Apparatus and method for controlling smart streetlamp through integrated analysis of environmental and traffic information based on IoT sensors

Abstract

The present invention relates to a smart street light control device and method through integrated analysis of environmental and traffic information based on IoT sensors. An electronic device according to one embodiment of the present invention includes a memory and a processor connected to the memory, wherein the processor collects sensing data including measurement values ​​for air quality, noise, and fine dust from an IoT sensor module installed in a street light, transmits the sensing data to a control terminal at a preset period or when an event occurs, generates current environmental state information and traffic condition information based on the sensing data through an artificial intelligence module, and controls the lighting color, brightness, and lighting pattern of a light-emitting module of the street light based on the current environmental state information and the traffic condition information.

Description

Technology Field

[0001] The present invention relates to a smart street light control device and method through integrated analysis of environmental and traffic information based on IoT sensors. Background Technology

[0003] Unless otherwise indicated in this specification, the contents described in this section are not prior art for the claims of this application, and are not to be recognized as prior art simply because they are included in this section.

[0004] Modern cities face complex urban environmental problems, such as air pollution, fine dust, noise pollution, and traffic congestion, due to population concentration. Consequently, there are ongoing attempts to utilize the densely installed street lighting infrastructure throughout the city not merely as lighting devices, but as core hubs of smart cities that collect urban data and protect the safety of citizens.

[0005] However, existing street light control systems remain passive, merely turning lights on and off according to sunset and sunrise times or adjusting light intensity based on ambient brightness via illuminance sensors. This approach has limitations in that it is difficult to immediately notify citizens of localized environmental changes, such as deteriorating air quality or increased fine dust concentrations.

[0006] In particular, although sudden situations on the road, such as traffic accidents or traffic congestion, are closely linked to changes in noise patterns or air quality, existing systems lack the capability to comprehensively collect and analyze such heterogeneous data. For example, there is a lack of active safety features, such as immediately detecting a traffic accident at night and sending a warning to following vehicles by changing the color or flashing pattern of streetlights, which frequently leads to secondary accidents.

[0007] Furthermore, existing environmental and traffic sensors often operate independently of the streetlight control system, resulting in inefficiency where collected data cannot be reflected in real-time for streetlight operation control and is utilized only for simple monitoring purposes.

[0008] Therefore, there is a growing need for intelligent street light control devices and methods that can collect air quality, noise, fine dust, and traffic information in real time through IoT sensors integrated into street lights, and analyze this data to intuitively inform the public of environmental pollution conditions or provide immediate visual warnings in the event of a traffic accident. Prior art literature

[0010] Korean Registered Patent No. 10-2842506 (July 31, 2025) The problem to be solved

[0011] One embodiment of the present invention provides a smart street light control device and method through integrated analysis of environmental and traffic information based on IoT sensors.

[0012] The technical problems to be solved by the present invention are not limited to those mentioned above, and other technical problems not mentioned will be clearly understood by those skilled in the art to which the present invention belongs from the description below. means of solving the problem

[0014] To achieve the above-mentioned purpose, an electronic device according to one embodiment of the present invention includes a memory and a processor connected to the memory, and the processor collects sensing data including measurement values ​​for air quality, noise, and fine dust from an IoT sensor module installed in a street light, generates current environmental state information and traffic situation information based on the sensing data through an artificial intelligence module, transmits the sensing data, current environmental state information, and traffic situation information to a control terminal at a preset period or when an event occurs, and controls the lighting color, brightness, and lighting pattern of the light-emitting module of the street light based on the current environmental state information and the traffic situation information.

[0015] At this time, the current environmental status information may include fine dust concentration and air quality values ​​extracted from the sensing data, and the traffic situation information may include the estimated traffic volume of the road derived by analyzing noise data included in the sensing data and the traffic accident probability value calculated through the artificial intelligence module.

[0016] At this time, the processor can compare the fine dust concentration included in the current environmental status information with a preset air quality standard value, and if the fine dust concentration exceeds the air quality standard value, control the light-emitting module so that the lighting color of the street light is illuminated in a preset color corresponding to the fine dust concentration.

[0017] At this time, the processor can calculate the estimated traffic volume by analyzing the waveform and decibel (dB) magnitude of the noise data included in the sensing data through the artificial intelligence module.

[0018] At this time, the processor analyzes the frequency spectrum image of the noise data through the artificial intelligence module to calculate a traffic accident occurrence probability value based on the similarity with a pre-learned accident noise pattern, and if the traffic accident occurrence probability value exceeds a preset threshold probability, it estimates that a traffic accident has occurred and transmits an accident occurrence notification to the control terminal, and controls the light-emitting module to light up with a preset lighting color and lighting pattern in response to the accident occurrence notification.

[0019] At this time, the processor can calculate an accident intensity index representing the physical impact of an accident based on the maximum decibel (dB) of an abnormal plosive sound detected in the noise data through the artificial intelligence module, derive an integrated risk index for the area where the street light is located based on the ratio of the fine dust concentration and the air quality standard value, the ratio of the average decibel of the noise data to the road noise standard value of the area where the street light is located, the traffic accident occurrence probability value, and the accident intensity index, and transmit the integrated risk index to the control terminal.

[0020] At this time, the above integrated risk index is derived by the following mathematical formula,

[0021]

[0022] IDI refers to the above-mentioned integrated risk index, D_curr refers to the currently measured fine dust concentration, D_limit refers to the above-mentioned air quality standard value corresponding to the fine dust advisory standard concentration issued under the Air Environment Conservation Act for the area where the street light is located, N_curr refers to the currently measured average decibel of noise, N_limit refers to the above-mentioned road noise standard value legally prescribed for the area where the street light is located, P_acc refers to the above-mentioned traffic accident occurrence probability value, and S_index may refer to the above-mentioned accident intensity index.

[0023] At this time, the above accident intensity index is derived by the following mathematical formula,

[0024]

[0025] S_index refers to the above-mentioned accident intensity index, S_max refers to the maximum decibel of the above-mentioned abnormal plosive sound, and S_ref may refer to a reference decibel corresponding to a normal vehicle horn sound.

[0026] At this time, when the processor transmits the accident occurrence notification to the control terminal, in controlling the lighting pattern of the street light, it may derive a target lighting interval, which is the blinking period of the street light based on the Integrated Risk Index (IDI), and control the light-emitting module of the street light to light according to the target lighting interval.

[0027] At this time, the above target lighting interval is derived by the following mathematical formula,

[0028]

[0029] T_int represents the above target lighting interval, T_std represents the preset standard lighting interval, and IDI may represent the above integrated risk index. Effects of the invention

[0031] As such, according to one embodiment of the present invention, a smart street light control device and method can be provided through integrated analysis of environmental and traffic information based on IoT sensors.

[0032] The effects obtainable from the present invention are not limited to those mentioned above, and other unmentioned effects will be clearly understood by those skilled in the art from the description below. Brief explanation of the drawing

[0034] Other aspects, features, and benefits of specific preferred embodiments of the present invention, as described above, will become more apparent from the following description in conjunction with the accompanying drawings. FIG. 1 is a conceptual diagram of a smart street light control device based on IoT sensor-based integrated analysis of environment and traffic information according to one embodiment of the present invention. FIG. 2 is a block diagram of an electronic device according to one embodiment of the present invention. Figure 3 is a diagram showing the difference in lighting color according to fine dust concentration according to one embodiment of the present invention. FIG. 4 is a diagram showing the operation of a light-emitting module for accident occurrence notification according to one embodiment of the present invention. FIG. 5 is a diagram showing a target lighting interval linked to an integrated risk index according to one embodiment of the present invention. FIG. 6 is a flowchart of a smart street light control method through integrated analysis of environment and traffic information based on IoT sensors according to an embodiment of the present invention. It should be noted that in the drawings above, similar reference numbers are used to illustrate identical or similar elements, features, and structures. Specific details for implementing the invention

[0035] Hereinafter, embodiments of the present invention will be described in detail with reference to the attached drawings.

[0036] In describing the embodiments, technical details that are well known in the technical field to which the present invention belongs and are not directly related to the present invention are omitted. This is intended to convey the essence of the present invention more clearly without obscuring it by omitting unnecessary explanations.

[0037] For the same reason, some components in the attached drawings have been exaggerated, omitted, or schematically depicted. Additionally, the size of each component does not entirely reflect its actual dimensions. Identical or corresponding components in each drawing have been assigned the same reference numbers.

[0038] The advantages and features of the present invention and the methods for achieving them will become clear by referring to the embodiments described below in detail together with the accompanying drawings. However, the present invention is not limited to the embodiments disclosed below but can be implemented in various different forms. These embodiments are provided merely to ensure that the disclosure of the present invention is complete and to fully inform those skilled in the art of the scope of the invention, and the present invention is defined only by the scope of the claims. Throughout the specification, the same reference numerals refer to the same components.

[0039] At this point, it will be understood that each block of the process flow diagrams and combinations of the flow diagrams can be executed by computer program instructions. Since these computer program instructions can be loaded into the processor of a general-purpose computer, a special-purpose computer, or other programmable data processing equipment, the instructions executed through the processor of the computer or other programmable data processing equipment create means to perform the functions described in the flow diagram block(s). Since these computer program instructions can also be stored in computer-available or computer-readable memory that can be directed toward the computer or other programmable data processing equipment to implement the function in a specific way, the instructions stored in computer-available or computer-readable memory can also produce a manufactured item containing instruction means to perform the function described in the flow diagram block(s). Since computer program instructions can be loaded onto a computer or other programmable data processing equipment, instructions that perform a series of operation steps on the computer or other programmable data processing equipment to create a process executed by the computer can also provide steps for executing the functions described in the flowchart block(s).

[0040] Additionally, each block may represent a module, segment, or part of code containing one or more executable instructions for executing a specified logical function(s). It should also be noted that in some alternative execution examples, the functions mentioned in the blocks may occur out of order. For instance, two blocks described in succession may actually be executed substantially simultaneously, or the blocks may be executed in reverse order according to their corresponding functions.

[0041] In this embodiment, the term "part" refers to a software or hardware component such as a field-programmable gate array (FPGA) or an application-specific integrated circuit (ASIC), and the "part" performs certain roles. However, the meaning of "part" is not limited to software or hardware. The "part" may be configured to reside in an addressable storage medium or configured to run one or more processors. Accordingly, as an example, the "part" includes components such as software components, object-oriented software components, class components, and task components, as well as processes, functions, attributes, procedures, subroutines, segments of program code, drivers, firmware, microcode, circuits, data, databases, data structures, tables, arrays, and variables. The functions provided within the components and "parts" may be combined into a smaller number of components and "parts" or further separated into additional components and "parts." In addition, the components and '~parts' may be implemented to play one or more CPUs within the device or secure multimedia card.

[0042] In describing the embodiments of the present invention in detail, the primary focus will be on examples of specific systems, but the main point claimed in this specification is applicable to other communication systems and services having a similar technical background without significantly departing from the scope disclosed in this specification, and this will be possible at the judgment of a person with skilled technical knowledge in the relevant technical field.

[0044] In addition, the user terminal described below may include a communication-capable desktop computer, laptop computer, notebook, smartphone, tablet PC, mobile phone, smart watch, smart glass, e-book reader, PMP (portable multimedia player), portable game console, navigation device, digital camera, DMB (digital multimedia broadcasting) player, digital audio recorder, digital audio player, digital video recorder, digital video player, PDA (Personal Digital Assistant), etc.

[0046] FIG. 1 is a conceptual diagram of a smart street light control device based on IoT sensor-based integrated analysis of environment and traffic information according to one embodiment of the present invention, and FIG. 2 is a block diagram of an electronic device (100) according to one embodiment of the present invention.

[0047] An electronic device (100) according to one embodiment includes a processor (110) and a memory (120). The processor (110) can perform at least one of the methods described above. The memory (120) can store information related to the method described above or store a program in which the method described above is implemented. The memory (120) may be volatile memory or non-volatile memory. The memory (120) may be referred to as a 'database', 'storage unit', etc.

[0048] The processor (110) can execute a program and control the electronic device (100). The code of the program executed by the processor (110) can be stored in memory (120). The device (100) can be connected to an external device (e.g., a personal computer or a network) through an input / output device (not shown) and exchange data.

[0049] At this time, the processor (110) can collect sensing data including measurements of air quality, noise, and fine dust from an IoT sensor module installed on a street light, and generate current environmental status information and traffic situation information based on the sensing data through an artificial intelligence module.

[0050] At this time, the current environmental status information may include fine dust concentration and air quality values ​​extracted from the sensing data, and the traffic situation information may include the estimated traffic volume of the road derived by analyzing noise data included in the sensing data and the traffic accident probability value calculated through the artificial intelligence module.

[0051] Looking more closely, the IoT sensor module may be a composite sensor device attached to or embedded in the main body of a street light to detect the environment and conditions around the street light. For example, the IoT sensor module may include at least one of a dust sensor that measures fine dust (PM10) and ultrafine dust (PM2.5), a gas sensor that measures the concentration of carbon dioxide or harmful gases in the atmosphere, a microphone sensor that collects ambient sounds, and an image sensor (CCTV) that captures traffic flow on the road.

[0052] In addition, the above sensing data may refer to a set of raw data collected in real time from each sensor included in the IoT sensor module. This may include fine dust concentration values ​​expressed as specific numerical values, noise waveforms and decibel (dB) levels, and video frames of vehicles traveling on the road. Through this, the processor can secure basic data for generating meaningful analysis data beyond simple measurement values.

[0053] At this time, the specific process and algorithm for deriving the probability value of the traffic accident occurrence from the sensing data (in particular, noise data) through the artificial intelligence module will be described in more detail later.

[0054] In addition, the processor transmits the sensing data, current environmental status information, and traffic situation information to a control terminal at a preset interval or when an event occurs, and can control the lighting color, brightness, and lighting pattern of the light-emitting module of the street light based on the current environmental status information and the traffic situation information.

[0055] Through this, drivers and pedestrians can immediately recognize the current road congestion, air quality, or the occurrence of accidents ahead solely through intuitive lighting changes (color, brightness, flashing, etc.) of streetlights, without having to check a separate smartphone or navigation system.

[0056] Therefore, drivers can perform safe driving by anticipating road conditions in advance, such as slowing down or taking detours. At the same time, by receiving precise data collected from individual streetlights in real time at remote control terminals (control centers), it becomes possible to monitor the entire city's environment and traffic flow macroscopically, and to enable efficient urban management, such as rapidly dispatching personnel or controlling traffic signals in the event of an emergency.

[0058] Figure 3 is a diagram showing the difference in lighting color according to fine dust concentration according to one embodiment of the present invention.

[0059] Referring to FIG. 3, the processor compares the fine dust concentration included in the current environmental state information with a preset atmospheric environment standard value, and

[0060] When the above fine dust concentration exceeds the above air quality standard value, the light-emitting module can be controlled so that the lighting color of the street light is turned on in a preset color corresponding to the above fine dust concentration.

[0061] To examine this in more detail, the above air quality standard values ​​may refer to the standard concentrations by grade of the Air Quality Index (AQI) notified by the Ministry of Environment or the relevant local government.

[0062] For example, the processor can control the lighting color of the street light to be blue when the fine dust (PM10) concentration is in the 'good' range of 0 to 30 μg / m³, green when it is in the 'normal' range of 31 to 80 μg / m³, yellow when it is in the 'bad' range of 81 to 150 μg / m³, and red when it is in the 'very bad' range of 151 μg / m³ or higher.

[0063] Through this, pedestrians and drivers using the road can intuitively perceive the current air quality status solely through changes in streetlight color, without having to check a smartphone or a separate display, and immediately take measures such as wearing a mask or refraining from going outside.

[0064] In addition, the processor can calculate the estimated traffic volume by analyzing the waveform and decibel (dB) magnitude of the noise data included in the sensing data through the artificial intelligence module.

[0065] Looking more closely, the artificial intelligence module mentioned above can be built based on machine learning or deep learning algorithms.

[0066] For example, to analyze the flow of time-series noise data, recurrent neural network (RNN) models such as LSTM (Long Short-Term Memory) or GRU (Gated Recurrent Unit) may be used, or convolutional neural network (CNN) models that convert noise waveforms into image-shaped spectrograms to analyze patterns may be utilized.

[0067] In addition, the calculation of the above estimated traffic volume can be based on the unique noise characteristics generated during vehicle operation.

[0068] Generally, as the density of vehicles on the road increases, the average decibel level of background noise rises, and temporary waveform peaks occur whenever an individual vehicle passes a streetlamp due to engine noise or tire friction noise.

[0069] The above artificial intelligence module analyzes the baseline changes and peak occurrence frequency of such noise in real time, and can estimate the number of vehicles passing through the road per unit time as a quantitative figure.

[0071] FIG. 4 is a diagram showing the operation of a light-emitting module for accident occurrence notification according to one embodiment of the present invention.

[0072] Referring to FIG. 4, the processor analyzes the frequency spectrum image of the noise data through the artificial intelligence module to calculate a probability value of a traffic accident based on similarity with a pre-learned accident noise pattern, and if the probability value of a traffic accident exceeds a preset threshold probability, it estimates that a traffic accident has occurred and transmits an accident occurrence notification to the control terminal, and controls the light-emitting module to light up with a preset lighting color and lighting pattern in response to the accident occurrence notification.

[0073] Looking at it in more detail, the calculation of the probability value of the traffic accident occurrence described above may begin with the artificial intelligence module converting the collected noise data into the time-frequency domain to generate a spectrogram image.

[0074] At this time, the artificial intelligence module utilizes a deep learning model, such as a convolutional neural network (CNN), to extract features that match sound patterns unique to accident situations, such as crash, skid, and scream, from the generated spectrogram image. Subsequently, the similarity between the extracted features and the pre-trained accident dataset can be quantified as a real value between 0 (0%) and 1 (100%) to calculate the probability of the traffic accident occurring.

[0075] In addition, the above threshold probability can be set by the manager of the present invention to minimize malfunctions and increase the actual accident detection rate, for example, it can be set to 0.8 (80%) or higher. However, this is not a fixed value, and it may be set variably, such as by lowering it to 0.6 (60%) to increase sensitivity in areas with a history of frequent accidents or during nighttime hours.

[0076] Furthermore, looking at an example of lighting control in response to the accident notification mentioned above, the processor can change the color of the streetlights to 'Red', which has the highest visibility, as soon as an accident is detected, and control them with a fast blinking pattern at 0.5-second intervals. Through this, it is possible to prevent secondary collisions by strongly warning subsequent vehicles approaching the accident site of the dangerous situation ahead, and also alert surrounding pedestrians.

[0077] In addition, the processor can calculate an accident intensity index representing the physical impact of an accident based on the maximum decibel (dB) of an abnormal plosive sound detected in the noise data through the artificial intelligence module, derive an integrated risk index for the area where the street light is located based on the ratio of the fine dust concentration to the air quality standard value, the ratio of the average decibel of the noise data to the road noise standard value of the area where the street light is located, the traffic accident probability value, and the accident intensity index, and transmit the integrated risk index to the control terminal.

[0078] Looking at it in more detail, the above integrated risk index can be derived by the following mathematical formula 1.

[0079] [Mathematical Formula 1]

[0080]

[0081] In this case, IDI refers to the integrated risk index, D_curr refers to the currently measured fine dust concentration, D_limit refers to the air quality standard value corresponding to the fine dust advisory standard concentration issued under the Air Environment Conservation Act for the area where the street light is located, N_curr refers to the average decibel of the currently measured noise, N_limit refers to the road noise standard value legally prescribed for the area where the street light is located, P_acc refers to the traffic accident occurrence probability value, and S_index may refer to the accident intensity index.

[0082] In addition, the above accident intensity index can be derived by the following mathematical formula 2.

[0083] [Mathematical Formula 2]

[0084]

[0085] In this case, S_index refers to the accident intensity index, S_max refers to the maximum decibel of the abnormal plosive sound, and S_ref may refer to a reference decibel corresponding to a normal vehicle horn sound.

[0086] Looking more closely, the above abnormal popping sound may include a loud 'bang' sound that occurs when vehicles collide, a sharp skid noise that occurs when tires rub strongly against the road surface, or repeated horn sounds in urgent situations.

[0087] At this time, the artificial intelligence module converts the unique frequency band and abrupt changes (peaks) of the waveform of such sound into an imaged spectrogram through time-frequency analysis, and inputs this into a convolutional neural network (CNN) model to detect accident-specific patterns that are clearly distinguishable from general road driving noise in real time.

[0088] Furthermore, the aforementioned integrated risk index is not merely a list of environmental pollution or noise levels, but rather serves as a comprehensive, quantified indicator of how dangerous the current road condition is for pedestrians and drivers. Under normal conditions, it increases gradually to reflect how much fine dust concentrations or traffic noise exceed standard limits, but it is designed to spike sharply at the moment when the probability of an accident is high or when actual impact sounds are detected.

[0089] Specifically, if we look at the design structure of the formula for deriving the above integrated risk index, the environmental factor and the noise factor are combined in a form where the squares of the ratios to the reference value are summed and then the square root is taken.

[0090] This is intended to ensure that severe pollution or extreme noise situations are not overlooked, by making the influence on the overall risk index increase exponentially as any single factor significantly exceeds the threshold.

[0091] In addition, the accident intensity index, which is proportional to the magnitude of the impact sound and the probability of an accident predicted by artificial intelligence, is combined through a multiplication operation. That is, in a calm state, only the preceding environmental and noise factors influence the index; however, as soon as signs of an accident are detected, the entire index is amplified by doubling by the probability and intensity, thereby acting as a trigger that enables the system to prioritize recognizing the emergency situation and immediately enter emergency control mode.

[0093] FIG. 5 is a diagram showing a target lighting interval linked to an integrated risk index according to one embodiment of the present invention.

[0094] Referring to FIG. 5, when the processor transmits the accident occurrence notification to the control terminal, in controlling the lighting pattern of the street light, it derives a target lighting interval, which is the blinking period of the street light based on the Integrated Risk Index (IDI), and can control the light-emitting module of the street light to light according to the target lighting interval.

[0095] At this time, the above target lighting interval can be derived by the following mathematical formula 3.

[0096] [Mathematical Formula 3]

[0097]

[0098] In this case, T_int represents the target lighting interval, T_std represents the preset standard lighting interval, and IDI may represent the integrated risk index.

[0099] Looking at it in more detail, the processor can intuitively convey the urgency of the situation to nearby pedestrians and drivers by accelerating the flashing speed of streetlights as accidents or environmental risks increase.

[0100] This is intended to go beyond simply turning on a warning light and more effectively draw people's attention by increasing the frequency of visual stimuli in proportion to the degree of danger.

[0101] For example, when a simple environmental standard is exceeded, it flashes slowly to give a warning, whereas when a serious traffic accident occurs and the integrated risk index increases rapidly, it flashes very quickly to induce immediate evasive maneuvers or stopping.

[0102] Specifically, if we look at the design structure of the formula for deriving the target lighting interval, it takes an inverse relationship where the standard lighting interval is divided by the integrated risk index. That is, as the integrated risk index (IDI) becomes greater than 1, the denominator increases, and the resulting target lighting interval (T_int) becomes smaller.

[0103] This means that if the risk level doubles, the flashing cycle is reduced by half, causing it to flash twice as fast, and as a result, it creates a visual effect where the streetlight sends a signal more urgently as the dangerous situation becomes more severe.

[0104] This dynamic control method can contribute to increasing the speed of human cognitive response compared to static warnings.

[0106] FIG. 6 is a flowchart of a smart street light control method through integrated analysis of environment and traffic information based on IoT sensors according to an embodiment of the present invention.

[0107] Referring to FIG. 6, a smart street light control method through integrated analysis of environmental and traffic information based on an IoT sensor according to an embodiment of the present invention can collect sensing data including measurement values ​​for air quality, noise, and fine dust from an IoT sensor module installed on a street light (S101).

[0108] In addition, the smart street light control method through integrated analysis of environment and traffic information based on IoT sensors according to one embodiment of the present invention can generate current environment status information and traffic situation information based on the sensing data through an artificial intelligence module (S103).

[0109] In addition, the smart street light control method through integrated analysis of environment and traffic information based on IoT sensors according to one embodiment of the present invention can transmit the sensing data, current environment status information and traffic situation information to a control terminal at a preset period or when an event occurs (S105).

[0110] In addition, the smart street light control method based on an IoT sensor-based integrated analysis of environment and traffic information according to one embodiment of the present invention can control the lighting color, brightness, and lighting pattern of the light-emitting module of the street light based on the current environment state information and the traffic situation information (S107).

[0112] In addition, the smart street light control method through integrated analysis of environmental and traffic information based on IoT sensors according to one embodiment of the present invention can be configured in the same way as the smart street light control device through integrated analysis of environmental and traffic information based on IoT sensors disclosed in FIGS. 1 to 5.

[0114] The embodiments described above may be implemented as hardware components, software components, and / or combinations of hardware and software components. For example, the devices, methods, and components described in the embodiments may be implemented using one or more general-purpose or special-purpose computers, such as, for example, a processor, a controller, an arithmetic logic unit (ALU), a digital signal processor, a microcomputer, a field programmable gate array (FPGA), a programmable logic unit (PLU), a microprocessor, or any other device capable of executing and responding to instructions. The processing unit may execute an operating system (OS) and one or more software applications executed on said operating system. Additionally, the processing unit may access, store, manipulate, process, and generate data in response to the execution of the software. For ease of understanding, the processing unit may be described as being used as a single unit, but those skilled in the art will understand that the processing unit may include multiple processing elements and / or multiple types of processing elements. For example, the processing unit may include multiple processors or one processor and one controller. Additionally, other processing configurations, such as parallel processors, are also possible.

[0115] The method according to the embodiment may be implemented in the form of program instructions that can be executed through various computer means and recorded on a computer-readable medium. The computer-readable medium may include program instructions, data files, data structures, etc., either alone or in combination. The program instructions recorded on the medium may be those specifically designed and configured for the embodiment, or they may be those known and available to those skilled in the art of computer software. Examples of computer-readable recording media include magnetic media such as hard disks, floppy disks, and magnetic tapes; optical recording media such as CD-ROMs and DVDs; magneto-optical media such as floptical disks; and hardware devices specifically configured to store and execute program instructions, such as ROM, RAM, and flash memory. Examples of program instructions include machine code, such as that generated by a compiler, as well as high-level language code that can be executed by a computer using an interpreter, etc. The hardware devices described above may be configured to operate as one or more software modules to perform the operation of the embodiment, and vice versa.

[0116] Software may include computer programs, code, instructions, or a combination of one or more of these, and may configure a processing unit to operate as desired or command the processing unit independently or collectively. Software and / or data may be permanently or temporarily embodied in any type of machine, component, physical device, virtual equipment, computer storage medium or device, or transmitted signal wave so as to be interpreted by the processing unit or to provide instructions or data to the processing unit. Software may be distributed over networked computer systems and may be stored or executed in a distributed manner. Software and data may be stored on one or more computer-readable recording media.

[0117] Although the embodiments have been described above with reference to the limited drawings, those skilled in the art can apply various technical modifications and variations based on the above. For example, suitable results may be achieved even if the described techniques are performed in a different order than described, and / or if the components of the described system, structure, device, circuit, etc. are combined or assembled in a form different from described, or replaced or substituted by other components or equivalents.

[0118] Therefore, other implementations, other embodiments, and equivalents to the claims also fall within the scope of the claims set forth below.

Claims

Claim 1 In an electronic device, memory; and a processor connected to said memory; The processor includes: collecting sensing data including measurements of air quality, noise, and fine dust from an IoT sensor module installed on a street light; generating current environmental status information and traffic situation information based on the sensing data through an artificial intelligence module; transmitting the sensing data, current environmental status information, and traffic situation information to a control terminal at a preset period or upon the occurrence of an event; controlling the lighting color, brightness, and lighting pattern of the light-emitting module of the street light based on the current environmental status information and the traffic situation information; the current environmental status information includes fine dust concentration and air quality values ​​extracted from the sensing data; the traffic situation information includes the estimated traffic volume of the road derived by analyzing noise data included in the sensing data and the traffic accident probability value calculated through the artificial intelligence module; the processor compares the fine dust concentration included in the current environmental status information with a preset air quality standard value; and if the fine dust concentration exceeds the air quality standard value, changes the lighting color of the street light to a preset color corresponding to the fine dust concentration. The processor controls the light-emitting module to be illuminated, and through the artificial intelligence module, analyzes the waveform and decibel (dB) magnitude of the noise data included in the sensing data to calculate the estimated traffic volume, and through the artificial intelligence module, analyzes the frequency spectrum image of the noise data to calculate a traffic accident occurrence probability value based on similarity with a pre-learned accident noise pattern, and if the traffic accident occurrence probability value exceeds a preset threshold probability, it is estimated that a traffic accident has occurred and an accident occurrence notification is transmitted to the control terminal, and in response to the accident occurrence notification, controls the light-emitting module to be illuminated with a preset lighting color and lighting pattern, and through the artificial intelligence module,An accident intensity index representing the physical impact of an accident is calculated based on the maximum decibel (dB) of abnormal plosive sounds detected in the above noise data, and an integrated risk index for the area where the street light is located is derived based on the ratio of the fine dust concentration and the above atmospheric environment standard value, the ratio of the average decibel of the above noise data to the road noise standard value of the area where the street light is located, the traffic accident occurrence probability value, and the above accident intensity index, and the integrated risk index is transmitted to the control terminal, wherein the integrated risk index is derived by the following mathematical formula, IDI represents the above integrated risk index, D_curr represents the currently measured fine dust concentration, D_limit represents the above air quality standard value corresponding to the fine dust advisory standard concentration issued under the Air Environment Conservation Act for the area where the above street light is located, N_curr represents the currently measured average decibel of noise, N_limit represents the above road noise standard value legally prescribed for the area where the above street light is located, P_acc represents the above traffic accident occurrence probability value, S_index represents the above accident intensity index, and the above accident intensity index is derived by the following mathematical formula, S_index represents the accident intensity index, S_max represents the maximum decibel of the abnormal plosive sound, and S_ref represents the reference decibel corresponding to a normal vehicle horn sound; and when the processor transmits the accident occurrence notification to the control terminal, in controlling the lighting pattern of the street light, the processor derives a target lighting interval, which is the blinking period of the street light based on the Integrated Risk Index (IDI), and controls the light-emitting module of the street light to light according to the target lighting interval, wherein the target lighting interval is derived by the following mathematical formula, An electronic device characterized in that T_int represents the target lighting interval, T_std represents the preset standard lighting interval, and IDI represents the integrated risk index. Claim 2 delete Claim 3 delete

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