Loss classification apparatus, loss classification system, loss classification method, and program

Abstract

To identify the type of a loss, out of multiple types of losses, the loss being generated in a factory, or the like.SOLUTION: A loss classification apparatus includes: an action information acquisition unit which acquires action information indicating who did what, when, and where; a loss information acquisition unit which acquires loss information indicating an occurrence of loss; and a classifying unit which classifies the generated loss into one of multiple types of losses, by referring to the action information, based on the acquired loss information.SELECTED DRAWING: Figure 2

Description

[Technical Field]

[0001] The present invention relates to a loss classification device, a loss classification system, a loss classification method, and a program. [Background technology]

[0002] There is technology for managing the operations of factories and other facilities. For example, Patent Document 1 discloses an equipment management system that manages the status of a series of processes executed by multiple processing devices, and that includes multiple data acquisition units that acquire equipment status data for each of the multiple processing devices, a data analysis unit that analyzes each of the equipment status data acquired by the multiple data acquisition units in order according to priority to detect whether an abnormality has occurred in the processing devices, an abnormality occurrence count accumulation unit that accumulates a variable indicating the number of times an abnormality has occurred in each of the multiple processing devices based on the analysis results by the data analysis unit, and a priority determination unit that determines the priority based on the variable accumulated in the abnormality occurrence count accumulation unit. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] International Publication No. 2013 / 094511 Summary of the Invention [Problem to be solved by the invention]

[0004] However, in Patent Document 1, although it is possible to detect the occurrence of some kind of abnormality, there is a problem in that when a loss such as a stoppage loss occurs, it is not possible to determine which of multiple types of loss it is. Thus, when a loss occurs in a factory or the like, there is a problem in that it is not possible to determine which of multiple types of loss it is.

[0005] The present invention has been made in view of the above circumstances, and its object is to provide a loss classification device, a loss classification system, a loss classification method, and a program that, when a loss occurs in a factory or the like, can determine which of multiple types of loss it is. [Means for solving the problem]

[0006] In order to solve the above-mentioned problems, one aspect of the present invention is a loss classification device that includes: a behavioral information acquisition unit that acquires behavioral information indicating when, who, where, and what was done; a loss information acquisition unit that acquires loss information indicating that a loss has occurred; and a classification unit that, based on the acquisition of the loss information, refers to the behavioral information and classifies the occurred loss as one of multiple types of loss.

[0007] Another aspect of the present invention is a loss classification system that includes a behavioral information acquisition unit that acquires behavioral information indicating when, who, where, and what was done; a loss information acquisition unit that acquires loss information indicating that a loss has occurred; and a classification unit that, based on the acquisition of the loss information, refers to the behavioral information and classifies the occurred loss as one of multiple types of loss.

[0008] Another aspect of the present invention is a loss classification method executed by a computer, the loss classification method including: a behavioral information acquisition step of acquiring behavioral information indicating when, who, where, and what was done; a loss information acquisition step of acquiring loss information indicating that a loss has occurred; and a classification step of classifying the occurred loss as one of multiple types of loss by referring to the behavioral information based on the acquisition of the loss information.

[0009] Another aspect of the present invention is a program for causing a computer to execute the following steps: a behavioral information acquisition step for acquiring behavioral information indicating when, who, where, and what was done; a loss information acquisition step for acquiring loss information indicating that a loss has occurred; and a classification step for classifying the occurred loss as one of multiple types of loss by referring to the behavioral information based on the acquisition of the loss information. [Effects of the Invention]

[0010] As described above, when a loss occurs in a factory or the like, it is possible to determine which of multiple types of loss it is. [Brief explanation of the drawings]

[0011] [Figure 1] 1 is a system configuration diagram showing an example of the configuration of a loss classification system SYS according to a first embodiment of the present invention. [Figure 2] 1 is a block diagram showing an example of the configuration of a loss classification device 1 according to the present embodiment. [Figure 3] FIG. 10 is an explanatory diagram showing an example of classification conditions according to the embodiment. [Figure 4] FIG. 10 is an explanatory diagram showing an example of a basic action according to the present embodiment. [Figure 5A] FIG. 10 is an explanatory diagram illustrating processing in a classification unit 163 according to the present embodiment. [Figure 5B] FIG. 10 is an explanatory diagram illustrating processing in a classification unit 163 according to the present embodiment. [Figure 6] FIG. 2 is a sequence diagram showing an example of processing in the loss classification system SYS according to the present embodiment. [Figure 7] 1 is a flowchart showing an example of classification processing in the loss classification device 1 according to the present embodiment. [Figure 8] 1 is a hardware configuration diagram showing an example of a hardware configuration of a loss classification device 1 according to the present embodiment. DETAILED DESCRIPTION OF THE INVENTION

[0012] (First embodiment) A first embodiment of the present invention will be described below with reference to the drawings.

[0013] The loss classification system SYS is a system that acquires behavioral information indicating when, who, where, and what was done, acquires loss information indicating that a loss has occurred, and, based on the acquired loss information, refers to the behavioral information to classify the occurred loss into one of multiple types of loss. The loss classification system SYS will be explained in more detail below.

[0014] FIG. 1 is a system configuration diagram showing an example of the configuration of a loss classification system SYS according to the first embodiment of the present invention. The loss classification system SYS includes a loss classification device 1, an output device 2, an imaging device C, a wireless tag SN, and a wireless receiver Rx. The loss classification device 1, the output device 2, the imaging device C, and the wireless receiver Rx are connected to each other via a network NW so that they can communicate with each other. The wireless tag SN and the wireless receiver Rx are also connected to each other so that they can communicate with each other.

[0015] The imaging device C is installed, for example, in a factory and constantly captures moving or still images. The captured moving or still images are captured in a manner that allows behavioral information indicating when, who, where, and what was done within the factory to be identified using AI (Artificial Intelligence) or machine learning. The imaging device C outputs the captured moving or still images to the loss classification device 1.

[0016] The wireless tag SN is a wireless tag owned by a worker H in the factory. The worker H includes people who are present in the factory, such as a maintenance worker, a main worker, and an assistant. The wireless tag is, for example, a UWB (Ultra Wideband) tag. The wireless tag SN may be attached to tools, parts, products, etc. in addition to the worker H. The wireless tag SN may be a wireless tag such as a ZigBee or NFC (Near Field Communication) tag. The wireless tag SN may also be a Bluetooth beacon, etc. In this case, the wireless receiver Rx may be a wireless receiver compatible with the wireless tag SN.

[0017] The wireless receiver Rx is a receiver that detects the proximity or contact of a wireless tag SN. Multiple wireless receivers Rx are installed in each device or each location of each device in a factory so that they can be identified. When the wireless receiver Rx detects a wireless tag SN, it outputs identification information of the wireless receiver Rx, identification information of the wireless tag SN, and the date and time of detection of the wireless tag SN to the loss classification device 1.

[0018] It should be noted that the loss classification system SYS does not need to include the other of the wireless tag SN and wireless receiver Rx and the imaging device C, as long as the behavioral information can be acquired using either one of them.

[0019] The output device 2 is an output device that outputs the loss classification result output from the loss classification device 1. The output device 2 is, for example, a display device.

[0020] The loss classification device 1 acquires behavioral information indicating when, who, where, and what was done based on video or still images captured by the imaging device C and / or detection of the wireless tag SN by the wireless receiver Rx. The loss classification device 1 also acquires loss information indicating the occurrence of a loss using publicly known technology. The loss information includes information on the date and time when the loss occurred. Losses include planned downtime, stoppage loss, performance loss, and defect loss. Planned downtime includes planned downtime loss. Stoppage losses include breakdown loss, switching adjustment loss, jig replacement loss, start-up loss, and shut-down loss. Performance losses include short-term stoppage idling loss and speed reduction loss. Defective losses include defect rework loss.

[0021] The loss classification device 1 classifies the generated loss into one of multiple types of loss by referring to the behavior information based on the acquired loss information. In this embodiment, an example of classifying the generated loss into one of the following types: breakdown loss in shutdown loss, switching adjustment loss, jig replacement loss, start-up loss, and shut-down loss will be described.

[0022] Next, the loss classification device 1 will be described in detail.

[0023] FIG. 2 is a block diagram showing an example of the configuration of the loss classification device 1 according to this embodiment. The loss classification device 1 includes a communication unit 10, an input unit 12, an output unit 14, a control unit 16, and a storage unit 18.

[0024] The communication unit 10 has a function of communicating with other devices in the loss classification system SYS via a network. The communication unit 10 outputs various information and signals received from other devices to the control unit 16. The communication unit 10 also transmits various information and signals output from the control unit 16 to other devices.

[0025] The input unit 12 is an input device such as a mouse, a keyboard, a touch panel, a power button, and a setting button.

[0026] The output unit 14 is an output device such as a display unit, a speaker, etc. In this embodiment, an example of output from the output device 2 will be described.

[0027] The control unit 16 has a function of controlling the loss classification device 1. The control unit 16 includes a behavior information acquisition unit 161 , a loss information acquisition unit 162 , a classification unit 163 , and an output control unit 164 .

[0028] The behavioral information acquisition unit 161 acquires moving images or still images from the imaging device C. The behavioral information acquisition unit 161 performs analysis of the moving images or still images using, for example, AI, to acquire behavioral information. The behavioral information acquisition unit 161 stores the acquired behavioral information in the storage unit 18. In this embodiment, the behavioral information acquisition unit 161 acquires behavioral information representing the worker (who) and their behavior (what they did (what they have)) through AI analysis of moving images or still images.

[0029] Furthermore, the behavior information acquisition unit 161 acquires behavior information from the wireless receiver Rx. The behavior information acquisition unit 161 stores the acquired behavior information in the storage unit 18. In this embodiment, the behavior information acquisition unit 161 acquires behavior information that indicates the date and time (when) and the place (where).

[0030] The behavior information acquisition unit 161 may acquire behavior information from the imaging device C and / or the wireless receiver Rx based on the acquisition of loss information, which will be described later.

[0031] The loss information acquisition unit 162 acquires loss information using known technology. The loss information is information indicating that a loss has occurred. The loss information includes information on the date and time when the loss occurred. The loss information acquisition unit 162 stores the loss information in the storage unit 18. The loss information acquisition unit 162 also outputs the loss information to the classification unit 163.

[0032] When loss information is input, the classification unit 163 acquires behavioral information from the storage unit 18 and compares the loss occurrence date and time in the loss occurrence information with the date and time (when) in the behavioral information. Furthermore, the classification unit 163 classifies (specifies) which of multiple types of loss the occurred loss is, based on the loss information, the behavioral information, and the classification conditions stored in the storage unit 18. The classification unit 163 outputs the classification result to the output control unit 164.

[0033] When the classification result is input, the output control unit 164 generates an output image and causes the output device 2 to output it as loss classification information.

[0034] The storage unit 18 stores behavior information 181, loss information 182, and classification conditions 183. The classification condition 183 is a first classification condition and / or second classification information, which will be described with reference to FIGS.

[0035] Fig. 3 is an explanatory diagram showing an example of classification conditions according to this embodiment, and Fig. 4 is an explanatory diagram showing an example of basic actions according to this embodiment. Here, Fig. 3 is an example of the first classification condition, and Fig. 4 is an example of the second classification condition.

[0036] In the example shown in FIG. 3, the first classification condition is information in which loss classifications are associated with classification conditions. The classification "failure loss" is classified according to the following conditions: "a downtime of 5 minutes or more during load time, and which is not a switching adjustment loss, jig replacement loss, start-up loss, or shut-down loss" or "a maintenance worker works with a toolbox (basic action)." The classification "switching adjustment loss" has the classification condition "classify from basic actions because the work is unique." The classification "jig replacement loss" is based on the classification condition "the downtime of the operation results from when the replacement popup is displayed based on the consumable operation count history until the count is reset after the replacement is completed." The classification condition for "startup loss" and "shutdown loss" is "downtime after planned shutdown is 5 minutes or more."

[0037] In this way, the first classification condition is information in which a classification condition is associated with each classification, and the classification unit 163 refers to the first classification condition to classify the occurred loss as one of multiple types of loss.

[0038] In the examples shown in FIGS. 5A and 5B, the second classification condition is information in which an identification number is associated with a basic action. Identification number "1" is the basic action "taking out a tool from the toolbox and holding it while performing maintenance work." Identification number "2" is the basic action "loosen the unit adjustment bolt by hand on the side of the cut section to change the position of the guillotine cut unit." Identification number "3" is the basic action "open a new box in front of the insertion point, take out the contents with both hands, and set it in the machine." Identification number "4" is the basic action of "pulling an empty bag into the machine from the side with both hands." Identification number "5" is the basic action "Set the waste liquid pipe from the side of the filling section to the tip of the filling section nozzle." Identification number "6" is the basic action "operate the filling machine touch panel." Identification number "7" is the basic action "remove the waste liquid pipe at the tip of the filling section nozzle from the side of the filling section."

[0039] In this way, the second classification condition is information in which a basic action is associated with each identification number, and the classification unit 163 refers to the second classification condition to classify the occurred loss as one of multiple types of loss.

[0040] Next, the processing of the classification unit 163 will be described in detail with reference to FIGS. 5A and 5B.

[0041] 5A and 5B are explanatory diagrams illustrating the processing in the classification unit 163 according to this embodiment. 5A and 5B, the major categories "failure loss," "switching adjustment loss," "jig replacement loss," and "startup loss / shutdown loss" are all losses classified as "stoppage loss." The classification unit 163 determines which of the conditions of "when," "where," "who," and "what" shown in FIGS. 5A and 5B applies based on the behavior information and loss information, and classifies the "major category" that satisfies each condition into which of multiple types of stoppage loss the occurred loss belongs.

[0042] Here, the numerical value stored in the "What was done" item column corresponds to the "identification number" of the "basic action" shown in FIG. 4. Furthermore, the "filling machine data" is operation history information such as pop-ups emitted from each device in the factory and input operations. When there is operation history information corresponding to the "filling machine data," the classification unit 163 classifies the "major items" that satisfy each condition by referring to the "filling machine data," in addition to determining which of the conditions of "when," "where," "who," and "what was done" shown in FIG. 5 applies based on the behavioral information and loss information, and classifies which of multiple types of stoppage losses the generated loss is.

[0043] In the examples shown in FIGS. 5A and 5B, by satisfying only one or two of the hatched items, it is possible to classify (identify) which of the multiple types of losses the stop loss of a "major item" is, without determining whether or not the conditions of all items are satisfied.

[0044] Next, the flow of the loss classification process in the loss classification system SYS will be described.

[0045] FIG. 6 is a sequence diagram showing an example of processing in the loss classification system SYS according to this embodiment. In step ST102, the wireless receiver Rx is always capable of detecting a wireless tag SN, and detects a wireless tag SN that is in proximity to or in contact with the wireless receiver Rx. In step ST103, the imaging device C continuously captures moving or still images of the inside of the factory. In step ST104, it is assumed that a stop loss occurs.

[0046] In step ST105, the imaging device C transmits image information of a moving image or a still image to the loss classification device 1 based on the occurrence of a stop loss. In step ST106, the wireless receiver Rx transmits behavior information to the loss classification device 1 based on the occurrence of the stop loss.

[0047] In step ST107, the loss classification device 1 refers to the behavioral information acquired by AI analysis of the image information, the behavioral information acquired from the wireless receiver Rx, and the classification conditions to classify the occurred stop loss as one of multiple types of stop losses, and generates classification information.

[0048] In step ST108, the loss classification device 1 transmits the classification information to the output device 2. In step ST109, the output device 2 outputs (displays) the classification information.

[0049] The above description deals with the case where the image information is transmitted in step ST105 and the behavior information is transmitted in step ST106 after the stop loss occurs in step ST104. However, this embodiment is not limited to this, and the image information is transmitted in step ST105 and the behavior information is transmitted in step ST106 before the stop loss occurs in step ST104, or the image information and the behavior information may be transmitted at all times regardless of whether the stop loss occurs in step ST104.

[0050] Furthermore, the image information in step ST105 and the behavior information in step ST106 may be image information and behavior information at the time when the stop loss occurs, or may be image information and behavior information after the stop loss occurs.

[0051] For example, in the case of image information and behavioral information at the time of occurrence of a stop loss, the aggregation time can be shortened even when the loss is classified by a predetermined period, for example, one day, one week, one month, etc. Furthermore, even when transmitting image information and behavioral information before and after a stop loss, by using image information and behavioral information after the stop loss occurs, the amount of data to be collected can be reduced, thereby shortening the aggregation time.

[0052] Next, an example of the classification process in the loss classification device 1 will be described.

[0053] FIG. 7 is a flowchart showing an example of the classification process in the loss classification device 1 according to this embodiment. In step S101, the loss classification device 1 acquires behavioral information based on AI analysis of moving images or still images acquired from the imaging device C, and / or behavioral information from the wireless receiver Rx. Next, the loss classification device 1 executes the process of step S103.

[0054] In step S103, the loss classification device 1 acquires loss information using a known technique, and then performs the process of step S105.

[0055] In step S105, the loss classification device 1 classifies the occurred loss into one of multiple types of loss based on the behavior information, the loss information, and the classification conditions, and generates classification information. Then, the loss classification device 1 executes the process of step S107.

[0056] In step S107, the loss classification device 1 causes the output device 2 to output the classification information, and the process in FIG. 7 ends.

[0057] Next, the hardware configuration of the loss classification device 1 will be described.

[0058] The loss classification device 1 includes a CPU 101, a drive unit 102, a storage medium 103, an input unit 104, an output unit 105, a ROM 106 (Read Only Memory), a RAM 107 (Random Access Memory), an auxiliary storage unit 108, and an interface unit 109.

[0059] The CPU 101, drive unit 102, input unit 104, output unit 105, ROM 106, RAM 107, auxiliary storage unit 108, and interface unit 109 are interconnected via a bus. The CPU 101 referred to here refers to a processor in general, and includes not only a device called a CPU in the narrow sense, but also, for example, a GPU, a DSP, etc. Furthermore, the CPU 101 referred to here is not limited to being realized by a single processor, but may be realized by combining multiple processors of the same or different types.

[0060] The CPU 101 reads and executes programs stored in the auxiliary storage unit 108, the ROM 106, and the RAM 107, and also reads various data stored in the auxiliary storage unit 108, the ROM 106, and the RAM 107 and writes the various data to the auxiliary storage unit 108 and the RAM 107, thereby controlling the loss classification device 1. The CPU 101 also reads various data stored in the storage medium 103 via the drive unit 102 and writes the various data to the storage medium 103. The storage medium 103 is a portable storage medium such as a magneto-optical disk, a flexible disk, or a flash memory, and stores various data. The drive unit 102 is a device that reads and writes data from and to a storage medium 103 such as an optical disk drive, a flexible disk drive, or a flash memory.

[0061] The input unit 104 is an input device such as a mouse, a keyboard, a touch panel, a power button, a setting button, and an infrared receiver. The output unit 105 is an output device such as a display unit, a speaker, or the like. The ROM 106 and RAM 107 store programs for operating the respective functional units of the loss classification device 1 and various data.

[0062] The auxiliary storage unit 108 is a hard disk drive, a flash memory, or the like, and stores programs for operating each functional unit of the loss classification device 1 and various data. The interface unit 109 has a communication interface and is connected to the network NW or other devices in the loss classification system SYS by wire or wirelessly.

[0063] For example, the communication unit 10 in the functional configuration of the loss classification device 1 in FIG. 2 above corresponds to the interface unit 109 in FIG. 8, the input unit 12 in FIG. 2 corresponds to the input unit 104 in FIG. 8, the output unit 105 in FIG. 2 corresponds to the output unit 105 in FIG. 8, the control unit 16 in FIG. 2 corresponds to the CPU 101 in FIG. 8, and the memory unit 18 in FIG. 2 corresponds to the memory medium 103, ROM 106, RAM 107, auxiliary memory unit 108, etc. in FIG. 8.

[0064] As described above, the loss classification device 1 according to this embodiment includes a behavioral information acquisition unit 161 that acquires behavioral information indicating when, who, where, and what was done, a loss information acquisition unit 162 that acquires loss information indicating that a loss has occurred, and a classification unit 163 that, based on the acquired loss information, refers to the behavioral information and classifies the occurred loss into which of multiple types of loss it belongs.

[0065] In this way, when a loss occurs in a factory or the like, it is possible to know which of multiple types of loss it is.

[0066] Furthermore, the classification unit 163 classifies the generated loss into one of a plurality of types of loss by referring to the behavior information and the classification conditions.

[0067] In this way, it is possible to classify the loss into which type of loss it is in accordance with the classification conditions set in advance.

[0068] In addition, the behavior information is acquired based on images or wireless tags.

[0069] In this way, it is possible to easily determine which type of loss a given loss is, without adding any complicated processing.

[0070] The embodiments of the present invention have been described in detail above with reference to the drawings, but the specific configuration is not limited to that described above, and various design changes can be made within the scope of the gist of the present invention.

[0071] The program running on the loss classification device 1 according to one aspect of the present invention may be a program that controls one or more processors, such as a central processing unit (CPU), to implement the functions described in the above-described embodiments and modifications of the present invention (a program that causes a computer to function). The term "computer" as used herein also includes quantum computers. Information handled by each of these devices may be temporarily stored in random access memory (RAM) during processing, and then stored in various storage devices, such as flash memory and hard disk drives (HDDs), and may be read, modified, or written by the CPU or the like as needed.

[0072] Note that a part or all of the loss classification device 1 in each of the above-described embodiments and modifications may be realized by a computer having one or more processors. In this case, a program for realizing the control function may be recorded on a computer-readable recording medium, and the program recorded on the recording medium may be read and executed by a computer system.

[0073] The term "computer system" used here refers to a computer system built into the loss classification device 1, including hardware such as an OS and peripheral devices. The term "computer-readable recording medium" refers to portable media such as flexible disks, optical magnetic disks, ROMs, and CD-ROMs, as well as storage devices such as hard disks built into the computer system.

[0074] Furthermore, the term "computer-readable recording medium" may include a medium that dynamically stores a program for a short period of time, such as a communication line when transmitting a program via a network such as the Internet or a communication line such as a telephone line, or a medium that stores a program for a fixed period of time, such as volatile memory within a computer system that serves as a server or client in such a case. The program may also be one that realizes part of the above-mentioned functions, or one that can realize the above-mentioned functions in combination with a program already stored in the computer system.

[0075] Furthermore, part or all of the loss classification device 1 in each of the above-described embodiments and modifications may be realized as an LSI, which is typically an integrated circuit, or as a chipset. Furthermore, each functional block of the loss classification device 1 in each of the above-described embodiments and modifications may be individually formed into a chip, or part or all of them may be integrated into a chip. The integrated circuit implementation method is not limited to LSI, and may be realized using a dedicated circuit and / or a general-purpose processor. Furthermore, if an integrated circuit implementation technology that can replace LSI emerges due to advances in semiconductor technology, it is also possible to use an integrated circuit based on that technology.

[0076] While the embodiments and modifications have been described above in detail with reference to the drawings as one aspect of the present invention, the specific configuration is not limited to the embodiments and modifications, and design changes within the scope of the present invention are also included. Furthermore, various modifications of one aspect of the present invention are possible within the scope of the claims, and embodiments obtained by appropriately combining the technical means disclosed in different embodiments are also included in the technical scope of the present invention. Furthermore, configurations in which elements described in the above embodiments and modifications are substituted with elements that achieve the same effect are also included. [Explanation of symbols]

[0077] 1. Loss Classifier 2 Output Devices 10. Communications Department 12 Input section 14 Output section 16 Control Unit 161 Behavioral Information Acquisition Unit 162 Loss Information Acquisition Unit 163 Classification Department 164 Output control section 18 Memory section 181 Behavioral Information 182 Loss Information 101 CPU 102 Drive section 103 Storage medium 104 Input section 105 Output section 106 ROM 107 RAM 108 Auxiliary storage 109 Interface section C. Imaging device H worker NW Network Rx Radio Receiver SN wireless tag SYS Loss Classification System

Claims

1. a behavioral information acquisition unit that acquires behavioral information indicating when, who, where, and what was done; a loss information acquisition unit that acquires loss information indicating that a loss has occurred; a classification unit that classifies the generated loss into one of a plurality of types of loss by referring to the behavior information based on the acquired loss information; A loss classification device comprising:

2. the classification unit classifies the generated loss into one of a plurality of types of loss by referring to the behavioral information and classification conditions; The loss classification device according to claim 1 .

3. The behavioral information is acquired based on an image or a wireless tag. The loss classification device according to claim 1 or 2.

4. a behavioral information acquisition unit that acquires behavioral information indicating when, who, where, and what was done; a loss information acquisition unit that acquires loss information indicating that a loss has occurred; a classification unit that classifies the generated loss into one of a plurality of types of loss by referring to the behavior information based on the acquired loss information; A loss classification system comprising:

5. 1. A computer-implemented method for loss classification, comprising: a behavioral information acquisition step of acquiring behavioral information indicating when, who, where, and what was done; a loss information acquisition step of acquiring loss information indicating that a loss has occurred; a classification step of classifying the generated loss into one of a plurality of types of loss by referring to the behavior information based on the acquired loss information; A loss classification method having

6. On the computer, a behavioral information acquisition step of acquiring behavioral information indicating when, who, where, and what was done; a loss information acquisition step of acquiring loss information indicating that a loss has occurred; a classification step of classifying the generated loss into one of a plurality of types of loss by referring to the behavior information based on the acquired loss information; A program to execute.

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