Control device, communication system, and control method

When the measurement values measured by a first sensor among a plurality of sensors installed in a dispersed manner at a specific location, in first cycles are determined to be abnormal values, a control device activates the first sensor in second cycles that are shorter than the first cycles. Moreover, when the abnormal values are included in the trend of temporal variation, the control device activates a plurality of second sensors, which is installed around the first sensor, in the second cycles. Moreover, when the measurement values measured by the first sensor and the plurality of second sensors in the second cycles are included in the trend of surface-direction distribution, the control device outputs the measurement values measured by the first sensor and the plurality of second sensors.

FIELD

The present invention relates to a control device, a communication system, and a control method.

BACKGROUND

As far as monitoring of infrastructure such as bridges, roads, and building structures is concerned, daily monitoring by visual inspection is carried out. As a result of performing daily monitoring, it becomes possible to perform asset management in which mainly qualitative observation is carried out regarding the changes occurring with respect to the normal situation. Thus, asset management is not about detecting abnormality, but to detect the signs of abnormality and accordingly take measures in an early stage. In recent years, a sensor network is being studied in which, using information obtained from a plurality of sensors installed at various locations, abnormality detection is carried out at the installation locations of the sensors. Moreover, conventionally, a technology is known in which, when abnormal measurement data is detected from a measurement device, if the abnormal value is detected only once, then the abnormal measurement data is destroyed on account of being determined as a measurement mistake or noise. Conventional technique is described in Japanese Laid-open Patent Publication No. S61-24791.

Meanwhile, the observation of the changes occurring with respect to the normal situation is largely dependent on the level of skill of the person doing visual inspection; and, although there are determinate numerical criteria regarding inspection details, it is a difficult task to thoroughly observe all aspects in the target objects spanning over a wide range. Moreover, regarding the task of screening in which, from among the target objects spanning over a wide range, the target objects that have not yet developed abnormality but that may develop abnormality in future are sampled and are observed as much as possible; it is difficult to achieve computerization of the task of screening. Particularly, an expert person is skilled in differentiating the type of noise or in differentiating whether or not a slight change hidden due to noise is a sign of possible abnormality in future. However, the differentiation performed by such an expert person is difficult to computerize.

SUMMARY

According to an aspect of an embodiment, a control device includes a first determining unit, a first activation instructing unit, a first identifying unit, a second determining unit, a second activation instructing unit, a second identifying unit, a third determining unit, and an output unit. The first determining unit determines whether or not measurement value measured by a first sensor among a plurality of sensors installed in a dispersed manner at specific location, in a first cycle, is an abnormal value. The first activation instructing unit activates the first sensor in a second cycle which is shorter than the first cycle when the measurement value is determined to be an abnormal value. The first identifying unit identifies trend of temporal variation in measurement values measured by the first sensor in the first cycle and the second cycle. The second determining unit determines whether or not the abnormal value is included in the trend of temporal variation. The second activation instructing unit activates a plurality of second sensors which is installed around the first sensor, in the second cycle, when the abnormal value is included in the trend of temporal variation. The second identifying unit identifies trend of surface-direction distribution of measurement values measured by the first sensor and the plurality of second sensors in the second cycle. The third determining unit determines whether or not measurement values measured by the first sensor and the plurality of second sensors in the second cycle are included in the trend of the surface-direction distribution. The output unit outputs measurement values measured by each of the first sensor and the plurality of second sensors, when measurement values measured by the first sensor and the plurality of second sensors in the second cycle are included in the trend of the surface-direction distribution.

DESCRIPTION OF EMBODIMENTS

An exemplary embodiment of a control device, a control system, and a control method disclosed in the application concerned is described below in detail with reference to the accompanying drawings. However, the technology disclosed herein is not limited by the embodiment described below.

Embodiment

FIG.1is a diagram illustrating an example of a communication system10. The communication system10includes a control device20, a collection device30, and a plurality of sensors40-1to40-4. The control device20and the collection device30are connected to a network11such as the Internet. In the following explanation, in the case of collectively referring to the sensors40-1to40-4without distinguishing therebetween, they are referred to as sensors40. In the communication system10illustrated inFIG.1, four sensors40are installed. However, the communication system10can alternatively have five more or sensors40installed therein. Moreover, in the communication system10illustrated inFIG.1, a single collection device30is installed. However, the communication system10can alternatively have two or more collection devices30installed therein.

The sensors40are installed in a dispersed manner within a predetermined area12, and perform wireless communication with the collection device30based on a wireless communication method such as Bluetooth (registered trademark). The area12in which the sensors40are installed represents the target area for monitoring, such as an area of a slope of a mountain or an area of a surface of a building structure such as a bridge or a road. For example, each sensor40measures various types of physical quantities such as vibrations, amount of rainfall, acceleration, or temperature at the installation location thereof.

When an activation instruction is received from the collection device30, each sensor40measures a physical quantity at the installation location thereof and wirelessly sends the measured value to the collection device30. Meanwhile, if any sensor40finds it difficult to perform direct wireless communication with the collection device30, it performs communication with the collection device30via the other sensors40.

The collection device30performs wireless communication with each sensor40based on a wireless communication method such as Bluetooth. The collection device30receives the measurement value sent from each sensor40; and sends the measurement values and sensor IDs, which enable identification of the respective sensors40, to the control device20via the network11. Moreover, when an activation instruction including a sensor ID is received from the control device20via the network11, the collection device30sends the activation instruction to the sensor40having the specified sensor ID.

The control device20controls the activation cycles of each sensor40via the network11and the collection device30. More particularly, the control device20issues an activation instruction for activation in first cycles to some of the sensors40installed within the area12, and activates the concerned sensors40in the first cycles. Then, based on the measurement values measured by the sensors40activated in the first cycles, the control device20determines whether or not any abnormal values are present among the measurement values.

If the measurement values of any sensor40are abnormal values, then the control device activates that sensor40in second cycles that are shorter than the first cycles, and thus collects the measurement values of the sensor40, from which the abnormal values are obtained, in shorter cycles. Then, based on the measurement values collected in shorter cycles, the control device20determines whether or not the measured abnormal values represent noise. If it is determined that the measured abnormal values do not represent noise, then the control device20further activates, in the second cycles, a plurality of other sensors40installed around the sensor40that measured the abnormal values.

Then, based on the measurement values measured by the sensors40activated in the second cycles, the control device20identifies trend of the distribution of the measurement values in the surface direction of the area in which the concerned sensors40are installed. Subsequently, if the measurement values measured by the sensors40are included in the identified trend of the surface-direction distribution, then the control device20sends the measurement values, which are obtained from the concerned sensors40, via the network11to a monitoring device that monitors the area12within which the sensors40are installed.

Meanwhile, a natural phenomenon or an abnormality in a building structure makes progress with continuity in the surface direction within an area of certain range. Hence, if the abnormal values measured by the sensor40at a particular site represents the abnormal values attributed to some natural phenomenon or deterioration in a building structure, then the measurement values that are obtained within the area of a predetermined range including the site of the sensor40which measured the abnormality values exhibit a distribution having continuity in the surface area. In that regard, in the present embodiment, when abnormal values not representing noise are obtained, the control device20further activates a plurality of other sensors40installed within a predetermined range including the sensor40that measured the abnormal values. Then, based on the measurement values measured by those sensors40, the control device20identifies the trend of the distribution of the measurement values in the surface direction. Subsequently, if the measurement values measured from the sensors40are included in the identified trend of the surface-direction distribution, then the control device20sends the measurement values measured by the sensors40to the monitoring device. As a result, the control device20can hold back from sending, to the monitoring device, the measurement values not indicating any signs of some natural, phenomenon or some abnormality of a building structure; and can reliably send, to the monitoring device, the measurement values indicating signs of some natural phenomenon or some abnormality of a building structure. As a result, while managing the management targets, the communication system10can hold down on unnecessary field investigation.

FIG.2is a diagram illustrating an example of the sensor40. The sensor40includes an antenna41, a wireless communication unit42, a control unit43, a collecting unit44, and a measuring unit45. The wireless communication unit42performs wireless communication with the collection device30and with the other sensors40via the antenna41.

The control unit43gets activated upon receiving an activation instruction from the collection device30via the wireless communication unit42, and instructs the collecting unit44to collect the measurement values. Subsequently, when the measurement values are output by the collecting unit44, the control unit43sends the measurement values, which are output by the collecting unit44, to the collection device30via the wireless communication unit42. Meanwhile, when the sensor40is not in the activated state, the control unit43controls the blocks of the sensor40, excluding the wireless communication unit42, in a low power consumption state.

When an instruction for collection of the measurement values is issued by the control unit43, the collecting unit44controls the measuring unit45and collects the measurement values therefrom. Then, the collecting unit44outputs the collected measurement values to the control unit43. The measuring unit45measures the measurement values under the control of the collecting unit44, and outputs the measurement values to the collecting unit44. For example, the measuring unit45measures the vibrations, the amount of rain, the acceleration, or the temperature at the installation location of the corresponding sensor40.

FIG.3is a diagram illustrating an example of the collection device30. The collection device30includes a wired communication unit31, a control unit32, a wireless communication unit33, and an antenna34. The wired communication unit31performs wired communication with the control device20via the network11. The wireless communication unit33performs wireless communication with the sensor40via the antenna34.

When an activation instruction including a sensor ID is received from the control device20via the wired communication unit31; the control unit32sends, via the wireless communication unit33, an activation instruction to the sensor40having the specified sensor ID. Moreover, when the measurement values from any sensor40are received via the wireless communication unit33; the control unit32sends the measurement values along with the sensor ID of the sensor40, which sent the measurement values, to the control device20via the wired communication unit31.

FIG.4is a diagram illustrating an example of the control device20. The control device20includes a data processing unit21, a Data Base (DB)22, and a sensor managing unit23. In the DB22, in a corresponding manner to each sensor ID, the measurement value measured by the sensor40having the concerned sensor ID is stored along with the timing of measurement of the measurement value. Moreover, for example, a range table220as illustrated inFIG.5is stored in the DB22.FIG.5is a diagram illustrating an example of the range table220. In the range table220, in a corresponding manner to each sensor ID, the sensor40having that sensor ID is stored along with the sensor IDs of the other sensors40installed around the concerned sensor40.

In the range table220illustrated inFIG.5, in a range “r=1” corresponding to a sensor ID “S001”, sensor IDs “S001”, “S010”, “S011”, and “S012” are included. Moreover, in the range table220illustrated inFIG.5, in a range “r=2” corresponding to the sensor ID “S001”, sensor IDs “S001”, “S010”, “S011”, “S012”, “S013”, and so on are included. In this way, in the range table220, in a range “r=n” corresponding to each sensor ID, greater the value of “n”, the greater is the number of sensor IDs included in the range “r=n”. In the range table220illustrated inFIG.5, information up to a range “r=R” is stored. Meanwhile, in the range “r=1”, at least four sensor IDs are included.

Returning to the explanation with reference toFIG.4, the sensor managing unit23includes an activation instructing unit230and an activation cycle managing unit231. The activation cycle managing unit231manages the activation cycles of each sensor40based on an instruction received from the data processing unit21, and outputs to the activation instructing unit230the sensor IDs of the sensors40for which the activation timing has arrived.

When a sensor ID is output from the activation cycle managing unit231, the activation instructing unit230sends an activation instruction including that sensor ID to the collection device30via the network11. Moreover, when a sensor ID and a measurement value is received from the collection device30via the network11, the activation instructing unit230stores the received measurement value in a corresponding manner to the measurement timing and the sensor ID in the DB22. Herein, the activation instructing unit230represents an example of a first activation instructing unit and a second activation instructing unit.

The data processing unit21includes an output unit210, a calculating unit211, and a determining unit212. The calculating unit211represents an example of a first identifying unit and a second identifying unit. The determining unit212represents an example of a first determining unit, a second determining unit, and a third determining unit. The determining unit212obtains, from the DB22, the measurement values measured in the first cycles of some of a plurality of sensors40installed in a dispersed manner within the area12. The first cycles are, for example, cycles spanning over a few hours to a few days. Then, the determining unit212determines whether or not the obtained measurement values are abnormal values.

For example, as illustrated inFIG.6, a sensor40a, which is one of a plurality of sensors40installed within the area12, is activated in the first cycles in the normal situation; and the measurement values measured by the sensor40aare sent to the control device20via the collection device30.FIG.6is a diagram illustrating an example of the sensor40athat is activated in the normal situation. From among the sensors40illustrated inFIG.6, a filled circle represents the sensor40athat is activated in the first cycles, and open circles represent the sensors40maintained in the low power consumption state. Meanwhile, in the example illustrated inFIG.6, although a single sensor40ais activated within the area12in the first cycles, there can be a plurality of sensors40ainstalled within the area12. Herein, the sensor40ais an example of a first sensor.

The determining unit212compares the measurement values measured by the sensor40ain the first cycle with a reference value calculated by performing statistical processing of the measurement values measured in the past. The reference value is an example of a statistical value. If a measurement value, which is obtained by the sensor40ain the first cycles, and the reference value have a difference D equal to or greater than a threshold value, then the determining unit212determines that the measurement value is an abnormal value. In the present embodiment, the reference value is, for example, the average value of the measurement values measured by the sensor40atill a predetermined point of time in the past. Alternatively, the reference value can be a value calculated based on the measurement values measured by such sensors40which are configured to measure the measurement values of other types. For example, if the sensor40ais configured to measure the temperature, then the reference value can be a value calculated based on the amount of sunlight measured by other sensors40.

FIG.7is a diagram illustrating an exemplary measurement value determined to be an abnormal value. For example, as illustrated inFIG.7, the sensor40ais activated in each first cycle Δt1and measures a measurement value60. Then, if the difference D between a measurement value Sn, which is obtained at a timing t0, and the reference value is equal to or greater than a threshold value, the determining unit212determines that the measurement value Snis an abnormal value.

When the measurement value Snis determined to be an abnormal value, the determining unit212instructs the activation cycle managing unit231to change the activation cycles from the first cycles Δt1to second cycles Δt2that are shorter than the first cycles Δt1. The second cycles Δt2are, for example, cycles spanning over a few minutes to a few tens of minutes. In response to the instruction received from the determining unit212, the activation cycle managing unit231changes the activation cycles of the sensor40afrom the first cycles Δt1to the second cycles Δt2. As a result, an activation result is sent to the sensor40aafter each second cycle Δt2, and the measurement value is collected from the sensor40aafter each second cycle Δt2.

Subsequently, the determining unit212instructs the calculating unit211to identify the trend of temporal variation in the measurement values measured by the sensor40ain the first cycles Δt1and in the second cycles Δt2. In response to the instruction received from the determining unit212, the calculating unit211identifies the trend of temporal variation in the measurement values measured by the sensor40ain the first cycles Δt1and in the second cycles Δt2.

More particularly, from the DB22, the calculating unit211obtains the measurement values measured in the first cycles Δt1, and obtains the measurement values that are measured in the second cycles Δt2between the period of time from the timing t0to a timing t1at which the period of time corresponding to the first cycle Δt1elapses. Then, based on the obtained measurement values, the calculating unit211identifies, as the trend of temporal variation in the measurement values, an approximation curve that approximates the temporal variation in the measurement values. For example, the calculating unit211identifies the approximation curve by fitting a predetermined function, which is represented by the order corresponding to the number of measurement values, in the chronological measurement values using the method of least square. Then, the calculating unit211outputs, to the determining unit212, the identified approximation curve as the trend of temporal variation in the measurement values.

Subsequently, the determining unit212determines whether or not the measurement value Sn, which is determined to be an abnormal value, is included in the trend of temporal variation in the measurement values as identified by the calculating unit211. If the measurement value Sn, which is determined to be an abnormal value, is not included in the trend of temporal variation in the measurement values as identified by the calculating unit211, then the determining unit212determines that the measurement value Snrepresents noise. Then, the determining unit212instructs the activation cycle managing unit231to reset the activation cycles of the sensor40afrom the second cycles Δt2to the first cycles Δt1. In response to the instruction received from the determining unit212, the activation cycle managing unit231resets the activation cycles of the sensor40afrom the second cycles Δt2to the first cycles Δt1.

More particularly, based on the approximation curve identified by the calculating unit211, the determining unit212identifies a measurement value Sn′ that is present on the approximation curve at the same timing as the timing of the measurement value Sndetermined to be an abnormal value. If a difference ΔS between the measurement value Snand the measurement value Sn′ is equal to or greater than a threshold value, then the determining unit212determines that the measurement value Sn, which is determined to be an abnormal value, is not included in the temporal variation in the measurement values as identified by the calculating unit211.

FIG.8is a diagram illustrating an example of the measurement value Snthat is determined to represent noise. For example, as illustrated inFIG.8, based on the measurement values measured by the sensor40ain the first cycles Δt1and in the second cycles Δt2up to the timing t1, an approximation curve61gets identified. In the example illustrated inFIG.8, the measurement value Sn, which is determined to be an abnormal value, and the measurement value Sn′, which is present at the same timing to on the approximation curve61as the timing of the measurement value Sn, have the difference ΔS equal to or greater than a threshold value. Hence, the determining unit212determines that the measurement value Sn, which is determined to be an abnormal value, is not included in the trend of temporal variation in the measurement values as identified by the calculating unit211; and determines that the measurement value Snrepresents noise.

FIG.9is a diagram illustrating an example of the measurement value Snthat is determined not to represent noise. For example, as illustrated inFIG.9, based on the measurement values measured by the sensor40ain the first cycles Δt1and in the second cycles Δt2up to the timing t1, an approximation curve62gets identified. In the example illustrated inFIG.9, the measurement value Sn, which is determined to be an abnormal value, and the measurement value Sn′, which is present at the same timing to on the approximation curve62as the timing of the measurement value Sn′, have the difference ΔS smaller than the threshold value. Hence, the determining unit212determines that the measurement value Sn, which is determined to be an abnormal value, is included in the trend of temporal variation in the measurement values as identified by the calculating unit211; and determines that the measurement value Sndoes not represent noise.

When the measurement value Sn, which is determined to be an abnormal value, is determined not to represent noise; the determining unit212extracts, from the range table220in the DB22, the sensor IDs included in the range “r=1” that corresponds to the sensor ID of the sensor40a. Then, the determining unit212instructs the activation cycle managing unit231to activate the sensors40having the extracted sensor IDs in the second cycles Δt2. In response to the instruction received from the determining unit212, the activation cycle managing unit231sets the activation cycles of the sensors40having the sensor IDs specified by the determining unit212to the second cycles Δt2. As a result, in each second cycle Δt2, an activation instruction is sent to the sensor40aand to a plurality of sensors40installed around the sensor40a, and the measurement values from those sensors40are collected in each second cycle Δt2.

FIG.10is a diagram illustrating an example of the sensors40that are activated when an abnormal value not representing noise is detected. When it is determined that the measurement value Sn, which is determined to be an abnormal value, is determined not to represent noise; for example, as illustrated inFIG.10, other sensors40bto40fthat are installed around the sensor40aare further activated in the second cycles Δt2. Herein, the sensors40bto40fare examples of second sensors. From among the sensors40illustrated inFIG.10, filled circles represent the sensors40ato40fthat are activated in the second cycles Δt2, and open circles represent the sensors40maintained in the low power consumption state. As a result, for example, as illustrated inFIG.11, in each second cycle Δt2, the measurement value is collected from the concerned sensors40.FIG.11is a diagram illustrating an example of the measurement values of each concerned sensor.

Subsequently, the determining unit212instructs the calculating unit211to identify the trend of the surface-direction distribution of the measurement values that are measured by the sensors40ato40fin the second cycles Δt2. In response to the instruction received from the determining unit212, the calculating unit211identifies the trend of the surface-direction distribution of the measurement values at each timing as measured by the sensors40ato40fin the second cycles Δt2.

More particularly, the calculating unit211obtains, from the DB22, the measurement values measured by each sensor40in the second cycles Δt2between the period of time from the timing t1to a timing t2at which the period of time corresponding to the first cycle Δt1elapses. Then, based on the measurement values measured by the sensors40ato40f, at each timing of measurement of the measurement values; the calculating unit211identifies, as the trend of the surface-direction distribution of the measurement values, an approximation curved surface that approximates the surface-direction distribution of the measurement values.FIG.12is a diagram illustrating an example of the approximation curved surface. For example, the calculating unit211identifies the approximation curved surface by fitting a predetermined function, which is represented by the order corresponding to the number of measurement values measured by the sensors40ato40f, at each measurement timing using the method of least square. Then, the calculating unit211, outputs, to the determining unit212, the identified approximation curved surface as the trend of the surface-direction distribution of the measurement values.

Subsequently, the determining unit212determines, at each timing of measurement of the measurement values, whether or not the measurement values measured by the sensors40are included in the trend of the surface-direction distribution of the measurement values as identified by the calculating unit211. For example, if the measurement values measured by the sensors40at all measurement timings are included in the distribution identified by the calculating unit211, then the determining unit212determines that the measurement values measured by the sensors40are included in the distribution identified by the calculating unit211. Alternatively, if the measurement values measured by the sensors40at such a number of measurement timings which is equal to or greater than a predetermined ratio of all measurement timings are included in the distribution identified by the calculating unit211, then the determining unit212can determine that the measurement values measured by the sensors40are included in the distribution identified by the calculating unit211.

If the measurement values measured by the sensors40are included in the trend of the surface-direction distribution, then the determining unit212outputs the sensor IDs of those sensors40to the output unit210. Then, the output unit210obtains, from the DB22, the measurement values corresponding to the sensor IDs output from the determining unit212; and outputs, to the monitoring device via the network11, an alert including the obtained measurement values and the sensor IDs. Thereafter, as needed, the output unit210obtains, from the DB22, the measurement values corresponding to the sensor IDs output by the determining unit212; and sends the measurement values to the monitoring device.

More particularly, for example, as illustrated inFIG.13, at each timing of measurement of the measurement value Sn, the determining unit212identifies the measurement value Sn′ that is present on an approximation curved surface63identified by the calculating unit211and that corresponds to a position (xn, yn) of each of the sensors40ato40f.FIG.13is a diagram for explaining an example of the difference between the approximation curved surface and the measurement value of each sensor. Then, at each timing of measurement of the measurement value Sn, the determining unit212calculates the difference ΔS between the measurement value Snmeasured by each of the sensors40ato40fand the identified measurement value Sn′. Subsequently, the determining unit212adds the difference ΔS calculated for the measurement value Snmeasured by each of the sensors40ato40f, and calculates a cumulative difference ΔS′. If the cumulative difference ΔS′ is smaller than a threshold value, then the determining unit212determines that the measurement values measured by the sensors40ato40fare included in the trend of the surface-direction distribution of the measurement values as identified by the calculating unit211.

On the other hand, if the measurement values measured by the sensors40ato40fare not included in the trend of the surface-direction distribution, then the determining unit212extracts, from the range table220in the DB22, the sensor IDs included in the range “r=2” that is associated to the sensor ID of the sensor40a. Then, the determining unit212instructs the activation cycle managing unit231to further activate the sensors40having the extracted sensor IDs in the second cycle Δt2. In response to the instruction received from the determining unit212, the activation cycle managing unit231sets the activation cycles of the sensors40having the sensor IDs specified by the determining unit212to the second cycles Δt2.

As a result, for example, as illustrated inFIG.14, in each second cycle Δt2, an activation signal is sent to the sensor40aand a plurality of sensors40bto40minstalled around the sensor40a, and the measurement values in each second cycle Δtzare collected from the sensors40bto40m.FIG.14is a diagram illustrating an example of the sensors that are activated when there is a large cumulative difference between the approximation curved surface and the measurement values of the sensors. For example, as illustrated inFIG.14, the sensor IDs of the sensors40ato40m, which are installed in a wider area than the area in which the sensors40ato40fhaving the sensor IDs included in the range “r=1” are installed, are included in the range “r=2”. Herein, the sensors40gto40mare examples of third sensors.

Subsequently, the determining unit212further instructs the calculating unit211to identify the trend of the surface-direction distribution of the measurement values measured by the sensors40ato40min the second cycles Δt2. In response to the instruction received from the determining unit212, the calculating unit211identifies the trend of the surface-direction distribution of the measurement values at each timing as measured by the sensors40ato40min the second cycles Δt2. Then, the determining unit212determines whether or not the measurement values measured by the sensors40ato40mare included in the trend of the surface-direction distribution of the measurement values as identified by the calculating unit211.

In this way, until a plurality of measurement values is included in the distribution identified by the calculating unit211, expansion of the range in which the sensors40are included and determination about whether or not the measurement values measured by a plurality of sensors40are included in the distribution identified by the calculating unit211is performed in a repeated manner. As a result of an increase in the number of activated sensors40, for example, as illustrated inFIG.15, the approximation curved surface identified by the calculating unit211becomes able to express more minute undulation.FIG.15is a diagram illustrating an example of the approximation curved surface. Thus, as a result of expanding the range in which the sensors40are included, the approximation curved surface identified by the calculating unit211approaches the trend of the surface-direction distribution of the measurement values Sn, and there is a decrease in the cumulative difference ΔS′ of the differences ΔS between the measurement values S and the measurement values Sn′ on the approximation curved surface.

However, it is also possible to think of a case in which the sensors40discretely output the measurement values not having any continuity in the surface direction. In the situation in which the sensors40discretely output the measurement values not having any continuity in the surface direction, it is highly likely that the measurement values of each sensor do no indicate any signs of some natural phenomenon or some abnormality of a building structure. In that case, even if the range associated to the sensor ID of the sensor40ais expanded until. “r=R” holds true, it is determined that the measurement values of a plurality of sensors40are not included in the trend of the distribution identified by the calculating unit211.

In such a case, the determining unit212instructs the activation cycle managing unit231to stop the sensors40. Then, regarding the sensor40to be activated in the first cycles in the normal situation, the determining unit212changes the sensor40ato some other sensor40, and instructs the activation cycle managing unit231to activate that other sensor40in the first cycles. In this way, even after the range associated to the sensor ID of the sensor40ais expanded until “r=R” holds true, if it is determined that the measurement values of a plurality of sensors40are not included in the distribution identified by the calculating unit211, the measurement values of the sensors40are not sent to the monitoring devices. As a result, the control device20holds back from sending, to the monitoring device, the measurement values not indicating any signs of some natural phenomenon or some abnormality of a building structure; and can reliably send, to the monitoring device, the measurement values indicating signs of some natural phenomenon or some abnormality of a building structure. As a result, while managing the management targets, the communication system10can hold down on unnecessary field investigation.

[Operations of Control Device20]

FIGS.16and17are flowcharts for explaining an example of the operations performed in the control device20.

Firstly, from among a plurality of sensors40, the determining unit212selects, for example, in a random manner, the sensor40ato be activated in the first cycles Δt1(S100). Then, the determining unit212instructs the activation cycle managing unit231to activate the selected sensor40ain the first cycles Δt1.

The activation cycle managing unit231determines whether or not one first cycle Δt1has elapsed (S101). If one first cycle Δt1has elapsed (Yes at S101), then the activation cycle managing unit231outputs the sensor ID of the sensor40ato the activation instructing unit230. Then, the activation instructing unit230sends an activation instruction, which includes the sensor ID output by the activation cycle managing unit231, to the collection device30via the network11.

Upon receiving the activation instruction from the control device20, the collection device30obtains the sensor ID from the activation instruction. Then, the collection device30wirelessly sends an activation instruction to the sensor40acorresponding to the obtained sensor ID. Upon receiving the activation instruction from the collection device30, the sensor40aobtains the measurement value Snthat is measured using the measuring unit45. Then, the sensor40awirelessly sends the measurement value S to the collection device30. Upon receiving the measurement value Snfrom the sensor40a, the collection device30sends the measurement value S and the sensor ID of the sensor40a, which sent the measurement value Sn, to the control device20via the network11.

Subsequently, the activation instructing unit230receives the sensor ID and the measurement value Snfrom the collection device30via the network11, and thus obtains the measurement value Sn(S102). Then, the activation instructing unit230stores the measurement value S; in a corresponding manner to the measurement timing and the sensor ID in the DB22.

Subsequently, the determining unit212refers to the measurement values stored in the DB22and calculates the difference D between the measurement value Sn, which is measured by the sensor40a, and the average value of the measurement values measured in the past (S103). Then, the determining unit212determines whether or not the calculated difference D is greater than a predetermined threshold value δ′ (S104). If the difference D is equal to or smaller than the threshold value δ′ (No at S104), then the activation cycle managing unit231again performs the operation at Step S01.

On the other hand, if the difference D is greater than the threshold value δ′ (Yes at S104), then the determining unit212determines that the measurement value Snmeasured by the sensor40ais an abnormal value. Then, the determining unit212instructs the activation cycle managing unit231to change the activation cycles of the sensor40afrom the first cycles Δt1to the second cycles Δt2. In response to the instruction received from the determining unit212, the activation cycle managing unit231changes the activation cycles of the sensor40afrom the first cycles Δt1to the second cycles Δt2. As a result, an activation instruction is sent to the sensor40ain each second cycle Δt2and, until the elapse of the first cycle Δt1, the measurement value is obtained from the sensor40ain each second cycle Δt2(S105).

Subsequently, the determining unit212instructs the calculating unit211to identify the trend of temporal variation in the measurement values measured by the sensor40ain the first cycles Δt1and in the second cycles Δt2. In response to the instruction received from the determining unit212; from the DB22, the calculating unit211obtains the measurement values measured in the first cycles Δt1, and obtains the measurement values measured in the second cycles Δt2between the period of time from the timing t0to the timing t1at which the period of time corresponding to the first cycle Δt1elapses. Then, based on the obtained measurement values, the calculating unit211identifies, as the trend of temporal variation in the measurement values, the approximation curve that approximates the temporal variation in the measurement values (S106).

Subsequently, based on the approximation curve identified by the calculating unit211, the determining unit212identifies the measurement value Sn′ that is present on the approximation curve at the same timing as the timing of the measurement value Sndetermined to be an abnormal value. Then, the determining unit212calculates the difference ΔS between the measurement value Snand the measurement value Sn′ (S107). Subsequently, the determining unit determines whether or not the difference ΔS is smaller than a predetermined threshold value δ0(S108).

If the difference ΔS is equal to or greater than the threshold value δ0(No at S108), then the determining unit212determines that the measurement value Sn, which is determined to be an abnormal value, is not included in the trend of measurement values as identified by the calculating unit211, and determines that the measurement value Snrepresents noise. Subsequently, the determining unit212instructs the activation cycle managing unit231to reset the activation cycles of the sensor40afrom the second cycles Δt2to the first cycles Δt1. In response to the instruction received from the determining unit212, the activation cycle managing unit231resets the activation cycles of the sensor40afrom the second cycles Δt2to the first cycles Δt1. Then, the activation cycle managing unit231again performs the operation at Step S101.

On the other hand, if the difference ΔS is smaller than the threshold value δ0(Yes at S108), then the determining unit212determines that the measurement value Sn, which is determined to be an abnormal value, is included in the trend of measurement values as identified by the calculating unit211, and determines that the measurement value Sndoes not represent noise. Then, the determining unit212initializes the variable n to one (S109illustrated inFIG.17).

Subsequently, the determining unit212refers to the range table220in the DB22, and extracts the sensor IDs included in the range “r=n” that is associated to the sensor ID of the sensor40aselected at Step S100. Then, the determining unit212instructs the activation cycle managing unit231to activate the sensors40having the extracted sensor IDs in the second cycles Δt2. In response to the instruction received from the determining unit212, the activation cycle managing unit231sets the activation cycle of the sensors40, which have the sensor IDs specified by the determining unit212, to the second cycles Δt2. As a result, in each second cycle Δt2, an activation instruction is sent by the activation instructing unit230to the sensor40aand to a plurality of sensors40installed around the sensor40a; and thus those sensors40are activated in each second cycle Δt2(S110). Then, the activation instructing unit230obtains, from each sensor40, the measurement values in each second cycle Δt2until the elapse of the first cycle Δt1(S111).

Then, the determining unit212instructs the calculating unit211to identify the trend of the surface-direction distribution of the measurement values measured by the sensors40in each second cycle Δt2. In response to the instruction received from the determining unit212, the calculating unit211obtains, from the DB22, the measurement values measured by the sensors40in each second cycle Δt2until the elapse in the period of time corresponding to the first cycle Δt1. Then, based on the measurement values measured by the sensors40, at each timing of measurement of the measurement values, the calculating unit211identifies, as the trend of the surface-direction distribution of the measurement values, an approximation curved surface that approximates the surface-direction distribution of the measurement values (S112).

Subsequently, at each timing of measurement of the measurement value Sn, for example, as illustrated inFIG.13, the determining unit212calculates the difference ΔS of the measurement value Snmeasured by each sensor40with the measurement value Sn′ on the approximation curved surface as identified by the calculating unit211. Then, the determining unit212adds the difference ΔS calculated for the measurement value Snmeasured by each sensor40, and calculates the cumulative difference ΔS′ (S113).

Subsequently, the determining unit212determines whether or not the cumulative difference ΔS′ calculated in at least one of the measurement timings is greater than the threshold value δ0(S114). If the cumulative difference ΔS′ calculated in at least one of the measurement timings is greater than the threshold value δ0(Yes at S114), then the determining unit212determines that the measurement values of a plurality of sensors40are not included in the trend of the surface-direction distribution. Then, the determining unit212increments the variable n by one (S115) and determines whether or not the value of the variable n is greater than a threshold value R (S116). If the value of the variable n is equal to or smaller than the threshold value R (No at S116), the determining unit212again performs the operation at Step S110. However, if the value of the variable n is greater than the threshold value R (Yes at S116), then the determining unit212instructs the activation cycle managing unit231to stop the sensors40. Subsequently, the determining unit212again performs the operation at Step S100. As a result of performing the operation at Step S100, the sensor40to be activated in the first cycles in the normal situation again gets selected in a random manner.

Meanwhile, if the cumulative differences ΔS′ calculated at all measurement timings are equal to or smaller than the threshold value δ0(No at S114), then the determining unit212determines that the measurement values measured by a plurality of sensors40are included in the trend of the surface-direction distribution. Then, the determining unit212outputs the sensor IDs of the sensors40to the output unit210. The output unit210obtains, from the DB22, the measurement values corresponding to the sensor IDs output by the determining unit212; and outputs an alert including the obtained measurement values and the sensor IDs to the monitoring device via the network11(S117). Then, the determining unit212updates the threshold value δ′ to be used at Step S104(S118). For example, the determining unit212multiplies a coefficient k, which is based on the measurement values of the sensors40configured to measure the measurement values of other types, to the threshold value δ′, and updates the threshold value δ′. Subsequently, the determining unit212again performs the operation at Step S100.

FIG.18is a diagram illustrating an example of the hardware of the control device20. For example, as illustrated inFIG.18, the control device20includes a memory200, a processor201, and an interface circuit202.

The interface circuit202is an interface for establishing a wired connection with the network11.

The memory200is used to store a program that is meant for implementing the functions of the data processing unit21and the sensor managing unit23, and to store the data referred to by the program. Moreover, the memory200is used to store the data of the DB22. The processor201reads the program from the memory200and executes it so that, for example, the functions of the data processing unit21and the sensor managing unit23are implemented.

Meanwhile, the program that is stored in the memory200need not always be stored therein from the beginning. Alternatively, for example, the program can be stored in a portable recording medium such as a memory card insertable in the control device20, and the control device20can obtain the program of the portion to be used in the processing from the portable recording medium and execute the program. Still alternatively, the program can be stored in some other computer or a server device, and the control device20can execute the program after obtaining it via a wireless communication line, a public line, the Internet, a LAN, or a WAN.

Given above is the explanation of the embodiment. The communication system10according to the embodiment includes a plurality of sensors40, the collection device30, and the control device20. The sensors40are installed in a dispersed manner at a specific location. The collection device30collects the measurement values measured by the sensors40. The control device20controls the sensors40based on the measurement values collected by the collection device30. The control device20includes the calculating unit211, the determining unit212, the activation instructing unit230, and the output unit210. The determining unit212determines whether or not the measurement values measured by the first sensor, from among the sensors40installed in a dispersed manner at specific locations, in the first cycles are abnormal values. If the measurement values are determined to be abnormal values, then the activation instructing unit230activates the first sensor in the second-cycles that are shorter than the first cycles. The calculating unit211identifies the trend of temporal variation in the measurement values measured by the first sensor in the first cycles and in the second cycles. Moreover, the determining unit212determines whether or not the abnormal values are included in the trend of temporal variation. If the abnormal values are included in the trend of temporal, variation, then the activation instructing unit230activates a plurality of second sensors, which are installed around the first sensor, in the second cycles. Moreover, the calculating unit211identifies trend of the surface-direction distribution of the measurement values measured by the first sensor and the second sensors in the second cycles. Furthermore, the determining unit212determines whether or not the measurement values measured by the first sensor and the second sensors in the second cycles are included in the trend of the surface-direction distribution. If the measurement values measured by the first sensor and the second sensors in the second cycles are included in the trend of the surface-direction distribution, then the output unit210outputs the measurement values measured by the first sensor and the second sensors. As a result, in the communication system10according to the present embodiment, from among the measurement values measured by a plurality of sensors40, the measurement values indicating signs of possible development into abnormality can be screened with accuracy.

Moreover, in the embodiment described above, based on the statistical value obtained from the measurement values measured by the first sensor in the first cycles, the determining unit212determines whether or not the measurement values measured by the first sensor in the first cycles are abnormal values. As a result, the determining unit212can accurately identify the abnormal values from among the measurement values measured by the first sensor.

Furthermore, in the embodiment described above, if the measurement values measured by the first sensor in the first cycles and the average value of the measurement values measured by the first sensor in the first cycles from the current point of time till a predetermined point of time in the past have the difference equal to or greater than a threshold value, then the determining unit212determines that the measurement values measured by the first sensor in the first-cycles are abnormal values. As a result, from among the measurement values measured by the first sensor, the determining unit212can identify the abnormal values with a simple method.

Moreover, in the embodiment described above, the calculating unit211identifies, as the trend of temporal variation, an approximation curve that approximates the temporal variation in the measurement values measured by the first sensor in the first cycles and in the second cycles. Furthermore, if the value present on the approximation curve, which is identified by the calculating unit211, at the timing of measurement of an abnormal value has a difference with the abnormal value to be smaller than a predetermined threshold value, then the determining unit212determines that the abnormal value is included in the trend of temporal variation. As a result, from among the measurement values measured by the first sensor, the determining unit212can accurately exclude noise that is not included in the trend of temporal variation in the measurement values.

Moreover, in the embodiment described above, the calculating unit211identifies, as the trend of the surface-direction distribution, an approximation curved surface that approximates the surface-direction distribution of the measurement values measured by the first sensor and the second sensors in the second cycle. Furthermore, the determining unit212adds, for a plurality of measurement values, the difference between the value present on the approximation curved surface identified by the calculating unit211and the corresponding measurement value; and, if the cumulative difference is smaller than a predetermined threshold value, determines that each measurement value is included in the trend of the surface-direction distribution. As a result, in the communication system10, from among the measurement values measured by a plurality of sensors40, the measurement values indicating signs of possible development into abnormality can be identified with accuracy.

Furthermore, in the embodiment described above, if is determined that none of the measurement values measured by the first sensor and the second sensors in the second cycles are included in the trend of the surface-direction distribution, then the activation instructing unit230further activates, in the second cycle, a plurality of third sensors installed around the area including the first sensor and the second sensors. The calculating unit211identifies the trend of the surface-direction distribution of the measurement values measured by the first sensor, the second sensors, and the third sensors in the second cycles. The determining unit212determines whether or not the measurement values measured by the first sensor, the second sensors, and the third sensors in the second cycles are included in the trend of the surface-direction distribution. If the measurement values measured by the first sensor, the second sensors, and the third sensors in the second cycles are included in the trend of the surface-direction distribution, then the output unit210outputs the measurement values measured by the first sensor, the second sensors, and the third sensors. As a result, in the communication system10, from among the measurement values measured by the sensors40, the measurement values indicating signs of possible development into abnormality can be identified with accuracy.

Meanwhile, the technology disclosed herein is not limited by the embodiment described above, and can be modified in various ways within the technical scope.

For example, in the embodiments described above, the control device20and the collection device30are configured to be separate devices. However, the technology disclosed herein is not limited to that case, and alternatively the control device20and the collection device30can be configured to be a single device. Moreover, when a plurality of collection devices30is installed in the communication system10, one of the collection devices30can be equipped with the functions of the control device20. Furthermore, the functions of the collection device30can be provided in one of the sensors40. Moreover, the functions of the control device20and the collection device30can be provided in one of the sensors40.

In the embodiment described above, in order to facilitate understanding of the control device20, the collection device30, and the sensors40according to the embodiment; the processing blocks of each of the control device20, the collection device30, and the sensors40are functionally separated according to the main processing details. Thus, the technology disclosed herein is not limited by the separation method and the names of the processing blocks. Regarding the processing blocks of each of the control device20, the collection device30, and the sensors40; either the processing blocks can be further broken up into smaller processing blocks according to the processing details or a plurality of processing blocks can be integrated into a single processing block. Moreover, the operations performed using each processing block can be implemented either as software-based operations or using dedicated hardware such as ASIC (Application Specific Integrated Circuit).

According to an aspect of the control device, the control system, and the control method disclosed in the application concerned enable accurate screening of such measurement values which, from among the measurement values measured by a plurality of sensors, indicate signs of possible development into abnormality.