JP2020135133A - Failure prevention system, failure prevention method and program - Google Patents

Abstract

To provide a system for preventing failure of freezing vehicle in transit.SOLUTION: A failure prevention system includes: a data acquisition part which acquires performance data of a freezing vehicle; a prediction part which predicts type and urgency of the failure occurring on a freezing vehicle based on operation data; and a guide part which guides countermeasure information on the failure according to the urgency predicted by the prediction part.SELECTED DRAWING: Figure 1

Description

The present invention relates to failure prevention systems, failure prevention methods and programs.

When transporting luggage in a freezer vehicle, the refrigerator may break down. As a countermeasure in the event of a refrigerated vehicle failure, for example, Patent Document 1 states that the control center monitors the measured value of a temperature sensor attached to the luggage of the refrigerated vehicle, and detects the failure of the refrigerated vehicle due to the temperature rise of the luggage. A system for transferring and delivering packages is disclosed. Further, Patent Document 2 discloses a method in which a management server arranges a substitute vehicle based on a request for arranging a substitute vehicle received from a frozen vehicle being transported. For example, when the management server receives the arrangement request, it compares the estimated delivery completion time of the undelivered product with the estimated delivery completion time by the alternative vehicle, and requests the dispatch of the alternative vehicle if the delivery by the alternative vehicle is completed earlier. .. In addition, the management server compares the time when the failed vehicle returns to the office with the estimated arrival time of the alternative vehicle, and if it is faster to return to the office than to wait for the arrival of the alternative vehicle, it returns to the office to the failed vehicle. Instruct.

JP-A-2009-161345 JP-A-2010-70267

If a refrigerated vehicle breaks down during the transportation of luggage, even if a replacement vehicle can be arranged, the luggage may be damaged. Therefore, from the viewpoint of preventing property damage, it is desirable to control so as to avoid failure of the refrigerated vehicle during transportation.

Therefore, an object of the present invention is to provide a failure prevention system, a failure prevention method, and a program capable of solving the above-mentioned problems.

According to one aspect of the present invention, the failure prevention system has a data acquisition unit that acquires operation data of the refrigerated vehicle, and a failure prediction that predicts the type and urgency of the failure that occurs in the refrigerated vehicle based on the operation data. It is provided with a unit and a guide unit for guiding countermeasure information for the failure according to the urgency predicted by the failure prediction unit.

According to one aspect of the present invention, the guide unit guides the user of the frozen vehicle with the countermeasure information for promoting maintenance of the frozen vehicle.

According to one aspect of the present invention, when the degree of urgency is high, the guide unit guides the user of the frozen vehicle with the countermeasure information for urging the switch from the frozen vehicle to the alternative vehicle.

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

According to one aspect of the present invention, the data acquisition unit acquires operation data regarding the compressor included in the refrigerator of the refrigerating vehicle, and the failure prediction unit determines the type and urgency of the failure that occurs in the compressor. Foresee.

According to one aspect of the present invention, the data acquisition unit acquires driving data regarding the engine of the refrigerated vehicle, and the failure prediction unit predicts the type and urgency of a failure that occurs 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 the operation data of the refrigerated vehicle and the information of the maintenance result for the refrigerated vehicle. It is further provided with a prediction model creation unit for creating or updating a prediction model.

According to one aspect of the present invention, the failure prevention system includes a refrigerating vehicle including a communication means for transmitting operation data indicating an operating state of the refrigerating machine included in the own vehicle, and a terminal device for receiving the countermeasure information. Further prepare.

According to one aspect of the present invention, the failure prevention method includes a step of acquiring driving data of a refrigerated vehicle, a step of predicting the type and urgency of a failure occurring in the refrigerated vehicle based on the driving data, and the above-mentioned step. It has a step of guiding countermeasure information for the failure according to the urgency predicted in the step of predicting.

According to one aspect of the present invention, the program uses a computer as a means for acquiring driving data of a refrigerated vehicle, a means for predicting the type and urgency of a failure occurring in the refrigerated vehicle based on the driving data, and the prediction. It functions as a means for guiding countermeasure information for the above-mentioned failure according to the urgency predicted by the means.

According to the present invention, it is possible to prevent the freezing vehicle from breaking down during the transportation of luggage.

It is a figure which shows an example of the failure prevention system in one Embodiment of this invention. It is a flowchart which shows an example of the failure prediction processing of the refrigerating vehicle in one Embodiment of this invention. It is a flowchart which shows an example of the transmission processing of the failure countermeasure information in one Embodiment of this invention. It is a flowchart which shows an example of the creation process of the failure prediction model in one Embodiment of this invention. It is a figure which shows an example of the hardware composition of the monitoring apparatus and the like in one Embodiment of this invention.

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

The refrigerating vehicle 20A includes an ECU 21A, a cold storage 22A, a refrigerator 30A, and a controller 31A.
The ECU 21 has a function of controlling the running 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 includes a communication means for communicating with the monitoring device 10 via a network. For example, the ECU 21A transmits information indicating the operating state of the engine measured by a sensor provided in the refrigerated vehicle 20A and traveling driving data such as a traveling distance to the monitoring device 10.

The refrigerator 30A is a device for cooling or heating the luggage transported by the freezing 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 luggage is loaded in the cold storage 22, and the controller 31A operates the refrigerator 30A so that the temperature inside the cold storage 22A becomes the set temperature. Further, the controller 31A includes a communication means for communicating with the monitoring device 10 via the network. For example, the controller 31A obtains 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 internal temperature and the outside temperature detected by the temperature sensor provided in the cold storage 22A. Refrigeration operation data such as the outside temperature detected by the detecting sensor and the fuel consumption consumed by the refrigerator 30A is transmitted to the monitoring device 10.

The freezing vehicle 20B is a substitute vehicle for the freezing vehicle 20A. The refrigerated vehicle 20B also has the same functions and configurations as the refrigerated vehicle 20A. The number of frozen vehicles and the number of alternative vehicles used for transporting luggage may be two or more. Hereinafter, when it is not necessary to distinguish between the refrigerated vehicle 20A and 20B, it is simply referred to as the refrigerated vehicle 20. The same applies to the ECU 21, the cold storage 22, the refrigerator 30, and the controller 31.

The terminal device 40 is a PC (personal computer) provided in a transportation company that transports luggage using the freezing vehicle 20A, a mobile terminal used by an employee of the transportation company, and 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 refrigerating vehicle 20A and maintenance guidance from the monitoring device 10. The person in charge of the transportation company requests the maintenance company for the maintenance of the refrigerated vehicle 20A or arranges for a substitute vehicle (for example, the refrigerated vehicle 20B) according to the guidance information received from the monitoring device 10.

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

The monitoring device 10 includes a failure prediction system 10a that predicts a 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. It is provided with 18. The failure prediction system 10a includes a data acquisition unit 11, a failure prediction unit 12, and a prediction model creation unit 13. The countermeasure guidance system 10b includes a countermeasure determination unit 15 and a guide unit 16.

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

Here, the prediction model is, for example, a discriminant that compares the refrigeration operation data and the value calculated based on the refrigeration operation data with a predetermined threshold value. For example, the failure prediction unit 12 substitutes the pressure and rotation speed of the discharge side and the suction side of the compressor into a predetermined calculation formula to calculate the load applied to the bearing, and further calculates the life of the bearing based on the determination formula. Then, the time and mileage until the bearing reaches the end of its life are calculated. The failure prediction unit 12 predicts that a failure will not occur if the calculated time to life is sufficiently long. For example, if the calculated time is 1 to 2 weeks, the failure prediction unit 12 predicts the failure type "compressor bearing failure" and the urgency "medium". For example, if the mileage until the failure occurs is the mileage reached by transportation for 1 to 2 days, the failure prediction unit 12 determines the failure type "compressor bearing failure" and the urgency "high". Foresee.

Further, the prediction model may be a classifier for discriminating between normality and failure of the refrigerator 30 which is constructed by collecting refrigerating operation data at the time of normal and failure and by machine learning or the like. Alternatively, it may be a classifier that classifies the freezing operation data into groups according to the 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. Further, 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 prediction model creation unit 13 may create a prediction model for each type of failure. One prediction model predicts the probability that the bearing or impeller of the compressor will fail 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. Further, another prediction model predicts the probability that the solenoid valve included in the refrigerant circuit will be damaged from the pressure and temperature of the refrigerant at each position of the refrigerant circuit, for example. Yet another prediction model predicts the probability that the engine of the vehicle will fail, for example, from the traveling driving data of the refrigerated vehicle 20. Further, the prediction model creation unit 13 creates a prediction model that predicts the time until various failures occur, based on, for example, data when a failure actually occurs in the compressor, solenoid valve, engine, or the like in the past. You may.

(Countermeasure guidance system)
The countermeasure determination unit 15 determines countermeasures for failures according to the type and urgency of the failure predicted by the failure prediction unit 12. For example, when it is predicted that the type of failure is "compressor bearing failure" and the urgency is "medium", the countermeasure determination unit 15 will take measures against the failure "replacement of the compressor" and the main body "refrigerating vehicle 20" to take the countermeasures. "User" and "Maintenance company of refrigerated vehicle 20" are determined. Further, for example, when the urgency of the failure is high, the countermeasure determination unit 15 determines the countermeasure "switch to an alternative vehicle" and the main body "user of the refrigerated vehicle 20" that takes the countermeasure regardless of the type of the failure. decide.

The guide unit 16 guides the user of the refrigerating vehicle 20 and the maintenance company about the countermeasures according to the type and urgency of the failure determined by the countermeasure determination unit 15. For example, when the urgency of failure is high, the guide unit 16 guides the user of the frozen vehicle 20 to refrain from using the frozen vehicle 20 and switch to an alternative vehicle. Further, regardless of the urgency of the failure, the guide unit 16 guides the refrigerated vehicle 20 to be stored in the maintenance company for maintenance such as inspection, maintenance, and parts replacement. Further, for example, when the countermeasure against the failure is "replacement of the compressor", the guide unit 16 informs the maintenance company of the refrigerating vehicle 20 about the model of the compressor used for the refrigerating vehicle 20 and the procurement of a new compressor. To do.

The communication unit 14 communicates with the ECU 21 and the controller 31, the terminal device 40, and the terminal device 50 of the refrigerating vehicle 20.
The input / output unit 17 receives input of various information. Further, 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 the traveling operation data and the freezing operation data acquired by the data acquisition unit 11. Hereinafter, the operation data will be referred to as a general term for a part or all of the traveling operation data and the freezing operation data.

FIG. 2 is a flowchart showing an example of failure prediction processing of a refrigerated vehicle according to an embodiment of the present invention.
First, the data acquisition unit 11 acquires driving data from the refrigerated vehicle 20 (step S11). For example, the data acquisition unit 11 includes the temperature inside the refrigerator, the set temperature, the outside 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, and the refrigerant in the heat exchanger during transportation from the controller 31. Refrigerating operation data such as pressure and temperature of the above is acquired together with the time via the communication unit 14. In addition, the data acquisition unit 11 acquires traveling operation data such as the mileage of the refrigerated vehicle 20 from the ECU 21 and the detection value of the sensor indicating the operating state of the engine together with the time via the communication unit 14. The data acquisition unit 11 writes and stores these 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 acquired by the data acquisition unit 11 and the prediction model (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 prediction model for the compressor, and the bearing and the impeller state are calculated. Calculate the probability that the impeller will fail, the time until the failure, the mileage until the failure, and the like. Further, the failure prediction unit 12 predicts the probability of failure of the solenoid valve, the time until failure, and the like 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. To do. Further, the failure prediction unit 12 predicts the engine failure probability, the time until failure, and the like based on the traveling driving data acquired by the data acquisition unit 11 and the prediction model for the vehicle engine. Further, the failure prediction unit 12 may calculate the time until failure based on the prediction model for each failure for calculating the time until failure.

Next, the failure prediction unit 12 compares the probability of failure with a predetermined threshold value (step S13). When the probability of failure is smaller than the threshold value (step S13; No), the failure prediction unit 12 predicts that no failure will occur (step S14). When the probability of failure is equal to or greater than the threshold value (step S13; Yes), the failure prediction unit 12 outputs the type and urgency of the failure 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 a failure occurs. For example, the failure prediction unit 12 has an urgency of "high" when the probability of failure is 80% or more, an urgency of "medium" when the probability of failure is 50% or more, and an urgency of "low" when the probability of failure is 50% or less. To decide. Alternatively, the failure prediction unit 12 sets the urgency level to "high" if the time until the failure occurs within 2 days, "medium" if the time is within 1 week, and the like. Then, the failure prediction unit 12 outputs the type of failure together with the degree of urgency. For example, when the failure prediction unit 12 calculates the probability of bearing failure as 80%, it outputs the failure type "compressor bearing failure" and the urgency "high" to the countermeasure guidance system 10b. The threshold value for calculating the degree of urgency may differ depending on the type of failure.

FIG. 3 is a flowchart showing an example of a failure countermeasure information transmission process according to the embodiment of the present invention.
First, the countermeasure determination unit 15 acquires the type and urgency of the failure 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). When the urgency is "high" (step S22; Yes), the countermeasure determination unit 15 tells the user of the refrigerated vehicle 20 that the refrigerated vehicle 20 is likely to break down early. Therefore, for example, the refrigerated vehicle 20 from the next time onward It is decided to refrain from using the vehicle and provide guidance to switch to an alternative vehicle (freezing vehicle 20B). For example, the guide unit 16 transmits an e-mail informing the type of failure, the degree of urgency, and switching to an alternative 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 Web page dedicated to the user.

When the urgency is not "high" (step S22; No), the countermeasure determination unit 15 predicts that the user of the refrigerated vehicle 20 is approaching the failure of the refrigerated vehicle 20, so that the alternative vehicle (frozen vehicle) 20B) Decide to guide you to prepare. The guidance unit 16 notifies the terminal device 40 of the type of failure, the degree of urgency, the guidance, and guidance on a user-dedicated Web page (step S24).

Further, the guide unit 16 guides the user of the refrigerated vehicle 20 for warehousing regardless of the urgency of failure (step S25). For example, the countermeasure determination unit 15 is a warehousing destination (maintenance to perform maintenance) that is a candidate for maintenance of the frozen vehicle 20 based on the location of the transportation company using the frozen vehicle 20, the transportation route, the type of predicted failure, and the like. The information of the factory) is output to the guide unit 16. The guidance unit 16 notifies the terminal device 40 of the warehousing guidance including the candidate of the warehousing destination, and provides guidance on the Web page.

Further, the guide unit 16 guides the maintenance company of the refrigerating vehicle 20 to prepare for maintenance (step S26). For example, the countermeasure determination unit 15 outputs identification information of the refrigerated vehicle 20, user information, failure type and urgency, information on replacement parts, and the like to the guide unit 16. The guide unit 16 notifies the terminal device 50 of the type of failure, arrangement of replacement parts and technicians, guidance for prompting preparation for maintenance, and the like. As a result, the maintenance company can prepare for the maintenance of the refrigerating vehicle 20.

FIG. 4 is a flowchart showing an example of a failure prediction model creation process according to the embodiment of the present invention.
First, the data acquisition unit 11 writes the operation data acquired from the refrigerated vehicle 20 that actually transported the luggage into the storage unit 18. For example, the storage unit 18 contains refrigerating operation data such as internal temperature during transportation, outside air temperature, humidity, refrigerant pressure and temperature, and compressor rotation speed transmitted from the controller 31, and refrigeration transmitted from the ECU 21. Traveling driving data such as the traveling distance of the vehicle 20 and the physical quantity indicating the state of the engine are accumulated (step S31).

Next, among the operation data stored in the storage unit 18, the person in charge associates the operation data of the refrigerated vehicle 20 in which a failure is predicted with the result of maintenance for the refrigerated vehicle 20 and the parts removed by replacement. Information on the analysis result (for example, evaluation of the progress of the failure) further analyzed by the expert is input to the monitoring device 10. The maintenance result is, for example, the result of the guidance shown in FIG. 3 and the inspection result by the maintenance company at the warehousing destination. 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, the location of the failure, and the degree of deterioration. The input / output unit 17 stores the analysis result and the operation data designated by the person in charge in the storage unit 18 in association with each other.

Next, the person in charge instructs the monitoring device 10 to create / update the prediction model. The input / output unit 17 receives the input of this instruction operation. Then, the prediction model creation unit 13 creates a prediction model (step S33). For example, the prediction model creation unit 13 adds the analysis result received in step S32 as label information to the corresponding refrigeration operation data, and other refrigeration operation data (that is, normal failure has not yet been predicted). The label information of "normal" is added to the refrigerating operation data of the refrigerated vehicle 20 in the state). The refrigeration operation data to which the label information is added is learning data. The prediction model creation unit 13 creates, for example, a prediction model that classifies both based on the label information added to the refrigeration operation data. Alternatively, the prediction model creation unit 13 classifies the accumulated refrigeration operation data into groups having the same characteristics only by the characteristics. Further, the prediction model creation unit 13 associates the type of failure with each group based on the label information attached to the refrigeration operation data included in the classified groups. For example, for a group classified based on the pressure of the refrigerant detected at a predetermined position in the refrigerant circuit, if the group contains a large amount of refrigeration operation data in which the deterioration of the solenoid valve has progressed, the group includes the solenoid valve. Correspond to the failure. For example, when the refrigerant operation data to be evaluated belongs to this group in step S12 of FIG. 2, the failure prediction unit 12 determines that the refrigerating vehicle 20 to be evaluated has a high probability of failure of the solenoid valve.
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).

To the driving data received and accumulated from the refrigerated vehicle 20 being transported in this way, the result of actually inspecting the refrigerated vehicle 20 is added and taken as learning data to be used for creating a prediction model. Can be done. By increasing the number of training data, the accuracy and reliability of the prediction model calculated by the prediction model creation unit 13 can be improved.

According to the present embodiment, the monitoring device 10 acquires refrigerating operation data indicating the operating state of the refrigerator 30 and traveling operation data indicating the traveling state of the refrigerating vehicle 20 while transporting the luggage using the refrigerating vehicle 20. .. Then, the monitoring device 10 predicts a failure that occurs in the refrigerating vehicle 20 including the refrigerator 30 before the failure actually occurs in the refrigerating vehicle 20. Further, the monitoring device 10 guides the user of the predicted failure and its countermeasure. As a result, it is possible to prevent the freezing vehicle 20 from breaking down during the transportation of the luggage by the freezing vehicle 20 and causing property damage to the luggage.

In addition, when a failure is predicted, the maintenance company of the refrigerating vehicle 20 is also informed of the predicted failure. As a result, the maintenance company can arrange parts and personnel and smoothly proceed with maintenance such as inspection and repair of the refrigerated vehicle 20 and parts replacement.

FIG. 5 is a diagram showing an example of a hardware configuration such as a monitoring device according to an embodiment of the present invention.
The computer 900 includes a CPU 901, a main storage device 902, an auxiliary storage 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 mounted on the computer 900. Each of the above-mentioned functions is stored in the auxiliary storage device 903 in the form of a program. The CPU 901 reads the program from the auxiliary storage device 903, expands it to the main storage device 902, and executes the above processing according to the program. Further, the CPU 901 secures a storage area in the main storage device 902 according to the program. Further, the CPU 901 secures a storage area for storing the data being processed in the auxiliary storage device 903 according to the program.

A program for realizing all or a 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 the recording medium is stored in the computer system. Processing by each functional unit may be performed by reading and executing. The term "computer system" as used herein includes hardware such as an OS and peripheral devices. In addition, the "computer system" includes a homepage providing environment (or display environment) if a WWW system is used. Further, the "computer-readable recording medium" refers to a portable medium such as a CD, DVD, or USB, or a storage device such as a hard disk built in a computer system. When this program is distributed to the computer 900 via a communication line, the distributed computer 900 may expand the program to the main storage device 902 and execute the above processing. Further, the above-mentioned program may be for realizing a part of the above-mentioned functions, and may further realize the above-mentioned functions in combination with a program already recorded in the computer system. .. The monitoring device 10 may be composed of a plurality of computers 900.

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

For example, in the above embodiment, an example of calculating the optimum refrigeration control parameter in the form of a so-called cloud service via a network has been given. For example, a program that exerts the function of the monitoring device 10 is implemented in the terminal device 40. On the other hand, the refrigerated vehicle 20 is provided with, for example, an external storage device, and the operation data collected during the traveling of the refrigerated vehicle 20 is stored in the external storage device, and the operation data stored in the external storage device is stored at a predetermined timing. The data may be transferred to the terminal device 40 so that the terminal device 40 executes the process described in the flowcharts of FIGS. 2 to 4.

1 ... Failure prevention system 10 ... Monitoring device 10a ... Failure prediction system 10b ... Countermeasure guidance system 11 ... Data acquisition unit 12 ... Failure prediction unit 13 ... Prediction model creation unit 14・ ・ ・ Communication unit 15 ・ ・ ・ Countermeasure decision unit 16 ・ ・ ・ Guidance unit 17 ・ ・ ・ Input / output unit 18 ・ ・ ・ Storage unit 20A, 20B ・ ・ ・ Refrigerated vehicle 21A, 21B ・ ・ ・ ECU
22A, 22B ... Cold storage 30A, 30B ... Refrigerator 31A, 31B ... Controller 40 ... Terminal equipment 50 ... Terminal equipment 900 ... Computer 901 ... CPU,
902 ... Main memory,
903 ... Auxiliary storage device,
904 ... Input / output interface 905 ... Communication interface

Claims (10)

A data acquisition unit that acquires operation data of frozen vehicles,
A failure prediction unit that predicts the type and urgency of a failure that occurs in the refrigerated vehicle based on the operation data.
A guide unit that guides countermeasure information for the failure according to the urgency predicted by the failure prediction unit, and a guide unit.
Failure prevention system equipped with. The guide unit guides the user of the frozen vehicle to the countermeasure information for promoting the maintenance of the frozen vehicle.
The failure prevention system according to claim 1. When the degree of urgency is high, the guide unit guides the user of the frozen vehicle with the countermeasure information for urging the switch from the frozen vehicle to the alternative vehicle.
The failure prevention system according to claim 1 or 2. The guide unit guides the maintenance company of the refrigerated vehicle to the countermeasure information for promoting maintenance according to the type of failure.
The failure prevention system according to any one of claims 1 to 3. The data acquisition unit acquires the operation data regarding the compressor included in the refrigerator of the refrigerating vehicle, and acquires the operation data.
The failure prediction unit predicts the type and urgency of a failure that occurs in the compressor.
The failure prevention system according to any one of claims 1 to 4. The data acquisition unit acquires the operation data regarding the engine of the refrigerated vehicle, and obtains the operation data.
The failure prediction unit predicts the type and urgency of a failure that occurs 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 urgency of a failure that occurs in the refrigerated vehicle based on the operation data and maintenance result information for the refrigerated vehicle.
The failure prevention system according to any one of claims 1 to 6, further comprising. A refrigerating vehicle equipped with a communication means for transmitting the operation data indicating the operating state of the refrigerating machine of the own vehicle, and
A terminal device that receives the countermeasure information and
The failure prevention system according to any one of claims 1 to 7, further comprising. Steps to acquire operation data of frozen vehicles and
Based on the driving data, a step of predicting the type and urgency of the failure occurring in the refrigerated vehicle, and
A step of guiding countermeasure information for the failure according to the urgency predicted in the step of predicting, and a step of guiding the countermeasure information for the failure.
Failure prevention method with. Computer,
Means of acquiring operation data of frozen vehicles,
A means for predicting the type and urgency of a failure occurring in the frozen vehicle based on the driving data.
A means for guiding countermeasure information for the failure according to the degree of urgency predicted by the predicting means,
A program to function as.

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