KR20240053386A - Control system for anti-icing device on road and method thereof - Google Patents

Abstract

The present invention relates to a control system and method for a road icing prevention device, and more specifically, to a sound wave sensor that transmits a sound wave signal to a preset detection area of the road and then receives the reflected signal, and uses artificial intelligence. By analyzing the signal obtained from the sonic sensor to determine the current weather condition, road surface condition, road surface temperature, and the amount of salt water sprayed from the salt water spray device, the heating device or salt water spray installed in the habitually icy section of the road Provided is a control system and method for a road icing prevention device that allows accurate control of the operation of the device.

Description

Control system and method of road icing prevention device {CONTROL SYSTEM FOR ANTI-ICING DEVICE ON ROAD AND METHOD THEREOF}

The present invention relates to a control system and method for a road icing prevention device, and more specifically, to a sound wave sensor that transmits a sound wave signal to a preset detection area of the road and then receives the reflected signal, and uses artificial intelligence. By analyzing the signal obtained from the sonic sensor to recognize the current weather condition, road surface condition, road surface temperature, and the amount of salt water sprayed from the salt water spray device, the heating device or salt water spray installed in the habitually icy section of the road It relates to a control system and method for a road icing prevention device that allows accurate control of the operation of the device.

In the United States, approximately 6 million traffic accidents occur every year. In particular, more than 1.2 million accidents occur on average each year in the United States alone due to slippery road surfaces, and more than 5,000 people die every year from these accidents. Accordingly, the slipperiness of road surfaces is emerging as a social problem that places a great burden on the government and insurance companies.

Korea is no exception. Black ice accidents occur every winter, with 6,500 cases occurring over the past five years and the number of deaths reaching approximately 200.

In Korea, the snow melting process is carried out in four stages, and all of these processes are carried out manually by humans, and it takes several hours to reach the final stage. In the case of the last black ice accident on the Sangju-Yeongcheon Expressway, it was a man-made disaster that occurred because the official in charge checked the forecast but did not request snow melting in advance.

These accidents are due to the absence of sensors that can detect the condition of the road surface in real time and a notification system using them.

Meanwhile, there are two major methods proposed so far to estimate the type of road surface or the friction coefficient of the road surface.

One is a direct method of finding out by causing physical friction, and the other is an indirect method of using radio waves or sound waves.

First, direct methods include wheel-based methods and vehicle dynamics-based methods.

What these two methods have in common is to estimate the friction coefficient of the road surface by applying the brakes regardless of the driver's intention and decelerating the vehicle.

In this method, you can only find out the friction coefficient by stepping on the road surface you want to know, and a vehicle dynamics model is essential. Additionally, since the brakes must be operated, there are limitations that affect ride comfort and fuel efficiency.

Meanwhile, indirect methods include methods using electromagnetic waves and sound waves. Both of these methods do not require physical friction, so they can be used independently of the vehicle dynamics model and have the advantage of knowing the condition of the road surface in advance without contacting the road surface. There is.

The electromagnetic wave-based method uses cameras, lidar, radar, etc., and although it can see quite far away, its accuracy is relatively low, and the system is very expensive, ranging from hundreds to thousands of dollars. Additionally, because the amount of data is large, it is difficult to create a data set.

Additionally, there is a method using an image sensor, which is one of the electromagnetic wave methods, and it uses a camera, an image sensor, to classify the type of road surface. However, image sensors are greatly affected by illumination, and black ice is not visible to the human eye, so vision sensors cannot detect it. Due to these limitations, the average performance of methods using image sensors is less than 70%. Additionally, not only an image sensor but also a high-performance image processing device is required, so the cost is relatively high.

In the case of black ice, just as it is invisible to the human eye, it cannot be detected even by an image sensor. In other words, there is a limitation in being unable to distinguish various types of road surfaces using the image sensor method.

Meanwhile, methods using the sound waves include a passive sensing method and an active sensing method. The passive sensing method records noise from tires for a certain period of time with a microphone and then classifies it using an artificial neural network. There is. In this method, the type of road surface can be known only for the surface over which the tire has passed, and there is a limitation in that classification takes a long time. Meanwhile, among active sensing methods, there is research to classify reflected road surfaces using only distance and reflection intensity information, but this method has the disadvantage of being weak to surrounding disturbances.

Meanwhile, many electric and hydrogen cars with light blue license plates are appearing on the roads these days. According to Bloomberg, it is predicted that by 2040, more than half of vehicles sold will be electric vehicles, and one-third of vehicles on the road will be electric vehicles.

To increase the mileage, which is the driving range of electric vehicles, vehicle manufacturers are actively using regenerative braking. However, it is not difficult to hear news about accidents caused by a car skidding due to regenerative braking. Symptoms such as loss of traction on the rear wheels or wheels locking occur due to regenerative braking.

Looking at Tesla's current regenerative braking settings, you can change it to standard and low, but there is no option to turn it off. The reason these accidents occur is because the control cycle of the regenerative braking module is 100ms, which is longer than other systems, and the reason why manufacturers do not allow the regenerative braking function to be turned off is because the vehicle's travelable distance drastically decreases.

It may be best to use regenerative braking depending on the condition of the road surface you are driving on, but this problem arises because the type of road surface cannot be known in advance before the vehicle passes.

In winter, the risk of accidents increases on road surfaces due to snow, freezing from snow or rainfall, black ice, etc., so devices or methods to melt snow or ice are used to solve problems such as freezing or snow on road surfaces.

As a conventional technology to prevent road surface icing, a technology was used to melt snow and ice by burying a heating device or heating wire in the upper part of the road and using the heat of the heating device or heating wire. However, this heating method requires a lot of initial cost, usage cost, and repair cost, so it is difficult to use in a large area and maintenance is difficult.

Meanwhile, methods using geothermal heat or solar energy for heat generation have also been developed, but the initial construction cost is excessive, making it difficult to use them universally.

In addition, spraying snow removers or salt water was used as a conventional method to prevent freezing, but the conventional method was spraying by vehicle, which is inefficient, requires high labor costs, and requires rapid snow removal measures appropriate for road surface conditions (weather conditions and road surface conditions). There was a problem that prevented it from being accomplished.

In addition, salt water spraying was carried out remotely when there was a forecast that the road surface would be slippery, but excessive salt water spray caused damage to nearby street trees and caused corrosion damage to vehicles passing near the salt water sprayer.

In addition, in the case of automatic salt spray devices operated without remote monitoring, there have been continued reports over the past few years that traffic accidents have occurred due to adverse installation effects or that they are ineffective in snow removal.

Meanwhile, the introduction of the road hot wire method was carefully considered because the installation and operation costs are very expensive compared to salt water spray devices. However, as awareness of the effectiveness of salt water spray devices decreases and demands for and interest in safety increase, bold introduction is in progress.

However, the existing heating system is said to collect temperature, snow amount, and ground conditions in real time through sensors in a nearby control box and automatically control the operation of the heating wire. However, it uses temperature and humidity sensors to detect moisture above a certain amount and temperature below a certain amount. It has a structure that operates automatically at a temperature of , and through communication with the control center, the presence or absence of a malfunction can be identified and the manager can remotely control it.

In addition, in the control method using temperature and humidity sensors, depending on the sensing conditions, malfunction may occur in sections that are not actually frozen, resulting in loss costs, and depending on the sensing conditions, it may lead to a fire due to overheating of the heating wire. There is.

Korean patent [10-2432286] discloses a road surface situation analysis and salt water spray automatic control system.

In Korea registered patent [10-2339364], an AI-type thermal spray system is disclosed.

Korean registered patent [10-2304126] discloses a heating cable for melting road ice black ice and snow and a road ice black ice and snow melting system using the same.

Korean registered patent [10-2333543] discloses a system and method for de-icing, snow removal, and road surface condition management during heat waves.

Korean registered patent [10-2432286] (registration date: 2022. 08. 09) Korean registered patent [10-2339364] (registration date: 2021. 12. 09) Korean registered patent [10-2304126] (registration date: 2021. 09. 14) Korean registered patent [10-2333543] (registration date: November 26, 2021)

Therefore, the present invention was created to solve the problems described above, and the object of the present invention is to provide a sound wave sensor that transmits a sound wave signal to a preset detection area of the road and then receives the reflected signal, and uses artificial intelligence. By analyzing the signal obtained from the sonic sensor and recognizing the current weather condition, road surface condition, road surface temperature, and the amount of salt water sprayed from the salt water spray device, the heating device or salt water installed in the habitually icy section of the road is recognized. The aim is to provide a control system and method for a road ice prevention device that allows accurate control of the operation of the injection device.

The purposes of the embodiments of the present invention are not limited to the purposes mentioned above, and other purposes not mentioned will be clearly understood by those skilled in the art from the description below. .

In order to achieve the above-described object, the control system of the road icing prevention device according to an embodiment of the present invention is for transmitting a sound wave signal to a preset point to detect road conditions and then receiving the reflected signal. Acoustic sensor (10); Detects road surface condition data at the preset point based on an artificial intelligence analysis model learned using the reflected signal acquired by the sonic sensor as an input signal, and detects the road surface condition data at the preset point. A control server 40 for generating a signal to control whether to operate or not; A communication unit 20 for transmitting the reflected signal obtained by the sound wave sensor to the control server; and the ice prevention device 100 controlled by the control server to perform operations to prevent ice on the road.

In addition, the control system of the road icing prevention device according to another embodiment of the present invention to achieve the above-described object transmits a sound wave signal to a preset point to detect road conditions and then receives the reflected signal. Acoustic sensor (10) for; Detects road surface condition data at the preset point based on an artificial intelligence analysis model learned using the reflected signal acquired by the sonic sensor as an input signal, and detects the road surface condition data at the preset point. A control unit 30 for generating a signal to control whether to operate or not; An ice prevention device 100 controlled by the control unit to perform operations to prevent ice on the road; and a communication unit 20 for transmitting the control signal generated by the control unit to the ice prevention device.

The freezing prevention device 100 is characterized in that it includes at least one of a heating device or a salt water spray device.

The artificial intelligence analysis model is a weather condition classification model that classifies weather conditions based on the reflected signal acquired by the sonic sensor, and a road surface type classification that classifies the type of road surface based on the reflected signal obtained by the sonic sensor. A model, and a road surface temperature regression model that learns the reflected signal obtained by the sonic sensor and the temperature of the road surface together and outputs the corresponding road surface temperature based on the reflected signal obtained by the sonic sensor. And, when the freezing prevention device 100 is the salt water injection device, the artificial intelligence analysis model learns the reflected signal acquired by the sound wave sensor and the injection amount (distribution degree) of salt water together, so that the sound wave sensor acquires It is characterized in that it further includes a salt water injection amount regression model that outputs the corresponding salt water injection amount (distribution degree) based on the reflected signal.

The road surface condition data includes weather conditions, road surface type, road surface temperature, and salt injection amount, and the control server 40 or the control unit 30 determines that the weather condition is "rain" or If it is “snow”, the classified road surface type is “wet road”, “snow-covered road” or “frozen road”, and the temperature of the detected road surface is less than 4 ℃, a control signal to operate the heating device After the heating device is activated, a control signal is generated to stop the operation of the heating device when the detected temperature of the road surface is 4° C. or higher, and the weather condition is “rain” or “snow.” , If the classified road surface type is “wet road”, “snow-covered road”, or “frozen road”, and the temperature of the detected road surface is less than 4 ℃, generate a control signal to operate the salt water injection device, , After the salt water spray device is activated, a control signal is generated to stop the operation of the salt water spray device when the detected salt water spray amount is 80% or more.

As the control server 40 or the control unit 30 detects that there is an abnormality in the state of the sound wave sensor, it transmits a notification message to the administrator terminal and transmits a status notification signal of the sound wave sensor to the control server. It is characterized by

In addition, a method of controlling a road icing prevention device according to an embodiment of the present invention to achieve the above-described object includes a data collection step (S510) of collecting measurement data from a sonic sensor; An artificial intelligence analysis model creation step (S520) of generating an artificial intelligence analysis model based on the collected data; A sound wave data acquisition step (S530) of transmitting a sound wave signal to a preset point for monitoring road conditions using the sound wave sensor and then receiving a reflected signal; A road surface condition data detection step (S540) of detecting road surface condition data at the preset point based on the artificial intelligence analysis model by using the reflected signal acquired by the sonic sensor as an input signal; and a control signal generation step (S550) of generating a control signal to control whether the ice prevention device operates based on the road surface condition data.

The artificial intelligence analysis model generation step (S520) includes a weather state classification model generation step of generating a weather state classification model that classifies the weather state based on the reflected signal acquired by the sonic sensor; A road surface type classification model generating step of generating a road surface type classification model that classifies the type of road surface based on the reflected signal acquired by the sonic sensor; And generating a road surface temperature regression model that learns the reflected signal acquired by the sonic sensor and the temperature of the road surface together to generate a road surface temperature regression model that outputs the temperature of the corresponding road surface based on the reflected signal acquired by the sonic sensor. Characterized in that it includes a step, and when the freezing prevention device is a salt water injection device, the artificial intelligence analysis model creation step (S520) is performed by combining the reflected signal acquired by the sonic sensor and the injection amount (distribution degree) of salt water. It is characterized by further comprising a salt water injection quantity regression model generation step of generating a salt water injection quantity regression model that learns together and outputs the corresponding salt water injection quantity (distribution degree) based on the reflected signal acquired by the sonic sensor.

The road surface condition data detection step (S540) is characterized by detecting the weather condition, type of road surface, road surface temperature, and salt injection amount, and the control signal generation step (S550) is performed when the weather condition is "rain". or “snow,” and the classified road surface type is “wet road,” “snow-covered road,” or “frozen road,” and the detected temperature of the road surface is less than 4° C., a control for operating the heating device. generating a signal; After the heating device is activated, generating a control signal to stop operation of the heating device when the detected temperature of the road surface is 4° C. or higher; If the weather condition is “rain” or “snow,” the classified road surface type is “wet road,” “snow-covered road,” or “frozen road,” and the detected temperature of the road surface is less than 4°C, Generating a control signal to operate the salt water injection device; and generating a control signal to stop the operation of the salt water spray device when the detected salt water spray amount is 80% or more after the salt water spray device is operated.

The control method of the road icing prevention device is characterized in that, when an abnormality in the state of the sonic sensor is detected, a notification message is transmitted to the administrator terminal, and a status notification signal of the sonic wave sensor is transmitted to the control server. It is characterized in that it further includes an abnormality detection notification step (S560).

In addition, according to one embodiment of the present invention, a computer-readable recording medium storing a program for implementing a control method of the road icing prevention device is provided.

In addition, according to an embodiment of the present invention, a program stored in a computer-readable recording medium is provided to implement the control method of the road icing prevention device.

According to an embodiment of the present invention, the control system and method for a road icing prevention device include a sound wave sensor that transmits a sound wave signal to a preset detection area of the road and then receives the reflected signal, and uses artificial intelligence. By analyzing the signal obtained from the sonic sensor to recognize the current weather condition, road surface condition, road surface temperature, and the amount of salt water sprayed from the salt water spray device, the heating device or salt water spray installed in the habitually icy section of the road It has the effect of accurately controlling the operation of the device.

In addition, according to the control system and method for a road icing prevention device according to an embodiment of the present invention, a sound wave sensor installed on the upper part of the road is provided to detect road surface condition data, so that the sensor provided on the road surface is It has the effect of solving the problem of being damaged by load and easily aging the asphalt around the sensor, thereby ensuring the durability of the road surface and service continuity.

In addition, according to the control system and method for a road ice prevention device according to an embodiment of the present invention, it can be linked to the existing snow melting equipment monitoring/control system in a plug-in manner without major modification. , It has the effect of increasing the operational efficiency of snow melting equipment based on more accurate road hazard notification than existing snow melting systems.

In addition, according to the control system and method for a road ice prevention device according to an embodiment of the present invention, various data (weather conditions, road surface temperature, snow amount, salt water spray amount, first salt water spray time, etc.) are recorded in real time. This has the effect of being able to easily check how road management has progressed and utilize it later.

In addition, according to the control system and method for a road ice prevention device according to an embodiment of the present invention, road ice is automatically managed based on road surface detection data using a sonic sensor, so skilled workers are not required. This has the effect of reducing road management costs.

1 is a diagram illustrating the operation of a control system for a road heating device according to an embodiment of the present invention.
Figure 2 is a configuration diagram of a control system for a road icing prevention device according to an embodiment of the present invention.
Figure 3 is a configuration diagram of a control system for a road ice prevention device according to another embodiment of the present invention.
4A to 4C are diagrams for explaining an artificial intelligence analysis model used in a control system for a road icing prevention device according to an embodiment of the present invention.
Figure 5 is a flowchart of an embodiment of a control method for a road icing prevention device according to the present invention.
Figure 6 is a detailed flowchart of one embodiment of the control signal generation step (S550) of Figure 5 when the road ice prevention device according to the present invention is a heating wire device.
Figure 7 is a detailed flowchart of an embodiment of the control signal generation step (S550) of Figure 5 when the road ice prevention device according to the present invention is a salt water spray device.

Since the present invention can make various changes and have various embodiments, specific embodiments will be illustrated in the drawings and described in detail. However, this is not intended to limit the present invention to specific embodiments, and should be understood to include all changes, equivalents, and substitutes included in the spirit and technical scope of the present invention.

When a component is said to be "connected" or "connected" to another component, it is understood that it may be directly connected to or connected to the other component, but that other components may exist in between. It should be.

On the other hand, when it is mentioned that a component is “directly connected” or “directly connected” to another component, it should be understood that there are no other components in between.

The terms used herein are merely used to describe specific embodiments and are not intended to limit the invention. Singular expressions include plural expressions unless the context clearly dictates otherwise. In this application, terms such as “comprise” or “have” are intended to designate the presence of features, numbers, processes, operations, components, parts, or combinations thereof described in the specification, but are not intended to indicate the presence of one or more other features. It should be understood that this does not exclude in advance the possibility of the existence or addition of elements, numbers, processes, operations, components, parts, or combinations thereof.

Unless otherwise defined, all terms used herein, including technical or scientific terms, have the same meaning as generally understood by a person of ordinary skill in the technical field to which the present invention pertains. Terms defined in commonly used dictionaries should be interpreted as having a meaning consistent with the meaning in the context of the related technology, and should not be interpreted as having an ideal or excessively formal meaning unless explicitly defined in the present application. No.

Hereinafter, the present invention will be described in more detail with reference to the attached drawings. Prior to this, the terms or words used in this specification and claims should not be construed as limited to their usual or dictionary meanings, and the inventor should appropriately define the concept of terms in order to explain his or her invention in the best way. Based on the principle of definability, it must be interpreted with meaning and concept consistent with the technical idea of the present invention. In addition, if there is no other definition in the technical and scientific terms used, they have meanings commonly understood by those skilled in the art to which this invention pertains, and the gist of the present invention is summarized in the following description and accompanying drawings. Descriptions of known functions and configurations that may be unnecessarily obscure are omitted. The drawings introduced below are provided as examples so that the idea of the present invention can be sufficiently conveyed to those skilled in the art. Accordingly, the present invention is not limited to the drawings presented below and may be embodied in other forms. Additionally, like reference numerals refer to like elements throughout the specification. It should be noted that like elements in the drawings are represented by like symbols wherever possible.

1 is a diagram for explaining the operation of a control system for a road heating device according to an embodiment of the present invention.

As shown in FIG. 1, the control system for a road heating device according to an embodiment of the present invention has a structure 1 located on the road, and the structure 1 includes a sound wave sensor 10 and a communication unit 20. ), etc., and the automatic control box 60 that controls the heating element 70 in the freezing prevention device 100 is controlled under the control of the control server 40 based on the detection data of the sonic sensor 10.

The sonic sensor 10 may be installed on the structure 1 so as to be located on the vehicle's travel path on the road and be perpendicular to the road surface.

The structure 1 means that the sonic sensor 10 can be installed on the road, such as a street light.

The sound wave sensor 10 transmits a sound wave signal to a preset point to detect road conditions and then receives a reflected signal.

Meanwhile, the communication unit 20 transmits data obtained through the sound wave sensor 10 to the control server 40.

In addition, the control server 40 can deliver a notification to the administrator terminal 50 when it detects whether the sound wave sensor 10 is broken (abnormal).

Among freezing prevention devices, the heating wire type can cause fires in the asphalt by operating the heating wire excessively longer than necessary, so the most important thing is how long the heating wire should be operated.

In the present invention, it is possible to precisely control the ice prevention device by detecting temperature changes in the road surface due to hot wire heating using a sonic sensor.

Specifically, an artificial intelligence model learned based on sound wave sensing data accumulated in a road surface environment with various temperatures is created, and the waveform of the sound wave sensor acquired based on the artificial intelligence model is analyzed to detect temperature changes in the road surface. It is possible to automatically control the operation of the heating device.

For example, after determining whether the road surface is dry or frozen through the waveform analysis of the sonic sensor, the heating element starts operating. If the road surface temperature is maintained above 4 degrees according to the sonic sensor output, that is, if the temperature is above the freezing point of water, the heating element is activated. to stop operation.

Additionally, according to the present invention, it is possible to detect how much salt water has been sprayed onto the road surface when the salt water injection device operates.

Specifically, we created an artificial intelligence model learned based on sound wave sensing data accumulated in a road surface environment where the degree of salt water injection was variously distributed, and analyzed the waveform of the sound wave sensor obtained based on the artificial intelligence model to determine whether salt water was present on the road surface. The degree of spraying (distribution) can be determined. If salt water is distributed on the road surface beyond the preset range, salt water spraying is stopped.

The present invention relates to road surface detection and interlocking control technology that enables the installed/operated road heating device or salt water spray device to operate in a timely manner. Road surface information is obtained using a temperature/humidity sensor mounted on a conventional road surface. It is possible to accurately control the operation of the heating device or salt water injection device by recognizing road surface conditions and determining snow melting conditions based on a sonic sensor instead of using a sonic sensor method.

A service that can be linked to existing snow melting equipment monitoring/control systems in a plug-in manner without major modifications, and can increase the operational efficiency of snow melting equipment based on more accurate road hazard notification than existing snow melting systems. can be provided.

The algorithm for determining whether to implement snow melting can be built in the control server (service server) 40 or in the control unit (MCU) provided with the sound wave sensor 10, and is used by the ice prevention device 100 through the communication unit. It may be delivered to the automatic control box 60.

Road surface condition detection is performed periodically and repeatedly until the sonic sensor needs to be restored due to a failure of the sonic sensor. After acquiring the reflected wave (sensor value) from the sound wave sensor 10, it is transmitted to the control server 40 (service server) through the communication unit. When the sensor state is normal, the control server 40 transmits the corresponding reflected wave to the control server 40 (service server). By analyzing using a big data-based artificial intelligence model, it is determined whether snow melting work is currently necessary and the operation of the corresponding ice prevention device is controlled.

Meanwhile, the abnormality of the sonic sensor is determined through the transmitted sensor value, and when the sensor state is abnormal, an operation stop command is transmitted to the automatic control box 60 of the ice prevention device 100, and the administrator terminal 50 A push alarm is sent for abnormalities, and a history of operation stoppages due to malfunctions is sent to the control server (not shown).

Figure 2 is a configuration diagram of a control system for a road icing prevention device according to an embodiment of the present invention.

As shown in FIG. 2, the control system of the road ice prevention device according to an embodiment of the present invention includes a sound wave sensor 10, a control server 40, a communication unit 20, and a freeze prevention device 100. Includes.

The sound wave sensor 10 transmits a sound wave signal to a preset point to detect road conditions and then receives a reflected signal.

The control server 40 detects road surface condition data at the preset point based on an artificial intelligence analysis model learned using the reflected signal acquired by the sound wave sensor 10 as an input signal, and detects road surface condition data at the preset point. A signal that controls whether or not the ice prevention device 100 operates is generated according to road surface condition data.

The road surface condition data includes weather conditions, road surface type, road surface temperature, and salt spray amount (spray degree, distribution).

The communication unit 20 transmits the reflected signal obtained by the sound wave sensor 10 to the control server 40.

The ice prevention device 100 is controlled by the control server 40 and performs operations to prevent ice on the road. The freezing prevention device 100 includes at least one of a heating device or a salt water spray device.

In detail, the control server 40 detects that the weather condition is “rain” or “snow” and the classified road surface type is “wet road” or “snowy road” or “frozen road”. Generating a control signal to operate the heating device when the temperature of a road surface is less than 4 ℃, and stopping the operation of the heating device when the detected temperature of the road surface is 4 ℃ or more after the heating device is operated. Generates control signals.

Meanwhile, the control server 40 determines that the weather condition is “rain” or “snow,” the classified road surface type is “wet road,” “snow-covered road,” or “frozen road,” and the detected When the temperature of the road surface is less than 4°C, a control signal is generated to operate the salt water injection device, and after the salt water injection device is activated, when the detected salt water injection amount is 80% or more, the salt water injection device is operated. Generates a control signal to stop.

Meanwhile, as the control server 40 detects that there is an abnormality in the state of the sound wave sensor 10, it transmits a notification message to the administrator terminal 50 and sends the sound wave sensor 10 to the control server 80. Sends a status notification signal.

Figure 3 is a configuration diagram of a control system for a road icing prevention device according to another embodiment of the present invention.

As shown in Figure 3, the control system of the road ice prevention device according to an embodiment of the present invention includes an acoustic sensor 10, a control unit 30, a communication unit 20, and an ice prevention device 100. Includes.

The sound wave sensor 10 transmits a sound wave signal to a preset point to detect road conditions and then receives a reflected signal.

The control unit 30 detects road surface condition data at the preset point based on an artificial intelligence analysis model learned using the reflected signal acquired by the sound wave sensor 10 as an input signal, and detects the road surface condition at the preset point. A signal that controls whether or not the ice prevention device 100 operates is generated according to the status data.

The road surface condition data includes weather conditions, road surface type, road surface temperature, and salt spray amount (spray degree, distribution).

The communication unit 20 transmits the control signal generated by the control unit 30 to the ice prevention device 100.

The ice prevention device 100 is controlled by the control unit 30 and performs operations to prevent ice on the road. The freezing prevention device 100 includes at least one of a heating device or a salt water spray device.

In detail, the control unit 30 determines that the weather condition is “rain” or “snow,” the classified road surface type is “wet road,” “snow-covered road,” or “frozen road,” and the detected A control that generates a control signal to operate the heating device when the temperature of the road surface is less than 4 ℃, and stops the operation of the heating device when the detected temperature of the road surface is 4 ℃ or more after the heating device is operated. generate a signal.

Meanwhile, the control unit 30 determines that the weather condition is “rain” or “snow,” the classified road surface type is “wet road,” “snow-covered road,” or “frozen road,” and the detected road surface When the temperature is less than 4 ° C, a control signal is generated to operate the salt water injection device, and after the salt water injection device is operated, the operation of the salt water injection device is stopped when the detected salt water injection amount is 80% or more. Generates a control signal that

Meanwhile, as the control unit 30 detects that there is an abnormality in the state of the sound wave sensor 10, it transmits a notification message to the administrator terminal 50 and sends a notification message to the control server 80. Sends status notification signals.

4A to 4C are diagrams for explaining an artificial intelligence analysis model used in a control system for a road icing prevention device according to an embodiment of the present invention.

The artificial intelligence analysis model is a weather condition classification model that classifies weather conditions based on the reflected signal acquired by the sound wave sensor 10, and the road surface based on the reflected signal obtained by the sound wave sensor 10. Includes a road surface type classification model that classifies the type.

In addition, when the freezing prevention device 100 is the heating device, the artificial intelligence analysis model learns the reflected signal obtained by the sonic sensor 10 and the temperature of the road surface together with the reflected signal obtained by the sonic sensor 10. It further includes a road surface temperature regression model that outputs the corresponding road surface temperature based on the signal.

When the freezing prevention device 100 is the salt water injection device, the artificial intelligence analysis model learns the reflected signal obtained by the sonic sensor 10 and the temperature of the road surface together to determine the reflected signal obtained by the sonic sensor 10. A road surface temperature regression model that outputs the temperature of the corresponding road surface based on the signal, and the reflected signal obtained by the sonic sensor 10 and the spray amount (distribution degree) of salt water are learned together to obtain the reflected signal obtained by the sonic sensor 10. It further includes a salt water injection amount regression model that outputs the corresponding salt water injection amount (distribution degree) based on the signal.

That is, the artificial intelligence analysis model according to the present invention basically includes a weather condition classification model, a road surface type classification model, and a road surface temperature regression model, and when the freezing prevention device 100 includes the salt water injection device, salt water It further includes an injection quantity regression model.

Figure 4a shows the structure of the road surface type classification model. The road surface type classification model is constructed by sampling the acquired signal (reflected wave) of the sonic sensor a total of T times over a certain period of time and learning the corresponding road surface type information together.

For example, learn 1000 pieces of data (x1, x2, ..., x1000) sampled in 1ms units for 1 second and the corresponding road surface type information.

As shown in Figure 4a, the acquisition signal (reflected wave) of the sound wave sensor is input and the corresponding road surface types are classified as dry road (dry road), wet road (wet road), frozen road (iced), and snow road (snow). ) can be classified into classes such as

Meanwhile, although not shown in the drawing, it may further include a weather condition classification model that classifies weather conditions based on the reflected signal acquired by the sonic sensor, and this also includes the acquired signal of the sonic sensor and weather information together. Learn and construct.

Figure 4b is a diagram for explaining a road surface temperature regression model that outputs the temperature of the corresponding road surface based on the reflected signal obtained by the sonic sensor in order to control the heating device, and Figure 4c is a diagram for controlling the salt water control device. , This is a diagram to explain the salt water injection amount regression model that outputs the corresponding salt water distribution amount (distribution degree) (%) based on the reflected signal acquired by the sonic sensor.

In Figure 4b, the relationship between the acquired data (X) of the sonic sensor and the temperature of the road surface is not a two-dimensional (plane) graph, but am.

Accordingly, the learned road surface temperature regression model outputs the corresponding road surface temperature based on the reflected signal obtained by the acoustic wave sensor.

In Figure 4c, as in Figure 4b, the relationship between the acquired data (X) of the sonic sensor and the salt water injection amount (distribution degree) is not a two-dimensional (plane) graph, but It is a formed dataset.

Accordingly, the learned salt water injection amount regression model outputs the corresponding salt water injection amount (distribution degree) based on the reflected signal acquired by the sonic sensor.

Figure 5 is a flowchart of an embodiment of a control method for a road ice prevention device according to the present invention.

First, collect measurement data from the sonic sensor (S510).

An artificial intelligence analysis model is created based on the collected data (S520).

The artificial intelligence analysis model creation step (S520) generates a weather state classification model that classifies weather conditions based on the reflected signal acquired by the sonic sensor, and the road surface based on the reflected signal obtained by the sonic sensor. Generates a road surface type classification model that classifies the type of road surface, learns the reflected signal obtained by the sonic sensor and the temperature of the road surface together, and outputs the temperature of the corresponding road surface based on the reflected signal obtained by the sonic sensor. Create a road surface temperature regression model.

On the other hand, when the freezing prevention device 100 is a salt water injection device, the artificial intelligence analysis model creation step (S520) is performed by learning the reflected signal acquired by the sonic sensor and the injection amount (distribution degree) of salt water together. Based on the reflected signal obtained by the sonic sensor, a salt water injection amount regression model is created that outputs the corresponding salt water injection amount (distribution degree).

That is, the artificial intelligence analysis model according to the present invention basically generates and includes a weather condition classification model, a road surface type classification model, and a road surface temperature regression model, and when the freezing prevention device 100 includes the salt water injection device. , further generate a salt water injection volume regression model.

Of course, the generated artificial intelligence analysis models must be mounted on the control server 40 or the control unit 30.

Afterwards, the sound wave sensor 10 is used to transmit a sound wave signal to a preset point for monitoring the condition of the road, and then the reflected signal is received (S530).

Thereafter, the control server 40 or the control unit 30 uses the reflected signal acquired by the transmitted sound wave sensor 10 as an input signal to determine the road surface condition at the preset point based on the artificial intelligence analysis model. Sensing data.

The road surface condition data detection step (S540) detects weather conditions, road surface type, road surface temperature, and salt injection amount (distribution degree).

Thereafter, the control server 40 or the control unit 30 generates a control signal for controlling whether to operate the ice prevention device based on the road surface condition data (S550).

Thereafter, as the control server 40 or the control unit 30 detects that there is an abnormality in the state of the sound wave sensor, a notification message is transmitted to the administrator terminal and the status of the sound wave sensor 10 is notified to the control server. Transmit the signal (S560).

Figure 6 is a detailed flowchart of one embodiment of the control signal generation step (S550) of Figure 5 when the road ice prevention device according to the present invention is a heating wire device.

When the freezing prevention device is a heating device, the control signal generation step (S550) first determines whether the detected weather condition is “rain” or “snow” (S610).

As a result of the determination in the determination step (S610), if the weather condition is not “rain” or “snow,” the process proceeds to step “S540” to detect road surface condition data.

Meanwhile, as a result of the judgment in the above judgment step (S610), if the weather condition is “rain” or “snow,” the classified road surface type is “wet road (wet road)”, “snow road (snow road)”, or “frozen road”. (iced)” (S620).

As a result of the determination in the above judgment step (S620), if the weather condition is “rain” or “snow,” the classified road surface type is “wet road (wet road)”, “snow road (snow)”, or “iced road (iced road).” )", otherwise, proceed to step "S540" to detect road surface condition data.

Meanwhile, as a result of the determination in the above judgment step (S620), if the weather condition is “rain” or “snow,” the classified road surface type is “wet road (wet road),” “snow road (snow road),” or “frozen road.” (iced)", it is determined whether the detected temperature of the road surface is less than 4 ℃ (S630).

As a result of the judgment in the determination step (S630), if the temperature of the road surface is not less than 4°C, the process proceeds to step “S540” to detect road surface condition data.

Meanwhile, as a result of the determination in the determination step (S630), if the temperature of the road surface is less than 4°C, a control signal to operate the heating device is generated (S640).

Afterwards, the process proceeds to the road surface condition data detection step (S540), where the road surface condition data is detected.

After the heating device is activated, it is determined whether the detected temperature of the road surface is 4°C or higher (S650).

As a result of the determination in the determination step (S650), if the temperature of the road surface is not 4° C. or higher, the process proceeds to step “S540” to detect road surface condition data.

Meanwhile, as a result of the determination in the determination step (S650), if the temperature of the road surface is 4°C or higher, a control signal to stop the operation of the heating device is generated (S660).

Afterwards, the process proceeds to the road surface condition data detection step (S540) to detect the road surface condition data.

That is, when the weather condition is “rain” or “snow,” the classified road surface type is “wet road,” “snow-covered road,” or “frozen road,” and the detected temperature of the road surface is less than 4°C. In this case, the heating device is operated, and after the heating device is operated, the operation of the heating device is stopped when the detected temperature of the road surface is 4° C. or higher.

Figure 7 is a detailed flowchart of an embodiment of the control signal generation step (S550) of Figure 5 when the road ice prevention device according to the present invention is a salt water spray device.

When the road ice prevention device is a salt water spray device, the control signal generation step (S550) first determines whether the detected weather condition is “rain” or “snow” (S710).

As a result of the determination in the determination step (S710), if the weather condition is not “rain” or “snow,” the process proceeds to step “S540” to detect road surface condition data.

Meanwhile, as a result of the determination in the above judgment step (S710), if the weather condition is “rain” or “snow,” the classified road surface type is “wet road (wet road),” “snow road (snow road),” or “frozen road.” (iced)” (S720).

As a result of the determination in the above judgment step (S720), if the weather condition is “rain” or “snow,” the classified road surface type is “wet road (wet road)”, “snow road (snow)”, or “iced road (iced road).” )", otherwise, proceed to step "S540" to detect road surface condition data.

Meanwhile, as a result of the determination in the above judgment step (S720), if the weather condition is “rain” or “snow,” the classified road surface type is “wet road (wet road)”, “snow road (snow road)” or “frozen road”. (iced)", it is determined whether the detected temperature of the road surface is less than 4 ℃ (S730).

As a result of the judgment in the determination step (S630), if the temperature of the road surface is not less than 4°C, the process proceeds to step “S540” to detect road surface condition data.

Meanwhile, as a result of the determination in the determination step (S730), if the temperature of the road surface is less than 4°C, a control signal to operate the salt water injection device is generated (S740).

Afterwards, the process proceeds to the road surface condition data detection step (S540), where the road surface condition data is detected.

After the salt water spray device is operated, it is determined whether the salt water spray amount (spray degree) is 80% or more (S750).

As a result of the judgment in the determination step (S750), if the salt water injection amount (injection degree) is not 80% or more, the process proceeds to step “S540” to detect road surface condition data.

Meanwhile, as a result of the determination in the determination step (S750), if the salt water spray amount (spray degree) is 80% or more, a control signal to stop the operation of the salt water spray device is generated (S760).

Afterwards, the process proceeds to the road surface condition data detection step (S540) to detect the road surface condition data.

That is, when the weather condition is “rain” or “snow,” the classified road surface type is “wet road” or “snowy road” or “frozen road,” and the detected temperature of the road surface is less than 4°C. The salt water injection device is operated, and after the salt water injection device is operated, the operation of the salt water injection device is stopped when the detected salt water injection amount is 80% or more.

Meanwhile, although it has been described that the salt water spray amount (spray degree, distribution) that stops the operation of the salt water spray device is 80% or more, it is not limited thereto.

Meanwhile, in order to control the operation of the salt water injection device, both the road surface temperature regression model and the salt water injection amount regression model are used together as an example, but it is also possible to control the operation of the salt water injection device using only the salt water injection amount regression model. do.

Above, as an example, a device for preventing freezing of roads includes a heating device or a salt water spray device, but the present invention is not limited to this, and it is also possible to control a system equipped with both a heating device and a salt water spray device.

In the above, the control method of the road ice prevention device according to an embodiment of the present invention has been described, but the computer-readable recording medium storing a program for implementing the control method of the road ice prevention device and the road ice prevention device Of course, a program stored in a computer-readable recording medium for implementing the control method can also be implemented.

In other words, those skilled in the art will be able to easily understand that the control method of the above-described road icing prevention device may be provided in a recording medium that can be read by a computer by tangibly implementing a program of commands for implementing the method. . In other words, it can be implemented in the form of program instructions that can be executed through various computer means and recorded on a computer-readable recording medium. The computer-readable recording medium may include program instructions, data files, data structures, etc., singly or in combination. Program instructions recorded on the computer-readable recording medium may be specially designed and configured for the present invention, or may be known and usable by those skilled in the computer software art. Examples of the computer-readable recording media include magnetic media such as hard disks, floppy disks, and magnetic tapes, optical media such as CD-ROMs and DVDs, and floptical disks. Included are magneto-optical media, and hardware devices specifically configured to store and perform program instructions, such as ROM, RAM, flash memory, USB memory, and the like. Examples of program instructions include machine language code, such as that produced by a compiler, as well as high-level language code that can be executed by a computer using an interpreter, etc. The hardware device may be configured to operate as one or more software modules to perform the operations of the present invention, and vice versa.

The present invention is not limited to the above-described embodiments, and the scope of application is diverse. Of course, various modifications and implementations are possible without departing from the gist of the present invention as claimed in the claims.

1: Structure 10: Sound wave sensor
20: communication unit 30: control unit
40: Control server 50: Administrator terminal
60: Control terminal box 70: Heating wire
80: Control server 100: Freezing prevention device
S510: Data collection step
S520: Artificial intelligence analysis model creation step
S530: Acquisition of sound wave data
S540: Road surface condition data detection step
S550: Control signal generation step
S560: Anomaly detection notification stage

Claims (10)

In the control system of the road ice prevention device,
A sound wave sensor 10 for transmitting a sound wave signal to a preset point to detect road conditions and then receiving the reflected signal;
Detects road surface condition data at the preset point based on an artificial intelligence analysis model learned using the reflected signal acquired by the sonic sensor as an input signal, and detects the road surface condition data at the preset point. A control server 40 for generating a signal to control whether to operate or not;
A communication unit 20 for transmitting the reflected signal obtained by the sound wave sensor to the control server; and
The ice prevention device 100 is controlled by the control server to perform operations to prevent ice on the road.
A control system for a road icing prevention device comprising a.
In the control system of the road ice prevention device,
A sound wave sensor 10 for transmitting a sound wave signal to a preset point to detect road conditions and then receiving the reflected signal;
Detects road surface condition data at the preset point based on an artificial intelligence analysis model learned using the reflected signal acquired by the sonic sensor as an input signal, and detects the road surface condition data at the preset point. A control unit 30 for generating a signal to control whether to operate or not;
An ice prevention device 100 controlled by the control unit to perform operations to prevent ice on the road; and
A communication unit 20 for transmitting the control signal generated by the control unit to the ice prevention device.
A control system for a road icing prevention device comprising a.
According to any one of paragraphs 1 and 2,
The freezing prevention device 100,
A control system for a road icing prevention device, comprising at least one of a heating device or a salt water spray device.
According to paragraph 3,
The artificial intelligence analysis model is,
A weather condition classification model that classifies weather conditions based on the reflected signal acquired by the sonic sensor, a road surface type classification model that classifies the type of road surface based on the reflected signal obtained by the sonic sensor, and the sonic sensor. Characterized by comprising a road surface temperature regression model that learns the acquired reflected signal and the temperature of the road surface together and outputs the corresponding road surface temperature based on the reflected signal acquired by the sonic sensor,
When the freezing prevention device 100 is the salt water spray device, the artificial intelligence analysis model is,
A salt water injection quantity regression model that learns the reflected signal acquired by the sonic sensor and the injection amount (distribution degree) of salt water together and outputs the corresponding salt water injection amount (distribution degree) based on the reflected signal acquired by the sonic sensor. A control system for a road icing prevention device further comprising:
According to clause 4,
The road surface condition data is,
Characterized by including weather conditions, road surface type, road surface temperature, and salt injection amount,
The control server 40 or the control unit 30,
If the weather condition is “rain” or “snow,” the classified road surface type is “wet road” or “snow-covered road” or “frozen road,” and the detected temperature of the road surface is less than 4 ℃, Generating a control signal to operate the heating device,
After the heating device is activated, if the detected temperature of the road surface is 4°C or higher, a control signal is generated to stop the operation of the heating device,
If the weather condition is “rain” or “snow,” the classified road surface type is “wet road,” “snow-covered road,” or “frozen road,” and the detected temperature of the road surface is less than 4°C, Generating a control signal to operate the salt water injection device,
A control system for a road icing prevention device, characterized in that, after the salt water spray device is activated, a control signal is generated to stop the operation of the salt water spray device when the detected salt water spray amount is 80% or more.
In paragraph 5.
The control server 40 or the control unit 30,
A control system for a road icing prevention device, characterized in that when an abnormality in the state of the sonic sensor is detected, a notification message is transmitted to an administrator terminal and a status notification signal of the sonic sensor is transmitted to a control server.
In the control method of a road ice prevention device,
A data collection step (S510) of collecting measurement data from the sonic sensor;
An artificial intelligence analysis model creation step (S520) of generating an artificial intelligence analysis model based on the collected data;
A sound wave data acquisition step (S530) of transmitting a sound wave signal to a preset point for monitoring road conditions using the sound wave sensor and then receiving a reflected signal;
A road surface condition data detection step (S540) of detecting road surface condition data at the preset point based on the artificial intelligence analysis model by using the reflected signal acquired by the sonic sensor as an input signal; and
A control signal generation step (S550) of generating a control signal to control whether the ice prevention device operates based on the road surface condition data.
A control method of a road ice prevention device comprising a.
In clause 7,
In the artificial intelligence analysis model creation step (S520),
A weather condition classification model generating step of generating a weather condition classification model that classifies weather conditions based on the reflected signal acquired by the sonic sensor;
A road surface type classification model generating step of generating a road surface type classification model that classifies the type of road surface based on the reflected signal acquired by the sonic sensor; and
A road surface temperature regression model generation step of learning the reflected signal acquired by the sonic sensor and the temperature of the road surface together to generate a road surface temperature regression model that outputs the temperature of the corresponding road surface based on the reflected signal acquired by the sonic sensor.
Characterized by including,
If the freezing prevention device is a salt water spray device, the artificial intelligence analysis model creation step (S520) is,
A salt water injection quantity regression model that learns the reflected signal acquired by the sonic sensor and the injection amount (distribution degree) of salt water together and outputs the corresponding salt water injection amount (distribution degree) based on the reflected signal acquired by the sonic sensor. A control method for a road icing prevention device, further comprising the step of generating a salt water spray amount regression model.
According to clause 8,
In the road surface condition data detection step (S540),
It is characterized by detecting weather conditions, road surface type, road surface temperature, and salt injection amount,
In the control signal generation step (S550),
If the weather condition is “rain” or “snow,” the classified road surface type is “wet road” or “snowy road” or “frozen road,” and the detected temperature of the road surface is less than 4 ℃, generating a control signal to operate the heating device;
After the heating device is activated, generating a control signal to stop operation of the heating device when the detected temperature of the road surface is 4° C. or higher;
If the weather condition is “rain” or “snow,” the classified road surface type is “wet road,” “snow-covered road,” or “frozen road,” and the detected temperature of the road surface is less than 4°C, Generating a control signal to operate the salt water injection device; and
After the salt water spray device is activated, generating a control signal to stop the operation of the salt water spray device when the detected salt water spray amount is 80% or more.
A control method for a road icing prevention device comprising:
According to clause 9,
As an abnormality in the state of the sonic sensor is detected, an anomaly detection notification step (S560) characterized in that a notification message is transmitted to the administrator terminal and a status notification signal of the sonic sensor is transmitted to the control server.
A method of controlling a road ice prevention device further comprising: