KR102825597B1 - System for predicting abnormal signs of a surveillance target using a sensor module and a transmission/reception method including an IoT laser water level meter - Google Patents

Abstract

The present invention relates to an abnormality prediction system using a sensor transmission/reception module including an IoT laser level meter. The system includes a sensor transmission/reception module (100) including an IoT laser level meter, which has an IoT laser level meter, at least one sensor module that can be linked with the IoT laser level meter, and collects status information by surrounding environment for each surveillance target; a gateway (200) that collects and controls status information by surrounding environment for each surveillance target from the sensor transmission/reception module (100); and a cloud server (300) that receives status information by surrounding environment for each surveillance target from the gateway (200), classifies and accumulates the status information by surrounding environment, calculates a reference status by surrounding environment, and compares it with the current status information by surrounding environment to determine whether there is an abnormality; and the sensor transmission/reception module (100) including the IoT laser level meter includes an object detection module (101) that has the IoT laser level meter, at least one sensor module that can be linked with the IoT laser level meter, and collects status information by each surveillance target; an external input/output module (102) that collects environmental information by each surveillance target; It includes a signal processing module (103) that signals-processes the status information and environment information of each surveillance target collected above; an IoT communication module (104) that transmits the signal-processed status information and environment information of each surveillance target to an external registration management server; a broadcast amplifier module (105) that converts the alarm text of each registered surveillance target into voice using its own TTS engine and outputs it when the status information of each signal-processed surveillance target's surrounding environment corresponds to the set risk information; and a control unit (106) that controls the target detection module (101) to the broadcast amplifier module (105) to monitor the status and environment of each surveillance target.

Description

{System for predicting abnormal signs of a surveillance target using a sensor module and a transmission/reception method including an IoT laser water level meter}

The contents disclosed in this specification relate to a sensor module including an IoT laser water level meter that can be universally used in fields, sewers, flooded areas, disasters, and catastrophes, a transmission/reception method, and a method for predicting abnormal signs of a surveillance target using the same.

[Korean] This result is the result of the local government-university cooperation-based regional innovation project, which was carried out with the support of the National Research Foundation of Korea and the Ministry of Education in 2024. (2022RIS-006)

[English] This research was supported by "Regional Innovation Strategy (RIS)" through the National Research Foundation of Korea(NRF) funded by the Ministry of Education(MOE)(2022RIS-006)

Unless otherwise indicated herein, the materials described in this section are not prior art to the claims of this application and their inclusion in this section is not an admission that they are prior art.

In general, water management in a paddy field is carried out by supplying water through a waterway from a reservoir, etc., and supplying it to the paddy field through a watergate connecting the waterway on the paddy ridge and the farmland, and cultivating the rice planted in the paddy field. Such a watergate is installed on the paddy ridge, for example, and uses an inflow gate that allows the water from the waterway to flow into the paddy field, or an outflow gate that discharges the water from the farmland to the waterway. When there is little water in the paddy field, the inflow gate is adjusted to allow it to flow in quickly, and when there is a lot of water, the outflow gate is adjusted to discharge it.

This method is more effective when regulated by the emergence, greening, and hardening stages of the rice, that is, by the growing season. In this method, there is a method of finely regulating the amount of water intake or drainage by controlling the inflow and outflow suitable for the growing season of the rice.

This automatically adjusts the water level of the paddy field by comprehensively considering the water level, temperature, water components, and water electrical conductivity of the paddy field according to the growing season of the rice planted in the paddy field, allowing farmers to cultivate rice easily and conveniently.

In particular, it provides various measurement, monitoring and control functions at a low price required in the information age by utilizing low-power laser level meters.

In addition, the present invention changes such water level gauges and sensors into devices equipped with various sensor modules that can be universally used for various disasters and calamities, thereby enabling comprehensive detection of various surveillance targets in various situations.

Meanwhile, existing IoT modules are installed for each object that is the subject of surveillance, that is, the subject of control and measurement, and networking is performed between the gateway and the management terminal through the modules to control and measure specific objects located remotely.

Since the existing circuits are designed to communicate only with specific control measurement targets, in such cases, when control measurement targets, i.e. objects, are added, the modules may need to be redesigned and developed to fit the added objects.

The prior art in this context is as follows.

Document 1 Korean Registration No. 10-2253854

Document 2 Korean Registration No. 10-2160560

Document 3 Korean Registration No. 10-0901656

Document 4 US Publication No. 2019-0342297

The disclosed content is intended to provide a sensor module and a transmission/reception method including an IoT laser water level meter that can be universally used in various flooded areas, disasters, and catastrophes, including water and sewage management.

Through this, we aim to provide a safe and efficient predictive operation system that accurately diagnoses various phenomena occurring during various surveillance operations through a cloud server and operates with optimal decision-making and control based on big data.

An abnormality prediction system using a sensor module and a transmission/reception method including an IoT laser water level meter according to an embodiment is provided.

First, modules with multiple functions are provided for use in various environments. Various manufacturers provide various modules that satisfy the user's requirements. For example, various analog, digital input/output modules, communication modules, etc. are used in a module format, allowing the user to create what they want.

In the embodiment, an integrated sensor including an IoT water level meter and its transmitting/receiving module are provided. It is characterized by being universally usable for various detection targets in various situations using a single sensor device, and comprehensively performing the status of the surveillance target, surrounding environment, control, and management operations.

Another embodiment is characterized in that, by using this, the cloud server receives and classifies information on the status and surrounding environment of the monitoring target, accumulates it, sets a reference status for each surrounding environment, and compares it with the current surrounding environment and status to determine whether there is an abnormality.

According to embodiments, a sensor module and a transmission/reception method including an IoT laser water level meter that can be universally used in various flooded areas, disasters, and catastrophes, including water and sewage management, are provided.

Through this, measurement, monitoring, and control functions for various surveillance targets can be easily and quickly established at a low cost required in the information age.

Conventionally, the circuit is designed to communicate only with the monitoring target of a specific control measurement. In this case, when the monitoring target, i.e., the object, is added, the module must be redesigned and developed to fit the added object. When redesigning and developing in this way, the development cost increases and the development time is long. In contrast, the embodiment can be used without redesign even if the objects of the monitoring are newly added, thereby reducing the development cost and shortening the development period.

Through this, various phenomena occurring during various surveillance operations are accurately diagnosed through a cloud server, and a safe and efficient predictive operation method is provided through optimal decision-making and control based on big data.

Figures 1 to 3 are conceptual diagrams of an abnormality sign prediction system using a sensor module and a transmission/reception method including an IoT laser water level meter according to the first embodiment.
Figures 4 and 5 are operation examples of the sensor module of Figure 1.
Figures 6 and 7 are conceptual diagrams of the second embodiment.

FIGS. 1 to 3 are drawings conceptually explaining an abnormality sign prediction system using a sensor module including an IoT laser level meter and a transmission/reception method according to a first embodiment.

FIG. 1 is a drawing illustrating a sensor transmission/reception module applied to the first embodiment, FIGS. 2a and 2b are system concept diagrams of the first embodiment, and FIG. 3 is a drawing explaining a cloud server applied to the system of FIG. 2.

As illustrated in FIGS. 1-3, the system according to the first embodiment can be universally used in various flooded areas, disasters, catastrophes, etc., including water and sewage management, by using a sensor transmission/reception module (100) including an IoT laser water level meter.

In the first embodiment, the sensor transceiver module (100) is configured to have a low-power laser level meter as the main body and a number of sensor modules mounted around it on a single board, and further includes a module for transmitting and receiving information detected by the sensor module.

The above sensor module is installed at least once around the water level gauge and can be linked with the low-power laser water level gauge. It can be used in areas other than water levels, such as flooded areas and waterworks, and in areas where water levels are related to temperature and humidity, as well as in disaster and catastrophe monitoring sites.

That is, the embodiment provides a sensor including an IoT laser level meter or a transmission/reception module thereof. Using a single sensor device, it can be universally used for various detection targets in various situations that are related to each other, and performs integrated operations such as the status of the surveillance target, the surrounding environment, control, and management.

This is desirable for small amounts of data at low bit rates, and in such cases, it is desirable to transmit data in the unlicensed ISM band within a radius of about 5 km using LoRa wireless transmission technology, which has very low power consumption.

The above sensor transceiver module (100) first provides modules with various functions for use in various environments. Various manufacturers provide various modules that satisfy the user's requirements. For example, various functions such as analog, digital input/output modules, communication modules, and diagnostic modules are applied in a module format, and through this, the user can create what he wants.

The embodiment applies this to provide an integrated sensor device including an IoT laser level meter or a sensor transceiver module thereof. It can be universally used for various surveillance targets in various situations using a single sensor device. And, it performs integrated operations of the status of the surveillance target, the surrounding environment, control, and management.

The embodiment uses these transceiver modules, i.e., the universal connection interface, to connect to a network. Based on the registered communication interface of each monitoring target, i.e., thing, when a specific thing is connected, the ID of the connected thing and the type of communication interface are set, and the control measurement information between the thing and the manager terminal is relayed based on the ID of the thing and the type of communication interface set above.

For example, when controlling an object remotely, the object to be controlled and measured is recognized based on the ID of the object being remotely controlled, and the object is interfaced with according to the corresponding communication interface.

Alternatively, when receiving measurement information of an object, the control measurement target object is recognized based on the object's ID and an interface is established with the object according to the type of communication interface of the object.

That is, the existing circuit is designed to communicate only with specific control measurement targets. In this case, when control measurement targets are added, the module must be redesigned and developed to fit the added objects. When redesigning and developing in this way, development costs increase and development time is required.

In comparison, the features of the present invention are as follows.

Even if new objects of control measurement are added, they can be used without redesign.

When there is a newly added control measurement target object, the ID and communication interface type information of the added object are uploaded and set, and when networking between the added object and the administrator terminal, the control measurement target object is recognized based on the ID of the object, and the control measurement information between the added object and the administrator terminal is relayed according to the corresponding communication interface type.

Specifically, the sensor transmission/reception module (100) according to the embodiment includes a power module (not shown), a serial communication module (120), a target detection module (101), an external input/output module (102), a signal processing module (103), an IoT communication module (104), a control unit (105), a storage unit (106), and a broadcast amplifier module (107).

The above power module (not shown) supplies power to the device itself. The power module includes a solar battery.

The above target detection module (101) collects status information of various surveillance targets and detects status information of externally registered surveillance targets. The above target detection module (101) uses a water level gauge and additionally mounts various sensor modules through a sensor board based on this. The sensor modules are determined differently depending on the surveillance target. For example, it is for detecting information such as water level of paddy field, nitrogen, phosphorus, potassium (NPK), electrical conductivity, temperature, etc., or water level of water supply and sewage, fire, vibration, noise, fine dust, etc. in disasters and calamities.

The above external input/output module (102) receives all surrounding A/D environmental information of the external registration monitoring target as one input module, and outputs all control signals to the external registration equipment as one output module.

The above signal processing module (103) may be equipped with an A/D module and a D/A module. The A/D module converts the analog environmental information of the input module into digital. The D/A module converts the status information of the surveillance target of the target detection module (101) and the surrounding environmental information of the surveillance target of the A/D module into an analog control signal.

The above IoT communication module (104) transmits status information of the target detection module (101) and environmental information of the A/D module to an external registration management server.

The above control unit (105) controls the power module or the IoT module (104) to collect status and environmental information of various monitoring targets in various situations and transmit this to the management server. In addition, surrounding equipment can be controlled accordingly.

The above storage unit (106) registers various settings and registration information under the control of the above control unit (105).

The above broadcast amplifier module (107) transmits a notification according to the status and environmental information of the monitoring target under the control of the above control unit (105).

The embodiment basically includes a detection module that receives status information of various surveillance targets and environmental information, an input module, a control signal to control equipment accordingly, an output module that transmits the corresponding information, and an IoT communication module.

It has an IoT communication module that performs IoT functions on its own. Through this, the control unit connects with the target detection module and input/output module to provide information on various surveillance targets to the manager via wireless communication through the gateway (200).

The above IoT communication module may be equipped with a wireless communication module, for example, a LoRa module, to provide various monitoring information to a nearby manager terminal, for example, a manager mobile phone.

The above IoT communication module (104) communicates with the gateway (200), and the gateway (200) is equipped with an Internet connection, so that the status of the surveillance target detected in real time and the surrounding environment information (e.g. water level, NPK, temperature, humidity, fine dust concentration, etc.) can be directly transmitted to the registration manager terminal app.

Therefore, the status of the surveillance target and surrounding environment information are transmitted to the administrator terminal through the IoT communication module.

For reference, the gateway (200) includes a key signal input unit (201) for receiving various administrator setting information, an interface unit (202) for transmitting and receiving information by connecting to the sensor transmission/reception module (100), a signal processing unit (203) for the information, a storage unit (204) for registering the administrator setting information, and a control unit (205) for controlling each unit.

The above input/output module includes AI and DI modules and comprehensively receives analog and digital surrounding environment information of various monitoring targets. For example, analog information such as water level, NPK, temperature, humidity, and fine dust around the monitoring target, and digital information of user operation of the switch are input.

The above output module transmits the control signal to an external registration device, for example, a water gate control device installed around the surveillance target, when a signal is input through the above input module and a control signal is generated by the control unit.

This sensor transceiver module (100) has its own TTS engine in the broadcast amplifier module (105) and can issue a voice alarm in different ways, for example, according to a voice comment, based on preset voice information for each data collected from the input module.

For example, if the ambient temperature, humidity, water level, or fine dust concentration level of the above-mentioned monitoring target exceeds a preset danger level, a voice alarm is triggered according to pre-registered voice information.

Figure 2a is an example installation diagram of the sensor transmission/reception module of Figure 1.

Referring to FIG. 2a, the sensor transceiver module (100) of FIG. 1 can be applied to, for example, a sensor placement in a water management system.

First, the water management system for discussion largely includes a number of water gate control devices installed in each field for a specific user.

The sensor device installed in the above-mentioned field detects the water level and temperature of the field water, and receives this from a remote gateway (200) to control the water gate control device.

The above sensor device is a sensor transmission/reception module (100) including an IoT laser water level meter in the embodiment.

Additionally, it may include a user terminal (300) located around a village or field, etc., which receives the water level and temperature of the field water through the gateway (200) and directly controls the water gate control device according to user setting information.

The above water gate control device operates the opening/closing valve of the water gate installed in each of a number of fields of a specific user. For reference, the above water gate is used to control water in reservoirs, etc. in the water channels of the fields.

The above sensor device is installed in each of the multiple fields of the specific user to detect the water level and temperature of the field water and transmit the detected water level and temperature to a remote registration gateway, and operates the water gate control device under the control of the registration gateway.

The above gateway (200) can be installed in a remote location, and can be installed one for each of a number of fields of a specific user, grouped into several groups. The gateway (200) can be installed by user and field. The gateway (200) first combines the water level and temperature of the field water, and registers the opening and closing levels of the water gate control valve according to the water level and temperature of each field water. Therefore, the opening and closing levels of the water gate control valve are extracted by comparing them with the water level and temperature of the field water received from the sensor device, and are controlled by the water gate control device.

The above user terminal (300) is a mobile communication terminal (310) or a user PC (320) held by a user in a village or around a rice field. The user terminal (300) receives information on the opening/closing level of the water gate control valve according to the water level and temperature of the rice field from the gateway (200) and changes it according to the user setting information. Therefore, the water gate control device is controlled through the gateway (200) according to this opening/closing level information.

Figure 2b is an example of installation in a disaster flooding situation, similar to Figure 2a above.

Fig. 2b can be applied to a sensor device using an IoT laser water level meter in a disaster flooding situation.

Additionally, as shown in Fig. 3, the gateway (200) installed in each of several areas including a number of fields or a plurality of fields includes a cloud server (not shown) that receives information on the opening and closing level of the water gate control valve according to the water level and temperature of the fields.

The cloud server receives, classifies, and accumulates information on the water level and temperature of the water in the paddy field and the opening/closing level of the water gate control valve for NPK from the gateway (200) every day and night according to the emergence, greening, and hardening stages of the rice planted in the paddy field, and sets a standard water level, standard temperature, and standard illuminance, and compares it with the current water level, temperature, and illuminance of the paddy field to determine whether there is an abnormality.

For example, if the water level and temperature of the current discussion both fall within the above-mentioned reference water level and reference temperature, it is determined to be a normal state, and if there is a difference in any one of them, it is determined to be an abnormal state.

The above rice growing period is from May to October, and is divided into the emergence, greening, and hardening stages. For example, the appropriate temperature for the emergence stage is 30 to 32°C, the appropriate temperature for the greening stage is 20 to 25°C, and the appropriate temperature for the hardening stage is 15 to 25°C.

The operations for setting the above reference water level and reference temperature are as follows.

First, the cloud server (400) classifies and accumulates information on the opening and closing level of the water gate control valve according to the water level and temperature of the discussed water, day and night, according to the emergence, greening, and hardening periods of rice, and defines a format for learning the water level pattern and temperature pattern. This information is collected and used by users by dividing it into multiple fields or multiple areas.

Next, according to the format defined above, a dataset on the opening/closing level information of the water level and temperature of the water gate control valve of the discussed water is detected every day and night for the emergence, greening, and hardening stages of the rice, and attributes for each piece of information are determined.

By normalizing the above-determined information, the water level and temperature patterns representing the characteristics of each day and night in the emergence, greening, and hardening stages of rice are obtained from the information on the opening and closing levels of the water gate control valve according to the water level and temperature of the discussed water, and are set as independent and dependent variables, respectively.

For example, the independent variables are the water level of the rice and the opening/closing level of the water gate control valve according to the temperature, and the day and night according to the emergence, greening, and hardening periods of the rice, and the dependent variables are the water level pattern, temperature pattern, and illuminance pattern. The water level pattern and temperature pattern can be used to determine the reference water level and reference temperature.

Therefore, the above-mentioned reference water level and reference temperature are set by learning with the above-mentioned independent and dependent variables.

In addition, in addition to the water level and temperature of the water discussed above, the water components and water electrical conductivity of the water discussed above are measured. The water components may be the same as the fertilizer components and include nitrogen, phosphorus, and potassium.

For reference, this is to solve the problem that the organic matter content itself is insufficient in the cultivated land that receives paddy water from the use of chemical fertilizers as mentioned above, and the soil hardens due to the formation of pores in the cultivated land, weakening the breathability and water retention, making it difficult for the roots of rice to spread out.

Therefore, the cloud server (400) receives information on the water level, temperature, water composition, and the opening/closing level of the water gate control valve according to the water electrical conductivity from the gateway (200).

The information on the opening/closing level of the water gate control valve according to the water level, temperature, water components, and water electrical conductivity of the above-mentioned rice paddy is received every day and night according to the emergence, greening, and hardening stages of the rice planted in the above-mentioned rice paddy, classified and accumulated to establish a standard water level, standard temperature, standard water components, and standard water electrical conductivity, and compared with the current water level, temperature, water components, and water electrical conductivity of the rice paddy to determine whether there is an abnormality.

The method for setting the reference water level, reference temperature, reference water component, and reference water electrical conductivity is the same as the method described above.

Through this, various phenomena occurring during reservoir operation are accurately diagnosed through a cloud server, and a safe and efficient prediction operation system is provided that operates with optimal decision-making and control based on big data.

Please note that this is just one example and may be applied differently to different surveillance targets.

Figure 4 is an example of the operation of the system of Figure 1. It is applied to the water management of the discussion and measures the water level of the discussion.

As shown in Fig. 4, the system of Fig. 1 first generates a laser basic signal for the external registration target, and outputs the generated laser signal to the target. Then, the reflected signal of the output laser signal is processed by cell average-constant false alarm probability (CA-CFAR) to recognize the target. This is possible with a signal processing unit.

Next, the reflected signal of the output laser signal is input. The input reflected signal is orthogonally modulated with a Gaussian distribution, and the modulated reflected signal is filtered to a set frequency band. An envelope is created from the filtered reflected signal with a Rayleigh distribution. From the envelope created in this way, a square law is detected with an exponential distribution, and the signal according to the detected square law is processed with CA-CFAR to recognize the corresponding object. The specific CA-CFAR processing is as shown in Fig. 4.

Figure 5 is a drawing explaining the CA-CFAR processing.

Referring to FIG. 5, the CA-CFAR processing is suitable for detecting a target signal, for example, water, from a signal containing background noise, interference signals, clutter, reverberation, etc. in a reflected signal, and is used in the embodiments.

The CFAR detector is based on the Neyman-Pearson detection theory, and estimates the average power of noise from the test cell, i.e., the surrounding cells within the reflected signal, and compares it with the threshold value obtained through the value to determine the presence or absence of the target water. The threshold value is adaptively determined according to the state of the surrounding noise so as to keep the false alarm probability of the test cell constant, and the performance of the detector varies depending on the type of algorithm used to estimate the power of the noise.

In other words, when detecting a cell where water exists, a guard cell can be placed to prevent the threshold from being raised by the signal component because the signal component may be included in the reference cell. However, if the number of guard cells is set too large, the distance between the test cell and the reference cell becomes too far, so it is necessary to adjust the appropriate cell size according to the result of the cross-ambiguity function.

The CFAR detector determines the threshold value (TZ) by multiplying the noise value estimated from the signal contained in the cells within the window by a constant value (T) determined to maintain a fixed false alarm probability.

It is known that the CA (cell averaging) CFAR algorithm shows optimal performance in a homogeneous noise environment.

The CFAR detector estimates the noise power of a test cell from the cells surrounding the test cell. If the noise contained in the surrounding cells is homogeneous, it can be assumed that the average power of each cell with an exponential distribution is the same. Therefore, in a homogeneous noise environment, the most accurate estimation method is to estimate the noise power contained in the test cell as the average of all cells. This CFAR technique is called CA CFAR and is one of the most widely used techniques. CA CFAR shows performance closer to the ideal case as the size of the window used for estimating the noise power increases.

There are cases where a homogeneous background noise signal includes an interference signal or multiple target signals in the cells within the window. When estimating the power of the noise using CA CFAR, the estimated value due to the interference signal will be larger than the actual power of the noise, and accordingly, the threshold value will be set high, which will lower the detection probability and lower the false alarm probability below the designed value. The CFAR detectors proposed for such cases include SO (smallest-of) CFAR, OS (ordered statistics) CFAR, and excision CFAR, and they utilize only the homogeneous noise signal by excluding the signals of cells with large values when estimating the noise power.

Figure 6 is a drawing explaining a second embodiment.

Referring to FIG. 6, in the second embodiment, first, the detection module (130) receives a reflection signal of a laser signal, orthogonally modulates it with a Gaussian distribution, and filters it into a specific frequency band.

Therefore, an envelope is created using a Rayleigh distribution from the above filtered reflection signal, a square law is detected using an exponential distribution from the envelope created in this way, and a signal according to the detected square law is processed using CA-CFAR to recognize the target.

If it is recognized as water in this way, the water level can be calculated from the input/output time difference of the signal.

In the second embodiment, in order to increase the accuracy of detection of the envelope of the Rayleigh distribution, the reflection signal is divided into three signals, and the envelope is detected by the sum of the amplitudes of these signals, thereby increasing the accuracy.

The above three signals are signals 1, 2, and 3, where the first signal is a signal having a frequency lower than the fundamental frequency of the laser output to the target, the second signal is a signal having the fundamental frequency, and the third signal is a signal having an integer multiple of the fundamental frequency.

Combining these three signals allows for accurate envelope detection.

To this end, the general-purpose IoT laser detection device (100) of the second embodiment first detects the first, second, and third signals described above from the filtered reflection signal.

The amplitudes of the first, second, and third signals detected above are calculated.

The above calculated amplitudes are added together.

So, we reduce the Rayleigh distribution by the above amplitude sum × 1/8n to create an envelope.

For example, when a reflected signal is acquired, FFT transform information for it is acquired.

The fundamental frequency of the above basic signal is obtained using a predetermined sampling frequency and frequency resolution. The above sampling frequency and frequency resolution vary depending on the movement of the target.

In addition, a first frequency lower than the fundamental frequency, a fundamental frequency, and a second frequency that is an integer multiple of the fundamental frequency are detected, and a first signal corresponding to the first frequency, a second signal corresponding to the fundamental frequency, and a third signal of the second frequency are specified.

Next, the amplitude representing the size of each of these first, second, and third signals can be calculated, and, for example, its effective value can be calculated.

Meanwhile, in the embodiment, the 1/8n is a value that reduces the Rayleigh distribution when detecting the envelope with the Rayleigh distribution of the corresponding reflection signal during the CA-CFAR operation when CA-CFAR is used to recognize the water to be measured for water level and the location of the water, and this is applied to detect the envelope.

This amplitude, that is, 1/8n used here, is a value that can lower the operating voltage while maintaining its frequency characteristics, considering that the laser is straight and highly directional. This is a method utilized in the existing frequency multiplexing, and it utilizes the point that the frequency multiplexing is set to an integer multiple, that is, 2N, 8N, and in consideration of the case where high frequencies are used, the value of 8N that can cover all is applied. For reference, this is used to set the minimum burst in frequency multiplexing, and a burst means a limited number of individual pulses or a limited number of vibrations.

On the other hand, as mentioned above, the laser is penetrative, so it may be necessary to increase accuracy by quickly reflecting when it comes into contact with the surface of the water to be measured, and to operate this in conjunction with low power.

To this end, in another embodiment, when outputting a laser to a target, the first, second, and third signals described above can be added together, and a laser basic signal can be generated by adding up this added value × 1/8n. Through this, even when outputting a laser to a target, the same effect as described above can be obtained.

Meanwhile, the amplitudes of the first, second, and third signals can be accurately calculated as follows in conjunction with surrounding signals.

That is, the amplitudes of the first, second, and third signals are each calculated as the square root of the sum of the squares of the fundamental amplitude of the corresponding signal and the amplitudes of the surrounding ±N signals.

For example, the amplitude of the first signal is calculated as the square root of the sum of the fundamental amplitude of the first signal and the amplitudes of the surrounding ±N signals, for example, five signals, squared. The same applies to the second and third signals.

Accuracy can be improved by extracting valid values from these surrounding ±N signals.

Among these data, error data is removed and a valid value is obtained by considering each deviation.

To this end, we apply a method of finding the average value of multiple measurements at the same point using the K-MEANS technique and a method of finding valid values between intervals using trend line analysis based on linear regression.

The above valid value extraction first finds the average value at each point using the K-MEANS technique to generate valid values. Then, the valid values are found through trend line analysis based on linear regression in the interval between each point.

First, when obtaining the above first, second, and third signals, the surrounding signals of each signal are measured at multiple points, for example, 9 points, in a specific first area for the detection target, and at multiple points, for example, 9 points, in a second area.

The above first and second areas are the main points where the target can be recognized on average in the detection target.

Next, among the 9 points in the first area and the 9 points in the second area, K points of actual surrounding signals are extracted for each point based on the K-MEANS technique and fixed as the center point to set the initial value.

The distance between the central points set in this way and the points of the remaining peripheral signals is calculated, and the points of the remaining peripheral signals are assigned to the nearest central point to perform clustering processing.

In the above processed cluster, the point of the remaining surrounding signals corresponding to the center is used as the center point, and the center point is updated for each cluster.

Therefore, by repeatedly performing the above-mentioned updated center point and finding the center point until it does not change, its surrounding signals are obtained as valid values.

The average of these valid values is calculated to obtain the valid value at each point. That is, the average value for multiple measurement values as mentioned in the example is calculated.

Meanwhile, by calculating the valid value for each section between each point in the first or second area, the valid value for the surrounding signal can be found for each section between each point. This finds the valid value between sections through trend line analysis based on linear regression.

That is, the valid values for each section between the nine points in the first area and the valid values for each section between the nine points in the second area are calculated through trend line analysis based on linear regression according to the equation below, thereby additionally obtaining valid values for surrounding signals for each section.

[Equation 1] y _i = x _i ^T β + ε _i (i = 1, ... , n)

(Here, y _i is the effective value for each interval between the nine points in the first or second region, x _i ^T is the predicted peripheral signal for each interval between each point (T is the transpose), β is the coefficient of x _i ^T , and ε _i is the error variable.)

On the other hand, another embodiment can determine what type of disaster the measurement target is by using the aforementioned points. Since the types of disasters are diverse, the embodiment is limited to the type of man-made disaster.

Disasters are broadly divided into natural disasters, social disasters, and man-made disasters. Social disasters are caused by fires, collapses, etc. Disasters refer to something larger than a certain scale, and the example divides collapse into damage, destruction, and collapse in more detail, considering various detection targets.

Here's how to categorize these man-made disasters into types:

First, the embodiment calculates the effective values of each of the first, second, and third signals.

The effective value of the first signal calculated above is multiplied by the ratio of the first signal × the weight of the first signal of the artificial disaster type for each artificial disaster type.

The ratio value of the first signal above is the original ratio of the fundamental frequency to the fundamental signal, and can be obtained, for example, as (fundamental frequency (%) / (fundamental frequency (%) + first frequency (%) + second frequency (%) + third frequency (%))). For reference, each frequency can be expressed as a cosine function, or as an exponential function added to a sine function.

Next, the effective value of the second signal calculated above is multiplied by the ratio value of the second signal × the weight of the second signal of the artificial disaster type for each artificial disaster type.

The effective value of the third signal calculated above is multiplied by the ratio of the third signal × the weight of the third signal of the artificial disaster type for each artificial disaster type.

The ratio values of the above second and third signals are also calculated identically to the ratio value of the above first signal.

Therefore, the ratio of the first signal multiplied by the above artificial disaster type × the weight of the first signal of the artificial disaster type) is added.

The ratio of the second signal multiplied by the above artificial disaster type × the weight of the second signal of the artificial disaster type) is added.

The ratio of the third signal multiplied by the above artificial disaster type × the weight of the third signal of the artificial disaster type) is added.

Extract the largest value from each summed value.

The artificial disaster type corresponding to the above extracted value is determined as the artificial disaster type of the target.

For example, the weights of the first, second, and third signals are as shown in Fig. 6.

Figure 7 is an example of the weights of the first, second, and third signals.

Referring to Figure 7, the weights of the first, second, and third signals are set differently for each type of artificial disaster.

The main types of man-made disasters are fire, pollution, damage, destruction, and collapse.

Specifically, for fire it is 10, 90, 10; for contamination it is 20, 30, 20; for damage it is 30, 20, 30; for destruction it is 40, 20, 40; and for collapse it is 50, 30, 50.

The weights of the first, second, and third signals for each type are values that take into account the fact that the first, second, and third signals in that type exhibit different signal characteristics.

For example, in the case of a fire, the first signal, i.e., a signal having a frequency lower than the fundamental frequency, and the second signal, i.e., a signal having an integer multiple of the fundamental frequency, may occur at relatively evenly low levels. On the other hand, the fundamental frequency occurs at a high value. In the case of contamination, similar to a fire, the first and second signals occur at relatively evenly low levels, and the fundamental frequency exhibits a relatively low value.

So, to determine whether the target of measurement corresponds to a fire, the effective value of the first signal × (ratio value of the first signal × 0.1) + the effective value of the second signal × (ratio value of the second signal × 0.9) + the effective value of the third signal × (ratio value of the third signal × 0.1) is calculated.

Similarly, contamination, damage, and breakdown collapse values are also calculated.

Therefore, the values obtained for each type of artificial disaster are compared, and the one with the highest value is determined as the type of artificial disaster for the measurement target.

Meanwhile, as another example, since various detection targets are used, the frequency of each target can be different, such as high frequency or low frequency.

To this end, the embodiment modulates high-frequency and low-frequency signals into low- and medium-frequency signals without information loss while maintaining mechanical characteristics using a Hilbert converter to easily detect a target.

The above Hilbert transform makes it easy to analyze signals by making high and low frequencies, which are difficult to analyze, smaller or larger than the original information while maintaining mechanical characteristics. This is all done by converting them into medium and low frequency signals in the same way.

For example, if the current signal is high frequency, the peak value is extracted from the signal, connected to an envelope, and the high frequency is attenuated and converted into mid-low frequency.

In addition, if the current signal is low frequency, the periodic peaks and valleys appearing in the signal are extracted, the distance between them is reduced to 1/2n, and the low frequency is expanded to create a low-to-mid frequency.

To elaborate, the above Hilbert transform is expressed in the conventional way by maintaining one input signal fm(t) = sin(wnt) and converting the other one into cos(w _m t) through the Hilbert transform.

fm(t) is delayed and synthesized with a sine signal sin(wct) having a carrier frequency wc to produce fmc. cos(w _m t) modulates the sustain signal fm(t) including sin(wct), and synthesizes sin(wct) with the result of a π/2 phase delay to produce f＾mc.

So, by adding fmc and f＾mc, a single sideband signal (SSB) with a specific amplitude can be created, that is, a low-mid frequency according to the embodiment.

To utilize this in an embodiment, for example, if the current signal is high-frequency, the envelope effect of the Hilbert transform is utilized. The envelope effect is to connect the peaks of a high-frequency signal to obtain a low-frequency signal. By taking only the positive peak values of the data, a signal of the same size as the original signal is reconstructed, and a signal of medium and low frequency is obtained while maintaining the mechanical characteristics included in the original signal.

If the current signal is low frequency, the period that appears in the signal is found, and the peaks and valleys of the period are extracted to reduce the distance between them to 1/2n, thereby obtaining a signal of medium and low frequency. Similarly, this can facilitate signal analysis by obtaining sound waves larger than the original information while maintaining the mechanical characteristics contained in the original signal.

100: Sensor Transmitter/Receiver Module Using IoT Laser Level Meter
200 : Gateway
300 : User terminal

Claims (6)

An IoT laser water level meter, a sensor transmission/reception module (100) including an IoT laser water level meter having at least one sensor module that can be linked with the IoT laser water level meter, and collecting status information on the surrounding environment for each monitoring target;
A gateway (200) that collects and controls the status information of each surrounding environment for each surveillance target in the above sensor transmission/reception module (100); and
A cloud server (300) that receives and classifies status information for each surveillance target from the gateway (200), accumulates the status information for each environment, calculates a reference status for each environment, and compares it with the current status information for each environment to determine whether there is an abnormality;

The sensor transmission/reception module (100) including the above IoT laser level meter
The IoT laser water level meter, a target detection module (101) having at least one sensor module that can be linked with the IoT laser water level meter and collecting status information for each monitoring target;
An external input/output module (102) that collects environmental information for each of the above surveillance targets;
A signal processing module (103) that processes the status information and environmental information of the collected surveillance targets;
An IoT communication module (104) that transmits status information and environmental information of the signal-processed surveillance target to an external registration management server;
If the status information of the surrounding environment for each of the above-mentioned signal-processed surveillance targets corresponds to the set risk information, a broadcast amplifier module (105) that converts the alarm text for each registered surveillance target into voice using its own TTS engine and outputs it; and
It includes a control unit (106) that controls the target detection module (101) to the broadcast amplifier module (105) to monitor the status and environment of each surveillance target;

The method of collecting status information of the monitoring target of the above control unit (106)
A first step of generating a laser base signal for the above surveillance target;
A second step of outputting the laser signal generated in the first step to the monitoring target; and
A third step of recognizing the surveillance target by performing CA-CFAR processing on the reflection signal of the laser signal output in the second step;

The third step above is
Step 3-1 for receiving a reflection signal of the laser signal output in the above step 2;
Step 3-2 of orthogonally modulating the reflection signal received in Step 3-1 into a Gaussian distribution;
Step 3-3 of filtering the reflected signal modulated in the above step 3-2 into a set frequency band;
Step 3-4 of creating an envelope with a Rayleigh distribution from the reflection signal filtered in Step 3-3 above;
Step 3-5 of detecting a square law with an exponential distribution in the envelope created in Step 3-4 above; and
Including a step 3-6 for recognizing the surveillance target by performing CA-CFAR processing on the signal according to the square law detected in the step 3-5 above;

Steps 3 and 4 above
Step 3-4-1 of detecting the first, second, and third signals from the reflection signals filtered in the above step 3-3;
In the above step 3-4-1, the first, second and third signals
The first signal is a signal having a frequency lower than the fundamental frequency of the basic signal, the second signal is a signal having a frequency that is an integer multiple of the frequency of the basic signal, and the third signal is a signal having a frequency that is an integer multiple of the frequency of the basic signal.

Step 3-4-2 for calculating the amplitude of the first, second, and third signals detected in the above steps 3-4-1;
Step 3-4-3 of adding the amplitudes produced in Step 3-4-2 above; and
An abnormality prediction system using a sensor transmission/reception module including an IoT laser water level meter, comprising a step 3-4-4 of reducing the Rayleigh distribution by 1/8n of the amplitude sum of the above steps 3-4-3 to create an envelope. delete In claim 1,
The above first step is
Step 1-1 of adding the first, second and third signals of the above step 3-4-1; and
An abnormality prediction system using a sensor transmission/reception module including an IoT laser level meter, comprising a step 1-2 for generating a laser base signal by multiplying the sum of the above steps 1-1 × 1/8n. In claim 1,
An abnormality prediction system using a sensor transmission/reception module including an IoT laser water level meter, characterized in that in the above-mentioned steps 3-4-2, the amplitudes of the first, second, and third signals are each calculated as the square root of the sum of the basic amplitude of the corresponding signal and the amplitudes of the surrounding ±N signals, respectively. In any one of claims 3 to 4,
In the above step 3-4-1, the step 3-7 for calculating the effective values of each of the first, second, and third signals;
Step 3-8 of multiplying the effective value of the first signal calculated in Step 3-7 by the ratio of the first signal × the weight of the first signal of the artificial disaster type for each artificial disaster type;
Step 3-9 multiplies the effective value of the second signal calculated in Step 3-7 by the ratio of the second signal × the weight of the second signal of the artificial disaster type for each artificial disaster type;
Step 3-10 of multiplying the effective value of the third signal calculated in Step 3-7 by the ratio of the third signal × the weight of the third signal of the artificial disaster type for each artificial disaster type;
In the above steps 3-8, 3-9, and 3-10, the step 3-11 of adding the ratio value of the first signal multiplied by the type of artificial disaster × the weight of the first signal of the type of artificial disaster;
In the above steps 3-8, 3-9, and 3-10, the step 3-12 of adding the ratio value of the second signal multiplied by the artificial disaster type × the weight of the second signal of the artificial disaster type;
In the above steps 3-8, 3-9, and 3-10, the step 3-13 of adding the ratio value of the third signal multiplied by the type of artificial disaster × the weight of the third signal of the type of artificial disaster;
Step 3-14, which extracts the largest value among the summed values in steps 3-11, 3-12, and 3-13 above; and
An abnormality prediction system using a sensor transmission/reception module including an IoT laser water level meter, comprising a step 3-15 of determining the artificial disaster type corresponding to the value extracted in the step 3-14 as the artificial disaster type of the monitoring target. In claim 5,
The weights of the first, second and third signals of the above artificial disaster types are
An abnormality prediction system using a sensor transmission/reception module including an IoT laser water level meter, characterized in that in case of fire, it is 10, 90, 10; in case of contamination, it is 20, 30, 20; in case of damage, it is 30, 20, 30; in case of destruction, it is 40, 20, 40; and in case of collapse, it is 50, 30, 50;