JP2019007832A - Area management system, method and program - Google Patents

Abstract

To contribute to improvements on inefficient area allocation in a distributed type navigation management system.SOLUTION: An area management system 500 of the present invention comprises: entanglement detection means 501 for calculating, on the basis of area information that indicates the state of occupied area allocation to a user who manages the navigation of a mobile entity in an occupied area allocated to the user himself and navigation information that indicates the navigation plan of each mobile entity by the user and that includes the departure position, departure time and arrival position of each mobile entity and information for evaluating a navigation route in the navigation plan, the degree of route improvement that is the degree of improvement on navigation route when at least some of the occupied areas of the selected users are changed for the navigation plan of each of mobile entities of at least two or more of selected users and detecting entanglement; and information output means 502 for outputting information to at least some of the selected users on the basis of the result of detection by the entanglement detection means 501.SELECTED DRAWING: Figure 22

Description

  The present invention relates to an area management system, an area management method, and an area management program for managing an area used for operation of a mobile object.

  The use of unmanned aircraft such as drones (Unmanned Aircraft System, UAS) for air transportation is under consideration. In order to utilize UAS, a mechanism for managing the UAS operation plan and the area used for it is required. Therefore, various methods of UAS operation management (UAS Traffic Management, UTM) have been studied.

  As a technique related to the operation management system that manages the operation of UAS, for example, there is a technique described in Patent Document 1.

  In the technique described in Patent Document 1, an automated guided vehicle (AGV) creates a route plan for each, and then repeatedly transmits and receives information and performs a reroute search at each AGV. By such a method, it is said that the weighting coefficient of the penalty function regarding the inconsistent route plan related to the collision or interference with other AGVs is gradually increased in each AGV, and the inconsistent route plan can be resolved in a distributed cooperative manner. Yes.

JP 2004-280213 A

  As an example of an operation management system that manages the operation of a mobile body, not just a UAS, a centralized operation management system in which a single control system centrally manages an operation plan and a region used for the operation of a large number of mobile objects Conceivable. However, this method has a problem that if a large number of approval requests are accumulated, it is difficult to approve promptly while considering the consistency of each operation plan, and all operations will be stopped at the time of failure. It is not preferable.

  Therefore, consider a distributed operation management system. Specifically, the authority to approve a mobile operation plan for the assigned area is delegated to each of the multiple operation management systems, and each operation management system operates each mobile object within the assigned area. Consider a distributed operation management system to manage.

  According to such a distributed operation management system, the operation plan approval process is performed for each operation management system while avoiding the conflict of moving objects between different operation management systems by allocating an area exclusively to each operation management system. Since the management system can be performed independently, the above problem can be avoided.

  However, if each operation management system autonomously applies for an area based on the operation plan of a mobile managed by the operation management system and obtains approval authority for that area, it may be an inefficient area allocation for multiple operation management systems. There is sex. If there is such inefficient area allocation, it is desirable to be able to contribute to the improvement by providing information on solutions that are beneficial to a plurality of operation management systems.

  The method described in Patent Document 1 is to perform distributed optimization to eliminate the infeasibility of path planning that each AGV independently plans, and performs overall optimization. If there is inefficient area allocation, it does not provide information on solutions that are beneficial to multiple flight management systems. For example, the reroute planning in the method described in Patent Document 1 is performed in order to avoid a collision with another moving body with respect to the shortest path planned by the moving body alone. The operation management of the moving body is a category of each operation management system in the above-described distributed operation management system. On the other hand, in the reroute plan in the distributed operation management system, a plurality of operation management systems are compared with the existing route optimized in the area allocated to itself. It is required to find a better route for the operation management system.

  Accordingly, an object of the present invention is to provide an area management system, an area management method, and an area management program that can contribute to improvement of inefficient area allocation in a distributed operation management system.

  The area management system according to the present invention includes area information indicating an allocation status of an occupied area to a user who manages the operation of a mobile body in an occupied area assigned to the area management information, and operation information indicating an operation plan of each mobile body by the user. The movement of a selected user that is at least two or more users selected based on the departure position, departure time, arrival position and operation information including information for evaluating the operation route in the operation plan. Tangle detection means for detecting tangles by calculating a route improvement degree that is an improvement degree of the operation route when at least a part of the occupied area of the selected user is changed for each operation plan of the body, and tangle detection And an information output means for outputting information to at least a part of the selected users based on the detection result of the means.

  An area management method according to the present invention includes: area information indicating an allocation status of an occupied area to a user who manages the operation of a mobile body in an occupied area assigned to the information processing apparatus; Two or more users selected at least based on the operation information indicating the plan, which is based on the departure information, the departure time, the arrival position, and the operation information including information for evaluating the operation route in the operation plan. For each operation plan of a selected user's mobile unit, calculate the route improvement degree, which is the improvement degree of the operation route when at least part of the occupied area of the selected user is changed, detect entanglement, and detect Based on the result, the information is output to at least a part of the selected users.

  The area management program according to the present invention stores, in a computer, area information indicating the allocation status of an occupied area to a user who manages the operation of a moving object in the occupied area assigned to the computer, and an operation plan for each moving object by the user. Selection that is at least two or more users selected based on the operation information including the operation information including the departure position, departure time, arrival position, and information for evaluating the operation route in the operation plan. An entanglement detection process for detecting entanglement by calculating a route improvement degree that is an improvement degree of an operation route when at least a part of the occupied area of the selected user is changed for each operation plan of the user's moving body; And based on the detection result in the tangle detection process, an information output process for outputting information to at least a part of the selected users is executed.

  ADVANTAGE OF THE INVENTION According to this invention, it can contribute to the improvement of inefficient area | region allocation in a distributed operation management system.

1 is a schematic configuration diagram of a mobile navigation system 100 including an area management system 10. FIG. 2 is a block diagram illustrating a configuration example of an area management system 10. FIG. It is explanatory drawing which shows the classification | category of a management object area | region. It is explanatory drawing which shows the example of generation | occurrence | production of a tangle. It is explanatory drawing which shows the example of determination of a tangle. 4 is a flowchart showing an example of the operation of the area management system 10. It is explanatory drawing which shows the example of a detection of the tangle using the improvement degree of the path length accompanying a region change. It is an image figure of a field. It is an image figure of a field. It is explanatory drawing which shows the example of the occupation state of an area | region, and the movement of a mobile body. It is explanatory drawing which shows the example of expression of the airspace in a tangle determination algorithm. It is explanatory drawing which shows the movement rule of the moving body with respect to the airspace group expressed by the graph. It is explanatory drawing which shows the pseudo code of the extraction algorithm of a transition possible state. It is explanatory drawing which shows the example of derivation | leading-out of a transition possible state. It is explanatory drawing which shows the pseudo code of the tangle determination algorithm of a 1st example. It is explanatory drawing which shows the example of the path | pass of the mobile body M1 and the mobile body M2 in an operation plan. It is explanatory drawing which shows an example of a transition possible state. It is explanatory drawing which shows an example of a path set. It is explanatory drawing which shows the example of a path search of the tangle determination algorithm of a 2nd example. It is explanatory drawing which shows the example of a determination of the tangle determination algorithm of a 2nd example. It is a schematic block diagram which shows the structural example of the computer concerning embodiment of this invention. It is a block diagram which shows the outline | summary of the area | region management system of this invention.

Embodiment 1. FIG.
Hereinafter, embodiments of the present invention will be described with reference to the drawings.

  FIG. 1 is a schematic configuration diagram of a mobile navigation system 100 including an area management system 10 of the present embodiment. As shown in FIG. 1, the area management system 10 of the present embodiment is based on the premise that there are a plurality of operation management systems 20 that perform operation management of mobile objects in the occupied area assigned to each area. And In addition, the area management system 10 not only assigns areas according to the area application from the operation management system 20, but also provides information for at least detection of tangles and improvement thereof for overall optimization.

  It should be noted that area allocation to the operation management system 20, area information indicating the area allocation status to the operation management system 20, management of operation information indicating the operation plan of each mobile unit reported from each operation management system 20, etc. It is assumed that it is performed separately, and in the present embodiment, it is possible to refer to information regarding these as appropriate.

  In the mobile navigation system 100, the area is exclusively allocated to each navigation management system 20. The operation management of the mobile body in the area after the assignment is delegated to the operation management system 20 of the assignment destination. Except for the allocation management system 20, the area cannot be used for the operation of the mobile unit unless it is negotiated to obtain the area itself. By imposing such restrictions on each of the operation management systems 20, a conflict of moving bodies between different operation management systems 20 is avoided.

  FIG. 2 is a block diagram illustrating a configuration example of the area management system 10 according to the present embodiment. The area management system 10 illustrated in FIG. 2 includes an entanglement detection unit 11 and an improvement information output unit 12.

  The entanglement detection unit 11 detects an area allocation that is inefficient as a result of allocating the occupied area to each of the plurality of operation management systems 20 as “entanglement”. The tangle detection unit 11 detects tangle based on the operation information and the area information.

  Here, the “occupied area” is an area that is being used or scheduled to be used by any user, and is an area that is allocated to the user for the time period by an application from the user or the like. In the present embodiment, the operation management system 20 that manages the operation of a moving object within the occupied area assigned to itself or the operator thereof is referred to as a “user”.

  As shown in FIG. 3, the management target areas of the area management system 10 are roughly divided into “usable areas” and “unusable areas”. The “available area” is further roughly divided into an “occupied area” and a “non-occupied area”. Here, the “available area” is an area that can be used for the operation of a mobile object. The “unusable area” is an area in which a general mobile body cannot be used for navigation due to physical conditions such as buildings and weather, or emergency response. In other words, the “unusable area” is an area other than the “usable area”. The “unoccupied area” is an area that is not occupied by any user in the “available area”. If any user makes a reservation, the “non-occupied area” becomes the “occupied area” of the user in that time zone.

  The area information includes, for example, information indicating the occupation status of the management target area in the time zone related to the obtainable operation plan. The area information may be, for example, information indicating the allocation status (including reservation) of the occupied area for each predetermined time period.

  The operation information includes a departure position, an arrival position, a departure time, a predetermined evaluation index of an operation route from the departure position to the arrival position, or information that can be calculated. Here, the “predetermined evaluation index” corresponds to what is desired to be improved in the present system. Examples include arrival time, route length, travel distance, travel time, energy consumption, distance to obstacles at each point on the route, and combinations thereof. In addition, the data format of operation information shall be predetermined.

  In this embodiment, “entanglement” is more specifically “when two or more users are specified, by changing the area allocation for the specified users, all of these users are currently registered for movement. It may be defined as “a state in which an improvement of the operation route is expected with respect to the body operation plan”. The change in area allocation is, for example, releasing or exchanging at least a part of the occupied area with another user. In addition, the improvement of the operation route is specifically the operation cost indicated by arrival time, route length, travel distance, travel time, energy consumption, distance to obstacles at each point on the route, and combinations thereof. It may be an improvement.

  The definition of “entanglement” is not limited to the above. For example, even if there is no improvement or disadvantage for some users or some mobile units, the mobile unit is currently being registered against the currently registered mobile plan due to the change in area allocation. When improvement of the operation route is expected for the entire user or the selected user as a whole, the state may be detected as “entanglement”.

  More simply, “entanglement” is changed to “the degree of improvement in the operation route of each mobile unit and / or the change in the user's occupation area with respect to the current operation plan of the mobile units of two or more specified users. It is also possible to set a state where the user's operational improvement degree based on the improvement degree satisfies a predetermined condition. In this case, as examples of the “predetermined conditions”, “all the improvement in the operation for each user based on the improvement in the operation route of the mobile object is positive for the designated user”, “the operation route in the entire mobile object” The improvement degree (total) is positive, "the operational improvement degree (total) for all the specified users is positive, and the improvement degree of the operation route of all mobiles is 0 or more. Is mentioned. Here, “the improvement degree of the operation route of the moving body” and “the improvement degree of the operation of the user” are assumed to indicate a deteriorated state with a negative value, a current state maintained with zero, and an improved state with a positive value.

  The improvement degree of the operation route of the mobile body may be, for example, the difference between the movement cost in the operation plan and the movement cost after the occupation area is changed. In addition, the user's operation improvement degree may be the total improvement or weighted sum of the improvement of the operation route of the moving body managed by the user.

  In addition, when the tangle is detected, the tangle detection unit 11 combines one or more improvement plans indicating the operation route of each moving body for eliminating the tangle and the operation improvement degree by each improvement plan. To detect.

  The improvement information output unit 12 outputs information for improving the current operation plan (hereinafter referred to as improvement information) based on the detection result by the tangle detection unit 11. Examples of the improvement information include information on improvement plans and information on the optimum route of each moving object. In addition to this, the improvement information output unit 12 may output, as the improvement information, information on the area to be exchanged and the operational improvement degree of the user accompanying the exchange. In addition, the improvement information output unit 12 can easily establish negotiations with respect to the improvement plan (for example, the minimum operational improvement degree among users) and the overall optimum degree (the total operational improvement degree of all users), It is also possible to evaluate fairness (difference in operational improvement between users (dispersion)), etc., and output in a ranking format or the like. Further, the improvement information output unit 12 can output the above information only to some of the users (selected users) used for tangling detection. By outputting these pieces of information to users who are involved in entanglement, the significance of area negotiation can be suggested to the users.

  Although not shown, the area management system 10 may include an information holding unit that holds area information and operation information, and a data reception unit that receives area information and operation information.

  FIG. 4 is an explanatory diagram showing an example of entanglement. As a premise of this example, each user possesses information on a departure place and a destination (arrival place) for a moving body managed by the user. Also, the area may become unavailable due to bad weather. FIG. 4A to FIG. 4D show an example in which entanglement occurs under such a premise.

  FIG. 4A shows the state of the management area at a certain time. FIG. 4A shows that a partial area is not available, and that the mobile unit M1 that is a mobile unit managed by the user A and the departure point (the mobile unit M2 that is a mobile unit managed by the user B) Start) and destination (Goal) are shown. Note that it is assumed that the available area is not assigned to any user at the time of FIG.

  First, the user A applies for an area related to the shortest route of the moving body M1 at that time. FIG. 4B shows an area allocation state after area application by the user A.

  After a while, it is assumed that the weather has recovered and the unavailable area becomes an available area. FIG.4 (c) shows the state of the area | region after a weather recovery.

  Next, the user B applies for an area related to the shortest route at that time of the moving body M2. FIG. 4D shows an area allocation state after the area application by the user B. The state shown in FIG. 4D is a state where entanglement has occurred.

  FIG. 5 is an explanatory diagram illustrating an example of determination of entanglement. FIG. 5A shows the state of FIG. 4D as it is. With respect to this state, as shown in FIG. 5B, when the area allocation of the user A and the user B is changed, both the moving distances of the moving bodies of both users can be shortened. In such a case, it is determined that the state of FIG. 5A is entangled, and the state of FIG. 5B is an improvement plan for the entanglement.

  As described above, if each user pursues his / her own profit in limited information, the situation may be lost. Therefore, when entanglement is detected, the user is prompted to exchange the region by providing information such as presenting the user with the improved state of entanglement as shown in FIG. 5B as an improvement plan.

  Next, the operation of the area management system 10 of this embodiment will be described. FIG. 6 is a flowchart showing an example of the operation of the area management system 10 of the present embodiment. In the example illustrated in FIG. 6, first, the tangle detection unit 11 selects two or more users (step S101).

  Next, the tangle detection unit 11 searches for a route for each operation of the moving body managed by the user using the non-occupied area when the occupied area of the selected user is released (step S102).

  Next, the tangle detection unit 11 calculates the improvement degree of the operation route of each mobile body based on the current operation plan and the search result (step S103).

  Next, the tangle detection unit 11 determines whether or not the improvement degree of the operation route of each moving body satisfies a predetermined condition (step S104). If the predetermined condition is satisfied (Yes in step S104), the tangle detection unit 11 determines that tangle has occurred (entanglement detection). At this time, the tangle detection unit 11 detects one or more improvement plans and the operational improvement degree by each improvement plan together with the detection of tangles (step S105).

  Next, the improvement information output unit 12 outputs the improvement information based on the detection result of the tangle detection unit 11 (step S106).

  On the other hand, when the improvement degree of the operation route of each mobile body does not satisfy the predetermined condition (No in step S104), the entanglement detection unit 11 determines that the entanglement due to the area allocation has not occurred, and the process ends. At this time, the tangle detection unit 11 may output that no tangle has occurred.

Next, the tangle detection method will be described in more detail with specific examples. FIG. 7 is an explanatory diagram illustrating an example of entanglement detection using a path length improvement degree associated with a region change. In the figure, s ⁱ represents the starting position of the mobile unit M1, and g ⁱ represents the arrival position of the mobile unit M2. Now, the operation routes of the mobile unit M1 managed by the user A, the mobile unit M2 managed by the user B, and the mobile unit M3 managed by the user C were in the state shown in the operation plan of FIG. To do. The route length of the moving body M1 in the operation plan is 10, the route length of the moving body M2 is 7, and the route length of the moving body M3 is 10.

  Assume that the tangle detection unit 11 selects the user A and the user B for such an operation plan (FIG. 7B). FIG. 7B shows the state of the area when the occupied areas of user A and user B are released. The white area is an unoccupied area. The entanglement detection unit 11 searches for each operation route using the non-occupied region based on the operation plans of the mobile unit M1 and the mobile unit M2 (FIG. 7C). Here, a route with a path length of 4 is found for the mobile body M1, and a route with a path length of 3 is found for the mobile body M2. The entanglement detection unit 11 compares the path lengths before and after the area release for the moving body M1 and the moving body M2, and detects entanglement because both are shorter.

  Next, some specific examples of the tangle determination algorithm are presented. Below, although a drone is illustrated as an example of a mobile body, a mobile body is not limited to a drone.

  8 and 9 are image diagrams of the area of this example. As shown in FIG. 8, in this example, a three-dimensional space is assumed as an area used for the operation of the moving body. In addition, a unit obtained by dividing the three-dimensional space into predetermined management units is defined as one unit of “area” (“air area”). Each airspace is assigned exclusively to the user.

  Also, as shown in FIG. 9, airspaces are assigned every time, and each user forms a route by expanding the airspace adjacent to the airspace where the drone is currently located from among the airspaces assigned to the user. In the following, for the sake of simplicity, an airspace extending in two dimensions is illustrated as an operation route of a mobile body, but those skilled in the art can easily imagine that the route can also be applied to an airspace extending in three dimensions. Will be done. If the moving body is a two-dimensional moving vehicle or the like, a two-dimensional space is assumed as an area used for moving the moving body, and the two-dimensional space is divided into predetermined management units. It is also possible to define it as one unit.

FIG. 10 is an explanatory diagram showing an example of the area occupation state and the movement of the moving object. FIG. 10A is an explanatory diagram showing a two-dimensional arrangement of the airspace according to the movement of this example. FIG. 10A shows four airspaces, a region a ₁ to a region a ₄ . As shown in the figure, the region a ₁ , the region a _2, and the region a ₄ are connected in a straight line in the + X direction in the drawing, and the region a ₃ is adjacent to the region a _{2 in} the + Y direction as a detour. doing.

  FIG. 10B is a table showing the allocation status of each area, and FIG. 10C is an explanatory diagram showing an example of movement of the moving object in the area arrangement. In FIG. 10C, a circle in the airspace represents a drone, and a number in the middle represents a drone identifier. Note that a circle 1 indicates a drone (mobile body M1) managed by the user A, and a circle 2 indicates a drone (mobile body M2) managed by the user B. As shown in FIG. 10B and FIG. 10C, drones managed by different users cannot enter one airspace. In consideration of the fact that the time across the airspace cannot be strictly controlled, even when a drone exists at the previous time and the drone has moved to another airspace at the current time, that airspace (one It is assumed that no other user's drone can enter the airspace where the drone existed at the previous time. Although FIG. 10 shows an example in which each drone travels one airspace per unit time, the moving speed of the drone is not limited to this example.

As shown in FIG. 10C, it is assumed that the moving body M1 is in the area a ₁ and the moving body M2 is in the area a ₄ at time t = 0. At this time, the area a ₁ is an area occupied by the user A, and the area a ₄ is an area occupied by the user B. The object position of the moving object M1 is a region a _1, the target position of the moving body M2 is a region a _4.

At time t = 1, the user A advances the moving object M1 in the region a ₂ on which the region a ₂ and its occupied area. User B, since the region a ₂ is in the occupied region of the user A, can not be advanced mobile M2 in the region a _2, keep in region a _4.

At time t = 2, the user A, since the region a ₄ is in the occupied region of the user B, thereby bypassing the mobile M1 to the area a ₃ on which the area a ₃ and its occupied area. User B, since that is the area occupied by the region a ₂ still user A, leaves a moving body M2 in the region a _4.

At time t = 3, user A, because it is a region occupied by the region a ₄ still user B, keep the moving object M1 in the area a _3. User B, since the region a ₂ is released, to move the moving body M2 in the region a ₂ on which the region a ₂ and its occupied area.

At time t = 4, user A, since the region a ₄ is released, but the area a ₂ is in the occupied region of the user B, keep the moving object M1 in the area a _3. User B moves the moving body M2 in the region a ₁ in terms of the region a ₁ and its occupied area (arrival).

At time t = 5, since the area a ₂ is released, the user A moves the moving body M1 to the area a ₂ after making the area a ₂ its own occupied area. Further, at time t = 6, the user A moves the moving object M1 in the area a ₄ on which the region a ₄ and its occupied area (arrival).

  In this way, when a certain airspace is occupied by any user, other users cannot use the airspace, and thus move while avoiding such airspace. In addition, the operation management system 20 which manages the mobile body should just perform the setting of a detour route, etc.

  Next, a specific example of the entanglement determination algorithm will be described with reference to FIGS. FIG. 11 is an explanatory diagram illustrating an example of airspace expression in the entanglement determination algorithm. As shown in FIG. 11, in the example shown below, the airspace (three-dimensional) is represented by a graph. More specifically, the coordinates of each space are made to correspond to the points on the graph, and the connection of the space coordinates to which the drone can move per unit time is made to correspond to the side. Then, each airspace is expressed as a set of coordinates. Note that a graph representing an empty space structure and each airspace are expressed as in the following equation (1). Here, V is a set of points, E is a set of edges, and m is the number of airspaces. Here, each airspace corresponds to a management unit of the above-described region.

  FIG. 12 is an explanatory diagram showing a moving rule of the moving object with respect to the airspace group expressed in a graph. As shown in FIG. 12, each moving body needs to occupy both spaces before and after movement when moving between airspaces. Also, different user moving bodies cannot simultaneously exist within one unit time for one airspace. In addition, even when a moving object exists at the previous time and moves at the time limit, it cannot be entered into the area.

  Next, a first example of an entanglement determination algorithm will be described. In the first example, the Dijkstra method is applied to a plurality of moving bodies managed by different users.

The tangle determination algorithm of the first example is for each drone to be determined, a departure point T _s ⁱ and an arrival time T _g indicated by the departure point / arrival point pair (s ⁱ , g ⁱ ) and the current operation plan. Enter ⁱ . Here, n is the number of drones to be determined. The determination target drone is, for example, a drone that is managed by a selected user in a time zone that is an entanglement detection target. In addition, i (i = 1,..., N) on the right shoulder of the departure point s, arrival point g, departure time T _s , and arrival time T _g represent a drone identifier. Further, the output is information on alternative routes (path information of each drone) when entanglement exists and when entanglement exists. Airspace information such as airspace units and allocation status can be referred to at any time.

  The tangle determination algorithm of the first example enumerates combinations of routes that can be taken for each selected drone when releasing the airspace of the selected user, and searches for a better route than the current state. Then, entanglement is determined based on the search result. At this time, the search time range is set from the first departure time to the last arrival time in the current operation plan of each drone to be determined.

  Before describing the details of the entanglement determination algorithm, the transitional state extraction algorithm used for the entanglement determination algorithm will be described below. In this algorithm, the following conditions are assumed as the drone movable state. First, for a drone that has not yet departed after the departure time, either use the drone's position as a starting point or leave the departure. At this time, it is assumed that the position of the drone that has not departed is not on the graph, for example, by giving special coordinates representing the state of not departing. Further, it is assumed that the drone at the arrival point is not on the graph by giving a special coordinate indicating the arrived state.

Further, the position of each drone is represented by d ⁱ , and is assumed to be s ⁰ before departure and g ⁰ after arrival. Further, the state of each drone at each time is represented by w = (d ¹ ,..., D ⁿ , t) using the position and time of the drone. In the following, for the sake of simplicity, each drone can move from an adjacent point, that is, a point connected by a side, to a point within the unoccupied area after the release of the selected user's occupied area. And

  Hereinafter, a more specific processing flow will be described. In the transition enable state extraction algorithm, when a state at time t is given, a transitionable state is extracted at time t + 1.

First, if there is drones are in arrival point in w at time t, the position of the drone in the state at time t + 1 and g ^0. Next, a case where d ¹ moves to each adjacent position (however, limited to an unoccupied region) and a case where it does not move are listed for the state. At this time, when the departure time has passed and the departure has not been made, the cases of leaving and not leaving are listed. Then, it listed for all conditions listed in the previous, and each adjacent position for d ² (but only in the unoccupied region) in the case of moving to, and if not move. At this time, when the departure time has passed and the departure has not been made, the cases of leaving and not leaving are listed.

Thereafter, the same processing is performed for d ³ ,. . . , D ⁿ Repeat. Finally, from all the states listed for ^dn , the state that cannot be changed due to the occupation condition is deleted, and the remaining state is output as a state that can be changed at time t + 1.

FIG. 13 shows a pseudo code of an extraction algorithm for a transitionable state. The transitionable state extraction algorithm shown in FIG. 13 inputs the state w at time t and outputs all the states at time t + 1 that can transition from the state w. First, Step. 1, w is substituted into St ⁽⁰⁾ as one of St indicating the transition possible state candidates. Then each i = 1,. . . , N, if d ⁱ = g ⁱ , St ⁽⁰⁾ . Let st _i = g ⁰ . Also, St ⁽⁰⁾ . _Let st _{n + 1} = t + 1.

Here, st _i represents the i-th element of St. In addition, St ⁽⁰⁾ is in a state of g ⁰ indicating that all the mobile bodies remain at the position t at time t + 1 or have already arrived. The number in () on the right shoulder of St represents the St label. The label corresponds to the identifier i of the moving object that is the derivation target of the transition destination in the subsequent Steps. Step. Reference numeral 1 denotes a process for generating a state in which all the moving bodies maintain the current state as transitionable state candidates after performing the process that has already arrived.

Step. In 2, St ⁽⁰⁾ is added to the candidate set ST [].

Step. In step 3, St having the smallest e (St) is selected from the ST elements. Here, e () is a function that returns a label. If the label of St selected at this time is i, and i = e (St)> n, Step. Proceed to 6. Step. 3 is a process of selecting elements from ST in ascending order of i. For example, if the only element of ST [] is St ⁽⁰⁾ , St ⁽⁰⁾ is selected as a result of returning e (St) = 0.

Step. 4, i indicating the derivation target of the transition destination is updated to i + 1, and the label of the selected St is e (St) = i. As a result, in the selected St, a candidate in which d ⁱ is also in the current position or does not leave is held in ST [] as St ⁽ⁱ⁾ . Furthermore, if d ⁱ in state w at time t is an element of V, for each of d ⁱ ′ satisfying that the side (d ⁱ , d ⁱ ′) is an element of E, based on the selected St, A new St ⁽ⁱ⁾ is generated. Specifically, first, the selected St is substituted into St ′, and St ′. St _i = d ⁱ ′ is added to ST [] as a new St ⁽ⁱ⁾ . Step. When there are a plurality of Sts selected in Step 3, a new St ⁽ⁱ⁾ reflecting the transition destination d ⁱ ′ is generated for each St. For example, when two Sts are selected and two transitional positions d ⁱ ′ are listed, four St ⁽ⁱ⁾ are generated. On the other hand, if d ⁱ = s ⁰ and t + 1 ≧ T _s ⁱ in the state w at time t, a new St ⁽ⁱ⁾ with a transition destination d ⁱ ′ = s ⁱ is generated based on the selected St. Specifically, the selected St is first substituted into St ′, and then St ′. St _i = s ⁱ is set as St ⁽ⁱ⁾ and added to ST [].

  Step. 5, Step. Return processing to 3. Thereafter, Step. Repeat steps 3-4.

  Step. In step 6, all the elements ST [] whose e (St) is n are selected. Among the selected Sts, all satisfying the condition (a) are output as transitionable states at time t + 1.

Condition (a):
Each i = 1,. . . , N, each j = 1,. . . , M for the selected St. If st _i is an element of a _j , ie a point in region a _j , then all k = 1,. . . , N, St ⁽⁰⁾ . st _k is not an element of a _j and St. st _k is not an element of a _j .

  The condition (a) is a condition (area exclusive condition) for deleting an area that overlaps another drone. Here, the exclusion condition of the area is applied for each i, that is, the drone unit. However, the exclusion condition may not be applied to the same drone by the user. In that case, k is changed to k = 1,. . . , N (excluding the moving body l of the same user as i and i). In this way, when one user manages the operation of a plurality of drones, exclusive control of the area can be performed on a user basis.

FIG. 14 is an explanatory diagram illustrating an example in which a region exclusion condition is applied. Assume that there are five points p1 to p5 arranged in a straight line. p1 belongs to the region _{a 1,} p2 and p3 belong to the region _{a 2,} p4 and p5 belongs to region _{a 3.} Now, state w = {p1, p4, t}. That is, at time t, the moving body M1 managed by the user A is located at p1, and the moving body M2 managed by the user B is located at p4. Consider a state in which transition to t + 1 is possible for this state w. Note that the areas a _{1 to} a ₃ are considered to be released at all the determination target times.

First, St ⁽⁰⁾ = {p1, p4, t + 1} is generated. Next, as St ⁽¹⁾ reflecting the transition possible state of the moving body M1, the pattern state {p2, p4, t + 1} in which the moving body M1 moves to an adjacent point, the state of the pattern in which the moving body M1 does not move {p1 , P4, t + 1} are generated. Next, as St ⁽²⁾ reflecting the possible transition state of the moving body M2, the state of the pattern in which the moving body M2 moves to an adjacent point and the moving body M2 move relative to the two St ⁽¹⁾ , respectively. A pattern state is generated. The generated state St ⁽²⁾ is {p2, p3, t + 1}, {p2, p5, t + 1}, {p2, p4, t + 1}, {p1, p3, t + 1}, {p1, p5, t + 1}, { p1, p4, t + 1}. If condition (a) is applied to this result, {p2, p3, t + 1} is excluded. The states that are finally output are four states in St ⁽²⁾ excluding {p2, p3, t + 1}.

  Next, details of the tangle determination algorithm of the first example will be described. First, each drone to be determined is assumed to be before departure, and t is assumed to be one time before the earliest departure time. Next, t + 1 states that can be transitioned from t are listed. The enumeration of the states may be performed using the transition state extraction algorithm already described.

  Next, states that can be transitioned at the next time are listed for all the listed states. Here, t is increased by 1, and the states that can be transitioned for each t until the latest arrival time are listed.

  When the enumeration of the states that can be transitioned to the latest arrival time is completed, the time t is reversed from the enumerated states to all states where t is the maximum and all drones have arrived. Thus, a path set indicating a combination of routes of each drone is derived. Here, one path set is derived for each of the targeted states.

  If there is a combination of routes in which the arrival time of the drone is earlier for each user in the derived path set, the path set is output assuming that entanglement exists. If there is no such thing, the fact that there is no entanglement is output.

FIG. 15 shows a pseudo code of the tangle determination algorithm of the first example. The tangle determination algorithm of this example inputs a departure point / arrival point pair (s ⁱ , g ⁱ ), a departure time T _s ⁱ and an arrival time T _g ⁱ indicated by the current operation plan, When there is entanglement, the information of the path set PS after the entanglement is eliminated is output.

First, Step. 1, S is a set of states before expansion (a state from which a path from that state to the next state is not derived), and W is a tree that records the expansion order. A state w ^{(t) in} which the position d ^{i of} each drone is set to s ⁰ before departure is generated by matching the time t with the time immediately before the earliest departure time of the drone to be determined. Here, the number in () on the right shoulder of w represents the time corresponding to the state. Let w ^{(t) be} the only element of S and W.

Step. In 2, the w having the smallest t is selected from the elements of S. Hereinafter, the time corresponding to the selected w is assumed to be t. At this time, if t is equal to or greater than the latest arrival time, Step. Proceed to step 5. If not, delete the selected w ^(t) from S.

Step. 3 lists all the states w ^{(t + 1)} that can be transitioned at the next time for each of the selected states w ^(t) . Then, the enumerated w ^{(t + 1)} is added to S and W. At this time, W is added with information on the state w ^(t) before the transition for each of w ^{(t + 1)} . Specifically, branches (w ^(t) , w ^{(t + 1)} ) are added in the tree structure. As a result, the state before the transition can be traced later.

  Step. 4, Step. Return processing to 2. Thereafter, until t exceeds the latest arrival time, Step. Repeat steps 2-3.

Step. 5, the state w ^{(tMAX) in} which t is the maximum and all drone positions have arrived (d ⁱ = g ⁰ for all i ⁾ is extracted from the elements of W, and for each of the extracted w ^(tMAX) Thus, a path set PS is generated by going back the branch until the time before the earliest departure time. Step. 5, the path set PS may be generated by going back the branch in W and connecting the states of the previous time. Here, tMAX and tMIN on the right shoulder of w represent the maximum time and the minimum time of w, which is an element of W, respectively. The pass set PS includes the positions at all times of all the drones to be determined. The pass set PS indicates the combination of passes for each drone.

Step. 6, output all path sets PS whose arrival time, which is the first time when d ⁱ = g ⁱ , is earlier than the input arrival time T _g ⁱ in all the drones among the formed path sets PS To do. If such a path set PS does not exist, information indicating that the tangle has not occurred is output.

Next, an example of path search and entanglement determination will be described with reference to FIGS. FIG. 16 is an explanatory diagram illustrating an example of paths of the moving body M1 and the moving body M2 in the operation plan. In this example, for the sake of simplicity, a case is considered in which each moving body can move between one region per unit time. That is, consider a case where the points and the areas correspond one-to-one. The moving body M1 is managed by the user A, and the moving body M2 is managed by the user B. Further, as shown in FIG. 16A, the area is divided into 4 × 5 20 square blocks, of which the central 6 squares are unusable areas. Further, as shown in FIG. 16B, a point number (any one of p1 to p14) is assigned to each area of the available area. Further, the starting position ^{s 1} of the moving object M1 is point p1, the arrival position ^{g 1} is set to a point pi 1, starting position ^{s 2} of the mobile M2 is point p8, arrival position ^{g 2} is set to the point p4. In addition, FIG. 16 (c) shows an area allocation state at the time of operation planning. According to the operation plan, the start time T _s ¹ and the arrival time T _g ¹ of the mobile body M1 are (T _s ¹ , T _g ¹ ) = (0, 10), the start time T _s ¹ and the arrival time of the mobile body M1. T _g ¹ is (T _s ² , T _g ² ) = (0, 10).

  Hereinafter, an example of the determination result of the entanglement determination algorithm when the area of the points p1 to p14 in the time zone from time t = 0 to 10 is released from such a state will be shown.

  FIG. 17 shows the state in which transition is possible from the time t = −1 immediately before the earliest departure time to the latest arrival time t = 10 among the mobile objects that are the determination targets in order from the smallest time. A part of the list is shown.

In the entanglement determination algorithm of the first example, from the states enumerated in this way, the state where the time is the maximum (in this example, tMAX = 10) and all the mobile objects to be determined have arrived is extracted. The path set PS (w ⁽¹⁰⁾ ,..., W ⁽⁻¹⁾ ) is formed by following the branches in the reverse order of the time until the time becomes the minimum (tMIN = −1). In FIG. 17, the state of the second stage from the top at t = 10 shows the same route as the route of each mobile object before the area release, but not all mobile objects have arrived. A path set PS based on the state w ^(tMAX) is not formed.

  The conditions for forming the path set PS differ depending on the definition of entanglement. For example, even when a part of the path having the same arrival time as the path before the area release is included, the case where the total arrival time is allowed to be positive for each user is not limited to the above. In such a case, for example, the search time range is expanded to the maximum allowable delay time to enumerate the states that can be transited, and after the path set PS is formed for the state in which all the mobile units have arrived, The output path set PS may be selected according to the definition of entanglement.

FIG. 18 is an explanatory diagram showing an example of a path set. The example shown in FIG. 18 is an example of the path set PS formed based on the state of the first stage from the top at t = 10 in FIG. According to the path set PS, it can be seen that both the user A and the user B can shorten the arrival time by changing the area allocation. FIG. 18A shows the route of each mobile unit included in the path set PS. FIG. 18C shows an example of area allocation in the path set PS. Here, 4 <10 = T _g ¹ and 4 <10 = T _g ² are satisfied, which is a WIN-WIN route for each user. The relationship between the path set PS of this example and the route of each mobile unit is as shown in FIG. That is, the root of the i-th moving body is obtained by connecting the i-th element d ⁱ in the state w ^{(t) at} each time, which is an element of the path set PS, in order of time. Note that the number of path sets to be output is not limited to one, and it is also possible to perform control such as evaluating the degree of improvement in operation and outputting the top n path sets PS for a set of paths that satisfy the conditions.

  Next, the tangle determination algorithm of the second example will be described. The second example is an algorithm based on A * (A star).

The entanglement determination algorithm of this example also has a departure time T _s ⁱ and an arrival time T _g ⁱ indicated by the departure point and arrival point pair (s ⁱ , g ⁱ ) and the current operation plan for each of the determined drones. Enter. Further, the output is information on an alternative route (path set) if there is entanglement and if entanglement exists.

  In the tangle determination algorithm of the first example based on the Dijkstra method, the following states are considered in order from the smallest current time, but the tangle determination algorithm of the second example based on A * is predicted arrival Consider the next state from the smallest time. If the predicted arrival time is close to the actual arrival time, it is only necessary to consider those that are likely to arrive earlier, so the processing time can be reduced. Theoretically, the predicted arrival time only needs to be estimated lower than the actual arrival time. Here, “predicted arrival time = current time + predicted time from the current state to arrival”.

  As a method of obtaining the predicted time until arrival, a method of obtaining the shortest route using the A * method without considering the movement of other moving objects, a method of calculating from the straight distance between the current location and the arrival point, etc. Can be mentioned. Note that there is no particular limitation as long as it is a method that can be estimated smaller than the actual arrival time.

  Hereinafter, a specific processing flow of the tangle determination algorithm of the second example will be exemplified.

  Step. At 0, first, as an initial state, a state is defined in which the position of each drone is before departure and the time t is the time immediately before the earliest departure time, and is added to the expanded tree W of the state.

  Step. In 1, a state in which the next transitionable state is not listed in W and the predicted time until arrival of all the drones is selected is selected. Based on the state, states that can transition at the next time are listed. At this time, if the minimum predicted arrival time, which is the minimum of the predicted arrival times, is greater than the latest input arrival time, information indicating that no entanglement has occurred is output. To finish.

  Step. At 2, if there is a state in which all drones have arrived at W, they are selected. Then, the path set PS indicating the combination of the routes of each drone is derived by tracing back the time t from this state. Here, one path set is derived for one selected state.

  Step. In step 3, if any of the derived path sets satisfies a predetermined condition, it is determined that there is entanglement, and the path set is output and the process ends. If there is no such thing, Step. Return to 1.

  FIG. 19 is an explanatory diagram of a path search example of the tangle determination algorithm of the second example. As shown in FIG. 19, in the process of enumerating the next state, the next state is enumerated for the state with the smallest predicted arrival time.

  FIG. 20 is an explanatory diagram illustrating a determination example of the tangle determination algorithm of the second example. As shown in FIG. 20, even if all transitionable states at each time are not listed, if all mobile units have arrived and a path set satisfying a predetermined condition is derived, entanglement is detected at that point. can do. Thus, even if a path set that passes through all the states is not formed, the process can be terminated when a path set that satisfies the condition is found on the way.

  Note that the tangle determination algorithm is not limited to the above example. For example, it is possible to output a path set that has no improvement or disadvantage for some users as a path set after entanglement elimination.

  In addition, when a plurality of waypoints are specified in the operation plan, it is possible to derive the transition destination state by using them as constraint conditions.

  Moreover, when deriving the state of the transition destination, it is possible to add the concept of speed to each moving body. In this case, the movable position may be changed according to the speed. For example, if the speed of the moving body is 1, it is only necessary to be able to move to a point (adjacent point) once straddling the side. Further, if the speed is 2, it is only necessary to be able to move to a point that straddles the side twice (adjacent point of the adjacent point). Further, if the speed is 3, it is only necessary to be able to move to a point that straddles the side three times (adjacent point of the adjacent point). In any case, the moving body may stop halfway as long as it is within a movable range.

  In addition, examples of the user selection method include a method for targeting all users from the beginning, a method for randomly selecting a combination of arbitrary users, and the like. It is also possible to determine users who are likely to have tangles using another algorithm and select those users.

  Another example of an algorithm is to search for the shortest route when assuming all non-occupied areas for mobiles with an operation plan, and evaluate the difference between the shortest expected arrival time and the arrival time in the operation plan. A method is mentioned. For example, based on the search results, list the n mobiles in the order in which the difference between the shortest expected arrival time and the arrival time in the operation plan is greater than or equal to a predetermined number or larger, and target users who manage those mobiles Also good.

Also, for example, based on the shortest route as a result of searching for the shortest route of each mobile object assuming that all of the users are non-occupied regions, other users who occupy the airspace on the route are listed. There is a method of performing the processing to be performed for other users in the same manner and using the result. It is also possible to repeatedly list other users who occupy the airspace on the shortest path, and target all the users who are finally listed.

  In the second example, when a path set satisfying the condition is derived, the path set is output and the processing is terminated. However, in the A * -based algorithm, a plurality of path sets that are considered to be improved It is also possible to output a path set of the optimum route. In this case, for example, the process of enumerating the next state may be continued until the maximum allowable delay time is reached.

  As described above, by using the entanglement detection unit 11 of this embodiment, when it is determined that there is entanglement, information on a path set (improvement plan) that is improved by changing the allocation of a part of the area, and the path set The information including the improvement degree of the operation route of each mobile body or the operation improvement degree of the user can be obtained. Here, the path set can output a plurality of outputs such as all optimum ones. The optimality here may be Pareto optimal.

  For example, as a result of determining entanglement for two users, together with entanglement, (first user operation improvement degree, second user operation improvement degree) = (4, 4), (3, 5), (6 , 3) and (3, 3) are obtained (pass set). In that case, the improvement information output unit 12 may output the solutions (4, 4), (3, 5), and (6, 3) as the improvement information.

  Further, when the improvement information is output in a ranking format or the like, the improvement information output unit 12 may evaluate each solution based on some criterion and rank each solution. For example, when the total optimum is used as a reference, the total improvement may be calculated and evaluated for the entire moving body or the entire user. For example, when the ease of negotiation is used as a reference, the minimum operational improvement degree of the target user may be calculated and evaluated. In addition, for example, when fairness is used as a reference, a difference or variance in operation improvement between users may be calculated and evaluated.

  Next, a configuration example of a computer according to the embodiment of the present invention will be shown. FIG. 21 is a schematic block diagram illustrating a configuration example of a computer according to the embodiment of the present invention. The computer 1000 includes a CPU 1001, a main storage device 1002, an auxiliary storage device 1003, an interface 1004, a display device 1005, and an input device 1006.

  The operation control system described above may be implemented in the computer 1000, for example. In that case, the operation of each component may be stored in the auxiliary storage device 1003 in the form of a program. The CPU 1001 reads out the program from the auxiliary storage device 1003 and develops it in the main storage device 1002, and executes the predetermined processing in the above embodiment according to the program.

  The auxiliary storage device 1003 is an example of a tangible medium that is not temporary. Other examples of the tangible medium that is not temporary include a magnetic disk, a magneto-optical disk, a CD-ROM, a DVD-ROM, and a semiconductor memory connected via the interface 1004. When this program is distributed to the computer 1000 via a communication line, the computer that has received the distribution may develop the program in the main storage device 1002 and execute the predetermined processing in the above embodiment.

  The program may be for realizing a part of predetermined processing in each embodiment. Furthermore, the program may be a difference program that realizes the predetermined processing in the above-described embodiment in combination with another program already stored in the auxiliary storage device 1003.

  The interface 1004 transmits / receives information to / from other devices. The display device 1005 presents information to the user. The input device 1006 accepts input of information from the user.

  Further, depending on the processing contents in the embodiment, some elements of the computer 1000 may be omitted. For example, the input device 1006 can be omitted if the input of information directly from the user is not accepted, and the display device 1005 can be omitted if the information is not directly presented to the user.

  In addition, some or all of the components of the system are implemented by general-purpose or dedicated circuits (Circuitry), processors, or combinations thereof. These may be constituted by a single chip or may be constituted by a plurality of chips connected via a bus. Moreover, a part or all of each component of this system may be implement | achieved by the combination of the circuit etc. which were mentioned above, and a program.

  When some or all of the constituent elements are realized by a plurality of information processing apparatuses and circuits, the plurality of information processing apparatuses and circuits may be centrally arranged or distributedly arranged. For example, the information processing apparatus, the circuit, and the like may be realized as a form in which each is connected via a communication network, such as a client and server system and a cloud computing system.

  Next, an outline of the area management system of the present invention will be described. FIG. 22 is a block diagram showing an outline of the area management system of the present invention. An area management system 500 shown in FIG. 22 includes an entanglement detection unit 501 and an information output unit 502.

  The entanglement detection means 501 (for example, the entanglement detection unit 11) includes area information indicating the allocation status of the occupied area to the user who manages the operation of the moving object within the occupied area assigned to the tangled detecting unit 501 and Two or more users selected at least based on the operation information indicating the operation plan, which is based on the operation information including the departure position, departure time, arrival position and information for evaluating the operation route in the operation plan. For each operation plan of the selected user's moving body, the route improvement degree, which is the improvement degree of the operation route when at least part of the occupied area of the selected user is changed, is calculated to detect entanglement.

  The information output unit 502 (for example, the improvement information output unit 12) outputs information to at least a part of the selected user based on the detection result by the tangle detection unit 501.

  According to the above configuration, it is possible to contribute to improvement of inefficient area allocation in the distributed operation management system.

  While the present invention has been described with reference to the present embodiment and examples, the present invention is not limited to the above embodiment and examples. Various changes that can be understood by those skilled in the art can be made to the configuration and details of the present invention within the scope of the present invention.

  The present invention can be suitably applied to an application for performing efficient area allocation in a distributed operation management system.

DESCRIPTION OF SYMBOLS 100 Mobile object navigation system 10 Area management system 11 Tangle detection part 12 Improvement information output part 20 Operation management system 1000 Computer 1001 CPU
1002 Main storage device 1003 Auxiliary storage device 1004 Interface 1005 Display device 1006 Input device 500 Area management system 501 Tangle detection means 502 Information output means

Claims (13)

The area information indicating the allocation status of the occupied area to the user who manages the operation of the moving body within the occupied area allocated to the user, and the operation information indicating the operation plan of each moving body by the user, Each of the mobile plans of the selected user that is at least two or more selected users based on the departure information, the departure time, the arrival position, and the operation information including information for evaluating the operation route in the operation plan On the other hand, a tangle detection means for detecting a tangle by calculating a route improvement degree that is an improvement degree of the operation route when at least a part of the occupied area of the selected user is changed,
An area management system comprising: information output means for outputting information to at least a part of selected users based on a detection result by the entanglement detection means. The tangle detection means detects tangle when the route improvement degree for each of the operation plans and / or the operation improvement degree for each selected user calculated based on the conditions satisfies a predetermined condition, and the condition The area management system according to claim 1, wherein an operation route that satisfies the conditions is detected as an improvement plan. The entanglement detecting means detects entanglement when the operational improvement degree is 0 or more in all of the selected users and the operational improvement degree is positive in at least a part of the selected users. Area management system. The area management system according to claim 2, wherein the entanglement detecting unit detects entanglement when the operation improvement degree is positive for all of the selected users. The entanglement detecting means detects entanglement when the operation improvement degree is positive for all selected users and the route improvement degree is 0 or more in all operation plans by the selected user. Area management system. The said information output means outputs the information containing the information of the at least 1 improvement plan, and the said operation improvement degree in the said improvement plan, when tangle is detected. The area management system described in 1. The information output means, when entanglement is detected, based on a result of evaluating the improvement plan based on a predetermined standard, information on at least one improvement plan in ranking form, and the operation in the improvement plan The area management system according to any one of claims 2 to 6, wherein information including an improvement degree is output. When the tangle is detected, the information output means outputs information including information on the occupied area to be exchanged between the selected users specified by the improvement plan and the operational improvement degree of the user after the exchange. The area management system according to any one of claims 2 to 7. The entanglement detecting means uses a non-occupied area when the occupied area of the selected user is released, and a combination of operation routes satisfying an exclusive area exclusive condition within a predetermined search time range for the set of operation plans. Any of the path sets which are is detected, and the entanglement is detected based on the route improvement degree for each of the operation routes calculated for all the searched path sets. The described area management system. The entanglement detecting means uses the non-occupied area when the occupied area of the selected user is released to enumerate the transitional states for the set of operation plans in ascending order of time within a predetermined search time range. As a result of selecting the ones that have a short predicted arrival time and enumerating the transition possible states, there is a path set that is a combination of flight routes that satisfy the exclusive condition of the occupied region and that satisfies the predetermined condition. The region management system according to any one of claims 1 to 8, wherein tangle is detected when searched. The area management system according to claim 9 or 10, wherein the entanglement detecting unit searches the path set using a Dijkstra method or an A star method. Information processing device
The area information indicating the allocation status of the occupied area to the user who manages the operation of the moving body within the occupied area allocated to the user, and the operation information indicating the operation plan of each moving body by the user, Each of the mobile plans of the selected user that is at least two or more selected users based on the departure information, the departure time, the arrival position, and the operation information including information for evaluating the operation route in the operation plan On the other hand, calculating the route improvement degree, which is the improvement degree of the operation route when at least a part of the occupied area of the selected user is changed, detects entanglement,
An area management method, comprising: outputting information to at least a part of selected users based on a result of detecting entanglement. On the computer,
The area information indicating the allocation status of the occupied area to the user who manages the operation of the moving body within the occupied area allocated to the user, and the operation information indicating the operation plan of each moving body by the user, Each of the mobile plans of the selected user that is at least two or more selected users based on the departure information, the departure time, the arrival position, and the operation information including information for evaluating the operation route in the operation plan In contrast, a tangle detection process for detecting tangles by calculating a route improvement degree that is an improvement degree of an operation route when at least a part of the occupied area of the selected user is changed, and a detection result in the tangle detection process An area management program for executing information output processing for outputting information to at least a part of selected users based on the above.

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