JP7079547B1 - Field evaluation device, field evaluation method and field evaluation program - Google Patents

Abstract

PROBLEM TO BE SOLVED: To provide a field evaluation device, a field evaluation method and a field evaluation program for objectively evaluating the work efficiency of a field on the premise of work by an automated driving machine. SOLUTION: A field evaluation device 1 has an information acquisition unit 11 for acquiring field information regarding the shape of one or a plurality of target fields, and a time calculation for calculating the working time of an automatic operation machine in the target field according to the field information. Based on the field evaluation unit 14 and the field information, which evaluates the work efficiency of the automatic driving machine in the target field based on the work time, the unit 13 and the field evaluation unit 14 generate an operation route in which the automatic driving machine moves in the target field. A route generation unit 12 is provided. The time calculation unit may calculate the working time by the automatic driving machine based on the route generated by the route generation unit. [Selection diagram] FIG. 4

Description

The present invention relates to a field evaluation device, a field evaluation method, and a field evaluation program.

In recent years, self-driving agricultural machines have become widespread. Such an agricultural machine automatically generates an operation route in the field based on the shape of the field. Therefore, as a result of generating various operation paths depending on the shape of the field, the work efficiency differs depending on the shape even if the area is the same. Therefore, on the premise of using an automatically operated agricultural machine, a technique for objectively expressing the efficiency of work in a field is required.

Patent Document 1 includes a first acquisition unit that acquires agricultural work data of agricultural work performed by an agricultural machine, and an evaluation calculation unit that calculates an agricultural work score for each field based on the agricultural work data acquired by the first acquisition unit. Agricultural work evaluation system with, is disclosed.

Patent Document 2 discloses a system for evaluating actually performed agricultural work based on working hours per area and the like.

In Patent Document 3, when evaluating a land based on its shape, a location / drawing input unit for inputting a target figure formed by a polygon that approximates the land to be evaluated, and an internal maximum included in the target figure. An evaluation system is disclosed that includes a shape depreciation rate determination unit having an internal maximum rectangle detection means for detecting a rectangle, and evaluates a target land based on the shape depreciation rate.

The systems described in Patent Documents 1 and 2 evaluate agricultural work, and have a different viewpoint from those for evaluating fields.

The system described in Patent Document 3 is not premised on route generation by an automated agricultural machine, and is a system for evaluating residential land in the first place, so it cannot be said that it is sufficient for evaluating the work efficiency of a field.

Patent Publication Japanese Patent Application Laid-Open No. 2017-068533 Patent Publication Japanese Patent Application Laid-Open No. 2017-129939 Patent Publication Japanese Patent Application Laid-Open No. 2008-040663

Provided is a field evaluation device that objectively evaluates the work efficiency of a field on the premise of work by an automatic driving machine.

In order to achieve the above object, the field evaluation device according to one aspect of the present invention has an information acquisition unit that acquires field information regarding the shape of one or a plurality of target fields, and the target field according to the field information. It includes a time calculation unit for calculating the working time of the automatic driving machine, and a field evaluation unit for evaluating the working efficiency of the automatic driving machine in the target field based on the working time.

Based on the field information, a route generation unit for generating an operation route for the automatic driving machine to move in the target field is further provided, and the time calculation unit is based on the route generated by the route generation unit. The working time by the automatic driving machine may be calculated.

The information acquisition unit acquires the field information regarding the plurality of target fields to be evaluated, and the route generation unit generates an inter-field route moving between the plurality of target fields, and the time calculation unit. May calculate the travel time of the inter-field route, and the field evaluation unit may evaluate the work efficiency based on the travel time and the work time.

A storage unit that stores the shape model of the field and the working time in association with each other is provided, and the time calculation unit approximates the shape of the target field to one or a plurality of the shape models by image identification processing, and the shape is described. The working time in the target field may be calculated based on the working time associated with the model.

The time calculation unit may calculate the working time in the target field based on at least one of the length around the target field, the number of vertices, and the uneven shape of the outer circumference of the target field. ..

The work is further provided with a second storage unit that stores the work and the type or movement mode of the automatic driving machine used for the work in association with each other, and the information acquisition unit is 1 or 1 to be evaluated in the target field. The time calculation unit may refer to the second storage unit and calculate the work time based on the type or the movement mode in the selected work.

In order to achieve the above object, the field evaluation method according to another aspect of the present invention includes an information acquisition process in which a computer acquires field information regarding the shape of one or a plurality of target fields, and the field evaluation method according to the field information. A time calculation process for calculating the working time of the automated driving machine in the target field and a field evaluation process for evaluating the working efficiency of the automated driving machine in the target field based on the working time are executed.

In order to achieve the above object, the field evaluation program according to still another aspect of the present invention responds to the information acquisition process for acquiring the field information regarding the shape of one or a plurality of target fields and the field information according to the field information. Then, a time calculation process for calculating the working time of the automatic driving machine in the target field and a field evaluation process for evaluating the working efficiency of the automatic driving machine in the target field based on the working time are executed. ..
The computer program can be stored in various data-readable recording media and provided, or can be provided for download via a network such as the Internet.

It is possible to objectively evaluate the work efficiency of the field on the premise of working with an automated driving machine.

The field evaluation apparatus according to the present invention is a perspective view of a drone which is an example of an automatic driving machine assumed to be used in work. It is an overall conceptual diagram of the field evaluation apparatus which concerns on this invention. It is a functional block diagram which the said drone has. It is a functional block diagram of the field evaluation apparatus which concerns on this invention, and the drone which is connected to the said field evaluation apparatus through a network. This is an example of a table stored in a storage unit of the field evaluation device. It is a schematic diagram which shows an example of the path generated in the field by the said field evaluation apparatus. It is a schematic diagram which shows another example of the path generated in the said field. It is a schematic diagram which shows the example of the inter-field route moving between a plurality of fields generated by the said field evaluation apparatus. An example showing the evaluation result of the field by the above-mentioned field evaluation device, (a) an example of a histogram showing the distribution of the evaluation value for each field, and (b) an example of a histogram showing the distribution of the evaluation value for each corporation that manages the field. Examples are (c) a histogram showing the distribution of evaluation values for each area having a field, and (d) an example of a scatter plot showing the relationship between the field area and efficiency and working time. It is a flowchart which shows the flow which the said field evaluation apparatus evaluates a field.

The field evaluation device 1 is a device that evaluates a field from the viewpoint of ease of work of an automatically moving agricultural machine. The field evaluation device 1 can evaluate one or a plurality of fields as target fields. The field evaluation device 1 acquires field information such as the number, area, or shape of the field, and also acquires the working time when the automatically operated agricultural machine works in this field. Further, the field evaluation device 1 calculates the work efficiency in the field based on this work time.

Hereinafter, embodiments for carrying out the present invention will be described with reference to the drawings. All figures are illustrations. In the following detailed description, certain details are given for illustration purposes and to facilitate a complete understanding of the disclosed embodiments. However, embodiments are not limited to these particular details. Also, for the sake of simplification of the drawings, well-known structures and devices are shown schematically.

First, the configuration of the drone will be described as an example of the automatic driving machine in which the field evaluation device according to the invention is supposed to be used in agricultural work.
As shown in FIG. 1, the rotor blades 101-1a, 101-1b, 101-2a, 101-2b, 101-3a, 101-3b, 101-4a, 101-4b (also called rotors) have the drone 100. It is a means for flying, and is equipped with eight aircraft (four sets of two-stage rotor blades) in consideration of the balance between flight stability, aircraft size, and power consumption. Each rotor 101 is arranged on all sides of the housing 110 by an arm protruding from the housing 110 of the drone 100. That is, the rotors 101-1a and 101-1b are left rearward in the direction of travel, the rotary blades 101-2a and 101-2b are forward left, the rotary blades 101-3a and 101-3b are rearward right, and the rotary blades 101- are forward right. 4a and 101-4b are arranged respectively. The drone 100 has the x direction in FIG. 1 as the traveling direction.

A grid-shaped propeller guard 115-1,115-2,115-3,115-4 forming a substantially cylindrical shape is provided on the outer periphery of each set of the rotor blade 101 to prevent the rotor blade 101 from interfering with foreign matter. The radial members that support the propeller guards 115-1,115-2,115-3,115-4 are not horizontal but have a wobbling structure. This is to encourage the member to buckle to the outside of the rotor blade in the event of a collision and prevent it from interfering with the rotor.

Rod-shaped legs extend downward from the axis of rotation of the rotor 101.

Motors 102-1a, 102-1b, 102-2a, 102-2b, 102-3a, 102-3b, 102-4a, 102-4b have rotary blades 101-1a, 101-1b, 101-2a, 101- It is a means to rotate 2b, 101-3a, 101-3b, 101-4a, 101-4b (typically an electric motor, but it may be a motor, etc.), and one machine is provided for one rotary blade. Has been done. Motor 102 is an example of a propulsion device. The upper and lower rotors (eg 101-1a and 101-1b) in one set and their corresponding motors (eg 102-1a and 102-1b) are for the stability of the drone's flight, etc. The axes are on the same straight line and rotate in opposite directions.

Nozzles 103-1 and 103-2 are means for spraying the sprayed material downward, and are provided with four nozzles. In the specification of the present application, the sprayed material generally refers to a liquid or powder sprayed on a field such as a pesticide, a herbicide, a liquid fertilizer, an insecticide, a seed, and water.

The tank 104 is a tank for storing the sprayed material, and is provided at a position close to the center of gravity of the drone 100 and at a position lower than the center of gravity from the viewpoint of weight balance. The hoses 105-1, 105-2, 105-3, 105-4 are means for connecting the tank 104 and the nozzles 103-1, 103-2, and are made of a hard material and serve to support the nozzles. May also serve as. The pump 106 is a means for discharging the sprayed material from the nozzle.

FIG. 2 shows an overall conceptual diagram of the flight control system of the drone 100. This figure is a schematic diagram, and the scale is not accurate. In the figure, the drone 100, the actuator 401, and the small mobile terminal 401a are each connected to the base station 404, and only the actuator 401 is connected to the farming cloud 405, but the connection relationship is an example. Not limited to. The drone 100, the actuator 401, the small mobile terminal 401a, and the base station 404 are each connected to the farming cloud 405. These connections may be wireless communication by Wi-Fi, mobile communication system, etc., or may be partially or wholly connected by wire.

The operation device 401 transmits a command to the drone 100 by the operation of the user 402, and also displays information received from the drone 100 (for example, position, amount of sprayed material, battery level, camera image, etc.). It is a means of the above, and may be realized by a portable information device such as a general tablet terminal that runs a computer program. The actuator 401 includes an input unit and a display unit as a user interface device. The drone 100 according to the present invention is controlled to perform autonomous flight, but may be capable of manual operation during basic operations such as takeoff and return, and in an emergency. In addition to the portable information device, an emergency operation device (not shown) having a function dedicated to emergency stop may be used. The emergency controller may be a dedicated device equipped with a large emergency stop button or the like so that an emergency response can be taken quickly. Further, apart from the operating device 401, the system may include a small mobile terminal 401a capable of displaying a part or all of the information displayed on the operating device 401, for example, a smart phone. Further, it may have a function of changing the operation of the drone 100 based on the information input from the small mobile terminal 401a. The small mobile terminal 401a is connected to, for example, the base station 404, and can receive information and the like from the farming cloud 405 via the base station 404.

The field 403 is a rice field, a field, or the like to be sprayed by the drone 100. In reality, the terrain of the field 403 is complicated, and the topographic map may not be available in advance, or the topographic map and the situation at the site may be inconsistent. Field 403 is usually adjacent to houses, hospitals, schools, other crop fields, roads, railroads, etc. In addition, there may be intruders such as buildings and electric wires in the field 403. Field 403 may include fields and paddy fields.

The base station 404 is a device that provides a master unit function for Wi-Fi communication, etc., and may also function as an RTK-GPS base station so that it can provide an accurate position of the drone 100 (Wi-). The base unit function of Fi communication and the RTK-GPS base station may be independent devices). Further, the base station 404 may be able to communicate with the farming cloud 405 by using a mobile communication system such as 3G, 4G, and LTE.

The farming cloud 405 is typically a group of computers operated on a cloud service and related software, and may be wirelessly connected to the actuator 401 by a mobile phone line or the like. The farming cloud 405 may be configured by a hardware device. The farming cloud 405 may acquire an image of the field 403 taken by the drone 100. In addition, the topographical information of the stored field 403 may be provided to the drone 100. In addition, the history of the flight and shot images of the drone 100 may be accumulated and various analysis processes may be performed.

The small mobile terminal 401a is, for example, a smart phone or the like. On the display of the small mobile terminal 401a, information on expected operations regarding the operation of the drone 100, more specifically, the scheduled time when the drone 100 will return to the departure / arrival point 406, and the work to be performed by the user 402 at the time of return Information such as contents is displayed as appropriate. Further, the operation of the drone 100 may be changed based on the input from the small mobile terminal 401a.

Normally, the drone 100 takes off from the departure / arrival point 406 outside the field 403 and returns to the departure / arrival point 406 after spraying the spray on the field 403 or when it becomes necessary to replenish or charge the spray. The flight route (invasion route) from the departure / arrival point 406 to the target field 403 may be stored in advance in the farming cloud 405 or the like, or may be input by the user 402 before the start of takeoff. The departure / arrival point 406 may be a virtual point defined by the coordinates stored in the drone 100, or may have a physical departure / arrival point.

FIG. 3 shows a block diagram showing a control function of an embodiment of the spraying drone according to the present invention. The flight controller 501 is a component that controls the entire drone, and may be an embedded computer including a CPU, a memory, related software, and the like. The flight controller 501 uses the input information received from the controller 401 and the input information obtained from various sensors described later, and the motors 102-1a and 102-1b via a control means such as ESC (Electronic Speed Control). , 102-2a, 102-2b, 102-3a, 102-3b, 104-a, 104-b to control the flight of the drone 100. The actual rotation speeds of the motors 102-1a, 102-1b, 102-2a, 102-2b, 102-3a, 102-3b, 104-a, 104-b are fed back to the flight controller 501, and normal rotation is performed. It is configured so that it can be monitored. Alternatively, the rotary blade 101 may be provided with an optical sensor or the like so that the rotation of the rotary blade 101 is fed back to the flight controller 501.

The software used by the flight controller 501 can be rewritten through a storage medium or the like for function expansion / change, problem correction, etc., or through communication means such as Wi-Fi communication or USB. In this case, protection is performed by encryption, checksum, digital signature, virus check software, etc. so that rewriting by unauthorized software is not performed. In addition, a part of the calculation process used by the flight controller 501 for control may be executed by another computer located on the controller 401, on the farming cloud 405, or elsewhere. Due to the high importance of the flight controller 501, some or all of its components may be duplicated.

The flight controller 501 communicates with the operation device 401 via the Wi-Fi slave unit function 503 and further via the base station 404, receives necessary commands from the operation device 401, and receives necessary information from the operation device 401. Can be sent to 401. In this case, the communication may be encrypted so as to prevent fraudulent acts such as interception, spoofing, and device hijacking. The base station 404 has the function of an RTK-GPS base station in addition to the communication function by Wi-Fi. By combining the signal from the RTK base station and the signal from the GPS positioning satellite, the flight controller 501 can measure the absolute position of the drone 100 with an accuracy of several centimeters. Since the flight controller 501 is so important, it may be duplicated / multiplexed, and each redundant flight controller 501 should use a different satellite to cope with the failure of a specific GPS satellite. It may be controlled.

The 6-axis gyro sensor 505 is a means for measuring the acceleration of the drone body in three directions orthogonal to each other, and further, a means for calculating the velocity by integrating the acceleration. The 6-axis gyro sensor 505 is a means for measuring the change in the attitude angle of the drone aircraft in the above-mentioned three directions, that is, the angular velocity. The geomagnetic sensor 506 is a means for measuring the direction of the drone aircraft by measuring the geomagnetism. The barometric pressure sensor 507 is a means for measuring barometric pressure, and can also indirectly measure the altitude of the drone. The laser sensor 508 is a means for measuring the distance between the drone aircraft and the ground surface by utilizing the reflection of the laser light, and may be an IR (infrared) laser. The sonar 509 is a means for measuring the distance between the drone aircraft and the ground surface by utilizing the reflection of sound waves such as ultrasonic waves. These sensors may be selected according to the cost target and performance requirements of the drone. In addition, a gyro sensor (angular velocity sensor) for measuring the inclination of the airframe, a wind power sensor for measuring wind power, and the like may be added. Further, these sensors may be duplicated or multiplexed. If there are multiple sensors for the same purpose, the flight controller 501 may use only one of them, and if it fails, it may switch to an alternative sensor for use. Alternatively, a plurality of sensors may be used at the same time, and if the measurement results do not match, it may be considered that a failure has occurred.

The flow rate sensor 510 is a means for measuring the flow rate of the sprayed material, and is provided at a plurality of locations on the path from the tank 104 to the nozzle 103. The liquid out sensor 511 is a sensor that detects that the amount of sprayed material is less than a predetermined amount.

The growth diagnosis camera 512a is a means for photographing the field 403 and acquiring data for image analysis. The growth diagnosis camera 512a is, for example, a multispectral camera, but may be a camera that receives visible light. The pathological diagnosis camera 512b is a means for photographing crops growing in the field 403 and acquiring data for pathological diagnosis. The pathological diagnosis camera 512b is, for example, a camera that receives visible light. The growth diagnosis camera 512a and the pathology diagnosis camera 512b may be realized by one hardware configuration.

The intruder detection camera 513 is a camera for detecting a drone intruder, and since the image characteristics and the orientation of the lens are different from the growth diagnosis camera 512a and the pathology diagnosis camera 512b, what are the growth diagnosis camera 512a and the pathology diagnosis camera 512b? Another device. The switch 514 is a means for the user 402 of the drone 100 to make various settings. The intruder contact sensor 515 is a sensor for detecting that the drone 100, in particular, its rotor or propeller guard part, has come into contact with an intruder such as an electric wire, a building, a human body, a standing tree, a bird, or another drone. .. The intruder contact sensor 515 may be replaced with a 6-axis gyro sensor 505. The cover sensor 516 is a sensor that detects that the operation panel of the drone 100 and the cover for internal maintenance are in the open state. The inlet sensor 517 is a sensor that detects that the inlet of the tank 104 is in an open state.

These sensors may be selected according to the cost target and performance requirements of the drone, and may be duplicated or multiplexed. Further, a sensor may be provided at the base station 404, the actuator 401, or some other place outside the drone 100, and the read information may be transmitted to the drone. For example, a wind sensor may be provided at the base station 404 to transmit information on the wind and wind direction to the drone 100 via Wi-Fi communication.

The flight controller 501 transmits a control signal to the pump 106 to adjust the discharge amount and stop the discharge. The current status of the pump 106 (for example, the number of revolutions) is fed back to the flight controller 501.

The LED 107 is a display means for informing the drone operator of the state of the drone. Display means such as a liquid crystal display may be used in place of or in addition to the LED. The buzzer is an output means for notifying the state of the drone (particularly the error state) by an audio signal. The Wi-Fi slave unit function 519 is an optional component for communicating with an external computer or the like for transferring software, for example, in addition to the actuator 401. Instead of or in addition to the Wi-Fi handset function, other wireless communication means such as infrared communication, Bluetooth (registered trademark), ZigBee (registered trademark), NFC, or wired communication means such as USB connection. You may use it. Further, instead of the Wi-Fi slave unit function, mutual communication may be possible by a mobile communication system such as 3G, 4G, and LTE. The speaker 520 is an output means for notifying the state of the drone (particularly the error state) by means of a recorded human voice, synthetic voice, or the like. Depending on the weather conditions, it may be difficult to see the visual display of the drone 100 in flight. In such cases, voice communication is effective. The warning light 521 is a display means such as a strobe light for notifying the state of the drone (particularly the error state). These input / output means may be selected according to the cost target and performance requirements of the drone, and may be duplicated / multiplexed.

● Outline of the field evaluation device As shown in FIG. 4, the field evaluation device 1 is communicably connected to each other through, for example, the drone 100 and the network NW. However, within the technical scope of the present invention, it is not always necessary to be connected to the drone 100, and it is sufficient if the field evaluation device 1 has a function of generating an operation path of an automated driving machine.

The field evaluation device 1 may have a hardware configuration or may be configured on the farming cloud 405. The field evaluation device 1 may be composed of a plurality of devices wirelessly or partially or wholly connected by wire.

● Functional part of the field evaluation device 1 The field evaluation device 1 has a computing device such as a CPU (Central Processing Unit) for executing information processing and a storage device such as RAM (Random Access Memory) and ROM (Read Only Memory). As a result, it mainly has an information acquisition unit 11, a route generation unit 12, a time calculation unit 13, a field evaluation unit 14, an output unit 15, a communication processing unit 16, and a storage unit 20 as software resources.

The storage unit 20 is an example of the first functional unit and the second functional unit, and is a functional unit that stores at least the information referred to in the field evaluation.
FIG. 5 is a diagram showing an example of the table T1 stored in the storage unit 20. In the table T1, the work and the type or movement mode of the automatic driving machine used for the work are stored in association with each other. The work items include, for example, tillage, puddling, rice planting, pesticide spraying and fertilizer spraying. The table T1 stores the types of automatic driving machines used for these operations. Autonomous driving machines refer to all machines that automatically generate driving routes and perform automatic driving. The automatic driving machine is, for example, an agricultural machine such as a cultivator, a tractor, or a rice transplanter. Further, the automatic driving machine may be a drone that performs agricultural work. The automatic driving machine is not limited to a machine that performs agricultural work, and may be a machine that comprehensively moves a predetermined area to perform work, for example, a cleaning robot.

As the type of the automatic driving machine, different types may be stored depending on the work, or there may be an automatic driving machine that can be used for both pesticide spraying and fertilizer spraying, as in the drone shown in the figure. Further, the table T1 may also store a manual movement mode in which the automatic driving machine is not used in each operation.

The movement mode indicates a mode for each work in each autonomous driving machine. The movement mode includes, for example, a work width, a movement speed during work, a movement speed when moving between fields, an assumed horsepower, and the like. As the moving speed at the time of moving between fields, in addition to the moving speed by the automatic operation, the moving speed assumed at the time of the manual operation by the operator may be stored separately from the moving speed by the automatic operation. In addition, the movement mode may include a movement speed when moving in the field without working. Further, the movement mode may include the speed at which the autonomous driving machine makes a turn, for example, a turn. For example, in the case of a machine used for a plurality of operations such as a drone, it may move in a different movement mode depending on the operation to be performed.

The information acquisition unit 11 is a functional unit that acquires field information regarding the shape of the target field. The field information may be, for example, coordinate data of the field published by a public institution such as the Ministry of Agriculture, Forestry and Fisheries, or survey data. Further, the field information may be survey data acquired in advance using an apparatus having GNSS. This survey data may be data acquired by patrolling the field while holding a smart phone having a GNSS function, a predetermined surveying device, or the like. Further, the survey data may be data obtained by flying a drone having a GNSS function. The survey data may be two-dimensional data or three-dimensional data in consideration of the height difference.

In addition, the field information may include information on an entry prohibited area where the automatic driving machine cannot enter. The no-entry area is a region defined in the horizontal direction and the height direction, which has an extension in the three-dimensional direction, and is, for example, a rectangular parallelepiped region drawn around an obstacle. The no-entry area may be a spherical area drawn around an obstacle.

The information acquisition unit 11 may call up the field information from an appropriate storage unit of the field evaluation device 1, or may acquire the field information from another information source through the network NW.

The information acquisition unit 11 may accept a plurality of fields separated from each other as target fields. The selection of a plurality of fields is performed when the same worker works on a plurality of fields using the same automatic driving machine, for example, when one land owner owns a plurality of scattered fields. Will be done in. According to such a configuration, it is possible to evaluate the work efficiency of the plurality of fields on the assumption that the work is performed on a plurality of fields at once.

Further, the information acquisition unit 11 may accept the selection of one or a plurality of works to be evaluated in the target field. The user who selects the work can select the work to be evaluated according to the use of the field and the type of work requiring evaluation. The information acquisition unit 11 may accept the work selection via the communication processing unit 16, or may accept the work selection by an appropriate input means provided in the field evaluation device 1.

The route generation unit 12 is a functional unit that generates an operation route in which the automatic driving machine moves in the target field based on the field information.

The route generation unit 12 may determine the area excluding the no-entry area from the field shape included in the field information among the target fields as the movement permission areas 70i and 80i (see FIGS. 6 and 7). The route generation unit 12 may have a different entry prohibited area for each type of autonomous driving machine. For example, in a land-running machine, it is necessary to avoid the obstacle regardless of the height of the obstacle, while the drone 100 flies in the air, so depending on the size of the obstacle in the height direction, the sky above the obstacle. It is possible to fly. The work efficiency of each machine type can be evaluated more accurately according to the configuration in which whether or not the area is prohibited from entering is determined based on the size of the obstacle in the height direction according to the type of the autonomous driving machine.

The route generation unit 12 generates an operation route in the movement permission area.
As shown in FIG. 6, for example, the route generation unit 12 generates an operation route that reciprocates in the movement permission area 70i and scans substantially comprehensively.

Further, as shown in FIG. 7, the route generation unit 12 may divide the movement permission area 80i into a plurality of areas and generate a route for each of the divided areas. More specifically, the route generation unit 12 divides the movement permission area 80i into one or a plurality of shaping areas 81i and one or a plurality of irregularly shaped areas 82i, 83i having a smaller area than the shaping area 81i. The route generation unit 12 divides the movement permission area 80i, especially when the movement permission area 80i has a concave polygonal shape when viewed from the sky. A concave polygon is a polygon in which at least one of the internal angles of the polygon is an angle exceeding 180 °, in other words, a polygon having a concave shape. When the recess is found, the route generation unit 12 divides the area. If no recess is found, it is determined that further division is unnecessary, and the process is terminated.

Further, the route generation unit 12 may further divide the shaping area 81i into an outer peripheral area 811i and an inner area 812i, and generate an operation route for each area. The outer peripheral area 811i is an annular area having a working width of an automated driving machine. The working width of the self-driving machine differs depending on the type of the self-driving machine and the work content. For example, when the drone 100 sprays a drug, the working width is the spray width of the drug. Further, the work width when the drone 100 monitors is the monitorable width when the drone is for monitoring. As described above, the working width is different from the housing width of the automatic driving machine.

The deformed areas 82i and 83i are areas in which the area per piece is smaller than the shaping area 81i, and the outer peripheral area 811i and the inner area 812i cannot be defined. The route generation unit 12 determines whether or not the route generation is possible for each of the areas 811i, 812i, 82i, and 83i to be formulated, and determines the area to be the target of the route generation. This is because the shaping area 81i and the deformed areas 82i and 83i may not be able to be operated due to their shapes. The route generation unit 12 determines whether or not the area is capable of route generation based on a predetermined value determined based on the operating performance of the autonomous driving machine. The driving performance of the self-driving machine includes the approaching distance required for the self-driving machine to reach constant speed operation and the stopping distance required from constant speed operation to stop. In addition, the operating performance of the autonomous driving machine includes the work width in chemical spraying and monitoring.

In the route generation unit 12, for example, the long side of the outer peripheral area 811i or the inner area 812i is less than a predetermined value determined based on the approach distance required for the autonomous driving machine to reach constant speed operation and the stop distance required for stopping. In this case, it may be decided not to generate a route in the outer peripheral area 811i or the inner area 812i. For example, when the long side of the outer peripheral area 811i or the inner area 812i is less than the total value of the approach distance and the stop distance, it is determined not to generate a route. Further, the route generation unit 12 does not generate a route when the shortest side of the outer peripheral area 811i or the inner area 812i is less than a predetermined value determined based on the working width of the automated driving machine. More specifically, when the shortest side of the outer peripheral area 811i or the inner area 812i is less than the working width of the autonomous driving machine, no route generation is performed. This is because if it is less than the predetermined value, the route cannot be generated.

Further, the route generation unit 12 determines whether or not the drone 100 can be operated for each of the determined irregular areas 82i and 83i. The route generation rule for the irregular areas 82i and 83i is a rule for generating a route that flies in one direction in the long side direction or a route that makes one round trip. Therefore, when the shortest side of the deformed areas 82i and 83i is less than a predetermined value determined based on the working width of the autonomous driving machine, the route generation unit 12 does not generate a route in the autonomous driving machine. ..

Also, if the long side of the deformed areas 82i and 83i is less than the predetermined value determined based on the approach distance required for the autonomous driving machine to reach constant speed operation and the stop distance required for stopping, route generation is not performed. Make a decision to that effect. For example, when the long side of the deformed areas 82i and 83i is less than the total value of the approach distance and the stop distance, no route generation is performed.

The route generation unit 12 shown in FIG. 5 generates an operation route in the route generation target area based on a predetermined route generation rule. The route generation unit 12 generates an operation route in each of the outer peripheral area 811i, the inner area 812i, and the deformed area 83i, and by connecting the generated operation routes, even if the operation route of the movement permission area 80i is generated. good. The order of concatenation is arbitrary. The route generation unit 12 generates an operation route based on a different route generation rule for each area. For example, the route generation unit 12 generates an orbiting route 811r that makes one orbit in the outer peripheral area 811i. Further, the route generation unit 12 reciprocates in the route generation target area a plurality of times in the inner area 812i, and scans the adjacent round-trip routes or the adjacent outward and return routes so as to spread or narrow from the outward route start point side to the outward route end point side. The round-trip operation path 812r may be generated. The route generation unit 12 may generate a path that moves in one direction toward the long side of the area or a modified area operation path 83r that makes one round trip in the deformed area 83i.

The route generation unit 12 generates different operation routes depending on the type of the automated driving machine. The route generation unit 12 refers to the information on the movement mode stored in the table T1 and applies this information as a coefficient to the route generation rule to generate an operation route. That is, the route generation unit 12 generates an operation route based on the same generation rule and a different coefficient for each type of the automatic driving machine. For example, an automated driving machine having a wide working width may have a smaller number of round trips than a machine having a narrow working width, but may not be able to generate a route in a narrow region and may not be able to work in the region. Further, the route generation unit 12 may generate an operation route based on a route generation rule different depending on the automatic driving machine. The route generation unit 12 can generate routes for various automated driving machines by appropriately adding and storing route generation rules generated based on a predetermined format.

The route generation unit 12 may generate a movement route in the field that moves without working in the field separately from an operation route that moves while working. The shorter the in-field movement route is, the more preferable it is, but it is difficult to operate the field in a so-called one-stroke stroke without duplication. It is normal.

Further, as shown in FIG. 8, when the information acquisition unit 11 has acquired the information that the information acquisition unit 11 evaluates a plurality of fields as the target fields, the route generation unit 12 moves between the plurality of fields F. That is, an inter-field path r is generated. The inter-field route r may be a route for moving from one field to another, or may be a concept indicating the entire route for patrolling a plurality of fields.

The time calculation unit 13 is a functional unit that calculates the working time of the automatic driving machine in the target field according to the field information. The time calculation unit 13 calculates the working time by the automatic driving machine based on the operation route generated by the route generation unit 12.

The time calculation unit 13 may calculate the working time by simulating the movement of the autonomous driving machine at least based on the path length. In this case, the working time is calculated by dividing the path length by the moving speed. Further, the time calculation unit 13 may calculate the working time in consideration of the number of turns or turns in the route. This is because it takes more time to change direction and turn than to go straight, and even if the path length is the same, the work time is longer on a route with a large number of turns.
The time calculation unit 13 may calculate the work time by measuring the time when the automatic driving machine actually moves along the route and performs the work.

Further, the time calculation unit 13 may calculate the work time by applying the manual work time to the area of the area where the operation route cannot be generated by the route generation unit 12. In this case, the time calculation unit 13 calculates the working time in the target field by adding up the working time of the automatic driving machine and the working time by hand. The work time calculated by the time calculation unit 13 becomes longer as the area where the operation route cannot be generated is larger.

When the inter-field route is generated by the route generation unit 12, the time calculation unit 13 may calculate the travel time of the inter-field route. For example, the time calculation unit 13 refers to the table T1 and calculates the movement time based on the movement path length between fields and the movement speed at the time of movement between fields. The time calculation unit 13 may adopt a moving speed by automatic operation as a moving speed when moving between fields, or may adopt a moving speed assumed in manual operation. Further, the time calculation unit 13 may accept an input for selecting which movement speed to adopt via the information acquisition unit 11. In addition, when there is an in-field movement route that moves in the field without working, the time calculation unit 13 calculates the movement time in the field based on the route and the movement speed when moving in the field. You may. The time calculation unit 13 calculates the working time in the target field by adding one or more of the moving time of the inter-field route, the moving time of the moving route in the field, and the manual working time to the working time of the automatic driving machine. calculate. The time calculation unit 13 calculates the working time longer as the moving time of the inter-field route, the moving time of the intra-field moving route, and the manual working time become longer.

The time calculation unit 13 may approximate the shape of the target field to one or a plurality of the shape models by image identification processing, and calculate the work time in the target field based on the work time associated with the shape model. For example, when the shape of the field can be approximated to a rectangle, the time calculation unit 13 shortens the working time because the route in the field is short and the overlapping routes are also short. Further, the time calculation unit 13 may calculate the working time based on the area of the target field that deviates from the shape model. More specifically, the larger the area deviating from the approximate model, the longer the working time may be calculated. Further, the time calculation unit 13 may calculate the working time according to the number of shape models applied to the target field. The route of the autonomous driving machine is assumed to be a route in the target field by, for example, being generated for each shape model and connecting a plurality of generated routes with a movement route in the field. Therefore, the larger the number of shape models that are close to the target field per piece, the longer the movement route in the field and the longer the working time.

The time calculation unit 13 may calculate the working time in the target field based on at least one of the length around the target field, the number of vertices, and the uneven shape of the outer circumference of the target field. For example, the time calculation unit 13 calculates the working time longer as the circumference of the target field is longer. This is because when the perimeter of the target field is long, it is assumed that the area of the target field is large or the shape is complicated. Since it is assumed that the shape of the target field is more complicated as the perimeter is longer with respect to the area, the time calculation unit 13 may calculate the working time based on the area and the perimeter. .. Further, the time calculation unit 13 calculates the working time longer as the number of vertices of the target field increases. This is because it is assumed that the shape of the target field becomes more complicated as the number of vertices increases. Furthermore, the time calculation unit 13 determines whether or not the concave shape is included in the outer circumference of the target field, and if the concave shape is included, the working time is calculated to be long. Further, the time calculation unit 13 may calculate the working time longer as the number of concave shapes increases. Since the route of the autonomous driving machine is assumed to be a route for reciprocating and scanning a rectangular field, it becomes necessary to divide the field and generate a route as the number of concave shapes increases. As a result, the movement route in the field becomes long, and it is expected that the working time will be long. According to such a configuration, the calculation processing load is reduced as compared with the configuration in which the route of the automatic driving machine is actually generated and the working time is calculated.

The time calculation unit 13 may refer to the storage unit 20 and calculate the work time based on the type or movement mode in the work for which the information acquisition unit 11 has accepted the selection. According to this configuration, the working time in the selected work can be calculated more accurately.

The field evaluation unit 14 is a functional unit that evaluates the work efficiency of the automated driving machine in the target field based on the work time of the target field. The field evaluation unit 14 calculates the evaluation value by, for example, dividing the working time by the field area. In addition, the field evaluation unit 14 evaluates the work efficiency based on the travel time and the work time. This movement time may include a movement time of movement between fields and a movement time of movement within a field.

That is, it can be said that a field in which the route traveled by the automatic driving machine can be simply drawn is a field with high work efficiency. Further, in the case where the in-field route or the inter-field route is long, or in the field where a route with many turns is required, the work time is longer and the evaluation value of the work efficiency is smaller than that of the above-mentioned field. Further, when the area where the work by the automatic driving machine is impossible is large, the work time increases as a result of the manual work, and the evaluation value of the work efficiency becomes small. According to such a configuration, it is possible to objectively evaluate the work efficiency according to the shape of the field on the premise of working with the automatic driving machine. As a result, it can be a factor to more accurately evaluate the productivity of the field in the evaluation of land assets and the calculation of credit risk. In addition, according to the configuration that can calculate the evaluation value according to the selected work, it is possible to help the crop selection by specifying the work suitable for the target field. Furthermore, since the degree of efficiency improvement expected by the introduction of the automatic driving machine is calculated, it can be used as an opportunity for the manager to introduce the automatic driving machine.

Further, in the table T1, when a plurality of automatic driving machines are associated with the same work, the route generation unit 12 generates different movement routes for each type of the automatic driving machine, and the time calculation unit 13 As a result of calculating the working hours, the field evaluation unit 14 can calculate the evaluation value for each type of the automatic driving machine in the target field. This evaluation value can also help the farm manager or the manufacturer / distributor of the autonomous driving machine to select the machine to be introduced into the field.

The output unit 15 is a functional unit that outputs the evaluation result of the target field determined by the field evaluation unit 14 to an appropriate display unit. The output unit 15 may be displayed on a terminal connected via the network NW, in addition to the display unit provided in the field evaluation device 1. The terminal may be an appropriate one such as a personal computer, a tablet terminal or a smart phone. The output unit 15 may display an evaluation value for each type of autonomous driving machine. Further, the output unit 15 may display the evaluation value for each selected target field. Further, the output unit 15 may display the evaluation value of the selected work on the display unit. The output unit 15 may display the asset valuation of land and the calculation result of credit risk. The output unit 15 may refer to a table that stores a plurality of relationships between the land asset evaluation result and the work efficiency evaluation value, and estimate and display the land asset evaluation value according to the evaluation value of the target field. good.

FIG. 8 is a diagram showing the generation result of the inter-field path r output by the output unit 15. In the figure, a plurality of fields F and an inter-field route r that moves between fields are numbered. The evaluation process is clarified by the configuration in which the output unit 15 outputs the generation result of the inter-field route.

FIG. 9 is a diagram showing an example of the evaluation result of the target field. FIG. 9A is an example of a histogram showing the distribution of evaluation values for each field, FIG. 9B is an example of a histogram showing the distribution of evaluation values for each corporation that manages the field, and FIG. 9C is an example of a histogram. , This is an example of a histogram showing the distribution of evaluation values for each area with fields. For example, the output unit 15 displays the histogram on an appropriate display unit, and then displays the display mode of the bar to which the evaluation target field belongs so as to be different from other bars. According to this configuration, the viewer can visually confirm the distribution or relative evaluation of the work efficiency. In addition, the manager of a field having a high evaluation value and the manager of a field having a low evaluation value may exchange information to contribute to the improvement of efficiency.

FIG. 9D is an example of a scatter plot showing the relationship between the field area and efficiency and working time. Plot P1 shows the relationship between the field area and the evaluation value of work efficiency, and plot P2 shows the relationship between the field area and the work time. As shown in the figure, even in a field having the same field area, the work efficiency and the work time differ depending on the shape of the field and vary. The output unit 15 may display the scatter plot on an appropriate display unit.

The communication processing unit 16 is a functional unit that transmits / receives information to / from an appropriate device through the network NW. The communication processing unit 16 may receive information regarding the selection of the field or work from, for example, an appropriate input device. The communication processing unit 16 may receive field information from a device for storing coordinate data or survey data of a field published by a public institution such as the Ministry of Agriculture, Forestry and Fisheries, or a device having a GNSS function. The communication processing unit 16 may receive information regarding the work, the machine type, and the movement mode thereof, and store the information in the table T1 of the storage unit 20. The communication processing unit 16 may receive the actual value of the working time in the target field from the automatic driving machine such as the drone 100. The communication processing unit 16 may transmit the evaluation result of the field to, for example, an output device having a display unit.

● Treatment flow As shown in FIG. 10, first, the selection of one or a plurality of target fields is accepted (S11). Then, the field information of the selected target field is acquired (S12). Then, the selection of one or a plurality of target works is accepted (S13). Then, the route generation unit 12 generates an operation route for the selected target field (S14). When a plurality of target fields are selected, an operation route in each target field is generated. In addition, the route generation unit 12 may generate an intra-field movement route. Then, the working time in the field generated in the target field is calculated (S15). Then, when a plurality of target fields are selected (Y in S16), the route generation unit 12 generates an inter-field route that moves between the target fields (S17). In addition, the time calculation unit 13 calculates the travel time required for the inter-field route (S18). Following step S18, or if there are not a plurality of target fields in step S16, the process proceeds to step S19. In step S19, the field evaluation unit 14 calculates an evaluation value of work efficiency.

(Technically significant effect of the present invention)
According to the field evaluation device according to the present invention, the work efficiency of the field can be objectively evaluated on the premise of the work by the automatic driving machine.

Claims (8)

An information acquisition unit that acquires field information regarding the shape of one or more target fields, and
A time calculation unit that calculates the working time of the automated driving machine in the target field according to the field information.
A field evaluation unit that evaluates the work efficiency of the automated driving machine in the target field based on the work time.
A field evaluation device.
Further, a route generation unit for generating an operation route for the automatic driving machine to move in the target field based on the field information is provided.
The time calculation unit calculates the working time by the automatic driving machine based on the route generated by the route generation unit.
The field evaluation device according to claim 1.
The information acquisition unit acquires the field information regarding the plurality of target fields to be evaluated, and obtains the field information.
The route generation unit generates an inter-field route that moves between the plurality of target fields.
The time calculation unit calculates the travel time of the inter-field route,
The field evaluation unit evaluates the work efficiency based on the travel time and the work time.
The field evaluation device according to claim 2.
It is equipped with a storage unit that stores the shape model of the field and the working time in association with each other.
The time calculation unit approximates the shape of the target field to one or a plurality of the shape models by image identification processing, and calculates the working time in the target field based on the working time associated with the shape model. ,
The field evaluation device according to any one of claims 1 to 3.
The time calculation unit calculates the working time in the target field based on at least one of the length around the target field, the number of vertices, and the uneven shape of the outer circumference of the target field.
The field evaluation device according to any one of claims 1 to 4.
A second storage unit that stores the work and the type or movement mode of the automatic driving machine used for the work in association with each other.
Further prepare
The information acquisition unit accepts the selection of one or a plurality of works to be evaluated in the target field, and receives the selection.
The time calculation unit refers to the second storage unit and calculates the work time based on the type or the movement mode in the selected work.
The field evaluation device according to any one of claims 1 to 5.
The computer
Information acquisition processing to acquire field information regarding the shape of one or more target fields, and
A time calculation process for calculating the working time of the automatic driving machine in the target field according to the field information, and a time calculation process.
A field evaluation process for evaluating the work efficiency of the automated driving machine in the target field based on the work time, and
The field evaluation method to carry out.
Against the computer
Information acquisition processing to acquire field information regarding the shape of one or more target fields, and
A time calculation process for calculating the working time of the automatic driving machine in the target field according to the field information, and a time calculation process.
A field evaluation process for evaluating the work efficiency of the automated driving machine in the target field based on the work time, and
A field evaluation program to carry out.

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