JP7204543B2 - Information processing device, information processing method, program, and electronic device - Google Patents

Description

TECHNICAL FIELD Embodiments of the present invention relate to an information processing apparatus, an information processing method, a program, and an electronic device.

2. Description of the Related Art Conventionally, there is known a technique of determining an abnormality such as a cooling abnormality by using a temperature detection result inside an electronic device. For example, a technique is disclosed in which the correlation between the cause of abnormality and the detected value of the sensor is derived in advance by simulation, and the cause of the abnormality is identified using the detected value of the sensor and the correlation.

However, the correlation derived from the preliminary simulation may change due to changes in conditions during actual driving, etc., making it difficult to accurately identify the cause of an abnormality in an electronic device.

JP 2008-34715 A JP 2017-54498 A

SUMMARY OF THE INVENTION It is an object of the present invention to provide an information processing apparatus, an information processing method, a program, and an electronic device that can accurately identify the cause of an abnormality in an electronic device. .

An information processing apparatus according to an embodiment is a first method for calculating an estimated value of a specific phenomenon of an electronic device according to a first detection value detected by each of a plurality of first sensors provided in the electronic device. a calculating unit; and a second detection value detected by a second sensor, which is the sensor that detects the specific phenomenon and is other than the first sensor, among the plurality of sensors provided in the electronic device. , the estimated value, a second calculation unit for calculating a difference between the estimated value, a specifying unit for specifying a cause of an abnormality related to the specific phenomenon according to the difference, and a plurality of the first detection values for determining the a third calculator for calculating an evaluation value for each of a plurality of factors affecting the specific phenomenon in the electronic device, wherein the first calculator calculates the plurality of evaluation values and the estimated value; and the plurality of calculated evaluation values are used to calculate the estimated value.

Schematic diagram of an information processing system. FIG. 2 is a block diagram of a functional configuration of an information processing device; FIG. 10 is a schematic explanatory diagram of an abnormality cause identification process; The diagram which shows the relationship between a 2nd detected value and an estimated value. FIG. 4 is a schematic diagram showing the data configuration of management information; FIG. 4 is a schematic diagram showing the data configuration of management information; 4 is a flowchart showing the flow of information processing; FIG. 10 is a schematic explanatory diagram of an abnormality cause identification process; 4 is a flowchart showing the flow of information processing; Block diagram of a hardware configuration example.

Details of the present embodiment will be described below with reference to the accompanying drawings.

FIG. 1 is a schematic diagram showing an example of an information processing system 1. As shown in FIG.

The information processing system 1 includes an electronic device 10 and an information processing device 20 . The electronic device 10 and the information processing device 20 are connected so as to be able to exchange data or signals.

The electronic device 10 is a device that includes one or more components and is driven by supplied power. The electronic device 10 is applied, for example, to various devices in which one or more electronic components are mounted in one rack. Specifically, the electronic device 10 is applied to various electronic devices such as a transmitter for digital broadcasting, a data relay device, a computer, and a server.

The electronic device 10 includes a housing 12 housing various components.

The housing 12 is a main body portion of the electronic device 10, and is an exterior housing various parts and devices. In the present embodiment, a case where the housing 12 is a box-shaped member with a hollow inside will be described as an example.

A part of the housing 12 is provided with an intake port 13A for taking in air from the outside of the housing 12 into the inside of the housing 12 . Further, a part of the housing 12 is provided with an exhaust port 13B for discharging the air inside the housing 12 to the outside of the housing 12 .

In this embodiment, a blower 15 is provided at the end of the housing 12 on the side of the intake port 13A. The blower 15 is arranged at a position capable of blowing the air F into the housing 12 from the air inlet 13A. A filter 17 is provided on the upstream side of the blower 15 in the direction in which the wind F flows. Therefore, in the present embodiment, by driving the blower 15 , the air outside the housing 12 is sent into the housing 12 through the filter 17 and the wind F is fed into the housing 12 . The heat generated inside the housing 12 is removed by the wind F sent into the housing 12 .

Air blower 15 may be called a fan or a blower, for example.

Note that the blower 15 may be arranged at a position capable of discharging the air inside the housing 12 to the outside of the housing 12 . That is, the blower 15 may be arranged at the end of the housing 12 on the side of the exhaust port 13B. Moreover, even if the blower 15 is arranged at both a position where the air F can be sent into the housing 12 and a position where the air inside the housing 12 can be discharged to the outside of the housing 12. good.

Disposed within housing 12 are one or more components 14 and one or more sensors 16 . In the present embodiment, a configuration in which a plurality of sensors 16 and a plurality of sensors 16 are arranged in housing 12 will be described as an example.

The component 14 is at least one of an electronic component and a member having various functions.

An electronic component is, for example, a component that operates according to supplied power. The electronic component includes a heat-generating component 14A that generates heat by being driven according to supplied power. Note that "driving" includes both electrical driving and mechanical driving. Electrical driving includes, for example, processing by a processor such as a CPU (Central Processing Unit). Mechanical drive includes, for example, motor drive.

The heat-generating component 14A is, for example, a processor such as a CPU or a GPU (Graphics Processing Unit). Note that the heat-generating component 14A may be any component that generates heat by being driven in accordance with supplied power, and is not limited to a CPU or GPU. For example, the heat generating component 14A may be a motor, an electronic circuit, or the like.

A member having various functions is, for example, a member having a cooling function. A member having a cooling function is, for example, a member made of a metal material with high thermal conductivity. Specifically, the member having a cooling function is the heat sink 14B, a heat radiation component, and the like.

The heat-generating components 14A arranged in the housing 12 are cooled by a member having a cooling function such as a heat sink 14B and the air F blown by the blower 15. FIG.

In the present embodiment, a configuration in which a heat-generating component 14A and a heat sink 14B are provided as components 14 in the housing 12 will be described as an example. Note that the housing 12 may have a configuration in which other parts are further provided.

In the present embodiment, as shown in FIG. 1, one or more heat generating components 14A are arranged on each of the substrates 18A and 18B in the housing 12. As shown in FIG.

The board 18 is an example of an electronic circuit board, and is sometimes called a motherboard or a main board.

In the present embodiment, a heat-generating component 14A1 and a heat-generating component 14A2 are arranged as the heat-generating component 14A on the substrate 18A. A heat sink 14B is arranged between the heat-generating component 14A1 and the substrate 18A. The heat sink 14B functions as a heat radiating member that radiates heat generated by the heat generating component 14A.

A heat-generating component 14A3 is arranged on the substrate 18B.

Note that the electronic device 10 may have a configuration in which another substrate 18 is further arranged inside the housing 12 . Further, the electronic device 10 may have a configuration in which the housing 12 further includes a plurality of heat-generating components 14A and other components 14 .

A plurality of sensors 16 (sensors 16a to 16h) are arranged inside the housing 12 .

The sensor 16 is a sensor capable of measuring physical quantities of environmental changes. For example, the sensor 16 detects physical quantities such as temperature, humidity, pressure, flow rate, acceleration, current, voltage, rotation speed, and the like, and outputs detection results as detection values. Physical quantities and detected values are represented by numerical values indicating, for example, temperature, humidity, pressure, flow rate, acceleration, current, voltage, number of revolutions, and the like. In addition, below, the detection value of the sensor 16 may be called the detection value X, and it demonstrates.

The sensor 16 includes, for example, a temperature sensor, a humidity sensor, a pressure sensor, a flow rate sensor of the wind F, a current sensor, a voltage sensor, a rotational speed sensor for detecting the rotational speed of the blower 15, and an acceleration sensor for detecting the rotational acceleration of the blower 15. , etc.

The sensor 16 may be arranged at a position where environmental fluctuations within the housing 12 can be measured.

In this embodiment, the sensors 16a to 16c and the sensors 16e to 16h are temperature sensors for detecting temperature. The sensor 16a is arranged at a position capable of detecting the temperature of the heat generating component 14A1. Specifically, a case where the sensor 16a is arranged in contact with the heat-generating component 14A1 will be described as an example.

The sensor 16b is arranged on the substrate 18A and is arranged at a position that is not in contact with the heat-generating components 14A1 and 14A2. The sensor 16g is arranged in contact with the heat generating component 14A2. The sensor 16h is installed on the substrate 18A and arranged at a non-contact position with respect to the heat-generating component 14A1. The sensors 16c, 16e, and 16f are arranged in a non-contact manner with respect to the component 14 inside the housing 12, and are arranged at positions different from each other.

A sensor 16 d is a sensor 16 that detects the rotation speed of the blower 15 . It should be noted that the sensor 16d only needs to be able to measure phenomena related to the driving of the blower 15 . Therefore, the sensor 16d may be a sensor that detects a physical quantity such as the amount of electric power (current value, voltage value) supplied to the blower 15 .

The information processing device 20 is a device that identifies the cause of an abnormality in the electronic device 10 . The information processing device 20 and each of the plurality of sensors 16 provided on the housing 12 are connected so as to be able to exchange data or signals. The information processing device 20 may be further connected to various electronic devices other than the sensor 16 mounted on the housing 12 so as to be able to exchange data or signals. For example, the information processing device 20 may be connected to the sensor 16, at least one of the plurality of parts 14, and the blower 15 so as to be able to exchange data or signals.

Next, an example of the functional configuration of the information processing device 20 will be described.

FIG. 2 is a block diagram showing an example of the functional configuration of the information processing device 20. As shown in FIG. For the sake of explanation, FIG. 2 also shows the information processing device 20 and the sensor 16 for exchanging data or signals with the information processing device 20 in the electronic device 10 .

The information processing device 20 and the plurality of sensors 16 are connected so as to be able to exchange data or signals. As described above, the information processing device 20 is connected to both the plurality of sensors 16 and various electronic devices other than the sensors 16 mounted on the housing 12 so as to be able to exchange data or signals. good too.

The information processing device 20 includes a control section 30 , a storage section 32 and an output section 34 . The control unit 30, the storage unit 32, and the output unit 34 are connected so as to be able to exchange data or signals.

The storage unit 32 stores various data. The storage unit 32 is, for example, a storage medium such as a known HDD (hard disk drive). In this embodiment, the storage unit 32 stores management information 36 in advance. Details of the data configuration of the management information 36 will be described later.

The output unit 34 outputs various information. In this embodiment, the output unit 34 outputs error information. Details of the error information will be described later.

The output unit 34 has at least one of a display function for displaying various information, a sound output function for outputting sound, and a communication function for communicating data with an external device. An external device is a device provided outside the electronic device 10 . The electronic device 10 and the external device may communicate with each other via a network or the like. For example, the output unit 34 is configured by combining at least one of a known display device, a known speaker, and a known communication device.

Next, the controller 30 will be described.

The control unit 30 includes an acquisition unit 30A, a third calculation unit 30B, a first calculation unit 30C, a second calculation unit 30D, an identification unit 30E, and an output control unit 30F.

The acquisition unit 30A, the third calculation unit 30B, the first calculation unit 30C, the second calculation unit 30D, the identification unit 30E, and the output control unit 30F are implemented by one or more processors, for example. For example, each unit described above may be implemented by causing a processor such as a CPU to execute a program, that is, by software. Each of the above units may be implemented by a processor such as a dedicated IC, that is, by hardware. Each of the above units may be implemented using both software and hardware. When multiple processors are used, each processor may implement one of the units, or may implement two or more of the units.

Acquisition unit 30A acquires detection value X of sensor 16 .

Here, in the present embodiment, the information processing apparatus 20 calculates an estimated value of the specific phenomenon of the electronic device 10 and uses it to identify the cause of abnormality. In the following description, the estimated value will be referred to as estimated value Ib, and the cause of abnormality will be referred to as cause of abnormality Ic.

A specific phenomenon means a specific phenomenon at a specific location within the housing 12 that causes a specific cause of abnormality Ic in the electronic device 10 . The specific phenomenon specifically indicates one of phenomena such as temperature, humidity, pressure, flow rate, acceleration, current, voltage, number of revolutions, etc. at a specific location within the housing 12 .

The specific location within housing 12 is within a predetermined space within housing 12 . Any one of the plurality of components 14 may be included within the space. Thus, for example, a particular location is an area within housing 12 that includes any component 14 . Specifically, the specific location is, for example, the heat-generating component 14A1, but is not limited to this. For example, the particular location may be any location on substrate 18A, or the like. If the specific location is the heat-generating component 14A1, the specific phenomenon is, for example, the temperature of the heat-generating component 14A1.

The type of specific phenomenon for which the estimated value Ib is to be calculated may be set in advance, or may be set by input by a user or the like through an operation instruction. Acquisition unit 30A acquires the type of specific phenomenon for which estimated value Ib is to be calculated, by receiving input from the user or reading setting content information from storage unit 32 . In the following description, the type of specific phenomenon for which the estimated value Ib is to be calculated may be simply referred to as the target specific phenomenon or specific phenomenon.

In the present embodiment, a case where the target specific phenomenon is the temperature of heat-generating component 14A1 included in electronic device 10 will be described as an example. Note that the target specific phenomenon is not limited to the temperature of the heat-generating component 14A1, and may be changed as appropriate.

Acquisition unit 30A acquires first detection value Xa and second detection value Xb as detection value X of sensor 16 .

The first detected value Xa is the detected value X of each of the plurality of first sensors 16A provided in the electronic device 10 .

Here, in the present embodiment, when the cause of abnormality Ic is specified, the control unit 30 operates the first sensor 16A, The sensors are classified into the second sensor 16B and the sensors 16 other than these.

The first sensor 16A is at least some of the sensors 16 provided in the electronic device 10 . Further, the first sensor 16A does not include the sensor 16 that directly detects the target specific phenomenon.

That is, when the specific phenomenon of interest is the temperature of the heat-generating component 14A1, the first sensor 16A does not include the sensor 16 that directly detects the temperature of the heat-generating component 14A1. In this case, the first sensor 16A is at least a sensor other than the sensor 16a among the plurality of sensors 16 (sensors 16a to 16h) provided in the housing 12. FIG. In the present embodiment, a case where sensor 16b, sensor 16c, sensor 16g, sensor 16h, and sensor 16f are used as first sensor 16A will be described as an example.

The acquisition unit 30A sets three or more sensors 16 other than the sensor 16a that directly detects the specific phenomenon as the first sensors 16A according to the specific phenomenon of interest, and from these first sensors 16A is obtained as the first detection value Xa. In addition, it is preferable that the acquisition unit 30A acquires three or more first detection values Xa. That is, it is preferable that the acquisition unit 30A acquires the detected value X received from each of the three or more first sensors 16A satisfying the above conditions as the first detected value Xa.

The second sensor 16B is a sensor 16 other than the first sensor 16A among the plurality of sensors 16 provided in the electronic device 10, and is a sensor 16 that directly detects a target specific phenomenon.

For example, when the target specific phenomenon is the temperature of the heat-generating component 14A1, the second sensor 16B detects the temperature becomes a sensor 16a that directly detects the .

Therefore, in this case, the acquisition unit 30A acquires the detection value X received from the sensor 16a, which is the second sensor 16B that directly detects the target specific phenomenon, as the second detection value Xb.

The third calculator 30B calculates an evaluation value for each of a plurality of factors affecting the specific phenomenon in the electronic device 10 according to the plurality of first detection values Xa acquired by the acquirer 30A. Below, the evaluation value of a factor may be referred to as an evaluation value Ia.

The evaluation value Ia is a factor that affects a specific phenomenon of interest. The evaluation value Ia is represented by a numerical value indicating the degree of influence on the target specific phenomenon. A factor with a higher evaluation value Ia indicates a greater degree of influence on the target specific phenomenon.

In other words, the evaluation value Ia of the factors affecting the target specific phenomenon is the evaluation value Ia of the event whose state changes during the operation of the electronic device 10 .

For example, the factors affecting the specific phenomenon are the contact state between the heat sink 14B and the heat-generating component 14A, the blocked state of the intake port 13A, the blocked state of the exhaust port 13B, the rotation speed of the blower 15, the air volume of the wind F sent out from the blower 15, They are the wind speed of the wind F sent out from the blower 15, the specific phenomenon used to identify the cause of the abnormality, the temperature inside the housing 12, the load on the heat generating component 14A, and the like. That is, the evaluation value Ia is the evaluation value Ia of these factors. Factors that affect the specific phenomenon vary depending on the type of the specific phenomenon, and are not limited to these.

The third calculator 30B calculates an evaluation value Ia for each of the multiple factors from the multiple first detection values Xa, for example, by the following method.

FIG. 3 is a schematic explanatory diagram of the identification processing of the cause of abnormality Ic according to the present embodiment. As shown in FIG. 3, the third calculator 30B calculates multiple evaluation values Ia from multiple first detection values Xa.

Specifically, for example, the third calculator 30B calculates the evaluation value Ia for each factor using the following formula (1).

Ia=p0+p1*Xa1+p2*Xa2+...+pn*Xan Formula (1)

In formula (1), Xa represents the first detected value Xa. The numbers (1 to n) adjacent to each Xa are the identifiers of the first sensors 16A. p0 to pn indicate coefficients. Ia indicates the evaluation value Ia of the factor.

The third calculator 30B may preliminarily derive the correlation between the factor evaluation value Ia and the plurality of first detection values Xa for each of the plurality of types of factors. Specifically, for example, the values of the counts p0 to Pn in Equation (1) above may be derived in advance for each factor. This correlation may be derived through experiments, simulations, machine learning, or the like. For machine learning, known machine learning methods such as convolutional neural networks (CNN), hierarchical Bayesian modeling, DNN (Deep Neural Network), data assimilation techniques such as particle filters, and the like may be used.

Formula (1) is given as an example of the function indicating the correlation for calculating the evaluation value Ia. However, the correlation formula for calculating the evaluation value Ia may be either a one-dimensional polynomial or a multidimensional polynomial. Further, the correlation formula for calculating the evaluation value Ia may be a highly nonlinear formula.

Then, the third calculation unit 30B substitutes each of the plurality of first detection values Xa into a previously derived function indicating the correlation (for example, formula (1) above) to obtain the evaluation value Ia for each factor. (Evaluation value Ia1 to Evaluation value Ian) can be calculated.

Note that the third calculator 30B preferably calculates the evaluation value Ia according to three or more first detection values Xa. That is, in the case of formula (1), the evaluation value Ia for each factor is calculated using the above formula (1) including at least the first detection value Xa1, the first detection value Xa2, and the first detection value Xa3. preferably. Also, the third calculator 30B may calculate the same number of evaluation values Ia as the number of the first detection values Xa used for calculation, which have different factors.

Therefore, for example, when the third calculator 30B calculates the evaluation value Ia from the three first detection values Xa, the third calculator 30B calculates the evaluation value Ia of the three factors different from each other. It will happen.

Returning to FIG. 2, the description is continued. Next, the first calculator 30C will be described. The first calculator 30C calculates an estimated value Ib of the specific phenomenon of the electronic device 10 according to the first detection value Xa detected by each of the plurality of first sensors 16A provided in the electronic device 10. do.

The estimated value Ib is a value obtained by estimating the physical quantity of the target specific phenomenon. For example, assume that the specific phenomenon of interest is the temperature of heat-generating component 14A1. In this case, the estimated value Ib of the specific phenomenon is the estimated value Ib of the temperature of the heat generating component 14A1.

The first calculator 30C calculates the estimated value Ib of the specific phenomenon using the plurality of first detection values Xa or the plurality of evaluation values Ia calculated by the third calculator 30B. In this embodiment, the first calculator 30C will be described on the assumption that one estimated value Ib is calculated from a plurality of first detected values Xa.

First, a method of calculating an estimated value Ib of a specific phenomenon using a plurality of first detected values Xa will be described.

In this case, for example, the first calculator 30C calculates the estimated value Ib of the specific phenomenon using the following equation (2).

Ib=q0+q1*Xa1+q2*Xa2+...+qn*Xan Formula (2)

In formula (2), Xa represents the first detection value Xa. The numbers (1 to n) adjacent to each Xa are the identifiers of the first sensors 16A. q0 to qn indicate coefficients. n represents an integer of 2 or more. Ib indicates the estimated value Ib.

The first calculator 30C may derive in advance the correlation between the estimated value Ib of the specific phenomenon and the plurality of first detected values Xa. Specifically, for example, the first calculator 30C may derive in advance the values of the counts q0 to qn of the above equation (2). This correlation may be derived through experiments, simulations, machine learning, or the like. For machine learning, known machine learning methods such as CNN, hierarchical Bayesian modeling, DNN, and data assimilation techniques such as particle filters may be used. In the above description, Equation (2) is given as an example of the function indicating the correlation for calculating the estimated value Ib from the first detected value Xa. However, the correlation formula for calculating the estimated value Ib from the first detected value Xa may be either a one-dimensional polynomial or a multidimensional polynomial. Further, the correlation formula for calculating the estimated value Ib from the first detected value Xa may be a highly nonlinear formula.

Then, the first calculation unit 30C substitutes each of the plurality of first detection values Xa into a previously derived function indicating the correlation (for example, the above equation (2)) to obtain an estimated value of the specific phenomenon Ib can be calculated.

Note that the first calculation unit 30C calculates the plurality of first detection values Xa , the estimated value Ib of the specific phenomenon may be calculated.

Next, a method of calculating the estimated value Ib of the specific phenomenon using a plurality of evaluation values Ia will be described.

In this case, the first calculator 30C receives the evaluation value Ia for each factor calculated from the plurality of first detection values Xa from the third calculator 30B. That is, the first calculator 30C receives the evaluation value Ia of each of the plurality of factors from the third calculator 30B. Then, the first calculator 30C calculates the estimated value Ib of the specific phenomenon using the evaluation value Ia of each of the plurality of factors.

For example, the first calculator 30C uses the previously derived correlation between the multiple evaluation values Ia and the estimated value Ib and the multiple evaluation values Ia calculated by the third calculator 30B to obtain the estimated value Calculate Ib.

Specifically, for example, the first calculator 30C calculates the estimated value Ib of the specific phenomenon using the following equation (3).

Ib=r0+r1*Ia1+r2*Ia2+...+rn*Ian Formula (3)

In formula (3), Ia indicates the evaluation value Ia of the factor. The numbers (1 to n) adjacent to each Ia are the identifiers of the first sensors 16A. r0 to rn indicate coefficients. n represents an integer of 2 or more. Ib indicates the estimated value Ib.

The first calculation unit 30C may preliminarily derive the correlation between the estimated value Ib of the specific phenomenon and the evaluation value Ia of each of the plurality of factors calculated from the plurality of first detection values Xa. . Specifically, for example, the first calculator 30C may derive in advance the values of the counts r0 to rn in the above equation (3). This correlation may be derived through experiments, simulations, machine learning, or the like. For machine learning, known machine learning methods such as CNN, hierarchical Bayesian modeling, DNN, and data assimilation techniques such as particle filters may be used. Equation (3) is given as an example of the function indicating the correlation for calculating the estimated value Ib from the evaluation value Ia. However, the correlation formula for calculating the estimated value Ib from the evaluation value Ia may be either a one-dimensional polynomial or a multidimensional polynomial. Further, the correlation formula for calculating the estimated value Ib from the evaluation value Ia may be a highly nonlinear formula.

Then, the first calculation unit 30C adds the evaluation of each of the plurality of factors calculated from each of the plurality of first detection values Xa1 to the previously derived function indicating the correlation (for example, the above formula (3)). By substituting the value Ia, the estimated value Ib of the specific phenomenon can be calculated.

Note that the first calculation unit 30C uses a learned model that indicates the correlation represented by the above equation (3) in addition to the function that indicates the correlation that is derived in advance, from the plurality of evaluation values Ia. An estimate Ib of the phenomenon may be calculated.

Description will be made with reference to FIG. In this way, the first calculator 30C estimates the specific phenomenon using the plurality of first detection values Xa or the evaluation value Ia for each factor calculated from the plurality of first detection values Xa. Calculate the value Ib.

Note that the first calculation unit 30C calculates the estimated value Ib from the evaluation value Ia among the calculation method for calculating the estimated value Ib from the first detection value Xa and the method for calculating the estimated value Ib from the evaluation value Ia. It is preferable to use a calculation method for calculating. This is because analysis of the evaluation value Ia makes it possible to derive which factor represented by the evaluation value Ia affects the abnormality of the specific phenomenon.

Returning to FIG. 2, the description is continued. The second calculation unit 30D calculates the difference ΔIb between the second detection value Xb detected by the second sensor 16B and the estimated value Ib of the specific phenomenon calculated by the first calculation unit 30C (Fig. 3). Specifically, the second calculator 30D calculates the absolute value of the difference between the second detection value Xb and the estimated value Ib as the difference ΔIb.

Here, as described above, the second sensor 16B is a sensor 16 other than the first sensor 16A among the plurality of sensors 16 provided in the electronic device 10, and directly detects the target specific phenomenon. It is the sensor 16 which detects. Therefore, it can be said that the second detection value Xb detected by the second sensor 16B is the actual measurement value of the target specific phenomenon.

On the other hand, the estimated value Ib of the specific phenomenon is derived from the first detection value Xa of the first sensor 16A, which is the sensor 16 other than the second sensor 16B among the plurality of sensors 16 provided in the electronic device 10. It is a value obtained by estimating the physical quantity of a specific phenomenon.

Therefore, the second calculator 30D calculates the difference ΔIb, which is the absolute value of the difference between the estimated value Ib of the specific phenomenon and the second detected value Xb, which is the measured value of the specific phenomenon.

The identifying unit 30E identifies the cause of abnormality Ic related to the specific phenomenon according to the difference ΔIb.

The first detection value Xa indirectly used to calculate the difference ΔIb used for deriving the cause of abnormality Ic is obtained from the first sensor 16A installed at a location that is not easily (almost) affected by the cause of abnormality Ic. is preferably the first detected value Xa of .

When the difference ΔIb is equal to or greater than the first threshold, the identification unit 30E determines that the target specific phenomenon indicates an abnormality. The first threshold may be derived in advance through experiments or the like and determined in advance according to the type of target specific phenomenon, the device configuration of the electronic device 10, and the like. Also, the first threshold value may be changed as appropriate by inputting an operation instruction by the user.

FIG. 4 is a diagram showing an example of the relationship between the second detected value Xb and the estimated value Ib.

The horizontal axis of FIG. 4 indicates the second detected value Xb, and the vertical axis indicates the estimated value Ib. In FIG. 4, the estimated value Ib and the second detected value Xb indicate temperature values in units of "°C".

FIG. 4 shows the results of an experiment conducted in each of an abnormal state in which a cooling abnormality occurred with respect to the heat-generating component 14A1 in the electronic device 10 and a normal state in which the cooling abnormality did not occur. 2 shows the relationship between the second detected value Xb and the estimated value Ib.

In FIG. 4, a plot 40 is a plot showing the relationship between the second detected value Xb and the estimated value Ib in the normal state. A plot 42 is a plot showing the relationship between the second detected value Xb and the estimated value Ib in an abnormal state.

As shown in FIG. 4 , when the electronic device 10 is in the normal state, the difference ΔIb between the second detected value Xb and the estimated value Ib is smaller than in the abnormal state, as shown in the plot 40. In the case of the example shown in FIG. 4, the difference ΔIb in the normal state was within ±5°C.

On the other hand, when electronic device 10 was in an abnormal state, difference ΔIb, which is the difference between second detected value Xb and estimated value Ib, was larger than in the normal state, as shown in plot 42 . In the example shown in FIG. 4, the difference ΔIb was 14° C. when the electronic device 10 was in an abnormal state.

When the estimated value Ib and the second detected value Xb have the relationship shown in FIG. 4, the identifying unit 30E sets, for example, the first threshold used when the target specific phenomenon is the temperature of the heat-generating component 14A1 to be: 10°C may be set in advance.

Returning to FIG. 2, the description is continued. The identification unit 30E may store the first threshold value in advance in the storage unit 32 in association with the identification information of the specific phenomenon. For the identification information of the specific phenomenon, for example, information that can uniquely identify the type of the specific phenomenon may be added. When identifying the cause of abnormality Ic, the identification unit 30E may read the first threshold value corresponding to the identification information of the target specific phenomenon from the storage unit 32 and use it for determination.

When the identification unit 30E determines that the difference ΔIb calculated by the second calculation unit 30D is equal to or greater than the first threshold, the identification unit 30E identifies the cause of abnormality Ic.

The anomaly cause Ic is the cause of an anomaly that affects a specific phenomenon of interest. The anomaly cause Ic includes at least one of an anomaly location and an anomaly content. The anomaly location is information indicating the location where an anomaly has occurred. The content of the abnormality indicates the content of the abnormality.

In the present embodiment, the identifying unit 30E identifies the abnormality cause Ic that causes the second detected value Xb to fluctuate more than the first detected value Xa. In other words, the identifying unit 30E determines the cause of abnormality Ic that has a higher influence on the second detection value Xb detected by the second sensor 16B that directly detects the specific phenomenon than the first detection value Xa. Identify.

Further, the identifying unit 30E preferably identifies the cause of abnormality Ic that cannot be directly identified from each of the plurality of first detection values Xa used to calculate the estimated value Ib of the specific phenomenon.

The cause of abnormality Ic is, for example, the location of the abnormality "between the heat-generating component and the heat sink" and the content of the abnormality "increase in thermal resistance". Further, the cause of abnormality Ic is, for example, the location of the abnormality "air inlet" and the content of the abnormality "decrease in flow rate". A flow rate drop means a flow rate drop of the wind F. Further, the cause of abnormality Ic is, for example, the location of the abnormality "heat generating component" and the content of the abnormality "abnormal heat generation". The cause of abnormality Ic is, for example, the location of abnormality "filter" and the content of abnormality "filter clogging".

It should be noted that the cause of abnormality Ic is preferably a cause of abnormality that affects a specific phenomenon such as the temperature of a local area below a predetermined range within the electronic device 10 . For example, the influence range of the abnormal location “between the heat-generating component and the heat sink” and the abnormality content “thermal resistance increase”, which is an example of the cause of abnormality Ic, is often limited to the periphery of the heat-generating component 14A (for example, the heat-generating component 14A1). . On the other hand, the abnormal location "air inlet" and the abnormal content "decrease in flow rate", which are examples of the abnormal cause Ic, have a wider range of influence than the abnormal location "between the heat-generating component and the heat sink". Therefore, when a plurality of causes of abnormality Ic can be identified, the identifying unit 30E preferably identifies the cause of abnormality Ic that affects the specific phenomenon in a more localized area.

When the cause of abnormality Ic affects a specific phenomenon in a local area, the estimated value Ib used to identify the cause of abnormality Ic is the area within the range affected by the cause of abnormality Ic in the electronic device 10. It can be said that it is the estimated value Ib of the region. Therefore, in this case, the specifying unit 30E can improve the accuracy of specifying the cause of abnormality Ic.

For example, assume that the cause of the abnormality Ic is the location of the abnormality "between the heat-generating component and the heat sink" and the contents of the abnormality "increase in thermal resistance". It is also assumed that this heat-generating component is the heat-generating component 14A1. In this case, the first calculator 30C uses the detection value X of any of the sensors 16 (for example, the sensor 16b, the sensor 16f, the sensor 16g, and the sensor 16h) as the first detection value Xa to calculate the specific phenomenon The estimated value Ib of is calculated. By this processing, the identifying unit 30E can improve the accuracy of identifying the cause of abnormality Ic. In this case, the sensor 16 used as the first detected value Xa is not limited to the sensor 16 arranged in the range affected by the heat generated by the heat-generating component 14A1. That is, the sensor 16 may be arranged in a range that is not affected by the heat generated by the heat generating component 14A1.

It should be noted that the cause of abnormality Ic is not limited to these as long as it satisfies the above conditions. The above conditions include a cause that has a higher influence on the second detection value Xb than the first detection value Xa, a cause that cannot be directly identified from each of the plurality of first detection values Xa, and a local at least one of the causes that affect the specific phenomenon in the relevant area.

The identification unit 30E preliminarily derives a correspondence relationship between the type of the specific phenomenon and the cause of abnormality Ic that satisfies at least one of the above conditions for each type of the specific phenomenon, and stores information indicating the correspondence relationship in the storage unit 32. should be stored in advance. Then, the specifying unit 30E may specify the cause of abnormality Ic using the information indicating the correspondence.

For example, the specifying unit 30E stores management information 36 in advance in the storage unit 32 as information indicating the correspondence.

The management information 36 is information that associates the type of specific phenomenon with the cause of abnormality Ic.

FIG. 5A is a schematic diagram showing an example of the data configuration of the management information 36A. The management information 36A is an example of the management information 36. FIG.

The management information 36A is information that associates the type of specific phenomenon with the cause of the abnormality. The data configuration of the management information 36A is not limited. For example, the management information 36A may be a table or a database.

In FIG. 5A, "A", "B", and "C" indicate identification information of types of specific phenomena. The management information 36A pre-stores, for each type of specific phenomenon, an abnormality cause Ic that exerts a large influence on the second sensor 16B that directly detects the specific phenomenon. The management information 36A only needs to store an abnormality cause Ic including at least one of an abnormality location and an abnormality content in association with the type of specific phenomenon. Further, the cause of abnormality Ic is a cause of abnormality that is not included in the evaluation value Ia of the type of the corresponding specific phenomenon. Therefore, in the management information 36A, an abnormality cause Ic that is not included in the evaluation value Ia of the corresponding type of specific phenomenon is registered for each type of specific phenomenon.

For example, assume that the target specific phenomenon is the temperature of the heat-generating component 14A and the difference ΔIb is equal to or greater than the first threshold. In this case, the cause of abnormality Ic is the performance deterioration of the cooling function for the heat-generating component 14A. Therefore, if the type of the specific phenomenon is, for example, the temperature of the heat-generating component 14A (assumed to be the type of specific phenomenon "A"), the management information 36A contains the abnormal location "air inlet" ( The intake port 13A) and the content of the abnormality "decrease in flow rate" may be associated in advance and stored. It should be noted that the information indicating the cause of abnormality Ic is preferably information that facilitates the maintenance work of the electronic device 10 according to the information provided by the user who has been provided with the information indicating the cause of abnormality Ic.

When the difference ΔIb is equal to or greater than the first threshold, the identification unit 30E reads the abnormality cause Ic corresponding to the type of specific phenomenon of the estimated value Ib used to calculate the difference ΔIb from the management information 36A. Then, the identification unit 30E may identify the cause of abnormality Ic by reading the cause of abnormality Ic.

The identification unit 30E may derive in advance the correspondence relationship between the type of specific phenomenon and the cause of the abnormality indicated in the management information 36A by simulation or experiment, and store it in the management information 36 in advance as the management information 36A. Then, the specifying unit 30E may specify the cause of abnormality Ic using the management information 36A.

The identification unit 30E identifies the cause of abnormality Ic by using a learned model that has learned the correspondence between the type of specific phenomenon and the cause of abnormality Ic indicated in the management information 36A, a function that indicates the correspondence, or the like. You may

FIG. 5A shows, as an example, the case where the management information 36 is information in which one type of specific phenomenon is associated with one cause of abnormality Ic. However, the management information 36 may be information in which one or a plurality of causes of abnormality Ic are associated with one type of specific phenomenon.

FIG. 5B is a schematic diagram showing an example of the data configuration of the management information 36B. The management information 36B is an example of the management information 36. FIG. As shown in FIG. 5B, the management information 36B may be information in which one type of specific phenomenon is associated with one or more causes of abnormality Ic. Similar to the management information 36A, in the management information 36B, an abnormality cause Ic that is not included in the evaluation value Ia of the corresponding type of specific phenomenon is registered for each type of specific phenomenon.

In this embodiment, when the management information 36A and the management information 36B are collectively described, they are simply referred to as the management information 36 for description.

Returning to FIG. 2, the description is continued. Note that the specifying unit 30E may determine whether or not each of the plurality of evaluation values Ia calculated by the third calculation unit 30B indicates an abnormality. Then, the identifying unit 30E may identify the cause of abnormality Ic using the difference ΔIb when determining that all the evaluation values Ia indicate no abnormality.

A second threshold may be used to determine whether the evaluation value Ia indicates an abnormality. The specifying unit 30E may specify the second threshold for each factor in advance and use it for determination. Then, the specifying unit 30E may store the second threshold value in advance in the storage unit 32 in association with the identification information of the factor. When determining whether or not the evaluation value Ia indicates an abnormality, the identification unit 30E may read the second threshold value corresponding to the identification information of the factor to be determined from the storage unit 32 and use it for determination.

Then, the specifying unit 30E may specify that the factor indicating the evaluation value Ia equal to or higher than the second threshold is abnormal.

Next, the output control section 30F will be described. The output control unit 30F outputs the error information to the output unit 34. FIG.

The error information includes information indicating the cause of abnormality Ic specified by the specifying unit 30E. The error information may further include information indicating a maintenance work procedure corresponding to the cause of abnormality Ic, items to be noted during maintenance work, a contact address for maintenance work service, and the like.

The output unit 34 outputs error information. If the output unit 34 has a display function, the output unit 34 displays error information. Moreover, when the output unit 34 has a sound output function, the output unit 34 displays a sound indicating error information.

Therefore, it is possible to easily provide information indicating the cause of abnormality Ic to the user. By checking the output error information, the user can perform maintenance work on the electronic device 10 according to the maintenance work procedure for the cause of abnormality Ic indicated in the error information. That is, the information processing device 20 can appropriately provide the user with information necessary for maintenance work of the electronic device 10 .

In addition, by performing maintenance work based on the error information, it is possible to reduce the frequency of occurrence of abnormalities in the electronic device 10, thereby reducing labor costs for maintenance workers and the cost of replacing unnecessary parts 14. be able to.

On the other hand, when the output unit 34 has a communication function, the output unit 34 transmits error information to an external device via a network or the like.

The external device that receives the error information uses the error information to remotely monitor the electronic device 10 that sent the error information, process the error information, analyze the error information, and send the error information to another external device. You may perform various processes, such as transferring to. For example, the external device can analyze the usage environment and usage method of the electronic device 10 by combining and analyzing the installation environment of the electronic device 10 and the error information. Therefore, by outputting the error information, the information processing apparatus 20 can provide information that can be used to improve the design criteria of the electronic device 10 . In addition, the information processing apparatus 20 can provide information that can be used to improve the usage environment of the electronic device 10 and to optimize the usage method.

Also, the external device to which the error information is sent, or another device to which the error information is transferred from the external device may be the server device of the service provider in charge of the maintenance work. In this case, the information processing device 20 can improve the efficiency of maintenance work.

Further, after the maintenance work, the electronic device 10 is operated again, and the information processing device 20 executes the above process, whereby the identification unit 30E of the electronic device 10 determines the difference ΔIb before the maintenance work and the difference ΔIb after the maintenance work. may be compared to further calculate a value indicating the degree of improvement due to maintenance work. In addition, the output control unit 30F may further output the calculated value indicating the degree of improvement to an external device or the like. In this case, the information processing device 20 can easily provide information that can be used for improvement studies of the electronic device 10 .

Next, the flow of information processing executed by the information processing device 20 will be described.

FIG. 6 is a flowchart showing an example of the flow of information processing executed by the information processing device 20. As shown in FIG. Note that the order of each of the plurality of steps can be arbitrarily changed, and is not limited to the example of FIG. Also, at least some of the steps may be executed in parallel.

FIG. 6 shows an example of using the management information 36A (see FIG. 5A) when identifying the cause of abnormality Ic.

The acquisition unit 30A sets a target specific phenomenon (step S100). For example, the acquisition unit 30A receives information indicating a specific phenomenon input by a user from an input device or the like, and sets the specific phenomenon indicated by the received information as a target specific phenomenon.

Next, acquisition unit 30A acquires first detection value Xa and second detection value Xb (step S102). Acquisition unit 30A receives detected value X from each of sensors 16 provided in electronic device 10 from second sensor 16B, which is sensor 16 that directly detects the specific phenomenon set in step S100. The detected value X is obtained as a second detected value Xb. In addition, the acquisition unit 30A obtains the detection value X received from each of the plurality of sensors 16 provided in the electronic device 10 from each of the first sensors 16A, which are the plurality of sensors 16 other than the second sensor 16B. The received detection value X is acquired as the first detection value Xa.

The third calculator 30B calculates an evaluation value Ia of each of the factors affecting the specific phenomenon set in step S100 according to the plurality of first detection values Xa acquired in step S102 ( step S104).

The first calculator 30C calculates the estimated value Ib of the specific phenomenon set in step S100 using the plurality of evaluation values Ia calculated in step S104 (step S106). Note that, as described above, the first calculator 30C may calculate the estimated value Ib from the first detected value Xa. In this case, the first calculator 30C may calculate the estimated value Ib from the plurality of first detected values Xa acquired in step S102.

Next, the specifying unit 30E and the output control unit 30F execute the processes of steps S108 and S110 for each evaluation value Ia of each of the plurality of factors calculated in step S104.

Specifically, the specifying unit 30E determines whether or not the evaluation value Ia is equal to or greater than the second threshold (step S108). If it is determined that the evaluation value Ia is less than the second threshold (step S108: No), this repetitive process is terminated. On the other hand, when it is determined that the evaluation value Ia is equal to or greater than the second threshold (step S108: Yes), the process proceeds to step 110. FIG.

In step S110, the output control unit 30F outputs to the output unit 34 error information indicating that the factor indicating the evaluation value Ia determined to be equal to or greater than the second threshold in step S108 is abnormal (step S110). Then, this iterative process ends.

When the processing of steps S108 and S110 is completed for all the evaluation values Ia of each of the plurality of factors calculated in step S104, the process proceeds to step S112.

In step S112, the specifying unit 30E determines whether or not all of the multiple evaluation values Ia calculated in step S104 are less than the second threshold (step S112). The specifying unit 30E makes a determination in step S112 by determining whether or not a negative determination was made in step S108 for all of the multiple evaluation values Ia calculated in step S104.

If a negative determination is made in step S112 (step S112: No), this routine ends. A negative determination in step S112 means that at least one of the evaluation values Ia for each of the factors calculated in step S104 is equal to or greater than the second threshold, and that error information has already been output in step S110. show.

On the other hand, if an affirmative determination is made in step S112 (step S112: Yes), the process proceeds to step S114. An affirmative determination in step S112 indicates that all of the evaluation values Ia for each of the factors calculated in step S104 are less than the second threshold and no error information is output in step S110.

In step S114, the second calculator 30D calculates the difference ΔIb between the second detection value Xb obtained in step S102 and the estimated value Ib calculated in step S106 (step S114).

Next, the specifying unit 30E determines whether or not the difference ΔIb calculated in step S114 is equal to or greater than the first threshold (step S116). If it is determined that the difference ΔIb is less than the first threshold (step S116: No), this routine is terminated. On the other hand, when determining that the difference ΔIb is greater than or equal to the first threshold (step S116: Yes), the identifying unit 30E determines that the target specific phenomenon indicates an abnormality (step S118).

The identifying unit 30E then identifies the cause of abnormality Ic (step S120). For example, the identification unit 30E identifies the abnormality cause Ic by reading the abnormality cause Ic corresponding to the type of the target specific phenomenon set in step S100 from the management information 36A.

The output control unit 30F outputs error information including information indicating the cause of abnormality Ic identified in step S120 to the output unit 34 (step S122). Then, the routine ends.

As described above, FIG. 6 shows an example of using the management information 36A (see FIG. 5A) when identifying the cause of abnormality Ic. That is, FIG. 6 shows, as an example, a form in which the identification unit 30E identifies an abnormality cause Ic using the management information 36A in which one abnormality cause Ic is associated with one type of specific phenomenon. . However, as described above, the identification unit 30E uses the management information 36B (see FIG. 5B) in which a plurality of causes of abnormality Ic are associated with one type of specific phenomenon to identify the cause of abnormality Ic. good too.

In this case, the identifying unit 30E may identify, at a time, a plurality of causes of abnormality Ic corresponding to the specific phenomenon set in step S100. In this case, the output control unit 30F may output error information including multiple causes of abnormality Ic to the output unit 34 .

In this case, the identifying unit 30E also selects, from among the plurality of causes of abnormality Ic corresponding to the target specific phenomenon set in step S100, the cause of abnormality Ic that was not identified when the affirmative determination was made in the previous step S116. You can choose one. At this time, information indicating the priority of selection may be registered in advance in the management information 36B in association with the cause of abnormality Ic. Then, the identification unit 30E may select one abnormality cause Ic in order of priority. Then, the output control unit 30F may output the unoutput causes of abnormality Ic to the output unit 34 one by one in order of priority.

As described above, the information processing device 20 of the present embodiment includes the first calculation unit 30C, the second calculation unit 30D, and the identification unit 30E. The first calculator 30C calculates an estimated value Ib of the specific phenomenon of the electronic device 10 according to the first detection value Xa detected by each of the plurality of first sensors 16A provided in the electronic device 10. do. The second calculator 30D detects a specific phenomenon with a second sensor 16B, which is a sensor 16 other than the first sensor 16A and detects a specific phenomenon. A difference ΔIb between the second detected value Xb and the estimated value Ib is calculated. The identifying unit 30E identifies the cause of abnormality Ic related to the specific phenomenon according to the difference ΔIb.

Here, conventionally, the correlation between the cause of abnormality and the detected value of the sensor is derived in advance by simulation, and the cause of abnormality is specified using the detected value of the sensor and the correlation. However, the correlation derived by the preliminary simulation may change due to changes in circumstances during actual driving. Moreover, at least some of the parts may be manufactured under manufacturing conditions different from those used in the simulation, making it difficult to derive a highly accurate correlation in advance. In addition, it is difficult to obtain in advance all data necessary for use in highly accurate simulations, and it is difficult to derive highly accurate correlations. Moreover, although the simulation results and the numerical analysis results were almost the same in some cases, these results were different from the experimental results in some cases. Specifically, since the correlations derived by simulation contain errors, even if the correlations were derived by conducting experiments, there were abnormal causes that could not be evaluated in prior experiments. That is, conventionally, the correlation between such an abnormality cause and a detected value was unknown. Further, the correlation between the factor evaluation value Ia and the estimated value Ib or the first detected value Xa is established only when the cause of the abnormality, which cannot be evaluated in prior experiments, is in a normal state. Therefore, it is difficult to accurately identify the cause of an abnormality in an electronic device with the conventional technology that identifies the cause of an abnormality using only simulation results.

On the other hand, the information processing apparatus 20 of the present embodiment calculates the difference ΔIb between the second detection value Xb and the estimated value Ib of the specific phenomenon. The second detection value Xb is the detection value X of the sensors 16 other than the first sensor 16A among the plurality of sensors 16 provided in the electronic device 10 . Also, the second detection value Xb is the detection value X of the second sensor 16B that detects the specific phenomenon. That is, the second detected value Xb is the measured value of the specific phenomenon. On the other hand, the estimated value Ib is the estimated value Ib of the specific phenomenon estimated from the first detected value Xa detected by the first sensor 16A other than the second sensor 16B. Then, the information processing device 20 identifies the cause of abnormality Ic using the difference ΔIb between the second detected value Xb, which is the measured value of the specific phenomenon, and the estimated value Ib, which is the estimated value of the specific phenomenon.

Thus, the information processing apparatus 20 of the present embodiment uses not only the estimated value Ib, but also the estimated value Ib and the second detected value Xb, which is the actually measured value. Then, the information processing device 20 identifies the cause of abnormality Ic according to the difference ΔIb between the estimated value Ib and the second detected value Xb. Therefore, even if it is difficult to predict in advance the correlation between the detection value X of the sensor 16 and the cause of abnormality Ic, the cause of abnormality Ic can be specified with high accuracy.

Therefore, the information processing apparatus 20 of the present embodiment can accurately identify the cause of abnormality Ic of the electronic device 10 .

In addition, in the information processing apparatus 20 of the present embodiment, the output control section 30F outputs error information including the cause of abnormality Ic to the output section 34 .

Therefore, the information processing apparatus 20 of the present embodiment can appropriately provide the user with information necessary for maintenance work of the electronic device 10 .

For example, it is assumed that the error information including the cause of the abnormality Ic is output as information indicating the location of the abnormality "between the heat-generating component and the heat sink" and the content of the abnormality "increased thermal resistance".

Here, heat dissipation performance may be improved by interposing a TIM (Thermal Interface Material) such as heat dissipating grease or a heat conductive sheet between the heat generating component 14A and the heat sink 14B. The TIM may deteriorate over time due to evaporation of the heat dissipation grease, etc., and its performance may deteriorate. However, it has been difficult to identify the cause of abnormality Ic by simulating such performance degradation in advance, because the simulation itself contains errors, and the identification accuracy is low. In addition, it is difficult to reproduce the abnormal state of the TIM by experiment, and it is also difficult to identify the cause of the abnormality Ic using the result of the previous experiment.

On the other hand, the information processing apparatus 20 of the present embodiment can accurately identify the cause of abnormality Ic and output it to the output unit 34 even in such a case. Therefore, the information processing apparatus 20 of the present embodiment can provide the user with appropriate information necessary for maintenance work, in addition to the above effects.

(Modification 1)
In the above embodiment, the information processing apparatus 20 generates one estimated value Ib The description has been given assuming the case of calculating .

However, the information processing apparatus 20 can perform a plurality of estimations of different types of the specific phenomenon from the plurality of first detection values Xa or the evaluation values Ia of each of the plurality of factors calculated from the plurality of first detection values Xa. A value Ib may be calculated. In this case, the information processing device 20 calculates the difference ΔIb between each of the plurality of estimated values Ib and the second detection value Xb in the same manner as in the above embodiment, and identifies the plurality of causes of abnormality Ic. Just do it.

FIG. 7 is a schematic explanatory diagram of the identification processing of the cause of abnormality Ic according to this modification.

In this case, acquisition unit 30A acquires first detection value Xa and second detection value Xb, as in the above-described embodiment. However, in this modified example, the acquisition unit 30A acquires the second detection value Xb (second detection value Xb1 to second detection value Xbn) detected in each of the plurality of types of specific phenomena. Note that n is an integer indicating the number of types of specific phenomena.

The third calculator 30B calculates a plurality of evaluation values Ia using a plurality of first detection values Xa in the same manner as in the above embodiment. Therefore, the third calculator 30B calculates a plurality of evaluation values Ia (evaluation value Ia1 to evaluation value Ian) as in the above embodiment. In addition, this n should just be a value of 2 or more. Also, as in the above embodiment, the number of evaluation values Ia and the number of first detection values Xa are the same number, and are at least two or more, preferably three or more.

As in the above embodiment, the first calculation unit 30C uses a plurality of evaluation values Ia (evaluation values Ia1 to Ian) to obtain an estimated value Ib (estimated value Ib1 to the estimated value Ibn). The number of estimated values Ib to be calculated may not match the number of first detected values Xa and evaluation values Ia.

Then, for each type of specific phenomenon, the identifying unit 30E obtains a second detected value Xb (second detected value Xb1 to second detected value Xbn), which is an actual measurement value of the specific phenomenon, and an estimated value Ib of the specific phenomenon. (estimated value Ib1 to estimated value Ibn) and the difference ΔIb (difference ΔIb1 to difference ΔIbn) can be calculated. Then, in the same manner as in the above-described embodiment, the specifying unit 30E determines that an abnormality occurs when the difference ΔIb (difference ΔIb1 to difference ΔIbn) is equal to or greater than the first threshold value for each type of specific phenomenon, and determines the cause of the abnormality Ic. (Abnormal cause Ic1 to Abnormal cause Icn) can be specified.

In this manner, the information processing apparatus 20 may identify multiple types of abnormality causes Ic by identifying the abnormality causes Ic for each of multiple types of specific phenomena.

Next, the flow of information processing executed by the information processing device 20 of this modified example will be described.

FIG. 8 is a flowchart showing an example of the flow of information processing executed by the information processing device 20. As shown in FIG. Note that the order of each of the plurality of steps can be arbitrarily changed, and is not limited to the example of FIG. Also, at least some of the steps may be executed in parallel.

Acquisition unit 30A sets a plurality of types of specific phenomena (step S200).

Next, the acquisition unit 30A acquires a plurality of first detection values Xa and a second detection value Xb detected for each of the plurality of types of specific phenomena (step S202).

The third calculator 30B calculates evaluation values for each of the plurality of factors affecting each of the plurality of types of specific phenomena set in step S200, according to the plurality of first detection values Xa acquired in step S202. Ia is calculated (step S204).

The first calculator 30C uses the multiple evaluation values Ia calculated in step S204 to calculate an estimated value Ib for each of the plurality of types of specific phenomena set in step S200 (step S206). Note that the first calculator 30C may calculate the estimated value Ib from the first detected value Xa, as in the above embodiment.

Next, the specifying unit 30E and the output control unit 30F execute the processes of steps S208 and S210 for each evaluation value Ia of each of the plurality of factors calculated in step S204. The processes of steps S208 and S210 are the same as steps S108 and S110 of the above embodiment (see FIG. 6).

Specifically, the specifying unit 30E determines whether or not the evaluation value Ia is equal to or greater than the second threshold (step S208). If it is determined that the evaluation value Ia is less than the second threshold (step S208: No), this repetitive process is terminated. On the other hand, when it is determined that the evaluation value Ia is equal to or greater than the second threshold (step S208: Yes), the process proceeds to step 210.

At step S210, the output control unit 30F outputs the error information to the output unit 34 (step S210). Then, this iterative process ends.

When the processing of steps S208 and S210 is completed for all the evaluation values Ia of the plurality of factors calculated in step S204, the process proceeds to step S212.

At step S212, the specifying unit 30E determines whether or not all of the plurality of evaluation values Ia calculated at step S204 are less than the second threshold (step S212). The processing of step S212 is the same as that of step S112 (see FIG. 6).

If a negative determination is made in step S212 (step S212: No), this routine ends.

On the other hand, if an affirmative determination is made in step S212 (step S212: Yes), the control unit 30 executes the processing of steps S214 to S222 for each type of specific phenomenon set in step S200.

Specifically, in step S214, the second calculator 30D calculates the difference ΔIb between the second detection value Xb obtained in step S202 and the estimated value Ib calculated in step S206 (step S214).

Next, the specifying unit 30E determines whether or not the difference ΔIb calculated in step S214 is equal to or greater than the first threshold (step S216). If it is determined that the difference ΔIb is less than the first threshold (step S216: No), this routine is terminated. On the other hand, when determining that the difference ΔIb is equal to or greater than the first threshold (step S216: Yes), the specifying unit 30E determines that the target type of specific phenomenon indicates an abnormality (step S218).

The identifying unit 30E then identifies the cause of abnormality Ic (step S220). For example, the identification unit 30E identifies the abnormality cause Ic by reading the abnormality cause Ic corresponding to the type of the target specific phenomenon set in step S200 from the management information 36A.

The output control unit 30F outputs error information including information indicating the cause of abnormality Ic identified in step S220 to the output unit 34 (step S222). Then, the routine ends.

In this way, the information processing apparatus 20 calculates a plurality of different specific phenomena from the plurality of first detection values Xa or the estimated values Ib of the plurality of factors calculated from the plurality of first detection values Xa. An estimated value Ib may be calculated.

In this case, the information processing device 20 can identify the abnormality cause Ic for each of the plurality of types of specific phenomena.

It should be noted that, in the above-described embodiment and modified example, a form in which the electronic device 10 and the information processing device 20 are separately configured is shown as an example (see FIG. 1). However, the electronic device 10 and the information processing device 20 may be configured integrally. That is, the electronic device 10 may be configured to include the information processing device 20 . In this case, for example, the information processing device 20 may be arranged inside the housing 12 of the electronic device 10 .

-Hardware configuration-
Next, the hardware configuration of the control unit 30 of the above embodiment and modification will be described.

FIG. 9 is a block diagram showing a hardware configuration example of the control section 30 of the above embodiment and modification.

The control unit 30 of the above embodiment and modifications includes a control device such as a CPU (Central Processing Unit) 51, a storage device such as a ROM (Read Only Memory) 52 and a RAM (Random Access Memory) 53, and is connected to a network. communication I/F 54 for performing communication, and a bus 61 for connecting each part.

The programs executed by the control unit 30 of the above-described embodiment and modified example are preinstalled in the ROM 52 or the like and provided.

The programs executed by the control unit 30 of the above embodiments and modifications are files in an installable format or an executable format stored on a CD-ROM (Compact Disk Read Only Memory), a flexible disk (FD), a CD-R (Compact Disk Recordable), DVD (Digital Versatile Disk), or other computer-readable recording medium, and provided as a computer program product.

Furthermore, the program executed by the control unit 30 of the above embodiments and modifications may be stored on a computer connected to a network such as the Internet, and may be provided by being downloaded via the network. . Further, the program executed by the control unit 30 of the above embodiment and modification may be provided or distributed via a network such as the Internet.

The program executed by the control unit 30 of the above embodiments and modifications can cause a computer to function as each part of the control unit 30 of the above embodiments and modifications. In this computer, the CPU 51 can read a program from a computer-readable storage medium into the main memory and execute it.

While several embodiments and variations of the invention have been described, these embodiments and variations are provided by way of example and are not intended to limit the scope of the invention. These novel embodiments and modifications can be implemented in various other forms, and various omissions, replacements, and modifications can be made without departing from the scope of the invention. These embodiments and modifications thereof are included in the scope and gist of the invention, and are included in the scope of the invention described in the claims and equivalents thereof.

10 Electronic device 12 Housing 14A Heat generating component 16 Sensor 16A First sensor 16B Second sensor 20 Information processor 30A Acquisition unit 30B Third calculation unit 30C First calculation unit 30D Second calculation unit 30E Identification unit 30F Output controller

Claims (10)

a first calculator that calculates an estimated value of a specific phenomenon of the electronic device according to a first detection value detected by each of a plurality of first sensors provided in the electronic device;
a second detection value detected by a second sensor, which is the sensor that detects the specific phenomenon and is other than the first sensor, among the plurality of sensors provided in the electronic device; and the estimated value. and a second calculator that calculates the difference between
an identifying unit that identifies the cause of an abnormality related to the specific phenomenon according to the difference;
a third calculator that calculates an evaluation value for each of a plurality of factors affecting the specific phenomenon in the electronic device according to the plurality of first detected values;
with
The first calculation unit calculates the estimated value using a pre-derived correlation between the plurality of evaluation values and the estimated value, and the plurality of calculated evaluation values.
Information processing equipment. The identification unit
determining that the specific phenomenon indicates an abnormality when the difference is equal to or greater than a first threshold, and identifying the cause of the abnormality;
The information processing device according to claim 1 . The identification unit
If the difference is greater than or equal to the first threshold,
Identifying the cause of the abnormality that causes a high variation in the second detection value compared to the first detection value;
The information processing apparatus according to claim 2 . The specifying unit is
When the difference is equal to or greater than the first threshold,
identifying the cause of the abnormality that cannot be directly identified from each of the plurality of first detection values used to calculate the estimated value of the specific phenomenon;
4. The information processing apparatus according to claim 2 or 3. The identification unit
If the difference is greater than or equal to the first threshold,
identifying the cause of the abnormality corresponding to the specific phenomenon from management information that associates the specific phenomenon with the cause of the abnormality including at least one of a location and content of the abnormality in the electronic device;
The information processing apparatus according to any one of claims 2 to 4. The third calculator,
calculating the evaluation value according to the three or more first detection values;
The information processing device according to claim 1 . The specific phenomenon is the temperature of a heat-generating component provided in the electronic device,
The information processing apparatus according to any one of claims 1 to 6 . A computer-executed information processing method comprising:
a first calculation step of calculating an estimated value of a specific phenomenon of the electronic device according to a first detection value detected by each of a plurality of first sensors provided in the electronic device;
a second detection value detected by a second sensor, which is the sensor that detects the specific phenomenon and is other than the first sensor, among the plurality of sensors provided in the electronic device; and the estimated value. and a second calculation step of calculating the difference between
an identifying step of identifying a cause of an abnormality related to the specific phenomenon in accordance with the difference;
a third calculating step of calculating an evaluation value for each of a plurality of factors affecting the specific phenomenon in the electronic device according to the plurality of first detected values;
including
In the first calculating step, the estimated value is calculated using a pre-derived correlation between the plurality of evaluation values and the estimated value, and the calculated plurality of evaluation values.
Information processing methods. A program for a computer to execute,
a first calculation step of calculating an estimated value of a specific phenomenon of the electronic device according to a first detection value detected by each of a plurality of first sensors provided in the electronic device;
a second detection value detected by a second sensor, which is the sensor that detects the specific phenomenon and is other than the first sensor, among the plurality of sensors provided in the electronic device; and the estimated value. and a second calculation step of calculating the difference between
an identifying step of identifying a cause of an abnormality related to the specific phenomenon in accordance with the difference;
a third calculating step of calculating an evaluation value for each of a plurality of factors affecting the specific phenomenon in the electronic device according to the plurality of first detected values;
including
In the first calculating step, the estimated value is calculated using a pre-derived correlation between the plurality of evaluation values and the estimated value, and the calculated plurality of evaluation values.
program. an electronic device,
a plurality of sensors;
a first calculator that calculates an estimated value of a specific phenomenon of the electronic device according to a first detection value detected by each of a plurality of first sensors that are a part of the plurality of sensors; ,
Calculating a difference between a second detection value detected by a second sensor, which is one of the plurality of sensors other than the first sensor and which detects the specific phenomenon, and the estimated value. a second calculator for
an identifying unit that identifies the cause of an abnormality related to the specific phenomenon according to the difference;
a third calculator that calculates an evaluation value for each of a plurality of factors affecting the specific phenomenon in the electronic device according to the plurality of first detected values;
with
The first calculation unit calculates the estimated value using a pre-derived correlation between the plurality of evaluation values and the estimated value, and the plurality of calculated evaluation values.
Electronics.

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