JP7467572B2 - Analysis data processing device, analysis data processing method, and analysis data processing program - Google Patents

Description

The present invention relates to a technique for detecting abnormal values contained in analytical data consisting of a time series, and a technique for processing the analytical data in order to analyze a desired phenomenon.

Positioning using positioning signals from GNSS (Global Navigation Satellite Systems) such as GPS (Global Positioning System) has been put to practical use. One highly accurate positioning method is positioning using the carrier phase of the positioning signal (interferometric positioning).

For example, Patent Document 1 describes static positioning, a type of positioning that uses carrier phase. Patent Document 1 describes a system that observes the displacement of a slope, and performs filtering and smoothing on the displacement data obtained by static positioning. This reduces the variation contained in the displacement data.

JP 2004-144623 A

However, the displacement data (analysis data) using the above-mentioned carrier phase (observation value) may become abnormal values due to changes in the reception environment of the positioning signal or fluctuations in integer value bias. An abnormal value here is a sudden change in the analysis data, which is a time series.

The presence of such abnormal values will adversely affect the values of the filtering and smoothing processes described above, resulting in errors in the values after these processes.

Therefore, the object of the present invention is to replace outliers contained in multiple analytical data consisting of time series.

The analysis data processing device of the present invention includes an abnormal value detection unit, a stability monitoring unit, a detection information generation unit, and an analysis data replacement unit. The abnormal value detection unit detects the occurrence of abnormal values using an abnormal value determination threshold for multiple analysis data consisting of a time series. The stability monitoring unit monitors whether the multiple analysis data is in an unstable state or a stable state using the frequency of occurrence of abnormal values. The detection information generation unit sets an abnormal value determination threshold for the stable state using analysis data in the stable state. The analysis data replacement unit replaces the analysis data. The stability monitoring unit sets a stability monitoring period in the unstable state, and if a predetermined number of analysis data within the abnormal value determination threshold are consecutive during the stability monitoring period, it determines that the state is stable. The analysis data replacement unit replaces the analysis data detected as an abnormal value by the abnormal value detection unit after it has been determined to be in a stable state with data within the normal value range.

In this configuration, analysis data detected as outliers is replaced.

This invention makes it possible to replace outliers contained in multiple analysis data consisting of time series.

FIG. 2 is a functional block diagram of an analysis data processing device according to an embodiment of the present invention; A functional block diagram of an observation system including an analysis data processing device according to the present embodiment. 1 is a flowchart showing the main processing of the analysis data processing device. Flowchart showing stability monitoring process Diagram to explain the stability monitoring principle Flowchart showing the process of detecting abnormal values in a stable state FIG. 1 is a diagram for explaining an abnormal value detection process in a stable state. 1A is a flowchart showing a main process of a replacement process for analysis data, and FIG. 1B is a flowchart showing a replacement process for data for extracting periodic noise. FIG. 1A is a diagram for explaining the concept of generating data for extracting periodic noise when there is analysis data from one sidereal day ago, and FIG. 1B is a diagram for explaining the concept of generating data for extracting periodic noise when there is no analysis data from one sidereal day ago. Diagram for explaining the concept of generating smoothed analysis data Flowchart showing backward substitution processing of analysis data A diagram for explaining the concept of backward substitution processing of analysis data.

The analysis data processing device according to the embodiment of the present invention will be described with reference to the drawings. FIG. 1 is a functional block diagram of the analysis data processing device according to the embodiment of the present invention. FIG. 2 is a functional block diagram of an observation system including the analysis data processing device according to the present embodiment.

As shown in FIG. 1, the analysis data processing device 41 according to this embodiment includes an abnormal value detection unit 411, a stability monitoring unit 412, a detection information generation unit 413, an analysis data replacement unit 414, a periodic noise extraction unit 415, a periodic noise removal unit 416, and a final smoothing processing unit 417. Each functional unit constituting the analysis data processing device 41 is realized by hardware such as one or more computing units and a program executed on the hardware.

The analysis data processing device 41 having the above configuration is used in the observation system 10 shown in FIG. 2. As a prerequisite for explaining the analysis data processing device 41, the observation system 10 will first be explained using FIG. 2.

As shown in FIG. 2, the observation system 10 includes observation station 21, observation station 22, reference station 30, and analysis device 40. In FIG. 3, there are two observation stations and one reference station, but this is not limited to this. The number of observation stations and the number of reference stations are appropriately determined depending on the location to be observed, etc. Also, while FIG. 2 shows an embodiment using GPS, other positioning systems such as GNSS can also be used.

The observation station 21 includes a GPS antenna 211 and a GPS receiver 212. The GPS antenna 211 receives positioning signals (GPS signals) transmitted from each of the multiple positioning satellites SAT1, SAT2, SAT3, and SAT4, and outputs them to the GPS receiver 212. The GPS receiver 212 observes the carrier phase integrated values of the positioning signals from each of the multiple positioning satellites SAT1, SAT2, SAT3, and SAT4. The GPS receiver 212 is connected to the GPS receiver 302 of the reference station 30, and transmits these carrier phase integrated values to the GPS receiver 302.

The observation station 22 includes a GPS antenna 221 and a GPS receiver 222. The GPS antenna 221 receives positioning signals (GPS signals) transmitted from each of the multiple positioning satellites SAT1, SAT2, SAT3, and SAT4, and outputs them to the GPS receiver 222. The GPS receiver 222 observes the carrier phase integrated values of the positioning signals from each of the multiple positioning satellites SAT1, SAT2, SAT3, and SAT4. The GPS receiver 222 is connected to the GPS receiver 302 of the reference station 30, and transmits these carrier phase integrated values to the GPS receiver 302.

The reference station 30 includes a GPS antenna 301 and a GPS receiver 302. The GPS antenna 301 receives positioning signals (GPS signals) transmitted from each of the multiple positioning satellites SAT1, SAT2, SAT3, and SAT4, and outputs them to the GPS receiver 302.

The GPS receiver 302 observes the carrier phase integrated values of the positioning signals from multiple positioning satellites SAT1, SAT2, SAT3, and SAT4.

The reference station 30 transmits its own carrier phase integrated value and the carrier phase integrated values of the observation stations 21 and 22 to the analysis data processing device 41 of the analysis device 40. These carrier phase integrated values of each station correspond to the "observation value" of the present invention.

The analysis device 40 includes an analysis data processing device 41 and a storage unit 42. The analysis data processing device 41 stores multiple pieces of analysis data in the storage unit 42. The storage unit 42 may be, for example, an FTP server. In this case, the reference station 30 directly stores multiple pieces of analysis data in the storage unit 42.

Next, the configuration and processing of the analysis data processing device 41 will be described. First, the functional units that make up the analysis data processing device 41 will be described using FIG. 1. The detailed processing executed by each functional unit will be described later using flow charts and explanatory diagrams.

As shown in FIG. 1, the analysis data processing device 41 includes an analysis data generation unit 410, an abnormal value detection unit 411, a stability monitoring unit 412, a detection information generation unit 413, an analysis data replacement unit 414, a periodic noise extraction unit 415, a periodic noise removal unit 416, and a final stage smoothing processing unit 417.

The analysis data generation unit 410 performs precise interferometric positioning using the carrier phase of the positioning signal to calculate the amount of displacement of the observation station 21 and the observation station 22 relative to the reference station 30.

Specifically, the analysis data generation unit 410 calculates the amount of displacement of the position of the observation station 21 using the carrier phase integrated value for each of the multiple positioning satellites observed by the reference station 30 and the carrier phase integrated value for each of the multiple positioning satellites observed by the observation station 21. The analysis data generation unit 410 also calculates the amount of displacement of the position of the observation station 22 using the carrier phase integrated value for each of the multiple positioning satellites observed by the reference station 30 and the carrier phase integrated value for each of the multiple positioning satellites observed by the observation station 22.

The analysis data generation unit 410 calculates the amount of displacement of the position of the observation station 21 and the amount of displacement of the position of the observation station 22 at a predetermined time interval (e.g., 1-minute intervals, 5-minute intervals, 30-minute intervals, or 1-hour intervals). Each of these displacement amounts is analysis data of the observation system 10. In other words, the analysis data is a numerical value.

The analysis data generation unit 410 outputs the analysis data to the abnormal value detection unit 411 and the analysis data replacement unit 414. The function of the analysis data generation unit 410 can also be provided in the reference station 30. However, by providing the analysis data generation unit 410 in the analysis device 40, more advanced statistical processing, such as dynamic changes to the judgment threshold value and processing using multiple judgment conditions and complex functions, can be performed, and highly accurate analysis data (e.g., displacement amount) can be generated more reliably.

The abnormal value detection unit 411 compares the analysis data with a threshold for detecting abnormal values. This threshold is provided by the detection information generation unit 413. The abnormal value detection unit 411 detects abnormal values in the analysis data, and outputs the abnormal value detection results to the stability monitoring unit 412, the detection information generation unit 413, and the analysis data replacement unit 414.

The stability monitoring unit 412 uses the abnormal value detection results, for example the frequency of occurrence of abnormal values, to monitor whether the time-series analysis data is in a stable or unstable state. The stability monitoring unit 412 outputs the stability monitoring results to the detection information generation unit 413 and the analysis data replacement unit 414.

The detection information generating unit 413 sets a threshold for determining abnormal values depending on the results of monitoring the stability of the analysis data, i.e., whether the time-series analysis data is in a stable or unstable state.

More specifically, the detection information generating unit 413 calculates a smoothed value (a low-pass filter calculation value, a least squares value, a Kalman filter calculation value, etc.) of the past analysis data and sets it as a reference value for abnormal value determination. Furthermore, the detection information generating unit 413 calculates the noise magnitude for the threshold from the statistical calculation value of the past analysis data. Note that the noise magnitude may be a fixed value. The detection information generating unit 413 sets a threshold for abnormal value determination using the reference value for abnormal value determination and the noise magnitude.

In this case, when the detection information generating unit 413 receives detection of an abnormal value in an unstable state, it resets the threshold value for determining abnormal values.

In addition, in a stable state, the detection information generating unit 413 does not use the analysis data determined to be an abnormal value to calculate the reference value for abnormal value determination, but uses a value obtained by replacing the analysis data determined to be an abnormal value (for example, the reference value for abnormal value determination (smoothed value) of the analysis data determined to be an abnormal value). Furthermore, in a stable state, the detection information generating unit 413 determines the fluctuation offset value based on the analysis data (output stage analysis data) after smoothing processing in the final stage smoothing processing unit 417. In this case, the detection information generating unit 413 sets the threshold value for abnormal value determination using the smoothed value, the magnitude of the noise, and the fluctuation offset value.

The detection information generation unit 413 outputs a threshold value for determining abnormal values to the abnormal value detection unit 411. In addition, the detection information generation unit 413 outputs the smoothed value of the past analysis data to the analysis data replacement unit 414.

When the analysis data replacement unit 414 receives information from the stability monitoring unit 412 that the analysis data is in a stable state, it replaces abnormal values contained in the analysis data. The analysis data replacement unit 414 performs two types of replacement.

As a first substitution, the analysis data substitution unit 414 performs substitution using analysis data from N sidereal days ago to generate data for periodic noise extraction. The analysis data substitution unit 414 outputs the data for periodic noise extraction to the periodic noise extraction unit 415.

As the second substitution, the analysis data substitution unit 414 performs substitution using the smoothed value from the detection information generation unit 413 to generate output data. The analysis data substitution unit 414 outputs the output data to the periodic noise removal unit 416.

The periodic noise extraction unit 415 extracts periodic noise from the periodic noise extraction data. The periodic noise extraction unit 415 outputs the periodic noise to the periodic noise removal unit 416.

The periodic noise removal unit 416 removes periodic noise from the output data using the periodic noise from the periodic noise extraction unit 415. The periodic noise removal unit 416 outputs the output data after the periodic noise removal to the final smoothing processing unit 417.

The final smoothing processor 417 smoothes the output data after removing periodic noise, and outputs the smoothed analysis data. The smoothing time constant of the final smoothing processor 417 is set to be significantly longer than the smoothing time constant of the detection information generator 413.

This smoothed analysis data is used to analyze the physical phenomena that cause fluctuations in the observed value (carrier phase).

As a specific example, the analysis data is the displacement of observation stations 21 and 22 relative to reference station 30, as described above. Displacement means the change in each position over time. Here, reference station 30 is placed in a location where the change in position is minimal, and observation stations 21 and 22 are placed on the ground (the slopes of mountains or cliffs, specific positions in volcanic areas, etc.) or artificial structures that are the objects of observation. Therefore, the observation data represents the displacement of the ground or artificial structures on which observation stations 21 and 22 are placed.

Although such ground displacements and displacements of artificial structures rarely result in fluctuations that are the primary objective of the observation target, it is still necessary to detect such fluctuations. Furthermore, the magnitude of noise in the observed values is usually large compared to the magnitude of displacement when almost no fluctuations are occurring.

By using the configuration of the observation data processing device of the present invention, even in observation systems where the main fluctuation of the observation target is small and the observation data has a large amount of noise, and where it is necessary to detect the main fluctuation, the effects of noise can be suppressed and the fluctuation of the main observation target can be reliably analyzed.

Next, the process (analysis data processing method) executed by the analysis data processing device 41 will be described. FIG. 3 is a flowchart showing the main process flow of the analysis data processing device. The details of each process will be described later.

The analysis data processing device 41 acquires multiple pieces of analysis data consisting of time series, detects abnormal values, and monitors stability (S101).

When the analysis data processing device 41 detects that the analysis data is in a stable state, it replaces abnormal values contained in the time-series analysis data with correction values for periodic noise extraction, and generates data for periodic noise extraction (S102). The analysis data processing device 41 extracts periodic noise from the data for periodic noise extraction (S103).

The analysis data processing device 41 removes periodic noise from the time-series analysis data (S104). At this time, the analysis data processing device 41 replaces abnormal values included in the time-series analysis data with smoothed values used as reference values for abnormal value detection to generate output data. The analysis data processing device 41 removes periodic noise from the output data.

The analysis data processing device 41 performs a smoothing process on the output data after removing periodic noise (S105).

Next, the stability monitoring process performed by the analysis data processing device 41 will be described. FIG. 4 is a flowchart showing the stability monitoring process. FIG. 5 is a diagram for explaining the stability monitoring principle. The horizontal axis of FIG. 5 is time (time when the analysis data is acquired), and the vertical axis of FIG. 5 is analysis data (numerical value). Note that the lines indicating the abnormal value determination threshold and the abnormal value determination reference value in FIG. 5 are drawn at approximate positions for ease of understanding, but may vary slightly at each time.

As shown in FIG. 4, the abnormal value detection unit 411 reads out multiple pieces of analysis data consisting of a time series from the storage unit 42. The abnormal value detection unit 411 acquires analysis data that is the target of stability monitoring from the multiple pieces of analysis data that have been read out (S301). The abnormal value detection unit 411 acquires the analysis data in order of earliest time.

The detection information generating unit 413 sets an abnormal value determination threshold for the analysis data (S302). Specifically, the detection information generating unit 413 calculates a smoothed value of analysis data from a time earlier than the analysis data for which the current abnormal value is to be detected, and sets the smoothed value as the abnormal value determination reference value. The smoothed value is calculated, for example, from a low-pass filter processed value, a simple moving average value, a value calculated using the least squares method, a value calculated using a Kalman filter, etc.

Note that for the analysis data at the initial time of stability monitoring, there is no analysis data at past times. For example, for the analysis data at time t1 in Figure 5, there is no analysis data at past times.

In addition, for the analysis data at the second time from the initial time of stability monitoring, only the analysis data at the immediately preceding time exists. For example, for the analysis data at time t2 in FIG. 5, only the analysis data at time t1 exists. In this case, the detection information generating unit 413 sets the analysis data at the immediately preceding time as the reference value for abnormal value determination.

The detection information generating unit 413 calculates the magnitude of noise for setting a threshold value from the statistical calculation value of the analysis data from the past time. The statistical calculation value is, for example, variance, standard deviation, etc. However, the detection information generating unit 413 sets a fixed value as the noise magnitude when there is no analysis data from the past time (time t1 in FIG. 5) or when there is very little analysis data from the past time (for example, time t2 in FIG. 5). Note that even when the number of analysis data from the past time is sufficient to calculate the statistical calculation value, the noise magnitude may be set to a fixed value.

The detection information generating unit 413 sets an upper threshold by adding the magnitude of the noise to the reference value for abnormal value determination, and sets a lower threshold by subtracting the magnitude of the noise from the reference value for abnormal value determination. In this case, it is preferable to dynamically set the reference value for abnormal value determination and the magnitude of the noise each time analysis data is acquired. This threshold for abnormal value determination is output to the abnormal value detection unit 411.

The abnormal value detection unit 411 compares the analysis data with the abnormal value determination threshold (S303). If the abnormal value detection unit 411 detects that the analysis data is within a monitoring normal value range based on the abnormal value determination threshold (S304: YES), it outputs this result to the stability monitoring unit 412, and the stability monitoring unit 412 performs a monitoring count (e.g., count up) in accordance with this result (S305). If the abnormal value detection unit 411 detects that the analysis data is outside the range of the abnormal value determination threshold (S305: NO), it outputs this result to the stability monitoring unit 412, and the stability monitoring unit 412 resets the count value (S307).

If the monitoring count value has not reached the stable state start threshold (S306: YES), the stability monitoring unit 412 waits for the result of the detection of an abnormal value at the next time. The stable state start threshold is a numerical value. For example, the stable state start threshold is based on the results of past stability judgments, and is set statistically based on the number of analysis data when the analysis data becomes stable. The stability monitoring unit 412 outputs to the detection information generating unit 413 that the period in which the stable state has not started is an unstable state.

When the monitoring count value reaches the stable state start threshold (S306: NO), the stability monitoring unit 412 ends the stability monitoring process. That is, the stability monitoring unit 412 detects that the analysis data has stabilized over time, and ends the stability monitoring process. The stability monitoring unit 412 outputs to the analysis data replacement unit 414 that the analysis data is in a stable state.

Note that the method is not limited to setting the initial value of the counter value to "0" or "1" and counting up the value up to the stable state start threshold, but it is also possible to use a method of setting the initial value of the counter value to a maximum value based on the stable state start threshold and counting down to "0" or "1".

The start of a stable state may also be detected using the degree of convergence of statistically calculated values (e.g., standard deviation or maximum value) of multiple consecutive analysis data.

The concept of stability monitoring by the analysis data processing device 41 is explained using Figure 5 as an example.

(Processing of analysis data at time t1)
There is no past analysis data for the analysis data at time t1. Therefore, the stability monitoring unit 412 initializes the count value CT. The stability monitoring unit 412 outputs information indicating that the stability monitoring unit 412 is in an unstable state to the detection information generating unit 413.

(Processing of analysis data at time t2)
For the analysis data at time t2, the only past analysis data is the analysis data at time t1. Therefore, the stability monitoring unit 412 sets the analysis data at time t1 as the reference value for abnormal value determination, and sets the threshold value for abnormal value determination using a fixed value or the like. The analysis data at time t2 is not within the normal value range for monitoring. Therefore, the stability monitoring unit 412 resets the count value CT. This returns the stability monitoring to the initial state. The stability monitoring unit 412 outputs to the detection information generation unit 413 that it is in an unstable state.

(Processing of analysis data at time t3)
Stability monitoring is initialized by the analysis data at time t2. Therefore, compared to the analysis data at time t3, the only past analysis data after stabilization initialization is the analysis data at time t2. Therefore, the stability monitoring unit 412 sets the analysis data at time t2 as the reference value for abnormal value determination, and sets the threshold value for abnormal value determination using a fixed value or the like. The analysis data at time t3 is within the normal value range for monitoring. Therefore, the stability monitoring unit 412 performs monitoring count-up. The stability monitoring unit 412 outputs to the detection information generating unit 413 that it is in an unstable state.

(Processing of analysis data at time t4)
For the analysis data at time t4, past analysis data after initialization of stability monitoring is analysis data at multiple times t2 and t3. Therefore, the stability monitoring unit 412 sets the smoothed value of the analysis data at multiple times t2 and t3 as the reference value for abnormal value determination, and sets the threshold value for abnormal value determination by a fixed value or a statistically calculated value. The analysis data at time t4 is within the normal value range for monitoring. Therefore, the stability monitoring unit 412 performs a monitoring count-up. The stability monitoring unit 412 outputs to the detection information generating unit 413 that it is in an unstable state.

(Processing of analysis data at time t5)
For the analysis data at time t5, past analysis data after initialization of stability monitoring is analysis data at multiple times t2, t3, and t4. Therefore, the stability monitoring unit 412 sets the smoothed value of the analysis data at multiple times t2, t3, and t4 as the reference value for abnormal value determination, and sets the threshold value for abnormal value determination by a fixed value or a statistically calculated value. The analysis data at time t5 is within the normal value range for monitoring. Therefore, the stability monitoring unit 412 performs a monitoring count-up. The stability monitoring unit 412 outputs to the detection information generating unit 413 that it is in an unstable state.

(Processing of analysis data at time t6)
For the analysis data at time t6, past analysis data after initialization of stability monitoring is analysis data at multiple times t2, t3, t4, and t5. Therefore, the stability monitoring unit 412 sets the smoothed value of the analysis data at multiple times t2, t3, t4, and t5 as the reference value for abnormal value determination, and sets the threshold value for abnormal value determination by a fixed value or a statistically calculated value. The analysis data at time t6 is not within the normal value range for monitoring. Therefore, the stability monitoring unit 412 resets the count value CT. This returns stability monitoring to the initial state. The stability monitoring unit 412 outputs to the detection information generating unit 413 that it is in an unstable state.

(Processing of analysis data at time t7)
Stability monitoring is initialized with the analysis data at time t6. Therefore, compared to the analysis data at time t7, the only analysis data that is past and after the initialization of stability monitoring is the analysis data at time t6. Therefore, the stability monitoring unit 412 sets the analysis data at time t6 as the reference value for abnormal value determination, and sets the threshold value for abnormal value determination using a fixed value or the like. The analysis data at time t7 is within the normal value range for monitoring. Therefore, the stability monitoring unit 412 performs a monitoring count-up. The stability monitoring unit 412 outputs to the detection information generating unit 413 that it is in an unstable state.

(Processing of analysis data at times t8, t9, t10, t11, and t12)
For each piece of analysis data at times t8, t9, t10, t11, and t12, the stability monitoring unit 412 sets the smoothed value of the analysis data at multiple past times as the reference value for abnormal value determination, and sets the abnormal value determination threshold value by a fixed value, a statistically calculated value, or the like. Each piece of analysis data at times t8, t9, t10, t11, and t12 is within the monitoring normal value range. Therefore, the stability monitoring unit 412 sequentially performs monitoring count-up for each piece of analysis data at times t8, t9, t10, t11, and t12. The stability monitoring unit 412 outputs to the detection information generating unit 413 that it is in an unstable state.

(Processing of analysis data at time t13)
For the analysis data at time t13, the past analysis data after stabilization initialization are analysis data at multiple times t6, t7, t8, t9, t10, t11, and t12. Therefore, the stability monitoring unit 412 sets the smoothed values of the analysis data at multiple times t6, t7, t8, t9, t10, t11, and t12 as the reference values for abnormal value determination, and sets the threshold value for abnormal value determination by a fixed value or a statistically calculated value, etc. The analysis data at time t13 is within the normal value range for monitoring. Therefore, the stability monitoring unit 412 performs a monitoring count-up. The stability monitoring unit 412 detects that the count value CT has reached the stable state start threshold value THA, and ends the stability monitoring process. The stability monitoring unit 412 outputs to the analysis data replacement unit 414 that it is in a stable state.

Next, the specific processing and principles of the analysis data processing device 41 in a stable state will be described. FIG. 6 is a flowchart showing the process of detecting abnormal values in a stable state. FIG. 7 is a diagram for explaining the process of detecting abnormal values in a stable state. The horizontal axis of FIG. 7 is time (the time when the observation result is calculated), and the vertical axis of FIG. 7 is the observation result.

The abnormal value detection unit 411 reads out multiple pieces of analysis data consisting of a time series from the storage unit 42. The abnormal value detection unit 411 acquires the analysis data for which an abnormal value is to be detected this time from the multiple pieces of analysis data that have been read out (S401). The abnormal value detection unit 411 acquires the analysis data in chronological order.

The detection information generating unit 413 sets an abnormal value determination threshold for the analysis data (S402). Specifically, the detection information generating unit 413 calculates a smoothed value of analysis data from the multiple pieces of acquired analysis data at a time earlier than the current analysis data. For example, a moving average value is used as the smoothed value. The detection information generating unit 413 sets the smoothed value as a reference value for abnormal value determination.

Note that, for the analysis data at the initial time after the start of the stable state, there is no analysis data at past times after the start of the stable state. For example, for the analysis data at time t13 in FIG. 5 and FIG. 7, there is no analysis data at past times after the start of the stable state. Here, the start time (initial time) of the stable state is the end time of the stability monitoring, and it can be determined that the analysis data is not an abnormal value. Therefore, the detection information generating unit 413 does not need to execute the setting process of the abnormal value determination threshold at the time of the first analysis data. Note that, it is also possible to calculate the abnormal value determination reference value at the initial time after the start of the stable state using multiple analysis data before the start of the stable state (stability monitoring period). For example, in the example of FIG. 5 and FIG. 7, the analysis data is stable from time t6 to time t12. At the initial time t13 after the start of the stable state, the analysis data from time t6 to time t12 may be used to calculate the abnormal value determination reference value for the analysis data at the initial time t13. Furthermore, by performing the backward substitution described below, the analysis data from time t1 to time t12 may be used to calculate the reference value for determining an abnormal value for the analysis data at the initial time t13.

In addition, for the analysis data for the second time from the initial time after the start of the stable state, only the analysis data for the time immediately preceding the start of the stable state exists. For example, for the analysis data at time t14 in Figures 5 and 7, only the analysis data for time t13 exists. In this case, the detection information generation unit 413 sets the analysis data for the immediately preceding time as the reference value for abnormal value determination.

In this case, similarly to the initial time, multiple pieces of analysis data from the stability monitoring period can also be used to calculate the reference value for abnormal value determination. For example, in the examples of Figures 5 and 7, the analysis data is stable from time t6 to time t12. At time t13 after the start of the stable state, the analysis data from time t6 to time t12 and the analysis data at the initial time t13 after the start of the stable state can be used to calculate the reference value for abnormal value determination for the analysis data at time t14. Furthermore, by performing backward substitution, which will be described later, the analysis data from time t1 to time t12 and the analysis data at the initial time t13 can be used to calculate the reference value for abnormal value determination for the analysis data at time t14.

At each subsequent time after the start of the stable state, the reference value for determining abnormal values can be calculated similarly using multiple pieces of observation data from the stability monitoring period. This allows the smoothed value set as the reference value for determining abnormal values to approach the true value. This improves the accuracy of detecting abnormal values.

The detection information generating unit 413 calculates the magnitude of noise for setting a threshold value from the statistical calculation value of the analysis data from past times. The statistical calculation value is, for example, variance, standard deviation, etc. However, the detection information generating unit 413 sets a fixed value as the noise magnitude when there is no analysis data from past times (time t13 in FIG. 7) or when there is very little analysis data from past times (for example, time t14 in FIG. 7). Note that even when the number of analysis data from past times is sufficient to calculate the statistical calculation value, the noise magnitude may be set to a fixed value.

The detection information generating unit 413 sets an upper threshold by adding the magnitude of the noise to the reference value for abnormal value determination, and sets a lower threshold by subtracting the magnitude of the noise from the reference value for abnormal value determination. In this case, it is preferable to dynamically set the reference value for abnormal value determination and the magnitude of the noise each time analysis data is acquired. This threshold for abnormal value determination is output to the abnormal value detection unit 411.

Furthermore, the detection information generating unit 413 can also expand the abnormal value determination threshold as follows. For example, the detection information generating unit 413 converts into a function a sudden change in the analysis data (sudden displacement such as an earthquake) or a change in the analysis data assumed to accurately obtain the analysis data. Alternatively, the detection information generating unit 413 converts into a function the speed of change or the acceleration of change obtained by differentiating the analysis data. These can be calculated using multiple past analysis data output from the final smoothing processing unit 417 of the analysis data processing device 41. The detection information generating unit 413 can also expand the normal value range based on the abnormal value determination threshold by using the value calculated from the function. This makes it possible to suppress erroneous determination of a sudden change in the analysis data due to a physical phenomenon to be analyzed as an abnormal value.

The abnormal value detection unit 411 compares the analysis data with the abnormal value determination threshold (S403). If the abnormal value detection unit 411 detects that the analysis data is within a normal value range for monitoring based on the abnormal value determination threshold (S404: YES), it outputs this result to the stability monitoring unit 412, and the stability monitoring unit 412 resets the count value for detecting the end of the stable state in response to this result (S420).

If the abnormal value detection unit 411 detects that the analysis data is outside the range of the abnormal value determination threshold (S404: NO), it detects that the analysis data is an abnormal value (S405). The abnormal value detection unit 411 outputs the abnormal value detection result to the stability monitoring unit 412, and the stability monitoring unit 412 performs a count-up to detect the end of the stable state (S406).

If the count value for detecting the end of a stable state has not reached the stable state end threshold (S407: YES), the stability monitoring unit 412 waits for the detection result at the next time. By sequentially performing such processing on multiple pieces of analysis data consisting of time series, the analysis data processing device 41 can detect sudden abnormal values due to observation errors, etc., which are different from changes in the analysis data due to the physical phenomenon being analyzed.

The stable state end threshold is a numerical value. For example, the stable state end threshold is set by the number of pieces of analysis data, based on the detection sensitivity of the analysis data fluctuations, the threshold setting, etc. Specifically, to increase the detection sensitivity of the analysis data fluctuations, the stability monitoring unit 412 sets the number of pieces of analysis data, i.e., the stable state end threshold, to a small value, and decreases the detection sensitivity of the analysis data fluctuations. On the other hand, to reduce the influence of the observation error, the stability monitoring unit 412 sets the number of pieces of analysis data, i.e., the stable state end threshold, to a large value.

When the count value for detecting the end of the stable state reaches the stable state end threshold (S407: NO), the stability monitoring unit 412 transitions to the stability monitoring state (S408). That is, the stability monitoring unit 412 detects that a fluctuation has occurred in the analysis data or that the stability of the analysis data has decreased, detects the end of the stable state, and transitions to the stability monitoring state. For example, when the ratio of the number of abnormal values is equal to or greater than a predetermined value, or the number of consecutive abnormal values is equal to or greater than a predetermined number, the stability monitoring unit 412 detects the end of the stable state and transitions to the stability monitoring state. Note that the stability monitoring unit 412 can detect the end of the stable state and transition to the stability monitoring state even when the degree of convergence of the statistical results of consecutive analysis data does not satisfy a predetermined convergence condition (for example, the convergence condition of variance or standard deviation) (convergence is difficult).

The detection information generating unit 413 does not use the analysis data detected as an abnormal value to calculate the reference value for abnormal value determination at a later time. Alternatively, the detection information generating unit 413 may use the reference value for abnormal value determination at the time of abnormal value detection of the analysis data detected as an abnormal value instead of the analysis data detected as an abnormal value to calculate the reference value for abnormal value determination at a later time.

The stability monitoring unit 412 may also detect the end of the stable state based on the reliability of the reference value for abnormal value judgment. As described above, when an abnormal value is detected, the reference value for abnormal value judgment is calculated without using the abnormal value or by replacing the abnormal value with the reference value for abnormal value judgment at that time. In this case, the reliability of the reference value for abnormal value judgment decreases as the number of abnormal values existing within a specified period increases. For this reason, the stability monitoring unit 412 obtains the number of abnormal values existing within a specified period from the detection information generating unit 413, and detects the end of the stable state when the number of abnormal values is equal to or greater than a specified value. Alternatively, the stability monitoring unit 412 uses the ratio of the number of abnormal values to the number of multiple analysis data within a specified period, and detects the end of the stable state when the ratio is equal to or greater than a specified value.

The concept of detecting abnormal values in a stable state by the analysis data processing device 41 is explained using FIG. 7 as an example. FIG. 7 shows a case where the end of a stable state is detected by the number of consecutive analysis data that are abnormal values. In the following explanation, a mode in which a smoothed value (a reference value for determining abnormal values) is calculated using a simple moving average value is shown, but a mode in which a smoothed value (a reference value for determining abnormal values) is calculated using a low-pass filter operation, a least squares operation, a Kalman filter operation, or the like may also be used.

(Processing of analysis data at time t13)
There is no past analysis data for the analysis data at time t13 after the start of the stable state, and the analysis data at time t13 is analysis data at the time stabilization ends. Therefore, the abnormal value detection unit 411 detects that the analysis data at time t13 is not an abnormal value, and the stability monitoring unit 412 resets the count value (ResetCT). As described above, the abnormal value detection unit 411 may use multiple pieces of analysis data during the stability monitoring period to detect whether the analysis data at time t13 is an abnormal value, and the stability monitoring unit 412 may perform counting processing according to this result.

(Processing of analysis data at time t14)
For the analysis data at time t14, the only past analysis data after the start of the stable state is the analysis data at time t13. Therefore, the detection information generating unit 413 sets the analysis data at time t13 as the reference value for abnormal value determination and sets the abnormal value determination threshold. The analysis data at time t14 is within the normal value range determined by the abnormal value determination threshold. Therefore, the abnormal value detecting unit 411 detects that the analysis data at time t14 is not an abnormal value, and the stability monitoring unit 412 resets the count value (ResetCT). As described above, the abnormal value detecting unit 411 may also use multiple analysis data during the stability monitoring period to detect whether the analysis data at time t14 is an abnormal value. Hereinafter, as described above, the abnormal value detecting unit 411 may also use multiple analysis data during the stability monitoring period to detect whether the analysis data at each time is an abnormal value.

(Processing of analysis data at times t15, t16, and t17)
For each of the analysis data at times t15, t16, and t17, the detection information generating unit 413 sets the smoothed value of the analysis data at the past times after the start of the stable state as the reference value for abnormal value determination, and sets the abnormal value determination threshold. The analysis data at times t15, t16, and t17 are each within the normal value range determined by the abnormal value determination threshold. Therefore, the abnormal value detecting unit 411 detects that the analysis data at times t15, t16, and t17 are not abnormal, and the stability monitoring unit 412 sequentially resets the count value (ResetCT).

(Processing of analysis data at time t18)
For the analysis data at time t18, the detection information generating unit 413 sets the smoothed value of the analysis data from multiple past times (times t13 to t17) since the start of the stable state as the reference value for abnormal value determination, and sets the abnormal value determination threshold. The analysis data at time t18 is not within the normal range determined by the abnormal value determination threshold. Therefore, the abnormal value detecting unit 411 determines that the analysis data at time t18 is an abnormal value, and the stability monitoring unit 412 counts up (CT:START).

(Processing of analysis data at times t19, t20, t21, and t22)
For each of the analysis data at times t19, t20, t21, and t22, detection information generating unit 413 sets the smoothed value of the analysis data at multiple past times after the start of the stable state as the abnormal value determination reference value, and sets the abnormal value determination threshold. At this time, detection information generating unit 413 replaces the analysis data at time t18 that was determined to be an abnormal value with the abnormal value determination reference value (smoothed value) at time t18 to calculate the smoothed value. As a result, the abnormal value determination reference value and abnormal value determination threshold are not affected by sudden abnormal values, and the abnormal value determination reference value and abnormal value determination threshold are set to stable values.

The analysis data at times t19, t20, t21, and t22 are each within the normal value range determined by the abnormal value determination threshold. Therefore, the abnormal value detection unit 411 detects that the analysis data at the multiple times t19, t20, t21, and t22 are not abnormal values, and the stability monitoring unit 412 sequentially resets the count value (ResetCT).

By performing this type of processing, the analysis data processing device 41 can accurately detect sudden abnormal values.

(Processing of analysis data at time t23)
For the analysis data at time t23, the detection information generating unit 413 sets the smoothed value of the analysis data at multiple past times (from time t13 to time t22) after the start of the stable state as the reference value for abnormal value judgment, and sets the abnormal value judgment threshold. At this time, the detection information generating unit 413 replaces the analysis data at time t18, which was judged to be an abnormal value, with the reference value for abnormal value judgment at time t18, and calculates the smoothed value. The analysis data at time t23 is not within the normal value range determined by the abnormal value judgment threshold. Therefore, the abnormal value detecting unit 411 detects that the analysis data at time t23 is an abnormal value, and the stability monitoring unit 412 counts up (CT: START).

(Processing of analysis data at times t24, t25, and t26)
For the analysis data at the times t24, t25, and t26, the detection information generating unit 413 sets the smoothed value of the analysis data at the times in the past after the start of the stable state transition as the reference value for abnormal value judgment, and sets the abnormal value judgment threshold. At this time, the detection information generating unit 413 replaces the analysis data at time t18, which is determined to be an abnormal value, with the reference value for abnormal value judgment at time t18, and replaces the analysis data at time t23, which is determined to be an abnormal value, with the reference value for abnormal value judgment at time t23, to calculate the smoothed value. Each of the analysis data at the times t24, t25, and t26 is not within the normal value range determined by the abnormal value judgment threshold. Therefore, the abnormal value detecting unit 411 sequentially detects that each of the analysis data at the times t24, t25, and t26 is an abnormal value, and the stability monitoring unit 412 sequentially counts up (adds the count value).

(Processing of analysis data at time t27)
For the analysis data at time t27, the detection information generating unit 413 sets the smoothed values of the analysis data at multiple past times after the start of the stable state as the reference value for abnormal value judgment, and sets the abnormal value judgment threshold. At this time, the detection information generating unit 413 replaces each of the analysis data at multiple times t18, t23, t24, t25, and t26 that are judged to be abnormal values with the reference values for abnormal value judgment at times t18, t23, t24, t25, and t26, respectively, to calculate the smoothed value. The analysis data at time t27 is not within the normal value range determined by the abnormal value judgment threshold. Therefore, the abnormal value detecting unit 411 detects that the analysis data at time t27 is an abnormal value, and the stability monitoring unit 412 counts up (adds the count value CT) successively.

The stability monitoring unit 412 detects that the count value has reached the stable state end threshold THB (CT=THB) and transitions to the stability monitoring state as described above.

In this way, by using the configuration and processing of this embodiment, the analysis data processing device 41 does not detect abnormal values in the analysis data based on the observed values when the observed values are unstable. Instead, the analysis data processing device 41 detects abnormal values in the analysis data when the observed values are stable. This allows the analysis data processing device 41 to reliably and accurately detect abnormal values contained in the analysis data.

The analysis data output from the analysis data processing device 41, i.e., the observation data smoothed with a large time constant, or the amount of change over time in the analysis data, etc., becomes monitoring information for the physical phenomenon being monitored. Notifications such as warnings for the physical phenomenon being monitored are generated based on this monitoring information. However, if there are abnormal values as described above, erroneous notifications may occur. For this reason, in the past, it was necessary for an operator to constantly monitor and eliminate abnormal values.

However, by using the configuration and processing of the analysis data processing device 41, it is possible to suppress false detection of abnormal values contained in the analysis data. This allows abnormal values to be reliably detected and eliminated. Therefore, abnormal values can be monitored and eliminated unmanned, and highly reliable notifications can be obtained.

In addition, by using the configuration and processing of this embodiment, when the time-series analysis data is in a stable state, if a fluctuation in the analysis data occurs due to a physical phenomenon or the like and stability monitoring is required again, stability monitoring can be resumed. Also, when the analysis data is in a stable state and a fluctuation occurs due to the physical phenomenon being monitored, erroneous detection of this analysis data as a sudden abnormal value is suppressed.

The analysis data processing device 41 can therefore accurately detect fluctuations in the analysis data and is effective in analyzing physical phenomena using the fluctuations in the analysis data.

(Analysis data replacement process)
The analytical data replacement process will be specifically described with reference to the drawings. Fig. 8A is a flow chart showing the main process of the analytical data replacement process. Fig. 8B is a flow chart showing the replacement process of the periodic noise extraction data.

When the analysis data replacement unit 414 receives information from the stability monitoring unit 412 that the state is stable (S501), it sequentially executes forward replacement (S502). If backward replacement is required (S503: YES), the analysis data replacement unit 414 executes backward replacement (S504). If backward replacement is not required (S503: NO), the analysis data replacement unit 414 omits the backward replacement.

(forward substitution)
The forward substitution is a process in which analysis data detected as an abnormal value in a stable state is replaced in the direction of time progression. The forward substitution includes substitution for generating data for extracting periodic noise and substitution for generating data for output.

The replacement for generating data for extracting periodic noise is performed by the process shown in FIG. 8(B) and with the concept shown in FIG. 9. FIG. 9 is a diagram for explaining the concept of generating data for extracting periodic noise. FIG. 9(A) shows a case where there is analysis data from one sidereal day ago, and FIG. 9(B) shows a case where there is no analysis data from one sidereal day ago. The horizontal axis in FIG. 9(A) and FIG. 9(B) is time, and the vertical axis is analysis data. In FIG. 9, dots (●) indicate analysis data.

As shown in FIG. 8B, when the analysis data replacement unit 414 obtains abnormal value detection information from the abnormal value detection unit 411, the analysis data replacement unit 414 detects whether or not there is analysis data for the abnormal value N days ago from among the multiple past analysis data stored. Here, n is an integer equal to or greater than 1, for example, n=1.

As shown at times d1t1 and d2t1 in FIG. 9A, if the analysis data replacement unit 414 detects that there is analysis data from N sidereal days ago (S511: YES), it replaces the abnormal value with the analysis data from N sidereal days ago (S512). By using the analysis data from N sidereal days ago to replace the abnormal value, the replaced analysis data has periodic noise that is approximately the same as the periodic noise at the time of the abnormal value. Therefore, the analysis data replacement unit 414 can perform replacement to more accurately include the periodic noise.

In addition, even if there is no analysis data from N days ago and the smoothed value of multiple analysis data older than that time is set at that time, the analysis data replacement unit 414 replaces the abnormal value with that smoothed value. Specifically, in the case of FIG. 9(B), if a smoothed value is set at time d1t1, the abnormal value at time d2t1 is replaced with the smoothed value at time d1t1.

As shown at times d1tn and d1tm in FIG. 9B, if the analysis data replacement unit 414 detects that analysis data from N days ago is not stored (S511: NO), it replaces the abnormal value with the analysis data immediately before the abnormal value (S513). Specifically, in the case of FIG. 9B, the abnormal value at time d1tn is replaced with the analysis data at the immediately preceding time d1tm. By using the analysis data immediately before the abnormal value to replace the abnormal value, the replaced analysis data has periodic noise similar to the periodic noise at the time of the abnormal value. Therefore, the analysis data replacement unit 414 can perform replacement so as to suppress the disturbance of the periodic noise.

The presence or absence of analysis data or smoothed values N sidereal days ago may be checked in order going back in time, and if analysis data or smoothed values are present, replacement may be performed. For example, if the analysis data replacement unit 414 detects that there is no analysis data one sidereal day ago and two sidereal days ago, but detects that there is analysis data or smoothed values three sidereal days ago, it does not determine that there is no analysis data, but uses the analysis data or smoothed values from three sidereal days ago for replacement.

The replacement for generating output data is performed based on the concept shown in Figure 10. Figure 10 is a diagram for explaining the concept of generating output data.

As part of the process of generating data for output, the analysis data replacement unit 414 replaces abnormal values with smoothed values of multiple analysis data after the start of the stable state input from the detection information generation unit 413. For example, as shown in FIG. 10, if an abnormal value is detected in the analysis data at time t56 after the start of the stable state, the analysis data replacement unit 414 replaces the analysis data (abnormal value) at time t56 after the start of the stable state with smoothed value A1. Also, as shown in FIG. 10, if an abnormal value is detected in the analysis data at time t59, the analysis data replacement unit 414 replaces the analysis data (abnormal value) at time t59 with smoothed value A2.

By performing this type of processing, the analysis data replacement unit 414 can replace abnormal values with stable correction values that are suitable for replacement for smoothing.

(Backward displacement)
The analysis data replacement unit 414 performs the backward replacement by the following method and concept. The backward replacement is the same for generating data for extracting periodic noise and generating data for output. As described above, the backward replacement may be performed only when necessary. However, by performing the backward replacement, the analysis data during the stability monitoring period can also be used for analysis, and the convergence of the smoothed value becomes faster, which is effective. Therefore, it is preferable that the analysis data replacement unit 414 performs the backward replacement. FIG. 11 is a flowchart showing the backward replacement process of the analysis data. FIG. 12 is a diagram for explaining the concept of the backward replacement of the analysis data.

The analysis data replacement unit 414 reads out multiple pieces of analysis data going back in time based on the stable state start time (S531).

The analysis data replacement unit 414 calculates a backward replacement reference value using multiple pieces of analysis data in the direction in which time advances compared to the analysis data targeted for backward replacement (S532). Specifically, the analysis data replacement unit 414 calculates smoothed values of multiple pieces of analysis data in the direction in which time advances compared to the analysis data targeted for backward replacement, and sets the smoothed values as the backward replacement reference value. Note that, at the beginning of the backward replacement, the analysis data at the start time of this detection can also be used as the backward replacement reference value.

The analysis data replacement unit 414 sets a backward replacement threshold value based on the backward replacement reference value. The backward replacement threshold value is set with the backward replacement reference value as the center value. For example, the backward replacement threshold value is set by a statistical calculation value such as the standard deviation of the analysis data calculated in advance.

The analysis data replacement unit 414 compares the analysis data to be replaced with the backward replacement threshold. When the analysis data replacement unit 414 detects that the analysis data to be replaced is within the normal value range for backward replacement based on the backward replacement threshold (S533: YES), it does not replace the analysis data.

When the analysis data replacement unit 414 detects that the analysis data to be replaced is not within the normal value range for backward replacement (S533: NO), it replaces the analysis data with the reference value for backward replacement (S534).

The analysis data replacement unit 414 sequentially reads out the analysis data going back in time, and performs the above-mentioned backward replacement while updating the backward replacement reference value and the backward replacement threshold value for each analysis data.

By performing such backward replacement, the analysis data replacement unit 414 can replace analysis data that is older than the start of the stable state, as shown in FIG. 12.

From time t12 to time t6 going backwards in time, each analysis data is within the normal value range defined by the backward replacement threshold. Therefore, each analysis data from time t12 to time t6 is not replaced and is used as is. In this case, as one goes backwards in time, the number and combination of analysis data on the time-advancing side changes based on the analysis data targeted for backward replacement, and the backward replacement threshold is updated sequentially in response to this change.

From time t5 to time t2 going backwards in time, each piece of analytical data is not within the normal value range determined by the backward replacement threshold. Therefore, each piece of analytical data from time t5 to time t2 is replaced with the backward replacement reference value at each time. In this case, the number and combination of analytical data on the time-progressing side changes with respect to the analytical data to be replaced backward, and the backward replacement threshold is updated sequentially in response to this change. Here, if there is analytical data (backward replacement reference value) replaced with respect to the time-progressing side with respect to the analytical data to be replaced backward, the replaced analytical data can be used to calculate the backward replacement reference value, and it is not necessary to use this replaced analytical data.

At time t1, the analysis data is within the normal range defined by the backward replacement threshold. Therefore, the analysis data at time t1 is not replaced and is used as is. At this time, the analysis data from time t2 to time t5 is used as the backward replacement reference value used for the analysis data at time t1. In this case, the analysis data replaced respectively (backward replacement reference value) is used.

By carrying out such processing, the analysis data replacement unit 414 can also use the analysis data before the actual detection. This allows the analysis data processing device 41 to increase the number of analysis data included in the periodic noise extraction data and the number of replaced analysis data, and the number of analysis data included in the output data and the number of replaced analysis data, while suppressing the adverse effects on the analysis caused by using analysis data before the start of the stable state.

(Processing after generation of data for periodic noise extraction and data for output)
The periodic noise extraction unit 415 performs a filter process on the periodic noise extraction data to extract periodic noise contained in the periodic noise extraction data. The filter process is, for example, a high-pass filter that passes components with a period of one sidereal day. As described above, the replaced analysis data used for the periodic noise extraction data contains periodic noise. Therefore, even if the periodic noise extraction data contains replaced analysis data, the periodic noise extraction unit 415 can accurately extract the periodic noise. The periodic noise extraction unit 415 outputs the extracted periodic noise to the periodic noise removal unit 416.

The periodic noise removal unit 416 removes periodic noise from the output data. Specifically, the periodic noise removal unit 416 synchronizes the output data with the periodic noise and subtracts the periodic noise from the output data. The periodic noise removal unit 416 outputs the output data after the periodic noise removal to the final stage smoothing processing unit 417.

The final smoothing processor 417 performs smoothing processing on the output data after periodic noise removal. Specifically, the final smoothing processor 417 calculates, at each time, the average value of multiple analysis data (including replaced data) before that time. This average value is, for example, a moving average value. The time length (smoothing time constant) used for this average value is significantly longer than the time length (smoothing time constant) of the smoothing processing used to calculate the above-mentioned reference value for abnormal value determination.

As described above, the replaced analysis data used in the output data is a smoothed value, so adverse effects on the smoothing process by the final smoothing processor 417, such as sudden abnormal values, are suppressed. Therefore, the final smoothing processor 417 can perform a highly accurate and stable smoothing process.

As a result, the analysis data after smoothing processing output from the analysis data processing device 41 of this embodiment improves the accuracy of analysis using the analysis data, enabling stable analysis to be achieved.

In the above explanation, there is analysis data for each time and this is an abnormal value, but there are cases where there is no analysis data (e.g., it cannot be obtained). In this case, the time when there is no analysis data can be treated as an abnormal value and the above-mentioned processing can be applied.

In addition, in the above explanation, an aspect was shown in which analysis data obtained by static positioning is used as interferometric positioning, but the above configuration and processing can also be applied to analysis data obtained by kinematic positioning such as RTK (real-time kinematic).

In the case of RTK positioning, it is also possible to use a method that does not require a reference point by receiving correction information, such as VRS-RTK (Virtual Reference Point) or QZSS RTK-PPP (RTK Precise Point Positioning). In the above explanation, the analysis device is described as being located separately from the receiver, but it may be located in a single GPS receiver, or in each observation station other than the reference point. In addition, even with the various RTK methods mentioned above, the analysis device may be located in a different location.

In the above explanation, each process is executed by a separate functional unit. However, if these processes are programmed and stored in a storage medium, and the program is executed by an information processing device such as a CPU, the above-mentioned effects can be achieved.

In addition, in the above explanation, an aspect of detecting and replacing abnormal values in the analysis data was shown, but it is also possible to use only abnormal values in the analysis data. In this case, the analysis data processing device only needs to include at least the abnormal value detection unit, stability monitoring unit, and detection information generation unit described above.

The above-mentioned processing was applied to the example of precise positioning using a GPS receiver, but it can also be applied to other sensor data, such as acceleration sensors and weather data. In this case, a sensor is placed instead of a GPS receiver, but the processing by the analysis device is the same.

10: Observation system 21, 22: Observation station 30: Reference station 40: Analysis device 41: Analysis data processing device 42: Memory unit 211, 221, 301: GPS antenna 212, 222, 302: GPS receiver 410: Analysis data generation unit 411: Abnormal value detection unit 412: Stability monitoring unit 413: Detection information generation unit 414: Analysis data replacement unit 415: Periodic noise extraction unit 416: Periodic noise removal unit 417: Final stage smoothing processing unit

Claims (8)

an abnormal value detection unit that detects occurrence of an abnormal value by using an abnormal value determination threshold for a plurality of analysis data, the analysis data being displacement data formed in a time series and using a carrier wave phase in a positioning signal ;
a stability monitoring unit that monitors whether the plurality of analysis data are in an unstable state or a stable state by using the frequency of occurrence of the abnormal value;
a detection information generating unit that sets the abnormal value determination threshold value in the stable state using the analysis data in the stable state;
an analysis data replacement unit that replaces the analysis data;
Equipped with
The stability monitoring unit
setting a stability monitoring period in the unstable state;
During the stability monitoring period, if a predetermined number of the analysis data are consecutive within the abnormal value determination threshold, the stability is determined to be in a stable state;
The analysis data replacement unit includes:
replacing the analysis data detected as the abnormal value by the abnormal value detection unit after the stable state is determined to be present with data within a normal value range ;
N is an integer of 1 or more,
For a plurality of analysis data consisting of a time series, when there is analysis data from N sidereal days prior to the time of the analysis data detected as the abnormal value, the abnormal value is replaced with the analysis data from N sidereal days prior, and data for extracting periodic noise is generated using the plurality of analysis data consisting of the time series including the analysis data subjected to the replacement and including noise with a sidereal day period.
Analytical data processing device. 2. The analysis data processing device according to claim 1 ,
The analysis data replacement unit includes:
When there is no analysis data N days prior to the time of the analysis data detected as the abnormal value, the abnormal value is replaced with the immediately preceding analysis data or a smoothed value, thereby generating the periodic noise extraction data.
Analytical data processing device. 3. The analysis data processing device according to claim 1 ,
a periodic noise extraction unit that extracts periodic noise contained in the plurality of analysis data by performing a filter process on the periodic noise extraction data,
Analytical data processing device. 4. The analysis data processing device according to claim 3 ,
a periodic noise removal unit that removes the periodic noise from the plurality of analysis data,
Analytical data processing device. 5. The analysis data processing device according to claim 4 ,
a final-stage smoothing processor that performs a smoothing process on the plurality of analysis data after the periodic noise has been removed,
Analytical data processing device. 6. The analysis data processing device according to claim 1,
The analysis data replacement unit includes:
The plurality of analysis data at a time before the start of the stable state is set as analysis data to be subjected to backward replacement;
A backward replacement threshold is set using the analysis data in a direction that advances in time relative to the analysis data that is the target of the backward replacement;
When it is detected that the analytical data to be replaced is not within a normal value range determined by the backward replacement threshold, the analytical data used for setting the backward replacement threshold is used to replace the analytical data to be replaced.
Analytical data processing device. Detecting occurrence of an abnormal value by using an abnormal value determination threshold for a plurality of analysis data , which are displacement data consisting of a time series and using a carrier wave phase in a positioning signal ;
Using the frequency of occurrence of the abnormal value, monitor whether the plurality of analysis data are in an unstable state or a stable state;
Using the analysis data in a stable state, a threshold for determining an abnormal value in the stable state is set;
setting a stability monitoring period in the unstable state;
During the stability monitoring period, if a predetermined number of the analysis data are consecutive within the abnormal value determination threshold, the stability is determined to be in a stable state;
replacing the analysis data detected as the abnormal value after the stable state is determined to be present with data within a normal value range ;
N is an integer of 1 or more,
For a plurality of analysis data consisting of a time series, when there is analysis data from N sidereal days prior to the time of the analysis data detected as the abnormal value, the abnormal value is replaced with the analysis data from N sidereal days prior, and data for extracting periodic noise is generated using the plurality of analysis data consisting of the time series including the analysis data subjected to the replacement and including noise with a sidereal day period.
Analytical data processing methods. Detecting occurrence of an abnormal value by using an abnormal value determination threshold for a plurality of analysis data , which are displacement data consisting of a time series and using a carrier wave phase in a positioning signal ;
Using the frequency of occurrence of the abnormal value, monitor whether the plurality of analysis data are in an unstable state or a stable state;
Using the analysis data in a stable state, a threshold for determining an abnormal value in the stable state is set;
setting a stability monitoring period in the unstable state;
During the stability monitoring period, if a predetermined number of the analysis data are consecutive within the abnormal value determination threshold, the stability is determined to be in a stable state;
replacing the analysis data detected as the abnormal value after the stable state is determined to be present with data within a normal value range ;
N is an integer of 1 or more,
For a plurality of analysis data consisting of a time series, when there is analysis data from N sidereal days prior to the time of the analysis data detected as the abnormal value, the abnormal value is replaced with the analysis data from N sidereal days prior, and data for extracting periodic noise is generated using the plurality of analysis data consisting of the time series including the analysis data subjected to the replacement and including noise with a sidereal day period.
An analysis data processing program that causes an information processing device to execute the processing.

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