KR102520452B1 - Method for remotely wireless update of firmware of measuring device for predicting collapse of slope land - Google Patents

Abstract

The present invention relates to a measuring instrument including an acceleration sensor, a moisture content sensor, and a sound wave sensor and capable of executing a risk calculation program for calculating the risk of collapse of a slope based on measurement data received from the sensors. In one embodiment, Calculating, by the risk calculation program, a primary risk for collapse risk based on acceleration-related measurement data applied to the instrument among the received measurement data, and if the primary risk is greater than or equal to a preset threshold, the Disclosed is a measuring instrument configured to perform the step of calculating a secondary risk for a collapse risk based on the received measurement data.

Description

Method for remotely wireless update of firmware of measuring device for predicting collapse of slope land}

The present invention relates to a system for predicting the risk of landslide or collapse on a slope or cut slope, and more particularly, predicts the risk of collapse of a slope using an instrument for predicting the risk of collapse of a slope using acceleration, acoustic waves, moisture content, etc. It relates to a method of remotely updating an instrument's firmware or software.

Accidents in which soil or the entire slope collapses, such as steep slopes, cut slopes, and landslides, are among the disasters that frequently occur. Most landslide and steep slope collapse detection instruments in Korea detect landslides after they have occurred and alert managers and danger zones. The cutting-edge technology of measuring instruments is developing, but analysis technology is not included, so it is built as a post-analysis system.

Although efforts have been made to predict collapse by detecting and measuring the risk of landslide or slope collapse in advance, it is still difficult to predict the risk in real time by installing it in an actual hazardous area because the prediction system is complicated or expensive. The situation is.

In addition, in order to increase the accuracy of the slope collapse risk, it is necessary to continuously update the firmware or software for calculating the slope collapse risk of each instrument. There is a need for a more efficient method for updating.

Prior Document 1: Korean Patent Publication No. 2018-0057817 (published on May 31, 2018) Prior Document 2: Korean Patent Registration No. 10-2091758 (published on March 20, 2020)

The present invention has been devised to solve this problem, and aims to provide a system for determining the grade for the risk of landslide collapse by measuring acceleration, interpreting the amount of impact and moving average value, and using moisture content and acoustic wave prediction techniques. .

In addition, the present invention provides an instrument update method capable of reducing update time by transmitting update data to the instrument while the instrument program is being executed and replacing only the program code to be replaced among all program codes stored in the memory.

According to one embodiment of the present invention, a measuring instrument including an acceleration sensor, a moisture content sensor, and a sound wave sensor and capable of executing a risk calculation program for calculating the risk of collapse of a slope based on measurement data received from the sensors, wherein the Calculating, by a risk calculation program, a primary risk for collapse risk based on acceleration-related measurement data applied to the instrument among the received measurement data, and receiving the primary risk if the primary risk is greater than or equal to a preset threshold Disclosed is a measuring instrument configured to execute a step of calculating a second degree of risk for a collapse risk based on measurement data.

According to one embodiment of the present invention, a method for updating a computer program that is stored in the memory of a microcontroller unit (MCU) of a measuring instrument installed on a slope and executed to calculate the risk of collapse of the slope, the program from a remote server. Receiving update information of; storing an update program reflecting the update in a first memory in the instrument; and copying the update program stored in the first memory to the MCU memory.

According to the present invention, the first degree of risk is determined based on the amount of impact caused by acceleration and the moving average value, and then the second degree of risk is determined by inputting the amount of impulse, the moving average value, the slope of the instrument, the moisture content, and the acoustic wave parameters into the artificial neural network algorithm as input data. By calculating , it is possible to evaluate and predict the slope collapse risk with high accuracy.

In addition, according to the present invention, update data is transmitted to the instrument while the program of the instrument is being executed, and when updating the program in the MCU memory, only the program code to be replaced among the entire program codes is replaced, thereby shortening the update time and improving the quality of the instrument program. There is an advantage in that the execution time can be further increased compared to the conventional method. If an error occurs during update operation or program execution, the most recent program code stored in the second memory can be copied back to the MCU memory at any time to initialize the program with the latest code, minimizing the dispatch of technical support personnel and stopping the execution of the instrument program. It has the advantage of being able to operate the instrument efficiently by minimizing time.

1 is a view showing a state in which a plurality of instruments are installed on a slope to predict the risk of collapse on a slope;
2 is a diagram illustrating a measuring instrument according to an embodiment of the present invention;
Figure 3 is a flow chart of a slope collapse risk prediction method according to an embodiment,
4 is a flowchart of an exemplary method for calculating a primary risk using acceleration;
5 is a diagram showing variables used in secondary risk calculation and classification of risk accordingly;
6 is a diagram for explaining parameters of acoustic waves;
7 is a diagram showing an exemplary method of calculating a secondary risk using an artificial neural network;
8 is a diagram for explaining a memory area of a measuring instrument according to an embodiment;
Fig. 9 is a flow diagram of an exemplary method for updating the instrument's software;
Fig. 10 illustrates an exemplary situation for updating the instrument's software;
11 is a diagram showing a first memory state for software update of a measuring instrument.

The above objects, other objects, features and advantages of the present invention will be easily understood through the following preferred embodiments in conjunction with the accompanying drawings. However, the present invention is not limited to the embodiments described herein and may be embodied in other forms. Rather, the embodiments introduced herein are provided so that the disclosed content will be thorough and complete and the spirit of the present invention will be sufficiently conveyed to those skilled in the art.

In the drawings of this specification, numerical values such as length, thickness, and area of components may be exaggeratedly displayed for effective description of technical contents. In this specification, singular forms also include plural forms unless specifically stated otherwise in a phrase. The expressions 'comprises', 'consists of', and 'consists of' used in the specification do not exclude the presence or addition of one or more other elements in addition to the elements mentioned in these expressions.

Hereinafter, the present invention will be described in detail with reference to the drawings. In describing specific embodiments below, a number of specific details have been prepared to more specifically describe the invention and aid understanding. However, readers who have knowledge in this field to the extent that they can understand the present invention can recognize that it can be used without these various specific details. In some cases, it is mentioned in advance that parts that are commonly known in describing the invention and are not greatly related to the invention are not described to prevent confusion in describing the present invention.

Figure 1 schematically shows a state in which a number of instruments are installed on a slope to predict the risk of collapse on a slope. As shown, in order to perform the collapse risk prediction method of the present invention, a plurality of instruments 10 are installed on the surface of the slope to be predicted. The measuring instrument 10 may be installed at predetermined intervals on the slope of the area subject to prediction of collapse risk.

Each measuring instrument 10 is composed of, for example, a cylindrical case, and includes various sensors such as an acceleration sensor and a sound wave sensor in the case. In addition, the measuring instrument 10 may further include a power supply unit for supplying power to these various sensors, a data processing circuit for processing data measured by the various sensors, and a transmission unit for transmitting the processed data to the repeater 20 .

In this regard, FIG. 2 illustrates a meter 10 according to one embodiment of the present invention. Figure 2 (a) is a perspective view of the exterior and Figure 2 (b) is a side cross-sectional view of some internal components. Referring to FIG. 2 , a meter 10 according to an embodiment includes a header 11 , an upper housing 12 , a lower housing 13 , and a lower weight 14 .

The upper housing 12 and the lower housing 13 are cylindrical members having an empty space therein. A PCB board 16 is accommodated inside the upper housing 12, and various types of sensors such as an acceleration sensor and a sound wave sensor and a data processing circuit may be mounted on the PCB 16. The acceleration sensor may measure the acceleration applied to the instrument 10 for a predetermined period of time, and the sound wave sensor may measure acoustic waves (acoustic elastic waves) inside the ground surface. An empty space is formed inside the lower housing 13, and this space serves as a wave guide that amplifies sound waves and guides them to the sound wave sensor.

Between the lower housing 13 and the lower weight 14, a moisture content sensor 15 for measuring the amount of moisture (moisture content) in the ground surface is interposed and installed. Since the moisture sensor 15 is away from the PCB board 16, the moisture sensor 15 and the PCB board 16 are connected by a cable 17, and through this, the measured data of the moisture sensor 15 is transmitted to the PCB board 16. can transmit The header 11 is a member that seals the upper housing 12 and has a connector pin electrically connected to the PCB board 16 formed on the upper end, and can communicate with the outside by wire by connecting a cable to the connector pin.

The data processing circuit mounted on the PCB substrate 16 may include, for example, a microcontroller unit (MCU). MCU may be implemented in the form of an integrated circuit chip having a microprocessor, memory (including main memory and auxiliary memory), input/output module, etc., and alternatively, some functions such as auxiliary memory such as flash memory are separate from the MCU. It may also be mounted on the PCB substrate 16 .

The data processing circuit may receive measurement values from various sensors such as an acceleration sensor, a moisture content sensor, and a sound wave sensor, and may generate measurement data from the measurement values. In one embodiment, the measurement data includes at least (i) a change in the amount of impulse applied to the instrument 10 based on the change in acceleration (I), (ii) a moving average of the change for a predetermined time (Ima), (iii) the instrument It may include the slope of (10), (iv) moisture content inside the surface of the slope, and (v) the number of acoustic wave peaks for a predetermined time, and each measurement data will be described later.

In one embodiment, the meter 10 and the repeater 20 may be connected by wire. One repeater 20 may be connected to each measuring instrument 10, or several measuring instruments 10 may be connected to one repeater 20. The repeater 20 may transmit measurement data received from one or more measuring instruments 10 to a server device (not shown) through a network such as an arbitrary wireless communication network and/or a mobile communication network. For example, each repeater 20 may communicate with a data remote server according to the TCP/IP protocol, and at this time, an LTE or 5G mobile communication network or data communication network may be used.

The remote server device can manage a landslide risk zone in a certain area as a unit risk area. For example, the server device may receive measurement data from the measuring instruments 10 installed in the dangerous area of this one unit and calculate the risk of the collapse risk of the slope of this dangerous area.

In one embodiment, it is also possible to calculate the risk level for the risk of collapsing the slope for each instrument 10. For example, it can be executed in the data processing circuit of each measuring instrument 10. Alternatively, in another alternative embodiment, the collapse risk prediction method of the present invention may be executed in the repeater 20 connected to each instrument 10, or the repeater 20 transmits the measurement data of each instrument 10 to a server device. may be transmitted wirelessly or wired, and the prediction method of the present invention may be executed in the server device.

In the following embodiments, it is assumed that a method for predicting a slope collapse risk is executed in a data processing circuit (eg, MCU) of each measuring instrument 10, and embodiments of the present invention will be described.

Figure 3 is a flow chart of a slope collapse risk prediction method according to an embodiment of the present invention. Referring to the figure, when the instrument 10 receives measurement data from various sensors such as an acceleration sensor, a moisture sensor, and a sound wave sensor, a first degree of risk is calculated based on the measurement data in step S10. In one embodiment, it is possible to calculate the primary risk for the collapse risk based on the measurement data related to the acceleration applied to the instrument 10, and at this time, the measurement data related to the acceleration is applied to the instrument 10 based on the amount of change in acceleration It includes the change amount (I) of the impulse and the moving average (Ima) of the change amount for a predetermined time.

After that, in step (S20), it is determined whether the primary risk calculated in step (S10) is equal to or greater than a predetermined threshold value. If the primary risk is less than the threshold value (S20_No), there is no risk of collapse, so it is possible to return to step S10 again without calculating the secondary risk and perform step S10 again after a predetermined period.

Regarding the threshold value, FIG. 5 is an exemplary chart showing the risk criterion used when calculating the risk using the measurement data related to acceleration. The values shown in FIG. 5 are exemplary values and, of course, this reference value may vary depending on specific embodiments or implementation situations. In one embodiment, the first risk is determined using the amount of change (I) of the amount of impact applied to the instrument 10 and the moving average (Ima) of the amount of change for a predetermined time (S10), and the first risk is compared with the threshold do. At this time, for example, referring to FIG. 5, the threshold of the change in impulse (I) can be set to 100 and the threshold of the moving average (Ima) can be set to 25, and the two measured values (ie, the change in impulse (I)) and moving average (Ima) are both below each threshold, it is determined that the risk is 'safe' and returns to step (S10), and if either of the two measured values is greater than each threshold, the risk may be higher than 'interest' It may proceed to step S30.

In this way, if it is determined in step S20 that the primary risk is equal to or greater than the threshold (S20_Yes), there is a risk of collapsing the slope, and in this case, the step S30 is performed to calculate the secondary risk. In one embodiment, the instrument 10 provides measurement data, that is, (i) the amount of change in impulse (I), (ii) the moving average of the amount of change (Ima), (iii) the slope of the instrument 10, (iv) the surface of the slope The secondary risk may be calculated based on the internal moisture content and (v) the number of acoustic wave peaks for a predetermined time. In one embodiment, the instrument 10 may calculate the secondary risk using an artificial neural network algorithm.

As a result of calculating the secondary risk, for example, as shown in FIG. 5, the risk is classified into five levels, that is, 'relief', 'interest', 'caution', 'danger', and 'alarm' levels, and belongs to any of these levels. It may be a way of displaying the For example, when a secondary risk is calculated using an artificial neural network, the artificial neural network uses the measurement data of (i) to (v) as input data, predicts which of the five levels the risk belongs to, and outputs it as output data. It is an algorithm that solves the classification problem.

In this way, if the secondary risk is calculated in step S30, a predetermined follow-up operation may be performed according to the calculated risk level. For example, according to the risk level, the risk can be comprehensively managed in real time by transmitting to a higher level device such as a server device or an integrated control system.

Figure 4 shows a specific and exemplary method of calculating the first degree of risk using the acceleration in Figure 3 (S10). Referring to FIG. 4, first, in step S110, variables and various parameters used in the slope collapse risk prediction method of the present invention are initialized. Here, the variable and various parameters may include, for example, the change amount (I) of the impulse amount, the moving average (Ima) of the change amount, the start time of the timer for measuring the time period, etc., but are not limited thereto.

After initializing these variables and various parameters in step S110, the acceleration sensor of the measuring instrument 10 measures acceleration for a predetermined period of time. In general, the fundamental force involved in the risk of collapse in slopes is gravity, and rainfall, rockfall, and changes in underground rocks due to earthquakes affected by gravity are involved in collapse. Free fall motion occurs due to gravity, which is called gravitational acceleration, and gravitational acceleration (9.8m/s2) is the sum of the x-axis, y-axis, and z-axis acceleration components and is expressed by Equation 1 as follows.

--- 수식1

--- Formula 1

The meaning of the above formula is that the sum of the accelerations acting in the three axial directions is 9.8 m/s2 regardless of the position at which the object is placed and the degree of twist, which is expressed as 1G. Therefore, the changed acceleration value in a place that can lead to a disaster due to external forces (earthquake, weathering, gravity, etc.) is a very important factor in determining the safety state.

The changed acceleration value can be converted into the amount of impact (force), which is exactly proportional (the relationship between F and a in F = ma is proportional), so the risk of collapse can be defined by the judgment based on the changed value of acceleration, that is, step S120 ), as shown in Equation 2 below, the difference in acceleration values at times (t1, t2) before and after a predetermined time period can be calculated and converted into the amount of change (I) in the amount of impact applied to the measuring instrument 10.

--- 수식2

--- Formula 2

In Equation 2 above, ax1, ay1, az1 are the accelerations of each component at the time period start time (t1), and ax2, ay2, az2 represent the acceleration values of each component at the time period end time (t2), respectively. The acceleration value of each component in Equation 2 above can be measured.

In one embodiment, the time period is 0.1 seconds, for example. That is, the amount of change (I) can be calculated according to Equation 2 above by measuring the acceleration value before and after 0.1 second. In another embodiment, the time period may be 0.01 second. That is, the acceleration value before and after 0.01 second can be measured and the amount of change (I) can be calculated according to Equation 2. In this case, the amount of change (I) for 0.1 second may be obtained by repeating the measurement of the acceleration value every 0.01 second 10 times and obtaining the arithmetic average.

Next, in step S130, a moving average Ima of the change amount is calculated using the change amount I calculated in step S120 above. "The moving average (Ima) of the change amount (I)" is an average of the change amount (I) for a predetermined time, for example, when the change amount (I) is calculated by the difference in acceleration values for 0.1 second, the change amount ( By calculating I) 10 times, the moving average (Ima) for 1 second can be obtained.

In one embodiment, N (N is an integer of 3 or more) are obtained for each predetermined time period (hereinafter referred to as "interval average") for the change amount (I), and a moving average (Ima) for the N interval averages is obtained. yield At this time, the moving average (Ima) is calculated as the arithmetic average of the [1st to (N-1)th moving averages] and the Nth section average, and the [1st to (N-1)th moving averages] ] can be calculated as the arithmetic mean of the [1st to (N-2)th moving averages] and the (N-1)th section average, and this process is [1st to 2nd moving averages] Repeat until the arithmetic average of and the third interval average is obtained.

For example, in one embodiment, if the variation (I) per 0.01 second is obtained 10 times and the arithmetic average is obtained, the 'interval average' of the variation (I) for 0.1 second can be obtained, and this interval average is obtained 10 times. calculated successively. That is, 10 interval average values for 0.1 second for the amount of change (I) are obtained, and a moving average (Ima) is obtained using the 10 interval average values.

At this time, in order to obtain a moving average from the 10 interval average values, first, among the 10 interval averages (when defined as the 1st to 10th interval averages from the previous order in time), the arithmetic average of the first and second interval averages is calculated. It is determined as 1 moving average, and then the arithmetic average of the first moving average and the third interval average is determined as the second moving average. Similarly, the arithmetic average of the second moving average and the fourth interval average is determined as the third moving average, and this calculation is repeated to calculate the final moving average (Ima) by arithmetic averaging of the eighth moving average and the tenth interval average. there is. If the moving average (Ima) of the variation (I) is calculated in this way, the change trend of the external force (earthquake, weathering, gravity, etc.) on the slope can be known, so the accuracy of predicting the collapse risk of the slope can be improved.

As described above, after calculating the amount of change (I) and the moving average (Ima) in steps S120 and S130, respectively, the first risk is calculated in step S140. For example, as described above with reference to FIG. 5 , the risk level of the first degree of risk may be determined according to exemplary values of the impulse change amount I and the moving average Ima shown in FIG. 5 .

In one embodiment, the risk level classification of FIG. 5 can be used when calculating the secondary risk. In the table of FIG. 5, the vertical direction represents the risk level. In FIG. 5, the risk level is exemplarily classified into five stages. Among the ratings of the risk level, 'safe' means that the current state is stable, and 'interest' may be, for example, a situation in which a slight deformation occurs on a slope or a rockfall occurs. In this state, visual observation may be difficult, and it may be due to animal movement, and continuous observation and investigation are required. 'Caution' of the risk level may be a situation in which damage to the surroundings is expected due to strong wind or rain, for example, and may require general investigation and action. 'Danger' of the risk level may be, for example, a situation in which it is determined that the slope is highly deformable, or a rockfall occurs in close proximity to the measuring instrument 10, and a detailed investigation may be required. This is a situation where a problem or slope collapse may occur, and a detailed investigation and prompt response are required. Classifying the risk level into five levels is exemplary, and the classification level or definition of each level may vary depending on the specific embodiment.

In the table of FIG. 5, the horizontal direction represents five types of measurement data. Among the measurement data, "angular change" indicates how much the measuring instrument 10 is tilted compared to the first installed state. An accelerometer or gyro sensor can be used to calculate the angle change (inclination). In the case of using an acceleration sensor, an inclination angle with respect to each axis can be obtained using a trigonometric function from the acceleration value of each component of an acceleration vector. For example, as shown in Equation 3 below, the inclination angle of each axis is compared with the angle at the time of initial installation of the measuring instrument 10 to obtain the midpoint of the inclination (φ) of the measuring instrument 10.

--- 수식3

--- Formula 3

In Equation 3 above, θx, θy, and θz are currently measured angles, and θxi, θyi, and θzi are angles at the time of initial installation of the instrument.

After that, in step S170, the long-term warning risk is calculated using the slope increment ( ) calculated in Equation 3. At this time, for example, the long-term warning risk level may be determined according to the risk level criterion shown in FIG. 5 . For example, if the slope increment Ф is less than 10 degrees, it may be determined as a 'safety' level, and if it is between 11 degrees and 30 degrees, it may be determined as an 'interest' level.

Acoustic acoustic waves (simply referred to as 'acoustic waves' or 'acoustic waves') among measurement data may be measured by the acoustic wave sensor of the measuring instrument 10 . The amount and frequency of acoustic waves tend to increase rapidly before a landslide or slope collapse occurs, and since a certain range around the sensor is measured, it is useful for capturing preliminary signs of landslides in a wider range than conventional measurement methods. can be used appropriately.

In general, a precursor phenomenon in which the number of occurrences of breaking sounds increases immediately before the collapse of the slope occurs, and at this time, the largest acoustic wave energy is generated. For example, FIG. 6 shows exemplary characteristic parameters of the acoustic wave. As shown in FIG. 6, characteristic parameters such as maximum amplitude, rise time, frequency, number of peaks, duration (Td), and energy (E) may be extracted from one measured acoustic wave waveform. As shown in FIG. 6, in general, when measuring acoustic waves, a voltage higher than the voltage level of background noise is set as a threshold value (At), and a waveform exceeding this threshold value (At) is detected as an effective acoustic wave. The maximum amplitude of the acoustic wave means the maximum value of the amplitude of the acoustic wave, the rise time is the time taken from the start of the acoustic wave to reach the maximum amplitude, and the number of peaks is the number of peaks in the acoustic wave per minute. am. Frequency can be calculated as the number of peaks per unit time. The duration (Td) is the time from the start of the acoustic wave to its extinction, and the energy (E) may mean the area of the envelope of the acoustic wave. In one embodiment of the present invention, the number of peaks is used as measurement data. That is, the number of acoustic wave peaks measured for a predetermined time (for example, 1 minute) is used as measurement data.

7 shows an artificial neural network algorithm for calculating a secondary risk according to an embodiment. Referring to the drawings, in one embodiment, an artificial neural network is composed of an input layer (Lin), one or more hidden layers (Lh1 and Lh2), and an output layer (Lout). The input layer Lin has five input nodes each of which uses the measurement data of (i) to (v) as input variables. In the illustrated embodiment, the artificial neural network has two hidden layers (Lh1 and Lh2), and each hidden layer is composed of four hidden nodes. The output layer (Lout) has five output nodes, and each output node corresponds to five risk levels of relief, interest, caution, danger, and warning for the risk of collapse of the slope, respectively.

The arrow connecting each input node and the hidden node, the arrow connecting each hidden node and the hidden node, and the arrow connecting each hidden node and the output node mean weight and bias. and the weight and bias are determined by learning from the learning data. In one embodiment, according to the risk level classification criterion of FIG. 5, past measurement data and correct answers (ie, risk level) for each measurement data may be prepared in advance as learning data and learned.

As described above, each input node of the input layer Lin corresponds to each measurement data (ie, the amount of change in impulse, the moving average of the amount of change, the slope, the moisture content, and the number of elastic wave peaks). In one embodiment, a measurement value of each measurement data may be directly input to each input node. However, in an alternative embodiment, the measurement data is pre-processed and then input to each input node. For example, such preprocessing may be, for example, unifying the units of measurement or normalizing such that the mean is 0 and the standard deviation is 1. The effect of unit difference, the effect of difference in maximum and minimum value, the effect of difference in average value, and the effect of difference in distribution are irrelevant to the nature of the data, and in order to learn weights and biases in artificial neural networks, it is necessary to It is desirable to leave only the essence of and remove elements that interfere with learning.

Meanwhile, in one embodiment, rainfall information and geological characteristics may be applied to an artificial neural network model. For example, as rainfall information, weights may be given to gradually increase the risk according to rainfall of 30mm, 40mm, 60mm, and 80mm per hour. In this case, for example, at least one input data value among 5 input data according to rainfall information It can be input to the input node by increasing or decreasing . Similarly, in the case of geological characteristics, a weight is applied to gradually increase the risk depending on whether the ground on which the instrument 10 is installed is soil (sand soil, clay soil), lipping rock (weathered rock), soft rock (blast rock), and hard rock (blast rock). And even in this case, for example, at least one input data value among five input data values can be increased or decreased according to geological characteristics and inputted to the input node.

In one embodiment, each output node of the output layer (Lout) corresponds to each of five risk levels of relief, interest, caution, danger, and warning for the risk of collapse of the slope, for example, depending on which output node has the highest output value. One of the five risk levels is determined. In an alternative embodiment, the risk level may be output as a probability by applying a Softmax activation function to the rear end of the output layer (Lout). In this case, the sum of all output values of the five output nodes becomes 1, and the largest output value becomes the level of the secondary risk calculated for the corresponding input data, and the output value at this time means the probability of the risk level. In this way, the secondary risk for the collapse risk of the slope is calculated by the artificial neural network algorithm, and the meter 10 may transmit the secondary risk calculation result to the server device through the relay 20. The server device may comprehensively analyze and determine the collapse risk of a specific slope area by receiving and synthesizing the secondary risk results received from each measuring instrument 10 .

Meanwhile, one or more application programs including various algorithms such as firmware or software for calculating the first risk and the second risk are stored in the memory of each measuring instrument 10, and regular/irregular programs are provided to increase risk calculation performance. An update is needed.

Exemplary methods of updating programs stored in instrument 10 will now be described with reference to FIGS. 8-11. First, FIG. 8 is a diagram for explaining a memory area of a measuring instrument 10 according to an embodiment. According to the illustrated embodiment, the measuring instrument 10 includes a microcontroller (MCU) memory 100 and a first memory 200 , and the second memory 300 may be included. The MCU memory 100 is, for example, a memory area built into an MCU chip and may be logically and/or physically partitioned into a boot program area 110 and an area 120 storing application programs. Alternatively, the application program area 120 may be implemented as a memory device separate from the MCU chip. The application programs stored in the application program area 120 may mean, for example, one or more computer programs for risk calculation. In this specification, the 'application program' stored in the application program area 120 may include arbitrary firmware or software. Also, in one embodiment, the MCU memory 100 may store an operating system (OS). The operating system may be stored, for example, in the boot program area 110 or the application program area 120, but is not separately shown in the drawings.

The first memory 200 is a volatile memory and is implemented as, for example, random access memory (RAM). For example, the first memory 200 may be a memory (main memory) built into the MCU chip or separate from the MCU chip. The second memory 300 is a non-volatile memory and may be implemented as, for example, a flash memory. For example, the second memory 300 may be a secondary memory built into the MCU chip or separate from the MCU chip.

Meanwhile, in the illustrated embodiment, for example, when the capacity of the MCU memory 100 is 1024 KB, the boot program area 110 and the application program area 120 may be logically and/or physically partitioned into 100 KB and 900 KB, respectively. The first memory 200 and the second memory 300 may each have a capacity sufficient to store at least all of the data of the application program area 120. Therefore, in this case, the first memory 200 and the second memory ( 300) Each may have a capacity of at least 900 KB or more. However, the capacity of each of the memories 100 , 200 , and 300 is exemplary for description, and it goes without saying that the capacity of the memories 100 , 200 , and 300 may vary according to specific implementation situations of the present invention.

Figure 9 is a flow chart of an exemplary method for updating the instrument's software and Figure 10 is a diagram illustrating an exemplary situation for updating the software. For convenience of explanation, it is assumed that an arbitrary application program (AP) (arbitrary firmware or software) stored in the application program area 120 of the MCU memory area 100 is updated as shown in FIG. 10 . The application program (AP) to be updated may be, for example, a risk calculation program that is executed in the measuring instrument 10 and can calculate the collapse risk of the slope.

As an example, as shown in FIG. 10, the program code of the application program (AP) is recorded in an area from address 0 to address B of the application program area 120 of the memory 100. At this time, since some functions of the application program (AP) are being updated, only some codes need to be replaced instead of changing the entire program code of the application program (AP). For example, as shown in FIG. 10, when updating the middle part ("AP_2") of the program code of the application program (AP), replace this middle part ("AP_2") with the update code (AP_U) and The code ("AP_1") and the later code ("AP_3") do not need to be changed.

In this way, in one embodiment of the present invention, only the code of the part to be updated (ie, the update code (AP_U)) among the entire program codes is updated to the existing program code. At this time, for convenience of description, let the length of the update code (AP_U) be Z, the length of the program code to be replaced (ie, deleted from the existing program code) by the update code (AP_U) be Y, and the length of the program code to be replaced It is assumed that the program code to be executed is a code starting from address A of the application program area 120 .

Therefore, as schematically shown in FIG. 10, the codes (AP_1 and AP_3) that do not require replacement among the programs (AP) are located in the memory area from addresses 0 to (A-1) of the application program area 120 of the memory and Addresses (A+Y) to B are recorded in the memory area, and the replacement target code (AP_2) is recorded in the memory area from A to (A+Y-1) in the middle. will understand

In the above exemplary situation, first, in step S210 of FIG. 9, the measuring instrument 10 receives the program update code (AP_U) and update information from the remote server. The "update code" (AP_U) refers to a program code portion to be updated among program codes of an application program (AP) to be updated stored in the MCU memory 100 . "Update information" may refer to metadata about the update code (AP_U) and may include, for example, an update version and an update date. In one embodiment, the update information includes the length (Z) of the update code (AP_U) (hereinafter also referred to as "replacement code length"), the start address of the code to be replaced by the update among the applications (AP) stored in the memory 100 ( A) (hereinafter also referred to as "replacement start address"), and information on the length (Y) of the code to be deleted by update among the applications (APs) (hereinafter also referred to as "deletion code length").

In this way, when the instrument 10 receives the replacement code length (Z), replacement start address (A), and deletion code length (Y) as update information, the MCU of the instrument 10 stores the memory (as shown in FIG. 10). 100), the program code (AP_1) from address 0 to (A-1) and the program code (AP_3) from address (A+Y) to B are not subject to update, but from address A to (A+Y -1) It can be determined that the program code (AP_2) up to the address is an update target.

After that, in step S20 , the measuring instrument 10 stores the updated application program (AP) in the first memory 200 . In this regard, FIG. 11 schematically shows an updated program (AP) stored in the first memory 200 . For convenience of explanation, it is assumed that the program AP is stored from address 0 of the first memory 200 .

Referring to FIGS. 10 and 11, in order to store the updated program (AP) in the first memory 200, a code part that does not need to be updated first, that is, in the case of FIG. 10, a program (AP) stored in the MCU memory 100 The program codes (AP_1) of addresses 0 to (A-1) of ) are copied and stored in the first memory 200. Thereafter, the update code (AP_U) received from the remote server is stored in the first memory 200, and the rest of the program (AP), that is, the program (AP) stored in the MCU memory 100 in FIG. The program code (AP_3) from address A+Y) to the last address is copied and stored in the first memory 200, and accordingly, the entire code of the program (AP) with the update reflected can be stored in the first memory 200. there is.

After storing the updated program (AP) in the first memory 200 as described above, in step S230, the update program (AP) stored in the first memory 200 may be stored in the second memory 300. there is. For example, all of the program (AP) codes stored in the first memory 200 may be copied and stored in the second memory 300 .

Meanwhile, in steps S210 to S230, the update code (AP_U) and update information received by the measuring instrument 10 from the remote server are first stored in the first memory 200 instead of immediately stored in the MCU memory 100. Since it is copied (S230) and stored in the second memory 300 (S220), the instrument 10 can continue to perform the original measurement operation and the corresponding calculation operation of the risk of collapse of the slope. That is, data for update can be received while the application program (AP) of the MCU memory 100 is being executed.

After that, in step S240, the updated application program (AP) stored in the first memory 200 is copied and stored in the MCU memory 100 to perform actual program update. At this time, preferably, the entire code of the program (AP) in the first memory 200 is not copied to the MCU memory 100, and the code (AP_2) of the updated area of the program (AP) and the memory address number thereafter It is only necessary to copy the code (AP_3) and replace the code of the corresponding area of the MCU memory 100 (ie, the code of the existing AP_2 and AP_3 areas of the MCU memory 100). Therefore, since the actual update time can be shortened, the program execution time can be maximized.

As described above, after the update of the program (AP) of the MCU memory 100 is completed, the program can be executed again to start measuring the instrument 10 and calculating the risk of collapse of the slope. When an error occurs during operation or, for example, an error occurs in the update step (S240), the program (AP) stored in the second memory 300 is copied again to the MCU memory 100, and the program is initialized and restarted. (Step S250).

As described above, those skilled in the art to which the present invention pertains can understand that various modifications and variations are possible from the description of this specification. Therefore, the scope of the present invention should not be limited to the described embodiments and should not be defined, but should be defined by not only the claims to be described later, but also those equivalent to these claims.

10: instrument 11: header
12: upper housing 13: lower housing
14: lower weight 15: moisture content sensor
16: PCB board 17: cable
20: repeater 100: MCU memory
200: first memory 300: second memory

Claims (7)

It includes a microcontroller unit (MCU), an MCU memory, an acceleration sensor, a moisture content sensor, and a sound wave sensor, and a risk calculation program for calculating the risk of collapse of a slope based on measurement data received from the sensors is stored in the MCU memory. As an instrument capable of performing
Calculating, by the risk calculation program, a primary risk for collapse risk based on acceleration-related measurement data applied to the instrument among the received measurement data, and if the primary risk is greater than or equal to a preset threshold, the It is configured to execute the step of calculating the secondary risk for the collapse risk based on the received measurement data,
The measurement data includes (i) a change in the amount of impact applied to the instrument based on the change in acceleration (I), (ii) a moving average of the change for a predetermined time (Ima), (iii) a slope of the instrument, (iv) ) the moisture content inside the surface of the slope, and (v) the number of acoustic wave peaks for a predetermined time,
The meter is configured to calculate a secondary risk using an artificial neural network algorithm,
The artificial neural network algorithm includes an input layer having five input nodes each of which uses the measurement data of (i) to (v) as input variables; at least one hidden layer composed of a plurality of nodes; And an output layer having a plurality of output nodes corresponding to each level of the risk level for the risk of collapsing the slope;
The risk calculation program is configured to reflect rainfall information and geological characteristics to input variables when input variables are input to the artificial neural network algorithm, but at least one of the input variables (i) to (v) is input according to the rainfall information. It is configured to input by increasing or decreasing the value, and to increase or decrease the input value of at least one of the input variables of (i) to (v) according to the type of ground on which the meter is installed,
The meter is configured to update the risk calculation program, the method for updating the risk calculation program,
Receiving update information of the program from a remote server (S210);
Storing the update program reflecting the update in the first memory 200 in the instrument (S220); and
Copying the update program stored in the first memory 200 to the MCU memory 100 (S240); characterized in that it comprises a measuring instrument. According to claim 1,
The instrument further includes a second memory 300,
The measuring instrument further comprises copying and storing the update program stored in the first memory 200 to the second memory 300 (S230). According to claim 1,
The update information includes an update code to be updated, a length (Z) of the update code, a start address (A) of a code to be replaced by the update in the program, and a length (Y) of a code to be deleted by the update in the program Characterized in that it contains information about, a measuring instrument. The method of claim 3, wherein the step of storing the update program in the first memory (200),
copying the code from addresses 0 to (A-1) of the program stored in the MCU memory 100 to a first memory;
copying the update code received from the remote server to a first memory; and
and copying the code from (A+Y) to the last address of the program stored in the MCU memory (100) to a first memory. According to claim 2,
An instrument, characterized in that the first memory (200) is a volatile random access memory (RAM) and the second memory (300) is a non-volatile memory. delete According to claim 1,
Characterized in that the meter is configured to update the risk calculation program periodically or aperiodically.

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