TW202423845A - Monitoring system, learning device, monitoring method, learning method, and program - Google Patents

Abstract

This monitoring system comprises: a determination unit that determines the internal state of a solid-liquid separation tank to be diagnosed from a supernatant water image representing the supernatant water inside the solid-liquid separation tank, using a first learning model that has learned the relationship between supernatant water images, which are images representing the supernatant water inside a solid-liquid separation tank for separating waste water into solid and liquid, and the internal state of the solid-liquid separation tank, on the basis of the supernatant water images and diagnostic results based on the supernatant water images; and an output unit that outputs information identifying the internal state of the solid-liquid separation tank to be diagnosed as determined by the determination unit using the supernatant water image of the solid-liquid separation tank and the first learning model.

Description

Monitoring system, learning device, monitoring method, learning method and program

The present invention relates to a monitoring system, a learning device, a monitoring method, a learning method, and a program. This application claims priority to Japanese Patent Application No. 2022-120673 filed in Japan on July 28, 2022, and the contents thereof are cited in this application.

Aerobic biological treatment (activated sludge) with aeration tanks and sedimentation tanks is a mature treatment form and is basically completed in terms of engineering as a facility. However, wastewater changes with changes in products, production steps, and production volume. When designing facilities based on assumed water quality and water volume, the inflow conditions (substrate, concentration, flow rate) are often different. In addition, since the shift to multi-variety manufacturing, the number of changes has increased and the size of the changes has increased, making the maintenance and management of complete facilities more complex and increasingly difficult. In addition, in response to the review of water quality and environmental standards, there is a demand for more stable treatment than before, and on the other hand, there is a demand to reduce treatment costs, that is, to reduce electricity costs, waste costs, and the amount of chemicals used. Therefore, a high level of management is required, such as accurately and quickly understanding the processing status and making appropriate adjustments based on the status, but such measurement and monitoring is not easy.

As for the technology for monitoring the processing state, the following technology is known: the technology for measuring the position (interface depth) of the interface formed by the floating turbidity in the liquid based on the result obtained by radiating ultrasonic pulses vertically relative to the liquid surface and receiving the reflected pulses (for example, refer to Patent Document 1). In addition, there is a technology for monitoring the state in a tank such as a solid-liquid separation tank (for example, refer to Patent Document 2). The technology includes: an analog/digital (A/D) converter that converts a received signal obtained by a sensor that emits ultrasound or light and receives ultrasound or light propagated in water containing a suspended sediment layer into a digital signal; a calculation unit that calculates the position of the interface in the tank based on the digital signal; a graphic conversion unit that converts the digital signal into pixel data; a memory that stores the pixel data and interface position data; and a display unit that displays the pixel data stored in the memory. In addition, there is known an interface level meter that can quickly and stably switch and display image information of multiple time widths (for example, refer to patent document 3). The interface level meter includes: an ultrasonic sensor; an A/D converter that converts the received signal obtained by the ultrasonic sensor into a digital signal; a calculation unit that calculates the position of the interface between the sludge accumulation layer and the supernatant based on the digital signal; a graphic conversion unit that converts the digital signal into pixel data corresponding to a specified color gradation; a memory having a memory area that acquires and stores pixel row data containing a plurality of pixel data at different time intervals; and a display unit having a display area that displays a plurality of pixel row data stored in any one of the memory areas based on the color gradation and a display area that displays the position of the interface calculated by the calculation unit.

In addition, there is a known technology for measuring the sedimentation state (for example, refer to patent document 4). The technology includes: an ultrasonic transmission/reception unit that transmits and receives ultrasound vertically downward from the water surface of the sedimentation tank; and a waveform processing unit that processes the reflected reception wave obtained by the ultrasonic transmission/reception unit. The waveform processing unit is used to measure the amount of floating matter under the water surface and/or the concentration distribution of sediment based on the intensity change of the reflected reception wave. In addition, there is a known technology for detecting the interface between layers in the sludge accumulation layer (for example, refer to patent document 5). In this technology, a sensor that emits ultrasound or light in the liquid in the solid-liquid separation tank and receives ultrasound or light propagated in the water containing the sludge accumulation layer is used to detect the position of the interface between the sludge accumulation layer and the supernatant based on the signal from the sensor, and detects the interface between the free sedimentation layer occupying the top layer in the sludge accumulation layer and the coagulation sedimentation layer below it. In this technology, the area where the received signal intensity distribution of the sensor located at the top of the sludge accumulation layer and in the depth direction of the tank is constant is set as the free sedimentation layer, and the position where the received signal intensity distribution begins to increase compared with the received signal intensity of the free sedimentation layer is set as the interface between the free sedimentation layer and the coagulation sedimentation layer. [Prior art literature] [Patent literature]

[Patent Document 1] Japanese Patent Publication No. 3-274484 [Patent Document 2] Japanese Patent Publication No. 2011-047761 [Patent Document 3] Japanese Patent Publication No. 2011-13084 [Patent Document 4] Japanese Patent Publication No. 4-264235 [Patent Document 5] Japanese Patent Publication No. 2011-47760

[The problem that the invention wants to solve]

In the above technology, images obtained by monitoring the processing state (hereinafter referred to as "monitoring images") are interpreted by humans. Humans diagnose the current processing state by interpreting the monitoring images. In addition, humans diagnose the cause of the result obtained by diagnosing the processing state based on experience. Furthermore, humans also judge the corresponding countermeasures. Experience is required to interpret the monitoring images, and sometimes the interpretation results differ depending on the person interpreting. The present invention is made in view of the above situation, and its purpose is to provide a monitoring system, learning device, monitoring method, learning method and program that can monitor the state inside the solid-liquid separation tank used to separate the solid and liquid of wastewater. [Means for solving the problem]

(1) One aspect of the present invention is a monitoring system comprising: a determination unit that determines the state of the interior of a solid-liquid separation tank as a diagnosis target based on a supernatant image representing the supernatant inside a solid-liquid separation tank for separating wastewater into solid and liquid, and a diagnosis result obtained based on the supernatant image, and uses a first learning model that has learned the relationship between the supernatant image and the state of the interior of the solid-liquid separation tank; and an output unit that outputs information for determining the state of the interior of the solid-liquid separation tank determined by the determination unit using the supernatant image of the solid-liquid separation tank as a diagnosis target and the first learning model. (2) One aspect of the present invention is a monitoring system comprising: a determination unit, based on an image representing the interior of a solid-liquid separation tank for separating solid and liquid wastewater, i.e., a monitoring image, and a diagnosis result of the interior of the solid-liquid separation tank obtained based on the monitoring image, and using a first learning model that has learned the relationship between the monitoring image and the state of the interior of the solid-liquid separation tank; A monitoring image of the interior of the solid-liquid separation tank as a diagnostic object is used to determine the state of the interior of the solid-liquid separation tank; and an output unit that outputs information used to determine the state of the interior of the solid-liquid separation tank determined by the monitoring image of the solid-liquid separation tank as a diagnostic object and the first learning model, wherein the monitoring image does not include an image when the measurement is poor, that is, an error image. (3) In one embodiment of the present invention, in the monitoring system described in (1), the supernatant image does not include an image when the measurement is poor, that is, an error image. (4) In one aspect of the present invention, in the monitoring system described in (1), there is a cause determination unit, which is based on the supernatant image and the information for determining the cause of the diagnosis result obtained based on the supernatant image, and uses a second learning model that has learned the relationship between the supernatant image and the information for determining the cause of the diagnosis result inside the solid-liquid separation tank. , based on the supernatant image of the solid-liquid separation tank as the diagnosis object, the information used to determine the cause of the state inside the solid-liquid separation tank is determined, and the output unit further outputs the information used to determine the cause of the state inside the solid-liquid separation tank determined by the cause determination unit using the supernatant image of the solid-liquid separation tank as the diagnosis object and the second learning model. (5) In one aspect of the present invention, in the monitoring system described in (1), there is a response method determination unit, which is based on the supernatant image and the information for determining the response method for the diagnosis result obtained based on the supernatant image, and uses a third learning model that has learned the relationship between the supernatant image and the information for determining the response method for the diagnosis result inside the solid-liquid separation tank. The output unit further outputs the information used to determine the response method for the state inside the solid-liquid separation tank determined by the response method determination unit using the supernatant image of the solid-liquid separation tank as the diagnosis object and the third learning model. (6) In one aspect of the present invention, in the monitoring system described in (1), a change sign deriving unit is included, wherein the change sign deriving unit is based on the supernatant image and the information for determining the change of the state inside the solid-liquid separation tank after the supernatant image is obtained, and uses a fourth learning method that has learned the relationship between the supernatant image and the information for determining the change of the state inside the solid-liquid separation tank. The learning model detects the sign of the change of the state inside the solid-liquid separation tank based on the supernatant image of the solid-liquid separation tank as the diagnosis object, and the output unit further outputs information for determining the sign of the change of the state inside the solid-liquid separation tank detected by the supernatant image of the solid-liquid separation tank as the diagnosis object and the fourth learning model. (7) In one embodiment of the present invention, in the monitoring system described in (1), the diagnosis result is generated based on either or both of the accumulation state of the solid matter contained in the supernatant image and the floating state of the solid matter. (8) In one aspect of the present invention, in the monitoring system described in (1), the determination unit determines whether the state inside the solid-liquid separation tank is normal, abnormal, or abnormal based on the supernatant image showing the inside of the solid-liquid separation tank as the diagnosis object. (9) In one aspect of the present invention, in the monitoring system described in (1), there is further a notification unit, which notifies the state inside the solid-liquid separation tank that the state inside the solid-liquid separation tank is either abnormal or abnormal when the determination unit determines that the state inside the solid-liquid separation tank is either abnormal or abnormal.

(10) One aspect of the present invention is a learning device having a learning unit that generates a first learning model representing the relationship between the supernatant image and the state inside the solid-liquid separation tank based on an image representing a supernatant inside a solid-liquid separation tank for separating wastewater from solid and liquid, i.e., a supernatant image, and a diagnosis result obtained based on the supernatant image of the state inside the solid-liquid separation tank, and by learning. (11) One aspect of the present invention is a learning device having a learning unit, the learning unit generating a first learning model representing the relationship between the monitoring image and the state of the solid-liquid separation tank by learning based on an image representing the interior of a solid-liquid separation tank for separating solid and liquid wastewater, i.e., a monitoring image, and a diagnosis result obtained from the monitoring image of the state of the interior of the solid-liquid separation tank, wherein the monitoring image does not include an image when the measurement is poor, i.e., an error image. (12) In one aspect of the present invention, in the learning device described in (10), the supernatant image does not include an image when the measurement is poor, i.e., an error image. (13) In one aspect of the present invention, in the learning device described in (10), the learning unit generates a second learning model representing the relationship between the supernatant image and the information for determining the cause of the diagnosis result formed based on the supernatant image by learning. (14) In one aspect of the present invention, in the learning device described in (10), the learning unit generates a third learning model that represents the relationship between the supernatant image and the information for determining the response method to the diagnosis result obtained based on the supernatant image, and the information for determining the response method to the diagnosis result inside the solid-liquid separation tank through learning. (15) In one aspect of the present invention, in the learning device described in (10), the learning unit generates a fourth learning model representing the relationship between the supernatant image and the information for determining the change of the state inside the solid-liquid separation tank after the supernatant image is obtained. (16) In one aspect of the present invention, in the learning device described in (10), the diagnosis result is generated based on either or both of the accumulation state of the solid matter contained in the supernatant image and the floating state of the solid matter.

(17) One aspect of the present invention is a monitoring method, which is executed by a monitoring system and has the following steps: Based on an image of the supernatant liquid inside a solid-liquid separation tank for separating wastewater into solid and liquid, that is, a supernatant liquid image and a diagnosis result obtained based on the supernatant liquid image, and using a first learning model that has learned the relationship between the supernatant liquid image and the state inside the solid-liquid separation tank, a step of determining the state inside the solid-liquid separation tank based on the supernatant liquid image that represents the supernatant liquid inside the solid-liquid separation tank as a diagnosis object; and a step of outputting information for determining the state inside the solid-liquid separation tank determined in the determination step using the supernatant liquid image of the solid-liquid separation tank as a diagnosis object and the first learning model.

(18) One aspect of the present invention is a learning method executed by a learning device and comprising the following steps: a step of generating a first learning model representing the relationship between the supernatant image and the state of the interior of the solid-liquid separation tank based on an image representing a supernatant liquid inside a solid-liquid separation tank for separating solid and liquid wastewater, i.e., a supernatant image, and a diagnosis result obtained based on the supernatant image, and by learning.

(19) One aspect of the present invention is a program that causes a computer of a monitoring system to execute the following steps: based on an image of a supernatant liquid inside a solid-liquid separation tank for separating solids from liquids in wastewater, that is, a supernatant liquid image and a diagnosis result obtained based on the supernatant liquid image, and using a first learning model that has learned the relationship between the supernatant liquid image and the state of the inside of the solid-liquid separation tank; type, a step of determining the state of the interior of the solid-liquid separation tank according to a supernatant image representing the supernatant inside the solid-liquid separation tank as a diagnosis object; and a step of outputting information used to determine the state of the interior of the solid-liquid separation tank determined by the supernatant image of the solid-liquid separation tank as a diagnosis object and the first learning model in the determination step.

(20) One aspect of the present invention is a program that causes a computer of a learning device to execute the following steps: a step of generating a first learning model representing the relationship between the supernatant image and the state of the interior of the solid-liquid separation tank based on an image representing the supernatant inside a solid-liquid separation tank for separating solid and liquid wastewater, i.e., a supernatant image, and a diagnosis result of the interior of the solid-liquid separation tank obtained based on the supernatant image, by learning. [Effect of the Invention]

The present invention has the effect of providing a monitoring system, a learning device, a monitoring method, a learning method and a program for monitoring the state inside a solid-liquid separation tank for separating solid and liquid from wastewater.

The monitoring system, monitoring method and program of this embodiment are described with reference to the drawings. The embodiment described below is only an example, and the embodiment to which the present invention is applied is not limited to the following embodiment. Furthermore, in all the drawings used to illustrate the embodiments, the same symbols are used for parts with the same functions, and repeated descriptions are omitted. In addition, the so-called "based on XX" mentioned in this application means "at least based on XX", and also includes the case where it is based on other elements in addition to XX. In addition, the so-called "based on XX" is not limited to the case of directly using XX, but also includes the case of the case based on the calculation or processing of XX. "XX" is an arbitrary element (for example, arbitrary information).

[Implementation] (Monitoring system) FIG. 1 is a diagram showing a configuration example of a monitoring system of an implementation of the present invention. The monitoring system 100 of the present implementation diagnoses the sludge accumulation state of a solid-liquid separation tank such as a settling tank and a concentration tank. In the present implementation, a sewage treatment facility 10 is described as an example of a facility including a solid-liquid separation tank. (Sewage treatment facility 10) An example of the sewage treatment facility 10 includes a front settling tank 11, a concentration tank 12, a storage tank 13, a dehydrator 14, a container 15, an aeration tank 16, a rear settling tank 17, a pump 18, and a facility control device 19. The front sedimentation tank 11 is connected to the aeration tank 16 via the flow path P1. Raw water is introduced into the front sedimentation tank 11. The front sedimentation tank 11 settles and separates the primary sludge (extracted sludge) from the introduced raw water. The treated water after settling and separation is introduced into the aeration tank 16 via the flow path P1. The aeration tank 16 is connected to the rear sedimentation tank 17 via the flow path P2. The aeration tank 16 aerobically treats the treated water introduced from the front sedimentation tank 11 by air aeration from the diffuser. The treated water aerobically treated in the aeration tank 16 is introduced into the rear sedimentation tank 17 via the flow path P2.

The post-sedimentation tank 17 is connected to the pump 18 via the flow path P3. The pump 18 is connected to the flow path P4. The flow path P4 is branched into the flow path P5 and the flow path P6. The flow path P5 is connected to the concentration tank 12, and the flow path P6 is connected to the aeration tank 16. The post-sedimentation tank 17 separates the treated water introduced from the aeration tank 16 into settled sludge (extracted sludge) and supernatant. The supernatant of the post-sedimentation tank 17 is discharged to the outside of the sewage treatment equipment 10 in the form of discharge water. In addition, a part of the sludge precipitated in the post-sedimentation tank 17 is introduced into the concentration tank 12 in the form of residual sludge via the pump 18, the flow path P4 and the flow path P5. The remaining part of the sludge precipitated in the rear sedimentation tank 17 is returned to the aeration tank 16 through the flow path P4 and the pipe P6 in the form of return sludge. By controlling the pump 18 using the equipment control device 19, a predetermined amount of sludge in the sludge precipitated in the rear sedimentation tank 17 is introduced into the flow path P4. In addition, the front sedimentation tank 11 is connected to the concentration tank 12 through the flow path P7. The extracted sludge from the front sedimentation tank 11 is introduced into the concentration tank 12 through the flow path P7. The concentration tank 12 is connected to the front sedimentation tank 11 through the flow path P8 and is connected to the storage tank 13 through the flow path P9.

In the concentration tank 12, the sludge introduced is separated into supernatant and concentrated sludge by gravity. The supernatant is returned to the front sedimentation tank 11 through the flow path P8. The concentrated sludge is extracted from the bottom of the concentration tank 12 and introduced into the storage tank 13 through the flow path P9. The storage tank 13 is connected to the dehydrator 14 through the flow path P10. The storage tank 13 temporarily stores the concentrated sludge introduced from the concentration tank 12. The concentrated sludge stored in the concentration tank 12 is introduced into the dehydrator 14. The dehydrator 14 is connected to the container 15 through the conveyor belt P11. The dehydrator 14 performs dehydration treatment on the concentrated sludge introduced from the storage tank 13. The dehydrated filter cakes produced by the dehydration treatment are introduced into the container 15 via the conveyor belt P11. The container 15 accommodates the dehydrated filter cakes introduced by the dehydrator 14 and carries out the accommodated dehydrated filter cakes.

(Monitoring system 100) The monitoring system 100 includes an ultrasonic sensor 20, a data processing device 30, a gateway device 31, an information processing device 40, a terminal device 45, and a monitoring device 50. The gateway device 31, the information processing device 40, the terminal device 45, and the monitoring device 50 are connected via a network NW. The network NW is a wireless or wired communication network. The network NW includes the Internet or an intranet. Specifically, the network NW is a communication network including a wide area network (WAN), a local area network (LAN), and the like. The WAN includes, for example, mobile phone networks, personal handy-phone systems (PHS) networks, public switched telephone networks (PSTN), dedicated communication line networks, and virtual private networks (VPN), etc.

The ultrasonic sensor 20 provides a pulse voltage from the ultrasonic transmitting circuit to the ultrasonic vibrator to transmit ultrasound into the water. In this embodiment, as an example, the ultrasonic sensor 20 is set in the rear sedimentation tank 17 and transmits ultrasound into the interior of the rear sedimentation tank. Here, the self-voltage [V] is converted into sound pressure [dB]. Ultrasonic waves are generated by the tiny vibration of the vibrator. An example of a vibrator is a ceramic element. The vibrator receives the reflected wave reflected by "something" in the water and is electrified, thereby generating an electromotive force, and the ultrasonic receiving circuit detects the voltage. Figure 2 is a diagram showing an example of an ultrasonic sensor. As shown in FIG2 , the ultrasonic sensor 20 includes an oscillating (transmitting) part 21 as a vibrator for transmission and a receiving part 22 as a vibrator for reception. In FIG2 , as an example, the ultrasonic sensor 20 includes two vibrators, the oscillating part 21 and the receiving part 22. However, a vibrator for transmission and a vibrator for reception can also be realized by one vibrator. The ultrasonic sensor 20 is installed at a predetermined height 27 of a post-sedimentation tank 17 (hereinafter also referred to as a treatment tank 25) for a suspended sludge accumulation layer 23 for storing sludge, etc. and a supernatant 24 of the suspended sludge accumulation layer 23 by a mechanism (not shown). By setting the ultrasonic sensor 20 in the processing tank 25 to be measured without changing the depth, the depth will not be changed. For example, in a tank with a depth of 5 m, 200 points can be allocated to 5 m to create an image database. In this case, each pixel is 2.5 cm and the display resolution is 2.5 cm. For example, there is a setting item for inputting the depth during measurement settings, and the depth direction data can be reduced or reduced based on the setting value. All data can also be stored and displayed in a reduced or reduced manner according to instructions, or partially expanded (full display) to expand a certain part. The oscillating unit 21 provides the electrical signal generated by the signal generating circuit (not shown) to the ultrasonic oscillator and transmits it toward the bottom of the processing tank 25.

The ultrasonic waves transmitted by the oscillation unit 21 are reflected by the interface 26 between the suspended sludge layer 23 and its supernatant 24, or the suspended sludge below the interface 26 or the bottom of the treatment tank 25. The reflected waves return one by one while generating a time difference (arrival time) proportional to the position (distance, depth) of the reflected object. The intensity of the reflected wave is related to the properties of the object (≒density), and its information is represented by sound pressure (dB). The reflected wave is received by the receiving unit 22. In the receiving unit 22, the sound pressure causes the oscillator to vibrate, generating a voltage corresponding to its intensity. Here, the sound pressure [dB] is converted into voltage [V]. The receiving unit 22 outputs the received signal to the data processing device 30. The data processing device 30 receives the reception signal output by the receiving unit 22 and converts the received reception signal into image data.

FIG3 is a diagram showing an example of a data processing device of the monitoring system of the present embodiment. In the example shown in FIG3, a case where a vibrator for transmission and a vibrator for reception are implemented by one vibrator is described. The data processing device 30 includes an ultrasonic transmission/reception circuit 32, a data conversion circuit 33, a data operation unit 34, and an image data storage unit 35. The ultrasonic transmission/reception circuit 32 generates an electrical signal for transmitting ultrasound and outputs the generated electrical signal to the ultrasonic sensor 20. The ultrasonic transmission/reception circuit 32 receives the electrical signal output by the ultrasonic sensor 20. The ultrasonic transmission/reception circuit 32 outputs the received electrical signal to the data conversion circuit 33. The data conversion circuit 33 obtains the electrical signal output by the ultrasonic transmitting/receiving circuit 32. The data conversion circuit 33 amplifies the obtained electrical signal. The data conversion circuit 33 performs shielding processing on the amplified electrical signal. The data conversion circuit 33 converts the signal intensity into a digital signal by digital processing based on the result of shielding processing on the amplified electrical signal. For example, the data conversion circuit 33 converts the electrical signal into, for example, 256 gray levels based on the signal intensity. The data conversion circuit 33 outputs the digital signal to the data operation unit 34.

The data calculation unit 34 obtains the digital signal output by the data conversion circuit 33. In addition, the data calculation unit 34 obtains temperature data from a thermocouple (not shown) provided in the ultrasonic sensor 20. The data calculation unit 34 uses the obtained temperature data to perform a correction operation of the speed of sound traveling in water. In addition, the data calculation unit 34 represents the position (= distance) of the signal as a function of time based on the obtained digital signal. In addition, the data calculation unit 34 calculates the change in reflection intensity (signal intensity) accompanying the passage of time after the ultrasonic sensor 20 transmits the ultrasonic wave based on the obtained digital signal. The data calculation unit 34 associates the signal intensity with the position information based on the result of expressing the position (= distance) of the signal as a function of time and the result of calculating the change in the reflection intensity (signal intensity) with the passage of time after the transmission of the ultrasound. The data calculation unit 34 temporarily stores (saves) the signal intensity and the position information in association. The data calculation unit 34 calculates the position (depth) of the interface 26 between the suspended sediment accumulation layer 23 and the supernatant 24. For example, the data calculation unit 34 derives the time until the reflection intensity exceeds the specified critical value and increases sharply based on the time passage of the reflection intensity of the ultrasound after the ultrasound sensor 20 transmits the ultrasound. The data calculation unit 34 calculates the distance to the interface 26 (the position of the interface 26) based on the derived time. The data calculation unit 34 outputs the digital data of the numerical value of the interface level to the image data storage unit 35.

The data operation unit 34 outputs the information obtained by associating the stored signal strength with the position information and the digital data of the numerical value of the interface level to the image data storage unit 35. Here, when the interface level cannot be determined, the data operation unit 34 can output information indicating a determination error to the image data storage unit 35. The data operation unit 34 can output the information obtained by associating the signal strength with the position information and the digital data of the numerical value of the interface level with the temperature data to the image data storage unit 35. FIG. 4 is a diagram showing an example of the operation of the monitoring system of the present embodiment. FIG. 4 shows an example of data output from the data operation unit 34 to the image data storage unit 35. An example of data output from the data operation unit 34 to the image data storage unit 35 is represented by an instantaneous value. FIG. 4 is a diagram showing the instantaneous value output from the data operation unit 34 to the image data storage unit 35 in two dimensions. In the data processing device 30, the data operation unit 34 transmits the digital signal to the monitoring device 50 via the gate device 31. Return to FIG. 1 to continue the explanation.

(Monitoring device 50) The monitoring device 50 is implemented by a device such as a personal computer, a server or an industrial computer. The monitoring device 50 includes a communication device 51, a recording device 52, an information processing unit 53, and a bus BL such as an address bus or a data bus for electrically connecting each structural component as shown in FIG. 1. The communication device 51 is implemented by a communication module. The communication device 51 communicates with other devices such as the data processing device 30 and the information processing device 40 via the network NW. The communication device 51 receives a digital signal transmitted by the data processing device 30. For example, the communication device 51 receives data measured during a specified time period in the past at a specified time interval. Specifically, the communication device 51 receives data corresponding to the past hour once an hour. In addition, the communication device 51 receives a monitoring image request for requesting the monitoring image transmitted by the terminal device 45. Here, the monitoring image is an image showing the change in reflection intensity (reception intensity) with the passage of time after the ultrasonic wave is transmitted to the solid-liquid separation tank. In response to the received monitoring image request, the communication device 51 transmits the monitoring image response output by the information processing unit 53 to the terminal device 45. In response to the transmitted monitoring image response, the communication device 51 receives the diagnosis result notification transmitted by the terminal device 45. The diagnosis result notification includes information indicating the monitoring image and information indicating the diagnosis result of the state inside the solid-liquid separation tank. In addition, the communication device 51 receives the tank state information request transmitted by the information processing device 40. The communication device 51 transmits the tank state information response output by the information processing unit 53 to the information processing device 40. The communication device 51 obtains the state notification information output by the information processing unit 53 and transmits the obtained state notification information to the information processing device 40.

The recording device 52 is implemented by, for example, a random access memory (RAM), a read only memory (ROM), a hard disk drive (HDD), a flash memory, or a hybrid memory device combining multiple of these. The program (monitoring application) executed by the monitoring device 50 is stored in the recording device 52. In addition, the pixel data output by the information processing unit 53 is stored in the recording device 52. The recording device 52 stores teacher data of the diagnosis result obtained by associating information representing the supernatant image with the diagnosis result of the inside of the solid-liquid separation tank (inside the tank) obtained from the supernatant image, and a learning model of the diagnosis result obtained by machine learning the relationship between the supernatant image and the state inside the solid-liquid separation tank based on the teacher data of the diagnosis result. Here, the monitoring image and the supernatant image contained in the monitoring image are explained. FIG5 shows an example of a monitoring image. The data processing device 30 transmits ultrasound from the ultrasound sensor 20 to the bottom of the water (= bottom direction), receives the reflected wave that hits the object within the range of the ultrasound, and converts the received reflected wave into a digital signal. The information processing unit 53 obtains the digital signal transmitted by the data processing device 30. Based on the obtained digital signal, the information processing unit 53 converts the intensity of the reflected wave into a color tone, and converts the time until the reflected wave returns into a distance to provide as position information, and combines the color tone and distance to obtain the distance from the sensor in the vertical direction (= water depth) and to obtain the distance in the horizontal direction (= time series) for continuous drawing. The continuous drawing is a monitoring image. As shown in FIG5, in the monitoring image, there are images corresponding to the bottom of the solid-liquid separation tank, the precipitated colorant, the sludge accumulation layer, the sludge interface, and the supernatant liquid. The supernatant liquid image is an image of the supernatant liquid contained in the monitoring image. Using FIG2 for illustration, the monitoring image is the measurement result of the sludge accumulation layer 23 and the supernatant liquid 24 obtained by the ultrasonic sensor 20, while the supernatant liquid image is the measurement result of only the supernatant liquid 24 obtained by the ultrasonic sensor 20. FIG6 is a diagram showing an example of teacher data. FIG6 shows teacher data of the diagnosis result. The teacher data of the diagnosis result is data obtained by associating the supernatant image with the diagnosis result of the state of the inside of the solid-liquid separation tank obtained from the supernatant image. In this embodiment, as an example, each of the multiple supernatant images is associated with any one of "normal", "abnormal" and "abnormal" as the diagnosis result. In the description of Figure 6, for convenience, the description is made using monitoring images. In Figure 6, (1) Since the supernatant has a sufficient depth, it is diagnosed as normal. (2) Since the depth of the supernatant is shallow, it is diagnosed as abnormal. (3) Since the flying of accumulated sludge is seen in the supernatant, it is diagnosed as abnormal. Return to Figure 1 to continue the explanation.

The information processing unit 53 functions as, for example, a graphics unit 54, a current state determination unit 55, and a learning unit 56. The graphics unit 54 obtains a digital signal received by the communication device 51. The graphics unit 54 converts the value of the obtained digital signal into pixel data. The graphics unit 54 stores the converted pixel data of the digital signal in the recording device 52. The graphics unit 54 obtains a supernatant image request received by the communication device 51. The graphics unit 54 obtains the pixel data stored in the recording device 52 based on the obtained supernatant image request. The graphics unit 54 creates an image of the supernatant contained in the monitoring image based on the obtained pixel data. The graphics unit 54 creates a supernatant liquid image response, which includes information representing the created supernatant liquid image and has the information processing device 40 as the destination. The graphics unit 54 outputs the created supernatant liquid image response to the communication device 51. The graphics unit 54 detects the interface between the supernatant liquid and the sludge accumulation layer, i.e., the sludge interface, based on the monitoring image, and creates pixel data in the vertical direction upward (in the direction where the water depth becomes shallower) from the sludge interface as the supernatant liquid image. The sludge interface is the interface 26 calculated by the data calculation unit 34. The graphics unit 54 can use the position of the interface 26 calculated by the data calculation unit 34 as the sludge interface. The graphics unit 54 obtains the tank status information request received by the communication device 51. The graphics unit 54 obtains the pixel data stored in the recording device 52 based on the obtained tank state information request, and creates a supernatant image based on the obtained pixel data. The graphics unit 54 creates a tank state information response, which includes information representing the created supernatant image and has the information processing device 40 as the destination. The graphics unit 54 outputs the created tank state information response to the communication device 51.

The current status determination unit 55 obtains the pixel data stored in the recording device 52, and creates a supernatant image based on the obtained pixel data. The current status determination unit 55 obtains the learning model of the diagnosis result stored in the recording device 52. The current status determination unit 55 determines the state of the inside of the solid-liquid separation tank of the created supernatant image based on the learning model of the obtained diagnosis result. When the determination result of the state inside the solid-liquid separation tank is abnormal or abnormal, the current status determination unit 55 creates state notification information, which includes information indicating the determination result of the state inside the solid-liquid separation tank and has the information processing device 40 as the destination. The current state determination unit 55 outputs the created state notification information to the communication device 51. The communication device 51 obtains the state notification information output by the current state determination unit 55 and transmits the obtained state notification information to the information processing device 40. When creating a supernatant image, the current state determination unit 55 can directly use the measured data or include the data measured over a long period of time in the display width limited by reduction. By including the data measured over a long period of time in the limited display width, changes over a longer period of time can be monitored. If it is assumed to be a still picture, the pixel data can be picked up at any appropriate interval and switched for display. In this embodiment, since measurement is always performed and new data is continuously added, there is a concern that the data processing will be delayed or blocked when the pixel data is picked up at any appropriate interval and switched for display. The measurement becomes unstable for the image display, which puts the cart before the horse. Therefore, in this embodiment, a plurality of pre-set display time widths are prepared, and data storage areas for time widths corresponding to the plurality of display time widths are created. In this embodiment, the interval (interval) for appending new data is specified, and an image database (data storage area (address)) corresponding to each of the multiple intervals is created.

The monitoring device 50 is operated to switch the display, and the display time width is selected. The current state determination unit 55 obtains data from the database corresponding to the selected time display width, and uses the obtained data to create a supernatant image. Assuming that the operation of switching the time display width is performed, data is obtained from the database corresponding to the selected time display width, and the supernatant image is created using the obtained data. By configuring in this way, the switching can be performed smoothly without processing the data of the database storing the data, and without causing a time lag in creating the supernatant image. The learning section 56 obtains the diagnosis result notification received by the communication device 51, and stores the teacher data of the diagnosis result obtained by associating the information representing the supernatant image contained in the obtained diagnosis result notification with the diagnosis result of the state of the inside of the solid-liquid separation tank (inside the tank) obtained from the supernatant image in the recording device 52. The learning section 56 obtains the teacher data of the diagnosis result stored in the recording device 52. The learning unit 56 performs machine learning (teacher-assisted learning) on the supernatant image and the diagnosis result of the state of the inside of the solid-liquid separation tank obtained from the supernatant image based on the teacher data of the obtained diagnosis result, and generates a learning model of the diagnosis result that associates the supernatant image with the state of the inside of the solid-liquid separation tank. For example, the learning unit 56 uses a convolutional neural network (CNN) to recognize the supernatant image. The supernatant image is classified into any one of normal, abnormal, and abnormal as the state of the inside of the solid-liquid separation tank based on the learning model of the diagnosis result and the information representing the supernatant image. The learning unit 56 stores the generated learning model of the diagnosis result in the recording device 52. The whole or part of the information processing unit 53 is a functional unit (hereinafter referred to as a software functional unit) implemented by a processor such as a central processing unit (CPU) executing a program such as a monitoring application stored in the recording device 52. Furthermore, the whole or part of the information processing unit 53 can be implemented by hardware such as a large scale integration (LSI), an application specific integrated circuit (ASIC) or a field programmable gate array (FPGA), or can be implemented by a combination of a software functional unit and hardware.

(Information processing device 40) The information processing device 40 is implemented by a device such as a personal computer, a server or an industrial computer. An example of the information processing device 40 is set in a monitoring center for remotely monitoring the sewage treatment equipment 10. When receiving the status information notification transmitted by the monitoring device 50, the information processing device 40 displays the judgment result of the solid-liquid separation tank contained in the received status information notification. In addition, the information processing device 40 creates a tank status information request based on the operator's operation of obtaining information on the status in the solid-liquid separation tank, and the tank status information request includes information on the status in the tank and has the monitoring device 50 as the destination. The information processing device 40 transmits the created tank status information request to the communication device 51. The information processing device 40 receives the tank status information response transmitted by the monitoring device 50 in response to the tank status information request. The information processing device 40 obtains the supernatant image contained in the received tank status information response. The information processing device 40 displays the obtained supernatant image.

(Terminal device 45) The terminal device 45 is implemented by a device such as a personal computer, a server or an industrial computer. An example of the terminal device 45 is set in a monitoring center for monitoring the sewage treatment equipment 10. When diagnosing the state inside the solid-liquid separation tank, the user creates a supernatant image request by operating the terminal device 45, and the supernatant image request includes information requesting a supernatant image and has the monitoring device 50 as a destination. The terminal device 45 creates the supernatant image request based on the user's operation. The terminal device 45 transmits the created supernatant image request to the monitoring device 50. The terminal device 45 receives a supernatant image response transmitted by the monitoring device 50 in response to the supernatant image request transmitted to the monitoring device 50. The terminal device 45 displays the monitoring image contained in the supernatant image response. The user diagnoses the state of the inside of the solid-liquid separation tank contained in the supernatant image with reference to the supernatant image displayed by the terminal device 45. The user creates a diagnosis result notification by operating the terminal device 45, and the diagnosis result notification includes information representing the supernatant image and the diagnosis result of the state of the inside of the solid-liquid separation tank and has the monitoring device 50 as the destination. The terminal device 45 creates the diagnosis result notification based on the user's operation. The terminal device 45 transmits the created diagnosis result notification to the monitoring device 50.

(Operation of the monitoring system) FIG. 7 is a diagram showing Example 1 of the operation of the monitoring system of the present embodiment. Referring to FIG. 7 , the following processing is described: the monitoring device 50 accumulates the diagnosis results of the state inside the solid-liquid separation tank contained in the diagnosis result notification transmitted by the terminal device 45, and performs machine learning based on the accumulated diagnosis results of the state inside the solid-liquid separation tank to generate a learning model of the diagnosis results. (Step S1-1) In the data processing device 30, the ultrasonic transmission/reception circuit 32 generates an electrical signal for transmitting ultrasound, and outputs the generated electrical signal to the ultrasonic sensor 20. (Step S2-1) In the data processing device 30, the ultrasonic transmitting/receiving circuit 32 receives the electrical signal output by the ultrasonic sensor 20. (Step S3-1) In the data processing device 30, the ultrasonic transmitting/receiving circuit 32 outputs the received electrical signal to the data conversion circuit 33. The data conversion circuit 33 obtains the electrical signal output by the ultrasonic transmitting/receiving circuit 32. The data conversion circuit 33 amplifies the obtained electrical signal. The data conversion circuit 33 performs shielding processing on the amplified electrical signal. The data conversion circuit 33 converts the signal intensity into a digital signal by digitally processing the signal intensity based on the result of shielding processing on the amplified electrical signal. The data operation unit 34 obtains the digital signal from the data conversion circuit 33, and performs temperature correction operation and interface level determination operation on the obtained digital signal in accordance with the position (distance) information. (Step S4-1) In the data processing device 30, the data operation unit 34 transmits the digital signal obtained by performing temperature correction operation and interface level determination operation on the position (distance) information to the monitoring device 50 via the gate device 31. (Step S5-1) In the monitoring device 50, the communication device 51 receives the digital signal transmitted by the data processing device 30. The graphics unit 54 obtains the digital signal received by the communication device 51. The graphics unit 54 converts the value of the acquired digital signal into pixel data. (Step S6-1) In the monitoring device 50, the graphics unit 54 stores the pixel data converted into the digital signal in the recording device 52. (Step S7-1) The terminal device 45 creates a supernatant image request.

(Step S8-1) The terminal device 45 transmits the created supernatant image request to the monitoring device 50. (Step S9-1) In the monitoring device 50, the communication device 51 receives the supernatant image request transmitted by the terminal device 45. The graphics unit 54 obtains the supernatant image request received by the communication device 51. The graphics unit 54 obtains the pixel data stored in the recording device 52 based on the obtained supernatant image request. The graphics unit 54 creates a supernatant image based on the obtained pixel data. The graphics unit 54 creates a supernatant image response, which includes information indicating the created supernatant image and has the terminal device 45 as a destination. (Step S10-1) In the monitoring device 50, the graphics unit 54 outputs the created supernatant image response to the communication device 51. The communication device 51 obtains the supernatant image response output by the graphics unit 54 and transmits the obtained supernatant image response to the terminal device 45. (Step S11-1) The terminal device 45 receives the supernatant image response transmitted by the monitoring device 50. The terminal device 45 displays the supernatant image by performing image processing on the information representing the supernatant image contained in the received supernatant image response. The terminal device 45 creates a diagnosis result notification, which includes the information representing the supernatant image and the result of diagnosing the supernatant image. (Step S12-1) The terminal device 45 transmits the created diagnosis result notification to the monitoring device 50. (Step S13-1) In the monitoring device 50, the communication device 51 receives the diagnosis result notification transmitted by the terminal device 45. The learning unit 56 obtains the diagnosis result notification received by the communication device 51, and stores the teacher data of the diagnosis result obtained by associating the information representing the supernatant image contained in the obtained diagnosis result notification with the diagnosis result of the state of the inside of the solid-liquid separation tank (inside the tank) obtained from the supernatant image in the recording device 52. (Step S14-1) In the monitoring device 50, the learning unit 56 obtains the teacher data of the diagnosis result stored in the recording device 52. The learning unit 56 performs machine learning on the supernatant image and the diagnosis result of the state inside the solid-liquid separation tank obtained from the supernatant image by using the teacher data of the obtained diagnosis result, thereby generating a learning model of the diagnosis result that associates the supernatant image with the state inside the solid-liquid separation tank. (Step S15-1) In the monitoring device 50, the learning unit 56 stores the generated learning model of the diagnosis result in the recording device 52.

Furthermore, the diagnosis result notification may also be the result of diagnosis based on the monitoring image instead of the supernatant image. That is, in step S7-1, the terminal device 45 creates a monitoring image request, in step S8-1, the monitoring image request created by the terminal device 45 is transmitted to the monitoring device 50, in step S9-1, the monitoring device 50 creates a monitoring image, and in step S10-1, the monitoring device 50 transmits the monitoring image response to the terminal device 45.

FIG8 is a diagram showing Example 2 of the operation of the monitoring system of the present embodiment. Referring to FIG8 , the following processing is described: the monitoring device 50 obtains the digital signal transmitted by the data processing device 30, and creates a supernatant image based on the obtained digital signal; the monitoring device 50 determines the state inside the solid-liquid separation tank based on the created supernatant image. Steps S1-2 to S6-2 can apply steps S1-1 to S6-1 of FIG7 , so the description here is omitted. (Step S7-2) In the monitoring device 50, the current state determination unit 55 obtains the pixel data stored in the recording device 52, and creates a supernatant image based on the obtained pixel data. (Step S8-2) In the monitoring device 50, the current state determination unit 55 obtains the learning model of the diagnosis result stored in the recording device 52. (Step S9-2) In the monitoring device 50, the current state determination unit 55 determines the state of the solid-liquid separation tank of the created supernatant image based on the learning model of the obtained diagnosis result. (Step S10-2) In the monitoring device 50, the current state determination unit 55 determines whether the determination result of the state inside the solid-liquid separation tank is abnormal or abnormal. When the current state determination unit 55 determines that the determination result of the state inside the solid-liquid separation tank is neither abnormal nor abnormal, that is, normal, the process ends. (Step S11-2) In the monitoring device 50, when the determination result of the state inside the solid-liquid separation tank is determined to be abnormal or abnormal, the current state determination unit 55 creates state notification information, which includes information indicating the determination result of the state inside the solid-liquid separation tank and has the information processing device 40 as the destination. (Step S12-2) In the monitoring device 50, the current status determination unit 55 outputs the created status notification information to the communication device 51. The communication device 51 obtains the status notification information output by the current status determination unit 55, and transmits the obtained status notification information to the information processing device 40.

Furthermore, in step S7-2, the monitoring device 50 creates a supernatant image, but this is only an example. For example, the monitoring device 50 only needs to create a monitoring image based on the pixel data, and in the subsequent steps, focus on the supernatant image by ignoring the portion deeper than the sludge interface.

FIG9 is a diagram showing Example 3 of the operation of the monitoring system of the present embodiment. Referring to FIG9, the following processing is described: the monitoring device 50 transmits information representing the image of the supernatant based on the tank state information request transmitted by the information processing device 40. Steps S1-3 to S6-3 can be applied to steps S1-1 to S6-1 of FIG7, so the description here is omitted. (Step S7-3) The information processing device 40 creates a tank state information request based on the user's operation. (Step S8-3) The information processing device 40 transmits the created tank state information request to the monitoring device 50. (Step S9-3) In the monitoring device 50, the communication device 51 receives the tank state information request transmitted by the information processing device 40. The graphics unit 54 obtains the tank state information request received by the communication device 51. The graphics unit 54 obtains the pixel data stored in the recording device 52 based on the obtained tank state information request, and creates a supernatant image based on the obtained pixel data. (Step S10-3) In the monitoring device 50, the graphics unit 54 creates a tank state information response, the tank state information response includes information indicating the created supernatant image and has the information processing device 40 as the destination. (Step S11-3) In the monitoring device 50, the graphics unit 54 outputs the created tank state information response to the communication device 51. The communication device 51 obtains the tank state information response output by the graphics unit 54, and transmits the obtained tank state information response to the information processing device 40. After step S11-3, the information processing device 40 receives the tank state information response transmitted by the monitoring device 50, and obtains the information representing the supernatant image contained in the received tank state information response. The information processing device 40 displays the supernatant image by performing image processing on the obtained information representing the supernatant image. By configuring in this way, the user of the information processing device 40 can confirm the state inside the solid-liquid separation tank. Furthermore, the monitoring device 50 can create a monitoring image instead of a supernatant image, and the tank state information response can include information representing the monitoring image. The information processing device 40 can display the monitoring image by performing image processing on the acquired information representing the monitoring image.

In the embodiment, as an example, the case where an ultrasonic sensor 20 is provided in the rear sedimentation tank 17 to determine the state inside the rear sedimentation tank 17 is described, but it is not limited to this example. For example, an ultrasonic sensor 20 can be provided in the front sedimentation tank 11 to determine the state inside the front sedimentation tank 11, and an ultrasonic sensor 20 can also be provided in the concentration tank 12 to determine the state inside the concentration tank 12. That is, an ultrasonic sensor 20 is provided in at least one of the rear sedimentation tank 17, the front sedimentation tank 11 and the concentration tank 12 to determine the internal state. In the embodiment, the case where a monitoring system 100 is connected to a sewage treatment equipment 10 is described, but it is not limited to this example. For example, a monitoring system 100 may be connected to multiple sewage treatment equipment 10, or multiple monitoring systems 100 may be connected to one sewage treatment equipment 10. Assuming that multiple sewage treatment equipment 10 are connected to the monitoring system 100, when an unstable state for which there is no experience occurs in equipment A, if there is experience of the unstable state occurring in equipment B, it is judged as "abnormal" and the possibility of output is high. That is, the monitoring device 50 can learn more, thereby increasing the number of cases that can be used for judgment. Therefore, the unstable state that can be judged as abnormal or abnormal can be increased. In the above-described embodiment, the case where the monitoring device 50 performs machine learning is described, but the present invention is not limited to this example. For example, a device independent of the monitoring device 50 may be used to implement a device that performs machine learning. In this case, the learning device obtains an image representing the interior of a solid-liquid separation tank for separating solid and liquid wastewater, that is, a supernatant image, and a diagnosis result of the interior of the solid-liquid separation tank obtained based on the supernatant image from the monitoring device 50. The learning device has a learning unit that generates a learning model of a diagnosis result representing the relationship between the supernatant image and the state inside the solid-liquid separation tank based on the supernatant image and the diagnosis result of the state inside the solid-liquid separation tank obtained based on the supernatant image by machine learning (machine learning with a teacher). In the embodiment, the case where the determination result of determining whether the state inside the solid-liquid separation tank is normal, abnormal, or abnormal based on the supernatant image is described, but it is not limited to this example. For example, the determination result of the state inside the solid-liquid separation tank can be determined based on the supernatant image, whether it is normal or abnormal, and the determination result of the state inside the solid-liquid separation tank can be classified into four or more types based on the supernatant image.

In the embodiment, when it is determined that the result of comparing the created supernatant image with the supernatant image of the past normal state has changed, the current state determination unit 55 can use the learning model of the diagnosis result stored in the recording device 52 to determine the state inside the solid-liquid separation tank. In the embodiment, the data processing device 30 can include a display switching operation unit 36 and an image data display unit 37. FIG. 10 is a diagram showing another example of the data processing device of the present embodiment. An example of the display switching operation unit 36 includes a display switching button. An example of the image data display unit 37 is a display. The image data display unit 37 can directly display the measured data, or can display the data for a long time in the display width limited by reducing the measured data. By displaying the data for a long time in the limited display width, the time change over a long time can be monitored. If the image data display unit 37 is assumed to display a still picture, the pixel data can be picked up at any appropriate interval and switched for display. In the case where the measurement is always performed and new data is continuously added as in the present embodiment, when the pixel data is picked up and switched for display, there is a concern that a delay or obstruction will occur in the data processing, and the measurement becomes unstable for the image display, which is putting the cart before the horse. Therefore, in this embodiment, a plurality of preset display time widths are prepared, and data storage areas for time widths corresponding to the plurality of display time widths are created, and intervals (intervals) for adding new data are specified, and image databases corresponding to the plurality of display time widths are created. Assuming that the image data display unit 37 includes 200 pixels in the vertical direction (=depth direction) and 240 pixels in the horizontal direction (=time sequence), when data is stored every 1 second, 4 minutes of display data are formed in the horizontal direction, and when data is stored every 10 seconds, 40 minutes of display data are formed in the horizontal direction. By deleting the oldest data while adding new data, it can be used in a limited data area. In addition, an area for storing data above 240 data required for display width can be set and displayed in a scrolling manner, so that changes from the past can be observed like a dynamic image. In this embodiment, these two data storage and display can be performed.

In the embodiment, the image data storage unit 35 of the data processing device 30 can be accessed from an external terminal to retrieve data stored in the image data storage unit 35. In the embodiment, the data operation unit 34 of the data processing device 30 can be accessed from an external terminal. In this case, the data stored in the data operation unit 34 can be retrieved and output to the external terminal according to the instruction from the external terminal. By configuring in this way, the external terminal can display the data output by the data operation unit 34, so that online monitoring can be performed. In addition, in this case, the latest data can be output from the data operation unit 34 to the external terminal according to the instruction of the external terminal. The range of output data can be set by the external terminal. By such a configuration, the external terminal can display data in sequence, so image monitoring can be performed by remote live broadcast. Specifically, by installing dedicated software in the external terminal, the inherent identification number (= password) assigned to the data operation unit 34 is checked in sequence between the data operation unit and the external terminal. Since one-to-one communication can be performed between the external terminal and the data operation unit, eavesdropping data theft caused by the setting of the signal distributor, etc. can be prevented.

In the embodiment, the data operation unit 34 of the data processing device 30 can be set to transmit data to an external terminal. The data operation unit 34 can transmit data to an external terminal based on the setting for transmitting data. By configuring in this way, the external terminal can be used for remote monitoring of the sewage treatment equipment 10. In the embodiment, the data operation unit 34 of the data processing device 30 can be set to output data to an external data server or recording medium. The data operation unit 34 outputs data to an external data server or recording medium based on the setting. The external data server creates a database based on the data output by the data operation unit 34. The external data server can display images based on the created database, and can also process the data. In the embodiment, when the data operation unit 34 of the data processing device 30 transmits data to the external terminal, for example, the data can be transmitted in accordance with the RS232C specification. In addition, when the data operation unit 34 of the data processing device 30 transmits data to the external terminal, the data can be directly transmitted between the external terminal, or a signal converter can be provided to convert the data into RS422, RS485 specifications or Universal Serial Bus (USB), LAN, or optical fiber transmission protocols for transmission. In addition, data can also be transmitted from the external terminal to the server regularly or irregularly. In this case, the central monitoring device can request data from the external terminal (small PC, etc.), and the external terminal can output the data. In this case, the external terminal can have the function of the monitoring device 50. The external terminal can obtain the data output by the data calculation unit 34, determine the state of the solid-liquid separation tank obtained from the supernatant image based on the obtained data, and output the determination result to the server. In addition, the server can also have the function of the current status determination unit 55 of the monitoring device. The external terminal obtains the data output by the data calculation unit 34 and transmits the obtained data to the server. The server obtains the data transmitted by the external device and determines the state of the interior of the solid-liquid separation tank obtained from the supernatant image based on the obtained data.

According to the monitoring system 100 of the present embodiment, the monitoring device 50 has: a determination unit as a current status determination unit 55, based on an image of a supernatant liquid inside a solid-liquid separation tank for separating solid and liquid wastewater, that is, a supernatant liquid image and a diagnosis result of the inside of the solid-liquid separation tank obtained based on the supernatant liquid image, and using a relationship between the supernatant liquid image and the state of the inside of the solid-liquid separation tank learned A first learning model as a learning model of the diagnosis result determines the state of the inside of the solid-liquid separation tank according to the supernatant image representing the supernatant inside the solid-liquid separation tank as the diagnosis object; and an output unit outputs information for determining the state of the inside of the solid-liquid separation tank determined by the determination unit using the supernatant image of the solid-liquid separation tank as the diagnosis object and the first learning model. By configuring in this way, the monitoring device 50 can use the first learning model that has learned the relationship between the supernatant image and the diagnosis result of the inside of the solid-liquid separation tank, and can determine the state of the inside of the solid-liquid separation tank based on the supernatant image representing the supernatant inside the solid-liquid separation tank as the diagnosis object, thereby monitoring the state of the inside of the solid-liquid separation tank. By using the first learning model, and determining the state of the inside of the solid-liquid separation tank based on the supernatant image representing the supernatant inside the solid-liquid separation tank as the diagnosis object, compared with the situation where people diagnose the inside of the solid-liquid separation tank based on experience, human experience is not required, and the deviation of the diagnosis result can be reduced. In addition, when a person diagnoses the inside of the solid-liquid separation tank based on experience, since the diagnosis is mainly made by observing the supernatant, the monitoring device 50 can make a diagnosis closer to the diagnosis made by a person than diagnosing the state of the inside of the solid-liquid separation tank based on an image including the supernatant and sludge accumulation layer of the solid-liquid separation tank. In the case of a system structure assuming a field completion type, since the size or characteristics of the tank will not change, the past and the present can be easily compared, so it is easy to distinguish between the stable state and the unstable state or abnormality. By acquiring case collections, latest cases, etc. from the outside (online/offline), and updating the acquired case collections, latest cases, etc., when an abnormality is detected, even if there is no past experience and no learning process, it is possible to output the judgment of what state the abnormality is in and the prompts for its response measures. By referring to the accessible database when an abnormality occurs, it is possible to infer what state it is in, so that the response measures can be obtained. In addition, it is possible to reduce the error of making judgments that are impossible to occur on site (equipment). Therefore, the risk of erroneous judgments can be reduced, and the time required for judgment can also be shortened. The risk of data hacking and system attacks can be reduced.

Furthermore, the diagnosis result obtained based on the monitoring image is generated based on either or both of the accumulation state of the solid matter and the floating state of the solid matter contained in the monitoring image. By configuring in this way, the monitoring device 50 can use the first learning model to determine the state inside the solid-liquid separation tank based on the supernatant image representing the supernatant inside the solid-liquid separation tank as the diagnosis object, wherein the first learning model learns the relationship between the supernatant image and the diagnosis result generated based on either or both of the accumulation state of the solid matter and the floating state of the solid matter contained in the monitoring image including the supernatant image. Furthermore, the determination unit determines whether the state of the solid-liquid separation tank is normal, abnormal, or abnormal based on the supernatant image showing the inside of the solid-liquid separation tank as the object of diagnosis. By configuring in this way, the monitoring device 50 can determine whether the state of the solid-liquid separation tank is normal, abnormal, or abnormal based on the supernatant image showing the inside of the solid-liquid separation tank as the object of diagnosis, based on the supernatant image and the diagnosis result of the inside of the solid-liquid separation tank obtained based on the supernatant image, and using the first learning model that has learned the relationship between the supernatant image and the diagnosis result of the inside of the solid-liquid separation tank. Furthermore, there is a notification unit, which notifies that the state inside the solid-liquid separation tank is either abnormal or abnormal when the determination unit determines that the state inside the solid-liquid separation tank is either abnormal or abnormal. By configuring in this way, when it is determined that the state inside the solid-liquid separation tank is either abnormal or abnormal, it can be notified that the state inside the solid-liquid separation tank is either abnormal or abnormal, so that the user can be informed that the state inside the solid-liquid separation tank needs to be dealt with.

[Variant 1 of the embodiment] (Monitoring system) FIG. 11 is a diagram showing a configuration example of a monitoring system of a variant 1 of the embodiment of the present invention. The monitoring system 100a of the variant 1 of the embodiment diagnoses the sludge accumulation state of a solid-liquid separation tank such as a sedimentation tank and a concentration tank. In the variant 1 of the embodiment, the sewage treatment equipment 10 is applied as an example of equipment including a solid-liquid separation tank in the same manner as in the embodiment.

(Monitoring system 100a) The monitoring system 100a includes an ultrasonic sensor 20, a data processing device 30, a gateway device 31, an information processing device 40a, a terminal device 45a, and a monitoring device 50a. The gateway device 31, the information processing device 40a, the terminal device 45a, and the monitoring device 50a are connected via a network NW. In the data processing device 30, the data operation unit 34 transmits a digital signal to the monitoring device 50a via the gateway device 31. (Monitoring device 50a) The monitoring device 50a is implemented by a device such as a personal computer, a server, or an industrial computer. The monitoring device 50a includes a communication device 51, a recording device 52, an information processing unit 53a, and a bus line BL such as an address bus or a data bus for electrically connecting each structural component as shown in FIG. 11. The program (monitoring application) executed by the monitoring device 50a is stored in the recording device 52. In addition, the pixel data output by the information processing unit 53a is stored in the recording device 52. The recording device 52 stores teacher data of the diagnosis result obtained by associating information representing the supernatant image with the diagnosis result of the state inside the solid-liquid separation tank obtained from the supernatant image, and a learning model of the diagnosis result obtained by machine learning the relationship between the supernatant image and the state inside the solid-liquid separation tank based on the teacher data of the diagnosis result. The recording device 52 stores the teacher data of the cause obtained by associating the information representing the supernatant image with the information for determining the cause of the diagnosis result of the state inside the solid-liquid separation tank obtained from the supernatant image, and the learning model of the cause obtained by machine learning the relationship between the supernatant image and the information for determining the cause of the state inside the solid-liquid separation tank based on the teacher data of the cause.

FIG. 12 is a diagram showing an example of teacher data. FIG. 12 shows teacher data of diagnosis results and teacher data of causes. The teacher data of diagnosis results is data obtained by associating a supernatant image with a diagnosis result of the state of the inside of the solid-liquid separation tank obtained from the supernatant image. The teacher data of causes is data obtained by associating a supernatant image with information for determining the cause of the diagnosis result of the state of the inside of the solid-liquid separation tank obtained from the supernatant image. In the description of FIG. 12, for convenience, the description is made by using monitoring images. In variation 1 of the embodiment, as an example, each of the plurality of supernatant images is associated with any one of "normal", "abnormal" and "abnormal" as a diagnosis result in the same manner as in the embodiment. Furthermore, each of the plurality of supernatant images is associated with an estimated result of the cause of the diagnosis result based on the diagnosis result. In FIG. 12 , (1) Since the supernatant has a sufficient depth, it is diagnosed as normal. In the case of a normal diagnosis, the estimated result of the cause of the diagnosis result is not memorized. (2) Since the depth of the supernatant is shallow, it is diagnosed as abnormal. In this case, the memory expansion is an example of the estimated result of the cause of the diagnosis result. The so-called expansion refers to the phenomenon that the sedimentation of sludge deteriorates and it is difficult to obtain the supernatant. (3) Since the accumulated sludge is seen flying in the supernatant, the diagnosis is abnormal. In this case, the memory is an example of the estimated result of the cause of the diagnosis result that the sludge input speed is fast, the sludge input amount is large, and the sludge interface is high. Return to Figure 11 and continue the explanation.

The information processing unit 53a functions as, for example, a graphics unit 54, a current state determination unit 55a, a learning unit 56a, and a cause determination unit 57. The current state determination unit 55a obtains pixel data stored in the recording device 52, and creates a supernatant image based on the obtained pixel data. The current state determination unit 55a obtains a learning model of the diagnosis result stored in the recording device 52. The current state determination unit 55a determines the state of the solid-liquid separation tank of the created supernatant image based on the learning model of the obtained diagnosis result. The learning unit 56a has the following functions in addition to the functions of the learning unit 56. The learning unit 56a obtains the diagnosis result notification received by the communication device 51, and stores the teacher data of the cause obtained by associating the information representing the supernatant image contained in the obtained diagnosis result notification with the estimated result of the cause forming the diagnosis result in the recording device 52. The learning unit 56a obtains the teacher data of the cause stored in the recording device 52. The learning unit 56a performs machine learning (teacher-assisted learning) on the supernatant image and the inferred result of the cause of the diagnosis result of the state of the inside of the solid-liquid separation tank formed by the supernatant image based on the acquired teacher data of the cause, and generates a learning model of the cause that associates the supernatant image with information for determining the cause of the state formed inside the solid-liquid separation tank. For example, the learning unit 56a uses a convolutional neural network to recognize the supernatant image. The supernatant image is classified into any one of the information for determining the cause of the state formed inside the solid-liquid separation tank based on the information representing the supernatant image by the learning model of the cause. The learning unit 56a stores the generated cause learning model in the recording device 52.

The cause determination unit 57 obtains information representing the supernatant image and the determination result of the state inside the solid-liquid separation tank from the current state determination unit 55a. When the determination result of the state inside the solid-liquid separation tank obtained is abnormal or abnormal, the cause determination unit 57 obtains the learning model of the cause stored in the recording device 52. The cause determination unit 57 determines the information used to determine the cause of the state inside the solid-liquid separation tank that forms the obtained supernatant image based on the obtained learning model of the cause. The cause determination unit 57 creates status notification information, which includes information indicating a supernatant image, information indicating the state inside the solid-liquid separation tank, and information indicating a determination result of information indicating the cause of the state inside the solid-liquid separation tank, and is destined for the information processing device 40a. The cause determination unit 57 outputs the created status notification information to the communication device 51. The communication device 51 obtains the status notification information output by the cause determination unit 57, and transmits the obtained status notification information to the information processing device 40a. All or part of the information processing unit 53a is a functional unit (hereinafter referred to as a software functional unit) implemented by, for example, a processor such as a CPU executing a program such as a monitoring application stored in the recording device 52. Furthermore, all or part of the information processing unit 53a can be implemented by hardware such as LSI, ASIC or FPGA, or by a combination of software functional units and hardware. The information processing device 40a can apply the information processing device 40.

(Terminal device 45a) Terminal device 45a is implemented by a device such as a personal computer, a server or an industrial computer. An example of terminal device 45a is set in a monitoring center for monitoring sewage treatment equipment 10. When diagnosing the state inside the solid-liquid separation tank, the user creates a supernatant image request by operating terminal device 45a, and the supernatant image request includes information requesting a supernatant image and has monitoring device 50a as a destination. Terminal device 45a creates a supernatant image request based on the user's operation. Terminal device 45a transmits the created supernatant image request to monitoring device 50a. The terminal device 45a receives the supernatant image response transmitted by the monitoring device 50a in response to the supernatant image request transmitted to the monitoring device 50a. The terminal device 45a displays the supernatant image contained in the supernatant image response. The user diagnoses the state of the inside of the solid-liquid separation tank contained in the supernatant image with reference to the supernatant image displayed by the terminal device 45a, and further infers the cause of the state of the inside of the solid-liquid separation tank. The user creates a diagnosis result notification by operating the terminal device 45a, and the diagnosis result notification includes information representing the image of the supernatant, the diagnosis result of the state inside the solid-liquid separation tank, and information for determining the cause of the diagnosis result and has the monitoring device 50a as the destination. The terminal device 45a creates the diagnosis result notification based on the user's operation. The terminal device 45a transmits the created diagnosis result notification to the monitoring device 50a.

(Operation of the monitoring system) FIG. 13 is a diagram showing Example 1 of the operation of the monitoring system of Modification 1 of the implementation form. Referring to FIG. 13 , the following processing is explained: the monitoring device 50a accumulates the diagnosis result of the state inside the solid-liquid separation tank contained in the diagnosis result notification transmitted by the terminal device 45a and the information for determining the cause of the diagnosis result, and performs machine learning based on the accumulated diagnosis result of the state inside the solid-liquid separation tank and the information for determining the cause of the diagnosis result, thereby generating a learning model of the diagnosis result and a learning model of the cause. Steps S1-4 to S10-4 can apply steps S1-1 to S10-1 of FIG. 7, so the description here is omitted. (Step S11-4) The terminal device 45a receives the supernatant image response transmitted by the monitoring device 50a. The terminal device 45a displays the supernatant image by performing image processing on the information representing the supernatant image contained in the received supernatant image response. The terminal device 45a creates a diagnosis result notification, which includes the diagnosis result of the state inside the solid-liquid separation tank and information for determining the cause of the diagnosis result and is destined for the monitoring device 50a. (Step S12-4) The terminal device 45a transmits the created diagnosis result notification to the monitoring device 50a.

(Step S13-4) In the monitoring device 50a, the communication device 51 receives the diagnosis result notification transmitted by the terminal device 45a. The learning unit 56a obtains the diagnosis result notification received by the communication device 51, and obtains the information representing the supernatant image contained in the obtained diagnosis result notification, the diagnosis result of the state inside the solid-liquid separation tank, and the information used to determine the cause of the formation of the diagnosis result. The learning unit 56a stores the teacher data of the diagnosis result obtained by associating the obtained information representing the supernatant image with the diagnosis result of the state inside the solid-liquid separation tank in the recording device 52. The learning unit 56a stores the teacher data of the cause obtained by associating the information representing the supernatant image with the information for determining the cause of the diagnosis result of the state inside the solid-liquid separation tank in the recording device 52. (Step S14-4) In the monitoring device 50a, the learning unit 56a obtains the teacher data of the diagnosis result stored in the recording device 52. The learning unit 56a performs machine learning on the supernatant image and the diagnosis result of the state inside the solid-liquid separation tank obtained from the supernatant image by using the teacher data based on the obtained diagnosis result, thereby generating a learning model of the diagnosis result obtained by associating the supernatant image with the state inside the solid-liquid separation tank. The learning unit 56a obtains the teacher data of the cause stored in the recording device 52. The learning unit 56a performs machine learning on the supernatant image and the information of the cause of the diagnosis result for determining the state inside the solid-liquid separation tank based on the acquired teacher data of the cause, and generates a learning model of the cause by associating the supernatant image with the information of the cause of the diagnosis result for determining the state inside the solid-liquid separation tank. (Step S15-4) In the monitoring device 50a, the learning unit 56a stores the generated learning model of the diagnosis result in the recording device 52. The learning unit 56a stores the generated learning model of the cause in the recording device 52.

Furthermore, the diagnosis result notification may also be the result of diagnosis based on the monitoring image instead of the supernatant image. That is, in step S7-4, the terminal device 45a creates a monitoring image request, in step S8-4, the monitoring image request created by the terminal device 45a is transmitted to the monitoring device 50a, in step S9-4, the monitoring device 50a creates a monitoring image, and in step S10-4, the monitoring device 50a transmits the monitoring image response to the terminal device 45a.

FIG. 14 is a diagram of Example 2 showing the operation of the monitoring system of Modification 1 of the embodiment. Referring to FIG. 14 , the following processing is described: the monitoring device 50a obtains the digital signal transmitted by the data processing device 30, and creates a supernatant image based on the obtained digital signal; the monitoring device 50a determines the state inside the solid-liquid separation tank based on the created supernatant image. Steps S1-5 to S6-5 can apply steps S1-1 to S6-1 of FIG. 7 , so the description here is omitted. (Step S7-5) In the monitoring device 50a, the current state determination unit 55a obtains the pixel data stored in the recording device 52, and creates a supernatant image based on the obtained pixel data. (Step S8-5) In the monitoring device 50a, the current state determination unit 55a obtains the learning model of the diagnosis result stored in the recording device 52. (Step S9-5) In the monitoring device 50a, the current state determination unit 55a determines the state of the solid-liquid separation tank of the created supernatant image based on the learning model of the obtained diagnosis result. (Step S10-5) In the monitoring device 50a, the cause determination unit 57 obtains the determination result of the state inside the solid-liquid separation tank from the current state determination unit 55a. The cause determination unit 57 determines whether the determination result of the state inside the solid-liquid separation tank obtained is abnormal or abnormal. The process ends when the cause determination unit 57 determines that the determination result of the state inside the solid-liquid separation tank obtained is neither abnormal nor abnormal.

(Step S11-5) In the monitoring device 50a, when the determination result of the state of the interior of the solid-liquid separation tank is determined to be abnormal or abnormal, the cause determination unit 57 obtains the learning model of the cause stored in the recording device 52. (Step S12-5) In the monitoring device 50a, the cause determination unit 57 determines information for determining the cause of the state of the interior of the solid-liquid separation tank that forms the obtained supernatant image based on the obtained learning model of the cause. (Step S13-5) In the monitoring device 50a, the cause determination unit 57 creates status notification information, which includes information indicating a supernatant image, information indicating a determination result of the state inside the solid-liquid separation tank, and information indicating a determination result of the cause of the state inside the solid-liquid separation tank, and has the information processing device 40a as a destination. (Step S14-5) In the monitoring device 50a, the cause determination unit 57 outputs the created status notification information to the communication device 51. The communication device 51 obtains the status notification information output by the cause determination unit 57, and transmits the obtained status notification information to the information processing device 40a. Furthermore, in step S7-5, the monitoring device 50 creates a supernatant image, but this is only an example. For example, the monitoring device 50 only needs to create a monitoring image based on pixel data, and in subsequent steps, focus on the supernatant image by ignoring the portion deeper than the sludge interface. The processing of the monitoring device 50a transmitting information representing the supernatant image based on the tank status information request transmitted by the information processing device 40a is omitted because Figure 9 can be applied.

In the variant 1 of the embodiment, the case where the monitoring system 100a is connected to one sewage treatment equipment 10 is described, but the present invention is not limited to this example. For example, the monitoring system 100a may be connected to a plurality of sewage treatment equipment 10, or a plurality of monitoring systems 100a may be connected to one sewage treatment equipment 10. Assuming that a plurality of sewage treatment equipment 10 are connected to the monitoring system 100a, when an unstable state with no experience occurs in the equipment of A, if there is experience that the unstable state occurs in the equipment of B, it is judged as "abnormal", and it is highly likely that information for determining the cause of the abnormality will be judged and output. That is, the monitoring device 50a can perform more learning, thereby increasing the number of cases that can be used for judgment. Therefore, the unstable state that can be judged as abnormal or abnormal can be increased. In the variant example 1 of the embodiment, the case where the monitoring device 50a performs machine learning is described, but it is not limited to this example. For example, a device independent of the monitoring device 50a can be used to implement a device that performs machine learning. In this case, regarding the learning device, in the learning device described in the embodiment, the learning device obtains the supernatant image and the information used to determine the cause of the diagnosis result obtained based on the supernatant image from the monitoring device 50a. The learning unit of the learning device generates a second learning model representing the relationship between the supernatant image and the information for determining the cause of the state inside the solid-liquid separation tank by machine learning (machine learning with a teacher) based on the supernatant image and the information for determining the cause of the diagnosis result obtained based on the supernatant image. In the variant 1 of the embodiment, the following situation is described, but not limited to this example: the determination result of the state inside the solid-liquid separation tank is normal, abnormal, or abnormal based on the supernatant image, and further, based on whether the determination result of the state inside the solid-liquid separation tank is abnormal or abnormal, the expansion, fast sludge input speed, large sludge input amount, and high sludge interface are memorized. For example, it can be classified into one or more causes based on whether the determination result of the state inside the solid-liquid separation tank is abnormal or abnormal.

According to the monitoring system 100a of the variant example 1 of the implementation form, in the monitoring device 50 of the implementation form, the monitoring device 50a includes a cause determination unit 57, and the cause determination unit 57 is based on the supernatant image and the information for determining the cause of the diagnosis result obtained according to the supernatant image, and uses a second learning model as a cause learning model that has learned the relationship between the supernatant image and the information for determining the cause of the diagnosis result formed inside the solid-liquid separation tank. The cause determination unit 57 determines the information for determining the cause of the state formed inside the solid-liquid separation tank according to the supernatant image of the solid-liquid separation tank as the diagnosis object. The output unit further outputs the information for determining the cause of the state inside the solid-liquid separation tank determined by the cause determination unit using the supernatant image of the solid-liquid separation tank as the diagnosis object and the second learning model. By configuring in this way, the monitoring device 50a can use the second learning model that has learned the relationship between the supernatant image and the information for determining the cause of the diagnosis result inside the solid-liquid separation tank, and determine the information for determining the cause of the state inside the solid-liquid separation tank based on the supernatant image of the solid-liquid separation tank as the diagnosis object, thereby monitoring the cause of the state inside the solid-liquid separation tank. By using the second learning model, the cause of the state inside the solid-liquid separation tank can be determined based on the supernatant image showing the inside of the solid-liquid separation tank as the diagnosis object. Compared with the case where a person diagnoses the cause of the state inside the solid-liquid separation tank based on experience, human experience is not required and the deviation of the diagnosis result can be reduced.

[Variant 2 of the embodiment] (Monitoring system) FIG. 15 is a diagram showing a configuration example of a monitoring system of a variant 2 of the embodiment of the present invention. The monitoring system 100b of the variant 2 of the embodiment diagnoses the sludge accumulation state of a solid-liquid separation tank such as a sedimentation tank and a concentration tank. In the variant 2 of the embodiment, the sewage treatment equipment 10 is applied as an example of equipment including a solid-liquid separation tank in the same manner as in the embodiment.

(Monitoring system 100b) The monitoring system 100b includes an ultrasonic sensor 20, a data processing device 30, a gateway device 31, an information processing device 40b, a terminal device 45b, and a monitoring device 50b. The gateway device 31, the information processing device 40b, the terminal device 45b, and the monitoring device 50b are connected via a network NW. In the data processing device 30, the data operation unit 34 transmits a digital signal to the monitoring device 50b via the gateway device 31.

(Monitoring device 50b) The monitoring device 50b is implemented by a device such as a personal computer, a server or an industrial computer. The monitoring device 50b includes a communication device 51, a recording device 52, an information processing unit 53b, and a bus BL such as an address bus or a data bus for electrically connecting each structural component as shown in FIG. 15. The program (monitoring application) executed by the monitoring device 50b is stored in the recording device 52. In addition, the pixel data output by the information processing unit 53b is stored in the recording device 52. The recording device 52 stores teacher data of the diagnosis result obtained by associating information representing the supernatant image with the diagnosis result of the inside of the solid-liquid separation tank obtained from the supernatant image, and a learning model of the diagnosis result obtained by machine learning the relationship between the supernatant image and the state inside the solid-liquid separation tank based on the teacher data of the diagnosis result. The recording device 52 stores the teacher data of the cause obtained by associating the information representing the monitoring image with the information for determining the cause of the diagnosis result of the inside of the solid-liquid separation tank obtained from the supernatant image, and the learning model of the cause obtained by machine learning the relationship between the supernatant image and the information for determining the cause of the state inside the solid-liquid separation tank based on the teacher data of the cause. The recording device 52 stores teacher data of a response method obtained by associating information representing a supernatant image with information for determining a response method for a diagnosis result of the solid-liquid separation tank obtained from the supernatant image, and a learning model of a response method obtained by machine learning the relationship between the supernatant image and the information for determining a response method for the state inside the solid-liquid separation tank based on the teacher data of the response method.

FIG16 is a diagram showing an example of teacher data. FIG16 shows teacher data of diagnosis results, teacher data of causes, and teacher data of countermeasures. The teacher data of diagnosis results is data obtained by associating a supernatant image with a diagnosis result of the state of the inside of a solid-liquid separation tank obtained from the supernatant image. The teacher data of causes is data obtained by associating a supernatant image with information for determining the cause of the diagnosis result of the state of the inside of a solid-liquid separation tank obtained from the supernatant image. The teacher data of countermeasures is data obtained by associating a supernatant image with information for determining a countermeasure to the state of the inside of a solid-liquid separation tank obtained from the supernatant image. In the description of FIG. 16 , for convenience, the description is made using monitoring images. In variant 2 of the embodiment, as an example, each of the plurality of monitoring images is associated with any one of “normal”, “abnormal” and “abnormal” as the diagnosis result in the same manner as the embodiment. Furthermore, each of the plurality of monitoring images is associated with the estimated result of the cause of the diagnosis result based on the diagnosis result. Furthermore, each of the plurality of monitoring images is associated with information for determining the response method to the diagnosis result based on the diagnosis result. In FIG. 16 , (1) since the supernatant has a sufficient depth, it is diagnosed as normal. In the case of a normal diagnosis, the presumed result and the countermeasures of the cause of the diagnosis are not memorized. (2) Since the depth of the supernatant is shallow, the diagnosis is abnormal. In this case, swelling is memorized as the presumed result of the cause of the diagnosis. Furthermore, the promotion of sludge extraction and the addition of sludge settling agents are memorized as the countermeasures to the presumed result of the cause of the diagnosis. (3) Since the flying of accumulated sludge is seen in the supernatant, the diagnosis is abnormal. In this case, the fast sludge addition speed, large sludge addition amount, and high sludge interface are memorized as an example of the presumed result of the cause of the diagnosis. Furthermore, the reduction of the input speed, the reduction of the input amount, and the promotion of sludge extraction are recorded as an example of a method of coping with the estimated result of the cause of the diagnosis result. Return to Figure 15 to continue the explanation.

The information processing unit 53b functions as, for example, a graphics unit 54, a current state determination unit 55a, a learning unit 56b, a cause determination unit 57b, and a response method determination unit 58. The learning unit 56b has the following functions in addition to the functions of the learning unit 56a. The learning unit 56b obtains the diagnosis result notification received by the communication device 51, and stores the teacher data of the response method obtained by associating the information representing the supernatant image contained in the obtained diagnosis result notification with the information used to determine the response method for the diagnosis result in the recording device 52. The learning unit 56b obtains the teacher data of the response method stored in the recording device 52. The learning unit 56b performs machine learning (teacher-assisted learning) on the supernatant image and the information on the countermeasure for determining the diagnosis result of the state inside the solid-liquid separation tank obtained from the supernatant image based on the teacher data of the countermeasure, thereby generating a learning model of the countermeasure that associates the supernatant image with the countermeasure for the state inside the solid-liquid separation tank. For example, the learning unit 56b uses a convolutional neural network to recognize the supernatant image. The supernatant image is classified into any one of the information on the countermeasure for determining the state inside the solid-liquid separation tank based on the information representing the supernatant image by the learning model of the countermeasure. The learning unit 56b stores the generated learning model of the response method in the recording device 52.

The cause determination unit 57b obtains information representing the supernatant image and the determination result of the state inside the solid-liquid separation tank from the current state determination unit 55a. When the determination result of the state inside the solid-liquid separation tank is abnormal or abnormal, the cause determination unit 57b obtains the learning model of the cause stored in the recording device 52. The cause determination unit 57b determines the cause of the state inside the solid-liquid separation tank that forms the obtained supernatant image based on the learned model of the cause. The response method determination unit 58 obtains information representing the supernatant image and the determination result of the state inside the solid-liquid separation tank from the current state determination unit 55a. When the result of the determination of the state of the interior of the solid-liquid separation tank is abnormal or abnormal, the response method determination unit 58 obtains the learning model of the response method stored in the recording device 52. The response method determination unit 58 determines the response method for the state of the interior of the solid-liquid separation tank of the obtained supernatant image based on the obtained learning model of the response method. The response method determination unit 58 obtains information for determining the cause of the state of the interior of the solid-liquid separation tank that forms the supernatant image from the cause determination unit 57b. The response method determination unit 58 creates state notification information, which includes information representing the supernatant image, information for determining the cause of the state inside the solid-liquid separation tank, and information for determining the response method for the state inside the solid-liquid separation tank, and is destined for the information processing device 40b. The response method determination unit 58 outputs the created state notification information to the communication device 51. The communication device 51 obtains the state notification information output by the cause determination unit 57b, and transmits the obtained state notification information to the information processing device 40b. All or part of the information processing unit 53b is a functional unit (hereinafter referred to as a software functional unit) implemented by a processor such as a CPU executing a program such as a monitoring application stored in the recording device 52. Furthermore, all or part of the information processing unit 53b can be implemented by hardware such as LSI, ASIC or FPGA, or by a combination of software function units and hardware. The information processing device 40b can apply the information processing device 40.

(Terminal device 45b) Terminal device 45b is implemented by a device such as a personal computer, a server or an industrial computer. An example of terminal device 45b is set in a monitoring center for monitoring sewage treatment equipment 10. When diagnosing the state inside the solid-liquid separation tank, the user creates a supernatant image request by operating terminal device 45b, and the supernatant image request includes information requesting a supernatant image and has monitoring device 50b as a destination. Terminal device 45b creates a supernatant image request based on the user's operation. Terminal device 45b transmits the created supernatant image request to monitoring device 50b. The terminal device 45a receives the supernatant image response transmitted by the monitoring device 50b in response to the supernatant image request transmitted to the monitoring device 50b. The terminal device 45b displays the supernatant image contained in the supernatant image response. The user diagnoses the state of the inside of the solid-liquid separation tank contained in the supernatant image with reference to the supernatant image displayed by the terminal device 45b, and further infers the cause of the state of the inside of the solid-liquid separation tank. The user creates a diagnosis result notification by operating the terminal device 45b, and the diagnosis result notification includes information representing the image of the supernatant, the diagnosis result of the state inside the solid-liquid separation tank, information for determining the cause of the diagnosis result, and information for determining the response method to the diagnosis result, and has the monitoring device 50b as the destination. The terminal device 45b creates the diagnosis result notification based on the user's operation. The terminal device 45b transmits the created diagnosis result notification to the monitoring device 50b.

(Operation of the monitoring system) FIG. 17 is a diagram showing Example 1 of the operation of the monitoring system of Modification 2 of the implementation form. Referring to FIG. 17 , the following processing is explained: the monitoring device 50b accumulates the diagnosis result of the state inside the solid-liquid separation tank contained in the diagnosis result notification transmitted by the terminal device 45b, information for determining the cause of the diagnosis result, and information for determining the response method to the diagnosis result. The following processing is described: the monitoring device 50b performs machine learning based on the accumulated diagnosis results of the state inside the solid-liquid separation tank, information for determining the cause of the diagnosis results, and information for determining the response method to the diagnosis results, and generates a learning model of the diagnosis results, a learning model of the cause, and a learning model of the response method. Steps S1-6 to S10-6 can apply steps S1-1 to S10-1 of Figure 7, so the description here is omitted. (Step S11-6) The terminal device 45b receives the supernatant image response transmitted by the monitoring device 50b. The terminal device 45b displays the supernatant image by performing image processing on the information representing the supernatant image contained in the received supernatant image response. The terminal device 45b creates a diagnosis result notification, which includes the diagnosis result of the state inside the solid-liquid separation tank, information for determining the cause of the diagnosis result, and information for determining the response method to the diagnosis result and has the monitoring device 50b as the destination. (Step S12-6) The terminal device 45b transmits the created diagnosis result notification to the monitoring device 50b.

(Step S13-6) In the monitoring device 50b, the communication device 51 receives the diagnosis result notification transmitted by the terminal device 45b. The learning unit 56b obtains the diagnosis result notification received by the communication device 51, and obtains the information representing the image of the supernatant liquid contained in the obtained diagnosis result notification, the diagnosis result of the state inside the solid-liquid separation tank, and the information for determining the response method to the diagnosis result. The learning unit 56b stores in the recording device 52 the teacher data of the diagnosis result obtained by associating the information representing the supernatant image with the diagnosis result of the state inside the solid-liquid separation tank, the teacher data of the cause obtained by associating the information representing the supernatant image with the information for determining the cause of the diagnosis result of the state inside the solid-liquid separation tank, and the teacher data of the response method obtained by associating the information representing the supernatant image with the information for determining the response method to the diagnosis result. (Step S14-6) In the monitoring device 50b, the learning unit 56b obtains the teacher data of the diagnosis result stored in the recording device 52. The learning unit 56b performs machine learning on the supernatant image and the diagnosis result of the state inside the solid-liquid separation tank obtained from the supernatant image by using the teacher data based on the obtained diagnosis result, thereby generating a learning model of the diagnosis result obtained by associating the supernatant image with the state inside the solid-liquid separation tank. The learning unit 56b obtains the teacher data of the cause stored in the recording device 52. The learning unit 56b performs machine learning on the supernatant image and the information on the cause of the diagnosis result for determining the state inside the solid-liquid separation tank based on the acquired teacher data of the cause, thereby generating a learning model of the cause that associates the supernatant image with the information on the cause of the diagnosis result for determining the state inside the solid-liquid separation tank. The learning unit 56b acquires the teacher data of the coping method stored in the recording device 52. The learning unit 56b performs machine learning on the supernatant image and the information on the countermeasure for determining the state inside the solid-liquid separation tank based on the obtained teacher data of the countermeasure, thereby generating a learning model of the countermeasure by associating the supernatant image with the information on the countermeasure for determining the state inside the solid-liquid separation tank. (Step S15-6) In the monitoring device 50b, the learning unit 56b stores the generated learning model of the diagnosis result, the learning model of the cause, and the learning model of the countermeasure in the recording device 52. Furthermore, the diagnosis result notification may also be the result of the diagnosis based on the monitoring image instead of the supernatant image. That is, in step S7-6, the terminal device 45b creates a monitoring image request, in step S8-1, the monitoring image request created by the terminal device 45b is transmitted to the monitoring device 50b, in step S9-1, the monitoring device 50b creates a monitoring image, and in step S10-1, the monitoring device 50b transmits the monitoring image response to the terminal device 45b.

FIG. 18 is a diagram of Example 2 showing the operation of the monitoring system of Modification 2 of the embodiment. Referring to FIG. 18 , the following processing is described: the monitoring device 50b obtains the digital signal transmitted by the data processing device 30 and creates a supernatant image based on the obtained digital signal; the monitoring device 50b determines the state inside the solid-liquid separation tank based on the created supernatant image. Steps S1-7 to S6-7 can apply steps S1-1 to S6-1 of FIG. 7 , so the description here is omitted. (Step S7-7) In the monitoring device 50b, the current state determination unit 55a obtains the pixel data stored in the recording device 52, and creates a supernatant image based on the obtained pixel data. (Step S8-7) In the monitoring device 50b, the current state determination unit 55a obtains the learning model of the diagnosis result stored in the recording device 52. (Step S9-7) In the monitoring device 50b, the current state determination unit 55a determines the state of the solid-liquid separation tank of the created supernatant image based on the learning model of the obtained diagnosis result. (Step S10-7) In the monitoring device 50b, the cause determination unit 57b obtains the determination result of the state inside the solid-liquid separation tank from the current state determination unit 55a. The cause determination unit 57b determines whether the determination result of the state inside the solid-liquid separation tank obtained is abnormal or abnormal. The process ends when the cause determination unit 57b determines that the determination result of the state inside the solid-liquid separation tank obtained is neither abnormal nor abnormal.

(Step S11-7) In the monitoring device 50b, when the judgment result of the state of the solid-liquid separation tank is determined to be abnormal or abnormal, the cause judgment unit 57b obtains the learning model of the cause stored in the recording device 52. (Step S12-7) In the monitoring device 50b, the cause judgment unit 57b judges the cause of the state of the solid-liquid separation tank that forms the obtained supernatant image based on the learned model of the cause. (Step S13-7) In the monitoring device 50b, the response method judgment unit 58 obtains the information representing the supernatant image and the judgment result of the state of the solid-liquid separation tank from the current state judgment unit 55a. When the result of the determination of the state of the interior of the solid-liquid separation tank is abnormal or abnormal, the response method determination unit 58 obtains the learning model of the response method stored in the recording device 52. (Step S14-7) In the monitoring device 50b, the response method determination unit 58 determines the response method for the state of the interior of the solid-liquid separation tank of the obtained supernatant image based on the obtained learning model of the response method. (Step S15-7) In the monitoring device 50b, the response method determination unit 58 obtains information for determining the cause of the state of the interior of the solid-liquid separation tank that forms the supernatant image from the cause determination unit 57b. The response method determination unit 58 creates state notification information, which includes information indicating a supernatant image, information for determining the cause of the state inside the solid-liquid separation tank, and information for determining a response method for the state inside the solid-liquid separation tank, and has the information processing device 40b as a destination. (Step S16-7) In the monitoring device 50b, the response method determination unit 58 outputs the created state notification information to the communication device 51. The communication device 51 obtains the state notification information output by the response method determination unit 58, and transmits the obtained state notification information to the information processing device 40b. Furthermore, in step S7-7, the monitoring device 50 creates a supernatant image, but this is only an example. For example, the monitoring device 50 only needs to create a monitoring image based on the pixel data, and in the subsequent steps, focus on the supernatant image by ignoring the portion deeper than the sludge interface. The processing of the monitoring device 50b transmitting information representing the supernatant image based on the tank status information request transmitted by the information processing device 40b is omitted because Figure 9 can be applied.

In the variation 2 of the embodiment, the case where the monitoring system 100b is connected to one sewage treatment equipment 10 is described, but it is not limited to this example. For example, the monitoring system 100b may be connected to a plurality of sewage treatment equipment 10, or a plurality of monitoring systems 100b may be connected to one sewage treatment equipment 10. In the case where the monitoring system 100b is connected to a plurality of sewage treatment equipment 10, when an unstable state with no experience occurs in the equipment of A, if there is experience that the unstable state occurs in the equipment of B, it is judged as "abnormal", and it is judged that there is a high possibility that information for determining the cause of the abnormality and information for determining the response method will be output. That is, the monitoring device 50b can perform more learning, thereby increasing the number of cases that can be used for judgment. Therefore, the number of unstable states that can be judged as abnormal or abnormal can be increased. In the variant example 2 of the embodiment, the case where the monitoring device 50b performs machine learning is described, but it is not limited to this example. For example, a device independent of the monitoring device 50b can be used to implement a device that performs machine learning. In this case, regarding the learning device, in the learning device described in the variant example 1 of the embodiment, the learning device obtains the supernatant image and information for determining the response method to the diagnosis result obtained based on the supernatant image from the monitoring device 50b. The learning unit of the learning device generates a third learning model that represents the relationship between the supernatant image and the information for determining the response method to the diagnosis result obtained based on the supernatant image by machine learning (machine learning with a teacher). In variant example 2 of the implementation form, the information processing device 40b can notify the operator of the sewage treatment equipment 10 of the response method contained in the status notification, and can also create control information for the equipment control device 19 to execute the response method, and transmit the created control information to the equipment control device 19. In the variant 2 of the embodiment, the following situation is described, but not limited to this example: the determination result of the state inside the solid-liquid separation tank is determined based on the supernatant image, whether it is normal, abnormal, or malfunction, and further, based on whether the determination result of the state inside the solid-liquid separation tank is abnormal or malfunction, the promotion of sludge extraction, the addition of sludge settling agents, etc., the reduction of the input speed, the reduction of the input amount, and the promotion of sludge extraction are memorized. For example, it can be classified into one or more coping methods based on whether the determination result of the state inside the solid-liquid separation tank is abnormal or malfunction. In the variant 2 of the embodiment, the following situation is described, but not limited to this example: in the variant 1 of the embodiment, there is further processing of determining information for determining a method for dealing with the state inside the solid-liquid separation tank based on the supernatant image of the solid-liquid separation tank. For example, in the embodiment, there may be further processing of determining information for determining a method for dealing with the state inside the solid-liquid separation tank based on the supernatant image of the solid-liquid separation tank.

According to the monitoring system 100b of the variant example 2 of the implementation form, in the monitoring device 50a of the implementation form, the monitoring device 50b includes a response method determination unit 58, and the response method determination unit 58 is based on the supernatant image and the information for determining the response method for the diagnosis result obtained according to the supernatant image, and uses the third learning model as a learning model of the response method that has learned the relationship between the supernatant image and the information for determining the response method for the diagnosis result inside the solid-liquid separation tank. The response method determination unit 58 determines the information for determining the response method for the state inside the solid-liquid separation tank according to the supernatant image of the solid-liquid separation tank as the diagnosis object. The output unit further outputs the information of the response method determination unit 58 for determining the response method for the state inside the solid-liquid separation tank determined by the third learning model using the supernatant image of the solid-liquid separation tank as the diagnosis object. By configuring in this way, the monitoring device 50b can use the third learning model that has learned the relationship between the supernatant image and the information for determining the response method for the diagnosis result inside the solid-liquid separation tank, and determine the information for determining the response method for the state inside the solid-liquid separation tank based on the supernatant image of the solid-liquid separation tank as the diagnosis object, thereby monitoring the response method for the state inside the solid-liquid separation tank. By using the third learning model and determining the response method to the state inside the solid-liquid separation tank based on the supernatant image of the solid-liquid separation tank as the diagnosis object, compared with the case where a person diagnoses the response method to the state inside the solid-liquid separation tank based on experience, human experience is not required and the deviation of the diagnosis result can be reduced.

[Variant 3 of the embodiment] (Monitoring system) FIG. 19 is a diagram showing a configuration example of a monitoring system of variant 3 of the embodiment of the present invention. The monitoring system 100c of variant 3 of the embodiment not only diagnoses the sludge accumulation state of a solid-liquid separation tank such as a sedimentation tank and a concentration tank, but also detects signs of changes. In variant 3 of the embodiment, the sewage treatment equipment 10 is applied as an example of equipment including a solid-liquid separation tank in the same manner as in the embodiment.

(Monitoring system 100c) The monitoring system 100c includes an ultrasonic sensor 20, a data processing device 30, a gateway device 31, an information processing device 40c, a terminal device 45c, and a monitoring device 50c. The gateway device 31, the information processing device 40c, the terminal device 45c, and the monitoring device 50c are connected via a network NW. In the data processing device 30, the data operation unit 34 transmits a digital signal to the monitoring device 50c via the gateway device 31.

(Monitoring device 50c) The monitoring device 50c is implemented by a device such as a personal computer, a server or an industrial computer. The monitoring device 50c includes a communication device 51, a recording device 52, an information processing unit 53c, and a bus BL such as an address bus or a data bus for electrically connecting each structural component as shown in FIG. 19. The program (monitoring application) executed by the monitoring device 50c is stored in the recording device 52. In addition, the pixel data output by the information processing unit 53c is stored in the recording device 52. The recording device 52 stores teacher data of the diagnosis result obtained by associating information representing the supernatant image with the diagnosis result of the state inside the solid-liquid separation tank obtained from the supernatant image, and a learning model of the diagnosis result obtained by machine learning the relationship between the supernatant image and the state inside the solid-liquid separation tank based on the teacher data of the diagnosis result. The recording device 52 stores the teacher data of the cause obtained by associating the information representing the supernatant image with the information for determining the cause of the diagnosis result of the state inside the solid-liquid separation tank obtained from the supernatant image, and the learning model of the cause obtained by machine learning the relationship between the supernatant image and the information for determining the cause of the state inside the solid-liquid separation tank based on the teacher data of the cause. The recording device 52 stores teacher data of a response method obtained by associating information representing a supernatant image with information for determining a response method for a diagnosis result of the state inside the solid-liquid separation tank obtained from the supernatant image, and a learning model of a response method obtained by machine learning the relationship between the supernatant image and the information for determining a response method for the state inside the solid-liquid separation tank based on the teacher data of the response method. The recording device 52 stores the changed teacher data obtained by associating the information representing the supernatant image with the information for determining the change of the state inside the solid-liquid separation tank after the supernatant image is obtained, and the changed learning model obtained by machine learning the relationship between the supernatant image and the information for determining the change of the state inside the solid-liquid separation tank based on the changed teacher data.

The information processing unit 53c functions as, for example, a graphics unit 54, a current state determination unit 55a, a learning unit 56c, a cause determination unit 57b, a response method determination unit 58c, and a change sign derivation unit 59. The learning unit 56c has the following functions in addition to the functions of the learning unit 56b. The learning unit 56c obtains the teacher data of the changes stored in the recording device 52. The learning unit 56c performs machine learning (teacher-assisted learning) on the supernatant image and the information for determining the change of the state of the inside of the solid-liquid separation tank after the supernatant image is obtained based on the obtained change teacher data, and generates a change learning model that associates the supernatant image with the information for determining the change of the state of the inside of the solid-liquid separation tank after the supernatant image is obtained. For example, the learning unit 56c uses a convolutional neural network to recognize the supernatant image. The supernatant image is classified into any one of the change information for determining the state of the inside of the solid-liquid separation tank after the supernatant image is obtained based on the change learning model and the information representing the supernatant image. The learning unit 56c stores the generated learning model of the change in the recording device 52. The response method determination unit 58c obtains information representing the supernatant image and the determination result of the state of the solid-liquid separation tank inside the image from the current state determination unit 55a. When the determination result of the state of the solid-liquid separation tank inside the obtained solid-liquid separation tank is abnormal or abnormal, the response method determination unit 58c obtains the learning model of the response method stored in the recording device 52. The response method determination unit 58c determines the response method for the state of the solid-liquid separation tank inside the obtained supernatant image based on the obtained learning model of the response method. The change sign derivation unit 59 obtains information representing the supernatant image from the current state determination unit 55a. The change sign deriving unit 59 obtains the learning model of the change stored in the recording device 52. The change sign deriving unit 59 derives the sign of the change of the solid-liquid separation tank of the obtained supernatant image based on the obtained learning model of the change. All or part of the information processing unit 53c is a functional unit (hereinafter referred to as a software functional unit) implemented by, for example, a processor such as a CPU executing a program such as a monitoring application stored in the recording device 52. Furthermore, all or part of the information processing unit 53c can be implemented by hardware such as LSI, ASIC or FPGA, or by a combination of software functional units and hardware. The information processing device 40c can apply the information processing device 40.

(Terminal device 45c) The terminal device 45c is implemented by a device such as a personal computer, a server or an industrial computer. An example of the terminal device 45c is set in a monitoring center for monitoring the sewage treatment equipment 10. When diagnosing the state inside the solid-liquid separation tank, the user creates a supernatant image request by operating the terminal device 45c, and the supernatant image request includes information requesting a supernatant image and has the monitoring device 50c as a destination. The terminal device 45c creates the supernatant image request based on the user's operation. The terminal device 45c transmits the created supernatant image request to the monitoring device 50c. The terminal device 45c receives the supernatant image response transmitted by the monitoring device 50c in response to the supernatant image request transmitted to the monitoring device 50c. The terminal device 45c displays the supernatant image contained in the supernatant image response. The user refers to the supernatant image displayed by the terminal device 45c to diagnose the state of the solid-liquid separation tank contained in the supernatant image, and then infers the cause of the state inside the solid-liquid separation tank, determines the response method to the state inside the solid-liquid separation tank, infers the state of the solid-liquid separation tank after obtaining the supernatant image, and determines its change. The user creates a diagnosis result notification by operating the terminal device 45c, and the diagnosis result notification includes information representing the supernatant image, the diagnosis result of the state inside the solid-liquid separation tank, information for determining the cause of the diagnosis result, information for determining the response method to the state inside the solid-liquid separation tank, and information for determining the change of the state inside the solid-liquid separation tank after the supernatant image is obtained, and has the monitoring device 50c as the destination. The terminal device 45c creates the diagnosis result notification based on the user's operation. The terminal device 45c transmits the created diagnosis result notification to the monitoring device 50c.

(Operation of the monitoring system) FIG. 20 is a diagram of Example 1 of the operation of the monitoring system of Modification 3 of the implementation form. Referring to FIG. 20, the following processing is explained: the monitoring device 50c accumulates the information representing the supernatant image and the diagnosis result of the state inside the solid-liquid separation tank contained in the diagnosis result notification transmitted by the terminal device 45c, the information for determining the cause of the diagnosis result, the information for determining the response method to the diagnosis result, and the information for determining the change of the state inside the solid-liquid separation tank after the supernatant image is obtained, and based on The accumulated information representing the supernatant image and the diagnosis result of the state inside the solid-liquid separation tank, the information used to determine the cause of the diagnosis result, the information used to determine the response method to the diagnosis result, and the information used to determine the change of the state inside the solid-liquid separation tank after the monitoring image is obtained are used for machine learning to generate a learning model of the diagnosis result, a learning model of the cause, a learning model of the response method, and a learning model of the change. Steps S1-8 to S10-8 can apply steps S1-1 to S10-1 of Figure 7, so the description here is omitted. (Step S11-8) The terminal device 45c receives the supernatant image response transmitted by the monitoring device 50c. The terminal device 45c displays the supernatant image by performing image processing on the information representing the supernatant image contained in the received supernatant image response. The terminal device 45c creates a diagnosis result notification, which includes information representing the supernatant image, the diagnosis result of the state inside the solid-liquid separation tank, information for determining the cause of the diagnosis result, information for determining the response method to the diagnosis result, and information for determining the change of the state inside the solid-liquid separation tank after the supernatant image is obtained, and has the monitoring device 50c as the destination.

(Step S12-8) The terminal device 45c transmits the created diagnosis result notification to the monitoring device 50c. (Step S13-8) In the monitoring device 50c, the communication device 51 receives the diagnosis result notification transmitted by the terminal device 45c. The learning unit 56c obtains the diagnosis result notification received by the communication device 51, and obtains information indicating the supernatant image contained in the obtained diagnosis result notification, the diagnosis result of the state inside the solid-liquid separation tank, information for determining the response method to the diagnosis result, and information for determining the change of the state inside the solid-liquid separation tank after obtaining the supernatant image. The learning unit 56c stores in the recording device 52 the teacher data of the diagnosis result obtained by associating the information representing the supernatant image with the diagnosis result of the state inside the solid-liquid separation tank, the teacher data of the cause obtained by associating the information representing the supernatant image with the information for determining the cause of the diagnosis result of the state inside the solid-liquid separation tank, the teacher data of the response method obtained by associating the information representing the supernatant image with the information for determining the response method to the diagnosis result, and the teacher data of the change obtained by associating the information representing the supernatant image with the information for determining the change of the state inside the solid-liquid separation tank after the supernatant image is obtained. (Step S14-8) In the monitoring device 50c, the learning unit 56c obtains the teacher data of the diagnosis result stored in the recording device 52. The learning unit 56c performs machine learning on the diagnosis result of the supernatant image and the state of the inside of the solid-liquid separation tank obtained from the supernatant image based on the teacher data of the obtained diagnosis result, and generates a learning model of the diagnosis result obtained by associating the supernatant image with the state of the inside of the solid-liquid separation tank. The learning unit 56c obtains the teacher data of the cause stored in the recording device 52. The learning unit 56c performs machine learning on the supernatant image and the information on the cause of the diagnosis result for determining the state inside the solid-liquid separation tank based on the acquired teacher data of the cause, thereby generating a learning model of the cause that associates the supernatant image with the information on the cause of the diagnosis result for determining the state inside the solid-liquid separation tank. The learning unit 56c acquires the teacher data of the coping method stored in the recording device 52. The learning unit 56c performs machine learning on the supernatant image and the information on the countermeasure for determining the state inside the solid-liquid separation tank based on the obtained teacher data on the countermeasure, thereby generating a learning model of the countermeasure by associating the supernatant image with the information on the countermeasure for determining the state inside the solid-liquid separation tank. The learning unit 56c obtains the teacher data of the changes stored in the recording device 52. The learning unit 56c performs machine learning on the supernatant image and the information for determining the change of the state inside the solid-liquid separation tank after the supernatant image is obtained based on the obtained change teacher data, thereby generating a change learning model that associates the supernatant image with the information for determining the change of the state inside the solid-liquid separation tank after the supernatant image is obtained.

(Step S15-8) In the monitoring device 50c, the learning unit 56c stores the generated learning model of the diagnosis result, the learning model of the cause, the learning model of the response method, and the learning model of the change in the recording device 52.

Furthermore, the diagnosis result notification may also be the result of diagnosis based on the monitoring image instead of the supernatant image. That is, in step S7-8, the terminal device 45c creates a monitoring image request, in step S8-8, the monitoring image request created by the terminal device 45c is transmitted to the monitoring device 50c, in step S9-8, the monitoring device 50c creates a monitoring image, and in step S10-8, the monitoring device 50c transmits the monitoring image response to the terminal device 45c.

FIG. 21 is a diagram of Example 2 of the operation of the monitoring system of Modification 3 of the implementation form. Referring to FIG. 21 , the following processing is explained: the monitoring device 50c obtains the digital signal transmitted by the data processing device 30, and creates a supernatant image based on the obtained digital signal; the monitoring device 50c determines the state inside the solid-liquid separation tank based on the created supernatant image. Steps S1-9 to S6-9 can apply steps S1-1 to S6-1 of FIG. 7, so the explanation here is omitted. (Step S7-9) In the monitoring device 50c, the current state determination unit 55a obtains the pixel data stored in the recording device 52, and creates a supernatant image based on the obtained pixel data. (Step S8-9) In the monitoring device 50c, the current state determination unit 55a obtains the learning model of the diagnosis result stored in the recording device 52. (Step S9-9) In the monitoring device 50c, the current state determination unit 55a determines the state of the solid-liquid separation tank of the created supernatant image based on the learning model of the obtained diagnosis result. (Step S10-9) In the monitoring device 50c, the cause determination unit 57b obtains the determination result of the state inside the solid-liquid separation tank from the current state determination unit 55a. The cause determination unit 57b determines whether the determination result of the state inside the solid-liquid separation tank obtained is abnormal or abnormal. When the cause determination unit 57b determines that the determination result of the state inside the solid-liquid separation tank obtained is neither abnormal nor abnormal, the process proceeds to step S15-9. (Step S11-9) In the monitoring device 50c, when the judgment result of the state of the inside of the solid-liquid separation tank is judged to be abnormal or abnormal, the cause judgment unit 57b obtains the learning model of the cause stored in the recording device 52. (Step S12-9) In the monitoring device 50c, the cause judgment unit 57b judges the cause of the state of the inside of the solid-liquid separation tank that forms the obtained supernatant image based on the learned model of the cause. (Step S13-9) In the monitoring device 50c, the response method judgment unit 58c obtains the information representing the supernatant image and the judgment result of the state of the inside of the image solid-liquid separation tank from the current state judgment unit 55a. When the obtained determination result of the state inside the solid-liquid separation tank is abnormal or abnormal, the response method determination unit 58c obtains the learning model of the response method stored in the recording device 52.

(Step S14-9) In the monitoring device 50c, the response method determination unit 58c determines the response method for the state of the solid-liquid separation tank inside the obtained supernatant image based on the obtained learning model of the response method. (Step S15-9) In the monitoring device 50c, the change sign derivation unit 59 obtains the learning model of the change stored in the recording device 52. In the monitoring device 50c, the change sign derivation unit 59 detects the sign of the change of the state of the solid-liquid separation tank inside the obtained supernatant image based on the obtained learning model of the change. (Step S16-9) In the monitoring device 50c, the change sign deriving unit 59 obtains information representing the supernatant image and the determination result of the state inside the image solid-liquid separation tank from the current state determination unit 55a. The change sign deriving unit 59 obtains information for determining the cause of the state inside the solid-liquid separation tank that forms the supernatant image from the cause determination unit 57b. The change sign output unit 59 creates status notification information, which includes information representing the supernatant image, the determination result of the state inside the solid-liquid separation tank of the image, information for determining the cause of the state inside the solid-liquid separation tank, information for determining the response method to the state inside the solid-liquid separation tank, and the detection result of the sign of the change of the state inside the solid-liquid separation tank, and the information processing device 40c is used as the destination. (Step S17-9) In the monitoring device 50c, the change sign output unit 59 outputs the created status notification information to the communication device 51. The communication device 51 obtains the status notification information output by the response method determination unit 58c, and transmits the obtained status notification information to the information processing device 40c. Furthermore, in step S7-9, the monitoring device 50 creates a supernatant image, but this is only an example. For example, the monitoring device 50 only needs to create a monitoring image based on pixel data, and in subsequent steps, focus on the supernatant image by ignoring the portion deeper than the sludge interface. The processing of the monitoring device 50c transmitting information representing the supernatant image based on the tank status information request transmitted by the information processing device 40c is omitted because Figure 9 can be applied.

In the modification 3 of the embodiment, the case where the monitoring system 100c is connected to one sewage treatment equipment 10 is described, but the present invention is not limited to this example. For example, the monitoring system 100c may be connected to a plurality of sewage treatment equipments 10, or a plurality of monitoring systems 100c may be connected to one sewage treatment equipment 10. Assuming that multiple sewage treatment equipment 10 are connected to the monitoring system 100c, when an unstable state that has no experience occurs in equipment A, if there is experience that the unstable state occurs in equipment B, it is judged as "abnormal", and it is highly likely that information for determining the cause of the abnormality, information for determining the response method, and information for determining the change in the state inside the solid-liquid separation tank after the supernatant image is obtained will be judged and output. That is, the monitoring device 50c can learn more, so the number of cases that can be used for judgment can be increased. Therefore, the unstable state that can be judged as abnormal or abnormal can be increased. In the modification example 3 of the embodiment, the case where the monitoring device 50c performs machine learning is described, but the present invention is not limited to this example. For example, a device independent of the monitoring device 50c can be used to implement a device that performs machine learning. In this case, regarding the learning device, in the learning device described in the modification example 2 of the embodiment, the learning device obtains the supernatant image from the monitoring device 50c and the information used to determine the change of the state of the inside of the solid-liquid separation tank after the supernatant image is obtained. The learning unit of the learning device generates a fourth learning model that represents the relationship between the supernatant image and the information for determining the change of the state inside the solid-liquid separation tank after the supernatant image is obtained by machine learning (machine learning with a teacher). In variant example 3 of the implementation form, the information processing device 40c can notify the operator of the sewage treatment equipment 10 of the information for determining the change of the state inside the solid-liquid separation tank contained in the state notification.

According to the monitoring system 100c of the variant example 3 of the implementation form, in the monitoring device 50b of the implementation form, the monitoring device 50c includes a change sign deriving unit 59, and the change sign deriving unit 59 is based on the supernatant image and the information for determining the change of the state inside the solid-liquid separation tank after the supernatant image is obtained, and uses the fourth learning model as a change learning model that has learned the relationship between the supernatant image and the information for determining the change of the state inside the solid-liquid separation tank, and detects the sign of the change of the state inside the solid-liquid separation tank according to the supernatant image of the solid-liquid separation tank as the diagnosis object. The output unit further outputs information for determining a sign of a change in the state of the solid-liquid separation tank detected by the fourth learning model using the supernatant image of the solid-liquid separation tank as the object of diagnosis. By configuring in this way, the monitoring device 50c can detect a sign of a change in the state of the solid-liquid separation tank based on the supernatant image of the solid-liquid separation tank as the object of diagnosis, thereby monitoring the change in the state of the solid-liquid separation tank. By using the fourth learning model, the signs of changes in the state inside the solid-liquid separation tank can be detected based on the supernatant image of the solid-liquid separation tank as the diagnosis object. Compared with the case where humans detect the signs of changes in the state inside the solid-liquid separation tank based on experience, human experience is not required and the deviation of the diagnosis result can be reduced.

[Variant 4 of the embodiment] (Monitoring system) FIG. 22 is a diagram showing a structural example of a monitoring system of a variant 4 of the embodiment of the present invention. In variant 3 of the embodiment, a monitoring system 100d of variant 4 of the embodiment connects a monitoring device 50d between a data processing device 30d and a gate device 31 without passing through a network NW. The monitoring system 100d of variant 4 of the embodiment diagnoses the sludge accumulation state of a solid-liquid separation tank such as a sedimentation tank and a concentration tank and detects signs of changes. In variant 4 of the embodiment, a sewage treatment device 10 is applied as an example of a device including a solid-liquid separation tank in the same manner as in the embodiment. In Figure 22, the sewage treatment equipment 10 is omitted.

(Monitoring system 100d) The monitoring system 100d includes an ultrasonic sensor 20, a data processing device 30d, a monitoring device 50d, a gateway device 31, an information processing device 40d, and a terminal device 45d. The gateway device 31, the information processing device 40d, and the terminal device 45d are connected via a network NW. In the data processing device 30d, the data operation unit 34 transmits a digital signal to the monitoring device 50d.

(Monitoring device 50d) FIG. 23 is a diagram showing an example of a monitoring device of a monitoring system of variation 4 of the present embodiment. The monitoring device 50d is implemented by a device such as a personal computer, a server, or an industrial computer. The monitoring device 50d includes a communication device 51, a recording device 52, an information processing unit 53d, and a bus BL such as an address bus or a data bus for electrically connecting each structural component as shown in FIG. 23. The program (monitoring application) executed by the monitoring device 50d is stored in the recording device 52. In addition, the pixel data output by the information processing unit 53d is stored in the recording device 52. The recording device 52 stores teacher data of the diagnosis result obtained by associating information representing the monitoring image with the diagnosis result of the state inside the solid-liquid separation tank obtained from the supernatant image, and a learning model of the diagnosis result obtained by machine learning the relationship between the supernatant image and the state inside the solid-liquid separation tank based on the teacher data of the diagnosis result. The recording device 52 stores the teacher data of the cause obtained by associating the information representing the supernatant image with the information for determining the cause of the diagnosis result of the state inside the solid-liquid separation tank obtained from the supernatant image, and the learning model of the cause obtained by machine learning the relationship between the supernatant image and the information for determining the cause of the state inside the solid-liquid separation tank based on the teacher data of the cause. The recording device 52 stores teacher data of a response method obtained by associating information representing a supernatant image with information for determining a response method for a diagnosis result of the state inside the solid-liquid separation tank obtained from the supernatant image, and a learning model of a response method obtained by machine learning the relationship between the supernatant image and the information for determining a response method for the state inside the solid-liquid separation tank based on the teacher data of the response method. The recording device 52 stores the changed teacher data obtained by associating the information representing the supernatant image with the information for determining the change of the state inside the solid-liquid separation tank after the supernatant image is obtained, and the changed learning model obtained by machine learning the relationship between the supernatant image and the information for determining the change of the state inside the solid-liquid separation tank based on the changed teacher data.

The information processing unit 53d functions as, for example, a graphics unit 54d, a current state determination unit 55d, a learning unit 56d, a cause determination unit 57d, a response method determination unit 58d, and a change sign derivation unit 59d. The graphics unit 54d obtains a digital signal received by the communication device 51. The graphics unit 54d converts the value of the obtained digital signal into pixel data. The graphics unit 54d stores the converted pixel data of the digital signal in the recording device 52. The graphics unit 54d obtains a supernatant image request received by the communication device 51. The graphics unit 54d obtains the pixel data stored in the recording device 52 based on the obtained supernatant image request. The graphics unit 54d creates a supernatant image based on the acquired pixel data. The graphics unit 54d creates a supernatant image response, which includes information representing the created supernatant image and has the information processing device 40d as a destination. The graphics unit 54d outputs the created supernatant image response to the communication device 51. The graphics unit 54d obtains the tank status information request received by the communication device 51. The graphics unit 54d obtains the pixel data stored in the recording device 52 based on the acquired tank status information request, and creates a supernatant image based on the acquired pixel data. The graphics unit 54d creates a tank state information response, which includes information representing the created supernatant image and has the information processing device 40d as the destination. The graphics unit 54d outputs the created tank state information response to the communication device 51.

The current status determination unit 55d obtains the pixel data stored in the recording device 52, and creates a supernatant image based on the obtained pixel data. The current status determination unit 55d obtains the learning model of the diagnosis result stored in the recording device 52. The current status determination unit 55d determines the state of the inside of the solid-liquid separation tank of the created supernatant image based on the learning model of the obtained diagnosis result. When the determination result of the state inside the solid-liquid separation tank is abnormal or abnormal, the current status determination unit 55d creates state notification information, which includes information indicating the determination result of the state inside the solid-liquid separation tank and has the information processing device 40d as the destination. The current state determination unit 55d outputs the created state notification information to the communication device 51. The communication device 51 obtains the state notification information output by the current state determination unit 55d and transmits the obtained state notification information to the information processing device 40d. When creating a supernatant image, the current state determination unit 55d can directly use the measured data, or can include the data measured over a long period of time in the display width limited by reduction. By including the data measured over a long period of time in the limited display width, changes over a longer period of time can be monitored. If it is assumed to be a still picture, pixel data can be picked up at any appropriate interval and switched for display. In the fourth variant of the present embodiment, since measurement is always performed and new data is continuously added, there is a concern that the data processing may be delayed or blocked when pixel data is picked up at any appropriate interval and switched for display. The measurement becomes unstable for the image display, which puts the cart before the horse. Therefore, in the fourth variant of the present embodiment, a plurality of pre-set display time widths are prepared, and data storage areas for time widths corresponding to the plurality of display time widths are created. In this embodiment, the interval (interval) for appending new data is specified, and an image database (data storage area (address)) corresponding to each of the multiple intervals is created.

The monitoring device 50d is operated to switch the display, and the display time width is selected. The current state determination unit 55d obtains data from the database corresponding to the selected time display width, and uses the obtained data to create a supernatant image. Assuming that the operation of switching the time display width is performed, data is obtained from the database corresponding to the selected time display width, and the supernatant image is created using the obtained data. By configuring in this way, the switching can be performed smoothly without processing the data in the database storing the data, and there will be no time lag in creating the supernatant image. The learning unit 56d obtains the diagnosis result notification received by the communication device 51, and causes the teacher data of the diagnosis result obtained by associating the information representing the supernatant image contained in the obtained diagnosis result notification with the diagnosis result of the state of the inside of the solid-liquid separation tank (inside the tank) obtained from the supernatant image to be stored in the recording device 52. The learning unit 56d obtains the teacher data of the diagnosis result stored in the recording device 52. The learning unit 56d performs machine learning (teacher-assisted learning) on the supernatant image and the diagnosis result of the state of the inside of the solid-liquid separation tank obtained from the supernatant image based on the teacher data of the obtained diagnosis result, and generates a learning model of the diagnosis result that associates the supernatant image with the state of the inside of the solid-liquid separation tank. For example, the learning unit 56d uses a convolutional neural network to recognize the supernatant image. The supernatant image is classified into any one of normal, abnormal, and abnormal as the state of the inside of the solid-liquid separation tank based on the learning model of the diagnosis result and the information representing the supernatant image. The learning unit 56d stores the generated learning model of the diagnosis result in the recording device 52. The learning unit 56d obtains the teacher data of the response method stored in the recording device 52. The learning unit 56d performs machine learning (teacher-led learning) on the supernatant image and the information of the response method for determining the diagnosis result of the state inside the solid-liquid separation tank obtained from the supernatant image based on the obtained teacher data of the response method, and generates a learning model of the response method that associates the supernatant image with the response method for the state inside the solid-liquid separation tank. For example, the learning unit 56d uses a convolutional neural network to recognize the supernatant image. The supernatant image is classified into any one of the information for determining the countermeasure method for the state inside the solid-liquid separation tank based on the information representing the supernatant image by using the countermeasure method learning model. The learning unit 56d stores the generated countermeasure method learning model in the recording device 52.

The learning unit 56d obtains the teacher data of the changes stored in the recording device 52. The learning unit 56d performs machine learning (teacher-assisted learning) on the supernatant image and the information for determining the changes in the state of the solid-liquid separation tank after the supernatant image is obtained based on the obtained teacher data of the changes, thereby generating a learning model of the changes that associates the supernatant image with the information for determining the changes in the state of the solid-liquid separation tank after the supernatant image is obtained. For example, the learning unit 56d uses a convolutional neural network to recognize the supernatant image. The learning unit 56d stores the generated changing learning model in the recording device 52, based on the information representing the supernatant image, to classify the supernatant image into any of the changing information for determining the state of the inside of the solid-liquid separation tank after the supernatant image is obtained.

The cause determination unit 57d obtains the information representing the supernatant image and the determination result of the state inside the solid-liquid separation tank from the current state determination unit 55d. When the determination result of the state inside the solid-liquid separation tank obtained is abnormal or abnormal, the cause determination unit 57d obtains the learning model of the cause stored in the recording device 52. The cause determination unit 57d determines the information for determining the cause of the state inside the solid-liquid separation tank that forms the obtained supernatant image based on the obtained learning model of the cause. The cause determination unit 57d creates status notification information, which includes information indicating a supernatant image, information indicating the state of the interior of the solid-liquid separation tank, and information indicating a determination result of information for determining the cause of the state of the interior of the solid-liquid separation tank, and is destined for the information processing device 40d. The cause determination unit 57d outputs the created status notification information to the communication device 51. The communication device 51 obtains the status notification information output by the cause determination unit 57d, and transmits the obtained status notification information to the information processing device 40d.

The response method determination unit 58d obtains information representing the supernatant image and the determination result of the state inside the solid-liquid separation tank from the current state determination unit 55d. When the determination result of the state inside the solid-liquid separation tank is abnormal or abnormal, the response method determination unit 58d obtains the learning model of the response method stored in the recording device 52. The response method determination unit 58d determines the response method for the state inside the solid-liquid separation tank of the obtained supernatant image based on the obtained learning model of the response method. The response method determination unit 58d obtains information for determining the cause of the state inside the solid-liquid separation tank that forms the supernatant image from the cause determination unit 57d. The response method determination unit 58d creates status notification information, which includes information indicating a supernatant image, information for determining the cause of the state inside the solid-liquid separation tank, and information for determining a response method for the state inside the solid-liquid separation tank, and has the information processing device 40d as a destination. The response method determination unit 58d outputs the created status notification information to the communication device 51.

The change sign deriving unit 59d obtains information representing the supernatant image from the current state determination unit 55d. The change sign deriving unit 59d obtains the learning model of the change stored in the recording device 52. The change sign deriving unit 59d derives the sign of the change of the solid-liquid separation tank of the obtained supernatant image based on the obtained learning model of the change. The whole or part of the information processing unit 53d is, for example, a functional unit (hereinafter referred to as a software functional unit) implemented by a processor such as a CPU executing a program such as a monitoring application stored in the recording device 52. Furthermore, the whole or part of the information processing unit 53c can be implemented by hardware such as LSI, ASIC or FPGA, or by a combination of software functional units and hardware. The information processing device 40d can apply the information processing device 40.

(Terminal device 45d) The terminal device 45d is implemented by a device such as a personal computer, a server or an industrial computer. An example of the terminal device 45d is set in a monitoring center for monitoring the sewage treatment equipment 10. When diagnosing the state inside the solid-liquid separation tank, the user creates a supernatant image request by operating the terminal device 45d, and the supernatant image request includes information requesting a supernatant image and has the monitoring device 50d as a destination. The terminal device 45d creates the supernatant image request based on the user's operation. The terminal device 45d transmits the created supernatant image request to the monitoring device 50d. The terminal device 45d receives the supernatant image response transmitted by the monitoring device 50d in response to the supernatant image request transmitted to the monitoring device 50d. The terminal device 45d displays the supernatant image contained in the supernatant image response. The user refers to the supernatant image displayed by the terminal device 45d to diagnose the state of the inside of the solid-liquid separation tank contained in the supernatant image, and then infers the cause of the state inside the solid-liquid separation tank, determines the response method to the state inside the solid-liquid separation tank, infers the state of the inside of the solid-liquid separation tank after obtaining the supernatant image, and determines its change. The user creates a diagnosis result notification by operating the terminal device 45d, and the diagnosis result notification includes information representing the supernatant image, the diagnosis result of the state inside the solid-liquid separation tank, information for determining the cause of the diagnosis result, information for determining the response method to the state inside the solid-liquid separation tank, and information for determining the change of the state inside the solid-liquid separation tank after the supernatant image is obtained, and has the monitoring device 50d as the destination. The terminal device 45d creates the diagnosis result notification based on the user's operation. The terminal device 45d transmits the created diagnosis result notification to the monitoring device 50d.

(Operation of the monitoring system) FIG. 24 is a diagram of Example 1 of the operation of the monitoring system of the variant 4 of the implementation form. Referring to FIG. 24, the following processing is explained: the monitoring device 50d accumulates the information representing the supernatant image and the diagnosis result of the state inside the solid-liquid separation tank contained in the diagnosis result notification transmitted by the terminal device 45d, the information for determining the cause of the diagnosis result, the information for determining the response method to the diagnosis result, and the information for determining the change of the state inside the solid-liquid separation tank after the supernatant image is obtained, and based on The accumulated information representing the supernatant image and the diagnosis result of the state inside the solid-liquid separation tank, the information used to determine the cause of the diagnosis result, the information used to determine the response method to the diagnosis result, and the information used to determine the change of the state inside the solid-liquid separation tank after the supernatant image is obtained are used for machine learning to generate a learning model of the diagnosis result, a learning model of the cause, a learning model of the response method, and a learning model of the change. Steps S1-10 to S10-10 can apply steps S1-1 to S10-1 in Figure 7, and steps S11-10 to S15-10 can apply steps S11-8 to S15-8 in Figure 20, so the description here is omitted.

FIG. 25 is a diagram of Example 2 of the operation of the monitoring system of Modification 4 of the implementation form. Referring to FIG. 25 , the following processing is explained: the monitoring device 50d obtains the digital signal transmitted by the data processing device 30d, and creates a supernatant image based on the obtained digital signal; the monitoring device 50d determines the state inside the solid-liquid separation tank based on the created supernatant image. Steps S1-11 to S6-11 can apply steps S1-1 to S6-1 of FIG. 7 , and steps S7-11 to S17-11 can apply steps S7-9 to S17-9 of FIG. 21 , so the explanation here is omitted. Regarding the processing of the monitoring device 50d transmitting information representing the supernatant image based on the tank status information request transmitted by the information processing device 40d, since Figure 9 can be applied, the description is omitted.

In the modification 4 of the embodiment, the monitoring device 50d is connected between the data processing device 30d and the gateway device 31 without passing through the network NW in the modification 3 of the embodiment, but the present invention is not limited to this example. For example, it can also be applied to the case where the monitoring device 50 is connected between the data processing device 30 and the gateway device 31 without passing through the network NW in the embodiment. It can also be applied to the case where the monitoring device 50a is connected between the data processing device 30 and the gateway device 31 without passing through the network NW in the modification 1 of the embodiment. The present invention can also be applied to the case where the monitoring device 50b is connected between the data processing device 30 and the gateway device 31 without passing through the network NW in the variation example 2 of the implementation form.

According to the monitoring system 100d of the variation 4 of the embodiment, the monitoring device 50d can be installed at the site where the sewage treatment equipment 10 is installed by connecting the monitoring device 50d between the data processing device 30 and the gate device 31 without going through the network NW. Therefore, compared with the case where the monitoring device 50d is installed at a location far away from the site where the sewage treatment equipment 10 is installed through the network NW, the data of the data processing device 30d can be monitored in real time. Since the judgment and derivation using the monitoring device 50d can be performed in real time, the time required for the judgment and derivation can be shortened. Since the time required for the judgment and derivation can be shortened, the status notification when it is judged to be abnormal or abnormal can be immediately performed. Since the monitoring device 50d can be installed at the site where the sewage treatment equipment 10 is installed, the risk of data hacking and system attack can be reduced compared to the case where the monitoring device 50d is installed at a location far away from the site where the sewage treatment equipment 10 is installed via the network NW. Since the monitoring device 50d can be installed at the site where the sewage treatment equipment 10 is installed, it is easier to determine the conditions unique to the site (equipment), determine the causes, determine the response methods, and derive the signs compared to the case where the monitoring device 50d is installed at a location far away from the site where the sewage treatment equipment 10 is installed via the network NW. The information processing device 40d installed in the monitoring center can remotely update the information (program, monitoring application, learning model, image data, AI analysis results) recorded in the recording device 52 of the monitoring device 50d installed on site via the network NW and the gateway device 31. The information (mainly pixel data, AI analysis results) recorded in the recording device 52 of the monitoring device 50d installed on site can be updated in the remotely installed information processing device 40d via the gateway device 31 and the network NW.

Next, another monitoring system according to another embodiment will be described. The configuration of the monitoring system according to another embodiment is the same as that of the monitoring system shown in FIG1 , but is different in the following aspects.

The recording device 52 stores teacher data of the diagnosis result obtained by associating information representing the monitoring image with the diagnosis result of the inside of the solid-liquid separation tank (inside the tank) obtained from the monitoring image, and a learning model of the diagnosis result obtained by machine learning the relationship between the monitoring image and the state inside the solid-liquid separation tank based on the teacher data of the diagnosis result. The teacher data of the diagnosis result is data obtained by associating the monitoring image with the diagnosis result of the state inside the solid-liquid separation tank obtained from the monitoring image. In this embodiment, as an example, each of the plurality of monitoring images is associated with any one of "normal", "abnormal" and "abnormal" as a diagnostic result. Using FIG6 for illustration, (1) since the supernatant and the sludge accumulation layer are separated and the solid-liquid separation is good, the diagnosis is normal. (2) since the sludge settling property is deteriorated and the sludge floats, the diagnosis is abnormal. (3) since the accumulated sludge is seen to be flying, the diagnosis is abnormal.

The graphics unit 54 obtains the monitoring image request received by the communication device 51. The graphics unit 54 obtains the pixel data stored in the recording device 52 based on the obtained monitoring image request. The graphics unit 54 creates a monitoring image based on the obtained pixel data. The graphics unit 54 creates a monitoring image response, which includes information representing the created monitoring image and has the information processing device 40 as the destination. The graphics unit 54 determines whether the created monitoring image is an error image. The graphics unit 54 outputs the monitoring image response including the monitoring image determined to be not an error image to the communication device 51. FIG26 is an example of an error image. An error image is a monitoring image created when the ultrasonic sensor 20 fails or is buried in sludge and cannot measure the treatment tank 25. An error image is an image in which the signal strength is weak both in the vertical direction and in the horizontal direction. For example, when the signal strength is less than a specified value from a specified depth within a certain period of time, the graphics unit 54 determines the monitoring image as an error image.

The graphics unit 54 acquires the pixel data stored in the recording device 52 based on the acquired slot status information request, and creates a monitoring image based on the acquired pixel data. The graphics unit 54 creates a slot status information response, which includes information indicating the created monitoring image and has the information processing device 40 as the destination.

The current status determination unit 55 obtains the pixel data stored in the recording device 52, and creates a monitoring image based on the obtained pixel data. The current status determination unit 55 determines whether the created monitoring image is an error image. The determination method is the same as the determination method using the graphics unit 54. When it is determined that it is not an error image, the current status determination unit 55 determines the state of the inside of the solid-liquid separation tank of the created monitoring image based on the learning model of the obtained diagnosis result.

The learning unit 56 obtains the diagnosis result notification received by the communication device 51, and stores the teacher data of the diagnosis result obtained by associating the information representing the monitoring image contained in the obtained diagnosis result notification with the diagnosis result of the state of the inside of the solid-liquid separation tank (inside the tank) obtained from the monitoring image in the recording device 52. The learning unit 56 performs machine learning (teacher-assisted learning) on the monitoring image and the diagnosis result of the state inside the solid-liquid separation tank obtained from the monitoring image based on the teacher data of the obtained diagnosis result, and generates a learning model of the diagnosis result that associates the monitoring image with the state inside the solid-liquid separation tank. For example, the learning unit 56 uses a convolutional neural network (CNN) to recognize the monitoring image. The monitoring image is classified into any one of normal, abnormal, and abnormal as the state inside the solid-liquid separation tank based on the learning model of the diagnosis result and information representing the monitoring image.

The information processing device 40 obtains the monitoring image contained in the received tank status information response. The information processing device 40 displays the obtained monitoring image. In the case of diagnosing the state inside the solid-liquid separation tank, the user creates a monitoring image request by operating the terminal device 45, and the monitoring image request includes information requesting a monitoring image and has the monitoring device 50 as the destination. The terminal device 45 creates the monitoring image request based on the user's operation. The terminal device 45 transmits the created monitoring image request to the monitoring device 50. The terminal device 45 receives a monitoring image response transmitted by the monitoring device 50 in response to the monitoring image request transmitted to the monitoring device 50. The terminal device 45 displays the monitoring image contained in the monitoring image response. The user diagnoses the state of the inside of the solid-liquid separation tank contained in the monitoring image with reference to the monitoring image displayed by the terminal device 45. The user creates a diagnosis result notification by operating the terminal device 45, and the diagnosis result notification includes information representing the monitoring image and the diagnosis result of the state inside the solid-liquid separation tank and has the monitoring device 50 as the destination.

That is, in another embodiment, the monitoring system uses a monitoring image instead of a supernatant image. In addition, it is determined whether the monitoring image is an error image. Furthermore, in the monitoring system, after determining whether the monitoring image is an error image, a supernatant image can be created based on the monitoring image. That is, the determination of whether it is an error image can be incorporated into the embodiment described above and its variants.

(Operation of the monitoring system) FIG. 27 is a diagram showing Example 1 of the operation of the monitoring system of another embodiment. Referring to FIG. 27, the following processing is described: the monitoring device 50 accumulates the diagnosis result of the state inside the solid-liquid separation tank contained in the diagnosis result notification transmitted by the terminal device 45 and the information for determining the cause of the diagnosis result, and performs machine learning based on the accumulated diagnosis result of the state inside the solid-liquid separation tank and the information for determining the cause of the diagnosis result, thereby generating a learning model of the diagnosis result and a learning model of the cause.

(Step S1-12) In the data processing device 30, the ultrasonic transmitting/receiving circuit 32 generates an electrical signal for transmitting ultrasound, and outputs the generated electrical signal to the ultrasonic sensor 20. (Step S2-12) In the data processing device 30, the ultrasonic transmitting/receiving circuit 32 receives the electrical signal output by the ultrasonic sensor 20. (Step S3-12) In the data processing device 30, the ultrasonic transmitting/receiving circuit 32 outputs the received electrical signal to the data conversion circuit 33. The data conversion circuit 33 obtains the electrical signal output by the ultrasonic transmitting/receiving circuit 32. The data conversion circuit 33 amplifies the obtained electrical signal. The data conversion circuit 33 performs shielding processing on the amplified electrical signal. The data conversion circuit 33 converts the signal intensity into a digital signal by digital processing based on the result of shielding processing on the amplified electrical signal. The data operation unit 34 obtains the digital signal from the data conversion circuit 33, and performs temperature correction operation related to the position (distance) information and interface level determination operation on the obtained digital signal. (Step S4-12) In the data processing device 30, the data operation unit 34 transmits the digital signal obtained by performing temperature correction operation related to the position (distance) information and interface level determination operation to the monitoring device 50 via the gate device 31. (Step S5-12) In the monitoring device 50, the communication device 51 receives the digital signal transmitted by the data processing device 30. The graphics unit 54 obtains the digital signal received by the communication device 51. The graphics unit 54 converts the value of the obtained digital signal into pixel data. (Step S6-12) In the monitoring device 50, the graphics unit 54 stores the pixel data converted into the digital signal in the recording device 52. (Step S7-12) The terminal device 45 creates a monitoring image request.

(Step S8-12) The terminal device 45 transmits the created monitoring image request to the monitoring device 50. (Step S9-12) In the monitoring device 50, the communication device 51 receives the monitoring image request transmitted by the terminal device 45. The graphics unit 54 obtains the monitoring image request received by the communication device 51. The graphics unit 54 obtains the pixel data stored in the recording device 52 based on the obtained monitoring image request. The graphics unit 54 creates a monitoring image based on the obtained pixel data. The graphics unit 54 creates a monitoring image response, which includes information indicating the created monitoring image and has the terminal device 45 as the destination. (Step S9a-12) The graphics unit 54 determines whether the created monitoring image is an error image. (Step S9b-12) If the created monitoring image is an error image, the graphics unit 54 ends the operation. In this way, the teacher data does not include an error image. If the created monitoring image is an error image, the graphics unit 54 can notify the terminal device 45 that the monitoring image is an error image. (Step S10-12) In the monitoring device 50, when the created monitoring image is not an error image, the graphics unit 54 outputs the created monitoring image response to the communication device 51. The communication device 51 obtains the monitoring image response output by the graphics unit 54, and transmits the obtained monitoring image response to the terminal device 45. (Step S11-12) The terminal device 45 receives the monitoring image response transmitted by the monitoring device 50. The terminal device 45 displays the monitoring image by performing image processing on the information representing the monitoring image contained in the received monitoring image response. The terminal device 45 creates a diagnosis result notification, which includes the information representing the monitoring image and the result of diagnosing the monitoring image. (Step S12-12) The terminal device 45 transmits the created diagnosis result notification to the monitoring device 50. (Step S13-12) In the monitoring device 50, the communication device 51 receives the diagnosis result notification transmitted by the terminal device 45. The learning unit 56 obtains the diagnosis result notification received by the communication device 51, and stores the teacher data of the diagnosis result obtained by associating the information representing the monitoring image contained in the obtained diagnosis result notification with the diagnosis result of the state of the inside of the solid-liquid separation tank (inside the tank) obtained from the monitoring image in the recording device 52. (Step S14-12) In the monitoring device 50, the learning unit 56 obtains the teacher data of the diagnosis result stored in the recording device 52. The learning unit 56 performs machine learning on the monitoring image and the diagnosis result of the state inside the solid-liquid separation tank obtained from the monitoring image by using the teacher data of the obtained diagnosis result, and generates a learning model of the diagnosis result obtained by associating the monitoring image with the state inside the solid-liquid separation tank. (Step S15-12) In the monitoring device 50, the learning unit 56 stores the generated learning model of the diagnosis result in the recording device 52.

FIG28 is a diagram showing Example 2 of the operation of another embodiment of the monitoring system. Referring to FIG28, the following processing is described: the monitoring device 50 obtains the digital signal transmitted by the data processing device 30, and creates a monitoring image based on the obtained digital signal; the monitoring device 50 determines the state inside the solid-liquid separation tank based on the created monitoring image. Steps S1-13 to S6-13 can apply steps S1-12 to S6-12 of FIG27, so the description here is omitted. (Step S7-13) In the monitoring device 50, the current state determination unit 55 obtains the pixel data stored in the recording device 52, and creates a monitoring image based on the obtained pixel data. (Step S8-13) In the monitoring device 50, the current state determination unit 55 obtains the learning model of the diagnosis result stored in the recording device 52. (Step S8a-13) The current state determination unit 55 determines whether the created monitoring image is an error image. (Step S9b-12) In the case where the created monitoring image is an error image, the current state determination unit 55 ends the operation. When the created monitoring image is an error image, the current status determination unit 55 outputs the status notification that the monitoring image is an error image to the information processing device 40 via the communication device 51. (Step S9-13) In the monitoring device 50, the current status determination unit 55 determines the state of the solid-liquid separation tank inside the created monitoring image based on the learning model of the obtained diagnosis result. (Step S10-13) In the monitoring device 50, the current status determination unit 55 determines whether the determination result of the state inside the solid-liquid separation tank is abnormal or abnormal. The process ends when the status determination unit 55 determines that the status of the inside of the solid-liquid separation tank is neither abnormal nor abnormal, that is, normal. (Step S11-13) In the monitoring device 50, when the status determination unit 55 determines that the status of the inside of the solid-liquid separation tank is abnormal or abnormal, the status determination unit 55 creates status notification information, which includes information indicating the status determination result of the inside of the solid-liquid separation tank and has the information processing device 40 as the destination. (Step S12-13) In the monitoring device 50, the status determination unit 55 outputs the created status notification information to the communication device 51. The communication device 51 obtains the status notification information output by the current status determination unit 55, and transmits the obtained status notification information to the information processing device 40.

FIG. 29 is a diagram showing Example 3 of the operation of the monitoring system of the present embodiment. Referring to FIG. 9 , the following processing is described: the monitoring device 50 transmits information representing the monitoring image based on the slot state information request transmitted by the information processing device 40. Steps S1-14 to S6-14 can apply steps S1-12 to S6-12 of FIG. 7 , so the description here is omitted. (Step S7-14) The information processing device 40 creates a slot state information request based on the user's operation. (Step S8-14) The information processing device 40 transmits the created slot state information request to the monitoring device 50. (Step S9-14) In the monitoring device 50, the communication device 51 receives the slot status information request transmitted by the information processing device 40. The graphics unit 54 obtains the slot status information request received by the communication device 51. The graphics unit 54 obtains the pixel data stored in the recording device 52 based on the obtained slot status information request, and creates a monitoring image based on the obtained pixel data. (Step S9a-14) The graphics unit 54 determines whether the created monitoring image is an error image. (Step S9b-14) When the created monitoring image is an error image, the graphics unit 54 terminates the operation. When the created monitoring image is an error image, the graphics unit 54 outputs the slot state information response including information that the monitoring image is an error image to the information processing device 40 via the communication device 51. (Step S10-14) In the monitoring device 50, the graphics unit 54 creates a slot state information response, which includes information indicating the created monitoring image and has the information processing device 40 as the destination. (Step S11-3) In the monitoring device 50, the graphics unit 54 outputs the created slot state information response to the communication device 51. The communication device 51 obtains the slot state information response output by the graphics unit 54, and transmits the obtained slot state information response to the information processing device 40. After step S11-3, the information processing device 40 receives the slot state information response transmitted by the monitoring device 50, and obtains the information representing the monitoring image contained in the received slot state information response. The information processing device 40 displays the monitoring image by performing image processing on the obtained information representing the monitoring image. By configuring in this way, the user of the information processing device 40 can confirm the state inside the solid-liquid separation tank.

The monitoring device 50 does not use the monitoring image, which is an error image, for creating a learning model. In addition, the monitoring device 50 does not make a judgment on the error image using the learning model. This prevents the error image from being misdiagnosed as normal by the learning model. In addition, in the case of creating a supernatant image based on the monitoring image, when the monitoring image is an error image, a portion of the error image with a small signal intensity is created as a supernatant image and used for the creation of the learning model or the judgment using the learning model, which may result in a misdiagnosis. Therefore, by removing the error image, it is possible to prevent the creation of a false supernatant image based on the error image that is not based on the supernatant measurement results.

The above is a detailed description of the implementation form of the present invention with reference to the drawings, but the specific structure is not limited to the implementation form, and also includes design changes within the scope of the subject matter of the present invention. For example, a computer program for realizing the functions of each device can be recorded in a computer-readable recording medium, and the computer system can read and execute the program recorded in the recording medium. Furthermore, the so-called "computer system" mentioned here can also include an operating system (OS) or hardware such as peripheral machines. In addition, the so-called "computer-readable recording media" refers to writable non-volatile memories such as floppy disks, magneto-optical disks, ROMs, flash memories, portable media such as digital versatile discs (DVDs), and storage devices such as hard disks built into computer systems.

Furthermore, the so-called "computer-readable recording medium" also includes a volatile memory (e.g., dynamic random access memory (DRAM)) inside a computer system that serves as a server or client and retains the program for a certain period of time when the program is transmitted via a network such as the Internet or a communication line such as a telephone line. In addition, the program can also be transmitted from a computer system that stores the program in a memory device, etc., to another computer system via a transmission medium or by a transmission wave in the transmission medium. Here, the "transmission medium" for transmitting the program refers to a medium that has the function of transmitting information, such as a network (communication network) such as the Internet or a communication line (communication line) such as a telephone line. In addition, the program can also be used to implement a part of the function. Furthermore, it can also be a so-called differential file (differential program) that can implement the function by combining with a program already recorded in a computer system. [Industrial Availability]

The present invention has the effect of providing a monitoring system, a learning device, a monitoring method, a learning method and a program for monitoring the state inside a solid-liquid separation tank for separating solid and liquid from wastewater.

2: vibrator 10: sewage treatment equipment 11: front sedimentation tank 12: concentration tank 13: storage tank 14: dehydrator 15: container 16: aeration tank 17: rear sedimentation tank 18: pump 19: equipment control device 20: ultrasonic sensor 21: oscillation (transmission) unit 22: receiving unit 23: suspended sediment accumulation layer 24: supernatant 25: treatment tank 26: interface 27: height 30, 30d: data processing device 31: gate device 32: ultrasonic transmission/reception circuit 33: data conversion circuit 34: data calculation unit 35: Image data storage unit 36: Display switching operation unit 37: Image data display unit 40, 40a, 40b, 40c, 40d: Information processing device 45, 45a, 45b, 45c, 45d: Terminal device 50, 50a, 50b, 50c, 50d: Monitoring device 51: Communication device 52: Recording device 53, 53a, 53b, 53c, 53d: Information processing unit 54, 54d: Graphical unit 55, 55a, 55d: Current status determination unit 56, 56a, 56b, 56c, 56d: Learning unit 57, 57b, 57d: Cause determination unit 58, 58c, 58d: Response method determination unit 59, 59d: Change sign output unit 100, 100a, 100b, 100c, 100d: Monitoring system BL: Bus line NW: Network P1, P2, P3, P4, P5, P6, P7, P8, P9, P10: Flow path P11: Conveyor belt S1-1, S2-1, S3-1, S4-1, S5-1, S6-1, S7-1, S8-1, S9-1, S10-1, S11-1, S12-1, S13-1, S14-1, S15-1, S1-2, S2-2, S3-2, S4-2, S5-2, S6-2, S7-2, S8-2, S 9-2, S10-2, S11-2, S12-2, S1 -3, S2-3, S3-3, S4-3, S5-3, S6-3, S7-3, S8-3, S9-3, S10-3, S11-3, S1-4, S2-4, S3-4, S4-4, S5-4, S6-4, S7-4, S8-4, S9-4, S10-4, S11-4, S12-4, S13- 4. S14-4, S15-4, S1-5, S2-5, S 3-5, S4-5, S5-5, S6-5, S7-5, S8-5, S9-5, S10-5, S11-5, S12-5, S13-5, S14-5, S1-6, S2-6, S3-6, S4-6, S5-6, S6-6, S7-6, S8-6, S9-6, S10-6, S11-6, S12-6, S13-6, S14-6, S15-6, S1 -7, S2-7, S3-7, S4-7, S5-7, S6-7, S7-7, S8-7, S9-7, S10-7, S11-7, S12-7, S13-7, S14-7, S15-7, S16-7, S1-8, S2-8, S3-8, S4-8, S5-8, S6-8, S7-8, S8- 8. S9-8, S10-8, S11-8, S12-8 , S13-8, S14-8, S15-8, S1-9, S2-9, S3-9, S4-9, S5-9, S6-9, S7-9, S8-9, S9-9, S10-9, S11-9, S12-9, S13-9, S14-9, S15-9, S16-9, S17-9, S1-10, S2-1 0, S3-10, S4-10, S5-10, S6-10 , S7-10, S8-10, S9-10, S10-10, S11-10, S12-10, S13-10, S14-10, S15-10, S1-11, S2-11, S3-11, S4-11, S5-11, S6-11, S7-11, S8-11, S9-11, S10-11, S11-11, S12-11, S13-11, S14- 11. S15-11, S16-11, S17-11, S1-12, S2-12, S3-12, S4-12, S5-12, S6-12, S7-12, S8-12, S9-12, S9a-12, S9b-12, S10-12, S11-12, S12-12, S13-12, S 14-12, S15-12, S1-13, S2-13, S 3-13, S4-13, S5-13, S6-13, S7-13, S8-13, S8a-13, S9-13, S10-13, S11-13, S12-13, S1-14, S2-14, S3-14, S4-14, S5-14, S6-14, S7-14, S8-14, S9-14, S9a-14, S9b-14, S10-14: Steps

FIG. 1 is a diagram showing an example of the structure of a monitoring system according to an embodiment of the present invention. FIG. 2 is a diagram showing an example of an ultrasonic sensor. FIG. 3 is a diagram showing an example of a data processing device of the monitoring system according to the present embodiment. FIG. 4 is a diagram showing an example of the operation of the monitoring system according to the present embodiment. FIG. 5 is a diagram showing an example of a monitoring image. FIG. 6 is a diagram showing an example of teacher data. FIG. 7 is a diagram showing an example 1 of the operation of the monitoring system according to the present embodiment. FIG. 8 is a diagram showing an example 2 of the operation of the monitoring system according to the present embodiment. FIG. 9 is a diagram showing an example 3 of the operation of the monitoring system according to the present embodiment. FIG. 10 is a diagram showing another example of the data processing device according to the present embodiment. FIG. 11 is a diagram showing a structural example of a monitoring system of a variant 1 of an implementation form of the present invention. FIG. 12 is a diagram showing an example of teacher data. FIG. 13 is a diagram showing an example 1 of the operation of the monitoring system of the variant 1 of the implementation form. FIG. 14 is a diagram showing an example 2 of the operation of the monitoring system of the variant 1 of the implementation form. FIG. 15 is a diagram showing a structural example of a monitoring system of a variant 2 of an implementation form of the present invention. FIG. 16 is a diagram showing an example of teacher data. FIG. 17 is a diagram showing an example 1 of the operation of the monitoring system of the variant 2 of the implementation form. FIG. 18 is a diagram showing an example 2 of the operation of the monitoring system of the variant 2 of the implementation form. FIG. 19 is a diagram showing a structural example of a monitoring system of a variant 3 of an embodiment of the present invention. FIG. 20 is a diagram showing an example 1 of an action of a monitoring system of a variant 3 of an embodiment. FIG. 21 is a diagram showing an example 2 of an action of a monitoring system of a variant 3 of an embodiment. FIG. 22 is a diagram showing a structural example of a monitoring system of a variant 4 of an embodiment of the present invention. FIG. 23 is a diagram showing an example of a monitoring device of a monitoring system of a variant 4 of the present embodiment. FIG. 24 is a diagram showing an example 1 of an action of a monitoring system of a variant 4 of an embodiment. FIG. 25 is a diagram showing an example 2 of an action of a monitoring system of a variant 4 of an embodiment. FIG. 26 is an example of an error image. FIG. 27 is a diagram showing example 1 of the operation of a monitoring system of another embodiment. FIG. 28 is a diagram showing example 2 of the operation of a monitoring system of another embodiment. FIG. 29 is a diagram showing example 3 of the operation of the monitoring system of this embodiment.

10: Sewage treatment equipment

11: Front sedimentation tank

12: Concentration tank

13: Storage tank

14: Dehydrator

15:Container

16: Aeration tank

17: Rear sedimentation tank

18: Pump

19: Equipment control device

20 Ultrasonic sensors

30Data processing devices

31: Gate device

40: Information processing device

45: Terminal device

50: Monitoring device

51: Communication device

52: Recording device

53: Information Processing Department

54: Graphics Department

55: Current situation assessment department

56: Study Department

100: Monitoring system

BL: Busbar

NW: Network

P1, P2, P3, P4, P5, P6, P7, P8, P9, P10: flow path

P11: Conveyor belt

Claims (20)

A monitoring system comprises: a determination unit, based on an image of a supernatant liquid inside a solid-liquid separation tank for separating wastewater into solid and liquid, i.e., a supernatant liquid image, and a diagnosis result obtained based on the supernatant liquid image, and using a first learning model that has learned the relationship between the supernatant liquid image and the state inside the solid-liquid separation tank, to determine the state inside the solid-liquid separation tank based on the supernatant liquid image representing the supernatant liquid inside the solid-liquid separation tank as a diagnosis object; and an output unit, outputting information for determining the state inside the solid-liquid separation tank determined by the determination unit using the supernatant liquid image of the solid-liquid separation tank as a diagnosis object and the first learning model. A monitoring system comprises: a determination unit, based on an image representing the interior of a solid-liquid separation tank for separating solid and liquid wastewater, i.e., a monitoring image, and a diagnosis result of the interior of the solid-liquid separation tank obtained based on the monitoring image, and using a first learning model that has learned the relationship between the monitoring image and the state of the interior of the solid-liquid separation tank, to determine the state of the interior of the solid-liquid separation tank based on the monitoring image representing the interior of the solid-liquid separation tank as a diagnosis object; and an output unit, outputting information for determining the state of the interior of the solid-liquid separation tank determined by the determination unit using the monitoring image of the solid-liquid separation tank as a diagnosis object and the first learning model, The monitoring image does not include images of poor measurement, i.e., error images. A monitoring system as described in claim 1, wherein the supernatant image does not include an image of a poor measurement, i.e., an error image. The monitoring system as described in claim 1 has: A cause determination unit, based on the supernatant image and the information for determining the cause of the diagnosis result obtained based on the supernatant image, and using a second learning model that has learned the relationship between the supernatant image and the information for determining the cause of the diagnosis result formed inside the solid-liquid separation tank, determines the information for determining the cause of the state formed inside the solid-liquid separation tank based on the supernatant image of the solid-liquid separation tank as the diagnosis object, The output unit further outputs the information for determining the cause of the state formed inside the solid-liquid separation tank determined by the cause determination unit using the supernatant image of the solid-liquid separation tank as the diagnosis object and the second learning model. The monitoring system as described in claim 1 has: A response method determination unit, based on the supernatant image and information for determining a response method for a diagnosis result obtained based on the supernatant image, and using a third learning model that has learned the relationship between the supernatant image and information for determining a response method for a diagnosis result inside the solid-liquid separation tank, determines the information for determining a response method for determining the state inside the solid-liquid separation tank based on the supernatant image of the solid-liquid separation tank as a diagnosis object, The output unit further outputs information used to determine the response method for the state inside the solid-liquid separation tank determined by the response method determination unit using the supernatant image of the solid-liquid separation tank as the diagnosis object and the third learning model. The monitoring system as described in claim 1 comprises: A change sign deriving unit, based on the supernatant image and information for determining the change of the state inside the solid-liquid separation tank after the supernatant image is obtained, and using a fourth learning model that has learned the relationship between the supernatant image and the information for determining the change of the state inside the solid-liquid separation tank, detects the sign of the change of the state inside the solid-liquid separation tank according to the supernatant image of the solid-liquid separation tank as the diagnosis object, The output unit further outputs the information for determining the sign of the change of the state inside the solid-liquid separation tank detected by the change sign deriving unit using the supernatant image of the solid-liquid separation tank as the diagnosis object and the fourth learning model. A monitoring system as described in claim 1, wherein the diagnosis result is generated based on either or both of the accumulation state of solid matter and the floating state of solid matter contained in the supernatant image. A monitoring system as described in claim 1, wherein the determination unit determines whether the state of the interior of the solid-liquid separation tank is normal, abnormal, or abnormal based on the supernatant image representing the interior of the solid-liquid separation tank as a diagnosis object. The monitoring system as described in claim 1 further comprises: A notification unit for notifying that the state inside the solid-liquid separation tank is either abnormal or abnormal when the determination unit determines that the state inside the solid-liquid separation tank is either abnormal or abnormal. A learning device comprises: A learning unit that generates a first learning model representing the relationship between the supernatant image and the state inside the solid-liquid separation tank based on an image representing the supernatant inside a solid-liquid separation tank for separating wastewater from solid and liquid, i.e., a supernatant image and a diagnosis result obtained based on the supernatant image of the state inside the solid-liquid separation tank, and by learning. A learning device comprises: A learning unit, based on a monitoring image representing an image of the interior of a solid-liquid separation tank for separating solid and liquid wastewater and a diagnosis result obtained from the monitoring image and the state of the interior of the solid-liquid separation tank, generates a first learning model representing the relationship between the monitoring image and the state of the interior of the solid-liquid separation tank by learning, The monitoring image does not include an image when the measurement is poor, that is, an error image. A learning device as described in claim 10, wherein the supernatant image does not include an image when the measurement is poor, i.e., an error image. A learning device as described in claim 10, wherein, the learning unit generates a second learning model representing the relationship between the supernatant image and the information for determining the cause of the diagnosis result formed based on the supernatant image, and the information for determining the cause of the diagnosis result formed inside the solid-liquid separation tank through learning. A learning device as described in claim 10, wherein, the learning unit generates a third learning model that represents the relationship between the supernatant image and the information for determining the response method to the diagnosis result obtained based on the supernatant image, and the information for determining the response method to the diagnosis result inside the solid-liquid separation tank through learning. A learning device as described in claim 10, wherein the learning unit generates a fourth learning model representing the relationship between the supernatant image and the information for determining the change of the state inside the solid-liquid separation tank after the supernatant image is obtained, based on the supernatant image and the information for determining the change of the state inside the solid-liquid separation tank. The learning device according to claim 10, wherein the diagnosis result is generated based on either or both of a state of accumulation of solid matter and a state of floating of solid matter contained in the supernatant image. A monitoring method is performed by a monitoring system and has the following steps: Based on an image of a supernatant liquid inside a solid-liquid separation tank for separating solid and liquid wastewater, that is, a supernatant liquid image and a diagnosis result obtained based on the supernatant liquid image, and using a first learning model that has learned the relationship between the supernatant liquid image and the state inside the solid-liquid separation tank, a step of determining the state inside the solid-liquid separation tank based on the supernatant liquid image representing the supernatant liquid inside the solid-liquid separation tank as a diagnosis object; and A step of outputting information for determining the state of the solid-liquid separation tank interior determined by the first learning model using the supernatant image of the solid-liquid separation tank as the object of diagnosis in the determination step. A learning method is performed by a learning device and comprises the following steps: Based on an image of a supernatant liquid inside a solid-liquid separation tank for separating solid and liquid wastewater, i.e., a supernatant liquid image, and a diagnosis result obtained based on the supernatant liquid image, a first learning model representing the relationship between the supernatant liquid image and the state inside the solid-liquid separation tank is generated by learning. A program that causes a computer of a monitoring system to execute the following steps: Based on an image of a supernatant liquid inside a solid-liquid separation tank for separating solid and liquid wastewater, that is, a supernatant liquid image, and a diagnosis result obtained based on the supernatant liquid image, and using a first learning model that has learned the relationship between the supernatant liquid image and the state inside the solid-liquid separation tank, a step of determining the state inside the solid-liquid separation tank based on a supernatant liquid image representing the supernatant liquid inside the solid-liquid separation tank as a diagnosis object; and A step of outputting information for determining the state of the solid-liquid separation tank interior determined by the first learning model using the supernatant image of the solid-liquid separation tank as the object of diagnosis in the determination step. A program causes a computer of a learning device to execute the following steps: Based on an image of a supernatant liquid inside a solid-liquid separation tank for separating solid and liquid wastewater, i.e., a supernatant liquid image, and a diagnosis result of the inside of the solid-liquid separation tank obtained based on the supernatant liquid image, a step of generating a first learning model representing the relationship between the supernatant liquid image and the state inside the solid-liquid separation tank by learning.

Images