Moving body introduction assistance system and moving body introduction assistance method

Abstract

A mobile body introduction assistance system includes a degree of congestion calculation unit and a unit for calculating the number of different types of agents operating. The degree-of-congestion calculation unit acquires degree-of-congestion information for a person moving within a building and a different type of agent that is a moving body different from the person. The different-type agent operation number calculation unit determines the operation number of different-type agents on the basis of the congestion degree information acquired by the congestion degree calculation unit.

Description

Technical Field

[0001] The invention relates to a mobile body introduction assistance system and a mobile body introduction assistance method. Background Art

[0002] When installing an elevator, the specifications such as the rated passenger capacity of the car and the number of cars are determined and installed so as to ensure sufficient transportation capacity for the flow of people at the most crowded time in the building in which the elevator is installed. In addition, in recent years, with the development of barrier-free and intelligent buildings, the opportunities for elevators to be used by not only people but also wheelchairs, personal mobility devices, service robots, etc. are increasing.

[0003] In addition, wheelchairs, personal mobility devices, and service robots (hereinafter referred to as "different types of agents") not only take longer to enter and exit the elevator than humans, but also occupy a larger area in the elevator than humans. In addition, the installation plan of the elevator is determined by assuming the situation of transporting people. Therefore, when the number of different types of agents increases, the transportation capacity of the elevator cannot be exerted as pre-assumed, and the service performance of the elevator decreases.

[0004] For example, Patent Document 1 describes a technique for controlling the movement of a moving object other than a person. Patent Document 1 describes a technique including an information storage unit, a congestion prediction unit, and an operation mode setting unit. Furthermore, Patent Document 1 describes that in the operation mode setting unit, a robot is set to any one of a plurality of operation modes having different effects on the degree of congestion in a service provision area. Prior art literature Patent Literature

[0005] Patent Document 1: Japanese Patent Publication No. 2019-940 Summary of the invention Technical problem to be solved by the invention

[0006] However, in the technology described in Patent Document 1, when congestion is predicted, the robot is made to wait in the waiting area and stops moving, so the service performance of the robot is reduced.

[0007] In view of the above problems, an object of the present invention is to provide a moving body introduction assistance system and a moving body introduction assistance method, which can ensure the transportation capacity of the elevator and suppress the reduction of service performance caused by different types of intelligent bodies. Technical solutions to technical problems

[0008] In order to solve the above-mentioned problems and achieve the present purpose, a mobile object introduction assistance system includes: a congestion calculation unit, which obtains congestion information of people moving in a building and mobile objects different from people, i.e., different types of intelligent objects; and a different type of intelligent object running number calculation unit, which determines the running number of different types of intelligent objects based on the congestion information obtained by the congestion calculation unit.

[0009] Furthermore, the moving object introduction support method includes the processes shown in the following (1) and (2). (1) A process of acquiring congestion degree information of humans moving in a building and moving objects other than humans, that is, different types of agents, by a congestion degree calculation unit. (2) A process of determining the number of different types of agents to be operated based on the congestion degree information acquired by the congestion degree calculation unit by the different types of agent operation number calculation unit. Effects of the Invention

[0010] According to the moving body introduction assistance system and the moving body introduction assistance method having the above-mentioned structure, the transportation capacity of the elevator can be ensured and the reduction in service performance caused by different types of intelligent bodies can be suppressed. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] Figure 1 This is a block diagram showing a schematic configuration of a movable body introduction support system according to a first embodiment example. Figure 2 This is a diagram showing an example of specification information of the moving object introduction support system according to the first embodiment example. Figure 3 This is a diagram showing an example of different types of agent specification information in the moving object introduction support system according to the first embodiment example. Figure 4 1 is a diagram showing traffic demand information of the moving object introduction support system according to the first embodiment example. Figure 5 This is a flowchart showing an example of operation of determining the number of different types of agents as an example of operation of the moving object introduction support system according to the first embodiment. Figure 6 This is a flowchart showing a method for calculating the congestion degree of the vehicle introduction support system according to the first embodiment example. Figure 7 This is a flowchart showing a method of outputting an operation performance report of the movable body introduction support system according to the first embodiment example. Figure 8 It is a diagram showing an operation performance report of the movable body introduction support system according to the first embodiment example. Fig. 9 FIG. 1 is a diagram showing another example of traffic demand information of the moving object introduction support system according to the first embodiment example. Fig.10 This is a block diagram showing a schematic configuration of a movable body introduction support system according to a second embodiment example. Fig.11 This is a flowchart showing an example of operation of determining the number of different types of agents as an example of operation of the moving object introduction support system according to the second embodiment. Fig.12 This is a block diagram showing a schematic configuration of a movable body introduction support system according to a third embodiment example. DETAILED DESCRIPTION

[0012] Below, refer to Figures 1 to 12 An embodiment example of a movable body introduction assisting system and a movable body introduction assisting method will be described. In addition, in each figure, the same reference numerals are given to common components.

[0013] 1. First Implementation Example 1-1. Structure of the mobile body introduction support system First, refer to Figures 1 to 4 The configuration of a movable body introduction assistance system and a movable body introduction assistance method according to a first embodiment example (hereinafter referred to as “this example”) will be described. Figure 1 : is a block diagram showing a structural example of the moving body introduction support system of this example.

[0014] like Figure 1 As shown, the mobile body introduction support system 1 includes a computing unit 2, a storage unit 3, an input unit 4, an output unit 5, and an instruction unit 6. Then, the computing unit 2, the storage unit 3, the input unit 4, the output unit 5, and the instruction unit 6 are connected so as to be able to send and receive information to each other via a network N1.

[0015] The calculation unit 2 is configured using, for example, a CPU (central processing unit), an MPU (microprocessor), a DSP (digital signal processor), a GPU (graphics processing unit), etc. The calculation unit 2 includes a congestion degree calculation unit 21 and a different type of agent operation number calculation unit 22 .

[0016] The congestion degree calculation unit 21 calculates the congestion degree in the building based on information stored in the storage unit 3 described later. Then, the congestion degree calculation unit 21 outputs the calculated congestion degree to the different types of agent operation number calculation unit 22.

[0017] Based on the congestion calculated by the congestion calculation unit 21, the different types of agent operation number calculation unit 22 calculates the operation number of different types of agents such as wheelchairs, personal mobile devices, and service robots, which are mobile objects other than humans. The different types of agent operation number calculation unit 22 outputs the calculated operation number to the output unit 5 and the instruction unit 6.

[0018] The storage unit 3 is, for example, a ROM (Read Only Memory), a RAM (Random Access Memory), a nonvolatile memory (NVRAM (Non Volatile RAM)), a hard disk drive, an SSD (Solid State Drive), or the like.

[0019] The storage unit 3 stores elevator specification information 31, different types of agent specification information 32, traffic demand information 33 which is information on the movement of people or different types of agents, and building specification information 34. The storage unit 3 also stores past operation information of the elevator.

[0020] Figures 2 to 4 1 is a diagram showing an example of various specification information stored in the storage unit 3 . Figure 2 An example of elevator specification information 31 and building specification information 34 is shown. Figure 3 An example of different types of agent specification information 32 is shown. Figure 4 An example of the traffic demand information 33 is shown.

[0021] like Figure 2 As shown, as the elevator specification information 31, for example, there are "ID", "type", "rated passenger capacity (persons)", "door width (mm)", "speed (m / s)", "acceleration (m / s)", 2 )”, “Door opening and closing time (s)”, etc. In addition, the building specification information 34 includes, for example, “Floor”, “Floor height (mm)”, “Service floor”, etc.

[0022] like Figure 3 As shown, as the different types of agent specification information 32, for example, a "label" indicating the type of the different types of agents can be cited. Moreover, for each type (label) of the different types of agents, "exclusive area in the car (person)", "entry time into the car (s)", "moving speed (m / s)", "weight (kg)", "width (mm)", "depth (mm)", "height (mm)" and the like are preset.

[0023] like Figure 4 As shown, the traffic demand information 33 is set with not only "traffic demand information of people" but also "traffic demand information of different types of agents". For each specified time period, "traffic demand information of people" and "traffic demand information of different types of agents" are set.

[0024] In the "transportation demand information of people", the number of people entering at the assumed departure place (floor) and the number of people leaving at the destination (floor) within a preset time period is stored. In addition, in the "transportation demand information of different types of agents", the number of cars entering at the assumed departure place (floor) and the number of cars leaving at the destination (floor) within a preset time period is stored. In addition, the transportation demand information 33 is updated at a specified time or every day.

[0025] The input unit 4 is an input interface for receiving information from the outside. Examples of the input unit 4 include various sensors installed in a building, a touch sensor, a keyboard, etc. The information input to the input unit 4 is output to the computing unit 2, the storage unit 3, etc. via the network N1.

[0026] The output unit 5 is an output interface for providing information to the outside. Examples of the output unit 5 include a speaker, an LED (Light Emitting Diode), a screen display device (a liquid crystal display, a projector, etc.), a printer, and a recording device for recording data on a recording medium.

[0027] The instruction unit 6 outputs the information calculated by the calculation unit 2 or the information input to the input unit 4 to the different types of agents. Then, based on the information output from the instruction unit 6, the different types of agents are operated.

[0028] 1-2. Operation example of the mobile object introduction support system Next, refer to Figures 5 and 6 An operation example of the movable body introduction support system 1 having the above-mentioned structure will be described. Figure 5 is a flowchart showing an example of actions for determining the number of operations of different types of agents. like Figure 5 As shown, the congestion degree calculation unit 21 of the operation unit 2 estimates (calculates) the congestion degree in the building based on the past elevator operation information and the traffic demand information 33 collected in the storage unit 3 (step S101). The details of the congestion degree calculation method will be described later.

[0029] Then, the congestion degree calculation unit 21 outputs the calculated congestion degree to the heterogeneous agent running number calculation unit 22. Next, the heterogeneous agent running number calculation unit 22 calculates the types and numbers of heterogeneous agents that can be operated based on the calculated congestion degree and the information of heterogeneous agents arranged in the building (heterogeneous agent specification information 32) (step S102). Then, the heterogeneous agent running number calculation unit 22 outputs the calculated information to the instruction unit 6.

[0030] Next, the instruction unit 6 outputs an operation instruction to the different types of agents based on the information calculated by the different types of agent operation number calculation unit 22 (step S103). Thus, the operation of determining the number of different types of agents to be operated by the mobile object introduction support system 1 is completed.

[0031] The instructions output to the different types of agents in step S103 include, for example, limiting the number of operations matching all floors of the building, limiting the number of operations on each floor, limiting the number of operations on a specific floor, limiting access to a specific floor, etc. Thus, the operation number determination action of the different types of agents performed by the mobile body introduction assistance system 1 is completed.

[0032] Thus, in the moving body introduction assistance system 1 of this example, the number of different types of agents to be operated is determined according to the congestion level, rather than stopping the movement of all different types of agents. This ensures the transportation capacity of the elevator and suppresses the degradation of service performance caused by different types of agents.

[0033] Next, we will refer to Figure 6 An example of a method for calculating the congestion degree will be described. Figure 6 This is a flowchart showing a method for calculating the congestion degree. like Figure 6 As shown, the congestion calculation unit 21 determines the information used to calculate the congestion degree from the collected past operation information of the elevator and the traffic demand information 33 (step S111). The information used to calculate the congestion degree includes "the previous day", "the same day of the previous month", "the information of the same day of the previous year", and "the information of the past X days". The past operation information includes, for example, the average riding rate (%) within X minutes, the average hall call duration (seconds), and the average hall call passing number (times).

[0034] Next, the congestion calculation unit 21 divides the operation information and traffic demand information 33 used for the congestion calculation determined in the process of step S111 into arbitrary time intervals (X minutes) (step S112). Then, the congestion calculation unit 21 converts the operation information and traffic demand information 33 divided in the process of step S112 into predetermined information (step S113). The information to be converted in step S113 is the average value of the divided information, calculation of the median value, clustering, etc.

[0035] Next, the congestion degree calculation unit 21 extracts arbitrary information from the operation information converted in the process of step S113, and calculates a plurality of evaluation values ​​according to a plurality of evaluation formulas (step S114). In addition, the congestion degree calculation unit 21 selects a congestion degree from the calculated plurality of congestion degrees based on a given judgment. Then, the congestion degree calculation unit 21 determines the selected congestion degree as the congestion degree from now to X minutes later (step S115). Thus, the congestion degree calculation operation performed by the congestion degree calculation unit 21 is completed.

[0036] In addition, as a method for calculating the congestion level, it is not limited to the above method, and various other calculation methods can be applied. For example, congestion level information corresponding to a time period is generated in advance as preset information of congestion level, and is stored in the storage unit 3. Moreover, the congestion level calculation unit 21 can obtain the congestion level corresponding to the time period from the congestion level information generated in advance and stored in the storage unit 3. In addition, the congestion level information stored in the storage unit 3 can be appropriately updated through actual operation or machine learning.

[0037] 1-3. How to output the operation performance report In addition, the mobile body introduction support system 1 of this example outputs a performance report of the number of operations of different types of agents output. Figure 7 and Figure 8 Describes how to output the operation performance report. Figure 7 is a flowchart showing a method for outputting an operation performance report. Figure 8 This is a diagram showing an example of an operation performance report.

[0038] like Figure 7 As shown, the congestion calculation unit 21 of the operation unit 2 calculates the congestion degree based on the collected elevator operation information and traffic demand information on the day after the business hours have ended (step S121). In addition, the processing in step S121 is not limited to being executed after the business hours have ended, and can be executed at a predetermined time, for example. In addition, the operation information collected in step S121 is, for example, Figure 8 The "elevator operation information" is shown.

[0039] Next, the calculation unit 2 calculates the evaluation of the number of operating agents and the appropriate number of operating agents of different types based on the (predicted) congestion calculated during the operation of the elevator, the actual congestion, and the operating performance of different types of agents (step S122). Figure 8The “other operation information” and “operation information of different types of agents” are shown. Examples of “other operation information” include “time period”, “selected congestion judgment index”, “calculated predicted congestion”, “actual congestion”, “evaluation of the number of operating agents”, “basis for judging the number of operating agents”, and “appropriate number of agents”. In addition, the “basis for judging the number of operating agents” is calculated based on the “operation information of different types of agents”.

[0040] As the "operating agent quantity evaluation", it is set whether the different types of agents are insufficient, excessive or appropriate. The "operating agent quantity evaluation" is calculated based on the difference between the target value pre-stored in the storage unit 3 and the operating performance of the different types of agents. Based on the calculated difference, the "operating agent quantity evaluation" sets whether the different types of agents are insufficient, excessive or appropriate.

[0041] Then, in the case of shortage or surplus, the number of different types of agents to be increased or decreased is set as the "appropriate number of agents". In addition, in the case of shortage or surplus, time information (waiting time, etc.) as a basis for judgment is output at the same time.

[0042] Examples of “operation information of different types of intelligent entities” include “identification (label) of different types of intelligent entities”, “time period”, “number of operations (units)”, “service performance”, “average waiting time (seconds)”, “average ride time (seconds)”, “average service completion time (seconds)”, “average number of charging times (times)”, “average power used (kWh)”, “average moving distance (floors)”, “maximum waiting time (seconds)”, “maximum ride time (seconds)”, “maximum service completion time (seconds)”, “maximum number of charging times (times)”, “maximum power used (kWh)”, “maximum moving distance (floors)”, etc.

[0043] Next, the output unit 5 outputs the Figure 8 The "actual operation performance of the elevator", "actual operation performance of different types of agents", and "other actual operation performance" shown are summarized and output as an actual operation performance report (step S123). Thus, the output operation of the actual operation performance report is completed.

[0044] The "actual operation performance of the elevator", "actual operation performance of different types of agents", and "other actual operation performance" may be output simultaneously, or the "other actual operation performance" may be calculated and output after the "actual operation performance of the elevator" and "actual operation performance of different types of agents". In addition, only the "evaluation of the number of operating agents", "basis for judging the number of operating agents", and "appropriate number of agents" may be output.

[0045] Thus, according to the mobile body introduction assistance system 1 of this example, by generating and outputting an operation performance report, appropriate information can be provided to the maintenance personnel of the elevator or service robot, the building owner, and the elevator user. For example, the operation performance of different types of intelligent bodies can be prompted to the maintenance personnel. For the building owner, it can be prompted whether the number of different types of intelligent bodies in operation is insufficient, excessive, or appropriate, etc. Moreover, the user of the elevator can be prompted with the situation and reason why the probability of riding with different types of intelligent bodies increases or decreases according to the time period. In this way, not only the service performance of different types of intelligent bodies can be improved, but also the convenience of the elevator can be improved.

[0046] 1-4. Other examples of traffic demand information Next, refer to Fig. 9 Another example illustrating traffic demand information. Fig. 9 is a diagram showing another example of traffic demand information. like Fig. 9 As shown, the traffic demand information includes "traffic demand information of people" and "traffic demand information of different types of intelligent entities". The "traffic demand information of people" and "traffic demand information of different types of intelligent entities" are set for each specified time period. In the "traffic demand information of people" and "traffic demand information of different types of intelligent entities", information on the moving direction of the car "up" and "down", the number of people boarding on each floor, and the number of people leaving are set.

[0047] In addition, the traffic demand information 33 is not limited to Figure 4 and Fig. 9 The example shown, and other various information can be set as the traffic demand information.

[0048] 2. Second Implementation Example Next, refer to Fig.10 and Fig.11 A movable body introduction support system according to a second embodiment example will be described. Fig.10 is a block diagram showing a moving body introduction support system according to a second embodiment example. Fig.11 1 is a flowchart showing an example of the operation of the movable body introduction support system according to the second embodiment example.

[0049] The mobile body introduction assistance system according to the second embodiment differs from the mobile body introduction assistance system according to the first embodiment in the configuration of the computing unit. Therefore, the same reference numerals are given to the common parts with the mobile body introduction assistance system according to the first embodiment, and repeated description is omitted.

[0050] like Fig.10As shown, the mobile body introduction assistance system 1A includes a calculation unit 2A, a storage unit 3, an input unit 4, an output unit 5, and an instruction unit 6. The calculation unit 2A includes a congestion degree calculation unit 21, a different type of agent operation number calculation unit 22, a learning unit 23, and a judgment unit 24. The learning unit 23 learns the congestion degree calculated by the congestion degree calculation unit 21 from past data and predicts the congestion degree. In addition, the learning unit 23 can learn the operation number based on the operation number calculated by the different type of agent operation number calculation unit 22 and the evaluation information input to the input unit 4. In addition, the learning unit 23 learns the actual traffic demand based on the actual human traffic volume, the operation performance of different types of agents, etc., and updates the traffic demand information 33.

[0051] The determination unit 24 selects one congestion degree based on a given determination index from among the plurality of congestion degrees calculated by the congestion degree calculation unit 21. Then, the calculation unit 2A sets the congestion degree selected by the determination unit 24 as the congestion degree from now until X minutes later.

[0052] Next, refer to Figure 1 An operation example of the movable body introduction support system 1A according to the second embodiment example will be described. Fig.11 is a flowchart showing an example of actions for determining the number of operations of different types of agents.

[0053] like Fig.11 As shown, the learning unit 23 uses the past elevator operation information and traffic demand information 33 collected in the storage unit 3 to learn the congestion estimation model through machine learning or the like (step S201). Next, the congestion calculation unit 21 calculates the congestion using the congestion estimation model learned by the learning unit 23 (step S202).

[0054] Furthermore, the different types of agent running number calculation unit 22 calculates the types and numbers of different types of agents that can be operated based on the calculated congestion degree and the information of different types of agents arranged in the building (different types of agent specification information 32) (step S203). Next, the instruction unit 6 outputs an operation instruction to the different types of agents based on the information calculated by the different types of agent running number calculation unit 22 (step S204). Thus, the operation of determining the number of different types of agents to be operated by the mobile body introduction support system 1A is completed.

[0055] According to the mobile body introduction support system 1A according to the second embodiment example, the learning unit 23 learns the congestion level estimation model, thereby improving the accuracy of the congestion level calculated by the congestion level calculation unit 21 and determining a more appropriate number of operations of different types of agents.

[0056] The other structures are the same as those of the movable body introduction assistance system 1 according to the first embodiment, and thus the description thereof is omitted. The same effects as those of the movable body introduction assistance system 1 according to the first embodiment can also be obtained by including a traction machine of the movable body introduction assistance system 1A having such a structure.

[0057] 3. Third Implementation Example Next, refer to Fig.12 , a movable body introduction assistance system according to a third embodiment example is described. Fig.12 The block diagram shows a vehicle introduction support system 1B according to the third embodiment. The same reference numerals are used for the common parts of the vehicle introduction support system 1 according to the first embodiment and the vehicle introduction support system 1A according to the second embodiment, and duplicate descriptions are omitted.

[0058] like Fig.12 As shown, the moving body introduction assistance system 1B is connected to the elevator control unit 100 that controls the driving of the elevator via the network N2. The elevator operation performance information and important traffic information are input from the elevator control unit 100 to the input unit 4 of the moving body introduction assistance system 1B via the network N2. Based on this information, the traffic demand information 33 in the storage unit 3 is updated.

[0059] In addition, a ride permission button 101 is provided at each floor where the elevator stops, for the passengers of the car to refuse or allow rides with different types of agents. The ride permission button 101 and the elevator control unit 100 are connected via the network N3. Then, the input information input to the ride permission button 101 is input to the moving body introduction support system 1B via the networks N2 and N3.

[0060] Based on the information from the ride-along button 101, the command unit 6 of the moving body introduction support system 1B outputs a command to the different type of agent. For example, when the different type of agent is refused to ride together, the command information is output to the different type of agent, thereby preventing the different type of agent from boarding the car. This can improve the convenience of people using the elevator.

[0061] The other structures are the same as those of the moving body introduction assistance system 1 according to the first embodiment, and thus their description is omitted. The moving body introduction assistance system 1B having the above structure can also achieve the same effects as those of the moving body introduction assistance system 1 according to the first embodiment.

[0062] The present invention is not limited to the embodiments described above and shown in the drawings, and various modifications can be made without departing from the gist of the invention described in the claims.

[0063] In addition, a part of the structure of a certain embodiment can be replaced with the structure of other embodiments, and the structure of other embodiments can also be added to the structure of a certain embodiment. In addition, with respect to a part of the structure of each embodiment, other structures can also be added, deleted, or replaced. In addition, the above-mentioned structures, functions, processing parts, processing units, etc. can also be implemented in hardware by designing a part or all of them, such as through integrated circuits. In addition, the above-mentioned structures, functions, etc. can also be implemented in software by interpreting and executing programs that implement their respective functions through a processor. Information on programs, tables, files, etc. that implement each function can be stored in a recording device such as a memory, a hard disk, an SSD (Solid State Drive), or a recording medium such as an IC card, an SD card, or a DVD. Description of symbols

[0064] 1, 1A, 1B mobile body introduction auxiliary system, 2, 2A operation unit, 3 storage unit, 4 input unit, 5 output unit, 6 command unit, 21 congestion calculation unit, 22 different types of intelligent body operation number calculation unit, 23 learning unit, 24 judgment unit, 31 elevator specification information, 32 different types of intelligent body specification information, 33 traffic demand information, 34 building specification information, 100 elevator control unit, 101 ride-along button.

Claims

1. A mobile body introduction assistance system, characterized in that: include: a congestion degree calculation unit that obtains congestion degree information of people moving in the building and moving objects other than people, that is, different types of intelligent objects; as well as A different type of agent running number calculation unit is configured to determine the running number of the different type of agents based on the congestion degree information obtained by the congestion degree calculation unit.

2. The movable body introduction assisting system according to claim 1, characterized in that: A storage unit is included, the storage unit stores traffic demand information, the traffic demand information is the movement amount information of the people moving in the building and the different types of intelligent bodies, The congestion degree calculation unit calculates the congestion degree information based on the traffic demand information.

3. The movable body introduction assisting system according to claim 2, characterized in that: The congestion degree calculation part calculates the congestion degree information based on past operation information of elevators installed in the building and the traffic demand information.

4. The movable body introduction assisting system according to claim 3, characterized in that: The congestion degree calculation unit calculates a plurality of congestion degrees by a plurality of calculation methods, and selects the congestion degree information used for the different-type agent operation number calculation unit from among the calculated plurality of congestion degrees based on a given determination use.

5. The movable body introduction assisting system according to claim 2, characterized in that: comprising a learning unit that learns a congestion estimation model based on past operation information of elevators installed in the building and the traffic demand information, The congestion degree calculation unit calculates the congestion degree information using the congestion degree estimation model learned by the learning unit.

6. The movable body introduction assisting system according to claim 1, characterized in that: The method includes a calculation unit that evaluates the number of operations of the different types of agents based on the actual operation performance of the different types of agents and a preset target value.

7. The movable body introduction assisting system according to claim 6, characterized in that: The calculation unit calculates a difference between the congestion degree information calculated by the congestion degree calculation unit and an actual congestion degree at each time, and outputs the difference as an operation performance report.

8. The movable body introduction assisting system according to claim 1, characterized in that: An output unit is included, which is connected to an elevator control unit that controls the driving of the elevator installed in the building, and outputs instruction information to the different types of agents based on information from the elevator control unit.

9. A mobile body introduction assisting method, characterized in that: include: Processing of obtaining, by a congestion calculation unit, congestion information of people moving in a building and moving objects other than people, i.e., different types of intelligent objects; as well as The process of determining the operating number of the different types of agents based on the congestion degree information acquired by the congestion degree calculation unit by the different types of agents operating number calculation unit.

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