Transport system, transport control method, trained model, learning system, learning method, and program

Abstract

To provide a transportation system, a transportation control method, a learned model, a learning system, a method, and a program, which are capable of efficiently suppressing a stay time from the end of use to the completion of return by a mobile robot, for a device to be lent in a device lending system.SOLUTION: In a transportation system 1, a host management device 10 uses learning data including collection result data containing a use end time at which the use of a device has ended and a collection completion time at which the device was collected as a return product after lending out the device, and data on a collection route along which a mobile robot collected the device, to store a learned model 124 that is machine-learned so as to input an end time prediction result obtained by predicting the use end time of the device being lent and output the collection route along which the mobile robot collected using the device being lent as a return product, inputs the end time prediction result to the learned model to acquire the collection route at which the mobile robot collected using the device being lent as a return product, and determines a mobile robot 20 which collects along the acquired route.SELECTED DRAWING: Figure 2

Description

[Technical Field]

[0001] The present disclosure relates to a transport system, a transport control method, a trained model, a learning system, a learning method, and a program. [Background technology]

[0002] Patent Document 1 discloses an information processing device that adjusts logistics bases in accordance with demand forecasts. This information processing device creates product demand information that indicates trends in product demand for each region based on the locations where each user's product-related behavior occurred and the number of behaviors performed at those locations, and determines a logistics base for the product based on the product demand information. Furthermore, this information processing device creates a transportation plan for transporting products to the logistics base for the product in advance based on the product demand information. Furthermore, this information processing device creates an inventory transfer plan for transferring product inventory in advance from other logistics bases where the product is in stock to the logistics base for the product based on the product demand information. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2021-140273 Summary of the Invention [Problem to be solved by the invention]

[0004] In equipment rental systems, inventory shortages can occur when demand for equipment rentals suddenly increases. One cause of inventory shortages, or a management issue for rental equipment, is the delay in returning equipment after the equipment has been used at the rental location, due to staff at the rental location having to decide whether to return the equipment. For example, if the equipment is medical equipment, the rental location's staff may have to make the decision because, depending on the ward, there may be situations where immediate loading is possible but not possible due to staff shortages or the presence of emergency patients.

[0005] Therefore, it is desirable to shorten such waiting time as much as possible. In particular, when a mobile robot is used to collect returned items, it is desirable to minimize the deterioration of the mobile robot and to save power as much as possible. Note that the technology described in Patent Document 1 cannot solve these problems even if rental equipment is used instead of products.

[0006] The present disclosure has been made to solve such problems, and provides a transport system, transport control method, trained model, and program that can efficiently reduce the residence time for equipment to be rented out in an equipment rental system, from the time use is finished until return by a mobile robot is completed, as well as a learning system, learning method, and program that can generate such a trained model. [Means for solving the problem]

[0007] The transport system according to the present disclosure is a transport system that transports rental equipment using a mobile robot, the transport system using learning data including: collection history data indicating collection history, including an end-of-use date when the rental equipment is finished and a collection completion date when the equipment is collected as a return item; and collection route data indicating a collection route along which the rental equipment is collected by the mobile robot. A trained model is input with learning data including: end-of-use date prediction results for the rental equipment; the trained model is input with the ... Therefore, the above-mentioned transport system can collect equipment via an efficient collection route, and as a result, the waiting time from when the equipment is no longer in use until it is returned by the mobile robot can be efficiently reduced.

[0008] The trained model may be a model that has been machine-learned to output the collection route that enables the collection of the plurality of devices. As a result, the transport system can acquire an efficient collection route for the plurality of devices, thereby more efficiently reducing the residence time from when the plurality of devices have been used until their return by the mobile robot is completed.

[0009] The collection history data may include first information, which is at least one of the time required for the mobile robot to collect the device, the distance traveled by the mobile robot, and the power consumption of the mobile robot, and the trained model may be a model trained by machine learning to output the collection route that minimizes the first information. With this configuration, the transport system obtains a collection route for the rented device based on a prediction of the end of use time, taking into account past collection history data including the first information, and determines the mobile robot that will be responsible for collection. Therefore, the transport system can collect the device via a collection route that is efficient in terms of at least one of time, distance traveled, and power consumption, and as a result, the waiting time from the end of use of the device until the mobile robot completes its return can be efficiently reduced from the above perspectives.

[0010] The collection history data may include first information, which is at least one of the time required for the mobile robot to collect the devices, the distance traveled by the mobile robot, and the power consumption of the mobile robot, and the trained model may be a model trained by machine learning to output the collection route for collecting the devices so as to minimize the first information when the collection time for the devices at the collection point is within a predetermined time. With this configuration, the transport system obtains a collection route capable of collecting the devices, taking into account past collection history data including the first information, based on a prediction result of the end of use of the rented devices, and determines the mobile robot to be responsible for collection. Therefore, the transport system can collect the devices using a collection route that is efficient in terms of at least one of time, travel distance, and power consumption, and as a result, the waiting time from the end of use of the devices until the mobile robot completes its return can be efficiently reduced from the above perspectives.

[0011] The device may be a medical device. In this way, the transport system can take into consideration the usage pattern of the medical device and efficiently reduce the residence time of the medical device from the time of use until the return by the mobile robot is completed.

[0012] The transport control method according to the present disclosure is a transport control method in which a computer controls the transport of rental equipment by a mobile robot in an equipment rental system, wherein the computer uses learning data including collection history data indicating collection history, including an end-of-use date when the equipment is finished being used after rental and a collection completion date when the equipment is collected as a return item, and collection route data indicating a collection route along which the mobile robot collects the equipment, to input an end-of-use prediction result that predicts the end-of-use date of the rental equipment and store a trained model that outputs a collection route along which the mobile robot will collect the rental equipment as a return item, the computer inputs the end-of-use prediction result that predicts the end-of-use date of the rental equipment into the trained model, obtains a collection route along which the mobile robot will collect the rental equipment as a return item, and the computer determines the mobile robot that will collect the rental equipment along the obtained collection route. In the transport control method, a collection route is obtained for the rental equipment based on the end-of-use prediction result, taking into account past collection history data, and a mobile robot that will collect the rental equipment is determined. Therefore, the above-mentioned transport control method can control the collection of equipment along an efficient collection route, and as a result, the waiting time from the end of use of the equipment until its return by the mobile robot can be efficiently reduced.

[0013] The trained model may be a model that has been machine-learned to output the collection route that enables the collection of the plurality of devices. In this way, the transport control method can obtain an efficient collection route for the plurality of devices, thereby more efficiently reducing the residence time from the end of use of the plurality of devices until the mobile robot completes returning the devices.

[0014] The collection history data may include first information, which is at least one of the time required for the mobile robot to collect the device, the distance traveled by the mobile robot, and the power consumption of the mobile robot, and the trained model may be a model that has been machine-learned to output the collection route that minimizes the first information. In the above-described transport control method, a collection route is obtained for the rented device by taking into account past collection history data, including the first information, from the prediction result of the end of use of the device, and a mobile robot that will be responsible for collection is determined. Therefore, the above-described transport control method can control the device to be collected along a collection route that is efficient from the perspective of at least one of time, distance traveled, and power consumption, and as a result, the waiting time from the end of use of the device until the mobile robot completes its return can be efficiently reduced from the above perspective.

[0015] The collection history data may include first information, which is at least one of the time required for the mobile robot to collect the devices, the distance traveled by the mobile robot, and the power consumption of the mobile robot, and the trained model may be a model trained by machine learning to output the collection route for collecting the devices so as to minimize the first information when the collection time for the devices at the collection point is within a predetermined time. In the above-mentioned transport control method, by this processing, a collection route capable of collecting the devices is obtained, taking into account past collection history data including the first information from the prediction result of the end of use of the rented devices, and a mobile robot to be responsible for collection is determined. Therefore, the above-mentioned transport control method can control the collection of the devices to be collected along a collection route that is efficient from the perspective of at least one of time, travel distance, and power consumption, and as a result, the residence time from the end of use of the devices to the completion of return by the mobile robot can be efficiently reduced from the above perspective.

[0016] The device may be a medical device. In this way, the transport control method can take into consideration the usage pattern of the medical device and efficiently reduce the residence time of the medical device from the time the medical device is used until the return by the mobile robot is completed.

[0017] A program according to the present disclosure is a program for causing a computer to execute transport control for transporting rental equipment by a mobile robot in an equipment rental system, the transport control including: learning data including collection record data indicating collection records, including an end-of-use date when the equipment was discontinued after rental and a collection completion date when the equipment was collected as a return; and collection route data indicating a collection route along which the equipment was collected by the mobile robot; inputting a prediction result of an end-of-use date for the rental equipment into a trained model that has been machine-learned to output a collection route along which the rental equipment will be collected as a return by the mobile robot; acquiring a collection route along which the rental equipment will be collected by the mobile robot as a return; and determining the mobile robot that will collect the equipment along the acquired collection route. Through this processing, the program acquires a collection route for the rental equipment based on the prediction result of the end-of-use date, taking into account past collection record data, and determining the mobile robot that will collect the equipment. Therefore, the above program can control the collection of equipment along an efficient collection route, and as a result, the waiting time from the time the equipment is no longer in use until it is returned by the mobile robot can be efficiently reduced.

[0018] The trained model may be a model that has been machine-learned to output the collection route that enables the collection of the plurality of devices. This allows the program to obtain an efficient collection route for the plurality of devices, thereby more efficiently reducing the residence time from the end of use of the plurality of devices until the mobile robot completes returning the devices.

[0019] The collection history data may include first information, which is at least one of the time required for the mobile robot to collect the device, the distance traveled by the mobile robot, and the power consumption of the mobile robot, and the trained model may be a model trained by machine learning to output the collection route that minimizes the first information. Through this processing, the program obtains a collection route for the rented device that takes into account past collection history data, including the first information, from the prediction result of the end of use of the device, and determines the mobile robot that will be responsible for collection. Therefore, the program can control the device to be collected along a collection route that is efficient from the perspective of at least one of time, distance traveled, and power consumption, and as a result, the waiting time from the end of use of the device until the mobile robot completes its return can be efficiently reduced from the above perspectives.

[0020] The collection history data may include first information, which is at least one of the time required for the mobile robot to collect the devices, the distance traveled by the mobile robot, and the power consumption of the mobile robot, and the trained model may be a model trained by machine learning to output the collection route for collecting the multiple devices so as to minimize the first information when the collection time for the multiple devices at the collection point is within a predetermined time. Through this processing, the program obtains a collection route capable of collecting the multiple devices, taking into account past collection history data including the first information from the prediction result of the end of use of the rented devices, and determines the mobile robot to be responsible for collection. Thus, the program can control the collection of the multiple devices to be collected along a collection route that is efficient from the perspective of at least one of time, distance traveled, and power consumption, and as a result, the waiting time from the end of use of the multiple devices until the mobile robot completes its return can be efficiently reduced from the above perspective.

[0021] The device may be a medical device. In this way, the program can take into consideration the usage pattern of the medical device and efficiently reduce the waiting time from when the medical device is used until the mobile robot completes returning it.

[0022] The trained model according to the present disclosure is a trained model that has been machine-learned to input a prediction result of the end of use of a rental device, which is a result of predicting the end of use of the rental device, and output a collection route for the rental device to be collected by the mobile robot as a return item, using training data including: collection record data indicating collection records, including the end of use date when the rental device is completed and the completion date when the device is collected by a mobile robot as a return item, after the rental device is rented out in an equipment rental system; and collection route data indicating the collection route for the rental device to be collected by the mobile robot as a return item. With this configuration, the trained model obtains a collection route for the rental device based on the prediction result of the end of use date, taking into account past collection record data. Therefore, the trained model can obtain a collection route that efficiently reduces the waiting time between the end of use of the device and the completion of its return by the mobile robot.

[0023] The learning system according to the present disclosure inputs learning data, including recovery record data indicating recovery records, including the end-of-use date when a device to be rented out in an equipment rental system is completed and the completion date when the device is collected by a mobile robot as a return item, into an untrained learning model and performs machine learning to generate a trained model that inputs an end-of-use prediction result, which is a prediction of the end-of-use date of the rented device, and outputs a recovery route for the rented device to be collected by the mobile robot as a return item. With the above-described configuration, the learning system can generate a trained model that can acquire a recovery route that efficiently reduces the dwell time between the end of use of the device and the completion of its return by the mobile robot.

[0024] The learning method according to the present disclosure inputs learning data, including recovery record data indicating recovery records, including the end-of-use date when a device to be rented out in an equipment rental system has ended and the completion date when the device is collected by a mobile robot as a return item, into an untrained learning model, and performs machine learning to generate a trained model that inputs an end-of-use prediction result, which is a prediction of the end-of-use date of the rented device, and outputs a recovery route for the rented device to be collected by the mobile robot as a return item. By performing the above-described processing, the learning method can generate a trained model that can acquire a recovery route that efficiently reduces the dwell time between the end of use of the device and the completion of its return by the mobile robot.

[0025] A program according to the present disclosure causes a computer to execute a learning process in which the program inputs learning data, including recovery record data indicating recovery records, including an end-of-use date when a rental device is rented out in an equipment rental system and a completion date when the device is collected by a mobile robot as a return, and recovery route data indicating a recovery route along which the device is collected by the mobile robot, into an untrained learning model and executes machine learning to generate a trained model that inputs an end-of-use prediction result, which is a prediction of an end-of-use date for the rental device, and outputs a recovery route along which the rental device is collected by the mobile robot as a return. Through this process, the program generates a trained model capable of acquiring a recovery route that efficiently reduces the waiting time between the end of use of the device and the completion of its return by the mobile robot. [Effects of the Invention]

[0026] According to the present disclosure, it is possible to provide a transport system, a transport control method, a trained model, and a program that can efficiently reduce the residence time of equipment to be rented out in an equipment rental system from the time of use to the time return by a mobile robot is completed, as well as a learning system, a learning method, and a program that can generate such a trained model. [Brief explanation of the drawings]

[0027] [Figure 1] 1 is a conceptual diagram for explaining an example of the overall configuration of a transport system according to an embodiment of the present invention; [Figure 2] FIG. 2 is a control block diagram illustrating an example of a transport system according to the present embodiment. [Figure 3] 3 is a control block diagram showing an example of the equipment rental system of FIG. 2. FIG. [Figure 4] FIG. 3 is a control block diagram showing an example of the electronic medical record system of FIG. 2. [Figure 5] 5 is a table showing an example of electronic medical record information stored in the electronic medical record system of FIG. 4. [Figure 6] 4 is a table showing an example of equipment rental information and provisional reservation information stored in the equipment rental system of FIG. 3. [Figure 7] 3 is a table showing an example of transported item information stored in the upper management device of FIG. 2. [Figure 8] FIG. 2 is a diagram showing an example of a movement path of a mobile robot. [Figure 9] FIG. 10 is a diagram showing another example of a movement path of the mobile robot. [Figure 10] 3 is a schematic diagram for explaining an example of a transport process in the upper management device of FIG. 2. FIG. [Figure 11] 11 is a diagram showing an example of a collection route acquired in the transport process of FIG. 10. FIG. [Figure 12] 10 is a flowchart illustrating an example of a transport method according to the present embodiment. [Figure 13]FIG. 3 is a block diagram showing an example configuration of a learning system that generates a trained model used in the upper management device of FIG. [Figure 14] FIG. 14 is a schematic diagram showing an example of a trained model generated by the training system of FIG. DETAILED DESCRIPTION OF THE INVENTION

[0028] The present invention will be described below through embodiments of the invention, but the invention according to the claims is not limited to the following embodiments. Furthermore, not all of the configurations described in the embodiments are necessarily essential as means for solving the problems.

[0029] <Embodiment> (Schematic configuration) The transport system according to this embodiment is a system in which a mobile robot transports equipment to be rented in an equipment rental system, and acquires a collection route for the rented equipment using a trained model. This trained model, the details of which will be described later, is a model that has been machine-learned to input a completion time prediction result and output a collection route using learning data including collection record data and collection route data.

[0030] The transport system then determines a mobile robot that will perform collection along the acquired collection route, and then controls the determined mobile robot to collect the device along the collection route.

[0031] This transport system calculates a collection route for the rented equipment based on the predicted end-of-use time and taking into account past collection data, and determines the mobile robot that will be responsible for collection. Therefore, this transport system can collect equipment using an efficient collection route, and as a result, it can efficiently reduce the amount of time the equipment remains in the facility until it is returned by the mobile robot.

[0032] First, an example of a transport system according to this embodiment will be described. Fig. 1 is a conceptual diagram for explaining an example of the overall configuration of a transport system 1 according to this embodiment. The transport system 1 according to this embodiment is a system that transports an object using a mobile robot that can move autonomously. Here, a mobile robot 20 as shown in Fig. 1 will be used as an example of the mobile robot, but the configuration and shape of the mobile robot 20 are not limited to this.

[0033] In addition to the mobile robot 20, the transport system 1 includes a host management device 10, a medical equipment rental system (hereinafter referred to as equipment rental system) 30, an electronic medical record system 40, a network 600, a communication unit 610, and a user terminal 400.

[0034] The mobile robot 20 is a transport robot that performs the task of transporting goods. The mobile robot 20 autonomously travels to transport goods within medical and welfare facilities such as hospitals, rehabilitation centers, nursing homes, and elderly care facilities. The mobile robot 20 may be a mobile robot that moves autonomously by referring to a map. The mobile robot 20 may also be a mobile robot that moves autonomously within a predetermined area, such as a part or all of the area of the map described above, or an area indicated by latitude and longitude. However, the mobile robot 20 may be configured to be able to move autonomously while sensing its surroundings, even outside the predetermined area, outside the entire area included in the map, or even in a configuration where no movement range is set.

[0035] A user U1, such as a user or assistant user of the transported item, or an administrator of the transported item, requests the mobile robot 20 to transport the item. When the transport request is made, the user U1 places the transported item in the mobile robot 20 at the requested location, or at the destination (origin) included in the transport request information. Of course, the transported item can also be stored by a storage robot or the like. Note that a mobile robot that carries and transports the transported item in an exposed state can also be used, but for simplicity of explanation, it is assumed that the transported item is stored in the mobile robot 20 and transported.

[0036] In this embodiment, it is sufficient that the mobile robot 20 is capable of transporting equipment to be lent as the transported object (hereinafter referred to as "rented equipment"). However, the mobile robot 20 may also transport equipment other than the rented equipment or transported objects other than equipment, such as medicines, consumables such as packets, specimens, hospital food, stationery, and other supplies.

[0037] User U1 can request the delivery of rental equipment according to the rental schedule (rental schedule). As will be described later, this rental schedule can be managed by the equipment rental system 30, and can be referenced by user U1 from the user terminal 400 to request delivery, and can also be referenced by the upper management device 10.

[0038] The mobile robot 20 autonomously moves to a set destination and delivers the rental equipment. In other words, the mobile robot 20 executes a luggage delivery task (hereinafter simply referred to as a task). In the following explanation, the location where the rental equipment is to be loaded is referred to as the delivery origin, and the location where the rental equipment is to be delivered is referred to as the delivery destination.

[0039] For example, let us say that the mobile robot 20 moves within a general hospital with multiple medical departments. The mobile robot 20 transports rental equipment between multiple medical departments. For example, the mobile robot 20 delivers rental equipment from the nurse's station of one medical department to the nurse's station of another medical department. Alternatively, the mobile robot 20 delivers rental equipment from its storage to the nurse's station of the medical department. Furthermore, if the destination is on a different floor, the mobile robot 20 may move using an elevator or the like. The mobile robot 20 is also responsible for returning the rental equipment to the storage, etc.

[0040] Examples of rental equipment include medical devices such as testing equipment and medical instruments. Medical devices include bedsore prevention devices, blood pressure monitors, transfusion pumps, syringe pumps, and other intravenous infusion devices, foot pumps, nurse call devices, bed exit sensors, foot pumps, low-pressure continuous inhalers, electrocardiogram monitors, drug infusion controllers, enteral nutrition pumps, ventilators, cuff pressure gauges, touch sensors, aspirators, nebulizers, pulse oximeters, blood pressure monitors, resuscitators, sterilization devices, and ultrasound machines. Other examples of medical devices include various intravenous infusion devices and various vital signs monitors. Note that multiple models of each type of medical device may be available for rental, such as transfusion pumps with different flow rates.

[0041] Some rental devices are also provided with stands. Examples of such rental devices with stands include low-pressure continuous suction devices, echo machines, electrocardiogram monitors (transmitters), electrocardiogram monitors (central monitors), electrocardiogram monitors (bedside monitors), ventilators, and nebulizers. Many rental devices with stands are powered by connecting to a commercial power source rather than by a battery, and are more likely to be stored in rental warehouses than rental devices without stands.

[0042] In addition, the above-mentioned rental devices do not require sterilization of the main body, or in many cases, only part of the device needs to be disinfected, and some rental devices can be equipped with disposable tools. Catheters, scalpels, scissors, etc. that require sterilization can also be handled as rental devices in this embodiment if the storage location and the place where they are sterilized are the same or close to each other.

[0043] In this embodiment, as shown in FIG. 1, the equipment rental system 30, the electronic medical record system 40, the mobile robot 20, and the user terminal 400 are connected to the host management device 10 via a network 600. The mobile robot 20 and the user terminal 400 are connected to the network 600 via a communication unit 610. The network 600 is a wired or wireless LAN (Local Area Network) or WAN (Wide Area Network). Furthermore, the host management device 10 is connected to the network 600 via a wired or wireless connection. The communication unit 610 is, for example, a wireless LAN unit installed in each environment. The communication unit 610 may also be a general-purpose communication device such as a WiFi router.

[0044] The user terminal 400 is, for example, a tablet computer or a smartphone, but may also be a stationary computer. The user terminal 400 may be any information processing device capable of wireless or wired communication.

[0045] User U1 or user U2 can make a transport request using the user terminal 400. For example, user U1 can access the equipment rental system 30 (or may go through the host management device 10) to refer to the schedule for the transport request from the user terminal 400, and can make a transport request for the rental equipment to the host management device 10 based on the results of the reference. The host management device 10, which has received this transport request, can make a transport request to the mobile robot 20.

[0046] In this way, various signals transmitted from the user terminals 400 of users U1 and U2 are first sent to the host management device 10 via the network 600, and then transferred from the host management device 10 to the target mobile robot 20. Similarly, various signals transmitted from the mobile robot 20 are first sent to the host management device 10 via the network 600, and then transferred from the host management device 10 to the target user terminal 400.

[0047] The host management device 10 is a server connected to each device and collects data from each device. Furthermore, the host management device 10 is not limited to a single physical device, but may include multiple devices that perform distributed processing. Furthermore, the host management device 10 may be distributed and located in edge devices such as mobile robots 20. For example, part or all of the transport system 1 may be mounted on the mobile robot 20.

[0048] The equipment rental system 30 is a system that manages a rental schedule (management information) that indicates the rental date and time and rental destination (usage location, user, etc.) for each rental device. The equipment rental system 30 can be a server connected to the host management device 10, and exchanges data with the host management device 10. This allows the host management device 10 to obtain the rental schedule for the rental devices managed by the equipment rental system 30. The equipment rental system 30 may be distributed to the host management device 10, or may be incorporated into the host management device 10.

[0049] The electronic medical record system 40 is a system that stores and manages electronic medical record data including information about patients (also referred to as patient information). For example, when a medical professional such as a doctor or nurse inputs patient information using a user terminal 400, the patient information is stored in the memory of the electronic medical record system 40. Furthermore, the medical professional can view and update the patient information stored in the electronic medical record system 40 through the user terminal 400.

[0050] The electronic medical record system 40 can be a server connected to the host management device 10, and exchanges data with the host management device 10. This allows the host management device 10 to obtain the electronic medical record data managed by the electronic medical record system 40. The electronic medical record system 40 can be distributed to the host management device 10, or can be incorporated into the host management device 10.

[0051] The upper management device 10 can also be configured to read medical conditions and surgery schedules from the electronic medical record data registered in the electronic medical record system 40, determine the equipment required for those conditions, and register the rental of rental equipment and other accessories in the equipment rental system 30.

[0052] The user terminal 400 and the mobile robot 20 may transmit and receive signals without going through the upper management device 10. For example, the user terminal 400 and the mobile robot 20 may transmit and receive signals directly via wireless communication. Alternatively, the user terminal 400 and the mobile robot 20 may transmit and receive signals via the communication unit 610.

[0053] User U1 or user U2 requests the delivery of rental equipment using user terminal 400. In the following explanation, user U1 is the person requesting delivery at the delivery source, and user U2 is the person expected to receive the equipment at the delivery destination (destination). Of course, user U2 at the delivery destination can also make the delivery request. Also, a user at a location other than the delivery source or delivery destination may make the delivery request.

[0054] When user U1 makes a transportation request, he / she uses the user terminal 400 to input the details of the rental equipment, the recipient of the rental equipment (hereinafter also referred to as the source of transportation), the delivery destination of the rental equipment (hereinafter also referred to as the destination of transportation), the estimated time of arrival at the source of transportation (received time of the rental equipment), the estimated time of arrival at the destination of transportation (transport deadline), etc. Hereinafter, this information will also be referred to as transportation request information. In the case of rental equipment that is the target of transportation in this embodiment, the source of transportation may be the storage location (equipment management location) of the rental equipment. The source of transportation may be the location where user U1 is located. The destination of transportation is the location where user U2 who is scheduled to use the equipment or the patient is located. User U1 can input transportation request information by operating the touch panel of user terminal 400.

[0055] The rental equipment in the transport request information can be specified using a rental schedule registered in the equipment rental system 30. For example, user U1 specifies the rental equipment from the user terminal 400, loads it onto the mobile robot 20 as needed, and makes a transport request to the host management device 10. Upon receiving the transport request, the host management device 10 refers to the equipment rental system 30, determines a transport schedule so that the rental equipment will be ready in time for the start time of use indicated in the rental schedule, and makes a transport request to the mobile robot 20, so that the equipment is transported according to the transport schedule.

[0056] Alternatively, the user U1 makes a transport request from the user terminal 400 while referring to the lending schedule, and the upper management device 10 determines the transport schedule by referring to the lending schedule and makes a transport request to the mobile robot 20, so that transport is carried out according to the transport schedule. In addition to these, various other transport request methods can be adopted.

[0057] These examples are based on the premise that a transport request is made after a rental schedule is registered based on a rental request (request for rental registration). On the other hand, rental equipment may be suddenly needed, in which case a rental schedule for the required time for that rental equipment is not registered. In such cases, user U1 can also send a transport request to the host management device 10 from the user terminal 400. Based on this transport request, the host management device 10 refers to the equipment rental system 30 to check whether there are any overlaps in the rental period, and if there are no problems, registers the request in the rental schedule and requests the mobile robot 20 to transport the equipment. In this case, the rental equipment can be loaded onto the mobile robot 20, for example, before or after the transport request is sent from the user terminal 400.

[0058] In either case, as described above, the user terminal 400 can transmit the transport request information input by the user U1 to the host management device 10. The host management device 10 is a management system that manages multiple mobile robots 20, and transmits operation commands to each mobile robot 20 to execute the transport task. At this time, the host management device 10 determines the mobile robot 20 that will execute the transport task for each transport request. Then, the host management device 10 transmits a control signal including the operation command to the mobile robot 20. The mobile robot 20 moves from the transport source to the transport destination in accordance with the operation command.

[0059] For example, the host management device 10 assigns a transport task to a mobile robot 20 at or near the transport source. Alternatively, the host management device 10 assigns a transport task to a mobile robot 20 heading towards the transport source or its vicinity. The mobile robot 20 assigned the task goes to the transport source to pick up the rental equipment. The transport source may be, for example, a storage location or the location of the user U1 who requested the task.

[0060] When the mobile robot 20 arrives at the destination, user U1 or another staff member loads the rental equipment onto the mobile robot 20. The mobile robot 20 carrying the rental equipment moves autonomously to the destination. The host management device 10 sends a signal to the user terminal 400 of user U2 at the destination. This allows user U2 to know that the rental equipment is being transported and the estimated arrival time. When the mobile robot 20 arrives at the set destination, user U2 can receive the rental equipment stored in the mobile robot 20. In this way, the mobile robot 20 performs the transport task.

[0061] In the overall configuration described above, the elements of the transport system can be distributed among the mobile robot 20, the user terminal 400, the equipment rental system 30, the electronic medical record system 40, and the host management device 10 to construct the transport system as a whole. It is also possible to configure the system by gathering all the essential elements required to transport the rental equipment into a single device. The host management device 10 controls one or more mobile robots 20.

[0062] (Control system of transport system 1) Fig. 2 is a control block diagram showing an example of a control system of the transport system 1. As shown in Fig. 2, the transport system 1 can include a host management device 10, a mobile robot 20, an equipment rental system 30, an electronic medical record system 40, and an environmental camera 300.

[0063] The transport system 1 efficiently controls a plurality of mobile robots 20 while autonomously moving the mobile robots 20 within a predetermined facility. For this purpose, a plurality of environmental cameras 300 are installed within the facility. For example, the environmental cameras 300 are installed in passageways, halls, elevators, entrances, etc. within the facility.

[0064] The environmental camera 300 captures images of the area in which the mobile robot 20 moves. In the transport system 1, the images captured by the environmental camera 300 and information based on them are collected by the host management device 10. Alternatively, the images captured by the environmental camera 300 may be sent directly to the mobile robot. The environmental camera 300 may be a surveillance camera installed in the corridors or entrances of a facility. The environmental camera 300 may be used to determine the distribution of congestion within the facility.

[0065] In the transportation system 1, the host management device 10 can plan a route based on, for example, transportation request information and generate route planning information. The route planning information can be generated as information that plans a transportation route corresponding to the transportation schedule described above. The host management device 10 instructs each mobile robot 20 on a destination based on the generated route planning information. The mobile robots 20 then autonomously move toward the destination specified by the host management device 10. The mobile robots 20 autonomously move toward the destination (destination) using sensors, a floor map, position information, etc. provided on the mobile robots themselves.

[0066] For example, the mobile robot 20 moves so as not to come into contact with surrounding equipment, objects, walls, and people (hereinafter collectively referred to as surrounding objects). Specifically, the mobile robot 20 detects the distance to the surrounding objects and moves while remaining at a certain distance (referred to as a distance threshold) or more from the surrounding objects. When the distance to the surrounding object becomes equal to or less than the distance threshold, the mobile robot 20 slows down or stops. In this way, the mobile robot 20 can move without coming into contact with surrounding objects. Because contact can be avoided, safe and efficient transportation becomes possible.

[0067] The upper management device 10 may include a calculation processing unit 11, a storage unit 12, a buffer memory 13, and a communication unit 14. The calculation processing unit 11 performs calculations for controlling and managing the mobile robot 20. The calculation processing unit 11 may be implemented as a device capable of executing a program, such as a central processing unit (CPU) of a computer. Various functions may also be realized by a program. Although FIG. 2 shows only the characteristic components of the calculation processing unit 11, namely, the end time prediction processing unit 110, the robot control unit 111, and the route planning unit 115, other processing blocks may also be included.

[0068] The end time prediction processing unit 110 inputs loaned device data indicating the medical device being loaned and electronic medical record data describing information indicating the need for use of the medical device into the trained model 120 stored in the memory unit 12, and obtains an end time prediction result, which is a result of predicting the end time of use of the loaned medical device, from the trained model 120. The end time prediction processing unit 110 passes the obtained end time prediction result to the route planning unit 115. Note that the end time prediction processing unit 110 can also be configured to notify the equipment rental system 30 of the end time prediction result via the communication unit 14.

[0069] Here, information indicating the need for the use of medical equipment can refer to information indicating the medical equipment itself, information indicating the surgery required for the patient, information indicating the patient's symptoms, information indicating treatment for the patient, or a combination of multiple of these pieces of information.

[0070] The robot control unit 111 performs calculations to remotely control the mobile robot 20 and generates a control signal. The robot control unit 111 generates the control signal based on route planning information 125, which will be described later, and other information. Furthermore, the control signal is generated based on various information obtained from the environmental camera 300 and the mobile robot 20. The control signal may include updated information such as a floor map 121, robot information 123, and robot control parameters 122, which will be described later. In other words, when various information is updated, the robot control unit 111 generates a control signal according to the updated information.

[0071] The route planning unit 115 plans a route for each mobile robot 20. When a transport task is input, the route planning unit 115 plans a route for transporting the rental equipment to the destination (destination) based on the transport request information. Specifically, the route planning unit 115 refers to route planning information 125 and robot information 123 already stored in the memory unit 12, and determines the mobile robot 20 that will execute the new transport task.

[0072] The starting point may be the current location of the mobile robot 20, the destination of the previous transport task, or the recipient of the rental equipment. The destination is the destination of the rental equipment, but may also be a waiting location, a charging location, a storage location, or the like. Here, the route planning unit 115 sets passing points for the mobile robot 20 from the starting point to the destination. The route planning unit 115 sets the order in which the passing points are to be passed for each mobile robot 20. Passing points are set, for example, at branching points, intersections, lobbies in front of elevators, and the surrounding areas. In addition, it may be difficult for mobile robots 20 to pass each other in narrow passages. In such cases, a passing point may be set just before the narrow passage. Candidates for passing points may be registered in advance in the floor map 121.

[0073] The route planning unit 115 determines which mobile robot 20 will perform each transport task from among the multiple mobile robots 20 so that the tasks can be executed efficiently as a whole system. The route planning unit 115 can, for example, preferentially assign transport tasks to waiting mobile robots 20 or mobile robots 20 that are close to the transport source. Instead of or in addition to such preferential assignment, the route planning unit 115 can also assign tasks according to other conditions, such as equalizing the degree of deterioration of the mobile robots 20, as will be described later.

[0074] The route planning unit 115 sets pass points including the starting point and the destination for the mobile robot 20 assigned a transportation task. For example, if there are two or more travel routes from the transportation origin to the transportation destination, pass points are set to enable the robot to travel in a shorter time. Therefore, the upper management device 10 updates information indicating the congestion status of the passages based on camera images, etc. Specifically, places where other mobile robots 20 are passing through or where there are many people are highly congested. Therefore, the route planning unit 115 sets pass points so as to avoid highly congested places.

[0075] There are cases where the mobile robot 20 can travel to the destination via either a counterclockwise route or a clockwise route. In such cases, the route planning unit 115 sets passing points so that the mobile robot 20 passes through the less congested route. By setting one or more passing points on the way to the destination, the route planning unit 115 allows the mobile robot 20 to travel on a less congested route. For example, when a path is divided at a branch point or an intersection, the route planning unit 115 sets passing points at the branch point, intersection, corner, and their surrounding areas as appropriate. This can improve transportation efficiency.

[0076] The route planning unit 115 may set passing points taking into consideration the congestion status of elevators, travel distance, etc. Furthermore, the upper management device 10 may estimate the number of mobile robots 20 or the number of people at the scheduled time when the mobile robots 20 are to pass a certain location. Then, the route planning unit 115 may set passing points according to the estimated congestion status. Furthermore, the route planning unit 115 may dynamically change passing points according to changes in the congestion status. The route planning unit 115 sets passing points in order for the mobile robots 20 to which a transportation task is assigned. The passing points may include the transportation origin and the transportation destination. As will be described later, the mobile robot 20 moves autonomously so as to pass through the passing points set by the route planning unit 115 in order.

[0077] As described above, the route planning unit 115 can determine the location of the mobile robot 20 and set the passing points. The route planning unit 115 can also perform similar processing when returning (collecting) a rental device that is currently being rented.

[0078] However, the route planning unit 115 is configured to be able to set a collection route, which is the transport route in this case, using the trained model 124 at the time of collection. In this case, the collection route that is set can also include passing points including the departure point and destination. The departure point here is the lending destination, and the destination is the storage location, maintenance location, or next lending destination.

[0079] The route planning unit 115 inputs the end time prediction result, which is the result of predicting the end time of use of the rental equipment currently on loan, into the trained model 124, and obtains a collection route for collecting the rental equipment currently on loan as a return item using the mobile robot 20. The input end time prediction result can be the end time prediction result obtained by the end time prediction processing unit 110 using the trained model 120 and passed to the route planning unit 115. In this way, the route planning unit 115 can automatically create a route plan for the collection route. However, the trained model 124 does not have to output the collection route itself, but can output only partial information about the collection route, and the route planning unit 115 can supplement the other information.

[0080] The route planning unit 115 can then execute processing to determine the mobile robot 20 that will collect the rental device along the acquired collection route, i.e., the mobile robot 20 that will be controlled to collect the rental device. This determination will be described later, but the route planning unit 115 can determine the mobile robot 20 based on predetermined conditions. Note that the determination of the mobile robot 20 can also be executed by the robot control unit 111.

[0081] The memory unit 12 is a memory unit that stores information necessary for managing and controlling the mobile robot 20, etc. In the example of FIG. 2, a trained model 120, a floor map 121, robot information 123, robot control parameters 122, a trained model 124, route planning information 125, and transported item information 126 are shown, but other information may be stored in the memory unit 12. The calculation processing unit 11 performs calculations using the information stored in the memory unit 12 when performing various processes. In addition, the various information stored in the memory unit 12 can be updated to the latest information.

[0082] The trained model 120 is a learning model that has been machine-learned using training data (hereinafter referred to as first training data) including rental history data, which includes the history of rental of medical devices as rental devices and the history of when the use of the medical devices has ended, and electronic medical record data, which describes information indicating the need for use of the rental medical devices. The trained model 120 has been machine-learned to input the electronic medical record data, which describes information indicating the need for use of the medical devices, and the loaned device data, which indicates the medical devices currently on loan, and output an end time prediction result, which is a prediction result of predicting the end time of use of the medical devices. In other words, the trained model 120 is a model consisting of an algorithm that predicts the end time prediction result from the electronic medical record data and the loaned device data. The algorithm, etc., is not important as long as such a prediction is possible. The trained model 120 and the trained model 124, which will be described later, can be updated at a predetermined timing as operation progresses and data is accumulated.

[0083] Here, the rental history data is data showing the rental history of medical equipment managed by the equipment rental system 30, including the history of when the use of the medical equipment has ended, and can be managed in the memory unit (memory unit 32 described below) of the equipment rental system 30. The end of use of a medical equipment can be obtained, for example, based on input from the user terminal 400 by user U2, and the same applies to the start of use of a medical equipment. In either case of the end of use or the start of use, the user terminal 400 can transmit the input result to the equipment rental system 30 via the network 600 directly or via the upper management device 10, and record it as rental history data together with the date and time. However, the start and end of use of a medical equipment can also be obtained by other methods. For example, if a medical device is powered by an electrical outlet, the start and end of use of the medical device can be determined from the power consumption detected by a sensor or the like attached to the electrical outlet at the rental location (usage location), and the determination result can be transmitted directly or via the host management device 10 to the equipment rental system 30 via the network 600, and recorded as rental history data together with the date and time. Alternatively, communication can be performed between the medical device and a predetermined rental location to determine the start and end of use of the medical device when the medical device approaches or moves away from the predetermined location, and the determination result can be transmitted directly or via the host management device 10 to the equipment rental system 30 via the network 600, and recorded as rental history data together with the date and time. This communication can be achieved, for example, by using a beacon that emits radio waves such as Bluetooth (registered trademark) or Bluetooth Low Energy (registered trademark) and a device that detects the radio waves, or by using an RFID (Radio Frequency Identification) tag such as an NFC (Near Field Communication) tag and a tag reader. Either the medical device or the predetermined location may be the sender or receiver, and the medical device may have such a communication function built-in or may have an external device with such a communication function attached.

[0084] The trained model 124 is a learning model that has been machine-learned using learning data (hereinafter referred to as second learning data) that includes collection history data that shows collection history, including the end-of-use date when the rental equipment ended after it was rented out and the completion date when it was collected as a returned item, and collection route data that shows the collection route taken to collect the rental equipment by the mobile robot 20.

[0085] Here, the end of use time can be obtained as explained for the end of use of rental history data. Similarly, the collection completion time can be obtained as an input result on the user terminal 400, or can be obtained by communication between the medical device and a predetermined location. However, the collection completion time only needs to be the collection completion date and time, and can be the date and time when the rental device is transported to the storage location or the next rental location (transport completion date and time), or, for example, the transport start date and time when such transport began. Therefore, when the collection completion time is obtained by communication between the medical device and a predetermined location, the predetermined location is the location where collection ended (such as the storage location or the next rental location).

[0086] The trained model 124 is machine-learned to input an end time prediction result, which is the result of predicting the end time of use of a rental device currently on loan, and output a collection route for the mobile robot 20 to collect the rental device as a return item. In other words, the trained model 124 is a model consisting of an algorithm that predicts a collection route from the end time prediction result. The algorithm, etc., is not important as long as it is capable of making such a prediction. Here, too, we explain the case where the device to be learned and predicted by the trained model 124 is a medical device. Therefore, the end time prediction result input to the trained model 124 can be the output result from the trained model 120, as described above.

[0087] The floor map 121 is map information of the facility in which the mobile robot 20 moves. This floor map 121 may be created in advance, or may be generated from information obtained from the mobile robot 20, or may be a pre-created basic map to which map correction information generated from information obtained from the mobile robot 20 has been added.

[0088] The robot information 123 describes the ID, model number, specifications, etc. of the mobile robot 20 managed by the upper management device 10. The robot information 123 may include location information indicating the current location of the mobile robot 20. The robot information 123 may include information indicating whether the mobile robot 20 is executing a task or is on standby. The robot information 123 may also include information indicating whether the mobile robot 20 is operating or out of order. The robot information 123 may also include information on rental equipment that can be transported and rental equipment that cannot be transported.

[0089] The robot control parameters 122 describe control parameters such as a threshold distance between the mobile robot 20 and a surrounding object managed by the host management device 10. The threshold distance is a margin distance for avoiding contact with a surrounding object, including a person. Furthermore, the robot control parameters 122 may include information regarding the strength of the operation of the mobile robot 20, such as an upper limit of the moving speed of the mobile robot 20.

[0090] The robot control parameters 122 may be updated depending on the situation. The robot control parameters 122 may include information indicating the availability and usage of storage space in the mobile robot 20. The robot control parameters 122 may include information on rental equipment that can be transported and rental equipment that cannot be transported. Of course, the robot control parameters 122 can also include information indicating whether or not it is possible to transport items other than rental equipment. The robot control parameters 122 associate the above various information with each mobile robot 20.

[0091] The route planning information 125 includes route planning information planned by the route planning unit 115. The route planning information 125 includes, for example, information indicating a transportation task. The route planning information 125 may include information such as the ID of the mobile robot 20 assigned the task, the starting point, the details of the rental equipment, the destination, the origin, the estimated time of arrival at the destination, the estimated time of arrival at the origin, and the arrival deadline. The route planning information 125 may associate the various information described above with each transportation task. The route planning information 125 may include at least a portion of the transportation request information input by the user U1 or the like for both rental transportation and return transportation, and for return transportation, may include at least a portion of the information included in the collection route output from the trained model 124.

[0092] Here, the route planning information 125 may include information about passing points for each mobile robot 20 and each transport task. For example, the route planning information 125 includes information indicating the order in which each mobile robot 20 passes through passing points. The route planning information 125 may include the coordinates of each passing point on the floor map 121 and information on whether or not the passing point has been passed.

[0093] The transported item information 126 is information about the rental equipment for which a transport request has been made. For example, it includes information such as the details (type) of the rental equipment, the origin of transport, and the destination of transport. Of course, the transported item information 126 may also include information about items other than rental equipment, and the same applies to items other than the transported item information 126 below. The transported item information 126 may also include the ID of the mobile robot 20 responsible for the transport. Furthermore, the transported item information 126 may include information indicating the status, such as in transport, before transport (before loading), or already transported, and this status may also include information indicating whether the transport is for rental or return. The transported item information 126 associates these pieces of information with each rental equipment. The transported item information 126 will be described later.

[0094] The route planning unit 115 can formulate a route plan by referring to various information stored in the storage unit 12. The route planning unit 115 can determine the mobile robot 20 that will execute a task based on, for example, the floor map 121, the robot information 123, the robot control parameters 122, and the route planning information 125. The route planning unit 115 can set pass points on the way to the destination and the order in which the pass points will be passed by by referring to the floor map 121, etc. Candidates for pass points are registered in advance in the floor map 121. The route planning unit 115 can set pass points depending on the congestion situation, etc. Furthermore, when tasks are processed consecutively, the route planning unit 115 may set the origin and destination as pass points.

[0095] Furthermore, two or more mobile robots 20 may be assigned to one transport task. For example, if the rental equipment is larger than the transport capacity of the mobile robot 20, one rental equipment is divided into two pieces and mounted on two mobile robots 20. Alternatively, if the rental equipment is heavier than the transport capacity of the mobile robot 20, one rental equipment is divided into two pieces and mounted on two mobile robots 20. In this way, one transport task can be shared and executed by two or more mobile robots 20. Of course, when controlling mobile robots 20 of different sizes, route planning may be performed so that a mobile robot 20 that can transport the rental equipment receives the rental equipment.

[0096] Furthermore, one mobile robot 20 may perform two or more transport tasks in parallel. For example, one mobile robot 20 may simultaneously carry two or more pieces of rental equipment and transport them sequentially to different destinations. Alternatively, one mobile robot 20 may carry one piece of rental equipment while carrying another piece of rental equipment. Furthermore, the destinations of the rental equipment carried at different locations may be the same or different. In this way, tasks can be carried out efficiently.

[0097] In such a case, the accommodation information indicating the usage status or availability of the accommodation space of the mobile robot 20 may be updated. In other words, the host management device 10 may manage the accommodation information indicating the availability status and control the mobile robot 20. For example, when the loading or receiving of the rental equipment is completed, the accommodation information is updated. When a transport task is input, the host management device 10 refers to the accommodation information and directs a mobile robot 20 with available space to load the rental equipment to receive it. This allows one mobile robot 20 to simultaneously perform multiple transport tasks, or allows two or more mobile robots 20 to share and perform a transport task. For example, a sensor may be installed in the accommodation space of the mobile robot 20 to detect the availability. Furthermore, the capacity and weight of each rental equipment may be registered in advance.

[0098] The buffer memory 13 is a memory that stores intermediate information generated during processing in the arithmetic processing unit 11. The communication unit 14 is a communication interface for communicating with multiple environmental cameras 300 and at least one mobile robot 20 installed in the facility where the transport system 1 is operated. The communication unit 14 can perform both wired and wireless communication. For example, the communication unit 14 transmits control signals necessary for controlling each mobile robot 20 to each mobile robot 20 based on instructions from the arithmetic processing unit 11. The communication unit 14 can also receive information collected by the mobile robot 20 and the environmental camera 300 and pass it on to the arithmetic processing unit 11. The communication unit 14 can also receive information such as a rental schedule from the equipment rental system 30 and pass it on to the arithmetic processing unit 11, or transmit information such as a rental schedule to the equipment rental system 30 for registration based on instructions from the arithmetic processing unit 11. The communication unit 14 can also receive electronic medical record information from the electronic medical record system 40 and pass it on to the arithmetic processing unit 11.

[0099] The mobile robot 20 may include a processing unit 21, a memory unit 22, a communication unit 23, a proximity sensor (e.g., a distance sensor group 24), a camera 25, a driving unit 26, a display unit 27, and an operation receiving unit 28. Note that while Fig. 2 shows only representative processing blocks included in the mobile robot 20, the mobile robot 20 also includes many other processing blocks that are not shown.

[0100] The communication unit 23 is a communication interface for communicating with the communication unit 14 of the higher-level management device 10. The communication unit 23 communicates with the communication unit 14 using, for example, wireless signals. The distance sensor group 24 is, for example, a proximity sensor, and outputs nearby object distance information that indicates the distance to an object or person present around the mobile robot 20. The distance sensor group 24 may include, for example, a front-rear distance sensor and a left-right distance sensor, and can measure the distance to surrounding objects in the front-rear direction and the left-right direction of the mobile robot 20.

[0101] The camera 25, for example, captures images for grasping the situation around the mobile robot 20. The camera 25, for example, captures an image of the area ahead in the direction of travel of the mobile robot 20. The camera 25 can also capture an image of a position marker provided on the ceiling of a facility, for example. The position marker may be used to allow the mobile robot 20 to grasp its own position.

[0102] The driving unit 26 drives the driving wheels provided on the mobile robot 20. The driving unit 26 may also include an encoder that detects the number of rotations of the driving wheels or their drive motors. The mobile robot's own position (current position) may be estimated based on the output of the encoder. The mobile robot 20 detects its own current position and transmits it to the host management device 10.

[0103] The display unit 27 and the operation reception unit 28 are realized by a touch panel display. The display unit 27 displays a user interface screen that serves as the operation reception unit 28. The display unit 27 may also display information indicating the destination of the mobile robot 20 and the status of the mobile robot 20. The operation reception unit 28 receives operations from the user. The operation reception unit 28 includes the user interface screen displayed on the display unit 27 as well as various switches provided on the mobile robot 20.

[0104] The arithmetic processing unit 21 performs calculations used to control the mobile robot 20. The arithmetic processing unit 21 can be implemented as a device capable of executing programs, such as a central processing unit (CPU) of a computer. Various functions can also be realized by programs. The arithmetic processing unit 21 includes a movement command extraction unit 211 and a drive control unit 212. Note that while FIG. 2 shows only representative processing blocks included in the arithmetic processing unit 21, processing blocks not shown in the figure are also included. The arithmetic processing unit 21 may search for routes between passing points.

[0105] The movement command extraction unit 211 extracts a movement command from a control signal provided by the upper management device 10. For example, the movement command includes information about the next passing point. For example, the control signal may include the coordinates of the passing point and information about the order in which to pass through the passing point. The movement command extraction unit 211 then extracts this information as a movement command.

[0106] Furthermore, the movement command may include information indicating that it is now possible for the mobile robot 20 to move to the next passing point. If the passage width is narrow, the mobile robots 20 may not be able to pass each other. Also, there may be cases where the passage is temporarily blocked. In such cases, the control signal includes a command to stop the mobile robot 20 at a passing point just before the place where it should stop. Then, after another mobile robot 20 has passed or it has become possible to pass, the upper management device 10 outputs a control signal to notify the mobile robot 20 that it is now possible to move. This causes the mobile robot 20, which had been temporarily stopped, to resume moving.

[0107] The drive control unit 212 controls the drive unit 26 to move the mobile robot 20 based on the movement command provided by the movement command extraction unit 211. For example, the drive unit 26 has drive wheels that rotate according to a control command value from the drive control unit 212. The movement command extraction unit 211 extracts a movement command so that the mobile robot 20 moves toward the passing point received from the host management device 10. The drive unit 26 then drives the drive wheels to rotate. The mobile robot 20 autonomously moves toward the next passing point. In this way, the mobile robot 20 passes through the passing points in order and arrives at the destination. The mobile robot 20 may also estimate its own position and transmit a signal to the host management device 10 indicating that it has passed a passing point. This allows the host management device 10 to manage the current position and transportation status of each mobile robot 20.

[0108] Here, the drive control unit 212 can identify its own position and recognize surrounding objects by analyzing image data output by the camera 25 and detection signals output by the distance sensor group 24. Then, based on the results of this analysis and the movement command, the drive control unit 212 can control the drive unit 26 to move the mobile robot 20. At this time, the drive control unit 212 can recognize surrounding objects and identify its own position by referring to the floor map 221 and robot control parameters 222.

[0109] The memory unit 22 stores a floor map 221, robot control parameters 222, and transported item information 226. Although FIG. 2 shows only a portion of the information stored in the memory unit 22, the information stored in the memory unit 22 also includes information other than the floor map 221, robot control parameters 222, and transported item information 226 shown in FIG. 2. The floor map 221 is map information of a facility in which the mobile robot 20 is to move. This floor map 221 is, for example, data obtained by downloading part or all of the floor map 121 from the upper management device 10. The floor map 221 may be one that has been created in advance. Furthermore, the floor map 221 may not be map information of the entire facility, but may be map information that partially includes the area in which the robot is to move.

[0110] The robot control parameters 222 are parameters for operating the mobile robot 20. The robot control parameters 222 include, for example, a distance threshold to a surrounding object. Furthermore, the robot control parameters 222 include an upper limit of the speed of the mobile robot 20.

[0111] Like the transported item information 126, the transported item information 226 includes information about the rental equipment. It can include information about the rental equipment (type, i.e., model), origin, and destination. The transported item information 226 can include information indicating the status, such as in transport, before transport (before installation), or already transported. This status can also include information indicating whether the transport is for rental or return. The transported item information 226 associates this information with each rental equipment. The transported item information 226 only needs to include information about the rental equipment transported by the mobile robot 20. Therefore, the transported item information 226 becomes part of the transported item information 126. In other words, the transported item information 226 does not need to include information about equipment transported by other mobile robots 20. The transported item information 126 will be described later.

[0112] The drive control unit 212 refers to the robot control parameters 222 and stops or decelerates the operation when the distance indicated by the distance information obtained from the distance sensor group 24 falls below the distance threshold. The drive control unit 212 controls the drive unit 26 so that the mobile robot 20 travels at a speed equal to or less than the upper speed limit. The drive control unit 212 limits the rotational speed of the drive wheels so that the mobile robot 20 does not travel at a speed equal to or greater than the upper speed limit.

[0113] FIG. 3 is a control block diagram showing an example of an equipment rental system 30 in the transportation system 1 of FIG. 2. As shown in FIG. 3, the equipment rental system 30 can include a processing unit 31, a storage unit 32, a buffer memory 33, and a communication unit 34. The processing unit 31 performs calculations for generating and managing a schedule for rental equipment. The processing unit 31 can be implemented as a device capable of executing a program, such as a central processing unit (CPU) of a computer. Various functions can also be realized by a program. Although FIG. 3 shows only the registration unit 311 and rental planning unit 312, which are characteristic of the processing unit 31, other processing blocks can also be included.

[0114] The registration unit 311 receives rental request information including the ID of the rental device, the start time of use, the end time of use, and the location of use, sent from the user terminal 400 in accordance with, for example, an operation by user U1, via the communication unit 34, and accepts the registration.

[0115] The registration unit 311 also receives, via the communication unit 34, rental provisional reservation information for the rental device to be provisionally reserved, including the rental device's ID, start time of use, end time of use, and location of use, which is transmitted from the user terminal 400 in accordance with an operation by user U1, for example, and accepts the provisional registration. The registration unit 311 also accepts, via the communication unit 34, a formal rental request or cancellation request for the accepted provisional registration, which is transmitted from the user terminal 400 in accordance with an operation by user U1, for example. However, this function of making a provisional reservation is not essential.

[0116] Based on the rental request information received by the registration unit 311, the rental planning unit 312 refers to equipment rental information 324 indicating an already planned rental schedule, provisional reservation information 325 indicating an already provisionally reserved rental schedule, and other rental request information and rental provisional reservation information requested at the same time, and also considers any cancellation requests requested at the same time to confirm that there are no overlaps. Of course, when determining whether there are overlaps, if there is no other individual medical equipment of the same type that is the rental target, it is processed as not overlapping. If there are no overlaps, the rental planning unit 312 generates a rental schedule for the rental equipment based on the received rental request information and updates the equipment rental information 324. Note that if the rental request information received by the registration unit 311 is information that overlaps in time with an existing rental schedule or the like (overlapping also taking into account transport time), the rental planning unit 312 sends the following reply. That is, the lending planning unit 312 returns a notification of the overlap to the sender of the lending request information (the user terminal 400 or the upper management device 10) via the communication unit .

[0117] As with the lending request information, the lending planning unit 312 also checks for overlaps with the lending tentative reservation information received by the registration unit 311 by referring to the already planned lending schedule, etc. based on the lending tentative reservation information. If there are no overlaps, the lending planning unit 312 generates a lending schedule for the lending equipment based on the received lending tentative reservation information and updates the tentative reservation information 325. Note that the equipment lending information 324 and the tentative reservation information 325 can share information by using a flag indicating whether the reservation is an official lending or a tentative reservation, or by adding a status indicating that the reservation is tentative.

[0118] Furthermore, in response to a formal request for provisional registration received by the registration unit 311, the lending planning unit 312 performs formal registration by moving the target information from the provisional reservation information 325 to the equipment lending information 324. In response to a cancellation request for provisional registration received by the registration unit 311, the lending planning unit 312 deletes the provisional reservation by deleting the target information from the provisional reservation information 325. As exemplified above by the registration unit 311 and the lending planning unit 312, the equipment lending system 30 can be provided with a reservation system for provisionally reserving the lending of medical equipment.

[0119] The memory unit 32 is a memory unit that stores information necessary for managing the rental of rental equipment and controlling the equipment rental system 30. In the example of Fig. 3, a floor map 321, mechanic information 322, equipment information 323, equipment rental information 324, and provisional reservation information 325 are shown, but other information may be stored in the memory unit 32. The calculation processing unit 31 performs calculations using the information stored in the memory unit 32 when performing various processes. In addition, the various information stored in the memory unit 32 can be updated to the latest information.

[0120] The equipment information 323 is information indicating the ID, model (product number), size, weight, etc. of the rental equipment, and may also include information indicating whether the equipment is currently on loan (i.e., inventory information indicating the stock status), and information indicating the time required for maintenance and storage location. At least a portion of the equipment information 323 necessary for transportation, or all of the equipment information 323, may be registered in the upper management device 10 as part of the transported item information 126. Among these, the inventory information may not be included as part of the equipment information 323, or may be included as part of the equipment information 323 and also as part of the equipment rental information 324.

[0121] The maintenance person information 322 is information associated with each rental device indicated by the device information 323, and may include information indicating the maintenance person who will maintain each rental device (such as the maintenance person's own ID or information indicating the type of maintenance person) and information indicating the notification destination for each maintenance person. In some cases, maintenance may be performed after a rental ends and before the next rental. The maintenance person information 322 can be stored to notify the maintenance person. The processing unit 11 can refer to the maintenance person information 322 and notify the maintenance person via the communication unit 14 when the medical device requiring maintenance is transported to a storage location after use. However, this notification can also be performed by the mobile robot 20. Such notification allows a maintenance person to go to the storage location to which the rental medical device is to be transported, as needed, to perform maintenance. At the storage location, a maintenance person, such as user U2, then performs maintenance, such as inspection, cleaning, and replacement of consumables, as needed, in preparation for the next use. In addition, maintenance technicians include clinical laboratory technicians, diagnostic radiologists, occupational therapists, physical therapists, clinical engineers, doctors, nurses, licensed practical nurses, and engineers from the manufacturers of the rental equipment.

[0122] The floor map 321 can be a part or all of the floor map 121. As described above, the equipment rental information 324 is information indicating a rental schedule for each rental equipment generated by the rental planning unit 312, and the provisional reservation information 325 is information indicating a provisional reservation for the rental equipment. The equipment rental information 324 and the provisional reservation information 325 will be described later.

[0123] The buffer memory 33 is a memory that stores intermediate information generated during processing in the arithmetic processing unit 31. The communication unit 34 is a communication interface for communicating with the host management device 10, and this communication interface can also be configured to communicate with the user terminal 400, the mobile robot 20, and the electronic medical record system 40. The communication unit 34 is capable of both wired and wireless communication. For example, the communication unit 34 can receive information such as loan request information and loan tentative reservation information from the host management device 10 or the user terminal 400 and pass it to the arithmetic processing unit 31, or can transmit information such as a loan schedule to the host management device 10 based on instructions from the arithmetic processing unit 31.

[0124] The communication unit 34 can also receive electronic medical record information from the electronic medical record system 40 and pass it to the arithmetic processing unit 31. In this case, the registration unit 311 of the arithmetic processing unit 31 can determine whether or not medical equipment needs to be loaned for surgery or other treatment based on the received electronic medical record information, and if so, generate medical equipment loan request information or provisional loan reservation information and pass it to the loan planning unit 312.

[0125] Here, when generating the electronic medical record information, if the electronic medical record information contains information directly indicating the medical equipment that needs to be rented, the registration unit 311 can generate rental request information or provisional rental reservation information for generating the equipment rental information 324 from the electronic medical record information. On the other hand, if the electronic medical record information does not contain information directly indicating such medical equipment, the registration unit 311 can select medical equipment corresponding to the symptom name, etc., in accordance with predetermined rules and generate rental request information or provisional rental reservation information. The registration unit 311 can also determine whether to generate rental request information or provisional rental reservation information in accordance with predetermined rules. For example, if the period until treatment is more than a predetermined period in the future, such as one month or one week, the registration unit 311 can generate rental request information in other cases. Alternatively, the registration unit 311 can generate rental request information for medical equipment related to a confirmed treatment, and generate provisional rental reservation information in other cases.

[0126] The lending planning unit 312 registers the equipment lending information 324 or the provisional reservation information 325 based on the lending request information or the provisional lending reservation information received in this way.

[0127] Alternatively, the communication unit 34 can receive medical equipment rental request information, rental provisional reservation information, etc. based on electronic medical record information from the electronic medical record system 40, and pass them to the arithmetic processing unit 31. In this case, the registration unit 311 of the arithmetic processing unit 31 accepts the received rental request information or rental provisional reservation information, and registers equipment rental information 324 or provisional reservation information 325 based on the information accepted by the rental planning unit 312.

[0128] However, as in the example in which the operation by the user U1 is followed, the registration by the registration unit 311 can be executed by a doctor, nurse, or the like determining whether it is necessary and performing the operation.

[0129] FIG. 4 is a control block diagram showing an example of the electronic medical record system 40 of FIG. 2. As shown in FIG. 4, the electronic medical record system 40 can include a processing unit 41, a storage unit 42, a buffer memory 43, and a communication unit 44. The processing unit 41 performs calculations for generating and managing electronic medical record data. The processing unit 41 can be implemented as a device capable of executing a program, such as a central processing unit (CPU) of a computer. Various functions can also be realized by a program. Although FIG. 4 shows only the registration unit 411, which is a characteristic of the processing unit 41, other processing blocks can also be included.

[0130] The registration unit 411 receives, via the communication unit 44, chart registration request information including the patient's ID, symptoms, treatment (including surgery), treatment schedule, treatment location, etc., transmitted from the user terminal 400 in response to an operation by the user U1, for example, accepts the registration, and stores it as electronic chart information 420 in the storage unit 42. The chart registration request information may also include the patient's name, chart ID, whether or not hospitalization is required and the schedule, and in the case of surgery, the staff or staff team, such as the surgeon.

[0131] The memory unit 42 is a memory unit that stores electronic medical record information 420 to be managed by the electronic medical record system 40 and other information necessary for controlling the electronic medical record system 40. Although the example in FIG. 4 shows electronic medical record information 420, other information may be stored in the memory unit 42. When performing various processes, the calculation processing unit 41 performs calculations using the other information stored in the memory unit 42. Furthermore, the various information stored in the memory unit 42 can be updated to the latest information.

[0132] The electronic medical record information 420 may include information requested to be registered as medical record registration request information. In addition, for example, the medical record ID and patient ID of the electronic medical record information 420 may be automatically assigned according to predetermined rules such as sequential numbers. The electronic medical record information 420 will be described later.

[0133] The buffer memory 43 is a memory that stores intermediate information generated during processing in the arithmetic processing unit 41. The communication unit 44 is a communication interface for communicating with the host management device 10, and this communication interface can also be configured to communicate with the user terminal 400, the mobile robot 20, and the equipment rental system 30. The communication unit 44 is capable of both wired and wireless communication. The communication unit 44 can receive medical record registration request information from the host management device 10 or the user terminal 400 and pass it to the arithmetic processing unit 41, and can transmit electronic medical record information 420 to the host management device 10 based on instructions from the arithmetic processing unit 41.

[0134] Furthermore, based on instructions from the processing unit 41, the communication unit 44 can also transmit, for example, electronic medical record information 420, or medical equipment rental request information or provisional rental reservation information based on the electronic medical record information 420, to the equipment rental system 30. In the latter case, the processing unit 41 references the electronic medical record information 420 to determine whether rental of medical equipment is necessary for surgery or other treatment, and if so, issues an instruction to the communication unit 44 to transmit medical equipment rental request information or provisional rental reservation information. Here, when the electronic medical record information 420 contains information directly indicating the medical equipment that needs to be rented, the processing unit 41 can generate rental request information or provisional rental reservation information for generating equipment rental information 324 from the electronic medical record information 420. On the other hand, when the electronic medical record information 420 does not contain information directly indicating such medical equipment, the processing unit 41 can select medical equipment corresponding to the symptom name, etc., according to predetermined rules, and generate rental request information or provisional rental reservation information.

[0135] Furthermore, the processing unit 41 can determine whether to generate loan request information or provisional loan reservation information according to predetermined rules, and can generate loan provisional reservation information if the treatment is more than a predetermined period of time in the future, such as one month or one week, and can generate loan request information in other cases. Alternatively, the processing unit 41 can generate loan request information for medical equipment related to a confirmed treatment, and can generate loan provisional reservation information in other cases.

[0136] (Electronic Medical Record Information 420) 5 is a table showing an example of electronic medical record information 420 stored in the electronic medical record system 40 of FIG. 4. As described above, the electronic medical record information 420 can include information requested for registration as medical record registration request information. For example, the electronic medical record information 420 can include a medical record ID, a patient ID, a patient name, symptoms, treatment (including surgery, medication, etc.), the date of the treatment, the location of the treatment, the necessity and date of hospitalization, the person scheduled for treatment, etc. The electronic medical record information 420 can also include information indicating the prognosis, that is, information indicating the progress of symptoms after treatment.

[0137] Here, the symptoms can include the name of a disease (disease name), an image showing the location of the disease, and the like. In the example table of FIG. 5, a link indicating the storage location of a file showing this image is described. In addition, in the case of surgery, the planned treatment person can be a staff member such as the surgeon or a team of staff members. Note that in FIG. 5, the planned treatment people are illustrated as users U1 and U2 who perform the registration operation of the electronic medical record information 420, that is, as planned users who arrange for transportation and collection. However, for the sake of simplicity, the planned treatment people may be different from the planned user, and the person who performs the registration operation of the electronic medical record information 420 may not be the planned user or the planned treatment person. Here, the planned treatment people and planned users are examples of staff information indicating at least one of the staff member who will use the medical device and the group to which the staff member belongs (for example, a group classified by ward, etc.).

[0138] The electronic medical record information 420 can include information that should be included in a normal medical record, not limited to the example in Figure 5. Furthermore, when medical equipment is required for surgery or other treatment, the electronic medical record information 420 can also include information that directly indicates the medical equipment.

[0139] (Equipment rental information 324, provisional reservation information 325, and delivery item information 126) An example of processing by the transport system 1 according to this embodiment will be described below, taking as an example a case where the information shown in Fig. 6 is stored as the equipment rental information 324 and the provisional reservation information 325. Fig. 6 is a table showing an example of the equipment rental information 324 and the provisional reservation information 325, and Fig. 7 is a table showing an example of the transported item information 126. Figs. 8 and 9 are diagrams showing examples of the movement route of a mobile robot.

[0140] As illustrated in FIG. 6, the equipment rental information 324 and provisional reservation information 325 may include the rental equipment's ID (equipment management number), name, whether maintenance is required, type of maintenance technician (or technician), destination (location of use), planned user, start time of use, and end time of use, as well as information indicating whether the equipment is officially rented or provisionally reserved. This information can be linked by the rental management number and managed as a table, as illustrated in FIG. 6. The start time of use and end time of use included here refer to the planned start time and planned end time of use, respectively. However, for equipment that has been started to be used and equipment that has been returned, these can be updated to the actual start time and end time of use, respectively. Such updates improve the accuracy of predictions that utilize the learning results. The equipment rental information 324 and provisional reservation information 325 can be distinguished by the information indicating whether the equipment is officially rented or provisionally reserved.

[0141] The delivery destination indicates the delivery destination (place of use) of the rental equipment, and can be extracted from the rental request information along with the start and end times of use. The intended user indicates the person who will use the rental equipment. For example, the intended user can be the name or ID of a patient, or the name and ID of a staff member such as a nurse or doctor. Of course, the intended user may include information on both patients and staff. The information on whether maintenance is required and the type of maintenance person (or maintenance person) can be information indicating whether the rental equipment requires maintenance (in this example, required or optional), and information indicating the type of maintenance person who will perform maintenance (or information indicating the ID and name of the maintenance person).

[0142] As described above, the equipment rental information 324 and the provisional reservation information 325 are generated based on the rental request information and the rental provisional reservation information, respectively, but at this time, they are generated by also referring to the equipment information 323 and the mechanic information 322. Note that the information on the mechanic type or the mechanic in the mechanic information 322 and the equipment rental information 324 is required when notifying the mechanic, and is therefore unnecessary in examples where notification is not performed.

[0143] As illustrated in FIG. 7 , the transported item information 126 may include the equipment control number, name, whether maintenance is required, the type of maintenance person (or maintenance person) indicating the notification destination, the transport origin, the transport destination, the planned user, the robot ID of the transporter, the status, the start time of use, and the end time of use. The transported item information 126 does not include information equivalent to the provisional reservation information 325. As illustrated in FIG. 7 , this information can be linked by the transport control number and managed as a table. Of course, the start time of use and the end time of use included here refer to the planned start time and planned end time of use, respectively. However, for equipment whose status is "Transported" that has started to be used and equipment whose status is "Returned," these can be updated to the actual start time and end time of use, respectively. Such updates can improve the accuracy of predictions that utilize the learning results. Furthermore, as described above, the status can also include information indicating whether the transport is for loan or return.

[0144] The origin of transport indicates the location where the mobile robot 20 will load the rental equipment. The destination of transport indicates the delivery destination (location of use) of the rental equipment. Note that, although an example is given in which there is one storage location serving as the origin of transport, it goes without saying that the number of storage locations is not limited to one, and the number of destinations is not limited to two. However, the origin of transport and destination of transport item information 126 become the rental location and return location, respectively, when the equipment is returned. The intended user indicates the person who will use the rental equipment. For example, the intended user is the name or ID of a patient. Alternatively, the intended user may be the name or ID of a staff member such as a nurse or doctor. Of course, the intended user may include information on both the patient and the staff member.

[0145] The transport item information 126 can be generated based on the transport request information as described above. Therefore, the transport item information 126 can be generated based on information including the equipment rental information 324 (and information about other transport items) and the mobile robot 20 determined based on the equipment rental information 324 and taking into account the task execution efficiency. The transport item information 126 can also be generated by the route planning unit 115 based on information obtained by the route planning unit 115 about the returned rental equipment. Specifically, the transport item information 126 about the returned rental equipment can also be generated based on information including the rental equipment to be returned related to the completion time prediction result input to the trained model 124 by the route planning unit 115 and the mobile robot 20 determined based on the acquired collection route and taking into account the task execution efficiency.

[0146] In the transported item information 126, the robot ID is the ID of the mobile robot 20 in charge of transporting the rental equipment. The robot ID is set based on a route plan that takes into account the efficiency of task execution. The status is information that indicates whether the rental equipment is before transport, in transport, or already transported. The status is updated when the mobile robot 20 loads the rental equipment and when receipt of the rental equipment is completed.

[0147] The transported item information 126 is then transmitted to each mobile robot 20 in charge of transporting the rental equipment. For example, the transported item information 226 of a mobile robot 20 includes information about the rental equipment that the mobile robot 20 is responsible for transporting. In other words, the transported item information of the rental equipment with the robot ID "BBB" does not need to be transmitted to the mobile robot 20 with the robot ID "AAA".

[0148] The transportation of the rental device E001 in Figures 6 and 7 will be described with reference to Figures 8 and 9. Note that in Figures 6 and 7, for convenience, the time is displayed as the current day, but in reality, the device is managed by date and time (year, month, date, and time). This is because, for example, some devices may be rented for several days or several months. Since transportation usually starts from storage location 800 (S001), Figure 7 shows an example in which such a route is set. Furthermore, the route itself is determined by the route planning unit 115 as described above, and is set in the corresponding mobile robot 20.

[0149] For the transport management number 001, as shown in FIG. 8, the mobile robot 20 (robot ID: AAA) first moves from pass point M1, which indicates the current time, toward pass point M2, which is the storage location 800 of the rental device E001. After that, the mobile robot 20 receives the rental device E001 at the storage location 800, and then moves through pass points M3 and M4 in order, taking route R to the destination G001 (M5). At the destination G001, the prospective user U001 will receive the rental device E001. After that, the mobile robot 20 can move as needed for other tasks.

[0150] The rental device E001 will be used at the delivery destination G001 until the end of use time of 15:30. However, the end of use time described here is a forecast and can be updated according to the output result from the trained model 120. Alternatively, this end of use time can be set as an output result predicted and output by the trained model 120 at the time of description. Thereafter, the rental device E001 will be returned, but since the rental device E001 is a device that requires maintenance, the return destination can be, for example, storage location 800.

[0151] In this case, the route planning unit 115 inputs the predicted end time of use of the rental device E001 to be returned from the transport source G001 to the storage location 800 into the trained model 124 to acquire a collection route. Furthermore, the route planning unit 115 determines the mobile robot 20 that will perform the collection, for example, a mobile robot 20 that is located near the transport destination G001. When returning the rental device E001, another mobile robot 20 (for example, robot ID: BBB) can be used. Note that the decision to return the rental device E001 can be made by a staff member such as a medical staff member, and a request for transportation to the storage location 800 can also be made. On the other hand, rental devices that do not require or require maintenance can be transported to the next destination and used. In this case, a staff member such as a medical staff member will determine the next destination and request transportation to the next destination.

[0152] In this manner, the mobile robot 20 that returns the rental device E001 and the recovery route are determined. In this case, as illustrated by route R in FIG. 9, the mobile robot 20 moves from pass point M1, which indicates the current time, toward the usage location G001 of the rental device E001, and receives the rental device E001 at the usage location G001. At the usage location G001, a user such as a prospective user U001 loads the rental device E001 onto the mobile robot 20. After receiving the rental device E001, the mobile robot 20 returns it to the storage location 800, and the route R in FIG. 9 and the subsequent route leading to the completion of the return can be acquired and determined as a recovery route by the route planning unit 115. Thereafter, the mobile robot 20 can move as needed for other tasks. The same applies to the transportation and return of the rental device E002 and other rental devices in FIGS. 6 and 7.

[0153] (Transportation process of this embodiment) An example of the transport process of this embodiment in the transport system 1 as described above will be described with reference to Fig. 10 and Fig. 11. Fig. 10 is a schematic diagram for explaining an example of the transport process in the upper management device 10 of Fig. 2, and Fig. 11 is a diagram showing an example of a collection route acquired in the transport process of Fig. 10.

[0154] In the transportation system 1 according to this embodiment, as described above, the electronic medical record information 420 is stored (registered) in the electronic medical record system 40, and information on some or all of the items of the electronic medical record information 420 can be transmitted to the upper management device 10 or obtained from the upper management device 10.

[0155] Furthermore, in the transport system 1 according to this embodiment, as described above, management information including the rental schedule (including the start time and end time of use), the location of use, and the inventory status is stored (registered) for each rental device transported as a transported item by the mobile robot 20. This management information can be stored in the memory unit 32 of the equipment rental system 30 as part or all of the equipment rental information 324 and tentative reservation information 325, and can also be stored in the memory unit 12 of the upper management device 10 as part or all of the transported item information 126.

[0156] As shown in Figure 10, the end time prediction processing unit 110 inputs loaned equipment data such as transported item information 126 and electronic medical record data such as electronic medical record information 420 into the trained model 120 stored in the memory unit 12, and obtains end time prediction results, which are prediction results (predicted values) predicting the end time of use of each medical device from the trained model 120.

[0157] Here, the transported item information 126 is exemplified as the on-rent equipment data input when predicting the completion time. This is because the transported item information 126 includes information about equipment currently being transported, if any, and information about equipment that has already been transported, if any. In this case, however, the information about medical equipment that has been returned in the transported item information 126 has been deleted by updating, or the completion time prediction processing unit 110 removes the information about the returned medical equipment before inputting it to the trained model 120. Note that the returned medical equipment here can include medical equipment that has begun to be returned, for example, by being loaded onto the mobile robot 20. Furthermore, if the transported item information 126 includes an item that is not to be rented, the completion time prediction processing unit 110 removes that information before inputting it to the trained model 120.

[0158] Alternatively, the currently rented equipment data input when predicting the end time can be, for example, the equipment rental information 324 in the equipment rental system 30. The end time prediction processing unit 110 receives, for example, the data in the table of FIG. 6, removes information related to tentative reservations, and then inputs the data to the trained model 120.

[0159] Furthermore, the on-rental equipment data input during completion time prediction is not limited to data indicating existing medical equipment that has not been returned or whose return has not yet begun, i.e., data indicating a list of loaned equipment, as described above. For example, the on-rental equipment data input during prediction may be one or more items designated by a staff member from the user terminal 400, by referring to the equipment rental information 324 in the equipment rental system 30 or the transported item information 126 in the memory unit 12. In this case, the completion time prediction processing unit 110 inputs information regarding the on-rental medical equipment designated by the user terminal 400 into the trained model 120.

[0160] In either input example, the information about the medical equipment on loan that is input into the trained model 120 can include the start time of use, and in particular, this start time of use may be the scheduled start time of use, but more accurate predictions can be made if the information is updated at the time use begins.

[0161] Furthermore, the input electronic medical record data is assumed to describe information indicating the need for use of a medical device, and this information can refer to information indicating the surgery required for the patient, information indicating the patient's symptoms, information indicating treatment for the patient, information indicating the medical device itself, or a combination of multiple pieces of information. Therefore, when information indicating the need for use of a medical device is implicitly or directly described in the electronic medical record information 420 as exemplified in the process of Figure 5, the input electronic medical record data corresponds to the data in the electronic medical record information 420 itself or data in the electronic medical record information 420 that includes that description.

[0162] However, the electronic medical record data input to the end time prediction processing unit 110 for end time prediction can be the current electronic medical record data, that is, data from the electronic medical record information 420 excluding information about medical equipment that has been returned after loaning has ended.

[0163] The information predicted by the end time prediction processor 110 for the end time of use of a certain medical device may include, for example, information identifying the medical device, information identifying the location of use, and the predicted end time and date of use. For example, the end time prediction processor 110 can output an infusion pump E002, its location of use G001, and "2021 / 10 / 5 14:00" as the predicted end time and date of use. Note that the predicted end time and date shown in the example is the same as "2021 / 10 / 5 14:00" described as the planned end time in the tables of FIG. 6 or FIG. 7. However, because this is a prediction result, the resulting prediction may differ from the planned time in these tables. Furthermore, while only one medical device is shown as an example here, as described for the loaned device data input during prediction, the end time prediction processor 110 can simultaneously predict the end times of use of other loaned medical devices and output the results.

[0164] Furthermore, the type of information predicted as the end time prediction result can be changed by performing predetermined processing such as changing the settings of output parameters when generating trained model 120. For example, if the data input to trained model 120 includes the actual start time of use, the output value can be the elapsed time from the start time of use, and end time prediction processing unit 110 can finally add them together to obtain the predicted end time of use.

[0165] Here, the trained model 120 will be described. As illustrated in FIG. 10, the trained model 120 is a model trained by machine learning by inputting first training data, which is past data, into the untrained model 120a. Specifically, as described as the processing in the end time prediction processing unit 110, the trained model 120 is a model trained by machine learning using the first training data to input loaned device data indicating medical devices currently on loan and electronic medical record data containing information indicating the need for use of the medical devices, and output end time prediction results. The trained model 120 can be updated by re-training as appropriate.

[0166] The first learning data is training data including rental record data and electronic medical record data, as exemplified by the equipment rental information 324 and the electronic medical record information 420. However, the equipment rental information 324 and the electronic medical record information 420, which are exemplified as the first learning data, may be stored separately as past data, as will be described below.

[0167] The rental history data included in the first learning data may be data indicating the rental history, including the history of when a medical device was rented out and when its use ended. The history of when the use of a medical device ended may include information indicating the time when the use of the medical device ended, and information indicating the time when the medical device was returned may be used instead. This information in the rental history data corresponds, for example, to information indicating the end time of treatment in the electronic medical record data.

[0168] Therefore, the rental history data includes, for example, information about medical equipment in the equipment rental information 324 that has been transported and whose destination is a storage location (storage location 800 (S001) in FIG. 8), and may also include information about medical equipment that is being transported if the destination is a storage location. In this way, the rental history data is data that includes information indicating the history of rental and return, or the history of return and the history of return that has begun. For example, the rental history data can be data about medical equipment in the equipment rental information 324 that has been returned (and return has begun), and in reality, this data can be stored as past history separately from the equipment rental information 324. Here, the trained model 120 can be configured to output a prediction result that takes into account the time required to prepare for return as the end time prediction result. In this case, the end time of use indicated by the rental history data can be, for example, the time when preparation for return is completed.

[0169] An example of rental history data will be described with reference to FIG. 6. For example, the rental history data may not include the record corresponding to the provisional reservation information 325 (the record with rental management number 003 in this example) among the equipment rental information 324 and provisional reservation information 325 shown in FIG. 6, and the maintenance person type (or maintenance person) is not required. Furthermore, the rental history data may not include the user or maintenance person (the person in charge of maintenance) who actually used or performed the equipment corresponding to the planned user or planned maintenance person, nor may it include whether maintenance is required. However, by including the user or maintenance person and whether maintenance is required, it becomes possible to make predictions that take into account the progress or delay of collection, etc., caused by the user or maintenance person, and the time when the equipment is unavailable for rental if maintenance is required. The rental history data can also be obtained by separately accumulating information on completed returns from the transported item information 126.

[0170] However, the rental history data may also include provisional reservation information 325. Specifically, as described above, the equipment rental system 30 may be equipped with a reservation system for provisionally reserving rental of medical equipment, and in such a configuration, the rental history data may include data in which information indicating medical equipment provisionally reserved in this reservation system, exemplified by part or all of the provisional reservation information 325, is associated with information (part of the equipment rental information 324) indicating the actual rental history based on the provisional reservation (the rental history of at least medical equipment for which return has begun). This allows the trained model 120 to predict in advance the end of use of medical equipment, even in response to provisional reservations in the equipment rental system 30.

[0171] Furthermore, the equipment rental system 30 may determine whether to rent a provisionally reserved medical device or a medical device for which a new rental request has been made, based on information indicating provisionally reserved medical devices that are not currently in a rental state, as exemplified by the provisional reservation information 325, and the acquired end time prediction result. Here, determining whether to rent refers to determining a rental schedule that avoids overlapping rentals. Furthermore, the trained model 120 can be configured to output a prediction result that takes into account the time required to prepare for return as the end time prediction result, as described above, and can also be configured to predict and output the time required for return. In either configuration, rentals can be performed according to a more efficient schedule, especially when used in conjunction with a configuration for determining a rental schedule.

[0172] The electronic medical record data included in the first learning data contains the same information items as the electronic medical record data input when predicting the end time, but is not current electronic medical record data but past electronic medical record data that describes information indicating the need for use of the loaned medical equipment. Here, too, the loaned medical equipment may include not only medical equipment that has been returned but also medical equipment that has begun to be returned (medical equipment that is being transported for return).

[0173] Returning to the description of the prediction process by the end time prediction processing unit 110, let us now turn to the following. The end time prediction processing unit 110 can also be configured to notify the equipment rental system 30 of the acquired end time prediction result via the communication unit 14. This notification can be executed by a notification processing unit (not shown) provided in the end time prediction processing unit 110 via the communication unit 14. The notification content includes the target medical equipment and the predicted end of use date and time, and if predictions are made for multiple medical equipment, information about all of them may also be included.

[0174] Upon receiving this notification, the equipment rental system 30 notifies at least one of the staff members in charge, such as the administrator, the rental staff member, and the staff member in charge of the return work. The notification destination may be registered in advance in the storage unit 32 as an email address, a short message number, or the like. The equipment rental system may be configured to include these notification destinations, in which case these notification destinations will be the notification destinations of the completion time prediction processing unit 110.

[0175] With this configuration, the host management device 10 can predict in advance when medical equipment will no longer be in use in the equipment rental system 30, rather than having staff at the rental location make the decision, and obtain the predicted results. In fact, when hospitals rent out medical equipment from storage locations to each location where it will be used, demand for medical equipment cannot be predicted in advance, and inventory shortages occur when demand suddenly increases. One factor that can cause inventory shortages of rental medical equipment is that the rental use of the medical equipment has ended at the rental location, but staff at the rental location still have to decide whether to transport the equipment for return, resulting in a delay.

[0176] However, the upper management device 10 in this embodiment can predict in advance the end of use of a medical device based on past rental history data and electronic medical record data. For example, the usage period of a medical device is actually determined by staff such as doctors or nurses based on the patient's symptoms and the progress of treatment. Therefore, the end of use period can be predicted in advance based on past electronic medical record data and rental history data, which is past usage history. In particular, for procedures such as intravenous drips, whose usage period can be estimated to some extent based on the fluid volume and injection rate, the end of use period of a medical device used in that procedure can be accurately predicted in advance. On the other hand, even for medical devices used in procedures whose required treatment time is difficult to estimate, accurate advance predictions can be made by using the trained model 120, which has learned many cases. Furthermore, to constantly improve prediction accuracy, it is recommended to retrain accumulated data and update the trained model 120.

[0177] As a result, the equipment rental system 30 can take measures to shorten the waiting time from the end of use until the return is completed as much as possible, and as a result, the waiting time can be suppressed, that is, shortened as much as possible, which can lead to a reduction in inventory shortages. As a measure for this, for example, the staff member who is notified as described above can immediately arrange transportation for the return using the user terminal 400, or the upper management device 10 can automatically set up transportation for the return immediately.

[0178] As described above, the loan history data, loaned device data, or electronic medical record data may include staff information indicating at least one of the staff member who will use the medical device and the group to which the staff member belongs. Although it is expected that the time required for treatment will actually vary depending on the staff member or group, by using staff information in this way, the upper management device 10 can make predictions taking into account the actions of the staff member, and more accurately predict when the medical device will end use in advance.

[0179] Furthermore, the completion time prediction processing unit 110 passes the acquired completion time prediction result to the route planning unit 115, and uses it to generate a collection route for collecting the medical devices as return items in order to reduce the above-mentioned retention time. This collection route will be described.

[0180] As described above, the transport system 1 according to this embodiment can obtain an end time prediction result. Then, in order to generate a collection route, which is a transport route at the time of collection, the route planning unit 115 inputs this end time prediction result into the trained model 124 as shown in Fig. 10 to obtain a collection route for collecting the medical equipment to be collected in the end time prediction result as a return item. The input end time prediction result can include, for example, information indicating the lender indicating the collection point, the start date and time of collection, the staff in charge of collection work, such as loading the loaned equipment onto the mobile robot 20, and of course, can also include information indicating the equipment to be collected.

[0181] Here, the trained model 124 will be described. As illustrated in FIG. 10, the trained model 124 is a model trained by machine learning by inputting second training data, which is past data, into the untrained model 124a. Specifically, as described as the processing in the route planning unit 115, the trained model 124 is a model trained by machine learning to input an end time prediction result, which is a result of predicting the end time of use of a rental device currently on loan, using the second training data, and output a collection route for collecting the rental device currently on loan as a return item using the mobile robot 20. The trained model 120 can be updated by re-learning as appropriate.

[0182] The second learning data is training data including collection record data and collection route data, as exemplified by the transported item information 126 and the route planning information 125. However, the route planning information 125 and the transported item information 126, which are exemplified as the second learning data, may be data that has been stored separately as past data, as will be described below.

[0183] The collection record data included in the second learning data may be data showing the collection record, including the end-of-use time when the use of the rental equipment has ended after rental and the collection completion time when the rental equipment is collected as a returned item. Here, the end-of-use time may be the end-of-use date and time. Furthermore, the collection completion time may be the collection completion date and time, and may be the date and time when the rental equipment is transported to a storage location or the next rental location (transport completion date and time), or may be the transport start date and time when such transport began, for example.

[0184] Therefore, the collection history data can include, for example, information on medical devices that have been transported and whose destination is a storage location (storage location 800 (S001) in FIG. 8) among the medical devices indicated in the delivery item information 126, and information on medical devices that have been transported and for which there exists another record in which the destination of that record is the delivery source. In this way, the collection history data is data that includes information indicating the history of loaned and returned medical devices. For example, the collection history data can be data on medical devices that meet such conditions in the delivery item information 126, and in reality, this data can be stored as past history separately from the delivery item information 126.

[0185] An example of collection history data will be described with reference to Fig. 7. As collection history data, for example, the type of maintenance person (or maintenance person) of the transported item information 126 shown in Fig. 7 is not necessary. Furthermore, the collection history data does not need to further include the user who actually used the item in correspondence with the planned user, but including the user makes it possible to make predictions that take into account progress or delays in collection, etc., caused by the user. In this case, predictions can be made by including information indicating the staff member in charge in the input end time prediction result.

[0186] The recovery route data included in the second learning data may be data indicating the recovery route taken by the mobile robot 20 to recover the rental equipment. This recovery route is exemplified by the route planning information 125 and may include passing points including the starting point and the destination. The starting point here is the rental destination, and the destination is the storage location, maintenance location, or next rental destination.

[0187] Returning to the description of the processing by the route planning unit 115. The route planning unit 115 inputs the end time prediction result into the trained model 124 as described above to obtain a collection route, and determines the mobile robot 20 that will collect the rental equipment along the collection route. In other words, the route planning unit 115 executes a process to determine the mobile robot 20 to be controlled in order to collect the rental equipment. This determination can be executed by the robot determination unit 115a provided in the route planning unit 115.

[0188] For example, the robot determination unit 115a can determine the mobile robot 20 based on a predetermined condition. The predetermined condition can be, for example, a condition that the mobile robot 20 is located at or near the origin of transportation, a condition that the degree of deterioration of the mobile robots 20 is uniform, or the like, so that the task can be executed efficiently as a whole system.

[0189] With this configuration, the upper management device 10 can efficiently reduce the amount of time that a device to be rented in the device rental system 30 stays there from the time that use ends until the mobile robot 20 completes returning it.

[0190] This effect will be explained in detail. When a hospital lends medical equipment from a storage location to each use location, it is impossible to predict the demand for medical equipment in advance, resulting in inventory shortages when demand suddenly increases. One cause of inventory shortages of loaned equipment, or a management issue for loaned equipment, is the occurrence of delays due to staff at the loaning location deciding whether to return the equipment after its use has ended. Taking the example of medical equipment as an example, the reason for the decision being made by staff at the loaning location is that, depending on the ward, there are situations where immediate loading can be performed and situations where it cannot be performed due to staff shortages or the presence of emergency patients. However, the upper management device 10 determines the collection route using a trained model 124 that has learned such situations through machine learning, thereby minimizing the delays.

[0191] Furthermore, when a mobile robot is used to collect returned items, the robot may pass through routes where general patients are present, and the congestion status of the route changes depending on the time of day. For example, a route that passes through areas where many people are waiting for tests takes longer to travel than a route with few or no people waiting for tests, and therefore collection takes longer. Furthermore, the workload at the collection center also changes depending on the time of day, so the collection time varies depending on the route passed and the order of collection points. Furthermore, when a mobile robot is used to collect returned items, it is desirable to minimize deterioration and achieve power saving as much as possible.

[0192] In contrast, the host management device 10 uses mobile robots 20 to collect returned items, but can calculate collection routes taking into account such differences in collection times, such as by automatically selecting a collection route based on a predicted end-of-use date for the equipment to minimize the time required for collection. Furthermore, the host management device 10 can at least one of appropriately selecting a collection route and appropriately selecting a mobile robot 20 to perform collection, taking into account the degree of deterioration and power consumption of the mobile robot 20. The former can be achieved by using collection history data that takes into account the degree of deterioration and power consumption of the mobile robot 20 when setting the collection route.

[0193] In this way, the host management device 10 can reduce the staying time by using a mobile robot and a recovery route that are efficient in terms of recovery time, power consumption and wear of the mobile robot 20. In other words, the host management device 10 can shorten the recovery time as much as possible, suppress deterioration of the mobile robot 20, and save power, thereby efficiently reducing the staying time.

[0194] Furthermore, because the decision to end use of a medical device is made by a doctor, nurse, or other staff member, the end-of-use prediction may be inaccurate, and the actual collection time may differ from the time the mobile robot 20 is dispatched. However, the upper management device 10 can reduce such prediction errors in the end-of-use prediction by updating the trained model 124, which is an algorithm for planning collection routes, based on past collection performance data, thereby making it possible to set an appropriate collection route.

[0195] Here, the equipment to be rented can be medical equipment as shown in the example, but is not limited to this. However, by setting the rental equipment to be medical equipment, the upper management device 10 can take into account the usage pattern of the medical equipment and efficiently reduce the waiting time from the end of use of the medical equipment to the completion of return by the mobile robot. Note that if the rental equipment is not medical equipment, a system that manages some information related to the target equipment, such as a system that compiles and manages questionnaires investigating rental demand for the equipment, can be provided instead of the electronic medical record system 40 to obtain the end time prediction results.

[0196] The trained model 124 may also be a model trained by machine learning to output a collection route for collecting multiple devices. Referring to FIG. 11 , an example of the output result of the trained model 124 will be described, in which the end time prediction result input to the trained model 124 when predicting a collection route includes information indicating the rental destination indicating the collection point, the start date and time of collection, and the staff member in charge of the collection work, such as loading the rental device onto the mobile robot 20. More specifically, an example of the output result of the trained model 124 will be described, taking as an example a case where the end time prediction result for the first device used in ward A and the end time prediction result for the second device used in ward B are both 16:00 on October 5, 2021.

[0197] In this case, the recovery route illustrated in Figure 11 can be output from the trained model 124. The recovery route in Figure 11 is a route in which mobile robot a, one of the mobile robots 20, departs from its current location at 16:00 on 2021 / 10 / 5, heads toward ward A, recovers the first device in ward A, moves to ward B after recovery, recovers the second device in ward B, and heads toward a return location (e.g., a storage location). In the example of Figure 11, the recovery route also includes information indicating estimated start times and estimated required times at each timing.

[0198] However, the value output from the trained model 124 may be, for example, only an estimated value of the required time. In the collection route illustrated in FIG. 11, the collection start time for the first ward can be calculated as the sum of the departure time of the mobile robot 20 and the travel time to the first ward, and the collection start times for subsequent wards can be calculated as the sum of the collection start time for the previous ward, the predicted required collection time, and the travel time between the wards. In either case, the travel time can be calculated based on the travel distance and the travel speed of the mobile robot 20, and more specifically, based on the travel distance and travel speed for each section of the route.

[0199] This allows the host management device 10 to acquire an efficient collection route for collecting multiple devices, thereby more efficiently reducing the waiting time from when the multiple devices have finished being used until they are returned by the mobile robot 20. In other words, with this configuration, when collecting rental devices from multiple locations within a medical institution using the mobile robot 20, the host management device 10 can minimize the time required for collection by appropriately selecting a collection route.

[0200] The collection record data may also include first information, which is at least one of the time required for the mobile robot 20 to collect the items, the distance traveled by the mobile robot 20, and the power consumption of the mobile robot 20. In this case, the trained model 124 may be generated as a machine-learned model that outputs a collection route that minimizes the value indicated by the first information. For example, the trained model 124 may be generated using, as training data, a data set in which the difference between the planned collection route and the actual collection record is less than or equal to a predetermined value for the first information. Furthermore, the collection route output in this case may also include a predicted value of the first information, as exemplified by the required time in FIG. 11 (however, FIG. 11 illustrates individual required times).

[0201] In this example, the host management device 10 obtains a collection route for the loaned device from the predicted end-of-use time, taking into consideration past collection performance data including the first information, and determines the mobile robot 20 that will be responsible for collection. Therefore, the host management device 10 configured as described above can collect the device along a collection route that can be considered efficient from the perspective of at least one of time, travel distance, and power consumption, and as a result, can efficiently reduce the residence time from the end of use of the device until the mobile robot 20 completes its return from the above perspectives.

[0202] Alternatively, the trained model 124 can be generated as a machine-learned model that outputs a collection route for collecting multiple devices so as to minimize the first information when the collection times for multiple devices at collection points are within a predetermined time. For example, the trained model 124 can be generated as a model that outputs a collection route that minimizes the value indicated by the first information when the predicted return times at multiple points are within a predetermined period. Furthermore, in this case, the output collection route can also include the predicted value of the first information.

[0203] In this example, the host management device 10 obtains a collection route that can collect multiple devices for the loaned devices, taking into consideration past collection performance data including the first information from the predicted end-of-use time for the devices, and determines the mobile robot 20 that will be responsible for collection. Therefore, the host management device 10 in this configuration can collect multiple devices using a collection route that can be considered efficient from the perspective of at least one of time, travel distance, and power consumption, and as a result, can efficiently reduce the residence time from the end of use of the multiple devices until the mobile robot 20 completes returning them from the above perspectives.

[0204] Furthermore, regardless of whether or not a condition such as within a predetermined time is adopted, the first information can be changed as appropriate by user settings, for example, from travel distance to power consumption, thereby allowing the user to set a collection route under desired conditions. In this case, such changes can be made by generating a trained model 124 for each combination of the first information, or by including information specifying the first information as one of the input parameters to the trained model 124. Furthermore, if the output collection route includes a predicted value of the first information, such a change corresponds to a change in the output value.

[0205] Although an example has been given in which the end time prediction result is input from the end time prediction processing unit 110 to the route planning unit 115, the present invention is not limited to this, and the end time prediction result obtained by other means may be input to the trained model 124. In other words, the end time prediction result may be a result of execution by another system, or in a simpler example, the end time predicted by a staff member such as a medical staff member may be input from the user terminal 400, etc.

[0206] Next, an example of the flow of the transport method according to this embodiment will be briefly described with reference to Fig. 12. Fig. 12 is a flowchart showing an example of the transport method according to this embodiment.

[0207] First, the upper management device 10 acquires the transported item information 126 by reading it from the storage unit 12 (S1001), and acquires the electronic medical record information 420 by receiving it from the electronic medical record system 40 (S1002). The order of steps S1001 and S1002 does not matter. Furthermore, the information acquired in both steps is at least information related to medical equipment that has not been returned or has not yet started to be returned, as described above.

[0208] Next, the upper management device 10 inputs the acquired equipment rental information 324 and electronic medical record information 420 into the trained model 120 to acquire the end time prediction result (S1003). Also, in step S1001, the upper management device 10 can acquire the equipment rental information 324 by receiving it from the equipment rental system 30, and input it into the trained model 120 in place of the transported item information 126 in step S1003 to acquire the end time prediction result.

[0209] Following step S1003, the host management device 10 inputs the end time prediction result into the trained model 124 and acquires a collection route (S1004). Note that the collection route acquired here may be for multiple devices. The host management device 10 then determines the mobile robot 20 that will perform collection along this collection route (S1005), and ends the process. This process can be performed for all devices currently on loan, but can also be performed, for example, for each device currently on loan. After step S1005, the host management device 10 controls the determined mobile robot 20 to perform collection.

[0210] (Learning System) 13 and 14, an example of the configuration of a learning system that generates the trained model 124 described above, and an example of processing in the learning system (an example of a learning method) will be described. FIG. 13 is a block diagram showing an example of the configuration of a learning system that generates the trained model 124 used in the upper management device 10 of FIG. 2. FIG. 14 is a schematic diagram showing an example of the trained model 124 generated by the learning system 80 of FIG. 13. Note that the untrained model 124a has the same configuration as shown in FIG. 14, but is a model in which the weighting coefficient is not determined.

[0211] 13 can include a control unit 81, an input unit 82, and a storage unit 83. The learning system 80 can be constructed using, for example, a computer such as a PC for AI (Artificial Intelligence) learning. However, the learning system 80 can be configured as a single device or with its functions distributed across multiple devices.

[0212] The control unit 81 controls the entire learning system 80. The control unit 81 can be realized, for example, by an integrated circuit, and can be realized, for example, by a processor, a working memory, and a non-volatile storage device. A control program executed by the processor is stored in this storage device, and the processor reads the program into the working memory and executes it, thereby fulfilling the functions of the control unit 81. The control program includes a learning program that executes learning. Note that the storage device can also utilize the memory unit 83.

[0213] The input unit 82 can be configured with at least one of an interface for inputting data and a communication interface for inputting data via communication from an external device. The input unit 82 inputs a data set of learning data (teacher data) 84 required for learning, and stores it in the memory unit 83 so that it can be referenced during learning. The memory unit 83 can store this teacher data 84, and can also store a learning model 85 as an unlearned model.

[0214] In the processing by the learning system 80, the control unit 81 inputs the training data 84 into a learning model 85 as an untrained model, performs machine learning based on the training data 84, and converts the learning model 85 into the trained model 124. As described above, the training data 84 includes collection record data and collection route data, as exemplified by the past transported item information 126 and the past route plan information 125, respectively. The trained model 124 is generated as a machine-learned model that receives the completion time prediction result as described above and outputs a collection route. With this configuration, the trained model 124 can acquire a collection route that efficiently reduces the residence time from the end of use of the equipment until the mobile robot 20 completes its return.

[0215] The learning model 85 can use, for example, a neural network 124n as shown in Fig. 14. The neural network 124n shown in Fig. 14 includes an input layer 124na, a hidden layer (intermediate layer) 124nb, and an output layer 124nc, and can also include a value corresponding to the output layer 124nc as correct answer data 124nd. For simplicity of explanation, the intermediate layer 124nb will be described as one layer, but there may be two or more intermediate layers 124nb.

[0216] The input layer 124na includes input nodes that use the explanatory variables x1, x2, x3, ... as input parameters. In the hidden layer 124nb, a node indicated by a value y1 assigns a weighting coefficient w 1 11 The value multiplied by the input parameter x2 and the weighting coefficient w 1 21 The value multiplied by the input parameter x3 and the weighting coefficient w 1 31 In the node of the hidden layer 124nb indicated by the value y2, the weight coefficient w is applied to the input parameter x1. 1 12 The value multiplied by the input parameter x2 and the weighting coefficient w 1 22 The value multiplied by the input parameter x3 and the weighting coefficient w 1 32 The same applies to the other nodes in the hidden layer 124nb.

[0217] The output layer 124nc includes an output node having a target variable z1 as an output parameter. In the output layer 124nc, the output node indicated by the value z1 is assigned a weighting coefficient w 2 11 The value y2 multiplied by the weighting factor w 2 21 The sum of these values is calculated and compared with the value t1 of the corresponding correct answer data 124nd.

[0218] According to this comparison, each weighting coefficient is calculated so that the comparison result becomes small, and the untrained neural network 124n is generated as the trained model 124. In other words, when the performance data is given as the supervised answer data 124nd, the control unit 81 adjusts each weighting coefficient so as to minimize the error between the value of the output node z1 of the output layer 124nc and the value t1 of the corresponding supervised answer data 124nd, and generates the trained model 124 as a result.

[0219] The training data 84 used when generating the trained model 124 can be a data set including collection record data and collection route data, as exemplified by the transported item information 126 and the route plan information 125, respectively. For example, part of the information for each item included in this data set is input as input parameters x1, x2, x3, ..., and the information for the remaining items can be set as the value t1 of the correct answer data 124nd. To give a more specific example, as described above, the explanatory variables can be input as input parameters, such as the rental destination indicating the collection point, the start date and time of collection, and the staff member in charge of the collection work, such as the work of loading the rental equipment onto the mobile robot 20, and the information on the required time can be set as the objective variable and the value t1 of the correct answer data 124nd.

[0220] The trained model 124 thus generated is updated by updating the track record and setting the updated track record as the correct answer data 124nd, and adjusting each weighting coefficient so as to minimize the error between the value of the output node of the output layer 124nc and the corresponding track record. In other words, the trained model 85 as the trained model 124 can be retrained based on a newly prepared data set if retraining is required.

[0221] When using the data set in the above example, the upper management device 10 can acquire a collection route and update the learning model 124 as follows.

[0222] First, the host management device 10 predicts the end time of use of each device using the trained model 120, etc., and acquires the end time prediction result. Next, based on the acquired end time prediction result, the host management device 10 determines whether the predicted date and time of devices at multiple locations is within a predetermined time (for example, within 15 minutes), and if it is within the predetermined time, decides that one mobile robot 20 will head to collect the devices from the multiple locations. Then, the host management device 10 calculates a pattern for the collection order from the multiple locations using the concept of permutation.

[0223] Next, the host management device 10 executes the following process for each calculated pattern. That is, for the collection points included in the pattern, the neural network 124n is used to input the collection point, collection start date and time, and the responsible staff member to obtain the required time for the collection operation at that point as a value z1 in the output layer 124nc, thereby estimating this required time in advance. The host management device 10 then determines the collection route by selecting the collection order pattern that minimizes the estimated required time. After collection, the trained model 124 is updated by updating each weighting coefficient based on the actual value t1 for the estimated value z1.

[0224] Here, input parameters can be added / deleted as appropriate at the discretion of the person constructing the model, etc., in order to prevent a decrease in prediction accuracy due to spurious correlation, etc. Furthermore, when planning a route for the first time, it is expected that there will be insufficient data to obtain the output of the neural network 124n, and in such a case, the shortest route can be found by using, for example, the Dijkstra algorithm, and then planning can be performed.

[0225] Furthermore, the learning process for the trained model 120 differs only in its algorithm, training data, etc., and a similar learning system can be used.

[0226] In this case, the processing by the learning system 80 is performed by the control unit 81 inputting the training data 84 into the learning model 85 as an untrained model, performing machine learning based on the training data 84, and converting the learning model 85 into the trained model 120. As described above, the training data 84 includes rental history data and electronic medical record data, as exemplified by the past equipment rental information 324 and the past electronic medical record information 420, respectively. The trained model 120 is generated as a machine-learned model that inputs the currently rented equipment data and electronic medical record data as described above and outputs an end-of-life prediction result that predicts the end-of-life date of the medical device. With this configuration, the trained model 120 is capable of predicting the end-of-life date of the medical device in the equipment rental system 30 in advance.

[0227] The learning model 85 can use, for example, a neural network 124n as shown in FIG. 14. Hereinafter, for convenience, the trained model 120 will be described by replacing reference numerals 124, etc. with reference numerals 120, etc. In this case, the neural network 120n is an example of the learning model 85 for medical devices of one model or model number (control number). As such, target medical devices may be handled collectively or individually. In this case, a neural network 120n is prepared for each type of medical device or each model number of medical device, and machine learning can be performed. Then, during prediction, the on-rent device data can be used as information to determine which of the multiple neural networks 120n after machine learning is to be used.

[0228] In this way, the trained model 120 can be generated as a different trained model for each type or model of medical device and stored as a set of trained models. In this case, the end time prediction processing unit 110 uses the currently rented device data as information for determining which type or model of medical device to predict, and obtains the end time prediction result using the trained model corresponding to the type or model indicated by the information. This allows the upper level management device 10 to more accurately predict the end time of medical device use in advance by taking into account the time required for transporting, ending use, and preparing for return of each medical device. This configuration is beneficial because the end time varies for each medical device, and it is also beneficial because the accuracy of end time prediction is expected to vary for each medical device.

[0229] In neural network 120n, the output node indicated by value z1 in output layer 120nc is compared with value t1 of corresponding supervised data 120nd, and each weighting coefficient is calculated based on this comparison so that the comparison result becomes smaller, thereby generating untrained neural network 120n as trained model 120. In other words, when a track record is given as supervised data 120nd, control unit 81 adjusts each weighting coefficient so as to minimize the error between the value of output node z1 of output layer 120nc and value t1 of corresponding supervised data 120nd, and generates trained model 120 as a result.

[0230] The training data 84 used to generate the trained model 120 can be a data set including electronic medical record data and actual rental data, as described above. For example, information on each item included in the electronic medical record data is input as input parameters x1, x2, x3, ..., and information on each item included in the actual rental data can be set as the value t1 of the correct answer data 120nd. For example, the value of the correct answer data 120nd can include a value indicating period information indicating the end date and time of use, and at the time of prediction (operation), the value of the corresponding node in the output layer 120c indicates the end time prediction result. Furthermore, as described above, the input parameters can include information directly indicating a medical device, but even if they do not, they can include information implicitly indicating the medical device, such as symptoms or treatment.

[0231] Although a simple example of electronic medical record data is shown in FIG. 5 , more detailed information can include the following items. For example, electronic medical record data can include, as patient information, a patient ID and / or name, age, gender, and the like that identify an individual patient, but not all of them. Furthermore, electronic medical record data can include, as information about hospitalization among treatment information, the date and time of admission, the department at admission, the hospitalization ward, the attending physician, the attending nurse, the name of the illness (disease name), the purpose of admission, the date of examination, the name of the examination, the name of the surgery, and the date of the surgery. Furthermore, electronic medical record data can include, for example, medical classification (Activities of Daily Living (ADL)), at least one of a nursing care plan and a nursing progress chart, and at least one of a clinical path and a path status. Furthermore, electronic medical record data can include information directly indicating the use of medical devices, as described above, and can also include information indicating the number of days since a procedure such as surgery, information indicating the severity, information indicating the doctor's or other doctor's judgment, and the like. However, electronic medical record data is not limited to including all of the above items; it can include only some of them, or additional items can be added. In particular, to prevent a decrease in prediction accuracy due to spurious correlations, etc., the information items to be included in the electronic medical record data can be added or deleted as appropriate at the discretion of the person building the model.

[0232] Furthermore, as described above, the electronic medical chart data included in the first learning data and the electronic medical chart data input at the time of prediction can also include information indicating that medical staff, such as doctors or nurses, have made a decision on the use of the medical device. This allows the upper level management device 10 to more accurately predict in advance when the use of the medical device will end, taking into account the track record of medical staff's decisions on the use of the medical device.

[0233] (others) Some or all of the processes in the above-described prediction system, upper management device 10, mobile robot 20, equipment rental system 30, electronic medical record system 40, learning system 80, etc. can be implemented as a computer program. Such a program includes a set of instructions (or software code) that, when loaded into a computer, causes the computer to perform one or more functions described in the embodiments. The program may be stored on a non-transitory computer-readable medium or a tangible storage medium. By way of example and not limitation, computer-readable media or tangible storage media include random-access memory (RAM), read-only memory (ROM), flash memory, solid-state drive (SSD) or other memory technology, CD-ROM, digital versatile disk (DVD), Blu-ray (registered trademark) disc or other optical disk storage, magnetic cassette, magnetic tape, magnetic disk storage, or other magnetic storage device. The program may also be transmitted on a transient computer-readable medium or communication medium. By way of example and not limitation, transient computer-readable media or communication media include electrical, optical, acoustic, or other forms of propagated signals.

[0234] The present disclosure is not limited to the above-described embodiments, and can be appropriately modified without departing from the spirit of the present disclosure. The present disclosure also includes implementations in which the examples in the above-described embodiments are appropriately combined.

[0235] For example, while the above embodiment mainly describes a system in which a mobile robot autonomously moves within a hospital, the above-described transport system can transport items, including not only medical equipment but also rental equipment, as cargo in hotels, restaurants, office buildings, event venues, or complexes. In other words, the transport system according to the above embodiment can be used to collect rental equipment other than medical equipment. Furthermore, while the description is based on the premise of transporting equipment within a single facility, the system can also be applied to transport between multiple facilities if the mobile robot is capable of moving between multiple facilities.

[0236] Furthermore, the above-described transport system is not limited to the use of the mobile robot 20 illustrated as an example, and mobile robots of various configurations can be used instead of or in addition to it. Furthermore, although the above-described transport system uses an example of an autonomously mobile robot, it can also be constructed as a system for transporting transported objects using a mobile robot remotely controlled by an operator. In this case, information indicating the operator should be included in the learning data, and the operator should also be determined when the mobile robot is selected. [Explanation of symbols]

[0237] 1. Transport system 10 Upper management device 11. Processing unit 12 Storage section 13 Buffer memory 14 Communications Department 20 Mobile Robot 21 Processing unit 22 Memory section 23 Communications Department 24 Range sensors 25 Camera 26 Drive unit 27 Display section 28 Operation reception section 30 Equipment Rental System 31 Processing unit 32 Storage section 33 Buffer Memory 34 Communications Department 40 Electronic Medical Record System 41 Processing unit 42 Storage section 43 Buffer Memory 44 Communications Department 110 End time prediction processing unit 111 Robot control unit 115 Route Planning Department 120, 124 trained models 120a, 124a Untrained model 121 Floor Map 122 Robot Control Parameters 123 Robot Information 124n Neural Network 124na input layer 124nb, hidden layer (intermediate layer) 124nc output layer 124th correct data 125 Route Planning Information 126 Transport Information 211 Movement command extraction part 212 Drive control unit 221 Floor Map 222 Robot Control Parameters 226 Transport Information 311 Registration Department 312 Lending Planning Department 321 Floor Map 322 Maintenance Information 323 Device information 324 Equipment Rental Information 325 Tentative reservation information 400 User Terminals 411 Registration Department 420 Electronic Medical Record Information 600 Network 610 Communication Unit

Claims

1. A transport system in which a mobile robot transports equipment to be rented in an equipment rental system, a trained model that has been machine-learned to input an end time prediction result that is a result of predicting the end time of use of the loaned device using learning data including recovery record data that indicates recovery records including the end time of use of the device when it has ended and the recovery completion time when it was recovered as a returned item after the device has been loaned out, and recovery route data that indicates the recovery route along which the device was recovered by the mobile robot, and to output a recovery route along which the loaned device will be recovered by the mobile robot as a returned item; inputting an end time prediction result, which is a result of predicting an end time of use of the loaned device, into the trained model, and acquiring a collection route for collecting the loaned device as a return item by the mobile robot; determining the mobile robot that will perform collection along the acquired collection route; Conveying system.

2. The trained model is a model trained by machine learning to output the recovery route that enables the recovery of multiple devices. The transport system according to claim 1 .

3. the collection record data includes first information that is at least one of a time required for the mobile robot to collect the items, a travel distance of the mobile robot, and power consumption of the mobile robot; The trained model is a model trained by machine learning to output the collection route that minimizes the first information. The transport system according to claim 1 or 2.

4. the collection record data includes first information that is at least one of a time required for the mobile robot to collect the items, a travel distance of the mobile robot, and power consumption of the mobile robot; The trained model is a model trained by machine learning to output the collection route for collecting the plurality of devices so as to minimize the first information when the collection time at the collection point for the plurality of devices is within a predetermined time. The transport system according to claim 1 or 2.

5. The device is a medical device. The transport system according to claim 1 or 2.

6. A transport control method in which a computer controls transport of a device to be rented in an equipment rental system by a mobile robot, comprising: the computer inputs an end time prediction result, which is a result of predicting the end time of use of the equipment currently on loan, using learning data including collection record data indicating collection records including the end time of use of the equipment after it has been loaned out and the completion time of collection when the equipment is collected as a returned item, and collection route data indicating the collection route along which the equipment is collected by the mobile robot, and stores a trained model that has been machine-learned to output a collection route along which the mobile robot will collect the equipment currently on loan as a returned item; The computer inputs an end time prediction result, which is a result of predicting an end time of use of the loaned device, into the trained model, and obtains a collection route for collecting the loaned device as a return item using the mobile robot; the computer determines the mobile robot that will perform collection along the acquired collection route; Transport control method.

7. The trained model is a model trained by machine learning to output the recovery route that enables the recovery of multiple devices. The transport control method according to claim 6.

8. the collection record data includes first information that is at least one of a time required for the mobile robot to collect the items, a travel distance of the mobile robot, and power consumption of the mobile robot; The trained model is a model trained by machine learning to output the collection route that minimizes the first information. The transport control method according to claim 6 or 7.

9. the collection record data includes first information that is at least one of a time required for the mobile robot to collect the items, a travel distance of the mobile robot, and power consumption of the mobile robot; The trained model is a model trained by machine learning to output the collection route for collecting the plurality of devices so as to minimize the first information when the collection time at the collection point for the plurality of devices is within a predetermined time. The transport control method according to claim 6 or 7.

10. The device is a medical device. The transport control method according to claim 6 or 7.

11. A program for causing a computer to execute transport control for transporting equipment to be rented by a mobile robot in an equipment rental system, The transport control includes: a trained model that has been machine-learned to input an end time prediction result that is a result of predicting the end time of use of the loaned device using learning data including: recovery record data that indicates recovery records including an end time of use when the device has ended after being loaned out and a recovery completion time when the device has been recovered as a returned item; and recovery route data that indicates a recovery route along which the device is recovered by the mobile robot; and inputting a predicted end time result of the prediction of the end time of the rental device, and acquiring a collection route for collecting the rental device as a return item by the mobile robot; determining the mobile robot that will perform collection along the acquired collection route; program.

12. The trained model is a model trained by machine learning to output the recovery route that enables the recovery of multiple devices. The program according to claim 11.

13. the collection record data includes first information that is at least one of a time required for the mobile robot to collect the items, a travel distance of the mobile robot, and power consumption of the mobile robot; The trained model is a model trained by machine learning to output the collection route that minimizes the first information.

13. The program according to claim 11 or 12.

14. the collection record data includes first information that is at least one of a time required for the mobile robot to collect the items, a travel distance of the mobile robot, and power consumption of the mobile robot; The trained model is a model trained by machine learning to output the collection route for collecting the plurality of devices so as to minimize the first information when the collection time at the collection point for the plurality of devices is within a predetermined time.

13. The program according to claim 11 or 12.

15. The device is a medical device.

13. The program according to claim 11 or 12.

16. This is a trained model that has been machine-learned using learning data including: collection record data that indicates collection records, including the end-of-use date when use of a rental device has ended and the completion date when the device is collected by a mobile robot as a returned item after it has been rented out in an equipment rental system; and collection route data that indicates the collection route along which the device was collected by the mobile robot.This trained model is used to cause a computer to input an end-of-use prediction result, which is the result of predicting the end-of-use date of the rental device, and output a collection route along which the rental device will be collected by the mobile robot as a returned item.

17. After a device to be rented out is rented out in an equipment rental system, learning data including recovery record data indicating recovery records including an end-of-use date when the use of the device ended and a recovery completion date when the device was recovered by a mobile robot as a returned item, and recovery route data indicating a recovery route along which the device was recovered by the mobile robot, are input into an untrained learning model, and machine learning is performed, An end time prediction result, which is a result of predicting the end time of use of the loaned device, is input, and a trained model is generated that outputs a collection route for collecting the loaned device as a return item by the mobile robot. Learning system.

18. After a device to be rented out is rented out in an equipment rental system, learning data including recovery record data indicating recovery records including an end-of-use date when the use of the device ended and a recovery completion date when the device was recovered by a mobile robot as a returned item, and recovery route data indicating a recovery route along which the device was recovered by the mobile robot, are input into an untrained learning model, and machine learning is performed, An end time prediction result, which is a result of predicting the end time of use of the loaned device, is input, and a trained model is generated that outputs a collection route for collecting the loaned device as a return item by the mobile robot. How to learn.

19. On the computer, After a device to be rented out is rented out in an equipment rental system, learning data including recovery record data indicating recovery records including an end-of-use date when the use of the device ended and a recovery completion date when the device was recovered by a mobile robot as a returned item, and recovery route data indicating a recovery route along which the device was recovered by the mobile robot, are input into an untrained learning model, and machine learning is performed, An end time prediction result, which is a result of predicting the end time of use of the loaned device, is input, and a trained model is generated that outputs a collection route for collecting the loaned device as a return item by the mobile robot. A program for executing the learning process.

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