JP2018169995A5 - - Google Patents

Description

System and method for supporting work using a drone

  The present invention relates to a system and a method for supporting works such as research, inspection, maintenance, processing, destruction, or construction of artificial or natural objects such as facilities, buildings, land, fields, forests, etc. The present invention relates to a system and a method that are suitable for supporting the work performed. As used herein, the term "drone" refers to a mobile object that can be steered without a crew (e.g., an aircraft, traveling vehicle, surface craft, submersible that can be steered wirelessly remotely or autonomously steered). Etc.).

  Techniques for using drones for various purposes are known. For example, the management system disclosed in Patent Literature 1 uses an unmanned aerial vehicle to specify the position of an object (for example, a parked vehicle in an indoor parking lot) existing in a building and acquire characteristic information.

  According to this management system, a flight path for specifying the position of the vehicle in the building is calculated based on the map in the building. Then, the unmanned aerial vehicle flies along the flight path. During the flight, a position-related information acquisition unit mounted on the unmanned aerial vehicle acquires information related to the position of the vehicle. Then, the position of the vehicle in the building is specified based on the acquired information, and the flight path for specifying the characteristics of each vehicle is calculated based on the result of the position specification. Thereafter, an unmanned aerial vehicle flies along the latter flight path. During the flight, a characteristic information acquisition unit mounted on the unmanned aerial vehicle acquires characteristic information of each vehicle. Then, the characteristic information of each vehicle is specified by associating the acquired characteristic information with the position of the specified vehicle.

  Patent Document 2 discloses a transmission line inspection system using an unmanned aerial vehicle. According to this system, an unmanned aerial vehicle takes an image of an inspection point of a transmission line (a steel tower, a site immediately below the transmission line, a tree close to the transmission line, etc.), and generates a three-dimensional image of the inspection point from the captured image. Based on the three-dimensional image, an abnormality at the inspection location is found. For example, the degree of corrosion of the plating is determined from the color, luminance, saturation, and the like of the tower, or a new change is grasped by comparison with a past three-dimensional image.

  Patent Literature 3 discloses a water level management system that grasps the water level of a paddy field from an image obtained by photographing the paddy field using an unmanned aerial vehicle, and transmits an instruction to open and close the floodgate.

  Patent Document 4 describes an inspection system that estimates deterioration of a structure such as a building or a bridge, creates an inspection plan, and determines whether repair is necessary. The inspection system automatically estimates the deterioration of the structure based on the design data and the situation data of the structure (use history, recent inspection results, repair results, etc.), and based on the estimation results, the unmanned aerial vehicle Automatically creates a first inspection plan for performing a simple inspection using photographing or the like. Then, the inspection system automatically creates a second inspection plan for performing a detailed inspection by a person based on the result of the performed simple inspection, and then, based on the result of the performed detailed inspection, Automatically determine if repair is required.

JP-A-2013-131713 JP 2005-265699 A JP 2016-220681 A JP-A-2017-071972

  In order to use a drone for a task, it is necessary to prepare a suitable travel plan for the task (for example, a flight plan for a flying drone) or manually pilot the drone to perform the task properly. is there. However, this is not so easy for many workers. For example, the availability of drones for agricultural work such as pesticide spraying will improve agricultural productivity, but it is not easy for ordinary farmers to do so.

  It is desirable that work be properly planned and implemented so that expected effects can be obtained. However, it is not easy for an individual worker or a designer of a system that supports the work to determine what kind of work is appropriate in various actual situations. Furthermore, for example, in the case of spraying a pesticide on a field, the same pesticide does not always have the expected effect on the same disease of the same crop. Repeated use of the same pesticide for the same disease will reduce its effectiveness, so it may be better to change the pesticide. Such circumstances make it more difficult to design a work plan to obtain the expected work effect.

  An object of the present invention is to make it easier for a user to use a mobile unit for a desired operation in a system that supports work using a mobile unit such as a drone.

  It is another object of the present invention to assist in designing a work plan to achieve expected work effects.

  According to one embodiment of the present invention, a drone operation support system for performing one or more operations using one or more drones includes a data server, the data server including one or more operation terminals used by a user. It is communicable or at least part of it is mounted on one of the operating terminals. The operation terminal used by the user can exchange data with the drone.

  Then, the data server receives a designation of a geographical region by the user from one of the one or more operation terminals, and stores the geographical region position based on the geographical region position based on the geographical region position. Moving plan creation means for creating a movement plan for controlling one of the one or more drones so as to perform a predetermined operation on the geographical area while moving with respect to the geographical area; And a movement plan providing means for providing the movement plan to one of the operation terminals so that the movement plan can be supplied to one of the drones through one of the operation terminals.

  According to this drone work support system, a user designates a desired geographical area from his / her operation terminal to a data server, and thereby creates a movement plan for causing the drone to execute work in the geographical area. It can be obtained from the data server to the operation terminal. Then, by supplying the movement plan to the drone from the operation terminal, the drone can be set in a state where the drone can execute the work in the geographical area.

  According to this drone work support system, a user can easily set the drone to work in his / her desired geographical area simply by operating his / her own operation terminal.

  The movement plan may include a movement path defining a path to be moved to perform the work on the geographical area, and a work position defining a position on the movement path at which the work is to be performed.

  As described above, by using the movement plan including the movement route and the work position, both the movement of the drone and the work performed during the movement can be more easily controlled.

  As an example of the above work, taking a photograph of a geographical region can be mentioned. When taking a photograph using a drone, the travel route in the travel plan includes a definition of a route for traveling to a plurality of areas arranged so as to be combined with each other to cover the entire geographical area. Can be included. Further, the work position in the movement plan can include a definition of a position where the photographing of each of the plurality of areas should be performed.

  With such a movement plan, it is easy to take a picture of the whole area covering the whole geographical area using the drone.

  Each drone used in this work support system includes a control instruction receiving unit that receives a control instruction from a radio control device operated by a user, an automatic control unit that autonomously moves according to a supplied movement plan, and an automatic control unit. It is possible to have a movement correcting means for correcting the moving position or the moving speed of the own device in response to a control instruction received from the wireless control device during the autonomous movement.

  A drone having such a configuration moves autonomously, that is, automatically, according to a movement plan. Then, the user can send an instruction from the wireless pilot to the drone to correct the moving position or the moving speed as necessary for danger avoidance or the like. As a result, even a user who is unfamiliar with the operation of the drone can easily move the drone correctly, and can overcome the drawback of automatic movement, which makes it difficult to respond to unexpected situations.

  The work support system includes a machine-readable computer program that can be installed and executed on the information processing terminal to cause a general-purpose information processing terminal (for example, a smartphone, a tablet terminal, or a personal computer) to function as the operation terminal. Can have. The computer program includes a geographical area designating means for providing a user with a geographical area designation to a data server, a travel plan input means for receiving a travel plan from the data server, and a travel for supplying the received travel plan to one of the drones. The general-purpose information processing terminal can be configured to operate as the plan output means.

  By downloading and installing such a computer program on a general-purpose information processing terminal used by the user, the user can easily use the general-purpose information processing terminal as an operation terminal of the drone operation support system.

  In this drone operation support system, the data server is capable of communicating with a plurality of operation terminals used by a plurality of users, respectively, and a plurality of data terminals respectively created based on a plurality of geographical areas designated by the plurality of users. May be configured to be able to store the movement plan of the vehicle.

  Then, when storing the plurality of travel plans, the data server may be configured to store each of the plurality of travel plans in association with one user who has designated one geographic region based on the plurality of travel plans. . In addition, the data server selects one or more travel plans associated with one user using one of the operation terminals from among the plurality of stored travel plans, and stores the one or more travel plans in one of the operation terminals. May be configured to provide.

  The drone operation support system configured as described above can be used by a plurality of users. Then, a movement plan for work in a geographical area designated by the user is selectively provided to the operation terminal of each user from various movement plans stored in the data server. Therefore, it is easy for each user to move the drone correctly in the geographical area designated by the user and perform the work.

  In this drone operation support system, the data server may be configured as follows.

  That is, the data server receives designation of a first geographical area by a user from one of the operation terminals, and stores first geographical area storage means for storing the position of the first geographical area, A first for controlling one of the drones to perform a first task on the first geographic area while moving relative to the first geographic area based on a location of the one geographic area; First movement plan creation means for creating a movement plan, and providing the first movement plan to one of the operation terminals so that the first movement plan can be supplied to one of the drones through one of the operation terminals. A first movement plan providing means for performing, and a work report creation means for receiving a result of a first work performed by one of the drones and creating a work report for notifying a user of the result of the first work And work report storage means for storing the work report; A work report providing means for providing a work report to one of the operation terminals and a work report from one of the operation terminals so that the user can know the result of the first work through one of the operation terminals. A second geographical area designated by the responding user and storing the position of the second geographical area, and moving with respect to the second geographical area. A second movement plan creating means for creating a second movement plan for controlling one of the drones to perform the second work on the second geographical area, and an operation terminal for executing the second movement plan And a second movement plan providing means for providing a second movement plan to one of the operation terminals so that the drone can be supplied to one of the drones.

  Utilizing the data server having such a configuration, the user performs the first operation on the first geographical region using the drone, and then refers to the report of the result of the first operation, A more complex and step-by-step process of designating a second geographic area to perform the second work on, and then using a drone to perform the second work on the second geographic area Can be easily implemented.

  As an example of the first work on the first geographical area, a survey of a region desired by the user (for example, photographing a field held by the user) can be cited. When conducting a regional survey, as the first travel plan, a survey travel route that defines a route to be traveled for the survey of the region and information collection for the survey of the region on the survey travel route (for example, A survey movement plan that includes a survey position that defines the position where the photographing is to be performed can be used.

  Also, in this case, as the work report, information collected by one of the drones that performed an area survey (for example, photography of a field) using the survey movement plan (for example, a large number of images taken everywhere in the field) Investigation report created from the photographic image can be used.

  In this case, the user refers to the survey report using one of the operation terminals, and, based on the survey report, performs predetermined actual work (for example, spraying pesticides) from within the area (for example, a field). A work site to be performed (for example, a site where a disease has occurred in a field) can be designated as the second geographical area.

  In this case, as the second movement plan, an actual work movement that defines a route to be moved to perform the actual work (for example, pesticide spraying) on the work place (for example, a place where a disease has occurred in a field). An actual work transfer plan including a path and an actual work position that defines a position where the actual work (eg, pesticide spraying) is to be executed on the actual work movement path can be adopted.

  Therefore, for example, a complicated and step-by-step work process such as taking a photograph of the entire area of the field using a drone, finding a place where the disease occurs from the photograph image, and spraying pesticides with the drone at that place, The user can easily execute.

  In this drone operation support system, the data server may further include the following elements.

  From one of the operation terminals, a user who has responded to the work report receives designation of a second work for the second geographical area, and stores the designated second work in association with the second geographical area. Second working storage means to perform. Performing machine learning using the information stored in the work report storage means, the second geographical area storage means, and the second work storage means, and for any second geographical area based on any work report Task learning means for generating an inference neural network for estimating the second task. Work inference means for using the inference neural network generated by the machine learning means to generate a second work proposal for a second geographic region desired by the user based on a work report desired by the user. Proposal providing means for providing a user with a proposal for the second work generated by the work inference means.

  According to the drone operation support system having such a configuration, the second geographical area is designated based on the survey report of the first geographical area, and the second geographical area to be performed on the second geographical area is specified. When the user repeatedly determines to determine the work, the result of the user's determination as to what work should be performed for what kind of investigation report is stored in the data server. Using the accumulated user judgment results, the work learning means creates an inference neural network by machine learning. Using the inference neural network, the work inference means estimates where and what work should be performed from the user's desired survey report, and outputs the estimated work as a proposal. This work proposal is provided to the user. This work proposal assists the user, and it becomes easy for the user to identify the work in which the expected effect is obtained.

  In this drone operation support system, one of the operation terminals can communicate with the first operation drone having the first operation device for performing the first operation, and one of the operation terminals performs the second operation. It may be able to communicate with a second working drone having a second working device for performing. Then, the first movement plan creating means of the data server creates the first movement plan so as to drive and control the first work device while moving the first work drone, Good. Further, the second movement plan creating means may be configured to create the second movement plan so as to drive and control the second work device while moving the second work drone.

  According to the drone operation support system having such a configuration, when performing a plurality of different operations, it is easy to use a plurality of drones having different configurations suitable for each operation.

  A work support system according to an embodiment of the present invention provides a user with a first database that accumulates past survey results of work targets and a survey result of a work target desired by the user in the first database. A work determining means for allowing a user to determine an actual work according to the survey result, a second database for accumulating the actual work determined by the user in association with the survey result, and accumulating in the first and second databases Machine learning means for creating an inference neural network for estimating an actual work corresponding to any survey result from machine survey data, and a machine Inference means for generating an actual work proposal by applying the inference neural network created by the learning means, and a user created by the inference means. Includes a proposal presenting means for presenting the actual work proposed according to The desired survey results to the user, the.

  According to this work support system, the investigation of the work target and the determination of the actual work based on the investigation result by the user are repeated by many users for many work targets or over many opportunities for each work target. As the accumulated data in the first and second databases are enriched, machine learning using those data is promoted, and an inference neural network that automatically estimates actual work from the survey results is created And its performance will improve. Then, the inference neural network is applied to the survey result desired by the user, and as a result, the estimated actual work proposal is provided to the user. This suggestion will be helpful to the user in determining an appropriate actual work. With this support, it becomes easier for the user to determine the actual work that achieves the expected effect.

  This work support system may further include a correction system that corrects the survey result.

FIG. 1 is a block diagram showing an overall configuration of a system for supporting work using a drone according to an embodiment of the present invention. 4 is a flowchart showing the flow of an example of overall control of the system. 5 is a flowchart showing a flow of a control example of registration of a local position in the system. FIG. 3 is an explanatory diagram showing an example of a method of specifying a local position and a data configuration example of the local position. 5 is a flowchart showing a flow of a control example of creation and setting of an investigation flight plan in the system. Explanatory drawing which shows the example of a relationship between an area and an investigation flight plan. Explanatory drawing which shows the example of a data structure of an investigation flight plan. 5 is a flowchart illustrating a flow of a control example of photographing using a research drone in the system. 5 is a flowchart illustrating a flow of a control example of creation and registration of a regional image from a captured image in the system. FIG. 3 is an explanatory diagram illustrating a data configuration example of a regional image. 4 is a flowchart showing a flow of a control example of registration of a work place and an actual work flight plan in the system. Explanatory drawing which shows the example of a data structure of a work place. Explanatory drawing which shows the example of a relationship between a work place and an actual work flight plan. Explanatory drawing which shows the example of a data structure of an actual work flight plan. 6 is a flowchart showing a flow of a control example of setting of an actual work flight plan in the same system. 5 is a flowchart showing a flow of an example of actual work control using an actual work drone in the same system. FIG. 7 is a block diagram showing the overall configuration of a work support system according to a second embodiment of the present invention. 9 is a flowchart showing the flow of an overall control example of the system according to the second embodiment. FIG. 3 is a block diagram showing a configuration example of a symptom analysis unit. FIG. 2 is a block diagram illustrating a configuration example of a work analysis unit. The figure which shows the example of a graphic interface at the time of selecting the area | region of a work place on the display screen of an operation terminal. The figure which shows the example of a graphic interface when specifying the symptom of a work place on the display screen of an operation terminal. The figure which shows the graphic interface at the time of deciding the actual work which should be performed to a work place on the display screen of an operation terminal. FIG. 9 is a block diagram showing another configuration example of the analysis unit. FIG. 11 is a block diagram showing another configuration example of the analysis unit. FIG. 3 is a plan view showing a planar design of an example of an image correction scale that can be used in photography by a drone. FIG. 27 is a diagram showing an example of dimensional conditions satisfied by the panel of each reference color in the example of the image correction scale shown in FIG. 26. FIG. 2 is a block diagram showing a configuration example of a system for correcting a regional image using an image correction scale. 29 is a flowchart illustrating an example of control performed by the correction system illustrated in FIG. 28.

  Hereinafter, an embodiment of the present invention will be described with reference to the drawings.

  In the following explanation, an example of using a drone configured as an unmanned aerial vehicle for field inspection and maintenance (for example, identifying a location where a disease or physiological disorder has occurred and spraying pesticides and fertilizers on the location). First, an embodiment of the present invention will be described. However, this application is merely an example for explanation, and is not intended to limit application of the embodiment to other applications.

  FIG. 1 shows an overall configuration of a system for supporting work using a drone according to an embodiment of the present invention.

  The work support system 1 shown in FIG. 1 is typically one that can be used by a plurality of different users. However, in the following, the description will be focused on a use scene of one of a plurality of users.

  As shown in FIG. 1, when using the work support system 1, the user can use different types of drones suitable for different works, for example, two types of drones 3 and 5. In the present embodiment, one of them is a research drone 3, which investigates the state of a geographical area desired by a user, that is, an area (for example, a field owned or used by the user), that is, a role of collecting information on the area. With. In the present embodiment, taking a picture of the area from the sky is adopted as a method of the area survey. However, photography is one example, and other investigation methods, for example, a radio wave radar, a sound wave radar, or an information collection method using various sensors may be adopted. Further, different survey methods may be combined.

  In the present embodiment, another type of drone is the actual work drone 5, and a certain kind of work to be performed in the area (hereinafter, referred to as actual work), for example, a disease has occurred in a vast field. It has the role of performing the task of spraying pesticides selectively to each location.

  Although the same type of drone may be used as the research drone 3 and the actual work drone 5, it is often desirable to use different types of drones in order to optimally fulfill their roles. It is also possible to use a plurality of survey drones 3 and / or use a plurality of actual drones 5 for one region. However, in the following, the description will be given taking a case where one user uses one research drone 3 and another actual work drone 5 as an example.

  The work support system 1 also has a data server 7, which is a computer system for managing and processing various data necessary for using the drones 3,5.

  Further, when using the work support system 1, the user uses the operation terminal 9. The operation terminal 9 has a function of performing data communication with the data server 7 through a communication network such as the Internet. The operation terminal 9 also has a function of transferring data to and from the drones 3 and 5 (for example, data can be transferred to and from the drones 3 and 5 by a wired or wireless communication method or via a portable data storage. ).

  A general-purpose portable information processing terminal having such a function (for example, a so-called mobile phone, smartphone, tablet terminal, mobile personal computer, or notebook personal computer, or the like) or another type of computer (for example, desktop Personal computer) can be employed as the operation terminal 9. In this case, a computer program for using the work support system 1 (for example, an application dedicated to the system, or a general-purpose web browser that can access a website that is an external interface of the data server 7) is used. The general-purpose terminal functions as the operation terminal 9 for the work support system 1 by being installed in the information processing terminal and executing the computer program on the terminal. Alternatively, hardware dedicated to the work support system 1 may be prepared as the operation terminal 9.

  Each user may always use only the same one operation terminal 9 when using the work support system 1, or may appropriately select one of the plurality of operation terminals 9 according to time and case. You can use it. Further, a plurality of different users can input desired information with reference to information regarding the same area (for example, the same field) using the respective operation terminals 9. In the following, description will be given taking a case where one user uses one operation terminal 9 as an example.

  The research drone 3 has a flight mechanism 11 for flying it, a GPS receiver 13 for measuring its three-dimensional position (latitude, longitude, altitude), a camera 15 for taking pictures, and their devices 11, 13 , 15 are controlled. In addition, the research drone 3 is provided with a radio controller 19 (a so-called propo) for a user to remotely control it by radio. The radio controller 19 can communicate with the controller 17 wirelessly, and transmits various control commands to the controller 17 in response to various user operations on the radio controller 19.

  The actual work drone 5 has a flight mechanism 21 for flying it, a GPS receiver 23 for measuring its three-dimensional position (latitude, longitude, altitude), a real work device 25 for performing actual work, and those devices. It has a controller 27 for controlling 21, 23, 25. When the actual work is pesticide spraying in a field, the actual work machine 25 is naturally a pesticide spraying device, but an auxiliary device such as a camera for photographing the situation of the actual work is additionally provided. Is also good. Further, the actual operation drone 5 is provided with a radio controller 29 (so-called propo) for a user to remotely control the drone 5 by radio. The wireless pilot 29 can communicate with the controller 27 wirelessly, and transmits various steering commands to the controller 27 in response to various user operations on the wireless pilot 29.

  The operation terminal 9 is arranged in the order of a normal step in which the user uses the area registration unit 31, the flight plan input unit 33, the flight plan output unit 35, the photographed image input unit 37, the photographed image output unit 39, and the photographing unit. It has information processing functional elements such as a position input unit 41, a photographing position output unit 43, an image display unit 45, a work place registration unit 47, a work result input unit 48, and a work result output unit 49.

  In response to the user input, the region registration unit 31 responds to the user's input by inputting the location of the geographic region desired by the user, that is, the position of the region (for example, the field held by the user) (for example, the latitude and longitude of the vertex of the contour of the region) (Referred to as an area location) in the data server 7.

  The flight plan input unit 33 downloads a flight plan desired by the user from the data server 7 in response to the user input. The flight plan output unit 35 transmits the downloaded flight plan to the drone 3 or 5 desired by the user in response to the user input, and sends the flight plan to the controller 17 or 27 of the drone 3 or 5. Let it be installed. Here, the flight plan is a kind of travel plan that defines a geographical route on which the drone is to fly.

  The captured image input unit 37 receives an image captured by the survey drone 3 (a large number of images are captured in one area) (hereinafter, referred to as a captured image) in response to a user input. The captured image output unit 39 transmits the received multiple captured images to the data server 7 in response to a user input.

  The photographing position input unit 41 responds to a user input, and collects the position and angle of the research drone 3 (or the camera 15) at the time of photographing each photographed image measured by the research drone 3 (hereinafter, the photographing position and angle are generically referred to). And called the shooting position). The photographing position output unit 43 transmits the received photographing position to the data server 7 in response to a user input.

  In response to the user input, the image display unit 45 downloads, from the data server 7, an image of the entire area (hereinafter, referred to as an area image) of the area registered by the user, and displays the image on the display screen of the operation terminal 9 (not shown). ). Here, each regional image is created by the data server 7 by combining a large number of captured images of the region.

  In response to the user's input, the work location registering unit 47 specifies one or more geographical small areas or locations (hereinafter referred to as “work locations”) on the displayed regional image where the actual work is to be performed in the region. Then, the work location is registered in the data server 7. For example, when the actual work is the spraying of pesticides on a field, from the regional image of a certain field, the user observes the color and shape of crops and leaves, and finds the location where the disease is found to be causing the disease, at the work location. It can be. When registering this work place, the user also specifies the contents of the work to be performed on the work place (for example, spraying the pesticide A and the pesticide B) and associates the work place with the work place to the data server 7. register.

  The work result input unit 48 receives a result of the actual work performed by the real work drone 5 (for example, a spray position and a spray amount of the pesticide) in response to the user input. The work result output unit 49 transmits the received work result to the data server 7 in response to the user input.

  The data server 7 stores databases such as a regional position database 51, a three-dimensional map database 53, a flight plan database 55, a photographed image database 57, a photographed position database 59, a regional image database 61, a work place database 63, and a work result database 65. Have. Further, the data server 7 has information processing functional elements such as a flight plan creating unit 71, a photographed image input unit 73, a photographed position input unit 75, a photographed image combining unit 77, and an analyzing unit 79.

  The local position database 51 responds to the local registration unit 31 of the operation terminal 9 to register and manage the local position of the region specified by the user (for example, the user's field).

  The three-dimensional map database 53 stores and manages a three-dimensional map defining the latitude, longitude, and altitude of each position. The three-dimensional map database 53 is used by the flight plan creating unit 71 to create a flight plan for the drones 3,5. Note that the data server 7 may use an external three-dimensional map on the Internet or the like without having the three-dimensional map database 53 in the server 7.

  Flight plan database 55 stores and manages flight plans for drones 3,5. The flight plan database 55 receives a request from the flight plan input unit 33 of the operation terminal 9 and sends the requested flight plan to the flight plan input unit 33. The flight plans managed by the flight plan database 55 are roughly classified into two types, for example, an investigation flight plan and an actual work flight plan. The investigation flight plan is a flight plan for investigating (for example, photographing) each registered area with the investigation drone 3. The actual work flight plan is a flight plan for performing actual work (for example, pesticide spraying) by the real work drone 5 at one or more work locations in each registered area.

  The captured image database 57 stores and manages captured images received from the captured image output unit 39 of the operation terminal 9. The photographing position database 59 stores and manages the photographing positions received from the photographing position output unit 43 of the operation terminal 9.

  The regional image database 61 stores and manages regional images that are created by combining captured images of the respective regions and that show the entire picture of each region. The area image database 61 receives the request from the image display unit 45 of the operation terminal 9 and sends the requested area image to the image display unit 45.

  The work location database registers and manages the work location specified by the user in response to the work location registration unit 47 of the operation terminal 9.

  The work result database 65 stores and manages the work results received from the work result output unit 49 of the operation terminal 9.

  The photographed image input unit 73 and the photographed position input unit 75 receive the photographed image and the photographed position passed from the survey drone 3 to the operation terminal 9 from the operation terminal 9 and register them in the photographed image database 57 and the photographed position database 59, respectively. .

  The flight plan creation unit 71 creates a survey flight plan for each region and an actual work flight plan for one or more work locations in each region, and registers the created flight plan in the flight plan database 55. The flight plan creation unit 71 may be a fully automatic tool that can create a flight plan in a fully automatic manner, or may be a semi-automatic tool that enables a person to operate the flight plan to create a flight plan. .

  The survey flight plan for each region is obtained by calculating the local position of each region in the local position database 51 (for example, the latitude and longitude of the vertex of the contour of the region) and the three-dimensional positions of various points in each region in the three-dimensional map database 53. (For example, latitude, longitude, and altitude). The actual work flight plan of one or more work places in each area is obtained by calculating the position of the work place in the work place database 63 (for example, the latitude and longitude of the vertex of the contour of the work place) and the three-dimensional map database 53. It is created using three-dimensional positions (for example, latitude, longitude, and altitude) of various points in each work place.

  The captured image input unit 73 receives the captured image of each area from the captured image output unit 39 of the operation terminal 9 and registers the received captured image in the captured image database 57. The shooting position input unit 75 receives the shooting position of each area from the shooting position output unit 43 of the operation terminal 9 and registers the received shooting position in the shooting position database 59.

  The photographed image combining unit 77 combines the photographed images of the respective regions in the photographed image database 57 based on the photographing positions of the respective regions in the photographing position database 59 to create a regional image representing the entire region. The status image is registered in the area image database 61. The regional image of each region serves as a survey report for notifying the user of the result of the survey (for example, photographing) of the region. By referring to the area image of each area, the user can determine which part in the area should be subjected to the actual work (for example, spraying the pesticide), and can specify that part.

  The analysis unit 79 analyzes the work result stored in the work result database 65. The analysis result of the work result can be used for improving the work method later or for other uses.

  FIG. 2 shows a flow of an overall control example of the work support system 1.

  As shown in FIG. 2, in the operation terminal 9, the user performs an operation for registering a desired area (for example, a field owned by the user) (step S1). The registration operation is, for example, an operation of displaying a map provided on the Internet on the screen of the operation terminal 9 and specifying a local location on the map. In response to the registration operation, the data server 7 registers the area by recording the area position of the area (step S2). After that, the data server 7 creates a survey flight plan for the area based on the area position (step S3).

  Thereafter, the user operates the operation terminal 9 to download the investigation flight plan of the area from the data server 7 to the operation terminal 9 and sends the investigation flight plan from the operation terminal 9 to the investigation drone 3 (step S4). . Then, the research flight plan is installed in the research drone 3 (step S5).

  The user takes the survey drone 3 to the area (for example, a field owned by the user), and operates the radio controller 19 to send takeoff and other auxiliary steering instructions to the survey drone 3 ( Step S6). The research drone 3 automatically and autonomously flies over the area in accordance with the research flight plan, repeatedly taking photographs of the surface area at various positions on the flight path, and obtains many captured images and The shooting position is recorded (step S7). The control of this flight is basically performed automatically and autonomously in accordance with the survey flight plan, and the maneuvering instruction from the wireless pilot 19 is used for auxiliary operations such as the start of takeoff and slight correction of the flight position or speed. Used for

  Many photographed images and photographing positions in the area are passed from the research drone 3 to the operation terminal 9 and sent from the operation terminal 9 to the data server 7 (step S8). The data server 7 combines the photographed images according to the respective photographing positions to create a regional image of the entire region (step S9).

  Thereafter, the user operates the operation terminal 9 to receive the regional image of the region from the data server 7 and display the image on the screen. Then, on the displayed regional image, a work location (for example, a disease in the field) is displayed. Is identified, and the server 7 requests the server 7 to register the work location (step S10). The data server 7 registers the work place, and creates an actual work flight plan (for example, a flight plan for spraying pesticides at the work place) at the work place (step S11).

  Thereafter, the user operates the operation terminal 9 to download the actual work flight plan from the data server 7 to the operation terminal 9, and sends the actual work flight plan to the actual work drone 5 from the operation terminal 9 (step S12). Then, the actual work flight plan is installed in the actual work drone 5 (step S13).

  The user takes the actual work drone 5 to the area (for example, a field owned by the user), and operates the wireless pilot 29 to send takeoff and other auxiliary operation instructions to the actual work drone. (Step S14). The actual work drone 5 basically performs an actual work (for example, sprays pesticides to a diseased part in a field) at a work place while flying over the work place according to an actual work flight plan, and records the work result. (Step S15). The control of this flight is basically performed automatically and autonomously in accordance with the actual flight plan, and the maneuvering instruction from the wireless pilot 19 is used for assisting such as starting takeoff and slightly modifying the flight position or speed. Is used regularly.

  The work result is passed from the actual work drone 5 to the operation terminal 9 and sent from the operation terminal 9 to the data server 7 (step S16). The data server 7 saves and analyzes the work result (step S17).

  As can be seen from the above control flow, the user can relatively easily use the drones 3 and 5 using the operation terminal 9 to perform a desired operation in a desired area.

  Hereinafter, the above control flow is divided into a plurality of parts, and each part will be described in more detail.

  FIG. 3 shows a flow of a control example of registration of an area position in the work support system 1.

  As shown in FIG. 3, at the operation terminal 9, a user inputs his / her user authentication information and requests a login to the data server 7 (step S21). The data server 7 authenticates the user and permits the user to log in (step S22).

  Then, the user displays a map on the operation terminal 9 (step S23), and specifies a desired area (for example, his own field) on the map (step S24). Further, the user inputs the name of the area on the operation terminal 9 (step S25).

  The map used here may be, for example, a map provided from the Internet or a map provided from the data server 7. As a method of designating a region, for example, a method in which a user designates a vertex of a contour of a desired region on a map can be adopted. Alternatively, a method of inputting a region name (for example, a place name or a place number) whose contour is defined can be adopted. When the vertices of the area are designated, the positions (for example, latitude and longitude) of those vertices are specified by the operation terminal 9.

  Then, the operation terminal 9 sends the designated area name and area location to the data server 7 (step S26). The data server 7 registers the area name, the area location, and the ID (identification name) of the area in the area location database 51 in association with the ID (identification name) of the user (step S27).

  FIG. 4 shows an example of the above-described method of specifying a local position and a data configuration example of the registered local position.

  As shown in the left part of FIG. 4, as a method of designating a certain area 81, the user can designate vertices A1 to A5 of a contour 83 of the area 81 on a map. In addition, the user may also specify the position of the departure / arrival point P1 at which the drone can be departed / arrived, within the area 81 or in the vicinity of the area 81. When these points are specified on the map, the positions of the specified points A1 to A5 and P1 (for example, the latitude and longitude (Xa1)) are determined based on the position coordinate system (for example, the coordinate system of latitude and longitude) of the map. , Ya1) to (Xa5, Ya5), (Xp1, Yp1)) are automatically specified by the operation terminal 9.

  Then, in the data server 7, as shown on the right side of FIG. 4, a region ID 87, a region name 88, a region position (for example, a set of the latitude and longitude of all vertices) 89, and a departure point position (for example, a departure point latitude) And longitude) 91 are stored in the local location database 51 as local data 93 in association with the user ID 85 of the user who specified them.

  FIG. 5 shows a flow of a control example of creation and setting of an investigation flight plan.

  As shown in FIG. 5, the data server 7 creates an investigation flight plan based on the local position and the departure / end point position of the region registered by a certain user in the local position database 51 (step S31). The research flight plan is to take the research drone 3 off the landing point, take a picture of all areas of the area while flying over the area, and take a picture of the drone 3 to land on the landing point. This is an operation control instruction. When preparing the survey flight plan, the data server 7 calculates the three-dimensional positions (eg, latitude, longitude, and altitude) of various points in the area provided from the three-dimensional map database 53 and calculates the flight route. decide.

  The data server 7 registers the survey flight plan in the flight plan database 55 in association with the user ID of the user and the area name of the area (step S32).

  Thereafter, the user logs in to the data server 7 from the operation terminal 9 (Steps S33 to S34). The data server 7 selects a survey flight plan associated with the user ID of the user from the flight plan database 55 in response to the user request, and sends a list of the selected survey flight plans to the operation terminal (step S1). S35). The operation terminal 9 displays the list (step S36).

  The user selects a desired survey flight plan from the displayed list and requests the data server 7, and the data server 7 sends the survey flight plan to the operation terminal, whereby the survey flight plan is stored in the operation terminal 9. It is downloaded (steps S37 to S38).

  Thereafter, the user supplies the research flight plan to the research drone 3 from the operation terminal 9 (step S39). The research drone 3 installs the received research flight plan in its own controller 17 (S40).

  FIG. 6 shows an example of a relationship between a registered area and an investigation flight plan.

  The research flight plan includes the flight path, flight speed, and shooting position. As shown in the left part of FIG. 6, the flight path 113 takes off from the departure point P1, flies over the area 81 so that all pictures of a plurality of areas covering the entire area 81 can be taken, and A three-dimensional route of the research drone 3 until landing at the departure / arrival point P1 is defined using, for example, a three-dimensional position (for example, latitude, longitude, and altitude) of a change point of the flight direction or the speed on the route.

  The flight speed defines a control target value of the flight speed in various sections on the flight path 101.

  As shown in the right part of FIG. 6 (enlarged view of the part surrounded by the dashed line in the left part), the photographing position defines all positions (illustrated by dots) on the flight path 101 where a photograph should be taken. .

  Each of the photographing positions is set such that the photographed image photographed there (indicated by a solid-line or broken-line rectangle in FIG. 6) and the photographed image photographed at the next photographing position are overlapped at their respective margins. Be placed. For example, in FIG. 6, a photograph image 103A photographed at a certain photographing position 103 overlaps with a photograph image 105A photographed at a photographing position 105 adjacent thereto at the upper edge portion and the lower edge portion thereof, and at the left side thereof. The photograph image 107A photographed at the photographing position 107 is overlapped at the left edge portion and the right edge portion. As a result, the photographic images photographed at all photographing positions are overlapped with each other at the overlapping portions and combined, so that a photographic image of the entire area 81 (that is, the position image of the area 81) can be synthesized. .

  FIG. 7 shows a data configuration example of the investigation flight plan.

  As shown in FIG. 7, the survey flight plan 109 includes an ID (identification name) 111 of the flight plan, a flight route (for example, the latitude Xr, longitude Yr, and altitude Zr of each change point on the route). ) And flight speed (target speed V from each change point to the next change point) 113, and shooting positions (for example, the latitude Xs, longitude Ys, altitude Zs, and shooting angle As of all shooting positions) 115 Is included. The survey flight plan 109 is managed in the flight plan database 55 in association with the user ID 85 and the area ID 87.

  FIG. 8 shows a flow of a control example of photographing using the investigation drone 3.

  After placing the survey drone 3 at the departure point in the target area, the user operates the radio controller 19 for the survey drone 3 to first instruct the survey drone 3 to start work (step S41). In response to the instruction, the research drone 3 takes off (step S42), and by the autopilot control by the controller 17 according to the research flight plan, while flying along the flight path of the flight plan, each photographing position. To execute photography (step S43). Then, the research drone 3 records the photographed image and the photographing position (for example, latitude, longitude, and altitude) at the time of photographing (step S44).

  While observing the flight of the research drone 3, the user needs to correct the flight state (for example, the flight position and the flight speed) (for example, the flight state of the research drone 3 is changed due to the influence of wind, etc.). Departure from the aircraft, or need to change the flight conditions to avoid collision or danger with other objects, etc.), operate the wireless pilot 19 to give an auxiliary steering instruction for making necessary corrections. Send to Survey Drone 3 (Step S45). The investigation drone 3 corrects the flight state (position or speed, etc.) in accordance with the correction instruction by processing the auxiliary instruction as, for example, an interrupt to the autopilot control (step S46).

  When the above-mentioned correction processing is completed, the investigation drone 3 returns to the automatic pilot control (step S47), and continues flying and photographing according to the investigation flight plan (S43). Then, after flying the entire route and photographing at all photographing positions, the research drone 3 lands at the landing point determined by the flight plan (step S48).

  The photographed image 117 and the photographing position 119 recorded on the investigation drone 3 are sent to the operation terminal 7 and recorded in the operation terminal 7 (steps S49 to S50). This data transfer may be performed by wireless communication during the flight, by wireless communication or wired communication after landing, or by taking out the data storage recording the photographed image 117 and the photographing position 119 from the investigation drone 3 and operating the operation terminal. 7 may be carried out.

  FIG. 9 shows a flow of a control example of creation and registration of a regional image from a captured image.

  The user operates the operation terminal 9 to log in to the data server 7 (steps S51 to S52). Thereafter, in response to the user request, the data server 7 selects an area name associated with the user ID from the area location database 51, and sends a list of the area names to the operation terminal 9 (step S53). On the operation terminal 9, the area name list is displayed, the user selects a desired area name from the list (step S54), and the photographed image 121 and the photographing position 123 acquired from the survey drone 3 are displayed in the area. The data is sent to the data server 7 in association with the area ID (step S55).

  The data server 7 registers the received photographed image and photographed position in the photographed image database 57 and the photographed position database 59 in association with the corresponding user ID and region ID (step S56). After that, the data server 7 reads out the photographed image and the photographing position of the region from the photographed image database 57 and the photographing position database 59, and combines the photographed images according to the photographing position, thereby forming a region covering the entire region of the region. The images are combined (step S57). The local image is registered in the local image database 61 in association with the user ID and the local ID (step S58).

  FIG. 10 shows a data configuration example of a regional image.

  As shown in FIG. 10, the area image 125 includes, for example, an ID (identification name) 127 of the area image, a shooting date and time 129, and image data 131 covering the entire area. The area image 125 is managed in the area image database 61 in association with the corresponding user ID 85 and area ID 87.

  FIG. 11 shows a flow of a control example of registration of a work place and an actual work flight plan.

  As shown in FIG. 11, the user logs in to the data server 7 from the operation terminal 9 (steps S61 to S62). Thereafter, in response to the user request, the data server 7 selects a local image associated with the user ID from the local image database 61, and sends a list of the local images to the operation terminal 9 (step S63).

  The operation terminal 9 displays a list of the local images, and the user selects a desired position image from the list (step S64). In response to the selection, the data server 7 reads the selected local image from the local image database 61 and sends it to the operation terminal 9 (step S65). The operation terminal displays the area image (step S66).

  The user looks at the area image (for example, an image of the field) displayed on the operation terminal 9, finds a place where the operation is to be performed (for example, a place where the pesticide is to be sprayed in the field, that is, a small area), and finds the place. The work location is designated on the area image (step S67). The designation of the work location can be performed, for example, by a method of designating the vertices of the outline of the work location. In this step S67, the user also specifies the contents of the work to be performed on the work place (for example, "work type is pesticide spraying, pesticides used are pesticide A and pesticide B").

  In response to the designation, the data server 7 determines the position of the work location (for example, the outline of the work location) based on the location of the work location on the regional image and the location of the region recorded in the regional location database 51. (The latitude and the longitude of the vertex) are specified, and the work place is registered in the work place database 63 (step S68). In this step S68, the database server 7 registers the work content specified by the user in the work place database 63 in association with the work place.

  After that, the data server 7 creates an actual work flight plan based on the registered positions of the work places, and the three-dimensional positions of the area and various points of the work places recorded in the three-dimensional map database 53. (Step S69). A real-world flight plan involves taking a real-world drone off from a departure point in the area, flying over one or more work points in the area, and performing actual work at each work point (eg, spraying pesticides). And an operation control instruction to the actual work drone 5 for returning to the departure point and landing.

  The data server 7 registers the actual work flight plan in the flight plan database 55 in association with the corresponding user ID, area ID, and work place ID (step S70).

  FIG. 12 shows an example of the data structure of a work location.

  As shown in FIG. 12, the work place 133 has its ID (identification name), the date and time when it is designated by the user, and the position of the vertex of the contour of the area (for example, the latitude Xa and the longitude Ya of each vertex). 139 and the like. The work place 133 is managed in the work place database 63 in association with the corresponding user ID 85, area ID 87, and area image ID 127. The work content 140 is managed in the work location database 63 in association with the work location 133. By managing the work contents 63, it is useful to evaluate the work contents later (for example, after carrying out actual work, conduct a survey flight with a drone to check the condition of the work place, and check the survey results and work contents Can be evaluated by analyzing).

  FIG. 13 shows an example of the relationship between a work location and an actual work flight plan.

  In the example illustrated in FIG. 13, three work points 141, 145, and 147 are designated in the area 81, and one actual work flight plan 143 is prepared for one of the work points 141. Another actual work flight plan 149 is prepared for the two work places 145 and 147. If there are multiple work sites, whether to create one actual work flight plan that covers all of those work sites, or create two or more actual work flight plans that share different work sites as shown in the example shown May be appropriately selected according to a user's request or a flight time.

  When a plurality of actual work flight plans are prepared for one area 81, work is performed by a plurality of actual work drones 5 based on the respective flight plans, or a different flight plan is used at a different date and time. Work can be performed.

  Each actual work flight plan includes a flight path and a flight speed of the actual work drone 5. For example, the illustrated actual work aeronometer 149 takes off from the departure point P1, goes to the first work place 145, goes around the work place 145 to perform the actual work (eg, pesticide spraying) over the entire area, and A flight path, such as going to the next work point 147, going around there, and returning to the departure point P1 to land is included.

  Each actual work flight plan further includes control instructions for driving and controlling the actual work device 25 while the actual work drone 5 is in flight. The control instruction includes, for example, a position (for example, a work start position and an end position) at which an actual operation (for example, pesticide spraying) is to be executed, and other control values (for example, a pesticide spray amount per unit time). May be included.

  FIG. 14 shows an example of the data configuration of the actual work flight plan.

  As shown in FIG. 14, the actual work flight plan 151 includes, for example, its ID (identification name) 153, flight path (for example, latitude Xr and longitude Yr of a change point at which the flight direction or speed is to be changed on the path). Altitude Zr), flight speed (for example, speed Vr from each change point to the next change point) 155, and work position (for example, latitude Xs and longitude Ys of work start position, altitude Zs, and work end position) Latitude Xe, longitude Ye and altitude Ze) and control values (for example, the target value C1 of the first control amount, the target value C2 of the second control amount, the target value C3 of the third control amount from each start position to the end position) , Etc.) 157 are included.

  Further, when a camera showing the work situation is mounted on the actual work drone 5, the position 159 (for example, the latitude Xs, the longitude Ys, the altitude Zs, and the shooting angle As of the shooting position) at which the camera should take a photo is also determined. , May be included in the actual work flight plan 151.

  The actual work item plan 151 having such a configuration is managed in the flight plan database 55 in association with the corresponding user ID 85, area ID 87, and work place ID 135.

  FIG. 15 shows a flow of a control example of setting of the actual work flight plan.

  As shown in FIG. 15, the user logs in to the data server 7 from the operation terminal 9 (steps S71 to S72). In response to the user request, the data server 7 selects an actual work flight plan associated with the user ID from the flight plan database 55, and sends a list of the selected actual work flight plans to the operation terminal (step S72). ). The operation terminal 9 displays the list (step S73).

  The user selects a desired actual work flight plan from the displayed list and requests the data server 7, and the data server 7 sends the actual work flight plan to the operation terminal, so that the actual work flight plan is transmitted to the operation terminal 9. Is downloaded (steps S74 to S75).

  Thereafter, the user sends the actual work flight plan from the operation terminal 9 to the actual work drone 5 (step S76). The actual work drone 5 installs the received actual work flight plan in its own controller 27 (S77).

  FIG. 16 shows a flow of a control example of actual work using an actual work drone.

  After placing the actual work drone 5 at the departure point in the target area, the user operates the radio control 29 for the actual work drone 5 to first instruct the real work drone 5 to start work (step S81). In response to the instruction, the actual work drone 5 takes off (step S82), and performs automatic flight control according to the actual work flight plan by the controller 27 while flying along the flight path of the flight plan. At the work position (for example, a section from each work start position to the end position), an actual work (for example, pesticide spraying) is executed (step S83). Then, the actual work drone 5 records the result of the performed work (for example, the start position and the end position of the pesticide application, the time, the application amount, and the like) (step S84).

  While observing the flight of the actual work drone 5, the user needs to correct the flight condition (for example, the flight position and the flight speed) (for example, the flight position of the actual work drone 5 or the flight position due to the influence of wind, etc.). When the speed deviates from the flight plan, the flight condition needs to be changed to avoid collision with other objects or danger, etc.), the radio control 29 is operated to assist in making necessary corrections. The actual operation instruction is sent to the actual work drone 3 (step S85). The actual work drone 5 corrects the flight state (position or speed, etc.) according to the correction instruction by processing the auxiliary instruction as, for example, an interrupt to the autopilot control (step S86).

  When the above-mentioned correction processing is completed, the actual work drone 5 returns to the automatic pilot control (step S87), and continues the flight and the actual work according to the actual work flight plan (S83). After that, the actual work drone 5 lands on the landing point determined by the flight plan after performing the actual work at all the work positions by flying the entire route (step S88).

  The work result 161 recorded in the actual work drone 5 is sent to the operation terminal 7 and recorded in the operation terminal 7 (steps S89 to S90). This data transfer may be performed by wireless communication during the flight, by wireless communication or wired communication after landing, or by detaching the data storage recording the work result 161 from the actual work drone 3 and attaching it to the operation terminal 7. This may be done by doing so.

  Thereafter, as already described with reference to FIG. 2, the work result 163 recorded in the operation terminal 7 is sent to the data server 7 (step S16), and the data server 7 sends the work result to the later Analysis is performed for effective use (step S17).

  FIG. 17 shows the overall configuration of a work support system according to the second embodiment of the present invention.

  The work support system 100 according to this embodiment facilitates setting of a work plan for obtaining an expected work effect in addition to the components of the work support system 1 according to the first embodiment shown in FIG. With some additional components for In the following description of the work support system 100 and reference drawings, elements common to the system 1 of the first embodiment are denoted by the same reference numerals, and redundant description is omitted.

  As shown in FIG. 17, in the work support system 100 according to the second embodiment, the data server 101 and the operation terminal 111 have the data server 7 and the operation terminal 9 of the work support system 1 according to the first embodiment, respectively. In addition to the components, it has some additional components.

  That is, the data server 101 includes the work plan database 103, the work plan proposal database 105, and the analysis unit 107 as additional components. The operation terminal 111 has, as additional components, a unique part detection unit 113, a work plan input unit 115, a proposal presentation unit 117, and a work selection unit 119.

  The singular part detection unit 113 of the operation terminal 111 automatically outputs the area survey result, that is, the area image (for example, an image of a registered specific field) displayed on the display screen of the operation terminal 111 by the image display unit 45. Then, a unique part (for example, an area in a field where a disease or the like is presumed to have a disease) is detected from the regional image. The unique part detection unit 113 displays the detected area of the unique part (for example, a frame line indicating the outline of the area) in the area image on the display screen.

  Note that the unique part detection unit 113 may be provided in the data server 101 instead of being provided in the operation terminal 111. For example, the analysis unit 107 of the data server 101 may include the unique part detection unit 113. The unique portion detection unit 113 is configured using a deep neural network like the symptom analysis unit 108 or the work analysis unit 109 in the analysis unit 107 to be described later, and an inference method of detecting a unique portion from a regional image by deep learning. May be machine-learned.

  The work plan input unit 115 of the operation terminal 111 is configured such that the image display unit 45 displays a regional image (for example, an image of a registered specific field) on a display screen of the operation terminal 111, and the user inputs a work place registration unit When a work location is specified in the local image using the 47, the user can input a work plan for the specified work location. That is, the work plan input unit 115 displays, on the display screen, a work plan input tool for the user to input an arbitrary work plan for each work location on the display screen. By operating the work plan input tool, the user can input an arbitrary work plan to the system 100 for each work place. When the input of the work plan is completed (for example, when the user requests the registration of the work plan on the display screen), the work plan input unit 115 sends the input work plan to the data server 101, and the work plan It is registered in the work plan database 103 of the data server 101 in association with the work place to be performed.

  Here, the work plan is data defining actual work to be performed at each work location. In this embodiment, as an example, the work plan for each work place includes a symptom name of the work place (for example, a name of a disease or a physiological disorder) and a name of an actual work to be executed in accordance with the symptom (for example, Agrochemicals, fertilizers, and other maintenance work to be performed at the work site). Alternatively, the work plan may include only the work name without the symptom name, or may include, in addition to the symptom name and the work name, additional information (for example, information or an image identifying a work place, or Pesticides and fertilizers).

  The work plan of each work place in each region registered in the work plan database 103 (for example, the symptom name and the actual work name for each unique place in each field) is obtained by the flight plan creation unit 71 of the data server 101 for each region. When creating a flight plan, it can be used as follows. That is, for example, when different work plans are registered for different work places in the same area, the flight plan creation unit 71 classifies work places given the same work plan (same work name) into the same group. Then, create one flight plan for each group, that is, different flight plans for different groups. For example, in one field, a work plan for applying pesticide C is registered at work points A and B, and a work plan for applying another pesticide F is registered at another work places D and E. The creating unit 71 creates a flight plan for applying the pesticide C to the group of the work places A and B, and creates a flight plan for spraying another pesticide F to the group of the other work places D and E. I do.

  When the user inputs a work plan for each work place using the work plan input unit 115, the proposal presenting unit 117 of the operation terminal 111 proposes a work plan for each work place from the work plan proposal database 105 of the data server 101. Is read, and each work plan proposal is displayed on the display screen of the operation terminal 111 in association with each work place.

  Here, the work plan proposal is a proposal of a work plan recommended for each work location, generated by the analysis unit 107 of the data server 101 by inference. The work plan proposal for each work place generated by the analysis unit 107 is stored in the work plan proposal database 105 in association with each work place. A more specific configuration and function of the analysis unit 107 will be described later.

  The work plan proposal displayed on the display screen of the operation terminal 111 serves as a user's reference when the user uses the work plan input unit 115 to input a work plan. A work plan proposal can help to determine a more appropriate work plan, especially for users with little knowledge or experience in determining a work plan. The higher the inference ability of the analysis unit 107, the higher the performance of the work plan proposal to assist the user. In order to enhance the inference ability of the analyzing unit 107, the analyzing unit 107 has a configuration described later.

  The work selection unit 119 of the operation terminal 111 selects a flight plan for a specific area (for example, a specific field) read by the flight plan input unit 33 from the flight plan database 55, which is to be executed by the actual work drone 5 this time. Let the user select a flight plan for his work. For example, when the flight plan for spraying pesticide C and the flight plan for spraying another pesticide F are read by the flight plan input unit 33 for the specific field, the work selection unit 119 The plan is displayed on a display screen, and the user is allowed to select a desired flight plan. The work selection unit 119 provides the selected flight plan to the flight plan output unit 35. The selected flight plan is supplied from the flight plan output unit 35 to the controller 27 of the actual work drone 5.

  The analysis unit 107 of the data server 101 obtains an image of each registered region (for example, each field) from the regional image database 61, the work place database 63, the regional position database 51, the work plan database 103, and the work result database 65, Location of each work place in each area, work plan (for example, symptom name and actual work name) for each work place, image of each area (each work place) after implementation of actual work based on each work plan, Data such as a work plan (especially symptom name) input by the user based on the image is read. The analysis unit 107 creates (that is, learns) an inference method for automatically generating a work plan proposal for each work location by performing machine learning using the read data. In other words, it includes improving the learned inference method). In addition, the analysis unit 107 uses the inference method created by the machine learning to create a work plan proposal according to the image from each work location, based on the image. The created work plan proposal for each work place is stored in the work plan proposal database 105 in association with each work place.

  Here, the proposal of the work plan for each work place includes, for example, a proposal of a symptom (for example, a disease name) estimated for the work place and an actual work recommended for the work place according to the symptom ( (E.g., names of pesticides and fertilizers).

  FIG. 18 shows a flow of an overall control example of the work support system 100.

  After the area image of the area desired by the user is registered in the data server 101 (step S9), the user can display the area image on the display screen of the operation terminal 111 (step S20). In this step S20, the operation terminal 111 automatically analyzes the area image to automatically detect the unique point, and displays the detected unique point in the area image on the display screen. Then, the user specifies the work place by his / her own eyes with reference to the displayed unique place, and requests the operation terminal 111 to register the specified work place. Then, the work location is notified from the operation terminal 111 to the data server 101 and registered in the data server 101 (step S21).

  When the work place is registered in the data server 101 in step S21, the analysis unit 107 of the data server 101 executes inference on the image of the registered work place, and automatically proposes a work plan for the work place. (Step S22). The operation terminal 111 receives the work plan proposal from the data server 111, and displays the work plan proposal on the display screen in association with the work location displayed on the display screen (step S23).

  The user determines a work plan (for example, a symptom name and an actual work name) for the work place with reference to the work plan proposal for the work place displayed on the operation terminal 111 and inputs the work plan to the operation terminal 111. The registration is requested to the operation terminal 111 (step S23). Then, the input work plan is notified to the data server 101 in association with the work place and registered there (step S25).

After actual work for a region is performed, (the there, the effect of the actual work has appeared) the area of the photographic image that region image is obtained is again captured, further based on the local image The work location newly identified by the user (the same location as the previous work location, or a location different from the previous location may be included, or there may be no work location) and the user for each work location Is input and registered, the inference unit 107 of the data server 101 executes the work plan of the actual work performed. And the image where the effect of the work appeared, the work location and the symptom name newly identified based on the image are received as teacher data, machine learning is performed, and the work plan inference method is automatically determined. Formed, that is to learn (step S26). The analysis unit 107 can apply the learned inference method to the inference in Step S22 to be executed later.

  With the above control, the inference method for generating the work plan proposal held by the data server 101 is improved to higher performance as photography and actual work in many areas are repeated. Thus, a more appropriate work plan proposal can be provided to the user. This makes it easier for the user to design a work plan to obtain the expected effects.

  FIG. 17 is referred to again. The analysis unit 107 of the data server 101 has a symptom analysis unit 108 and a work analysis unit 109. The symptom analysis unit 108 analyzes an image of each work place (a part corresponding to each work place in the regional image), estimates a symptom of the work place, and estimates the estimated symptom (for example, disease name or physiological condition). The failure plan is stored in the work plan proposal database 105 in association with the work location. The work analysis unit 109 estimates an actual work (for example, a pesticide name or a fertilizer name) that is recommended to be applied to the work place from the symptoms of each work place, and uses the estimated actual work as a work proposal. Is stored in the work plan proposal database 105 in association with the work place. The symptom proposal and the work proposal for each work location constitute a work plan proposal for that work location.

  Each of the symptom analysis unit 108 and the work analysis unit 109 can be configured to perform machine learning and inference suitable for each purpose using, for example, a neural network.

  19 and 20 show configuration examples of the symptom analysis unit 108 and the work analysis unit 109, respectively.

  As shown in FIG. 19, the symptom analysis unit 108 has the following two types of deep neural networks (hereinafter, abbreviated as DNNs). One is a symptom learning DNN 121, and the other is a symptom inference DNN 123.

  The symptom learning DNN 121 inputs a large number of images and symptoms of a large number of work locations, images and symptoms from the past to the present, and histories of actual work (change history) as teacher data 125, and performs machine learning such as deep learning. Perform learning, thereby learning inference methods to infer symptoms from images (ie, create an inference neural network).

  The symptom inference DNN 123 has an inference method (that is, an inference neural network) created at a certain point in the past by machine learning by the symptom learning DNN 121, and converts the image and history data of each work location into the inference method (inference neural network). Network) to perform inference, and output symptom suggestion data 129 for the work location.

  The symptom learning DNN 121 and the symptom inference DNN 123 may be configured as different hardware or different computer software. In this case, the inference method created by performing a certain amount of learning during a certain period of time in the symptom learning DNN 121 is copied to the symptom inference DNN 123, and thereafter, the symptom inference DNN 123 can execute the inference method. By repeating such duplication after an appropriate period, the inference performance of the symptom inference DNN 123 improves over time.

  Alternatively, the symptom learning DNN 121 and the symptom inference DNN 123 may be configured as the same hardware or the same computer software. In that case, the same hardware or computer software can operate as the symptom learning DNN 121 at one time and operate as the symptom inference DNN 123 at another time. By alternately repeating the learning and the inference in different time zones in this way, the learning result of the previous time zone is repeatedly used for the inference in the next time zone. Therefore, the symptom inference DNN123 is performed with time. Inference performance is improved. Alternatively, the same hardware or computer software may be configured to perform learning and inference concurrently (ie, operate as symptom learning DNN 121 and symptom inference DNN 123 concurrently).

  As shown in FIG. 20, the work analysis unit 109 also has two types of DNNs, similarly to the above-mentioned symptom analysis unit 108. These are a work learning DNN 131 and a work inference DNN 133.

  The work learning DNN 131 detects the symptoms of a large number of work places, the actual work applied thereto, the image of the work place newly photographed after performing the actual work, and the symptom newly determined based on the image, Inputting a large amount as the teacher data 135 and performing machine learning, for example, deep learning, thereby learning an inference method for inferring the actual work to be applied thereto from the symptoms (that is, an inference neural network). Make up).

  The work inference DNN 133 has an inference method (that is, an inference neural network) created at a certain point in the past by machine learning by the work learning DNN 131, and inputs the symptoms of each work location to the inference method (inference neural network). Then, inference is performed, and work proposal data 139 for the work location is output.

  The work learning DNN 131 and the work inference DNN 133 may be configured as different hardware or different computer software, like the above-described symptom learning DNN 121 and symptom inference DNN 123. In this case, the work state learning DNN 131 copies the inference method created by performing a certain amount of learning in a certain period to the work inference DNN 13, and thereafter, the work inference DNN 133 can execute the inference method. By repeating such duplication after an appropriate period, the inference performance of the work inference DNN 133 improves over time.

  Alternatively, the work learning DNN 131 and the work inference DNN 133 may be configured as the same hardware or the same computer software. In that case, the same hardware or computer software can operate as the work learning DNN 131 in a certain time zone and operate as the work inference DNN 133 in another time zone. By alternately repeating the learning and the inference in different time zones in this way, the learning result of the previous time zone is repeatedly used for the inference in the next time zone. Therefore, the work inference DNN 133 with time elapses. Inference performance is improved. Alternatively, the same hardware or computer software may be configured to execute learning and inference concurrently (that is, operate as the work learning DNN 131 and the work inference DNN 133 concurrently).

  FIGS. 21 to 23 show examples of a graphic interface for the user to specify a work place and a work plan on the display screen of the operation terminal 111. FIG.

  FIG. 21 shows an example of an image of the interface 200 when a work location is specified. An image of a region desired by the user (for example, a field) is displayed in the region image window 201 (in this example, a part of the field is enlarged and displayed so that the state of each crop can be understood). When the user operates the region selection button 203, the region image analysis is performed by the unique portion detection unit 113, the unique portion in the region is automatically detected, and the detected unique portion is determined by the region selection tool. 213. The user can refer to this suggestion when visually finding a work place. The user can operate the area selection tool 213 as necessary to specify a work location. The user can input the name of the work place using the work place name input tool 205. By operating the registration button 211, the user can register the work location (the position of the area specified by the area selection tool 211) and its name in the data server 101.

  FIG. 22 shows an example of an image of a graphic interface used when specifying a symptom of a work place. Once the work sites 221 and 223 are registered, the data server 101 estimates the symptoms from the images of the work sites 221, 223 and generates a symptom proposal. The symptom suggestions 225 and 227 are displayed on the regional image window 201 in association with the respective work locations 221 and 223. The user can refer to the symptom suggestion when specifying the symptom of each work place 221 and 223. The user can operate the symptom input tool 207 to specify the symptom name of each work place 221 or 223. By operating the registration button 211, the user can register the symptom names of the respective work locations 221 and 223 in the data server 101.

FIG. 23 shows an example of an image of the graphic interface when an actual work for each work location is determined. Once the symptoms of the work places 221 and 223 are registered, the data server 101 estimates a recommended actual work from the symptoms of each work place 221 and 223 to generate a work proposal. The work proposed 235 and 237 is displayed is associated with in each work place 221 and 223 on the regional image window. The user can refer to the work proposal when specifying the actual work at each work place 221, 223. The user can operate the actual work input tool 209 to determine the actual work name for each work place 221, 223. By operating the registration button 211, the user can register the actual work name for each work place 221 and 223 in the data server 101.

  FIG. 24 shows another configuration example of the analysis unit 107 of the data server 101.

  In the configuration example of FIG. 24, the analysis unit 107 can output the work plan proposal of each work place, that is, the symptom proposal and the actual work proposal together without being separated. That is, the analysis unit 107 includes the work plan learning DNN 141 and the work plan inference DNN 143.

  The work plan learning DNN 141 includes images and work plans (many symptoms and actual work names) of a large number of work places, images and symptoms of the work places after actual work is performed based on the work plans, and past to present. A large amount of images, symptoms, and actual work histories (change history) of the work place are input as teacher data 145, and machine learning, for example, deep learning is performed, thereby inferring a work plan from the images. Learn inference methods for creating (in other words, creating an inference neural network).

The work plan inference DNN 143 has an inference method (that is, an inference neural network) created at a certain point in the past by machine learning by the work plan symptom learning DNN 141, and infers an image of each work place and the data of the history described above. A method (inference neural network) is input to perform inference, and a work plan proposal 149 for the work location is output.
The work plan learning DNN 141 and the work plan inference DNN 143 may be configured as different hardware or different computer software, or may be configured as the same hardware or the same computer software.

  FIG. 25 shows another configuration example of the analysis unit 107.

  In the configuration example of FIG. 25, the analysis unit 107 proposes a work plan for each region, not for each work site in the region (including the position of one or more work sites in the region and the symptom of each work site). Name and actual work name). That is, the analysis unit 107 includes the work plan learning DNN 151 and the work plan inference DNN 153.

  The work plan learning DNN 151 includes a whole image of a large number of areas, positions of each work place therein, their symptom names and actual work names, and an overall image, work places and symptoms after the actual work in those areas, and And inputting a large amount of the entire image, work location, symptom, and actual work history (change history) of the area from the past to the present as the teacher data 155, and executing machine learning, for example, deep learning. Then, learn an inference method for inferring a work plan (locations of work points in the area, symptom names and actual work names of each work place) from the entire image of the area (that is, create an inference neural network).

  The work plan inference DNN 153 has an inference method (that is, an inference neural network) created at a certain point in the past by machine learning using the work plan symptom learning DNN 151, and stores an image of a work place in each region and the data of the history. An input is made to the inference method (inference neural network) to perform inference, and a work plan proposal 159 for the area is output.

  The work plan learning DNN 151 and the work plan inference DNN 153 may be configured as different hardware or different computer software, or may be configured as the same hardware or the same computer software.

  FIG. 17 is referred to again. The regional image database 61, the work location database 63, the regional position database 51, the flight plan database 55, the work plan database, the work result database, the data stored in the work plan proposal database 105 of the data server 101, and the analysis unit 107 The inference method created by learning, that is, the inference neural network, can be used for various useful uses other than the use displayed on the operation terminal 111 to assist the user. Therefore, the data stored in these databases and all or any part of the inference neural network can be output from the data server 101 to the outside.

  As described above, the work support systems according to the two embodiments of the present invention have been described. However, these work support systems apply data collected by drone surveys (for example, images of a field such as a field obtained by photographing). A correction system for performing data correction for removing included noise (for example, an actual error such as a color tone or brightness of a photographic image caused by environmental conditions such as weather or time zone at the time of shooting) may be provided. For example, the work support systems 1 and 100 according to the two embodiments described above may include such a correction system in, for example, the data server 7 or 101 or the operation terminal 9 or 111, for example.

  Hereinafter, an example of such a correction system will be described. The correction system exemplified below is for correcting a regional image obtained by photographing with a drone, and can be provided in the work support systems 1 and 100 according to the above-described two embodiments.

  FIG. 26 shows a planar design of an example of an image correction scale used to utilize the correction system.

  The image correction scale 161 shown in FIG. 26 is, for example, a rectangular flat plate, and has a surface shown in FIG. Is placed.

  The image correction scale 161 has a plurality of (eight in this example, but other numbers) reference color scales 163C1 to 163C7, 163G. Among them, some of the reference color scales 163C1 to 163C7 (seven in this example, but other numbers may be used) are colored with different predetermined chromatic colors, respectively. The scale is called 163C. The remaining reference color scale 163G (one in this example, but any other number) may be colored in a predetermined achromatic color (for example, pure white or a predetermined gray color). This is hereinafter referred to as “gray scale”.

  The different chromatic colors applied to the plurality of reference color scales 163C1 to 163C7 of the color scale 163C are predetermined in order to determine the state of the target area (that is, the tone values of element colors such as RGB are predetermined). T) different reference colors. For example, when the application area of the image correction scale 161 is a rice field, the reference colors are different colors of a predetermined greenish color to determine the state of the rice from the leaf color of the rice. An identification code is defined in advance for each reference color, and each of the reference color scales 163C1 to 163C7 is accompanied by a display of the identification code. The reference color of the gray scale 163C is also an achromatic color having a predetermined element color tone value, and the display of the predetermined identification code is attached to the gray scale 163C.

  All the reference color scales 163C1 to 163C7, 163G are, for example, perfectly circular. When taking a picture of an area, the drone takes an image of not only the area but also the vicinity of the area, and therefore also takes an image correction scale 161. The regional image obtained by the photographing includes not only the region but also an image around the region and an image of the image correction scale 161. Regardless of the direction of the image of the image correction scale 161 in the local image, if each of the reference color scales 163C1 to 163C7 and 163G is a perfect circle, each of the reference colors is selected from the local image obtained by shooting. It is easy to identify and extract the images of the scales 163C1 to 163C7 and 163G by image processing.

  FIG. 27 shows an example of dimensional conditions satisfied by the panel on which each reference color scale is displayed in the example of the image correction scale shown in FIG.

  As shown in FIG. 27, each reference color scale 167 in the image correction scale 161 is a perfect circle as described above. Each reference color scale 167 is displayed on the surface of the unit panel 165, and the unit panel 1 is, for example, a square. An identification code 169 of the reference color of the reference color scale 167 is displayed in a background area on the surface of the unit panel 165 outside the reference color scale 167 (which has a predetermined color different from the reference color). By integrating a predetermined number of unit panels 165 on which reference color scales 167 of different colors are drawn, a flat image correction scale 161 as shown in FIG. 26 is configured.

  The size of the unit panel 165 is selected so as to include a circumscribed square of the reference color scale 167 (illustrated by a dash-dotted auxiliary line) (that is, the circumscribed square does not protrude outside the unit panel 165). The display position of the identification code 169 on the unit panel 165 exists outside the circumscribed square regardless of the direction of the circumscribed square as shown by the dashed auxiliary line (that is, the circumscribed square). ).

  Accordingly, in the image processing for identifying the reference color scale 167 from the image captured by the drone, the circumscribed square includes the background color and the reference color in the circumscribed square regardless of the direction in which the unit panel 165 is oriented in the image. Since there are only two color regions, it is easy to accurately identify and extract the reference color scale 167.

  FIG. 28 shows a configuration example of a correction system for correcting a local image using the image of the image correction scale 161 shown in FIG.

  As shown in FIG. 28, the correction system 171 includes an image input unit 173, a color scale search unit 177, a color scale pixel value storage unit 179, a gray scale search unit 181, a gray scale pixel value storage unit 183, and a reference pixel value storage unit. 185, a local polygon data storage unit 187, an image separation unit 189, a first color tone adjustment unit 191, a second color tone adjustment unit 193, an image synthesis unit 195, and a color analysis unit 197.

  The image input unit 173 inputs a regional image of the target region obtained by photographing. In the work support systems 1 and 100 according to the two embodiments described above (see FIGS. 1 and 17), the area image of the target area can be input from the area image database 61.

  The color scale search unit 177 identifies the image of each of the reference color scales 163C1 to 163C7 of the color scale 163C from the input area image by image processing, and processes all the pixels in the identified reference color scale image. (For example, a set of RGB values) is extracted. The color scale pixel value storage unit 179 stores all pixel value sets in the extracted reference color scale image.

  The grayscale search unit 181 identifies an image of the grayscale 163G from the input regional image by image processing, and sets a value set of all pixels in the identified grayscale image (for example, an RGB value Set) to extract. The grayscale pixel value storage unit 183 stores all pixel value sets of the extracted grayscale image.

  The reference pixel value storage unit 185 stores a predetermined pixel value set (for example, a set of RGB values) of all the reference color scales 163C1 to 163C7 and 163G of the image correction scale 161 (hereinafter, referred to as a reference pixel value set). I do.

  The area polygon data storage unit 187 stores data (hereinafter, referred to as area polygon data) indicating the shape and position of the target area (for example, the geographical coordinates of the contour of the target area). In the work support systems 1 and 100 according to the above-described two embodiments (see FIGS. 1 and 17), the area polygon data of the target area exists in the area position database 51.

  The image separating unit 189 converts the local image (for example, an image obtained by photographing a field) input from the image input unit 173 into a target area (for example, an image of the field) using the local polygon data 187. And a portion other than the target area (for example, an image of a peripheral area near the field photographed together with the field).

  The first color tone adjustment unit 191 performs a process of correcting the color of the image of the target area from the image separation unit 189. In this correction processing, the pixel value set of each of the reference color scales 163C1 to 163C7 of the color scale 163C from the color scale pixel storage unit 179 and the reference color scales 163C1 to 163C7 of the color scale 163C from the reference pixel value storage unit 185 are set. The reference pixel value set is used.

  The second color tone adjustment unit 193 performs a process of correcting the color of the image of the part other than the area from the image separation unit 189. In this correction process, a grayscale 163G pixel value set from the grayscale pixel storage unit 183 and a grayscale 163G reference pixel value set from the reference pixel value storage unit 185 are used.

  The image combining unit 195 combines the corrected image of the target area and the corrected image of the part other than the target area to combine the corrected area image.

  The color analysis unit 197 analyzes the pixel value of each part of the corrected area image, and generates information for a later use, for example, detection of a unique region. For example, an image of a target area portion in the area image is divided into small sections, and statistical values such as an average value and a standard deviation of a pixel value set of all pixels in each small section are calculated. Since these statistical values are useful for finding out, for example, a peculiar area in which a disease or a physiological disorder of a crop in a field has occurred, for example, the work support system 100 according to the above-described second embodiment (see FIG. 17) Can be used in the analysis unit 107 or the unique part detection unit 113 of FIG.

  FIG. 29 shows a flow of an example of control performed by the correction system 171 shown in FIG.

  As shown in FIG. 29, a local image obtained by photographing is input, and an image of the color scale 163C (that is, an image of the reference color scales 163C1 to 163C7) is searched from the input local image (step S101). ), The extracted pixel values of the image of the color scale 163C (that is, the pixel value sets of all the pixels of the images of the reference color scales 163C1 to 163C7) are extracted and stored (step S102). Then, based on the stored pixel value of the color scale 163C and a predetermined corresponding reference pixel value (that is, a reference pixel value set of the reference color scales 163C1 to 163C7 from the reference pixel value storage unit 185), A first correction function for correcting the former color tone to match the latter color tone is calculated (step S103).

  Further, a grayscale 163G image is searched from the input area image (step S104), and the found pixel values of the grayscale 163G (that is, pixel value sets of all pixels of the grayscale 163G image) are stored (step S104). Step S105). Then, based on the stored pixel value of the gray scale 163G and a predetermined reference pixel value of the gray scale 163G (that is, a reference pixel value set of the gray scale 163G from the reference pixel value storage unit 185), the former is used. A second correction function is calculated to correct the color tone of the image to match the latter color tone (step S106).

  Then, the image of the target area is separated from the input area image, and the color tone of the image is corrected using the first correction function (step S107). Further, an image of a portion other than the target area is separated from the input area image, and the color tone of the image is corrected using the second correction function (step S108). Accordingly, even if the color tone of the regional image obtained by photographing is shifted from the actual regional image, the image of the regional portion (for example, an image of a rice field) indicates the actual state of the region based on the color scale 163C. The correction is performed based on the image so that the evaluation can be more appropriately performed, and the image of the portion other than the target area is corrected so that the color tone looks more natural.

  Then, the corrected area part and the image of the other part are combined, and the corrected area image is synthesized (step S109).

  Then, the corrected area image is analyzed, and a predetermined statistical value (for example, an average pixel value set of a number of small areas into which the area image is subdivided, a standard deviation thereof, and the like) is calculated (step S110). The calculated statistics can be used for later work, for example, to find a specific area of the target area.

  Although some embodiments of the present invention have been described above, these embodiments are merely examples for explanation, and do not purport to limit the scope of the present invention to only these embodiments. The present invention can be implemented in various forms different from the above embodiment.

  For example, the present invention is not limited to the application of agricultural chemicals in a field, but can also be applied to work support systems for various other uses. For example, the present invention can be applied to a wide range of applications, such as transferring objects using a drone in a material storage area, monitoring and maintaining transmission lines and towers with a drone, or photographing a photograph or a moving image of a district or place desired by a user.

  Depending on the application, a process including only the actual flight may be performed instead of a process including the two-stage flight of the survey flight and the actual flight as in the above-described embodiment. For example, if you want to shoot a video of a certain place, the user specifies the place on the operation terminal, the server makes a flight plan for shooting the place, and the user downloads the flight plan and installs it on the drone. And a simpler process, such as performing a drone shooting flight.

  In some applications, processes involving more stages of flight may be performed. For example, in the case of pesticide spraying in a field, after a certain period has elapsed from the day on which the pesticide was sprayed, to investigate the effect of the pesticide, a survey flight (in this case, a survey flight of the entire area may be performed, A more complex process, such as conducting a survey flight that focuses on the work location), repeating survey flights and pesticide spraying regularly, or performing a sowing or fertilizing flight. It can be performed by applying the present invention.

Further, a part or all of the above-described data server may be implemented in an operation terminal used by a user. For example, a software tool for creating a flight plan is installed on the operation terminal 7 so that a user can create a flight plan by himself using the tool, or the tool automatically creates a flight plan It may be like.
In addition, the present invention may be applied to a case in which a drone that can perform a traveling method other than flying, for example, a taxi, a surface navigation, an underwater diving, or the like is used as a drone according to the type or situation of a work target.

DESCRIPTION OF SYMBOLS 1 Work support system 3 Survey drone 5 Actual work drone 7 Data server 9 Operation terminal 17, 27 Controller 19, 29 Radio controller 31 Area registration part 33 Flight plan input part 35 Flight plan output part 37 Photographed image input part 39 Photographed image Output unit 41 Shooting position input unit 43 Shooting position output unit 45 Image display unit 47 Work place registration unit 48 Work result input unit 49 Work result output unit 51 Regional position database 53 3D map database 55 Flight plan database 57 Shooting image database 59 Shooting Position database 61 Regional image database 63 Work location database 65 Work result database 71 Flight plan creation unit 73 Photographed image input unit 75 Photographed position input unit 77 Photographed image coupling unit 79 Analysis unit 100 Work support system 101 Data server 111 Operation terminal 103 Work plan Database 105 Work plan proposal database 107 Analysis unit 108 Symptom analysis unit 109 Work analysis unit 113 Unique part detection unit 115 Work plan input unit 117 Proposal presentation unit 119 Work selection unit 121 Symptom learning deep neural network 123 Symptom inference deep neural network 131 Work learning Deep neural network 133 Work inference Deep neural network 141, 151 Work plan learning Deep neural network 143, 153 Work plan inference deep neural network 161 Image correction scale 163C Color scale 163G Gray scale 165 Unit panel 167 Reference color scale 169 Identification code 171 Correction system

Claims (22)

In a drone operation support system configured to be able to communicate with one or more drones, or to be able to communicate with one or more users using the one or more drones,
A first target storage unit that receives a first target specification from at least one of the users and stores a position of the first target,
Storing a first movement plan configured to control at least one of the drones so that at least one of the drones performs a first work and a movement according to the position of the first object; One movement plan storage means,
First movement plan providing means for providing the stored first movement plan to at least one of the users or at least one of the drones;
First work result storage means for receiving and storing the result of the first work performed on the first target,
Work report creation means for creating a work report representing a result of the stored first work,
Work report providing means for providing the created work report to at least one of the users,
A second target specifying tool configured so that at least one of the users can specify a second target according to the result of the first work displayed in the work report, at least one of the users Means for providing a second target identification tool to be provided to one person;
A second target storage unit that receives the specification of the second target from at least one of the users and stores a position of the second target,
Storing a second movement plan configured to control at least one of the drones such that at least one of the drones performs a movement according to the position of the second object and a second task; Second movement plan storage means,
A second movement plan providing unit for providing the stored second movement plan to at least one of the users or at least one of the drones. A work content specifying tool configured so that at least one of the users can specify the work content of the second work in accordance with the result of the first work displayed in the work report, Means for providing a work content identification tool to be provided to at least one of
Work content storage means for receiving the work content from at least one of the users and storing the work content,
The drone operation support system according to claim 1, further comprising: A symptom identification tool configured so that at least one of the users can identify the symptom of the second target according to the result of the first operation displayed in the operation report, at least one of the users Means for providing a symptom identification tool to be provided to one person,
Symptom storage means for receiving the identification of the symptom from at least one of the users and storing the symptom;
The drone operation support system according to claim 1, further comprising: In response to the result of the first operation, a unique part is automatically detected from the first object, and the detected unique part is provided to at least one of the users as a proposal of a second object. The drone operation support system according to any one of claims 1 to 3, further comprising a second target proposal unit that performs the operation. Receiving a result of the first work, automatically generating a work content proposal for the second work, and further comprising a work content proposal means for providing the work content proposal to at least one of the users; The drone operation support system according to any one of claims 1 to 4. 2. The apparatus according to claim 1, further comprising: a symptom suggestion unit configured to automatically generate a symptom suggestion of the second target in response to the result of the first operation, and provide the symptom suggestion to at least one of the users. The drone operation support system according to any one of claims 1 to 5. A first movement plan that creates the first movement plan based on the stored position of the first object and provides the created first movement plan to the first movement plan storage unit; The drone operation support system according to any one of claims 1 to 6, further comprising a creation unit. A second movement plan that creates the second movement plan based on the stored position of the second target and provides the created second movement plan to the second movement plan storage unit The drone operation support system according to any one of claims 1 to 7, further comprising a creation unit. As the one or more drones, a first working drone having a first working device for performing the first work, and a second working drone having a second working device for performing the second work, If used by the user,
The first travel plan is configured to control the first work drone;
The second travel plan is configured to control the second work drone;
A drone operation support system according to any one of claims 1 to 8. The first operation includes investigating, inspecting, maintaining, processing, destroying or constructing the first object;
The drone operation support system according to claim 1, wherein the second operation includes investigating, inspecting, maintaining, processing, destroying, or constructing the second object. The first operation includes photographing the first object;
The work report includes a display of a photographic image of the first subject taken by the photography,
The drone operation support system according to claim 1, wherein the second target specifying tool is configured so that at least one of the users can specify the second target on the photographic image. In a drone operation support system configured to be able to communicate with one or more drones, or to be able to communicate with one or more users using the one or more drones,
A first target storage unit that receives a first target specification from at least one of the users and stores a position of the first target,
A first movement plan storing a first movement plan configured to control at least one of the drones such that at least one of the drones performs movement and a first task according to a position of the first object. Moving plan storage means,
First movement plan providing means for providing the stored first movement plan to at least one of the users or at least one of the drones;
First work result storage means for receiving and storing the result of the first work performed on the first target,
Work report creation means for creating a work report representing a result of the stored first work,
Work report providing means for providing the created work report to at least one of the users,
A second target specifying tool configured so that at least one of the users can specify a second target according to the result of the first work displayed in the work report, at least one of the users Means for providing a second target identification tool to be provided to one person;
A second target storage unit that receives the specification of the second target from at least one of the users and stores a position of the second target,
A second target information processing means for performing information processing relating to a second work corresponding to the stored position of the second target, and providing a result of the information processing to at least one of the users or at least one of the drones; And a drone work support system. In a drone operation support system configured to be able to communicate with one or more drones, or to be able to communicate with one or more users using the one or more drones,
First work result storage means for receiving and storing the result of the first work performed on the first target,
Work report creation means for creating a work report representing a result of the stored first work,
Work report providing means for providing the created work report to at least one of the users,
A second target specifying tool configured so that at least one of the users can specify a second target according to the result of the first work displayed in the work report, at least one of the users Means for providing a second target identification tool to be provided to one person;
A second target storage unit that receives the specification of the second target from at least one of the users and stores a position of the second target,
Storing a second movement plan configured to control at least one of the drones such that at least one of the drones performs a movement according to the position of the second object and a second task; Second movement plan storage means,
A second movement plan providing unit for providing the stored second movement plan to at least one of the users or at least one of the drones. In a drone operation support system configured to be able to communicate with one or more drones, or to be able to communicate with one or more users using the one or more drones,
First work result storage means for receiving and storing a result of a first work performed on a first target by at least one of the drones,
Work report creation means for creating a work report representing a result of the stored first work,
Work report providing means for providing the created work report to at least one of the users,
A second target specifying tool configured so that at least one of the users can specify a second target according to the result of the first work displayed in the work report, at least one of the users Means for providing a second target identification tool to be provided to one person;
A second target storage unit that receives the specification of the second target from at least one of the users and stores a position of the second target,
A drone operation support system comprising: an information processing unit that performs information processing based on the stored position of the second target and outputs a result of the information processing. A drone operation support method performed by a computer system configured to be communicable with one or more drones or with one or more users using the one or more drones,
Receiving a first target specification from at least one of the users, and storing a position of the first target;
Storing a first movement plan configured to control at least one of the drones such that at least one of the drones performs a first work and a movement according to the position of the first object; ,
Providing the stored first travel plan to at least one of the users or at least one of the drones;
Receiving and storing the result of the first operation performed on the first target,
Creating a work report representing a result of the stored first work;
Providing the created work report to at least one of the users;
A second target specifying tool configured so that at least one of the users can specify a second target according to the result of the first work displayed in the work report, at least one of the users Steps to provide to one person,
Receiving the identification of the second object from at least one of the users, storing the position of the second object,
Storing a second movement plan configured to control at least one of the drones such that at least one of the drones performs a second work and a movement according to the position of the second object. ,as well as,
Providing the stored second travel plan to at least one of the users or at least one of the drones;
Drone work support method having A drone operation support method performed by a computer system configured to be communicable with one or more drones or with one or more users using the one or more drones,
Receiving a first target specification from at least one of the users, and storing a position of the first target;
Storing a first movement plan configured to control at least one of the drones such that at least one of the drones performs a first work and a movement according to the position of the first object; ,
Providing the stored first travel plan to at least one of the users or at least one of the drones;
Receiving and storing the result of the first operation performed on the first target,
Creating a work report representing a result of the stored first work;
Providing the created work report to at least one of the users;
A second target specifying tool configured so that at least one of the users can specify a second target according to the result of the first work displayed in the work report, at least one of the users Steps to provide to one person,
Receiving the identification of the second object from at least one of the users, and storing the position of the second object; and
Performing drone information processing relating to a second operation according to the stored position of the second object, and providing a result of the information processing to at least one of the users or at least one of the drones Method. A drone operation support method performed by a computer system configured to be communicable with one or more drones or with one or more users using the one or more drones,
Receiving and storing the result of the first operation performed on the first object;
Creating a work report representing a result of the stored first work;
Providing the created work report to at least one of the users;
A second target specifying tool configured so that at least one of the users can specify a second target according to the result of the first work displayed in the work report, at least one of the users Steps to provide to one person,
Receiving the identification of the second object from at least one of the users, storing the position of the second object,
Based on the stored position of the second target, performing information processing for a second work on the second target,
Providing the result of the information processing to at least one of the users or at least one of the drones;
Storing a second movement plan configured to control at least one of the drones so that at least one of the drones performs a second work and a movement according to the position of the second object; ,as well as,
Providing the stored second travel plan to at least one of the users or at least one of the drones;
Drone work support method having A drone operation support method performed by a computer system configured to be communicable with one or more drones or with one or more users using the one or more drones,
Receiving the result of a first operation performed on a first object by at least one of the drones;
Creating a work report representing a result of the stored first work;
Providing the created work report to at least one of the users;
A second target specifying tool configured so that at least one of the users can specify a second target according to the result of the first work displayed in the work report, at least one of the users Steps to provide to one person,
Receiving the identification of the second object from at least one of the users and storing the position of the second object; and performing information processing based on the stored position of the second object, Outputting the result of
Drone work support method having A machine-readable computer program for causing a computer system configured to be able to communicate with one or more drones or to communicate with one or more users using the one or more drones to execute the drone operation support method. The drone work support method,
Receiving a first target specification from at least one of the users, and storing a position of the first target;
Storing a first movement plan configured to control at least one of the drones such that at least one of the drones performs a first work and a movement according to the position of the first object; ,
Providing the stored first travel plan to at least one of the users or at least one of the drones;
Receiving and storing the result of the first operation performed on the first target,
Creating a work report representing a result of the stored first work;
Providing the created work report to at least one of the users;
A second target specifying tool configured so that at least one of the users can specify a second target in accordance with the result of the first work displayed in the work report, at least one of the users Steps to provide to one person,
Receiving the identification of the second object from at least one of the users, storing the position of the second object,
Storing a second movement plan configured to control at least one of the drones so that at least one of the drones performs a second work and a movement according to the position of the second object; ,as well as,
Providing the stored second travel plan to at least one of the users or at least one of the drones;
A computer program having: A machine-readable computer program for causing a computer system configured to be able to communicate with one or more drones or to communicate with one or more users using the one or more drones to execute the drone operation support method. The drone work support method,
Receiving a first target specification from at least one of the users, and storing a position of the first target;
Storing a first movement plan configured to control at least one of the drones such that at least one of the drones performs a first work and a movement according to the position of the first object; ,
Providing the stored first travel plan to at least one of the users or at least one of the drones;
Receiving and storing the result of the first operation performed on the first target,
Creating a work report representing a result of the stored first work;
Providing the created work report to at least one of the users;
A second target specifying tool configured so that at least one of the users can specify a second target in accordance with the result of the first work displayed in the work report, at least one of the users Steps to provide to one person,
Receiving the identification of the second object from at least one of the users, and storing the position of the second object; and
A computer program having a step of performing information processing relating to a second operation according to a stored position of the second target, and providing a result of the information processing to at least one of the users or at least one of the drones. A machine-readable computer program for causing a computer system configured to be able to communicate with one or more drones or to communicate with one or more users using the one or more drones to execute the drone operation support method. The drone work support method,
Receiving and storing the result of the first operation performed on the first object;
Creating a work report representing a result of the stored first work;
Providing the created work report to at least one of the users;
A second target specifying tool configured so that at least one of the users can specify a second target according to the result of the first work displayed in the work report, at least one of the users Steps to provide to one person,
Receiving the identification of the second object from at least one of the users, storing the position of the second object,
Storing a second movement plan configured to control at least one of the drones such that at least one of the drones performs a second work and a movement according to the position of the second object. ,as well as,
Providing the stored second travel plan to at least one of the users or at least one of the drones;
A computer program having: A machine-readable computer program for causing a computer system configured to be able to communicate with one or more drones or to communicate with one or more users using the one or more drones to execute the drone operation support method. The drone work support method,
Receiving the result of a first operation performed on a first object by at least one of the drones;
Creating a work report representing a result of the stored first work;
Providing the created work report to at least one of the users;
A second target specifying tool configured so that at least one of the users can specify a second target according to the result of the first work displayed in the work report, at least one of the users Steps to provide to one person,
Receiving the identification of the second object from at least one of the users and storing the position of the second object; and performing information processing based on the stored position of the second object, Outputting the result of
A computer program having: