Display data output device, display data output method and display data output program

Abstract

To provide a display data output device, a display data output method and a display data output program that are able to easily analyze relations between time-series data.SOLUTION: A display data output device includes a generator that generates display data and an output unit that outputs the display data generated by the generator. The generator generates the display data including: a first region in which first time-series data related to an explanatory variable and second time-series data related to an objective variable corresponding to the explanatory variable are displayed; a second region in which one or more identifiers respectively corresponding to one or more conversion equations for converting time-series data are displayed; and a third region in which the first time-series data converted based on a first conversion equation identified by a first identifier among the one or more identifiers is displayed.SELECTED DRAWING: Figure 12

Description

[Technical Field]

[0001] The present invention relates to a display data output device, a display data output method, and a display data output program. [Background technology]

[0002] For example, in a treatment facility such as a water treatment facility that treats drinking water, sewage, etc. (hereinafter simply referred to as a treatment facility), predictions are made on time-series data (hereinafter simply referred to as time-series data) measured by a measuring device. In such a plant facility, for example, various setting values ​​required for operation are set based on the prediction results for the time-series data to be predicted (see Patent Document 1). [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Application Publication No. 10-133737 Summary of the Invention [Problem to be solved by the invention]

[0004] In the plant facility described above, it is desirable to improve the prediction accuracy of the time series data to be predicted by, for example, analyzing the relationship between multiple types of time series data measured by multiple measuring devices (hereinafter simply referred to as multiple types of time series data). Therefore, in the plant facility described above, it is desirable to have a method that enables easy analysis of the relationship between multiple types of time series data. [Means for solving the problem]

[0005] A display data output device according to the present disclosure includes a generator that generates display data and an output unit that outputs the display data generated by the generator, wherein the generator generates the display data having a first area that displays first time series data related to explanatory variables and second time series data related to a target variable corresponding to the explanatory variables, a second area that displays one or more identifiers corresponding to one or more transformation formulas that transform time series data, and a third area that displays the first time series data transformed based on the first transformation formula identified by a first identifier among the one or more identifiers. [Effects of the Invention]

[0006] According to the display data output device, the display data output method, and the display data output program of the present disclosure, it becomes possible to easily analyze the relationship between time-series data. [Brief explanation of the drawings]

[0007] [Figure 1] FIG. 1 is a diagram illustrating an example of the configuration of a processing system 1000 according to the first embodiment. [Figure 2] FIG. 2 is a diagram illustrating an example of the configuration of the processing facility 20 according to the first embodiment. [Figure 3] FIG. 3 is a diagram illustrating the hardware configuration of the information processing device 1. As shown in FIG. [Figure 4] FIG. 4 is a block diagram of the functions of the information processing device 1 according to the first embodiment. [Figure 5] FIG. 5 is a flowchart illustrating the data analysis process according to the first embodiment. [Figure 6] FIG. 6 is a flowchart illustrating the data analysis process according to the first embodiment. [Figure 7] FIG. 7 is a diagram illustrating a specific example of the first time series data 131. As shown in FIG. [Figure 8] FIG. 8 is a diagram illustrating a specific example of the second time series data 132. As shown in FIG. [Figure 9]FIG. 9 is a diagram illustrating the data analysis process according to the first embodiment. [Figure 10] FIG. 10 is a diagram illustrating the data analysis process according to the first embodiment. [Figure 11] FIG. 11 is a diagram illustrating the data analysis process according to the first embodiment. [Figure 12] FIG. 12 is a diagram illustrating a specific example of the display data OP2. [Figure 13] FIG. 13 is a diagram illustrating a specific example of the display data OP2a. [Figure 14] FIG. 14 is a diagram illustrating the data analysis process according to the first embodiment. [Figure 15] FIG. 15 is a diagram illustrating a specific example of the history data 133 in the first modified example. DETAILED DESCRIPTION OF THE INVENTION

[0008] Hereinafter, embodiments of the present disclosure will be described with reference to the drawings. However, such descriptions should not be interpreted in a limiting sense, and do not limit the subject matter described in the claims. Furthermore, various changes, substitutions, and modifications can be made without departing from the spirit and scope of the present disclosure. Furthermore, different embodiments can be combined as appropriate.

[0009] [Water treatment system 1000 according to the first embodiment] First, a description will be given of an example of the configuration of a processing system 1000 according to the first embodiment. Fig. 1 is a diagram illustrating an example of the configuration of a processing system 1000 according to the first embodiment.

[0010] As shown in FIG. 1, the processing system 100 includes, for example, an information processing system 10 and a processing facility 20.

[0011] Specifically, the treatment facility 20 is, for example, various types of water treatment facilities installed in a sewage treatment plant. An example of the configuration of the treatment facility 20 will be described below.

[0012] [Processing equipment 20 in the first embodiment] FIG. 2 is a diagram illustrating an example of the configuration of the processing facility 20 according to the first embodiment.

[0013] As shown in FIG. 2, the treatment facility 20 includes, for example, a primary sedimentation tank 210, a reaction tank 220, a final sedimentation tank 230, a thickening tank 240, a thickening device 250, a digestion tank 260, and a heater 270.

[0014] The primary sedimentation tank 210 separates and settles organic matter and suspended matter contained in water to be treated, such as sewage (hereinafter also simply referred to as water to be treated).The primary sedimentation tank 210 then discharges the separated organic matter and suspended matter as primary settled sludge to the concentration tank 240, and discharges the water to be treated from which the organic matter and suspended matter have been separated to the reaction tank 220.

[0015] The reaction tank 220 treats the water to be treated by biological treatment (hereinafter simply referred to as biological treatment), such as a standard activated sludge process or a circulating nitrification-denitrification process. Specifically, the reaction tank 220 has a denitrification tank (not shown) in which anaerobic denitrifying bacteria produce nitrogen from nitrite nitrogen and nitrate nitrogen (denitrification). The reaction tank 220 also has a nitrification tank (not shown) located downstream of the denitrification tank, in which aerobic nitrifying bacteria nitrify ammonia nitrogen to produce nitrite nitrogen and nitrate nitrogen. Continuous aeration (air blowing) is performed in the nitrification tank, for example, by an air supply device (not shown). The reaction tank 220 then discharges the water to be treated, which has undergone biological treatment, to a final sedimentation tank 230.

[0016] The final settling tank 230, for example, separates and settles sludge contained in the water to be treated discharged from the reaction tank 220, and discharges the separated sludge as activated sludge. The final settling tank 230 then supplies, for example, a portion of the activated sludge to the thickener 250 as excess sludge, and returns the activated sludge other than the excess sludge to the reaction tank 220 as returned sludge. The final settling tank 230 also discharges, for example, the treated water from which the sludge has been separated.

[0017] The thickening tank 240 thickens, for example, the primary sludge discharged from the primary sedimentation tank 210 and supplies the thickened sludge to the digestion tank 260 .

[0018] The thickener 250 thickens excess sludge discharged from the final settling tank 230 and supplies the thickened sludge to the digester 260, for example.

[0019] The digestion tank 260 is a tank that stores, for example, anaerobic bacteria and sludge. The anaerobic bacteria in the digestion tank 260 anaerobically digest (decompose) organic matter in the sludge, including the primary sludge supplied from the thickening tank 240 and the excess sludge supplied from the thickener 250, through a biological reaction to produce digested sludge. The anaerobic bacteria in the digestion tank 260 also produce digestion gases, such as methane gas, during the digestion process.

[0020] The heater 270 heats, for example, excess sludge before it is supplied to the digestion tank 260. Specifically, the heater 270 is, for example, a heat exchanger that heats (raises the temperature of) the excess sludge before it is supplied to the digestion tank 260 using the heat retained in a heat medium (a fluid such as water or heat transfer oil).

[0021] The treatment facility 20 may further include, for example, a sterilization treatment device (not shown) that sterilizes the treated water discharged from the final sedimentation tank 230. The treatment facility 20 may then discharge the treated water that has been sterilized by the sterilization treatment device.

[0022] In addition, the following description will be given assuming that the treatment facility 20 is a water treatment facility installed in a sewage treatment plant, but the present invention is not limited to this. Specifically, the treatment facility 20 may be, for example, a water treatment facility installed in a water purification facility. The treatment facility 20 may also be, for example, an incineration facility that incinerates sludge discharged from a water purification plant or a sewage treatment plant. Furthermore, the treatment facility 20 may also be, for example, an incineration facility that incinerates garbage.

[0023] Returning to FIG. 1, the information processing system 10 includes, for example, an information processing device 1 (hereinafter also referred to as a display data output device 1) and an operation terminal 5.

[0024] The operation terminal 5 is, for example, one or more PCs (Personal Computers) or mobile terminals such as smartphones, and is a terminal through which an operator of the processing equipment 20 (hereinafter simply referred to as the operator) inputs necessary information, etc. into the information processing device 1.

[0025] The information processing device 1 is, for example, a physical machine or a virtual machine, and performs a process (hereinafter also referred to as a data analysis process) to identify the relationship between multiple pieces of time-series data measured in the processing facility 20. Then, the information processing device 1 performs a process (hereinafter also referred to as a data prediction process) to predict time-series data that will be a target variable from time-series data that will be an explanatory variable based on the analysis results of the data analysis process, for example.

[0026] Specifically, in the data analysis process, the information processing device 1 in this embodiment acquires, for example, time series data (hereinafter also referred to as first time series data) measured by a measuring device M1 provided in the treatment facility 20. The first time series data is time series data used as an explanatory variable, and is, for example, time series data on the air flow rate (air flow rate per unit time) for the water to be treated in the reaction tank 220. The information processing device 1 also acquires, for example, time series data (hereinafter also referred to as second time series data) measured by a measuring device M2 provided in the treatment facility 20. The second time series data is time series data used as a response variable, and is, for example, time series data on the ammonia concentration of the water to be treated at the outlet of the reaction tank 220. Then, the information processing device 1 outputs, for example, the acquired first time series data and the acquired second time series data to an output device.

[0027] Thereafter, the information processing device 1 receives, for example, an input of an identifier (hereinafter also referred to as a first identifier) ​​indicating a transformation formula for transforming the output first time series data. Then, the information processing device 1 performs, for example, a transformation corresponding to the received input identifier on the first time series data. Furthermore, the information processing device 1 outputs, for example, the acquired second time series data and the transformed first time series data to an output device.

[0028] That is, the information processing device 1 in this embodiment outputs, for example, the first time series data and the second time series data in a form that can be viewed by an operator. Then, for example, the operator compares the output first time series data with the second time series data to identify a transformation that is considered to be effective for the first time series data (hereinafter also referred to as an effective transformation), and inputs an identifier corresponding to the identified effective transformation into the information processing device 1. For example, an effective transformation is a transformation that is predicted to enable a simple comparison with the second time series data when performed on the first time series data. In other words, an effective transformation is, for example, a transformation that changes the shape of the first time series data into a shape suitable for predicting the second time series data, or a transformation that can reproduce a causal relationship existing between an event indicated by the first time series data and an event indicated by the second time series data.

[0029] Thereafter, the information processing device 1 outputs again the first time series data and the second time series data that have been subjected to the conversion corresponding to the input accepted identifier in a form that can be viewed by the worker. Then, the worker, for example, compares the output converted first time series data with the output converted second time series data again to determine whether the conversion performed on the first time series data was effective.

[0030] As a result, the information processing device 1 according to the present embodiment can allow the operator to specify an effective transformation that is preferable to be performed on the first time series data. This allows the operator to easily analyze the relationship between the first time series data and the second time series data. Specifically, the operator can easily analyze the relationship between the first time series data and the second time series data by performing the specified effective transformation on the first time series data.

[0031] Therefore, when the information processing device 1 performs data prediction processing to predict second time series data (future second time series data) from first time series data, for example, it is possible to improve the prediction accuracy of the second time series data by performing prediction using the first time series data after performing effective transformations identified in the data analysis processing.

[0032] Note that, although the following description will be given assuming that one information processing device 1 executes the data analysis process, the present invention is not limited to this. Specifically, the functions required to execute the data analysis process may be distributed among a plurality of information processing devices 1 (hereinafter simply referred to as a plurality of information processing devices 1) that can access each other via a network such as the Internet. The data analysis process may be executed by, for example, the plurality of information processing devices 1 cooperating with each other.

[0033] [Hardware configuration of information processing system] Next, a description will be given of the hardware configuration of the information processing system 10. Fig. 3 is a diagram illustrating the hardware configuration of the information processing device 1.

[0034] 3, the information processing device 1 includes a CPU 101 (hereinafter also referred to as a generator 101) which is a processor, a memory 102, a communication device 103, a storage medium 104, and an output device 105. Each unit is connected to one another via a bus 106.

[0035] The storage medium 104 has, for example, a program storage area (not shown) that stores a program 110 for performing data analysis processing. The storage medium 104 also has, for example, a storage unit 130 (hereinafter also referred to as information storage area 130) that stores information used when performing data analysis processing. The storage medium 104 may be, for example, a hard disk drive (HDD) or a solid state drive (SSD).

[0036] The CPU 101 performs data analysis processing by executing a program 110 loaded from the storage medium 104 to the memory 102, for example.

[0037] The communication device 103 accesses each of the operation terminals 5 via a network NW such as the Internet, for example.

[0038] The output device 105 (hereinafter also referred to as the output unit 105) is, for example, a display, and outputs data (hereinafter also referred to as display data) indicating the processing results of the data analysis processing performed by the CPU 101.

[0039] The display data may be outputted, for example, from an output device (not shown) of the operation terminal 5.

[0040] [Functions of information processing systems] Next, a description will be given of the functions of the information processing system 10. Fig. 4 is a block diagram of the functions of the information processing device 1 in the first embodiment.

[0041] As shown in FIG. 4, the information processing device 1 realizes various functions including a data acquisition unit 111, a data management unit 112, a first data output unit 113, an identifier reception unit 114, a data conversion unit 115, and a second data output unit 116 by organically cooperating with hardware such as a CPU 101 and a memory 102 and a program.

[0042] 4, the information processing device 1 stores, for example, first time series data 131, second time series data 132, and history data 133 in the information storage area 130. The history data 133 will be described later.

[0043] The data acquiring unit 111 acquires, for example, first time-series data 131 measured by a measuring device M1 provided in the treatment facility 20. For example, if the first time-series data 131 is time-series data about the amount of air sent to the water to be treated in the reaction tank 220, the measuring device M1 is a flow meter that measures the flow rate of air (oxygen) supplied to the reaction tank 220. Specifically, the data acquiring unit 111 acquires, for example, multiple measurement values ​​measured by the measuring device M1 within a predetermined period of time from the measuring device M1 as the first time-series data 131. Then, the data management unit 112 stores, for example, the first time-series data 131 acquired by the data acquiring unit 111 in the information storage area 130.

[0044] Furthermore, the data acquiring unit 111 acquires, for example, second time series data 132 measured by a measuring device M2 provided in the treatment facility 20. For example, if the second time series data 132 is time series data on the ammonia concentration of the water to be treated in the reaction tank 220, the measuring device M2 is a concentration meter that measures the ammonia concentration of the water to be treated at the outlet of the reaction tank 220. Specifically, the data acquiring unit 111 acquires, for example, multiple measurement values ​​measured by the measuring device M2 within a predetermined period of time from the measuring device M2 as the second time series data 132. Then, the data management unit 112 stores, for example, the second time series data 132 acquired by the data acquiring unit 111 in the information storage area 130.

[0045] The data acquiring unit 111 may acquire each measurement value from the measuring device M1, for example, each time the measuring device M1 measures a measurement value. The data managing unit 112 may sequentially store the measurement values ​​acquired by the data acquiring unit 111 in the information storage area 130 as part of the first time-series data 131. The data acquiring unit 111 may acquire each measurement value from the measuring device M2, for example, each time the measuring device M2 measures a measurement value. The data managing unit 112 may sequentially store the measurement values ​​acquired by the data acquiring unit 111 in the information storage area 130 as part of the second time-series data 132.

[0046] The first data output unit 113 outputs, for example, the first time series data 131 acquired by the data acquisition unit 111 and the second time series data 132 acquired by the data acquisition unit 111 to the output device 105. That is, the first data output unit 113 makes, for example, the first time series data 131 acquired by the data acquisition unit 111 and the second time series data 132 acquired by the data acquisition unit 111 available for viewing by an operator.

[0047] The first data output unit 113 may output only the time series data specified by the operator, for example, from the first time series data 131 and the second time series data 132. The first data output unit 113 may also output the first time series data 131 and the second time series data 132 in sequence, for example.

[0048] The identifier receiving unit 114 receives, for example, an input of an identifier indicating a transformation formula for transforming the first time series data 131 output by the first data output unit 113. That is, the identifier receiving unit 114 waits until, for example, an operator who has viewed the first time series data 131 and the second time series data 132 output by the first data output unit 113 inputs an identifier corresponding to a transformation that is predicted to be an effective transformation for the first time series data 131.

[0049] The data conversion unit 115 performs conversion on the first time series data 131 in accordance with the identifier received by the identifier receiving unit 114, for example.

[0050] The second data output unit 116 outputs, for example, the first time series data 131 converted by the data conversion unit 115 and the second time series data 132 acquired by the data acquisition unit 111 to the output device 105.

[0051] The second data output unit 116 may output only the time series data specified by the operator from the converted first time series data 131 and second time series data 132. The second data output unit 116 may output the converted first time series data 131 and second time series data 132 in sequence.

[0052] In the following, a case will be described in which the first time series data 131 and the second time series data 132 are stored in the information storage area 130 in a distinguished state, but the present invention is not limited to this. Specifically, the first time series data 131 stored in the information storage area 130 may be used as the second time series data 132 in the data analysis process, for example. Furthermore, the second time series data 132 stored in the information storage area 130 may be used as the first time series data 131 in the data analysis process, for example.

[0053] [Data Analysis Processing in the First Embodiment] Next, the data analysis process in the first embodiment will be described. Figures 5 and 6 are flowcharts explaining the data analysis process in the first embodiment. Figures 7 to 14 are diagrams explaining the data analysis process in the first embodiment.

[0054] [Data acquisition process] First, a description will be given of the data analysis process, specifically, the process of acquiring the first time series data 131 and the second time series data 132 (hereinafter also referred to as the data acquisition process). Fig. 5 is a flowchart illustrating the data acquisition process.

[0055] As shown in FIG. 5(A), the data acquiring unit 111 acquires, for example, first time-series data 131 measured by a measuring device M1 provided in the processing facility 20 (step S1 in FIG. 5(A)).

[0056] Then, the data management unit 112 stores, for example, the first time-series data 131 acquired in step S1 in the information storage area 130 (step S2 in FIG. 5(A)).

[0057] Note that, for example, a plurality of measuring devices M1 may be attached to the processing equipment 20. Then, in step S1, the data acquiring unit 111 may acquire a plurality of first time series data 131 (a plurality of types of first time series data 131) from each of the plurality of measuring devices M1. Specific examples of the first time series data 131 will be described below.

[0058] [Example of the first time series data 131] Fig. 7 is a diagram illustrating a specific example of the first time series data 131. Specifically, Fig. 7 is a diagram illustrating a specific example of three types of first time series data 131 acquired from three types of measurement devices M1, respectively.

[0059] 7 includes, for example, items such as "date and time" in which the date and time of measurement by each measuring device M1 is set, and "water level" in which a measurement value measured by the measuring device M1, which is a water level meter that measures the water level of the water to be treated in the reaction tank 220, is set. In addition, the first time series data 131 shown in Fig. 7 includes, for example, items such as "air flow rate" in which a measurement value measured by the measuring device M1, which is a flow meter that measures the flow rate of air (oxygen) supplied to the reaction tank 220, is set, and "air temperature" in which a measurement value measured by the measuring device M1, which is a thermometer provided outside the reaction tank 220, is set.

[0060] Specifically, in the first time-series data 131 shown in FIG. 7, the first row of data has, for example, "6 / 1 12:00" set as the "date and time", "10 (m)" set as the "water level", and "10000 (m)" set as the "airflow rate". 3 / h)" is set and "Temperature" is set to "20 (℃)".

[0061] In the first time-series data 131 shown in FIG. 7, for example, "6 / 2 12:00" is set as the "date and time", "12 (m)" is set as the "water level", and "15000 (m)" is set as the "airflow rate". 3 / h)" is set, and "temperature" is set to "15 (℃)". Explanation of other data included in FIG. 7 will be omitted.

[0062] Returning to FIG. 5(B), the data acquiring unit 111 acquires, for example, second time-series data 132 measured by a measuring device M2 provided in the processing facility 20 (step S11 in FIG. 5(B)).

[0063] Then, the data management unit 112 stores, for example, the second time-series data 132 acquired in step S11 in the information storage area 130 (step S12 in FIG. 5(B)).

[0064] Note that, for example, a plurality of measuring devices M2 may be attached to the processing equipment 20. Then, in step S11, the data acquiring unit 111 may acquire a plurality of second time series data 132 (a plurality of types of second time series data 132) from each of the plurality of measuring devices M2. Specific examples of the second time series data 132 will be described below.

[0065] [Example of the second time series data 132] Fig. 8 is a diagram illustrating a specific example of the second time series data 132. Specifically, Fig. 8 is a diagram illustrating a specific example of one type of second time series data 132 acquired from one type of measurement device M2.

[0066] The second time series data 132 shown in Figure 8 has, for example, items such as "date and time," in which the measurement date and time by the measuring device M2 is set, and "NH4-N concentration," in which the measurement value measured by the measuring device M2, which is a concentration meter that measures the ammonia concentration of the treated water at the outlet of the reaction tank 220, is set.

[0067] Specifically, in the second time series data 132 shown in FIG. 8, the first row of data has, for example, the "date and time" set to "6 / 1 12:00" and the "NH4-N concentration" set to "0.2 (mg / L)."

[0068] 8, the second row of data has, for example, "6 / 2 12:00" set as the "date and time" and "0.4 (mg / L)" set as the "NH4-N concentration." Explanation of the other data included in FIG. 8 will be omitted.

[0069] [Main processing] Next, the main processing of the data analysis processing (hereinafter also simply referred to as the main processing) will be described. Fig. 6 is a flowchart illustrating the main processing.

[0070] As shown in FIG. 6, the first data output unit 113 displays (outputs) on the output device 105, for example, at least one of the first time series data 131 stored in the information storage area 130 and the second time series data 132 stored in the information storage area 130 (step S21 in FIG. 6).

[0071] That is, the first data output unit 113 makes at least one of the waveform of the first time series data 131 stored in the information storage area 130 and the waveform of the second time series data 132 stored in the information storage area 130 viewable by the worker.

[0072] 9, the first data output unit 113 generates display data OP1 including, for example, a solid line graph G1 indicating the waveform of the first time series data 131 specified by the worker and a dashed line graph G2 indicating the waveform of the second time series data 132 specified by the worker, and displays the display data OP1 on the output device 105. Hereinafter, of the areas included in the display data OP1, an area where at least the solid line graph G1 and the dashed line graph G2 are displayed will also be referred to as a first area.

[0073] In addition, the worker may repeatedly view multiple first time series data 131 and multiple second time series data 132, for example, by repeatedly specifying at least one of the first time series data 131 and the second time series data 132.

[0074] Next, the identifier receiving unit 114 waits until it receives inputs, for example, designation of first time series data 131 (hereinafter also referred to as first time series data 131a) to be used as an explanatory variable, designation of second time series data 132 (hereinafter also referred to as second time series data 132a) to be used as a target variable, and designation of an identifier for a transformation formula that transforms the first time series data 131a (NO in step S22 of Figure 6).

[0075] That is, for example, the worker repeatedly views a plurality of first time series data 131 and a plurality of second time series data 132, and then determines the first time series data 131a and the second time series data 132a to be analyzed. Then, for example, the worker specifies the determined first time series data 131a and the second time series data 132a, and specifies an identifier corresponding to a conversion that can be predicted to be effective for the first time series data 131a. Note that the worker may specify, as an effective conversion, a conversion that can be predicted to make the waveform of the first time series data 131a closer to the waveform of the second time series data 132a.

[0076] More specifically, the worker may specify, for example, as an identifier for the transformation of the first time series data 131a, an identifier corresponding to a transformation that slides the time axis of the first time series data 131a (hereinafter also referred to as time series slide), an identifier corresponding to a transformation that introduces a phase lag into the fluctuations of the measurement values ​​in the first time series data 131a (hereinafter also referred to as first-order lag), an identifier corresponding to a transformation that excludes outliers from each measurement value in the first time series data 131a (hereinafter also referred to as outlier exclusion), an identifier corresponding to a transformation that subtracts each measurement value in other first time series data 131 from each measurement value in the first time series data 131a, or an identifier corresponding to a transformation that multiplies each measurement value in the first time series data 131a by each measurement value in other first time series data 131.

[0077] For example, when the data converter 115 receives inputs of the first time series data 131a, the second time series data 132a, and an identifier indicating a conversion formula for converting the first time series data 131a (YES in step S22 of FIG. 6), the data converter 115 performs a conversion corresponding to the specified identifier on the specified first time series data 131a (step S23 of FIG. 6).

[0078] Thereafter, the second data output unit 116 displays (outputs) on the output device 105, for example, at least one of the first time series data 131 after conversion in step S23 and the second time series data 132a specified in step S22 (step S24 in Figure 6).

[0079] That is, the second data output unit 116 makes at least one of the first time series data 131 converted in step S23 and the second time series data 132a specified in step S22 available for viewing by the worker.

[0080] 10, the second data output unit 116 generates display data OP1 including, for example, a solid line graph G1 (hereinafter also referred to as solid line graph G1a) showing the waveform of the first time series data 131a after the conversion in step S23, and a dashed line graph G2 (hereinafter also referred to as dashed line graph G2) showing the waveform of the second time series data 132a specified in step S22, and displays the generated display data on the output device 105. Hereinafter, of the areas included in the display data OP1, the area where at least the solid line graph G1a is displayed is also referred to as a third area.

[0081] 11 , the second data output unit 116 may generate display data OP1 including, for example, a solid line graph G1a showing the waveform of the first time series data 131a after the conversion in step S23 and a solid line graph G1 (hereinafter also referred to as solid line graph G1b) showing the waveform of the first time series data 131a specified in step S22, and output the display data OP1 to the output device 105. That is, the second data output unit 116 may generate, for example, not only display data OP1 including the waveforms of the first time series data 131a and the second time series data 132a, but also display data OP1 including the waveforms of the first time series data 131a before conversion and the waveforms of the first time series data 131a after conversion, and output the display data OP1 to the output device 105.

[0082] In step S22, the identifier accepting unit 114 may display, on the output device 105, display data OP2 that can accept, for example, the designation of the first time series data 131a, the designation of the second time series data 132a, and the designation of an identifier indicating a conversion formula for converting the first time series data 131a. Hereinafter, a region included in the display data OP2 in which an identifier indicating a conversion formula for converting the first time series data 131a can be designated (selected) will also be referred to as a second region. Hereinafter, the display data OP1 and the display data OP2 will also be collectively referred to simply as display data OP. In this case, the operator may, for example, designate the first time series data 131a, the second time series data 132a, and the identifier indicating the conversion formula for converting the first time series data 131a for the display data OP2. A specific example of the display data OP2 will be described below.

[0083] [Example of display data OP2] FIG. 12 is a diagram illustrating a specific example of the display data OP2.

[0084] 12 includes, for example, a pull-down menu INa (a pull-down menu INa corresponding to "Select time series data") for selecting the first time series data 131a, a pull-down menu INb (a pull-down menu INb corresponding to "Select identifier") for selecting an identifier indicating a conversion formula for converting the first time series data 131a, and a button INc for displaying a window box (not shown) for setting parameters for performing the conversion corresponding to the identifier selected in the pull-down menu INb. Also, the display data OP2 shown in FIG. 12 includes, for example, a button INf (a button INf corresponding to "Perform conversion") for performing the conversion corresponding to the identifier selected in the pull-down menu INb on the first time series data 131a selected in the pull-down menu INa, a check box INg for selecting each time series data to be analyzed, and a button INh (a button INh corresponding to "Check trend") for displaying (outputting) each time series data selected in the check box INg in a comparable state.

[0085] The time series data displayed in the check box INg (selectable time series data) may include, for example, the first time series data 131a selected in the pull-down menu INa, the second time series data 132, and the converted first time series data 131a converted by pressing the button INf. The time series data displayed in the check box INg may also include, for example, other time series data (not shown) designated in advance by the operator.

[0086] Specifically, as shown in Fig. 12, when the operator designates the first time series data 131 regarding the airflow rate for the water to be treated in the reaction tank 220 as the first time series data 131a, the operator designates "Airflow Rate" in the pull-down menu INa. Furthermore, when the operator performs a conversion to slide the time axis of the first time series data 131a designated in the pull-down menu INa, the operator designates "Time Series Slide" in the pull-down menu INb. Furthermore, the operator designates parameters such as the slide width on the time axis of the first time series data 131a in a window box displayed by pressing the button INc.

[0087] In this case, the window box may be automatically set with parameters that can suppress the deviation between the first time series data 131a specified in the pull-down INa and the second time series data 132a previously specified by the operator (for example, the second time series data 132a previously selected in the checkbox INg) to a predetermined threshold or less (for example, minimum).

[0088] Thereafter, the operator performs conversion of the first time-series data 131a by, for example, pressing the button INf.

[0089] Then, for example, when specifying the second time series data 132 regarding the ammonia concentration of the water to be treated at the outlet of the reaction tank 220 as the second time series data 132a, the operator specifies "ammonia concentration" in the check box INg, as shown in Fig. 12. Furthermore, when comparing the converted first time series data 131a with the second time series data 132a, for example, the operator specifies "air flow rate x time series slide" indicating the converted first time series data 131a (first time series data 131a after time series slide has been performed), as shown in Fig. 12.

[0090] Furthermore, the operator, for example, by pressing button INh, generates display data OP1 including a solid line graph G1a showing the waveform of the converted first time series data 131a and a dashed line graph G2 showing the waveform of the converted second time series data 132a, as shown in FIG. 10, and outputs the data to the output device 105, thereby checking whether the conversion performed on the first time series data 131a was valid.

[0091] Specifically, for example, if the relationship between the waveform of the first time series data 131a after conversion and the waveform of the second time series data 132a can be read, the operator may determine whether the conversion performed on the first time series data 131a has brought the waveform of the first time series data 131a and the waveform of the second time series data 132a closer to each other, and may confirm whether the conversion performed on the first time series data 131a was effective.

[0092] In addition, the operator may determine that the conversion performed on the first time series data 131a was effective when, for example, it is possible to read the relationship between the waveform of the converted first time series data 131a and the waveform of the converted second time series data 132a.

[0093] Here, the display data OP2 may be, for example, capable of accepting multiple designations of an identifier indicating a transformation formula for transforming the first time series data 131a. Specifically, the display data OP2 may be, for example, capable of accepting designations of an identifier indicating a transformation formula for further transforming the converted first time series data 131a.

[0094] That is, for example, if the operator determines that the conversion performed on the first time series data 131a was not effective, the operator may attempt to further convert the converted first time series data 131a. Therefore, the display data OP2 may be capable of accepting not only the specification of an identifier indicating a conversion formula for converting the unconverted first time series data 131a, but also the specification of an identifier indicating a conversion formula for further converting the converted first time series data 131a. Below, a specific example of display data OP2 (hereinafter also referred to as display data OP2a) that can accept multiple specifications of an identifier indicating a conversion formula for converting the first time series data 131a will be described.

[0095] [Example of display data OP2a] Fig. 13 is a diagram illustrating a specific example of display data OP2a. Specifically, Fig. 13 is a diagram illustrating a specific example of display data OP2a that can accept twice the designation of an identifier indicating a transformation formula for converting the first time series data 131a. Note that, although the following description will be given of display data OP2a that can accept twice the designation of an identifier indicating a transformation formula for converting the first time series data 131a, this is not limiting. Specifically, the display data OP2a may be, for example, capable of accepting three or more designations of an identifier indicating a transformation formula for converting the first time series data 131a.

[0096] The display data OP2a shown in FIG. 13 further includes, in addition to the screen components of the display data OP2 described in FIG. 12, a pull-down INd (a pull-down INd corresponding to "Select Identifier (Re)") that allows the selection of an identifier indicating a conversion formula for further converting the converted first time series data 131a, and a button INe that displays a window box (not shown) that allows the setting of parameters for performing the conversion corresponding to the identifier selected in the pull-down INd.

[0097] 13, the operator selects "Air Flow Rate" in the pull-down menu INa to specify the first time series data 131 relating to the air flow rate for the water being treated in the reaction tank 220 as the first time series data 131a, and then selects "Time Series Slide" in the pull-down menu INb to slide the time axis of the first time series data 131a, and then further selects "First Order Lag" in the pull-down menu INd. Then, the operator specifies the necessary parameters in a window box that is displayed by pressing the button INe, for example.

[0098] Thereafter, the operator reconverts the converted first time-series data 131a by, for example, pressing the button INf.

[0099] For example, when specifying the second time series data 132 regarding the ammonia concentration of the water to be treated at the outlet of the reaction tank 220 as the second time series data 132a, the operator specifies "ammonia concentration" in the check box INg, as shown in Fig. 13. Furthermore, when comparing the reconverted first time series data 131a with the second time series data 132a, the operator specifies "air flow rate × time series slide × first-order lag" which indicates the reconverted first time series data 131a (first time series data 131a after time series slide and first-order lag) as shown in Fig. 13.

[0100] Furthermore, by pressing button INh, for example, the operator generates display data OP1 including a solid line graph G1 (hereinafter also referred to as solid line graph G1c) showing the waveform of the first time series data 131a after reconversion and a dashed line graph G2a showing the waveform of the second time series data 132a, as shown in FIG. 14, and outputs the generated data to the output device 105, thereby checking whether the combination of the two conversions performed on the first time series data 131a was a valid conversion.

[0101] As a result, for example, if the relationship between the waveforms of the first time series data 131a and the second time series data 132a after reconversion can be read and the waveforms of the first time series data 131a and the second time series data 132a after reconversion are determined to be sufficiently close to each other, the operator may determine that the combination of the two conversions performed on the first time series data 131a was effective and terminate the data analysis process.On the other hand, for example, if the operator determines that the waveforms of the first time series data 131a and the second time series data 132a after reconversion are not yet sufficiently close to each other, the operator may perform the data analysis process again.

[0102] That is, the information processing device 1 in this embodiment allows the worker to repeatedly convert the first time series data 131 and check the converted first time series data 131. This allows the worker to identify the relationship between the first time series data 131 and the second time series data 132 through trial and error, for example.

[0103] As described above, the information processing device 1 in this embodiment includes, for example, a generator 101 that generates display data OP, and an output unit 105 that outputs the display data OP generated by the generator 101. The generator 101 generates display data OP having, for example, a first area that displays first time series data 131 related to explanatory variables and second time series data 132 related to a target variable corresponding to the explanatory variables, a second area that displays one or more identifiers corresponding to one or more transformation formulas that transform the time series data, and a third area that displays the first time series data 131 transformed based on a transformation formula (hereinafter also referred to as a first transformation formula) identified by a specific identifier (hereinafter also referred to as a first identifier) ​​from the one or more identifiers.

[0104] Specifically, the generator 101 generates display data OP having a third area that displays the first time-series data 131 converted based on the first conversion formula identified by the specified first identifier, for example.

[0105] More specifically, the generator 101 generates display data OP having a third area that displays first time series data 131 converted based on, for example, a first conversion formula identified by a specified first identifier and a specified parameter (hereinafter also referred to as the first parameter).

[0106] That is, the information processing device 1 in this embodiment outputs, for example, the first time series data 131 and the second time series data 132 in a form viewable by an operator. Then, the operator, for example, compares the output first time series data 131 and the second time series data 132 to identify a conversion that is considered to be an effective conversion for the first time series data 131, and inputs an identifier of the identified conversion to the information processing device 1. Furthermore, the information processing device 1, for example, re-outputs the first time series data 131 and the second time series data 132 after the conversion corresponding to the input identifier in a form viewable by the operator. Thereafter, the operator, for example, compares the output converted first time series data 131 and the second time series data 132 again to determine whether the conversion performed on the first time series data 131 was effective.

[0107] As a result, the information processing device 1 in this embodiment can allow the worker to specify, for example, an effective conversion that is preferable to be performed on the first time series data 131. Therefore, the worker can easily analyze the relationship between the first time series data 131 and the second time series data 132 by performing, for example, the specified effective conversion on the first time series data 131.

[0108] In particular, the information processing device 1 in this embodiment, for example, by performing a time series slide on the first time series data 131, can easily analyze the relationship between the first time series data 131 and the second time series data 132 even if there is a time lag (a lag on the time axis) between the first time series data 131 and the second time series data 132.

[0109] Specifically, for example, if the measuring device M2 is installed at a position on the outlet side of the water to be treated in the reaction tank 220 (i.e., a position away from the position where air is blown in the reaction tank 220), a time lag may occur between the ammonia concentration measured by the measuring device M2 and the air flow rate measured by the measuring device M1. That is, in this case, even if the air flow rate measured by the measuring device M1 changes, it may take some time for the ammonia concentration measured by M2 to change due to the change in the air flow rate. Therefore, for example, if the first time series data 131 represents the air flow rate for the water to be treated in the reaction tank 220 and the second time series data 132 represents the ammonia concentration of the water to be treated at the outlet of the reaction tank 220, the information processing device 1 can perform a time series slide to slide the time at which the first time series data 131 was measured to a later time, thereby converting the first time series data 131 to simulate changes in the state that actually occur in the reaction tank 220.

[0110] Therefore, in this case, the information processing device 1 can strengthen the correlation between the first time series data 131 and the second time series data 132, for example, and can easily grasp the causal relationship that exists between the first time series data 131 (explanatory variable) and the second time series data 132 (objective variable). Therefore, the information processing device 1 can easily analyze the relationship between the first time series data 131 and the second time series data 132, for example.

[0111] Furthermore, when the information processing device 1 in this embodiment performs data prediction processing to predict second time series data 132 (future second time series data 132) from first time series data 131, for example, it is possible to improve the prediction accuracy of the second time series data 132 by performing prediction using the first time series data 131 after performing effective transformation identified in the data analysis processing.

[0112] Specifically, for example, when the data prediction process is performed using a learning model (not shown), the operator can improve the prediction accuracy of the second time series data 132 by generating the learning model used in the data prediction process by learning training data (not shown) including the first time series data 131 and the second time series data 132 after effective transformation.

[0113] [Modification (1) of the data analysis process in the first embodiment] Next, a modified example of the first embodiment (hereinafter also referred to as the first modified example) will be described. Figure 15 is a diagram illustrating the first modified example.

[0114] The information processing device 1 in this modification generates history data 133, which is information indicating the history of the conversion of the first time-series data 131 in step S23 of FIG. 6, for example.

[0115] Specifically, for example, each time the first time series data 131 is converted in step S23 of FIG. 6, the data management unit 112 generates history data 133 for the conversion and stores the generated history data 133 in the information storage area 130.

[0116] Then, for example, when outputting the first time series data 131 (hereinafter also referred to as new first time series data 131) and the second time series data 132 (hereinafter also referred to as new second time series data 132) in step S21 of Figure 6 performed next time or later, the first data output unit 113 refers to the history data 133 stored in the information storage area 130, identifies an identifier (hereinafter also referred to as second identifier) ​​indicating a conversion formula corresponding to the combination of data types (hereinafter also simply referred to as data types) for each of the new first time series data 131 and the new second time series data 132, and displays (outputs) the identified identifier on the output device 105.

[0117] Specifically, the first data output unit 113 generates, for example, display data OP1 that further includes the specified identifier and displays it on the output device 105. Hereinafter, among the areas included in the display data OP1, an area that displays an identifier corresponding to the combination of the data type of the new first time series data 131 and the data type of the new second time series data 132 will also be referred to as a fourth area.

[0118] [Example of historical data 133] Next, a description will be given of the history data 133. Fig. 15 is a diagram illustrating a specific example of the history data 133 in the first modified example.

[0119] The history data 133 shown in FIG. 15 has, for example, the following items: “Data type (1)” in which the data type of the first time series data 131 is set; “Data type (2)” in which the data type of the second time series data 132 is set; and “Identifier” in which an identifier indicating the conversion formula used in the conversion performed on the first time series data 131 is set.

[0120] Specifically, in the history data 133 shown in Figure 15, for example, in the first row of data, "air flow rate" is set as "data type (1)," "NH4-N concentration" is set as "data type (2)," and "time series slide" is set as "identifier."

[0121] That is, the data in the first row of the history data 133 shown in FIG. 15 indicates that, for example, when the first time series data 131 corresponding to "air flow rate" is designated as the first time series data 131a (the first time series data 131 to be analyzed) and the second time series data 132 corresponding to "NH4-N concentration" is designated as the second time series data 132a (the second time series data 132 to be analyzed), a conversion corresponding to "time series slide" has been performed on the first time series data 131a.

[0122] In addition, in the history data 133 shown in Figure 15, for example, in the data in the second row, "sewage inflow volume" is set as the "data type (1)", "NH4-N concentration" is set as the "data type (2)", and "first-order lag" is set as the "identifier".

[0123] That is, the data in the second row of the history data 133 shown in Fig. 15 indicates that, for example, when first time series data 131 corresponding to "sewage inflow amount" is designated as first time series data 131a (first time series data 131 to be analyzed) and second time series data 132 corresponding to "NH4-N concentration" is designated as second time series data 132a (second time series data 132 to be analyzed), a conversion corresponding to "first order lag" has been performed on first time series data 131a. Explanation of the other data included in Fig. 15 will be omitted.

[0124] 6 is output in step S21 is "air flow rate," and the data type of the new second time series data 132 output in step S21 is "NH4-N concentration," the first data output unit 113 identifies a "time series slide" corresponding to the combination of "air flow rate" and "NH4-N concentration" by referring to the data in the first row of the history data 133 shown in FIG. 15. Thereafter, the first data output unit 113 outputs, for example, information indicating the identified "time series slide" in addition to the new first time series data 131 and the new second time series data 132.

[0125] That is, in step S21, for example, the information processing device 1 in this modification also presents information indicating the results of conversions performed in the past to the worker as a recommendation.

[0126] In other words, in this modified example, in step S21, the information processing device 1 uses knowledge about conversions (so-called domain knowledge) possessed by other workers to identify a conversion that can be determined to be preferable to perform on the first time series data 131 stored in the information storage area 130, and presents information indicating the identified conversion as a recommendation.

[0127] 15 has been described as including one identifier indicating a transformation formula used in the transformation performed on the first time-series data 131, but the present invention is not limited to this. Specifically, the history data 133 may include, for example, a plurality of identifiers indicating a plurality of transformation formulas used in the transformation performed on the first time-series data 131. The history data 133 may also include, for example, parameters set when the transformation corresponding to each identifier is performed. In other words, the information processing device 1 may display, for example, a plurality of identifiers as information indicating the results of transformations performed in the past, or may display each of the identifiers and the parameters.

[0128] As described above, the information processing device 1 in this modification further includes an information storage area 130 that stores, for example, a first identifier, and history data 133 that associates the data type of the first time series data 131 with the data type of the second time series data 132. The generator 101 then refers to the information storage area 130 in which the history data 133 is stored, and generates display data OP that includes a fourth area that displays a second identifier corresponding to a combination of the data type of the new first time series data 131 related to the explanatory variables and the data type of the new second time series data 132 related to the objective variable corresponding to the explanatory variables.

[0129] This allows the information processing device 1 in this modification to share the knowledge and know-how of each worker, for example, and therefore allows the information processing device 1 to efficiently identify the relationship between the first time series data 131 and the second time series data 132, for example.

[0130] Note that the history data 133 in this modification may further include, for example, other information related to the converted first time-series data 131. Specifically, the history data 133 may include, for example, information indicating the treated water volume (scale) of the treatment facility 20 where the first time-series data 131 was measured. The first data output unit 113 may specify, in step S21 of FIG. 6, an identifier (second identifier) ​​to be displayed on the output device 105 by, for example, referring to only data included in the history data 133 stored in the information storage area 130, whose other information satisfies a predetermined condition. Specifically, the first data output unit 113 may specify, for example, an identifier (second identifier) ​​to be displayed on the output device 105 by, for example, referring to only data included in the history data 133 stored in the information storage area 130, about another treatment facility (not shown) whose difference in treated water volume between the treatment facility 20 that is the target of the data analysis process and the other treatment facility 20 is equal to or less than a predetermined threshold.

[0131] This allows the information processing device 1 in this modification to more efficiently identify the relationship between the first time series data 131 and the second time series data 132, for example. [Explanation of symbols]

[0132] 1: Information processing device 5: Operation terminal 10: Information processing system 20: Processing equipment 101:CPU 102:Memory 103: Communication device 104: Storage medium 105: Output device 106: Bus 111: Data acquisition unit 112: Data management unit 113: First data output unit 114: Request receiving unit 115: Data conversion unit 116: Second data output unit 130: Memory unit 131: First time series data 132: Second time series data 133: Historical data 1000: Processing system M1: Measuring device M2: Measuring device NW: Network OP: Display data OP1: Display data OP2: Display data OP2a: Display data

Claims

1. a generator for generating display data; an output unit that outputs the display data generated by the generator, a display data output device, wherein the generator generates the display data having a first area displaying first time series data related to explanatory variables and second time series data related to a response variable corresponding to the explanatory variables, a second area displaying one or more identifiers corresponding to one or more transformation formulas for transforming time series data, and a third area displaying the first time series data transformed based on a first transformation formula identified by a first identifier among the one or more identifiers.

2. 2. The display data output device according to claim 1, wherein the generator generates the display data having the third area displaying the first time series data converted based on each of the first conversion formula identified by the specified first identifier and a specified first parameter.

3. further comprising a storage unit configured to store the first identifier, a data type of the first time series data, and a data type of the second time series data in association with each other; 2. The display data output device according to claim 1, wherein the generator refers to the storage unit and generates the display data including a fourth area displaying a second identifier corresponding to a combination of a data type of new first time series data related to the explanatory variable and a data type of new second time series data related to a target variable corresponding to the explanatory variable.

4. Generate display data; outputting the generated display data; A display data output method in which processing is executed by a computer, a display data output method, wherein the generating process generates the display data having a first area displaying first time series data related to explanatory variables and second time series data related to a target variable corresponding to the explanatory variables; a second area displaying one or more identifiers corresponding to one or more transformation formulas for transforming time series data; and a third area displaying the first time series data transformed based on a first transformation formula identified by a first identifier among the one or more identifiers.

5. Generate display data; outputting the generated display data; A display data output program for causing a computer to execute a process, a display data output program for generating display data having a first area displaying first time series data related to explanatory variables and second time series data related to a response variable corresponding to the explanatory variables; a second area displaying one or more identifiers corresponding to one or more transformation formulas for transforming time series data; and a third area displaying the first time series data transformed based on a first transformation formula identified by a first identifier among the one or more identifiers.

Images