JP7288310B2 - FAILURE PREVENTION SYSTEM, FAILURE PREVENTION METHOD AND PROGRAM - Google Patents

Description

The present invention relates to a failure prevention system, failure prevention method, and program.

During the transportation of goods in a refrigerated vehicle, the refrigerator may malfunction. As a countermeasure against a failure of a refrigeration vehicle, for example, Patent Document 1 discloses that a control center monitors the measurement values of a temperature sensor attached to cargo in a refrigeration vehicle, and detects a failure of the refrigeration vehicle due to an increase in the temperature of the cargo. Disclosed is a system for transporting packages by transferring them to a carrier. Further, Patent Literature 2 discloses a method in which a management server arranges a substitute vehicle based on a substitute vehicle arrangement request received from a refrigerated vehicle that is in transit. For example, when receiving an arrangement request, the management server compares the estimated delivery completion time of the undelivered product with the estimated delivery completion time by the substitute vehicle, and if the delivery completion by the substitute vehicle is earlier, it requests dispatch of the substitute vehicle. . In addition, the management server compares the time at which the damaged vehicle returns to the office with the expected arrival time of the substitute vehicle, and if it is faster to return to the office than to wait for the arrival of the substitute vehicle, return to the office to the damaged vehicle. give instructions.

Japanese Patent Application Laid-Open No. 2009-161345 Japanese Unexamined Patent Application Publication No. 2010-70267

If the refrigeration vehicle breaks down while the cargo is being transported, there is a possibility that the cargo will be damaged even if a replacement vehicle can be arranged. Therefore, from the viewpoint of property damage prevention, it is desirable to control the refrigeration vehicle to avoid failure during transportation.

SUMMARY OF THE INVENTION Accordingly, it is an object of the present invention to provide a failure prevention system, a failure prevention method, and a program capable of solving the above problems.

According to one aspect of the present invention, a failure prevention system includes a data acquisition unit that acquires operating data of a refrigerated vehicle; and a guide unit for guiding countermeasure information for the failure according to the degree of urgency predicted by the failure prediction unit , wherein the guide unit, when the degree of urgency is higher than a predetermined threshold, causes the refrigeration The user of the refrigerated vehicle is informed of the countermeasure information that prompts the user to switch from the vehicle to the alternative vehicle.

According to one aspect of the present invention, a failure prevention system includes a data acquisition unit that acquires operating data of a refrigerated vehicle; and a guide unit that guides countermeasure information for the failure according to the degree of urgency predicted by the failure prediction unit. The user of the refrigerated vehicle is informed of the countermeasure information that prompts the preparation of a substitute vehicle for the vehicle.

According to one aspect of the present invention, the guide section guides the user of the refrigerated vehicle with the countermeasure information that prompts maintenance of the refrigerated vehicle.

According to one aspect of the present invention, the guide section guides a maintenance company of the refrigerated vehicle with the countermeasure information for prompting maintenance according to the type of failure.

According to one aspect of the present invention, the data acquisition unit acquires operation data relating to a compressor included in a refrigerator of the refrigeration vehicle, and the failure prediction unit determines the type and degree of urgency of failure occurring in the compressor. predict.

According to one aspect of the present invention, the data acquisition section acquires operating data relating to the engine of the refrigerated vehicle, and the failure prediction section predicts the type and urgency of failure occurring in the engine.

According to one aspect of the present invention, the failure prevention system predicts the type and urgency of a failure occurring in the refrigerated vehicle based on operating data of the refrigerated vehicle and information on maintenance results for the refrigerated vehicle. and a prediction model creating unit that creates or updates a prediction model to be used.

According to one aspect of the present invention, the failure prevention system includes a refrigerated vehicle including communication means for transmitting operating data indicating the operating state of a refrigerator provided in the own vehicle, and a terminal device for receiving the countermeasure information. Prepare more.

According to one aspect of the present invention, a failure prevention method is a failure prevention method executed by a computer, comprising: acquiring operating data of a refrigerated vehicle; a step of predicting the type and degree of urgency of the fault; and a step of guiding countermeasure information to the failure according to the degree of urgency predicted in the predicting step. If the degree of urgency predicted in the step is high, the user of the refrigerated vehicle is informed of the countermeasure information that prompts the user to switch from the refrigerated vehicle to a substitute vehicle.

According to one aspect of the present invention, the program comprises: means for acquiring operating data of a refrigerated vehicle; means for predicting the type and degree of urgency of a failure occurring in the refrigerated vehicle based on the operating data; means for guiding countermeasure information to the failure according to the degree of urgency predicted by the means for predicting, and the means for guiding functions from the refrigeration vehicle when the degree of urgency predicted by the means for predicting is high. The user of the refrigerated vehicle is informed of the countermeasure information that prompts the user to switch to the alternative vehicle.

Advantageous Effects of Invention According to the present invention, it is possible to prevent a refrigeration vehicle from breaking down during transportation of cargo.

It is a figure which shows an example of the failure prevention system in one Embodiment of this invention. 4 is a flowchart showing an example of failure prediction processing for a refrigerated vehicle according to an embodiment of the present invention; 6 is a flow chart showing an example of transmission processing of troubleshooting information in one embodiment of the present invention. 4 is a flow chart showing an example of processing for creating a failure prediction model according to an embodiment of the present invention; It is a figure showing an example of hardware constitutions, such as a monitoring device in one embodiment of the present invention.

A failure prevention system for a refrigerated vehicle according to an embodiment of the present invention will be described below with reference to FIGS. 1 to 5. FIG.
FIG. 1 is a diagram showing an example of a failure prevention system according to one embodiment of the present invention.
As shown in FIG. 1, the failure prevention system 1 includes a monitoring device 10, a refrigeration vehicle 20A, a terminal device 40, and a terminal device 50. As shown in FIG. In the failure prevention system 1, the monitoring device 10 predicts the occurrence of a failure before the refrigeration vehicle 20A actually fails, and guides the user of the refrigeration vehicle 20A when the failure is likely to occur and the type of failure. System.

The refrigerated vehicle 20A includes an ECU 21A, a refrigerator 22A, a refrigerator 30A, and a controller 31A.
The ECU 21 has a function of controlling travel of the refrigerated vehicle 20A. For example, the ECU 21A controls the engine, brakes, steering, etc. based on the driver's operation. The ECU 21A has communication means for communicating with the monitoring device 10 via a network. The ECU 21A transmits, to the monitoring device 10, for example, information indicating the operating state of the engine measured by a sensor provided in the refrigerated vehicle 20A and travel operation data such as travel distance.

Refrigerator 30A is a device that cools or heats cargo to be transported by refrigerated vehicle 20A. The refrigerator 30A includes a refrigerant circuit including a compressor (not shown), an outdoor heat exchanger, an indoor heat exchanger, an expansion valve, and the like. The controller 31A controls the refrigerator 30A. The cargo is loaded in the cold storage 22, and the controller 31A operates the refrigerator 30A so that the temperature inside the cold storage 22A reaches the set temperature. Further, the controller 31A has communication means for communicating with the monitoring device 10 via a network. For example, the controller 31A detects information such as the temperature and pressure of the refrigerant in the refrigerant circuit detected by various sensors, the number of revolutions of the compressor, and the inside temperature and outside temperature detected by the temperature sensor provided in the cold storage 22A. Refrigerating operation data such as the outside temperature detected by the detecting sensor and the amount of fuel consumed by the refrigerator 30A are transmitted to the monitoring device 10 .

The refrigerated vehicle 20B is a substitute vehicle for the refrigerated vehicle 20A. The refrigerating vehicle 20B also has the same function and configuration as the refrigerating vehicle 20A. It should be noted that the number of refrigeration vehicles and the number of alternative vehicles used for transporting packages may be two or more. Hereinafter, the refrigerating vehicles 20A and 20B are simply referred to as refrigerating vehicles 20 when there is no need to distinguish between them. The same applies to the ECU 21, the refrigerator 22, the refrigerator 30, and the controller 31.

The terminal device 40 is a PC (personal computer) installed in a transportation company that transports packages using the refrigerated vehicle 20A, a mobile terminal used by an employee of the transportation company, or the like. The terminal device 40 is communicably connected to the monitoring device 10 via a network. The terminal device 40 receives information such as failure prediction of the refrigerated vehicle 20A and maintenance guidance from the monitoring device 10 . According to the guide information received from the monitoring device 10, the person in charge of the transportation company requests maintenance of the refrigerated vehicle 20A from the maintenance company, or arranges for a substitute vehicle (for example, the refrigerated vehicle 20B).

The terminal device 50 is a PC or the like installed in a maintenance company that maintains the refrigerated vehicle 20A. The terminal device 50 is communicably connected to the monitoring device 10 via a network. The terminal device 50 receives information necessary for maintenance of the refrigerated vehicle 20 (type of failure, replacement parts, failure prediction timing, etc.) from the monitoring device 10 . The person in charge of the maintenance company arranges parts and personnel necessary for maintenance of the refrigeration vehicle 20 according to the information received from the monitoring device 10 .

The monitoring device 10 includes a failure prediction system 10a that predicts failure of the refrigerated vehicle 20, a countermeasure guidance system 10b that guides appropriate countermeasures against the predicted failure, a communication unit 14, an input/output unit 17, and a storage unit. 18. The failure prediction system 10 a includes a data acquisition section 11 , a failure prediction section 12 and a prediction model creation section 13 . The countermeasure guidance system 10 b includes a countermeasure determination section 15 and a guidance section 16 .

(Failure prediction system)
The data acquisition unit 11 acquires traveling operation data and refrigeration operation data from the refrigeration vehicle 20 .
The failure prediction unit 12 predicts the type and degree of urgency of failure occurring in the refrigerator 30 based on the refrigeration operation data acquired by the data acquisition unit 11 and a predetermined prediction model. For example, the failure prediction unit 12 predicts the type of failure, "failure of compressor bearing," and the degree of urgency, "medium." Further, the failure prediction unit 12 predicts the type of failure (for example, engine failure) and the degree of urgency of the refrigerated vehicle 20 based on the traveling data acquired by the data acquisition unit 11 and a predetermined prediction model.

Here, the prediction model is, for example, refrigerating operation data or a judgment formula for comparing a value calculated based on the refrigerating operation data with a predetermined threshold value. For example, the failure prediction unit 12 calculates the load applied to the bearing by substituting the pressure and rotation speed on the discharge side and the suction side of the compressor into a predetermined calculation formula, and further calculates the service life of the bearing based on the determination formula. to calculate the time and travel distance until the bearing reaches the end of its life. The failure prediction unit 12 predicts that failure will not occur if the calculated time until the life is sufficiently long. For example, if the calculated time is one to two weeks, the failure prediction unit 12 predicts that the type of failure is "failure of compressor bearing" and the degree of urgency is "medium". For example, if the travel distance until the failure occurs is the travel distance that can be reached by transportation for one to two days, the failure prediction unit 12 determines the failure type “compressor bearing failure” and the urgency level “high”. And predict.

Further, the prediction model may be a classifier that is constructed by machine learning or the like by collecting refrigeration operation data in normal times and in failures, and that distinguishes between normality and failure of the refrigerator 30 . Alternatively, it may be a classifier that classifies the refrigeration operation data into groups for each type of failure. For example, the failure prediction unit 12 inputs the refrigeration operation data acquired by the data acquisition unit 11 into the prediction model, and predicts the type of failure depending on which group it belongs to. Further, for example, the failure prediction unit 12 predicts the urgency of failure based on the distance from the boundary information separating each group.

The prediction model creation unit 13 creates a prediction model by machine learning or the like. In addition, the prediction model creation unit 13 updates the prediction model by adding the refrigeration operation data acquired by the data acquisition unit 11 to the learning data. The predictive model creation unit 13 may create a predictive model for each type of failure. One prediction model predicts the probability of failure of the bearings or impellers of the compressor from, for example, the pressure and rotation speed of the compressor in a predetermined period, the total operating time of the compressor, and the like. Another prediction model predicts, for example, the probability of breakage of an electromagnetic valve provided in the refrigerant circuit from the pressure and temperature of the refrigerant at each position in the refrigerant circuit. Still another prediction model predicts the probability that the engine of the vehicle will fail, for example, from the running data of the refrigerated vehicle 20 . In addition, the predictive model creation unit 13 creates a predictive model for predicting the time until various failures occur based on data when failures actually occurred in the compressor, solenoid valve, engine, etc. in the past, for example. may

(Measure guidance system)
The countermeasure determination unit 15 determines countermeasures for the failure according to the type of failure predicted by the failure prediction unit 12 and the degree of urgency. For example, when it is predicted that the type of failure is "compressor bearing failure" and the degree of urgency is "medium", the countermeasure determination unit 15 selects the countermeasure "compressor replacement" for the failure and the entity that performs the countermeasure "refrigeration vehicle 20 user" and "maintenance company of refrigerated vehicle 20" are determined. Further, for example, when the degree of urgency of the failure is high, the countermeasure determination unit 15 selects the countermeasure "switching to a substitute vehicle" and the subject "user of the refrigerated vehicle 20" who takes the countermeasure regardless of the type of the failure. decide.

The guide unit 16 guides the user of the refrigerated vehicle 20 and the maintenance company about countermeasures according to the type of failure and the degree of urgency determined by the countermeasure determination unit 15 . For example, when the urgency of the failure is high, the guidance unit 16 guides the user of the refrigerated vehicle 20 to refrain from using the refrigerated vehicle 20 and switch to an alternative vehicle. In addition, regardless of the degree of urgency of the failure, the guide unit 16 guides the refrigerated vehicle 20 to enter the maintenance company and perform maintenance such as inspection, maintenance, and parts replacement. Further, for example, if the countermeasure against the failure is "compressor replacement", the guide unit 16 guides the maintenance company of the refrigeration vehicle 20 about the type of compressor used in the refrigeration vehicle 20 and procurement of a new compressor. do.

The communication unit 14 communicates with the ECU 21 and the controller 31 of the refrigeration vehicle 20 , the terminal device 40 and the terminal device 50 .
The input/output unit 17 receives input of various information. Also, the input/output unit 17 outputs various information to a monitor or the like.
The storage unit 18 stores various information. For example, the storage unit 18 stores traveling operation data and frozen operation data acquired by the data acquisition unit 11 . A part or all of the running operation data and the refrigerating operation data are hereinafter collectively referred to as operation data.

FIG. 2 is a flow chart showing an example of refrigerated vehicle failure prediction processing according to an embodiment of the present invention.
First, the data acquisition unit 11 acquires operating data from the refrigerated vehicle 20 (step S11). For example, the data acquisition unit 11 obtains from the controller 31 the internal temperature during transportation, the set temperature, the outside air temperature, the pressure and temperature of the refrigerant on the discharge side and the suction side of the compressor, the rotation speed of the compressor, the refrigerant in the heat exchanger, Refrigerating operation data such as pressure and temperature are obtained through the communication unit 14 along with the time. The data acquisition unit 11 also acquires travel operation data, such as the distance traveled by the refrigerated vehicle 20 and detection values of sensors indicating the operating state of the engine, from the ECU 21 via the communication unit 14 along with the time. The data acquisition unit 11 writes and stores the acquired operation data in the storage unit 18 .

Next, the failure prediction unit 12 predicts whether or not a failure will occur in the refrigerator 30 based on the refrigeration operation data and the prediction model acquired by the data acquisition unit 11 (step S12). For example, the failure prediction unit 12 calculates the life of the compressor bearing and the state of the impeller based on the compressor pressure, rotation speed, total operating time, etc. in a predetermined period and the compressor prediction model. The probability of impeller failure, the time until failure, or the distance traveled until failure is calculated. In addition, the failure prediction unit 12 predicts the probability of failure of the solenoid valve, the time until failure, etc. based on the pressure and temperature of the refrigerant detected by the sensors provided at each position of the refrigerant circuit and the prediction model for the solenoid valve. do. Further, the failure prediction unit 12 predicts the failure probability of the engine, the time until failure, etc., based on the driving data acquired by the data acquisition unit 11 and the prediction model for the engine of the vehicle. Further, the failure prediction unit 12 may calculate the time to failure based on a prediction model for each failure for calculating the time to failure.

Next, the failure prediction unit 12 compares the probability of failure with a predetermined threshold (step S13). If the probability of failure is smaller than the threshold (step S13; No), the failure prediction unit 12 predicts that no failure will occur (step S14). If the probability of failure is greater than or equal to the threshold (step S13; Yes), the failure prediction unit 12 outputs the type of failure and the degree of urgency to the countermeasure guidance system 10b (step S15). For example, the failure prediction unit 12 calculates the degree of urgency based on the probability of failure and the time until failure occurs. For example, the failure prediction unit 12 assigns a "high" urgency when the failure probability is 80% or more, a "medium" urgency when the failure probability is 50% or more, and a "low" urgency when the failure probability is less than 50%. and decide. Alternatively, the failure prediction unit 12 sets the urgency level to "high" if the time until failure occurs is within two days, and sets the urgency level to "medium" if the time is within one week. Then, the failure prediction unit 12 outputs the type of failure along with the degree of urgency. For example, when the failure prediction unit 12 calculates that the probability of bearing failure is 80%, it outputs the failure type "compressor bearing failure" and the urgency level "high" to the countermeasure guidance system 10b. Note that the threshold for calculating the degree of urgency may differ for each type of failure.

FIG. 3 is a flow chart showing an example of processing for transmitting troubleshooting information according to an embodiment of the present invention.
First, the countermeasure determination unit 15 acquires the type of failure and the degree of urgency from the failure prediction unit 12 (step S21). Next, the countermeasure determination unit 15 determines whether or not the degree of urgency is "high" (step S22). If the degree of urgency is "high" (step S22; Yes), the countermeasure determination unit 15 informs the user of the refrigeration vehicle 20 that there is a high possibility that the refrigeration vehicle 20 will break down early. It decides to refrain from using , and to provide guidance to switch to a substitute vehicle (refrigerated vehicle 20B). The guidance unit 16 transmits, for example, the type of failure, the degree of urgency, and an e-mail that guides switching to a substitute vehicle to the terminal device 40 via the communication unit 14 (step S23). Further, for example, the guidance unit 16 may display the guidance on a user-dedicated Web page.

If the degree of urgency is not "high" (step S22; No), the countermeasure determination unit 15 predicts that the user of the refrigerated vehicle 20 is about to break down. 20B). The guidance unit 16 notifies the terminal device 40 of the type of failure, the degree of urgency, and the above guidance, and provides guidance on the user-dedicated Web page (step S24).

In addition, the guide unit 16 guides the user of the refrigerated vehicle 20 to enter the warehouse regardless of the urgency of the failure (step S25). For example, the countermeasure determination unit 15 selects a storage destination (maintenance site for maintenance) that is a candidate for maintenance of the refrigerated vehicle 20 based on the location of the transportation company that uses the refrigerated vehicle 20, the transportation route, the type of predicted failure, and the like. (factory) to the guidance unit 16. The guidance unit 16 notifies the terminal device 40 of the warehousing guidance including the candidate of the warehousing destination, and provides the guidance on the web page.

In addition, the guidance unit 16 guides maintenance preparations for the refrigerated vehicle 20 (step S26). For example, the countermeasure determination unit 15 outputs identification information of the refrigeration vehicle 20, user information, the type and degree of urgency of the failure, information on replacement parts, and the like to the guidance unit 16. FIG. The guidance unit 16 notifies the terminal device 50 of the type of failure, the arrangement of replacement parts and engineers, and guidance for urging maintenance preparations. This allows the maintenance company to prepare for maintenance of the refrigerated vehicle 20 .

FIG. 4 is a flow chart showing an example of processing for creating a failure prediction model according to an embodiment of the present invention.
First, the data acquisition unit 11 writes in the storage unit 18 the operation data acquired from the refrigeration vehicle 20 that actually transported the cargo. For example, the storage unit 18 stores refrigerating operation data transmitted from the controller 31 during transportation, such as the internal temperature, the outside air temperature, the humidity, the pressure and temperature of the refrigerant, the rotation speed of the compressor, and the refrigerating operation data transmitted from the ECU 21. Driving data such as the distance traveled by the vehicle 20 and the physical quantity indicating the state of the engine are accumulated (step S31).

Next, the person in charge associates the operation data of the refrigeration vehicle 20 for which a failure was predicted among the operation data accumulated in the storage unit 18, and the result of the maintenance of the refrigeration vehicle 20 and the parts removed for replacement. Information on analysis results (for example, an evaluation of the progress of the failure, etc.) further analyzed by the expert is input to the monitoring device 10 . The result of maintenance is, for example, the result of the guide shown in FIG. 3 and the result of inspection by the maintenance company at the receiving end. The input/output unit 17 receives inputs such as maintenance results and analysis results (step S32). The analysis results include information such as the type of failure, location of failure, and degree of deterioration. The input/output unit 17 associates the analysis result with the operating data specified by the person in charge and stores them in the storage unit 18 .

Next, the person in charge instructs the monitoring device 10 to create/update the prediction model. The input/output unit 17 receives input of this instruction operation. Then, the prediction model creation unit 13 creates a prediction model (step S33). For example, the predictive model creation unit 13 adds the analysis result received in step S32 as label information to the corresponding refrigerating operation data, and other refrigerating operation data "Normal" label information is added to the refrigeration operation data of the refrigeration vehicle 20 in the state. The refrigerating operation data to which label information is added is learning data. The prediction model creation unit 13 creates a prediction model for classifying both based on label information added to the refrigeration operation data, for example. Alternatively, the prediction model creating unit 13 classifies the accumulated refrigerating operation data into groups having the same features only by their features. Further, the predictive model creating unit 13 associates each group with the type of failure based on the label information attached to the refrigerating operation data included in the classified groups. For example, if a certain group classified based on the refrigerant pressure detected at a predetermined position in the refrigerant circuit contains a large amount of refrigeration operation data in which the deterioration of the solenoid valve has progressed, the solenoid valve Match the fault. For example, when the refrigerant operation data to be evaluated belongs to this group in step S12 of FIG.
The prediction model creation unit 13 updates the prediction model created last time with the prediction model created this time. The prediction model creation unit 13 stores the updated prediction model in the storage unit 18 (step S34).

In this way, the operation data received and stored from the refrigerated vehicle 20 during transportation is added with the results of inspections and the like of the refrigerated vehicle 20 actually performed, and taken in as learning data to be used for creating a prediction model. can be done. By increasing the number of learning data, the accuracy and reliability of the prediction model calculated by the prediction model creation unit 13 can be enhanced.

According to this embodiment, the monitoring device 10 acquires refrigeration operation data indicating the operating state of the refrigerator 30 and traveling operation data indicating the traveling state of the refrigeration vehicle 20 while the cargo is being transported using the refrigeration vehicle 20. . Then, the monitoring device 10 predicts a failure occurring in the refrigerated vehicle 20 including the refrigerator 30 before the failure actually occurs in the refrigerated vehicle 20 . Furthermore, the monitoring device 10 guides the user about the predicted failure and its countermeasures. As a result, it is possible to prevent the refrigeration vehicle 20 from breaking down during transportation of the cargo by the refrigeration vehicle 20 and causing property damage to the cargo.

In addition, when a failure is predicted, the predicted failure is also notified to the maintenance company of the refrigeration vehicle 20. - 特許庁As a result, the maintenance company can arrange parts and personnel, and smoothly perform maintenance such as inspection, repair, and parts replacement of the refrigerated vehicle 20 .

FIG. 5 is a diagram showing an example of a hardware configuration such as a monitoring device according to an embodiment of the invention.
A computer 900 includes a CPU 901 , a main memory device 902 , an auxiliary memory device 903 , an input/output interface 904 and a communication interface 905 .
The monitoring device 10 , the terminal device 40 , and the terminal device 50 described above are implemented in the computer 900 . Each function described above is stored in the auxiliary storage device 903 in the form of a program. The CPU 901 reads out the program from the auxiliary storage device 903, develops it in the main storage device 902, and executes the above processing according to the program. Also, the CPU 901 secures a storage area in the main storage device 902 according to the program. In addition, the CPU 901 secures a storage area for storing data being processed in the auxiliary storage device 903 according to the program.

A program for realizing all or part of the functions of the monitoring device 10, the terminal device 40, and the terminal device 50 is recorded on a computer-readable recording medium, and the program recorded on this recording medium is transferred to the computer system. By reading and executing, the processing by each functional unit may be performed. The "computer system" here includes hardware such as an OS and peripheral devices. The "computer system" also includes the home page providing environment (or display environment) if the WWW system is used. The term "computer-readable recording medium" refers to portable media such as CDs, DVDs, and USBs, and storage devices such as hard disks incorporated in computer systems. Moreover, when this program is delivered to the computer 900 via a communication line, the computer 900 receiving the delivery may develop the program in the main storage device 902 and execute the above process. Further, the program may be for realizing part of the functions described above, or may be capable of realizing the functions described above in combination with a program already recorded in the computer system. . Note that the monitoring device 10 may be configured by a plurality of computers 900 .

In addition, it is possible to appropriately replace the components in the above-described embodiments with well-known components without departing from the scope of the present invention. Moreover, the technical scope of the present invention is not limited to the above-described embodiments, and various modifications can be made without departing from the scope of the present invention.

For example, in the above embodiment, an example of calculating the optimum refrigeration control parameters in the form of a so-called cloud service via a network was given, but for example, a program that exhibits the functions of the monitoring device 10 may be installed in the terminal device 40. On the other hand, the refrigerated vehicle 20 is provided with, for example, an external storage device, the driving data collected while the refrigerated vehicle 20 is traveling is stored in the external storage device, and the driving data stored in the external storage device is retrieved at a predetermined timing. It may be transferred to the terminal device 40, and the terminal device 40 may execute the processing described in the flow charts of FIGS.

DESCRIPTION OF SYMBOLS 1... Failure prevention system 10... Monitoring apparatus 10a... Failure prediction system 10b... Countermeasure guidance system 11... Data acquisition part 12... Failure prediction part 13... Prediction model preparation part 14 Communication unit 15 Countermeasure determination unit 16 Guide unit 17 Input/output unit 18 Storage unit 20A, 20B Refrigeration vehicle 21A, 21B ECU
22A, 22B... Cooler 30A, 30B... Freezer 31A, 31B... Controller 40... Terminal device 50... Terminal device 900... Computer 901... CPU,
902 Main storage device,
903 Auxiliary storage device,
904 Input/output interface 905 Communication interface

Claims (10)

a data acquisition unit that acquires operating data of the refrigerated vehicle;
a failure prediction unit that predicts the type and degree of urgency of a failure occurring in the refrigerated vehicle based on the operating data;
a guidance unit that guides countermeasure information for the failure according to the degree of urgency predicted by the failure prediction unit;
with
When the degree of urgency is higher than a predetermined threshold, the guide unit guides the user of the refrigerated vehicle with the countermeasure information that prompts switching from the refrigerated vehicle to a substitute vehicle.
failure prevention system. a data acquisition unit that acquires operating data of the refrigerated vehicle;
a failure prediction unit that predicts the type and degree of urgency of a failure occurring in the refrigerated vehicle based on the operating data;
a guidance unit that guides countermeasure information for the failure according to the degree of urgency predicted by the failure prediction unit;
with
When the degree of urgency is not higher than a predetermined threshold, the guide unit guides the user of the refrigeration vehicle with the countermeasure information that prompts the preparation of a substitute vehicle for the refrigeration vehicle.
failure prevention system . The guide unit guides a user of the refrigerated vehicle with the countermeasure information that prompts maintenance of the refrigerated vehicle.
The failure prevention system according to claim 1 or 2 . The guide unit guides the maintenance contractor of the refrigeration vehicle with the countermeasure information that prompts maintenance according to the type of the failure.
The failure prevention system according to any one of claims 1 to 3. The data acquisition unit acquires the operating data related to a compressor included in a refrigerator of the refrigeration vehicle,
The failure prediction unit predicts the type and urgency of failure occurring in the compressor,
The failure prevention system according to any one of claims 1 to 4. The data acquisition unit acquires the operating data related to the engine of the refrigerated vehicle,
The failure prediction unit predicts the type and urgency of a failure occurring in the engine,
The failure prevention system according to any one of claims 1 to 5. a prediction model creation unit that creates or updates a prediction model that predicts the type and degree of urgency of a failure occurring in the refrigerated vehicle based on the operation data and information on the maintenance result of the refrigerated vehicle;
7. The fault prevention system of any one of claims 1-6, further comprising: a refrigeration vehicle comprising communication means for transmitting the operation data indicating the operation state of a refrigerator provided in the own vehicle;
a terminal device that receives the countermeasure information;
The failure prevention system of any one of claims 1-7, further comprising: A computer-implemented failure prevention method comprising:
obtaining operating data of the refrigerated vehicle;
a step of predicting the type and degree of urgency of failure occurring in the refrigerated vehicle based on the operating data;
a step of guiding countermeasure information for the failure according to the degree of urgency predicted in the predicting step;
has
In the step of providing guidance, when the degree of urgency predicted in the step of predicting is high, the user of the refrigeration vehicle is guided to the countermeasure information that prompts the user to switch from the refrigeration vehicle to an alternative vehicle.
Failure prevention method. the computer,
means for obtaining operational data of a refrigerated vehicle;
means for predicting the type and degree of urgency of a failure occurring in the refrigerated vehicle based on the operating data;
means for guiding countermeasure information for the failure according to the degree of urgency predicted by the predicting means;
function as
When the degree of urgency predicted by the predicting means is high, the guiding means guides the user of the refrigerated vehicle with the countermeasure information that prompts switching from the refrigerated vehicle to a substitute vehicle.
program.

Images