Flight plan management device, flight plan management method, and program

Abstract

In the present invention, a device information acquisition means acquires device information of a mobile body. A preparation state acquisition means acquires the preparation state of an operator. A possible departure time prediction means predicts a possible departure time of the mobile body on the basis of the preparation state. A flight plan management means manages a flight plan on the basis of the results of the prediction.

Description

Flight plan management device, flight plan management method, and recording medium

[0001] The present disclosure relates to managing flight plans for mobile objects.

[0002] In order to fly a drone, it is necessary to reserve the space-time (three-dimensional space and time) to be used. Therefore, if there is uncertainty in the drone's flight plan and the space-time to be used is not determined, a large reservation slot may be set as a buffer. However, setting a large reservation slot reduces the space utilization efficiency and is undesirable from a public perspective. On the other hand, if the reservation slot is set small, a new reservation is required when the flight plan changes, which is time-consuming. Patent Document 1 describes how a flight plan for flying an unmanned aerial vehicle can be submitted quickly and easily.

[0003] JP 2014-040231 A

[0004] However, even with Patent Document 1, it is not necessarily possible to flexibly respond to changes in the flight plan.

[0005] One object of the present disclosure is to provide a flight plan management system that adjusts the time and space used depending on the state of preparation before flight.

[0006] In order to solve the above problem, in one aspect of the present disclosure, an operation plan management device includes: an equipment information acquisition means for acquiring equipment information of a mobile object; a readiness state acquisition means for acquiring the readiness state of an operator; an available departure time prediction means for predicting the available departure time of the mobile object based on the equipment information and the readiness state; and an operation plan management means for managing an operation plan based on the result of the prediction.

[0007] In another aspect of the present disclosure, an operation plan management method includes acquiring equipment information of a mobile object, acquiring a readiness state of an operator, predicting a possible departure time of the mobile object based on the equipment information and the readiness state, and managing an operation plan based on the result of the prediction.

[0008] In yet another aspect of the present disclosure, a recording medium records a program that causes a computer to execute a process of acquiring equipment information of a mobile object, acquiring the operator's readiness status, predicting a possible departure time of the mobile object based on the equipment information and the readiness status, and managing an operation plan based on the result of the prediction.

[0009] According to the present disclosure, it is possible to adjust the space-time used depending on the state of preparation before flight.

[0010] 1 shows the overall configuration of an operation plan management system according to a first embodiment; FIG. 2 is a block diagram showing the hardware configuration of a terminal device; FIG. 3 is a block diagram showing the hardware configuration of a server; FIG. 4 is a block diagram showing the functional configuration of a server; FIG. 5 is an example of an equipment information and operation plan input screen; FIG. 6 is an example of a preparation status input screen; FIG. 7 is an example of an operation plan; FIG. 8 is an example of an operation plan adjustment; FIG. 9 is another example of an operation plan adjustment; FIG. 10 is an example of a display of an adjusted operation plan transmitted by the server; FIG. 11 is a flowchart of operation plan adjustment processing; FIG. 12 is a block diagram showing the functional configuration of an operation plan management device according to a second embodiment; FIG. 13 is a flowchart of processing by an operation plan management device according to a second embodiment.

[0011] First Embodiment [Overall Configuration] Fig. 1 shows the overall configuration of a flight operation plan management system to which a flight operation plan management device according to the present disclosure is applied. The flight operation plan management system 1 includes a drone 5, a server 100, and a terminal device 200. The server 100 is an example of a flight operation plan management device. The server 100 and the terminal device 200 can communicate with each other via wired or wireless communication. The terminal device 200 and the drone 5 can communicate with each other via wireless communication. It is also assumed that there are a plurality of drones 5 and a plurality of terminal devices 200.

[0012] The terminal device 200 is operated by the drone operator or the like. Information such as the equipment information, operation plan, and preparation status of the drone 5 is input to the terminal device 200. The equipment information is information about the drone 5 itself, and includes information such as the model. The equipment information is transmitted from the drone 5 to the terminal device 200. The operation plan is a flight plan for the drone 5, and includes information such as the departure date and time and flight route. The operator registers the operation plan in advance in the server 100 as a reservation of the time and space to be used. The preparation status is information indicating the preparation status of the drone 5 before flight, and includes information such as the inspection status of the drone 5 and the loading status of cargo. The operation plan and preparation status are input by the operator to the terminal device 200.

[0013] The server 100 manages operation plans for multiple drones in a database. The server 100 also predicts the drone's possible departure time and adjusts the operation plan. The possible departure time is the time when the drone's pre-flight preparations are complete and the drone is ready to depart. Specifically, the server 100 receives information such as the drone's 5 equipment information, operation plan, and preparation status from the terminal device 200. The server 100 then predicts the drone's possible departure time using a prediction model prepared in advance. If the predicted possible departure time differs from the departure time in the operation plan, the server 100 changes the contents of the operation plan and updates the database. The server 100 makes the updated contents of the database available to other operators. The server 100 also transmits the changed operation plan to the terminal device 200.

[0014] Here, we will explain predictive models. A predictive model is information that represents the relationship between explanatory variables and dependent variables. A predictive model is a component for estimating the results of an estimation target by calculating a dependent variable based on the explanatory variables. A predictive model is generated by executing a learning algorithm using training data, for which the dependent variable value has already been obtained, and arbitrary parameters as input. A predictive model may be, for example, a function c that maps an input x to a correct answer y. A predictive model may estimate a numerical value of an estimation target, or may estimate a label of an estimation target. A predictive model may output a variable that describes the probability distribution of a dependent variable. A predictive model may also be referred to as a "learning model," "analysis model," "AI model," "trained model," "inference model," or "prediction formula." Note that explanatory variables are variables used as inputs in a predictive model. Explanatory variables may also be referred to as "features" or "characteristics." Furthermore, the learning algorithm for generating a predictive model is not particularly limited and may be an existing learning algorithm. For example, the learning algorithm may be a random forest, a support vector machine, a naive Bayes, a neural network, a piecewise linear model using FAB inference (Factorized Asymmetric Bayesian Inference), or a neural network. A piecewise linear model technique using FAB inference is disclosed in, for example, U.S. Patent Publication US 2014 / 0222741 A1. Furthermore, the prediction model is not limited to one generated by a learning algorithm. The prediction model may be a model that predicts a possible departure time based on predetermined rules.

[0015] In this way, the server 100 predicts possible departure times and adjusts time-space reservation slots, allowing the operator to focus on pre-flight preparations.

[0016] 2 is a block diagram showing the hardware configuration of the terminal device 200. The terminal device 200 is, for example, a PC or a tablet. As shown in the figure, the terminal device 200 includes an interface (I / F) 211, a processor 212, a memory 213, a recording medium 214, a database (DB) 215, a display unit 216, and an input unit 217.

[0017] The I / F 211 transmits and receives data to and from external devices. Specifically, the terminal device 200 receives device information of the drone 5 from the drone 5 via the I / F 211. The terminal device 200 also transmits device information, an operation plan, a preparation status, and the like of the drone 5 to the server 100 via the I / F 211.

[0018] The processor 212 is a computer such as a CPU (Central Processing Unit), and executes a pre-prepared program to control the entire terminal device 200. The processor 212 may be a GPU (Graphics Processing Unit), a TPU (Tensor Processing Unit), a DSP (Digital Signal Processor), an MPU (Micro Processing Unit), an FPU (Floating point number Processing Unit), a PPU (Physics Processing Unit), a quantum processor, or an FPGA (Field-Programmable Gate Array).

[0019] The memory 213 is configured by a ROM (Read Only Memory), a RAM (Random Access Memory), etc. The terminal device 200 may also use the memory 213 as a working memory while the processor 212 is executing various processes.

[0020] The recording medium 214 is a non-volatile, non-transitory recording medium such as a disk-shaped recording medium or a semiconductor memory, and is configured to be detachable from the terminal device 200. The recording medium 214 records various programs to be executed by the processor 212. When the terminal device 200 executes various processes, the programs recorded on the recording medium 214 are loaded into the memory 213 and executed by the processor 212.

[0021] The DB 215 stores data used and data generated by the terminal device 200. Specifically, the DB 215 stores device information transmitted from the drone 5, an operation plan input by the operator, and the like.

[0022] The display unit 216 is, for example, a liquid crystal display, and displays a screen for the operator to input the flight plan and preparation status. The display unit 216 also displays information transmitted from the server 100. The input unit 217 is, for example, an input device such as a keyboard or a mouse, and is used by the operator to input the flight plan and preparation status.

[0023] 3 is a block diagram showing the hardware configuration of the server 100. As shown in the figure, the server 100 includes an interface (I / F) 111, a processor 112, a memory 113, a recording medium 114, and a database (DB) 115.

[0024] The I / F 111 transmits and receives data to and from external devices. Specifically, the server 100 receives information such as equipment information, operation plans, and preparation status of the drone 5 from the terminal device 200 via the I / F 111. The server 100 also transmits the changed operation plan to the terminal device 200 via the I / F 111.

[0025] The processor 112 is a computer such as a CPU, and executes a program prepared in advance to control the entire server 100. The processor 112 may be a GPU, a TPU, a quantum processor, or an FPGA. The processor 112 executes the flight plan adjustment process, as described below.

[0026] The memory 113 is configured by a ROM, a RAM, etc. The memory 113 is also used as a working memory while the processor 112 is executing various processes.

[0027] The recording medium 114 is a non-volatile, non-transitory recording medium such as a disk-shaped recording medium or a semiconductor memory, and is configured to be detachable from the server 100. The recording medium 114 records various programs to be executed by the processor 112. When the server 100 executes various processes, the programs recorded on the recording medium 114 are loaded into the memory 113 and executed by the processor 112.

[0028] The DB 115 stores data used by the server 100. Specifically, the DB 115 stores operation plans for multiple drones. The DB 115 also stores a prediction model that predicts possible departure times. The server 100 may include an input unit such as a keyboard or mouse for an administrator to give instructions or input, and a display unit such as a liquid crystal display.

[0029] 4 is a block diagram showing the functional configuration of the server 100. Functionally, the server 100 includes a device information acquisition unit 11, an operation plan acquisition unit 12, a preparation status acquisition unit 13, a departure time prediction unit 14, an operation plan management unit 15, and an operation plan presentation unit 16.

[0030] The terminal device 200 acquires information from the drone and the operator. Specifically, the terminal device 200 acquires the device information transmitted by the drone and the operation plan and preparation status input by the operator, and transmits them to the server 100.

[0031] 5 and 6 are examples of input screens of the terminal device 200. The terminal device 200 transmits data entered on the input screen to the server 100. FIG. 5 shows an example of an input screen for device information and a flight plan. FIG. 5 displays basic information 21 and a route 22 on the input screen 20. The basic information 21 includes a user ID, drone device information, and a flight plan. The drone device information is, for example, information such as the drone model. The flight plan is, for example, information about the cargo to be loaded onto the drone and the drone's departure date and time. The terminal device 200 may accept information received from the drone as device information, or may accept direct input from the operator. The route 22 is the drone's flight route. The route 22 is part of the flight plan. The terminal device 200 may accept the setting of the route 22 from the operator, or may accept the departure point and arrival point as settings from the operator and generate an optimal flight route.

[0032] FIG. 6 shows an example of a preparation status input screen. FIG. 6 displays a checklist 31 within the input screen 30. The checklist 31 includes items, check details, and progress. The operator inspects the drone according to the check details for each item. Then, when the operator completes the inspection, the operator checks the progress column for the corresponding item. When the operator updates the checklist 31, the terminal device 200 transmits the updated preparation status to the server 100 each time.

[0033] 4 , the server 100 receives device information, an operation plan, and a preparation status from the terminal device 200. The device information acquisition unit 11 accepts the device information from the terminal device 200. The operation plan acquisition unit 12 accepts the operation plan from the terminal device 200. The preparation status acquisition unit 13 accepts the preparation status from the terminal device 200.

[0034] The device information acquisition unit 11 acquires information on the performance and specifications of the drone, such as its maximum speed and maximum flight time, from a database prepared in advance, based on the drone model included in the device information. The device information acquisition unit 11 then outputs the device information, including the performance and specifications, to the departure time prediction unit 14 and the flight plan management unit 15.

[0035] The flight plan acquisition unit 12 outputs the flight plan to the departure time prediction unit 14 and the flight plan management unit 15. In addition, the preparation status acquisition unit 13 outputs the preparation status to the departure time prediction unit 14 and the flight plan management unit 15.

[0036] The departure time prediction unit 14 acquires device information from the device information acquisition unit 11, acquires an operation plan from the operation plan acquisition unit 12, and acquires a preparation state from the preparation state acquisition unit 13. The departure time prediction unit 14 then predicts the time when the drone can depart. Specifically, the departure time prediction unit 14 calculates the time required for the drone to depart (hereinafter also referred to as "required departure time") based on the preparation state. The departure time prediction unit 14 then predicts the time when the drone can depart by adding the required departure time to the current time. The required departure time (t) is calculated, for example, by the following formula using the progress (X) of each preparation, the required time (α) for each preparation, the leeway time (tx), and a constant term (t0): t = (α1 × X1) + (α2 × X2) + (α3 × X3) + tx + t0 (1)

[0037] X1 to X3 indicate the progress of each preparation item. For example, X1 indicates the progress of equipment maintenance. If the equipment maintenance is complete, X1 = 0; if the equipment maintenance is incomplete, X1 = 1. X2 indicates the progress of battery check. If the battery check is complete, X2 = 0; if the battery check is incomplete, X2 = 1. X3 indicates the progress of luggage loading. If loading is complete, X3 = 0; if loading is incomplete, X3 = 1. α1 to α3 indicate the required time for each preparation item. Note that α1 to α3, tx, and t0 are determined based on past performance. For example, the required time and departure time for each preparation item are collected from past performance, and the collected data is used as training data to train a model. The departure time prediction unit 14 then predicts the values ​​of α1 to α3, tx, and t0 using the generated model and calculates the required departure time.

[0038] For example, if only equipment maintenance (X1) has been completed, and α1 = "-3", α2 = "-4", α3 = "-1", margin time (tx) = "1", and constant term (t0) = "15", the time required until departure (t) can be calculated as follows: t = (-3 x 0) + (-4 x 1) + (-1 x 1) + 1 + 15 = 10 (minutes)

[0039] The departure time prediction unit 14 predicts that the possible departure time is the time obtained by adding 10 minutes to the current time. The departure time prediction unit 14 then outputs the possible departure time to the flight operation plan management unit 15.

[0040] The method for predicting the possible departure time is not limited to the above method. For example, the departure time prediction unit 14 may use hidden semi-Markov models (HSMM) to predict the time required for all pre-flight preparations to be completed based on the current state.

[0041] The flight operation plan management unit 15 acquires device information from the device information acquisition unit 11, acquires a flight operation plan from the flight operation plan acquisition unit 12, acquires a preparation state from the preparation state acquisition unit 13, and acquires an available departure time from the departure time prediction unit 14. Based on the flight operation plan and the available departure time, the flight operation plan management unit 15 determines whether the available departure time is within the range of the flight operation plan. If the available departure time is not within the range of the flight operation plan, the flight operation plan management unit 15 adjusts the flight operation plan. Then, the flight operation plan management unit 15 updates the flight operation plan stored in the database 115 based on the adjusted flight operation plan (hereinafter also referred to as the "adjusted flight operation plan"). The flight operation plan management unit 15 also outputs the adjusted flight operation plan to the flight operation plan presentation unit 16.

[0042] 7 to 10 show examples of adjustment of flight plans by the flight plan management unit 15.

[0043] FIG. 7 shows an example of a flight plan. FIG. 7 includes a departure time 41, a space-time route 42, and a flight plan space 43. The departure time 41 is the departure time of the drone as determined in the flight plan. The space-time route 42 indicates the space-time route of the drone, i.e., the trajectory of the drone in space-time. The space-time route 42 is generated from the geographical route from the starting point to the end point, the departure time of the drone, and the drone's speed. The flight plan space 43 is a space generated by adding a buffer (excess space) to the space-time route 42. In FIG. 7, the drone's departure time 41 is 11:30, and a buffer is provided within a range of five minutes before and after the departure time. The operator registers the flight plan space in the database 115 in advance and performs pre-flight preparations to fly the drone within the range of the flight plan space.

[0044] FIG. 8 illustrates an example of adjusting a flight schedule when a delay occurs in the flight schedule. In addition to a departure time 41 and a flight schedule space 43, FIG. 8 also includes a predicted time 44, a predicted space-time route 45, and a predicted flight schedule space 46. The predicted time 44 is the possible departure time predicted by the departure time prediction unit 14. The predicted space-time route 45 indicates the space-time route of the drone at the possible departure time. The predicted space-time route 45 is generated from the geographical route from the starting point to the end point, the possible departure time of the drone, and the drone's speed. The predicted flight schedule space 46 is a space generated by adding a buffer to the predicted space-time route 45. In FIG. 8, the predicted time 44 is 11:50, so the operator cannot fly the drone within the flight schedule space 43. Therefore, the flight schedule management unit 15 generates the predicted flight schedule space 46 based on the predicted time 44. The predicted flight schedule space 46 is an example of the adjusted flight schedule described above.

[0045] Figure 9 shows an example of adjusting a flight schedule when the flight schedule is advanced. In Figure 9, the predicted time 44a is earlier than the departure time 41, so the drone can fly earlier. Therefore, the flight schedule management unit 15 generates a predicted flight schedule space 46a based on the predicted time 44a. The predicted flight schedule space 46a is an example of the adjusted flight schedule described above.

[0046] Figure 10 shows an example of adjusting a flight schedule when another flight schedule conflicts with the possible departure time. Figure 10 includes a departure time 41, a flight schedule space 43, a predicted time 44b, a predicted flight schedule space 46b, and another flight schedule space 47. The other flight schedule space 47 is a flight schedule space registered in the database 115 by another person. In Figure 10, the predicted time 44b is included within the range of the other flight schedule space 47. In such a case, the flight schedule management unit 15 generates a predicted flight schedule space 46b outside the range of the other flight schedule space 47 so as not to conflict with the other flight schedule space 47. The predicted flight schedule space 46b is an example of the adjusted flight schedule described above.

[0047] Returning to FIG. 4 , the flight operation plan presentation unit 16 generates display data based on the adjusted flight operation plan acquired from the flight operation plan management unit 15 and transmits the display data to the terminal device 200 .

[0048] In the above configuration, the equipment information acquisition unit 11 and the operation plan acquisition unit 12 are examples of equipment information acquisition means, the preparation state acquisition unit 13 is an example of preparation state acquisition means, the departure time prediction unit 14 is an example of possible departure time prediction means, and the operation plan management unit 15 and the operation plan presentation unit 16 are examples of operation plan management means.

[0049] 11 shows a display example of an adjusted flight plan transmitted by the server 100. In this example, an adjusted time 23, which is the adjusted departure time, is displayed on the device information and flight plan input screen 20a. By looking at the adjusted time 23, the operator can understand that there is a difference between the original flight plan and the actual preparation status, or that the drone departure time has been changed.

[0050] [Operation Plan Adjustment Processing] Next, the operation plan adjustment processing described above will be explained. Fig. 12 is a flowchart of the operation plan adjustment processing performed by the server 100. This processing is realized by the processor 112 shown in Fig. 3 executing a prepared program and operating as each element shown in Fig. 4.

[0051] First, the device information acquisition unit 11 acquires device information from the terminal device 200 and outputs it to the departure time prediction unit 14 and the flight operation plan management unit 15 (Step S11). The flight operation plan acquisition unit 12 acquires the flight plan from the terminal device 200 and outputs it to the departure time prediction unit 14 and the flight operation plan management unit 15 (Step S12). The preparation status acquisition unit 13 acquires the preparation status from the terminal device 200 and outputs it to the departure time prediction unit 14 and the flight operation plan management unit 15 (Step S13).

[0052] Next, the departure time prediction unit 14 predicts the possible departure time of the drone based on the preparation state (step S14). The departure time prediction unit 14 outputs the predicted possible departure time to the flight plan management unit 15.

[0053] Next, the flight operation plan management unit 15 determines whether all pre-flight preparations by the operator have been completed (step S15). If the pre-flight preparations have not been completed (step S15: No), the flight operation plan management unit 15 determines whether the possible departure time is within the flight operation plan range based on the flight operation plan and the possible departure time (step S16). If the possible departure time is within the flight operation plan range (step S16: Yes), the process returns to step S13. On the other hand, if the possible departure time is outside the flight operation plan range (step S16: No), the flight operation plan management unit 15 modifies the flight operation plan and registers the modified flight operation plan in the database 115. Then, the flight operation plan presentation unit 16 presents the modified flight operation plan to the operator (step S17).

[0054] In this way, the flight plan is revised as necessary until all pre-flight preparations by the operator are completed, and when all pre-flight preparations are completed (step S15: Yes), the flight plan adjustment process ends.

[0055] [Modifications] Next, modifications of the first embodiment will be described. The following modifications can be applied to the first embodiment in appropriate combinations. (Modification 1) In the first embodiment described above, drone operation plans are managed, but the objects of management are not limited to drones and may include various unmanned aerial vehicles and unmanned guided vehicles that fly under external control.

[0056] (Variation 2) In the first embodiment described above, the server 100 updates the database 115 based on the adjusted flight schedule and then transmits the adjusted flight schedule to the terminal device 200. However, the application of the present disclosure is not limited to this. For example, the server 100 may first transmit the adjusted flight schedule to the terminal device 200 to request approval for the change to the flight schedule, and update the database 115 only when the operator approves.

[0057] (Variation 3) In the first embodiment, the server 100 adjusts the flight schedule space by shifting the time so as not to conflict with other flight schedule spaces, but the method for adjusting the flight schedule is not limited to this. For example, the server 100 may generate a flight route that does not conflict with the flight routes of other flight schedules and propose the flight route to the operator.

[0058] 13 is a block diagram showing the functional configuration of an operation plan control device 50 according to a second embodiment. The operation plan control device 50 according to the second embodiment includes an equipment information acquisition unit 51, a preparation state acquisition unit 52, an available departure time prediction unit 53, and an operation plan management unit 54.

[0059] 14 is a flowchart of the processing by the operation plan control device 50. The equipment information acquisition means 51 acquires equipment information of a mobile object (step S51). The preparation state acquisition means 52 acquires the preparation state of the operator (step S52). The possible departure time prediction means 53 predicts the possible departure time of the mobile object based on the equipment information and the preparation state (step S53). The operation plan management means 54 manages the operation plan based on the prediction result (step S54).

[0060] According to the flight plan control device 50 of the second embodiment, it is possible to adjust the space-time to be used depending on the state of preparation before departure.

[0061] A part or all of the above-described embodiments can be described as, but not limited to, the following supplementary notes.

[0062] (Supplementary Note 1) An operation plan management device comprising: an equipment information acquisition means for acquiring equipment information of a mobile body; a readiness state acquisition means for acquiring the readiness state of an operator; an available departure time prediction means for predicting the available departure time of the mobile body based on the equipment information and the readiness state; and an operation plan management means for managing an operation plan based on the result of the prediction.

[0063] (Supplementary Note 2) The flight plan management device according to Supplementary Note 1, further comprising: a scheduled departure time acquisition means for acquiring a scheduled departure time of the moving object; and the flight plan management means for comparing the scheduled departure time with the possible departure time and adjusting the departure time of the moving object.

[0064] (Supplementary Note 3) The flight plan management device according to Supplementary Note 2, wherein the flight plan management means outputs a result of the adjustment to a terminal device of the operator.

[0065] (Supplementary Note 4) The flight plan management device according to Supplementary Note 3, wherein the flight plan management means registers the result of the adjustment as a scheduled departure time when the operator approves the result of the adjustment.

[0066] (Appendix 5) The operation plan management device described in Appendix 1, wherein when adjusting the operation plan of the mobile body based on the possible departure time, the operation plan management means adjusts the operation plan of the mobile body so that it does not conflict with the operation plans of other mobile bodies.

[0067] (Appendix 6) The operation plan management device described in Appendix 1, wherein when adjusting the operation plan of the mobile body based on the possible departure time, the operation plan management means proposes an alternative route to the operator of the mobile body so that the operation plan of the mobile body does not conflict with the operation plans of other mobile bodies.

[0068] (Supplementary Note 7) An operation plan management method that acquires equipment information of a mobile object, acquires the readiness state of an operator, predicts a possible departure time of the mobile object based on the equipment information and the readiness state, and manages an operation plan based on the result of the prediction.

[0069] (Appendix 8) A recording medium having recorded thereon a program that causes a computer to execute the following processes: acquire equipment information of a mobile object; acquire the operator's readiness status; predict the possible departure time of the mobile object based on the equipment information and the readiness status; and manage an operation plan based on the result of the prediction.

[0070] Although the present disclosure has been described above with reference to the embodiments and examples, the present disclosure is not limited to the above-described embodiments and examples. Various modifications that can be understood by a person skilled in the art can be made to the configuration and details of the present disclosure within the scope of the present disclosure.

[0071] 5 Drone 11 Equipment information acquisition unit 12 Flight plan acquisition unit 13 Preparation status acquisition unit 14 Departure time prediction unit 15 Flight plan management unit 16 Flight plan presentation unit 100 Server 200 Terminal device

Claims

1. device information acquisition means for acquiring device information of a mobile object; a readiness status acquisition means for acquiring a readiness status of an operator; a departure time prediction means for predicting a departure time of the mobile object based on the device information and the preparation state; an operation plan management means for managing an operation plan based on the result of the prediction; An operation plan management device comprising:

2. a scheduled departure time acquisition means for acquiring a scheduled departure time of the moving body, 2. The flight plan management device according to claim 1, wherein the flight plan management means compares the scheduled departure time with the possible departure time and adjusts the departure time of the moving object.

3. 3. The flight plan management device according to claim 2, wherein the flight plan management means outputs the result of the adjustment to a terminal device of the operator.

4. 4. The flight plan management device according to claim 3, wherein the flight plan management means registers the result of the adjustment as a scheduled departure time when the operator approves the result of the adjustment.

5. 2. The operation plan management device according to claim 1, wherein when adjusting the operation plan of the mobile body based on the possible departure time, the operation plan management means adjusts the operation plan of the mobile body so that it does not conflict with the operation plans of other mobile bodies.

6. 2. The operation plan management device according to claim 1, wherein, when adjusting the operation plan of the mobile body based on the possible departure time, the operation plan management means proposes an alternative route to the operator of the mobile body so that the operation plan of the mobile body does not conflict with the operation plans of other mobile bodies.

7. A flight plan management method executed by a computer, comprising: Acquires device information of the mobile device, Obtain the operator's readiness status; predicting a possible departure time of the mobile object based on the device information and the preparation state; A flight plan management method for managing flight plans based on the results of the prediction.

8. Acquires device information of the mobile device, Obtain the operator's readiness status; predicting a possible departure time of the mobile object based on the device information and the preparation state; A program that causes a computer to execute a process for managing flight plans based on the results of the prediction.