Machine learning device and anomaly identification device

Abstract

To provide a machine learning device and abnormality identification device for automatically determining the rust and corrosion states of ironware installed in a manhole.SOLUTION: A machine learning device that constitutes a learning model for identifying an abnormality in a manhole is provided. A first learning unit out of first to fourth learning units comprises first input data acquisition means 111 for acquiring, as first input data, site photographs containing facilities in the manhole as subjects, first label acquisition means 112 for acquiring, as a first label, results of determination of rust and corrosion states of ironware included in the site photographs containing the facilities in the manhole as the subjects, and first learning model construction means 113 for performing supervised learning using a combination of the first input data and the first label data as teacher data to construct a first learning model for determining the rust and corrosion states of ironware included in a new site photograph containing facilities in a manhole as the subjects.SELECTED DRAWING: Figure 2

Description

[Technical field]

[0001] The present invention relates to a machine learning device and an anomaly identification device. [Background technology]

[0002] Inspections inside manholes that house underground power transmission cables mainly involve measuring the amount of cable movement using measuring equipment, visually checking the deterioration of metal fittings, and visually checking for cracks in the manhole body and water leaks at the pipe opening.

[0003] When measuring the amount of cable movement, the measurement value may vary depending on the person who measures it, as the position where the cable bending radius is maximum is first predicted by visual inspection, and then the actual position where it is maximum is found by applying a measuring tool. In addition, it is necessary to manage it to the order of a few millimeters, and there is a large margin of error depending on the person who measures it.

[0004] The deterioration of metal fittings is judged based on samples of rust color and any corrosive organisms that have developed, but the judgment may vary depending on the inspector.

[0005] When it comes to cracks in the manhole body or leaks from the pipe opening, tiny cracks or traces of water leakage can be overlooked with human visual inspection alone, and the problem may have progressed significantly by the time of the next inspection.

[0006] For example, Patent Document 1 discloses a technology for identifying cracks on the surface of concrete using machine learning. However, the technology disclosed in Patent Document 1 cannot automatically identify not only cracks in the manhole body, but also the rust and corrosion state of metal fittings installed in the manhole. [Prior art documents] [Patent documents]

[0007] [Patent Document 1] JP 2020-038132 A Summary of the Invention [Problem to be solved by the invention]

[0008] The present invention has been made in consideration of the above-mentioned problems, and aims to provide a machine learning device and anomaly identification device that automatically identifies the rust and corrosion state of metal objects installed in manholes. [Means for solving the problem]

[0009] In order to achieve the above object, the present invention has the following configuration.

[0010] (1) A machine learning device that constructs a learning model for identifying abnormalities inside a manhole, the machine learning device comprising: a first input data acquisition means that acquires an on-site photo that includes equipment inside the manhole as a subject as first input data; a first label acquisition means that acquires a judgment result of a rust / corrosion state of metal objects included in the on-site photo of the manhole as a first label; and a first learning model construction means that constructs a first learning model for judging the rust / corrosion state of the metal objects included in a new on-site photo that includes the equipment inside the manhole as a subject by performing supervised learning using a pair of the first input data and the first label as teacher data.

[0011] According to (1), it is possible to provide a machine learning device that automatically identifies the rust and corrosion condition of metal fittings installed inside manholes.

[0012] (2) In the machine learning device described in (1), it is preferable that the device further comprises a second input data acquisition means for acquiring an on-site photo including the equipment inside the manhole as a subject as second input data, a second label acquisition means for acquiring as a second label a determination result of a location of a crack in a wall included in the on-site photo including the equipment inside the manhole as a subject, and a second learning model construction means for constructing a second learning model for determining the location of a crack in a wall included in a new on-site photo including the equipment inside the manhole as a subject by performing supervised learning using a pair of the second input data and the second label as teacher data.

[0013] According to (2), it will be possible to provide a machine learning device that can automatically identify cracks in manhole structures and water leaks at pipe openings.

[0014] (3) In the machine learning device described in (1) or (2), it is preferable to further include a third input data acquisition means for acquiring first on-site condition data as third input data, the first on-site condition data including the water level in the manhole, the pH and hydrogen sulfide concentration of the water stored in the manhole, the installation date and time of the metal to be installed in the manhole, and information related to the material of the metal; a third label acquisition means for acquiring the corrosion level of the metal a predetermined period after the date and time indicated by the third input data as a third label; and a third learning model construction means for constructing a third learning model for predicting the corrosion level of the metal a predetermined period after the date and time indicated by new first on-site condition data by performing supervised learning using a pair of the third input data and the third label as teacher data.

[0015] According to (3), it is possible to provide a machine learning device that automatically predicts the corrosion level of metal fittings installed inside manholes.

[0016] (4) In the machine learning device described in (1) to (3), it is preferable to further include a fourth input data acquisition means for acquiring, as fourth input data, second on-site situation data including the water level in the manhole, the pH and hydrogen sulfide concentration of the water stored in the manhole, the installation date and time of the metal fittings installed in the manhole, information related to the material of the metal fittings, and an on-site photo including the equipment in the manhole as a subject; a fourth label acquisition means for acquiring, as a fourth label, the on-site photo taken a predetermined period after the date and time indicated by the fourth input data; and a fourth learning model construction means for constructing a fourth learning model for generating a predicted photo including the equipment in the manhole as a subject after the predetermined period after the date and time indicated by the new second on-site situation data by performing supervised learning using a pair of the fourth input data and the fourth label as teacher data.

[0017] According to (4), it is possible to provide a machine learning device that automatically generates predictive photos that include equipment inside a manhole as a subject.

[0018] (5) An anomaly identification device using the first learning model constructed by a machine learning device described in any one of (1) to (4), comprising: an on-site photo acquisition means for acquiring a new on-site photo including equipment inside the manhole as a subject; and a rust / corrosion state determination means for determining the rust / corrosion state of the metal fittings based on the on-site photo and the first learning model.

[0019] According to (5), it is possible to provide an abnormality identification device that automatically determines the rust and corrosion state of metal fittings installed inside manholes.

[0020] (6) In the anomaly identification device described in (5), it is preferable that the anomaly identification device further uses the second learning model constructed by the machine learning device described in (2), and further comprises an on-site photo acquisition means for acquiring a new on-site photo including the equipment inside the manhole as a subject, and a crack determination means for determining a location of a crack in a wall included in the new on-site photo including the equipment inside the manhole as a subject, based on the on-site photo and the second learning model.

[0021] According to (6), it is possible to provide an abnormality identification device that automatically detects cracks in a manhole body or water leakage from a pipe opening.

[0022] (7) In the abnormality identification device described in (6), it is preferable to further include a cable abnormality determination means for determining an abnormality in a cable installed in the manhole from an on-site photograph that includes the equipment inside the manhole as a subject.

[0023] According to (7), it is possible to provide an abnormality identification device that automatically determines the amount of movement of a cable inside a manhole body.

[0024] (8) In the abnormality identification device described in (7), it is preferable to identify an abnormality inside the manhole based on at least one of the rust / corrosion state of the metal fittings, the location of the crack, and the amount of movement of the cable.

[0025] According to (8), it is possible to provide an anomaly identification device that automatically identifies abnormalities within a manhole body.

[0026] (9) In the anomaly identification device described in (5) to (8), it is preferable that the anomaly identification device further uses the third learning model constructed by the machine learning device described in (3), and further includes a first on-site condition data acquisition means for acquiring new first on-site condition data of the manhole, and a corrosion level prediction means for predicting the corrosion level of the metal fittings a predetermined period after the date and time indicated by the new first on-site condition data based on the first on-site condition data and the third learning model.

[0027] According to (9), it is possible to provide an anomaly identification device that automatically predicts the corrosion level of metal fittings installed inside manholes.

[0028] (10) In the anomaly identification device described in any one of (5) to (9), the anomaly identification device further uses the fourth learning model constructed by the machine learning device described in (4), and preferably further includes a second on-site situation data acquisition means for acquiring new second on-site situation data for the manhole, and a predicted photo generation means for generating a predicted photo including, as a subject, equipment in the manhole the specified period after the date and time indicated by the new second on-site situation data, based on the second on-site situation data and the fourth learning model.

[0029] According to (10), it is possible to provide an anomaly identification device that automatically generates a predicted photograph that includes equipment inside a manhole as a subject. Effect of the Invention

[0030] According to the present invention, it is possible to automatically identify the state of rust and corrosion of metal fittings installed inside a manhole. [Brief description of the drawings]

[0031] [Figure 1] 1 is a diagram showing an overall configuration of an anomaly identification system according to an embodiment of the present invention; [Diagram 2] 2 is a functional block diagram of a first learning unit included in the machine learning device according to the embodiment of the present invention. FIG. [Diagram 3] 1 is a functional block diagram of an anomaly identification device according to an embodiment of the present invention. [Figure 4] 4 is a flowchart showing an operation of the machine learning device according to the embodiment of the present invention. [Diagram 5] 4 is a flowchart showing an operation of the machine learning device according to the embodiment of the present invention. [Figure 6] 4 is a flowchart showing an operation of the machine learning device according to the embodiment of the present invention. [Figure 7] 4 is a flowchart showing an operation of the machine learning device according to the embodiment of the present invention. [Figure 8] 4 is a flowchart showing an operation of the anomaly identification device according to the embodiment of the present invention. [Figure 9] 4 is a flowchart showing an operation of the anomaly identification device according to the embodiment of the present invention. [Figure 10] 4 is a flowchart showing an operation of the anomaly identification device according to the embodiment of the present invention. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0032] Hereinafter, an embodiment of the present invention will be described with reference to FIGS.

[0033] [Configuration of one embodiment] First, a configuration of an anomaly identification system 100 according to this embodiment will be described. Fig. 1 is a block diagram showing the anomaly identification system according to this embodiment. As shown in Fig. 1, the anomaly identification system 100 includes a machine learning device 10 and an anomaly identification device 20.

[0034] Here, the machine learning device 10 and the anomaly identification device 20 are paired one-to-one and connected to be able to communicate with each other. Although not shown in FIG. 1, the machine learning device 10 and the anomaly identification device 20 may be connected to each other via a network. The network may be, for example, a local area network (LAN), the Internet, a public telephone network, or a combination of these. There are no particular limitations on the specific communication method in the network, whether it is a wired connection or a wireless connection, etc. Alternatively, the machine learning device 10 and the anomaly identification device 20 may be directly connected via a connection unit, rather than communicating using a network.

[0035] The machine learning device 10 constructs, by supervised learning, a learning model to be used in the anomaly identification device 20. To this end, the machine learning device 10 includes a first learning unit 11 to a fourth learning unit 14, as shown in FIG.

[0036] The first learning unit 11 constructs a first learning model for judging the rust / corrosion state of metal objects contained in a new on-site photo that includes an equipment inside a manhole as a subject. Note that this "equipment inside a manhole" also includes the inner wall of the manhole.

[0037] Fig. 2 is a functional block diagram of the first learning unit 11. As shown in Fig. 2, the first learning unit 11 includes a first input data acquisition unit 111, a first label acquisition unit 112, a first learning model construction unit 113, and a first learning model storage unit 114.

[0038] The first input data acquisition unit 111 acquires an on-site photograph including the equipment in the manhole as a subject as the first input data. Here, it is preferable that the on-site photograph including the equipment in the manhole as a subject includes a junction box, a cable alignment, a pipe entrance, and metal fittings installed in the manhole.

[0039] The first label acquisition unit 112 acquires, as the first label, the judgment result of the rust / corrosion state of the metal objects included in the site photograph that includes the facility inside the manhole as a subject. Note that this judgment result may be, for example, the rust / corrosion state of the metal objects included in the site photograph that includes the facility inside the manhole as a subject, obtained by a skilled worker visually inspecting the site photograph that is used as the first input data.

[0040] Here, the corrosion state of the metal may include a corrosion level that indicates the degree of corrosion of the metal. For example, the following four levels, Level A to Level D, may be used as the corrosion level.

[0041] Level A is the level where crevice corrosion has occurred and has caused significant damage to the cables inside the manhole. More specifically, it is the level where immediate repairs are required because within one year, there will be violations of technical standards for electrical equipment, insufficient design strength, and commercial operation will be impossible.

[0042] Level B is a level where damage to the cable is expected due to the occurrence of crevice corrosion. More specifically, it is a level where repairs are necessary or detailed investigation is required, and it is likely to progress to Level A status within one year.

[0043] Level C is a condition where pitting and corrosion have occurred on the surface, and there is a possibility that the cable may be damaged before the next inspection. More specifically, compared to Level B, there are fewer products of corrosion, and it is thought that the cable will progress to Level A in a few years, so continued monitoring is required.

[0044] Level D is a condition in which no pitting or corrosion has occurred, or pitting or corrosion has occurred on the surface, but it is unlikely that there will be any damage by the time of the next inspection, so follow-up observation is required.

[0045] The first learning model construction unit 113 performs supervised learning using the pair of the first input data and the first label as teaching data to construct a first learning model for determining the rust / corrosion state of metal objects contained in site photos that include equipment inside a manhole as a subject, and transmits the constructed first learning model to the anomaly recognition device 20.

[0046] The first learning model construction unit 113 can be realized, for example, by using a Support Vector Machine (hereinafter also referred to as SVM).

[0047] In this case, the first learning model construction unit 113 uses a binarized label indicating whether or not the data corresponds to a specific corrosion level as the first label, and calculates a hyperplane that separates the space including the first input data so as to maximize the margin regarding whether or not the data corresponds to the specific corrosion level. Furthermore, the first learning model construction unit 113 can use the coefficients of this hyperplane as parameters of a learning model that the anomaly identification device 20 described below uses to determine the corrosion level.

[0048] The first learning model memory unit 114 stores the learning model constructed by the first learning model construction unit 113.

[0049] The second learning unit 12 constructs a second learning model for determining the location of a crack in a wall included in a new on-site photo that includes equipment inside a manhole as a subject. The second learning unit 12 includes a second input data acquisition unit 121, a second label acquisition unit 122, a second learning model construction unit 123, and a second learning model storage unit 124. Note that the configuration of the second learning unit 12 is similar to the configuration of the first learning unit 11 shown in FIG. 2, and therefore is not shown in the figure.

[0050] The second input data acquisition unit 121 acquires, as second input data, a site photograph that includes the facility inside the manhole as a subject.

[0051] The second label acquisition unit 122 acquires, as the second label, the determination result of the occurrence location of the crack in the wall included in the site photograph including the facility in the manhole as a subject. Note that this determination result may be, for example, the occurrence location of the crack included in the site photograph obtained by a skilled worker visually inspecting the site photograph including the facility in the manhole as a subject used as the second input data.

[0052] The "location where the crack has occurred" may include not only the location where the crack has occurred, but also the location where water is leaking from the crack.

[0053] The second learning model construction unit 123 performs supervised learning using the pair of the second input data and the second label as teaching data to construct a second learning model for determining the location of a crack in a wall contained in a new site photo that includes equipment inside a manhole as a subject, and transmits the constructed second learning model to the anomaly identification device 20.

[0054] The second learning model memory unit 124 stores the learning model constructed by the second learning model construction unit 123.

[0055] The third learning unit 13 constructs a third learning model for predicting the corrosion level of metal fittings a predetermined period after the date and time indicated by the new first on-site condition data. The third learning unit 13 includes a third input data acquisition unit 131, a third label acquisition unit 132, a third learning model construction unit 133, and a third learning model storage unit 134. Note that the configuration of the third learning unit 13 is similar to the configuration of the first learning unit 11 shown in FIG. 2, and therefore is not shown in the figure.

[0056] The third input data acquisition unit 131 acquires, as third input data, the first on-site condition data including the water level in the manhole, the pH and hydrogen sulfide concentration of the water stored in the manhole, the installation date and time of the metal fittings to be installed in the manhole, and information related to the material of the metal fittings.

[0057] The third label acquisition unit 132 acquires, as a third label, the corrosion level of the metal object a predetermined period after the date and time indicated by the third input data.

[0058] For example, based on historical data consisting of the water level in a manhole at a specified date and time, the pH and hydrogen sulfide concentration of the water stored in the manhole, the installation date and time of the metal fittings installed in the manhole, information related to the material of the metal fittings, and the corrosion level of the metal fittings at the specified date and time, the third input data acquisition unit 131 may acquire the water level in the manhole at a first date and time, the pH and hydrogen sulfide concentration of the water stored in the manhole, the installation date and time of the metal fittings installed in the manhole, and information related to the material of the metal fittings as third input data, and the third label acquisition unit 132 may acquire the corrosion level of the metal fittings in the same manhole at a second date and time after a specified period of time has passed as the third label.

[0059] The third learning model construction unit 133 performs supervised learning using the pair of the third input data and the third label as teaching data to construct a third learning model for predicting the corrosion level of metal fittings a specified period of time after the date and time indicated by the new first on-site condition data, and transmits the constructed third learning model to the anomaly identification device 20. In addition, the third learning model construction unit 133 may set multiple types of "specified periods" as this "specified period" and construct multiple learning models corresponding to each of these multiple types of "specified periods."

[0060] The third learning model memory unit 134 stores the learning model constructed by the third learning model construction unit 133.

[0061] The fourth learning unit 14 constructs a fourth learning model for predicting site photos a predetermined period after the date and time indicated by the new second site situation data. The fourth learning unit 14 includes a fourth input data acquisition unit 141, a fourth label acquisition unit 142, a fourth learning model construction unit 143, and a fourth learning model storage unit 144. Note that the configuration of the fourth learning unit 14 is similar to the configuration of the first learning unit 11 shown in FIG. 2, and therefore is not shown in the figure.

[0062] The fourth input data acquisition unit 141 acquires, as fourth input data, second on-site condition data including the water level in the manhole, the pH and hydrogen sulfide concentration of the water stored in the manhole, the installation date and time of the metal fittings to be installed in the manhole, information related to the material of the metal fittings, and on-site photographs that include the equipment in the manhole as a subject.

[0063] The fourth label acquisition unit 142 acquires, as the fourth label, a site photo that includes, as a subject, the equipment inside the manhole, taken a predetermined period after the date and time indicated by the fourth input data.

[0064] For example, based on historical data consisting of the water level in a manhole, the pH and hydrogen sulfide concentration of the water stored in the manhole, the installation date and time of the metal fittings installed in the manhole, information related to the material of the metal fittings, and on-site photographs including the equipment in the manhole as a subject at a specified date and time, the fourth input data acquisition unit 141 may acquire the water level in the manhole, the pH and hydrogen sulfide concentration of the water stored in the manhole, the installation date and time of the metal fittings installed in the manhole, and the on-site photographs including the equipment in the manhole as a subject at a first date and time as fourth input data, and the fourth label acquisition unit 142 may acquire a on-site photograph of the same manhole at a second date and time after a specified period of time has passed, including the equipment in the manhole as a subject, as the fourth label.

[0065] It is preferable that the on-site photo including the equipment inside the manhole as a subject included in the fourth input data and the on-site photo of the manhole after a predetermined period of time included in the fourth label are taken at the same angle. Alternatively, the on-site photo including the equipment inside the manhole after a predetermined period of time may be corrected to be taken at the same angle as the on-site photo including the equipment inside the manhole as a subject included in the fourth input data, and then used as the fourth label.

[0066] The fourth learning model construction unit 143 performs supervised learning using the pair of the fourth input data and the fourth label as teaching data to construct a fourth learning model for predicting site photos a predetermined period after the date and time indicated by the new second on-site situation data, and transmits the constructed fourth learning model to the anomaly identification device 20. In addition, the fourth learning model construction unit 143 may set multiple types of "specified periods" as this "specified period" and construct multiple learning models corresponding to each of these multiple types of "specified periods."

[0067] The fourth learning model memory unit 144 stores the learning model constructed by the fourth learning model construction unit 143.

[0068] 3 is a functional block diagram of the abnormality identification device 20. The abnormality identification device 20 includes a control unit 21, a storage unit 22, and a display unit .

[0069] The control unit 21 is a part that controls the entire anomaly identification device 20, and realizes various functions in this embodiment by appropriately reading and executing various programs from a storage area such as a ROM, a RAM, a flash memory, or a hard disk (HDD). The control unit 21 may be a CPU. The control unit 21 includes an on-site photo acquisition unit 211, a first on-site situation data acquisition unit 212, a second on-site situation data acquisition unit 213, a rust / corrosion determination unit 214, a crack determination unit 215, a cable anomaly determination unit 216, an anomaly identification unit 217, a corrosion level prediction unit 218, and a predicted photo generation unit 219.

[0070] The site photo acquisition unit 211 acquires new site photos including the equipment in the manhole as a subject, separately from site photos including the equipment in the manhole as a subject, which are included in the teacher data used when machine learning is performed by the machine learning device 10. For example, a worker may operate an input device such as a keyboard or a mouse provided in the anomaly identification device 20 to load a new site photo including the equipment in the manhole as a subject into the anomaly identification device 20, and then the site photo acquisition unit 211 may acquire the new site photo that has been loaded.

[0071] The first local situation data acquisition unit 212 acquires new first local situation data of the manhole separately from the first local situation data included in the teacher data used when machine learning was performed by the machine learning device 10. For example, a worker may operate an input device such as a keyboard or a mouse provided in the anomaly identification device 20 to load new first local situation data of the manhole into the anomaly identification device 20, and then the first local situation data acquisition unit 212 may acquire the new first local situation data that has been loaded.

[0072] The second local situation data acquisition unit 213 acquires new second local situation data of the manhole separately from the second local situation data included in the teacher data used when machine learning was performed by the machine learning device 10. For example, a worker may operate an input device such as a keyboard or a mouse provided in the anomaly identification device 20 to load new second local situation data of the manhole into the anomaly identification device 20, and then the second local situation data acquisition unit 213 may acquire the new loaded second local situation data.

[0073] The rust / corrosion determination unit 214 determines the rust / corrosion state of metal fittings in the manhole based on the on-site photograph acquired by the on-site photograph acquisition unit 211 and the first learning model.

[0074] The crack determination unit 215 determines the location of a crack in the wall included in the new site photo of the manhole, based on the site photo acquired by the site photo acquisition unit 211 and the second learning model.

[0075] The cable abnormality determination unit 216 determines an abnormality in the cable provided in the manhole from a site photograph that includes the equipment in the manhole as a subject and is acquired by the site photograph acquisition unit 211.

[0076] For example, the allowable bending radius of the cable is determined based on the type of cable, and if the on-site photo acquired by the on-site photo acquisition unit 211 is a 3D photo, the difference between the survey data calculated using the 3D photo and the allowable bending radius of the cable is calculated, and if the difference is within a specified value, the cable is determined to be normal, and if it exceeds the specified value, the cable is determined to be abnormal.

[0077] It is preferable that the cable abnormality determination unit 216 determines an abnormality in the cable from an on-site photo at the present time, particularly at the time of abnormality determination by the abnormality identification unit 217 described below.

[0078] The abnormality identification unit 217 identifies abnormalities inside the manhole based on the rust / corrosion state of the metal fittings determined by the rust / corrosion determination unit 214, the location of the crack determined by the crack determination unit 215, and the abnormality determination related to the cable determined by the cable abnormality determination unit 216. In particular, with regard to the rust / corroded condition of metal fittings, if there is a location where rust or corrosion has occurred and the cable is in contact with the cable and damage to the cable is anticipated, the anomaly identification unit 217 will identify the location of rust or corrosion as an abnormality inside the manhole.

[0079] The corrosion level prediction unit 218 predicts the corrosion level of metal fittings a predetermined period after the date and time indicated by the first on-site condition data, based on the first on-site condition data acquired by the first on-site condition data acquisition unit 212 and the third learning model. This "predetermined period" corresponds to the time lag between the date and time indicated by the third input data and the date and time indicated by the third label in the teacher data used when generating the third learning model.

[0080] The predicted photo generating unit 219 generates a predicted photo including, as a subject, the equipment in the manhole a predetermined period after the date and time indicated by the second local situation data acquired by the second local situation data acquiring unit 213, based on the fourth learning model and the second local situation data acquired by the second local situation data acquiring unit 213. This "predetermined period" corresponds to the time lag between the date and time indicated by the fourth input data and the date and time indicated by the fourth label in the teacher data used when generating the fourth learning model.

[0081] The storage unit 22 stores the first to fourth learning models acquired from the machine learning device 10. The storage unit 22 may also store an on-site photo including the equipment in the manhole as a subject acquired by the on-site photo acquisition unit 211, first on-site situation data acquired by the first on-site situation data acquisition unit 212, second on-site situation data acquired by the second on-site situation data acquisition unit 213, determination results by the rust / corrosion determination unit 214, the crack determination unit 215, and the cable abnormality determination unit 216, an identification result by the abnormality identification unit 217, a prediction result by the corrosion level prediction unit 218, and a predicted photo generated by the predicted photo generation unit 219.

[0082] The display unit 23 is a monitor that displays the judgment results by the rust / corrosion judgment unit 214, the crack judgment unit 215, and the cable abnormality judgment unit 216, the identification result by the abnormality identification unit 217, the prediction result by the corrosion level prediction unit 218, and the predicted photo generated by the predicted photo generation unit 219.

[0083] [Operation of the second embodiment] Next, a description will be given of the operation of the anomaly identification system 100 according to this embodiment. First, the operation of the machine learning device 10 will be described with reference to FIGS.

[0084] 2.1 Operation of the Machine Learning Device 10 FIG. 4 is a flowchart showing the operation of the first learning unit 11. In step S11, the first input data acquisition unit 111 acquires, as first input data, a site photo that includes the facility inside the manhole as a subject.

[0085] In step S12, the first label acquisition unit 112 acquires, as a first label, the judgment result of the rust / corrosion state of metal objects included in an on-site photograph that includes the facility inside the manhole as a subject.

[0086] In step S13, the first learning model construction unit 113 sets the pair of the first input data and the first label as teacher data.

[0087] In step S14, the first learning model construction unit 113 performs supervised learning using the training data in step S13.

[0088] In step S15, if the machine learning is completed (S15: YES), the process proceeds to step S16. If the machine learning is not completed yet (S15: NO), the process proceeds to step S11.

[0089] In step S16, the first learning model construction unit 113 transmits the constructed first learning model to the anomaly identification device 20. After that, all processing ends.

[0090] FIG. 5 is a flowchart showing the operation of the second learning unit 12. In step S21, the second input data acquisition unit 121 acquires, as second input data, a site photo that includes the facility inside the manhole as a subject.

[0091] In step S22, the second label acquisition unit 122 acquires, as a second label, the determination result of the location where a crack has occurred in a wall included in the on-site photo that includes the facility inside the manhole as a subject.

[0092] In step S23, the second learning model construction unit 123 sets the pair of the second input data and the second label as teacher data.

[0093] In step S24, the second learning model construction unit 123 performs supervised learning using the training data in step S23.

[0094] In step S25, if the machine learning is completed (S25: YES), the process proceeds to step S26. If the machine learning is not completed yet (S25: NO), the process proceeds to step S21.

[0095] In step S26, the second learning model construction unit 123 transmits the constructed second learning model to the anomaly identification device 20. After that, all processing ends.

[0096] FIG. 6 is a flowchart showing the operation of the third learning unit 13. In step S31, the third input data acquisition unit 131 acquires, as the first on-site condition data of the manhole, the water level in the manhole, the pH and hydrogen sulfide concentration of the water stored in the manhole, the installation date and time of the metal fittings to be installed in the manhole, and information related to the material of the metal fittings as third input data.

[0097] In step S32, the third label acquisition unit 132 acquires, as a third label, the corrosion level of the metal object a predetermined period after the date and time indicated by the third input data.

[0098] In step S33, the third learning model construction unit 133 sets the pair of the third input data and the third label as teacher data.

[0099] In step S34, the third learning model construction unit 133 performs supervised learning using the training data in step S33.

[0100] In step S35, if the machine learning is completed (S35: YES), the process proceeds to step S36. If the machine learning is not completed yet (S35: NO), the process proceeds to step S31.

[0101] In step S36, the third learning model construction unit 133 transmits the constructed third learning model to the anomaly identification device 20. After that, all processing ends.

[0102] FIG. 7 is a flowchart showing the operation of the fourth learning unit 14. In step S41, the fourth input data acquisition unit 141 acquires, as fourth input data, second on-site condition data including the water level in the manhole, the pH and hydrogen sulfide concentration of the water stored in the manhole, the installation date and time of the metal fittings to be installed in the manhole, information related to the material of the metal fittings, and on-site photographs that include the equipment in the manhole as subjects.

[0103] In step S42, the fourth label acquisition unit 142 acquires, as the fourth label, a site photo that includes, as a subject, the equipment inside the manhole, a predetermined period after the date and time indicated by the fourth input data.

[0104] In step S43, the fourth learning model construction unit 143 sets the pair of the fourth input data and the fourth label as teacher data.

[0105] In step S44, the fourth learning model construction unit 143 performs supervised learning using the training data in step S43.

[0106] In step S45, if the machine learning is completed (S45: YES), the process proceeds to step S46. If the machine learning is not completed yet (S45: NO), the process proceeds to step S41.

[0107] In step S46, the fourth learning model construction unit 143 transmits the constructed fourth learning model to the anomaly identification device 20. After that, all processing ends.

[0108] 2.2 Operation of the anomaly identification device 20 Next, the operation of the abnormality identification device 20 will be described with reference to FIGS.

[0109] First, the abnormality identification method performed by the abnormality identification device 20 will be described with reference to FIG.

[0110] In step S51, the site photo acquisition unit 211 acquires new site photos.

[0111] In step S52, the rust / corrosion determination unit 214 determines the rust / corrosion state of metal fittings in the manhole based on the on-site photo acquired by the on-site photo acquisition unit 211 and the first learning model.

[0112] In step S53, the crack determination unit 215 determines the location of a crack in a wall contained in a new site photo that includes equipment inside a manhole as a subject, based on the site photo acquired by the site photo acquisition unit 211 and the second learning model.

[0113] In step S54, the cable abnormality determination unit 216 determines whether there is an abnormality in the cable from the current on-site photograph.

[0114] In step S55, the abnormality identifying unit 217 identifies an abnormality inside the manhole based on the rust / corrosion state of the metal parts determined by the rust / corrosion determining unit 214, the location of the crack determined by the crack determining unit 215, and the cable abnormality determined by the cable abnormality determining unit 216. After that, all processing is terminated.

[0115] Next, a corrosion level prediction method by the anomaly identifying device 20 will be described with reference to FIG.

[0116] In step S61, the first local condition data acquisition unit 212 acquires new first local condition data for the manhole, separate from the first local condition data included in the teacher data used when machine learning was performed by the machine learning device 10.

[0117] In step S62, the corrosion level prediction unit 218 predicts the corrosion level of the metal object a predetermined period after the date and time indicated by the first on-site condition data, based on the first on-site condition data acquired by the first on-site condition data acquisition unit 212 and the third learning model. Then, all processing is terminated.

[0118] Next, a method for generating a predicted photograph by the anomaly identification device 20 will be described with reference to FIG.

[0119] In step S71, the second local condition data acquisition unit 213 acquires new second local condition data for the manhole, separate from the second local condition data included in the teacher data used when machine learning was performed by the machine learning device 10.

[0120] In step S72, the predicted photo generating unit 219 generates a predicted photo including the facility in the manhole as a subject, a predetermined period after the date and time indicated by the second local situation data, based on the second local situation data acquired by the second local situation data acquiring unit 213 and the fourth learning model. Then, all the processes are terminated.

[0121] [3. Modifications] Although the embodiment of the present invention has been described above, the present invention is not limited to the above-described embodiment. Furthermore, the effects described in the present embodiment are merely a list of the most preferable effects resulting from the present invention, and the effects of the present invention are not limited to those described in the present embodiment.

[0122] [3.1 Variation 1] In the above embodiment, the machine learning device 10 and the anomaly identification device 20 are shown as separate entities, but this is not limiting. For example, the machine learning device 10 may be incorporated into the housing of the anomaly identification device 20, thereby realizing an anomaly identification system 100 in an integrated manner.

[0123] [3.2 Variation 2] In the above embodiment, the first input data acquisition unit 111 of the first learning unit 11 acquires a site photo including the equipment in the manhole as a subject as the first input data, and the second input data acquisition unit 121 of the second learning unit 12 acquires a site photo including the equipment in the manhole as a subject as the second input data, but is not limited to this. For example, the first input data acquisition unit 111 and the second input data acquisition unit 121 may acquire, as the first input data or the second input data, one or more of data inside the manhole (dimensions of the manhole, distance from the wall surface of the junction box, position of the pipe entrance), dimensions of the junction box and metal fittings, in addition to the site photo including the equipment in the manhole as a subject.

[0124] The anomaly identification method by the anomaly identification system 100 is realized by software. When realized by software, the programs constituting this software are installed on a computer. Furthermore, these programs may be recorded on removable media and distributed to users, or may be distributed by being downloaded to the user's computer via a network. [Explanation of symbols]

[0125] 10 Machine Learning Device 11 First Study Section 12 Second Learning Section 13 Third Study Section 14 Fourth Study Section 20 Anomaly Identification Device 21 Control section 22 Memory section 23 Display section 111 First input data acquisition unit 112 First label acquisition unit 113 First Learning Model Construction Unit 114 First learning model memory unit 121 Second input data acquisition unit 122 Second Label Acquisition Unit 123 Second Learning Model Construction Section 124 Second learning model memory unit 131 Third input data acquisition unit 132 3rd Label Acquisition Section 133 Third Learning Model Construction Section 134 Third Learning Model Memory Unit 141 4th input data acquisition unit 142 4th Label Acquisition Section 143 4th Learning Model Construction Section 144 Fourth Learning Model Memory Unit 211 On-site Photo Acquisition Department 212 1st Field Condition Data Acquisition Division 213 2nd Field Condition Data Acquisition Division 214 Rust and corrosion determination section 215 Crack Judgment Section 216 Cable abnormality determination unit 217 Anomaly Identification Unit 218 Corrosion Level Prediction Department 219 Predictive Photo Generation Unit

Claims

1. A machine learning device that constructs a learning model for identifying anomalies in a manhole, A first input data acquisition means for acquiring a site photograph including the facility inside the manhole as a subject as first input data; a first label acquisition means for acquiring, as a first label, a judgment result of a rust / corrosion state of a metal object included in an on-site photograph including the facility inside the manhole as a subject; a first learning model construction means for constructing a first learning model for determining a rust / corrosion state of the metal object included in a new on-site photo that includes the equipment inside the manhole as a subject by performing supervised learning using a pair of the first input data and the first label as training data; a third input data acquisition means for acquiring, as third input data, first on-site status data including information on the water level in the manhole, the pH and hydrogen sulfide concentration of the water stored in the manhole, the installation date and time of the metal fittings installed in the manhole, and the material of the metal fittings; a third label acquisition means for acquiring a corrosion level of the metal object a predetermined period after the date and time indicated by the third input data as a third label; a third learning model construction means for constructing a third learning model for predicting the corrosion level of the metal object after the predetermined period from the date and time indicated by the new first on-site condition data by performing supervised learning using a pair of the third input data and the third label as training data; A machine learning device comprising:

2. A machine learning device that constructs a learning model for identifying anomalies in a manhole, A first input data acquisition means for acquiring a site photograph including the facility inside the manhole as a subject as first input data; a first label acquisition means for acquiring, as a first label, a judgment result of a rust / corrosion state of a metal object included in an on-site photograph including the facility inside the manhole as a subject; a first learning model construction means for constructing a first learning model for determining a rust / corrosion state of the metal object included in a new on-site photo that includes the equipment inside the manhole as a subject by performing supervised learning using a pair of the first input data and the first label as training data; a fourth input data acquisition means for acquiring, as fourth input data, second on-site status data including the water level in the manhole, the pH and hydrogen sulfide concentration of the water stored in the manhole, the installation date and time of the metal fittings installed in the manhole, information related to the material of the metal fittings, and on-site photographs including the equipment in the manhole as subjects; a fourth label acquisition means for acquiring the on-site photo taken a predetermined period after the date and time indicated by the fourth input data as a fourth label; a fourth learning model construction means for constructing a fourth learning model for generating a predicted photograph including, as a subject, equipment in the manhole, the predetermined period after the date and time indicated by the new second on-site situation data, by performing supervised learning using a pair of the fourth input data and the fourth label as training data; A machine learning device comprising:

3. A second input data acquisition means for acquiring a site photo including the equipment inside the manhole as a subject as second input data; a second label acquisition means for acquiring, as a second label, a determination result of a location where a crack has occurred in a wall included in a site photograph including the facility inside the manhole as a subject; a second learning model construction means for constructing a second learning model for determining a location of a crack in a wall included in a new on-site photo including the equipment inside the manhole as a subject by performing supervised learning using a pair of the second input data and the second label as teacher data; The machine learning device according to claim 1 or 2, further comprising:

4. An anomaly identification device using the first learning model constructed by the machine learning device according to any one of claims 1 to 3, An on-site photo acquisition means for acquiring a new on-site photo including the equipment inside the manhole as a subject; A rust / corrosion state determination means for determining the rust / corrosion state of the metal object based on the on-site photograph and the first learning model; An anomaly identification device comprising:

5. An anomaly identification device further using the second learning model constructed by the machine learning device according to claim 3, An on-site photo acquisition means for acquiring a new on-site photo including the equipment inside the manhole as a subject; A crack determination means for determining a location of a crack in a wall included in a new on-site photo including the equipment inside the manhole as a subject based on the on-site photo and the second learning model; The anomaly identification device according to claim 4 , further comprising:

6. The abnormality identification device according to claim 4 or claim 5, further comprising a cable abnormality determination means for determining an abnormality in a cable provided in the manhole from an on-site photograph that includes the equipment in the manhole as a subject.

7. The present invention further comprises a cable abnormality determination means for determining an abnormality in a cable provided in the manhole from an on-site photograph including the equipment in the manhole as a subject, 6. The abnormality identification device according to claim 5, which identifies an abnormality in the manhole based on at least one of the rust / corrosion state of the metal fittings, the location of the crack, and the amount of movement of the cable.

8. An anomaly identification device further using the third learning model constructed by the machine learning device according to claim 1, A first on-site condition data acquisition means for acquiring new first on-site condition data of the manhole; a corrosion level prediction means for predicting a corrosion level of the metal object after a predetermined period from a date and time indicated by new first on-site condition data based on the first on-site condition data and the third learning model; The anomaly identification device according to claim 4 , further comprising:

9. An anomaly identification device further using the fourth learning model constructed by the machine learning device according to claim 2, A second on-site situation data acquisition means for acquiring new second on-site situation data of the manhole; and a predicted photo generation means for generating a predicted photo including, as a subject, equipment in the manhole after the predetermined period from the date and time indicated by the new second on-site situation data, based on the second on-site situation data and the fourth learning model. The anomaly identification device according to claim 4 , further comprising:

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