Ridge management method, ridge management system, and ridge management program

Abstract

To determine positions and shapes of ridges having various shapes set in a farm field.SOLUTION: A ridge management method includes determining map information which shows a small region 510 with a display format corresponding to operation information on a work device 30 at the small region 510 set in a farm field 500. Moreover, the ridge management method includes determining a rail track 600 of the work device 30 during at least a part of a period corresponding to the operation information. Furthermore, the ridge management method includes outputting display information which shows an image which displays the rail track 600 of the work device 30 superimposed on the map information. Determining the rail track 600 of the work device 30 may include determining the rail track 600 of the work device 30 during a period including a work period in which the work device 30 is working in the farm field 500, during the period corresponding to the operation information.SELECTED DRAWING: Figure 6

Description

[Technical field]

[0001] The present invention relates to a ridge management method, a ridge management system, and a ridge management program. [Background technology]

[0002] 2. Description of the Related Art In recent years, for the purpose of cultivation management, information on work carried out in a farm field has been provided to a user, for example, an owner or worker of the farm field, using information and communication technology.

[0003] For example, Patent Document 1 discloses a technique for determining the positions of ridges arranged in parallel by linearly approximating the movement trajectory of an agricultural machine. [Prior art documents] [Patent documents]

[0004] [Patent Document 1] Patent No. 5821970 Summary of the Invention [Problem to be solved by the invention]

[0005] However, in farm fields, there are also curved ridges. In addition, there are cases where the ridges are not parallel to each other. With the technology described in Patent Document 1, it is difficult to detect such curved ridges or ridges that are not arranged parallel to each other.

[0006] In view of the above circumstances, one of the objects of the present disclosure is to determine the positions and shapes of ridges of various shapes set in a farm field. Other objects can be understood from the following description and the description of the embodiment. [Means for solving the problem]

[0007] The means for solving the problems will be described below using the numbers and symbols used in the description of the embodiment of the invention. These numbers and symbols are added in parentheses for reference purposes to show an example of the correspondence between the description of the claims and the description of the embodiment of the invention. Therefore, the description in parentheses should not be interpreted as limiting the scope of the claims.

[0008] A ridge management method according to one embodiment for achieving the above object includes classifying one or more ridges (510) worked on by the working device (30) into a type category including straight ridges formed in a straight line and curved ridges formed in a curved line, based on position information of the working device (30). The ridge management method also includes determining a ridge shape (600) representing the shape of the one or more ridges (510) based on the type category.

[0009] A ridge management system (1000) according to one embodiment for achieving the above object includes a type classification unit (160) and a shape determination unit (170). The type classification unit (160) classifies one or more ridges (510) worked on by the working device (30) into type categories including straight ridges formed in a straight line and curved ridges formed in a curved line, based on position information of the working device (30). The shape determination unit (170) determines a ridge shape (600) representing the shape of the one or more ridges (510) based on the type category.

[0010] A ridge management program (410) according to one embodiment for achieving the above object causes the calculation device (120, 220) to classify one or more ridges (510) worked on by the working device (30) into a type category including straight ridges formed in a straight line and curved ridges formed in a curved line, based on position information of the working device (30). The ridge management program (410) also causes the calculation device (120, 220) to determine a ridge shape (600) representing the shape of the one or more ridges (510) based on the type category. Effect of the Invention

[0011] According to the above embodiment, the user can easily grasp information about the ridges. [Brief description of the drawings]

[0012] [Figure 1] FIG. 1 is a schematic diagram of a ridge management system in one embodiment. [Diagram 2] 1 is a diagram for explaining the shape of a ridge determined by a ridge management system in one embodiment. FIG. [Diagram 3] FIG. 1 is a diagram showing a configuration of a ridge management device in one embodiment. [Figure 4] A diagram showing functional blocks executed by a ridge management system in one embodiment. [Diagram 5] FIG. 2 is a diagram illustrating a configuration of a terminal according to an embodiment. [Figure 6] 1 is a flowchart showing processing by a ridge management system in one embodiment. [Figure 7] 4A to 4C are diagrams for explaining the process by which the ridge management device in one embodiment determines the shape of a ridge. [Figure 8] 11 is a diagram for explaining an approximation function that represents a ridge by the ridge management device in one embodiment. FIG. [Figure 9] A diagram showing functional blocks executed by a ridge management system in one embodiment. [Figure 10] 1 is a flowchart showing processing by a ridge management system in one embodiment. [Figure 11] A figure for explaining an approximation function that represents the shape of ridges grouped by the ridge management device in one embodiment. [Figure 12] A diagram for explaining the process by which a ridge management device in one embodiment determines a ridge type classification. [Figure 13] A diagram for explaining the process of grouping ridges by the ridge management device in one embodiment. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0013] (Embodiment 1) A ridge management system 1000 according to this embodiment of the present invention will be described with reference to the drawings. In this embodiment, as shown in Fig. 1, the ridge management system 1000 includes a ridge management device 100 and a terminal 200. The ridge management device 100 is communicatively connected to an operation device 30 and the terminal 200 via a network 20, for example the Internet.

[0014] The operating device 30 performs work while moving, for example, along the ridges in the farm field 500. The operating device 30 includes, for example, a tractor, a transplanter, and the like. The operating device 30 includes a measuring device that measures its own position, for example, a GNSS (Global Navigation Satellite System) receiver, a quantum compass, and the like. The operating device 30 measures its own position at each time, and outputs position information indicating the measured position and time to the ridge management device 100 as operation information.

[0015] Furthermore, the working device 30 may acquire status information indicating the state of the working device 30 at each time, such as speed, direction of travel, steering angle, and ON / OFF status of various clutches, and output the information to the ridge management device 100. When the working device 30 is a vehicle, such as a tractor, that pulls a work machine (such as a ridge-making work machine), the status information may include information such as the PTO (power take-off) rotation speed when transmitting power to the work machine, and the hitch height and lift arm angle that indicate the attitude of the work machine. The status information is, for example, included in operation information and output from the working device 30 to the ridge management device 100.

[0016] As shown in FIG. 2, the ridge management device 100 determines a ridge shape 600 representing the shape and position of one or more ridges 510 installed in the field 500 based on the operation information acquired from the working device 30. For example, the ridge management device 100 determines a ridge shape 600 (e.g., the first ridge shape 600-1, the second ridge shape 600-2, etc.) representing ridges 510 formed in a straight line (e.g., the first ridge 510-1, the second ridge 510-2, etc.). Also, the ridge management device 100 determines a ridge shape 600 (e.g., the third ridge shape 600-3, the fourth ridge shape 600-4, etc.) representing ridges 510 formed in a curved shape (e.g., the third ridge 510-3, the fourth ridge 510-4, etc.).

[0017] The ridge information representing the determined ridge shape 600 is displayed by the terminal 200. For example, the terminal 200 displays an image representing the ridge shape 600 on a map. A user of the terminal 200, such as the owner of the field 500, the manager of the work, etc., can easily grasp the shape of the ridge 510 provided in the field 500 based on the ridge information displayed on the terminal 200. Also, the terminal 200 may display information regarding the work in the field 500, such as the yield in the ridge 510, etc., together with the ridge shape 600. Thereby, the user can easily grasp the information in each ridge 510 and the corresponding ridge 510.

[0018] (Configuration of the Ridge Management System) The configuration of the ridge management device 100 included in the ridge management system 1000 shown in FIG. 1 will be described. As shown in FIG. 3, the ridge management device 100 includes an input / output device 110, an arithmetic device 120, a communication device 130, and a storage device 140. The ridge management device 100 is a computer including, for example, a cloud server or the like. Information for the arithmetic device 120 to execute processing is input to the input / output device 110. Also, the input / output device 110 outputs the result of the processing executed by the arithmetic device 120. The input / output device 110 includes various input devices and output devices, and includes, for example, a keyboard, a mouse, a microphone, a display, a speaker, a touch panel, etc. The input / output device 110 may be omitted.

[0019] The communication device 130 is connected to the network 20 and communicates with each device via the network 20. For example, the communication device 130 transfers operation information acquired from the maintenance device 30 to the calculation device 120. Also, the communication device 130 transfers a signal generated by the calculation device 120 to the terminal 200. Also, the communication device 130 may acquire information from other devices that are not connected via the network 20. For example, the communication device 130 may acquire information from other devices via any storage medium, such as a memory card or a Universal Serial Bus (USB) memory. Also, the communication device 130 may acquire information from other devices that are directly connected via a USB or the like. The communication device 130 includes various interfaces, such as a Network Interface Card (NIC) and a USB.

[0020] The storage device 140 stores various data for determining the shape of the ridges 510 in the field 500, such as the field data 400, and the ridge management program 410. The storage device 140 is used as a non-transitory tangible storage medium for storing the ridge management program 410. The ridge management program 410 may be provided as a computer program product recorded on a computer-readable storage medium 1, or may be provided as a computer program product downloadable from a server.

[0021] The farm field data 400 stores information about the farm field 500, for example, information about a ridge shape 600 that represents the shape of the ridge 510. The farm field data 400 may also store information that represents the position, area, etc. of the farm field 500.

[0022] The arithmetic device 120 reads out and executes the ridge management program 410 from the storage device 140, and performs various data processing to determine the shape of the ridges 510. For example, the arithmetic device 120 includes a central processing unit (CPU) and the like.

[0023] By reading and executing the ridge management program 410, the calculation device 120 cooperates with the storage device 140 to realize a data storage unit 150, a type classification unit 160, a shape determination unit 170, and an output unit 180, as shown in FIG. 4. The data storage unit 150 stores the field data 400. The type classification unit 160 classifies one or more ridges 510 into type categories including straight ridges formed in a straight line and curved ridges formed in a curved line. The shape determination unit 170 determines a ridge shape 600 representing the shape of the one or more ridges 510. The output unit 180 outputs ridge information representing the ridge shape 600 to the terminal 200.

[0024] The shape determining unit 170 includes a straight ridge determining unit 171 and a curved ridge determining unit 172. The straight ridge determining unit 171 determines the shape of straight ridges formed in a straight line. The curved ridge determining unit 172 determines the shape of curved ridges formed in a curved line.

[0025] Next, the configuration of the terminal 200 shown in Fig. 1 will be described. As shown in Fig. 5, the terminal 200 includes an input / output device 210, a calculation device 220, a communication device 230, and a storage device 240. The terminal 200 includes, for example, a computer, a tablet, a mobile phone, and the like. Information for the calculation device 220 to execute processing is input to the input / output device 210. In addition, the input / output device 210 outputs the result of the calculation device 220 executing the processing. The input / output device 210 includes various input devices and output devices, and includes, for example, a keyboard, a mouse, a microphone, a display, a speaker, a touch panel, and the like.

[0026] The communication device 230 is connected to the network 20 and communicates with each device via the network 20. The communication device 230 transfers, for example, ridge information acquired from the ridge management device 100 to the calculation device 220. The communication device 230 may also output information to other devices that are not connected via the network 20. For example, the communication device 230 may output information to other devices via any storage medium, such as a memory card or a Universal Serial Bus (USB) memory. The communication device 230 may also acquire information from other devices directly connected via a USB or the like. The communication device 230 includes various interfaces, such as a transceiver used for wireless communication, such as a wireless Local Area Network (LAN) or a cellular network, a network interface card (NIC), and a USB.

[0027] The storage device 240 stores various data for displaying the ridge information acquired from the ridge management device 100, for example, a display program 420. The storage device 240 is used as a non-transitory tangible storage medium for storing the display program 420. The display program 420 may be provided as a computer program product recorded in a computer-readable storage medium 2, or may be provided as a computer program product downloadable from a server.

[0028] The arithmetic device 220 reads out and executes the display program 420 from the storage device 240, and performs various data processing for displaying the ridge information. For example, the arithmetic device 220 includes a central processing unit (CPU; Central Processing Unit) and the like.

[0029] The arithmetic device 220 reads and executes the display program 420, thereby implementing the display unit 250 in cooperation with the storage device 240 as shown in Fig. 4. The display unit 250 cooperates with the input / output device 210 to display the ridge information.

[0030] (Operation of the Field Management System) The operation of the field management system 1000 will be described. An operator moves to the field 500 with the working device 30, for example, a tractor, in order to perform work in the field 500. For example, the operator starts a driving device of the working device 30, such as an engine or a motor. When the driving device is started, the working device 30 acquires operation information, specifically, position information and status information. The working device 30 outputs the acquired operation information to the field management device 100. For example, the working device 30 sequentially outputs the operation information to the field management device 100. Also, when the driving device is stopped, the working device 30 may output the operation information acquired from when the driving device was started until it stopped to the field management device 100.

[0031] When the arithmetic unit 120 of the field management device 100 acquires operation information from the working device 30, it reads and executes the field management program 410. When the arithmetic unit 120 reads and executes the field management program 410, it starts the process shown in FIG. 6, which is part of the field management method.

[0032] In step S110, the type classification unit 160 realized by the arithmetic unit 120 extracts the operation information when moving along the field 510. As shown in FIG. 7, the working device 30 moves along the track 620 while performing work. Therefore, the position information of the working device 30 represents the positioning position 610 on the track 620. Also, when performing work, the working device 30 moves along the field 510. Therefore, the type classification unit 160 extracts the operation information when the working device 30 performs work from the operation information of the working device 30.

[0033] For example, the type classification unit 160 determines a period during which the operating device 30 performed a task based on the operation information. For example, the type classification unit 160 determines a period during which the operating device 30 performed a task based on the speed of the operating device 30 represented in the operation information. The type classification unit 160 may extract operation information in which the speed of the operating device 30 falls within a predetermined range, and determine a period during which the operating device 30 performed a task based on the time at which the extracted operation information was measured. For example, the type classification unit 160 determines a period during which the operating device 30 performed a task as a period during which the operating device 30 performed a task, the period during which the operating device 30 performed a task being a continuous period of time represented in the extracted operation information. The type classification unit 160 may determine a period longer than a predetermined period, among the periods during which the time represented in the extracted operation information is a continuous period, as a period during which the operating device 30 performed a task.

[0034] The type classification unit 160 may also extract a period in which the operation information indicates that work is being performed. For example, when the working device 30 is a device that pulls a working machine, such as a tractor, the type classification unit 160 may determine a period in which the working machine, such as a ridge-forming rotary, is lowered as a period in which the working device 30 is performing work. When the working device 30 is a transplanter, the type classification unit 160 may determine a period in which the work clutch is engaged as a period in which the working device 30 is performing work.

[0035] Furthermore, the type classification unit 160 may exclude a period during which the working device 30 is turning from a period during which the working device 30 is performing work. For example, the working device 30 moves along the ridge 510 while performing work, and when it reaches an end of the ridge 510, turns to perform work on another ridge 510. For this reason, the type classification unit 160 may exclude operation information corresponding to a period during which the working device 30 is turning. For example, when the operation information of the working device 30 indicates that the traveling direction (e.g., azimuth angle) changes by a threshold value or more within a predetermined period, the type classification unit 160 may exclude the period corresponding to the operation information as a period during which the working device 30 is turning.

[0036] In step S120 shown in FIG. 6, the type classification unit 160 classifies the ridges 510 into type categories including straight ridges formed in a straight line and curved ridges formed in a curved line based on the operation information. For example, the type classification unit 160 determines a period during which continuous work was performed as a period during which movement was performed along one ridge 510, and determines whether the trajectory 620 along which the working device 30 moved during that period is straight or curved. When the trajectory 620 along which the working device 30 moved is straight, the type classification unit 160 classifies the ridges 510 worked on during that period as straight ridges. When the trajectory 620 along which the working device 30 moved is curved, the type classification unit 160 classifies the ridges 510 worked on during that period as curved ridges.

[0037] For example, the type classification unit 160 extracts operation information for a period during which tasks were performed continuously, and determines whether the positioning positions 610 represented by the extracted operation information are arranged in a straight line. For example, the type classification unit 160 determines an approximation line for the positioning positions 610 represented by the extracted operation information, and determines whether the positioning positions 610 are arranged in a straight line based on the degree of agreement indicating the degree to which the approximation line agrees with the positioning positions 610.

[0038] Here, the approximation line may be expressed by a linear function. For example, as shown in FIG. 8, the type classification unit 160 extracts two positioning positions 610 with the largest interval among the positioning positions 610 corresponding to the extracted operation information, and determines a first direction 650 in which a straight line connecting the extracted positioning positions 610 extends. The type classification unit 160 determines a linear function representing a position in a second direction 660 perpendicular to the first direction 650 as an approximation line for the determined position in the first direction 650. For example, the linear function represents a position in the second direction 660, for example, y, with a position in the first direction 650 as a variable, for example, variable x, as shown in Equation (1). In other words, the linear function corresponds to a function representing an approximation line when the first direction 650 is the x-axis and the second direction 660 is the y-axis. In addition, for example, the origin of the linear function represents one of the two positioning positions 610 with the largest interval among the corresponding positioning positions 610. In this case, the origins of the linear functions representing the ridge shapes 600 of the ridges 510 are different from each other.

number

[0039] For example, in the example shown in FIG. 8, when determining the ridge shape 600 corresponding to the first ridge 510-1, the type classification unit 160 extracts the first positioned position 610-1 and the second positioned position 610-2, which are the two positions with the largest interval, from among the positioned positions 610 corresponding to the first ridge 510-1. The type classification unit 160 determines a first direction 650 in which a straight line connecting the extracted first positioned position 610-1 and the second positioned position 610-2 extends. The type classification unit 160 determines a linear function representing a position in a second direction 660 perpendicular to the first direction 650 as an approximation line for the determined position in the first direction 650. In addition, the origin of the linear function is determined to be, for example, the first positioned position 610-1.

[0040] Furthermore, when determining the ridge shape 600 corresponding to the third ridge 510-3, the type classification unit 160 extracts the third positioned position 610-3 and the fourth positioned position 610-4, which are the two positioned positions with the largest distance between them, from among the positioned positions 610 corresponding to the third ridge 510-3. The type classification unit 160 determines a first direction 650 in which a straight line connecting the extracted third positioned position 610-3 and the fourth positioned position 610-4 extends. The type classification unit 160 determines a linear function representing a position in a second direction 660 perpendicular to the first direction 650 as an approximation line for the determined position in the first direction 650. Furthermore, the origin of the linear function is determined to be, for example, the fourth positioned position 610-4.

[0041] The degree of agreement may be determined based on the sum of distances from the positioning position 610 to a linear function (approximation line). For example, the degree of agreement may represent a coefficient of determination of the linear function for the positioning position 610 with respect to the extracted operation information.

[0042] When the degree of agreement is equal to or greater than a predetermined threshold, the type classification unit 160 determines that the positioning positions 610 represented by the operation information are arranged in a straight line, in other words, the ridges 510 worked on during that period are straight ridges. When the degree of agreement is less than a predetermined threshold, the type classification unit 160 determines that the positioning positions 610 represented by the operation information are not arranged in a straight line, in other words, the ridges 510 worked on during that period are curved ridges. In this way, the type classification unit 160 classifies the ridges 510 into type categories based on the degree of agreement of the linear function for the positioning positions 610.

[0043] In step S130 of FIG. 6, the straight ridge determination unit 171 determines the shape of the ridge 510 classified as a straight ridge. For example, the straight ridge determination unit 171 determines a part of an approximation line of the positioning position 610 as a ridge shape 600 representing the shape of the ridge 510. For example, the straight ridge determination unit 171 determines an approximation line of a range corresponding to two positioning positions 610 with the largest distance between them as the ridge shape 600. For example, the straight ridge determination unit 171 determines an approximation line between the two positioning positions 610 with the largest distance between them in the first direction 650 as the ridge shape 600. For example, as shown in FIG. 8, the ridge shape 600 corresponding to the first ridge 510-1 represents a range from the first positioning position 610-1 to the second positioning position 610-2 in the first direction 650 among the approximation lines.

[0044] In step S140 of FIG. 6, the curved ridge determination unit 172 determines the shape of the ridge 510 classified as a curved ridge. For example, the curved ridge determination unit 172 determines a polynomial function of degree 2 or higher that represents an approximation curve for the positioning positions 610, and determines a part of the curve represented by the determined polynomial function as the ridge shape 600 that represents the shape of the ridge 510. For example, the curved ridge determination unit 172 determines a polynomial function that represents a position in a second direction 660 perpendicular to the first direction 650 for a position in a first direction 650 in which a straight line connecting two positioning positions 610 that are the largest apart among the positioning positions 610 corresponding to the extracted operation information extends. The curved ridge determination unit 172 determines, as the ridge shape 600, a range sandwiched between the two positioning positions 610 that are the largest apart in the first direction 650 among the curves represented by the determined polynomial function. Also, for example, the origin of the polynomial function represents one of the two positioned positions 610 with the largest interval among the corresponding positioned positions 610. In this case, the origins of the polynomial functions representing the ridge shapes 600 of the ridges 510 are different from each other.

[0045] The curved ridge determination unit 172 determines, for example, a plurality of polynomial functions of one variable with the position in the first direction 650 as a variable, as a plurality of approximation functions for the positioned position 610. For example, the curved ridge determination unit 172 determines, as the approximation functions for the positioned position 610, a quadratic function of one variable, a cubic function of one variable, and a quartic function of one variable.

[0046] The curved ridge determination unit 172 determines information criteria for the determined multiple approximation functions, and determines the ridge shape 600 using the approximation function corresponding to the smallest information criterion. For example, the curved ridge determination unit 172 determines an information criterion for a quadratic function of one variable determined as the approximation function, an information criterion for a cubic function of one variable, and an information criterion for a quartic function of one variable. The curved ridge determination unit 172 represents the ridge shape 600 using a function corresponding to the smallest information criterion among the determined information criteria, for example, a cubic function of one variable. For example, the curved ridge determination unit 172 determines, as the ridge shape 600, a range between two positioning positions 610 with the largest distance in the first direction 650 among the cubic functions of one variable. For example, in the example shown in Fig. 8, the curved ridge determination unit 172 determines the range from the third positioned position 610-3 to the fourth positioned position 610-4 in the first direction 650 among the approximation functions with the smallest information criterion as the ridge shape 600 corresponding to the third ridge 510-3. The determined ridge shape 600 is associated with the corresponding field 500 and stored in the field data 400. The information criterion is an index indicating the quality of the approximation curve representing the positioned position 610. The curved ridge determination unit 172 uses any information criterion, such as the Akaike Information Criterion (AIC) or the Bayes Information Criterion (BIC).

[0047] 6, the output unit 180 outputs ridge information representing the determined ridge shape 600 to the terminal 200. For example, the ridge information represents the ridge shape 600 representing the position and shape of the ridge 510, and an image showing the field 500 on a map.

[0048] In step S160, the display unit 250 of the terminal 200 displays the ridge information acquired from the ridge management device 100 on the input / output device 210. For example, the display unit 250 displays an image representing a field 500 and a ridge shape 600 on a map, as shown in Fig. 2. The display unit 250 may also display information about work on the ridge 510, such as information representing the work time, harvest yield, etc.

[0049] This allows the user to easily understand the shape and position of the ridges 510 in the field 500.

[0050] (Embodiment 2) The ridges 510 may be substantially identical in shape to other ridges 510 provided in the same field 500, but may be located at different positions. For example, as shown in FIG. 2, a first ridge 510-1 is provided so that it is substantially identical in shape to a second ridge 510-2, but located at different positions. A third ridge 510-3 is provided so that it is substantially identical in shape to a fourth ridge 510-4, but located at different positions. In addition, a plurality of ridges 510, such as a fifth ridge 510-5 and a sixth ridge 510-6, may be provided so that they have similar shapes, but have different lengths of the ridges 510, specifically, the length in the direction in which the ridges 510 extend, and different positions.

[0051] For this reason, the ridge management system 1000 may group ridges 510 that have similar shapes, and represent the shapes of the grouped ridges 510 using an approximation function that shows the same shape but different positions.

[0052] (Configuration of ridge management system) The configuration of the ridge management system 1000 of this embodiment is the same as that of embodiment 1, except for the shape determination unit 170B realized by the arithmetic device 120 of the ridge management device 100, as shown in Figure 9. For this reason, a detailed description of the configuration of the ridge management system 1000 will be omitted, except for the shape determination unit 170B.

[0053] The shape determination unit 170B includes a straight ridge determination unit 171B, a curved ridge determination unit 172B, and a ridge classification unit 173. The straight ridge determination unit 171B determines, for example, a shape of a straight ridge represented by one shape for the grouped ridges 510 and the position of the ridges 510. The curved ridge determination unit 172B determines, for example, a shape of a curved ridge represented by one shape for the grouped ridges 510 and the position of the ridges 510. The ridge classification unit 173 groups ridges 510 having similar shapes among one or more ridges 510 provided in the field 500.

[0054] (Operation of the ridge management system) The operation of the ridge management system 1000 will now be described. As in embodiment 1, when the drive device is started, the working device 30 acquires operation information and outputs the acquired operation information to the ridge management device 100. When the calculation device 120 of the ridge management device 100 acquires the operation information from the working device 30, it reads and executes the ridge management program 410. When the calculation device 120 reads and executes the ridge management program 410, it starts the processing shown in Figure 10, which is part of the ridge management method.

[0055] The processes in steps S110 and S120 are similar to those in the first embodiment, and therefore the description thereof will be omitted.

[0056] In step S125, the ridge classification unit 173 groups each ridge 510 based on position information corresponding to the period of movement along each ridge 510. For example, the ridge classification unit 173 groups each ridge 510 based on the extension direction of the approximate straight line determined in step S120. For example, the ridge classification unit 173 groups the ridges 510 using any clustering method, such as the k-means method or group average method, based on the extension direction of the approximate straight line. Here, the extension direction of the approximate straight line represents, for example, an angle with an arbitrary reference direction, such as the extension direction of a latitude line or a longitude line.

[0057] Furthermore, the ridge classification unit 173 may group each ridge 510 by adding other information in addition to the extension direction of the approximation line. For example, the ridge classification unit 173 may group each ridge 510 based on the extension direction of the approximation line and the type classification of the ridge 510 determined in step S120. Furthermore, the ridge classification unit 173 may group each ridge 510 based on the extension direction of the approximation line and the time when the operation information corresponding to each ridge 510 was measured, for example, the average of the time. The ridge classification unit 173 may classify the ridges 510 based on three or more pieces of information.

[0058] In step S130B, the straight ridge determination unit 171B determines the shape of the ridges 510 classified as straight ridges. For example, the straight ridge determination unit 171B determines a shape corresponding to the grouped ridges 510 and a ridge shape 600 representing the positions of each of the grouped ridges 510. For example, the straight ridge determination unit 171B selects any one of the grouped ridges 510 (e.g., the first ridge 510-1) from the grouped ridges 510, as shown in FIG. 11. The straight ridge determination unit 171B determines the ridge shape 600 of the selected ridge 510 based on the positioning position 610 corresponding to the selected one ridge 510, for example, each of the positioning positions 610 from the first positioning position 610-1 to the second positioning position 610-2. For example, the ridge shape 600 is represented by an approximation function in which the first direction 650 is the x-axis and the second direction 660 is the y-axis.

[0059] The rib shapes 600 of the other grouped ribs 510 are determined, for example, by translating the rib shape 600 of the selected rib 510. For example, the straight line rib determination unit 171B translates an approximation function representing the rib shape 600 of the selected rib 510 (e.g., the first rib 510-1) to a position corresponding to the other rib 510 (e.g., the second rib 510-2) and determines the translated approximation function as the rib shape 600 of the other rib 510. For example, the straight line rib determination unit 171B translates the mathematical expression (1) represented by the rib shape 600 of the first rib 510-1 to determine an approximation function that approximates the positioned position 610 corresponding to the second rib 510-2 (e.g., each of the positioned positions 610 from the fifth positioned position 610-5 to the sixth positioned position 610-6). For example, the straight line ridge determination unit 171B determines an approximation function obtained by modifying the value b in formula (1) representing the ridge shape 600 of the first ridge 510-1 so as to approximate the positioning position 610 corresponding to the second ridge 510-2 as the ridge shape 600 of the second ridge 510-2. For example, the value b is determined without changing the coefficient a of the approximation function of the second ridge 510-2 from the coefficient a of the approximation function of the first ridge 510-1.

[0060] For example, the ridge shapes 600 of the other grouped ridges 510 represent a part of an approximation function determined in parallel with the approximation function of the selected ridge 510. For example, the straight line ridge determination unit 171B determines, as the ridge shape 600, an approximation straight line in the range corresponding to the two positioning positions 610 with the largest distance between them, as in step S130.

[0061] In step S140B shown in FIG. 10, the curved ridge determination unit 172B determines the shape of the ridge 510 classified as a curved ridge. For example, similar to step S130B, the curved ridge determination unit 172B determines a ridge shape 600 representing one shape corresponding to the grouped ridges 510 and the position of each grouped ridge 510. For example, the curved ridge determination unit 172B selects any one of the grouped ridges 510. Similar to embodiment 1, the curved ridge determination unit 172B determines the ridge shape 600 of the selected ridge 510 based on the positioning position 610 corresponding to the selected one ridge 510. For example, as shown in FIG. 8, a ridge shape 600 of a selected ridge 510 (eg, a third ridge 510-3) is represented by an approximation function in which a first direction 650 is the x-axis and a second direction 660 is the y-axis.

[0062] The rib shapes 600 of the other grouped ribs 510 are determined, for example, by translating the rib shape 600 of the selected rib 510, similar to step S130B. For example, the curved rib determination unit 172B translates an approximation function representing the rib shape 600 of the selected rib 510 (e.g., the third rib 510-3) to a position corresponding to the other rib 510, and determines the translated approximation function as the rib shape 600 of the other rib 510. For example, the curved rib determination unit 172B translates an approximation function (e.g., a polynomial function) represented by the rib shape 600 of the selected rib 510, and determines an approximation function that approximates the positioning position 610 corresponding to the other rib 510. For example, the curved ridge determination unit 172B determines, as the ridge shape 600 of the other ridge 510, an approximation function obtained by modifying the polynomial function represented by the ridge shape 600 of the selected ridge 510 by translating y with respect to variable x and / or variable x with respect to y to approximate the positioning position 610 corresponding to the other ridge 510. The curved ridge determination unit 172B may further determine, as the ridge shape 600 of the other ridge 510, an approximation function obtained by scaling the polynomial function represented by the ridge shape 600 of the selected ridge 510 in the x direction and / or y direction to approximate the positioning position 610 corresponding to the other ridge 510.

[0063] For example, similar to step S130B, the ridge shapes 600 of the other grouped ridges 510 represent a part of the approximation function determined in parallel with the approximation function of the selected ridge 510. For example, the curved ridge determination unit 172B determines, as the ridge shape 600, an approximation curve of the range corresponding to the two positioning positions 610 with the largest distance between them among the corresponding positioning positions 610.

[0064] The processes in steps S150 and S160 shown in FIG. 10 are similar to those in the first embodiment, and therefore the description thereof will be omitted.

[0065] As a result, the display unit 250 of the terminal 200 displays the ridges 510 that have been formed to have the same shape in a similar shape. Because the display unit 250 displays the same configuration of the ridges 510 in the field 500 as the user envisions, the user can more easily understand the state of the field 500.

[0066] (Modification) The above-described embodiments and modifications are merely examples, and the configurations described in each embodiment and modification may be arbitrarily modified and / or combined as long as the functions are not impaired. Furthermore, some of the functions described in the embodiments and modifications may be omitted as long as the necessary functions can be realized.

[0067] For example, in step S140 of FIG. 6 or step S140B of FIG. 10, the curved ridge determination unit 172, 172B may express an approximation curve of the positioning position 610 corresponding to one ridge 510 by an arbitrary function. For example, the curved ridge determination unit 172, 172B may determine a polynomial function, a power function, an exponential function, a logarithmic function, or the like as the approximation curve. For example, first, the curved ridge determination unit 172, 172B determines a plurality of approximation functions (e.g., two or more of a polynomial function, a power function, an exponential function, and a logarithmic function) corresponding to the positioning position 610. Then, the curved ridge determination unit 172, 172B may determine one approximation function from the determined plurality of approximation functions based on a degree of agreement indicating a degree of agreement with the positioning position 610.

[0068] In this case, the curved ridge determination units 172 and 172B may determine the type of approximation curve to be determined, for example, a polynomial function, a power function, an exponential function, a logarithmic function, or the like, based on a change in the positioning position 610 in the second direction 660 which is the y-axis as the positioning position moves in the first direction 650 which is the x-axis. For example, when the positioning position 610 in the second direction 660 increases or decreases as the positioning position moves in the first direction 650, the curved ridge determination units 172 and 172B select a polynomial function and determine an approximation curve for the positioning position 610. When the positioning position 610 in the second direction 660 monotonically increases or decreases as the positioning position moves in the first direction 650, and the change becomes large, the curved ridge determination units 172 and 172B select an exponential function or a power function and determine an approximation curve for the positioning position 610. If the positioned position 610 in the second direction 660 monotonically increases or decreases as the positioned position 610 moves in the first direction 650, the change becomes smaller, and the curved ridge determination units 172, 172B select a logarithmic function or a power function to determine an approximation curve for the positioned position 610. If the positioned position 610 in the second direction 660 monotonically increases or decreases as the positioned position 610 moves in the first direction 650, the change becomes smaller, and the curved ridge determination units 172, 172B select a power function to determine an approximation curve for the positioned position 610.

[0069] For example, in step S140 of Fig. 6 or step S140B of Fig. 10, when the difference in the information criterion is small, the curved ridge determination units 172, 172B may use an approximation function with the fewest parameters, for example, coefficients, to represent the ridge shape 600. For example, the curved ridge determination units 172, 172B extract an approximation function whose difference from the smallest information criterion is equal to or less than a threshold value among the information criterions for a plurality of approximation functions, and use the approximation function with the fewest parameters among the extracted approximation functions to represent the ridge shape 600. The parameters represent values ​​that change the amount of change in y relative to the amount of change in variable x in the approximation function, for example, the coefficients of a polynomial function, the coefficients and exponents of a power function, the coefficients and bases of an exponential function, the coefficients and bases of a logarithmic function, etc.

[0070] 6 or step S140B in FIG. 10, the curved ridge determination unit 172, 172B may determine a ridge shape 600 represented by a plurality of line segments as the shape of a curved ridge. For example, the curved ridge determination unit 172 may extract a portion of the positioning position 610 as an extracted position, and determine a plurality of straight line segments connecting the extracted positions in the order of the measurement times as the ridge shape 600. For example, the curved ridge determination unit 172 may extract the positioning positions 610 such that, when the positioning positions 610 are arranged in the order of the measurement times, the number of positioning positions 610 sandwiched between two extracted positions whose measurement times are adjacent is a predetermined value (e.g., five).

[0071] Furthermore, the curved ridge determination units 172 and 172B may determine a plurality of straight line segments representing the shape of the ridge 510 based on the determined approximation curve, and determine the determined plurality of line segments as the ridge shape 600. For example, the curved ridge determination units 172 and 172B may determine a line connecting points including positions corresponding to extreme values ​​and inflection points in the approximation function with straight line segments as the ridge shape 600. For example, the curved ridge determination units 172 and 172B may determine a line connecting positions corresponding to extreme values ​​and inflection points in the approximation function and a part of the positioning position 610 with straight line segments as the ridge shape 600. In this case, the curved ridge determination units 172 and 172B determine a line segment connecting the positioning positions 610 whose measurement times are adjacent to each other among the part of the positioning positions 610. The curved ridge determination units 172 and 172B extract the line segment that is closest to the extreme value or the inflection point from the determined line segments. The curved ridge determination units 172 and 172B exclude the extracted line segment from the determined multiple line segments, and determine the multiple line segments that are added to the two line segments that connect the positioning positions 610 at both ends of the extracted line segment and the extreme value or the inflection point as the ridge shape 600.

[0072] For example, in step S120 of FIG. 6 or FIG. 10, the type classification unit 160 may determine an approximation line and an approximation curve, for example, a plurality of polynomial functions, for the positioning position 610, and determine a type category based on the determined approximation line or approximation curve and the positioning position 610. For example, the type classification unit 160 determines a plurality of approximation functions including a linear function based on the positioning position 610 corresponding to one ridge 510. The plurality of approximation functions represent, for example, an approximation line and one or more approximation curves. The type classification unit 160 determines an information criterion of the determined plurality of approximation functions, and determines a type category based on the determined information criterion. For example, when the information criterion of the linear function (approximation line) is the smallest, the type classification unit 160 determines the corresponding ridge 510 as a straight ridge. When the information criterion of an approximation function (approximation curve) other than the linear function is the smallest, the type classification unit 160 determines the corresponding ridge 510 as a curved ridge.

[0073] In this case, the type classification unit 160 may classify the curved ridges further finely according to the one or more determined approximation curves. The type classification unit 160 classifies the ridges 510 into a type category corresponding to an approximation curve having the smallest information amount criterion among the type categories corresponding to the one or more approximation curves.

[0074] In this case, if the difference in the information amount criteria is small, the type classification unit 160 may classify the ridge 510 into a type category corresponding to an approximate straight line or approximate curve with the fewest parameters, for example, coefficients. For example, the type classification unit 160 extracts an approximate straight line or approximate curve whose difference from the smallest information amount criteria among the information amount criteria for the approximate straight line and a plurality of approximate curves is equal to or less than a threshold. Among the extracted approximate straight lines or approximate curves, the type classification unit 160 classifies the ridge 510 into the approximate straight line or approximate curve with the fewest parameters.

[0075] The type classification unit 160 may also classify the curved ridges into polynomial ridges that resemble the shape of a polynomial function and non-polynomial ridges that resemble the shape of a function other than a polynomial, such as an exponential function, a logarithmic function, or a power function. For example, the type classification unit 160 determines an approximation line and an approximation curve for the non-polynomial ridge based on the positioning position 610 corresponding to one ridge 510. For example, the type classification unit 160 determines one or more approximation curves that respectively approximate one or more of an exponential function, a logarithmic function, and a power function as the approximation curve for the non-polynomial ridge. The type classification unit 160 determines the degree of agreement between the approximation line or one or more approximation curves and the positioning position 610. When all the degrees of agreement are less than a threshold, the type classification unit 160 classifies the ridge 510 as a polynomial ridge. When at least one degree of agreement is equal to or greater than a threshold and the degree of agreement of the approximation line is the largest, the type classification unit 160 classifies the ridge 510 as a straight ridge. When at least one degree of agreement is equal to or greater than a threshold and the degree of agreement of the approximation line is not the largest, the type classification unit 160 classifies the ridge 510 as a non-polynomial ridge.

[0076] In this case, for example, in step S140 in Fig. 6 or step S140B in Fig. 10, when the ridge 510 is classified as a non-polynomial ridge, the curved ridge determination unit 172, 172B determines the ridge shape 600 based on the degree of similarity. For example, the curved ridge determination unit 172, 172B represents the ridge shape 600 using a function corresponding to the approximation curve with the greatest degree of similarity.

[0077] For example, in step S120 of FIG. 6 or FIG. 10, the type classification unit 160 may determine the type category using a learning model that has been trained to determine the type category of the ridge 510 corresponding to a plurality of positioning positions 610 from the positioning positions 610. For example, as shown in FIG. 12, the type classification unit 160 inputs an input image 700 representing a positioning position 610 corresponding to one ridge 510 (e.g., each of the positioning positions 610 from the third positioning position 610-3 to the fourth positioning position 610-4) to the learning model. The learning model outputs the type category corresponding to the ridge 510 of the positioning position 610 represented in the input image 700. The type classification unit 160 classifies the ridge 510 corresponding to the type category output by the learning model. The learning model may output not only straight ridges and curved ridges, but also type categories that further classify curved ridges, such as type categories corresponding to exponential functions, type categories corresponding to logarithmic functions, etc. The learning model may further output the degree of the corresponding polynomial.

[0078] For example, in step S125 of FIG. 10, the ridge classification unit 173 may group the ridges 510 in the field 500 using any method. For example, the ridge classification unit 173 may group the ridges 510 based on the direction of a line connecting two positioned positions 610 that are the largest apart among the positioned positions 610 corresponding to one ridge 510. The ridge classification unit 173 may also perform grouping using the range of the traveling direction of the working device 30 at each positioned position 610. The ridge classification unit 173 may also perform grouping using a statistical value of the speed at the positioned positions 610 corresponding to the ridge 510, such as an average value or a median value. The ridge classification unit 173 may also perform grouping using the time when the positioned positions 610 corresponding to the ridge 510 were measured, such as an average value or a median value of each time when each positioned position 610 was measured. The ridge classification unit 173 may classify the ridges 510 using at least one of the direction in which the approximate line extends, the direction of the line connecting the two positioning positions 610, the range of the traveling direction of the working implement 30, a statistical value of the speed of the working implement 30, and the time at which the positioning position 610 was measured.

[0079] The ridge classification unit 173 may also group the ridges 510 based on a plurality of partial contours 520 constituting the contour of the field 500. For example, the ridges 510 may be formed in a shape obtained by translating a part of the contour of the field 500. For this reason, in the example shown in FIG. 13, the ridge classification unit 173 may group the ridges 510 based on a first partial contour 520-1, a second partial contour 520-2, and a third partial contour 520-3 constituting the contour of the field 500. Here, the first partial contour 520-1 represents the contour of the field 500 from the first vertex 530-1 to the second vertex 530-2, and the second partial contour 520-2 represents the contour of the field 500 from the second vertex 530-2 to the third vertex 530-3. Moreover, the third partial contour 520-3 represents the contour from the third vertex 530-3 to the first vertex 530-1. The partial contour 520 is registered in advance for each farm field 500 by the user, for example.

[0080] In this case, the ridge classification unit 173 translates the partial contour 520 so as to approximate the positioning position 610 corresponding to one ridge 510, and determines the degree of match between the translated partial contour 520 and the positioning position 610. The ridge classification unit 173 classifies the corresponding ridge 510 into a group corresponding to the partial contour 520 with the largest determined degree of match.

[0081] The ridge classification unit 173 may also classify the ridges 510 based on the direction of the angle range in which the number of travel directions of the working device 30 represented by the operation information is the largest. For example, the ridge classification unit 173 determines a histogram for the travel directions of the working device 30 represented by the operation information of the field 500, and determines the angle range with the largest number of degrees. The ridge classification unit 173 then determines a third direction representative of the angle range, for example, a fifth direction that is spaced by the same angle from the median direction of the angle range and a fourth direction perpendicular to the third direction. For example, the angle between the third direction and the fifth direction and the angle between the fourth direction and the fifth direction represent 45 degrees. The ridge classification unit 173 classifies the ridges 510 of the field 500 into a group in which the ridges 510 extend in the third direction, a group in which the ridges 510 extend in the fourth direction, and a group in which the ridges 510 extend in the fifth direction.

[0082] In addition, when grouping the ridges 510 without using type classification, the ridge classification unit 173 may group the ridges 510 prior to step S120.

[0083] Furthermore, in step S110, before extracting the operation information when moving along the ridge 510, the ridge classification unit 173 may classify the operation information into operation information corresponding to ridges 510 having similar shapes. For example, the ridge classification unit 173 may group the operation information based on the traveling direction of the working device 30 represented in the operation information and the time when the operation information was measured. For example, the ridge classification unit 173 may group the operation information by any clustering using the traveling direction of the working device 30 represented in the operation information and the time when the operation information was measured. The ridge classification unit 173 may further group the operation information using the speed of the working device 30. The ridge classification unit 173 may use the amount of change in the traveling direction of the working device 30 to group the operation information. For example, the amount of change in the traveling direction represents the angle by which the traveling direction changes in a predetermined period of time. For example, the amount of change in the traveling direction may represent the angle between the traveling direction of the working device 30 represented in the operation information and the traveling direction of the working device 30 represented in the operation information measured immediately before that operation information.

[0084] For example, in step S130B shown in FIG. 10, the straight-line ridge determination unit 171B may select one ridge 510 from the grouped ridges 510 based on the degree of agreement of the approximation straight line of each ridge 510. For example, the straight-line ridge determination unit 171B determines an approximation straight line for each grouped ridge 510. The straight-line ridge determination unit 171B determines a degree of agreement representing the degree to which the approximation straight line agrees with the corresponding positioning position 610 for each ridge 510. The straight-line ridge determination unit 171B selects the ridge 510 with the largest degree of agreement, and determines the ridge shape 600 of the selected ridge 510. The ridge shapes 600 of the other grouped ridges 510 are determined based on the ridge shape 600 of the selected ridge 510.

[0085] Similarly, in step S140B shown in FIG. 10, the curved ridge determination unit 172B may select one ridge 510 from the grouped ridges 510 based on the degree of coincidence of the approximation curves of each ridge 510.

[0086] For example, the speed and traveling direction of the working device 30 at the positioned position 610 may be determined based on the position information. For example, the speed of the working device 30 may be determined based on the distance between two positioned positions 610 whose measured times are adjacent to each other and the measured time difference. Furthermore, the traveling direction of the working device 30 may represent the direction from the positioned position 610 measured earlier to the positioned position 610 measured later, between the two positioned positions 610 whose measured times are adjacent to each other.

[0087] Furthermore, the x-axis of the approximation function, for example, the approximation line or curve, may be set in any direction. For example, the x-axis may be set parallel to a meridian or latitude line.

[0088] The origin of the approximation function may be determined at any position. For example, the origin may be set at any position in the field 500, or may be set at one point common to each ridge 510 in the field 500. The origin may be set at a vertex 530 (e.g., a first vertex 530-1) as shown in FIG. 13 . The origin may be set at the geometric center of the field 500.

[0089] The user may also modify the ridge shape 600 displayed on the input / output device 210 of the terminal 200. For example, the display unit 250 of the terminal 200 may display an image showing the ridge shape 600 represented by the determined approximation function and a ridge shape 600 represented by another approximation function. The user may select the ridge shape 600 that best matches the corresponding ridge 510 from the multiple ridge shapes 600 displayed.

[0090] Further, the straight ridge determination units 171 and 171B and the curved ridge determination units 172 and 172B of the ridge management device 100 may compare the degree of coincidence with respect to the positioning position 610 corresponding to the determined approximate function with a threshold value. The result of the comparison is included in the ridge information by the output unit 180 and output to the terminal 200. For example, when the degree of coincidence is smaller than the threshold value, the display unit 250 of the terminal 200 emphasizes and displays the corresponding ridge 510 as being likely to deviate from the shape of the ridge 510 compared to other ridges 510. Further, the display unit 250 may display a message indicating the ridge 510 whose degree of coincidence is smaller than the threshold value, prompting the user to confirm the ridge shape 600.

[0091] For example, the terminal 200 may execute part or all of the processes of the ridge management device 100. Further, the ridge management device 100 may execute part or all of the processes of the terminal 200. The ridge management program 410 may include a display program 420. The ridge management system 1000 may display the display information on an external terminal that does not include the terminal 200 and is not included in the ridge management system 1000.

[0092] (Appendix) The ridge management method, the ridge management system, and the ridge management program described in each embodiment can be described as follows.

[0093] The ridge management method according to the first aspect is classifying, based on the position information of the working device, one or more ridges worked by the working device into a type classification including a straight ridge formed in a straight line and a curved ridge formed in a curved shape; determining a ridge shape representing the shape of the one or more ridges based on the type classification; and including.

[0094] The ridge management method according to the second aspect is the ridge management method according to the first aspect, wherein classifying the one or more ridges into a type classification includes determining an approximate straight line for a plurality of positions represented by the position information; determining a first degree of agreement representing a degree to which the approximation line agrees with the plurality of positions represented by the position information; classifying the one or more ridges into the type category based on the first degree of agreement; Includes.

[0095] A ridge management method according to a third aspect is a ridge management method according to the second aspect, The curved ridge includes a polynomial ridge whose shape is represented by a polynomial and a non-polynomial ridge whose shape is represented by one or more functions other than a polynomial; Classifying the one or more ridges into the type category includes determining one or more approximation curves represented by one or more functions other than the polynomial for a plurality of positions represented by the position information; determining a second degree of match representing a degree to which the one or more approximation curves match the positions represented by the position information; Further comprising: Classifying the ridge into the type category based on the first degree of agreement, classifying the one or more ridges into the type category based on the first degree of coincidence of the approximation straight line and the second degree of coincidence of the one or more approximation curves. Includes.

[0096] A ridge management method according to a fourth aspect is a ridge management method according to any one of the first aspects, Classifying the one or more ridges into the type category includes determining a plurality of approximation functions, including a linear function, for a plurality of positions represented by the position information; determining the type classification based on an information criterion for the plurality of approximation functions; Includes.

[0097] A ridge management method according to a fifth aspect is a ridge management method according to any one of the first and fourth aspects, Determining the ridge shape includes: determining a polynomial function of degree 2 or higher that represents a ridge shape for the curved ridge among the one or more ridges; Includes.

[0098] A ridge management method according to a sixth aspect is a ridge management method according to any one of the first to fourth aspects, Determining the ridge shape includes: determining a plurality of straight line segments representing the ridge shape for the curved ridge among the one or more ridges; Includes.

[0099] A ridge management method according to a seventh aspect is a ridge management method according to the sixth aspect, An end point of at least one of the plurality of straight line segments represents an extreme value or an inflection point of a function that represents the shape of the curved ridge.

[0100] A ridge management method according to an eighth aspect is a ridge management method according to any one of the first to seventh aspects, Determining the ridge shape includes: determining an approximation function for said ridge shape; Including, The approximation function represents a position in a second direction perpendicular to the first direction, using a position in a first direction in which a corresponding one of the one or more ridges extends as a variable.

[0101] A ridge management method according to a ninth aspect is the ridge management method according to the eighth aspect, The origin of the approximation function differs between a first ridge and a second ridge among the one or more ridges.

[0102] A ridge management method according to a tenth aspect is a ridge management method according to the ninth aspect, The origin of the first ridge represents one of two positions having the largest distance between them among a plurality of positions for the position information.

[0103] A ridge management method according to an eleventh aspect is a ridge management method according to any one of the first to tenth aspects, Determining the ridge shape includes: grouping the one or more ridges based on a direction in which each of the one or more ridges extends; The ridge shape of one of the one or more grouped ridges represents a shape obtained by translating the ridge shape of another of the one or more grouped ridges.

[0104] A ridge management method according to a twelfth aspect is a ridge management method according to any one of the first to eleventh aspects, Displaying ridge information representing the ridge shape. Further includes:

[0105] A ridge management method according to a thirteenth aspect is a ridge management method according to any one of the first to eleventh aspects, determining a degree of match representing the degree to which the determined ridge shape matches a plurality of positions represented by the position information; Displaying ridge information representing the ridge shape and information representing confirmation of the ridge shape of a ridge among the one or more ridges, the ridge shape of which has the degree of agreement smaller than a threshold value; Further includes:

[0106] A ridge management system according to a fourteenth aspect of the present invention comprises: a type classification unit that classifies one or more ridges worked on by the working device into a type category including straight ridges formed in a straight line and curved ridges formed in a curved line based on position information of the working device; a shape determining unit that determines a ridge shape representing a shape of the one or more ridges based on the type classification; Equipped with.

[0107] A ridge management program according to a fifteenth aspect, classifying one or more ridges worked on by the working device into a type category including straight ridges formed in a straight line and curved ridges formed in a curved line based on position information of the working device; determining a ridge shape representing a shape of the one or more ridges based on the type classification; The calculation device executes the following. [Explanation of symbols]

[0108] 1, 2: Memory medium 20: Network 30: Working device 100: Ridge management device 110: Input / output device 120: Arithmetic unit 130: Communication device 140: Memory device 150: Data storage section 160: Type classification section 170: Shape determination section 171: Straight ridge determination section 172: Curved ridge determination section 173: Ridge classification section 180: Output section 200: Terminal 210: Input / output device 220: Arithmetic unit 230: Communication device 240: Memory device 250: Display section 400: Field data 410: Ridge management program 420: Display program 500: Field 510: Ridge 520: Partial contour 530: Vertex 600: Ridge shape 610: Positioning position 620: Orbit 650: First direction 660: Second direction 700: Input image 1000: Ridge management system

Claims

1. classifying one or more ridges worked on by the working device into a type category including straight ridges formed in a straight line and curved ridges formed in a curved line based on position information of the working device; determining a ridge shape representing a shape of the one or more ridges based on the type classification; A furrow management method comprising:

2. Classifying the one or more ridges into the type category includes: determining an approximation line for a plurality of positions represented by the position information; determining a first degree of coincidence representing a degree to which the approximation line coincides with the plurality of positions represented by the position information; classifying the one or more ridges into the type category based on the first degree of agreement; The ridge management method according to claim 1, comprising:

3. The curved ridge includes a polynomial ridge whose shape is represented by a polynomial and a non-polynomial ridge whose shape is represented by one or more functions other than a polynomial; Classifying the one or more ridges into the type category includes: determining one or more approximation curves represented by one or more functions other than the polynomial for a plurality of positions represented by the position information; determining a second degree of match representing a degree to which the one or more approximation curves match the plurality of positions represented by the position information; Further comprising: Classifying the ridge into the type category based on the first degree of agreement, classifying the one or more ridges into the type category based on the first degree of coincidence of the approximation straight line and the second degree of coincidence of the one or more approximation curves; The ridge management method according to claim 2, comprising:

4. Classifying the one or more ridges into the type category includes: determining a plurality of approximation functions, including a linear function, for a plurality of positions represented by the position information; determining the type classification based on an information criterion for the plurality of approximation functions; The ridge management method according to claim 1, comprising:

5. Determining the ridge shape includes: determining a polynomial function of degree 2 or higher that represents a ridge shape for the curved ridge among the one or more ridges; The ridge management method according to any one of claims 1 to 4, comprising:

6. Determining the ridge shape includes: determining a plurality of straight line segments representing the ridge shape for the curved ridge among the one or more ridges; The ridge management method according to any one of claims 1 to 4, comprising:

7. At least one end point of the plurality of straight line segments represents an extreme value or an inflection point of a function that represents the shape of the curved ridge. The ridge management method according to claim 6.

8. Determining the ridge shape includes: determining an approximation function for said ridge shape; Including, The approximation function represents a position in a second direction perpendicular to the first direction, using a position in a first direction in which a corresponding ridge among the one or more ridges extends as a variable. A ridge management method according to any one of claims 1 to 4.

9. The origin of the approximation function is different between a first ridge and a second ridge among the one or more ridges. The ridge management method according to claim 8.

10. The origin of the first ridge represents one of two positions having the largest interval among a plurality of positions for the position information. The ridge management method according to claim 9.

11. Determining the ridge shape includes: grouping the one or more ridges based on a direction in which each of the one or more ridges extends; The ridge shape of one of the one or more grouped ridges represents a shape obtained by translating the ridge shape of another of the one or more grouped ridges. A ridge management method according to any one of claims 1 to 4.

12. Displaying ridge information representing the ridge shape. The ridge management method according to any one of claims 1 to 4, further comprising:

13. determining a degree of match representing the degree to which the determined ridge shape matches a plurality of positions represented by the position information; Displaying ridge information representing the ridge shape and information representing confirmation of the ridge shape of a ridge among the one or more ridges, the ridge shape of which has a degree of coincidence smaller than a threshold value; The ridge management method according to any one of claims 1 to 4, further comprising:

14. a type classification unit that classifies one or more ridges worked on by the working device into a type category including straight ridges formed in a straight line and curved ridges formed in a curved line based on position information of the working device; a shape determining unit that determines a ridge shape representing a shape of the one or more ridges based on the type classification; A ridge management system equipped with:

15. classifying one or more ridges worked on by the working device into a type category including straight ridges formed in a straight line and curved ridges formed in a curved line based on position information of the working device; determining a ridge shape representing a shape of the one or more ridges based on the type classification; A ridge management program that causes a computing device to execute the above.

Images