JP2015111324A - Ground surface height fluctuation prediction system - Google Patents

Abstract

PROBLEM TO BE SOLVED: To provide a ground surface height fluctuation prediction system for predicting the height of a ground surface in accordance with the fluctuation of the actual height of the ground surface, and generating output data capable of easily determining whether or not frequent measurement is required for a measurement spot.SOLUTION: A ground surface height fluctuation prediction system 1 includes: a database 11 for recording ground surface height data in which the height of a ground surface on at least one measurement spot calculated in accordance with measurement performed in the past is associated with the past time point where the measurement has been performed; and an arithmetic part 12 for acquiring the ground surface height data from the database 11, and for applying a predetermined mathematical model with time as variables to the ground surface height data for each measurement spot to calculate a prediction expression indicating the temporal fluctuation of the height of the ground surface at the measurement spot. The arithmetic part 12 generates output data expressing the fluctuating situation of the height of the ground surface at the measurement spot with a predetermined method on the basis of the prediction expression.

Description

  The present invention relates to a ground height fluctuation prediction system for predicting ground height fluctuation.

  The land where the structure is installed will gradually sink due to the weight of the structure. At this time, if the land on which the structure is installed sinks unevenly (that is, uneven settlement occurs), there is a risk that the structure will collapse or break. Therefore, for example, for tanks that store hazardous materials such as oil and gas, the unequal settlement rate (the value obtained by dividing the difference between the maximum and minimum values of the subsidence amount by the tank diameter) exceeds a predetermined value. It is regulated so as not to become.

  Such uneven settlement can be prevented by periodically measuring the height of the ground and performing work such as jacking up as necessary. In this case, the more frequently the measurement is performed, the unequal subsidence and its sign can be detected earlier, so that unequal subsidence can be reliably prevented.

  However, the situation of settlement varies depending on the location. Therefore, if there are places where the measurement should be performed frequently (for example, a place where subsidence is progressing or a place where the subsidence speed is high, etc.), there is no need to perform the measurement frequently. There are also places (for example, places where subsidence has converged, places where the subsidence speed is low, and places where fluctuations in the height of the ground are small). Therefore, simply increasing the frequency of measurement only results in unnecessary measurement, resulting in a decrease in work efficiency. Moreover, in a plant having a vast site, the number of points to be measured (hereinafter referred to as “measurement points”) is enormous, and therefore it is practically difficult to frequently measure all the measurement points.

  Here, in order to effectively prevent uneven settlement without reducing work efficiency, it is necessary to optimize the frequency of measurement at each measurement point. And in order to optimize the frequency of measurement for every measurement point, it is necessary to predict the fluctuation | variation state of the height of the ground in each measurement point. In addition, it is necessary to predict the fluctuation state of the ground height in order to systematically carry out the work for preventing uneven settlement.

  For example, in Patent Document 1, a Swedish sounding test (a test in which a screw point is screwed into the ground and the hardness of the ground is directly measured) is performed at several measurement points on the land where the structure is installed. Based on the results, the amount of subsidence at each measurement point after the structure is installed is predicted, and the in-plane distribution of the amount of subsidence in the entire land is predicted based on the amount of subsidence to generate a three-dimensional representation of the subsidence image. A ground analysis device has been proposed. The user who has confirmed the settlement image diagram generated by the ground analysis device can easily recognize the prediction result of unequal settlement that occurs when the structure is installed.

JP 2002-54128 A

  However, the ground analysis device proposed in Patent Document 1 predicts the amount of settlement based only on the hardness of the ground measured before installing the structure on the land. For this reason, in this ground analysis device, the predicted settlement amount may deviate significantly from the actual settlement amount. Furthermore, even if the user confirms the settlement image generated by the ground analysis device, it is impossible to grasp the fluctuation in the height of the ground after the structure is installed. Therefore, it is difficult for the user to determine whether or not the measurement point requires frequent measurement.

  Therefore, the present invention performs prediction according to actual ground height fluctuations and generates output data that can easily determine whether or not the measurement point requires frequent measurement. An object is to provide a height fluctuation prediction system.

  In order to achieve the above object, the ground height fluctuation prediction system of the present invention includes a ground height at at least one measurement point obtained by a measurement performed in the past, a past time point at which the measurement was performed, A database for recording the ground height data associated with each other, and obtaining the ground height data from the database, and applying a predetermined mathematical model with time as a variable to the ground height data for each measurement point And a calculation unit that calculates a prediction formula indicating a variation in the height of the ground at the measurement point, and the calculation unit calculates a fluctuation state of the height of the ground at the measurement point based on the prediction formula. Output data expressed by a predetermined method is generated.

  According to this ground height fluctuation prediction system, the arithmetic unit predicts the ground height fluctuation at the measurement point based on the ground height data obtained from the past measurement. It is possible to make predictions in line with fluctuations in Furthermore, since the calculation unit generates output data indicating the fluctuation state of the ground height at the measurement point, the user who confirms the output data can easily determine whether the measurement point requires frequent measurement. It becomes possible to judge.

  Furthermore, in the ground height fluctuation prediction system having the above characteristics, it is preferable that the calculation unit calculates the prediction formula by applying the mathematical model composed of an exponential function with time as a variable to the ground height data. .

  According to this ground height fluctuation prediction system, the calculation unit calculates a prediction formula using an exponential function that accurately applies to ground height fluctuations (for example, long-term fluctuations of about several decades). It becomes possible to accurately predict fluctuations in the height of the ground. Further, by using the exponential function as the mathematical model, the calculation unit can calculate the prediction formula not only when the ground height decreases but also when the ground height increases.

  Furthermore, in the ground height fluctuation prediction system having the above characteristics, when T is time, A, B, and C are constants, and H is the height of the ground at the measurement point at time T, the calculation unit is set to “H = It is preferable to calculate the prediction formula by applying the mathematical model of “A × exp (−B × T) + C” to the ground height data and obtaining the constants A, B, and C, respectively.

  According to the ground height fluctuation prediction system, a simple function that changes monotonically as “H = A × exp (−B × T) + C” is used as a mathematical model. Therefore, the prediction formula obtained by applying the function to the ground height data can grasp the mode of change (that is, the variation tendency of the ground height) only by confirming the constants A, B, and C. It becomes a simple formula. Therefore, the calculation unit can determine the fluctuation tendency of the height of the ground at the measurement point by a simple calculation process.

  Further, in the ground height fluctuation prediction system having the above characteristics, the calculation unit may calculate a tendency of fluctuation of the ground height at the measurement point based on at least the constants A and B in the prediction formula calculated for the measurement point. It is preferable to determine and generate the output data expressing the determination result by a predetermined method.

  According to the ground height fluctuation prediction system, the calculation unit calculates the height of the ground at the measurement point based on the constants A and B that briefly indicate the change of the prediction formula (that is, the ground height fluctuation tendency). The fluctuation tendency of the height is determined. Therefore, the calculation unit can effectively determine the fluctuation tendency of the ground height at the measurement point.

  Furthermore, in the ground height fluctuation prediction system having the above characteristics, the calculation unit calculates the ground height at the measurement point based on the signs of the constants A and B in the prediction formula calculated for the measurement point. It is preferable to determine the fluctuation tendency and generate the output data expressing the determination result by a predetermined method.

  According to this ground height fluctuation prediction system, the calculation unit determines the measurement point based on the positive and negative signs of the constants A and B indicating the outline of the change of the prediction formula (that is, the fluctuation tendency of the ground height). The tendency of fluctuation of the ground height is determined. Therefore, the calculation unit can more effectively determine the tendency of the ground height variation at the measurement point.

  Furthermore, in the ground height variation prediction system having the above characteristics, the calculation unit may include a variation tendency of the ground height at the measurement point where both the constants A and B of the prediction formula are positive, and the prediction formula. It is preferable to determine that the fluctuation tendency of the height of the ground at the measurement point where the constants A and B are both negative is different.

  According to the ground height fluctuation prediction system, the calculation unit has different fluctuation trends when the ground height decreasing speed gradually decreases and when the ground height decreasing speed gradually increases. It becomes possible to judge.

  Further, in the ground height fluctuation prediction system having the above characteristics, the calculation unit is configured to determine that the tendency of the ground height fluctuation at the measurement point where the constants A and B of the prediction formula are both positive is a current decrease rate. It is preferable to determine so as to differ according to at least one of the decrease convergence degree.

  According to this ground height fluctuation prediction system, when the computing unit converges while the ground height decreases, it is classified finely according to the degree of the ground height reduction at the present time, and each of them varies differently. It can be determined as a trend.

  Further, in the ground height fluctuation prediction system having the above characteristics, the calculation unit is configured to detect a fluctuation tendency of the ground height at the measurement point where the constants A and B of the prediction formula are both positive or negative, and the prediction. It is preferable that the constants A and B of the formula are determined so that the tendency of the ground height to vary at the measurement point where one of the constants A and B is positive and the other is negative.

  According to the ground height fluctuation prediction system, the calculation unit can determine that the fluctuation tendency is different between the case where the ground height decreases and the case where the ground height increases.

  Furthermore, in the ground height fluctuation prediction system having the above characteristics, the database records the ground height data at a plurality of the measurement points, and records map data indicating the positions of the measurement points. And the calculation unit is configured to calculate a ground height variation state at the plurality of measurement points based on the map data acquired from the database and the prediction formula calculated for each measurement point. It is preferable that the variation situation mapping data shown for each position is generated as the output data.

  According to this ground height fluctuation prediction system, the calculation unit generates fluctuation situation mapping data indicating the fluctuation situation of the ground height at the measurement point for each position of the measurement point. Thus, the user can easily determine which measurement point needs frequent measurement.

  Furthermore, in the ground height fluctuation prediction system having the above characteristics, when the plurality of ground height data obtained for the measurement point are discontinuous at a specific time point, the calculation unit The ground height data after the specific time and the ground height data before the specific time corrected to be continuous with the ground height data after the specific time. It is preferable that the prediction formula at the measurement point is calculated by applying the mathematical model to the corrected ground height data combining.

  According to this ground height fluctuation prediction system, even when the ground height data is discontinuous due to an earthquake or the like, the calculation unit calculates the prediction formula using the corrected ground height data. It becomes possible to accurately predict fluctuations in the height of the ground at the measurement point.

  According to the ground height fluctuation prediction system having the above characteristics, the calculation unit predicts the ground height fluctuation at the measurement point based on the ground height data obtained by the past measurement. It is possible to make predictions according to height fluctuations. Furthermore, since the calculation unit generates output data indicating the fluctuation state of the ground height at the measurement point, the user who confirms the output data can easily determine whether the measurement point requires frequent measurement. It becomes possible to judge.

The block diagram which shows an example of a structure of the elevation fluctuation | variation prediction system which concerns on embodiment of this invention. The graph which shows an example of altitude data and the prediction formula obtained when a calculating part processes the said altitude data. The graph which shows an example of specific measurement point fluctuation situation data. The figure which shows an example of fluctuation status mapping data. The graph which shows an example of the determination method of a fluctuation tendency. The graph which shows an example of the altitude data containing a discontinuous part, and the prediction formula obtained by a calculation part processing the said altitude data. 7 is a graph showing an example of corrected altitude data obtained by correcting the altitude data of FIG. 6 and a prediction formula obtained by processing the corrected altitude data by a calculation unit. The graph which shows an example of the prediction formula obtained when a calculating part processes only the falling part of the first half of elevation data. The graph which shows an example of the prediction formula obtained when a calculating part processes only the latter convergence part of altitude data.

  Hereinafter, a ground height fluctuation prediction system according to an embodiment of the present invention will be described with reference to the drawings. In the following, for the purpose of concrete explanation, as an embodiment of the present invention, fluctuations in altitude (the height of the ground surface obtained by surveying with reference to the level mark, where the average sea level is 0) are predicted. An example of the altitude fluctuation prediction system (ground height fluctuation prediction system) will be described.

<Configuration of altitude fluctuation prediction system>
First, an example of the configuration of the elevation variation prediction system according to the embodiment of the present invention will be described with reference to FIG. FIG. 1 is a block diagram showing an example of the configuration of an elevation variation prediction system according to an embodiment of the present invention.

  As shown in FIG. 1, the elevation variation prediction system 1 according to the embodiment of the present invention includes a database 11, a calculation unit 12, and an output unit 13.

  The database 11 includes elevation data (ground height data) in which elevations at at least one measurement point obtained by measurement performed in the past are associated with past points in time when the measurement was performed, and measurement points. And map data indicating the respective positions. The database 11 has a configuration including a recording device such as a hard disk or a semiconductor memory capable of recording various data.

  For example, the altitude data includes information indicating the measurement point at which the measurement was performed, information on the altitude at the measurement point determined by the measurement, information on the time when the measurement was performed (for example, date), Each of these is generated when a person inputs in association with a computer or the like.

  Any method may be used for the method of generating altitude data and the method of recording altitude data by the database 11. For example, an automatic measurement device that automatically generates altitude data is placed at a measurement point, and this automatic measurement device automatically performs measurement at a predetermined timing and obtains information for identifying itself and measurement. The elevation data may be automatically generated by associating the obtained elevation information with the information at the time of measurement. Further, in this case, the automatic measurement device may automatically transmit the altitude data, and the altitude fluctuation prediction system 1 may receive the altitude data and record it in the database 11. In addition, a configuration in which the database 11 records the altitude data by connecting the portable terminal or the like to the automatic measuring apparatus and receiving the altitude data and then transferring the altitude data from the portable terminal or the like to the database 11. There may be.

  Further, for example, the map data is data including information indicating positions (for example, coordinates) of a plurality of measurement points. The map data may include information indicating the position of equipment in the facility such as a structure (tank, building, etc.) or a passage (road, bridge, etc.). Moreover, the information (for example, measurement point [1-1]: the ground under the 1st tank, measurement point [1-2]: which shows the correspondence of a measurement point and equipment to one or both of map data and altitude data): The ground below the first tank, the measurement point [2-1]: the ground below the second tank, etc.) may be included.

  The calculation unit 12 obtains altitude data from the database 11 and processes it to calculate a prediction formula indicating temporal fluctuations in altitude at the measurement point. Furthermore, the calculating part 12 produces | generates the output data which show the fluctuation | variation state of the altitude in a measurement point based on the said prediction formula. The computing unit 12 has a configuration including a computing device such as a CPU (Central Processing Unit) and a temporary storage device such as a memory.

  The altitude fluctuation status indicated by the output data is, for example, the altitude altitude fluctuation (prediction formula) and altitude fluctuation tendency (specifically, for example, decrease, increase, acceleration decrease, acceleration increase, convergence) Current and future such as current altitude, altitude after a certain time, altitude after a certain time, final altitude (altitude after infinite time), etc. Is included.

  As the output data, for example, the calculation unit 12 indicates specific measurement point fluctuation state data that is data indicating the fluctuation state of the altitude of a specific measurement point, and indicates the fluctuation state of altitude at a plurality of measurement points for each position of the measurement point. Fluctuation state mapping data. At this time, the calculation unit 12 generates specific measurement point fluctuation status data based on the prediction formula of the specific measurement point (details will be described later). Moreover, the calculating part 12 produces | generates fluctuation situation mapping data based on the map data acquired from the database 11, and the prediction formula calculated for every measurement point (it mentions later for details). Note that the calculation unit 12 may generate any kind of data as output data as long as the data indicates an altitude fluctuation state. However, for the sake of concrete explanation, the calculation unit 12 is described above. Two types of data are generated.

  The output unit 13 includes an image display device such as a liquid crystal display, for example, and displays output data generated by the calculation unit 12 as an image. Then, the user confirms the output data by viewing the image displayed on the output unit 13. Thereby, the user can confirm the fluctuation | variation state of the altitude in a measurement point.

  As described above, in the altitude fluctuation prediction system 1, the calculation unit 12 predicts the fluctuation of the ground height at the measurement point based on the altitude data obtained by past measurement. It is possible to make predictions in line with fluctuations in Furthermore, since the calculation part 12 produces | generates the output data which shows the fluctuation | variation state of the ground height in a measurement point, the user who confirmed this output data can easily determine whether it is a measurement point which requires frequent measurement. It becomes possible to judge.

  Moreover, since the calculation part 12 produces | generates the fluctuation situation mapping data which showed the fluctuation situation of the height of the ground in a measurement point for every position of a measurement point, the user who confirmed this fluctuation situation mapping data performs frequent measurement. It is possible to easily determine which measurement point is necessary.

  Each process of the altitude fluctuation prediction system 1 (particularly the arithmetic unit 12) is performed by arithmetic processing using computer hardware resources (CPU, memory, etc.) and software resources (OS: Operating System, various drivers, etc.). Is called. Specifically, such arithmetic processing is realized in software by a CPU or the like executing a program for executing the above-described processing. For example, the processing in the arithmetic unit 12 is realized by temporarily storing altitude data and map data in a memory and performing arithmetic processing according to a predetermined program on the data stored in the memory by the CPU. The

<Calculation method of prediction formula>
Next, the calculation method of the prediction formula by the calculating part 12 is demonstrated with reference to drawings. FIG. 2 is a graph showing an example of altitude data and a prediction expression obtained by processing the altitude data by the calculation unit. In FIG. 2, the elevation data is indicated by white circles and the prediction formula is indicated by a thick solid line. In FIG. 2, for convenience of comparison between the elevation data and the prediction formula, the elevation data is connected by a thick broken line, and points on the prediction formula at the same time as the elevation data are indicated by black circles.

  As illustrated in FIG. 2, the calculation unit 12 calculates a prediction formula by fitting (fitting) a mathematical model formed of an exponential function having time as a variable to altitude data. Specifically, for example, the computing unit 12 calculates a prediction formula by applying a mathematical model of the following formula (1) to the elevation data. In FIG. 2, the calculation unit 12 exemplifies a prediction formula calculation method when the following formula (1) is applied to altitude data.

    H = A × exp (−B × T) + C (1)

  In the above formula (1), H is the altitude at the measurement point (for example, the unit is meters). Further, T is time, and is elapsed time when the time point when the most previous altitude data is measured is 0 (for example, the unit is days). A, B, and C are constants.

  The calculation unit 12 applies the above formula (1) to the altitude data and obtains the constants A, B, and C, respectively, thereby obtaining the prediction formula (specific values obtained for the constants A, B, and C of the above formula (1)). Formula that substitutes a typical numerical value). At this time, the arithmetic unit 12 calculates the constants A, B, and C using an arbitrary fitting method such as a least square method.

  As is clear from the above equation (1), the constant A indicates the total amount of change in altitude (the amount of change in altitude from T = 0 to T = ∞) when the change in altitude converges (B> 0). Value. The constant B is a time constant and is a value related to the altitude fluctuation speed. The constant C is a value indicating the final altitude (T = ∞ altitude) when the altitude change converges (B> 0).

  As a result of intensive studies by the inventor of the present application, it has been found that if an exponential function with time as a variable is adopted as a mathematical model, it can be accurately applied to altitude fluctuations (for example, long-term fluctuations of several decades). It was. Therefore, as described above, the arithmetic unit 12 can predict the change in altitude with high accuracy by calculating the prediction formula using the mathematical model composed of the exponential function. Further, by using the exponential function as the mathematical model, the calculation unit 12 can calculate the prediction formula not only when the altitude decreases but also when the altitude increases.

  Further, when a simple function having a monotonic change of “H = A × exp (−B × T) + C” is used as a mathematical model, a prediction formula obtained by applying the function to the altitude data is a constant A, B and It becomes a simple formula that can grasp the mode of change (that is, the altitude fluctuation tendency) only by confirming C (the details will be described later with a specific example). Therefore, the calculation unit 12 can determine the altitude fluctuation tendency at the measurement point by a simple calculation process.

<Generation method of output data>
Next, a method for generating output data based on the calculated prediction formula by the calculation unit 12 will be described with reference to the drawings.

[Generation method of specific measurement point fluctuation status data]
First, an example of a method for generating specific measurement point variation situation data will be described with reference to the drawings. FIG. 3 is a graph showing an example of specific measurement point variation status data. Note that the specific measurement point variation situation data illustrated in FIG. 3 is data that represents the prediction formula as a graph. Moreover, the thick solid line shown in FIG. 3 is a prediction formula, and this prediction formula is the same as the prediction formula calculated from the altitude data shown in FIG.

  As shown in FIG. 3, the calculating part 12 produces | generates the data which show a prediction formula as specific measurement point fluctuation situation data. For example, the calculation unit 12 generates specific measurement point change situation data indicating the temporal change of the past, present, and future altitudes at the measurement point using a prediction formula.

  In this case, the user who has confirmed the specific measurement point variation situation data can easily determine whether or not the measurement point requires frequent measurement.

  In addition, although the specific measurement point fluctuation situation data of this example shows only a prediction formula, it may show a prediction formula and altitude data together like FIG. Moreover, the calculating part 12 may produce | generate the specific measurement point fluctuation status data which showed the present time and the future (or only the future) temporal fluctuation | variation of the altitude at a measurement point by the prediction formula.

[Method of generating fluctuation mapping data]
Next, an example of a method for generating variation state mapping data by the calculation unit 12 will be described with reference to the drawings. FIG. 4 is a diagram illustrating an example of the fluctuation state mapping data. Note that the variation state mapping data illustrated in FIG. 4 is generated for the purpose of particularly confirming the decreasing tendency of the altitude (that is, the tendency of settlement of the ground).

  The fluctuation status mapping data illustrated in FIG. 4 is created by the calculation unit 12 by superimposing the altitude fluctuation tendency at each measurement point on map data indicating the positions of structures, passages, and the like in the facility. It is.

  The fluctuation state mapping data illustrated in FIG. 4 shows the fluctuation tendency at each measurement point as “accelerated decrease point” where the altitude is decreasing at an accelerated rate (illustrated by a black circle), reaching the standard convergence period. "Decrease point before convergence" (illustrated by circles with grid-like hatching), "Decrease point after convergence (large decrease rate)" where the standard convergence period has been reached but the altitude decrease rate is large (inside "Black double circles", standard convergence period is reached, but altitude decrease rate is small "decrease point after convergence (low decrease rate)" (shown by double circles), "others not applicable" The points are generated by a method of classifying them into five types (points indicated by white circles). Note that the standard convergence period means that a series of elevation fluctuations have converged to some extent when a prediction formula that converges at T = ∞ has been calculated (for example, 90% of the total elevation fluctuation amount (constant A) ends) This is a later period and is a period in which the degree of convergence of fluctuation is large.

  Next, the determination method of the fluctuation tendency in each measurement point by the calculating part 12 is demonstrated with reference to drawings. FIG. 5 is a graph showing an example of a method for determining a fluctuation tendency. Further, the variation tendency determination method illustrated in FIG. 5 is performed by the calculation unit 12 when the variation state mapping data illustrated in FIG. 4 is generated.

  In the determination method illustrated in FIG. 5, the calculation unit 12 determines the fluctuation tendency mainly based on the positive and negative signs of the constants A and B of the prediction formula.

  As shown in FIG. 5, when both constants A and B of the prediction formula are positive or both negative, the prediction formula decreases in altitude (see FIGS. 5A and 5B). On the other hand, when one of the constants A and B is positive and the other is negative, the prediction formula increases the altitude (see FIGS. 5C and 5D). Utilizing this fact, the calculation unit 12 determines the measurement point where the altitude decreases and the measurement point where the altitude increases as different fluctuation trends. Specifically, for the measurement point where the altitude decreases, the calculation unit 12 “acceleration decrease point”, “decrease point before convergence”, “decrease point after convergence (large decrease speed)” and “decrease point after convergence (decrease) “Small speed)”, and the measurement point where the elevation increases is determined as “other point”.

  Also, as shown in FIG. 5, when both the constants A and B of the prediction formula are positive, the prediction formula becomes a prediction formula that gradually decreases the altitude decrease rate (see FIG. 5A). On the other hand, when the constants A and B of the prediction formula are both negative, the prediction formula is such that the altitude decrease rate gradually increases (see FIG. 5B). Utilizing this fact, the calculation unit 12 determines, as different fluctuation trends, measurement points where the altitude decrease rate gradually decreases and measurement points where the altitude decrease rate gradually increases. Specifically, the calculation unit 12 determines that “decrease point before convergence”, “decrease point after convergence (large decrease rate)”, and “decrease point after convergence (low decrease rate)” for measurement points where the decrease rate of altitude gradually decreases. The measurement points where the altitude decrease rate gradually increases are determined as “accelerated decrease points”.

  The “acceleration decrease point” at which the altitude decrease rate gradually increases is a measurement point where the altitude may decrease significantly in a short period of time. Therefore, it can be said that the “accelerated decrease point” is a measurement point that requires frequent measurement.

  Further, the calculation unit 12 determines and classifies the variation tendency finely based on at least one of the altitude decrease rate and the decrease convergence degree at the measurement point where both the constants A and B of the prediction formula are positive. At this time, the calculation unit 12 uses, for example, the differential value of the prediction formula at the current time as the altitude decrease rate at the current time. Further, the calculation unit 12 determines, for example, whether or not the standard convergence period has been reached as the degree of decrease in the altitude at the current time point.

  When creating the fluctuation state mapping data illustrated in FIG. 4, the calculation unit 12 determines that the decrease convergence degree is small at the measurement point where both the constants A and B of the prediction formula are positive (the standard convergence period must be reached). If the decrease convergence degree is large (if the standard convergence period has been reached), then “decrease point after convergence (large decrease rate)” or “decrease point after convergence (low decrease rate) ) ”. Further, the calculation unit 12 may measure the measurement point that has reached the standard convergence period if the current altitude decrease rate is large (for example, within the top 25% of the measurement points that have reached the standard convergence period). For example, if it is determined as “decreasing point after convergence (high decreasing rate)” and the current altitude decreasing rate is small (for example, if it does not enter the top 25% of the measuring points that have reached the standard convergence period) ) Judge as “decrease point after convergence (low decrease speed)”.

  The “decrease point before convergence” that has not reached the standard convergence period is a measurement point where the altitude may greatly decrease in the future. Therefore, it can be said that the “decrease point before convergence” is a measurement point that requires frequent measurement.

  On the other hand, “decrease point after convergence (large decrease rate)” and “decrease point after convergence (low decrease rate)” are measurement points where the possibility that the altitude will greatly decrease in the future is low. However, among such measurement points, the “decrease point after convergence (large decrease rate)” at which the altitude decrease rate is large at the present time can be said to be a measurement point that requires frequent measurement. On the other hand, it can be said that the “decrease point after convergence (low decrease rate)” at which the altitude decrease rate is small is a measurement point that requires slightly frequent measurement.

  In this way, the calculation unit 12 determines the altitude fluctuation tendency at the measurement point based on the constants A and B that simply indicate the mode of change of the prediction formula (that is, the altitude fluctuation tendency), thereby measuring the measurement point. It is possible to effectively determine the fluctuation tendency of the altitude at. In particular, the calculation unit 12 determines the altitude fluctuation tendency at the measurement point based on the positive and negative signs of the constants A and B indicating the outline of the change of the prediction formula (that is, the altitude fluctuation tendency). It is possible to determine the altitude fluctuation tendency at the measurement point more effectively.

  When calculating the prediction formula, the accuracy of fitting the mathematical model (for example, root mean square error (RMSE)) is lower than a predetermined level (for example, RMSE is larger than a predetermined value). About the measurement point, the calculating part 12 does not need to perform the determination method mentioned above. In this case, the calculation unit 12 may determine the altitude fluctuation tendency at the measurement point where the accuracy of the mathematical model fitting is low as “other point”, or as another fluctuation tendency such as “determination impossible”. May be.

  4 and 5, the calculation unit 12 indicates the fluctuation tendency at the measurement point as “accelerated decrease point”, “decrease point before convergence”, “decrease point after convergence (large decrease rate)”, “after convergence”. Although the case of classifying into five points of “decreasing point (low decreasing speed)” and “other points” is illustrated, it may be classified into a smaller fluctuation trend or more than this. May be. Moreover, you may classify | categorize the fluctuation tendency in a measurement point by methods other than this.

  For example, the measurement point at which the altitude increases, which has been determined as the “other point” in the above example by the calculation unit 12, is the same as the measurement point at which the altitude is decreased, the “accelerated increase point”, “the increase point before convergence” ”,“ Increased point after convergence (large increase speed) ”and“ increase point after convergence (low increase speed) ”. Contrary to the above example, the calculation unit 12 determines the measurement point at which the altitude decreases as “other points”, and sets the measurement point at which the altitude increases as “acceleration increase point” and “pre-convergence increase point”. ”,“ Increased point after convergence (large increase speed) ”and“ increase point after convergence (low increase speed) ”.

  Moreover, the calculating part 12 may produce | generate the data which showed the fluctuation | variation state of the altitude in several measurement points of the formats (for example, list etc.) other than a map for every position of a measurement point.

<Calculation of prediction formula using corrected altitude data>
Next, a case where a prediction formula is calculated using corrected altitude data obtained by correcting altitude data will be described with reference to the drawings. FIG. 6 is a graph showing an example of elevation data including a discontinuous portion and a prediction formula obtained by processing the elevation data by the calculation unit. FIG. 7 is a graph showing an example of corrected altitude data obtained by correcting the altitude data in FIG. 6 and a prediction formula obtained by processing the corrected altitude data by the calculation unit. 6 and 7 show the altitude data and the prediction formula in the same manner as in FIG. However, in FIG. 7, the corrected altitude data is indicated by white squares.

  In some cases, the altitude at the measurement point may change or the altitude at the reference point may change due to an earthquake or the like. In this case, as shown in FIG. 6, a discontinuous portion may occur in the elevation data. And if the calculating part 12 calculates a prediction formula using the altitude data containing such a discontinuous part, the precision of fitting of the numerical formula model with respect to altitude data will become low (RMSE will become high).

  Therefore, in such a case, as shown in FIG. 7, the calculation unit 12 applies a mathematical model to the corrected altitude data (corrected ground height data) to calculate a prediction formula at the measurement point. Here, the corrected altitude data is corrected so as to be continuous with altitude data after the discontinuous time point (1995 in the figure) and the altitude data after that time point. This is a combination of altitude data before that time.

  As described above, when the calculation unit 12 calculates the prediction formula using the corrected ground height data, it is possible to accurately predict the fluctuation of the ground height at each measurement point.

  It should be noted that detection and correction of discontinuous portions in the elevation data may be performed by a person or automatically using a predetermined arithmetic device. Further, one of the detection and correction of the discontinuous portion may be performed by a person, and the other may be automatically performed by an arithmetic device.

<Calculation of prediction formula using some altitude data>
Next, the case where a prediction formula is calculated using a part of altitude data will be described with reference to the drawings. FIG. 8 is a graph showing an example of a prediction formula obtained by the calculation unit processing only the first-half falling portion of the elevation data. FIG. 9 is a graph showing an example of a prediction formula obtained by the calculation unit processing only the latter half of the elevation data. 8 and 9 are graphs showing a case where a prediction formula is calculated using only a part of the elevation data shown in FIG. 8 and 9 show the altitude data and the prediction formula in the same manner as in FIG.

  As shown in FIG. 8, even when the calculation unit 12 calculates the prediction formula using only the elevation data in the first-half falling portion (the portion before the one-dot chain line in the figure) where the amount of decrease in elevation is large, It is possible to calculate a prediction formula that approximates the altitude data to some extent. In FIG. 8, a discontinuous portion (1995) exists as in FIG. For this reason, the accuracy of fitting the mathematical model to the elevation data is high for the part before the discontinuous part, but the mathematical model fitting to the elevation data is applied to the part after the discontinuous part. The accuracy is slightly lower.

  On the other hand, as shown in FIG. 9, even when the prediction formula is calculated using only the elevation data in the latter half of the convergence portion (the portion after the one-dot chain line in the figure) where the amount of decrease in elevation is small, Thus, it is possible to calculate a prediction formula that approximates to some extent.

  Therefore, when the altitude data is small (for example, when only several years have passed since the installation of the structure, or when the altitude data starts to be accumulated after several decades after the installation of the structure) However, with the altitude fluctuation prediction system 1, it is possible to calculate a prediction formula that approximates the altitude data to some extent.

  However, as shown in FIG. 8 and FIG. 9, rather than using a part of the elevation data, it is better to apply the mathematical model to the elevation data by using all the existing elevation data as shown in FIG. 2. The accuracy can be increased (the RMSE can be decreased). Therefore, in the altitude fluctuation prediction system 1, calculating the prediction formula using all the existing altitude data is preferable because the altitude fluctuation status (for example, final altitude and fluctuation tendency) can be obtained with high accuracy.

<Deformation, etc.>
[1] In the above-described embodiment, the altitude fluctuation prediction system 1 uses the specific measurement point fluctuation status data (see FIG. 3) indicating the prediction formula at a specific measurement point, and the fluctuation indicating the altitude fluctuation tendency at each measurement point. Although the situation mapping data (see FIG. 4) is illustrated as being generated, the altitude fluctuation prediction system 1 may generate any output data as long as it indicates the altitude fluctuation situation at the measurement point. .

  For example, the altitude fluctuation prediction system 1 may generate output data indicating the current altitude, or output data indicating the altitude after a predetermined time has elapsed (for example, after 10 years), Output data indicating the final altitude (elevation after infinite time) may be generated.

[2] In the above-described embodiment, the altitude variation prediction system 1 generates variation status mapping data (see FIG. 4) indicating the altitude variation status at each measurement point. Etc.) Fluctuation situation mapping data indicating a fluctuation situation in units may be generated. For example, in this case, the altitude fluctuation prediction system 1 may generate fluctuation situation mapping data indicating the fluctuation situation of the unequal settlement rate of equipment.

[3] When the calculation unit 12 calculates the prediction formula at each measurement point, the time point at which the elapsed time T becomes 0 may be unified.

[4] In the above-described embodiment, it is assumed that there are a plurality of measurement points, but only one measurement point may be used. In this case, the altitude fluctuation prediction system 1 generates output data indicating only the altitude fluctuation status at the measurement point.

[5] In the above-described embodiment, for the sake of concrete explanation, the altitude fluctuation prediction system 1 predicts altitude fluctuation. However, the altitude change prediction system 1 can predict even the change in the height of the ground other than the altitude, similarly to the change in altitude. For example, the altitude fluctuation prediction system 1 (ground height fluctuation prediction system) according to the embodiment of the present invention can detect the altitude fluctuation even if the altitude fluctuation is based on an arbitrary point that is not a benchmark. Similarly, it can be predicted.

  The ground height fluctuation prediction system of the present invention can be suitably used for a ground height fluctuation prediction system that predicts ground height fluctuation in a facility such as a plant, for example.

1: Altitude fluctuation prediction system (ground height fluctuation prediction system)
11: Database 12: Calculation unit 13: Output unit

Claims (10)

A database that records ground height data associated with the height of the ground at at least one measurement point determined by a measurement performed in the past and the past time point at which the measurement was performed;
Obtaining the ground height data from the database, and applying a predetermined mathematical model with time as a variable to the ground height data for each measurement point, the temporal height of the ground at the measurement point A calculation unit that calculates a prediction formula indicating fluctuation,
The said calculating part produces | generates the output data which expressed the fluctuation | variation state of the ground height in the said measurement point by the predetermined method based on the said prediction formula, The ground height fluctuation | variation prediction system characterized by the above-mentioned.   The ground height variation according to claim 1, wherein the calculation unit calculates the prediction formula by applying the mathematical model including an exponential function with time as a variable to the ground height data. Prediction system. When T is time, A, B and C are constants, and H is the height of the ground at the measurement point at time T,
The calculation unit applies the mathematical model that is “H = A × exp (−B × T) + C” to the ground height data, and obtains the constants A, B, and C, respectively, thereby obtaining the prediction formula. The ground height variation prediction system according to claim 2, wherein the system is calculated.   The calculation unit determines a tendency of fluctuation in ground height at the measurement point based on at least the constants A and B in the prediction formula calculated for the measurement point, and expresses the determination result by a predetermined method. The ground height variation prediction system according to claim 3, wherein the output data is generated.   The arithmetic unit determines a tendency of the ground height to fluctuate at the measurement point based on the positive and negative signs of the constants A and B in the prediction formula calculated for the measurement point, and uses the determination result as a predetermined method. The ground height fluctuation prediction system according to claim 4, wherein the output data expressed by:   The calculation unit is configured to change the ground height at the measurement point where both the constants A and B of the prediction formula are positive, and at the measurement point where both the constants A and B of the prediction formula are negative. The ground height fluctuation prediction system according to claim 5, wherein the ground height fluctuation tendency is determined so as to be different from the ground height fluctuation tendency.   The calculation unit is configured such that the tendency of the ground height to fluctuate at the measurement point where both the constants A and B of the prediction formula are positive differs according to at least one of the current decrease rate and the decrease convergence degree. The ground height variation prediction system according to claim 5, wherein the ground height variation prediction system is determined.   In the calculation unit, the constant A and B of the prediction formula are both positive or negative, and the fluctuation tendency of the ground height at the measurement point and one of the constants A and B of the prediction formula are positive. The ground height variation prediction system according to any one of claims 5 to 7, wherein the ground height variation tendency at the measurement point where the other is negative is determined to be different. The database records the ground height data at a plurality of the measurement points, and records map data indicating the positions of the measurement points,
The calculation unit is configured to calculate a ground height variation state at the plurality of measurement points based on the map data acquired from the database and the prediction formula calculated for each measurement point. The ground height fluctuation prediction system according to any one of claims 1 to 8, wherein fluctuation state mapping data shown for each of the data is generated as the output data. When the plurality of ground height data obtained for the measurement point is discontinuous at a specific time point,
The calculation unit is configured to input the ground height data after the specific time and the ground height data after the specific time before the specific time corrected to be continuous. The prediction formula at the measurement point is calculated by applying the mathematical model to the corrected ground height data obtained by combining the ground height data. The ground height fluctuation prediction system according to item 1.

Images