JP2025114085A - Management system, management device, management method, and program - Google Patents

Abstract

To provide a management system which facilitates management of an object used in a facility.SOLUTION: A management system according to the present disclosure includes: means for acquiring image data indicating a predetermined area; means for specifying at least one person and at least one object included in the image data; means for analyzing a relationship between at least one person and at least one object; means for managing work information in which information related to a worker and information related to a work object are associated with each other when an analysis result is obtained that the image data includes the worker and the work object related to the work of the worker; and means for outputting the work information.SELECTED DRAWING: Figure 1

Description

This disclosure relates to a management system, a management device, a management method, and a program.

At logistics centers, cargo is sometimes received and shipped using pallets. If the pallets used for receiving and shipping cargo are rental pallets, the business that rents the pallets must accurately manage the number of pallets in order to return them.

Cited document 1 discloses identifying an ID tag attached to a pallet. Furthermore, cited document 1 discloses that the ID information of the ID tag identified by the terminal is transmitted to an information management server, and the information management server manages the ID information.

Japanese Patent Application Laid-Open No. 2019-101693

Pallets used in logistics centers are generally rented out by multiple businesses. When managing pallets rented out from multiple businesses using ID tags, managers at the logistics center must manage pallets with the same ID tag attachment location and information set on the ID tag across multiple businesses. Standardizing the ID tag attachment location and information set on the ID tag for pallets from multiple businesses places a burden on the businesses that rent out the pallets, making it difficult. This creates the problem of making it difficult for managers at the logistics center to manage pallets rented from various businesses.

The purpose of this disclosure is to provide a management system, management device, management method, and program that can facilitate the management of items used within a facility.

The management system according to the present disclosure comprises means for acquiring image data showing a predetermined area, means for identifying at least one person and at least one object contained in the image data, means for analyzing the relationship between the at least one person and the at least one object, means for managing work information that associates information related to the worker and the work object when an analysis result indicates that the image data includes a worker and a work object related to the worker's work, and means for outputting the work information.

The management device according to the present disclosure includes an identification unit that identifies at least one person and at least one object included in image data showing a predetermined area; an analysis unit that analyzes the relationship between the at least one person and the at least one object; a management unit that, when the analysis result indicates that the image data includes a worker and a work object related to the worker's work, manages work information that associates the worker with information related to the work object; and an output unit that outputs the work information.

The management method disclosed herein identifies at least one person and at least one object contained in image data showing a specified area, analyzes the relationship between the at least one person and the at least one object, and, if the analysis results indicate that the image data contains a worker and a work object related to the worker's work, manages work information that associates the worker with information related to the work object and outputs the work information.

The program disclosed herein causes a computer to identify at least one person and at least one object included in image data showing a specified area, analyze the relationship between the at least one person and the at least one object, and, if the analysis results indicate that the image data includes a worker and a work object related to the worker's work, manage work information that associates information related to the worker and the work object, and output the work information.

This disclosure provides a management system, management device, management method, and program that can facilitate the management of items used within a facility.

FIG. 1 is a diagram illustrating an example of the configuration of a management system. FIG. 2 is a flowchart showing the flow of a management method executed in the management system. FIG. 3 shows a configuration diagram of a pallet management system. FIG. 4 shows image data in which people and objects have been identified. FIG. 5 shows image data taken at a site where workers are retrieving pallets. FIG. 6 is a table showing people and things identified by the identification means. FIG. 7 shows a table in which management companies are associated with each identified pallet. FIG. 8 shows work information that associates a person who is determined to be a worker who performed loading work in a work area with a pallet or the like that is determined to be a work object in the work area. FIG. 9 shows information managed by the management means. FIG. 10 shows a pallet slip that reflects work information. FIG. 11 is a block diagram showing an example of the configuration of a management system.

(Embodiment 1)
FIG. 1 is a diagram showing an example of the configuration of a management system 10. The management system 10 may include at least one computer device. The computer device may be software or a module in which processing is performed by a processor executing a program stored in a memory. When the management system 10 includes two or more computer devices, the respective computer devices may communicate with each other via a network. The management system 10 may be configured on the cloud. Alternatively, the management system 10 may be configured within a facility such as a logistics center.

The management system 10 has an acquisition means 11, an identification means 12, an analysis means 13, a management means 14, and an output means 15. The acquisition means 11, the identification means 12, the analysis means 13, the management means 14, and the output means 15 may be an acquisition unit, an identification unit, an analysis unit, a management unit, and an output unit, respectively. The acquisition unit, the identification unit, the analysis unit, the management unit, and the output unit may be software or modules whose processing is performed by a processor executing a program stored in memory. Alternatively, the acquisition unit, the identification unit, the analysis unit, the management unit, and the output unit may be hardware such as a circuit or chip.

The acquisition means 11, identification means 12, analysis means 13, management means 14, and output means 15 may be provided in a single computer device, or may be distributed across two or more computer devices.

The acquisition means 11 acquires image data indicating a predetermined area. The acquisition unit may be used as a means for acquiring image data indicating a predetermined area. The predetermined area may be an area imaged by an imaging device. The predetermined area may be, for example, an indoor area or an outdoor area. An indoor area may be a logistics center, distribution center, factory, etc. The predetermined area may also be a mixed area of outdoor areas where delivery trucks, etc. are parked and indoor areas where packages are sorted, etc.

The image data is data generated by an imaging device and may be a two-dimensional image or a three-dimensional image. The acquisition means 11 may be, for example, an imaging device. Alternatively, the acquisition means 11 may be an acquisition unit that acquires image data from an imaging device. The acquisition means 11 may acquire image data from an imaging device via a network, or may acquire image data from an imaging device connected via a cable, etc.

The identification means 12 identifies at least one person and at least one object contained in the image data. The identification unit may be used as a means for identifying at least one person and at least one object contained in the image data.

For example, the identification means 12 may identify at least one person and at least one object using image analysis technology. Image analysis technology may be, for example, facial recognition technology, facial authentication technology, etc. In other words, the identification means 12 may identify a person by recognizing or authenticating the face of the person appearing in image data. The identification means 12 may also identify at least one person or at least one object using AI (Artificial Intelligence) technology. Specifically, the identification means 12 may use a trained model that has been trained using image data showing the characteristics of an object as training data. The trained model may be a learning model that takes image data as input and outputs the name of the object contained in the image data.

Alternatively, the identification means 12 may identify an object by performing semantic segmentation, which assigns a label of the identified object to each pixel included in the image data. The identification means 12 may assign a label indicating the object to the pixels that make up the image data.

When identifying a person, the identification means 12 may identify the person's name, gender, age, etc. When identifying an object, the identification means 12 may identify the object's name, color, material, etc.

The analysis means 13 analyzes the relationship between at least one person and at least one object. The analysis unit may be used as a means for analyzing the relationship between at least one person and at least one object. Analyzing the relationship may mean determining whether the person and the object are related to the same task. In other words, analyzing the relationship may mean determining whether the person and the object are related to a worker and a work object related to the worker's task.

A worker may be, for example, a person engaged in work at a logistics center, distribution center, factory, etc. The work of a worker may be the work or task that the worker is to perform at a logistics center, distribution center, factory, etc. The work of a worker may be, for example, sorting luggage, loading luggage onto a truck, unloading luggage from a truck, etc. The work of a worker includes a variety of tasks and is not limited to sorting luggage, etc. The work object may be an object required when the worker performs the work. For example, the work object may be luggage, a pallet on which the luggage is placed, a cart for carrying the luggage, etc. However, the work object is not limited to these objects.

When the management means 14 obtains an analysis result indicating that the image data contains a worker and a work object related to the worker's work, it manages work information that associates information related to the worker and the work object. The management means may also be used as a means for managing work information.

Information related to the work object may be, for example, the name of the work object, the size of the work object, the number of work objects, the management company of the work object, the material of the work object, etc. Associating may also be referred to as associating.

The output means 15 outputs the work information. The output unit may be used as a means for outputting the work information. Outputting the work information may mean, for example, outputting the work information to a display unit such as a display. Alternatively, the output means 15 may output paper on which the work information is printed. Alternatively, the output means 15 may transmit the work information to another computer device via a network. The work information may, for example, be in the form of a table showing workers and work objects related to the work object.

Next, the flow of the management method executed in the management system 10 will be explained using Figure 2. Figure 2 is a flowchart showing the flow of the management method executed in the management system 10.

First, the acquisition means 11 acquires image data showing a predetermined area (S11). Next, the identification means 12 identifies at least one person and at least one object contained in the image data (S12). Next, the analysis means 13 analyzes the relationship between the at least one person and at least one object (S13). Next, if the analysis result indicates that the image data contains a worker and a work object related to the worker's work, the management means 14 manages work information that associates information related to the worker and the work object (S14). Next, the output means 15 outputs the work information (S15).

As described above, the management system 10 analyzes the relationships between people and objects contained in image data. Furthermore, as a result of analyzing the relationships, the management system 10 manages work information that associates information related to workers and work objects that are related to the workers' work.

In this way, by analyzing image data, management system 10 can easily manage work information that associates workers included in the image data with work objects related to the worker's work. For example, management system 10 can manage the number of work objects handled by a worker. Furthermore, even if the image data includes multiple work objects managed by different management companies, management system 10 can manage the number of work objects for each management company by analyzing the image data.

(Embodiment 2)
Next, a method and system for efficiently managing pallets will be described. Fig. 3 shows a configuration diagram of a pallet management system. The pallet management system includes a management device 20, a camera 30, and a network 40.

The management device 20 may be a computer device that operates when a processor executes a program stored in memory. In FIG. 3, the camera 30 captures an image of the work area 50. The camera 30 transmits image data showing the work area 50 to the management device 20 via the network 40. The camera 30 may be mounted on a communication terminal such as a smartphone. The network 40 may be an open network such as the Internet, or a closed network that is confined to a facility or the like that includes the work area 50. The network may be, for example, an IP network.

The management device 20 may be located on the cloud or in a cloud environment. Alternatively, the management device 20 may be located on a closed network that is closed to a facility or the like that includes the work area 50. A management device 20 located on a closed network that is closed to a facility or the like that includes the work area 50 may be referred to as, for example, an edge terminal.

The work area 50 is used as a place where workers load cargo onto a truck. For example, workers load cargo and pallets onto the truck from the side or back of the truck bed. Alternatively, the work area 50 may be used as a place where workers unload cargo and pallets from a truck. Alternatively, the work area 50 may be used as a place where workers load only recovered pallets onto a truck. Alternatively, the work area 50 may be used as a place where workers unload pallets that do not have cargo on them from a truck. Alternatively, the work area 50 may be used as a place where workers load cargo into a container or unload cargo from a container.

The camera 30 may be connected to the network 40 via a wired line or a wireless line. The management device 20 may also be connected to the network 40 via a wired line or a wireless line.

The camera 30 may be a device used as the acquisition means 11 in FIG. 1. The management device 20 may be a device equipped with the functions realized by the identification means 12, analysis means 13, management means 14, and output means 15 in FIG. 1. In other words, the management device 20 may have an identification unit, an analysis unit, a management unit, and an output unit. Alternatively, the management device 20 may be a group of devices consisting of multiple computer devices. In this case, the functions realized by the identification means 12, analysis means 13, management means 14, and output means 15 may be distributed across or installed in multiple devices. The functions realized by the identification means 12, analysis means 13, management means 14, and output means 15 may be distributed across computer devices and edge terminals located in a cloud environment.

The identification means 12 in the management device 20 receives image data transmitted from the camera 30. The identification means 12 then identifies people and objects contained in the image data. The image data is data representing the work area 50. Here, the person and object identification process performed by the identification means 12 will be explained using Figure 4. Figure 4 shows image data in which people and objects have been identified.

The image data 51 shown in FIG. 4 may be a two-dimensional image. Alternatively, the image data 51 may be frame images constituting video data obtained by photographing the work area 50.

The identification means 12 identifies the truck 61, luggage 62, pallet 63, luggage 64, pallet 65, cart 66, person 71, and person 72 contained in the image data 51. Identifying may also be referred to as identifying or recognizing.

Image data 51 is an image captured when the loading platform door of truck 61 is open. Therefore, image data 51 shows luggage 62 and pallet 63 loaded on truck 61. In FIG. 4, luggage 62 and pallet 63 represent one luggage and one pallet, but all luggage loaded on truck 61 are referred to as luggage 62, and all pallets are referred to as pallet 63. Each luggage and pallet loaded on the truck may also be referred to as luggage 62_1-n (n is a positive integer) and pallets 63_1-n.

The identification means 12 analyzes the image data 51 using image analysis technology. For example, the identification means 12 may recognize people and objects contained in the image data 51 by extracting feature quantities of the people and objects contained in the image data 51. Feature quantities may be, for example, histograms of oriented gradients (HOG) or scaled invariance feature transform (SIFT).

Furthermore, the identification means 12 may identify the types or names of people and objects contained in the image data 51 by analyzing the extracted features. Analyzing the features may involve classifying the features according to the feature values or patterns indicated by the feature values. The example in Figure 4 shows that the identification means 12 has identified two people, a truck, multiple packages and pallets contained in the truck, a dolly, and the packages and pallets loaded on the dolly.

Alternatively, the identification means 12 may identify a person by recognizing the person's face using facial recognition technology or facial authentication technology. In facial recognition technology or facial authentication technology, for example, the identification means 12 may extract facial features. Furthermore, the identification means 12 may analyze the facial features to identify the faces of two people. For example, if the facial features of person 71 and person 72 are registered in advance in the management device 20, the identification means 12 may determine whether the person included in image data 51 corresponds to person 71 or person 72.

Alternatively, the identification means 12 may identify an object using identification information attached to or provided on the object. For example, the identification means 12 may identify multiple objects by reading a two-dimensional code such as a barcode or a QR (Quick Response) code.

Alternatively, the identification means 12 may perform image analysis using AI technology. In image analysis using AI technology, images of the work area 50, images of vehicles, images of pallets, images of luggage, etc. may be used to extract features from each image, and a trained model that has learned to further analyze the features may be used. Alternatively, the identification means 12 may identify people and objects included in the image data 51 by performing semantic segmentation. Identifying people and objects included in the image data 51 may mean assigning a label indicating the name of the object to each pixel that makes up the image data 51.

So far, the operation or function of the identification means 12 has been explained using image data 51 taken of the scene where workers are loading luggage onto a truck. Here, we will explain image data 52 taken of the scene where workers are retrieving a pallet. Figure 5 is image data taken of the scene where workers are retrieving a pallet. Image data 52 shows a person 71 loading only the pallet onto a truck 61. The identification means 12 identifies people and objects contained in image data 52, just as it does in image data 51 taken of the scene where workers are loading luggage onto a truck, for image data 52 taken of the scene where workers are retrieving a pallet.

If the identified items are pallets and luggage, the identification means 12 may further identify the number of pallets and luggage. The identification means 12 may also identify the type of pallet. Specifically, the identification means 12 may identify the management company of the pallet identified as the type of pallet. For example, the identification means 12 may identify the pallet and also the management company of the pallet by using a learning model that has learned images of pallets for each management company.

Alternatively, the identification means 12 may identify the management company of a pallet by determining predetermined reference values for the features of the pallet for each management company and comparing the predetermined reference values for the features with the features extracted from the image data 51. Alternatively, if the pallet indicates identification information that differs for each management company, the identification means 12 may identify the management company of the pallet by reading the identification information. Furthermore, if the color of the pallet differs for each management company, the identification means 12 may identify the management company of the pallet based on the color of the pallet. Alternatively, the identification means 12 may identify the management company of each pallet by determining whether the number of holes, shape of the holes, etc. of the pallet match the number of holes and shape of the holes of pallets of each pre-registered management company.

The identification means 12 may manage or organize the identified people and objects as shown in FIG. 6. FIG. 6 is a table showing the people and objects identified by the identification means 12. The identification means 12 may generate the tabular data shown in FIG. 6. The person column in FIG. 6 shows people 71 and 72. The object column in FIG. 6 shows pallets, carts, and luggage. The management company column in FIG. 6 shows Company A and Company B as the management companies for the pallets. For carts and luggage, information corresponding to the management company has not been identified, so no company information is shown. The number column in FIG. 6 shows the number of each object.

Alternatively, the identification means 12 may associate a management company with each pallet. Figure 7 shows a table in which a management company is associated with each identified pallet. Pallets 63_1 to 63_6 represent pallets 63 loaded onto truck 61. Parcels 62_1 to 62_6 represent parcels 62 loaded onto truck 61.

The analysis means 13 analyzes the relationships between people and objects identified by the identification means 12. Here, we will explain an example in which the analysis means 13 determines whether the people and objects identified by the identification means 12 are related to the loading work being performed in the work area 50. For example, the analysis means 13 determines whether the person's actions correspond to actions that a worker might perform when performing work in the work area 50.

The analysis means 13 may, for example, analyze the objects operated by person 71 and person 72, the direction of movement of person 71 and person 72, etc. Specifically, the analysis means 13 may determine whether person 71 and person 72 are moving toward truck 61, and further whether they are moving away from truck 61.

The analysis means 13 may determine that the person 71 and the person 72 are moving toward the truck 61 if the direction of the person's faces or the direction in which the person's toes are pointing are facing toward the truck 61. The analysis means 13 may also determine that the person 71 and the person 72 are moving away from the truck 61 if the direction of the person's faces or the direction in which the person's toes are pointing are facing away from the truck 61.

Furthermore, the analysis means 13 may determine that the person 71 and the person 72 are moving toward the truck 61 if the positions of the heads of the person 71 and the person 72 are closer to the truck 61 than the positions of the centers of gravity of the person 71 and the person 72.Furthermore, the analysis means 13 may determine that the person 71 and the person 72 are moving away from the truck 61 if the positions of the centers of gravity of the person 71 and the person 72 are closer to the truck 61 than the positions of the heads of the person 71 and the person 72.

The analysis means 13 may determine that a person determined to be moving toward truck 61 or away from truck 61 is related to the loading of luggage being performed in the work area 50. In the example of FIG. 4, the analysis means 13 may determine that person 71 is related to the loading of luggage being performed in the work area 50. On the other hand, the analysis means 13 may determine that person 72 is not related to the loading of luggage being performed in the work area 50. Person 72 may, for example, be a security guard guarding the work area 50.

Furthermore, the analysis means 13 may determine that the cart 66 operated by the person 71, and further the luggage 64 and pallet 65 loaded on the cart 66, are also related to the luggage loading work being performed in the work area 50. The person 71 has already been determined to be related to the luggage loading work being performed in the work area 50.

Alternatively, the analysis means 13 may determine that a person 71 moving with a dolly 66, or a person 71 moving with a dolly 66 and a pallet 65, or a person 71 moving with a dolly 66, a pallet 65, and luggage 64, is involved in luggage loading work. Furthermore, the analysis means 13 may determine that a dolly 66, a pallet 65, and luggage 64 moving with a person 71 are involved in luggage loading work.

Furthermore, the analysis means 13 may determine that the luggage 62 and pallet 63 stored in the truck 61 are also related to the luggage loading work being carried out in the work area 50. The luggage 62 and pallet 63 being stored in the truck 61 may mean that the area in which the luggage 62 and pallet 63 exist overlaps with the area identified as the truck 61.

The analysis means 13 may use AI technology to analyze the relationships between people and objects. In analysis using AI technology, a trained model that has trained images showing workers performing work in the work area 50 and the work object may be used. Learning images showing workers may mean learning the movements of the workers, the direction of movement of the workers, etc.

The management means 14 associates the person 71, pallet, cart, and luggage determined to be involved in the loading of luggage, and manages them as work information. Figure 8 shows work information that associates the person 71 determined to be the worker who performed the loading of luggage in the work area 50 with the pallet, etc. determined to be the work object in the work area 50.

In addition to the work information shown in FIG. 8, the management means 14 may also manage the information shown in FIG. 9. FIG. 9 shows information managed by the management means 14. FIG. 9 shows that camera 30 is being used to photograph work area 50. It also shows that truck 61 is parked in work area 50. FIG. 9 also shows that camera 31 is being used to photograph work area 80. It also shows that vehicle 91 is parked in work area 80. The work area 80 and vehicle 91 are not shown in the illustration.

The location where the camera 30 is installed is predetermined. Therefore, the management means 14 may manage information associating the camera 30 with the work area 50 in advance, as shown in FIG. 9. The management means 14 may also add the truck 61 identified by the identification means 12 using image data acquired from the camera 30 to the line of vehicles associated with the work area 50 shown in FIG. 9. If it is predetermined that the vehicle parked in the work area 50 is a truck 61, the management means 14 may also manage information related to the camera, work location, and vehicle in advance.

The output means 15 outputs the work information using a predetermined format. Figure 10 shows a pallet slip that reflects the work information. The pallet slip in Figure 10 indicates the number of pallets handled by the worker 71 who performed the loading work in the work area 50, as well as the pallet management company.

The work location information shown in Figure 9 is set for the work location on the pallet slip. The vehicle information shown in Figure 9 is set for the vehicle on the pallet slip. The worker information shown in Figure 8 is set for the worker on the pallet slip. The management company and number information shown in Figure 8 is set for the management company and number on the pallet slip.

The output means 15 may output a pallet slip as a work report from a person 71 who has completed work in the work area 50. For example, the output means 15 may receive input about the person 71 and the work area 50, and generate a pallet slip that reflects the information in Figure 8 associated with the person 71 and the information in Figure 9 associated with the work area 50.

As explained above, the management device 20 manages the number of pallets handled by the person 71 who worked in the work area 50 as a worker. Furthermore, the management device 20 outputs a pallet slip indicating the number of pallets handled by the person 71. This allows for more accurate management of the number of pallets handled by the person 71 compared to when the number of pallets handled by the person 71 is self-reported by the person 71. As a result, it becomes possible to prevent pallets from being lost. Furthermore, even in the event of a pallet being lost, it is possible to clarify who is responsible for the loss of the pallet, as the worker who handled the pallet is managed in association with the pallet.

(Embodiment 3)
Next, a process related to the evaluation of a worker will be described. When identifying an object, the identification means 12 may identify the state of the object. The state of the object may be, for example, whether a pallet, luggage, cart, etc. is not damaged, or whether the pallet and luggage are neatly stored in the truck. "Neatly stored" may be rephrased as "arranged in an orderly manner." Alternatively, "neatly stored" may be rephrased as "stored within a specific area," or "no luggage protruding from a specific area," or the like.

The identification means 12 may identify the condition of an object using a trained model that has been trained by inputting images showing an object in a damaged state and an object in an undamaged state. The identification means 12 may also identify the condition of an object using a trained model that has been trained by inputting images showing luggage and pallets arranged in an orderly manner inside a truck and images showing luggage and pallets arranged in an orderly manner.

The analysis means 13 may analyze the number and extent of damage, etc., and evaluate the work content. The evaluation of the work content may be indicated, for example, using a score, or may be indicated using multiple levels, such as high and low ratings. The analysis means 13 may evaluate the work content using a learning model that inputs image data of work objects such as pallets, loading platforms, and carts, and outputs a score according to the number and extent of damage, etc. The management means 14 may associate and manage the evaluation of the work content with the people 71 determined to be involved in the loading work being performed in the work area 50. Furthermore, the output means 15 may reflect the worker's evaluation of the work content in the slip when outputting a pallet slip or another slip, etc.

Furthermore, the image data on which the work content has been evaluated may be used as training data for a learning model for evaluating the work content. The learning model for evaluating the work content may be, for example, a learning model for determining a score for the work content.

As described above, the management device 20 performs a work evaluation of the worker contained in the image data. Information related to the work evaluation can be used to assess the worker's future training, salary, etc.

Figure 11 is a block diagram showing an example configuration of the management system 10 and management device 20 (hereinafter referred to as the management system 10, etc.) described in the above-mentioned embodiment. Referring to Figure 11, the management system 10, etc. includes a network interface 1201, a processor 1202, and a memory 1203. The network interface 1201 may be used to communicate with a network node. The network interface 1201 may include, for example, a network interface card (NIC) that complies with the IEEE 802.3 series. IEEE stands for Institute of Electrical and Electronics Engineers.

The processor 1202 reads and executes software (computer programs) from the memory 1203 to perform the processing of the management system 10, etc., described using flowcharts. The processor 1202 may be, for example, a microprocessor, an MPU, or a CPU. The processor 1202 may include multiple processors.

Memory 1203 is composed of a combination of volatile memory and non-volatile memory. Memory 1203 may also include storage located remotely from processor 1202. In this case, processor 1202 may access memory 1203 via an I/O (Input/Output) interface (not shown).

In the example of FIG. 11, memory 1203 is used to store software modules. The processor 1202 reads and executes these software modules from memory 1203, thereby performing processing of the management system 10 and the like described in the above-described embodiment.

As explained using FIG. 11, each of the processors included in the management system 10, etc., in the above-described embodiments executes one or more programs containing instructions for causing a computer to execute the algorithms explained using the drawings.

In the above examples, the program includes instructions (or software code) that, when loaded into a computer, cause 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 technologies. Furthermore, computer-readable media or tangible storage media include CD-ROMs, digital versatile discs (DVDs), Blu-ray® discs, or other optical disk storage, magnetic cassettes, magnetic tapes, magnetic disk storage, or other magnetic storage devices. The program may also be transmitted on a transitory computer-readable medium or a communication medium. By way of example and not limitation, transitory computer-readable media or communication media include electrical, optical, acoustic, or other forms of propagated signals.

The present disclosure has been described above with reference to the embodiments, but the present disclosure is not limited to the above-described embodiments. Various modifications that would be understood by a person skilled in the art can be made to the configuration and details of the present disclosure within the scope of the present disclosure. Furthermore, each embodiment can be combined with other embodiments as appropriate.

The drawings are merely examples for illustrating one or more embodiments. Each drawing may not relate to only one particular embodiment, but may also relate to one or more other embodiments. As will be understood by those skilled in the art, various features or steps described with reference to any one drawing may be combined with features or steps shown in one or more other figures, for example, to create an embodiment not explicitly shown or described. Not all features or steps shown in any one drawing are necessary to illustrate an exemplary embodiment, and some features or steps may be omitted. The order of steps described in any drawing may be changed as appropriate.

A part or all of the above-described embodiments can be described as, but not limited to, the following supplementary notes.
(Appendix 1)
means for acquiring image data representing a predetermined area;
means for identifying at least one person and at least one object included in the image data;
means for analyzing a relationship between the at least one person and the at least one object;
a means for managing work information that associates information related to the worker and the work object when an analysis result indicates that the image data includes a worker and a work object related to the worker's work;
and means for outputting the work information.
(Appendix 2)
The work object is
2. The management system of claim 1, wherein the pallet is used to transport luggage.
(Appendix 3)
The information relating to the work content by the worker is
3. The management system according to claim 1, wherein the information indicates at least one of the number and type of the work objects.
(Appendix 4)
The analyzing means includes:
A management system described in any one of appendices 1 to 3, which determines whether the actions of the at least one person correspond to actions that the worker may perform when performing work.
(Appendix 5)
The actions that the worker may perform when performing the work include:
The management system described in Appendix 4 includes at least one of the following actions: moving away from a vehicle transporting the work object, moving toward the vehicle, and moving together with the work object.
(Appendix 6)
The work is
3. The management system of claim 2, including at least one of an operation of storing the pallet with cargo loaded in a vehicle, an operation of unloading the pallet with cargo loaded from the vehicle, an operation of storing a pallet without cargo loaded in a vehicle, and an operation of unloading a pallet without cargo loaded from the vehicle.
(Appendix 7)
The analyzing means includes:
7. The management system of claim 1, wherein the work performed by the worker is evaluated.
(Appendix 8)
The analyzing means includes:
The management system described in any one of appendix 1 to 7, which evaluates the work performed by the worker using a learning model that inputs the image data including the work object and outputs an evaluation of the work content performed by the worker.
(Appendix 9)
an identification unit that identifies at least one person and at least one object included in image data showing a predetermined area;
an analysis unit that analyzes a relationship between the at least one person and the at least one object;
a management unit that manages work information that associates information related to the worker and the work object when an analysis result indicates that the image data includes a worker and a work object related to the worker;
an output unit that outputs the work information.
(Appendix 10)
Identifying at least one person and at least one object included in image data representing a predetermined area;
analyzing a relationship between the at least one person and the at least one object;
When an analysis result is obtained that the image data includes a worker and a work object related to the work of the worker, managing work information that associates information related to the worker and the work object;
A management method that outputs the work information.
(Appendix 11)
Identifying at least one person and at least one object included in image data representing a predetermined area;
analyzing a relationship between the at least one person and the at least one object;
When an analysis result is obtained that the image data includes a worker and a work object related to the work of the worker, managing work information that associates information related to the worker and the work object;
and outputting the work information.

Some or all of the elements (e.g., configurations and functions) described in Supplementary Notes 2 to 8 that are dependent on Supplementary Notes 1 may also be dependent on Supplementary Notes 9 and 10 in the same dependent relationship as Supplementary Notes 2 to 8. Some or all of the elements described in any Supplementary Note may be applied to various hardware, software, recording means for recording software, systems, and methods.

REFERENCE SIGNS LIST 10 Management system 11 Acquisition means 12 Identification means 13 Analysis means 14 Management means 15 Output means 20 Management device 30 Camera 40 Network 50 Work area 51 Image data 52 Image data 61 Truck 62 Baggage 63 Pallet 64 Baggage 65 Pallet 66 Cart 71 Person 72 Person

Claims (10)

means for acquiring image data representing a predetermined area;
means for identifying at least one person and at least one object included in the image data;
means for analyzing a relationship between the at least one person and the at least one object;
a means for managing work information that associates information related to the worker and the work object when an analysis result indicates that the image data includes a worker and a work object related to the worker's work;
and means for outputting the work information. The work object is
The management system according to claim 1, wherein the object is a pallet used for transporting cargo. The information relating to the work content by the worker is
The management system according to claim 1 , wherein the information indicates at least one of the number and type of the work objects. The analyzing means includes:
The management system according to claim 1 or 2, further comprising: determining whether or not the motion of the at least one person corresponds to a motion that the worker is likely to perform when performing the work. The actions that the worker may perform when performing the work include:
The management system according to claim 4 , further comprising at least one of an action of moving away from a vehicle transporting the work object, an action of moving towards the vehicle, and an action of moving together with the work object. The work is
3. The management system according to claim 2, comprising at least one of an operation of storing the pallet with cargo loaded in a vehicle, an operation of unloading the pallet with cargo loaded from a vehicle, an operation of storing a pallet without cargo loaded in a vehicle, and an operation of unloading a pallet without cargo loaded from a vehicle. The analyzing means includes:
The management system according to claim 1 or 2, wherein the work performed by the worker is evaluated. an identification unit that identifies at least one person and at least one object included in image data showing a predetermined area;
an analysis unit that analyzes a relationship between the at least one person and the at least one object;
a management unit that manages work information that associates information related to the worker and the work object when an analysis result indicates that the image data includes a worker and a work object related to the worker;
an output unit that outputs the work information. Identifying at least one person and at least one object included in image data representing a predetermined area;
analyzing a relationship between the at least one person and the at least one object;
When an analysis result is obtained that the image data includes a worker and a work object related to the work of the worker, managing work information that associates information related to the worker and the work object;
A management method that outputs the work information. Identifying at least one person and at least one object included in image data representing a predetermined area;
analyzing a relationship between the at least one person and the at least one object;
When an analysis result is obtained that the image data includes a worker and a work object related to the work of the worker, managing work information that associates information related to the worker and the work object;
and outputting the work information.