Information processor for diagnosing the status of the humidity sensor

Abstract

The present invention relates to an information processor. The information processor includes a diagnosis unit for diagnosing the state of a humidity sensor, and when the number of days in which the ratio of detection data indicating 100% humidity of the humidity sensor in all detection data of one day is equal to or greater than a reference ratio has continued for a reference period of time, the diagnosis unit diagnoses that the humidity sensor is in a deteriorated state.

Description

Technical Field

[0001] The present invention relates to an information processor including a diagnosis unit for diagnosing a state of a humidity sensor. Background Art

[0002] Humidity sensors are commonly used in various applications. In the process of using humidity sensors, due to various factors, the detected data value from the humidity sensor may deviate from the true value. Therefore, it is necessary to make an appropriate response to this situation.

[0003] JP2005-062199A describes a technique for automatically calibrating a humidity sensor. The humidity sensor disclosed in JP2005-062199A reaches a degraded state when used continuously for a long time, so that the detection data continuously shows an excessively high value. Once the humidity sensor reaches such a degraded state, it is difficult to resolve the degraded state. Furthermore, even in the case where the degraded state can be resolved, it takes a long time to resolve the degraded state. Therefore, when using a humidity sensor, it is desirable to appropriately sense the degraded state of the humidity sensor.

[0004] In view of this problem, an object of the present invention is to provide an information processor capable of appropriately sensing a degradation state of a humidity sensor. Summary of the invention

[0005] In order to solve the above problem, the information processor includes a diagnostic unit for diagnosing the state of the humidity sensor, and when the number of days in which the proportion of the detection data indicating 100% humidity of the humidity sensor in all the detection data of a day is equal to or greater than a reference proportion has continued for a reference period of time, the diagnostic unit diagnoses that the humidity sensor is in a degraded state.

[0006] The present invention makes it possible to appropriately sense the degradation state of the humidity sensor. BRIEF DESCRIPTION OF THE DRAWINGS

[0007] The drawings described herein are for illustrative purposes only of selected embodiments and not all possible implementations, and are not intended to limit the scope of the present invention.

[0008] Figure 1 is a schematic diagram showing a schematic structure of an information processing system according to an embodiment mode of the present invention.

[0009] Figure 2 is a block diagram showing an example of a functional configuration of an information processing server according to an embodiment mode of the present invention.

[0010] Figure 3 : is a flowchart showing an example of a process flow related to diagnosis of the state of the humidity sensor performed by the information processing server according to the embodiment mode of the present invention.

[0011] Figure 4 : is a schematic diagram showing an example of transition of detection data of the humidity sensor according to the embodiment mode of the present invention. DETAILED DESCRIPTION

[0012] The preferred embodiment mode of the present invention will be described in detail below with reference to the accompanying drawings. The dimensions, materials and other specific values ​​indicated in this embodiment mode are only for the purpose of helping to understand the description of the present invention, and unless otherwise specifically indicated, they do not limit the present invention. It should be noted that components having substantially the same function or configuration in this specification and the accompanying drawings have the same reference numerals and will not be given a repeated description thereof, and components not directly related to the present invention are omitted in the accompanying drawings.

[0013] Will refer to Figure 1 The configuration of an information processing system 1 according to an embodiment mode of the present invention is described.

[0014] Figure 1 1 is a schematic diagram showing a schematic configuration of the information processing system 1 .

[0015] like Figure 1 As shown in FIG. 1 , the information processing system 1 includes an information processing server 10, a user terminal 20, a sensor device 30, and a weather information server 40. The information processing server 10, the user terminal 20, the sensor device 30, and the weather information server 40 can communicate via a wireless communication network. The information processing system 1 is a system for supporting a user U1 who is a farmer to cultivate agricultural products at a cultivation site C1. It should be noted that Figure 1 An example is shown in which the cultivation site C1 is a plastic greenhouse, but the cultivation site C1 may also be a cultivation site other than a plastic greenhouse (for example, an uncovered bare cultivation site, etc.).

[0016] It should be noted that the following description relates to a case where the information processing server 10 corresponds to an example of the information processor according to the present invention, but the information processor according to the present invention may also be another device (e.g., the user terminal 20) other than the information processing server 10. In addition, the functions of the information processor according to the present invention may also be implemented by a plurality of devices (e.g., the information processing server 10 and the user terminal 20). For example, the functions of the information processor according to the present invention may also be implemented by cloud computing.

[0017] Using the information acquired from each device, i.e., the user terminal 20, the sensor device 30, and the weather information server 40, the information processing server 10 transmits valuable information on the cultivation of agricultural products at the cultivation site C1 to the user terminal 20. The information transmitted from the information processing server 10 is displayed by the user terminal 20 to inform the user U1.

[0018] Specifically, the information processing server 10 uses the information acquired from each device to predict the pest risk to the agricultural products, and sends the result of the pest risk prediction to the user terminal 20. The pest risk is the risk of an outbreak of pests (i.e., disease damage, which is damage to agricultural products caused by diseases, or insect damage, which is damage to agricultural products caused by insects). In addition, the information processing server 10 may also send information acquired from the sensor device 30 (specifically, various types of detection data related to the cultivation site C1 detected by the sensor device 30) to the user terminal 20. Note that the detailed configuration of the information processing server 10 will be described later.

[0019] The user terminal 20 is an information processing terminal (specifically a smart phone) used by the user U1. Figure 1 An example in which the user terminal 20 is a smartphone is shown, but the user terminal 20 may be an information processing terminal other than a smartphone (for example, a tablet terminal or a stationary personal computer, etc.).

[0020] The user terminal 20 has a function of receiving an input operation performed by the user U1, and transmits input information (which is information input by the user U1) to the information processing server 10. The input information from the user U1 includes, for example, information indicating the address of the cultivation site C1, the size of the cultivation site C1, the type of agricultural product, the planting density of the agricultural product, the cultivation start time, the harvest time, or the history of chemical application (agricultural chemicals), etc. In addition, the user terminal 20 has a function of visually displaying information, and displays the information received from the information processing server 10.

[0021] The sensor device 30 is installed in the cultivation site C1 and includes a plurality of sensors. In this embodiment mode, the sensor device 30 includes at least a humidity sensor 31 for detecting the humidity in the cultivation site C1. In addition, the sensor device 30 includes, as sensors other than the humidity sensor 31, for example: a temperature sensor for detecting the air temperature in the cultivation site C1; a carbon dioxide concentration sensor for detecting the carbon dioxide concentration in the cultivation site C1; or a solar radiation sensor for detecting the amount of solar radiation in the cultivation site C1, etc.

[0022] The sensor device 30 transmits detection data from sensors provided in the sensor device 30 to the information processing server 10. For example, the sensor detects various physical quantities at detection times separated by preset time intervals, and the sensor device 30 transmits detection data from the sensor to the information processing server 10 at the detection times.

[0023] The weather information server 40 provides weather information to an external device. Specifically, the weather information server 40 transmits weather information in an area including the cultivation site C1 to the information processing server 10 according to a request from the information processing server 10. The weather information is information related to the weather, and includes, for example, information indicating the temperature of the outside air (i.e., the outside air temperature), the humidity of the outside air, the amount of solar radiation or the amount of rainfall, and the like.

[0024] Will refer to Figure 2 The configuration of the information processing server 10 according to the embodiment mode of the present invention is described.

[0025] The information processing server 10 includes, for example: a CPU (Central Processing Unit) as an operation processing device; a ROM (Read Only Memory), which is a memory element for storing programs and calculation parameters used by the CPU; and a RAM (Random Access Memory), which is a storage element for temporarily storing parameters that vary appropriately according to the implementation of the CPU, etc.

[0026] Figure 2 is a block diagram showing an example of the functional structure of the information processing server 10 .

[0027] like Figure 2 As shown in FIG. 1 , the information processing server 10 includes, for example, a communication unit 11, a control unit 12, and a memory unit 13. It should be noted that the communication unit 11 corresponds to an example of an output unit according to the present invention.

[0028] The communication unit 11 communicates with the devices in the information processing system 1. Specifically, the communication unit 11 receives information transmitted from these devices, namely, the user terminal 20, the sensor device 30, and the weather information server 40, and outputs the acquired information to the control unit 12 and the memory unit 13. In addition, the communication unit 11 transmits information generated by the control unit 12 to the user terminal 20.

[0029] The control unit 12 performs various types of processing for generating information to be sent to the user terminal 20. Figure 2 As shown in FIG. 1 , the control unit 12 includes, for example, a prediction unit 12 a , a diagnosis unit 12 b , and a correction unit 12 c which work in cooperation with a program.

[0030] The prediction unit 12a predicts the pest risk to agricultural products. Specifically, the prediction unit 12a predicts the pest risk to agricultural products by using a pre-learned prediction model. When information sent from each device, i.e., the user terminal 20, the sensor device 30, and the weather information server 40 is input, the prediction model outputs the pest risk to agricultural products. The prediction model can be constructed according to an existing algorithm such as a support vector machine, or it can be a time series model.

[0031] The diagnosis unit 12b diagnoses the state of the humidity sensor 31 in the sensor device 30. Specifically, the diagnosis unit 12b diagnoses whether the humidity sensor 31 is in a degraded state. Here, the humidity sensor 31 includes a humidity sensitive material provided between electrodes and formed of a polymer material, which produces moisture absorption (i.e., adsorption of water molecules) and moisture desorption (i.e., release of water molecules). The humidity sensor 31 detects humidity based on changes in electrical signals passing through the humidity sensitive material during moisture absorption and moisture desorption. The humidity sensor 31 may be a resistive humidity sensor that detects humidity by using a change in resistance of a humidity sensitive material during moisture absorption and moisture desorption, or it may be a capacitive humidity sensor that detects humidity by using a change in capacitance of a humidity sensitive material during moisture absorption and moisture desorption. However, if the humidity sensor 31 is used continuously for a long period of time, moisture may be retained in the humidity sensitive material, which may cause a degraded state in which the detection data continuously shows an excessively high value.

[0032] When the humidity sensor 31 has reached a degraded state, the value of the detection data from the humidity sensor 31 deviates from the true value. Here, the detection data of the humidity sensor 31 is used for the prediction of the pest risk performed by the prediction unit 12a. When the humidity sensor 31 has reached a degraded state, the accuracy of predicting the pest risk is therefore reduced. When it has been diagnosed that the humidity sensor 31 is in a degraded state, information indicating that the humidity sensor 31 is in a degraded state is correspondingly sent to the user terminal 20 through the communication unit 11, and, for example, the user U1 is prompted to replace the humidity sensor 31.

[0033] When it has been diagnosed that the humidity sensor 31 is in a degraded state, the correction unit 12c performs correction to reduce the value of the detection data of the humidity sensor 31. As a result, when the humidity sensor 31 has reached a degraded state, the value of the detection data of the humidity sensor 31 can be brought closer to the true value. When it has been diagnosed that the humidity sensor 31 is in a degraded state, the prediction unit 12a can predict the pest risk based on the detection data of the humidity sensor 31 corrected by the correction unit 12c. As a result, during the period before the humidity sensor 31 is replaced, the decrease in the prediction accuracy regarding the pest risk can be suppressed. It should be noted that the details of the processing related to the correction of the detection data of the humidity sensor 31 performed by the correction unit 12c will be described later.

[0034] The memory unit 13 stores information used in the processing performed by the control unit 12. Specifically, the memory unit 13 stores information transmitted from each device, that is, the user terminal 20, the sensor device 30, and the weather information server 40.

[0035] As indicated above, the diagnosis unit 12b diagnoses the state of the humidity sensor 31 in the information processing server 10. Here, when the number of days in which the ratio of the detection data indicating 100% humidity of the humidity sensor 31 in all the detection data of one day is equal to or greater than the reference ratio has continued for a reference period of time, the diagnosis unit 12b diagnoses that the humidity sensor 31 is in a degraded state. As a result, the degraded state of the humidity sensor 31 can be appropriately sensed. The details of the processing related to such diagnosis of the state of the humidity sensor 31 performed by the information server 10 will be described later.

[0036] The above-mentioned reference ratio and reference period are appropriately set to values ​​that can be experimentally determined when the humidity sensor 31 reaches a degraded state. For example, the reference ratio and reference period can be set based on the results of trend analysis of detection data obtained by preparing a degraded humidity sensor 31 in advance and collecting detection data from the humidity sensor 31.

[0037] Will refer to Figure 3 and Figure 4 The operation of the information processing server 10 according to the embodiment mode of the present invention will be described.

[0038] Figure 3 : is a flowchart showing an example of a process flow related to diagnosis of the state of the humidity sensor 31 performed by the information processing server 10. For example, Figure 3 The processing flow illustrated in FIG. 1 is triggered as a result of detection data being sent from the humidity sensor 31 to the information processing server 10 . Figure 3 Steps S101 and S105 in correspond to Figure 3 The start and end of the processing flow shown in FIG.

[0039] It should be noted that the following description relates to an example in which, when the percentile value corresponding to the difference between 100% and the reference ratio with respect to all detection data of the humidity sensor 31 for one day is that the number of days with 100% humidity has continued for the reference period, the number of days in which the ratio of the detection data indicating 100% humidity of the humidity sensor 31 among all detection data of one day is equal to or greater than the reference ratio is considered to have continued for the reference period. However, whether the number of days in which the ratio of the detection data indicating 100% humidity of the humidity sensor 31 among all detection data of one day is equal to or greater than the reference ratio has continued for the reference period can also be determined by another method that does not employ a percentile value.

[0040] When to start Figure 3 , the diagnosis unit 12b first determines in step S102 whether the number of days with percentile values ​​corresponding to the difference between 100% and the reference ratio for all detection data of one day of the humidity sensor 31 has continued for a reference period of time. When it is determined that the number of days with percentile values ​​of 100% humidity has continued for the reference period of time (step S102 / YES), the diagnosis unit 12b diagnoses that the humidity sensor 31 is in a degraded state, and the processing proceeds to step S103. On the other hand, if it has been determined that the number of days with percentile values ​​of 100% humidity has not continued for the reference period of time (step S102 / NO), the diagnosis unit 12b determines that the humidity sensor 31 is not in a degraded state, and Figure 3 The processing flow shown in the figure ends.

[0041] In step S102, taking the case where the reference ratio is 60% and the reference period is 10 days, when the number of days where the 40th percentile value of the detection data of the humidity sensor 31 is 100% humidity has continued for 10 days, the diagnosis unit 12b diagnoses that the humidity sensor 31 is in a degraded state. In this example, when the number of days where the ratio of the detection data of the humidity sensor 31 indicating 100% humidity in all the detection data of one day is equal to or greater than 60% has continued for 10 days, the humidity sensor 31 is diagnosed to be in a degraded state. The following description relates to an example where the reference ratio is 60% and the reference period is 10 days, but the reference ratio and the reference period are not particularly limited to this example.

[0042] Specifically, the diagnosis unit 12b can identify the 40th percentile value of the past few days by using all the detection data of the past few days of the humidity sensor 31. The diagnosis unit 12b can therefore determine whether the 40th percentile value of the past few days is 100% humidity or less than 100% humidity. If the 40th percentile value is 100% humidity at all times in the last 10 days, it is determined as "yes" in step S102. It should be noted that, for example, it can be determined whether the 40th percentile value of the past few days is 100% humidity after the detection of all the detection data of one day of the humidity sensor 31 has ended.

[0043] For example, when the humidity sensor 31 detects humidity every 10 minutes, all detection data of the humidity sensor 31 for one day constitute 144 items of data. However, the time intervals at which the humidity sensor 31 detects humidity do not have to be every 10 minutes, and the intervals do not have to be equal.

[0044] Figure 4 2 is a schematic diagram showing an example of transition of detection data of the humidity sensor 31 . Figure 4 The horizontal axis A1 in the graph indicates time, while the vertical axis A2 indicates humidity and the 40th percentile value R1. Figure 4 In FIG. 1 , the solid line L1 shows the transition of the detection data of 19 days from the first day D1 to the 19th day D19. Figure 4 In FIG. 4 , the 40th percentile value R1 of the detection data of each day is indicated by a point. It should be noted that the detection data of the humidity sensor 31 decreases during the day and increases at night.

[0045] exist Figure 4 In the example illustrated in , the 40th percentile value R1 rises from the second day D2 to the sixth day D6, reaching 100% humidity on the sixth day D6. The 40th percentile value R1 is 100% humidity for several days after the sixth day D6. Here, the 40th percentile value R1 of the detection data of the humidity sensor 31 is 100% humidity for 10 days in the period P1 from the sixth day D6 to the fifteenth day D15. Therefore, from the sixteenth day D16 onwards, it is diagnosed in step S102 that the humidity sensor 31 is in a degraded state.

[0046] It should be noted that even if the number of days in which the 40th percentile value is 100% humidity has continued for 10 days - there may be one or two days during which the 40th percentile value R1 is less than 100% humidity, the diagnosis unit 12b can also determine that the number of days in which the 40th percentile value is 100% humidity has continued for 10 days. In other words, even if the number of days in which the ratio of detection data indicating 100% humidity is equal to or greater than the reference ratio has continued for a reference period - there are several days during which the ratio of detection data indicating 100% humidity is less than the reference ratio, the diagnosis unit 12b can still determine that the number of days in which the reference ratio is reached or exceeded has continued for the reference period when the number of days in which the reference ratio is not reached is equal to or less than the predetermined number of days.

[0047] When it is determined as “YES” in step S102 , the communication unit 11 transmits information indicating that the humidity sensor 31 is in a degraded state to the user terminal 20 in step S103 .

[0048] For example, in step S103, the communication unit 11 causes the user terminal 20 to display the information that has been sent (i.e., the information indicating that the humidity sensor 31 is in a degraded state). In this way, the user U1 is notified of the information indicating that the humidity sensor 31 is in a degraded state. Thus, the user U1 is prompted to replace the humidity sensor 31. It should be noted that the user terminal 20 may display the information every time the information indicating that the humidity sensor 31 is in a degraded state is sent from the information processing server 10, or it may display the information only a set number of times a day or at a set time point.

[0049] In step S104 following step S103, the correction unit 12c performs correction to reduce the value of the detection data of the humidity sensor 31. Thereafter, Figure 3 The processing flow shown in the figure ends.

[0050] For example, in the correction of step S104, the correction unit 12c may subtract a predetermined value (e.g., 10%) from the value of the detection data of the humidity sensor 31, thereby reducing the value of the detection data. In addition, for example, the correction unit 12c may multiply the value of the detection data of the humidity sensor 31 by a predetermined ratio (e.g., 0.9), thereby reducing the value of the detection data. The predetermined value for subtraction and the predetermined ratio for multiplication are appropriately set in such a way that the value of the detection data of the humidity sensor 31 can be close to the true value. For example, a normal humidity sensor 31 that is not in a degraded state and a humidity sensor 31 that is in a degraded state may be prepared in advance, and based on a comparison result of the detection data of the normal humidity sensor 31 and the detection data of the degraded humidity sensor 31 obtained by collecting detection data from each humidity sensor 31 (specifically, the result of comparing the detection data items whose indication value is less than 100% humidity), the predetermined value for subtraction and the predetermined ratio for multiplication may be set.

[0051] It should be noted that the predetermined value for subtraction and the predetermined ratio for multiplication may be fixed values, or may be values ​​that vary according to parameters. Here, for correction, from the viewpoint of making the value of the detection data of the humidity sensor 31 appropriately close to the true value, the correction unit 12c preferably changes the degree of decrease in the value of the detection data of the humidity sensor 31 based on the degree of degradation of the humidity sensor 31. Specifically, for correction, the higher the degree of degradation of the humidity sensor 31, the correction unit 12c preferably increases the degree of decrease in the value of the detection data of the humidity sensor 31 (for example, increases the predetermined value for subtraction or decreases the predetermined ratio for multiplication). It should be noted that the predetermined value for subtraction and the predetermined ratio for multiplication may vary according to parameters other than the degree of degradation (for example, air temperature, etc.).

[0052] For example, the correction unit 12c may identify the degree of degradation of the humidity sensor 31 based on the transition of the detection data of the humidity sensor 31. For example, when the number of days in which the ratio of the detection data indicating 100% humidity of the humidity sensor 31 in all the detection data of one day is equal to or greater than a predetermined ratio (which is greater than a reference ratio) has continued for a predetermined period of time, the correction unit 12c may determine that the degree of degradation of the humidity sensor 31 is particularly high.

[0053] For the correction of the humidity sensor 31, preferably, the correction unit 12c estimates the excess humidity associated with the detection data indicating 100% humidity based on the transition of the past detection data of the humidity sensor 31, and uses a value obtained by reducing the estimated excess humidity, which is a hypothetical humidity exceeding 100% humidity, as a correction value. Assuming that the value of the detection data of the deteriorated humidity sensor 31 may fluctuate within a numerical range exceeding 100% humidity based on the detection data of the normal humidity sensor 31, the excess humidity constitutes the value of the detection data indicating the deteriorated humidity sensor 31.

[0054] exist Figure 4 In the example illustrated in , for example, as indicated above, the humidity sensor 31 is diagnosed to be in a degraded state from the 16th day D16 onwards, and the detection data is corrected. Figure 4 , a two-dot chain line L2 indicates a transition of the excess humidity estimated from the detection data of the 16th day D16 to the 19th day D19. Specifically, the correction unit 12c estimates the excess humidity by using a pre-learned estimation model. When information indicating a transition of past detection data of the humidity sensor 31 is input, the estimation model outputs the excess humidity associated with the detection data indicating 100% humidity. The estimation model may be constructed based on an existing algorithm such as a support vector machine, or it may be, for example, a time series model.

[0055] also, Figure 4 The dotted line L3 in indicates the transition of the value obtained by reducing the excess humidity of the detection data from the 16th day D16 to the 19th day D19. For example, the correction unit 12c can reduce the excess humidity by subtracting a predetermined value (for example, 10%) from the excess humidity of the detection data. In addition, for example, the correction unit 12c can reduce the excess humidity by multiplying the excess humidity of the detection data by a predetermined ratio (for example, 0.9). The predetermined value for subtraction and the predetermined ratio for multiplication are appropriately set in the same manner as in step S104 in such a way that the value of the detection data of the humidity sensor 31 can be close to the true value, and they can be fixed values ​​or can be values ​​that vary according to parameters. Here, in Figure 4 In the example illustrated in , a portion of the value indicated by the dotted line L3 exceeds 100% humidity. For example, when the value obtained by reducing the excess humidity exceeds 100% humidity, the correction unit 12c therefore uses 100% humidity as the correction value.

[0056] As described above, the excess humidity is a value that fluctuates within a numerical range exceeding 100% humidity according to the detection data of the normal humidity sensor 31. Here, for the correction of the humidity sensor 31, from the viewpoint of correctly estimating the excess humidity, the correction unit 12c preferably estimates the excess humidity based on the degree of deterioration of the humidity sensor 31. Specifically, the higher the degree of deterioration of the humidity sensor 31, the correction unit 12c preferably estimates a larger value of excess humidity.

[0057] Advantages of the information processing server 10 according to the embodiment mode of the present invention will be described.

[0058] In the information processing server 10 according to this embodiment mode, when the number of days in which the ratio of the detection data indicating 100% humidity of the humidity sensor 31 in all the detection data of a day is equal to or greater than the reference ratio has continued for a reference period, the diagnosis unit 12b diagnoses that the humidity sensor 31 is in a degraded state. As indicated above, the detection data of the degraded humidity sensor 31 continues to show an excessively high value. Therefore, it is possible that the detection data of the humidity sensor 31 shows 100% humidity. As indicated above, when the number of days in which the ratio of the detection data indicating 100% humidity of the humidity sensor 31 in all the detection data of a day is equal to or greater than the reference ratio has continued for a reference period, the humidity sensor 31 is diagnosed to be in a degraded state, whereby the degraded state of the humidity sensor 31 can be appropriately sensed.

[0059] Furthermore, in the information processing server 10 according to this embodiment mode, the diagnosis unit 12 b preferably diagnoses that the humidity sensor 31 is in a deteriorated state when the number of days of the percentile value corresponding to the difference between 100% and the reference ratio with respect to all the detection data of one day of the humidity sensor 31 has continued for a reference period of time. In this way, it is possible to appropriately determine whether the number of days in which the ratio of the detection data of the humidity sensor 31 indicating 100% humidity in all the detection data of one day is equal to or greater than the reference ratio has continued for the reference period of time.

[0060] Furthermore, in the information processing server 10 according to this embodiment mode, the output unit (e.g., the communication unit 11) preferably outputs information indicating that the humidity sensor 31 is in a degraded state when the humidity sensor 31 has been diagnosed to be in a degraded state. In this way, the user U1 can be notified of the information indicating that the humidity sensor 31 is in a degraded state, and the user U1 can be prompted to replace the humidity sensor 31.

[0061] It should be noted that the above text describes an example in which the communication unit 11 of the information processing server 10 is used as an output unit, but when the function of the information processor according to the present invention is implemented by the user terminal 20, for example, the display control unit (a functional unit for controlling the operation of the display device) of the user terminal 20 may correspond to the output unit. In this case, when it has been diagnosed that the humidity sensor 31 is in a degraded state, the display control unit of the user terminal 20, for example, causes the display device to display the fact that the humidity sensor 31 is in a degraded state.

[0062] Furthermore, in the information processing server 10 according to this embodiment mode, the correction unit 12c preferably performs correction to reduce the value of the detection data of the humidity sensor 31 when the humidity sensor 31 has been diagnosed to be in a degraded state. In this way, the value of the detection data of the degraded humidity sensor 31 can be made close to the true value. Therefore, before the humidity sensor 31 is replaced, the processing using the detection data of the humidity sensor 31 (for example, the prediction of the pest risk using the correction unit 12c) can be appropriately performed.

[0063] Furthermore, in the information processing server 10 according to the present embodiment mode, the correction unit 12c preferably changes the degree of reduction of the value of the detection data of the humidity sensor 31 based on the degree of deterioration of the humidity sensor 31, so as to correct the humidity sensor 31. In this way, the value of the detection data of the deteriorated humidity sensor 31 can be appropriately brought close to the true value. Therefore, before the humidity sensor 31 is replaced, the processing using the detection data of the humidity sensor 31 (for example, the prediction of the pest risk using the correction unit 12c) can be appropriately performed.

[0064] Furthermore, in the information processing server 10 according to this embodiment mode, the correction unit 12c preferably estimates the excess humidity associated with the detection data indicating 100% humidity based on the transition of the past detection data of the humidity sensor 31 when correcting the humidity sensor 31, and uses a value obtained by reducing the estimated excess humidity, which is an imaginary humidity exceeding 100% humidity, as a correction value. In this way, the value of the detection data indicating 100% humidity in the detection data of the deteriorated humidity sensor 31 can be appropriately close to the true value. Therefore, before the humidity sensor 31 is replaced, the processing using the detection data of the humidity sensor 31 (for example, the prediction of the pest risk using the correction unit 12c) can be appropriately performed.

[0065] Furthermore, in the information processing server 10 according to this embodiment mode, the correction unit 12c preferably estimates the excess humidity based on the degree of degradation of the humidity sensor 31 when calibrating the humidity sensor 31. In this way, the excess humidity can be accurately estimated. Therefore, the value of the detection data indicating 100% humidity among the detection data of the degraded humidity sensor 31 can be appropriately close to the true value.

[0066] Furthermore, in the information processing server 10 according to this embodiment mode, preferably, when the humidity sensor 31 is diagnosed to be in a deteriorated state, the prediction unit 12a predicts the pest risk based on the detection data of the humidity sensor 31 corrected by the correction unit 12c. In this way, before the humidity sensor 31 is replaced, the decrease in the prediction accuracy of the pest risk can be suppressed.

[0067] The preferred embodiment mode of the present invention is described above with reference to the accompanying drawings, but the present invention is of course not limited to the above embodiment mode, and it goes without saying that various revised examples or modified examples within the scope described in the patent claims also belong to the technical scope of the present invention.

[0068] For example, the above description relates to the humidity sensor 31 used in the information processing system 1 for supporting the user U1 as a farmer in cultivating agricultural products at the cultivation site C1, but the humidity sensor according to the present invention may also be a humidity sensor for other purposes.

[0069] In addition, for example, the above describes processing related to diagnosis of the degradation state of the humidity sensor 31, but the humidity sensor 31 in the above processing can also be replaced by another sensor (e.g., a soil moisture sensor, etc.) for which the upper limit of the detection data is 100%.

[0070] In addition, for example, the processing described in this specification with the help of flowcharts does not have to be implemented in the order shown in the flowcharts. Multiple processing steps can be implemented in parallel. In addition, additional processing steps can be adopted, or some of the processing steps can be omitted.

[0071] Furthermore, for example, a series of control processes provided by the above-described information processing server 10 can be realized by using any software, hardware, or a combination of software and hardware. The programs constituting the software are stored in advance in a recording medium provided inside or outside the information processor.

[0072] Reference Symbols

[0073] 1 Information Processing System

[0074] 10 Information processing server (information processor)

[0075] 11 Communication unit (output unit)

[0076] 12 Control Unit

[0077] 12a Prediction Unit

[0078] 12b Diagnostic unit

[0079] 12c Calibration Unit

[0080] 13 Memory Cells

[0081] 20 User Terminal

[0082] 30 Sensor devices

[0083] 31 Humidity sensor

[0084] 40 Weather Information Server

[0085] C1 Cultivation site

[0086] U1 User

Claims

1. An information processor (10), comprising a diagnosis unit (12b) for diagnosing a state of a humidity sensor (31), wherein when the number of days in which the ratio of detection data indicating 100% humidity of the humidity sensor (31) in all detection data of a day is equal to or greater than a reference ratio has continued for a reference period of time, the diagnosis unit (12b) diagnoses that the humidity sensor (31) is in a degraded state; The information processor (10) further includes a correction unit (12c) for performing correction to reduce the value of the detection data of the humidity sensor (31) when it has been diagnosed that the humidity sensor (31) is in a degraded state; Wherein for the correction, the correction unit (12c) estimates excess humidity associated with detection data indicating 100% humidity based on transitions in past detection data of the humidity sensor (31), and uses a value obtained by reducing the estimated excess humidity as a correction value, the excess humidity being an imaginary humidity exceeding 100% humidity.

2. An information processor according to claim 1, wherein when the percentile value corresponding to the difference between 100% and the reference ratio of all detection data of the humidity sensor (31) for one day is the number of days with 100% humidity that has continued for the reference period, the diagnostic unit (12b) diagnoses that the humidity sensor (31) is in a degraded state.

3. The information processor according to claim 1 or 2, comprising an output unit (11) for outputting information indicating that the humidity sensor (31) is in a degraded state when it has been diagnosed that the humidity sensor (31) is in a degraded state.

4. The information processor according to claim 1, wherein for the correction, the correction unit (12c) changes the degree of decrease of the value of the detection data of the humidity sensor (31) based on the degree of degradation of the humidity sensor (31).

5. The information processor according to claim 1, wherein for the correction, the correction unit (12c) estimates the excessive humidity based on a degree of degradation of the humidity sensor (31).

6. The information processor according to any one of claims 4 to 5 comprises a prediction unit (12a), which is used to predict the pest risk to agricultural products based on the detection data of the humidity sensor (31), wherein when the humidity sensor (31) has been diagnosed to be in a degraded state, the prediction unit (12a) predicts the pest risk based on the detection data of the humidity sensor (31) corrected by the correction unit (12c).

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