Smart Heat Trace Control System Using a Black Ice Prediction Algorithm

Abstract

A smart heating cable control system for preventing freezing of a road surface according to the present disclosure for achieving the aforementioned technical problem comprises: a sensor unit for measuring environmental information of a target road section; a data collection unit for collecting and preprocessing data obtained from the sensor unit; a freezing prediction calculation unit for analyzing the possibility of freezing of the road surface using sensing data obtained from the sensor unit; and a heating cable control unit for variably controlling the power supply of a heating cable device installed on the road based on the freezing probability calculated by the freezing prediction calculation unit.

Description

Technology Field

[0001] The present invention relates to the field of road safety management technology, and specifically to a multi-sensor-based road surface condition analysis technology that comprehensively detects the temperature, humidity, dew point temperature, surface heat distribution, and reflection characteristics of a road surface, an algorithm technology that predicts the possibility of black ice occurrence in advance using weather forecast data and a freezing phase transition index, and a smart heating cable control technology that actively adjusts the driving power or PWM control amount of a heating cable device according to the predicted freezing probability. Background Technology

[0002] 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.

[0003] Black ice, which forms on roads during the winter, is a phenomenon in which a thin layer of ice difficult to identify with the naked eye develops on the road surface. It is one of the major causes of reduced braking performance and skidding accidents. In particular, areas where road surface temperatures drop rapidly due to surrounding environmental conditions—such as bridges, tunnel entrances and exits, roads adjacent to rivers, and shaded sections in mountainous regions—have a high risk of black ice formation, making them an important management target from a traffic safety perspective.

[0004] Conventionally, to solve the problem of road surface freezing, a method has been primarily used in which heating wires are embedded in the road surface and activated to prevent freezing when the road surface temperature drops below a certain standard. However, most of these methods are structured to control the heating wires on / off based on conditions where the road surface temperature or ambient temperature drops below 0°C, resulting in the problem of the heating wires being unnecessarily activated even when actual freezing does not occur. Consequently, there were limitations such as increased unnecessary power consumption and reduced operational efficiency of the heating wire equipment.

[0005] Furthermore, existing road surface condition monitoring technologies have often been limited to measuring road conditions using single sensors, such as temperature and humidity sensors. This simplistic sensing method presented a problem in accurately determining the likelihood of fine ice layers or black ice forming on the road surface. In particular, since black ice forms as a result of the complex interaction of various environmental factors—including road surface temperature, atmospheric humidity, dew point temperature, solar radiation, and surrounding terrain conditions—it is difficult to accurately predict freezing based solely on temperature criteria.

[0006] Recently, road surface condition detection technologies utilizing various sensors, such as thermal imaging sensors, optical sensors, and LiDAR sensors, are being researched to analyze road environments more precisely. Additionally, algorithm-based approaches that predict the likelihood of freezing by combining meteorological and environmental data are being proposed. In particular, research is being conducted to determine the likelihood of road freezing using a freezing index that comprehensively considers the difference between road surface temperature and atmospheric dew point temperature, relative humidity, and freezing threshold conditions. This approach is known to provide higher prediction accuracy than existing simple threshold-based control methods.

[0007] However, conventional technology has had limitations in that it has not sufficiently presented techniques to organically link and utilize such freezing prediction information with the control of actual road heating systems. In other words, even when the possibility of road surface freezing is predicted in advance, heating system control often still relies on simple temperature-based driving methods, resulting in a lack of control technologies capable of actively responding to freezing before it occurs or optimizing power consumption.

[0008] Therefore, there is a need to develop a new smart heating cable control system that can prevent black ice formation while minimizing energy consumption by precisely detecting road surface temperature, humidity, surface heat distribution, and reflection characteristics using multiple sensors, calculating the probability of road surface freezing in advance through a black ice prediction algorithm that reflects weather data and freezing phase transition conditions, and variably controlling the output power of the heating cable in proportion to the predicted freezing probability. Prior art literature

[0009] 1. Korean Patent Registration No. 10-2838372 (July 21, 2025) 2. Korean Patent Publication No. 10-2018-0001927 (January 5, 2018) The problem to be solved

[0010] The present invention has been devised to solve the problems of the conventional technology described above, and aims to provide a smart heating wire control system capable of predicting black ice occurring on road surfaces in advance and actively responding to it.

[0011] Specifically, one embodiment of the present invention aims to provide a multi-sensor-based road surface condition detection technology that includes not only a temperature and humidity sensor but also a thermal imaging sensor and a LiDAR sensor, in order to overcome the accuracy limitations of a single-sensor-based road surface condition determination method, such as a temperature and humidity sensor.

[0012] In addition, another objective of the present invention is to overcome the limitations of the conventional simple threshold-based control method that drives the heating wire based on whether the road surface temperature drops below 0°C, and to provide a black ice prediction algorithm that can more accurately predict the possibility of road surface freezing by calculating a freezing phase transition index that reflects various physical environmental conditions such as atmospheric relative humidity, the difference between the road surface temperature and the atmospheric dew point temperature, and the freezing threshold.

[0013] In addition, another objective of the present invention is to provide a smart heating cable control technology that calculates the probability of future road surface freezing by comprehensively analyzing road surface condition data obtained from multiple sensors and external weather forecast data, and variably controls the amount of power supplied to the heating cable device in proportion to the freezing probability.

[0014] In particular, another objective of the present invention is to provide a system that reduces the possibility of black ice formation and improves energy efficiency by minimizing unnecessary heating wire operation, by actively adjusting the output power of the heating wire to maintain the latent heat of the road surface before freezing occurs, rather than a method of removing ice using the heating wire after freezing actually occurs.

[0015] Furthermore, the objective of the present invention is to provide an intelligent road icing response system capable of more precisely determining the risk of road surface icing by combining environmental factors such as topographical characteristics around the road, the presence of shaded areas, proximity to rivers, road elevation, and solar radiation with sensing data.

[0016] Meanwhile, the technical problems of the present invention are not limited to those mentioned above, and other unmentioned technical problems will be clearly understood by a person skilled in the art from the description below. means of solving the problem

[0017] A smart heating cable control system for preventing freezing of a road surface according to the present disclosure for achieving the aforementioned technical objectives comprises: a sensor unit for measuring environmental information of a target road section; a data collection unit for collecting and preprocessing data obtained from the sensor unit; a freezing prediction calculation unit for analyzing the possibility of freezing of the road surface using sensing data obtained from the sensor unit; and a heating cable control unit for variably controlling the power supply of a heating cable device installed on the road based on the freezing probability calculated by the freezing prediction calculation unit.

[0018] The above-mentioned freezing prediction calculation unit calculates a freezing phase transition index using the relationship between road surface temperature, atmospheric relative humidity, and atmospheric dew point temperature based on the above-mentioned sensing data, and can calculate the probability of road surface freezing for a future time interval using the above-mentioned freezing phase transition index and weather prediction data. Effects of the invention

[0019] According to the means for solving the problem described above in the present disclosure, by using multiple sensors including a thermal imaging sensor and a lidar sensor in addition to a temperature and humidity sensor to comprehensively detect the temperature distribution, reflection characteristics, and minute surface changes of a road surface, it is possible to determine the road surface condition more precisely than in conventional single-sensor-based methods and to provide the effect of more accurately identifying minute freezing phenomena such as black ice.

[0020] In addition, according to the aforementioned means for solving the problem of the present disclosure, by using a freezing phase transition index that comprehensively considers the relative humidity of the atmosphere, the difference between the road surface temperature and the atmospheric dew point temperature, and the freezing critical point, the possibility of freezing on the road surface is predicted in advance, thereby providing the effect of improving the accuracy of freezing prediction compared to the conventional method of driving a heating wire based simply on whether the temperature drops below 0℃.

[0021] Furthermore, according to the aforementioned means for solving the problem of the present disclosure, by variably controlling the power supply of the heating wire device in a pulse width modulation (PWM) manner in proportion to the freezing probability calculated based on multiple sensing data, it is possible to proactively respond to maintain the latent heat of the road surface at a stage prior to freezing, rather than removing ice after it actually occurs, thereby providing the effect of effectively reducing the possibility of black ice formation.

[0022] In addition, according to the aforementioned means for solving the problem of the present disclosure, unnecessary operation of the heating cable can be minimized by adjusting the output power of the heating cable in stages according to the probability of freezing, thereby providing the effect of reducing power consumption of the road heating cable system and improving energy operation efficiency.

[0023] Furthermore, according to the aforementioned means for solving the problem of the present disclosure, the risk of road surface freezing can be comprehensively determined by combining road surface sensing data with environmental information such as topographical characteristics, the presence of shaded areas, proximity to rivers, road elevation, and solar radiation, thereby providing the effect of more accurately managing black ice risk sections that occur repeatedly in specific areas.

[0024] In addition, according to the aforementioned means for solving the problem of the present disclosure, since the possibility of black ice occurrence can be predicted in advance and the heating wire can be actively controlled, it provides the effect of improving road traffic safety and contributing to the prevention of traffic accidents caused by road icing in winter.

[0025] Furthermore, according to the aforementioned means for solving the problem of the present disclosure, by integrally implementing multi-sensor-based road surface detection technology, an ice prediction algorithm, and a heating wire active control technology, it can be utilized in the construction of smart road infrastructure and provides the effect of offering scalability that allows it to be utilized in conjunction with an Intelligent Transportation System (ITS) or a smart city-based road safety management system in the future.

[0026] The effects obtainable from the exemplary embodiments of the present disclosure are not limited to those mentioned above, and other unmentioned effects can be clearly derived and understood by those skilled in the art to which the exemplary embodiments of the present disclosure belong from the description below. That is, unintended effects resulting from the implementation of the exemplary embodiments of the present disclosure can also be derived by those skilled in the art from the exemplary embodiments of the present disclosure. Brief explanation of the drawing

[0027] FIG. 1 is a diagram showing a smart heating wire control system for preventing freezing of a road surface according to an embodiment. Figure 2 is a diagram showing the sensor configuration according to an embodiment. FIG. 3 is a flowchart illustrating the freezing prediction, heating wire control, and ice-making efficiency-based feedback control process according to an embodiment. FIG. 4 is a flowchart illustrating the process of determining the heating wire control range based on the length of the black ice section according to an embodiment. FIG. 5 is a flowchart illustrating the traffic volume-based freezing risk correction and heating cable control process according to an embodiment. Figure 6 is a diagram showing a smart heating wire control process according to an embodiment. Specific details for implementing the invention

[0028] Hereinafter, various embodiments of the present disclosure are described in conjunction with the accompanying drawings. As various embodiments of the present disclosure may be subject to various modifications and may have various forms, specific embodiments are illustrated in the drawings and described in detail. However, this is not intended to limit the various embodiments of the present disclosure to specific forms, and it should be understood that they include all modifications and / or equivalents and substitutions that fall within the spirit and scope of the various embodiments of the present disclosure. In relation to the description of the drawings, similar reference numerals have been used for similar components.

[0029] In various embodiments of the present disclosure, terms such as “comprising” or “having” are intended to indicate the existence of the features, numbers, steps, actions, components, parts, or combinations thereof described in the specification, and should be understood as not precluding the existence or addition of one or more other features, numbers, steps, actions, components, parts, or combinations thereof.

[0030] In various embodiments of the present disclosure, expressions such as “or” include any and all combinations of the words listed together. For example, “A or B” may include A, may include B, or may include both A and B.

[0031] Expressions such as "first," "second," "first," or "second" used in various embodiments of the present disclosure may modify various components of the various embodiments, but do not limit such components. For example, such expressions do not limit the order and / or importance of such components and may be used to distinguish one component from another.

[0032] When it is mentioned that a component is "connected" or "joined" to another component, it should be understood that the component may be directly connected or joined to the other component, but that a new component may also exist between the component and the other component.

[0033] In the embodiments of the present disclosure, terms such as "module," "unit," "part," etc. are used to refer to a component that performs at least one function or operation, and such component may be implemented in hardware or software, or in a combination of hardware and software. Additionally, a plurality of "modules," "units," "parts," etc. may be integrated into at least one module or chip and implemented as at least one processor, except where each needs to be implemented in specific individual hardware.

[0034] Terms such as those defined in commonly used dictionaries should be interpreted as having meanings consistent with their meanings in the context of the relevant technology, and should not be interpreted in an ideal or overly formal sense unless explicitly defined in the various embodiments of the present disclosure.

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

[0036] FIG. 1 is a diagram showing a smart heating wire control system for preventing freezing of a road surface according to an embodiment.

[0037] Referring to FIG. 1, a smart heating wire control system for preventing freezing of a road surface according to an embodiment may be configured to include a sensor unit (110), a data collection unit (120), a freezing prediction calculation unit (130), a heating wire control unit (140), and a central control server (200).

[0038] The sensor unit (110) measures environmental information of the target road section. The sensor unit (110) is configured to measure environmental information of the target road section and may be configured to collect various environmental data in real time to determine the possibility of freezing of the road surface. The sensor unit (110) may include at least one of a temperature and humidity sensor for measuring the ambient temperature and relative humidity around the road, a thermal imaging sensor for detecting the heat distribution and skin temperature of the road surface, and a LiDAR sensor for detecting changes in the reflective characteristics and minute surface condition changes of the road surface.

[0039] Specifically, the temperature and humidity sensor may be configured to generate basic environmental information for determining road surface freezing conditions by measuring ambient temperature and relative humidity data, and the thermal imaging sensor may be configured to detect the temperature distribution of the road surface in the form of a thermal image to identify minute changes in the road surface temperature or local cooling areas. Additionally, the lidar sensor may be configured to detect whether a fine layer of moisture or ice is formed on the road surface by emitting laser pulses toward the road surface and analyzing the scattered signals returning to measure changes in reflectance, distance changes, or diffuse reflection characteristics of the road surface. In this way, the sensor unit (110) is configured to acquire various sensing data regarding the road surface and surrounding environment using a plurality of sensors, and the sensing data is transmitted to the data collection unit or freezing prediction calculation unit described later to be utilized for analyzing the possibility of freezing on the road surface and performing a black ice prediction algorithm. Additionally, the sensor unit (110) may be configured to further include an auxiliary sensor for additionally acquiring environmental data related to the elevation of the road, solar radiation, presence of surrounding water systems, or terrain information as needed.

[0040] The data collection unit (120) collects and preprocesses data obtained from the sensor unit. Specifically, the data collection unit (120) may be configured to receive various sensing data generated from the temperature and humidity sensor, thermal imaging sensor, and lidar sensor included in the sensor unit (110) in real time or periodically, and may be configured to store or manage the received data along with time information. At this time, the data collection unit (120) may perform a process of synchronizing data and aligning it based on the same time standard by taking into account differences in the data collection period, data format, or data transmission method of the data received from each sensor.

[0041] Additionally, the data collection unit (120) may be configured to perform a data cleansing process to remove noise, missing values, or outliers that may be included in the collected sensing data. For example, the data collection unit (120) may be configured to remove noise from the sensing data or correct or exclude data outside the normal range using at least one of a moving average filter, a median filter, or a threshold-based filtering method.

[0042] Additionally, the data collection unit (120) may be configured to extract average skin temperature or temperature distribution information of the road surface using thermal image data acquired from a thermal image sensor, and to generate feature values ​​related to the reflectance or surface condition of the road surface using reflection signal data acquired from a LiDAR sensor. The feature data generated in this way may be integrated with ambient temperature and relative humidity data acquired from a temperature and humidity sensor to form a single environmental data set.

[0043] In addition, the data collection unit (120) may form a data set by linking auxiliary data, such as weather data received from an external weather server or past road surface environment data, together with the collected sensing data, and the data set may be transmitted to the freezing prediction calculation unit (130) described later and used as input data for analyzing the possibility of road surface freezing. Through this, the data collection unit (120) may be configured to perform the role of improving the accuracy of road surface freezing prediction by collecting various environmental data obtained from a plurality of sensors, refining and sorting it, and processing it into a form suitable for performing a freezing prediction algorithm.

[0044] The freezing prediction calculation unit (130) analyzes the possibility of freezing on the road surface using sensing data obtained from the sensor unit. The freezing prediction calculation unit (130) may be configured to calculate the risk of freezing on the road surface by comprehensively considering various physical conditions related to the road surface environment, and to perform an algorithm to predict the possibility of black ice occurrence based on this. Specifically, the freezing prediction calculation unit (130) can calculate environmental variables related to road surface temperature, ambient temperature, relative humidity, and dew point temperature using sensing data transmitted from the data collection unit (120). At this time, the freezing prediction calculation unit (130) can calculate the dew point temperature using ambient temperature and relative humidity data, and determine the possibility of moisture condensation or freezing occurring on the road surface by analyzing the difference between the dew point temperature and the road surface temperature.

[0045] Additionally, the above-mentioned freezing prediction calculation unit (130) may be configured to calculate a freezing phase transition index indicating the possibility of road surface freezing using the road surface temperature, relative humidity, dew point temperature difference, and freezing threshold conditions. The freezing phase transition index is a value calculated by reflecting the conditions under which moisture on the road surface transitions from a liquid state to a solid state, and if the index value is greater than or equal to a preset reference value, it may be determined that there is a high possibility of road surface freezing.

[0046] In addition, the above-mentioned freezing prediction calculation unit (130) can analyze the heat distribution information of the road surface obtained from the thermal imaging sensor included in the sensor unit (110) and the road surface reflectance change or surface condition information obtained from the lidar sensor to additionally determine whether a fine layer of moisture or ice is formed on the road surface. Through this, freezing analysis reflecting the actual physical condition of the road surface can be performed, rather than a simple temperature-based judgment.

[0047] Additionally, the above-mentioned freezing prediction calculation unit (130) may be configured to predict the probability of future freezing of the road surface by combining weather prediction data received from an external weather server, for example, changes in atmospheric temperature, changes in humidity, or precipitation prediction information for a certain period of time. At this time, the above-mentioned freezing prediction calculation unit (130) may calculate the probability of freezing of the road surface by setting a prediction time window for a certain period of time and calculating the possibility of freezing occurring due to environmental changes during the time interval.

[0048] In addition, the above-mentioned freezing prediction calculation unit (130) can correct the freezing risk by additionally considering environmental information such as topographical characteristics of the target road section, whether it is a shaded area, whether it is adjacent to a river, road elevation, and solar radiation. For example, it may be configured to reflect a weighted freezing risk for sections where the road surface temperature remains relatively low, such as shaded areas or areas adjacent to a river. In this way, the above-mentioned freezing prediction calculation unit (130) is configured to analyze the road surface environment based on multi-sensing data and to calculate the probability of freezing on the road surface by comprehensively utilizing the freezing phase transition index and weather prediction data, and the calculated freezing probability information can be transmitted to the heating wire control unit (140) described later and utilized for driving control of the heating wire device.

[0049] The heating wire control unit (140) variably controls the power supply of the heating wire device installed on the road based on the freezing probability calculated by the freezing prediction calculation unit. Specifically, the heating wire control unit (140) receives a freezing probability value transmitted from the freezing prediction calculation unit (130) and can control the operating state of the heating wire device by determining whether the freezing probability value corresponds to a preset reference range. For example, if the freezing probability value is less than a certain reference value, the heating wire device can be kept in a non-operating state, and if the freezing probability value is greater than or equal to the reference value, the heating wire device can be operated to raise the temperature of the road surface.

[0050] Additionally, the heating wire control unit (140) may be configured to adjust the amount of power supplied to the heating wire device in stages according to the magnitude of the freezing probability value. For example, when the freezing probability is predicted to be at a low level, the heating wire device may be controlled to supply a predetermined amount of heat to maintain the latent heat of the road surface, and when the freezing probability increases to an intermediate level or higher, the amount of power supplied to the heating wire device may be increased to more actively raise the road surface temperature.

[0051] Additionally, the heating wire control unit (140) may be configured to control the power supplied to the heating wire device using Pulse Width Modulation (PWM). The PWM control method allows for fine control of the amount of heat generated by the heating wire by adjusting the duty ratio of the power supplied to the heating wire device in proportion to the freezing probability value. For example, when the freezing probability is predicted to be about 50%, the heating wire device may be controlled to continuously supply low heat corresponding to a portion of the total power to maintain the latent heat of the road surface, and when the freezing probability rises to a higher level, the duty ratio may be increased to gradually increase the amount of heat generated by the heating wire.

[0052] In addition, the heating wire control unit (140) may be configured to perform an active pre-heating control mode that operates the heating wire in advance to maintain the temperature of the road surface before freezing occurs, rather than a method for removing the ice layer after it has actually formed on the road surface. Through this, the latent heat of the road surface is maintained to prevent freezing caused by a rapid drop in temperature and to reduce the possibility of black ice formation. The heating wire control unit (140) according to the embodiment may be configured to minimize unnecessary power consumption while preventing road surface freezing in advance by variably controlling the power supply of the heating wire device in proportion to the freezing probability calculated by the freezing prediction calculation unit (130). In addition, the heating wire control unit (140) may be configured to control heating wire devices installed in multiple road sections individually or in groups as needed.

[0053] Figure 2 is a diagram showing the sensor configuration according to an embodiment.

[0054] Referring to FIG. 2, the sensor unit (110) according to the embodiment may be configured to include a temperature and humidity sensor (111), a thermal imaging sensor (112), and a LiDAR sensor (113). The temperature and humidity sensor (111) measures the temperature and humidity of the atmosphere. The temperature and humidity sensor (111) is configured to measure the ambient temperature and relative humidity around a target road section and may be configured to generate basic environmental data for determining the possibility of road surface freezing. Specifically, the temperature and humidity sensor (111) may be configured to generate ambient temperature data by measuring the ambient temperature around the road and simultaneously generate humidity data by measuring the relative humidity, which indicates the water vapor content in the atmosphere. The ambient temperature data and relative humidity data may be used as key environmental variables to determine the possibility of moisture condensation or freezing occurring on the road surface.

[0055] In addition, the ambient temperature and relative humidity data measured by the above temperature and humidity sensor (111) can be transmitted to the freezing prediction calculation unit described later and used to calculate the dew point temperature. For example, the freezing prediction calculation unit can calculate the ambient dew point temperature using the above ambient temperature and relative humidity data, and determine the possibility of moisture condensation or freezing occurring on the road surface by analyzing the difference between the dew point temperature and the road surface temperature.

[0056] Additionally, the temperature and humidity sensor (111) may be configured to measure ambient temperature and relative humidity data at regular intervals and transmit the generated data to a data collection unit, and the data may be used as input data for a road surface freezing prediction algorithm. Through this, changes in the atmospheric environment around the road can be continuously monitored, and the possibility of road surface freezing can be analyzed more accurately. In the embodiment, the temperature and humidity sensor (111) measures the ambient temperature and relative humidity around the road to generate environmental data necessary for road surface freezing analysis, and the data may be used as basic data for road surface freezing prediction and heating wire control.

[0057] A thermal imaging sensor (112) detects the heat distribution of the road surface. The thermal imaging sensor (112) is configured to detect the heat distribution of the road surface of a target road section and can be configured to generate thermal information related to the road surface condition by measuring the surface temperature and temperature distribution changes of the road surface. Specifically, the thermal imaging sensor (112) can be configured to detect infrared radiation emitted from the road surface and measure the temperature distribution of the road surface in the form of a thermal image. Through this, not only the average temperature of the road surface but also locally formed low-temperature areas or cooling areas can be identified, and this temperature distribution information can be used as important data to determine the possibility of freezing occurring on the road surface.

[0058] Additionally, the thermal imaging sensor (112) may be configured to continuously measure the surface temperature of the road surface to generate a temperature change pattern over time, and the temperature change data may be transmitted to the freezing prediction calculation unit described later to be used to analyze the cooling rate or temperature decrease trend of the road surface. For example, if the temperature of the road surface continuously decreases for a certain period of time, the freezing prediction calculation unit may determine that the possibility of road surface freezing increases.

[0059] Additionally, the thermal imaging sensor (112) may be configured to generate temperature distribution data of the road surface and transmit it to a data collection unit, and the data collection unit may be configured to analyze the thermal imaging data to extract the average skin temperature of the road surface or the temperature value of a specific area. The road surface temperature data extracted in this way can be used as input data for a freezing prediction algorithm along with the ambient temperature and relative humidity data obtained from the temperature and humidity sensor. In this way, the thermal imaging sensor (112) may be configured to precisely detect the heat distribution and skin temperature of the road surface to generate data for analyzing the thermal condition of the road surface, thereby enabling a more accurate determination of the possibility of freezing on the road surface.

[0060] The lidar sensor (113) detects the reflective properties of the road surface and minute surface changes.

[0061] The lidar sensor (113) is configured to detect reflection characteristics and minute surface changes on the road surface of a target road section, and may be configured to generate sensing data to detect changes in surface conditions, such as a moisture layer or an ice layer formed on the road surface. Specifically, the lidar sensor (113) may be configured to emit a laser pulse toward the road surface and receive and analyze a signal reflected back from the road surface. By analyzing the reflection time, reflection intensity, and scattering pattern of the emitted laser pulse, the lidar sensor can detect not only distance information to the road surface but also changes in the reflectivity or surface condition of the road surface.

[0062] Additionally, the lidar sensor (113) may be configured to generate data to determine whether a fine layer of moisture, frost, or ice is formed on the road surface by analyzing the reflection signal characteristics of the road surface. For example, when a thin layer of ice is formed on the road surface, the diffuse reflection characteristics or reflection intensity pattern of the laser signal may change, and the lidar sensor can detect such changes in reflection characteristics to identify changes in the road surface condition.

[0063] In addition, the lidar sensor (113) can detect minute changes in height or surface roughness of the road surface by scanning laser signals within a certain angle range, and through this, can analyze physical structural changes such as water film or thin ice layer formed on the road surface. This data is transmitted to the data collection unit described later, undergoes a preprocessing process, and can be used as input data for analyzing the possibility of road surface freezing in the freezing prediction calculation unit. In this way, the lidar sensor (113) generates data for analyzing the physical condition of the road surface by detecting reflection characteristics, distance information, and minute surface condition changes of the road surface using laser-based sensing technology, and can be configured to perform the role of more accurately determining the possibility of black ice formation that is difficult to identify with the naked eye.

[0064] Additionally, the freezing prediction calculation unit (130) calculates a freezing phase transition index based on the sensing data using the relationship between the road surface temperature, atmospheric relative humidity, and atmospheric dew point temperature. Specifically, the freezing prediction calculation unit (130) may be configured to analyze road surface temperature, atmospheric temperature, and relative humidity data using the sensing data transmitted from the data collection unit (120). At this time, the freezing prediction calculation unit (130) may calculate the atmospheric dew point temperature using the atmospheric temperature and relative humidity data, and the dew point temperature is a value representing the temperature at which water vapor in the air begins to condense, and can be utilized as important reference information for determining the possibility of road surface freezing.

[0065] Additionally, the above freezing prediction calculation unit (130) can determine the likelihood of moisture condensation or freezing occurring on the road surface by analyzing the difference between the road surface temperature and the atmospheric dew point temperature. For example, if the road surface temperature approaches or falls below the atmospheric dew point temperature, it may be determined that the likelihood of water vapor in the air condensing or freezing on the road surface increases.

[0066] In addition, the above freezing prediction calculation unit (130) may be configured to calculate a freezing phase transition index representing the conditions under which liquid moisture on the road surface transitions into solid ice by comprehensively considering the relationship between the road surface temperature, atmospheric relative humidity, and atmospheric dew point temperature.

[0067] In the example, the freezing phase transition index can be calculated according to Equation 1.

[0068] Mathematical formula 1

[0069] IPI=α·(Td-Ts)+β·RH

[0070] In Equation 1, IPI is the freezing phase transition index, Ts is the road surface temperature, Td is the atmospheric dew point temperature, and RH is the atmospheric relative humidity. α and β are weighting coefficients that reflect the influence of each environmental variable.

[0071] The above freezing phase transition index (IPI) can be calculated by reflecting the difference between the road surface temperature and the atmospheric dew point temperature, as well as atmospheric relative humidity. It can be defined so that the index value increases as the road surface temperature approaches or decreases from the dew point temperature, and as atmospheric relative humidity increases. If the index value exceeds a preset threshold, it can be determined that there is a high probability of freezing occurring on the road surface. The above freezing phase transition index is an indicator that simultaneously reflects the thermal state of the road surface and the moisture conditions in the atmosphere; if the index value increases above a preset reference value, it can be determined that there is a high probability of freezing occurring on the road surface.

[0072] In addition, the freezing prediction calculation unit (130) can predict the possibility of road surface freezing for a certain period of time by analyzing the trend of change of the freezing phase transition index over time, and the calculated index value can be used as an indicator representing the risk of road surface freezing. The freezing phase transition index information can be transmitted to the heating wire control unit (140) described later and reflected in the driving control of the heating wire device. In this way, the freezing prediction calculation unit (130) can be configured to analyze the possibility of road surface freezing more precisely and predict the possibility of black ice occurrence in advance by calculating the freezing phase transition index based on the physical relationship between road surface temperature, atmospheric relative humidity, and atmospheric dew point temperature.

[0073] Additionally, the freezing prediction calculation unit (130) calculates the probability of road surface freezing for a future time interval using the freezing phase transition index and weather prediction data. Specifically, the freezing prediction calculation unit (130) may be configured to use the freezing phase transition index calculated based on the sensing data obtained from the sensor unit (110) as an input variable, and to analyze the possibility of road surface freezing occurring during a future time interval by combining it with weather prediction data received from an external weather server or a weather information provision system. The weather prediction data may include at least one of the following weather variables: changes in atmospheric temperature, changes in relative humidity, whether precipitation occurs, or changes in atmospheric pressure during a future time interval.

[0074] In the example, the probability value of road surface freezing can be calculated through Equation 2.

[0075] Mathematical formula 2

[0076]

[0077] In mathematical formula 2, Pf is the probability of freezing, IPI is the freezing phase transition index, k is a coefficient that controls the sensitivity to change in probability, and θ is the threshold value for freezing occurrence.

[0078] The above road surface freezing probability Pf can be calculated based on the freezing phase transition index IPI, and the freezing probability can be defined to increase as the index value approaches or exceeds a preset freezing threshold θ. The above formula can be configured to convert the possibility of freezing into a probability value in the range of 0 to 1 using a logistic function, and the calculated freezing probability value can be used as a reference value for controlling the driving power of the heating device in the heating wire control unit.

[0079] Additionally, the above-mentioned freezing prediction calculation unit (130) may be configured to predict changes in road surface conditions according to environmental changes during the time interval by setting a preset prediction time window. For example, the above-mentioned freezing prediction calculation unit (130) may calculate the probability of road surface freezing occurring at a future point in time by analyzing the trend of road surface temperature changes and atmospheric environment changes based on a time interval ranging from a few minutes to tens of minutes in the future, and by calculating the possibility of change in the freezing phase transition index.

[0080] In addition, the freezing prediction calculation unit (130) may be configured to calculate the probability value of liquid moisture on the road surface transitioning to an ice state by comprehensively analyzing the current value of the freezing phase transition index, the rate of change over time, and atmospheric environment change information included in the weather prediction data. The freezing probability value may be expressed as a probability value in the range of 0 to 1 or as a percentage, and as the value increases, it may indicate a state where freezing is more likely to occur on the road surface.

[0081] In addition, the above-mentioned freezing prediction calculation unit (130) may be configured to correct the freezing probability value by additionally reflecting environmental correction information such as topographical characteristics of the road section, whether there is a shaded area, whether there is proximity to a river, road elevation, or solar radiation information. Through this, a more accurate freezing prediction can be performed by considering the local cooling environment that may occur in a specific road section. In this way, the above-mentioned freezing prediction calculation unit (130) is configured to calculate the probability of road surface freezing for a future time period by comprehensively analyzing the freezing phase transition index and weather prediction data, and the calculated freezing probability information is transmitted to the heating wire control unit and can be used for power control of the heating wire device installed on the road.

[0082] The heating wire control unit (140) drives the heating wire to maintain the latent heat of the road surface before the occurrence of road surface freezing by variably controlling the power supplied to the heating wire device in proportion to the freezing probability using a pulse width modulation (PWM) method. Specifically, the heating wire control unit (140) may be configured to receive a road surface freezing probability value transmitted from the freezing prediction calculation unit (130) and to variably control the amount of power supplied to the heating wire device according to the magnitude of the freezing probability value. At this time, the heating wire control unit (140) can actively respond to the possibility of road surface freezing by controlling the amount of power supplied to the heating wire device to increase as the freezing probability increases.

[0083] Additionally, the heating wire control unit (140) may be configured to control the power supplied to the heating wire device using a Pulse Width Modulation (PWM) method. The PWM control method may be implemented by generating a pulse signal of a constant period and adjusting the duty ratio of the pulse signal to control the average amount of power delivered to the heating wire device. For example, when the probability of freezing is low, the duty ratio may be set low to control the supply of power to the heating wire device at a low temperature level, and when the probability of freezing increases, the duty ratio may be increased to control the gradual increase in the amount of heat generated by the heating wire device.

[0084] In addition, the heating wire control unit (140) may be configured to perform an active pre-heating control method that drives the heating wire to maintain the thermal state of the road surface before freezing occurs, rather than a method for removing ice after actual freezing occurs on the road surface. For example, when the probability of freezing is predicted to be above a certain level, the heating wire device may be controlled to continuously supply a certain level of low heat to maintain the latent heat of the road surface, thereby preventing a rapid drop in the road surface temperature and suppressing the conversion of moisture into ice.

[0085] Additionally, the heating wire control unit (140) can control the operating state of the heating wire device in stages according to the trend of changes in the probability of freezing. For example, in a stage where the probability of freezing is low, the heating wire can be driven in a latent heat maintenance mode, and in a stage where the probability of freezing is high, it can be controlled to switch to a heating mode that actively raises the road surface temperature. In the embodiment, the heating wire control unit (140) can drive the heating wire to maintain the latent heat of the road surface in a stage prior to road surface freezing by variably controlling the power supplied to the heating wire device in proportion to the probability of freezing value using a PWM method. This allows for efficient management of the heating wire system's energy consumption while reducing the possibility of black ice formation.

[0086] Additionally, the freezing prediction calculation unit (130) calculates a freezing phase transition index (IpI) using the road surface temperature (Ts), ambient temperature (Ta), ambient dew point temperature (Td), and relative humidity (RH), and determines the possibility of freezing on the road surface based on the index value. Specifically, the freezing prediction calculation unit (130) may calculate the ambient dew point temperature (Td) using the road surface temperature (Ts), ambient temperature (Ta), and ambient relative humidity (RH) among the sensing data transmitted from the data collection unit (120), or may utilize dew point temperature information obtained through external weather data or sensing data. The dew point temperature (Td) refers to the temperature at which water vapor in the atmosphere begins to condense and can be used as an important environmental variable for determining the possibility of freezing on the road surface.

[0087] Additionally, the freezing prediction calculation unit (130) may be configured to calculate a freezing phase transition index (IpI) using the physical relationship between the road surface temperature (Ts), the ambient temperature (Ta), the ambient dew point temperature (Td), and the relative humidity (RH). The freezing phase transition index (IpI) is an indicator representing the possibility of liquid moisture transitioning into solid ice on the road surface, and may be defined as a value that simultaneously reflects the thermal state of the road surface and the moisture conditions in the atmosphere. For example, the freezing prediction calculation unit (130) may be configured to calculate the freezing phase transition index (IPI) by reflecting the difference between the road surface temperature (Ts) and the ambient dew point temperature (Td) and the ambient relative humidity (RH), and the index value may be set to increase as the road surface temperature approaches or falls below the dew point temperature and the relative humidity increases. In addition, the above-mentioned freezing prediction calculation unit (130) may be configured to additionally consider the cooling conditions of the road surface by reflecting the change trend of the ambient temperature (Ta).

[0088] Additionally, the above-mentioned freezing prediction calculation unit (130) may be configured to determine the likelihood of freezing on the road surface by comparing the calculated freezing phase transition index (IPI) with a preset reference value. For example, if the index value increases above a set threshold value, it may be determined that there is a high likelihood of freezing occurring on the road surface, and the result of this determination may be used to calculate the probability of freezing on the road surface or to generate a control signal for the heating wire control unit.

[0089] In this way, the above-mentioned freezing prediction calculation unit (130) can be configured to calculate a freezing phase transition index (IPI) using the road surface temperature (Ts), ambient temperature (Ta), ambient dew point temperature (Td), and relative humidity (RH), and to predict the possibility of black ice occurrence in advance by determining the possibility of freezing on the road surface based on the index value.

[0090] Subsequently, the freezing prediction calculation unit (130) calculates the freezing risk by applying a reliability weight factor for each sensor to the sensing data obtained from the sensor unit. Specifically, the freezing prediction calculation unit (130) may be configured to receive the sensing data obtained from the temperature and humidity sensor, thermal imaging sensor, and lidar sensor included in the sensor unit (110) as input data, and to set a reliability weight factor for each sensor by considering the measurement accuracy of the data obtained from each sensor, reliability according to environmental conditions, or data quality. For example, the freezing prediction calculation unit (130) may be configured to apply a first weight factor to the temperature and humidity sensor data that performs ambient temperature and relative humidity measurements, apply a second weight factor to the thermal imaging sensor data that detects the heat distribution of the road surface, and apply a third weight factor to the lidar sensor data that detects the reflection characteristics of the road surface or minute surface condition changes. At this time, the weight factor may be set based on the measurement accuracy of the sensor, the installation environment, data reliability, or the results of past data analysis.

[0091] In the example, the risk of road surface freezing can be calculated through Equation 3.

[0092] Mathematical formula 3

[0093]

[0094] In Equation 3, Rf is the Road Freezing Risk, St is the temperature and humidity sensor-based freezing risk index, Sh is the thermal imaging sensor-based road temperature risk index, and Sl is the lidar sensor-based road surface condition risk index. w1, w2, and w3 are the reliability weight factors for each sensor data.

[0095] The above road surface icing risk Rf can be calculated by applying sensor-specific reliability weights to sensing data obtained from multiple sensors. For example, the icing risk can be calculated by applying weights w1, w2, and w3 to the temperature and humidity sensor-based icing risk index St, the thermal imaging sensor-based road surface temperature risk index Sh, and the LiDAR sensor-based surface condition risk index Sl, respectively. In this case, the weights can be set according to the measurement accuracy, data reliability, or environmental conditions of each sensor, and the calculated road surface icing risk value can be used as a reference value for determining the possibility of road surface icing and for controlling heating cables.

[0096] In addition, the freezing prediction calculation unit (130) may be configured to integrate and analyze sensing data obtained from each sensor using the sensor-specific weights, and to calculate the road surface freezing risk using the data values ​​to which the weights are applied. For example, if the reliability of data measured by a specific sensor is determined to be high, a higher weight may be assigned to the corresponding sensor data to reflect it in the calculation of the freezing risk; conversely, if the reliability of the sensor data is low, the weight may be adjusted to reduce the influence of the corresponding data. Furthermore, the freezing prediction calculation unit (130) may be configured to dynamically adjust the sensor-specific weights by analyzing changes in weather conditions, fluctuation patterns of sensor data, or sensor status information. Through this, the freezing risk can be calculated more accurately even when the data reliability of a specific sensor changes under specific environmental conditions. In the embodiment, the freezing prediction calculation unit (130) may be configured to calculate the road surface freezing risk more accurately by integrating and analyzing multiple sensing data by applying sensor-specific reliability weights, thereby enabling a more reliable prediction of the possibility of black ice occurrence.

[0097] Additionally, the freezing prediction calculation unit (130) is configured to predict the possibility of road surface freezing after a certain period of time using weather prediction data received from an external weather server. Specifically, the freezing prediction calculation unit (130) may be configured to receive weather prediction data related to a target road section by communicating with an external weather server or a weather information providing system. The weather prediction data may include at least one of weather variables such as changes in atmospheric temperature, changes in relative humidity, whether precipitation will occur, wind speed, atmospheric pressure, or solar radiation over a certain period of time in the future.

[0098] Additionally, the freezing prediction calculation unit (130) may be configured to analyze changes in the road surface environment over a future period of time by combining current road surface temperature, atmospheric temperature, and relative humidity data obtained from the sensor unit (110) with the weather prediction data. For example, the freezing prediction calculation unit (130) may estimate the possibility of temperature change on the road surface by considering the atmospheric temperature downward trend, humidity increase, or precipitation prediction information included in the weather prediction data, and calculate the possibility of moisture condensation or freezing occurring on the road surface.

[0099] Additionally, the above-mentioned freezing prediction calculation unit (130) may be configured to predict the road surface condition after a certain period of time based on a preset prediction time interval. For example, the above-mentioned freezing prediction calculation unit (130) may analyze environmental changes during a time interval ranging from a few minutes to tens of minutes to predict the possibility that the road surface temperature will reach a freezing critical condition after that time.

[0100] Additionally, the freezing prediction calculation unit (130) may be configured to calculate the risk of road surface freezing or the probability of freezing based on the weather prediction data, and the calculated prediction result may be transmitted to the heating wire control unit (140) and used as reference information for controlling the operation time and power supply of the heating wire device. In the embodiment, the freezing prediction calculation unit (130) may be configured to predict changes in the road surface environment after a certain period of time using weather prediction data received from an external weather server, and to preemptively respond to the occurrence of black ice by determining in advance the possibility of road surface freezing based on this.

[0101] Additionally, the freezing prediction calculation unit (130) may be configured to calculate a terrain correction index that corrects the freezing risk using at least one of the terrain information of the target road section, the presence of shaded areas, the presence of adjacent water systems, road elevation, and solar radiation information. Specifically, the freezing prediction calculation unit (130) may be configured to collect data regarding the topographical characteristics and environmental conditions of the section based on location information of the target road section, or to query from a stored database. The terrain information may be information indicating the road slope, terrain structure, or cooling environment caused by surrounding terrain, and such information may be utilized as a factor influencing changes in road surface temperature.

[0102] In addition, the above-mentioned freezing prediction calculation unit (130) may be configured to determine whether the target road section corresponds to a shaded area where sunlight is blocked by buildings, mountains, or structures, etc., and if it is determined to be a shaded area, a correction value that increases the risk of freezing may be applied by considering the possibility of a decrease in road surface temperature due to a decrease in solar radiation.

[0103] Additionally, the freezing prediction calculation unit (130) may be configured to determine whether the target road section is adjacent to a water system such as a river, lake, or wetland, and if an adjacent water system exists, it may be configured to correct the freezing risk by considering an increase in ambient humidity or a local cooling effect. Additionally, the freezing prediction calculation unit (130) may be configured to reflect the characteristics of atmospheric temperature decrease that may occur in high-altitude areas by utilizing road elevation information, and may be configured to consider the influence of solar radiation received by the road surface by utilizing solar radiation information. The freezing prediction calculation unit (130) may be configured to calculate a terrain correction index based on such terrain and environmental information, and to correct the previously calculated freezing risk or freezing probability using the terrain correction index. For example, for road sections with environmental conditions where freezing is highly probable, such as sections that are shaded areas and have an adjacent water system, the freezing risk may be weighted and reflected. In the embodiment, the freezing prediction calculation unit (130) can be configured to more accurately predict the possibility of road surface freezing by calculating a terrain correction index that reflects the topographical characteristics of the target road section and surrounding environmental conditions, and by reflecting this in the freezing risk calculation process, by considering the local cooling environment that may occur in a specific area.

[0104] Additionally, the lidar sensor (113) is configured to detect changes in road surface reflectivity and the formation of an ice layer by emitting laser pulses toward the road surface and analyzing the scattered signals. Specifically, the lidar sensor (113) may be configured to emit laser pulses of a certain wavelength toward the road surface, receive signals that are reflected or scattered and return from the road surface, and analyze the reflection intensity, reflection time, or scattering pattern of the corresponding signals. Through this, the lidar sensor (113) can detect changes in the reflectivity and surface condition of the road surface, and these changes in reflection characteristics can be used as information to determine the presence of a moisture layer or an ice layer formed on the road surface.

[0105] Additionally, the lidar sensor (113) may be configured to detect minute physical changes on the road surface by analyzing the reflection signal characteristics of the laser pulse. For example, when a thin layer of ice forms on the road surface, the reflection characteristics or scattering pattern of the road surface may change, and the lidar sensor (113) may determine the change in the road surface condition by detecting such changes in reflection intensity or diffuse reflection characteristics.

[0106] Additionally, the lidar sensor (113) may be configured to measure minute distance changes or surface roughness changes of the road surface by scanning laser pulses in multiple angular directions, thereby detecting structural changes such as minute ice layers or water films formed on the road surface. In the embodiment, the lidar sensor (113) may be configured to detect the physical condition of the road surface by analyzing changes in reflectance, scattering characteristics, and surface condition of the road surface using laser-based sensing technology, and thereby generate sensing data to determine whether black ice, which is difficult to identify with the naked eye, has formed or the possibility of road surface freezing.

[0107] Additionally, the thermal imaging sensor (112) is configured to detect minute temperature distribution changes on the road surface and generate road surface skin temperature data. Specifically, the thermal imaging sensor (112) may be configured to detect infrared energy emitted from the road surface and measure the temperature distribution of the road surface in the form of a thermal image. The thermal image data includes temperature information at each point on the road surface, thereby enabling the identification of not only the average skin temperature of the road surface but also locally formed low-temperature areas or cooling areas.

[0108] Additionally, the thermal imaging sensor (112) may be configured to detect minute temperature distribution changes on the road surface in real time or periodically to generate a temperature change pattern over time. For example, if the temperature drops faster in a specific area of ​​the road surface than in surrounding areas, the thermal imaging sensor (112) may identify that area as a cooling concentration area, and this information may be used as important data to determine the possibility of road surface freezing.

[0109] Additionally, the thermal imaging sensor (112) may be configured to transmit the thermal imaging data to a data collection unit (120), and the data collection unit (120) may be configured to analyze the thermal imaging data to extract feature data such as the average surface temperature of the road surface, temperature distribution values, or temperature change rates. The extracted road surface surface temperature data may be transmitted to a freezing prediction calculation unit (130) described later and used as input data for analyzing the possibility of road surface freezing. In the embodiment, the thermal imaging sensor (112) may be configured to precisely analyze the thermal condition of the road surface by detecting minute temperature distribution changes on the road surface and generating road surface surface temperature data, thereby allowing for a more accurate determination of the possibility of freezing occurring on the road surface.

[0110] Additionally, the heating wire control unit (140) sets a plurality of power level control modes according to the freezing probability value to control the output power of the heating wire in stages. Specifically, the heating wire control unit (140) may be configured to receive a freezing probability value transmitted from the freezing prediction calculation unit (130) and to set a driving mode of the heating wire device according to the range of the freezing probability value. For example, if the freezing probability value corresponds to a low range, a first control mode may be applied to maintain the heating wire in a non-driving state or drive it at a minimum power level, and if the freezing probability value increases above a certain standard, a second control mode may be applied to supply low heat to the heating wire device.

[0111] In addition, if the above freezing probability value rises above an intermediate level, a third control mode that increases the output power of the heating wire device to a higher level may be applied, and if the freezing probability value is determined to be at a very high level, a fourth control mode that drives the heating wire device to a maximum output level may be applied.

[0112] In addition, the heating wire control unit (140) can manage power supply levels corresponding to each power level control mode by pre-setting them, and can be configured to switch the control modes stepwise according to changes in the freezing probability value. Through this, the output power of the heating wire device can be efficiently adjusted in response to the risk of road surface freezing.

[0113] Additionally, the heating wire control unit (140) may be configured to implement the power step control mode using a pulse width modulation (PWM) method, and can precisely control the amount of heat generated by the heating wire device by adjusting the duty cycle of the power supplied to the heating wire in stages according to the freezing probability value. In an embodiment, the heating wire control unit (140) may be configured to minimize unnecessary power consumption while actively responding to the possibility of road surface freezing by setting a plurality of power step control modes according to the freezing probability value and controlling the output power of the heating wire device in stages.

[0114] Additionally, the heating wire control unit (140) drives the heating wire device into a pre-heating state when the freezing probability is greater than or equal to a preset reference value. Specifically, the heating wire control unit (140) receives a freezing probability value transmitted from the freezing prediction calculation unit (130) and may be configured to compare the freezing probability value with a preset reference value. At this time, if the freezing probability value is determined to be greater than or equal to the reference value, it is determined that there is a high probability of road surface freezing occurring within a certain period of time, and the heating wire device can be controlled to drive into a pre-heating state.

[0115] Additionally, the above preheating state may refer to a control state in which a heating device is operated at a stage prior to actual freezing occurring on the road surface to maintain the temperature of the road surface above a certain level. For example, the heating device control unit (140) may control the heating device to gradually increase the temperature of the road surface or maintain the latent heat of the road surface by supplying a predetermined amount of power when the probability of freezing is determined to be above a set reference value.

[0116] In addition, the heating wire control unit (140) may be configured to control the output power of the heating wire device in a pulse width modulation (PWM) manner while in a preheated state, thereby allowing the road surface temperature to be maintained stably while efficiently adjusting the amount of power supplied to the heating wire device.

[0117] Additionally, the heating wire control unit (140) may be configured to maintain or release a preheating state depending on changes in the freezing probability value, and may be controlled to stop the operation of the heating wire device or reduce the output power when the freezing probability decreases again below a reference value. In an embodiment, the heating wire control unit (140) may be configured to maintain the temperature of the road surface at a stage prior to road surface freezing by operating the heating wire device in a preheating state when the freezing probability is above a preset reference value, thereby reducing the possibility of black ice formation.

[0118] Additionally, the heating wire control unit (140) controls the heating wire in a low-temperature maintenance mode to maintain the latent heat of the road surface when the freezing probability reaches a preset range. Specifically, the heating wire control unit (140) may be configured to receive a freezing probability value transmitted from the freezing prediction calculation unit (130) and to determine whether the freezing probability value corresponds to a preset specific range. In this case, if the freezing probability value corresponds to the preset range, it is determined that there is a possibility of freezing occurring on the road surface but immediate high-output heating is not required, and the heating wire can be controlled to operate in a low-temperature maintenance mode.

[0119] The above low-temperature maintenance mode may refer to a control state in which a relatively low level of power is continuously supplied to the heating wire device to prevent a rapid decrease in the thermal energy, i.e., latent heat, possessed by the road surface, rather than a high-power heating method intended to rapidly raise the temperature of the road surface. For example, the heating wire control unit (140) may control the device to supply a predetermined low-temperature when the freezing probability value falls within an intermediate range, thereby preventing the road surface temperature from rapidly dropping below the freezing threshold temperature.

[0120] In addition, the heating wire control unit (140) may be configured to control the power supplied to the heating wire device in a low-temperature maintenance mode using pulse width modulation (PWM), thereby allowing the heating wire's duty cycle to be maintained at a low level while stably maintaining the road surface's thermal condition.

[0121] Additionally, the heating wire control unit (140) may be configured to perform a transition between a low-temperature maintenance mode and another power control mode depending on changes in the freezing probability value. For example, if the freezing probability value increases further, it may switch to a heating mode that increases the output power of the heating wire device, and conversely, if the freezing probability value decreases, it may control the low-temperature maintenance mode to be deactivated or the operation of the heating wire device to be stopped. In an embodiment, the heating wire control unit (140) may be configured to prevent a rapid drop in road surface temperature by controlling the heating wire to a low-temperature maintenance mode to maintain the latent heat of the road surface when the freezing probability reaches a preset range, thereby reducing the possibility of black ice formation.

[0122] Additionally, the data collection unit (120) may be configured to perform data filtering and outlier removal algorithms to remove noise from the sensor data. Specifically, the data collection unit (120) may be configured to collect sensing data received from the temperature and humidity sensor (111), the thermal imaging sensor (112), and the LiDAR sensor (113), and to perform a data preprocessing process to remove external environmental influences, sensor errors, or noise components generated during the communication process that may be included in the sensing data.

[0123] Additionally, the data collection unit (120) may apply a data filtering algorithm to ensure the stability of the sensing data. For example, the data collection unit (120) may be configured to mitigate instantaneous fluctuations or noise in the sensing data by applying at least one of a moving average filter, a median filter, or a low-pass filter. Additionally, the data collection unit (120) may be configured to perform an outlier removal algorithm to detect and remove abnormal data values ​​that fall outside the normal range among the sensing data. For example, the data collection unit (120) may be configured to determine data that falls outside the average value or standard deviation range of the sensing data as an outlier using a preset threshold criterion or a statistical analysis method, and to remove or correct the corresponding data.

[0124] In addition, the data collection unit (120) may be configured to generate refined sensing data through the filtering and outlier removal process and to transmit the refined data to the freezing prediction calculation unit (130) for use as input data for analyzing the possibility of road surface freezing.

[0125] In the embodiment, the data collection unit (120) can be configured to improve the reliability of the sensing data by performing a data filtering and outlier removal algorithm on the sensing data, thereby increasing the accuracy of the road surface freezing prediction.

[0126] Additionally, the above-mentioned freezing prediction operation unit (130) includes a self-learning module that automatically corrects parameters of the freezing prediction algorithm using past road surface freezing occurrence data and weather data. Specifically, the above-mentioned freezing prediction operation unit (130) may be configured to store or receive from an external database learning data that includes environmental data such as road surface temperature, ambient temperature, relative humidity, dew point temperature, and weather conditions related to past freezing cases on the road surface. The past data may be stored along with whether freezing actually occurred at a specific time and a specific road section, thereby allowing for the analysis of data patterns related to freezing conditions.

[0127] Furthermore, the self-learning module may be configured to automatically adjust parameters used in the freezing prediction algorithm using the past road surface freezing occurrence data and weather data. For example, the self-learning module may be configured to optimize algorithm parameters, such as weighting coefficients used to calculate the freezing phase transition index, thresholds used to calculate the freezing probability, or reliability weights for sensor data, based on training data. Additionally, the self-learning module may be configured to calculate a prediction error by comparing and analyzing the actual occurrence of road surface freezing with the freezing prediction results calculated by the freezing prediction algorithm, and to iteratively correct algorithm parameters to minimize the prediction error. Through this, the freezing prediction model suitable for the weather characteristics or road environment of a specific region can be continuously improved.

[0128] In addition, the self-learning module may be configured to retrain the freezing prediction algorithm based on data accumulated over a certain period or data during a specific season, thereby enabling it to respond to long-term environmental changes or changes in climate characteristics. As such, the self-learning module included in the freezing prediction operation unit (130) may be configured to continuously improve the accuracy of road surface freezing prediction by automatically correcting the parameters of the freezing prediction algorithm using past road surface freezing occurrence data and weather data.

[0129] Additionally, the freezing prediction calculation unit (130) includes a risk section analysis module that identifies a section at risk of black ice occurrence using data collected from a plurality of road sections. Specifically, the freezing prediction calculation unit (130) may be configured to store or receive environmental data, such as road surface temperature, ambient temperature, relative humidity, dew point temperature, road surface reflectivity characteristics, and surface condition data, collected from sensor units (110) installed in a plurality of road sections. The data may be accumulated and stored together with time information and road section identification information, thereby allowing for the analysis of environmental change patterns in each road section.

[0130] In addition, the risk section analysis module may be configured to analyze the history of road surface freezing, the pattern of change in the freezing phase transition index value, or the freezing probability value occurring in each road section based on data collected from the plurality of road sections. Through this, it is possible to identify whether freezing occurs repeatedly or whether a pattern with a high probability of freezing appears in a specific road section. Furthermore, the risk section analysis module may be configured to calculate statistical indicators such as the frequency of freezing, the duration of freezing, or the average value of the probability of freezing by road section, and may be configured to identify road sections with a high risk of black ice occurrence using said statistical indicators.

[0131] In addition, the above-mentioned risk section analysis module may be configured to more accurately identify risk sections with a high probability of black ice occurrence by analyzing environmental information such as topographical characteristics of the target road section, the presence of shaded areas, the presence of adjacent water systems, road elevation, and solar radiation. As such, the risk section analysis module included in the above-mentioned freezing prediction calculation unit (130) may be configured to identify risk sections for black ice occurrence by comprehensively analyzing environmental data collected from multiple road sections, and to perform freezing prediction and heating wire control more intensively for the identified risk sections.

[0132] Additionally, the heating wire control unit (140) is configured to perform a PWM control algorithm that dynamically adjusts the duty ratio of the power supplied to the heating wire in proportion to the freezing probability value. Specifically, the heating wire control unit (140) may be configured to receive the road surface freezing probability value transmitted from the freezing prediction calculation unit (130) as an input variable and to dynamically adjust the duty ratio of the power supplied to the heating wire device according to the magnitude of the freezing probability value. At this time, the duty ratio refers to the ratio of the time during which power is supplied to the heating wire device during one cycle of the PWM signal, and the average amount of power supplied to the heating wire device may be set to increase as the duty ratio value increases.

[0133] Additionally, the heating wire control unit (140) may be configured to gradually increase the duty cycle of the PWM signal as the freezing probability value increases, thereby controlling the heating amount of the heating wire device to increase in stages. For example, when the freezing probability value is low, a low duty cycle may be applied to control the supply of power to the heating wire device at a low heat level, and when the freezing probability value is high, the duty cycle may be increased to control the expansion of the heating amount of the heating wire device. Furthermore, the heating wire control unit (140) may be configured to update the duty cycle of the PWM signal in real time according to changes in the freezing probability value, thereby dynamically adjusting the power supply of the heating wire device in response to changes in the road surface environment. In an embodiment, the heating wire control unit (140) can efficiently control the heating amount of the heating wire device in response to the possibility of road surface freezing by performing a PWM control algorithm that dynamically adjusts the duty cycle of the power supplied to the heating wire in proportion to the freezing probability value, and thereby can be configured to prevent road surface freezing while improving the energy efficiency of the heating wire system.

[0134] Additionally, the central task server (200) manages data collected from sensor units and heating devices installed in multiple road sections. Specifically, the central control server (200) may be configured to be connected via a communication network to the sensor units (110) and heating devices installed in multiple road sections, and may be configured to receive sensing data such as road surface temperature, ambient temperature, relative humidity, road surface reflection characteristics, and road surface condition data generated in each road section. Additionally, the central control server (200) may be configured to collect operational data such as the operating status of the heating device, power supply status, or heating device control information in each road section.

[0135] Additionally, the central control server (200) may include a database for storing and managing data received from the plurality of road sections, and may be configured to comprehensively manage environmental changes and road surface condition information for each road section based on the data stored in the database. Furthermore, the central control server (200) may be configured to monitor the risk of road surface freezing or the probability of freezing for each road section by analyzing data collected from the plurality of road sections, and may be configured to generate control information for controlling the heating device in a specific road section when the risk of freezing increases in that section. Additionally, the central control server (200) may be configured to analyze freezing occurrence patterns for each road section or identify dangerous sections based on sensor data and heating device operation data for each road section, thereby supporting the efficient management of black ice risk sections in the road management system. In this way, the central control server (200) can be configured to efficiently monitor road surface conditions and perform heating wire control and road safety management functions to respond to road surface freezing by integrally managing and analyzing data collected from sensor units and heating wire devices installed in multiple road sections.

[0136] In addition, the central control server (200) analyzes the freezing risk of multiple road sections and remotely controls the heating cable driving policy. Specifically, the central control server (200) may be configured to analyze the freezing risk of each road section based on road surface temperature, ambient temperature, relative humidity, road surface reflection characteristics, and road surface condition data collected from sensor units (110) installed in multiple road sections. The central control server (200) can determine the possibility of freezing occurring in a specific road section by calculating or comparing and analyzing the freezing phase transition index, freezing probability, or freezing risk index for each road section using the collected data.

[0137] Additionally, the central control server (200) may be configured to set a driving policy for the heating device to be applied to each road section based on the analysis results. For example, if it is determined that the risk of freezing is high in a specific road section, a control policy may be set to drive the heating device in that section in a preheated state or increase the output power, and for sections with a low risk of freezing, a control policy may be set to lower the output power of the heating device or stop the operation.

[0138] In addition, the central control server (200) may be configured to transmit a set heating cable driving policy to a heating cable control unit (140) installed in each road section via a communication network to remotely control the driving status or power supply status of the heating cable device. Through this, heating cable devices installed in multiple road sections can be centrally managed and controlled in an integrated manner.

[0139] In addition, the central control server (200) may be configured to continuously analyze sensor data and freezing prediction information collected in real time to dynamically update the heating cable driving policy, thereby allowing the heating cable control strategy to be flexibly adjusted according to weather or environmental changes. In the embodiment, the central control server (200) may be configured to analyze the freezing risk of multiple road sections and remotely control the heating cable driving policy based thereon, thereby enabling efficient response to road surface freezing and preventing the occurrence of black ice.

[0140] In addition, in the embodiment, a freezing prediction control method that additionally reflects the cooling rate of the road surface may be applied to more precisely predict the possibility of black ice occurrence. Specifically, the freezing prediction calculation unit (130) may be configured to calculate the rate of change of the road surface temperature over time by analyzing road surface temperature data obtained from the sensor unit (110) at regular time intervals. For example, the freezing prediction calculation unit (130) may calculate the rate of decrease or cooling rate of the road surface temperature using road surface temperature data measured at regular time intervals, and this rate of temperature change may be used as an additional judgment factor to determine the possibility of the road surface reaching a frozen state.

[0141] Additionally, the freezing prediction calculation unit (130) may be configured to correct the probability of freezing on the road surface by comprehensively analyzing the rate of change in the road surface temperature, the freezing phase transition index (IPI), and weather prediction data. For example, even if the road surface temperature does not approach the freezing threshold temperature, if the rate of decrease in the road surface temperature is faster than a certain standard, it may be determined that there is a high probability of reaching a freezing state within a short period of time in the future, and the freezing probability value may be increased.

[0142] Additionally, the heating cable control unit (140) may be configured to control the timing of operation and the amount of power supplied to the heating cable device using the corrected freezing probability value. For example, if the rate of decrease in road surface temperature is rapid, the heating cable may be controlled to operate in a preheated state even before the freezing probability rises above a certain level. Furthermore, the central control server (200) may be configured to analyze road surface temperature change rate data collected from multiple road sections and identify road sections where rapid cooling phenomena occur repeatedly as black ice risk sections. Through this, a more preemptive heating cable control policy can be applied to road sections with a fast cooling rate, such as bridge sections, tunnel entrances, or shaded areas. According to the present embodiment, by reflecting not only the absolute value of the road surface temperature but also the temporal rate of change of the road surface temperature in the freezing prediction algorithm, the possibility of black ice occurrence can be predicted earlier, and thereby, the heating cable device can be controlled more efficiently to prevent freezing of the road surface.

[0143] In addition, in the embodiment, a heating wire control method that incorporates road traffic volume data into a freezing prediction algorithm may be applied to more accurately determine the possibility of black ice occurrence. Specifically, the freezing prediction calculation unit (130) of the present embodiment may be configured to calculate a freezing phase transition index using environmental data such as road surface temperature, ambient temperature, relative humidity, and dew point temperature obtained from the sensor unit (110), and additionally collect traffic volume data of vehicles traveling on the target road section. The traffic volume data may be provided from a traffic volume sensor, an image-based vehicle detection device, or an external traffic management system.

[0144] FIG. 3 is a flowchart illustrating the freezing prediction, heating wire control, and ice-making efficiency-based feedback control process according to an embodiment.

[0145] Referring to FIG. 3, the control method of the present embodiment may be composed of a series of processes that predict the possibility of road surface freezing based on environmental data and traffic volume data, control the heating wire accordingly, evaluate the de-icing efficiency, and perform subsequent control.

[0146] First, in the environmental data collection step (S310) following the start phase, environmental data such as road surface temperature, ambient temperature, relative humidity, and dew point temperature can be collected through the sensor unit (110). Additionally, if necessary, vehicle traffic volume data may be collected together from a traffic volume sensor or an external system. Next, in the freezing prediction and traffic volume analysis step (S320), the freezing prediction calculation unit (130) may be configured to calculate a freezing phase transition index using the collected environmental data and traffic volume data, and to predict the possibility of freezing on the road surface.

[0147] Subsequently, the freezing possibility determination step (S330) may be configured to determine whether the predicted freezing possibility is greater than or equal to a preset reference value. If the determination result indicates that the freezing possibility is greater than or equal to the reference value, the process proceeds to the heating wire control step (S340), and the heating wire control unit (140) may be controlled to operate the heating wire device. After the heating wire control, the ice-making efficiency measurement step (S350) may be performed, and in this step, the ice-making efficiency may be calculated by analyzing changes in road surface conditions due to the operation of the heating wire, the degree of freezing clearance, or the degree of temperature rise. Next, the ice-making efficiency determination step (S360) may be configured to determine whether the ice-making efficiency is greater than or equal to a preset reference value.

[0148] Based on the above judgment, if the de-icing efficiency is greater than or equal to a reference value, a heating wire output reduction step (S370) may be performed to reduce or control the output power of the heating wire device in stages. Subsequently, a low-heat maintenance mode maintenance step (S380) may be performed to operate the heating wire in a low-output state to maintain the latent heat of the road surface.

[0149] Meanwhile, if the ice-making efficiency is below a reference value, an ice-making efficiency cause diagnosis step (S390) may be performed to identify the cause of the decrease in ice-making efficiency by analyzing sensor data, environmental conditions, traffic volume, or heating element operation status. Subsequently, in the cause-specific correction control step (S395), correction control such as increasing heating element output, changing control mode, resetting correction coefficients, or expanding control intervals may be performed according to the diagnosis results.

[0150] Finally, once the above series of processes is completed, the process can proceed to the termination stage. The smart heating wire control system according to the embodiment can simultaneously improve energy efficiency and ice-making performance while adaptively responding to environmental changes.

[0151] FIG. 4 is a flowchart illustrating the process of determining the heating wire control range based on the length of the black ice section according to an embodiment. Referring to FIG. 4, the control method of the present embodiment may be configured to identify a section where black ice may occur using road surface temperature change characteristics and to differentially set the heating wire control range according to the length of the section. First, in the road surface temperature measurement step (S410) following the start step, the road surface temperature of the target road section may be measured in real time through the sensor unit (110). The road surface temperature data may be collected at regular time intervals and used for analyzing the rate of change over time. Next, in the road surface temperature drop rate determination step (S420), the freezing prediction calculation unit (130) may be configured to calculate the temperature drop rate using the measured road surface temperature data and determine whether the drop rate is greater than or equal to a preset reference value. If the drop rate is greater than or equal to the reference value, it may be determined that there is a high probability of freezing occurring.

[0152] Subsequently, the black ice risk section identification step (S430) may be configured to identify sections among the target road sections that have a high probability of black ice occurrence based on the above judgment result. Additionally, if necessary, a heating cable control step (S440) may be performed to operate a heating cable device corresponding to the risk section. Next, the black ice length determination step (S450) may be configured to determine whether the continuous length of the identified black ice section is greater than or equal to a preset standard length.

[0153] Based on the above judgment, if the length of the black ice section is greater than or equal to the reference length, a continuous danger section determination step (S455) may be performed to identify the section as a continuous freezing danger section. Subsequently, in the heating wire control range expansion step (S460), the heating wire control range may be expanded to include not only a plurality of heating wire sections included in the continuous danger section but also adjacent sections in front and behind.

[0154] Meanwhile, if the length of the black ice section is less than the reference length, a local risk section determination step (S465) may be performed to identify the section as a local freezing risk section. Subsequently, in the minimum adjacent section selection control step (S470), control may be exercised to selectively operate only the heating wire device corresponding to the local risk section or the heating wire device within the adjacent minimum range. Finally, when the above series of processes is completed, the process may proceed to a termination step. The smart heating wire control system according to the present invention enables improved freezing response performance while maintaining energy efficiency.

[0155] FIG. 5 is a flowchart illustrating the traffic volume-based freezing risk correction and heating cable control process according to an embodiment.

[0156] Referring to FIG. 5, the control method of the present embodiment may be configured to correct the risk of freezing using vehicle traffic volume information and to determine whether to operate the heating device and the output level based on the corrected risk. First, in the vehicle traffic volume recognition step (S510) following the start step, vehicle traffic volume data of the target road section may be collected through a traffic volume sensor, an image-based vehicle detection device, or an external traffic management system. Next, in the vehicle traffic volume judgment step (S520), the freezing prediction calculation unit (130) may be configured to determine whether the vehicle traffic volume is below a preset reference value. As a result of the judgment, if the vehicle traffic volume is below the reference value, it may be determined that there is a high possibility that the risk of freezing will increase due to insufficient heat exchange effect by the vehicle, and accordingly, a correction coefficient application step (S540) that increases the risk may be performed. On the other hand, when the vehicle traffic volume is above a reference value, the risk of freezing may be reduced due to frictional heat and heat exchange effects caused by vehicle driving, so a step of applying a correction factor (S530) to reduce the risk of freezing may be performed. Subsequently, in the step of recalculating the corrected risk of freezing (S550), the risk of freezing may be recalculated by reflecting the applied correction factor. Next, in the step of determining the corrected risk (S560), it may be configured to determine whether the recalculated risk of freezing is above a preset reference value. If, as a result of the determination, the corrected risk of freezing is above the reference value, a step of driving the heating wire device (S570) may be performed to control the heating wire control unit (140) to drive the heating wire device, and subsequently, in the step of increasing the output power (S580), the output power of the heating wire device may be increased or the PWM duty ratio may be adjusted upward to enable active de-icing.

[0157] Meanwhile, if the corrected freezing risk is below a reference value, a low-temperature maintenance mode step (S575) may be performed to operate the heating cable in a low-output state or to restrict the operation of the heating cable if necessary, and in the control cycle extension step (S585), the sensing and control update cycle may be increased to reduce the energy consumption of the system. Finally, when the above series of processes is completed, the process may proceed to a termination step. The smart heating cable control system according to the present invention is configured to reflect the actual road environment to simultaneously improve freezing response performance and energy efficiency.

[0158] The smart heating wire control method will be described in order below. Since the operation (function) of the smart heating wire control method according to the embodiment is essentially the same as the function of the smart heating wire control system, descriptions that overlap with FIGS. 1 to 5 will be omitted.

[0159] Figure 6 is a diagram showing a smart heating wire control process according to an embodiment.

[0160] Referring to FIG. 6, in step S110, environmental information of the target road section is measured. In step S120, data obtained from the sensor unit is collected and preprocessed. In step S130, the possibility of freezing of the road surface is analyzed using the sensing data obtained from the sensor unit. In step S140, the power supply of the heating device installed on the road is variably controlled based on the freezing probability calculated by the freezing prediction calculation unit.

[0161] Meanwhile, the methods according to the various embodiments of the present invention described above can be implemented in the form of an application or software program that can be installed on an existing electronic device.

[0162] In addition, the whole or part of the method may be composed of multiple software function modules and implemented on an operating system (OS). Alternatively, each step may be composed of a single software function module, or each step may be combined to form a single software function module and implemented on an operating system. Therefore, even if all of the embodiments of the present disclosure are not implemented as a single software function module, if multiple software function modules implement each step of the present disclosure and multiple software function modules are implemented on a single operating system, it can be understood that the method of the present disclosure has been implemented.

[0163] In addition, the methods according to the various embodiments of the present invention described above can be implemented solely through software upgrades or hardware upgrades of existing electronic devices. Furthermore, the various embodiments of the present invention described above can also be performed through an embedded server equipped in an electronic device or an external server of the electronic device.

[0164] Meanwhile, according to one embodiment of the present invention, the various embodiments described above may be implemented as software comprising instructions stored on a computer-readable recording medium using software, hardware, or a combination thereof. In some cases, the embodiments described herein may be implemented as the processor itself. According to the software implementation, embodiments such as the procedures and functions described herein may be implemented as separate software modules. Each of the software modules may perform one or more functions and operations described herein.

[0165] Meanwhile, a computer or a similar device may include a device according to the disclosed embodiments, which is capable of calling instructions stored from a storage medium and operating according to the called instructions. When said instructions are executed by a processor, the processor may perform a function corresponding to said instructions directly or by using other components under the control of said processor. The instructions may include code generated or executed by a compiler or an interpreter.

[0166] A computer-readable recording medium may be provided in the form of a non-transitory computer-readable recording medium. Here, "non-transitory" simply means that the storage medium does not contain a signal and is tangible, without distinguishing whether data is stored semi-permanently or temporarily on the storage medium. In this context, a non-transitory computer-readable medium refers to a medium that stores data semi-permanently and is readable by a device, rather than a medium that stores data for a short moment, such as registers, caches, or memory. Specific examples of non-transitory computer-readable media may include CDs, DVDs, hard disks, Blu-ray discs, USBs, memory cards, and ROMs.

[0167] As described above, exemplary embodiments have been disclosed in the drawings and specification. Although specific terms have been used to describe the embodiments in this specification, they are used only for the purpose of explaining the technical concept of this disclosure and are not intended to limit the meaning or the scope of this disclosure as defined in the claims. Therefore, those skilled in the art will understand that various modifications and equivalent alternative embodiments are possible therefrom. Accordingly, the true technical scope of protection of this disclosure should be determined by the technical concept of the appended claims.

Claims

Claim 1 A smart heating cable control system for preventing freezing of a road surface comprises: a sensor unit for measuring environmental information of a target road section; a data collection unit for collecting and preprocessing data obtained from the sensor unit; a freezing prediction calculation unit for analyzing the possibility of freezing of the road surface using sensing data obtained from the sensor unit; and a heating cable control unit for variably controlling the power supply of a heating cable device installed on the road based on the freezing probability calculated by the freezing prediction calculation unit. The smart system comprises: a freezing prediction computation unit that calculates a freezing phase transition index using the relationship between road surface temperature, atmospheric relative humidity, and atmospheric dew point temperature based on the sensing data; calculates a road surface freezing probability for a future time interval using the freezing phase transition index and weather prediction data; a heating wire control unit that drives a heating wire to maintain the latent heat of the road surface in a stage prior to road surface freezing by variably controlling the power supplied to the heating wire device in proportion to the freezing probability using a pulse width modulation (PWM) method; after the heating wire control, calculates a de-icing efficiency by analyzing at least one of the road surface condition change, the degree of freezing resolution, or the degree of temperature rise resulting from the operation of the heating wire; if the de-icing efficiency is greater than or equal to a preset reference value, reduces or controls the output power of the heating wire device in stages; and if the de-icing efficiency is less than the preset reference value, analyzes at least one of sensor data, environmental conditions, traffic volume, and the operating status of the heating wire to identify the cause of the decrease in de-icing efficiency, and performs correction control including increasing the heating wire output and changing the control mode according to the identified cause. Heating wire control system. Claim 2 A smart heat wire control system according to claim 1, wherein the sensor unit comprises one or more of: a temperature and humidity sensor for measuring the temperature and humidity of the atmosphere; a thermal imaging sensor for detecting the heat distribution of the road surface; or a LiDAR sensor for detecting the reflective characteristics and minute surface changes of the road surface. Claim 3 A smart heating cable control system according to claim 1, wherein the freezing prediction calculation unit calculates a freezing phase transition index (IPI) using road surface temperature (Ts), ambient temperature (Ta), ambient dew point temperature (Td), and relative humidity (RH), determines the possibility of freezing on the road surface based on the index value, and calculates the freezing risk by applying a reliability weight factor per sensor to the sensing data obtained from the sensor unit. Claim 4 delete Claim 5 A smart heating cable control system according to claim 1, wherein the freezing prediction calculation unit is configured to calculate a terrain correction index that corrects the freezing risk using at least one of terrain information of a target road section, whether there is a shaded area, whether there is an adjacent water system, road elevation, and solar radiation information. Claim 6 In paragraph 2, the smart heating wire control system is configured such that the lidar sensor emits laser pulses toward the road surface and analyzes the scattered signals to detect changes in road surface reflectivity and whether an ice layer has formed. Claim 7 In paragraph 2, the smart heating wire control system is configured such that the thermal imaging sensor detects minute temperature distribution changes on the road surface and generates road surface surface temperature data. Claim 8 A smart heating wire control system according to claim 1, wherein the heating wire control unit is configured to operate the heating wire device in a pre-heating state when the freezing probability is greater than or equal to a preset reference value. Claim 9 A smart heating wire control system according to claim 1, wherein the heating wire control unit is configured to control the heating wire in a low-temperature maintenance mode to maintain the latent heat of the road surface when the probability of freezing reaches a preset range. Claim 10 In claim 1, the above-mentioned freezing prediction calculation unit is a smart heating cable control system comprising a self-learning module that automatically corrects parameters of a freezing prediction algorithm using past road surface freezing occurrence data and weather data.

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