Intelligent street lamp management and control method and device, computer equipment and storage medium

Abstract

The invention relates to the technical field of street lamp management and control, and discloses an intelligent street lamp management and control method and device, computer equipment and a storage medium, and the method comprises the steps: receiving perception data transmitted by a sensor on a street lamp, and according to the image data, extracting pedestrian flow data and traffic flow data of a traffic section corresponding to the street lamp which uploads the sensing data, and according to the environment data, the pedestrian flow data and the traffic flow data, adjusting the illumination brightness and the on-off state of the street lamp according to the environment data, the pedestrian flow data and the traffic flow data. Therefore, the energy utilization efficiency of the street lamp is improved.

Description

Technical Field

[0001] The present invention relates to the field of street lamp control technology, and specifically to a smart street lamp control method, device, computer equipment and storage medium. Background Art

[0002] Streetlights are one of the main facilities on urban roads. At present, traditional streetlight control systems mainly use timed lighting or control the switch status of streetlights according to lighting conditions. Streetlight control is not intelligent enough. Streetlights will continue to be turned on when no one is around, resulting in energy waste.

[0003] Therefore, there is an urgent need for a new type of smart street lamp control method to enhance the intelligence of street lamp management and thus improve the energy efficiency of street lamps. Summary of the invention

[0004] In view of this, the present invention provides a smart street lamp control method, device, computer equipment and storage medium to solve the problem that the current street lamp control is not intelligent enough, resulting in low energy utilization efficiency of street lamps.

[0005] In a first aspect, the present invention provides a smart street lamp management and control method, including: receiving perception data transmitted by sensors on street lamps, the perception data including environmental data and image data; extracting pedestrian flow data and vehicle flow data of a traffic section corresponding to the street lamp that uploaded the perception data based on the image data; and adjusting the lighting brightness and switch status of the street lamp based on the environmental data, pedestrian flow data and vehicle flow data.

[0006] The intelligent street lamp control method of the present invention receives perception data transmitted by sensors on street lamps, the perception data including environmental data and image data, extracts pedestrian flow data and vehicle flow data of the traffic section corresponding to the street lamp that uploaded the perception data according to the image data, adjusts the lighting brightness and switch status of the street lamp according to the environmental data, pedestrian flow data and vehicle flow data, and can turn on the street lamp and adjust the lighting brightness of the street lamp when there are pedestrians or vehicles on the road, avoiding wasting energy by turning on the street lamp when there is no one, thereby improving the energy utilization efficiency of the street lamp.

[0007] In an optional embodiment, after extracting the pedestrian and vehicle flow data of the traffic section corresponding to the street lamp that uploaded the perception data based on the image data, it also includes: using an autoregressive moving average model to predict the pedestrian and vehicle flow data for future time periods; and planning in advance the lighting brightness and switching status of the street lamps in the future time periods based on the pedestrian and vehicle flow data for the future time periods.

[0008] In this way, the future flow of people and vehicles is predicted through the autoregressive moving average model, and the lighting brightness and switching status of street lamps can be planned in advance, achieving more accurate and forward-looking street lamp management, reducing energy waste and improving response efficiency.

[0009] In an optional embodiment, after receiving the perception data transmitted by the sensor on the street lamp, it includes: using the autoregressive moving average model to obtain the current perception data prediction value; comparing the difference between the current perception data prediction value and the actual perception data value uploaded by the street lamp, and outputting a sensor abnormality alarm if the difference is greater than a preset threshold.

[0010] In this method, the autoregressive moving average model is used to predict the perception data and compare it with the actual data, so that sensor anomalies can be discovered in time, the reliability and stability of the system can be improved, and the maintenance costs and safety hazards caused by sensor failures can be reduced.

[0011] In an optional embodiment, the smart street lamp management and control method also includes: receiving fault warning information transmitted by the street lamp based on monitoring the street lamp's own parameters; identifying the fault type and extracting the street lamp's location information based on the fault warning information; and sending the fault type and location information to the maintenance terminal.

[0012] In this way, by receiving fault warning information and identifying the fault type, the faulty street light can be quickly located and maintenance personnel can be notified, thus shortening the fault response time and improving maintenance efficiency and the reliability of the street light system.

[0013] In an optional embodiment, after receiving the perception data transmitted by the sensor on the street lamp, it also includes: sharing the perception data to the smart city management platform; receiving the city data shared by the smart city management platform; and adjusting the lighting brightness and switch status of the street lamp according to the city data.

[0014] In this way, the perception data is shared to the smart city management platform, and the city data is received, which can realize the collaborative work of street lighting control and other smart city management platforms, improve the overall smart management level of the city, and optimize resource allocation.

[0015] In an optional implementation, the smart city management platform includes a smart city traffic management system; correspondingly, the perception data is shared to the smart city management platform, including: identifying whether there are abnormal road events based on the image data, and obtaining road safety identification results; and sending the road safety identification results to the smart city traffic management system.

[0016] In this way, the road safety identification results of the smart city traffic management system can further improve the safety and responsiveness of the street lighting system, especially in abnormal road events, so that the lighting strategy can be adjusted in time to ensure traffic safety.

[0017] In an optional embodiment, the environmental data includes light intensity; correspondingly, the lighting brightness and switching state of the street lamp are adjusted according to the environmental data, pedestrian flow data and vehicle flow data, including: comparing the light intensity with a preset light intensity; when the light intensity is less than the preset light intensity and the pedestrian flow data is greater than zero or the vehicle flow data is greater than zero, controlling the street lamp to turn on the lighting; adjusting the lighting brightness of the street lamp according to the sum of the pedestrian flow data and the vehicle flow data, wherein the greater the sum of the pedestrian flow data and the vehicle flow data, the greater the lighting brightness of the street lamp.

[0018] In this way, street lighting is adjusted according to light intensity, pedestrian flow data and vehicle flow data, which can achieve energy-saving effects and reduce energy consumption while ensuring road safety.

[0019] In the second aspect, the present invention provides a smart street light management and control device, including: a data receiving module, used to receive perception data transmitted by sensors on street lights, the perception data including environmental data and image data; a traffic extraction module, used to extract pedestrian flow data and vehicle flow data of a traffic section corresponding to the street light that uploaded the perception data based on the image data; a street light control module, used to adjust the lighting brightness and switch status of the street light based on the environmental data, pedestrian flow data and vehicle flow data.

[0020] In a third aspect, the present invention provides a computer device, comprising: a memory and a processor, the memory and the processor are communicatively connected to each other, computer instructions are stored in the memory, and the processor executes the smart street light control method of the first aspect or any corresponding embodiment thereof by executing the computer instructions.

[0021] In a fourth aspect, the present invention provides a computer-readable storage medium having computer instructions stored thereon, the computer instructions being used to enable a computer to execute the smart street light management and control method of the first aspect or any corresponding embodiment thereof. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] In order to more clearly illustrate the specific implementation methods of the present invention or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some implementation methods of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0023] Figure 1 is a flow chart of a smart street light control method according to an embodiment of the present invention;

[0024] Figure 2 is a structural block diagram of a smart street light control device according to an embodiment of the present invention;

[0025] Figure 3 It is a schematic diagram of the hardware structure of a computer device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0026] 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 those skilled in the art without creative work are within the scope of protection of the present invention.

[0027] With the rapid development of science and technology and the continuous improvement of people's quality of life, traditional street light control systems have problems such as difficulty in centralized management, high energy consumption, and expensive maintenance costs. In addition, the current street light control system lacks intelligence, adaptability, and energy saving. It cannot adjust the lighting brightness according to real-time environmental conditions, nor can it achieve remote monitoring and fault self-diagnosis. Therefore, a new type of smart street light control method is needed to improve street light management efficiency, reduce energy consumption costs, and achieve intelligent control and remote monitoring.

[0028] In summary, the defects of the existing street lamp control system mainly include: lack of intelligent management, unable to adjust the lighting brightness according to the real-time environmental conditions, resulting in energy waste; difficult and costly maintenance, requiring manual inspection and repair; lack of remote monitoring function, difficult to detect and deal with faults in time; the centralized management of the system is inflexible and cannot adapt to the actual needs of different road sections.

[0029] In view of this, an embodiment of the present invention provides a smart street lamp management and control method to address the shortcomings of the traditional system and improve the intelligence, efficiency and energy saving of street lamp management.

[0030] According to an embodiment of the present invention, an embodiment of a smart street light management and control method is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0031] In this embodiment, a smart street light control method is provided, which can be used in mobile terminals, servers, etc. For example, the smart street light control method of the embodiment of the present invention can be executed in a cloud computing and data analysis platform. The cloud computing and data analysis platform is responsible for receiving, storing, processing and analyzing the massive perception data from smart street lights. Through data analysis, functions such as real-time monitoring of street light status, fault warning, and energy consumption management can be realized. In addition, big data analysis can also help city managers understand information such as urban traffic conditions and environmental quality, and provide a scientific basis for urban planning and management.

[0032] In one example, the main functions of the cloud computing and data analysis platform include: massive perception data storage and data outlet analysis. In terms of storage, the ABP framework structure is specifically adopted to implement a layered architecture (domain layer, application layer, presentation layer and infrastructure layer) to achieve PB-level data storage and efficient expansion.

[0033] Figure 1 is a flow chart of a smart street light control method according to an embodiment of the present invention. Figure 1 As shown, the process includes the following steps:

[0034] Step S101, receiving perception data transmitted by a sensor on a street lamp, where the perception data includes environmental data and image data.

[0035] Specifically, street lamps are integrated with a variety of sensors, such as high-definition cameras, environmental perception sensors (including temperature and humidity sensors, PM2.5 sensors, noise sensors, etc.), traffic flow sensors, etc. Real-time perception of multi-dimensional information such as road traffic conditions, air quality, road conditions, vehicle flow, pedestrian flow, and weather conditions is achieved to obtain perception data.

[0036] Data collection methods include but are not limited to:

[0037] 1. Use high-definition cameras, WIFI probes and other equipment to capture image data, and then capture road scenes and pedestrian flow data in real time.

[0038] 2. Monitor environmental quality parameters such as temperature, humidity, noise, PM2.5, and light intensity and light deviation at the location of street lamps through environmental perception sensors.

[0039] The collected perception data provides the basis for the intelligent control of street lights and also provides important decision support for urban traffic management departments. For example, according to traffic flow and pedestrian activities, smart street lights can automatically adjust the brightness and switching time to achieve energy saving and meet lighting needs.

[0040] After preliminary processing, the perception data is uploaded to the cloud computing and data analysis platform through the wireless communication module (such as LoRa, NB-IoT, etc.) on the street lamp. The cloud computing and data analysis platform thus receives the perception data.

[0041] Step S102, extracting pedestrian flow data and vehicle flow data of the traffic section corresponding to the street lamp that uploaded the perception data based on the image data.

[0042] Specifically, image recognition technology is used to identify human images and vehicle images in image data, and then count the pedestrian flow data and vehicle flow data of the traffic section where the street light is located.

[0043] Step S103, adjusting the lighting brightness and switch status of the street lamp according to the environmental data, the pedestrian flow data and the vehicle flow data.

[0044] Specifically, the cloud computing and data analysis platform sends the decision results to the intelligent control and execution system, which can also uniformly control all street lights through broadcast commands.

[0045] The intelligent control and execution system is responsible for remotely controlling and adjusting the smart street lights according to the instructions of the cloud computing and data analysis platform. The intelligent control and execution system can realize the dimming and light control of single lights or groups, and define lighting strategies according to scene requirements (such as timed execution, event triggering, interval triggering, sunrise and sunset time, etc.).

[0046] In one example, a single lamp controller is installed at the bottom of the street lamp to support individual control of a single lamp. It can be specially adjusted according to the control requirements and connected to the intelligent control and execution system using 4G connection. Through the intelligent control and execution system, the smart street lamp can automatically adjust the brightness and switching time according to actual needs, achieving energy saving and meeting lighting needs while also improving the management efficiency of the street lamp.

[0047] Street lighting control methods include:

[0048] Dimming control: Dynamically adjust the lighting brightness and on / off status of street lamps based on factors such as road environment, traffic flow, and pedestrian flow.

[0049] Energy-saving management: Energy saving and consumption reduction of street lighting system can be achieved by optimizing dimming strategies.

[0050] In one example, the environmental data includes light intensity;

[0051] Correspondingly, step S103 includes.

[0052] Step S1031, comparing the light intensity with the preset light intensity;

[0053] Step S1032, when the light intensity is less than the preset light intensity and the pedestrian flow data is greater than zero or the vehicle flow data is greater than zero, control the street lamp to turn on the lighting;

[0054] Step S1033, adjusting the lighting brightness of the street lamp according to the sum of the pedestrian flow data and the vehicle flow data, wherein the greater the sum of the pedestrian flow data and the vehicle flow data, the greater the lighting brightness of the street lamp.

[0055] By adjusting street lighting according to light intensity, pedestrian flow data and vehicle flow data, energy saving can be achieved and energy consumption can be reduced while ensuring road safety.

[0056] The smart street lamp control method of the embodiment of the present invention receives perception data transmitted by sensors on street lamps, the perception data including environmental data and image data, extracts pedestrian flow data and vehicle flow data of the traffic section corresponding to the street lamp that uploaded the perception data based on the image data, adjusts the lighting brightness and switch status of the street lamp based on the environmental data, pedestrian flow data and vehicle flow data, and can turn on the street lamp and adjust the lighting brightness of the street lamp when there are pedestrians or vehicles on the road, avoiding wasting energy when the street lamp is turned on when there is no one, thereby improving the energy utilization efficiency of the street lamp.

[0057] In some embodiments, after extracting pedestrian flow data and vehicle flow data of a traffic section corresponding to a street lamp that uploaded the perception data according to the image data in step S102, the method further includes:

[0058] Step S1021, using an autoregressive moving average model to predict the pedestrian flow data and vehicle flow data for a future time period;

[0059] Step S1022, planning the lighting brightness and switch status of the street lamps in the future time period in advance according to the pedestrian flow data and vehicle flow data in the future time period.

[0060] Specifically, the cloud computing and data analysis platform deploys an autoregressive integrated moving average model (ARIMA), which can use the historical data of the variable itself to predict itself. Its prediction algorithm is:

[0061]

[0062] Among them, y t is the current value, which can be the current pedestrian flow data or the current vehicle flow data according to the actual situation, μ is the constant term, p is the number of autoregressive terms, q is the number of moving average terms, and r i is the autoregressive correlation coefficient, ∈ t is the error value corresponding to the current value, θ i is the moving average correlation coefficient, yt-i is the historical value, ∈ t-i is the error value corresponding to the historical value.

[0063] Furthermore, based on the current value and historical value, the value at the next time point can be calculated, that is, the value of the future time period can be obtained, including the pedestrian flow data and vehicle flow data of the future time period, thereby providing a data basis for early control of street light status.

[0064] In this way, the future flow of people and vehicles is predicted through the autoregressive moving average model, and the lighting brightness and switching status of street lamps can be planned in advance, achieving more accurate and forward-looking street lamp management, reducing energy waste and improving response efficiency.

[0065] In some embodiments, in step S101, after receiving the sensing data transmitted by the sensor on the street lamp, the method includes:

[0066] Step S1011, using an autoregressive moving average model to obtain a predicted value of current perception data;

[0067] Step S1012, compare the difference between the current perception data prediction value and the actual perception data value uploaded by the street lamp, and output a sensor abnormality alarm if the difference is greater than a preset threshold.

[0068] Specifically, the perception data includes temperature, humidity, noise, light intensity, etc. Taking light intensity as an example, the light sensor collects light intensity data in real time and sends it to the cloud computing and data analysis platform. At the same time, the cloud computing and data analysis platform predicts the current predicted value of the light intensity based on the historical value of the light intensity data, and compares the actual value and predicted value of the light intensity data collected in real time. If the difference is greater than the preset threshold, it means that the light sensor may have a fault, resulting in data abnormality. At this time, the sensor abnormality alarm is output and the maintenance personnel have been notified to troubleshoot the problem.

[0069] In this method, the autoregressive moving average model is used to predict the perception data and compare it with the actual data, so that sensor anomalies can be discovered in time, the reliability and stability of the system can be improved, and the maintenance costs and safety hazards caused by sensor failures can be reduced.

[0070] In some embodiments, the smart street light management and control method further includes:

[0071] Step S104, receiving fault warning information transmitted by the street lamp and obtained based on monitoring parameters of the street lamp itself;

[0072] Step S105, identifying the fault type according to the fault warning information and extracting the location information of the street lamp;

[0073] Step S106: Send the fault type and location information to the maintenance terminal.

[0074] Specifically, the street lamp's own parameters include but are not limited to voltage parameters, current parameters, and temperature parameters.

[0075] The street lamp's own parameters are monitored in real time to determine the working status of the street lamp. Once a fault or abnormality is found, the early warning mechanism is triggered immediately, and the maintenance personnel are notified through the remote monitoring platform for processing. Specifically, if the voltage parameter, current parameter and temperature parameter deviate from the corresponding normal value, it is considered that the street lamp has a fault or abnormality, and the street lamp generates fault warning information and sends the fault warning information to the cloud computing and data analysis platform.

[0076] The cloud computing and data analysis platform stores the street lamp identification and corresponding location information of each street lamp. After receiving the fault warning information, the cloud computing and data analysis platform determines the location information of the street lamp according to the street lamp identification in the fault warning information. Based on the previous fault problem records, the street lamp fault type is identified and sent to the maintenance terminal through SMS, email or APP push to notify the management personnel to handle it, thereby improving maintenance efficiency.

[0077] Through the GIS geographic information platform or the smart street light cloud platform, street light managers can remotely manage city street lights at any workstation and monitor street lights 24 hours a day. Once a fault is found, the system will immediately activate the alarm system and provide a maintenance plan to ensure that the street lights maintain optimal operation. This not only improves the maintenance efficiency of street lights, but also reduces maintenance costs.

[0078] The street light management personnel receive the fault type and location information through the maintenance terminal, and the maintenance terminal provides the street light management personnel with real-time street light status monitoring and fault warning functions.

[0079] The maintenance terminal displays information such as the geographical location, working status and energy consumption data of street lights through a graphical interface, which makes it easy for managers to efficiently monitor the operating status of the street light system and supports remote control and configuration of street lights.

[0080] In this way, by receiving fault warning information and identifying the fault type, the faulty street light can be quickly located and maintenance personnel can be notified, thus shortening the fault response time and improving maintenance efficiency and the reliability of the street light system.

[0081] In some embodiments, after receiving the sensing data transmitted by the sensor on the street lamp in step S101, the method further includes:

[0082] Step S1013, sharing the sensing data to the smart city management platform;

[0083] Step S1014, receiving city data shared by the smart city management platform;

[0084] Step S1015, adjusting the lighting brightness and switch status of the street lamp according to the city data.

[0085] Specifically, the smart city management platform includes the smart city traffic management system, traffic signal system, environmental monitoring system, etc.

[0086] The cloud computing and data analysis platform is compatible with common IoT protocols such as Modbus, UDP / TCP, OCP, MQTT, HTTP, etc., and can be integrated and interconnected with multiple smart city management platforms to achieve a wider range of functions and applications. It supports access to all IoT devices that support these protocols, such as smart cameras, traffic lights, environmental monitoring stations, etc., to form an intelligent network ecosystem of "people-vehicle-road-environment".

[0087] By interconnecting with other smart city management platforms, data resources can be shared and the level of urban management can be jointly improved. While sending the cloud computing and data analysis platform's own perception data to the smart city management platform, the smart city management platform's urban data can also be used to adjust the brightness and switching of street lights.

[0088] For example, the smart city management platform is a traffic signal system, and the city data shared by the traffic signal system is the special road sections for real-time traffic control and the corresponding street light control instructions for the special road sections. Specifically, when the traffic light system fails, the street light switch can be used as a traffic light indicator. When the street light on a special road section is turned on, it indicates that the light is green, and when the street light is turned off, it indicates that the light is red. The corresponding street light control instructions are generated according to the traffic command requirements. The cloud computing and data analysis platform controls the switch of the street light according to the special road sections and the corresponding street light control instructions to implement traffic control on the special road sections.

[0089] In addition, cloud computing and data analysis platforms can be linked with smart city traffic management systems to achieve unified control and management of street lamp energy consumption; they can also be integrated with urban security systems, environmental monitoring systems, etc. to provide cities with comprehensive services and guarantees.

[0090] For example, smart city management platforms include smart city traffic management systems;

[0091] Correspondingly, step S1013, sharing the sensing data to the smart city management platform, includes:

[0092] Step A1, identifying whether there is a road abnormality event based on the image data, and obtaining a road safety identification result;

[0093] Step A2: Send the road safety identification result to the smart city traffic management system.

[0094] The image data is recognized through a neural network model. If an abnormal road event is identified, the road safety identification result will be sent to the smart city traffic management system. The smart city traffic management system can quickly locate the location of the abnormal road event and take measures to avoid further expansion of the accident.

[0095] The road safety identification results of the smart city traffic management system can further improve the safety and responsiveness of the street lighting system, especially in abnormal road events, so that the lighting strategy can be adjusted in time to ensure traffic safety.

[0096] Furthermore, the cloud computing and data analysis platform provides an open API interface to facilitate third-party application developers to develop more innovative services based on the smart street light system, such as intelligent navigation, emergency response, etc.

[0097] In this way, the perception data is shared to the smart city management platform, and the city data is received, which can realize the collaborative work of street lighting control and other smart city management platforms, improve the overall smart management level of the city, and optimize resource allocation.

[0098] Furthermore, in the process of smart street light control, security and privacy protection are crucial. Smart street light control methods need to adopt advanced data encryption technology and access control mechanisms to ensure the security of perception data, monitoring data, etc. during transmission and processing. At the same time, it is also necessary to strictly abide by relevant laws and regulations to protect the privacy rights and interests of users.

[0099] In terms of security and privacy protection, we have taken a number of measures to ensure data security and user privacy protection. Specific measures include:

[0100] a) Data encryption: Encrypted communication technology is used, and multiple encryption processes are applied to the transmitted and stored data, including MD5, 3DES symmetric key encryption algorithm, AES256 algorithm, and RSA algorithm. The appropriate encryption algorithm is selected according to the characteristics of the data and security requirements to prevent data leakage and illegal access.

[0101] b) Access control: Apply the casbin permission control framework and establish a strict access control mechanism so that only authorized users can access sensitive data and system resources.

[0102] c) Privacy Protection Policy: Develop and implement a strict privacy protection policy that clearly defines the rules and restrictions on data collection, use, and sharing.

[0103] d) Regular audits: Conduct security audits and vulnerability scans on the system regularly to promptly identify and fix potential security risks.

[0104] In summary, the smart street lamp management and control method of the embodiment of the present invention realizes the intelligent management and control of the street lamp system, which can effectively improve the level and efficiency of urban lighting management.

[0105] This embodiment provides a smart street light control device. Figure 2 As shown, including:

[0106] The data receiving module 201 is used to receive the sensing data transmitted by the sensor on the street lamp, and the sensing data includes environmental data and image data;

[0107] The traffic extraction module 202 is used to extract the pedestrian flow data and vehicle flow data of the traffic section corresponding to the street lamp that uploaded the perception data according to the image data;

[0108] The street lamp control module 203 is used to adjust the lighting brightness and switch status of the street lamp according to the environmental data, the pedestrian flow data and the vehicle flow data.

[0109] The intelligent street light control device of the present invention receives perception data transmitted by sensors on street lights, the perception data including environmental data and image data, extracts pedestrian flow data and vehicle flow data of the traffic section corresponding to the street lights that uploaded the perception data based on the image data, adjusts the lighting brightness and switch status of the street lights based on the environmental data, pedestrian flow data and vehicle flow data, and can turn on the street lights and adjust the lighting brightness of the street lights when there are pedestrians or vehicles on the road, avoiding wasting energy by turning on the street lights when there are no people, thereby improving the energy utilization efficiency of the street lights.

[0110] In some optional implementations, the smart street light management and control device further includes:

[0111] The future traffic prediction module is used to predict the pedestrian and vehicle traffic data in the future time period using the autoregressive moving average model;

[0112] The street light advance control module is used to plan the lighting brightness and switch status of street lights in future time periods in advance based on the pedestrian flow data and vehicle flow data in future time periods.

[0113] In some optional implementations, the smart street light management and control device further includes:

[0114] The current data prediction module is used to obtain the current perception data prediction value using an autoregressive moving average model;

[0115] The difference comparison module is used to compare the difference between the current perception data prediction value and the actual perception data value uploaded by the street lamp. If the difference is greater than a preset threshold, a sensor abnormality alarm is output.

[0116] In some optional implementations, the smart street light management and control device further includes:

[0117] An early warning information receiving module is used to receive fault early warning information transmitted by the street lamp based on monitoring parameters of the street lamp itself;

[0118] A location extraction module is used to identify the fault type and extract the location information of the street lamp according to the fault warning information;

[0119] The early warning information sending module is used to send the fault type and location information to the maintenance terminal.

[0120] In some optional implementations, the smart street light management and control device further includes:

[0121] Data sharing module, used to share the perception data to the smart city management platform;

[0122] A city data receiving module is used to receive city data shared by the smart city management platform;

[0123] The shared data adjustment module is used to adjust the lighting brightness and switch status of street lamps according to urban data.

[0124] In some optional implementations, the smart city management platform includes a smart city traffic management system; correspondingly, the data sharing module includes:

[0125] A road abnormal event recognition module is used to identify whether there is a road abnormal event based on image data and obtain a road safety recognition result;

[0126] The road safety identification result sending module is used to send the road safety identification result to the smart city traffic management system.

[0127] In some optional embodiments, the environmental data includes light intensity;

[0128] Correspondingly, the street light control module 203 includes:

[0129] A light intensity comparison module, used to compare the light intensity with a preset light intensity;

[0130] A street light turn-on adjustment module is used to control the street light to turn on lighting when the light intensity is less than the preset light intensity and the pedestrian flow data is greater than zero or the vehicle flow data is greater than zero;

[0131] The street lamp brightness adjustment module is used to adjust the street lamp lighting brightness according to the sum of the pedestrian flow data and the vehicle flow data, wherein the greater the sum of the pedestrian flow data and the vehicle flow data, the greater the street lamp lighting brightness.

[0132] The further functional description of each of the above modules and units is the same as that of the above corresponding embodiments and will not be repeated here.

[0133] The smart street light control device in this embodiment is presented in the form of a functional unit, where the unit refers to an ASIC (Application Specific Integrated Circuit) circuit, a processor and memory that executes one or more software or fixed programs, and / or other devices that can provide the above functions.

[0134] The embodiment of the present invention also provides a computer device having the above Figure 3 The smart street light control device shown.

[0135] See also Figure 3 , Figure 3 is a schematic diagram of the structure of a computer device provided by an optional embodiment of the present invention, such as Figure 3 As shown, the computer device includes: one or more processors 10, a memory 20, and interfaces for connecting various components, including high-speed interfaces and low-speed interfaces. Various components are connected to each other using different buses for communication, and can be installed on a common mainboard or installed in other ways as needed. The processor can process the instructions executed in the computer device, including instructions stored in or on the memory to display the graphical information of the GUI on an external input / output device (such as, a display device coupled to the interface). In some optional embodiments, if necessary, multiple processors and / or multiple buses can be used together with multiple memories and multiple memories. Similarly, multiple computer devices can be connected, and each device provides some necessary operations (for example, as a server array, a group of blade servers, or a multi-processor system). Figure 3 A processor 10 is taken as an example.

[0136] The processor 10 may be a central processing unit, a network processor or a combination thereof. The processor 10 may further include a hardware chip. The hardware chip may be a dedicated integrated circuit, a programmable logic device or a combination thereof. The programmable logic device may be a complex programmable logic device, a field programmable gate array, a general purpose array logic or any combination thereof.

[0137] The memory 20 stores instructions executable by at least one processor 10, so that the at least one processor 10 executes the method shown in the above embodiment.

[0138] The memory 20 may include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application required for at least one function; the data storage area may store data created according to the use of the computer device, etc. In addition, the memory 20 may include a high-speed random access memory, and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some optional embodiments, the memory 20 may optionally include a memory remotely arranged relative to the processor 10, and these remote memories may be connected to the computer device via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0139] The memory 20 may include a volatile memory, such as a random access memory; the memory may also include a non-volatile memory, such as a flash memory, a hard disk or a solid state drive; the memory 20 may also include a combination of the above types of memory.

[0140] The computer device also includes an input device 30 and an output device 40. The processor 10, the memory 20, the input device 30 and the output device 40 may be connected via a bus or other means. Figure 3 The example of connecting through bus is taken in the following.

[0141] The input device 30 can receive input digital or character information, and generate key signal input related to the user settings and function control of the computer device, such as a touch screen, a keypad, a mouse, a track pad, a touch pad, an indicator bar, one or more mouse buttons, a trackball, a joystick, etc. The output device 40 may include a display device, an auxiliary lighting device (e.g., an LED) and a tactile feedback device (e.g., a vibration motor), etc. The above-mentioned display device includes but is not limited to a liquid crystal display, a light emitting diode, a display and a plasma display. In some optional embodiments, the display device can be a touch screen.

[0142] The embodiment of the present invention also provides a computer-readable storage medium. The method according to the embodiment of the present invention can be implemented in hardware, firmware, or can be implemented as a computer code that can be recorded in a storage medium, or can be implemented as a computer code that is originally stored in a remote storage medium or a non-temporary machine-readable storage medium and will be stored in a local storage medium through a network download, so that the method described herein can be stored in such software processing on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. Among them, the storage medium can be a magnetic disk, an optical disk, a read-only storage memory, a random access memory, a flash memory, a hard disk or a solid-state hard disk, etc.; further, the storage medium can also include a combination of the above types of memories. It can be understood that a computer, a processor, a microprocessor controller, or programmable hardware includes a storage component that can store or receive software or computer code. When the software or computer code is accessed and executed by a computer, a processor, or hardware, the method shown in the above embodiment is implemented.

[0143] A part of the present invention may be applied as a computer program product, such as a computer program instruction, which, when executed by a computer, can call or provide the method and / or technical solution according to the present invention through the operation of the computer. Those skilled in the art should understand that the existence of the computer program instruction in a computer-readable medium includes, but is not limited to, a source file, an executable file, an installation package file, etc., and accordingly, the way in which the computer program instruction is executed by the computer includes, but is not limited to: the computer directly executes the instruction, or the computer compiles the instruction and then executes the corresponding compiled program, or the computer reads and executes the instruction, or the computer reads and installs the instruction and then executes the corresponding installed program. Here, the computer-readable medium may be any available computer-readable storage medium or communication medium accessible to the computer.

[0144] Although the embodiments of the present invention have been described in conjunction with the accompanying drawings, those skilled in the art may make various modifications and variations without departing from the spirit and scope of the present invention, and such modifications and variations are all within the scope defined by the appended claims.

Claims

1. A smart street light control method, characterized in that: include: Receiving perception data transmitted by a sensor on a street lamp, wherein the perception data includes environmental data and image data; Extracting pedestrian flow data and vehicle flow data of the traffic section corresponding to the street lamp that uploaded the sensing data according to the image data; The lighting brightness and switch status of the street lamp are adjusted according to the environmental data, the pedestrian flow data and the vehicle flow data.

2. The smart street light control method according to claim 1, characterized in that: After extracting the pedestrian flow data and vehicle flow data of the traffic section corresponding to the street lamp that uploaded the perception data according to the image data, the method further includes: Predicting the pedestrian flow data and the vehicle flow data in a future time period using an autoregressive moving average model; The lighting brightness and switch status of the street lamps in the future time period are planned in advance according to the pedestrian flow data and the vehicle flow data in the future time period.

3. The smart street light control method according to claim 1, characterized in that: After receiving the perception data transmitted by the sensor on the street lamp, it includes: The autoregressive moving average model is used to obtain the predicted value of the current perception data; The difference between the current perception data prediction value and the actual perception data value uploaded by the street lamp is compared, and if the difference is greater than a preset threshold, a sensor abnormality alarm is output.

4. The smart street light control method according to claim 1, characterized in that: Also includes: Receive fault warning information transmitted by street lamps based on monitoring parameters of the street lamps themselves; Identify the fault type and extract the location information of the street lamp according to the fault warning information; The fault type and the location information are sent to a maintenance terminal.

5. The smart street light control method according to claim 1, characterized in that: After receiving the perception data transmitted by the sensor on the street lamp, it also includes: Sharing the perception data to a smart city management platform; Receiving city data shared by the smart city management platform; The lighting brightness and switch status of the street lamp are adjusted according to the city data.

6. The smart street light control method according to claim 5, characterized in that: The smart city management platform includes a smart city traffic management system; Correspondingly, the perception data is shared to the smart city management platform, including: Identify whether there is an abnormal road event based on the image data, and obtain a road safety identification result; The road safety identification result is sent to the smart city traffic management system.

7. The smart street light control method according to claim 1, characterized in that: The environmental data includes light intensity; Correspondingly, adjusting the lighting brightness and switch state of the street lamp according to the environmental data, the pedestrian flow data and the vehicle flow data includes: Comparing the light intensity with a preset light intensity; When the illumination intensity is less than the preset illumination intensity and the pedestrian flow data is greater than zero or the vehicle flow data is greater than zero, controlling the street lamp to turn on illumination; The lighting brightness of the street lamp is adjusted according to the size of the sum of the pedestrian flow data and the vehicle flow data, wherein the greater the sum of the pedestrian flow data and the vehicle flow data, the greater the lighting brightness of the street lamp.

8. A smart street light control device, characterized in that: include: A data receiving module, used to receive the perception data transmitted by the sensor on the street lamp, wherein the perception data includes environmental data and image data; A flow extraction module, used to extract, based on the image data, pedestrian flow data and vehicle flow data of the traffic section corresponding to the street lamp that uploaded the perception data; The street lamp control module is used to adjust the lighting brightness and switch status of the street lamp according to the environmental data, the pedestrian flow data and the vehicle flow data.

9. A computer device, characterized in that: include: A memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the smart street light control method according to any one of claims 1 to 7 by executing the computer instructions.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a computer to execute the smart street light management and control method according to any one of claims 1 to 7.

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