JP7333870B2 - Equipment data processing device, equipment data processing system, equipment data processing method, and equipment data processing program - Google Patents

Description

The present disclosure relates to an equipment data processing device, an equipment data processing system, and equipment data processing that associates inspection data obtained from an inspection device that inspects equipment to be inspected, which is equipment to be inspected among a plurality of equipment, with the equipment to be inspected. It relates to a method and an equipment data processing program.

In the past, facility inspections were performed directly by workers, but in recent years, inspection devices such as drones equipped with measuring instruments have come to automatically perform inspections in order to improve efficiency. When an inspection is performed automatically by an inspection device, it is necessary to identify the equipment to be inspected by the inspection device and link inspection data indicating the results of the inspection to the data of the identified equipment.

As one of techniques for identifying an object, a technique using three-dimensional point cloud data of the object is known. For example, Patent Literature 1 discloses that a matching object is extracted from three-dimensional point cloud data obtained by measuring the inside of a building when specifying a position within the building, and the extracted matching object and the building are identified as three-dimensional data. Techniques are disclosed for identifying measured locations by comparison with dimensional model data.

JP 2019-101694 A

However, the technique described in Patent Literature 1 is a technique for specifying a position within a building, and is not a technique for linking inspection data obtained from an inspection device that inspects the inspection target facility with the data of the inspection target facility. . In addition, when the technology described in Patent Document 1 is used to associate inspection data with data of equipment to be inspected, an object for matching is extracted from the 3D point cloud data when determining the equipment to be inspected. processing is required, and the processing is overloaded.

The present disclosure has been made in view of the above, and an object thereof is to obtain an equipment data processing device capable of linking inspection data to equipment to be inspected while reducing processing.

In order to solve the above-described problems and achieve the object, the equipment data processing device of the present disclosure includes a characteristic point point cloud data storage unit, an equipment point cloud data storage unit, a data acquisition unit, an equipment determination unit, A linking unit and a characteristic part detection unit are provided. The feature location point cloud data storage unit stores three-dimensional point cloud data of each feature location of a plurality of facilities. The facility point cloud data storage unit stores overall three-dimensional point cloud data for each of the plurality of facilities. The data acquisition unit acquires three-dimensional point cloud data of the facility to be inspected and inspection data of the facility to be inspected from an inspection device that inspects the facility to be inspected. The facility determination unit determines the three-dimensional points acquired by the data acquisition unit based on the result of comparison between the three-dimensional point cloud data acquired by the data acquisition unit and the three-dimensional point cloud data of the characteristic locations of each of the plurality of facilities. It is determined whether or not the group data is three-dimensional point cloud data of any one of a plurality of facilities. When the equipment determination unit determines that the 3D point cloud data acquired by the data acquisition unit is the 3D point cloud data of one facility among the plurality of facilities, the linking unit acquires the 3D point cloud data by the data acquisition unit. Link the obtained inspection data to the data of one piece of equipment. The feature point detection unit detects the feature points of each of the plurality of facilities based on the result of comparison between the reference point cloud data and the overall three-dimensional point cloud data of each of the plurality of facilities stored in the facility point cloud data storage unit. is detected, and three-dimensional point cloud data of each of the determined characteristic points of the plurality of facilities is stored in the characteristic point point cloud data storage unit.

Advantageous Effects of Invention According to the present disclosure, it is possible to link inspection data to an inspection target facility while reducing processing.

1 is a diagram showing an example of a configuration of an equipment data processing system according to a first embodiment; FIG. A diagram showing an example of a configuration of an inspection device according to a first embodiment. 1 is a diagram showing an example of a configuration of an equipment data processing device according to a first embodiment; FIG. FIG. 4 is a diagram showing an example of an equipment point cloud data table including overall point cloud data of each equipment stored in the equipment point cloud data storage unit according to the first embodiment; A diagram showing an example of a characteristic point point cloud data table containing characteristic point point cloud data of each piece of equipment stored in the characteristic point point cloud data storage unit according to the first embodiment. 4 is a diagram showing an example of facility ledger data stored in the facility ledger data storage unit according to the first embodiment; FIG. FIG. 4 is a diagram for explaining a method of generating reference point cloud data by a reference data generation unit according to the first embodiment; FIG. 4 is a diagram for explaining a method of generating feature point cloud data by a feature point detection unit according to the first embodiment; FIG. 4 is a diagram for explaining a method of determining equipment to be inspected by the equipment determining unit according to the first embodiment; Flowchart showing an example of pre-processing by the processing unit of the facility data processing device according to the first embodiment Flowchart showing an example of ledger linking processing by the processing unit of the equipment data processing device according to the first embodiment 1 is a diagram showing an example of a hardware configuration of an equipment data processing device according to a first embodiment; FIG. The figure which shows an example of a structure of the equipment data processing apparatus concerning Embodiment 2. Flowchart showing an example of pre-processing by the processing unit of the equipment data processing device according to the second embodiment The figure which shows an example of a structure of the equipment data processing apparatus concerning Embodiment 3. FIG. 11 is a diagram for explaining a method of generating feature point cloud data by a feature point detection unit according to the third embodiment; Flowchart showing an example of pre-processing by the processing unit of the facility data processing device according to the third embodiment The figure which shows an example of a structure of the equipment data processing apparatus concerning Embodiment 4. FIG. 11 is a diagram for explaining a method of changing the allowable range by the allowable range changing unit according to the fourth embodiment; Flowchart showing an example of ledger linking processing by the processing unit of the facility data processing device according to the fourth embodiment The figure which shows an example of a structure of the equipment data processing apparatus concerning Embodiment 5. Flowchart showing an example of characteristic portion editing processing by the processing unit of the equipment data processing device according to the fifth embodiment

A facility data processing device, a facility data processing system, a facility data processing method, and a facility data processing program according to embodiments will be described below in detail with reference to the drawings.

Embodiment 1.
1 is a diagram illustrating an example of a configuration of an equipment data processing system according to a first embodiment; FIG. As shown in FIG _. 1, the equipment data processing system 100 according to the first embodiment includes an inspection device 2 that inspects equipment 1 ₁ , 1 ₂ , . 1 ₁ , _{1 2} , . n is an integer of 3 or more, for example. The inspection device 2 and the equipment data processing device 3 are connected via a network 5 . The network 5 includes, for example, a mobile phone communication network and a wide area data communication network.

The equipment 1 _{1 ,} 1 ₂ , . Equipment installed on utility poles is, for example, a transformer or a switch. The equipment 1 ₁ , 1 _{2 ,} . . _. , _1n may be referred to as facility ₁ hereinafter when they are not individually distinguished.

The inspection device 2 is, for example, a flying object such as a drone equipped with multiple sensors, a vehicle equipped with multiple sensors, or an autonomous mobile robot equipped with multiple sensors. The inspection device 2 is configured to be able to wirelessly communicate with a wireless base station included in the network 5 , and wirelessly transmits the generated data to the facility data processing device 3 via the network 5 .

Specifically, the inspection device 2 generates inspection data Dc indicating the result of inspection of the equipment 1 and three-dimensional point cloud data Dd of the equipment 1, and converts the generated inspection data Dc and three-dimensional point cloud data Dd into It transmits the processing target data Dp including the data to the facility data processing device 3 via the network 5 . The three-dimensional point group data Dd is data representing the facility 1 by a three-dimensional point group including a plurality of three-dimensional points, and includes data of a plurality of three-dimensional points.

The facility data processing device 3 acquires the processing target data Dp including the inspection data Dc and the three-dimensional point cloud data Dd from the inspection device 2 via the network 5 . _The facility data processing device 3 _stores feature location point cloud _data Df ₁ , Df ₂ , . , and compares the three-dimensional point cloud data Dd included in the processing target data Dp acquired from the inspection device 2 with each feature point cloud data Df ₁ , Df ₂ , . . . , Df.

The equipment data processing device 3, based on the result of comparison between the three-dimensional point cloud data Dd and each feature location point cloud data Df ₁ , Df ₂ , . The facility 1 to be inspected is determined by determining which _facility 1 among 1 , 1 _{2 ,} . The equipment data processing device 3 links the inspection data Dc included in the processing target data Dp acquired from the inspection device 2 to the data of the equipment 1 determined as the equipment 1 to be inspected in the equipment ledger data.

In this way, the equipment data processing system 100 converts the three-dimensional point cloud data Dd into a feature point cloud without extracting a matching object from the three-dimensional point cloud data Dd when determining the equipment 1 to be inspected. By comparing the data Df ₁ , Df ₂ , . The facility data processing system 100 will be described in more detail below.

FIG. 2 is a diagram illustrating an example of the configuration of the inspection device according to the first embodiment; As shown in FIG. 2 , the inspection device 2 includes a communication section 10 , a three-dimensional shape measuring instrument 11 , a sensor group 12 and an inspection data generation section 13 . In addition, in FIG. 2, the configuration for movement among the configurations of the inspection device 2 is not shown. If the inspection device 2 is a drone, it includes an airframe, a plurality of rotors, a plurality of motors that drive the plurality of rotors, a plurality of controllers that control the motors, and the like. The communication unit 10 , the three-dimensional shape measuring instrument 11 , the sensor group 12 , and the inspection data generation unit 13 are mounted on the body of the inspection device 2 .

The communication unit 10 wirelessly transmits and receives data to and from the equipment data processing device 3 via the network 5 . Note that the communication unit 10 may be configured to transmit and receive data to and from the equipment data processing device 3 via, for example, a USB (Universal Serial Bus), a wireless LAN (Local Area Network), or Bluetooth (registered trademark). .

The three-dimensional shape measuring instrument 11 includes, for example, a laser scanner or an optical cutting sensor. A laser scanner irradiates an object with a laser beam, measures the time it takes for the laser beam irradiated to the object to return, and converts the measured time into a distance to measure the three-dimensional shape of the object. A light section sensor measures the three-dimensional shape of an object by a light section method.

The three-dimensional shape measuring instrument 11 generates three-dimensional point cloud data representing the equipment 1 as a three-dimensional point cloud including a plurality of three-dimensional points. The 3D point cloud data of the facility 1 includes data of a plurality of 3D points. The three-dimensional point cloud data of the facility 1 generated by the three-dimensional shape measuring instrument 11 is data representing the entirety of the facility 1 with a plurality of three-dimensional points. The three-dimensional shape measuring instrument 11 generates three-dimensional point cloud data of the facility 1 when the operation mode of the inspection device 2 is the inspection mode or the preprocessing mode.

The sensor group 12 includes multiple sensors such as a two-dimensional image measuring instrument, a non-contact temperature sensor, a microphone, and a non-contact vibration sensor. Two-dimensional image instruments include, for example, line cameras, area cameras, light section sensors, or thermography. A line camera is a camera that captures an image of one line at a time. An area camera is a camera that captures images for a plurality of columns at once. The light section sensor measures the reflection intensity image obtained by measuring the reflection intensity of the laser beam irradiated to the object as a two-dimensional image of the object. Such a reflection intensity image is a monochrome image in which reflection intensity is represented by gradation.

When the operation mode of the inspection device 2 is the inspection mode, the inspection data generation unit 13 generates inspection data Dc of the facility 1 based on the detection results of the plurality of sensors included in the sensor group 12 for the facility 1 . The inspection data Dc of the facility 1 includes, for example, captured image data of the facility 1, temperature data of the facility 1, sound data emitted from the facility 1, vibration data of the facility 1, and the like. Note that the inspection data generator 13 can also include the three-dimensional point group data Dd generated by the three-dimensional shape measuring instrument 11 in the inspection data Dc.

When the operation mode of the inspection device 2 is the inspection mode, the communication unit 10 transmits the three-dimensional point cloud data Dd of the facility 1 generated by the three-dimensional shape measuring instrument 11 and the point cloud generated by the inspection data generation unit 13. Data to be processed Dp including the data is transmitted to the equipment data processing device 3 via the network 5 .

Further, when the operation mode of the inspection device 2 is the pre-processing mode, the communication unit 10 associates the 3D point cloud data of the facility 1 generated by the 3D shape measuring instrument 11 with the facility ID (IDentifier) and distributes the data to the network 5. to the equipment data processing device 3 via. For example, every time the 3D point cloud data of the facility 1 is generated, the communication unit 10 stores the facility ID transmitted from a device (not shown) to the inspection device 2 as the 3rd point of the facility 1 generated by the 3D shape measuring instrument 11 . It is transmitted to the facility data processing device 3 via the network 5 in association with the dimensional point cloud data.

Next, the configuration of the equipment data processing device 3 will be described. 3 is a diagram illustrating an example of a configuration of the equipment data processing device according to the first embodiment; FIG. As shown in FIG. 3 , the facility data processing device 3 includes a communication section 20 , a storage section 21 and a processing section 22 .

The communication unit 20 transmits and receives data to and from the inspection device 2 via the network 5 . Note that the communication unit 20 may be configured to transmit and receive data to and from the inspection device 2 via USB, wireless LAN, or Bluetooth, for example.

The storage unit 21 includes an equipment point cloud data storage unit 31, a feature location point cloud data storage unit 32, a processing target data storage unit 33, an equipment ledger data storage unit 34, and a reference point cloud data storage unit 35. .

The equipment point cloud data storage unit 31 stores overall point cloud data, which is data of a three-dimensional point cloud of the entire equipment 1 . Such overall point cloud data is, for example, three-dimensional point cloud data generated by the inspection device 2 in the preprocessing mode and transmitted from the inspection device 2 to the facility data processing device 3. 3D point cloud data generated by conversion processing from Computer-Aided Design) data. Further, the overall point cloud data of each facility 1 may be three-dimensional point cloud data generated by an external device (not shown) other than the inspection device 2 and transmitted to the facility data processing device 3 . Hereinafter, the overall three-dimensional point cloud of the facility 1 may be referred to as the overall three-dimensional point cloud.

FIG. 4 is a diagram showing an example of an equipment point cloud data table that is stored in the equipment point cloud data storage unit according to the first embodiment and includes overall point cloud data of each piece of equipment. The facility point cloud data table 50 shown in FIG. 4 includes “equipment ID”, “equipment name”, and “whole point cloud data” for each facility 1 . “Equipment ID” is identification information unique to each facility 1 . “Equipment name” is information indicating the name of the facility 1 . "Three-dimensional point cloud data" is overall point cloud data, and includes information on the three-dimensional coordinates of each of a plurality of three-dimensional points forming the overall three-dimensional point cloud.

In the facility point cloud data table 50 shown in FIG. 4, the name of the facility 1 with the facility ID "P ₁ " is "facility A ₁ ", and the overall point cloud data is "overall point cloud data Dw ₁ ". . The name of the facility 1 with the facility ID "P ₂ " is "facility A ₂ ", the overall point cloud data is "overall point cloud data Dw ₂ ", and the facility with the facility ID "P _n " The name of 1 is "equipment A _n ", and the total point cloud data is "entire point cloud data Dw _n ". . . , Dw _n may be referred to as overall point cloud data _Dw when _they are not individually distinguished. The facility 1 with the facility ID "P ₁ " is the facility 1 ₁ , the facility 1 with the facility ID "P ₂ " is the facility 1 ₂ , and the facility 1 with the facility ID "P _n " is the facility 1 _n. is. Note that the facility point cloud data table 50 may be configured without the "equipment name".

The feature location point cloud data storage unit 32 shown in FIG. 3 stores feature location point cloud data, which is three-dimensional point cloud data of feature locations, in each piece of equipment 1 . Such feature location point cloud data is generated by the processing unit 22 . A characteristic part is, for example, a characteristic part that originally existed in the facility 1, or a deformation or damage that occurred in the facility 1 after the facility 1 was installed.

FIG. 5 is a diagram showing an example of a characteristic point point cloud data table including characteristic point point cloud data of each piece of equipment stored in the characteristic point point cloud data storage unit according to the first embodiment. The feature location point cloud data table 51 shown in FIG. 5 includes “equipment ID”, “equipment name”, and “feature location point cloud data” for each facility 1 . The “equipment ID” and “equipment name” are the same as the “equipment ID” and “equipment name” of the facility point cloud data table 50 . “Three-dimensional point cloud data” is characteristic point point cloud data, and includes information on the three-dimensional coordinates of each of a plurality of three-dimensional points representing characteristic points unique to each piece of equipment 1 . Note that the feature location point cloud data table 51 may be configured without the “equipment name”.

In the characteristic point point cloud data table 51 shown in FIG. 5, the characteristic point point cloud data of the facility 1 with the facility ID "P ₁ " is "characteristic point point cloud data Df ₁ ". Further, the feature point point cloud data of the facility 1 with the facility ID "P ₂ " is "feature point point cloud data Df ₂ ", and the feature point point cloud data of the facility 1 with the facility ID "P _n " is It is "characteristic point point cloud data Df _n ". . _. , _Dfn may be referred to as feature point point cloud data Df when _they are individually indicated without distinction.

The processing target data storage unit 33 shown in FIG. 3 stores processing target data Dp including inspection data Dc and three-dimensional point cloud data Dd transmitted from the inspection device 2 and received by the communication unit 20 . The processing target data Dp is temporarily stored in the processing target data storage unit 33 by the processing unit 22 . The processing target data Dp is stored in the processing target data storage unit 33 for each piece of equipment 1 to be inspected.

The facility ledger data storage unit 34 shown in FIG. 3 stores facility ledger data. The facility ledger data includes data of the facility 1 and inspection data Dc generated by the inspection device 2 . 6 is a diagram of an example of equipment ledger data stored in an equipment ledger data storage unit according to the first embodiment; FIG. The equipment ledger data 52 shown in FIG. 6 is data in a table format, and includes “equipment ID”, “equipment name”, and “inspection data” for each equipment 1 .

The “equipment ID” and “equipment name” are the same as the “equipment ID” and “equipment name” of the characteristic point point cloud data table 51 . “Inspection data” is inspection data generated by the inspection device 2 . In the facility ledger data 52 shown in FIG. 6, the inspection data of the facility 1 with the facility ID "P ₁ " is "inspection data Dc ₁ ". The inspection data of the facility 1 with the facility ID "P ₂ " is "inspection data Dc ₂ ", and the inspection data of the facility 1 with the facility ID "P _n " is "inspection data Dc _n ".

The processing unit 22 shown in FIG. 3 includes a data acquisition unit 41 , a reference data generation unit 42 , a feature location detection unit 43 , an equipment determination unit 44 and a linking unit 45 . The data acquisition unit ₄₁ stores the overall point cloud data Dw ₁ , Dw ₂ , . memorize to The _data acquisition unit 41 stores the overall point cloud data Dw ₁ , Dw ₂ , . can also be memorized.

The reference data generator 42 acquires the overall point cloud data Dw ₁ , Dw _{2 ,} . Calculate the average of The equipment 1 of the same type is, for example, the equipment 1 having the same or similar functions or the equipment 1 having a similar appearance. In the following description, the average of the entire point cloud data Dw may be referred to as reference point cloud data Dwa.

The reference point cloud data Dwa is generated, for example, by dividing the space of the three-dimensional coordinate system into a plurality of coordinate areas AR ₁ to AR _m , and for each of the plurality of divided coordinate areas AR ₁ to AR _m . It is done by calculating the average coordinates. The reference point cloud data Dwa is data of a three-dimensional point cloud including average coordinates of three-dimensional points in each of the coordinate areas AR ₁ to AR _m .

Hereinafter, the three-dimensional point group represented by the reference point cloud data Dwa may be referred to as a reference three-dimensional point group, and the three-dimensional points included in the reference three-dimensional point group may be referred to as average three-dimensional points. m is, for example, an integer of 10,000 or more, but may be an integer of less than 10,000. The coordinate areas AR ₁ to AR _m are set so that the interval between two adjacent three-dimensional points among the plurality of three-dimensional points whose data are included in the overall point cloud data Dw is larger than the interval between the two adjacent three-dimensional points.

Here, it is assumed that the number of three-dimensional points included in the coordinate area _AR1 is 100 among the plurality of three-dimensional points whose data are included in the overall point cloud data Dw of the plurality of facilities 1 of the same type. In this case, the reference data generation unit 42 calculates the average of the coordinates of 100 three-dimensional points as the average coordinates of the three-dimensional points in the coordinate area _AR1 , and the reference data generation unit 42 calculates the three-dimensional coordinates of the coordinate area _AR1 . The average coordinates of the three-dimensional points in each of the coordinate areas AR ₂ to AR _m are calculated by a method similar to the method of calculating the average coordinates of the points. The reference data generation unit 42 generates reference point cloud data Dwa including information on average coordinates of three-dimensional points in each of the coordinate areas AR ₁ to AR _m . Note that the reference data generation unit 42 can also set the three-dimensional points whose positions are the same among the facilities 1 of the same type as the average three-dimensional point. The method of generating the reference point cloud data Dwa is not limited to the method described above.

7 is a diagram for explaining a method of generating reference point cloud data by a reference data generation unit according to the first embodiment; FIG. As shown in FIG. 7, the reference _data generator 42 generates reference point cloud data Dwa from the overall point cloud data Dw ₁ , Dw ₂ , . Stored in the group data storage unit 35 .

The feature point detection unit 43 shown in FIG. 3 acquires the overall point cloud data Dw of each facility 1 from the facility point cloud data storage unit 31 and acquires the reference point cloud data Dwa from the reference point cloud data storage unit 35 . The feature location detection unit 43 performs comparison processing for comparing each of the overall point cloud data Dw and the reference point cloud data Dwa for each piece of equipment 1 of the same type. In this comparison processing, the feature location detection unit 43 compares the overall three-dimensional point cloud represented by the overall point cloud data Dw and the reference three-dimensional point cloud represented by the reference point cloud data Dwa. Then, the feature point detection unit 43 determines whether or not there is a feature point, which is a point having features with respect to the reference three-dimensional point group, in the entire three-dimensional point group.

For example, the feature point detection unit 43 compares the coordinates of a plurality of 3D points included in the overall 3D point group with the coordinates of a plurality of average 3D points included in the reference 3D point group. Then, the feature point detection unit 43 detects a plurality of three-dimensional points each located at a distance equal to or greater than a preset distance from the nearest average three-dimensional point among the plurality of average three-dimensional points included in the reference three-dimensional point group. Determine whether there is a set. The characteristic point detection unit 43 detects, as characteristic points, a set of a plurality of three-dimensional points each located at a distance equal to or greater than a preset distance from the nearest average three-dimensional point.

The feature location detection unit 43 extracts the data of the three-dimensional point cloud, which is a set of a plurality of three-dimensional points of the feature locations from the entire three-dimensional point cloud, as the feature location point cloud data Df from the overall point cloud data Dw. The characteristic point point cloud data Df thus obtained is stored in the characteristic point point cloud data storage unit 32 .

FIG. 8 is a diagram for explaining a method of generating characteristic point point cloud data by the characteristic point detection unit according to the first embodiment; In the example shown in FIG. 8, the overall three-dimensional point cloud represented by the overall point cloud data _Dw1 has a dent in part compared to the reference 3-dimensional point cloud. is detected as a feature point. The characteristic point detection unit 43 extracts the three-dimensional point cloud data of the concave portion detected as the characteristic point as characteristic point point cloud data _Df1 , and stores the extracted characteristic point point cloud data _Df1 as characteristic point point cloud data. stored in the unit 32;

The equipment determination unit 44 shown in FIG. The facility 1 to be inspected is determined based on the result of comparison with the characteristic point point cloud data Df. Then, the facility determination unit 44 associates the inspection data Dc included in the processing target data Dp with the facility ID of the determined facility 1 and adds the inspection data Dc of the determined facility 1 to the facility ledger data 52 .

FIG. 9 is a diagram for explaining a method of determining equipment to be inspected by the equipment determination unit according to the first embodiment. _The facility determination unit 44 acquires the characteristic point point cloud data Df ₁ , Df ₂ , . . . , Df _n from the characteristic point point cloud data storage unit ₃₂ , , _Dfn and the three-dimensional point cloud data Dd included in the data Dp to be processed are compared.

The equipment determination unit 44 determines that _part of the three- _dimensional point cloud data _Dd is It is determined whether or not it matches with any of the feature location point cloud data Df among the plurality of facilities 1 ₁ , 1 ₂ , . . . , 1 _n . Specifically, the facility determination unit 44 selects the three-dimensional point cloud data Dd from among the three-dimensional point clouds of the plurality of feature locations indicated by the feature location point cloud data Df ₁ , Df ₂ , . . . , Df _n . Determine whether there is a 3D point cloud of features that matches a portion of the indicated 3D point cloud. Here, the part of the three-dimensional point cloud refers to a plurality of three-dimensional points existing at positions corresponding to the positions of the three-dimensional point cloud of the characteristic locations in the three-dimensional point cloud represented by the three-dimensional point cloud data Dd. is a set.

For example, if a part of the three-dimensional point cloud represented by the three-dimensional point cloud data Dd is within a preset allowable range from the three-dimensional point cloud represented by the feature location point cloud data Df, the equipment determination unit 44 , it is determined that the feature point cloud data Df matches a part of the three-dimensional point cloud data Dd. In addition, the equipment determination unit 44 presets from the three-dimensional point group indicated by the characteristic point point group data Df among a plurality of three-dimensional points forming a part of the three-dimensional point group indicated by the three-dimensional point group data Dd. It can also be determined that a part of the 3D point group matches when the ratio of the 3D points existing within the defined allowable range is equal to or greater than a preset threshold value.

The equipment determination unit 44 determines the equipment ID associated with the characteristic point point cloud data Df including the three-dimensional point cloud data that matches a part of the three-dimensional point cloud indicated by the three-dimensional point cloud data Dd. Extract from the data table 51 . The facility determination unit 44 determines the facility 1 having the facility ID extracted from the characteristic point point cloud data table 51 as the facility 1 to be inspected. Then, the facility determination unit 44 associates the inspection data Dc included in the processing target data Dp with the facility ID of the determined facility 1 and adds the inspection data Dc of the determined facility 1 to the facility ledger data 52 . The facility determination unit 44 determines whether or not the three-dimensional point cloud represented by the three-dimensional point cloud data Dd includes the three-dimensional point cloud of the feature location represented by the feature location point cloud data Df. is not limited to the examples.

In the example shown in FIG. 9, since the feature location point cloud data _Df1 and the 3D point cloud data Dd match, the equipment determination unit 44 determines that the 3D point cloud data Dd is the 3D point cloud data of the equipment ₁₁ . Determine that there is. In this case, the equipment determination unit 44 associates the inspection data Dc included in the processing target data Dp with the equipment ID “P ₁ ” of the identified equipment 1 ₁ , and stores the equipment ledger data as inspection data Dc ₁ of the equipment 1 ₁ . It is added to the equipment ledger data 52 of the unit 34 .

Next, processing by the processing unit 22 of the equipment data processing device 3 will be described using a flowchart. 10 is a flowchart illustrating an example of preliminary processing by a processing unit of the equipment data processing device according to Embodiment 1. FIG.

As shown in FIG. 10, the processing unit 22 of the facility data processing device 3 acquires the overall point cloud data Dw of each facility 1 from the inspection device 2 or an external device (not shown), and acquires the acquired overall point cloud of each facility 1. The data Dw is stored in the facility point cloud data storage unit 31 (step S10).

Next, the processing unit 22 acquires the overall point cloud data Dw of each facility 1 stored in the facility point cloud data storage unit 31 from the facility point cloud data storage unit 31, and obtains the overall point cloud data Dw for each facility type. is calculated to generate reference point cloud data Dwa (step S11).

Next, the processing unit 22 compares each of the overall point cloud data Dw of the plurality of facilities 1 with the corresponding reference point cloud data Dwa of the facility type, and detects characteristic locations of each facility 1 (step S12). Then, the processing unit 22 extracts the three-dimensional point cloud data of the detected feature points from the overall point cloud data Dw of each facility 1 as the feature point point cloud data Df, and converts the extracted feature point point cloud data Df into feature point points. It is stored in the group data storage unit 32 (step S13). After completing the process of step S13, the processing unit 22 ends the process shown in FIG.

11 is a flowchart illustrating an example of ledger linking processing by the processing unit of the facility data processing apparatus according to the first embodiment; FIG. The processing shown in FIG. 11 is executed, for example, for each processing target data Dp.

As shown in FIG. 11, the processing unit 22 of the facility data processing device 3 acquires the processing target data Dp from the inspection device 2 (step S20). The processing target data Dp includes inspection data Dc of the equipment 1 to be inspected and three-dimensional point cloud data Dd of the equipment 1 to be inspected.

Next, the processing unit 22 acquires the characteristic point point cloud data Df of each facility 1 from the characteristic point point cloud data storage unit 32, and stores the acquired characteristic point point cloud data Df and the three-dimensional point cloud data Dd of each facility 1. are compared to determine the facility 1 to be inspected (step S21).

Next, the processing unit 22 links the inspection data Dc to the data of the facility 1 determined as the facility 1 to be inspected in the facility ledger data 52 (step S22). When the facility ledger data 52 is in the state shown in FIG. 6 and the facility 1 identified as the facility 1 to be inspected is the facility ₁₁ with the facility ID “P ₁ ”, the data of the facility 1 to which the inspection data Dc is linked. is the facility ID "P ₁ " or the facility name "A ₁ ", but is not limited to such examples. When the process of step S22 ends, the processing unit 22 ends the process shown in FIG.

12 is a diagram illustrating an example of a hardware configuration of the equipment data processing device according to the first embodiment; FIG. As shown in FIG. 12 , the facility data processing device 3 includes a computer having a processor 101 , memory 102 and communication device 103 .

Processor 101 , memory 102 , and communication device 103 can send and receive information from each other, eg, via bus 104 . Storage unit 21 is implemented by memory 102 . The communication unit 20 is implemented by the communication device 103 . The processor 101 reads out and executes a program stored in the memory 102, thereby performing functions such as the data acquisition unit 41, the reference data generation unit 42, the feature location detection unit 43, the equipment determination unit 44, and the linking unit 45. Execute. The processor 101 is an example of a processing circuit, for example, and includes one or more of a CPU (Central Processing Unit), a DSP (Digital Signal Processor), and a system LSI (Large Scale Integration).

The memory 102 includes one or more of RAM (Random Access Memory), ROM (Read Only Memory), flash memory, EPROM (Erasable Programmable Read Only Memory), and EEPROM (Registered Trademark) (Electrically Erasable Programmable Read Only Memory). include. The memory 102 also includes a recording medium in which a computer-readable program is recorded. Such recording media include one or more of nonvolatile or volatile semiconductor memories, magnetic disks, flexible memories, optical disks, compact disks, and DVDs (Digital Versatile Disks). The facility data processing device 3 may include integrated circuits such as ASIC (Application Specific Integrated Circuit) and FPGA (Field Programmable Gate Array).

As described above, the equipment data processing device 3 according to the first embodiment includes the characteristic point point cloud data storage unit 32 , the data acquisition unit 41 , the equipment determination unit 44 and the linking unit 45 . The feature location point cloud data storage unit 32 stores three-dimensional point cloud data of each feature location of the plurality of facilities 1 . The data acquisition unit 41 acquires the three-dimensional point cloud data Dd of the facility 1 and the inspection data Dc of the facility 1 from the inspection device 2 that inspects the facility 1 . The equipment determination unit 44 acquires the It is determined whether or not the obtained three-dimensional point cloud data Dd is the three-dimensional point cloud data of any one of the plurality of facilities 1 . When the equipment determination unit 44 determines that the three-dimensional point cloud data Dd acquired by the data acquisition unit 41 is the three-dimensional point cloud data of one facility 1 out of the plurality of facilities 1, the linking unit 45 , the inspection data Dc acquired by the data acquisition unit 41 are linked to the data of one piece of equipment 1 . As a result, the equipment data processing device 3, when determining the equipment to be inspected, can process the three-dimensional point cloud data Dd of the plurality of equipment 1 without extracting the matching object from the three-dimensional point cloud data Dd. It compares with the 3D point cloud data of each feature location. Therefore, the equipment data processing device 3 can associate the inspection data Dc with the equipment 1 to be inspected while reducing the processing load. For example, the processing load can be reduced.

The equipment data processing device 3 also includes an equipment point cloud data storage unit 31 and a feature location detection unit 43 . The equipment point cloud data storage unit 31 stores overall point cloud data Dw, which is three-dimensional point cloud data for each of the plurality of equipment 1 . The feature point detection unit 43 detects each of the plurality of facilities 1 based on the result of comparison between the overall point cloud data Dw and the reference point cloud data Dwa for each of the plurality of facilities 1 stored in the facility point cloud data storage unit 31. is detected, and characteristic point point cloud data Df, which is three-dimensional point cloud data of each of the detected characteristic points of the plurality of facilities 1, is stored in the characteristic point point cloud data storage unit 32. FIG. As a result, the equipment data processing device 3 can generate the characteristic point point cloud data Df, so that the user of the equipment data processing device 3 can save the trouble of generating the characteristic point point cloud data Df.

The facility data processing device 3 also includes a reference data generation unit 42 . The reference data generation unit 42 generates reference point cloud data Dwa based on the overall point cloud data Dw of the plurality of facilities 1 stored in the facility point cloud data storage unit 31 . The feature point detection unit 43 compares the overall point cloud data Dw of each of the plurality of facilities 1 stored in the facility point cloud data storage unit 31 with the reference point cloud data Dwa generated by the reference data generation unit 42, and compares the Based on this, the feature points of each of the plurality of facilities 1 are detected. In this manner, since the facility data processing device 3 generates the reference data generation unit 42, the user of the facility data processing device 3 can save the trouble of generating the characteristic point point cloud data Df.

Embodiment 2.
The facility data processing system according to the second embodiment differs from the facility data processing system 100 according to the first embodiment in that the facility data processing device does not have a reference data generation unit. In the following, constituent elements having functions similar to those of the first embodiment are given the same reference numerals and explanations thereof are omitted, and differences from the equipment data processing device 3 of the first embodiment are mainly explained.

13 is a diagram illustrating an example of a configuration of an equipment data processing device according to a second embodiment; FIG. As shown in FIG. 13, the equipment data processing device 3A according to the second embodiment differs from the equipment data processing device 3 according to the first embodiment in that a processing unit 22A is provided instead of the processing unit 22. FIG. The processing unit 22A differs from the processing unit 22 in that it does not include the reference data generation unit 42 and includes a data acquisition unit 41A instead of the data acquisition unit 41 .

The data acquisition unit 41A acquires from the communication unit 20 the reference point cloud data Dwa for each equipment type that is transmitted from an external device (not shown) and received by the communication unit 20, and the acquired reference point cloud data Dwa for each equipment type. It is stored in the reference point cloud data storage unit 35 . The reference point cloud data Dwa is ideal three-dimensional point cloud data generated based on design drawing data or CAD data of the equipment 1, and is generated by an external device. For example, the reference point cloud data Dwa is three-dimensional point cloud data generated by converting CAD data of the facility 1 .

14 is a flowchart illustrating an example of preliminary processing by a processing unit of the equipment data processing device according to the second embodiment; FIG. Since the processes of steps S32 and S33 shown in FIG. 14 are the same as the processes of steps S12 and S13 shown in FIG. 10, description thereof is omitted.

As shown in FIG. 14, the processing unit 22A of the facility data processing device 3A acquires the reference point cloud data Dwa for each facility type from an external device (not shown), and uses the acquired reference point cloud data Dwa for each facility type as a reference. It is stored in the point cloud data storage unit 35 (step S30).

In addition, the processing unit 22A acquires the overall point cloud data Dw of each facility 1 from the inspection device 2 or an external device (not shown), and stores the acquired overall point cloud data Dw of each facility 1 in the facility point cloud data storage unit 31. It is stored (step S31).

A hardware configuration example of the equipment data processing device 3A according to the second embodiment is the same as the hardware configuration of the equipment data processing device 3 shown in FIG. The processor 101 can execute the functions of the data acquisition unit 41A, the feature location detection unit 43, the equipment determination unit 44, and the linking unit 45 by reading and executing the programs stored in the memory 102. FIG.

As described above, the facility data processing device 3A according to the second embodiment includes the reference point cloud data storage unit 35 that stores the reference point cloud data Dwa. The feature point detection unit 43 compares the overall point cloud data Dw of each of the plurality of facilities 1 stored in the facility point cloud data storage unit 31 with the reference point cloud data Dwa stored by the reference point cloud data storage unit 35. Based on the results, the feature points of each of the plurality of facilities 1 are detected. As a result, the facility data processing device 3A does not need to generate the reference point cloud data Dwa, so the preprocessing can be performed at high speed and with a low load.

Embodiment 3.
The facility data processing system according to the third embodiment is similar to the facility according to the first embodiment in that the facility data processing device sets three-dimensional point groups of feature points that are not similar between the facilities 1 as feature point point group data. Differs from data processing system 100 . In the following, constituent elements having functions similar to those of the first embodiment are given the same reference numerals and explanations thereof are omitted, and differences from the equipment data processing device 3 of the first embodiment are mainly explained.

15 is a diagram illustrating an example of a configuration of an equipment data processing device according to a third embodiment; FIG. As shown in FIG. 15, the facility data processing device 3B according to the third embodiment differs from the facility data processing device 3 according to the first embodiment in that a processing unit 22B is provided instead of the processing unit 22. FIG. The processing unit 22B is different from the processing unit 22 in that it further includes a similar feature location detection unit 46 and includes a feature location detection unit 43B instead of the feature location detection unit 43 .

Similar to the feature location detection unit 43 , the feature location detection unit 43</b>B detects the feature location of each of the plurality of facilities 1 . The similar feature point detection unit 46 converts the feature point point cloud data Df of one facility 1 out of the plurality of facilities 1 detected by the feature point detection unit 43B into the feature points of the remaining facilities 1 out of the plurality of facilities 1. It is determined whether or not it is similar to any of the point cloud data Df. For example, when the similarity between the three-dimensional point groups indicated by the two characteristic point point cloud data Df is greater than or equal to the similarity determination threshold value, the similar characteristic point detection unit 46 detects the two characteristic point point cloud data Df. It is determined that they are similar to each other.

When the similar feature location detection unit 46 determines that the feature location of one facility 1 is similar to the feature location of the remaining facilities 1, the feature location detection unit 43B detects the three-dimensional point cloud data of the entire facility 1. Based on the result of comparison with the reference point cloud data Dwa, feature locations different from the determined feature locations are detected from the three-dimensional point cloud data of one piece of equipment 1 .

The characteristic point detection unit 43B detects that the 3D point cloud data of the characteristic point different from the determined characteristic point is not similar to any of the 3D point cloud data of the remaining characteristic points of the facility 1, and the similar characteristic point detection unit 46 If it is determined, the three-dimensional point cloud data of the feature location different from the determined feature location is stored in the feature location point cloud data storage unit 32 as the feature location point cloud data Df.

FIG. 16 is a diagram for explaining a method of generating characteristic point point cloud data by a characteristic point detection unit according to the third embodiment; FIG. 16 shows an example in which the overall three-dimensional point cloud of the facility _1-2 indicated by the overall point cloud data _Dw2 is compared with the reference point cloud data Dwa. As shown in FIG. 16, the feature location detection unit 43B compares the overall three-dimensional point cloud of the equipment ₁₂ with the reference point cloud data Dwa to detect a feature location F1.

In the example shown in FIG. 16, since the three-dimensional point cloud of the feature point F1 in the facility _1-2 is similar to the three-dimensional point cloud represented by the feature point point cloud data _Df1 of the facility _1-1 , the feature point detection unit 43B , the overall three-dimensional point cloud of the facility ₁₂ and the reference point cloud data Dwa are again compared to detect a feature point F2 different from the feature point F1. The 3D point cloud of the feature point F2 in the facility _1-2 is not similar to the 3D point cloud indicated by the feature point point cloud data _Df1 of the facility _1-1 . Therefore, the feature location detection unit 43B stores the three-dimensional point cloud data of the feature location F2 in the facility _1-2 in the feature location point cloud data storage unit ₃₂ as the feature location point cloud data Df2 of the facility _1-2 .

17 is a flowchart illustrating an example of preliminary processing by a processing unit of the equipment data processing device according to the third embodiment; FIG. Since the processes of steps S40 and S41 shown in FIG. 17 are the same as the processes of steps S10 and S11 shown in FIG. 10, description thereof is omitted.

As shown in FIG. 17, the processing unit 22B of the facility data processing device 3B selects one unselected facility 1 from among the plurality of facilities 1 after completing the process of step S41, and calculates the overall score of the selected facility 1. The group data Dw is acquired from the equipment point cloud data storage unit 31 (step S42).

The processing unit 22B performs comparison processing for comparing the overall point cloud data Dw of the facility 1 selected in step S42 and the corresponding reference point cloud data Dwa (step S43). In the process of step S43, the reference point cloud data Dwa corresponding to the entire point cloud data Dw of the facility 1 selected in step S42 is the reference point cloud data Dwa of the facility 1 of the same type as the facility 1 selected in step S42.

The processing unit 22B detects one characteristic location of the equipment 1 selected in step S42 based on the result of the comparison processing in step S43 (step S44). The processing unit 22B determines whether or not another piece of equipment 1 has a characteristic portion similar to the characteristic portion of the piece of equipment 1 detected in step S44 (step S45). If the processing unit 22B determines that another facility 1 has a feature point similar to the feature point of the facility 1 detected in step S44 (step S45: Yes), the process proceeds to step S43.

When the processing unit 22B determines that there is no feature point similar to the feature point of the facility 1 detected in step S44 (step S45: No), it determines whether there is an unselected facility 1. (step S46). When the processing unit 22B determines that there is an unselected facility 1 (step S46: Yes), the process proceeds to step S42.

When the processing unit 22B determines that there is no unselected facility 1 (step S46: No), the processing unit 22B converts the three-dimensional point cloud data of the characteristic locations of each facility 1 determined in step S44 into the overall point cloud data of the plurality of facilities 1. Characteristic point point cloud data Df is extracted from the corresponding overall point cloud data Dw out of Dw, and the extracted characteristic point point cloud data Df is stored in the characteristic point point cloud data storage unit 32 (step S47). End the process.

A hardware configuration example of the equipment data processing device 3B according to the third embodiment is the same as the hardware configuration of the equipment data processing device 3 shown in FIG. By reading and executing the programs stored in the memory 102, the processor 101 obtains a data acquisition unit 41, a reference data generation unit 42, a feature location detection unit 43B, an equipment determination unit 44, a linking unit 45, and a similar feature location. The function of the detection unit 46 can be executed.

Note that the facility data processing device 3B may be configured without the reference data generation unit 42, like the facility data processing device 3A according to the second embodiment. In this case, the same data as the reference point cloud data storage unit 35 of the facility data processing device 3A is stored in the reference point cloud data storage unit 35 of the facility data processing device 3B.

As described above, the facility data processing device 3B according to the third embodiment includes the similar feature location detection unit 46. FIG. The similar feature point detection unit 46 converts the feature point point cloud data Df of one facility 1 out of the plurality of facilities 1 determined by the feature point detection unit 43B into the feature points of the remaining facilities 1 out of the plurality of facilities 1. It is determined whether or not it is similar to any of the point cloud data Df. If the similar feature point detection unit 46 determines that the feature point point cloud data Df of one facility 1 is similar to the feature point point cloud data Df of any of the remaining facilities 1, the feature point detection unit 43B detects one Based on the result of comparison between the feature point cloud data Df of the facility 1 and the reference point cloud data Dwa, feature points different from the feature points determined to be similar are detected from the overall point cloud data Dw of one facility 1. As a result, the facility data processing device 3B can prevent erroneous discrimination of the inspection target facility due to the presence of a plurality of facilities 1 having similar characteristic locations, and can improve the accuracy of discrimination of the inspection target facility.

Embodiment 4.
The equipment data processing system according to the fourth embodiment differs from the equipment data processing system according to the third embodiment in that the allowable range used for determining the equipment to be inspected in the equipment data processing device is changed. In the following, constituent elements having functions similar to those of the third embodiment are denoted by the same reference numerals, and descriptions thereof are omitted, and differences from the facility data processing device 3B of the third embodiment are mainly described.

FIG. 18 is a diagram illustrating an example of a configuration of an equipment data processing device according to a fourth embodiment; As shown in FIG. 18, the equipment data processing device 3C according to the fourth embodiment differs from the equipment data processing device 3B according to the third embodiment in that a processing unit 22C is provided instead of the processing unit 22B. The processing section 22C differs from the processing section 22B in that it further includes an allowable range changing section 47 .

The allowable range change unit 47 changes the allowable range used by the facility determination unit 44 to determine the feature location when the feature location of the facility 1 changes due to aging or the like. For example, when the equipment determination unit 44 determines that the three-dimensional point cloud data Dd is not within the allowable range of each characteristic point of the plurality of facilities 1, the allowable range changing unit 47 changes the allowable range of each characteristic point. do. The change of the characteristic location by the allowable range changing unit 47 is performed so as to expand the allowable range. The expansion of the allowable range is set, for example, to a range in which the feature locations change over time.

The facility determination unit 44 determines whether any of the allowable ranges of the plurality of characteristic locations changed by the allowable range changing unit 47 includes a part of the three-dimensional point cloud data Dd. Equipment 1 having a characteristic portion corresponding to an allowable range including a part of Dd is determined to be the equipment 1 to be inspected. The linking unit 45 links the inspection data Dc of the processing target data Dp to the data of the facility 1 determined by the facility determination unit 44 to be subject to inspection.

19 is a diagram for explaining a method of changing the allowable range by the allowable range changing unit according to the fourth embodiment; FIG. In the example shown within a frame 90 in FIG. 19, the facility determining unit 44 compares the three-dimensional point cloud data Dd included in the processing target data Dp of the facility _1-1 with the characteristic point point cloud data _{Df 1 of} the facility 1-1. However, part of the three-dimensional point cloud represented by the three-dimensional point cloud data Dd does not match the three-dimensional point cloud represented by the feature point point cloud data _Df1 .

Therefore, although the three-dimensional point cloud data Dd is the three-dimensional point cloud data of the facility _1-1 , the facility determination unit 44 cannot determine that the facility _1-1 is the facility 1 to be inspected. In this case, the allowable range changing unit 47 expands the allowable range used by the equipment determination unit 44 .

As a result, the allowable range is expanded as shown in a frame 91 in _FIG . Since it matches the group, the equipment determination unit 44 can determine that the equipment _1-1 is the equipment 1 to be inspected.

It should be noted that the allowable range changing unit 47 can reduce the threshold value for similarity degree determination in the similar characteristic location detecting unit 46 when the allowable range used in the equipment determining unit 44 is expanded. As a result, the similar feature spot detection unit 46 detects a part of the three-dimensional group indicated by the three-dimensional point cloud data Dd of one piece of equipment 1 by the equipment determination unit 44, and the three-dimensional It is possible to suppress matching with the point cloud.

Further, the facility data processing device 3C may be configured without the reference data generation unit 42, similarly to the facility data processing device 3A according to the second embodiment. In this case, the same data as the reference point cloud data storage unit 35 of the facility data processing device 3A is stored in the reference point cloud data storage unit 35 of the facility data processing device 3C.

The allowable range changing unit 47 can also change the method of enlarging the allowable range according to the shape of the feature location. For example, if the shape of the feature location is concave, the allowable range changing unit 47 expands the allowable range in the direction of increasing the dent, and if the shape of the feature location is convex, increases the allowable range in the direction of increasing the protrusion. can be expanded.

20 is a flowchart illustrating an example of ledger linking processing by the processing unit of the facility data processing apparatus according to the fourth embodiment; FIG. Since the process of step S60 shown in FIG. 20 is the same as the process of step S20 shown in FIG. 11, description thereof is omitted.

The processing unit 22C of the facility data processing device 3C performs a comparison process for comparing each characteristic point point cloud data Df and the three-dimensional point cloud data Dd (step S61). The processing unit 22C determines whether the three-dimensional point cloud indicated by the three-dimensional point cloud data Dd includes any of the feature locations of the facilities 1 ₁ , 1 ₂ , . . . , 1 _n (step S62 ). In the process of step S62, the processing unit 22C, for example, preliminarily selects a part of the three-dimensional point cloud represented by the three-dimensional point cloud data Dd from the three-dimensional point cloud represented by any one of the plurality of characteristic location point cloud data Df. If it is within the set allowable range, it is determined that the characteristic point point cloud data Df matches a part of the three-dimensional point cloud.

Next, when the processing unit 22C determines that the three-dimensional point cloud indicated by the three-dimensional point cloud data Dd does not include any feature location among the equipment 1 ₁ , 1 ₂ , . . . , 1 _n ( Step S62: No), the allowable range used in step S61 is expanded (step S63), and the process proceeds to step S61.

If the processing unit 22C determines that the three-dimensional point cloud indicated by the three-dimensional point cloud data Dd includes any of the features of the facilities 1 ₁ to 1 _n (step S62: Yes), in step S62 The facility 1 of the characteristic location determined to be included is determined as the facility 1 to be inspected (step S64). Then, the processing unit 22C links the inspection data Dc to the data of the facility 1 determined as the facility 1 to be inspected in the facility ledger data 52 (step S65). 22 C of process parts complete|finish the process shown in FIG. 20, when the process of step S65 is complete|finished.

A hardware configuration example of the facility data processing device 3C according to the fourth embodiment is the same as the hardware configuration of the facility data processing device 3 shown in FIG. By reading and executing the programs stored in the memory 102, the processor 101 performs a data acquisition unit 41, a reference data generation unit 42, a feature location detection unit 43B, an equipment determination unit 44, a linking unit 45, and a similar feature location detection. The functions of the unit 46 and the allowable range changing unit 47 can be executed.

As described above, the equipment data processing device 3C according to the fourth embodiment includes the allowable range changing unit 47. FIG. If the equipment determination unit 44 determines that the three-dimensional point cloud data Dd acquired by the data acquisition unit 41 is not within the allowable range of any of the plurality of facilities 1, the allowable range changing unit 47 changes the allowable range to change As a result, the equipment data processing device 3C can accurately determine the equipment to be inspected even when the feature location changes with the passage of time, such as aging.

Embodiment 5.
The facility data processing system according to the fifth embodiment differs from the facility data processing system according to the fourth embodiment in that feature point point cloud data can be edited. In the following, constituent elements having functions similar to those of the fourth embodiment are denoted by the same reference numerals, and descriptions thereof are omitted, and differences from the facility data processing device 3C of the fourth embodiment are mainly described.

FIG. 21 is a diagram illustrating an example of a configuration of an equipment data processing device according to a fifth embodiment; As shown in FIG. 21, the equipment data processing device 3D according to the fifth embodiment has an input unit 23 and a display unit 24, and has a processing unit 22D in place of the processing unit 22C. It is different from the facility data processing device 3C. Hereinafter, the user of the equipment data processing device 3D is simply referred to as the user.

The input unit 23 includes, for example, a mouse and a keyboard, and may be a touch pad. The display unit 24 is, for example, a liquid crystal display or an organic EL (ElectroLuminescence) display. The processing section 22D differs from the processing section 22C in that it further includes a feature editing section 48 .

The feature point editing unit 48 edits part or all of the feature point point cloud data Df of each piece of equipment 1 generated by the feature point detection unit 43B. For example, the characteristic point editing unit 48 acquires the characteristic point point cloud data Df of the facility to be edited selected by the input operation from the characteristic point point cloud data storage unit 32 based on the input operation to the input unit 23 by the user. , edit a part or all of the acquired characteristic point point cloud data Df.

The feature location editing unit 48 displays a point cloud image, which is a three-dimensional point cloud image of the feature locations of the facility to be edited, on the display unit 24 based on the feature location point cloud data Df of the facility to be edited. The user can change part or all of the characteristic point point cloud data Df of the facility to be edited by performing an input operation to the input unit 23 while referring to the point cloud image displayed on the display unit 24. can.

Further, based on the input operation to the input unit 23 by the user of the equipment data processing device 3D, the characteristic point editing unit 48 converts the characteristic point point cloud data Df of the facility to be edited selected by the input operation into the characteristic point detection unit 43B. can also be generated in

In this case, the feature location editing unit 48 acquires the feature location point cloud data Df of the facility to be edited generated by the feature location detection unit 43B from the feature location detection unit 43B, and displays the acquired feature location point cloud data Df on the display unit. 24. The characteristic point editing unit 48 changes part or all of the characteristic point point cloud data Df of the facility to be edited based on the user's input operation to the input unit 23, and changes the characteristic point point cloud data Df after the change. It is stored in the location point cloud data storage unit 32 to update the characteristic location point cloud data Df of the facility to be edited.

22 is a flowchart illustrating an example of characteristic portion editing processing by the processing unit of the equipment data processing apparatus according to the fifth embodiment; FIG. As shown in FIG. 22, the processing unit 22D acquires the entire point cloud data Dw of the facility to be edited selected by the user's input operation to the input unit 23 (step S70).

Next, the processing unit 22D generates characteristic point point cloud data Df based on the overall point cloud data Dw and the reference point cloud data Dwa acquired in step S70 (step S71). The processing unit 22D changes the characteristic point point cloud data Df according to the user's input operation to the input unit 23, and updates the characteristic point point cloud data Df stored in the characteristic point point cloud data storage unit 32 (step S72). ). The processing unit 22D ends the processing shown in FIG. 22 when the processing of step S72 is completed.

A hardware configuration example of the facility data processing device 3D according to the fifth embodiment is the same as the hardware configuration of the facility data processing device 3 shown in FIG. By reading and executing the programs stored in the memory 102, the processor 101 performs a data acquisition unit 41, a reference data generation unit 42, a feature location detection unit 43B, an equipment determination unit 44, a linking unit 45, and a similar feature location detection. The functions of the section 46, the allowable range changing section 47, and the characteristic portion editing section 48 can be executed.

Further, the facility data processing device 3D may be configured without the reference data generation unit 42, like the facility data processing device 3A according to the second embodiment. In this case, the same data as the reference point cloud data storage unit 35 of the facility data processing device 3A is stored in the reference point cloud data storage unit 35 of the facility data processing device 3D.

As described above, the facility data processing device 3D according to the fifth embodiment includes the characteristic portion editing section 48. FIG. The feature location editing unit 48 edits the feature location point cloud data Df of each of the plurality of facilities 1 determined by the feature location detection unit 43B. As a result, the facility data processing device 3D can, for example, change the allowable range by the allowable range changing unit 47 so that the facility to be inspected cannot be accurately identified because the change in the feature location is large. The characteristic point point cloud data Df can be changed according to the change of the point. Therefore, the equipment data processing device 3D can appropriately set the characteristic point point cloud data Df, and can improve the accuracy of determining the equipment to be inspected.

In the example described above, the feature location detection units 43 and 43B generate the 3D point cloud data of one feature location as the feature location point cloud data Df, but the 3D point cloud data of a plurality of feature locations are generated as the feature location point cloud data Df. can also be generated as feature location point cloud data Df.

The configurations shown in the above embodiments are only examples, and can be combined with other known techniques, or can be combined with other embodiments, without departing from the scope of the invention. It is also possible to omit or change part of the configuration.

₁ , _{1 1} , 1 2 , . 12 sensor group, 13 inspection data generation unit, 21 storage unit, 22, 22A, 22B, 22C, 22D processing unit, 23 input unit, 24 display unit, 31 facility point cloud data storage unit, 32 feature location point cloud data storage unit , 33 processing target data storage unit, 34 facility ledger data storage unit, 35 reference point cloud data storage unit, 41, 41A data acquisition unit, 42 reference data generation unit, 43, 43B characteristic location detection unit, 44 facility determination unit, 45 Linking unit 46 Similar feature location detection unit 47 Allowable range change unit 48 Feature location editing unit 50 Facility point cloud data table 51 Feature location point cloud data table 52 Facility ledger data 100 Facility data processing system.

Claims (9)

a feature location point cloud data storage unit that stores three-dimensional point cloud data of each feature location of a plurality of pieces of equipment;
an equipment point cloud data storage unit that stores overall three-dimensional point cloud data for each of the plurality of equipment;
a data acquisition unit that acquires three-dimensional point cloud data of the equipment to be inspected and inspection data of the equipment to be inspected from an inspection device that inspects the equipment to be inspected;
The three-dimensional data acquired by the data acquisition unit based on a result of comparison between the three-dimensional point cloud data acquired by the data acquisition unit and the three-dimensional point cloud data of the characteristic locations of each of the plurality of facilities a facility determination unit that determines whether or not the point cloud data is the three-dimensional point cloud data of any of the plurality of facilities;
When the 3D point cloud data acquired by the data acquisition unit is determined to be the 3D point cloud data of one of the plurality of facilities by the facility determination unit, acquired by the data acquisition unit a linking unit that links the inspection data obtained to the data of the one piece of equipment;
Detecting a feature point of each of the plurality of facilities based on a comparison result between the overall three-dimensional point cloud data of each of the plurality of facilities stored in the facility point cloud data storage unit and the reference point cloud data. and a feature location detection unit that stores three-dimensional point cloud data of the determined feature locations of each of the plurality of facilities in the feature location point cloud data storage unit. a reference data generation unit that generates the reference point cloud data based on the three-dimensional point cloud data of the plurality of facilities stored in the facility point cloud data storage unit;
The feature location detection unit is
The plurality of 2. The equipment data processing device according to claim 1 , wherein the characteristic locations of each of the equipment are detected. A reference point cloud data storage unit that stores the reference point cloud data,
The feature location detection unit is
Based on the result of comparison between the overall three-dimensional point cloud data of each of the plurality of facilities stored in the facility point cloud data storage unit and the reference point cloud data stored by the reference point cloud data storage unit, The facility data processing device according to claim 1 , wherein the feature location of each of the plurality of facilities is detected. Which of the three-dimensional point cloud data of the characteristic points of one of the plurality of facilities determined by the characteristic point detection unit is the three-dimensional point cloud data of the characteristic points of the remaining facilities of the plurality of facilities? A similar feature location detection unit that determines whether or not it is similar to
The feature location detection unit is
When the similar feature point detection unit determines that the 3D point cloud data of the feature points of the one facility is similar to the 3D point cloud data of any of the remaining facilities, Characteristic points different from the characteristic points determined to be similar from the three-dimensional point cloud data of the one piece of equipment are detected based on the result of comparison between the overall three-dimensional point cloud data and the reference point cloud data. The equipment data processing device according to any one of claims 1 to 3 . 5. The apparatus according to any one of claims 1 to 4, further comprising a feature point editing unit that edits three-dimensional point cloud data of the feature points of each of the plurality of facilities determined by the feature point detection unit. Equipment data processor as described. an allowable range for changing the allowable range when the facility determining unit determines that the three-dimensional point cloud data acquired by the data acquiring unit is not within the allowable range of any of the characteristic locations of the plurality of facilities; The equipment data processing device according to any one of claims 1 to 5 , further comprising a changing unit. A facility data processing device according to any one of claims 1 to 6 ;
A facility data processing system comprising: the inspection device; A computer-executed facility data processing method comprising:
a first step of acquiring three-dimensional point cloud data of the equipment to be inspected and inspection data of the equipment to be inspected from an inspection device that inspects the equipment to be inspected;
Based on the result of comparison between the three-dimensional point cloud data acquired in the first step and the three-dimensional point cloud data of the characteristic locations of each of the plurality of facilities, the three-dimensional data acquired in the first step a second step of determining whether the point cloud data is three-dimensional point cloud data of any one of the plurality of facilities;
When the 3D point cloud data acquired in the first step is determined to be the 3D point cloud data of one of the plurality of facilities in the second step, the first a third step of linking the inspection data acquired by the step to the data of the one piece of equipment;
Based on the result of comparison between the overall three-dimensional point cloud data of each of the plurality of facilities and the reference point cloud data, the feature locations of each of the plurality of facilities are detected and the features of each of the plurality of facilities determined. and a fourth step of storing three-dimensional point cloud data of the location . a first step of acquiring three-dimensional point cloud data of the equipment to be inspected and inspection data of the equipment to be inspected from an inspection device that inspects the equipment to be inspected;
Based on the result of comparison between the three-dimensional point cloud data acquired in the first step and the three-dimensional point cloud data of the characteristic locations of each of the plurality of facilities, the three-dimensional data acquired in the first step a second step of determining whether the point cloud data is three-dimensional point cloud data of any one of the plurality of facilities;
When the 3D point cloud data acquired in the first step is determined to be the 3D point cloud data of one of the plurality of facilities in the second step, the first a third step of linking the inspection data acquired by the step to the data of the one piece of equipment;
Based on the result of comparison between the overall three-dimensional point cloud data of each of the plurality of facilities and the reference point cloud data, the feature locations of each of the plurality of facilities are detected and the features of each of the plurality of facilities determined. and a fourth step of storing the three-dimensional point cloud data of the location .

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