KR101962961B1 - System for analyzing road condition using surface multi-sensor - Google Patents

Abstract

Disclosed is a system for determining a road situation using a surface multi-sensor, which comprises: a surface multi-sensor installed below a surface of the road; a control unit collecting information from the surface multi-sensor and transmitting the collected information to a server; and a server storing the information received from the control unit in a database, generating learning data based on the information stored in the database, learning an artificial intelligence model by using the learning data, and transmitting the information on the learned artificial intelligence model to the control unit.

Description

SYSTEM FOR ANALYZING ROAD CONDITION USING SURFACE MULTI-SENSOR [0002]

The present invention relates to a system and method for determining the state of a road using a surface multi-sensor.

Real-time monitoring of road conditions is essential for safe and smooth vehicle operation. However, most of the conventional monitoring means often use expensive equipment, and monitoring results and feedback thereof have been difficult to be performed in real time.

Recently, a variety of sensor devices with stable and low price have been produced and sold, and IoT technology capable of connecting various sensor terminals through a network has been developed and distributed.

IoT (Internet of Things) is also referred to as the Internet of things, and refers to a technology or environment in which sensors and communication modules are attached to objects so that data can be exchanged with servers and the like through a network in real time. The existing IoT has been understood as a model controlled by the central server, but recently, the IoT system based on cloud computing has also been widely developed and provided.

Cloud services have been developed and provided with optimized technologies for processing and storing data in response to environmental changes such as miniaturization of devices such as smart phones and enhancement of processing tasks.

As a result, customers using cloud services were able to maintain a reasonable cost structure through the usage-based payment structure. However, problems such as server stability, security problems and response speed still remain.

Especially in the industrial field, IoT often needs to maintain the urgency, accuracy and security of vast amounts of data that need to be processed and stored. In this case, data processing is limited only by current cloud computing technology.

This introduces the concept of fog computing, which extends cloud computing and services to the end of the network (edge, edge), and introduces the concept of wider use of individual devices (edges) rather than a centralized network system.

Edge Computing has the advantage that it can process data directly from IoT objects, so that it has lower computational power than cloud computing but has a faster response speed and does not need broadband communication. Edge computing means placing processors and data near the end (endpoint) rather than centrally (in the cloud).

Edge computing is a technology that provides immediate performance and stability over the cloud as data can be instantly analyzed and applied to the field at the end (edge) where the data is collected.

Therefore, it is required to develop a system capable of judging the road condition in real time and providing feedback by utilizing these technologies.

Registered Patent Publication No. 10-1703001, Registered on January 31, 2017

SUMMARY OF THE INVENTION The present invention provides a system and method for determining the state of a road using a surface multi-sensor.

The problems to be solved by the present invention are not limited to the above-mentioned problems, and other problems which are not mentioned can be clearly understood by those skilled in the art from the following description.

According to an aspect of the present invention, there is provided a system for determining the state of a road using a surface multi-sensor, the system including: a surface multi-sensor installed under the surface of the road; A control unit for transmitting the collected information to a server, and information received from the control unit in a database, generating learning data based on information stored in the database, and learning an artificial intelligence model using the learning data And transmitting information about the learned artificial intelligence model to the control unit.

The control unit may transmit first information collected from the surface multisensor to the server, and the server may extract one or more parameters from the first information, input the extracted parameters to the artificial intelligence model To determine the state of the road, and to transmit information on the state of the road to the control unit.

The control unit may acquire an artificial intelligence model based on the information on the artificial intelligence model received from the server when information on the state of the road can not be received from the server, Extract one or more parameters, and input the extracted parameters to the artificial intelligence model to determine the state of the road.

The surface multi-sensor may further include a housing, an external temperature sensor inserted into the upper surface of the housing, and a surface lower end temperature sensor disposed under the upper surface of the housing; Wherein an outer temperature sensor is inserted into the groove and an opening of the groove is coated with a material having a thermal conductivity equal to or higher than a predetermined reference value, And the server can determine whether or not the road is freeze based on information collected from the surface multi-sensor for detecting ice.

In addition, the information received from the controller may include first temperature information sensed by the external temperature sensor, second temperature information sensed by the surface lower end temperature sensor, and second temperature information sensed at a time point when the first temperature information and the second temperature information are sensed Wherein the server extracts one or more parameters from the information received from the control unit and labels the one or more parameters using information on whether or not the road surface is freezing , Generates learning data including the labeled information, and learns the artificial intelligence model using the generated learning data.

The server may acquire at least one weight information corresponding to the learning result of the artificial intelligence model and transmit the weight information to the controller, and the controller may acquire the artificial intelligence model using the weight information can do.

The server determines whether the road is freezing. If the road is determined not to be frozen, the server predicts the possibility of freezing and freezing of the road, transmits the predicted information to the controller, The control unit may control the artificial intelligence model and the first information obtained by the control unit during a predetermined time range including the predicted freezing time when the predicted freezing probability exceeds a predetermined reference value, It is possible to monitor whether or not it is freezing.

The system may further include a multi-gas sensor connected to the control unit, wherein, when the road is frozen while monitoring whether or not the road is freezing, the first information and the multi- The controller determines whether or not the frozen state of the road is maintained, progressed, or solved by using the weather information received from the sensor. If it is determined that the frozen state of the road is to be maintained or proceeded, And when it is determined that the freezing state of the road is to be canceled, the freezing state of the road is determined, and the determination result of the freezing state can be provided to the server.

The surface multisensor may include a first sensor installed on one side of an expansion joint of the bridge including the road and a second sensor installed on another side of the expansion joint facing the one side, Wherein the first sensor and the second sensor include a housing and a distance sensor exposed to the outside of the housing, the distance sensor measuring a distance between the first sensor and the second sensor, And the server can determine the status of the bridge based on information collected from a surface multi-sensor for monitoring the expansion joint of the bridge.

According to an aspect of the present invention, there is provided a method of determining a road condition using a surface multi-sensor, the method comprising: receiving information collected from a surface multi- , Storing the received information in a database, generating learning data based on the information stored in the database, learning an artificial intelligence model using the learning data, and transmitting the learned artificial intelligence And transmitting information about the model to the control unit.

Other specific details of the invention are included in the detailed description and drawings.

According to the disclosed embodiments, it is possible to provide a road condition monitoring system that ensures stability and reliability using a relatively inexpensive sensor.

In addition, it is possible to monitor the state of the road in real time based on the IoT system, to collect information collected from each sensor terminal, and to collect various information by analyzing the collected information using the AI model .

In addition, by using an edge computing system, it is possible to cope with an unstable network communication, and in the case where a quick response is required, it is possible to analyze information collected on the road in real time and cope with it.

The effects of the present invention are not limited to the above-mentioned effects, and other effects not mentioned can be clearly understood by those skilled in the art from the following description.

1 is a diagram illustrating a system for determining the state of a road using a surface multi-sensor according to an exemplary embodiment of the present invention.
2 is a view showing a structure of a surface multi-sensor according to an embodiment.
3 is a diagram showing an example in which a surface multi-sensor and a surface multi-sensor are disposed.
FIG. 4 is a view showing a surface multi-sensor further including a motion detection sensor according to an embodiment.
5 is a view showing a multi-gas sensor according to an embodiment.
6 is a diagram illustrating a surface multi-sensor including a distance sensor in accordance with one embodiment.
Figs. 7 and 8 are views showing a surface multi-sensor provided with a stretch joint of a bridge therebetween.
9 is a diagram illustrating an intelligent alternator according to an embodiment.
10 is a diagram illustrating an example of a method of determining a deformation of a structure using an intelligent aligner including a motion detection sensor.
11 is a flowchart illustrating a method of determining the state of a road using a surface multi-sensor according to an embodiment.

BRIEF DESCRIPTION OF THE DRAWINGS The advantages and features of the present invention, and the manner of achieving them, will be apparent from and elucidated with reference to the embodiments described hereinafter in conjunction with the accompanying drawings. It should be understood, however, that the invention is not limited to the disclosed embodiments, but may be embodied in many different forms and should not be construed as limited to the embodiments set forth herein, Is provided to fully convey the scope of the present invention to a technician, and the present invention is only defined by the scope of the claims.

The terminology used herein is for the purpose of illustrating embodiments and is not intended to be limiting of the present invention. In the present specification, the singular form includes plural forms unless otherwise specified in the specification. The terms " comprises "and / or" comprising "used in the specification do not exclude the presence or addition of one or more other elements in addition to the stated element. Like reference numerals refer to like elements throughout the specification and "and / or" include each and every combination of one or more of the elements mentioned. Although "first "," second "and the like are used to describe various components, it is needless to say that these components are not limited by these terms. These terms are used only to distinguish one component from another. Therefore, it goes without saying that the first component mentioned below may be the second component within the technical scope of the present invention.

Unless defined otherwise, all terms (including technical and scientific terms) used herein may be used in a sense that is commonly understood by one of ordinary skill in the art to which this invention belongs. In addition, commonly used predefined terms are not ideally or excessively interpreted unless explicitly defined otherwise.

As used herein, the term "part" or "module" refers to a hardware component, such as a software, FPGA, or ASIC, and a "component" or "module" performs certain roles. However, "part" or " module " is not meant to be limited to software or hardware. A "module " or " module " may be configured to reside on an addressable storage medium and configured to play back one or more processors. Thus, by way of example, "a" or " module " is intended to encompass all types of elements, such as software components, object oriented software components, class components and task components, Microcode, circuitry, data, databases, data structures, tables, arrays, and variables, as used herein. Or " modules " may be combined with a smaller number of components and "parts " or " modules " Can be further separated.

The terms spatially relative, "below", "beneath", "lower", "above", "upper" And can be used to easily describe a correlation between an element and other elements. Spatially relative terms should be understood in terms of the directions shown in the drawings, including the different directions of components at the time of use or operation. For example, when inverting an element shown in the figures, an element described as "below" or "beneath" of another element may be placed "above" another element . Thus, the exemplary term "below" can include both downward and upward directions. The components can also be oriented in different directions, so that spatially relative terms can be interpreted according to orientation.

In this specification, a computer means all kinds of hardware devices including at least one processor, and may be understood as meaning a software configuration that operates in the corresponding hardware device according to the embodiment. For example, the computer may be understood to include, but is not limited to, smartphones, tablet PCs, desktops, laptops, and user clients and applications running on each device.

Hereinafter, embodiments of the present invention will be described in detail with reference to the accompanying drawings.

1 is a diagram illustrating a system for determining the state of a road using a surface multi-sensor according to an exemplary embodiment of the present invention.

In this specification, the type of the road 1 is not limited. For example, the road 1 is not limited to the kind of roads, national roads, highways, etc., and the material of the road 1 is also not limited to asphalt, soil, sand, metal (iron plate).

The road 1 may be an open road, a mountain road, a tunnel, or the road 1 may be a road of a bridge.

Referring to Fig. 1, railings 2 of road 1 are shown. The railings 2 are shown according to one embodiment and can mean guard rails of the road 1, may mean a safety barrier, may mean guard rails of a bridge or a previously installed guardrail , But is not limited thereto.

Depending on the embodiment, there may be no handrail 2 on the road 1. In this case, the structures installed on the handrail 2 can be installed on the ground or underground.

In one embodiment, the road 1 may be provided with one or more surface multisensors 100 and 200. For example, surface multisensors 100 and 200 may be installed below the surface of road 1.

In one embodiment, the surface multisensors 100 and 200 may be installed on the road 1 at predetermined intervals, but the types of the surface multisensors 100 and 200 installed on the road 1, Is not limited.

For example, the surface multisensors 100 and 200 may be embedded at a predetermined depth from the surface of the road 1. As another example, when the surface multi-sensors 100 and 200 have a predetermined space under the ground surface of the road 1 (for example, the space provided for installing the sensor or the road 1 is covered with an iron plate or the like) , Or may be installed in the space.

The surface multi-sensors 100 and 200 are connected to the respective controllers 310 and 210 in a wired or wireless manner. According to a preferred embodiment, the surface multi-sensors 100 and 200 are connected to the respective controllers 310 and 210, And may be connected to the control units 310 and 210.

In one embodiment, the surface multi-sensors 100 and 200 may be coupled to the multi-gas sensor 300. For example, the surface multi-sensor 100 and the multi-gas sensor 300 may be connected in various configurations such as one-to-one, one-to-many, many-to-one and many-to-many.

Similarly, the control units 210 and 310 may also be connected in various configurations such as one-to-one, one-to-many, many-to-one and many-to-many with each of the surface multisensors 100 and 200, Further, the control unit may be built in or integrated into each surface multi-sensor.

In one embodiment, the control unit 310 may be connected to one or more multi-gauges sensors 300, embedded in the multi-gauges sensor 300, or integrated with the multi-gauges sensors 300.

In one embodiment, the controller 310 may transmit information collected from the surface multisensor 100 and the multi-weather sensor 300 to the server 10. According to an embodiment, the control unit 310 may directly process the information collected from the surface multi-sensor 100 and the multi-gas sensor 300. [ In this case, the control unit 310 can transmit the information processing result to the server 10.

For example, the processing of the information collected from the surface multi-sensor 100 and the multi-gas sensor 300 and the method of obtaining the specific information therefrom are not limited, but at least part or all of the method may be performed by the control unit 310 and / Server 10, respectively.

In one embodiment, the server 10 determines the state of the road based on the information collected from the surface multi-sensor 100 and the multi-gas sensor 300, transmits the determination result to the outside, (310). The control unit 310 determines the state of the road based on the information received from the server 10 and determines the state of the road 1 directly instead of the server 10 according to the embodiment, It is possible to determine the situation of the road 1 by processing the information directly in real time during transmission and reception.

The control unit 310 may include one or more configurations capable of outputting the status of the road 1 and may include an output device such as a display, a speaker, and an LED installed in the road 1 or the rail 2 And information on the state of the road is output by using the information, thereby allowing the driver to obtain the information.

In one embodiment, the control unit 310 may transmit information on the state of the road 1 to a user terminal (e.g., a smartphone or a navigation device of a driver) used by the driver using a Wi-Fi or a beacon.

In one embodiment, the server 10 may process information collected using an artificial intelligence model. For example, the server 10 performs learning on an artificial intelligence model using machine learning, inputs information collected from the surface multi-sensor 100 to the learned artificial intelligence model, And the like).

In one embodiment, the server 10 stores information received from the control unit 310 in a database, generates learning data based on the information stored in the stored database, and learns the AI model using the generated learning data .

In one embodiment, the server 10 may classify the information received from the controller 310, extract one or more features from the classified information, generate one or more parameters from the extracted features, The learning data can be generated by performing labeling based on the information previously confirmed for the group. However, the type of learning data and the method of generating learning data are not limited thereto.

In one embodiment, the server 10 may send information about the learned artificial intelligence model to the controller 310. [

For example, the server 10 may send information about one or more weights used in the learned artificial intelligence model to the control unit 310. [ The control unit 310 can acquire an artificial intelligence model using information received from the server 10. [

In one embodiment, the server 10 may be understood as a type of computer as described above, and in accordance with an embodiment, the server 10 may refer to a cloud server.

In most cases, data generated in a smart device such as the control unit 310 or IoT objects is stored and processed in a main server through a cloud technology.

Cloud services have been developed and provided with optimized technologies for processing and storing data in response to environmental changes such as miniaturization of devices such as smart phones and enhancement of processing tasks.

As a result, customers using cloud services were able to maintain a reasonable cost structure through the usage-based payment structure. However, problems such as server stability, security problems and response speed still remain.

Especially in the industrial field, IoT often needs to maintain the urgency, accuracy and security of vast amounts of data that need to be processed and stored. In this case, data processing is limited only by current cloud computing technology.

This introduces the concept of fog computing, which extends cloud computing and services to the end of the network (edge, edge), and introduces the concept of wider use of individual devices (edges) rather than a centralized network system.

Edge Computing has the advantage that it can process data directly from IoT objects, so that it has lower computational power than cloud computing but has a faster response speed and does not need broadband communication. Edge computing means placing processors and data near the end (endpoint) rather than centrally (in the cloud).

Edge computing is a technology that provides immediate performance and stability over the cloud as data can be instantly analyzed and applied to the field at the end (edge) where the data is collected.

In one embodiment, edge computing may be applied to the system shown in FIG.

That is, the control unit 310 can function as an " edge " of edge computing.

However, the system according to the disclosed embodiment can utilize cloud and edge computing together. For example, the server 10 may learn the artificial intelligence model and transmit the learning results to the control unit 310 periodically.

Also, the server 10 can collect and update the artificial intelligence model periodically by combining the information received from the controller 310.

The collected information is basically transmitted to the server 10 through the control unit 310, and the server 10 stores the information in the database. In addition, the server 10 may analyze the data using the artificial intelligence model and transmit the analysis result to the control unit 310.

For example, the control unit 310 may immediately analyze the collected information to determine the state of the road 1, and may provide a special When it is determined that there is no abnormal situation, the server 10 can receive the analyzed information and determine the state of the road 1. According to an embodiment, the control unit 310 compares the analyzed information with the analyzed information in the server 10, updates itself on the basis of the comparison result, or transmits the comparison result to the server 10 The server 10 may assist in updating the AI model.

As another example, the edge can directly analyze and process data when communication with the server 10 is unstable or impossible.

When the server 10 obtains information indicating that an abnormal situation occurs on the road 1 or an abnormal situation is predicted, the control unit 310 analyzes and processes the data directly for quick response, ) Can be monitored and responded to.

In one embodiment, the surface multi-sensor 100 may include one or more temperature sensors. For example, the surface multi-sensor 100 may include two temperature sensors provided at different positions, and may be a sensor device used to determine whether or not the road 1 is freezing. The specific configuration of the sensor device used for determining whether or not the road 1 is freezing will be described later.

At this time, it is possible to determine whether or not the road 1 is frozen based on the temperature information sensed by the two temperature sensors included in the surface multi-sensor 100.

In one embodiment, the icing of the road may take the form of a visible white ice, but it may take a form that is hard to see visually, such as black ice. In this case, it may cause accidents caused by slipping of the vehicle. Therefore, it is necessary to check the freezing position, provide information to the driver, or transfer it to the relevant authorities to eliminate the freezing condition, .

In one embodiment, the server 10 extracts one or more parameters from the temperature information sensed by the two temperature sensors included in the surface multisensor 100, and determines whether the surface of the road (1) , And generate learning data including the labeled information.

The server 10 can learn the artificial intelligence model using the generated learning data. Also, the server 10 can transmit information about the learned artificial intelligence model to the control unit 310, and the controller 310 can acquire the learned artificial intelligence model based on the information. The information on the learned artificial intelligence model may include one or more pieces of weight information corresponding to the learning result of the artificial intelligence model. In addition, the controller 310 may acquire an artificial intelligence model using the weight information.

The server 10 can use the learned model to determine whether or not the road is freezing from the temperature information measured by the surface multi-sensor 100, and if the road is not frozen, the possibility of freezing of the road and the freezing time can be predicted have.

The server 10 may transmit judgment information on whether or not the road is freezing, or prediction information on the possibility of freezing of the road and the freezing time, to the control unit 310. [ The control unit 310 determines whether or not the information that the road has been frozen is received from the server 10 or information indicating that the road will soon be frozen, for example, when the possibility of freezing of the road exceeds a predetermined reference value, The information collected from the surface multi-sensor 100 may be processed by the control unit 310 to monitor whether the road is frozen or not. For example, the controller 310 monitors whether or not the road 1 is frozen using the information collected from the surface multi-sensor 100 for a predetermined time range before and after the frozen point of the road predicted by the server 10 .

In one embodiment, when the road 1 is determined to be frozen, the control unit 310 can transmit the freezing information of the road to the server 10 and the outside. For example, the control unit 310 may output or transmit a message indicating that the road at the location where the surface multi-sensor 100 is installed is freezing by using the output device.

The controller 310 or the server 10 determines whether or not the road 1 has been frozen by comparing the time at which the road was frozen, the degree of freezing of the road, the direction of the freezing of the road, It is possible to predict the freezing time of the road.

For example, when it is determined that the road 1 is frozen, the control unit 310 determines whether or not the freezing is solved based on the change in temperature and temperature measured by the surface multisensor 100, It can be judged.

In addition, although freezing can be naturally solved by a change in temperature, it can be solved by the pressure applied by the automobile passing through the freezing portion, the friction applied to the ice, and the chemicals applied for freezing the ice.

When the progress of the freezing or the disappearance of the ice is confirmed, the control unit 310 can grasp the cause thereof and can more accurately predict the direction of the freezing on the basis of the detected cause. In one embodiment, the control unit 310 obtains ambient temperature information from the multi-gas sensor 300. [ The control unit 310 determines whether or not the ice making process is proceeding based on the transition of the temperature and the temperature around the surface multisensor 100 and on the direction of ice and freezing determined based on the information sensed by the surface multisensor 100 The cause of the resolution can be determined.

For example, if the ambient temperature is getting lower or the temperature is kept below a certain reference value, it can be predicted that the ice will proceed accordingly. If the ambient temperature condition coincides with the progress of the icing, it is determined that there are no other external factors, and the progress of the icing can be predicted based on the temperature.

Conversely, even if the ambient temperature is low, it can be judged that freezing is eliminated. In this case, it can be determined that freezing is resolved by the vehicle running on the road or chemicals applied to the road, and the cause can be more specifically judged based on the degree of freezing, speed and pattern.

In this case, the control unit 310 can more accurately predict the progress of the icing based on the determined cause.

In one embodiment, the surface multi-sensor 100 may include at least one distance sensor. The surface multi-sensor 100 measures the distance between different surface multi-sensors 100 and 200 using a distance sensor, and can determine the state of the road based on the distance. For example, the road 1 may be a road of a bridge. In this case, one or more surface multisensors 100 and 200 may be installed at different positions of the bridge, and may be installed with stretching seams of bridges in between, depending on the embodiment.

The control unit 310 or the server 10 can determine the expansion, contraction, and warping of the bridge, and accordingly, whether the expansion joint is expanded or compressed based on the distance information received from the one or more surface multi-sensors 100 and 200 .

In one embodiment, the controller 310 or the server 10 may compare the distance information between one or more surface multisensors 100 and 200 to a preset reference value to determine whether an abnormal situation has occurred in the bridge. Further, it is possible to judge whether or not an abnormal situation occurs in the bridge based on the rate of change of the distance between one or more surface multisensors 100 and 200, or the risk of occurrence of an abnormal situation.

In another embodiment, the server 10 may learn an artificial intelligence model based on distance information obtained from one or more surface multisensors 100 and 200. The server 10 can extract one or more parameters based on the distance information obtained from the one or more surface multisensors 100 and 200 and labels the extracted parameters based on the abnormality of the previously observed bridge Data can be generated.

According to the embodiment, the learning data may further include parameters extracted from the vibration, motion, or weather condition information of the bridge.

The server 10 can learn the artificial intelligence model based on the generated learning data and transmit the learned artificial intelligence model information to the controller 310. [ Similarly, the control unit 310 can acquire the learned artificial intelligence model based on the received information.

The controller 310 can determine the status of the bridge based on the information collected from the surface multisensor 100 in the control unit 310 and the server 10 as in the case of monitoring whether or not the road is freezing, And the communication with the server 10 is not smooth, the situation of the bridge can be monitored by analyzing the data directly.

For example, if real-time monitoring is required, the distance between one or more surface multisensors 100 and 200 may be less than a preset reference range, A case in which the distance is judged to be out of a predetermined reference range within a predetermined time, and the like.

In this case, the controller 310 monitors the distance between one or more surface multi-sensors 100 and 200 in real time, and can determine whether an abnormal situation of the bridge occurs based on the distance.

In addition, the control unit 310 can determine the cause of an abnormal situation in the bridge based on the information collected from the multi-wake-up sensor 300, or if it is scheduled to occur. For example, the control unit 310 can determine the linkage between the meteorological information including the ambient temperature and humidity of the bridge and the abnormal situation occurring in the bridge (for example, excessive contraction or expansion of the expansion joint).

In one embodiment, the control unit 310 may obtain weather forecast information. Based on the weather forecast information, the control unit 310 can predict the direction of the excessive expansion or contraction of the expansion joint of the road or the expansion joint of the bridge. For example, when the road is frozen and the weather is clear after a predetermined time and the temperature is predicted to rise, it is predicted that freezing will also be solved. On the contrary, if the weather is cloudy and the temperature is predicted to decrease, Can be expected to be strengthened. When it is predicted that freezing will be enhanced or that it is predicted that the freezing will not be solved, the control unit 310 can externally request a means for freezing the ice.

In addition, when the expansion joint of the bridge is excessively opened or closely adhered, information on the change in temperature and humidity over time is acquired, and it is determined whether or not the distance between the expansion joints is naturally restored to the predetermined reference range, The required time can be predicted.

The server 10 can acquire information on whether a road or a bridge has a problem and whether or not the problem can be solved based on the information obtained from the control unit 310. [ If it is determined that the problem will not be solved naturally, the server 10 may send a message to an organization capable of solving the problem, and may request control of the road or bridge according to an embodiment.

In one embodiment, the system may further include intelligent selectors 400 and 402 that provide the driver with notifications according to road conditions.

In one embodiment, the intelligent extenders 400 and 402 may be installed at predetermined intervals on the road, and may be installed at predetermined intervals on both sides of the road. The intelligent extenders 400 and 402 may be installed in the railing 2 of the road 1, but are not limited thereto.

In one embodiment, the intelligent aligners 400 and 402 may include a light emitting portion capable of reflecting light of a vehicle, but capable of outputting light of a specific color. The concrete structure and utilization method of the intelligent aligner will be described later.

In one embodiment, the intelligent aligners 400 and 402 can output lights of different colors, depending on whether the road 1 is frozen, as determined based on the information collected in the surface multisensor 100. [ The intelligent extenders 400 and 402 may output lights of different colors according to the state of the road determined by the control unit that has obtained the information collected from the surface multi-sensor 100. In this embodiment, And at least one of the controllers 210 and 310 and the server 10 shown in FIG.

The control unit may transmit information on the state of the road to the intelligent extenders 400 and 402 and the intelligent extenders 400 and 402 may output the corresponding color.

In another embodiment, the control unit may communicate color information based on the state of the road to the intelligent controllers 400 and 402, and the intelligent controllers 400 and 402 may output the color received from the controller.

In one embodiment, the intelligent aligners 400 and 402 can output lights of different colors, respectively, depending on whether or not they are freezing on the road 1 where the intelligent aligners 400 and 402 are located. For example, if the surface of the road 1 on which the surface multisensor 100 is installed is determined to be frozen based on the information collected by the surface multisensor 100, the intelligent controller 402 responds to the freezing condition It is possible to output the light of the color.

In one embodiment, the server 10 may obtain positional information of each of the intelligent controllers 400 and 402 and information about the colors output from the intelligent controllers 400 and 402, respectively.

The server 10 can generate map information in which the colors output by each of the intelligent controllers 400 and 402 are displayed at the positions of the intelligent controllers 400 and 402, respectively. For example, the map information may include a map image or may include navigation information. The server 10 can provide the generated map information to the driver's terminal of the driver traveling on the road 1. [ For example, the driver terminal may include, but is not limited to, a smartphone of the driver or a navigation device installed in a vehicle operated by the driver.

In displaying the navigation information, the driver terminal may display the color information received from the server 10 together with the image of the road displayed in the navigation. Therefore, even when the driver does not watch the intelligent controllers 400 and 402, it is possible to easily determine the freezing state of the road in the traveling direction through navigation.

The intelligent aligners 400 and 402 are installed at predetermined intervals on both sides of a structure (for example, a bridge or the like) including the road 1 and can also be utilized for determining the state of the structure . Specific methods for utilizing the intelligent extenders 400 and 402 will be described later.

Hereinafter, the structure and use method of the surface multi-sensor will be described in detail with reference to the drawings.

2 is a view showing a structure of a surface multi-sensor according to an embodiment.

Referring to FIG. 2, the surface multi-sensor 100 is shown in a cylindrical shape, but the shape of the surface multi-sensor 100 is not limited thereto.

In one embodiment, the surface multi-sensor 100 includes a housing having a space therein and a cable for outputting at least one sensor and sensor information installed in the housing.

In one embodiment, the housing may be comprised of an upper housing 110 and a lower housing 120, as shown in FIG. 2, and may be coupled to one housing using coupling means 112.

In one embodiment, the upper housing 110 and the lower housing 120 are coupled to each other, and a space in which one or more sensors can be disposed may be provided inside the coupled housing.

The surface multisensor 100 further includes a waterproof ring 130 coupled between the upper housing 110 and the lower housing 120 to prevent water from penetrating into the interior of the surface multisensor 100.

In one embodiment, the surface multi-sensor 100 is installed below the surface of the road and can be used to determine the freezing of the road using a temperature sensor.

The surface multisensor 100 includes an external temperature sensor 150 inserted into the top surface of the top housing 110 and a surface bottom temperature sensor 140 disposed below the top surface of the top housing 110 .

The surface multi-sensor 100 transmits information sensed by the two temperature sensors to the outside, and accordingly, the surface multi-sensor 100 includes a cable 152 for transmitting the sensed information from the external temperature sensor 150 to the outside, And a cable 142 for transmitting the sensed information from the surface lower temperature sensor 140 to the outside.

The surface multisensor 100 also includes a connection hose tube 160 that surrounds and protects the cables 142 and 152.

In one embodiment, cables 142 and 152 are connected to control 310 along connection hose tube 160.

3 is a diagram showing an example in which a surface multi-sensor and a surface multi-sensor are disposed.

Referring to FIG. 3, a surface multi-sensor 100 is installed below the surface of the road 1. The surface multisensor 100 includes an external temperature sensor 150 inserted into the upper surface of the upper housing 110 and a surface lower temperature sensor 140 disposed below the upper surface of the upper housing 110.

The external temperature sensor 150 and the surface lower end temperature sensor 140 each have different distances from the road surface 1, so that different temperatures are sensed from each sensor. According to the disclosed embodiment, it is possible to determine the icing state of the road 1 based on the difference between the two temperature sensors.

To this end, the external temperature sensor 150 and the surface lower end temperature sensor 140 are disposed at different positions of the surface multisensor 100, respectively. For example, the external temperature sensor 150 is disposed outside the housing 100. In one embodiment, the external temperature sensor 150 may be disposed to protrude from the top of the housing 100. However, in this case, the external temperature sensor 150 may be easily broken by an external impact.

3, a groove 114 into which the external temperature sensor 150 can be inserted is provided on the upper surface of the housing 100 (that is, the upper surface of the upper housing 110) 150 may be inserted into the groove 114. The depth of the groove 114 may be set to be greater than the height of the external temperature sensor 150.

In addition, the opening of the groove 114 may be coated to protect the external temperature sensor 150 inserted in the groove 114 from impact or foreign matter. However, the opening of the groove 114 may be coated with the material 116 having a thermal conductivity of a predetermined reference value or more so that the external temperature can be easily transferred to the external temperature sensor 150. For example, the opening of the groove 114 may be coated with platinum. Further, the opening of the groove 114 may be coated with gold or silver, and is not limited to a specific material.

In addition, the surface lower end temperature sensor 140 may be installed below the upper surface of the housing 100, that is, the upper housing 110. A predetermined distance is generated between the external temperature sensor 150 and the surface lower end temperature sensor 140 and the temperature is measured at the external temperature sensor 150 and the surface lower end temperature sensor 140 by the thermal conductivity of the housing 100 There will be a difference in temperature.

In the disclosed embodiment, it is determined whether or not the road is freeze based on the temperature measured by the external temperature sensor 150 and the temperature measured by the surface lower temperature sensor 140 and the difference therebetween. The temperature change of the outside is transmitted to the surface multi-sensor 100 installed on the lower surface of the road through the road. At this time, there occurs a difference in whether the surface of the road is frozen and the degree of heat transfer depending on the degree of freezing . In addition, the degree of heat transfer to the outer temperature sensor 150 and the surface lower end temperature sensor 140 also varies.

In one embodiment, this difference, which occurs depending on whether or not the road is freezing and the degree of freezing, can be first analyzed based on a predetermined database.

For example, when there is a database in which existing measured information on the temperature measured by the external temperature sensor 150 and the surface lower temperature sensor 140 and the difference therebetween is recorded according to whether the road is freeze and the degree of freezing, The control unit of the multi-sensor 100 (the control unit is understood as a concept including both the control unit 310 directly connected to the surface multi-sensor 100 and the server 10 connected through the network) It is possible to determine whether or not the current road is freezing and the degree of freezing by comparing the temperature measured by the currently measured external temperature sensor 150 and the temperature measured by the surface lower end temperature sensor 140.

Furthermore, these differences, which occur depending on the degree of freezing of the road and the degree of freezing, can be analyzed by learning based on the AI model.

For example, when there is a database in which existing measured information on the temperature measured by the external temperature sensor 150 and the surface bottom temperature sensor 140 and the difference therebetween is recorded according to whether the road is freezing and the degree of freezing, Extracts one or more features based on the information stored in the database, extracts one or more parameters from the respective features, and determines, for the extracted parameters, whether or not the observed road is freezing at the time corresponding to the parameter and the degree of freezing So that learning data can be generated.

The control unit can learn the artificial intelligence model by using the generated learning data. Accordingly, the controller determines the temperature measured by the external temperature sensor 150 and the surface lower end temperature sensor 140 according to the degree of freezing of the road and the degree of freezing, An artificial intelligence model capable of outputting the degree of freezing of the road and the degree of freezing can be obtained.

Thereafter, the control unit extracts one or more parameters from the temperature measured by the external temperature sensor 150 and the surface bottom temperature sensor 140, and the difference, inputs the extracted parameters to the learned artificial intelligence model, And information on the degree of freezing can be obtained.

In one embodiment, the control unit can predict the possibility of freezing of the road surface and the time of freezing, based on the temperature measured by the external temperature sensor 150 and the surface lower temperature sensor 140 and the difference therebetween.

For example, when the control unit determines that the road surface is not frozen at present based on the temperature measured at the external temperature sensor 150 and the temperature measured at the surface lower temperature sensor 140 and the difference therebetween, The time can be predicted.

As described above, the possibility of freezing of the road surface and the time of freezing may also be judged by a database-based comparison or may be predicted based on the learned artificial intelligence model.

In addition, when the current road is frozen, the controller can determine the direction of the freezing progress of the road and the traveling speed of the road. For example, the controller can determine whether the road is frozen or not to be frozen, based on the temperature measured by the external temperature sensor 150 and the temperature measured by the surface lower temperature sensor 140, Can be judged.

In one embodiment, the control unit calculates the temperature and the difference between the temperature measured by the external temperature sensor 150 and the surface lower temperature sensor 140, as well as the weather information received from the weather sensor 300 or the external server, The degree of freezing, the possibility of freezing, the time of freezing, the traveling direction, and the traveling speed.

For example, when the ambient temperature received from the multi-gas sensor 300 is lower than a preset reference temperature, the controller can determine that the freezing will proceed further. According to the weather information received from the external server, , It can be judged that the freezing will further proceed. Likewise, if the ambient temperature is higher than a predetermined reference temperature, it can be determined that the freezing will be eliminated, and if it is predicted that the temperature will rise in accordance with the weather information received from the external server, it can be determined that the freezing will be eliminated. In this case, the control unit can predict the time point at which the ice is released. For example, the prediction of the freezing time can be determined based on the freezing state of the present road and the surrounding temperature or the speed of eliminating the ice according to the predicted temperature.

In one embodiment, when it is determined that the icing state is maintained or progressed, the control unit may transmit a freezing state to the server or an external related organization, and may request follow-up action or control of entry to the road accordingly.

FIG. 4 is a view showing a surface multi-sensor further including a motion detection sensor according to an embodiment.

Referring to FIG. 4, the surface multi-sensor 100 may further include substrates 170 and 172 that include additional sensors. In one embodiment, the substrates 170 and 172 may be disposed in a space provided within the housing 100.

For example, the surface multi-sensor 100 may further include a motion detection sensor disposed on the substrates 170 and 172. The motion detection sensor is for sensing various motions of the surface multi-sensor 100 and may include, for example, a vibration sensor, a gyro sensor, a compass sensor, an acceleration sensor, and the like.

The surface multi-sensor 100 can detect vibrations and movements of roads and bridges using a motion detection sensor.

For example, vibration due to a vehicle traveling on the road can be transmitted to the surface multi-sensor 100, and in the case of a bridge, movement due to shaking, contraction or expansion of the bridge can be transmitted to the surface multi- .

In one embodiment, the surface multi-sensor 100 can acquire information about roads, bridges, and vehicles that travel therefrom using a motion sensor, which is collected from two temperature sensors as described above in accordance with an embodiment Information can be used together to obtain the icing condition of the road and the additional information related thereto.

In one embodiment, the control unit can recognize the vehicle driving the road on which the surface multi-sensor 100 is installed based on the information collected from the motion detection sensor. For example, when vibration is detected in the surface multi-sensor 100, the control unit may determine that the vehicle is traveling on the road on which the surface multi-sensor 100 is installed.

Further, the surface multi-sensor 100 can determine the position of the vehicle based on the intensity of the vibration. For example, as the vibration becomes stronger, it can be determined that the vehicle is close to the surface multi-sensor 100, and accordingly, the direction or movement of the vehicle can be determined based on the increase and decrease of the vibration.

In addition, the control unit can determine whether the vehicle is traveling normally based on the vibration sensed by the surface multisensor (100). For example, the control unit can determine whether or not the wheels of the vehicle are slipping due to freezing, and can be used to determine whether or not the road is freezing according to the degree of slipping. It can also be used to judge.

In addition, the surface multisensor 100 can acquire additional information based on the intensity of the vibration as well as the pattern or change. For example, the surface multi-sensor 100 can determine whether or not the road is freezing and the degree of freezing based on the intensity of the vibration. For example, when the intensity of the vibration sensed by the surface multi-sensor 100 gradually increases, it can be determined that the vehicle is approaching. At this time, the intensity of the vibration will be detected most when the vehicle passes over the surface multi-sensor 100, and then decrease again. If the maximum value of the vibration sensed at this time is smaller than usual, it can be judged that the tire is slightly slipped due to the freezing and thus the vibration is reduced.

That is, the control unit can determine the degree of freezing of the road and the degree of freezing based on the intensity of the vibration sensed from the traveling vehicle. For example, the controller can determine that the icing is severe as the maximum value of the vibration sensed according to the running of the vehicle is smaller.

In one embodiment, the control unit may determine whether a vehicle has been slipped based on the vibration sensed from the surface multi-sensor 100. [ For example, when the vibration linearly increases and decreases according to the approach of the vehicle, it can be determined that the vehicle is traveling normally, but it is possible to detect that a slight slip occurs on the basis of the maximum value of the vibration, It is possible to judge whether or not it is freezing.

However, when the vibration to be sensed decreases sharply, that is, when the vibration sensed by the surface multisensor 100 increases linearly and suddenly is not sensed, or when a value smaller than a predetermined reference value is detected, It can be judged that a slip in which the wheels of the vehicle slip largely due to the freezing of the road has occurred in the case where the vehicle speed is different (sharply) decreased from the increase and decrease pattern of the vibration.

For example, the control unit can determine whether or not the road is freeze based on the information collected from the two temperature sensors included in the surface multi-sensor 100, and based on the information collected from the motion detection sensor, Or to determine whether or not the road is freezing based on the information collected from the temperature sensor. When it is determined that the road is freezing, the surface multi-sensor 100 can judge whether or not a slip occurs in the vehicle running on the road.

According to the embodiment, when it is determined that slip has occurred, the surface multi-sensor 100 can determine whether an accident has occurred. For example, when a slip occurrence is detected in the first surface multisensor, the first surface multisensor can transmit a slip occurrence to the surrounding surface multisensor through the server or other communication means. Thereafter, when vibration is detected by the first surface multi-sensor or another surface multi-sensor in the vicinity, it is possible to determine whether or not an accident has occurred based on the pattern of the vibration.

For example, when the vibration is short and large, it can be estimated as vibration due to the collision, and the position of the surface multi-sensor in which the vibration is sensed is located in the traveling direction of the vehicle judged as the occurrence of the slip from the first surface multi- And the vibration is detected within a predetermined time from the occurrence of the slip, the controller may determine that an accident caused by the slip has occurred.

In this case, the control unit can notify the occurrence of the accident through the server, and the server requests the image capturing to the camera device installed at the position judged as an accident, acquires the captured image, can do.

The camera device may be a CCTV installed in the corresponding location, or may be an accident camera device provided in the multi-gas sensor 300, but the present invention is not limited thereto.

5 is a view showing a multi-gas sensor according to an embodiment.

In one embodiment, the multi-gas sensor 300 may be installed on the railing 2 of the road 1 as shown in Fig. 1 and may be installed on the ground around the road 1, It is not limited.

5, the multi-gas sensor 300 may include a solar panel 310, a wind speed and weather instrument 320, a lightning rod 330, and a right and left design 340, The type of the meteorological measuring device and the peripheral device included in the gas meter are not limited thereto. For example, the multi-gas sensor 300 may include, but is not limited to, a hygrometer, a camera device, a display, an LED, a motion sensor, an infrared sensor, an ultrasonic sensor,

In one embodiment, the multi-gas sensor 300 may further include a battery capable of storing power generated from the solar panel 310.

In addition, the multi-gas sensor 300 may include an IoT wireless communication module capable of communicating with the server 10. For example, the multi-gas sensor 300 may include the control unit 310 described above.

The multi-gas sensor 300 may be connected to the surface multisensor 100 or may be connected to the surface multisensor 100 through the control unit 310. The control unit 310 can transmit the information collected from the multi-gas sensor 300 and the surface multisensor 100 to the server 10 and can function as an IoT edge to process the directly collected information.

In one embodiment, the multi-weather sensor 300 may further include a Bluetooth (beacon) or Wi-Fi module capable of communicating with a driver's terminal (smartphone, navigation, etc.). For example, the multi-gas sensor 300 may output information on road conditions using a display, an LED, or the like, or may provide information to a terminal of a driver using a communication module.

6 is a diagram illustrating a surface multi-sensor including a distance sensor in accordance with one embodiment.

In one embodiment, the surface multisensor 100 shown in FIG. 6 measures the distance between different surface multisensors and can be used to determine the state of the bridge based thereon. That is, the surface multi-sensor 100 shown in Fig. 6 can be installed in a bridge including a road.

Referring to FIG. 6, the surface multi-sensor 100 may further include a distance sensor 180 capable of measuring a distance. In one embodiment, the distance sensor 180 may be an ultrasonic sensor. In another embodiment, the distance sensor 180 may be an infrared distance measurement sensor. In another embodiment, the distance sensor 180 may be a distance sensor in the form of a signal generating unit and a signal receiving unit for receiving a signal generated from the signal generating unit and measuring the distance, and is not limited to a specific type.

In one embodiment, the distance sensor 180 is disposed in a space inside the housing, and the housing may be provided with an opening 190 for allowing the distance sensor 180 to be exposed to the outside.

Although the surface multisensor 100 shown in FIG. 6 is shown as including both a temperature sensor, a motion detection sensor, and a distance sensor, the surface multisensor 100 according to the disclosed embodiment includes a temperature sensor, One or more of the sensors may be included, and the configuration is not limited.

In one embodiment, the distance sensor 180 is used to measure the distance between the surface multisensors 100 and 200 installed at different locations.

In one embodiment, when the distance sensor 180 is composed of a signal generating unit and a signal receiving unit, the signal generating unit and the signal receiving unit may be provided in different surface multi-sensors, respectively. In this case, Surface multisensors can be installed facing each other.

When the distance sensor 180 is an ultrasonic or infrared sensor, the distance sensor 180 included in the surface multisensor 100 is connected to each of the surface multisensors 100 and 200 in the direction in which the other surface multisensor 200 is viewed. Can be installed.

In the case of the surface multisensor 100 including the distance sensor 180, it may be installed at the lower end of the bridge or may be installed in a specific space inside the bridge so that the distance between the different surface multisensors can be measured.

For example, the bridge has a space in which an empty space is covered with an iron plate or the like. In this case, the surface multi-sensor 100 can be installed at the lower end of the steel plate.

In one embodiment, the control unit may determine the state of the bridge based on distance information received from the surface multisensor (100). For example, if the distance between the surface multisensors installed at different locations of the bridge is reduced or increased, this may mean that the bridge is contracting or expanding.

Figs. 7 and 8 are views showing a surface multi-sensor provided with a stretch joint of a bridge therebetween.

In the case of bridges, it includes a stretch joint (3), which means a device that can be stretched tightly to compensate for the expansion or contraction of concrete on both sides of the bridge due to the ambient conditions of the bridge (such as weather).

In one embodiment, a plurality of surface multisensors 100 and 200 are installed across the stretch seam 3 and a distance sensor 180 is used to measure the distance between the surface multisensors 100 and 200 .

The position where the surface multi-sensors 100 and 200 are installed is not limited and may be installed above the expansion joint 3 as shown in Fig. 7 and may be provided below the expansion joint 3 as shown in Fig. As shown in FIG.

That is, due to the contraction or expansion of the bridge, the distance between the surface multisensors 100 and 200 provided between the expansion joints 3 will be different, and the control unit will be based on the distance between the surface multisensors 100 and 200 It is possible to judge whether or not a problem of a bridge occurs.

For example, when the distance between the surface multisensors 100 and 200 is out of a preset range, the control unit can judge that there is a problem in the bridge.

For example, when the distance between the surface multisensors 100 and 200 is equal to or less than a preset reference value, the controller can determine that the expansion joint 3 is excessively compressed due to the expansion of the bridge. In fact, when the expansion joint 3 is excessively compressed, the concrete in the vicinity of the expansion joint 3 or the expansion joint 3 may protrude from the road due to the pressure.

In this case, not only the running of the vehicle can be disturbed but also the tire may be crushed or cause an accident, so it is necessary to quickly grasp the situation and take measures.

That is, when the distance between the surface multisensors 100 and 200 is out of the predetermined range, the controller determines that an abnormal situation has occurred in the bridge, and transmits the determination result to the outside. For example, the control unit may transmit the determination result to the server 10. [

According to the embodiment, the control unit transmits distance information between the surface multisensors 100 and 200 to the server 10, and the server 10 determines the state of the bridge based on the distance information, Lt; / RTI >

As described above, the server 10 operates as an edge, and the control unit operates as an edge, and the server 10 or the control unit analyzes the distance information between the surface multi-sensors 100 and 200 according to the situation to determine the state of the bridge can do.

In one embodiment, the range that is the basis for determining the abnormal situation of the bridge based on the distance between the surface multisensors 100 and 200 may be determined based on a distance at which a problem may actually occur in the bridge, , It can be set to a range smaller than the distance at which a problem may actually occur in the bridge.

In one embodiment, the control unit may determine whether the bridge is a problem based on a change in distance and distance between the surface multisensors 100 and 200, and may predict the possibility of a problem with the bridge and its point of view according to an embodiment .

For example, the controller can judge and predict the artificial intelligence model based on a database-based comparison or a learned artificial intelligence model, and the method is similar to the above-described freezing determination method.

For example, the control unit can collect distance information between the surface multisensors 100 and 200, and store the state information of the bridges in the database. Then, the controller compares the distance information between the surface multisensors 100 and 200 with the information stored in the database to determine whether there is a problem with the bridge.

Further, the control unit can determine the possibility of causing a problem with the bridge after a predetermined time based on the information on the change in the distance between the surface multisensors 100 and 200.

In addition, when it is determined that there is a problem in the bridge, the control unit can predict whether or not the problem has been solved and the time to solve the problem. For example, if the distance between the surface multisensors 100 and 200 shows a tendency to increase when the distance between the current surface multisensors 100 and 200 is equal to or less than the preset reference value, And it is possible to predict when the problem will be solved based on the increasing speed of the distance.

Further, based on the weather information and the weather forecast information, the control unit can determine whether the time point of occurrence of the problem becomes faster, the problem becomes worse, the problem is solved, and the like. For example, when the weather is hot, the concrete expands, and thus the distance between the surface multisensors 100 and 200 can be shortened.

In this case, the controller can acquire information on the temperature change in the future based on the obtained weather information, for example, it can be predicted that the problem caused by the expansion of the concrete will also be solved when the temperature is determined to decrease.

Further, when the distance between the surface multisensors 100 and 200 becomes equal to or less than a predetermined threshold value, problems (for example, joints or protrusion of concrete) that can not be restored to the bridge can occur. In this case, even if the distance between the surface multi-sensors 100 and 200 is increased, the control unit can transmit the restoration request signal and the entrance control request signal to the road.

In another embodiment, the control unit can use the artificial intelligence model to determine whether the bridge is problematic, depending on the distance between the surface multisensors 100 and 200.

For example, the controller extracts one or more parameters from the distance information between the surface multisensors 100 and 200, inputs the extracted parameters to the learned artificial intelligence model, and determines whether there is a problem of the bridge, that is, It is possible to judge whether or not the problem of (3) occurs.

In addition, the control unit can determine the possibility of occurrence of a problem of the bridge, the timing of occurrence of the problem, the possibility of solving the problem, and the timing of solving the problem as the output of the artificial intelligence model.

The artificial intelligence model learning method can be used as the artificial intelligence model learning method for detecting the freezing. For example, the control unit may collect distance information between the surface multisensors 100 and 200, extract one or more features from the collected information, and extract one or more parameters from each feature. The control unit may acquire information on whether or not an abnormality occurs in the bridge at a time point corresponding to each parameter, and may generate learning data by labeling the parameter with each parameter.

The control unit can learn the artificial intelligence model using the generated learning data. The artificial intelligence model can be used to determine whether there is a problem in the bridge, possibility of problem, point of view, possibility of resolving the problem, and timing of resolution.

In one embodiment, the control unit may obtain additional information using a motion sensor included in the surface multi-sensor.

For example, the surface multi-sensor 100 can determine the vibration and movement of a bridge by using a motion detection sensor. For example, the surface multi-sensor 100 can determine the movement due to the expansion or contraction of the bridge, and when a concrete or joint protrudes due to a problem in the expansion joint 3, have.

Based on the information sensed by the motion detection sensor, the control unit can determine whether or not an abnormality of the expansion joint 3, an abnormality occurrence and an abnormality occurrence time are detected based on the information detected from the motion detection sensor, Verified and calibrated with the results determined based on the distance between the surface multi-sensors 100 and 200.

In one embodiment, the surface multi-sensor 100 may obtain information about the vehicle that is driving the road using a motion sensor. As described above, when the vehicle travels on the road, the vibration sensed by the surface multisensor 100 gradually increases, and then gradually decreases after the maximum value.

In one embodiment, the control unit can determine the running state of the vehicle based on the vibration sensed by the surface multisensor (100). For example, the control unit can determine whether the vehicle is traveling on a flat ground or a road with irregularities. In this case, the unevenness may mean a joint or a concrete protruding due to the expansion of the bridge.

In one embodiment, if the distance between the surface multisensors 100 and 200 is less than or equal to a preset reference value, the control unit may perform a monitoring operation to confirm whether a real problem has occurred.

For example, the monitoring operation may include determining a running condition of the vehicles traveling on the bridge using a motion detection sensor. For example, it is normal that the vibration of the vehicle gradually increases and then disappears and disappears. However, when the vibration caused by the running of the vehicle is greatly reduced instantaneously, the vibration is not detected for a moment, The control unit can judge that the bridge is uneven so that the vehicle runs on the unevenness and does not generate vibration momentarily, or that the vehicle vibrates irregularly without being linear.

The vibration pattern according to the state of the road and the running state of the vehicle can be collected through experiments or observations, and can be stored in a database. In one embodiment, the control unit can determine the state of the road and hence the running state of the vehicle based on a simple comparison with the database or a similar range comparison. In another embodiment, the control unit learns the artificial intelligence model based on the database and utilizes it to learn the artificial intelligence model from the information collected in the surface multisensor.

In one embodiment, the vibration sensed by the surface multi-sensor 100 may be due to the running of one vehicle, but the vibrations generated by the plurality of vehicles may be received in a superimposed manner. Although the above-described embodiments have been described on the basis of the case where one vehicle runs on the surface multi-sensor 100 for the convenience of explanation, vibration due to running of a plurality of vehicles may be received depending on the situation.

In this case, the control unit can determine the traveling state of the vehicle traveling on the road based on the change in the vibration received from the surface multi-sensor 100. [ For example, when vibrations due to driving are simultaneously received in two vehicles, the total vibration may be sharply reduced even when slip occurs in one of the two vehicles. For example, in this case, the vibration of a vehicle during reception of the vibration of two vehicles or a part thereof may be reduced, and the control unit may determine the occurrence of the slip based on this.

In one embodiment, the control unit can separate each vibration based on the characteristics of the vibration that each vehicle generates. For example, different vibration patterns may occur depending on the type of the vehicle, the shape of the wheels, the weight of the vehicle, and the traveling speed, and the vibration may increase or decrease depending on the position of the vehicle. The control unit can separate the vibration signal for each vehicle from the vibration signal in which different vibration patterns are superimposed. In one embodiment, the control unit may compare the vibration signals obtained from the plurality of surface multi-sensors and isolate the vibration signals for each vehicle therefrom.

In one embodiment, a method of extracting a vibration signal for each vehicle from a vibration signal in which the vibration signals due to the running of a plurality of vehicles are superimposed can also be performed based on the AI model.

In another embodiment, an artificial intelligence model capable of determining whether or not the above-mentioned slippage, slip, running disturbance due to unevenness, or the like occurs from a vibration signal in which vibrations due to running of a plurality of vehicles are generated can be learned.

For example, learning data may be generated by extracting one or more features and parameters from a vibration signal in which vibration due to running of a plurality of vehicles is superimposed, and labeling the type of the problem according to the actual observation have. The control unit can learn the artificial intelligence model based on the generated learning data, and the learned artificial intelligence model can be generated based on the parameters extracted from the vibration signals in which the vibrations due to the running of the plurality of vehicles are superimposed, And the type thereof, or the likelihood of occurrence.

9 is a diagram illustrating an intelligent alternator according to an embodiment.

Referring to Fig. 9, an intelligent controller 400 is shown. The intelligent delineators 400 may be installed at predetermined intervals in the railing 2 of the road 1 as shown in FIG. 1, but are not limited thereto.

Referring to FIG. 9, the intelligent controller 400 includes a solar film 410. In one embodiment, the intelligent controller 400 may be powered using power generated from the solar film 410. The intelligent controller 400 includes a power supply and a battery that store power generated from the solar light film 410 and supply power to the intelligent controller 400.

In one embodiment, the intelligent controller 400 includes an LED module 420. The LED module 420 includes one or more LEDs capable of outputting one or more colors.

In one embodiment, the intelligent controller 400 can output lights of different colors depending on the icing state of the road 1. [ For example, the intelligent controller 400 may output red when the road 1 is frozen, yellow when the road 1 is expected to freeze or risk freezing, and green when there is no risk of freezing, But is not limited thereto.

In one embodiment, the intelligent controller 400 may include reflective means for reflecting the light of the vehicle. For example, the LED module 420 may include a reflector capable of reflecting the light of the vehicle, and the reflector may be provided with one or more apertures or lenses for transmitting the light of the LED to the outside.

In another embodiment, the housing of the intelligent controller 400 may serve as a reflector. That is, the housing of the intelligent controller 400 may be made of a material that easily reflects the light of the vehicle.

In one embodiment, the angle of the LED module 420 may vary depending on the situation. The intelligent controller 400 may include a power section capable of changing the angle of the LED module 420. [

For example, when the surroundings are bright (such as during the day), the LED module 420 may be disposed in a direction facing the vehicle to be driven. Therefore, the LED module 420 can emit a color light according to the freezing state of the road in the direction of the driver of the traveling vehicle.

As another example, when the surroundings are dark (night or the like), the LED module 420 may be disposed in an oblique direction with respect to the traveling vehicle, or may be disposed in the surface direction of the road. If the strong LED light is emitted toward the driver when the surroundings are dark, the LED module 420 may be disposed in an oblique direction with respect to the traveling vehicle because it may interfere with the field of view. As another example, the LED module 420 may be arranged to emit light in the direction of the surface of the road. When the surroundings are dark, the road reflects the light emitted from the LED module 420, So that the driver can intuitively transmit information on whether or not the road is freezing.

In addition, when the road is frozen, the light of the LED module 420 is reflected more clearly, thereby making it possible to transmit the frost state of the road more clearly and intuitively.

In one embodiment, the intelligent controller 400 includes a communication module. For example, the intelligent terminator 400 may include an IoT communication module, wherein the IoT communication module may include, but is not limited to, Cat.M, RoLa, LTE-M, and the like.

In one embodiment, the intelligent controller 400 may further include an icing sensor, a temperature sensor, a humidity sensor, a fog correction sensor, and the like. The multi-gas sensor 300 and the surface multi-sensor 100, As shown in FIG. In this case, the intelligent controller 400 may directly collect information without a separate sensor device, and output the color light corresponding to the state of the road by controlling the LED module 420 based on the collected information.

In one embodiment, the intelligent controller 400 may further include a motion detection sensor. As described above, the motion detection sensor may include a vibration sensor, a gyro sensor, a compass sensor, and the like, and may include an altitude sensor according to an embodiment.

10 is a diagram illustrating an example of a method of determining a deformation of a structure using an intelligent aligner including a motion detection sensor.

Referring to FIG. 10, intelligent adapters 400, 402, and 404 installed at predetermined intervals in a structure including a road 1 and an example of determining deformation of a structure using the intelligent extenders 400 and 402 are shown.

Referring to Figures 10 (a) to 10 (c), illustrative examples are shown in which deformation of a structure occurs and determination is made using the intelligent aligners 400, 402, 404, 406 and 408.

In one embodiment, intelligent aligners 400, 402, and 404 are installed at predetermined intervals on one side of the structure, and the control unit is connected to the intelligent aligner 400, 402, Information about the position and motion of each of the sensors 400, 402, and 404 may be collected.

For example, the control unit may obtain information about the height and motion of the intelligent controllers 400, 402, and 404 from the altitude sensors provided in the intelligent controllers 400, 402, and 404. An acceleration sensor, a gyro sensor, or the like may be used to determine the motion of the intelligent aligners 400, 402, and 404.

As shown in FIG. 10 (a), when a change occurs in the heights of the intelligent controllers 400, 402, and 404, the controller can determine that the structure is bent. For example, when the height of the intelligent aligner 402 in the middle of the intelligent extenders 400, 402, and 404 is increased, the controller can determine that the corresponding position of the structure is bent.

In addition, intelligent controllers 400, 404, 406, and 408 are installed at predetermined intervals on both sides of the structure, and the control unit controls the intelligent derriers 400, 404, 406, Information about the position and movement of each of the players 400, 404, 406, and 408 may be collected.

For example, as shown in Fig. 10 (b), the difference in height between the intelligent selectors 400 and 406, 404 and 408 of the intelligent selectors 400, 404, 406, Or if a change occurs, the control unit can judge that the structure is warped.

Further, the intelligent controllers 400, 402, 404, 406, and 408 are installed at predetermined intervals on both sides of the structure, and the control unit controls the movement of the intelligent relays 400, 402, 404, 406, Information about the position and movement of each of the intelligent aligners 400, 402, 404, 406, and 408 may be collected from the sensors.

The height of the intelligent controller 402 in the center of the intelligent controllers 400, 402, 404, 406, and 408 rises, for example, as shown in FIG. 10 (c) When a height difference or change occurs between the controllers 400 and 406, 404 and 408, the control unit can judge that the structure is warped and warped at the same time.

11 is a flowchart illustrating a method of determining the state of a road using a surface multi-sensor according to an embodiment.

11 is described as a server, but at least some or all of the steps shown in FIG. 11 may be performed partially or entirely in the server or the control unit described in this specification, Subject is not limited.

In step S510, the server receives information collected from the surface multi-sensor installed under the surface of the road from the control unit of the surface multi-sensor.

In step S520, the server stores the received information in the database.

In step S530, the server generates learning data based on the information stored in the database.

In step S540, the server learns the artificial intelligence model using the learning data.

In step S550, the server transmits information about the learned artificial intelligence model to the controller.

FIG. 11 is a timing diagram illustrating operations performed by the server 10 and the control unit 310 shown in FIG. Therefore, the embodiments described as the operations of the server 10 and the control unit 310 in Figs. 1 to 10 can also be applied to the method shown in Fig.

The steps of a method or algorithm described in connection with the embodiments of the present invention may be embodied directly in hardware, in software modules executed in hardware, or in a combination of both. The software module may be a random access memory (RAM), a read only memory (ROM), an erasable programmable ROM (EPROM), an electrically erasable programmable ROM (EEPROM), a flash memory, a hard disk, a removable disk, a CD- May reside in any form of computer readable recording medium known in the art to which the invention pertains.

The components of the present invention may be embodied as a program (or application) and stored in a medium for execution in combination with a computer which is hardware. The components of the present invention may be implemented in software programming or software components, and similarly, embodiments may include various algorithms implemented in a combination of data structures, processes, routines, or other programming constructs, such as C, C ++ , Java (Java), assembler, and the like. Functional aspects may be implemented with algorithms running on one or more processors.

While the present invention has been described in connection with what is presently considered to be practical exemplary embodiments, it is to be understood that the invention is not limited to the disclosed embodiments, but, on the contrary, You will understand. Therefore, it should be understood that the above-described embodiments are illustrative in all aspects and not restrictive.

1: Road
2: Railing
10: Server
100: Surface multisensor
200: Surface multisensor
210:
300: Multi-weather sensor
310:

Claims (10)

Surface multisensor installed below surface of road;
A controller for collecting information from the surface multisensor and transmitting the collected information to a server; And
Wherein the control unit stores information received from the control unit in a database, generates learning data based on information stored in the database, learns an artificial intelligence model using the learning data, To the control unit; Lt; / RTI >
Wherein,
Transmitting the first information collected from the surface multi-sensor to the server,
The server comprises:
Extracting one or more parameters from the first information, inputting the extracted parameters to the artificial intelligence model to determine the state of the road, transmitting information on the state of the road to the controller,
Wherein,
Acquiring an artificial intelligence model based on the information on the artificial intelligence model received from the server and extracting one or more parameters from the first information when the information about the state of the road can not be received from the server , Inputting the extracted parameters to the artificial intelligence model to determine the state of the road,
The surface multi-
housing;
An external temperature sensor inserted into the upper surface of the housing; And
A surface lower end temperature sensor disposed below the upper surface of the housing; Lt; / RTI >
Wherein a surface of the housing is provided with a groove into which an external temperature sensor is inserted, the external temperature sensor is inserted into the groove, and the opening of the groove is coated with a material having a thermal conductivity of a predetermined reference value or more, Lt; / RTI >
The server comprises:
Determining whether or not the road is frozen based on information collected from the freezing surface sensing multi-sensor,
The information received from the controller may include at least one of first temperature information sensed by the external temperature sensor, second temperature information sensed by the surface lower end temperature sensor, the road information at the time point when the first temperature information and the second temperature information are sensed Information on whether or not the surface is freezing,
The server comprises:
Extracting one or more parameters from the information received from the controller, labeling the one or more parameters using information on whether or not the road surface is freezing, generating learning data including the labeled information,
Learning the artificial intelligence model using the generated learning data,
A system that judges the situation of a road by using surface multi-sensor. delete delete The method according to claim 1,
The surface multi-
And a motion detection sensor disposed in a space inside the housing,
Wherein,
If it is determined that the road surface has been frozen,
And a controller for recognizing a vehicle traveling on the road surface based on information received from the motion detection sensor,
A system that judges the situation of a road by using surface multi-sensor. 5. The method of claim 4,
Wherein,
And a control unit for recognizing a vehicle traveling on the road surface based on the vibration detected by the motion detection sensor and determining a change in vibration in accordance with a distance between the vehicle and the surface multi-sensor, And determining whether the vehicle has been slipped based on a change in the vibration,
A system that judges the situation of a road by using surface multi-sensor. The method according to claim 1,
The server comprises:
Acquiring one or more pieces of weight information corresponding to learning results of the artificial intelligence model, transmitting the weight information to the controller,
Wherein,
Acquiring the artificial intelligence model using the weight information,
A system that judges the situation of a road by using surface multi-sensor. The method according to claim 6,
The server comprises:
Determining whether or not the road is freezing; estimating the possibility of freezing and freezing of the road when the road is determined not to freeze; transmitting the predicted information to the control unit;
Wherein,
Determining whether or not the road is frozen using the artificial intelligence model and the first information obtained by the controller during a predetermined time range including the predicted freezing time when the predicted freezing probability exceeds a predetermined reference value, Monitoring,
A system that judges the situation of a road by using surface multi-sensor. 8. The method of claim 7,
The system comprises:
A multi-gas sensor connected to the controller; Further comprising:
Wherein,
Progress of the road, and whether or not the road is frozen, by using the first information and the weather information received from the multi-gas sensor, when it is determined that the road is frozen while monitoring whether or not the road is freezing However,
Wherein the control unit transmits a determination result of the icing condition to the server when the icing condition of the road is determined to be maintained or progressed,
Determining whether a freezing state of the road is to be solved, determining a time point at which the freezing state of the road is solved, and transmitting a determination result of the time point to the server,
A system that judges the situation of a road by using surface multi-sensor. The method according to claim 1,
The surface multi-
A first sensor installed on one side of the expansion joint of the bridge including the road; And
A second sensor installed on another side surface of the expansion joint facing the one side surface; Lt; / RTI >
Wherein the first sensor and the second sensor comprise:
housing; And
A distance sensor that is exposed to the outside of the housing; Lt; / RTI >
Wherein the distance sensor comprises a surface multisensor for monitoring the expansion joint of a bridge measuring a distance between the first sensor and the second sensor,
The server comprises:
And determining a state of the bridge based on information collected from a surface multi-sensor for monitoring the expansion joint of the bridge,
A system that judges the situation of a road by using surface multi-sensor. delete

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