Information processing system, information processing method, and program

Abstract

To provide an information processing system or the like for facilitating management of an event related to a water system.SOLUTION: A relationship model information acquisition unit, a data set identification unit, and an influence degree identification unit are provided, the relationship model information acquisition unit acquires relationship model information indicating a relationship between an index relating to an event created in advance and two or more parameters, the data set identification unit identifies a data set in which the index relating to the event is associated with the two or more parameters, and the influence degree identification unit identifies an influence degree with respect to the event for each parameter associated in the data set by comparing the relationship model information with the data set.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The present invention relates to an information processing system, an information processing method, and a program. [Background technology]

[0002] Various products, including paper products, are manufactured using aqueous processes. In these processes, problems are predicted in advance in order to prevent or mitigate the occurrence of operational problems and adverse effects on products. In this regard, techniques for predicting product quality, etc. in papermaking processes, etc., are known. For example, Patent Document 1 discloses a technique for predicting expected results, etc., using predetermined parameter information and relationship model information. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-123880 Summary of the Invention [Problem to be solved by the invention]

[0004] However, when a problem occurs in any process, it is important to be able to identify the specific cause with a high degree of accuracy. From this perspective, technology that is easy for system users to use is desired.

[0005] In view of the above circumstances, the present invention provides an information processing system and the like that facilitates the management of events involving water systems. [Means for solving the problem]

[0006] According to one aspect of the present invention, there is provided an information processing system that performs information processing on events in a water system or derived from a water system, the information processing system comprising: a relationship model information acquisition unit; a dataset identification unit; and an influence identification unit; the relationship model information acquisition unit acquires relationship model information created in advance, which indicates the relationship between an index related to the event and two or more parameters, wherein the two or more parameters are parameters selected from the group consisting of water quality parameters, control parameters, and result parameters, the water quality parameters are parameters related to the water quality of the water system, the control parameters are parameters related to the control conditions of the water system, equipment related to the water system, or raw materials added to the water system, and the result parameters are parameters that do not correspond to indicators and relate to results that arise in the water system, equipment related to the water system, or raw materials added to the water system, or that derive from the water system, equipment related to the water system, or raw materials added to the water system; the dataset identification unit identifies a dataset in which the index related to the event is associated with the two or more parameters; and the influence identification unit compares the relationship model information with the dataset to identify the influence on the event for each parameter associated in the dataset.

[0007] According to the above aspect, an information processing system or the like is provided that facilitates the management of events involving water systems.

[0008] Furthermore, it may be provided in the following aspects.

[0009] (1) An information processing system for performing information processing on an event in a water system or derived from the water system, comprising a relationship model information acquisition unit, a data set identification unit, and an impact identification unit, wherein the relationship model information acquisition unit acquires relationship model information indicating a relationship between an index related to the event and two or more parameters, which are created in advance, and wherein the two or more parameters are parameters selected from the group consisting of a water quality parameter, a control parameter, and a result parameter, the water quality parameter is a parameter related to the water quality of the water system, and the control parameter is a parameter related to the water system, equipment related to the water system, or raw materials added to the water system. the result parameters are parameters not corresponding to the indicators, relating to results arising in the water system, equipment related to the water system, or raw materials added to the water system, or derived from the water system, equipment related to the water system, or raw materials added to the water system; the dataset identification unit identifies a dataset in which the indicators related to the event are associated with the two or more parameters; and the impact identification unit compares the relationship model information with the dataset to identify the impact on the event for each parameter associated in the dataset.

[0010] (2) In the information processing system described in (1) above, the influence identification unit identifies the influence by performing a predetermined calculation process on each of the parameters, and the calculation process includes at least one of differential processing, statistical processing, and processing using a trained model.

[0011] (3) In the information processing system described in (1) or (2) above, the index and the two or more parameters associated in the dataset are obtained more recently than the index and the two or more parameters associated in the relationship model information.

[0012] (4) In the information processing system described in any one of (1) to (3) above, the relationship model information is a model obtained by regression analysis, time series analysis, decision tree, neural network, Bayesian, clustering, classification, or ensemble learning between the pre-check result corresponding to the index or an index related to the pre-check result and the two or more parameters.

[0013] (5) An information processing system according to any one of (1) to (4) above, further comprising a parameter information acquisition unit and an estimation unit, wherein the parameter information acquisition unit acquires two or more parameters selected from the group consisting of the water quality parameters, the control parameters, and the result parameters as parameter information, the estimation unit estimates the index based on the acquired parameter information and the relationship model information, and the dataset associates the index estimated by the estimation unit with the two or more parameters acquired by the parameter information acquisition unit.

[0014] (6) An information processing system according to any one of (1) to (5) above, further comprising a countermeasure presentation unit, which presents countermeasure information relating to the corresponding parameter according to the impact level identified by the impact level identification unit.

[0015] (7) An information processing system according to any one of (1) to (6) above, wherein the water system is a water system in a process for producing paper products and / or materials related to the production of paper products.

[0016] (8) In the information processing system described in (7) above, the two or more parameters include, as the water quality parameters, one or more selected from the group consisting of pH of the water system, electrical conductivity, oxidation-reduction potential, zeta potential, turbidity, temperature, foam height, biochemical oxygen demand (BOD), chemical oxygen demand (COD), total organic carbon (TOC), inorganic carbon, absorbance, color, appearance, whiteness, transparency, particle size distribution, degree of aggregation, amount of foreign matter, suspended solids (SS), foam area on the water surface, area of ​​dirt in the water, amount of bubbles, amount of glucose, amount of organic acid, amount of active alkali, total amount of titratable alkali, metal or metal ion content, non-metal ion content, amount of starch, amount of calcium, total chlorine content, amount of free chlorine, amount of dissolved oxygen (DO), cation demand, amount of hydrogen sulfide, degree of sulfidation, amount of hydrogen peroxide, ash concentration, respiration rate of microorganisms in the system, viable cell count, spore count, and ATP.

[0017] (9) In the information processing system described in (7) or (8) above, the two or more parameters are, as the control parameters, the operating speed (machining speed) of the paper machine, the filter cloth rotation speed of the raw material dehydrator, the filter cloth rotation speed of the washer, the amount or unit of chemicals added to the water system, the amount or unit of chemicals added to the raw material added to the water system, the amount or unit of chemicals added to equipment related to the water system, the amount or unit of steam used for heating, the temperature of the steam used for heating, the pressure of the steam used for heating, the flow rate from the seed box, the nip pressure of the press part, the felt vacuum pressure of the press part, the blending ratio of the papermaking raw materials, the amount of the papermaking raw materials, Broken paper blend amount or unit consumption, screen opening of papermaking raw material, gap distance between the rotor and stator of the beater, freeness, degree of beating, draw, line pressure of the press part, gravity dewatering amount of the wire part or a parameter equivalent thereto, forced dewatering amount of the wire part or a parameter equivalent thereto, gravity dewatering amount of the press part, a parameter equivalent to gravity dewatering amount of the press part, a parameter for controlling gravity dewatering amount of the press part, forced dewatering amount of the press part, a parameter equivalent to forced dewatering amount of the press part, a parameter for controlling forced dewatering amount of the press part concentration of interlayer adhesive, amount or unit consumption of interlayer adhesive, amount of shower water or unit consumption, new water consumption unit, water consumption unit, timing or number of times of cleaning the wire part, timing or number of times of cleaning the press part, timing or number of times of cleaning the dryer part, timing or number of times of changing paper machine tools, amount of new water replenishment, amount of screen rejects or parameters to control it, screen differential pressure or equivalent parameters, amount of cleaner rejects or parameters to control it, amount of fuel for heating or unit consumption, flotator concentration, flotator air volume or or a parameter equivalent thereto, froth amount of a flotator or a parameter equivalent thereto, load of a disperser or a parameter equivalent thereto, number of times of felt singeing, flow rate of fluid flowing through the water system, air temperature, amount of steam for heating or consumption unit, amount of calcium carbonate added or consumption unit, amount of calcium bicarbonate added or consumption unit, flow rate or concentration of clarified green liquor, flow rate or concentration of crude green liquor, flow rate or concentration of diluted black liquor, flow rate or concentration of strong black liquor, flow rate or concentration of white liquor, amount of black liquor injected in the black liquor treatment system, black liquor injection concentration in the black liquor treatment system, pulp production amount, mud filter moisture content,An information processing system comprising one or more items selected from the group consisting of lime input amount or current unit in a slaking / causticizing system, lime production amount in a calcination system, fuel consumption amount, fuel consumption unit, causticization rate in a slaking / causticizing system, caustic input amount or unit consumption in a cooking system, black liquor generation amount in a black liquor treatment system, solid amount when evaporating black liquor in a black liquor treatment system, evaporation multiple when evaporating black liquor in a black liquor treatment system, boiler economizer temperature in a black liquor treatment system, moisture content of sludge in a green liquor treatment system, specific gravity of black liquor, specific gravity of green liquor, specific gravity of white liquor, specific gravity of weak liquor, specific gravity of dregs, specific gravity of calcium carbonate before calcination, and specific gravity of calcium oxide after calcination.

[0018] (10) In the information processing system described in any one of (7) to (9) above, the two or more parameters are, as the result parameters, the unit weight (basis weight) of the paper product, the yield rate, the white water concentration, the moisture content of the paper product, the amount of steam or the basic unit in the equipment for manufacturing the paper product, the steam temperature in the equipment for manufacturing the paper product, the steam pressure in the equipment for manufacturing the paper product, the thickness of the paper product, the ash concentration in the paper product, the type of defect in the paper product, the number of defects in the paper product, An information processing system including one or more items selected from the group consisting of timing of paper breakage, freeness, degree of beating, amount of aeration, air temperature inside the dryer, splicing rate, number of splices, amount of paper waste, number of defects, defect index, outside air temperature including indoor air temperature, outside humidity including indoor humidity, moisture content of wet paper, moisture content of product, temperature of the dryer rolls and cylinders, measurements from five senses sensors, amount of fuel or fuel consumption rate, calcination rate in the calcination system, causticization rate in the slaking / causticizing system, amount of caustic added in the cooking system, and amount of lime added in the slaking / causticizing system.

[0019] (11) In the information processing system described in any one of (7) to (10) above, the index relates to the quality or energy amount of the paper product and / or materials related to the production of the paper product, the quality relates to one or more selected from the group consisting of the number of defects, paper strength, joint rate, sizing degree, air permeability, smoothness, ash content, color tone, whiteness, texture, odor, causticization rate, burn rate, kappa number, freeness, and moisture content of the paper product and / or materials related to the production of the paper product, and the energy amount relates to one or more selected from the group consisting of steam amount, steam consumption rate, fuel amount, and fuel consumption rate when producing the paper product and / or materials related to the production of the paper product.

[0020] (12) An information processing method executed by an information processing system, the method comprising a step of executing processing of each part of the information processing system described in any one of (1) to (11) above.

[0021] (13) A program for causing a computer to execute the processing of each part of the information processing system described in any one of (1) to (11) above. Of course, this is not the case. [Brief explanation of the drawings]

[0022] [Figure 1] 1 is a diagram showing the overall configuration of an information processing system 1. FIG. [Figure 2] FIG. 2 is a diagram illustrating a hardware configuration of an information processing device 2. [Figure 3] FIG. 2 is a diagram showing the hardware configuration of a user terminal 3. [Figure 4] 1 is an example of a papermaking process to which the information processing system 1 can be applied. [Figure 5] FIG. 2 is a functional block diagram showing functions of the information processing device 2. [Figure 6] 1 is an activity diagram showing the flow of information processing using the information processing device 2 and the like. [Figure 7]This is a plot of the number of trouble occurrences A versus an index a related to the expected outcome for a total of 30 sets of data. [Figure 8] FIG. 10 is an explanatory diagram showing an example of a method for calculating an influence degree using a defect index as an index (expected result). [Figure 9] 10 is a graph schematically showing the degree of influence of each parameter on an event. [Figure 10] 1 is a schematic diagram showing the transition of the actual measured value (broken line) of ORP (an example of a parameter) of raw material system 1 in response to an event in a water system, and the degree of influence of ORP of raw material system 1 on the event. DETAILED DESCRIPTION OF THE INVENTION

[0023] Hereinafter, embodiments of the present invention will be described. Note that various features shown in the following embodiments can be combined with each other.

[0024] That is, the information processing system of this embodiment is as follows. An information processing system for performing information processing on events in or arising from a water system, The system includes a relationship model information acquisition unit, a data set identification unit, and an influence identification unit, the relationship model information acquisition unit acquires relationship model information that is created in advance and indicates a relationship between an index related to the event and two or more parameters; wherein the two or more parameters are each selected from the group consisting of a water quality parameter, a control parameter, and a result parameter; the water quality parameter is a parameter related to the water quality of the water system, the control parameters are parameters relating to control conditions of the aqueous system, equipment related to the aqueous system, or raw materials added to the aqueous system; The result parameter is a parameter that does not correspond to the index and relates to a result that occurs in the water system, equipment related to the water system, or raw materials added to the water system, or that derives from the water system, equipment related to the water system, or raw materials added to the water system, the dataset specifying unit specifies a dataset in which the index related to the event and the two or more parameters are associated with each other; The influence degree identification unit identifies the influence degree on the event for each parameter associated in the dataset by comparing the relationship model information with the dataset.

[0025] Incidentally, the program for realizing the software appearing in one embodiment may be provided as a non-transitory computer-readable medium, or may be provided so that it can be downloaded from an external server, or may be provided so that the program is started on an external computer and its functions are realized on a client terminal (so-called cloud computing).

[0026] Furthermore, various information processing according to an embodiment may realize input and output corresponding to the input. Here, the form of information referenced in such information processing (hereinafter referred to as reference information) is not limited as long as an output is obtained as a result of the input. The reference information may be, for example, rule-based information such as a database, a lookup table, or a predetermined function (including a decision formula such as a regression formula constructed using a statistical method), a trained model that has previously trained the correlation between input and output, or a large-scale language model that can output a desired result by inputting a prompt.

[0027] In one embodiment, a "unit" may include, for example, a combination of hardware resources implemented by a circuit in the broad sense and software information processing that can be specifically realized by these hardware resources. In one embodiment, various information is handled, and this information is represented, for example, by physical values ​​of signal values ​​representing voltage and current, high and low signal values ​​as a binary bit set consisting of 0 or 1, or quantum superposition (so-called quantum bits), and communication and calculations can be performed on a circuit in the broad sense.

[0028] Furthermore, a circuit in the broad sense is a circuit realized by at least an appropriate combination of a circuit, circuitry, processor, memory, etc. The processor may be a general-purpose processor or a dedicated circuit. That is, it includes an application specific integrated circuit (ASIC), a programmable logic device (e.g., a simple programmable logic device (SPLD), a complex programmable logic device (CPLD), and a field programmable gate array (FPGA)), etc.

[0029] 1. Hardware Configuration In this section, the hardware configuration of the information processing system 1 according to this embodiment will be described.

[0030] The information processing system 1 of this embodiment is a system used to process information about events occurring in or arising from a water system. Hereinafter, "events occurring in or arising from a water system" may be simply referred to as "events." Here, the information processing system 1 of this embodiment is equipped with an information processing device 2 and a user terminal 3, which are connected via a communication line. Note that the communication line here includes the Internet, wireless, etc., and mediates the exchange of data between devices connected to the line. Furthermore, in the information processing system 1 of this embodiment, the measurement device 4 is configured to be able to measure various parameters in the water system W. The parameters measured by this measurement device 4 are configured to be able to be transmitted to the information processing device 2.

[0031] In this specification, a system exemplified as information processing system 1 is one that is made up of one or more devices or components. Therefore, even an information processing device 2 alone is an example of a system, and a system that also includes a user terminal 3, a water system W to which the system is applied, and a measuring device 4 may also be called a system. Below, we will continue to explain each device that can constitute information processing system 1.

[0032] [Information processing device 2] 2 is a diagram showing the hardware configuration of the information processing device 2. The information processing device 2 has a communication unit 21, a storage unit 22, and a control unit 23, and is configured by electrically connecting these units via a communication bus 20. Each unit provided in the information processing device 2 will be described below.

[0033] (Communications Department 21) The communication unit 21 is configured to be able to transmit various electrical signals from the information processing device 2 to external components. The communication unit 21 is also configured to be able to receive various electrical signals from the external components to the information processing device 2. Note that the communication unit 21 may have a network communication function, thereby enabling communication of various information between the information processing device 2 and external devices via a communication line.

[0034] (Storage unit 22) The memory unit 22 stores various pieces of information defined above. This can be implemented, for example, as a storage device such as a solid state drive (SSD) that stores various programs and the like related to the information processing device 2 executed by the control unit 23, or as a memory such as a random access memory (RAM) that stores temporarily required information (arguments, arrays, etc.) related to the program operations. The memory unit 22 stores various programs, variables, etc. related to the information processing device 2 executed by the control unit 23.

[0035] (Control unit 23) The control unit 23 is, for example, a central processing unit (CPU) not shown. The control unit 23 realizes various functions related to the information processing device 2 by reading out predetermined programs stored in the storage unit 22. In other words, information processing by software stored in the storage unit 22 is specifically realized by the control unit 23, which is an example of hardware, and can be executed as each functional unit included in the control unit 23. These will be described in more detail in the next section. Note that the control unit 23 is not limited to being single, and multiple control units 23 may be provided for each function. A combination of these may also be used.

[0036] [User terminal 3] FIG. 3 is a diagram showing the hardware configuration of the user terminal 3. The user terminal 3 is typically a terminal used by a person who performs operations related to the water system W. In this specification, a person who performs such operations may be simply referred to as a "user." The user terminal 3 has a communication unit 31, a memory unit 32, a control unit 33, a display unit 34, and an input unit 35, and these components are electrically connected within the user terminal 3 via a communication bus 30. Descriptions of the communication unit 31, memory unit 32, and control unit 33 will be omitted as they are substantially the same as the communication unit 21, memory unit 22, and control unit 23 in the information processing device 2 described above.

[0037] (Display section 34) The display unit 34 may be, for example, included in the housing of the user terminal 3 or may be externally attached. The display unit 34 displays a graphical user interface (GUI) screen that can be operated by the user. This is preferably implemented by selectively using display devices such as a CRT display, a liquid crystal display, an organic EL display, and a plasma display depending on the type of the user terminal 3.

[0038] (Input unit 35) The input unit 35 may be included in the housing of the user terminal 3, or may be externally attached. For example, the input unit 35 may be implemented as a touch panel integrated with the display unit 34. A touch panel allows the user to input tapping, swiping, and the like. Of course, a switch button, a mouse, a QWERTY keyboard, or the like may be used instead of a touch panel. That is, the input unit 35 accepts an operation input made by the user. The input is transferred as a command signal to the control unit 33 via the communication bus 30, and the control unit 33 can execute predetermined control or calculation as necessary.

[0039] [Measuring device 4] The measuring device 4 is configured to be able to measure various parameters related to the water system W. In other words, this measuring device 4 is a measuring device related to predetermined parameters. In this embodiment, predetermined calculation processing is performed using two or more parameters selected from the group consisting of water quality parameters, control parameters, and result parameters, and the measuring device 4 acquires parameters that form the basis of this calculation. Note that while only a single measuring device 4 is shown in FIG. 1, multiple (multiple types of) measuring devices 4 may be applied to the information processing system 1.

[0040] The measuring device 4 may be selected from a variety of sensors, depending on the parameters to be measured. Examples of the measuring device 4 that can be used include a pH meter, an electrical conductivity meter, an oxidation-reduction potential meter, a turbidity meter, a thermometer, a level meter for measuring bubble height, a COD meter, a UV meter, a particle size distribution meter, an aggregation sensor, a digital camera (or a digital video camera), an internal bubble sensor, an absorptiometer, a freeness meter, a dissolved oxygen meter, a zeta potential meter, a residual chlorine meter, a hydrogen sulfide meter, a retention / freeness meter, a color sensor, a hydrogen peroxide meter, and a five-sense sensor. The five-sense sensor here may include an image sensor, a light intensity sensor, an acoustic sensor, an ultrasonic sensor, a gas component sensor, an odor sensor, a liquid component sensor, a tactile sensor, a pressure sensor, a temperature sensor, a humidity sensor, a displacement sensor, and the like.

[0041] In addition, control parameters, etc., may be directly input for controlling the device and used as they are, or such data may be transmitted and received from the device, or the device operator may record the control parameters on a device other than the device.

[0042] [Water-based W] The aqueous system W according to this embodiment may be any of various industrial processes involving water. That is, the type of aqueous system W is not particularly limited, but, for example, it may be an aqueous system in a process for producing paper products and / or materials related to the production of paper products. Hereinafter, these may be collectively referred to as "paper products, etc." Furthermore, in this specification, "materials related to the production of paper products" does not refer to the paper products themselves, but rather to various raw materials and intermediates used in the production of paper products. Specifically, raw materials used in the production of paper products include white water and calcium oxide generated during the paper production process. Furthermore, intermediates used in the production of paper products include, for example, pulp produced before the papermaking process. Specifically, processes for producing white liquor, calcium oxide, and pulp, which are materials related to paper products, include the cooking process, washing process, black liquor concentration process, and causticizing process. Furthermore, the aqueous system of interest may be an aqueous system other than that used in the production of paper products, etc., and may include, for example, various pipes, heat exchangers, storage tanks, kilns, cleaning equipment, etc. An example of a process that is a target of the information processing system 1 of this embodiment is shown in FIG. 4. FIG. 4 is an example of a papermaking process to which the information processing system 1 can be applied. In the papermaking process shown in FIG. 4, various systems are shown, including a raw material system, a preparation / papermaking system, a recovery system, and a drainage system. The measuring device 4 described above can acquire parameters from various elements present in such a papermaking process. Note that the application of the information processing system 1 is not limited to the process shown in the figure, and predetermined elements may be added or deleted as appropriate.

[0043] 2. Functional configuration In this section, the functional configuration of this embodiment will be described. Fig. 5 is a functional block diagram showing the functions of the information processing device 2. As described above, information processing by software (stored in the storage unit 22) is specifically realized by hardware (control unit 23), and can be executed as each functional unit included in the control unit 23.

[0044] Specifically, the information processing device 2 (control unit 23) may include, as its respective functional units, a parameter information acquisition unit 231, a relationship model information acquisition unit 232, an estimation unit 233, a dataset identification unit 234, an impact identification unit 235, a countermeasure presentation unit 236, a relationship model information creation unit 237, and a memory management unit 238. Note that these respective functional units may be increased, omitted, or integrated as appropriate depending on the application to which the information processing device 2 is applied, etc.

[0045] (Parameter information acquisition unit 231) The parameter information acquisition unit 231 is configured to be able to execute a parameter information acquisition step. In the parameter information acquisition step, the parameter information acquisition unit 231 acquires, as parameter information, two or more parameters selected from the group consisting of water quality parameters, control parameters, and result parameters. Here, water quality parameters are parameters related to the water quality of the water system. Control parameters are parameters related to the control conditions of the water system, equipment related to the water system, or raw materials added to the water system. Result parameters are parameters that have a meaning different from expected results and are parameters that do not correspond to indicators related to results arising in the water system, equipment related to the water system, or raw materials added to the water system, or derived from the water system, equipment related to the water system, or raw materials added to the water system. When acquiring these parameters, the parameter information acquisition unit 231 is configured to acquire various information via the communication unit 21 from a measurement device 4 that is capable of measuring at least some of the parameters, for example.

[0046] (Relationship model information acquisition unit 232) The relationship model information acquisition unit 232 is configured to be able to execute a relationship model information acquisition step. In the relationship model information acquisition step, the relationship model information acquisition unit 232 acquires relationship model information that indicates the relationship between an index related to an event and two or more parameters, which has been created in advance. The two or more parameters in the relationship model information are parameters selected from the group consisting of water quality parameters, control parameters, and result parameters. The water quality parameters, control parameters, and result parameters have been explained above in connection with the parameter information acquisition unit 231, and therefore explanation thereof will be omitted. Details of this model will be explained later.

[0047] (Guessing part 233) The estimation unit 233 is configured to be able to execute an estimation process. In the estimation process, the estimation unit 233 estimates an index (an expected result or a related index) based on the acquired parameter information and relationship model information. Although not described in the embodiment, the estimated index may be presented to a user via, for example, the user terminal 3. The estimated index is configured to be recognizable by a user, etc. That is, the estimation unit 233 creates display information and controls it so that it is visible to a user, etc. The display information may be visual information itself, such as a screen, an image, an icon, or text, generated in a manner that is visible to a user, or may be rendering information for displaying visual information, such as a screen, an image, an icon, or text, on various devices or terminals.

[0048] (Dataset identification unit 234) The dataset specifying unit 234 is configured to be able to execute a dataset specifying step. In the dataset specifying step, the dataset specifying unit 234 specifies a dataset in which an index related to an event is associated with two or more parameters. This dataset may be any of various datasets in which the above-mentioned index is associated with the above-mentioned two or more parameters. However, typically, the dataset specified by the dataset specifying unit 234 is one in which an index (expected result or related index) estimated by the estimation unit 233 is associated with two or more parameters acquired by the parameter information acquisition unit 231. In other words, the dataset specifying unit 234 specifies the index (expected result or related index) estimated by the estimation unit 233 and two or more parameters associated with the index acquired by the parameter information acquisition unit 231. The specified dataset can be used in the impact specification unit 235.

[0049] (Influence Identification Department 235) The influence identification unit 235 is configured to be able to execute an influence identification step. In the influence identification step, the influence identification unit 235 identifies the influence of each parameter associated in the dataset on an event by comparing the relationship model information with the dataset. Note that the influence refers to the influence of each parameter included in the dataset on an event. For example, when an event (e.g., a problem in a manufacturing process) occurs, if the influence of a parameter in the dataset is relatively high, it can be determined that the parameter is the main cause of the event (the aforementioned problem). Note that the influence identification unit 235 calculates (estimates or predicts) the influence of each parameter associated in the dataset by a predetermined calculation process. From this perspective, the influence identification unit 235 may also be referred to as an "influence estimation unit" or an "influence estimation unit."

[0050] (Countermeasure presentation unit 236) The countermeasure presentation unit 236 is configured to be able to execute a countermeasure presentation step. In the countermeasure presentation step, the countermeasure presentation unit 236 presents countermeasure information related to the corresponding parameter according to the impact level identified by the impact level identification unit 235. The impact level identification unit 235 identifies the impact level, allowing the user to understand the main factors causing the event, and the countermeasure presentation unit 236 further improves the usability of the system by presenting the countermeasures to the user. The countermeasure information is presented, for example, by being output to the user terminal 3. The countermeasure information may be visual information itself generated in a manner that is visible to the user, such as characters, numerical values, diagrams, photos, videos, screens, images, icons, text, etc., or may be rendering information for displaying the visual information on various devices or terminals, for example.

[0051] (Relationship model information creation unit 237) The relationship model information creation unit 237 is configured to be able to execute a relationship model information creation step. In the relationship model information creation step, the relationship model information creation unit 237 creates or updates relationship model information used in the above-mentioned estimation step, etc.

[0052] (Memory Management Department 238) The memory management unit 238 is configured to be able to execute a memory management process. In the memory management process, the memory management unit 238 is configured to manage various pieces of information to be stored that are related to the information processing system 1 of this embodiment. Typically, the memory management unit 238 is configured to store information handled by the information processing device 2 in a memory area. This memory area is exemplified by the memory unit 22 of the information processing device 2 or the memory units of various devices and terminals, but this memory area does not necessarily have to be within the information processing system 1, and the memory management unit 238 can also manage various pieces of information to be stored in an external storage device or the like.

[0053] 3. Details of data processing In Section 3, an information processing method executed by the information processing device 2 etc. will be described with reference to an activity diagram etc. Fig. 6 is an activity diagram showing the flow of information processing using the information processing device 2 etc.

[0054] [Activity A101: Get parameter information] 6, in this embodiment, the parameter information acquisition unit 231 of the information processing device 2 acquires parameter information (activity A101). As described above, the parameter information includes two or more parameters selected from the group consisting of water quality parameters, control parameters, and result parameters.

[0055] The parameter information is related to the water system W to be estimated and includes two or more parameters selected from the group consisting of water quality parameters, control parameters, and result parameters. Note that the water system W here is not limited to one that exists in a single tank or flow path or one that has a continuous flow, but also includes one that has multiple tanks or flow paths, specifically, one in which the flow path branches or the water from multiple flow paths merges, or one in which water is moved in batches from tank to tank, or in which treatment is performed along the way. Furthermore, if the water system related to the water quality parameters, control parameters, or result parameters is divided into tanks, etc., it is sufficient to use the water quality parameters, control parameters, or result parameters for a part of the water system, or the water quality parameters, control parameters, or result parameters for the entire water system may be used.

[0056] The water quality parameters are not particularly limited as long as they relate to the water quality of the water system W. Furthermore, the control parameters are not particularly limited as long as they relate to the control conditions of the water system W, equipment related to the water system W, or the raw materials added to the water system W. Furthermore, the result parameters are not particularly limited as long as they are parameters having a meaning different from the expected result and relate to the results arising in the water system W, equipment related to the water system W, or the raw materials added to the water system W, or derived from the water system W, equipment related to the water system W, or the raw materials added to the water system W. Note that "parameters that do not correspond to indicators" in the result parameters include cases where the evaluation indicators (e.g., physical quantities) are different (e.g., when one is length and the other is mass), cases where the evaluation indicator is the same but the evaluation target is different (e.g., the mass of paper and the mass of an additive), and cases where the measurement points are different (e.g., the redox potential of the papermaking raw material system and the redox potential of the papermaking system), but do not include cases where the meaning of the results is different (e.g., when the redox potential is positive and when the redox potential is negative).

[0057] Below, specific examples of water quality parameters, control parameters, and result parameters will be described when the aqueous system W is an aqueous system in a process for producing paper products, etc. These parameters may relate to the papermaking process shown in Figure 4, for example, or may relate to the cooking system, black liquor treatment system, green liquor treatment system, slaking / causticizing system, and calcination system (lime calcination process) in a pulp production system, as described in JP 2022-12850 A.

[0058] That is, the parameter information may include, as water quality parameters, one or more selected from the group consisting of pH, electrical conductivity, oxidation-reduction potential, zeta potential, turbidity, temperature, foam height, biochemical oxygen demand (BOD), chemical oxygen demand (COD), total organic carbon (TOC), inorganic carbon, absorbance, color, appearance, whiteness, transparency, particle size distribution, degree of aggregation, amount of foreign matter, suspended solids (SS), foam surface area, soiled area in water, amount of air bubbles, amount of glucose, amount of organic acid, amount of active alkali, total titratable alkali, metal or metal ion content, non-metal ion content, amount of starch, amount of calcium, total chlorine content, amount of free chlorine, amount of dissolved oxygen (DO), cation demand, amount of hydrogen sulfide, sulfidity, amount of hydrogen peroxide, ash concentration, respiration rate of microorganisms in the system, viable cell count, spore count, and ATP. These water quality parameters are acquired from any location in the water system W.

[0059] Of the above water quality parameters, "appearance" may be obtained from an RGB color sensor or camera images. "Active alkali content" and "total titratable alkali content" may indicate the alkali content evaluated as sodium content, the alkali content evaluated as calcium content, or the total alkali content. Furthermore, the metal in the "metal or metal ion content" can include heavy metals such as iron (Fe), lead (Pb), gold (Au), platinum (Pt), silver (Ag), copper (Cu), chromium (Cr), cadmium (Cd), mercury (Hg), zinc (Zn), arsenic (As), manganese (Mn), cobalt (Co), nickel (Ni), molybdenum (Mo), tungsten (W), tin (Sn), bismuth (Bi), uranium (U), and plutonium (Pu); alkali metals such as sodium (Na) and potassium (K); alkaline earth metals such as calcium (Ca) and barium (Ba); and base metals such as magnesium (Mg) and aluminum (Al). The "metal or metal ion content" may refer to the content of a specific (one) metal or metal ion, or the content of multiple metals. Furthermore, "non-metal ions" can include ions containing heteroatoms such as silicon (Si), phosphorus (P), sulfur (S), and nitrogen (N) as constituent components, as well as halogen ions such as chlorine (Cl) and bromine (Br).

[0060] The parameter information also includes, as control parameters, the operating speed of the paper machine (machine speed), the filter cloth rotation speed of the raw material dehydrator, the filter cloth rotation speed of the washer, the amount or unit of chemicals added to the water system, the amount or unit of chemicals added to the raw material added to the water system, the amount or unit of chemicals added to equipment related to the water system, the amount or unit of steam used for heating, the temperature of steam used for heating, the pressure of steam used for heating, the flow rate from the seed box, the nip pressure of the press part, the felt vacuum pressure of the press part, the blending ratio of the papermaking raw materials, the amount or unit of broken paper used in the papermaking raw materials, the opening of the screen for the papermaking raw materials, and the rotor and stator of the beater. gap distance between the wire part and the sheet, freeness, degree of beating, draw, line pressure of the press part, gravity dewatering rate of the wire part or a parameter equivalent thereto, forced dewatering rate of the wire part or a parameter equivalent thereto, gravity dewatering rate of the press part, a parameter equivalent to gravity dewatering rate of the press part, a parameter for controlling gravity dewatering rate of the press part, forced dewatering rate of the press part, a parameter equivalent to forced dewatering rate of the press part, a parameter for controlling forced dewatering rate of the press part, concentration of interlayer adhesive, amount of interlayer adhesive or consumption unit, amount of shower water or consumption unit, new water consumption unit, water consumption unit, timing or frequency of cleaning of the wire part, timing or frequency of cleaning of the press part, timing or frequency of cleaning of the dryer part, timing or frequency of changing tools in the paper machine, amount of new water replenishment, amount of screen rejects or a parameter for controlling it, screen differential pressure or a parameter equivalent thereto, amount of cleaner rejects or a parameter for controlling it, amount of fuel for heating or consumption unit, floatator concentration, amount of floatator air or a parameter equivalent thereto, amount of floatator froth or a parameter equivalent thereto load of disperser or equivalent parameters, number of felt singes, flow rate of fluid flowing through the water system, air temperature, amount of steam for heating or unit consumption, amount of calcium carbonate added or unit consumption, amount of calcium bicarbonate added or unit consumption, flow rate or concentration of clarified green liquor, flow rate or concentration of crude green liquor, flow rate or concentration of diluted black liquor, flow rate or concentration of strong black liquor, flow rate or concentration of white liquor, amount of black liquor injected in the black liquor treatment system, black liquor injection concentration in the black liquor treatment system, pulp production, mud filter moisture, amount of lime input or current unit in the slaking / causticizing system, lime production in the calcination system, fuel consumption,The control parameters may include one or more parameters selected from the group consisting of fuel consumption rate, causticization rate in the slaking / causticizing system, caustic input or consumption rate in the cooking system, black liquor production rate in the black liquor treatment system, solids amount during evaporation of black liquor in the black liquor treatment system, evaporation multiple during evaporation of black liquor in the black liquor treatment system, boiler economizer temperature in the black liquor treatment system, sludge moisture content in the green liquor treatment system, specific gravity of black liquor, specific gravity of green liquor, specific gravity of white liquor, specific gravity of weak liquor, specific gravity of dregs, specific gravity of calcium carbonate before calcination, and specific gravity of calcium oxide after calcination. These control parameters are obtained from various equipment related to the aqueous system W. In this case, when the aqueous system W is an aqueous system used in the process of producing paper products, examples of the equipment include equipment such as wires and felts in a paper machine that directly add chemicals, and equipment such as the dry part (dryer) of the papermaking system. Regarding the above control parameters, the type of "fuel" can be set as appropriate, and may be, for example, heavy oil or petroleum coke. Furthermore, the location where the "temperature" in the "temperature of the economizer of the boiler in the black liquor treatment system" is obtained may be any location within the device. For example, it may be the inlet of the device, the inside of the device, or the outlet of the device.

[0061] The parameter information may also include, as result parameters, one or more selected from the group consisting of unit weight of the paper product (basis weight), yield rate, white water consistency, moisture content of the paper product, amount of steam or unit consumption in the equipment for manufacturing the paper product, steam temperature in the equipment for manufacturing the paper product, steam pressure in the equipment for manufacturing the paper product, thickness of the paper product, ash concentration in the paper product, type of defect in the paper product, number of defects in the paper product, time of paper breakage in the process, freeness, degree of beating, aeration amount, air temperature inside the dryer, splicing rate, number of splices, amount of paper loss, number of defects, defect index, outside air temperature including indoor air temperature, outside humidity including indoor humidity, moisture content of wet paper, moisture content of the product, temperature of the dryer rolls and cylinders, measurements from the five senses sensors, amount of fuel or unit consumption of fuel, calcination rate in the calcination system, causticization rate in the slaking / causticizing system, amount of caustic input in the cooking system, and amount of lime input in the slaking / causticizing system. Among these, the amount of steam in the equipment for manufacturing paper products can be, for example, the amount of steam in the paper machine dryer, the amount of steam in the kraft pulp black liquor evaporator, the amount of steam in the black liquor heater in the kraft pulp digester, or the amount of steam injected to heat the pulp raw material or white water. These result parameters are obtained from various equipment related to the water system W.

[0062] Although some parameters essentially indicate the same thing, they may be classified as two or more of water quality parameters, control parameters, and result parameters depending on the purpose. For example, in a black liquor evaporator, black liquor is heated by indirect heat exchange with steam generated from a boiler, and as a result of the heating, process steam is generated from the black liquor. This process steam is used to heat (concentrate) the concentrated black liquor in the next process. The amount of process steam generated is a result parameter because it is generated from the black liquor, and is used as a control parameter because it is used to heat (concentrate) the concentrated black liquor in the next process. Furthermore, the steam generated from the boiler to heat the black liquor is also used as a control parameter. Note that the two or more parameters acquired by the parameter information acquisition unit are not all substantially identical. For example, in the case where the entire amount of process steam generated from the black liquor is used to heat (concentrate) the concentrated black liquor in the next process, the use of the process steam generated from the black liquor as a result parameter and the amount of steam used to heat (concentrate) the concentrated black liquor as a control parameter is excluded. In such a case, the process steam generated from the black liquor as a result parameter and the amount of steam used to heat (concentrate) the concentrated black liquor as a control parameter are substantially the same. However, when a portion of the process steam generated from the black liquor is used to heat (concentrate) the concentrated black liquor in the next process, the process steam generated from the black liquor as a result parameter and the amount of steam used to heat (concentrate) the concentrated black liquor as a control parameter can be used as two parameters. In such a case, the process steam generated from the black liquor as a result parameter and the amount of steam used to heat (concentrate) the concentrated black liquor as a control parameter are not substantially the same.Furthermore, when the total amount of process steam generated from the black liquor described above is used to heat (concentrate) the concentrated black liquor in the next step, the two parameters used are the process steam generated from the black liquor as a result parameter and the amount of steam used to heat (concentrate) the concentrated black liquor as a control parameter, and if other parameters such as the pH of the water system as a water quality parameter are further combined, the multiple parameters may be substantially the same. In addition, for example, freeness and beating degree are also the same parameters, but can be included in both the control parameter and the result parameter.

[0063] These parameters may be quantitative or qualitative. When qualitative parameters are used, they may be assigned numerical values ​​and treated as quantitative data.

[0064] Note that the terms water quality parameters, control parameters, and result parameters each encompass multiple parameters. Regarding "acquiring two or more parameters selected from the group consisting of water quality parameters, control parameters, and result parameters as parameter information," the two or more parameters included in the parameter information can be independently selected from the water quality parameters, control parameters, and result parameters. Two or more parameters may be selected from only water quality parameters, control parameters, and result parameters (e.g., water pH and temperature), or two or more parameters may be selected from a combination of two or three of water quality parameters, control parameters, and result parameters (e.g., pH, press pressure in the press part, and thickness of the paper product). However, identical parameters (e.g., the pH of water at point A and the pH of water at point A) should not be selected (however, for example, the pH of water at point A and the pH of water at point B, which are measured at different points, may be selected).

[0065] The order in which activity A101 here and activity A102, which will be described later, are performed is arbitrary; activity A101 can be performed before activity A102, activity A102 can be performed before activity A101, or both activities can be performed in parallel (simultaneously).

[0066] [Activity A102: Obtaining Relationship Model Information] In activity A102, the relationship model information acquisition unit 232 acquires relationship model information. The relationship model information is created in advance and indicates the relationship between an index and two or more parameters. In a typical example, the relationship model information here is created by the relationship model information creation unit 237.

[0067] The term "prior" refers to a period before the index is estimated, and may be any period before the index is estimated, whether during actual operation of the water system W or before actual operation. In addition, in one embodiment, the "index" is quantitative and can be understood as, for example, something used to evaluate the status of an event. If the index indicates that something is not good (if the value is bad), the event is evaluated as not being good, for example, as being prone to trouble in the manufacturing process, or as being at risk of a decline in the quality of the manufactured product. In the embodiment, the index can be expressed as an expected result or an index related to the expected result. In the following, the "index related to the expected result" may be referred to as a "related index" to distinguish it from the generic "index." Furthermore, the term "event" refers to an event occurring in the water system W or derived from the water system W, and represents concepts such as, for example, a situation in the manufacturing process of a given product, the quality of the manufactured product, etc. In the embodiment, as an example, the event refers to a situation in which there is concern about trouble in the manufacturing process or a decline in product quality, but the event is not limited to this and may also be applied to a situation in which a favorable situation in the manufacturing process or a situation in which product quality can be ensured.

[0068] The "probable outcomes" of the relationship model refer to various events related to the water system W. The estimation unit 233 of the information processing device 2 of this embodiment estimates expected results (indexes), and the expected results related to the relationship model information also correspond to the results estimated by this estimation unit 233. The expected results can be set appropriately depending on the estimation target, but when the water system W is a water system in a process for manufacturing paper products, the expected results may be as shown below. That is, in this embodiment, the expected result (index) may relate to the quality or energy amount of paper products and / or materials related to the manufacture of paper products. Here, the quality may relate to one or more of the following: the number of defects, paper strength, joint rate, sizing degree, air permeability, smoothness, ash content, color tone, whiteness, formation, odor, causticization rate, burnt rate, kappa number, freeness, and moisture content of paper products and / or materials related to the manufacture of paper products. Furthermore, the energy amount may relate to one or more of the following: steam volume, steam consumption rate, fuel volume, and fuel consumption rate when manufacturing paper products and / or materials related to the manufacture of paper products. As mentioned above, "materials related to the manufacture of paper products" does not refer to the paper products themselves, but rather to various raw materials and intermediates used in the manufacture of paper products. In other words, raw materials used in the manufacture of paper products include white water, calcium oxide, and the like generated during the paper product manufacturing process. That is, the causticization rate and burnt rate described above may relate to these raw materials. Furthermore, intermediates used in the production of paper products include, for example, pulp produced before the papermaking process. The above-mentioned expected results may be evaluation items related to such pulp. The expected results may be the items listed above themselves, or a combination of the items listed above or related items. For example, an example of a related item related to the above-mentioned calcination rate is the amount of fuel used to achieve a predetermined calcination rate (typically, the amount of fuel used in the kiln when obtaining calcium oxide used in the caustic slaking process from calcium carbonate). The type of fuel for the "fuel amount" in the expected results (index) is not particularly limited, and may be heavy oil, petroleum coke, etc.

[0069] Furthermore, the expected result in this embodiment may correspond to the result parameter described above. In this case, the result parameter is not usually included in the parameter information (two or more parameters).

[0070] Examples of relationship model information include, but are not limited to, a function showing the relationship between an expected result or related index and two or more parameters, a lookup table, or a trained model of the relationship between an expected result or related index and two or more parameters.

[0071] It is assumed that two or more parameters included in the parameter information acquired by the parameter information acquisition unit 231 and two or more parameters included in the parameter information used in the relationship model information are common to each other. As described above, the water quality parameters, control parameters, and result parameters each encompass a plurality of parameters. "Two or more of the parameters are common" may mean that two or more of the water quality parameters, control parameters, and result parameters (e.g., two or more water quality parameters only; pH ​​and temperature of water) are common to each other, or that a combination of water quality parameters, control parameters, and result parameters (e.g., one water quality parameter and one control parameter; e.g., pH of water and pressing pressure of the pressing part) is common to each other, or that all of the water quality parameters, control parameters, and result parameters (e.g., one water quality parameter, one control parameter, and one result parameter) are common to each other.

[0072] Furthermore, the "related indicator" in the indicators may be an indicator that has a certain correlation with the expected result (for example, a function of two or more parameters), and may not be a generally known indicator but may be one that is independently created by the user (operator, etc.) of the information processing system.

[0073] The relationship model information is created, for example, as follows. That is, before estimating expected results or related indices, preliminary measurement results corresponding to the expected results or preliminary measurement indices related to the preliminary results are measured. Also, two or more parameters, each of which is a water quality parameter, a control parameter, and a result parameter, are measured in the same water system. Multiple sets of data on these preliminary measurement results or preliminary measurement indices and parameters are prepared, for example, by changing the day or time of measurement, so that the preliminary measurement results or preliminary measurement indices and parameters fluctuate. Next, the preliminary measurement results or preliminary measurement indices are assumed to be functions of two or more parameters, and the function form and coefficients are determined by comparing them with the preliminary measurement results or preliminary measurement indices, thereby constructing relationship model information. Here, the relationship model information may be a model obtained by performing predetermined processing on an index related to the preliminary measurement results or preliminary confirmation results corresponding to the index and two or more parameters. The processing methods that can be used here include regression analysis (linear model, generalized linear model, generalized linear mixed model, ridge regression, lasso regression, elastic net, support vector regression, projection pursuit regression, principal component regression, etc.), time series analysis (VAR model, SVAR model, ARIMAX model, SARIMAX model, state space model, HMM model, etc.), decision trees (decision tree, regression tree, random forest, XGBoost, Light GBM, etc.), neural networks (simple perceptron, multilayer perceptron, DNN, CNN, RNN, LSTM, GAN, VAE, etc.), Bayes (naive Bayes, Bayesian optimization, Bayesian network, etc.), clustering (k-means, k-means++, etc.), classification (k-nearest neighbor method, support vector machine, etc.), ensemble learning (Boosting, Adaboost, etc.), etc.

[0074] In one embodiment, the relationship model information is preferably a model obtained by regression analysis of a pre-check result corresponding to a forecast result or an index related to the pre-check result with two or more parameters. Note that the number of sample sets used in the regression analysis is not particularly limited.

[0075] It is preferable to create the relationship model information for the same water system as the water system for which the expected results are to be estimated. Furthermore, for example, when the water quality of the water system changes significantly even within the same equipment (for example, when the pulp, which is the raw material for papermaking, is changed in the papermaking system of a paper mill), it is preferable to create and use the relationship model information for the water system after the water quality change.

[0076] From this perspective, during operation of the water system W, expected results or related indicators and two or more parameters may be measured regularly or irregularly, and relationship model information may be created each time, or data may be added to update the relationship model information.

[0077] Such relationship model information may be created and updated, for example, by the function of the relationship model information creation unit 237 included in the information processing device 2. That is, the relationship model information creation unit 237 can create relationship model information suitable for the information processing of this embodiment by performing predetermined arithmetic processing on the acquired expected results or related indicators and two or more parameters. Note that this relationship model information may also be created manually by, for example, an operator.

[0078] An example of creating relationship model information will be described below. Specifically, the case will be described where an index a related to the predicted result is calculated using water quality parameter x, water quality parameter y, and control parameter z, and the index a related to this predicted result and the number of times a trouble occurs A (number of times / day) as the predicted result are calculated.

[0079] When creating the relationship model information, the water quality parameter x, water quality parameter y, and control parameter z shown in Table 1 below are measured, and the number of times a trouble occurs A is also measured, obtaining a total of 30 sets of data. The obtained data is shown in Table 1 below. Of course, the number of sets of data is not limited to this and may be set as appropriate.

[0080] The indicator a related to the expected outcome is "parameters" x, y, z, and b nare the coefficients of x, y, and z, and a0 and b0 are constants, and are expressed by the following equation (1).

number

[0081] A negative binomial regression analysis was performed based on the actual measured value of the number of times a problem occurred and the index a related to the predicted outcome in equation (1). The index a related to the predicted outcome calculated from this is also shown in Table 1. Figure 7 is a plot of the number of times a problem occurred versus the index a related to the predicted outcome for a total of 30 sets of data. The correlation coefficient between the number of times a problem occurred and the index a related to the predicted outcome was r = 0.78 (p < 0.05), indicating a strong correlation.

[0082] [Table 1]

[0083] In addition, the function of the index a related to the expected results used in the regression analysis, and the water quality parameter x, water quality parameter y, and control parameter z is not limited to the above formula (1), and general formula (2) can also be used.

number

[0084] [Activity A103: Estimating Indicators] After acquiring parameter information in activity A101 and relationship model information in activity A102, the estimation unit 233 estimates an index (expected result or related index). Here, it is preferable that the estimation result is associated with each parameter included in the parameter information and presented to the user. Typically, such an estimation result can be output by inputting the acquired parameter information into the relationship model information.

[0085] [Activity A104: Identify a dataset] The data set identifying unit 234 identifies a data set in which an index (probable outcome or related index) related to an event is associated with two or more parameters.

[0086] Here, the parameter is a parameter associated with the index, or in other words, may have a correlation with the value of the index. Note that the selection of parameters is made, for example, based on the knowledge of the user or through analysis by an arbitrary information processing device, and if this selection is appropriate, the correlation with the index will be appropriate accordingly, and it can be expected that the accuracy of the index and the accuracy of the influence degree described below will also be improved.

[0087] As described above, a dataset is data in which an index related to an event and two or more parameters are paired. In an exemplary embodiment, the index and two or more parameters associated in the dataset are preferably obtained more recently than the index and two or more parameters associated in the relationship model information. In other words, the data (index and two or more parameters) constituting the dataset may be new data that has not necessarily been used to create the relationship model information. That is, the dataset of this embodiment may be a dataset based on the most recently obtained parameter information and the corresponding index. The corresponding index here may typically be a value output by inputting the most recently obtained parameter information into the relationship model information. This ensures real-time calculation of the impact of activity A105 (described later) and improves the accuracy of the impact calculation.

[0088] However, the data set identified by the data set identifying unit 234 is not limited to this. For example, a dataset identified by the dataset identifying unit 234 may be used to create the relationship model information, and then this dataset may be used to calculate the degree of influence. That is, in an embodiment, the dataset identified by the dataset identifying unit 234 may be part of the datasets that make up the relationship model information, and does not necessarily need to be independent of the data in the relationship model information. Note that if the two are independent, it is expected that the accuracy of calculating the degree of influence will be improved.

[0089] In addition, in the present embodiment, an index related to an event estimated by the estimation unit 233 is used as the dataset, but this is not limited to this. An index related to an event may be simulated by utilizing a statistical or machine learning method, and this simulated index may be identified as the dataset. For example, a method called Permutation Importance may be used to apply two or more new parameters to an arbitrary range of datasets used to create the relationship model information and simulatedly evaluate them to generate simulated indexes, and these simulated indexes may be identified by the dataset identification unit 234.

[0090] [Activity A105: Identify impact] The influence degree identification unit 235 compares the relationship model information acquired in the activity A102 with the data set identified in the activity A104, thereby identifying the influence degree on the event for each parameter included in the data set. The influence level refers to the level of influence of each parameter included in the data set on an event. For example, when an event (such as a problem in a manufacturing process) occurs, if the influence level of a parameter in the data set is relatively high, it can be determined that the parameter is the main factor causing the event (the aforementioned problem).

[0091] Although various methods for identifying the degree of influence may be used, in a typical embodiment, the influence identification unit 235 identifies the degree of influence by performing a predetermined arithmetic process on each parameter. This predetermined arithmetic process includes at least one of differential processing, statistical processing, and processing using a trained model.

[0092] Here, a case where difference processing is used as the calculation process for calculating the degree of influence will be described as an example. Fig. 8 is an explanatory diagram showing an example of a method for calculating the degree of influence using a defect index as an index (expected result). Fig. 9 is a graph schematically showing the degree of influence of each parameter on an event. In addition, in the description here, a case where the parameters of the data set are six (parameter P1 to parameter P6) as shown in Fig. 9 will be described as an example.

[0093] In the defect index ID as an index (expected result) shown in Fig. 8, t0, t1, t2, t3, t4, t5, and t6 are predetermined. The equation of the defect index ID shown in Fig. 8 and the values ​​of t0, etc. correspond to the relationship model information. Also, a and b are constants and can be set appropriately. The value of the defect index ID itself corresponds to the index (expected result).

[0094] Each influence V(n) of the parameter Pn (n=1 to 6) can be expressed as ID(p)-ID(n). In this way, the influence is calculated (specified) by the difference process related to the defect index ID. FIG. 8 shows an example of calculation of the influence V(1), which is the value of the influence of the parameter P1.

[0095] ID(p) is calculated using parameters that are more recently obtained than the parameters included in the relationship model information. The newly obtained parameters are, for example, new parameters that are obtained separately from the parameters used to create the relevance model information by the relevance model information creation unit described above.

[0096] Then, ID(p) is obtained by substituting the parameter values ​​of the data set into the defect index ID formula. Specifically, in the formula for ID(p), A(p1) is the newly obtained value for parameter P1, B(p2) is the newly obtained value for parameter P2, C(p3) is the newly obtained value for parameter P3, D(p4) is the newly obtained value for parameter P4, E(p5) is the newly obtained value for parameter P5, and F(p6) is the newly obtained value for parameter P6. These values ​​A(p1) to F(p6) are typically values ​​acquired at the same time, but in an exemplary embodiment, they may be values ​​acquired at different times. For example, an exemplary embodiment is one in which value A(p1) is a value acquired X hours ago, while value B(p2) is acquired as an average value for a time period from Y days ago to Z hours ago.

[0097] As shown in FIG. 8, ID(1) can be calculated based on the reference value A(s1) for parameter P1 and parameters (values ​​B(p2) to F(p6)) obtained more recently than the parameters included in the relationship model information. The reference value A(s1) can be set based on various perspectives, such as historical average values ​​during the relationship model creation period, target reference values, design values, and numerical values ​​from periods of good operation. The same applies to the reference values ​​B(s2) to F(s6) for the other parameters P2 to P6. In other words, the reference values ​​described here are based on the relationship model information. Note that the term ID(1) is the same as ID(p) except for the reference value A(s1), as indicated by the arrow Ar in FIG. 8.

[0098] Although not shown in Figure 8, the influence V(2), which is the value of the influence of parameter P2, can be calculated by calculating ID(p) - ID(2). The value of ID(p) has been explained above and will not be repeated here, but for ID(2), the value of B(p2) becomes the reference value B(s2). Everything else is the same as ID(p). Note that the influence V(3) and subsequent values, which are the value of the influence of parameter P3, can be calculated in the same manner.

[0099] In this way, the degree of influence is determined by comparing the relationship model information acquired in activity A102 with the data set determined in activity A104. ID(p) is a value that takes into account the data set, but ID(n) takes into account not only the data set mentioned above but also the reference value, so relationship model information is also taken into account. In one example of an embodiment (differential processing), the influence V(n) of each parameter is given by ID(p)-ID(n), so it can be said that the influence is a value obtained by comparing the dataset with the relationship model.

[0100] In the above description, a case has been described as an example in which parameter values ​​(value A(p1) to value F(p6)) obtained more recently than the parameters included in the relationship model information are used to calculate ID(p) or ID(n). In other words, the newly obtained parameters (value A(p1) to value F(p6)) used to calculate ID(p) or ID(n) are the values ​​of parameters P1 to P6 themselves, but are not limited to this. For example, moving average values ​​may be used instead of the values ​​themselves. Furthermore, a composite value (composite parameter value) of two or more parameters may be used as the parameter (value A(p1) to value F(p6)) used to calculate the defect index described here. In this case, the composite parameter related to this composite value may be used to specify the composite value when calculating the impact.

[0101] Although the above describes an example in which differential processing is used as the calculation processing, statistical processing and processing such as a trained model can also be used. For example, Shap, Permutation Importance, Feature Importance, an impulse response function, etc. may be used for this processing. These processings may be combined with the above-described differential processing to calculate the influence degree with higher reliability and robustness.

[0102] In the above description, only one reference value is set for each ID(n), but this is not limited to this. For example, when calculating the value of one ID(n), multiple reference values ​​such as reference value A(s1) and reference value B(s2) may be used. In this case, the influence of a combination of two or more parameters is calculated. In other words, it is possible to identify the influence related to the interaction of parameters.

[0103] When the influence degree identification unit 235 identifies the influence degree of each parameter as described above, the influence degree is displayed to the user via the display unit 34 of the user terminal 3 in a format such as that shown in Fig. 9. That is, the influence degree identification unit 235 can output the calculated influence degree to the user terminal 3 as information (impact degree information) that allows the user to understand the calculated influence degree, and identify the influence degree. The format of the impact information is not particularly limited, and may be, for example, visual information itself generated in a visible form such as numerical values ​​or a graph as shown in Fig. 9, or rendering information for displaying the visual information. Also, instead of visual information, it may be audio information, or both. In the example of FIG. 9, the user can visually understand that the parameter with the greatest influence as a factor causing the event is parameter P2, "ORP (oxidation-reduction potential) of raw material system 1."

[0104] The system will be easier for users to use if they can visually grasp the factors that cause events as a production flow diagram in addition to the graph shown in Fig. 9. For example, as shown in Fig. 4, the influence identification unit 235 can identify a parameter with a large influence (in the example of Fig. 4, parameter P2, "ORP of raw material system 1") by highlighting the parameter on the production flow diagram.

[0105] Note that "identification" does not necessarily include the output of numerical values ​​or graphs (output of visual information) or the output of audio (output of audio information). In other words, "identification" may include, for example, only the calculation of the impact degree through the arithmetic processing described in FIG. 8. For example, if there is no particular problem with the manufacturing process or product quality, it is not necessarily necessary to notify the user. Also, there are cases where the user is not interested in the magnitude of the impact degree of the parameter for the event, but is interested in countermeasures for the event, as will be described later.

[0106] The timing at which the impact identification unit 235 identifies the impact may be regular or irregular. When identifying the impact periodically, for example, intervals of several seconds to several tens of minutes may be set. The impact identification unit 235 may also identify the impact based on a manual instruction from the user (an instruction from the user to identify the impact). Furthermore, when the value of the index calculated by the estimation unit 233 exceeds a preset threshold, the impact identification unit 235 may identify the impact of each parameter.

[0107] FIG. 10 is a schematic diagram showing the transition of the actual measured value (broken line) of ORP (one example of a parameter) of raw material system 1 in response to an event in the water system and the degree of influence of the ORP of raw material system 1 on the event. 10, the influence degree identification unit 235 may output a screen showing the actual measurement value and the influence degree over time to the user terminal 3. This allows the user to grasp the change over time in the actual measurement value related to the parameter and the influence degree of the parameter, making the system easier for the user to use.

[0108] In this manner, in the embodiment, the degree of influence is determined for the parameters (parameters P1 to P6) related to the data set. This makes it possible to identify with a high degree of accuracy which of all parameters (all factors) is the most influential factor in a currently occurring event (for example, a problem in a manufacturing process), making the system easier for system users to use and facilitating the management of events involving water systems.

[0109] [Activity A106: Present countermeasure information] The countermeasure presentation unit 236 presents countermeasure information according to the impact level identified by the impact level identification unit 235 in activity A105. As described above, for example, if the "ORP of raw material system 1" of the parameter P2 has the greatest impact as a factor causing an event, the countermeasure information presented will be countermeasure information for optimizing the situation of "ORP of raw material system 1."

[0110] The countermeasure information is, for example, stored in a database by the storage management unit 238 of the information processing device 2. The countermeasure information is associated with one or more countermeasures to be taken when the calculated impact level is high. That is, in an example embodiment, each of parameters P1 to P6 is associated with at least one piece of countermeasure information to be presented when the impact level is high. As described above, the presented countermeasure information may be visual information itself generated in a manner visible to the user, such as characters, numbers, diagrams, photos, videos, screens, images, icons, text, etc., or may be rendering information for displaying the visual information on various devices or terminals. When there are multiple highly impactful parameters, the countermeasure information presented by the countermeasure presentation unit 236 may correspond to these multiple highly impactful parameters. Typically, when the impact levels of parameters P1 and P2 are high, one or more countermeasures that can effectively control both parameters P1 and P2 may be presented.

[0111] Furthermore, when the countermeasure presentation unit 236 is capable of presenting a plurality of pieces of countermeasure information, the display order may be based on the score, or the display order may be random, and only the score may be displayed. The score indicates the degree to which the countermeasure information is recommended. The score may be, for example, a score set in advance by the user. The score may also reflect the results and effects of the countermeasures taken by the user based on the countermeasure information. In this case, the countermeasure presentation unit 236 only needs to be able to receive feedback on the results and effects of the countermeasures taken by the user based on the countermeasure information. In other words, the user takes countermeasures based on the countermeasure information, and the countermeasure presentation unit 236 receives input of a score according to the results and effects of the countermeasures.

[0112] As described above, according to the information processing device 2 (information processing system 1) of this embodiment, the function of the influence identification unit 235 can effectively identify the influence of parameters on events in the water system W. This allows a person involved in a process related to the water system W to easily take operational measures, etc.

[0113] 4. Variations In Section 4, a modified example of the information processing method of the information processing system 1 and the like described above will be described.

[0114] The above-described embodiment has been described as a configuration of the information processing system 1, but an information processing method executed by the information processing system may also be provided, which includes steps of executing processing of each unit of the information processing system. Also, a program for causing a computer to execute processing of each unit of the information processing system 1 may also be provided.

[0115] In the above-described embodiment, an information processing method using relevance model information is described, but the information associated when creating the relevance model information is not limited to the above. In other words, the relevance model information used in this embodiment may be associated with various other conditions, such as weather conditions, regional conditions, and conditions related to the age of the facility.

[0116] In the above embodiment, the information processing system 1 performs various storage and control operations, but multiple external devices may be used instead of the information processing system 1. That is, various types of information may be distributed and stored in multiple external devices using blockchain technology or the like.

[0117] In the above embodiment, the information processing device 2 and the user terminal 3 are configured to function as separate devices, but the user terminal 3 itself may have various functions as the control unit 23 of the information processing device 2. In other words, the user terminal 3 may be a standalone computer that performs processes such as acquiring various information, making inferences, and identifying the impact level.

[0118] Although the embodiments of the present invention have been described above, these are merely examples of the present invention, and various other configurations may be adopted. Furthermore, the present invention is not limited to the above-described embodiments, and modifications and improvements within the scope of achieving the object of the present invention are included in the present invention. [Explanation of symbols]

[0119] 1: Information processing system 2: Information processing equipment 3: User terminal 4: Measuring equipment 20: Communication bus 21: Communications Department 22: Storage section 23: Control section 30: Communication bus 31: Communications Department 32: Storage section 33: Control section 34:Display section 35: Input section 231: Parameter information acquisition unit 232: Relationship model information acquisition unit 233: Guessing part 234: Dataset identification part 235: Impact Identification Department 236: Countermeasure presentation section 237: Relationship model information creation unit 238: Memory Management Department W: Water system

Claims

1. An information processing system for performing information processing on events in or arising from a water system, The system includes a relationship model information acquisition unit, a data set identification unit, and an influence identification unit, the relationship model information acquisition unit acquires relationship model information that is created in advance and indicates a relationship between an index related to the event and two or more parameters; wherein the two or more parameters are each selected from the group consisting of a water quality parameter, a control parameter, and a result parameter; the water quality parameter is a parameter related to the water quality of the water system, the control parameters are parameters relating to control conditions of the aqueous system, equipment related to the aqueous system, or raw materials added to the aqueous system; The result parameter is a parameter that does not correspond to the index and relates to a result that occurs in the water system, equipment related to the water system, or raw materials added to the water system, or that derives from the water system, equipment related to the water system, or raw materials added to the water system, the dataset specifying unit specifies a dataset in which the index related to the event and the two or more parameters are associated with each other; The influence degree identification unit identifies the influence degree on the event for each parameter associated in the dataset by comparing the relationship model information with the dataset.

2. 2. The information processing system according to claim 1, the influence degree identification unit identifies the influence degree by performing a predetermined calculation process on each of the parameters; An information processing system, wherein the arithmetic processing includes at least one of differential processing, statistical processing, and processing using a trained model.

3. 2. The information processing system according to claim 1, An information processing system, wherein the indicator and the two or more parameters associated in the dataset are obtained more recently than the indicator and the two or more parameters associated in the relationship model information.

4. 2. The information processing system according to claim 1, an information processing system, wherein the relationship model information is a model obtained by regression analysis, time series analysis, decision tree analysis, neural network analysis, Bayesian analysis, clustering analysis, classification, or ensemble learning between a pre-check result corresponding to the indicator or an indicator related to the pre-check result and the two or more parameters.

5. 2. The information processing system according to claim 1, The apparatus further includes a parameter information acquisition unit and an estimation unit, the parameter information acquisition unit acquires, as parameter information, two or more parameters selected from the group consisting of the water quality parameter, the control parameter, and the result parameter; the estimation unit estimates the index based on the acquired parameter information and the acquired relationship model information; An information processing system, wherein the dataset is one in which the index estimated by the estimation unit is associated with the two or more parameters acquired by the parameter information acquisition unit.

6. 2. The information processing system according to claim 1, It also has a countermeasure presentation section, The countermeasure presentation unit presents countermeasure information related to a corresponding parameter in accordance with the degree of impact identified by the impact level identification unit.

7. 2. The information processing system according to claim 1, The information processing system, wherein the aqueous system is an aqueous system in a process for producing paper products and / or materials related to the production of paper products.

8. 8. The information processing system according to claim 7, The two or more parameters include, as the water quality parameters, one or more selected from the group consisting of pH, electrical conductivity, oxidation-reduction potential, zeta potential, turbidity, temperature, bubble height, biochemical oxygen demand (BOD), chemical oxygen demand (COD), total organic carbon (TOC), inorganic carbon, absorbance, color, appearance, whiteness, transparency, particle size distribution, degree of aggregation, amount of foreign matter, suspended solids (SS), foam area on the water surface, area of ​​dirt in the water, amount of air bubbles, amount of glucose, amount of organic acid, amount of active alkali, total amount of titratable alkali, metal or metal ion content, non-metal ion content, amount of starch, amount of calcium, total chlorine amount, amount of free chlorine, amount of dissolved oxygen (DO), cation demand, amount of hydrogen sulfide, degree of sulfidation, amount of hydrogen peroxide, ash concentration, respiration rate of microorganisms in the system, viable cell count, spore count, and ATP of the water system.

9. 8. The information processing system according to claim 7, The two or more parameters include, as the control parameters, the operating speed (machine speed) of the papermaking machine, the filter cloth rotation speed of the raw material dehydrator, the filter cloth rotation speed of the washer, the amount or unit of chemicals added to the water system, the amount or unit of chemicals added to the raw material added to the water system, the amount or unit of chemicals added to the equipment related to the water system, the amount or unit of steam used for heating, the temperature of steam used for heating, the pressure of steam used for heating, the flow rate from the seed box, the nip pressure of the press part, the felt vacuum pressure of the press part, the blending ratio of the papermaking raw materials, the amount or unit of broken paper used in the papermaking raw materials, the opening of the screen of the papermaking raw materials, the beating speed of the papermaking raw materials, the amount or unit of broken paper used in the papermaking raw materials, the beating speed of the papermaking raw materials, the amount or unit of broken paper used in the papermaking raw materials, the amount or unit of broken paper used in the papermaking raw materials, the opening of the screen of the papermaking raw materials, the beating speed of the papermaking raw materials, the amount or unit of broken paper used in ... gap distance between the rotor and stator of the machine, freeness, degree of beating, draw, line pressure of the press part, gravity dewatering rate of the wire part or a parameter equivalent thereto, forced dewatering rate of the wire part or a parameter equivalent thereto, gravity dewatering rate of the press part, a parameter equivalent to gravity dewatering rate of the press part, a parameter for controlling gravity dewatering rate of the press part, forced dewatering rate of the press part, a parameter equivalent to forced dewatering rate of the press part, a parameter for controlling forced dewatering rate of the press part, concentration of interlayer adhesive, amount or consumption unit of interlayer adhesive, amount of shower water or consumption unit, new water consumption unit, water consumption unit, timing or frequency of cleaning of the wire part, timing or frequency of cleaning of the press part, timing or frequency of cleaning of the dryer part, timing or frequency of changing tools on the paper machine, amount of new water replenishment, amount of screen rejects or a parameter for controlling it, screen differential pressure or a parameter equivalent thereto, amount of cleaner rejects or a parameter for controlling it, amount of fuel for heating or consumption unit, flotator concentration, amount of flotator air or a parameter equivalent thereto, flotator flow amount of water or a parameter equivalent thereto, disperser load or a parameter equivalent thereto, number of times of felt singeing, flow rate of fluid flowing through the water system, air temperature, amount of steam for heating or unit consumption, amount of calcium carbonate added or unit consumption, amount of calcium bicarbonate added or unit consumption, flow rate or concentration of clarified green liquor, flow rate or concentration of crude green liquor, flow rate or concentration of diluted black liquor, flow rate or concentration of strong black liquor, flow rate or concentration of white liquor, amount of black liquor injected in the black liquor treatment system, black liquor injection concentration in the black liquor treatment system, pulp production volume, mud filter moisture, amount of lime input or current unit in the slaking / causticizing system,An information processing system comprising one or more items selected from the group consisting of lime production amount in a calcination system, fuel consumption amount, fuel consumption unit, causticization rate in a slaking / causticizing system, caustic input amount or consumption unit in a cooking system, black liquor generation amount in a black liquor treatment system, solid amount when evaporating black liquor in a black liquor treatment system, evaporation multiple when evaporating black liquor in a black liquor treatment system, boiler economizer temperature in a black liquor treatment system, moisture content of sludge in a green liquor treatment system, specific gravity of black liquor, specific gravity of green liquor, specific gravity of white liquor, specific gravity of weak liquor, specific gravity of dregs, specific gravity of calcium carbonate before calcination, and specific gravity of calcium oxide after calcination.

10. 8. The information processing system according to claim 7, The two or more parameters include, as the result parameters, one or more selected from the group consisting of unit weight (basis weight) of the paper product, yield rate, white water concentration, moisture content of the paper product, amount of steam or unit consumption in the equipment for manufacturing the paper product, steam temperature in the equipment for manufacturing the paper product, steam pressure in the equipment for manufacturing the paper product, thickness of the paper product, ash concentration in the paper product, type of defect in the paper product, number of defects in the paper product, timing of paper breakage in the process, freeness, degree of beating, aeration amount, air temperature inside the dryer, splicing rate, number of splices, amount of broken paper, number of defects, defect index, outside air temperature including indoor air temperature, outside humidity including indoor humidity, moisture content of wet paper, moisture content of product, temperature of the dryer roll / cylinder, measurements from five senses sensors, amount of fuel or unit consumption of fuel, calcination rate in the calcination system, causticization rate in the slaking / causticizing system, amount of caustic added in the cooking system, and amount of lime added in the slaking / causticizing system.

11. 8. The information processing system according to claim 7, The indicator relates to the quality or energy content of the paper product and / or materials associated with the production of the paper product; The quality is one or more selected from the group consisting of the number of defects, paper strength, joint rate, sizing degree, air permeability, smoothness, ash content, color tone, whiteness, formation, odor, causticization rate, burnt rate, kappa number, freeness, and moisture content of the paper product and / or materials related to the production of the paper product; An information processing system in which the amount of energy relates to one or more selected from the group consisting of steam amount, steam consumption rate, fuel amount, and fuel consumption rate when producing the paper product and / or materials related to the production of the paper product.

12. An information processing method executed by an information processing system, A method comprising the step of executing processing of each part of the information processing system according to any one of claims 1 to 11.

13. A program, A program for causing a computer to execute processing of each unit of the information processing system according to any one of claims 1 to 11.

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