target high pole lamp can be decomposed into continuous image frames frame by frame. Each image frame represents a time point in the video. The video is decomposed into image frames, which provides a continuous image data stream for subsequent target detection and analysis, and each frame can represent the status at a specific time point. Moreover, the specific time point can be controlled according to the time granularity required by the system, for example: the time interval is divided into a time granularity of 50ms to obtain image data at a specific time point. The motion trajectory database also includes the image acquisition time, image section, and weather when the image is acquired, which are all factors that affect the total number of traveling vehicles and pedestrians. Specific targets include but are not limited to obstacles, subsidence areas, and accident sites. These factors are factors that affect traffic safety, and continuous monitoring can be achieved using high pole lights as a carrier.

[0074] S4. Based on the motion trajectory information of the moving vehicles and pedestrians in the motion trajectory database, determine the total number of moving vehicles and pedestrians within the predetermined distance range of the target high pole lamp, and combine the motion trajectory database information to form a neural network training sample library. According to the neural network training sample library, a pedestrian and vehicle flow prediction model based on a neural network is constructed. The training sample library is shown in Table 1.

[0075] Table 1: Training sample library

[0076]

[0077] S5. Based on the pedestrian and vehicle flow prediction model, the total number of vehicles and pedestrians is predicted, and the target high pole light brightness value and light coverage value are determined. At the same time, the pedestrian and vehicle flow information is wirelessly pushed to the GIS map system as the subsequent vehicle road condition broadcast data.

[0078] In this embodiment, the switch control and operation monitoring of the high pole lamp specifically include:

[0079] Calculate the time of darkness and daybreak in the current time period, control the automatic turning on of lights at night and automatic turning off of lights at dawn, adjust the time of turning on and off lights in advance, manually correct the longitude and latitude, correct the equipment time synchronization, and set different on and off times of lighting circuits. And by judging whether the communication with the cloud platform server is normal, the equipment automatically switches to the local mode when offline and performs the operation of automatically turning on and off lights at the preset offline longitude and latitude.

[0080] By remotely opening and closing all circuits or a single circuit of the device, monitoring the working status of the relay, and monitoring the voltage and current values ​​of the device through the circuit monitoring module, it is determined whether the lamp circuit is actually opened or closed. By setting the current, voltage, power, and normal working range values ​​of the lamp equipment in different circuits, the faults of the lamps and circuits are determined, and the database server records the fault and notifies the data monitoring screen and the lighting control background. It automatically combines the configured trigger alarm conditions to realize real-time alarm and generate a fault work order for the lamp, and automatically distributes the work order to the preset operation and maintenance management personnel, and notifies the operation and maintenance personnel to handle the fault in time through mobile terminal message reminders and SMS notifications.

[0081] Among them, the control of automatically turning on the lights at night and automatically turning off the lights at dawn includes two control methods: local control and remote control. The local control is through the lighting control cloud box local control button switch control circuit relay to open and close; the remote control is through the lighting control background or mobile phone terminal, issuing switch instructions, using MQTT communication protocol attributes to issue attributes, and finally the cloud box control mainboard receives the instructions, controls the high-power relay integrated module to open or close, and then controls the switch of the light.

[0082] In this embodiment, it also includes: the cloud box main control board receives the real-time working parameters of the circuit monitored by the monitoring module set on the corresponding circuit loop of the lamp, records the power consumption current, voltage, and power information of the circuit and calculates the power consumption of the corresponding high pole lamp, reports the power consumption to the lighting management cloud platform wirelessly through GPS and 4G / 5G Internet of Things integration module, and stores the power consumption data of the device through the database server. Through the electricity fee configuration module of the lighting control background, the electricity price information is input to automatically calculate the electricity cost, and support annual, monthly, weekly, and daily electricity consumption statistics query.

[0083] In this embodiment, after the lighting control background receives the real-time working parameter data, it also includes statistical processing of the data, and statistics the overall power consumption, daily power consumption, year-on-year power consumption, month-on-month power consumption, and time-sharing power consumption of single equipment, single circuit, multiple equipment, projects, and regional equipment, and displays the equipment energy efficiency information in real time through the data monitoring large screen.

[0084] In this embodiment, when the smart lighting cloud box is in an online communication state, multiple smart lighting cloud boxes that need to be adjusted are selected through the lighting control background, the switch control instructions or device parameters are adjusted, and the attributes are sent to different cloud box control mainboards through the MQTT protocol, and the cloud box control mainboard executes the corresponding parameter data.

[0085] In this embodiment, the server time is also sent to the cloud box control mainboard of the smart lighting cloud box through the lighting control background. The cloud box control mainboard automatically synchronizes the server time and manually adjusts the device time when the synchronization fails.

[0086] In this embodiment, the specific target is detected based on the environmental video image, specifically:

[0087] Specific target images of the road are collected to build a feature database, and representative pictures in the feature database are selected as training sets and test sets. Dangerous features are annotated on the photos in the training set. Dangerous features include obstacles, collapse areas, and accident scenes.

[0088] Perform image preprocessing on the sample images of the training set; preprocessing includes image scaling, cropping, brightness adjustment, and data enhancement, including rotation, translation, and flipping.

[0089] The preprocessed training set sample images are processed through the Yolov5 algorithm, and the target obstacles are selected using the labelimg software to start training the specific target image dataset.

[0090] A specific target image dataset is used to train the basic YOLOv5 network model to obtain a trained YOLOv5 network model.

[0091] The trained YOLOv5 network model is used to recognize specific target images, and the output result of the trained YOLOv5 network model is used as the recognition result to complete the detection of specific target images; among them, the basic YOLOv5 network model is the YOLOv5s network model, and the loss function in the training process is the WIoU loss function.

[0092] In this embodiment, a pedestrian and vehicle flow prediction model based on a neural network is constructed according to a neural network training sample library, specifically:

[0093] The neural network training sample library is divided into training samples and test samples, the learning samples are used as BP neural network input vectors, and the total number of moving vehicles and pedestrians is used as the target output; the data samples of a period of time in the neural network training sample library (including image acquisition time, image location, and weather when the image is acquired) are divided into two parts, the first 75% are used as training samples for training the network, and the second 25% are used as test samples for testing the network.

[0094] Set the initial weights and initial thresholds of the training samples, set the minimum expected error of the BP neural network, the learning rate and the number of cycles, and calculate the output of the hidden layer and output layer of the BP neural network and the deviation between the target value and the actual output.

[0095] Determine whether the deviation is within the allowable range. If not, optimize the output layer and hidden layer weights through the swarm particle algorithm until the error is within the allowable range, and complete the training of the pedestrian and vehicle flow prediction model based on the neural network. When the deviation is not within the allowable range, the error signal is propagated back along the original path, and the network connection weights of each layer of neurons are gradually adjusted until the error is less than the specified accuracy. At this time, the first group of learning is completed, and the next group of learning is entered until the connection weights are within the specified range for the prediction errors of all training groups, and the optimal weights at this time are output. The more training samples there are, the more sufficient the network learns, the greater the network experience value, and the higher the prediction accuracy. Train the network repeatedly, and when the error is less than 10%, stop training and start prediction.

[0096] Input the test sample into the pedestrian and vehicle flow prediction model based on the neural network constructed in step 33, and predict the total number of vehicles and pedestrians to verify the pedestrian and vehicle flow prediction model; for example, after the network training is completed, use another 25% of the data samples to test the network to check whether the model meets the requirements. Use the trained BP neural network to predict the total number of vehicles and pedestrians corresponding to the other 25% of the data samples, and compare the error between the model prediction value and the actual observation value. When the prediction error of the BP neural network model for each group of test data is lower than the specified level, it passes the test and can be used to predict the total number of vehicles and pedestrians. At this time, the correlation coefficient between the model prediction value and the measured value is 0.90, and the maximum error of the model prediction is 10%, which passes the test.

[0097] The relevant parameters of the total number of moving vehicles and pedestrians to be predicted are input into the verified pedestrian and vehicle flow prediction model to predict the total number of moving vehicles and pedestrians in the future time series.

[0098] In this embodiment, based on the pedestrian and vehicle flow prediction model, the total number of vehicles and pedestrians is predicted, and the target high pole lamp brightness value and the light coverage value are determined, specifically:

[0099] Based on the pedestrian and vehicle flow prediction model, the number x of vehicles and pedestrians within the predetermined distance range of the target high pole lamp is obtained, and the corresponding target high pole lamp required brightness value is used as the required value L(x), and the current brightness value of the target high pole lamp is used as the comparison value L. The target high pole lamp required brightness value L(x) is calculated according to the formula:

[0100]

[0101] Among them, L max represents the maximum brightness of the target high pole lamp, λ represents the brightness attenuation parameter of the target high pole lamp, x represents the total number of moving vehicles and pedestrians, and ξ represents the preset reference value of the total number of moving vehicles and pedestrians.

[0102] Compare the demand value L(x) with the comparison value L: when L(x)>L, the brightness of the target high pole lamp is enhanced; when L(x)<L, the brightness of the target high pole lamp is reduced; when L(x)=L, the brightness of the target high pole lamp is not changed.

[0103] Based on the number x of vehicles and pedestrians within the predetermined distance range of the target high pole lamp, the traffic flow density value ρ is calculated, where ρ = x / S, where S = πL2, and L is the distance within the predetermined distance range.

[0104] Based on the traffic flow density value ρ, the light coverage value is adjusted, wherein the light coverage radius R and the traffic flow density value ρ satisfy R=K*ρ, K is a constant; wherein, specific methods for adjusting the light coverage value include adjusting the light pitch angle, adjusting the light beam angle, and adjusting the light height from the ground.

[0105] In summary, the present invention can issue instructions by operating the lighting management platform, mobile phone APP, and lighting intelligent control cloud box to control the on-site lamps individually, in batches, and by plan. It can also control them in different time periods and regions to achieve intelligent monitoring and control and refined management of high pole lighting. At the same time, it can realize map functions, equipment inspections, lighting control, power over-reading, gateway inspections, equipment time calibration, electricity price clocks, batch parameter adjustment, energy efficiency management, alarms and other functions, as well as traffic flow monitoring, safety monitoring, lighting illumination and coverage control functions based on image recognition and neural network technology, which is of great significance for improving lighting efficiency, reducing energy consumption, and realizing intelligent management of urban traffic.

[0106] The above is a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A high pole lamp lighting intelligent control system, characterized in that: It includes multiple smart lighting cloud boxes, a lighting management cloud platform, a lighting control background, a data monitoring screen, a mobile terminal and a database server. The multiple smart lighting cloud boxes are electrically connected to the corresponding circuit loops of multiple lamps respectively, the smart lighting cloud boxes are wirelessly connected to the lighting management cloud platform, the lighting management cloud platform is wirelessly connected to the lighting control background, the data monitoring screen, and the database server, and the lighting management cloud platform is wirelessly connected to the mobile terminal; The intelligent lighting cloud box includes a cloud box control mainboard and a liquid crystal display screen electrically connected to the cloud box control mainboard, a GPS and 4G / 5G Internet of Things integrated module, a power module, a high-power relay integrated module, a local on / off light button, a local / remote selection button, a 4-position aviation plug socket and an image acquisition module, wherein the GPS and 4G / 5G Internet of Things integrated module is wirelessly connected to the lighting management cloud platform; The power module is electrically connected to the leakage protection switch, the GPS and 4G / 5G Internet of Things integrated module is electrically connected to the GPS positioning antenna interface and the 4G / 5G Internet of Things antenna interface, and the high-power relay integrated module is electrically connected to the input / output terminal block; Among them, the lighting management cloud platform and lighting control background are servers or industrial computers.

2. A control method for the high pole lamp lighting intelligent control system according to claim 1, characterized in that: include: The real-time working parameters of the circuits monitored by the monitoring modules set on the corresponding circuit loops of multiple lamps are sent to the cloud box control mainboard of the intelligent lighting cloud box. The cloud box control mainboard wirelessly sends the real-time working parameter data to the lighting management cloud platform through GPS and 4G / 5G Internet of Things integrated modules. The lighting management cloud platform then forwards the real-time working parameter data to the lighting control background, data monitoring large screen, mobile terminal and database server; The lighting management cloud platform is linked with the smart lighting cloud box. The GPS and 4G / 5G IoT integrated modules of the smart lighting cloud box automatically obtain longitude and latitude information. The cloud box control mainboard controls the GPS and 4G / 5G IoT integrated modules to wirelessly report progress data to the lighting management cloud platform. The real-time location of the equipment is retrieved through Amap or Baidu Maps, and the operating status, power consumption data, circuit working status, real-time current, voltage and power information of the circuit lighting status, and historical usage data of the high pole lamp are displayed. The high pole lamp can also be switched on and off and monitored for operation. The image acquisition module acquires environmental video images at a predetermined distance, extracts and stores the motion trajectory information of moving vehicles and pedestrians, and establishes a motion trajectory database; at the same time, it detects specific targets based on environmental video images, and performs remote sound and light warning broadcasts for vehicles and pedestrians if dangerous factors are detected; wherein the motion trajectory database includes the image acquisition time, image area, and weather at the time of image acquisition; the specific targets include but are not limited to obstacles, subsidence areas, and accident sites; Based on the motion trajectory information of the moving vehicles and pedestrians in the motion trajectory database, determine the total number of moving vehicles and pedestrians within a predetermined distance range of the target high pole lamp, and form a neural network training sample library in combination with the motion trajectory database information; According to the neural network training sample library, a neural network-based pedestrian and vehicle flow prediction model is constructed; Based on the pedestrian and vehicle flow prediction model, the total number of traveling vehicles and pedestrians is predicted, and the brightness value of the target high pole lamp and the light coverage value are determined; at the same time, the pedestrian and vehicle flow information is wirelessly pushed to the GIS map system as the subsequent vehicle road condition broadcast data.

3. The intelligent control method for high pole lamp lighting according to claim 2 is characterized in that: The switch control and operation monitoring of the high pole lamp specifically includes: Calculate the time of darkness and daybreak in the current time period, control the automatic turning on of lights at night and automatic turning off of lights at daybreak, adjust the time of turning on and off lights in advance, manually correct the longitude and latitude, correct the synchronization of equipment time, and set different switching times of lighting circuits; and automatically switch the equipment offline to local mode to execute the preset offline longitude and latitude automatic switching operation by judging whether it is communicating normally with the cloud platform server; By remotely opening and closing all circuits or a single circuit of the equipment, monitoring the working status of the relay, and monitoring the voltage and current values ​​of the equipment through the circuit monitoring module, it is determined whether the circuit of the lamp is actually opened or closed; by setting the current, voltage, power and normal working range values ​​of the lamp equipment in different circuits, the fault of the lamp and the circuit is determined, and the database server records the fault and notifies the data monitoring screen and the lighting control background, automatically combining the configured trigger alarm conditions to realize real-time alarm and generate a fault work order for the lamp, and automatically dispatching the work order to the preset operation and maintenance management personnel, and notifying the operation and maintenance personnel to handle the fault in time through mobile terminal message reminders and SMS notifications; Among them, the control of automatically turning on the lights at night and automatically turning off the lights at dawn includes two control methods: local control and remote control. The local control is through the lighting control cloud box local control button switch control loop relay to turn on and off; the remote control is through the lighting control background or mobile phone terminal, issuing switch instructions, using MQTT communication protocol attributes to issue attributes, and finally the cloud box control mainboard receives the instructions, controls the high-power relay integrated module to turn on or off, and then controls the switch of the light.

4. The intelligent control method for high pole lamp lighting according to claim 2 is characterized in that: Also includes: The cloud box main control board receives the real-time working parameters of the circuit monitored by the monitoring module set on the corresponding circuit loop of the lamp, records the current, voltage, and power information of the circuit, and calculates the power consumption of the corresponding high pole lamp. It wirelessly reports the power consumption to the lighting management cloud platform through GPS and 4G / 5G Internet of Things integrated modules, and stores the device power consumption data through the database server; through the electricity fee configuration module of the lighting control background, the electricity price information is input to automatically calculate the electricity cost, and it supports annual, monthly, weekly, and daily electricity consumption statistics inquiries.

5. The intelligent control method for high pole lamp lighting according to claim 2 is characterized in that: It also includes statistical processing of data after the lighting control background receives real-time working parameter data, and statistics on the overall power consumption, daily power consumption, year-on-year power consumption, month-on-month power consumption, and time-sharing power consumption of single equipment, single circuit, multiple equipment, projects, and regional equipment, and displays the equipment energy efficiency information in real time through the data monitoring screen.

6. The intelligent control method for high pole lamp lighting according to claim 2 is characterized in that: It also includes when the smart lighting cloud box is in an online communication state, selecting multiple smart lighting cloud boxes that need to be adjusted through the lighting control background, adjusting switch control instructions or device parameters, and sending attributes to different cloud box control main boards through the MQTT protocol, and the cloud box control main board executes the corresponding parameter data.

7. The intelligent control method for high pole lamp lighting according to claim 2 is characterized in that: It also includes sending the server time to the cloud box control mainboard of the smart lighting cloud box through the lighting control background. The cloud box control mainboard automatically synchronizes the server time and manually adjusts the device time when the synchronization fails.

8. The intelligent control method for high pole lamp lighting according to claim 2 is characterized in that: The specific target is detected based on the environment video image, specifically: Collect specific target images of the road to build a feature database, select representative pictures in the feature database as training sets and test sets, and annotate the photos with dangerous features in the training set, wherein the dangerous features include obstacles, collapse areas, and accident scenes; Perform image preprocessing on the training set sample images; The preprocessing includes image scaling, cropping, brightness adjustment, and data enhancement, including rotation, translation, and flipping; The preprocessed training set sample images are processed through the Yolov5 algorithm, and the target obstacles are selected using the labelimg software to start training the specific target image dataset; A specific target image dataset is used to train the basic YOLOv5 network model to obtain a trained YOLOv5 network model; The trained YOLOv5 network model is used to recognize specific target images, and the output result of the trained YOLOv5 network model is used as the recognition result to complete the detection of specific target images; among them, the basic YOLOv5 network model is the YOLOv5s network model, and the loss function in the training process is the WIoU loss function.

9. The intelligent control method for high pole lamp lighting according to claim 2 is characterized in that: The pedestrian and vehicle flow prediction model based on the neural network is constructed according to the neural network training sample library, specifically: Dividing the neural network training sample library into training samples and test samples, using the learning samples as BP neural network input vectors, and using the total number of running vehicles and pedestrians as target output; Set the initial weights and initial thresholds of the training samples, set the minimum expected error of the BP neural network, the learning rate and the number of cycles, and calculate the output of the hidden layer and output layer of the BP neural network and the deviation between the target value and the actual output; Determine whether the deviation is within the allowable range. If not, optimize the output layer and hidden layer weights by using a swarm particle algorithm until the error is within the allowable range, thereby completing the training of the pedestrian and vehicle flow prediction model based on a neural network. Input the test samples into the constructed pedestrian and vehicle flow prediction model based on neural network, and predict the total number of moving vehicles and pedestrians to verify the pedestrian and vehicle flow prediction model; The relevant parameters of the total number of moving vehicles and pedestrians to be predicted are input into the verified pedestrian and vehicle flow prediction model to predict the total number of moving vehicles and pedestrians in the future time series.

10. The intelligent control method for high pole lamp lighting according to claim 2, characterized in that: The total number of vehicles and pedestrians is predicted based on the pedestrian and vehicle flow prediction model, and the brightness value and the near and far coverage value of the target high pole lamp are determined, specifically: Based on the pedestrian and vehicle flow prediction model, the number x of vehicles and pedestrians within the predetermined distance range of the target high pole lamp is obtained, and the corresponding target high pole lamp required brightness value is used as the required value L(x), and the current brightness value of the target high pole lamp is used as the comparison value L. The target high pole lamp required brightness value L(x) is calculated according to the formula: Among them, L max represents the maximum brightness of the target high pole lamp, λ represents the brightness attenuation parameter of the target high pole lamp, x represents the total number of moving vehicles and pedestrians, and ξ represents the preset reference value of the total number of moving vehicles and pedestrians; Compare the demand value L(x) with the comparison value L: when L(x)>L, the target high pole lamp is operated to increase the brightness; when L(x)<L, the target high pole lamp is operated to reduce the brightness; when L(x)=L, the brightness of the target high pole lamp is not changed; Based on the number x of vehicles and pedestrians within the predetermined distance range of the target high pole lamp, calculate the traffic flow density value ρ, where ρ = x / S, where S = πL2, and L is the distance within the predetermined distance range; Based on the traffic flow density value ρ, the light coverage value is adjusted, wherein the light coverage radius R and the traffic flow density value ρ satisfy R=K*ρ, K is a constant; wherein, specific methods for adjusting the light coverage value include adjusting the light pitch angle, adjusting the light beam angle, and adjusting the light height from the ground.

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