Information processing apparatus and information processing system

Abstract

To improve a technique for monitoring an operation state of a liquid treatment apparatus.SOLUTION: An information processing device that includes a control unit and determines an abnormality related to a liquid treatment device, the liquid treatment device including a tank that stores a liquid, a scraping unit that scrapes a floating substance floating on a liquid surface of the liquid to the outside of the tank, and a pump that feeds a gas into the liquid, wherein the control unit acquires an image including a region of the liquid surface, calculates an area of a region of the floating substance that may be included in the image, and determines the abnormality based on the area of the region of the floating substance.SELECTED DRAWING: Figure 4

Description

[Technical Field]

[0001] The present disclosure relates to an information processing device and an information processing system. [Background technology]

[0002] In the manufacturing industry, liquid treatment devices such as pressurized flotation tanks are primarily used to treat wastewater. For example, in a pressurized flotation tank, substances with low specific gravity can be made to float as suspended matter from the liquid stored in the tank by adding chemicals and pumping air using a pump. Furthermore, the suspended matter can be separated from the water by scraping it out of the tank using a scraping unit.

[0003] There has been a need for improved technology to monitor whether liquid treatment devices are operating properly. Meanwhile, a method for determining water quality abnormalities from images captured by a surveillance camera is known (for example, Patent Document 1). [Prior art documents] [Patent documents]

[0004] [Patent Document 1] Japanese Patent Publication No. 2023-44875 Summary of the Invention [Problem to be solved by the invention]

[0005] The technology described in Patent Document 1 determines water quality abnormalities based on feature values ​​calculated from the turbidity of the water surface. However, there were cases where abnormalities could not be detected using this technology. As such, there was room for improvement in determining abnormalities in liquid treatment devices.

[0006] In view of the above circumstances, an object of the present disclosure is to improve the technology for monitoring the operating status of a liquid treatment device. [Means for solving the problem]

[0007] (1) An information processing device according to an embodiment of the present disclosure includes: An information processing device having a control unit and determining an abnormality related to a liquid processing device, The liquid treatment device includes a tank for storing a liquid, a scraping unit for scraping out floating matter floating on the surface of the liquid to the outside of the tank, and a pump for feeding gas into the liquid, The control unit acquiring an image including the liquid surface region; Calculating the area of ​​the floating matter region that may be included in the image; The abnormality is determined based on the area of ​​the floating matter region.

[0008] (2) An information processing device according to an embodiment of the present disclosure is the information processing device according to (1), The control unit Calculating the liquid level from the image; The abnormality is determined based on the liquid level.

[0009] (3) An information processing device according to an embodiment of the present disclosure is the information processing device according to (2), The control unit If the liquid level is equal to or greater than a predetermined threshold, it is determined that the liquid treatment device is abnormal.

[0010] (4) An information processing device according to an embodiment of the present disclosure is the information processing device according to any one of (1) to (3), The control unit determines that the pump is abnormal if the area of ​​the region of suspended matter does not exceed a predetermined first threshold value for a predetermined first period.

[0011] (5) An information processing device according to an embodiment of the present disclosure is the information processing device according to any one of (1) to (4), The control unit determines that the scraping unit is abnormal if the area of ​​the floating matter region does not fall below a predetermined second threshold value within a predetermined second period.

[0012] (6) An information processing device according to an embodiment of the present disclosure is the information processing device according to any one of (1) to (5), When the length of the period during which there is no change over time in the area of ​​the floating matter region exceeds a predetermined third threshold, the control unit determines that the pump or the scraping unit is abnormal.

[0013] (7) An information processing device according to an embodiment of the present disclosure is the information processing device according to any one of (2) to (6), The control unit Calculating the area of ​​the wall surface region of the tank included in the image; The liquid level is calculated based on the area of ​​the wall surface region.

[0014] (8) An information processing device according to an embodiment of the present disclosure is the information processing device according to any one of (2) to (6), The control unit calculating a width of the wall surface area of ​​the tank measured in a direction in which the wall surface area of ​​the tank in the image expands or contracts in response to the change in the liquid level; The liquid level is calculated based on the width.

[0015] (9) An information processing device according to an embodiment of the present disclosure is the information processing device according to any one of (2) to (6), The control unit Calculating the area of ​​the scraped-out portion that may be included in the image; The liquid level is calculated based on the area of ​​the scraping portion.

[0016] (10) An information processing device according to an embodiment of the present disclosure is the information processing device according to (7), The control unit In calculating the area of ​​the floating matter region, if the image includes an overlapping region in which the floating matter is hidden by the scraping unit, the area of ​​the overlapping region is added to the area of ​​the floating matter region to calculate the area of ​​the floating matter region; When calculating the area of ​​the wall surface region, if the image includes an overlapping region where the wall surface is hidden by the scraping portion, the area of ​​the overlapping region is added to the area of ​​the wall surface region to calculate the area of ​​the wall surface region.

[0017] (11) An information processing system according to an embodiment of the present disclosure includes: An information processing system including a liquid treatment device and an information processing device that determines an abnormality related to the liquid treatment device, The liquid treatment device includes a tank for storing a liquid, a scraping unit for scraping out floating matter floating on the surface of the liquid to the outside of the tank, and a pump for feeding air into the liquid, the information processing device has a control unit, The control unit acquiring an image including the liquid surface region; Calculating the area of ​​the floating matter region that may be included in the image; The abnormality is determined based on the area of ​​the floating matter region. [Effects of the Invention]

[0018] According to an embodiment of the present disclosure, techniques for monitoring the operational status of liquid treatment devices can be improved. [Brief explanation of the drawings]

[0019] [Figure 1] FIG. 1 is a schematic diagram of an information processing system including an information processing device that executes a program according to an embodiment of the present disclosure. [Figure 2] FIG. 2 is a diagram for explaining the liquid treatment device and the imaging device. [Figure 3] FIG. 3 is a diagram showing a schematic image relating to a liquid treatment device. [Figure 4] FIG. 4 is a block diagram showing the configuration of the information processing device. [Figure 5] FIG. 5 is a flowchart illustrating an example of processing executed by the information processing device. [Figure 6]FIG. 6 is a diagram for explaining a specific example of calculation of the area of ​​a floating matter region. [Figure 7] FIG. 7 is a flowchart showing an example of a process that the information processing device optionally executes in addition to the process shown in FIG. [Figure 8] FIG. 8 is a diagram showing a schematic image before and after the liquid level is increased. [Figure 9] FIG. 9 is a flowchart illustrating an example of processing executed by an information processing device to generate a trained model. [Figure 10] FIG. 10 is a graph showing the change in the area of ​​the oil region and the change in the water level. DETAILED DESCRIPTION OF THE INVENTION

[0020] Hereinafter, an information processing device according to an embodiment of the present disclosure will be described with reference to the drawings.

[0021] In each drawing, the same or corresponding parts are denoted by the same reference numerals. In the description of this embodiment, the description of the same or corresponding parts will be omitted or simplified as appropriate.

[0022] 1 shows a schematic diagram of an information processing system 1 including an information processing device 10 according to this embodiment. The information processing system 1 includes the information processing device 10, an imaging device 20, and a liquid treatment device 30. The information processing device 10 and the imaging device 20 are connected so as to be able to communicate with each other. The imaging device 20 is also installed so as to be able to capture images related to the liquid treatment device 30.

[0023] The information processing device 10 is any device used by a user. For example, a personal computer, a server computer, a general-purpose electronic device, or a dedicated electronic device can be employed as the information processing device 10. The information processing device 10 may also be a server device installed in a data center, for example. For example, the information processing device 10 may be a server belonging to a cloud computing system or other computing system. Note that while FIG. 1 shows an example in which the information processing system 1 includes one information processing device 10, this is not limiting. The information processing system 1 may also include two or more information processing devices 10. The information processing device 10 can acquire images captured by the imaging device 20.

[0024] The imaging device 20 is any device capable of capturing an image of a target. The imaging device 20 includes a high-speed camera, such as a CCD (Charge-Coupled Device) camera or a CMOS (Complementary Metal-Oxide-Semiconductor) camera. While FIG. 1 illustrates an example in which the information processing system 1 includes one imaging device 20, the present invention is not limited to this. The information processing system 1 may include two or more imaging devices 20. Furthermore, in the present embodiment, an example in which the information processing device 10 and the imaging device 20 are separate entities will be described, but the present invention is not limited to this. That is, the information processing device 10 and the imaging device 20 may be integrated.

[0025] Next, a liquid treatment device 30 and an imaging device 20 according to the present disclosure will be described with reference to FIG.

[0026] The liquid treatment device 30 is a device used to treat a liquid 2 and includes a tank 4, a scraping unit 5, and a pump 6. The tank 4 stores the liquid 2. The liquid 2 includes floating matter 3 floating on the liquid surface. The scraping unit 5 serves to scrape the floating matter 3 out of the tank 4. In the present disclosure, examples of the liquid treatment device include, but are not limited to, a pressurized flotation device. Similarly, examples of the tank 4 include, but are not limited to, a pressurized flotation tank. Examples of the liquid 2 include, but are not limited to, water, particularly wastewater. Examples of the floating matter 3 include, but are not limited to, oil. In addition, in FIG. 2, the tank 4 is approximately cylindrical and the scraping unit 5 is installed so as to be rotatable about the central axis of the tank 4, but the shape and operating principle of the tank and the scraping unit are not limited thereto.

[0027] A pump 6 sends gas 7 into the liquid 2 through pressurized liquid 33. In a pressurized tank 31, the gas 7 pressurized by the pump 6 dissolves in the liquid 32, producing a pressurized liquid 33. When the pressurized liquid 33 is injected into the liquid 2, tiny bubbles are generated in the liquid 2 upon release of pressure, which can promote the floating of the floating matter 3.

[0028] If the amount of suspended matter 3 is small, there is a risk that impurities may not be sufficiently separated from the liquid 2. On the other hand, if the amount of suspended matter 3 is large, the processing speed of the liquid 2 by the liquid treatment device 30 may not be able to keep up, and impurities may not be sufficiently separated from the liquid 2. Therefore, by appropriately controlling the operating conditions of the liquid treatment device, the amount of suspended matter 3 can be controlled and the liquid treatment device can be operated effectively. Examples of operating conditions to be controlled include the operating speed (e.g., rotational speed) of the scraping unit 5. In the case of a pressurized flotation tank, examples of operating conditions include the operating conditions of the pump 6.

[0029] On the other hand, if there is an abnormality in the liquid treatment device, the amount of floatable matter 3 cannot be controlled, and the amount of floatable matter 3 may become excessively large or excessively small. While there is a method for visually checking the amount of floatable matter, it requires a lot of work to travel to the tank. Furthermore, since there is no indicator for quantitatively checking the amount of floatable matter, judgment is subjective. Therefore, the technology disclosed herein determines abnormalities from an image including the liquid surface area. Using an information processing device 10 to treat liquid using a liquid treatment device 30 improves the convenience of workers when monitoring the operating status of the liquid treatment device.

[0030] The imaging device 20 captures an image including the liquid surface area of ​​the liquid 2. The image captured by the imaging device 20 can be used to calculate the area of ​​the floating matter area, which will be described later, and to generate a trained model. The imaging device 20 may be attached above the liquid surface, for example, to a deck spanning the top of the tank 4. Note that the installation position of the imaging device 20 is not limited in the present disclosure. Furthermore, the installation angle (photographing direction) of the imaging device 20 is not limited, but may be, for example, facing downward or diagonally downward. The imaging device 20 is preferably installed so as to capture an image including the liquid surface area of ​​the liquid 2 and the wall surface area of ​​the tank 4.

[0031] Fig. 3 schematically shows an image 100 relating to a liquid treatment device 30. Image 100 was captured by imaging device 20 and includes a liquid surface region 110. In Fig. 3, image 100 also includes a floating matter region 120, a scraping portion region 140, and a tank wall surface region 130. However, in the present disclosure, the floating matter region, the scraping portion region, and the wall surface region may not be included in the image relating to the liquid treatment device.

[0032] 4 is a diagram showing a configuration of the information processing device 10 according to an embodiment of the present disclosure. Each component of the information processing device 10 according to an embodiment of the present disclosure will be described in detail with reference to FIG.

[0033] As shown in FIG. 4, the information processing device 10 includes a control unit 11, a storage unit 12, a communication unit 13, an input unit 14, and an output unit 15.

[0034] The control unit 11 includes at least one processor, at least one dedicated circuit, or a combination thereof. The processor is a general-purpose processor such as a central processing unit (CPU) or a graphics processing unit (GPU), or a dedicated processor specialized for a specific process. The dedicated circuit is, for example, a field-programmable gate array (FPGA) or an application specific integrated circuit (ASIC). The control unit 11 executes processes related to the operation of the information processing device 10 while controlling each unit of the information processing device 10.

[0035] The storage unit 12 includes at least one semiconductor memory, at least one magnetic memory, at least one optical memory, or a combination of at least two of these. The semiconductor memory is, for example, a random access memory (RAM) or a read only memory (ROM). The RAM is, for example, a static random access memory (SRAM) or a dynamic random access memory (DRAM). The ROM is, for example, an electrically erasable programmable read only memory (EEPROM). The storage unit 12 functions as, for example, a main storage device, an auxiliary storage device, or a cache memory. The storage unit 12 stores data used in the operation of the information processing device 10 and data obtained by the operation of the information processing device 10.

[0036] The communication unit 13 includes at least one external communication interface. The communication interface may be either a wired communication interface or a wireless communication interface. In the case of wired communication, the communication interface is, for example, a LAN (Local Area Network) interface or a USB (Universal Serial Bus). In the case of wireless communication, the communication interface is, for example, an interface compatible with mobile communication standards such as LTE (Long Term Evolution), 4G (4th generation), or 5G (5th generation), or an interface compatible with short-range wireless communication such as Bluetooth (registered trademark). The communication unit 13 receives data used in the operation of the information processing device 10 and transmits data obtained by the operation of the information processing device 10.

[0037] The input unit 14 includes at least one input interface. The input interface may be, for example, a physical key, a capacitance key, a pointing device, or a touch screen integrated with a display. The input interface may also be, for example, a microphone that accepts voice input, or a camera that accepts gesture input. The input unit 14 accepts an operation to input data used in the operation of the information processing device 10. The input unit 14 may be connected to the information processing device 10 as an external input device instead of being provided in the information processing device 10. Any connection method may be used, for example, a Universal Serial Bus (USB), a High-Definition Multimedia Interface (HDMI) (registered trademark), or Bluetooth (registered trademark).

[0038] The output unit 15 includes at least one output interface. The output interface is, for example, a display that outputs information as a video. The display is, for example, an LCD (liquid crystal display) or an organic EL (electro luminescence) display. The output unit 15 displays and outputs data obtained by the operation of the information processing device 10. The output unit 15 may be connected to the information processing device 10 as an external output device instead of being provided in the information processing device 10. As a connection method, any method such as USB, HDMI (registered trademark), or Bluetooth (registered trademark) can be used.

[0039] The functions of the information processing device 10 are realized by executing a program according to this embodiment on a processor corresponding to the information processing device 10. That is, the functions of the information processing device 10 are realized by software. The program causes a computer to execute the operations of the information processing device 10, thereby causing the computer to function as the information processing device 10. That is, the computer functions as the information processing device 10 by executing the operations of the information processing device 10 in accordance with the program.

[0040] In this embodiment, the program can be recorded on a computer-readable recording medium. The computer-readable recording medium includes non-transitory computer-readable media, such as a magnetic recording device, an optical disc, a magneto-optical recording medium, or a semiconductor memory. The program can be distributed, for example, by selling, transferring, or lending a portable recording medium, such as a DVD (digital versatile disc) or a CD-ROM (compact disc read only memory), on which the program is recorded. The program can also be distributed by storing the program in the storage of an external server and transmitting the program from the external server to another computer. The program can also be provided as a program product.

[0041] Some or all of the functions of the information processing device 10 may be implemented by a dedicated circuit equivalent to the control unit 11. In other words, some or all of the functions of the information processing device 10 may be implemented by hardware.

[0042] (Operation of information processing device) The operation of the information processing device 10 according to this embodiment will be described with reference to Fig. 5. Fig. 5 is a flowchart showing an example of processing executed by the information processing device 10 according to this embodiment.

[0043] Step S110: The control unit 11 of the information processing device 10 acquires an image 100 including the liquid surface area of ​​the liquid 2 as an image related to the liquid processing device 30. Any method can be used to acquire the image 100. For example, the control unit 11 may acquire the image 100 captured by the imaging device 20. The control unit 11 may receive the image 100 via the communication unit 13, or may receive it without via the communication unit 13. The memory unit 12 may store the image 100.

[0044] Step S120: The control unit 11 calculates the area of ​​the floating matter region 120 that may be included in the image 100. The storage unit 12 may store the calculated area. At this time, the area may be stored in association with the time when the image 100 was captured.

[0045] The method for calculating the area of ​​the floating matter region 120 is not limited. However, the area of ​​the floating matter region 120 may be calculated from first information that identifies the floating matter region 120 in the image 100. Furthermore, the control unit 11 may generate the first information by inference using a trained model. That is, the first information may be generated by inputting input data based on the image 100 into the trained model. By using the trained model, it is possible to classify the image into two or more regions (e.g., the floating matter region 120, the wall region 130, etc.) with higher accuracy. The trained model will be described later.

[0046] The input data to be input to the trained model may be image data. The image data may be data of the image 100 itself, or may be data obtained by subjecting the image 100 to image processing.

[0047] The storage unit 12 may store the input data and the trained model. Alternatively, one or both of the input data and the trained model may be stored in an external device separate from the information processing device 10 and received from the external device via the communication unit 13.

[0048] The method for calculating the area of ​​the floatable matter region 120 from the first information is not limited. For example, if the floatable matter region 120 is identified in pixel units, the area of ​​the floatable matter region 120 may be the number of pixels in the floatable matter region 120. Furthermore, the area of ​​the floatable matter region 120 may be, for example, the floatable matter region ratio. The floatable matter region ratio may be determined, for example, by equation (1). [Number 1] (Floating matter area ratio) = (Number of pixels in the floating matter area) / (Reference area) (1) The reference area may be determined arbitrarily. For example, the reference area may be the number of pixels in image 100. Alternatively, the reference area may be the area of ​​liquid surface region 110 when it is assumed that scraped-out region 140 and floating matter region 120 do not exist in image 100, specifically, the number of pixels in liquid surface region 110 in image 100. Furthermore, the reference area may be the area of ​​the entire liquid surface when the entire liquid surface of liquid 2 is interpolated from image 100, specifically, the number of pixels corresponding to that area.

[0049] When a plurality of regions 120 of floating matter are included in an image, the area of ​​the regions 120 of floating matter may be calculated as the total area.

[0050] A specific example of calculating the area of ​​a floating matter region will be described with reference to Fig. 6. The method described below can also be applied to calculating the area of ​​a wall surface, which will be described later.

[0051] The image shown in FIG. 6 includes not only a floating matter region 121 but also an overlapping region 122 where floating matter 3 floating on the liquid surface is hidden by the scraping unit 5. In this case, the area of ​​the floating matter region may be calculated as is, or may be calculated as a value obtained by adding the area of ​​the overlapping region to the area of ​​the floating matter region. That is, as the "number of pixels in the floating matter region" in the above formula (1), the number of pixels in the floating matter region 121 may be used, or the number of pixels in region 123 obtained by adding together the floating matter region 121 and the overlapping region 122 may be used. For example, if the area of ​​the floating matter region 121 is 12.8% and the area of ​​the overlapping region 122 is 0.4% of the entire image 100, "12.8%" may be used as the second information, or "13.2%" obtained by calculating 12.8% + 0.4% may be used as the second information.

[0052] The control unit 11 may estimate an overlapping region 122 or its area where the floating matter 3 floating on the liquid surface is hidden by the scraping unit 5. That is, the control unit 11 may generate information identifying the overlapping region or information based on the area of ​​the overlapping region. The memory unit 12 may store the information. The overlapping region may be estimated based on information identifying the scraping unit region 140 that may be included in the image 100 and the first information. For example, when the floating matter region and the scraping unit region contact each other, the overlapping region may be estimated so that the tangent line is included in the boundary line of the overlapping region. In FIG. 3, the floating matter region 120 and the scraping unit region 140 do not contact each other, so it is estimated that no overlapping region exists. In FIG. 6, the floating matter region 121 and the scraping unit region 140 contact each other. Therefore, the overlapping region 122 may be estimated by connecting the ends of the tangent line (the dashed dotted line in the figure) with a virtual line (straight or curved). The control unit 11 may generate information that identifies the scraping area 140 that may be included in the image 100 based on the image 100, and may generate the information by, for example, inputting input data based on the image 100 into a trained model.

[0053] Step S130: Control unit 11 of information processing device 10 determines an abnormality related to liquid processing device 30 based on the area of ​​the floating matter region. Control unit 11 may control memory unit 12 to store the determination result. Control unit 11 may also control output unit 15 to output the determination result.

[0054] Specific criteria for determination are not limited. However, for example, if the amount of floating matter is excessively small, it is considered that a pump malfunction is causing a problem in the supply of gas to promote the floating of the floating matter. Therefore, if the area of ​​the floating matter region does not exceed a predetermined first threshold value within a predetermined first period, it may be determined that the pump 6 is abnormal. For example, if the area of ​​the floating matter region does not exceed 10% within 30 minutes, it may be determined that the pump 6 is abnormal. Furthermore, if the amount of floating matter is excessively large, it is considered that a problem is occurring in the operation of scraping the floating matter out of the tank due to a malfunction of the scraping unit. Therefore, if the area of ​​the floating matter region does not fall below a predetermined second threshold value within a predetermined second period, it may be determined that the scraping unit 5 is abnormal. For example, if the area of ​​the floating matter region does not fall below 90% within 30 minutes, it may be determined that the scraping unit 5 is abnormal. Here, the area of ​​the floating matter region is expressed as 100% (reference area) of the area of ​​the liquid surface region 110 in the image 100 when it is assumed that the scraped-out region 140 and the floating matter region 120 do not exist in the image 100. Also, the second threshold value is usually larger than the first threshold value.

[0055] Furthermore, if the amount of detected floating matter is always constant, it is considered that some other abnormality has occurred in the liquid treatment device. Therefore, if the length of the period during which there is no change in the area of ​​the floating matter region over time exceeds a predetermined third threshold, the pump 6 or the scraping unit 5 may be determined to be abnormal. For example, if the length of the period during which there is no change in the area of ​​the floating matter region over time exceeds 30 minutes, the pump 6 or the scraping unit 5 may be determined to be abnormal. In addition to determining an abnormality regarding the liquid treatment device 30, the imaging device 20 may also be determined to be abnormal based on similar criteria. For example, if there is no change in the area of ​​the floating matter region over time between the time when the scraping unit 5 passes through the imaging area of ​​the image 100 and the time when the next scraping unit passes through the imaging area, the pump 6 or the scraping unit 5 may be determined to be abnormal. Here, "no change over time" includes not only cases where there is no change over time, but also cases where there is very little change over time (for example, the difference between the maximum and minimum values ​​is within 1% when the liquid surface region 110 in the image 100 is set to 100%).

[0056] Furthermore, if the change in the area of ​​the floating matter region per unit time is outside a predetermined range, the pump 6 may be determined to be abnormal. For example, if the change in the area of ​​the floating matter region per unit time is excessively large or excessively small, the pump 6 or the scraping unit 5 may be determined to be abnormal. Furthermore, if the decrease in the area of ​​the floating matter region is small (below a predetermined threshold) during the period when the scraping unit 5 is scraping out floating matter 3 from the imaging region, for example, between the time when the scraping unit 5 passes through the imaging region and the time when the next scraping unit passes through the imaging region, the scraping unit 5 may be determined to be abnormal. If the area of ​​the floating matter region does not fall below a predetermined threshold during that period, the scraping unit 5 may be determined to be abnormal. Similarly, if the increase in the area of ​​the floating matter region is small (below a predetermined threshold) during the period when the scraping unit 5 is not scraping out floating matter 3 from the imaging region, for example, between the time when the scraping unit 5 passes through the imaging region and the time when the next scraping unit passes through the imaging region, the pump 6 may be determined to be abnormal. If the area of ​​the region of suspended matter does not exceed a predetermined threshold during that period, it may be determined that there is an abnormality in the pump 6. As will be described later, these conditions may be combined with a condition relating to the liquid level.

[0057] Another aspect of the operation of the information processing device 10 according to this embodiment will be described with reference to Fig. 7. Fig. 7 is a flowchart showing an example of processing that the information processing device 10 according to this embodiment optionally executes in addition to the processing shown in Fig. 5.

[0058] Step S140: The control unit 11 calculates the liquid level of the liquid 2 from the image 100. The storage unit 12 may store the calculated liquid level. At this time, the calculated liquid level may be stored in association with the time when the image 100 was captured.

[0059] FIG. 8 shows a schematic diagram of an image 101 before the liquid level increases and an image 102 after the increase. The area of ​​the wall surface region 130 decreases and becomes thinner as the liquid level increases. Furthermore, the area of ​​the scraping portion region 140 decreases as the liquid level increases because the portion of the scraping portion 5 that is immersed in the liquid 2 increases. Therefore, the liquid level can be calculated based on the shape of the wall surface region 130 or the scraping portion region 140.

[0060] The liquid level can be calculated based on, for example, the wall surface region 130. Specifically, the liquid level can be calculated based on the area of ​​the wall surface region 130. To calculate the liquid level, a table that associates the liquid level with the area of ​​the wall surface region 130 at that liquid level may be used. The table may be stored in the storage unit 12. The area of ​​the wall surface region 130 may be calculated from second information that identifies the wall surface region 130 in the image 100. Note that the liquid level may be calculated based on the second information.

[0061] For example, the control unit 11 may generate second information that identifies the wall area 130 in the image 100 by inference using the trained model. That is, the second information may be generated by inputting input data based on the image 100 into the trained model. The generation of the second information may be performed simultaneously with the generation of the first information.

[0062] The method for calculating the area of ​​the wall surface region 130 from the second information is not limited. For example, if the wall surface region 130 is identified in pixel units, the area of ​​the wall surface region 130 may be the number of pixels in the wall surface region 130. Furthermore, the area of ​​the wall surface region 130 may be, for example, the wall surface area ratio. The wall surface area ratio may be determined, for example, by equation (2). [Number 2] (Wall area ratio) = (Number of pixels in the wall area) / (Reference area) (2) The reference area may be determined arbitrarily. The reference area in the above formula (1) and the reference area in the above formula (2) may be the same or different.

[0063] When a plurality of wall surface regions 130 are included in the image, the area of ​​the wall surface regions 130 may be calculated as the total area.

[0064] In some cases, the image contains an overlapping area where the tank wall is hidden by the scraped-out portion. In such cases, the area of ​​the wall area may be calculated as is, or may be calculated by adding the area of ​​the overlapping area to the area of ​​the wall area.

[0065] The liquid level can also be calculated based on, for example, the width 150. The width 150 is the width measured in the direction in which the wall surface area 130 expands or contracts in response to changes in the liquid level, and the control unit may calculate the width 150. The width 150 may be calculated from the second information. To calculate the liquid level, a table that associates the liquid level with the width 150 at that liquid level may be used. The table may be stored in the memory unit 12.

[0066] Furthermore, the liquid level can be calculated based on, for example, the region 140 of the scraping portion. Specifically, the liquid level can be calculated based on the area of ​​the region 140 of the scraping portion. To calculate the liquid level, a table that associates the liquid level with the area of ​​the region 140 of the scraping portion at that liquid level may be used. The table may be stored in the storage unit 12. The area of ​​the region 140 of the scraping portion may be calculated from third information that identifies the region 140 of the scraping portion in the image 100. Note that the liquid level may be calculated based on the third information.

[0067] For example, the control unit 11 may generate third information that identifies the scraped-out area 140 in the image 100 by inference using the trained model. That is, the third information may be generated by inputting input data based on the image 100 into the trained model. The generation of the third information may be performed simultaneously with the generation of the first information.

[0068] The method for calculating the area of ​​scraping portion region 140 from the third information is not limited. For example, when scraping portion region 140 is specified in pixel units, the area of ​​scraping portion region 140 may be the number of pixels in scraping portion region 140. Furthermore, the area of ​​scraping portion region 140 may be, for example, the area ratio of the wall surface. The area ratio of the wall surface may be determined, for example, by equation (3). [Number 3] (Ratio of scraped area) = (Number of pixels in scraped area) / (Reference area) (3) The reference area may be determined arbitrarily. The reference area in the above formula (1) and the reference area in the above formula (3) may be the same or different.

[0069] Step S150: Control unit 11 of information processing device 10 determines whether there is an abnormality in liquid processing device 30 based on the liquid level. Control unit 11 may control memory unit 12 to store the determination result. Control unit 11 may also control output unit 15 to output the determination result.

[0070] The specific criteria for determination are not limited. For example, the control unit 11 may determine that the liquid treatment device 30 is abnormal if the liquid level is equal to or higher than a predetermined threshold. The control unit 11 may also determine that the liquid treatment device 30 is abnormal if the liquid level is equal to or lower than a predetermined threshold. Specifically, when an empty state of the tank 4 is represented as 0% and a full state of the tank 4 is represented as 100%, the control unit 11 may determine that the liquid treatment device 30 is abnormal if the liquid level is equal to or higher than 95% or equal to or lower than 5%. Furthermore, the control unit 11 may determine that the liquid treatment device 30 is abnormal if the liquid level exceeds the reference level, based on the height of a location in the tank 4 from which suspended matter is discharged, because wastewater may be leaking from that location. The control unit 11 may also determine that the liquid treatment device 30 is abnormal if the liquid level does not exceed a predetermined threshold within a predetermined period of time. The control unit 11 may also determine that the liquid treatment device 30 is abnormal if the liquid level does not fall below a predetermined threshold within a predetermined period of time. The control unit 11 may also determine that the liquid treatment device 30 is abnormal if the liquid level falls below a predetermined threshold and continues to fall for a predetermined period of time. For example, if the liquid level falls below the height of the point in the tank 4 where floating matter is discharged and the liquid level continues to fall for a predetermined period of time, the control unit 11 may determine that the liquid treatment device 30 is abnormal, as there is a possibility that flotation separation is not being performed properly.

[0071] According to this method, there is no need to install a separate gauge to check the liquid level, which reduces the cost of installing the gauge and also eliminates the need to check the gauge separately.

[0072] The criteria for determining abnormality in steps S130 and S150 will be described in detail below.

[0073] In step S130 or step S150, control unit 11 may acquire information other than image 100 related to liquid treatment device 30 and combine the information to determine an abnormality related to liquid treatment device 30. Examples of information other than image 100 include the operating status of scraping unit 5, the operating status of pump 6, etc.

[0074] Furthermore, step S130 and step S150 may be combined. That is, control unit 11 may determine an abnormality in liquid treatment device 30 based on information other than the area of ​​floating matter region 120, the liquid level, and arbitrarily acquired image 100.

[0075] For example, the control unit 11 may determine that the scraping unit 5 is abnormal when all of the following conditions (1) to (3) are met. (1) The scraping unit 5 is in operation. (2) The liquid level is sufficiently high. (3) The area of ​​the region 120 of suspended matter does not fall below a second predetermined threshold (for example, 60%) for a second predetermined period (for example, 180 seconds). Here, the determination of whether the liquid level is sufficiently high may be made, for example, by determining whether the liquid level falls below a predetermined threshold (e.g., 50%) within a predetermined period (e.g., 30 seconds). By setting condition (2), it is possible to determine whether there is an abnormality in the scraping unit 5 when the scraping unit 5 is in contact with the liquid. In this way, the abnormality determination criteria described in relation to step S130 may be combined with conditions related to information other than the image 100 and conditions related to the liquid level.

[0076] (Generating a trained model) Next, a technique for generating a trained model that can be used to generate additional output information according to the present disclosure will be described with reference to the accompanying drawings. The trained model receives an image including a liquid surface area as input and outputs output data including information identifying areas of floating matter that may be included in the image. The output data preferably includes one or both of information identifying areas of the tank wall that may be included in the image and information identifying areas of scraped-out portions that may be included in the image. The trained model can be generated by machine learning using, as a training image, an image obtained by performing preprocessing to change the brightness of a captured image including a liquid surface area.

[0077] FIG. 9 is a flowchart showing an example of processing executed by the information processing device 10 according to this embodiment to generate a trained model.

[0078] Step S210: The control unit 11 acquires an image that has been subjected to preprocessing to change the brightness of the captured image including the liquid surface area. For example, the control unit 11 may acquire the image from a host computer or the like via the communication unit 13. The control unit 11 may also acquire the captured image including the liquid surface area from an imaging device. In this case, the control unit 11 may perform preprocessing to change the brightness of the acquired captured image. The control unit 11 may refer to a database to perform the preprocessing. The storage unit 12 may store the database. The control unit 11 may also refer to an input based on a user operation via the input unit 14 to perform the preprocessing.

[0079] Examples of brightness that may be changed in preprocessing include brightness in the HSV color space, luminance in the HLS color space, and luminance in the YUV color space. Preprocessing may also be processing that sets the average and variance of the brightness distribution of the captured images to predetermined values. Preprocessing may also be processing that makes the brightness uniform across multiple captured images.

[0080] Step S220: The control unit 11 acquires information identifying a region of floating matter that may be included in the acquired image. For example, the control unit 11 may acquire the information from a host computer or the like via the communication unit 13. Alternatively, the control unit 11 may acquire the information based on a user operation via the input unit 14.

[0081] In order to further use the trained model to generate third information, the control unit 11 may further acquire one or both of information identifying the area of ​​the tank wall that may be included in the acquired image and information identifying the area of ​​the scraping portion.

[0082] Step S230: The control unit 11 generates a trained model by machine learning using the acquired image as a teacher image. The trained model is generated by associating the teacher image with the information acquired in step S220.

[0083] The trained model may be, for example, a trained model for segmentation, such as a trained model generated by a machine learning algorithm such as Mask R-CNN, Unet, or UperNet. [Example]

[0084] The present disclosure will be specifically described below using examples.

[0085] First, a trained model for classifying image regions was generated. The trained model used input images containing the surface area of ​​wastewater, and output data that could be included in the image, including information identifying the area of ​​oil floating on the surface of the liquid and information identifying the area of ​​the wall surface.

[0086] In a flotation device such as that shown in Figure 2, an image including the liquid surface area of ​​the wastewater was captured using an imaging device and used as a training image. Annotation information was also prepared that included information identifying the oil area and the wall surface area corresponding to the training image. Using the training image and annotation information as training data, a trained model was generated by machine learning. Mask R-CNN, a type of deep learning algorithm, was used as the machine learning algorithm.

[0087] Next, the generated trained model was used to monitor the levitation device in order to detect abnormalities.

[0088] In a pressurized flotation device such as that shown in Figure 2, a camera was used to capture video images of the wastewater liquid surface. Frames were extracted from the video at predetermined time intervals. The images obtained by extraction were used to identify oil areas and wall areas, and the area of ​​each area was calculated using the trained model described above. Figure 10 shows the progress of the oil area expressed as an area percentage, and the progress of the water level calculated from the area of ​​the wall area. Using the data shown in Figure 10 and the anomaly determination criteria described above, anomalies related to the liquid treatment device can be determined.

[0089] Although the present disclosure has been described based on the drawings and examples, it should be noted that those skilled in the art can easily make various modifications and alterations based on the present disclosure. Therefore, it should be noted that these modifications and alterations are included in the scope of the present disclosure. For example, the functions included in each means or step can be rearranged so as not to be logically inconsistent, and multiple means or steps can be combined or divided into one.

[0090] 5, 7, and 9 show examples in which steps are processed serially, but the present invention is not limited to this. For example, each step may be processed in parallel.

[0091] Furthermore, for example, in the above-described embodiment, an embodiment is also possible in which the configuration and operation of the information processing device 10 are distributed among a plurality of computers that can communicate with each other. [Explanation of symbols]

[0092] 1. Information Processing Systems 2 liquid 3 Floating objects 4 Tank 5. Scraping section 6. Pump 7 Gases 10. Information processing equipment 11 Control section 12 Storage section 13 Communications Department 14 Input section 15 Output section 20 Imaging device 30 Liquid treatment equipment 31 Pressurized Tank 32 liquid 33 Pressurized Liquids Images 100, 101, and 102 110 Liquid Surface Area 120 Floating Area 121 Floating Area 122 overlapping area 123 areas 130 Wall Area 140 scraping area 150 width

Claims

1. An information processing device having a control unit and determining an abnormality related to a liquid processing device, The liquid treatment device includes a tank for storing a liquid, a scraping unit for scraping out floating matter floating on the surface of the liquid to the outside of the tank, and a pump for feeding gas into the liquid, The control unit acquiring an image including the liquid surface region; Calculating the area of ​​the floating matter region that may be included in the image; An information processing device that determines the abnormality based on the area of ​​the floating matter region.

2. The control unit Calculating the liquid level from the image; The information processing device according to claim 1 , wherein the abnormality is determined based on the liquid level.

3. The control unit The information processing device according to claim 2 , wherein the liquid processing device is determined to be abnormal when the liquid level is equal to or greater than a predetermined threshold value.

4. The information processing device according to claim 1 , wherein the control unit determines that the pump is abnormal when the area of ​​the region of floating matter does not exceed a predetermined first threshold value within a predetermined first period.

5. The information processing device according to claim 1 , wherein the control unit determines that the scraping unit is abnormal when the area of ​​the floating matter region does not fall below a predetermined second threshold value within a predetermined second period.

6. The information processing device according to claim 1 , wherein the control unit determines that the pump or the scraping unit is abnormal when the length of a period during which there is no change over time in the area of ​​the floating matter region exceeds a predetermined third threshold value.

7. The control unit Calculating the area of ​​the wall surface region of the tank included in the image; The information processing device according to claim 2 , wherein the liquid level is calculated based on an area of ​​the wall surface region.

8. The control unit calculating a width of the wall surface area of ​​the tank measured in a direction in which the wall surface area of ​​the tank in the image expands or contracts in response to the change in the liquid level; The information processing device according to claim 2 , wherein the liquid level is calculated based on the width.

9. The control unit Calculating the area of ​​the scraped-out portion that may be included in the image; The information processing device according to claim 2 , wherein the liquid level is calculated based on an area of ​​the scraping portion.

10. The control unit In calculating the area of ​​the floating matter region, if the image includes an overlapping region in which the floating matter is hidden by the scraping unit, the area of ​​the overlapping region is added to the area of ​​the floating matter region to calculate the area of ​​the floating matter region; 8. The information processing device according to claim 7, wherein, in calculating the area of ​​the wall surface region, if the image includes an overlapping region in which the wall surface is hidden by the scraping unit, the area of ​​the wall surface region is calculated as the value obtained by adding the area of ​​the overlapping region to the area of ​​the wall surface region.

11. An information processing system including a liquid treatment device and an information processing device that determines an abnormality related to the liquid treatment device, The liquid treatment device includes a tank for storing a liquid, a scraping unit for scraping out floating matter floating on the surface of the liquid to the outside of the tank, and a pump for feeding air into the liquid, the information processing device has a control unit, The control unit acquiring an image including the liquid surface region; Calculating the area of ​​the floating matter region that may be included in the image; An information processing system that determines the abnormality based on the area of ​​the floating matter region.

Images