FORM 2
THE PATENTS ACT, 1970
(39 of 1970)
&
THE PATENTS RULES, 2003
COMPLETE SPECIFICATION
[See section 10, Rule 13]
WATER TREATMENT PLANT AND METHOD FOR OPERATING WATER TREATMENT PLANT;
MITSUBISHI ELECTRIC CORPORATION, A CORPORATION ORGANISED
AND EXISTING UNDER THE LAWS OF JAPAN, WHOSE ADDRESS IS 7-3,
MARUNOUCHI 2-CHOME, CHIYODA-KU, TOKYO 1008310, JAPAN
THE FOLLOWING SPECIFICATION PARTICULARLY DESCRIBES THE
INVENTION AND THE MANNER IN WHICH IT IS TO BE PERFORMED

DESCRIPTION
Field
[0001] The present invention relates to a water
treatment plant which performs treatment of water such as
clean water or sewage and a method for operating the water
treatment plant.

Background
[0002] In a water treatment plant, water treatment
control is performed while changing a control target value
depending on environmental changes. For example, by
changing the control target value along with changes in a
water treatment environment such as seasonal temperature
difference, the flow rate of inflow water, and the quality
of inflow water, water treatment control depending on
changes in the water treatment environment is performed in
the water treatment plant.
[0003] The control target value is changed by an
operator on the basis of past experience and the like, and
specialized expertise is required for performing the change.
Patent Literature 1 proposes a technique which uses an
artificial intelligent (AI) device for controlling a sewage
treatment device so that experience of an operator can be
reflected in changing a control target value depending on
environmental changes. In such a technique, detection
values of multiple sensors which detect the flow rate,
temperature, biochemical oxygen demand (BOD), NH4+, and the
like of inflow water to the sewage treatment device are
input to the AI device, and the sewage treatment device is
controlled on the basis of an output of the AI device.
Citation List
Patent Literature
[0004] Patent Literature 1: Japanese Patent Application
Laid-open No. 2004-25160
Summary
Technical Problem
[0005] In such a conventional water treatment plant
described above, water treatment control using an AI device
is performed with the use of numerical values of the flow
rate, temperature, BOD, NH4+ and the like of inflow water
as indices. However, such a conventional water treatment
plant described above has room for improvement. For
example, there may be a case where effective water
treatment control cannot be performed in such a
conventional water treatment plant described above with
respect to a change in a water treatment environment of a
water treatment device, the change not appearing in a
numerical value detected by a sensor.
[0006] The present invention has been made in view of
the above, and an object thereof is to obtain a water
treatment plant capable of performing more effective water
treatment control with respect to a change in a water
treatment environment.
Solution to Problem
[0007] A water treatment plant according to the present
invention is a water treatment plant which performs water
treatment using a water treatment device, and includes an
imaging device, a processing device, and a control device.
The imaging device images a water treatment environment of
the water treatment device and outputs image data obtained
by imaging. The processing device causes an arithmetic
device which performs an arithmetic operation using one or
more calculation models generated by machine learning to
execute the arithmetic operation employing the image data
output from the imaging device as input data of the one or
more calculation models. The control device controls the
water treatment device on the basis of information output
from the arithmetic device by executing the arithmetic
operation.
Advantageous Effects of Invention
[0008] The present invention achieves an effect that it
is possible to provide a water treatment plant capable of
performing more effective water treatment control with
respect to a change in a water treatment environment.

Brief Description of Drawings
[0009] FIG. 1 is a diagram illustrating an outline of a
water treatment plant according to a first embodiment.
FIG. 2 is a diagram illustrating an example
configuration of the water treatment plant according to the first embodiment.
FIG. 3 is a diagram illustrating example
configurations of multiple sensor groups according to the
first embodiment.
FIG. 4 is a diagram illustrating an example
configuration of a processing device according to the first
embodiment.
FIG. 5 is a diagram illustrating an example of a data
table stored in a storage device according to the first
embodiment.
FIG. 6 is a diagram illustrating an example
configuration of an arithmetic device according to the
first embodiment.
FIG. 7 is a diagram illustrating an example
configuration of a control device according to the first
embodiment.
FIG. 8 is a flowchart illustrating an example of a
series of processes of the processing device according to
the first embodiment.
FIG. 9 is a flowchart illustrating an example of a
series of processes of the arithmetic device according to
the first embodiment.
FIG. 10 is a flowchart illustrating an example of a
series of processes of the control device according to the
first embodiment.
FIG. 11 is a diagram illustrating an example of a
hardware configuration of the processing device according
to the first embodiment.
Description of Embodiments
[0010] Hereinafter, a water treatment plant and a method
for operating the water treatment plant according to an
embodiment of the present invention will be described in
detail with reference to the drawings. The present
invention is not limited to the embodiment.
[0011] First Embodiment.
FIG. 1 is a diagram illustrating an outline of a water
treatment plant according to a first embodiment. As
illustrated in FIG. 1, a water treatment plant 1 according
to the first embodiment includes a water treatment device
10, an imaging device 20, a processing device 30, an
arithmetic device 40, and a control device 50. The
arithmetic device 40 is an example of an AI device.
[0012] The water treatment device 10 is, for example, a
device which performs treatment of water such as clean
water or sewage, and includes a device to be controlled
such as a pump or a blower which controls a water treatment
state. An example of the water treatment device 10 is not
limited to the device according to the first embodiment
which includes the device to be controlled such as a pump
or a blower, and a grit chamber, a primary settling basin,
a sludge-reducing device, and the like of the water
treatment plant may be used.
[0013] The control device 50 controls the water
treatment device 10. The imaging device 20 images a water
treatment environment of the water treatment device 10 and
outputs image data of the water treatment environment
obtained by imaging. The water treatment environment of
the water treatment device 10 includes at least one of a
water treatment environment inside the water treatment
device 10 and a water treatment environment outside the
water treatment device 10. The processing device 30
acquires image data from the imaging device 20.
[0014] The processing device 30 causes the arithmetic
device 40 to execute an arithmetic operation employing the
acquired image data as input data, and acquires a result of
the arithmetic operation by the arithmetic device 40 from
the arithmetic device 40. The arithmetic device 40
includes a calculation model generated by machine learning.
Such a calculation model receives an input of the image
data of the imaging device 20, and outputs information on a
control target value of the device to be controlled, for
example. The control target value is, for example, a
target value of the amount of control of the device to be
controlled such as a pump or a blower which controls a
water treatment state of the water treatment device 10.
30 [0015] The arithmetic device 40 performs an arithmetic
operation using the above-described calculation model and
employing the image data acquired from the processing
device 30 as input data, and outputs information including
the result of the arithmetic operation by the arithmetic
device 40 to the processing device 30. The processing
device 30 outputs the information acquired from the
arithmetic device 40 to the control device 50. The control
device 50 controls the water treatment device 10 on the
basis of the information output from the processing device
30. For example, in a case where the information output
from the arithmetic device 40 is the information on the
control target value of the device to be controlled, the
control device 50 can control the water treatment device 10
by outputting control information including the control
target value to the device to be controlled of the water
treatment device 10. The arithmetic device 40 is, for
example, AI called artificial intelligence or the like, and
contributes to estimation of a preferable control target
value of the device to be controlled through machine
learning based on the input image data.
[0016] As described above, in the water treatment plant
1, the water treatment control can be performed using the
arithmetic device 40 and employing an image of the water
treatment environment of the water treatment device 10 as a
new index. Therefore, in the water treatment plant 1, it
is possible to perform, with the use of the arithmetic
device 40, for example, water treatment control which has
been performed by an operator of the water treatment plant
1 on the basis of the image of the water treatment
environment of the water treatment device 10 and on the
basis of past experience or knowledge of the operator, and
to perform effective water treatment control.
[0017] In the above-described first embodiment, the
example has been described in which the image data of the
imaging device 20 is output to the arithmetic device 40 via
the processing device 30, and the result of the arithmetic
operation by the arithmetic device 40 is output to the
processing device 30, thereby controlling the control
device 50. However, the present invention is not limited
to the example. For example, modification may be made so
that a function of the processing device 30 is incorporated
into at least one of the arithmetic device 40 and the
control device 50 to omit the processing device 30. In
this modification, for example, the processing device 30
which is separate from at least one of the arithmetic
device 40 and the control device 50 can be omitted, so that
an effect of increasing the degree of freedom in device
configuration is achieved.
[0018] Hereinafter, the water treatment plant 1
according to the first embodiment will be described in
detail. FIG. 2 is a diagram illustrating an example
configuration of the water treatment plant according to the
first embodiment. In the following, sewage treatment will
be described as an example of water treatment performed by
the water treatment device 10.
[0019] As illustrated in FIG. 2, the water treatment
plant 1 according to the first embodiment includes the
above-described water treatment device 10, imaging devices
201 to 203, sensor groups 211 to 213, the processing device
30, the arithmetic device 40, the control device 50, a
storage device 61, a display device 62, and an input device
63. In the following description, the imaging devices 201
to 203 may be referred to as the imaging device 20 when
they are indicated without being distinguished from each
other, and the sensor groups 211 to 213 may be referred to
as the sensor group 21 when they are indicated without
being distinguished from each other.
[0020] The processing device 30, the arithmetic device
40, the control device 50, the storage device 61, the
display device 62, and the input device 63 are communicably
connected to each other via a communication network 64.
The communication network 64 is, for example, a local area
network (LAN), a wide area network (WAN), a bus, or a leased line.
[0021] The water treatment device 10 illustrated in FIG.
2 is a sewage treatment device which treats sewage. Such a
water treatment device 10 includes a primary settling tank
11 which stores sewage as inflow water from sewerage and
the like, and settles a solid substance and the like in the
sewage, the solid substance being relatively easy to sink,
a treatment tank 12 which aerobically treats supernatant
water in the primary settling tank 11, and a final settling
tank 13 which separates a liquid mixture containing
activated sludge flowing from the treatment tank 12 into
supernatant water and activated sludge. The supernatant
water in the final settling tank 13 is discharged as
treated water from the final settling tank 13.
[0022] In the treatment tank 12, the supernatant water
which flows therein from the primary settling tank 11
contains organic matter, and the organic matter contained
in the supernatant water is treated, for example, by
digestion by aerobic microorganisms such as phosphorus
accumulating bacteria, nitrifying bacteria, and
denitrifying bacteria.
[0023] The water treatment device 10 further includes a
blower 14 which blows air into the treatment tank 12 to
dissolve the air in the liquid mixture containing activated
sludge, and a pump 15 which is provided on a pipe which
connects the final settling tank 13 and the treatment tank
12, and returns the activated sludge to the treatment tank
12 from the final settling tank 13. Each of the blower 14
and the pump 15 is an example of the device to be
controlled described above, and hereinafter, the blower 14
and the pump 15 may be referred to as the device to be
controlled when they are indicated without being
distinguished from each other.
[0024] The multiple imaging devices 201, 202, and 203
image water treatment environments of the water treatment
device 10 which are objects to be imaged different from
each other. The imaging device 201 images a water
treatment environment which is an object to be imaged
inside the primary settling tank 11. The object to be
imaged inside the primary settling tank 11 is, for example,
a state of water, a state of bubbles, or a state of
settlings in the primary settling tank 11.
[0025] The imaging device 202 images a water treatment
environment which is an object to be imaged inside the
treatment tank 12. The object to be imaged inside the
treatment tank 12 is, for example, a state of activated
sludge or a state of water in the treatment tank 12. The
state of activated sludge includes, for example, the amount
or distribution of the activated sludge. The state of
activated sludge may be, for example, the amount of each
microorganism.
[0026] The imaging device 203 images a water treatment
environment which is an object to be imaged inside the
final settling tank 13. The object to be imaged inside the
final settling tank 13 is, for example, a state of
supernatant water or a state of settlings in the final
settling tank 13. In the following description, the
primary settling tank 11, the treatment tank 12, and the
final settling tank 13 may be referred to as a tank when
they are indicated without being distinguished from each
other. The objects to be imaged which are imaged by the
imaging device 20 are not limited to the above-described
11
examples, and the imaging device 20 can also image a state
of an inner wall of the tank, a state of surroundings of
the tank, or the like as the object to be imaged. Although
the imaging devices 201, 202, and 203 illustrated in FIG. 2
image the state or environment inside the water treatment
device 10 as the water treatment environment of the water
treatment device 10, an imaging device may be provided
which images a state or environment outside the water
treatment device 10 illustrated in FIG. 2.
[0027] The imaging device 20 is, for example, a digital
camera, or a digital microscope. The imaging device 20 may
be, for example, a digital camera for a microscope. In
such a case, when the operator of the water treatment plant
1 places water in the tank or the like under the microscope,
the imaging device 20 can image a microscopic image thereof.
The number of imaging devices 20 is not limited to three,
and may be two or less, or four or more. Hereinafter, the
operator of the water treatment plant 1 will be simply
referred to as the operator.
[0028] The multiple sensor groups 211 to 213 detect
various characteristics indicating the water treatment
environment of the water treatment device 10. For example,
the sensor group 211 detects an inflow water characteristic
which is a characteristic of inflow water to the primary
settling tank 11. The sensor group 212 detects an intratreatment-
tank characteristic which indicates a state of
the treatment tank 12. The sensor group 213 detects a
treated water characteristic which is a characteristic of
treated water discharged from the final settling tank 13.
[0029] FIG. 3 is a diagram illustrating example
configurations of the multiple sensor groups according to
the first embodiment. As illustrated in FIG. 3, the sensor
group 211 includes a flow rate sensor 221 which detects the
inflow amount of inflow water, a BOD sensor 222 which
detects the BOD of the inflow water, a water temperature
sensor 223 which detects the temperature of the inflow
water, and an NH3 sensor 224 which detects the NH3
concentration in the inflow water. The sensor group 211
may include a sensor for detecting the NH4+ or ammoniacal
nitrogen concentration in the inflow water instead of or in
addition to the NH3 sensor 224.
[0030] The sensor group 212 includes a dissolved oxygen
sensor 231 which detects the amount of dissolved oxygen in
the treatment tank 12, an active microorganism
concentration sensor 232 which detects the active
microorganisms concentration in the treatment tank 12, and
a BOD sensor 233 which detects the BOD in the treatment tank 12. In addition, the sensor group 212 further
includes multiple sensors each of which detects one of the
ammoniacal nitrogen concentration, a nitrate nitrogen
concentration, a total nitrogen concentration, a phosphate
phosphorus concentration, and a total phosphorus
concentration.
[0031] The sensor group 213 includes a flow rate sensor
241 which detects the outflow amount of treated water, a
BOD sensor 242 which detects the BOD of the treated water,
and a total nitrogen concentration sensor 243 which detects
the total nitrogen concentration in the treated water.
[0032] The sensor groups 211 to 213 may include a sensor
which detects an object other than the above-described
objects to be detected, or may not include a part of the
multiple sensors described above. Hereinafter, data of
numerical values detected by each sensor in the sensor
groups 211 to 213 will be referred to as numerical data. In
addition, image data and numerical data may be referred to
as detection data when they are indicated without being
distinguished from each other.
[0033] The processing device 30 acquires image data
output from the imaging device 20 and numerical data output
from the sensor group 21, and stores the acquired image
data and numerical data in the storage device 61. The
processing device 30 causes the arithmetic device 40 to
execute an arithmetic operation employing data selected
between the image data output from the imaging device 20
and the numerical data output from the sensor group 21 as
input data, and acquires information including a result of
the arithmetic operation by the arithmetic device 40. The
processing device 30 transmits the information output from
the arithmetic device 40 to the control device 50, and
stores the information output from the arithmetic device 40
in the storage device 61.
[0034] In addition, the processing device 30 can display
the image data output from the imaging device 20 on the
display device 62. The operator can determine, for example,
on the basis of an image of the inside of the tank
displayed on the display device 62, whether there is a sign
of a future unfavorable intra-tank state in the water
treatment device 10. The term “future” here means, for
example, several hours ahead or one or more days ahead.
[0035] The future unfavorable intra-tank state includes,
for example, a state where the removal of organic matter
becomes insufficient, a state where the removal of nitrogen
becomes insufficient, and a state where a filtration
membrane (not illustrated) becomes easily clogged. In
addition, the sign of the future unfavorable intra-tank
state includes, for example, a state where the number of
microorganisms which inhibit water treatment is increasing,
or a state where the distribution of microorganisms which
perform water treatment exhibits a specific distribution.
In the water treatment plant 1 according to the first
embodiment, it is possible to determine the sign of the
future unfavorable intra-tank state described above on the
basis of the image data of the imaging device 20.
Therefore, it is possible to contribute to improvement in
diversification and accuracy of grounds for determination
of signs, as compared with determination of signs using
numerical data only. Hereinafter, the sign of the future
unfavorable intra-tank state may be simply referred to as
the sign.
[0036] In a case where the operator determines that the
image of the inside of the tank displayed on the display
device 62 indicates the above-described sign, by operating
the input device 63, the operator can generate or update a
calculation model included in the arithmetic device 40
employing, as learning data, image data at a time when an
environmental change indicating the sign occurs.
[0037] FIG. 4 is a diagram illustrating an example
configuration of the processing device according to the
first embodiment. As illustrated in FIG. 4, the processing
device 30 includes a communication unit 31, a storage unit
32, and a control unit 33. The communication unit 31 is
connected to the communication network 64. The control
unit 33 can transmit and receive data to and from each of
the arithmetic device 40, the control device 50, the
storage device 61, the display device 62, and the input
device 63 via the communication unit 31 and the
communication network 64.
[0038] The control unit 33 includes a data processing
unit 34, a display processing unit 35, an arithmeticoperation
request unit 36, an acceptance processing unit 37,
and a switching unit 38. The data processing unit 34
repeatedly acquires image data output from the imaging
device 20 and numerical data output from the sensor group
21, and stores the acquired image data and numerical data
in the storage device 61.
[0039] The data processing unit 34 stores the image data
acquired from each imaging device 20 in the storage device
61 in association with time. In addition, the data
processing unit 34 stores the numerical data acquired from
each sensor in the storage device 61 in association with
time. Furthermore, the data processing unit 34 acquires
information output from the arithmetic device 40, outputs
the acquired information to the control device 50, and
stores the acquired information in the storage device 61.
[0040] FIG. 5 is a diagram illustrating an example of a
data table stored in the storage device according to the
15 first embodiment. The data table illustrated in FIG. 5
includes image data, numerical data, and control target
values for each time. In FIG. 5, image data IM1(t0),
IM1(t1),..., IM1(tm),..., and IM1(tn) are image data of the
imaging device 201. In addition, image data IM2(t0),
IM2(t1),..., IM2(tm),..., and IM2(tn) are image data of the
imaging device 202.
[0041] Furthermore, image data IM3(t0), IM3(t1),...,
IM3(tm),..., and IM3(tn) are image data of the imaging
device 203. Note that m and n are natural numbers, and n>m
is established. Although FIG. 5 illustrates numerical data
of one sensor, i.e. NU1(t0), NU1(t1),...,NU1(tm),..., and
NU1(tn) only, the data table also includes numerical data
of the rest of sensors.
[0042] In addition, the data table illustrated in FIG. 5
includes information on the control target value of each
device to be controlled output to the control device 50 by
the processing device 30 at each time. In FIG. 5, control
target values RV1(t0), RV1(t1),..., RV1(tm),..., and
RV1(tn) are control target values of the blower 14. In
addition, control target values RV2(t0), RV2(t1),...,
RV2(tm),..., and RV2(tn) are control target values of the
pump 15.
[0043] Returning to FIG. 4, the description of the
control unit 33 will be continued. The display processing
unit 35 displays the image data and the numerical data
acquired by the data processing unit 34 on the display
device 62. In addition, the display processing unit 35 can
10 acquire, from the storage device 61, the information input
by the operator operating the input device 63, and can
display the acquired information on the display device 62.
[0044] The arithmetic-operation request unit 36 outputs,
to the arithmetic device 40 via the communication network
15 64, data necessary for inputting a calculation model which
satisfies a selection condition described later, out of the
image data and the numerical data acquired by the data
processing unit 34.
[0045] For example, in a case where the calculation
20 model which satisfies the selection condition is a
calculation model for an image, the arithmetic-operation
request unit 36 outputs the image data acquired by the data
processing unit 34 to the arithmetic device 40. In
addition, in a case where the calculation model which
25 satisfies the selection condition is a calculation model
for a sensor, the arithmetic-operation request unit 36
outputs the numerical data acquired by the data processing
unit 34 to the arithmetic device 40.
[0046] Furthermore, in a case where the calculation
30 models which satisfy the selection conditions are the
calculation model for an image and the calculation model
for a sensor, the arithmetic-operation request unit 36
outputs the image data and the numerical data acquired by
17
the data processing unit 34 to the arithmetic device 40.
It is also possible for the arithmetic-operation request
unit 36 to acquire the data necessary for inputting the
calculation model which satisfies the selection condition
5 from the storage device 61 and to output the acquired data
to the arithmetic device 40.
[0047] The arithmetic-operation request unit 36 outputs
detection data to the arithmetic device 40, thereby causing
the arithmetic device 40 to execute an arithmetic operation
10 employing the detection data as input data. The data
processing unit 34 acquires information indicating a result
of the arithmetic operation output from the arithmetic
device 40, and outputs the acquired information to the
control device 50. The information output from the
15 arithmetic device 40 includes, for example, control
information including the control target value of the
device to be controlled, and the control device 50 controls
the water treatment device 10 by controlling the device to
be controlled provided in the water treatment device 10 on
20 the basis of the information output from the processing
device 30.
[0048] The acceptance processing unit 37 accepts
selection of image data for generating and updating
multiple calculation models included in the arithmetic
25 device 40 on the basis of an operation on the input device
63 performed by the operator. The arithmetic-operation
request unit 36 acquires image data, the selection of which
has been accepted by the acceptance processing unit 37,
from the storage device 61. In addition, the arithmetic30
operation request unit 36 acquires, from the storage device
61, information on the control target value of each device
to be controlled associated with time when the selected
image data was acquired.
18
[0049] The arithmetic-operation request unit 36
transmits learning data in which the selected image data
and object-to-be-controlled data are associated with each
other, to the arithmetic device 40 via the communication
5 network 64. In the learning data, the object-to-becontrolled
data associated with the selected image data is
data including the control target values acquired from the
storage device 61 and the type of each device to be
controlled. For example, in a case where the selected
10 image data are image data IM1(tm), IM2(tm), and IM3(tm) at
a time tm illustrated in FIG. 5, the object-to-becontrolled
data includes control target values RV1(tm) and
RV2(tm) illustrated in FIG. 5.
[0050] The acceptance processing unit 37 can also accept
15 information on a period for selecting time-series image
data stored in the storage device 61, on the basis of the
operation on the input device 63 performed by the operator.
For example, the acceptance processing unit 37 can accept
an operation on the input device 63 for selecting image
20 data for the past year.
[0051] The arithmetic-operation request unit 36 acquires,
from the storage device 61, time-series image data output
from the imaging device 20 during the period accepted by
the acceptance processing unit 37. In addition, the
25 arithmetic-operation request unit 36 acquires, from the
storage device 61, data of time-series control target
values set in each device to be controlled during the
period accepted by the acceptance processing unit 37. The
arithmetic-operation request unit 36 transmits learning
30 data including the acquired time-series image data and data
of time-series control target values to the arithmetic
device 40 via the communication network 64.
[0052] In addition, as will be described later, in a
19
case where the calculation model for an image is, for
example, a recurrent neural network which outputs
information on a score indicating the degree whether an
environmental change indicating the above-described sign
5 has occurred, the operator can select correct data and
incorrect data. For example, the operator can select, as
the correct data, image data imaged by the imaging device
20 in a state where there is the above-described sign in
the water treatment device 10. In addition, the operator
10 can select, as the incorrect data, for example, image data
imaged by the imaging device 20 at a time when there is no
above-described sign.
[0053] The switching unit 38 can operate in a manual
switching mode in which the selection condition is changed
15 on the basis of the operation on the input device 63
performed by the operator. For example, in a case where
the acceptance processing unit 37 accepts a selection
condition switching operation performed by the operator
when the operation mode of the switching unit 38 is the
20 manual switching mode, the switching unit 38 changes the
selection condition set in the storage unit 32.
[0054] In addition, the switching unit 38 can also
operate in an automatic switching mode in which the
selection condition is automatically changed. For example,
25 in a case where the operation mode of the switching unit 38
is the automatic switching mode and the selection condition
is set at the calculation model for a sensor, the switching
unit 38 determines whether a first switching condition is
satisfied. If it is determined that the first switching
30 condition is satisfied, the switching unit 38 changes the
selection condition set in the storage unit 32 from the
calculation model for a sensor to the calculation model for
an image. As a result, the calculation model used in the
20
arithmetic device 40 is changed to the calculation model
for an image.
[0055] For example, in a case where a numerical value
indicated by numerical data of one or more specific sensors
5 included in the multiple sensor groups 21 is outside a
preset range continuously for a preset period of time or
longer, the switching unit 38 can determine that the first
switching condition is satisfied. The first switching
condition is not limited to conditions of the detection
10 results of the sensors, and may be a condition of, for
example, time of day, season, weather, or any other
condition.
[0056] In addition, in a case where the operation mode
of the switching unit 38 is the automatic switching mode
15 and the calculation model for an image is set as the
selection condition, the switching unit 38 determines
whether a second switching condition is satisfied. If it
is determined that the second switching condition is
satisfied, the switching unit 38 changes the selection
20 condition set in the storage unit 32 from the calculation
model for an image to the calculation model for a sensor.
As a result, the calculation model used in the arithmetic
device 40 is changed to the calculation model for a sensor.
[0057] For example, in a case where a numerical value
25 indicated by numerical data of one or more specific sensors
included in the multiple sensor groups 21 is inside a
preset range continuously for a preset period of time or
longer, the switching unit 38 can determine that the second
switching condition is satisfied. The second switching
30 condition is not limited to conditions of the detection
results of the sensors, and may be a condition of, for
example, time of day, season, weather, or any other
condition.
21
[0058] The operation mode of the switching unit 38 can
be changed on the basis of an operation performed by the
operator. In addition, the switching unit 38 can change
the calculation models alternately between the calculation
5 model for a sensor and the calculation model for an image.
For example, the switching unit 38 can set the calculation
model for a sensor in a first period T1, and can set the
calculation model for an image in a second period T2 which
comes alternately with the first period T1. In such a case,
10 it is possible to mainly perform water treatment control
with numerical values while performing water treatment
control with images by making the second period T2 shorter
than the first period T1.
[0059] Next, the arithmetic device 40 will be described.
15 FIG. 6 is a diagram illustrating an example configuration
of the arithmetic device according to the first embodiment.
As illustrated in FIG. 6, the arithmetic device 40 includes
a communication unit 41, a storage unit 42, and a control
unit 43.
20 [0060] The communication unit 41 is connected to the
communication network 64. The control unit 43 can transmit
and receive data to and from each of the imaging device 20,
the processing device 30, the control device 50, the
storage device 61, and the input device 63 via the
25 communication unit 41 and the communication network 64.
[0061] The storage unit 42 stores multiple calculation
models. The multiple calculation models stored in the
storage unit 42 include the above-described calculation
model for an image and calculation model for a sensor.
30 [0062] The calculation model for an image is, for
example, a convolutional neural network which receives
inputs of multiple image data output from multiple imaging
devices 20 and outputs control target values of multiple
22
devices to be controlled. With the use of the
convolutional neural network, as compared to a case of
using a general neural network, learning of image data is
efficiently performed by sharing weights, which makes it
5 possible to acquire highly accurate results. In
consideration of the variety of system architecture, the
calculation model for an image may be a neural network
other than the convolutional neural network.
[0063] The calculation model for a sensor is, for
10 example, a neural network which receives inputs of multiple
numerical data output from multiple sensors provided in the
multiple sensor groups 211 to 213 and outputs control
target values of multiple devices to be controlled. The
calculation model for a sensor is a neural network suitable
15 for an arithmetic operation of numerical data, unlike the
convolutional neural network which is a calculation model
for an image. In addition, for example, the calculation
model for a sensor may be a calculation model generated by
a learning algorithm such as linear regression or logistic
20 regression. The calculation model for a sensor may be a
convolutional neural network because the degree of freedom
in device configuration is increased.
[0064] The control unit 43 includes an acquisition
processing unit 44, an arithmetic processing unit 45, an
25 output processing unit 46, and a learning processing unit
47. The acquisition processing unit 44 acquires detection
data from the processing device 30 via the communication
network 64 and the communication unit 41. The detection
data from the processing device 30 includes image data,
30 numerical data, or image data and numerical data, as
described above.
[0065] The arithmetic processing unit 45 reads, from the
storage unit 42, a calculation model corresponding to the
23
detection data acquired by the acquisition processing unit
44, inputs the detection data to the read calculation model
to perform an arithmetic operation using the calculation
model, thereby acquiring an output of the calculation model.
5 For example, in a case where the detection data acquired by
the acquisition processing unit 44 is image data, the
arithmetic processing unit 45 inputs the image data to the
calculation model for an image to perform an arithmetic
operation using the calculation model for an image, and
10 acquires an output of the calculation model for an image.
[0066] In addition, in a case where the detection data
acquired by the acquisition processing unit 44 is numerical
data, the arithmetic processing unit 45 inputs the
numerical data to the calculation model for a sensor to
15 perform an arithmetic operation using the calculation model
for a sensor, and acquires an output of the calculation
model for a sensor.
[0067] In addition, in a case where the detection data
acquired by the acquisition processing unit 44 includes
20 image data and numerical data, the arithmetic processing
unit 45 uses both the calculation model for an image and
the calculation model for a sensor. That is, the
arithmetic processing unit 45 inputs the image data out of
the image data and the numerical data to the calculation
25 model for an image to perform an arithmetic operation using
the calculation model for an image, and acquires
information output from the calculation model for an image.
Furthermore, the arithmetic processing unit 45 inputs the
numerical data out of the image data and the numerical data
30 to the calculation model for a sensor to perform an
arithmetic operation using the calculation model for a
sensor, and acquires information output from the
calculation model for a sensor.
24
[0068] The output processing unit 46 outputs, as the
output information of the arithmetic device 40, information
acquired by the arithmetic operation using each calculation
model in the arithmetic processing unit 45 to the
5 processing device 30 from the communication unit 41. The
information output from each calculation model is
information on the control target values of the multiple
devices to be controlled described above.
[0069] In the case where the detection data acquired by
10 the acquisition processing unit 44 includes image data and
numerical data, the output processing unit 46 can select
one of information output from the calculation model for a
sensor and information output from the calculation model
for an image to output the selected information to the
15 processing device 30 from the communication unit 41.
[0070] For example, in a case where a difference between
the control target value output from the calculation model
for an image and the control target value output from the
calculation model for a sensor is a preset value or larger,
20 the output processing unit 46 selects the control target
value output from the calculation model for an image and
outputs the control target value to the processing device
30. In addition, in a case where the difference between
the control target value output from the calculation model
25 for an image and the control target value output from the
calculation model for a sensor is smaller than the preset
value, the output processing unit 46 selects the control
target value output from the calculation model for a sensor
and outputs the control target value to the processing
30 device 30.
[0071] In the case where the detection data acquired by
the acquisition processing unit 44 includes image data and
numerical data, the arithmetic processing unit 45 can
25
perform, for each device to be controlled, an arithmetic
operation of an average value of the control target value
output from the calculation model for a sensor and the
control target value output from the calculation model for
5 an image. The output processing unit 46 can output, as
output information, control information including the
average value of the control target values for each device
to be controlled obtained by the arithmetic operation by
the arithmetic processing unit 45.
10 [0072] The calculation model for an image may include a
recurrent neural network in addition to the convolutional
neural network described above. In such a case, the
arithmetic processing unit 45 inputs time-series image data
imaged by the imaging device 20 to the recurrent neural
15 network, and acquires, from the recurrent neural network,
data of an image predicted to be imaged by the imaging
device 20 after the elapse of a time Ta. The time Ta is,
for example, 12 hours or longer. Then, the arithmetic
processing unit 45 inputs the data of the image predicted
20 to be imaged by the imaging device 20 after the elapse of
the time Ta to the convolutional neural network, and
acquires information output from the convolutional neural
network.
[0073] In addition, the calculation model for an image
25 may include the recurrent neural network only. Such a
recurrent neural network receives an input of, for example,
time-series image data imaged by the imaging device 20, and
outputs information on a score indicating the degree
whether an environmental change indicating the above30
described sign has occurred. Such a recurrent neural
network is stored for each type of sign in the storage unit
42. In the storage unit 42, control information, which is
information in which the type and the control target value
26
of each device to be controlled are associated with each
other, is stored for each type of sign. Such control
information can be stored in the storage unit 42 by the
operator operating the input device 63, for example.
5 [0074] The arithmetic processing unit 45 can input the
time-series image data imaged by the imaging device 20 to
the recurrent neural network for each type of sign to
acquire information on a score output from each recurrent
neural network. The arithmetic processing unit 45 acquires,
10 from the storage unit 42, control information including the
type and the control target value of the device to be
controlled associated with the type of sign of which score
is equal to or higher than a threshold. In addition, in a
case where there are multiple types of sign of which scores
15 are equal to or higher than the threshold, the arithmetic
processing unit 45 acquires, from the storage unit 42,
control information including the type and the control
target value of the device to be controlled associated with
the type of sign of which score is highest. The arithmetic
20 processing unit 45 outputs the acquired control information
including the type and the control target value of the
device to be controlled to the processing device 30 from
the communication unit 41 as output information of the
arithmetic device 40.
25 [0075] The learning processing unit 47 can generate and
update the above-described calculation model for an image
on the basis of the learning data output from the
processing device 30. The learning processing unit 47
stores the generated or updated calculation model for an
30 image in the storage unit 42.
[0076] For example, in a case where the calculation
model for an image includes a convolutional neural network,
the learning processing unit 47 can generate or update the
27
calculation model for an image by optimizing the
convolutional neural network on the basis of the image data
and the object-to-be-controlled data included in the
learning data.
5 [0077] In a case where the calculation model for an
image includes the recurrent neural network, the learning
processing unit 47 can generate or update the calculation
model for an image by optimizing the recurrent neural
network on the basis of the learning data including the
10 time-series image data.
[0078] The neural network in the arithmetic device 40 is
an artificial neural network. The artificial neural
network is a calculation model in which perceptrons are
hierarchically arranged, each of the perceptrons obtaining
15 a weighted sum of input signals, applying a non-linear
function called an activation function thereto, and
outputting a result of the application. The output out of
the perceptron can be expressed by the following equation
(1), in which the input is expressed by X=(x1, x2,..., xn),
20 the weight is expressed by W=(w1, w2,..., wn), the
activation function is expressed by f(.), and the elementwise
product of vectors is expressed by *.
[0079] out=f(X*W)...(1)
[0080] In the convolutional neural network, perceptrons
25 each receive a two-dimensional signal corresponding to an
image as an input, calculate a weighted sum of the inputs,
and pass results of the calculation to the next layer. As
the activation function, a sigmoid function or a rectified
linear unit (ReLU) function is used.
30 [0081] The above-described perceptrons are
hierarchically arranged in the artificial neural network,
and an identification result is calculated by processing an
input signal in each layer. In a final layer, for example,
28
if the type of task in the artificial neural network is a
regression task, an output of the activation function is
used as it is as an output of the task, and if the type of
task is a classification task, a softmax function is
5 applied regarding the final layer, and a result of the
application is used as an output of the task.
[0082] In a case of the convolutional neural network, an
artificial network is configured as a map of twodimensional
signals. It can be considered that each of the
10 two-dimensional signals corresponds to the perceptron.
With respect to a feature map of the previous layer, the
weighted sum is calculated and the activation function is
applied, and a result thereof is output.
[0083] The above-described process is called a
15 convolution operation in the convolutional neural network,
and in addition thereto, a pooling layer for performing a
pooling process may be inserted in each layer. The pooling
layer performs downsampling by performing an averaging
operation or a maximum operation on a feature map.
20 [0084] Learning by such an artificial neural network is
performed by back propagation, and for example, a known
stochastic gradient descent method is used. The back
propagation is a framework in which an output error of the
artificial neural network is propagated from the final
25 layer to previous layers in sequence to update weights.
[0085] Next, the control device 50 illustrated in FIG. 2
will be described. The control device 50 can control the
water treatment device 10 by controlling the blower 14, the
pump 15, and the like. For example, by controlling the
30 blower 14 to adjust the amount of air blown into a liquid
mixture containing activated sludge, the control device 50
can control the concentration of dissolved oxygen in the
liquid mixture containing activated sludge. In addition,
29
by controlling the pump 15, the control device 50 adjusts
the flow rate of activated sludge returned to the treatment
tank 12 from the final settling tank 13.
[0086] FIG. 7 is a diagram illustrating an example
5 configuration of the control device according to the first
embodiment. As illustrated in FIG. 7, the control device
50 includes a communication unit 51, a storage unit 52, a
control unit 53, and an input/output unit 54. The
communication unit 51 is connected to the communication
10 network 64. The control unit 53 can transmit and receive
data to and from the processing device 30 via the
communication unit 51 and the communication network 64.
[0087] The control unit 53 includes an input processing
unit 55, a blower control unit 56, and a pump control unit
15 57. The input processing unit 55 acquires control
information output from the processing device 30 via the
communication unit 51, and stores the acquired control
information in the storage unit 52. The control
information stored in the storage unit 52 includes a
20 control target value of the blower 14 and a control target
value of the pump 15.
[0088] The blower control unit 56 reads the control
target value of the blower 14 stored in the storage unit 52.
In addition, the blower control unit 56 acquires numerical
25 data indicating the amount of dissolved oxygen detected by
the dissolved oxygen sensor 231 from the storage device 61
or the dissolved oxygen sensor 231. The blower control
unit 56 generates a control signal by proportional integral
(PI) control or proportional integral differential (PID)
30 control on the basis of the control target value of the
blower 14 and the acquired amount of dissolved oxygen. The
blower control unit 56 outputs the generated control signal
to the blower 14 from the input/output unit 54. The blower
30
14 adjusts the amount of air blown into the treatment tank
12 on the basis of the control signal output from the
input/output unit 54 of the control device 50.
[0089] The pump control unit 57 reads the control target
5 value of the pump 15 stored in the storage unit 52. In
addition, the pump control unit 57 acquires, from a sensor
(not illustrated) via the input/output unit 54, numerical
data indicating the flow rate of the activated sludge to
the treatment tank 12 from the final settling tank 13. The
10 pump control unit 57 generates a control signal by PI
control or PID control on the basis of the control target
value of the pump 15 and the acquired flow rate of the
activated sludge. The pump control unit 57 outputs the
generated control signal to the pump 15 from the
15 input/output unit 54. The pump 15 adjusts the flow rate of
the activated sludge to the treatment tank 12 from the
final settling tank 13 on the basis of the control signal
output from the input/output unit 54 of the control device
50.
20 [0090] Next, an operation of the water treatment plant 1
will be described with reference to a flowchart. FIG. 8 is
a flowchart illustrating an example of a series of
processes of the processing device according to the first
embodiment, and the series of processes is repeatedly
25 executed by the control unit 33 of the processing device 30.
[0091] As illustrated in FIG. 8, the control unit 33 of
the processing device 30 determines whether a selection
condition switching operation has been accepted from the
operator (step S10). If it is determined that the
30 selection condition switching operation has been accepted
(step S10: Yes), the control unit 33 switches the selection
condition by changing the selection condition stored in the
storage unit 32 to a selection condition depending on the
31
switching operation (step S11).
[0092] When the process of step S11 ends, or if it is
determined that the selection condition switching operation
has not been accepted (step S10: No), the control unit 33
5 determines whether selection of image data has been
accepted from the operator (step S12). If it is determined
that the selection of image data has been accepted (step
S12: Yes), the control unit 33 outputs learning data
including the selected image data to the arithmetic device
10 40 (step S13).
[0093] When the process of step S13 ends, or if it is
determined that the selection of the image data has not
been accepted (step S12: No), the control unit 33
determines whether the detection data has been acquired
15 (step S14). If it is determined that the detection data
has been acquired (step S14: Yes), the control unit 33
determines whether the operation mode is the automatic
switching mode (step S15).
[0094] If it is determined that the operation mode is
20 the automatic switching mode (step S15: Yes), the control
unit 33 performs an automatic switching process (step S16).
In step S16, when the control unit 33 determines that the
first switching condition is satisfied in a state where the
calculation model for a sensor is set as the selection
25 condition, the control unit 33 sets the calculation model
for an image as the selection condition. In addition, when
the control unit 33 determines that the second switching
condition is satisfied in a state where the calculation
model for an image is set as the selection condition, the
30 control unit 33 sets the calculation model for a sensor as
the selection condition.
[0095] When the process of step S16 ends, or if it is
determined that the operation mode is not the automatic
32
switching mode (step S15: No), the control unit 33 acquires
detection data corresponding to the selection condition
from the storage device 61, and outputs the acquired
detection data to the arithmetic device 40 (step S17). In
5 step S17, for example, in a case where the set selection
condition is the calculation model for an image, the
detection data corresponding to the selection condition is
image data. In addition, in a case where the set selection
condition is the calculation model for a sensor, the
10 detection data corresponding to the selection condition is
numerical data.
[0096] Next, the control unit 33 acquires output
information output from the arithmetic device 40 in
response to step S17 (step S18), and outputs the acquired
15 output information to the control device 50 (step S19).
Such output information includes the control information as
described above. When the process of step S19 ends, or if
it is determined that the detection data has not been
acquired (step S14: No), the control unit 33 ends the
20 processes illustrated in FIG. 8.
[0097] FIG. 9 is a flowchart illustrating an example of
a series of processes of the arithmetic device according to
the first embodiment, and the series of processes is
repeatedly executed by the control unit 43 of the
25 arithmetic device 40.
[0098] As illustrated in FIG. 9, the control unit 43 of
the arithmetic device 40 determines whether the detection
data has been acquired from the processing device 30 (step
S20). If it is determined that the detection data has been
30 acquired (step S20: Yes), the control unit 43 executes an
arithmetic process using a calculation model and employing
the acquired detection data as an input of the calculation
model (step S21), and transmits output information of the
33
calculation model to the processing device 30 (step S22).
[0099] When the process of step S22 ends, or if it is
determined that the detection data has not been acquired
(step S20: No), the control unit 43 determines whether the
5 learning data has been acquired from the processing device
30 (step S23). If it is determined that the learning data
has been acquired from the processing device 30 (step S23:
Yes), the control unit 43 executes a learning process of
the calculation model using the learning data (step S24).
10 [0100] When the process of step S24 ends, or if it is
determined that the learning data has not been acquired
(step S23: No), the control unit 43 ends the processes
illustrated in FIG. 9.
[0101] FIG. 10 is a flowchart illustrating an example of
15 a series of processes of the control device according to
the first embodiment, and the series of processes is
repeatedly executed by the control unit 53 of the control
device 50.
[0102] As illustrated in FIG. 10, the control unit 53 of
20 the control device 50 determines whether the control
information has been acquired from the processing device 30
(step S30). If it is determined that the control
information has been acquired (step S30: Yes), the control
unit 53 controls each device to be controlled on the basis
of the acquired control information (step S31). When the
process of step S31 ends, or if it is determined that the
control information has not been acquired (step S30: No),
the control unit 53 ends the processes illustrated in FIG..
[0103] FIG. 11 is a diagram illustrating an example of a hardware configuration of the processing device according to the first embodiment. As illustrated in FIG. 11, the processing device 30 includes a computer including 101, a memory 102, and an interface circuit 103.
[0104] The processor 101, the memory 102, and the
interface circuit 103 can transmit and receive data to and
from each other via a bus 104. The communication unit 31 is realized by the interface circuit 103. The storage unit is realized by the memory 102. The processor 101
executes functions of the data processing unit 34, the display processing unit 35, the arithmetic-operation request unit 36, the acceptance processing unit 37, and the switching unit 38 by reading and executing programs stored in the memory 102. The processor 101 is an example of a processing circuit, and includes one or more of a central processing unit (CPU), a digital signal processor (DSP), and system large scale integration (LSI).
[0105] The memory 102 includes one or more of a random access memory (RAM), a read only memory (ROM), a flash memory, and an erasable programmable read only memory
(EPROM). In addition, the memory 102 includes a recording medium in which the above-described programs readable by the computer are recorded. Such a recording medium includes one or more of a non-volatile or volatile semiconductor memory, a magnetic disk, a flexible memory, an optical disk, a compact disc, and a DVD.
[0106] In a case where the control unit 33 of the processing device 30 is realized by dedicated hardware, the control unit 33 is, for example, a single circuit, a
composite circuit, a programmed processor, a parallel programmed processor, an application specific integrated circuit (ASIC), a field programmable gate array (FPGA), or
a combination thereof.
[0107] The arithmetic device 40 also includes a hardware
configuration similar to the hardware configuration
illustrated in FIG. 11. The communication unit 41 is realized by the interface circuit 103. The storage unit 42 is realized by the memory 102. The processor 101 executes
functions of the acquisition processing unit 44, the arithmetic processing unit 45, the output processing unit 46, and the learning processing unit 47 by reading and executing the programs stored in the memory 102. In a case where the control unit 43 is realized by dedicated hardware, the control unit 43 is a single circuit, a composite circuit, a programmed processor, a parallel programmed processor, an ASIC, an FPGA, or a combination thereof.
[0108] The control device 50 also includes a hardware
configuration similar to the hardware configuration
illustrated in FIG. 11. The communication unit 51 and the
input/output unit 54 are realized by the interface circuit
103. The storage unit 52 is realized by the memory 102. The processor 101 executes functions of the input processing unit 55, the blower control unit 56, and the pump control unit 57 by reading and executing programs stored in the memory 102. In a case where the control unit 53 is realized by dedicated hardware, the control unit 53 is a single circuit, a composite circuit, a programmed processor, a parallel programmed processor, an ASIC, an FPGA, or a combination thereof.
[0109] In the example described above, the information output from the arithmetic device 40 is output to the control device 50 from the processing device 30, but a
configuration may be employed in which the information output from the arithmetic device is directly input to the control device 50 without the processing device 30.
[0110] In a case where the calculation model for an image includes the recurrent neural network, it is possible to output, to the processing device 30 for each type of
sign, information on a sign score which is a score indicating the degree whether there is the sign of the future unfavorable intra-tank state in the water treatment device 10. In such a case, the display processing unit of the processing device 30 can display the acquired sign
score for each type of sign on the display device 62.
[0111] In the above-described example, the calculation model which employs image data only as input data has been described as an example of the calculation model for an image, but the calculation model for an image may be a calculation model which employs, in addition to image data, numerical data or other data as input data.
[0112] Although the convolutional neural network which receives inputs of multiple image data and outputs multiple control target values has been described above as an example of the calculation model for an image, the calculation model for an image is not limited to the
example described above. For example, the convolutional neural network can be provided for each control target value as the calculation model for an image. The
convolutional neural network can be provided for each imaging device as the calculation model for an image. In addition, the convolutional neural network can be provided for each imaging device and each device to be controlled as the calculation model for an image.
[0113] In the above-described example, in a case where
the calculation model for an image includes the recurrent neural network only, the control information, which is information in which the type and the control target value of each device to be controlled are associated with each other, is stored for each type of sign, but there is not limitation to such an example. For example, the arithmetic device 40 can also generate or update the recurrent neural network by performing machine learning on the basis of the time-series image data and the time-series control target values stored in the storage device 61. In such a case, the recurrent neural network outputs the control target values from the time-series image data. As a result, effective water treatment can be performed, for example, even in a case where there is a sign in the water treatment plant 1, the sign being one of multiple signs of the future unfavorable intra-tank state and being not yet recognized by the operator.
[0114] In the above-described example, the blower 14 and the pump 15 have been described as examples of the device to be controlled which is controlled by using the arithmetic device 40, but the device to be controlled which is controlled by using the arithmetic device 40 may include devices other than the blower 14 and the pump 15.
[0115] As described above, the water treatment plant 1
according to the first embodiment includes the water
treatment device 10 which performs water treatment, the
imaging device 20, the processing device 30, the arithmetic
device 40, and the control device 50. The imaging device images a water treatment environment of the water treatment device 10 and outputs image data obtained by
imaging. The processing device 30 causes the arithmetic
device 40 which performs an arithmetic operation using one or more calculation models generated by machine learning to execute the arithmetic operation employing the image data output from the imaging device 20 as input data of the one
or more calculation models. The control device 50 controls the water treatment device on the basis of output information output from the arithmetic device 40 by
executing the arithmetic operation. Therefore, in the water treatment plant 1, it is possible to perform, with the use of the arithmetic device 40, for example, water treatment control which has been performed by the operator of the water treatment plant 1 on the basis of an image of the water treatment environment of the water treatment device and on the basis of past experience or knowledge of the operator. Therefore, more effective water treatment control can be performed with respect to a change in the water treatment environment.
[0116] In addition, the one or more calculation models
include a convolutional neural network employing image data as input data. The processing device 30 causes the arithmetic device 40 to execute an arithmetic operation using the convolutional neural network. The convolutional neural network is an example of the calculation model for an image. As described above, by preparing the
convolutional neural network employing image data as input data and causing the arithmetic device 40 to execute the arithmetic operation using the convolutional neural network
on the image data output from the imaging device 20, the water treatment device 10 can be accurately controlled.
[0117] The water treatment plant 1 includes a sensor
which detects a characteristic indicating the water
treatment environment of the water treatment device 10 and
outputs numerical data of the detected characteristic. The
arithmetic device 40 includes a neural network for a sensor which employs numerical data output from the sensor as input data. The neural network for a sensor is an example
of the calculation model for a sensor described above. The processing device 30 causes the arithmetic device 40 to execute an arithmetic operation using the neural network
for a sensor. As described above, by detecting the characteristic indicating the water treatment environment of the water treatment device with a sensor 2,
outputting numerical data of the detected characteristic from the sensor 2, preparing the neural network for a sensor which employs the numerical data output from the sensor 2 as input data, and causing the arithmetic device to execute the arithmetic operation using the neural network for a sensor on the numerical data output from the sensor 2, it is possible to control the water treatment device 10 using a detection result of the sensor.
[0118] The processing device includes the switching unit 38 which performs switching between the use of the convolutional neural network and the use of the neural
network for a sensor to cause the arithmetic device 40 to execute the arithmetic operation. As a result, the water treatment device 10 can be accurately controlled, for
example, by performing switching between the water treatment control using the image imaged by the imaging device and the water treatment control using the detection result by the sensor depending on the situation.
[0119] In addition, the processing device 30 includes
the acceptance processing unit 37 which accepts selection of one or more image data from multiple image data imaged by the imaging device The arithmetic device executes machine learning of one or more calculation models on the basis of the one or more image data accepted by the acceptance processing unit 37. As a result, for example,
the calculation models included in the arithmetic device can be updated, and the water treatment device can be accurately controlled.
[0120] The control device 50 controls each device to be controlled provided in the water treatment device by proportional-integral control or proportional-integralderivative
control. As a result, the water treatment device can be accurately controlled.
[0121] The water treatment device 10 includes the
devices to be controlled which are objects to be controlled by the control device 50. The processing device 30 causes the arithmetic device 40 to execute an arithmetic operation
to generate control target values RV1 and RV2 of the devices to be controlled. The control device 50 controls the water treatment device 10 employing the control target
values RV1 and RV2 caused to be generated by the processing
device as output information. As a result, the devices
to be controlled provided in the water treatment device can be accurately controlled.
[0122] The configurations described in the embodiment
above are merely examples of the content of the present invention and can be combined with other known technology and part thereof can be omitted or modified without departing from the gist of the present invention.
Reference Signs List
[0123] 1 water treatment plant; 10 water treatment
device; 11 primary settling tank; 12 treatment tank; 13 final settling tank; 14 blower; 15 pump; 20, 201, 202, 203
imaging device; 21, 211, 212, 213 sensor group; 221 flow
rate sensor; 222 BOD sensor; 223 water temperature sensor;
224 NH3 sensor; 231 dissolved oxygen sensor; 232 active
microorganism concentration sensor; 233 BOD sensor; 241
flow rate sensor; 242 BOD sensor; 243 total nitrogen
concentration sensor; 30 processing device; 31, 41, 51
communication unit; 32, 42, 52 storage unit; 33, 43, 53
control unit; 34 data processing unit; 35 display
processing unit; 36 arithmetic-operation request unit; 37
acceptance processing unit; 38 switching unit; 40
arithmetic device; 44 acquisition processing unit; 45
arithmetic processing unit; 46 output processing unit; 47
learning processing unit; 50 control device; 54
input/output unit; 55 input processing unit; 56 blower
control unit; 57 pump control unit; 61 storage device; 62
display device; 63 input device; 64 communication network.

We Claim:
1. A water treatment plant that performs water treatment
5 using a water treatment device, the water treatment plant
comprising: an imaging device to image a water treatment environment of the water treatment device and to output image data obtained by imaging; a processing device to cause an arithmetic device that performs an arithmetic operation using one or more calculation models generated by machine learning to execute the arithmetic operation employing the image data output from the imaging device as input data of the one or more
calculation models; and a control device to control the water treatment device on a basis of output information output from the arithmetic device by executing the arithmetic operation.
2. The water treatment plant according to claim 1, wherein
the arithmetic device includes, as the calculation model, a convolutional neural network employing the image data as input data, and the processing device causes the arithmetic device to execute an arithmetic operation using the convolutional neural network on the image data output by the imaging device.
3. The water treatment plant according to claim 2,
comprising: a sensor to detect a characteristic that indicates a
water treatment environment of the water treatment device and to output numerical data of the detected characteristic, wherein the arithmetic device includes a neural network for a sensor that employs numerical data output from the sensor as input data, and the processing device causes the arithmetic device to execute an arithmetic operation using the neural network for a sensor on the numerical data output from the sensor.
4. The water treatment plant according to claim 3,
Wherein the processing device includes a switching unit to perform switching between
use of the convolutional neural network and use of the neural network for a sensor to cause the arithmetic device to execute the arithmetic operation.
5. The water treatment plant according to any one of
claims 1 to 4, wherein the processing device includes an acceptance processing unit to accept selection of one or more image data among a plurality of
image data imaged by the imaging device, and the arithmetic device executes machine learning of the one or more calculation models on a basis of the one or more image data
accepted by the acceptance processing unit.
6. The water treatment plant according to any one of
claims 1 to 5, wherein the control device controls a device to be controlled provided in the
water treatment device by proportional-integral control or proportional-integral-derivative control.
7. The water treatment plant according to any one of claims 1 to 6, wherein the arithmetic device is AI.
8. The water treatment plant according to any one of
claims 1 to 7, wherein the water treatment device includes a device to be
controlled that is an object to be controlled by the
control device, the processing device causes the arithmetic device to
execute the arithmetic operation to generate a control
target value of the device to be controlled, and the control device controls the water treatment device using the control target value caused to be generated by
the processing device as the output information.
9. A method for operating a water treatment plant that performs water treatment using a water treatment device, the method comprising: an imaging step of imaging a water treatment environment of the water treatment device and outputting image data obtained by imaging; a processing step of causing an arithmetic device that
performs an arithmetic operation using one or more calculation models generated by machine learning to execute the arithmetic operation employing the image data output in the imaging step as input data of the one or more calculation models; and a control step of controlling the water treatment device on a basis of output information output from the arithmetic device by executing the arithmetic operation.
10. The method for operating a water treatment plant
according to claim 9, comprising: a convolutional neural network preparation step of
preparing, as the calculation model, a convolutional neural
network employing the image data as input data for the
arithmetic device; and a convolutional neural network execution step of causing the arithmetic device to execute an arithmetic operation using the convolutional neural network on the image data output in the imaging step.
11. The method for operating a water treatment plant according to claim 10, comprising:
a numerical data output step of detecting a characteristic that indicates a water treatment environment of the water treatment device by a sensor and outputting numerical data of the detected characteristic; a neural network for a sensor preparation step of preparing, as the calculation model, a neural network for a sensor employing the numerical data output in the numerical data output step as input data for the arithmetic device, and a neural network for a sensor execution step of causing the arithmetic device to execute an arithmetic operation using the neural network for a sensor on the numerical data output in the numerical data output step.
12. The method for operating a water treatment plant
according to claim 11, comprising: a switching step of performing switching between the
convolutional neural network and the neural network for a sensor used by the arithmetic device to cause the arithmetic device to execute the arithmetic operation.
13. The method for operating a water treatment plant
according to any one of claims 9 to 12, comprising: a selection step of accepting selection of one or more image data from a plurality of the image data imaged in the
imaging step; and a machine learning execution step of executing machine
learning of the one or more calculation models on a basis
of the one or more image data selected in the selection
step.
14. The method for operating a water treatment plant
according to any one of claims 9 to 13, wherein
in the control step, a device to be controlled provided in the water
treatment device is controlled by proportional-integral control or proportional-integral-derivative control.
15. The method for operating a water treatment plant
according to any one of claims 9 to 14, comprising: an AI preparation step of preparing AI as the arithmetic device.
16. The method for operating a water treatment plant
according to any one of claims 9 to 15, wherein the water treatment device includes a device to be controlled that is an object to be controlled, and
the method comprises: a control target value generation step of causing a
control target value of the device to be controlled to be generated as output information output from the arithmetic cdevice by executing the arithmetic operation; and a control target value control step of controlling the water treatment device employing the control target value generated in the control target value generation step as
the output information.