Flush water exchange prediction system, flush water exchange prediction device, flush water exchange prediction method, computer program

Abstract

To provide a washing water replacement prediction device which can efficiency predict the replacement timing (time) of washing water with high accuracy.SOLUTION: A washing water replacement prediction system S comprises: a conductivity sensor 9 which acquires conductivity data of washing water in a washing tank; a conductivity data storage unit 103 which stores the acquisition result; an operation start time and operation stop time calculation unit 106 which calculates the operation start time t1 at which washing processing is started and the operation stop time t2 at which the washing processing is stopped on the basis of the stored conductivity data; a regression line calculation unit 107 which acquires the conductivity data in a period from the operation start time t1 to the most recent operation stop time t2 after that and derives the regression line expressing transition of the conductivity; and a replacement time prediction unit 108 which predicts the replacement timing of the washing water on the basis of the derivation result.SELECTED DRAWING: Figure 1

Description

Technical Field

[0001] The present invention relates to a technique for predicting the timing of replacement of washing water used in a washing process such as a sheet metal washing process.

Background Art

[0002] Generally, as a method for measuring the dirt of washing water used in a washing process such as a sheet metal washing process, a method of measuring the conductivity is known. <00,00010>For example, in the method for monitoring washing water disclosed in Patent Document 1, the change rate between the measured conductivity of the washing water and the conductivity of the previous time is calculated and compared with the average change rate of about the last 10 times. Then, by determining whether a rapid change has occurred, the timing when the conductivity that requires replacement is reached is predicted.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] A method of detecting a large amount of rapid change as in the monitoring method disclosed in Patent Document 1 is considered effective when the amount of change in the normal conductivity is not large. However, in the case of sheet metal washing, the change in the conductivity of the washing water is mixed with operation and stop, and there are also a washing state and a waiting state for washing during the operation time. Therefore, since the value changes frequently (see the graph shown in FIG. 4), there remains a problem that the replacement time (time) of the washing water cannot be efficiently predicted with high accuracy by the conventional technology.

[0005] The primary objective of this invention is to provide a flush water replacement prediction system and flush water replacement prediction device that can efficiently predict the timing of flush water replacement with high accuracy. Furthermore, the invention provides a method and computer program for this purpose. [Means for solving the problem]

[0006] The present invention, which solves the above problems, is a water replacement prediction system for predicting the timing of water replacement in a water washing tank during a sheet metal washing process, comprising: conductivity acquisition means for acquiring conductivity data of the water washing water in the water washing tank; storage means for storing the acquisition results of the conductivity acquisition means; calculation means for calculating the start time when the washing process is started and the stop time when the washing process is stopped based on the conductivity data stored in the storage means; derivation means for acquiring the conductivity data from the storage means from the start time to the most recent stop time thereafter and deriving a regression line representing the change in conductivity; and prediction means for predicting the timing of water replacement based on the derivation results of the derivation means.

[0007] Furthermore, the present invention provides a method for predicting the timing of water replacement in a washing tank during a sheet metal washing process, comprising the steps of acquiring conductivity data of the water in the washing tank and Acquired conductivity data The method is characterized by comprising: a step of storing the conductivity data; a step of calculating the start time when the cleaning process is started and the stop time when the cleaning process is stopped based on the stored conductivity data; a step of acquiring the conductivity data from the start time to the most recent stop time and deriving a regression line representing the change in conductivity; and a step of predicting the timing of the water change based on the derived result.

[0008] Furthermore, the computer program is a computer program that causes the computer to function as a water exchange prediction device that predicts when to replace the water in a water rinsing tank during the cleaning process of a sheet metal cleaning process, characterized in that the computer functions as: conductivity acquisition means for acquiring conductivity data of the water in the water rinsing tank; storage means for storing the acquisition results of the conductivity acquisition means; calculation means for calculating the start time when the cleaning process is started and the stop time when the cleaning process is stopped based on the conductivity data stored in the storage means; derivation means for acquiring the conductivity data from the storage means from the start time to the most recent stop time thereafter and deriving a regression line representing the change in conductivity; and prediction means for predicting when to replace the water based on the derivation results of the derivation means.

[0009] Furthermore, the water washing water replacement prediction device is a water washing water replacement prediction device that predicts the timing of replacing the water washing water in a water washing tank during the washing process of a sheet metal washing process, and is characterized by comprising: a storage means for acquiring conductivity data of the water washing water in the water washing tank and storing the acquisition results; a calculation means for calculating the start time when the washing process is started and the stop time when the washing process is stopped based on the conductivity data stored in the storage means; a derivation means for acquiring the conductivity data from the storage means from the start time to the most recent stop time thereafter and deriving a regression line representing the change in conductivity; and a prediction means for predicting the timing of replacing the water washing water based on the derivation results of the derivation means. [Effects of the Invention]

[0010] According to the present invention, it is possible to provide a flush water replacement prediction system and a flush water replacement prediction device that can efficiently predict the timing (time) for replacing flush water with high accuracy. Furthermore, a method and a computer program for this can also be provided. [Brief explanation of the drawing]

[0011] [Figure 1]A diagram for explaining an example of the overall configuration including the washing water replacement prediction device according to the present embodiment. [Figure 2] A diagram for explaining an example of the functional configuration of the washing water replacement prediction device 1 according to the present embodiment. [Figure 3] (a) and (b) are diagrams for explaining an example of the conductivity data stored in the conductivity data storage unit (or the server for storing sensor data). [Figure 4] A diagram for explaining the operating time, start time of operation, and stop time of operation. [Figure 5] A diagram for explaining an example of a method for calculating the start time of operation by the start time of operation / stop time of operation calculation unit. [Figure 6] A diagram for explaining an example of a method for calculating the stop time of operation by the start time of operation / stop time of operation calculation unit. [Figure 7] A flowchart showing an example of the processing procedure for calculating the start time of operation and the stop time of operation. [Figure 8] A diagram for explaining an example of a method for deriving a regression line by the regression line calculation unit. [Figure 9] A diagram for explaining an example of a method for calculating the replacement time (time) by the replacement time prediction unit. [Figure 10] A flowchart showing an example of the processing procedure for calculating the replacement time (time) of the washing water. [Figure 11] A diagram for explaining an example of a method for calculating the threshold value of the first slope. [Figure 12] A diagram for explaining an example of a method for calculating the threshold value of the second slope and the threshold value of the difference. [Figure 13] A diagram for explaining an example of a method for determining various parameters required for calculating the start time of operation and the stop time of operation using the operation determination threshold value h. [Figure 14] A diagram for explaining an example of the overall configuration including the washing water replacement prediction device, which is different from FIG. 1. [Figure 15] A diagram for explaining the difference in conductivity due to the installation position of the conductivity sensor in the washing tank.

Best Mode for Carrying Out the Invention

[0012] Hereinafter, taking as an example the case where the present invention is applied as a washing water replacement prediction system (washing water replacement prediction device) for predicting the replacement timing of the washing water in a washing tank in a washing process such as a sheet metal washing process, embodiments will be described while referring to the drawings. Note that a PC (personal computer) may be connected so as to be able to exchange various information via a communication network, and may be configured to function as a washing water replacement prediction device having a function of predicting the replacement timing of the washing water.

[0013] [Example of Embodiment] FIG. 1 is a diagram for explaining an example of the overall configuration including the washing water replacement prediction system S according to the present embodiment. In the overall configuration shown in FIG. 1, it includes a washing machine 3, an inlet 4, an outlet 8, and a washing water replacement prediction system S. The washing water replacement prediction system S includes a washing water replacement prediction device 1, an operation terminal 2, a conductivity sensor 9, a wireless communication module 10, and a gateway 11. Note that the washing water replacement prediction device 1 and the operation terminal 2 may be configured as an information processing system or an information processing device having an information processing function such as a personal computer.

[0014] The washing machine 3 includes a washing tank 5, a washing water tank 6, and a drying area 7. An object to be washed (for example, a sheet metal product) is input from the inlet 4, sequentially processed through the washing tank 5, the washing water tank 6, and the drying area 7, and then carried out from the outlet 8. The washing tank 5 stores, for example, a cleaning liquid of an alkaline solution. The washing water tank 6 stores washing water. The washing water replacement prediction device 1 according to the present embodiment predicts the replacement timing of the washing water in the washing water tank 6.

[0015] The conductivity sensor 9 is attached to the washing water tank 6 and is a sensor that measures the ease of electric current flow in the washing water in the washing water tank 6. Note that the conductivity sensor 9 is installed, for example, at the lower part of the washing water tank 6 (for example, inside a pipe through which water is circulated). The wireless communication module 10 works in cooperation with the gateway 11 to transmit the conductivity data acquired (detected) by the conductivity sensor 9 to the flush water exchange prediction device 1. Alternatively, the conductivity data detected by the conductivity sensor 9 may be stored in, for example, a sensor data storage server 12 (see Figure 14) so ​​that the flush water exchange prediction device 1 can retrieve it as needed.

[0016] Furthermore, the configuration of the wireless communication module 10 and gateway 11 is not limited to this configuration, as long as it is capable of acquiring conductivity data detected by the conductivity sensor 9 in the water exchange prediction device 1.

[0017] The operation terminal 2 is, for example, an input device such as a keyboard or a pointing device, and accepts input operations directed at the water exchange prediction device 1 by the user of the water exchange prediction device 1. The operation terminal 2 may also have a function to display prediction results output by the water exchange prediction device 1 via a display device such as a display on the operation terminal 2.

[0018] Figure 2 is a diagram illustrating an example of the functional configuration of the water exchange prediction device 1 according to this embodiment. The flush water exchange prediction device 1 consists of a network interface 101, a conductivity data acquisition unit 102, a conductivity data storage unit 103, an operation reception unit 104, a main control unit 105, an operation start time / operation stop time calculation unit 106, a regression line calculation unit 107, an exchange time prediction unit 108, and a prediction graph display unit 109. The functional configuration of each unit will be described in detail below.

[0019] The network interface 101 controls the exchange of various information from external devices connected to the water exchange prediction device 1, for example, or the exchange of information via wireless communication. The conductivity data acquisition unit 102 receives the acquisition results from the conductivity sensor 9, which functions as a conductivity acquisition means, via the network interface 101. The conductivity data storage unit 103 functions as a storage means for storing the acquisition results of the conductivity sensor 9, which functions as a conductivity acquisition means. The operation reception unit 104 receives input operations from the operation terminal 2 via the network interface 101.

[0020] The main control unit 105 controls each component of the flush water exchange prediction device 1. The main control unit is realized through the cooperation of hardware resources of a computer device having a processor and internal memory implemented in the flush water exchange prediction device 1 and a predetermined computer program. The flush water exchange prediction device 1 also has an RTC (Real Time Clock) module that outputs time data representing the year, month, day and time, and a synchronization clock for control operations. Thus, the main control unit 105 functions as a control means that comprehensively controls each process performed by the water washing and water exchange prediction device 1.

[0021] The start time / stop time calculation unit 106 functions as a calculation means for calculating the start time t1 when the cleaning process is started and the stop time t2 when the cleaning process is stopped, based on the conductivity data stored in the conductivity data storage unit 103. Details of the specific calculation methods for the start time t1 and stop time t2 will be described later.

[0022] The regression line calculation unit 107 acquires conductivity data from the conductivity data storage unit 103 between the start time t1 and the most recent stop time t2, and functions as a derivation means that derives a regression line representing the trend of conductivity from the acquired results.

[0023] The replacement time prediction unit 108 functions as a prediction means that predicts the timing of water replacement based on the derivation results of the regression line calculation unit 107. The prediction graph display unit 109 generates a graph of prediction results, etc., which is displayed on a display, for example, on the operating terminal 2. The prediction graph display unit 109 may also be configured as a display device for the water exchange prediction device 1 that generates and displays the aforementioned graphs, etc.

[0024] Figure 3 is a diagram illustrating an example of conductivity data stored in the conductivity data storage unit 103 (or sensor data storage server 12). Figure 3(a) shows the conductivity data stored in the conductivity data storage unit 103 (or sensor data storage server 12), and Figure 3(b) is a graph of the stored conductivity data. Note that the higher the conductivity, the more contaminated the water is.

[0025] The conductivity data acquired via the conductivity sensor 9, conductivity data acquisition unit 102, etc., is associated with time data representing the time of acquisition and stored in the conductivity data storage unit 103 (or sensor data storage server 12). As shown in Figure 3(a), the information stored in the conductivity data storage unit 103 (or sensor data storage server 12) consists of items such as "time" and "conductivity".

[0026] Figure 4 is a diagram illustrating the operating time, the start time t1, and the stop time t2. The graph in Figure 4 has conductivity on the vertical axis and time on the horizontal axis. Hereafter, the period during which the washing machine 3 is in operation will be referred to as "operating time." The conductivity sensor 9 is assumed to be installed at the bottom of the washing tank 6 (for example, inside the piping that circulates the water).

[0027] As shown in Figure 4, the conductivity data is a mix of operating time data and non-operating time data. During operating time, the system is in a state of washing (water contamination increases) and a state of waiting to wash (water contamination decreases due to filtration), so the conductivity changes are a mix of increases and decreases. Outside of operating time, the conductivity changes upward. This is not because the water contamination has increased, but rather because the stirring stops and the water contamination settles at the bottom of the washing tank 6, and the system is affected by the settled contamination. Therefore, when predicting when to change the washing water in the washing tank, data outside of operating time is noise because it does not accurately measure the water contamination. One of the features of the present invention is that it predicts the timing for replacing the water in the washing tank based on the operating time from the start time t1 to the most recent stop time t2.

[0028] Figure 5 is a diagram illustrating an example of how the start time t1 is calculated by the start time / stop time calculation unit 106. Specifically, a sharp decline occurs at the start of operation, and by detecting this, the start time t1 is calculated. In the graph shown in Figure 5, the vertical axis represents conductivity and the horizontal axis represents time.

[0029] An arbitrary value is set in advance as the threshold for the slope used for determination (first slope threshold k1). The slope of the graph representing the change in conductivity data over a predetermined time (from t to t+5 minutes in the graph) is calculated. If this slope is less than or equal to the first slope threshold k1, time t is set as the start time t1. The slope of the graph representing the change in conductivity can be calculated, for example, using the least squares method.

[0030] In this way, the start time / stop time calculation unit 106 compares the slope of a graph representing the change in conductivity of the conductivity data over a predetermined time (t to t+5 minutes) with a first slope threshold k1, and calculates the start time t1 based on the comparison result. Since the first slope threshold k1 is a negative value, if we compare the absolute values ​​of the slopes, it would be "when the absolute value of the conductivity slope is greater than or equal to the absolute value of the first slope threshold k1". Thus, the start time / stop time calculation unit 106 determines the start time (t) of a predetermined period (t to t+5 minutes) as the start time t1 when the absolute value of the slope of the graph representing the change in conductivity is greater than or equal to the absolute value of the first slope threshold k1.

[0031] Figure 6 is a diagram illustrating an example of how the start time / stop time calculation unit 106 calculates the stop time t2. Specifically, the conductivity gradually increases after the shutdown time, and by detecting this, the shutdown time t2 is calculated. In the graph shown in Figure 6, the vertical axis represents conductivity and the horizontal axis represents time.

[0032] The calculation method is performed by first setting arbitrary values ​​as the difference threshold d and the slope threshold (second slope threshold k2) for determination. The maximum and minimum values ​​of the conductivity data and the slope of the graph representing the change in conductivity data are determined for a predetermined time (from t to t+30 minutes in the graph). If the difference between the maximum and minimum values ​​is less than or equal to the difference threshold d, and the slope of the graph is greater than or equal to 0 and less than or equal to the second slope threshold k2, then time t is set to the shutdown time t2. The slope of the graph representing the change in conductivity is calculated, for example, using the least squares method.

[0033] In this manner, the start-up / stop-up time calculation unit 106 calculates the start-up / stop-up time t2 based on the difference between the maximum and minimum conductivity values ​​of the conductivity data over a predetermined period (t to t+30 minutes), and the result of comparing the slope of the graph representing the change in conductivity with a second slope threshold k2. This is because, when the washing machine 3 is in operation, the conductivity value may fluctuate significantly within 30 minutes (a predetermined time), potentially causing the slope calculated using the least squares method to be greater than or equal to the second slope threshold k2. Therefore, to improve the accuracy of the calculation of the shutdown time t2, the maximum and minimum values ​​of the graph are used in addition to the slope.

[0034] Thus, the start time / stop time calculation unit 106 determines the start time (t) of a predetermined period as the stop time t2 when the difference is less than or equal to the difference threshold d, the slope of the graph representing the change in conductivity is 0 or greater, and less than or equal to the second slope threshold. The procedure for calculating the start time and stop time will be explained below.

[0035] Figure 7 is a flowchart showing an example of the processing procedure for calculating the start time and stop time of operation. Note that each process shown in Figure 7 is primarily controlled by the main control unit 105. The explanation will proceed assuming that the first slope threshold k1, the second slope threshold k2, and the difference threshold d mentioned above are pre-set by the user.

[0036] The main control unit 105 determines whether it has received input to start calculating the start time t1 and the stop time t2 via the operation terminal 2, and starts processing if it has received such input. The main control unit 105 sets the initial value of (time)t to the beginning time of the conductivity data (S101). The main control unit 105 derives the "slope" of the conductivity from (time)t to t+5 minutes (S102).

[0037] The main control unit 105 determines whether the "slope" derived in step S102 is less than or equal to the first slope threshold k1 (S103). If it is less than or equal to the first slope threshold k1 (S103: Yes), the main control unit 105 saves the value of (time) t as the start time t1 (S104) and proceeds to step S107. The information of the start time t1 is stored in a readable format in a storage device such as the conductivity data storage unit 103 (or the sensor data storage server 12).

[0038] If not (S103: No), the main control unit 105 advances the time (time) t by 1 minute (S105). The main control unit 105 determines whether (time)t has reached the end time of the conductivity data (S106). If it determines that it has reached the end time (S106:Yes), the main control unit 105 terminates processing. Otherwise (S106:No), the main control unit 105 proceeds to the processing in step S102.

[0039] The main control unit 105 derives the "difference between the maximum and minimum values" and the "slope" of the conductivity from (time) t to t+30 minutes (S107). The main control unit 105 determines whether the difference between the maximum and minimum values ​​is less than or equal to the difference threshold d (S108). If it is determined that it is less than or equal to the difference threshold d (S108: Yes), the main control unit 105 determines whether the "slope" is greater than or equal to 0 and less than or equal to the second slope threshold k2 (S109). If not (S108: No), the main control unit 105 advances the time (time) t by 1 minute (S111).

[0040] If the main control unit 105 determines that the second slope threshold k2 is less than or equal to (S109: Yes), it saves the value of (time)t as the shutdown time t2 (S110) and proceeds to step S102. Otherwise (S109: No), the main control unit 105 advances the time of (time)t by 1 minute (S111). The information of the shutdown time t2 is stored in a readable format in a storage device such as the conductivity data storage unit 103 (or the sensor data storage server 12).

[0041] The main control unit 105 determines whether (time) t has reached the end time of the conductivity data (S112). If it determines that it has reached the end time (S112: Yes), the main control unit 105 terminates processing. Otherwise (S106: No), the main control unit 105 proceeds to step S107. Through this series of processes, the start time t1 and stop time t2 are determined.

[0042] Figure 8 is a diagram illustrating an example of how the regression line is derived by the regression line calculation unit 107. In the graph shown in Figure 8, the vertical axis represents conductivity and the horizontal axis represents time. Specifically, the regression line calculation unit 107 reads the start time t1 and stop time t2 stored in a storage device such as the conductivity data storage unit 103 (or the sensor data storage server 12). Then, it extracts conductivity data for the operating time from start time t1 to stop time t2. The least squares method is applied to the extracted data to derive a regression line.

[0043] In this manner, the regression line calculation unit 107 acquires conductivity data from the conductivity data storage unit 103 for the period from the start time t1 to the most recent stop time t2, and derives a regression line representing the trend of conductivity from the acquired results. Furthermore, as shown in Figure 8, the regression line can be derived with greater accuracy by using conductivity data from multiple operating times.

[0044] Figure 9 illustrates an example of how the replacement timing (time) is calculated by the replacement time prediction unit 108. In the graph shown in Figure 9, the vertical axis represents conductivity and the horizontal axis represents time. Note that an arbitrary value is set in advance as the replacement threshold th.

[0045] As shown in Figure 9, the exchange time prediction unit 108 compares the slope of the regression line with a predetermined exchange threshold th and predicts the time when the regression line intersects the exchange threshold th (the time it is reached) as the exchange time. The procedure for calculating the exchange time will be described below.

[0046] Figure 10 is a flowchart showing an example of the processing procedure for calculating the timing (time) for changing the flush water. Each process shown in Figure 10 is primarily controlled by the main control unit 105. The aforementioned exchange threshold th is assumed to be pre-set by the user.

[0047] The main control unit 105 determines whether or not it has received input for predicting and executing the timing of water change via the operation terminal 2, and starts processing if it has received such input. The main control unit 105 acquires conductivity data from the time of the previous exchange to the predicted execution time (S201). The main control unit 105 calculates and saves the start time t1 and the stop time t2 (S202). The calculation and saving of the start time t1 and the stop time t2 are as described above using Figure 7.

[0048] The main control unit 105 reads the saved start time t1 and stop time t2 (S203). The main control unit 105 extracts conductivity data (conductivity data for operating time) from the start time t1 to the stop time t2 (S204). The main control unit 105 derives a regression line from the extracted conductivity data (S205). The main control unit 105 outputs the time at which the derived regression line reaches the exchange threshold th as the exchange time (S206). Through this series of processes, the timing (time) for changing the flush water is determined.

[0049] The information output as the replacement time is displayed via the prediction graph display unit 109, for example, on the display of the operation terminal 2.

[0050] Thus, in the flush water replacement prediction device 1 according to this embodiment, only the conductivity sensor 9 is used, and data other than operating time, which is noise during prediction, can be excluded to predict the timing (time) of flush water replacement with high accuracy.

[0051] As explained above, it is necessary to pre-set various parameters required for calculating the start and stop times of operation (the threshold for the first slope k1, the threshold for the second slope k2, and the threshold for the difference d). However, it can be difficult to manually determine appropriate values. This section describes a method for determining various parameters by first operating the cleaning device 2 for a certain period, manually recording the start time t1, the second slope threshold k2, etc., as basic data, and then using the recorded time and conductivity data.

[0052] Figure 11 is a diagram illustrating an example of a method for calculating the first slope threshold k1. As shown in Figure 11, the recorded information consists of items such as "time" (recording the start time t1) and "slope from t1 to t1+5 minutes".

[0053] The first slope threshold k1 is determined by manually recording several start times t1 and calculating the "slope" of a graph representing the change in conductivity data from t1 to t1+5 minutes for each recorded start time t1. The largest of the calculated "slope" (the one with the smallest absolute value of the slope) is determined as the first slope threshold k1. For example, in the record shown in Figure 11, the smallest "slope" occurs when the slope is -0.6 at "2021 / 2 / 8 09:00". Therefore, the first slope threshold k1 is determined to be k1 = -0.6.

[0054] Figure 12 illustrates an example of a method for calculating the second slope threshold k2 and the difference threshold d. As shown in Figure 12, the recorded information consists of items such as the "time" at which the shutdown time t2 was recorded, the "slope from t2 to t2+30 minutes", and the "difference between the maximum and minimum values ​​from t2 to t2+30 minutes".

[0055] The second slope threshold k2 is determined by manually recording several downtimes t2, and for each recorded downtime t2, calculating the "slope" of the graph representing the change in conductivity data from t2 to t2+30 minutes later, as well as the difference between the maximum and minimum values. The maximum value of the "slope" obtained in this way is determined as the second slope threshold k2, and the maximum value of the difference is determined as the difference threshold d.

[0056] For example, in the record shown in Figure 12, the maximum slope occurs when the slope is 0.09 at "2021 / 2 / 8 11:00". Also, the maximum difference occurs when the slope is 0.06 at "2021 / 2 / 4 13:00". Therefore, the threshold for the second slope k2 is determined to be k2 = 0.09, and the threshold for the difference d = 0.06.

[0057] In this way, various parameters necessary for calculating the start and stop times of operation can be determined to be more appropriate values.

[0058] Furthermore, as mentioned above, calculating the start and stop times requires determining the "slope," which is computationally intensive and load-intensive. In addition, various parameters necessary for calculating the start and stop times (the threshold k1 for the first slope, the threshold k2 for the second slope, and the threshold d for the difference) must be set in advance. This section describes a method for calculating the start time t1 and stop time t2 with less computation and fewer parameters (specifically, only the operation determination threshold h) while minimizing the decrease in calculation accuracy.

[0059] Figure 13 illustrates an example of a method for determining various parameters necessary for calculating the start and stop times of operation using an operation determination threshold h. The graph shown in Figure 13 has conductivity on the vertical axis and time on the horizontal axis. The start time t1 and stop time t2 are calculated by arbitrarily setting an operating threshold h. If the conductivity value exceeds the operating threshold h for a certain period of time (e.g., 2 hours) or longer, the time when the excess began is set as the stop time t2. Even during operating hours (when conductivity changes in a mix of rising and falling), the conductivity may rise and exceed the operating threshold h, but if it is during operating hours, the conductivity will fall again, and the excess of the operating threshold h will be temporary. Therefore, the accuracy of the calculation is improved by using the start time of the excess when the operating threshold h is exceeded for a certain period of time or longer, rather than simply the time when the operating threshold h is exceeded. Furthermore, the start time t1 is defined as the time when the conductivity value falls below the operation determination threshold h during the period when the washing machine 3 is considered to be stopped, that is, the time after the operation stop time t2 calculated above. In this way, it is possible to calculate the start time t1 and stop time t2 with less computation and fewer parameters (only the operation determination threshold h) while minimizing the decrease in calculation accuracy.

[0060] Figure 14 is a diagram illustrating an example of an overall configuration including the flush water exchange prediction device 1, which differs from Figure 1. The difference between this overall configuration and the one shown in Figure 1, which includes the flush water exchange prediction device 1, is that the overall configuration shown in Figure 14 includes a sensor data storage server 12. In this case, the flush water exchange prediction device 1 will acquire conductivity data detected by the conductivity sensor 9 via the sensor data storage server 12.

[0061] The timing at which the flush water exchange prediction device 1 acquires conductivity data from the sensor data storage server 12 can be configured to acquire it periodically (for example, acquiring one day's worth of conductivity data once a day), or it can be configured to acquire all the conductivity data from the previous exchange time to the prediction execution time at the time of prediction execution.

[0062] In the preceding explanation, we assumed that the conductivity sensor 9 is installed in the lower part of the washing tank 6 (for example, inside the piping that circulates the water). Figure 15 is a diagram illustrating the difference in conductivity depending on the installation position of the conductivity sensor 9 in the wash tub 6. Figure 15(a) is a graph showing the change in conductivity when the sensor is installed at the bottom of the wash tub 6, and Figure 15(b) is a graph showing the change in conductivity when the sensor is installed at the top of the wash tub 6.

[0063] If the sensor is installed at the bottom of the washing tank 6, as shown in Figure 15(a), when the washing machine 3 stops operating, the washing water is not stirred and the dirt in the water settles at the bottom, so it is thought that the conductivity will increase due to the influence of the settled dirt. Furthermore, if the conductivity sensor 9 is installed at the top of the washing tank 6, as shown in Figure 15(b), when the washing machine 3 is stopped, the dirt settles, reducing the amount of dirt at the top of the washing tank 6, and thus the conductivity is expected to decrease. The following describes how to calculate the start time t1 and stop time t2 when the conductivity sensor 9 is installed on top of the washing tank 6 (Figure 15(b)).

[0064] When the conductivity sensor 9 is installed on top of the washing tank 6, the start time t1 of operation can be calculated by detecting the rapid increase in conductivity that occurs when the washing machine 3 starts operating. Specifically, an arbitrary value is set in advance as the threshold for the slope used for determination (first slope threshold k1). The slope of the graph representing the change in conductivity of the conductivity data over a predetermined time (t to t+5 minutes) is calculated. If that slope is greater than or equal to the first slope threshold k1, time t is set as the start time t1.

[0065] Furthermore, when the conductivity sensor 9 is installed on top of the washing tank 6, the shutdown time t2 can be calculated by detecting the gradual decrease in conductivity that occurs after the washing machine 3 stops operating. Specifically, arbitrary values ​​are set in advance as the difference threshold d for calculation and the slope threshold (second slope threshold k2) for determination. The maximum and minimum conductivity values ​​of the conductivity data and the slope of the graph representing the change in conductivity data are determined for a predetermined time (from t to t+30 minutes). If the difference between the maximum and minimum values ​​is less than or equal to the difference threshold d, and the slope of the graph is between the second slope threshold k2 and 0, then time t is set as the shutdown time t2. In this case, the second slope threshold k2 is a negative value, so if we compare the absolute values ​​of the slopes, it would be "when the absolute value of the conductivity slope is greater than or equal to 0 and less than or equal to the absolute value of the second slope threshold k2."

[0066] The above description is intended to illustrate the present invention in more detail, and the scope of the present invention is not limited to these examples. Various forms that do not depart from the spirit of the present invention are also included, and for example, parts of the embodiments described above may be combined as appropriate. For example, the predetermined time of 5 minutes or 30 minutes as described above is not the only example.

[0067] Furthermore, the present invention may also be a method that includes the procedures for each process performed in an information processing system or an information processing device. Furthermore, the present invention may also be a computer program or application software that causes a computer to execute the procedures for each process performed in the information processing system or information processing device described above. This computer program can be distributed via various recording media, or via a network or cloud-based database. This computer program becomes executable when installed on a computer having a storage device such as ROM, thereby realizing the information processing system or information processing device described above. Furthermore, the present invention may also be a method that includes the procedures for each process performed in the information processing system or information processing device described above.

[0068] Furthermore, the present invention may also be a computer program that causes a computer to execute the procedures of each process performed in the information processing system and information processing device described above. This computer program can be distributed via various recording media or a network. When this computer program is installed on a computer having a storage device such as ROM, it becomes executable and realizes the information processing system and information processing device described above. [Explanation of Symbols]

[0069] 1...Water washing water exchange prediction device, 2...Operation terminal, 3...Washing machine, 4...Inlet, 5...Washing tank, 6...Water washing tank, 7...Drying area, 8...Outlet, 101...Network interface, 102...Conductivity data acquisition unit, 103...Conductivity data storage unit, 104...Operation reception unit, 105...Main control unit, 106...Operation start time / operation stop time calculation unit, 107...Regression line calculation unit, 108...Exchange time prediction unit, 109...Prediction graph display unit, S...Water washing water exchange prediction system.

Claims

1. A water replacement prediction system for predicting when to replace the water in a water washing tank during the cleaning process of a sheet metal cleaning process, Conductivity acquisition means for acquiring conductivity data of the wash water in the wash tank, A storage means for storing the acquisition results of the conductivity acquisition means, A calculation means that calculates the start time when the cleaning process is started and the stop time when the cleaning process is stopped, based on the conductivity data stored in the storage means. A derivation means for acquiring conductivity data from the storage means from the start time of operation to the most recent stop time of operation thereafter, and deriving a regression line representing the trend of conductivity, The present invention is characterized by having a prediction means that predicts the timing of replacing the flush water based on the derivation result of the derivation means. Flush water exchange prediction system.

2. The calculation means is characterized by comparing the slope of a graph representing the change in conductivity of the conductivity data over a predetermined period from a predetermined start time to an end time with a first slope threshold, and calculating the start time of operation based on the comparison result. The flush water exchange prediction system according to claim 1.

3. The calculation means is characterized in that the start time of the predetermined time is set as the start time of operation when the absolute value of the slope of the graph representing the change in conductivity is equal to or greater than the absolute value of the first slope threshold. The flush water exchange prediction system according to claim 2.

4. The calculation means is characterized by calculating the shutdown time based on the difference between the maximum and minimum values ​​of the conductivity data over a predetermined time, and the result of comparing the slope of a graph representing the trend of the conductivity with a second slope threshold. The flush water exchange prediction system according to claim 2.

5. The calculation means is characterized in that the start time of the predetermined time is set to the shutdown time when the difference value is less than or equal to a predetermined threshold, the slope of the graph representing the change in conductivity is 0 or greater, and less than or equal to the second slope threshold, The flush water exchange prediction system according to claim 4.

6. The derivation means is characterized by acquiring conductivity data from the storage means for a plurality of operating start times up to the most recent operating stop time, and deriving a regression line representing the change in conductivity based on this conductivity data. A water exchange prediction system for washing toilets according to any one of claims 1 to 5.

7. The prediction means is characterized by comparing the slope of the regression line with a predetermined exchange threshold and predicting the time when the regression line intersects the exchange threshold as the exchange time. A water exchange prediction system for washing toilets according to any one of claims 1 to 5.

8. A method for predicting when to replace the water in a water washing tank during the cleaning process of a sheet metal cleaning process, A step of acquiring conductivity data of the water used for washing in the washing tank, A step of storing the acquired conductivity data, A step of calculating the start time when the cleaning process is initiated and the stop time when the cleaning process is stopped, based on the stored conductivity data. A step of acquiring the conductivity data from the start time of operation to the most recent stop time of operation thereafter and deriving a regression line representing the change in conductivity, The method is characterized by comprising the step of predicting the timing of replacing the flush water based on the derived result. A method for predicting water replacement during flushing.

9. A computer program that enables a computer to function as a water exchange prediction device that predicts when to replace the water in a water washing tank during the cleaning process of a sheet metal cleaning process, The aforementioned computer, Conductivity acquisition means for acquiring conductivity data of the water used for washing in the washing tank, A storage means for storing the acquisition result of the conductivity acquisition means, A calculation means that calculates the start time when the cleaning process is started and the stop time when the cleaning process is stopped, based on the conductivity data stored in the storage means. A derivation means for acquiring conductivity data from the storage means from the start time of operation to the most recent stop time of operation thereafter, and deriving a regression line representing the trend of conductivity, The method is characterized by functioning as a prediction means for predicting the timing of replacing the flush water based on the derivation result of the derivation means. Computer program.

10. A water exchange prediction device for predicting the timing of water exchange in a water washing tank during the cleaning process of a sheet metal cleaning process, A storage means for acquiring conductivity data of the water used for washing in the washing tank and storing the acquired results, A calculation means that calculates the start time when the cleaning process is started and the stop time when the cleaning process is stopped, based on the conductivity data stored in the storage means. A derivation means for acquiring conductivity data from the storage means from the start time of operation to the most recent stop time of operation thereafter, and deriving a regression line representing the trend of conductivity, The present invention is characterized by having a prediction means that predicts the timing of replacing the flush water based on the derivation result of the derivation means. Water exchange prediction device for flushing.

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