JP2023129089A - Cargo handling vehicle management system, server, cargo handling vehicle management method, and program - Google Patents

Abstract

To make it possible to utilize results of analyzing a behavior of a cargo handling vehicle by linking an onboard device with a beacon installed within a facility.SOLUTION: A cargo handling vehicle management system 1 includes an onboard device 10 and a server 80 that analyzes data collected by the onboard device 10. The onboard device 10 has: a beacon receiving unit 15 that receives radio waves containing a beacon ID emitted from a beacon B installed in a predetermined area within a facility; a time acquisition unit (RTC unit 21, CPU 11) that acquires time information when a cargo handling vehicle has entered a predetermined area based on the intensity of received radio waves; an extraction unit (CPU 11) that extracts the beacon ID from the received radio waves; and a recording unit 17 that records the time information and the beacon ID in chronological order. The server 80 has: a calculation unit (CPU 81) that identifies a moving route between a plurality of areas based on the time information and the identification information recorded by the recording unit and calculates the number of times the cargo handling vehicle has moved along the moving route; and an output unit (communication unit 82) that outputs calculation results.SELECTED DRAWING: Figure 1

Description

The present invention relates to a cargo handling vehicle management system, a server, a cargo handling vehicle management method, and a program.

In warehouses, factories, etc., cargo is transported by forklifts. BACKGROUND ART Technologies for efficiently transporting cargo in warehouses and the like have been proposed (see, for example, Patent Documents 1 to 3).
Patent Document 1 discloses an article position management system that manages articles using wireless tags on pallets on which packages are placed. Patent Document 2 discloses an information processing system that analyzes the operating status of a user or a forklift based on the reception status of a first signal, a second signal, and a third signal. The first signal indicates the current position of the mobile terminal, the second signal indicates the loading status of the article on the forklift, and the third signal indicates the riding status of the user on the forklift. Patent Document 3 discloses a work management system that notifies a worker that loading work has been completed, depending on the state of radio wave reception from a beacon carried by the worker.

All of the techniques disclosed in Patent Documents 1 to 3 use beacon signals to determine the location. There is also a technique shown in Patent Document 4 that uses beacon signals. Patent Document 4 discloses an operation management device that records truck operation data using a beacon carried by a vehicle crew member.

JP 2018-127298 Publication JP2020-19628A JP 2021-33551 Publication Japanese Patent Application Publication No. 2020-140356

However, in Patent Documents 1 to 4 mentioned above, there is room for improvement in the point of utilizing a beacon signal emitted from a beacon placed in a warehouse in combination with data collected by an on-vehicle device.

The present invention has been made in view of the above-mentioned circumstances, and its purpose is to make it possible to utilize the results of analyzing the behavior of cargo handling vehicles by linking an on-vehicle device and a beacon installed in a facility. There is a particular thing.

In order to achieve the above-mentioned object, the present invention has the following features.
A cargo handling vehicle management system comprising an onboard device mounted on a cargo handling vehicle and a server that analyzes data collected by the onboard device,
The onboard device is
a receiving unit that receives radio waves including identification information of the transmitters, which are emitted from transmitters installed in a plurality of areas within the facility;
a time acquisition unit that acquires time information indicating an entry time when the cargo handling vehicle entered any one of the plurality of areas based on the intensity of the received radio waves;
an extraction unit that extracts the identification information from the received radio waves;
a recording unit that records the acquired time information and the extracted identification information in chronological order;
The server is
a calculation unit that specifies a travel route between the plurality of areas based on the plurality of time information and the identification information recorded by the recording unit and calculates the number of times the cargo handling vehicle has traveled along the travel route; ,
an output unit that outputs the calculated result;
Cargo handling vehicle management system.

Moreover, in order to achieve the above-mentioned object, the server according to the present invention has the following features.
A server that analyzes data collected by onboard equipment installed on a cargo handling vehicle,
The onboard device is
a receiving unit that receives radio waves including identification information of the transmitters, which are emitted from transmitters installed in a plurality of areas within the facility;
a time acquisition unit that acquires time information indicating an entry time when the cargo handling vehicle entered any one of the plurality of areas based on the intensity of the received radio waves;
an extraction unit that extracts the identification information from the received radio waves;
a recording unit that records the acquired time information and the extracted identification information in chronological order;
The server is
a calculation unit that specifies a travel route between the plurality of areas based on the plurality of time information and the identification information recorded by the recording unit and calculates the number of times the cargo handling vehicle has traveled along the travel route; ,
an output unit that outputs the calculated result;
server.

Moreover, in order to achieve the above-mentioned object, the cargo handling vehicle management method according to the present invention has the following features.
A cargo handling vehicle management method in a cargo handling vehicle management system comprising an onboard device mounted on a cargo handling vehicle and a server that analyzes data collected by the onboard device, the method comprising:
Receive radio waves containing identification information of the transmitters emitted from transmitters installed in multiple areas within the facility,
Obtaining time information indicating an entry time when the cargo handling vehicle entered any one of the plurality of areas based on the intensity of the received radio waves;
Extracting the identification information from the received radio waves,
Recording the acquired time information and the extracted identification information in chronological order,
Based on the plurality of recorded time information and the identification information, specifying a travel route between the plurality of areas, calculating the number of times the cargo handling vehicle has traveled along the travel route,
output the calculated results,
Cargo handling vehicle management method.

Moreover, in order to achieve the above-mentioned object, the program according to the present invention has the following features.
A program for causing a computer to execute the above cargo handling vehicle management method.

According to the present invention, the results of analyzing the behavior of a cargo handling vehicle can be utilized by linking an onboard device with a beacon installed in a facility.

The present invention has been briefly described above. Furthermore, the details of the present invention will be further clarified by reading the mode for carrying out the invention (hereinafter referred to as "embodiment") described below with reference to the accompanying drawings. .

FIG. 1 is a diagram showing the configuration of a cargo handling vehicle management system according to an embodiment of the present invention. FIG. 2 is a side view showing the appearance of the forklift during operation and an example of cargo. FIG. 3 is a side view showing a typical example of the relationship between a camera and a forklift. FIG. 4 is a flowchart showing an example of the operation of the onboard device. FIG. 5 is a flowchart showing an example of the operation of the on-vehicle device. FIG. 6 is a diagram showing an example of image analysis. FIG. 7 is a flowchart showing an example of the operation of the on-vehicle device. FIG. 8 is a diagram showing an example 1 of installing fixed beacons in a warehouse. FIG. 9 is a diagram for explaining an example of the PDCA cycle in the cargo handling vehicle management system. FIG. 10 is a diagram illustrating an example of how data stored in a server is utilized. FIG. 11 is a graph showing an example of an analysis result by the server. FIG. 12 is a graph showing an example of an analysis result by the server. FIG. 13 is a graph showing an example of an analysis result by the server. FIG. 14 is a graph showing an example of an analysis result by the server. FIG. 15 is a diagram showing an example of an analysis result by the server. FIG. 16 is a graph showing an example of the evaluation results by the server. FIG. 17 is a graph showing an example of evaluation results by the server. FIG. 18 is a graph showing an example of an analysis result by the server. FIG. 19 is a graph showing an example of an analysis result by the server. FIG. 20 is a graph showing an example of an analysis result by the server. FIG. 21 is a diagram showing a second example of installing fixed beacons in a warehouse. FIG. 22 is a screen example of a dashboard showing an example of an analysis result by the server. FIG. 23 is a screen example of a dashboard showing an example of an analysis result by the server. FIG. 24 is a screen example of a dashboard showing an example of an analysis result by the server. FIG. 25 is a screen example of a dashboard showing an example of an analysis result by the server. FIG. 26 is a screen example of a dashboard showing an example of an analysis result by the server. FIG. 27 is a screen example of a dashboard showing an example of an analysis result by the server. FIG. 28 is a screen example of a dashboard showing an example of an analysis result by the server. FIG. 29 is a screen example of a dashboard showing an example of an analysis result by the server. FIG. 30 is a screen example of a dashboard showing an example of an analysis result by the server. FIG. 31 is a screen example of a dashboard showing an example of an analysis result by the server.

Specific embodiments of the present invention will be described below with reference to each figure.

<System configuration>
FIG. 1 shows the configuration of a cargo handling vehicle management system 1 in an embodiment of the present invention. The cargo handling vehicle management system shown in FIG. 1 is mainly used for driving support when carrying cargo by driving a cargo handling vehicle such as a forklift in a facility such as a warehouse.

The cargo handling vehicle management system 1 shown in FIG. 1 includes an onboard device 10 that is installed on each cargo handling vehicle as equipment on the customer side, and is used to manage each cargo handling vehicle, workers, work contents, etc. It includes an office PC 30 (communication terminal) installed in a predetermined office. The cargo handling vehicle management system 1 also includes a server 80 as equipment on the analysis service provider side, which performs analysis based on various data collected from the onboard equipment 10 and transmits the analysis results to the office PC 30. The cargo handling vehicle management system 1 includes a plurality of office PCs 30 and a plurality of on-vehicle devices 10 installed in offices of a plurality of customers. For example, each customer can use the analysis service provided by the server 80 by using a dedicated application that is cargo handling vehicle management software that is pre-installed in each office PC 30.

Although details will be described later, the vehicle-mounted device 10 of this embodiment has functions useful for safe driving and efficient operation in addition to the normal drive recorder function. Specifically, the onboard device 10 receives radio waves emitted from a plurality of beacons (transmitters) installed within the facility, monitors and records the transportation status (operation status) of the cargo handling vehicle. Furthermore, the on-vehicle device 10 has a battery management function and is useful for extending battery life and reducing the number of battery replacements.

Note that the on-vehicle device 10 and the office PC 30 do not need to be connected via a network, and in that case, the on-vehicle device 10 is configured to read a memory card 65 that holds data recorded by the on-vehicle device 10. Make it.

The office PC 30 is composed of a general-purpose computer device installed in the office, and manages information such as the operating status of cargo handling vehicles and specific points where contact accidents are likely to occur. In the example of FIG. 1, data communication performed between the onboard device 10 and the office PC 30 is relayed by the base station 71, the server 80 (analysis device), and the network 70. Wireless communication between the base station 71 and the onboard device 10 may be performed using a mobile communication network (mobile line network) such as LTE (Long Term Evolution)/5G (5th Generation), or a wireless LAN (Local Area Network), and either one can be used selectively. Further, the network 70 is a network (packet communication network) such as the Internet, and relays data communication between the office PC 30 and the server 80.

The vehicle-mounted device 10 includes various interfaces (I/F) 12A, 12B, 13, 14, 16, 19, and 29 to enable input or output of various signals.
The speed I/F 12A has a function of inputting a vehicle speed pulse signal output from a vehicle speed sensor 51 mounted on the vehicle side. The engine rotation I/F 12B has a function for inputting an engine rotation pulse signal output from the vehicle side. The external input I/F 13 is used to input various external signals, and has a function for inputting the battery voltage value output from the battery voltmeter 53 and operation signals indicating the lifting/lowering operation of the cargo holding part (claw) of the forklift. has.

The sensor input I/F 14 is used to input signals from various sensors. In the example of FIG. 1, the G sensor 28 and the gyro sensor 52 are connected to the sensor input I/F 14.
The G sensor 28 detects the magnitude of acceleration in various directions applied to the vehicle in which the on-vehicle device 10 is mounted. Based on the output of the G sensor 28, the control unit 11 detects sudden deceleration or sudden acceleration of the forklift 90. The gyro sensor 52 detects the rotational angular velocity around the pitch axis, yaw axis, and roll axis of the forklift 90 on which the on-vehicle device 10 is mounted.
It is possible to output signals indicating changes in pitch angle, yaw angle, and roll angle. Based on the outputs of the gyro sensor 52 and the G sensor 28, the control unit 11 detects a sharp turn of the forklift 90.

The analog input I/F 29 is used for inputting various analog signals. The battery voltage value output from the battery voltmeter 53 can be input to the analog input I/F 29 . The control unit 11 monitors the voltage status of the battery based on the output of the battery voltmeter 53. Furthermore, the output of a thermometer that measures the temperature inside and outside the premises can be input to the analog input I/F 29. The control unit 11 can use the output of the thermometer, for example, to manage the driver's physical condition (detect abnormality).
Camera I/F 16 has a function for connecting cameras 23A and 23B. That is, it has a function of taking in video signals output from the cameras 23A and 23B and converting them into predetermined digital image data suitable for computer processing.

The audio I/F 19 has a function of generating a predetermined audio signal that can be used to alert the user by audio.

The control unit 11 that implements the main functions of the on-vehicle device 10 is composed of an electronic circuit mainly including a microcomputer (CPU) processor. This microcomputer executes a program stored in advance in the nonvolatile memory 26A, etc.
It realizes the control function of the on-vehicle device 10, which will be described later.

Each of the above-mentioned interfaces 12A, 12B, 13,
14, 16, and 29 are connected. Further, a speaker 20 is connected to the output of the control unit 11 via an audio I/F 19 .

Also, a beacon receiving section 15, a recording section 17, a display section 27, a power supply section 25, a communication section 24, a nonvolatile memory 26A, a volatile memory 26B, a card I/F 18, an RTC section 21, a switch input section 22, and a GPS receiving section 9. is connected to the control section 11.

Beacon receiving section 15 receives radio waves from beacons located within a predetermined range via antenna 15a. Beacons include driver beacons, fixed beacons, and mobile beacons.

The driver beacon is, for example, a card-shaped beacon carried by the driver, and emits a beacon signal including the driver ID. When a driver in possession of a driver beacon approaches the on-vehicle device 10 (for example, sits in the driver's seat), the control unit 11 automatically recognizes the driver ID and identifies the driver. In other words, the driver simply gets into the driver's seat with a driver beacon that stores his or her driver ID, and the onboard device 10 recognizes the driver ID. data management becomes possible.

Beacons BA, BB, BC, ... (hereinafter referred to as beacon B), which are fixed beacons, are fixedly placed in predetermined areas RA, RB, RC, ... (hereinafter referred to as area R) within the facility. (See Figure 8.) Beacon B is used to obtain information representing the current location of forklift 90. Each beacon B has unique identification information (beacon ID). Each beacon B emits a beacon signal (radio wave) containing the beacon ID at advertising intervals set on the dedicated application. Each area RA shown in FIG. 8 represents a range where the radio waves of each beacon B can reach, that is, a detection range where the reception strength is equal to or higher than a predetermined value. The office PC 30 and server 80 know the correspondence between each piece of identification information of beacon B and each position in the facility where each beacon B is installed, and can specify the position of beacon B in the facility from the identification information. . In addition, in order to grasp information on the current position, the onboard device 10 may perform positioning based on a signal received by the GPS receiving unit 9, which will be described later, in addition to the beacon signal from the beacon B.

The mobile beacon is a beacon possessed by someone other than the driver, and is used to detect when someone else approaches the forklift 90. A mobile beacon emits a beacon signal that includes identification information that identifies an individual, such as a driver ID or worker ID.

The recording unit 17 is used, for example, to automatically record image data of videos output by the cameras 23A and 23B and hold it for a certain period of time.
The display unit 27 can be used to display visible information such as characters necessary for operating the on-vehicle device 10, information for calling attention to driving operations, etc. so that the driver can visually recognize them.

The power supply unit 25 generates stable power supply based on the power supply supplied from the vehicle side,
The generated power source power is supplied to each circuit in the on-vehicle device 10 including the control unit 11.
The communication unit 24 provides a wireless communication function for data communication between the vehicle-mounted device 10 and the base station 71.

The nonvolatile memory 26A is constituted by a semiconductor memory, and holds in advance programs executable by the microcomputer of the control unit 11, various constant data, tables, etc. necessary for control.
The volatile memory 26B is used to temporarily hold data generated by the control unit 11 during processing.

A memory card 65 owned by the driver is removably connected to the card I/F 18. The control unit 11 can read data from the memory card 65 attached to the card I/F 18, and can also write various data generated by the control unit 11 to the memory card 65 via the card I/F 18.

The RTC (real time clock) section 21 is constituted by an integrated circuit having a clock function. In other words, the RTC unit 21 can generate information about the current time and grasp elapsed time.
The switch input section 22 is used to input signals representing the states of various switches necessary for operating the on-vehicle device 10.

The GPS receiving unit 9 receives radio waves from multiple GPS (Global Positioning System) satellites.
It is received via antenna 9a. Based on the plurality of reception signals received by the GPS reception unit 9,
Position information representing the current position of the forklift 90 can be calculated and obtained. Furthermore, the movement of the cargo handling vehicle can be detected using position information based on the signal received by the GPS receiving unit 9.

When the control unit 11 receives the radio waves emitted from the beacon B, in other words, when the intensity (reception intensity) of the radio waves received by the beacon reception unit 15 is equal to or higher than a predetermined value, the control unit 11 receives the current time generated by the RTC unit 21. Get information about. In addition to the strength of the radio waves received by the beacon receiver 15 (reception strength), the control unit 11 controls the RTC unit based on the transmission timing (time interval) of the radio signal, the frequency band of the signal, and the power value at the time of transmission (transmission power). It is also possible to obtain the current time information generated in step 21. The control unit 11 extracts a beacon ID from the radio waves whose reception strength is equal to or higher than a predetermined value, and acquires time information indicating the entry time at which the cargo handling vehicle entered the area R corresponding to this beacon ID. The time information acquired by the control unit 11 and the extracted beacon ID (hereinafter also referred to as “beacon ID acquisition result”) are recorded in a file generated by the onboard device 10 and recorded in the recording unit 17. Time information and beacon ID are recorded in this on-vehicle device generation file, for example, at 0.5 second intervals. The onboard device generation file includes vehicle data such as vehicle speed based on input signals from various I/Fs collected by the onboard device 10, and trigger information, which will be described later, including video data. In the onboard device generation file, information on each item recorded in association with each time information is recorded for each record. The onboard device generation file includes records generated at intervals of, for example, 0.5 seconds.

As a specific example, the onboard device generation file includes information on the date and time (date, time), driver ID (crew authentication information), and work start time acquired from the onboard device 10 or the crew authentication information. The onboard equipment generation file also includes information on the entry time when the cargo handling vehicle entered the area, the exit time when the cargo handling vehicle left the area, trigger information, and the remaining amount of the onboard battery at each time. The onboard device generation file recorded in the recording unit 17 is transmitted to the server 80 by the communication unit 24 once a day, for example, after the cargo handling vehicle has finished its work.

Incidentally, instead of being transmitted to the server 80 by the communication unit 24, the onboard device generated file recorded in the recording unit 17 is saved in the memory card 65 and read into the office PC 30 once a day, for example. It may also be transmitted from the PC 30 to the server 80.

The office PC 30 is a PC that runs on a general-purpose operating system. The office PC 30 can be used as a management device for understanding and managing the driving situation including dangerous behavior of the cargo handling vehicle, the operating situation, and the like.
The office PC 30 includes a control unit (CPU) 31, a communication unit 32, a display unit 33, a storage unit 34, a card I/F 35, an operation unit 36, an output unit 37, an audio I/F 38, and an external I/F 48.

The control unit 31 centrally controls each unit of the office PC 30. The communication unit 32 can communicate with the server 80 via the network 70. The display unit 33 can display various information that can be used to manage the operation of each cargo handling vehicle. The storage unit 34 can acquire and manage onboard equipment generation files generated by the onboard equipment 10 mounted on each cargo handling vehicle, various analysis results and evaluation results provided from the server 80, and the like.

A memory card 65 is removably inserted into the card I/F 35. The card I/F 35 is used to input various data recorded by the onboard device 10 from the memory card 65. The operation unit 36 has a keyboard, a mouse, etc., and accepts operations by the administrator of the office PC 30. The output unit 37 outputs various data. A microphone 41 and a speaker 42 are connected to the audio I/F 38. The administrator can also make voice calls using the microphone 41 and speaker 42.

External storage devices (not shown) such as an operation data database (DB) and a hazard map database (DB) can be connected to the external I/F 48 . The hazard map DB can hold data representing dangerous behavior (near misses) and locations where accidents have occurred by cargo handling vehicles such as forklifts.

The server 80 includes a control unit (CPU) 81, a communication unit 82, and a storage unit 83, and analyzes data collected by the onboard device 10 and included in the onboard device generation file. The communication unit 82 communicates with the vehicle-mounted device 10 via the base station 71. Further, the communication unit 82 may communicate with the office PC 30 via the network 70. The storage unit 83 is a memory capable of storing various data, and stores the onboard equipment generation file transmitted from the onboard equipment 10. Furthermore, the storage unit 83 may store operation data transmitted from the office PC 30. The control section 81 controls each section of the server 80 in an integrated manner. The server 80 receives each on-vehicle device generation file for each cargo handling vehicle transmitted from the plurality of office PCs 30 through the communication unit 82 and stores it in the storage unit 83 . Note that the server 80 may be a so-called cloud server configured by a plurality of servers on the Internet.

The control unit 81 analyzes the behavior of each cargo handling vehicle by analyzing the received onboard equipment generation file. The onboard equipment generation file stored in the storage unit 83 includes time information indicating the time when the cargo handling vehicle entered any area R, and the beacon ID of any beacon B corresponding to the area R. recorded in series. The control unit 81 determines the travel route from one beacon B to the next beacon B traveled by the cargo handling vehicle from the time series data of the beacon ID acquisition results based on the onboard device generation file stored in the storage unit 83. Identify. The control unit 81 also calculates the number of times the cargo handling vehicle has traveled along each travel route. Details of the analysis results will be described later.

Note that beacon B has radio wave disturbance due to its characteristics, so there is a possibility that the beacon ID acquisition result changes at short intervals. Therefore, the number of consecutive acquisitions is arbitrarily set, and based on the setting, only when the same beacon ID is continuously acquired, there is an onboard device 10 within the detection range of the beacon B, that is, a cargo handling vehicle equipped with the onboard device 10. deemed to have been done.

Each analysis result (various reports, near-miss data, etc.) is stored in the storage unit 83, and is transmitted by the communication unit 82 to each office PC 30 or a communication terminal such as a mobile terminal owned by an administrator or the like.

Further, the control unit 81 of the server 80 generates prediction information regarding battery replacement based on the battery information indicating the voltage status and operating status of the battery of the forklift 90, which is received from the office PC 30. This prediction information is stored in the storage section 83 and transmitted to the office PC 30 by the communication section 82.

With the above configuration, the on-vehicle device 10 and multiple beacons B installed in the facility are linked in communication, and information such as safe driving conditions, work time, number of work times, and time in a specific area R is communicated with the on-board device 10. Collect by 10. The on-vehicle device 10 collects this information as driving record data and video data by linking this information with information that identifies the driver, and transmits the data to the server 80 .

The server 80, which has received various information from the onboard equipment 10, sends the analysis results that visualize the behavior of the cargo handling vehicle by person (by driver) in the specific area R, that is, the operating status, together with video data, to the office PC 30 and the administrator. The information is sent to a mobile terminal (hereinafter also referred to as office PC 30, etc.). For example, the server 80 calculates, for each driver, the travel time from one beacon B to another beacon B and the residence time in the loading/unloading area, and transmits it to the office PC 30 or the like together with the video data.

The administrator can check the analysis results and video data displayed on the office PC 30, etc., and standardize safe and optimal working hours in the specific area R. Therefore, the manager can smooth out the time required for movement and loading/unloading by instructing drivers who are taking more than the average time for movement and loading/unloading. Additionally, for drivers who work quickly but have safety issues, guidance can be provided using video data, etc., in order to level the playing field. In this way, it is possible to optimally level the work in the area from both aspects of work efficiency and safety. Further, the calculated number of times of movement from beacon B to other beacons B, etc. can also be utilized to optimize the layout within the warehouse.

<Specific examples of cargo handling vehicles>
Figure 2 shows the appearance of a forklift during operation and an example of its cargo. In FIG. 2, the left side represents the forward direction of the forklift, the right side represents the backward direction, and the direction perpendicular to the paper surface represents the width direction of the forklift (left-right direction with respect to the traveling direction of the forklift).

As shown in FIG. 2, the forklift 90 has a plurality of (two in this embodiment) claws 91 and a backrest 92 that protrude long in front of the driver's seat. The claw 91 and the backrest 92 are supported by the mast 93 in a manner that they can be moved up and down in the vertical direction along the mast 93.
Further, the forklift 90 can move the positions of the claws 91 and the backrest 92 in the vertical direction by driving a predetermined elevating mechanism. In addition, the forklift 90 has
A drive mechanism is provided to change the inclination angle of the mast 93 that supports the claws 91 and the backrest 92, so that the tilt angles of the claws 91 and the backrest 92 can be adjusted. Furthermore, the forklift 90 has a head guard 94 that covers the upper part of the driver's seat.

On the other hand, various cargoes 100 to be transported by the forklift 90 are generally stored on a pallet 110, which is a platform placed on the ground 98. Therefore, when actually transporting this cargo 100, the forklift 90 is moved forward toward the pallet 110 with the claws 91 lowered downward as shown in FIG. 2, and the claws 91 are passed inside the pallet 110. Make it. When the forklift 90 lifts the claw 91 upward in this state, the cargo 100 placed on the pallet 110 can be lifted together with the pallet 110. Then, by moving the forklift 90 with the cargo 100 and the pallet 110 lifted, the cargo 100 and the pallet 110 can be transported.

By the way, when driving the forklift 90, the driver is prohibited from double-operating the claws 91 to raise and lower them while driving to prevent accidents, and is also required to keep track of the condition of the claws 91 at all times. is necessary. In addition to recording images based on normal triggers (sudden turning, sudden deceleration, sudden acceleration), the on-vehicle device 10 performs this double operation, detects the presence or absence of cargo held by the claws 91, detects falling cargo, and records images in the width direction of the forklift 90. It has a function of detecting the shaking of the luggage (hereinafter also simply referred to as "the shaking of the luggage").

<Camera installation position, shooting range>
FIG. 3 shows a typical example of the relationship between the forklift 90 and the camera 23A for photographing the driver's appearance, the camera 23B for photographing the nails 91, luggage, and the like.

In the example shown in FIG. 3, the camera 23A built into the on-vehicle device 10 is attached to the front of the head guard 94, and the shooting direction is adjusted so that it faces a direction inclined downward at about 45 degrees with respect to the horizontal direction. It has been done. In other words, since the area of the photographing range 23a shown in FIG. 3 is to be photographed, the driver of the forklift 90 can be photographed with the camera 23A. Further, in the example shown in FIG. 3, the camera 23B is installed at the top of the mast 93, and the photographing direction is adjusted so that it faces in a direction inclined downward by about 45 degrees with respect to the horizontal direction. In other words, since the area of the photographing range 23b shown in FIG. 3 is to be photographed, the claw 91 of the forklift 90, the pallet 110 to be transported, and the cargo 100 can be photographed with the camera 23B. The photographing range 23b of the camera 23B is desirably set to a wide range of about 120 degrees, for example, so that substantially the entire area of the luggage 100 and the pallet 110 can be photographed at the same time.

<Example of operation of onboard equipment>
FIG. 4 shows an example of the operation of the on-vehicle device 10 shown in FIG. 1. The on-vehicle device 10 is mounted on the forklift 90 shown in FIGS. 2 and 3, and the control section 11 performs the operation shown in FIG. 4 in conjunction with each functional section of the on-vehicle device 10. The control unit 11 has a function of monitoring the transportation status of cargo, and a trigger generation function of generating a trigger for recording information such as an image representing the situation in the vicinity of the forklift 90 when the transportation status satisfies a predetermined condition. and has. FIG. 4 shows the operation of recording information representing the operating status of the forklift 90 (presence or absence of cargo) and the situation near the forklift 90 when dangerous behavior (double operation, falling cargo, shaking of the cargo) occurs. .

When the driver in possession of the driver beacon is authenticated, the on-vehicle device 10 starts the operation shown in FIG. 4. The onboard device 10 transports luggage (baggage 100 and pallets 110) based on inputs from speed I/F 12A, engine rotation I/F 12B, external input I/F 13, sensor input I/F 14, and camera I/F 16. Monitor the situation (step S1). Details of the processing in S1 will be described later.

The onboard device 10 monitors the transportation situation until it detects at least one of double operation, the presence or absence of a load, falling of the load, and shaking of the load in the width direction of the forklift 90, and if detected (YES in S2). ), a trigger is generated (S3). The onboard device 10 displays the trigger type indicating whether the event that occurred is double operation, presence or absence of luggage, falling luggage, or shaking of luggage, the time at which the event occurred, and the point in time at which the event occurred. The position information of the forklift 90 (own vehicle) is temporarily recorded in the recording unit 17 as trigger information. The trigger information is
It may be recorded on the memory card 65 or the volatile memory 26B.

Next, the onboard device 10 uses image (video) data representing the situation near the forklift 90 taken by the camera 23B before and after the time when the event occurred, and input signals from various I/Fs, etc. Vehicle data such as vehicle speed is recorded in association with trigger information (S4). Note that the vehicle data includes operation data that will be described later. The data recorded in the processes of S3 and S4 may be collectively referred to as trigger information. The trigger information recorded in the processes of S3 and S4 may be transmitted to the server 80 by wireless communication. Further, for example, when the operating status including image data taken by the cameras 23A and 23B is transmitted to the server 80 in real time, the onboard device 10 transmits only the trigger information recorded in the process of S3 to the server 80. Good too. Note that even if the driver does not have a driver beacon, the onboard device 10 can start the above-mentioned operation, but if driver authentication is performed, trigger information can be recorded and managed for each driver. Become.

<Trigger detection by image analysis>
With reference to FIGS. 5 and 6, the operation of the vehicle-mounted device 10 to detect a trigger by image analysis will be described. FIG. 5 shows the details of the luggage transportation status monitoring process (S1) shown in FIG. , shows the operation of detecting falling luggage and shaking of luggage. FIG. 6 shows one frame (image 201) of an image taken by the camera 23B.

The onboard device 10 analyzes the travel detection range 201a in the image 201 (S21). As shown in FIG. 6, the running detection range 201a is set in two areas near the left and right ends above the image 201. The on-vehicle device 10 detects whether the forklift 90 is traveling based on changes that appear in both traveling detection ranges 201a in the images 201 of a plurality of consecutive frames. By looking at changes in the two travel detection ranges 201a on the left and right, it is possible to prevent erroneous detections due to reflections of moving objects, etc. When the vehicle-mounted device 10 detects that the vehicle is running (Yes in S22), the process proceeds to S23.

Next, the onboard device 10 analyzes the baggage detection ranges 201b-1 to 201b-3 in the image 201 (S23). The baggage detection ranges 201b-1, 201b-2, and 201b-3 are set in predetermined areas including at least a portion of the claw 91 in the image 201. In the image 201, baggage detection ranges 201b-1, 201b-2, and 201b-3 are set from the tip of the claw 91 to the backrest 92, respectively. The on-vehicle device 10 recognizes a pattern corresponding to a baggage (baggage 100 or pallet 110) in each of baggage detection ranges 201b-1 to 201b-3. Based on the recognition result, the onboard device 10 identifies whether or not there is luggage on the claw 91, that is, the presence or absence of luggage. Here, in the image 201,
The area occupied by the recognized baggage is referred to as the baggage area. If there is no luggage area in any of the luggage detection ranges 201b-1 to 201b-3 (No in S24), the onboard device 10 detects the absence of luggage (S25). The onboard device 10 detects the presence of luggage when at least a part of the luggage area exists in at least one of the luggage detection ranges 201b-1 to 201b-3 (Yes in S24) (S26). .

When the onboard device 10 detects the presence of luggage, it determines whether part of the luggage area is missing or whether the position or size of the luggage area has changed (S27). If there is neither omission nor change, the onboard device 10 detects double operation, falling luggage, and no shaking of the luggage (S28). If a part of the luggage area is missing, the on-vehicle device 10 detects that the luggage has fallen due to collapse of the luggage or the like (S29). When the position or size of the baggage area on the image 201 changes from that in the previous frame image, if the size changes (YES at S30), the onboard device 10 detects that there is a double operation. (S31). Since a change in the size of the baggage area on the image 201 means a change in the distance between the camera 23B fixed to the mast 93 and the baggage, the onboard device 10 can detect whether the claw 91 is raised or lowered while driving.
That is, double operation is detected. On the other hand, if the size of the baggage area on the image 201 does not change, that is, the size of the baggage area on the image 201 does not change, and the position of the image 201 in the left-right direction (width direction of the forklift 90) changes. If so (NO in S30), the on-vehicle device 10 moves to the process of S32. In S32, since the position of the cargo area in the width direction of the forklift 90 has changed, the onboard equipment 10 detects the shaking of the cargo in the width direction of the forklift 90 (S32). In the process shown in FIG. 5, the onboard device 10 detects the presence of luggage (S26) and then detects the presence of double operation (S31).
The presence of double operation may be detected without detecting the presence or absence of luggage. For example, the onboard device 10 is
When it is detected that the forklift 90 is traveling, the area of the claw 91 in the image 201 is detected, and if the size of the area of the claw 91 changes, it is determined that the claw 91 is raised or lowered, that is, there is double operation. can be detected.

<Trigger detection by operation signal>
Referring to FIG. 7, the operation of the vehicle-mounted device 10 to detect a trigger (double operation) based on an operation signal will be described. The onboard device 10 acquires operation signals input from the speed I/F 12A, the engine rotation I/F 12B, the external input I/F 13, and the sensor input I/F 14 (S41). When the onboard device 10 detects an operation signal indicating that the forklift 90 is traveling (Yes in S42), the process proceeds to S43.

When the vehicle-mounted device 10 does not detect the operation signal indicating the raising and lowering of the claw 91 (No in S43), it detects that there is no double operation (S44). When the vehicle-mounted device 10 detects an operation signal indicating the raising and lowering of the claw 91 (Yes in S43), it detects that there is a double operation (S45). In this way, the onboard device 10 can detect double operation without performing image analysis.

As described above, the presence or absence of a double operation can be detected by both the image analysis shown in FIG. 5 and the analysis based on the operation signal shown in FIG. 7. Note that to detect whether or not the forklift 90 is traveling, analysis based on the signal (GPS signal) received by the GPS receiving unit 9 can be used instead of the above-described image analysis and analysis based on the operation signal. Further, the presence or absence of double operation may be detected using at least any two of image analysis, analysis using operation signals, and analysis using GPS signals.

In FIGS. 5 and 7, when double operation, presence or absence of luggage, falling luggage, and shaking of luggage are detected (S25, S26, S29, S31, S32, S45), as shown in FIG. , a trigger is generated, and trigger information is recorded (S3, S4). Trigger information is sent to server 80.

<Analysis by server>
The server 80 that has received the trigger information stores the trigger information in the database 85. The control unit 81 of the server 80 refers to the database 85 and performs analysis regarding the operation of the forklift 90 based on trigger information of triggers that occur within a predetermined period. The analysis by the server 80 is based on the operation of one forklift 90 for one day or multiple days, and/or the operation of multiple forklifts 90 for one day or multiple days, which are stored in the database 85. This is done based on information regarding the operation of The server 80 allows the office PC 30 to view the analysis results in response to a request from the office PC 30 via the network 70 . Examples of the analysis results include instructions for operating the forklift 90, analysis reports, rankings, heat maps, near-miss maps, and the like.

As explained above, according to the on-vehicle device 10, the server 80, and the cargo handling vehicle management system 1 of this embodiment, it is possible to perform recording triggered by an event specific to a cargo handling vehicle such as the forklift 90. For example, when it is detected that the claw 91 is raised and lowered while the forklift 90 is running, the presence or absence of a load, the fall of the load, or the occurrence of shaking of the load, a trigger is generated and the situation near the forklift 90 is activated. It is possible to record images showing the Therefore, the manager can easily grasp the dangerous behavior and operating status of the forklift 90. Therefore, safe driving support and efficient operation support are possible. Furthermore, since battery management based on the operating time and battery voltage of the forklift 90 becomes possible, appropriate battery management helps extend battery life and reduce the number of battery replacements.

Further, according to the present embodiment, by accumulating information collected from the on-vehicle devices 10 of a plurality of customers who operate forklifts and using it as an analysis target, the service provider who operates the server 80 can improve safe driving, efficient driving, etc. It will be possible to provide more specialized advice and suggestions regarding operation.

<Example of behavior analysis of cargo handling vehicle>
Next, with reference to FIGS. 8 to 20, an analysis example in which a fixed beacon is mainly used will be described as an example of behavior analysis of the forklift 90. The following includes a description of the cargo handling vehicle management method in the cargo handling vehicle management system 1. Note that this cargo handling vehicle management method is executed by a program installed in advance in the onboard equipment 10, the server 80, the office PC 30, and the like.

FIG. 8 is a diagram showing an example of installing fixed beacons in a warehouse. The warehouse shown in FIG. 8 is provided with a plurality of unloading/loading yards 101 where trucks are parked and unloading/loading is performed, and a temporary storage area 102 is provided adjacent to the unloading/loading yard 101. The temporary storage area 102 is a place where cargo unloaded from trucks parked at the unloading/loading area 101 is temporarily placed. A storage area 103 adjacent to the temporary storage area 102 is provided in the warehouse. The storage area 103 includes a plurality of areas RB, RC, RD, RE, RF, and RG, and the luggage placed in the temporary storage area 102 is stored in one of the areas RB, RC determined according to the delivery destination of the luggage, etc. , RD, RE, RF, and RG. Beacons BA, BB, BC, BD, BE, BF, and BG that cover each area R are fixedly installed in each area RA, RB, RC, RD, RE, RF, and RG, respectively. Each area R may be provided with a predetermined shelf, or items may be placed on the floor.

When a forklift 90 traveling in a warehouse enters area RA, an on-vehicle device 10 mounted on the forklift 90 receives radio waves emitted from a beacon BA, and acquires the time at which the radio waves are received as time information. The onboard device 10 also extracts the beacon ID of the beacon BA from the received radio wave, and records the time information and beacon ID in the onboard device generation file. While the forklift 90 stays in the area RA, the on-vehicle device 10 receives a radio wave including the beacon ID from the beacon BA, and records it in the on-vehicle device generation file along with time information indicating the time when the radio wave was received. If the onboard device 10 could not receive radio waves from the beacon BA, that is, the reception strength of the radio waves was less than a predetermined value, the onboard device generates information indicating this time as the exit time when the forklift 90 left the area RA. Record in file. Similarly, the time when the forklift 90 that left area RA entered area RB, etc. is recorded in the onboard equipment generation file.

In this way, the beacon ID acquisition result is recorded as time-series data in the onboard device generation file, and is transmitted from the onboard device 10 to the server 80 after the forklift 90 finishes its work, for example, after the day's work is finished. The server 80 accumulates the onboard device generation files received from the plurality of onboard devices 10 in the database 85 and uses them for behavior analysis of the forklift 90. Note that the onboard device 10 may transmit the onboard device generated file to the server 80 in real time.

Next, referring to FIG. 9, the server 80 performs analysis and evaluation using the onboard device generation file acquired and generated by the onboard device 10, and the service provider makes an improvement proposal based on the evaluation results. , a series of steps (PDCA cycle) for restarting business operations in an improved state will be explained. FIG. 9 is a diagram for explaining an example of the PDCA cycle in the cargo handling vehicle management system 1.

First, the server 80 acquires operation data of the forklift 90 in the warehouse using the onboard device 10 and the beacon B (processing "C" shown in FIG. 9). The operation data includes the number of times of entering and exiting a specific area R, the time of entering and exiting, the length of stay in a specific area, the number of left and right turns or the number of back and forth changes between areas, crew information based on driver ID (work characteristics), and driver safety. Includes a trigger list indicating driving status and the presence/absence of luggage. The number of right and left turns means the number of times the steering wheel is switched left and right. Further, the operation data includes video data around the forklift 90. Additionally, the server 80 grasps various data indicating external factors such as weather, day of the week, season, time of day, loading/unloading style, work plan for the day (work overcrowding situation), presence/absence of unexpected work, and contents. .

Next, the server 80 analyzes the work efficiency and safe driving of the forklift 90 based on the data acquired through cooperation between the onboard device 10 and the beacon B (processing “A” shown in FIG. 9). . The server 80 analyzes the acquired data, stores the analysis results in a database 85, and outputs them to the office PC 30 or the like. Based on the data acquired by the plurality of onboard devices 10, the server 80 outputs analysis results indicating, for example, the work time and travel time for each driver and the number of times the driver enters and exits a specific area.

The server 80 calculates location information within the warehouse from the beacon ID. The server 80 calculates the stay time, work time, or travel time from the entry time when the forklift 90 entered the predetermined area and the exit time when the forklift 90 exited the predetermined area. The server 80 calculates the number of times the forklift 90 enters and exits each area from the entry time when the forklift 90 entered the area and the exit time when the forklift 90 exited the area. The server 80 is
The operating time is calculated from the operating data or the time of the crew authentication information acquired by the on-vehicle device 10, and the operating time is linked to information indicating which driver or which forklift 90 the operating time is associated with. The server 80 calculates the number of right/left turns or forward/backward switching from the trigger information of the on-vehicle device 10, and associates it with information indicating which driver or which forklift 90 is involved. The server 80 calculates the near-miss occurrence status, that is, the position and number of near-miss occurrences, from the trigger information of the on-vehicle device 10, and understands the near-miss occurrence status by linking it to information indicating which driver or which forklift 90.

After confirming the analysis results displayed on the office PC 30, etc., the manager considers indicators of work efficiency and safe driving, and sets standard work hours.

The data acquired by the server 80 can be utilized to confirm the working time in the specific area R in the warehouse and to investigate the staying time and safe driving conditions. For example, the administrator can determine that a particular driver is dangerous if he or she drives roughly, even if the driver works quickly. Further, the data acquired by the server 80 can be utilized to confirm the number of stops at each area R and to investigate the efficiency of the destination where the luggage is picked up. For example, the administrator can determine whether many pickup locations are far away, resulting in inefficiency. Further, the data acquired by the server 80 can be utilized to identify near-miss areas within the warehouse based on the operating conditions and investigate dangers lurking within the warehouse. For example, if there are many near-misses, the manager can determine that there are dangerous areas within the warehouse and consider changing the layout.

Next, the administrator or service provider makes an improvement proposal based on the analysis result by the server 80 (processing "P" shown in FIG. 9). Based on the standard working hours, the manager or service provider sets work procedures in the temporary storage area 102 and changes to storage locations for luggage, and makes a proposal for leveling the work.

Thereafter, the business is performed again in an improved state according to the improvement proposal (processing "D" shown in FIG. 9). For example, in a new layout in which the cargo storage area 103 is changed from area RG to area RC, work is performed using the forklift 90 according to a predetermined standard work. At this time, the server 80 acquires various data during the business (processing "C" shown in FIG. 9), verifies it (processing "A" shown in FIG. 9), and executes the PDCA cycle, which is a series of processing. repeat.

From the second PDCA cycle onward, target values for work efficiency and safe driving indicators are updated and set. The server 80 can therefore further improve work efficiency and safety in the cargo handling vehicle management system 1.

FIG. 10 is a diagram showing an example of how data stored in the database 85 of the server 80 is used. The server 80 uses the data accumulated in the database 85 to aggregate and process data in accordance with each package: standard package, custom package, external consulting package, and real-time package, or to provide data to a collaborating consulting company.

The standard package includes data sets set in advance by the service provider, such as data aggregated and processed by vehicle, driver, area (beacon), etc., and data that visualizes these data. including. The visualized data included in the standard package includes, for example, a graph showing the number of visits (times) for each beacon B, travel time (minutes) for each driver from area R4 to R1, and working time for each driver at the temporary storage area 102. (minutes) included.

The custom package includes various data customized according to the customer's requests, and includes data that visualizes the data.

11 to 20 are graphs showing the results of analyzing data acquired by the server 80.

FIG. 11 shows near-miss information for each beacon BA, beacon BB, beacon BC, and beacon BD. The near-miss information includes the number of times the forklift 90 made a sudden turn, the number of times it suddenly accelerated, and the number of times it suddenly decelerated. By combining the near miss information obtained from the onboard device 10 and the information obtained from the beacon B, it becomes possible to visualize areas where near misses frequently occur and investigate the causes of frequent near misses. Therefore, safety can be improved.

FIG. 12 shows the travel time (minutes) for each driver from the area RB where the beacon BD is installed in the storage location 103 to the temporary storage area 102, that is, the area RA where the beacon BA is installed. FIG. 12 shows the first to fifth travel times and average travel times for each driver, as well as the overall average. By understanding the travel time and number of trips from area RB where the beacon BD is installed to the temporary storage area 102, the manager can change the layout of the luggage storage area in the warehouse, or, for example, change the driver's behavior based on the overall average as a target value. Work can be leveled. Therefore, it is possible to improve the efficiency of work.

FIG. 13 shows the working time (minutes) for each driver in the temporary storage area 102. The stay time of the onboard device 10 in the temporary storage area 102, which is the area RA where the beacon BA is installed, is regarded as the working time in the temporary storage area 102. By understanding the work time (loading, unloading, etc.) at the temporary storage area, the manager can visualize the work time and provide guidance to drivers on how to level it out. Therefore, it is possible to improve the efficiency of work.

FIG. 14 shows the travel time between specific areas for each driver. The graph shown in FIG. 14 shows, from left to right, a route from area A to area B, a route from area A to area C, a route from area A to area D, a route from area A to area E, and a route from area A to area C. The travel time of each crew member on the route from A to Area F is shown. According to this graph, it is possible to understand the variation in travel time for each route, the minimum value, maximum value, average value, etc., which can be useful for leveling out the driver's work.

FIG. 15 is a diagram showing a near-miss occurrence situation in a warehouse. The server 80 calculates the area in the warehouse where each beacon B is installed from the data acquired from the onboard device 10, and superimposes marks M11, M12, and M13 on the map of the warehouse at the locations where the near miss occurred. Output the displayed map. The marks M11, M12, and M13 use the size of the circle to indicate the number of near misses, and the color of the circle to indicate the type of near miss (sudden acceleration, sudden deceleration, sudden turn, human approach, etc.). Further, the mark M13 has a cross inside a circle, indicating that, as shown in graph G, sudden acceleration occurs frequently and is an area that requires attention. According to the map shown in FIG. 14 in which the marks M11, M12, and M13 are displayed in a superimposed manner, it is easy to intuitively grasp the occurrence situation of a near miss.

As described with reference to FIGS. 11 to 15, the server 80 visualizes the occurrence of near misses, the travel time of the forklift 90 between areas, and the stay time of the forklift 90 in a specific area (temporary storage area 102). Therefore, the service provider can grasp the current operating status of the customer. Additionally, by providing training to drivers based on the visualized data, customers can improve safety and further streamline their operations. Further, the server 80 calculates the number of visits (number of entrances and exits) of the forklift 90 to a specific area by totaling the entry and exit times for beacons installed in the specific area, which are included in the onboard device generation file. I can do it. Note that, for example, if the areas are provided adjacent to each other, the server 80 may calculate the number of times a forklift enters and exits a specific area by counting only the times of entry into the area. As a specific example, if entry into area B is detected after entry into area A, that is, if the onboard device 10 receives radio waves from a beacon installed in area B, the server 80 It is determined that the person has left area A and entered area B, and the number of times the user has entered and left area is counted.

16 and 17 are diagrams showing the evaluation results of work efficiency and safety by the server 80.

FIG. 16 shows the analysis results and evaluation results for the first to fourth weeks when the above-described PDCA cycle was performed multiple times as a driver (worker) driving evaluation.

The analysis results AC1, AC2, AC3, and AC4 (hereinafter referred to as analysis results AC) shown in Figure 16 show the number of times (times) entering and leaving beacon B, and the number of times the driver stayed in beacon B during the first to fourth weeks of the PDCA cycle. It is a graph visualizing time (minutes) and the number of trigger recordings (times). The target values for the number of entrances and exits, the number of trigger records, and the stay time shown in each analysis result AC are determined by the administrator. According to the analysis result AC, it is possible to understand the number of entrances and exits of each driver, the number of recorded triggers, and the length of stay while comparing them with target values.

Evaluation results EC1, EC2, EC3, and EC4 (hereinafter referred to as evaluation results EC) shown in FIG. 16 are evaluation results obtained by comparing each analysis result with a target value. In each evaluation result EC, evaluation points are added when the number of entrances and exits is lower than the target value, points are deducted when it is greater, points are added when the number of trigger recordings is less than the target value, points are deducted when it is greater, and the stay time If it is shorter than the target value, points will be added; if it is longer, points will be deducted. Each evaluation result EC evaluates the driver's work efficiency and safety in ranks A to C based on the total result of adding and/or subtracting evaluation points for the number of entrances and exits, the number of recorded triggers, and the stay time.

The evaluation result EC1 for the first week of the PDCA cycle has an evaluation score of -3, and is ranked C as it is considered inefficient and unsafe. The evaluation result EC2 for the second week of the PDCA cycle has an evaluation score of -1, and is ranked B as being inefficient but safe. The evaluation result EC3 for the third week of the PDCA cycle has an evaluation score of 0, and is ranked B because it is efficient but not safe. The evaluation result EC4 for the fourth week of the PDCA cycle has an evaluation score of +3 and is ranked A as being efficient and safe.

As shown in FIG. 16, by evaluating the driver's work efficiency and safety in ranks A to C from the acquired data, it is possible to improve both efficiency and safety.

FIG. 17 shows analysis results and evaluation results for the first to fourth weeks when the above-described PDCA cycle was performed multiple times as an evaluation of layout efficiency and safety. FIG. 17 shows an example where area A in a warehouse where beacon A is installed is used as a temporary storage area.
The analysis results ACa1, ACa2, ACa3, and ACa4 (hereinafter referred to as analysis results ACa) shown in FIG. It is a graph visualizing the number of times (times) and the number of trigger recordings (times). The plurality of routes are a route from beacon A to beacon B, a route from beacon A to beacon F, and a route from beacon A to beacon G. The target values of travel time, number of right/left turns, and number of trigger records shown in each analysis result ACa are determined by the administrator. According to the analysis result ACa, the travel time, number of left and right turns, and number of trigger records on each route can be grasped while comparing with target values.

Evaluation results ECa1, ECa2, ECa3, and ECa4 (hereinafter referred to as evaluation results ECa) shown in FIG. 17 are evaluation results obtained by comparing each analysis result with a target value. In each evaluation result ECa, evaluation points are added when the travel time is longer than the target value, points are deducted when it is shorter, points are added when the number of left and right turns is less than the target value, and points are deducted when it is more than the target value, and the number of trigger records is If it is less than the target value, points will be added; if it is more, points will be deducted. Each evaluation result ECa evaluates the work efficiency and safety of the layout in ranks A to C based on the total result of adding and/or subtracting evaluation points for travel time, number of left/right turns, and number of trigger records.

The evaluation result ECa1 for the first week of the PDCA cycle has an evaluation score of -3, and is ranked C as it is considered inefficient and unsafe. The evaluation result EC2a for the second week of the PDCA cycle has an evaluation score of 0, and is ranked B as being inefficient but safe. The evaluation result EC3a for the third week of the PDCA cycle has an evaluation score of 0, and is ranked B because it is efficient but not safe. The evaluation result EC4a for the fourth week of the PDCA cycle has an evaluation score of +3 and is ranked A as being efficient and safe.

As shown in FIG. 17, by evaluating the work efficiency and safety of the layout in ranks A to C from the acquired data, it is possible to improve both efficiency and safety.

Note that the target value may be updated after the second week of the PDCA cycle. For example, based on the analysis and evaluation results of the first week of the PDCA cycle, managers can set new target values and update the target values for the second week, thereby making further improvements in terms of efficiency and safety. can be achieved. Furthermore, regarding the evaluation method, evaluation points and number of ranks can be set as appropriate.

18 to 20 show other examples of visualization by the server 80.

FIG. 18 is a graph showing the operating status of each forklift 90, visualized by displaying the presence or absence of cargo separately. The graph in FIG. 18 can be used to manage the operation of the forklift 90.

FIG. 19 is a graph that visualizes the safety of roads inside warehouses. FIG. 19 shows that the acceleration g values in the X direction, Y direction, and Z direction, as well as the rotational angular velocity around the yaw axis, have reached the threshold values GX1, GX2, GY1, GY2, GZ1, GZ2, YA1, and YA2 from the trigger information. It visualizes whether it has exceeded or fallen below. Locations where the acceleration or rotational angular velocity is greater than or below the threshold are locations where the road surface is damaged. The server 80 determines where the acceleration or rotational angular velocity is large in the graph of FIG. Identify your location within. In this way, the graph in FIG. 19 can be used to help plan maintenance within the warehouse.

FIG. 20 is a graph that visualizes the voltage value of the battery. According to the graph of FIG. 27, it can be easily determined whether or not the voltage of the batteries BT1 and BT2 of each forklift 90 exceeds the upper and lower threshold values V1 and V2 and approaches the maximum value or the minimum value. Therefore, by instructing a driver whose voltage value exceeds the threshold value V1 or V2 during work so that the voltage falls between the threshold value V2 and V1, it is possible to extend the life of the battery.

In the above embodiment, an example was shown in which the server 80 analyzes the behavior of the forklift 90 and evaluates the analysis results, but the office PC 30 or the like may perform part or all of the processing performed by the server 80. .

For example, the on-vehicle device 10 can detect changes in the physical condition of the driver by analyzing images taken of the driver while riding a forklift, and notify the driver of any abnormalities, so that the on-vehicle device 10 can detect changes in the driver's physical condition and notify the driver of abnormalities. Helpful for driver health management.

Furthermore, the service provider may provide the analyzed and acquired data to an external specialist, such as a campus layout improvement consultant or a healthcare consultant. Collaboration between service providers and external specialists makes it possible to provide high value-added services to customers.

<Data analysis example>
Next, an example of analysis of various data collected by the onboard device 10 will be described with reference to FIGS. 21 to 31. The data collected by the on-vehicle device 10 is saved as an on-vehicle device generated file for each record at intervals of, for example, 0.5 seconds. The following includes a description of the cargo handling vehicle management method in the cargo handling vehicle management system 1. Note that this cargo handling vehicle management method is executed by a program installed in advance in the onboard equipment 10, the server 80, the office PC 30, and the like.

<Overview of data analysis>
The purpose of the data analysis is to quantitatively confirm the operating status of the forklift 90 and to understand the characteristics of the crew members. Regarding the operation status of the forklift 90, we can understand the trends of the operation status by month, day of the week, and time of day, quantitatively understand the issues with the warehouse layout and work flow, and quantitatively evaluate the improvement effects. The purpose is to confirm. It also aims to understand the characteristics of crew members and utilize them for personnel evaluation and guidance of crew members.

The data analysis described below includes analysis performed using fixed beacons for each area where fixed beacons are installed, and analysis performed without using fixed beacons. Various analysis results are displayed on the screen of the display unit 33 in the office PC 30, for example, as a dashboard including one or more tables or graphs. By visually checking the dashboard, the administrator can understand the analysis results and check the progress towards goals such as safety indicators, for example.

Types of data analysis include operation analysis, work analysis, and time series analysis. Operational analysis means analyzing operational efficiency in terms of ratios. Job analysis means analyzing measurements in a specific job. Time series analysis means analyzing changes in measured values over time.

Items used for data analysis include sensor values and calculated values. The sensor values include a G-sensor X-axis value indicating sudden acceleration/deceleration of the forklift 90, a G-sensor Y-axis value indicating left-right shaking of the forklift 90, a gyro sensor yaw angle value indicating a sudden turn of the forklift 90, and a battery voltage. Contains value.

The calculated values include each value of working time, residence time, cargo handling time, number of cargo handling, operating time, number of entries, and route. The working time is the time during which the forklift 90 was working, that is, the time from when the ignition is turned on until it is turned off, and corresponds to the total number of records in the target operation day file among the onboard equipment generated files. The stay time is the time during which the radio waves of the beacons installed in each area were detected, and corresponds to the total number of records for each area in "installed beacon acquisition results" described later. The cargo handling time is the time during cargo handling work, that is, the time when it is determined that there is cargo (cargo present) during loading and unloading of pallets, and corresponds to the total number of records of "cargo presence = 1", which will be described later.

The number of cargo handling is the number of times the forklift 90 changes from the state without cargo to the state with cargo, and corresponds to the total number of records of "cargo presence/absence_cumulative=1". The item “cargo presence/absence_total” indicates the number of records in which the state of “cargo presence” is consecutive. That is, the number of cargo handling is the result of counting the period from when the forklift 90 inserts the claw into the pallet to when it releases the claw as one cargo handling, and is expressed by the numerical value of "cargo presence/absence_cumulative=1".

The operating time is the time during which the forklift 90 is traveling, and corresponds to the number of records of "travelling = 1". The number of entries is the number of times radio wave detection from any area beacon is started, and corresponds to the total number of records of "installed beacon acquisition result_cumulative=1". The route is the number of times for each moving route between areas, and corresponds to the number of beacon ID values before and after the "installed beacon acquisition result" is switched as the moving route (="before_after"). That is, the "movement route" means moving from one of two areas to the other. In addition, when a radio wave is not detected from any area beacon, it is recorded as beacon ID:0, for example.

FIG. 21 is a diagram showing an example of installing fixed beacons in a warehouse. The warehouse shown in FIG. 21 is provided with a temporary storage area 112, an elevator 114, and a plurality of shelves 116. The temporary storage area 112 is a place where, for example, cargo that has been unloaded from a truck and brought into a warehouse is temporarily placed. Luggage is placed on each of the plurality of shelves 116, but the shelves 116 may not be provided and the load may be placed on the floor. The warehouse includes a plurality of areas R1, R2, R3, R4, and R5 (hereinafter sometimes referred to as area R), and each area R includes beacons B1, B2, and B2 that cover each area R, respectively. B3, B4, and B5 (hereinafter sometimes referred to as beacons B) are each fixedly installed. The package placed in the temporary storage area 112 is placed on one of the shelves 116 determined according to the destination of the package.

In the example shown in FIG. 21, the interior of the warehouse is partitioned into left and right sections by passages extending along the broken line L1. Beacons B1 and B5 are arranged on the left side of the broken line L1, and beacons B2, B3, and B4 are placed on the right side of the broken line L1. Beacon B can be placed at a position where detailed analysis is desired, and the number and location of beacons B can be set as appropriate, such as placing additional beacons at positions B6 and B7.

When a forklift 90 traveling in a warehouse enters an area R1, an on-vehicle device 10 mounted on the forklift 90 receives radio waves emitted from a beacon B1, and acquires the time at which the radio waves are received as time information. The onboard device 10 also extracts the beacon ID of the beacon B1 from the received radio wave, and records the time information and beacon ID in the onboard device generation file. While the forklift 90 stays in the area R1, the onboard device 10 receives a radio wave including the beacon ID from the beacon B1, and sets the numerical value corresponding to this beacon ID together with time information indicating the time when the radio wave was received. It is recorded in the onboard device generated file as "Beacon acquisition result". The onboard device 10 records the number of records in which the same beacon ID is recorded continuously in the onboard device generation file as "installed beacon acquisition result_total". If the onboard device 10 could not receive radio waves from the beacon B1, that is, the reception strength of the radio waves was less than a predetermined value, the onboard device generates information indicating this time as the exit time when the forklift 90 left the area R1. Record in file. Similarly, the time when the forklift 90 that left area R1 entered area R2, etc. is recorded in the onboard equipment generation file. When the installation beacon acquisition result switches from B1 to B2, for example, the onboard device 10 counts "1_2" as the "route".

In this way, the above-mentioned calculated values and sensor values including the beacon ID acquisition result are recorded as time-series data in the onboard device generation file. The onboard device generation file includes the onboard device serial number, office code, vehicle code, and crew ID. Since the vehicle code is recorded in the master database in association with the vehicle type information, it is possible to determine from the vehicle code whether the forklift 90 is a reach vehicle or a counter vehicle. Furthermore, since the vehicle code is associated with the crew ID in the crew database, it is possible to identify the other from either the vehicle code or the crew ID. The onboard device generation file is transmitted from the onboard device 10 to the server 80 after the forklift 90 finishes its work, for example, after the day's work is finished. The server 80 accumulates the onboard device generation files received from the plurality of onboard devices 10 in the database 85 and uses them for behavior analysis of the forklift 90. Note that the onboard device 10 may transmit the onboard device generated file to the server 80 in real time.

Next, with reference to FIGS. 22 to 31, the results of data analysis performed by the server 80 using the onboard device generation file acquired and generated by the onboard device 10 will be described.

FIG. 22 shows an example of a cargo handling dashboard. The dashboard 401 shown in FIG. 22 includes a graph 4011 showing the ratio of cargo handling time to working time, a graph 4012 showing the average working time per cargo handling, and a map 4013. Dashboard 401 includes areas 4014, 4015, 4016, and 4017 for setting conditions for analysis. Area 4014 is an area for specifying the target month, area 4015 is the target vehicle code, area 4016 is the correspondence between the vehicle code and display color, and area 4017 is the target installation beacon ID.

A graph 4011 shows the ratio between the working time of each vehicle and the cargo handling time on each date. The graph 4011 includes a plurality of bar graphs arranged in the vertical direction, and includes two bar graphs for each date, in which the data of the reach vehicle in the upper row and the counter vehicle in the lower row are shown differentiated by color coding, for example. The horizontal axis of the graph 4011 indicates working time and cargo handling time.

Graph 4012 shows the average working time per cargo handling for each vehicle as a line graph, the top two graphs show data for reach vehicles, and the bottom two graphs show data for counter vehicles. The average working time per cargo handling is the result of dividing the cargo handling time by the number of cargo handling, and is shown on the right vertical axis (unit: minutes). The horizontal axis shows the date, and the vertical axis on the left side shows the number of cargo handled as shown in the vertical bar graph. This number of cargo operations indicates the number of cargo operations per day calculated from the number of cargo operations for one week.

The map 4013 is a map showing the warehouse layout with circular symbols showing the length of stay in each area superimposed on the map. The longer the stay time, the larger the circle will be displayed.

According to the dashboard 401, the operating status, work time reduction and average work amount in cargo handling work can be grasped on a monthly basis. Therefore, it can be used for setting target values for the next month and creating shifts.

From the dashboard 401, the ratio of cargo handling time to working time is approximately 40%. This ratio is slightly higher for reach vehicles, but since counter vehicles handle more cargo than counter vehicles, it can be seen that reach vehicles take longer to load cargo, but handle less cargo than counter vehicles. It can also be seen that the working time per cargo handling is similarly shorter for counter vehicles.

FIG. 23 shows an example of a work analysis dashboard. The dashboard 402 shown in FIG. 23 includes an operating time graph 4021, route count tables 4022 and 4023, and a route count map 4024. Dashboard 402 includes areas 4025, 4026, 4027, and 4028 for setting conditions for analysis. An area 4025 is an area for specifying the target month, an area 4026 is an area for specifying the target date, an area 4027 is an area for specifying the target time, and an area 4028 is an area for specifying the correspondence between the vehicle code and display color.

The operating time graph 4021 shows the operating time of each vehicle on a specified date, with the upper row showing data for reach vehicles and the lower row showing data for counter vehicles. In the operating time graph 4021, the horizontal axis indicates time, and the presence or absence of cargo is displayed by color. The operating hours of each vehicle on the specified date are shown, with the upper row showing data for reach vehicles and the lower row showing data for counter vehicles. In the operating time graph 4021, the horizontal axis indicates time, and the presence or absence of cargo is displayed by color.

Route count tables 4022 and 4023 display the number of times a vehicle has used a travel route by time period. The route count table 4022 shows data on reach vehicles, and the route count table 4023 shows data on counter vehicles, and the number of times a travel route is used is displayed for each vehicle. For example, "1_2" indicates the route from area R1 to area R2 shown in FIG. 21. The number of times a travel route has been used in each time period is displayed numerically, and the display is color-coded according to the number of times the route has been used. The route count tables 4022 and 4023 display the number of times each travel route is used, but may also be shown as a ratio of the number of times each route is used in each time period to the total number of uses of all routes.

The route count map 4024 is a map showing the layout of the warehouse with symbols corresponding to each movement route and the number of times each movement route is used superimposed. According to the route count map 4024, travel routes that are frequently used by time zone can be confirmed on the map, making detailed analysis possible.

According to the dashboard 402, frequently used routes and work efficiency can be grasped. Therefore, it can be used for guidance on work improvement, changing the internal layout of the refrigerator, and measuring the effects after changing the layout. According to the dashboard 402, both the reach vehicle and the counter vehicle have routes “1_4”, “4_1”, “4_5”, and “5_4” that cross the broken line L1 shown in FIG. 21 and move left and right in the warehouse. It can be seen that there are almost no Reach vehicles have many routes compared to counter vehicles, but it can be seen that routes ``1_5'' and ``5_1'' are used noticeably more frequently.

FIG. 24 shows an example of a cargo handling dashboard. The dashboard 403 shown in FIG. 24 includes a graph 4031 showing the ratio of cargo handling time to working time, a graph 4032 showing the number of cargo handling by time zone and the average working time per cargo handling, a working time graph 4033, and a map 4034. Dashboard 403 includes areas 4035, 4036, and 4037 for setting conditions for analysis. Area 4035 is an area for specifying the target month, area 4036 is the target time period, and area 4037 is the target installation beacon ID.

A graph 4031 is similar to the graph 4011, and shows the ratio between the working time of each vehicle and the cargo handling time on each date. The graph 4031 includes a plurality of bar graphs arranged in the vertical direction, and includes two bar graphs for each date, with the data of the reach vehicle in the upper row and the counter vehicle in the lower row being distinguished by color coding, for example. The horizontal axis of the graph 4031 indicates working time and cargo handling time.

Graph 4032 shows the average working time per cargo handling for each vehicle as a line graph, the top three graphs show data for reach vehicles, and the bottom three graphs show data for counter vehicles. The average working time per cargo handling is the result of dividing the cargo handling time by the number of cargo handling, and is shown on the right vertical axis (unit: minutes). The horizontal axis shows the date, and the vertical axis on the left side shows the number of cargo handled as shown in the vertical bar graph.

The operating time graph 4033 is a graph similar to the operating time graph 4021.
Map 4034 is a map similar to map 4013.

According to the dashboard 403, similarly to the dashboard 401, it is possible to grasp the operating status, work time reduction, and average amount of work in cargo handling work on a monthly basis. Therefore, it can be used for setting target values for the next month and creating shifts.

FIG. 25 shows an example of a work analysis dashboard. The dashboard 404 shown in FIG. 25 includes route count tables 4041 and 4042, a pie chart 4043, and a graph 4044. Dashboard 404 includes areas 4045 and 4046 for setting conditions for analysis. Area 4045 is an area for specifying the target month, and area 4046 is an area for specifying the target year, month, and day.

The route count tables 4041 and 4042 are graphs similar to the route count tables 4022 and 4023, and display the number of times the travel route used by the vehicle is used by time period. The route count table 4041 is data for reach vehicles, and the route count table 4042 is data for counter vehicles, which are displayed for each vehicle. For example, "1_2" indicates the route from area R1 to area R2 shown in FIG. 21. The number of times a travel route has been used in each time period is displayed numerically, and the display is color-coded according to the number of times the route has been used. The route count tables 4022 and 4023 display the number of times each travel route is used, but may also be shown as a ratio of the number of times each route is used in each time period to the total number of uses of all routes.

The pie chart 4043 is a pie chart that shows the presence or absence of cargo, and by specifying the vehicle, date, time zone, and travel route, it shows the ratio of the state with cargo to the state without cargo among the travel time under the specified conditions. shows.

The graph 4044 is a line graph showing the presence or absence of cargo, with the horizontal axis showing time and the vertical axis showing the value indicating the presence or absence of cargo. The graph 4044 can display, for example, the presence or absence of cargo for each record.

According to the dashboard 404, frequently used routes and work efficiency can be grasped. Therefore, it can be used for guidance on work improvement, changing the internal layout of the refrigerator, and measuring the effects after changing the layout. For example, if the number of uses for a particular time period and route shown in the route count table 4041 is extremely high, by specifying the corresponding cell on the screen, the percentage of cargo presence can be displayed in the pie chart 4043 and graph 4044. It can be displayed. Therefore, more detailed analysis becomes possible.

FIG. 26 is an example of a cargo handling dashboard. The dashboard 405 shown in FIG. 26 includes a graph 4051 showing the ratio of cargo handling time to working time, and a graph 4052 showing the average working time per cargo handling. Dashboard 405 includes areas 4053, 4054, and 4055 for setting conditions for analysis. An area 4053 is an area for specifying the target month, an area 4054 is an area for specifying the target vehicle code, and an area 4055 is an area for specifying the display color of the line graph and bar graph included in the graph 4052.

Graph 4051 shows the ratio of work time to cargo handling time on each date for each vehicle. The graph 4051 shows data for reach vehicles on the left and counter vehicles on the right. Graph 4051 includes a plurality of bar graphs arranged in the vertical direction, and each bar graph indicates the ratio of work time to cargo handling time in each time period. The horizontal axis of the graph 4011 indicates working time and cargo handling time.

Graph 4052 is similar to graph 4012, and shows the average working time per cargo handling for each vehicle as a line graph, with the upper graph showing data for reach vehicles and the lower graph showing data for counter vehicles. The average working time per cargo handling is the result of dividing the cargo handling time by the number of cargo handling, and is shown on the right vertical axis (unit: minutes). The horizontal axis shows the date, and the vertical axis on the left side shows the number of cargo handled as shown in the vertical bar graph.

According to the dashboard 405, the operating status, work time reduction and average work amount in cargo handling work can be grasped on a monthly basis. Therefore, it can be used for setting target values for the next month and creating shifts.

FIG. 27 shows an example of a cargo handling dashboard. The dashboard 406 shown in FIG. 27 includes a graph 4061 showing the average number of cargo handling by time zone and a working time graph 4062. Dashboard 406 includes areas 4063, 4064, 4065, and 4066 for setting conditions for analysis. Area 4063 is an area for specifying the target month, area 4064 is the target vehicle code, area 4065 is the target year, month and day, and area 4066 is the target time zone.

In the graph 4061, the horizontal axis shows time, the vertical axis shows the average number of cargo handling, and shows the average number of cargo handling for each time period for each vehicle.

The operating time graph 4062 shows the operating time of each vehicle on the specified date, with the upper row showing the data for the reach vehicle and the lower row showing the data for the counter vehicle. In the operating time graph 4062, the horizontal axis indicates time, and the presence or absence of cargo is displayed by color. The operating time graph 4062 shows the operating status of the forklift 90, including the presence or absence of cargo, during the target time period specified in the area 4066.

According to the dashboard 406, the average amount of work and operating status by time period can be grasped. Therefore, more detailed analysis than the dashboard 401 is possible.

FIG. 28 is an example of a standard dashboard. Dashboard 407 shown in FIG. 28 includes graphs 4071, 4072, and 4073. Dashboard 407 includes areas 4074, 4075, 4076, and 4077 for setting conditions for analysis. An area 4074 is an area for specifying the target month, an area 4075 is an area for specifying the correspondence between the target vehicle code and display color, an area 4076 is an area for specifying the target year, month and day, and an area 4077 is an area for specifying the target time.

Graph 4071 is a scatter diagram in which the horizontal axis indicates the value of the G sensor X axis and the vertical axis indicates the value of the gyro sensor yaw angle. The graph 4072 is a scatter diagram in which the horizontal axis indicates the value of the G sensor Y axis and the vertical axis indicates the value of the gyro sensor yaw angle. In graphs 4071 and 4072, data for each vehicle is displayed in different colors, for example.

In the graph 4073, the horizontal axis represents time, the vertical axis represents battery voltage value, the upper graph represents data for the reach vehicle, and the lower graph represents data for the counter vehicle.

According to the dashboard 407, it is possible to grasp the correspondence between sudden acceleration/deceleration, side-to-side shaking, and sharp turns of the forklift 90 and changes in the battery voltage value for each vehicle. Further, since the crew member corresponding to the vehicle can be identified based on the correspondence between the vehicle code and the crew member ID, the dashboard 407 can be utilized for personnel evaluation and guidance of the crew member.

FIG. 29 shows an example of a cargo handling dashboard. Dashboard 408 shown in FIG. 29 includes graphs 4081, 4082, and 4083. Dashboard 408 includes areas 4084, 4085, and 4086 for setting conditions for analysis. Area 4084 is an area for specifying the target business office code, area 4085 is the target vehicle code, and area 4086 is the area for specifying the target year and month.

Graph 4081 shows the ratio of cargo handling time to daily working time by crew, date, and vehicle. The top two bar graphs show data on vehicles and crew members belonging to office A, and the other bar graphs show data on vehicles and crew members belonging to office B. The horizontal axis shows the average working time by date (unit: hours) for each month.

Graph 4082 shows the number of cargo handled by crew, date, and vehicle. The top two bar graphs show data on vehicles and crew members belonging to office A, and the other bar graphs show data on vehicles and crew members belonging to office B. The horizontal axis shows the average number of cargo handled by date for each month.

Graph 4083 shows the average working time per cargo handling. The top two bar graphs show data on vehicles and crew members belonging to office A, and the other bar graphs show data on vehicles and crew members belonging to office B. The horizontal axis shows the monthly average working time per cargo handling (unit: seconds).

According to the dashboard 408, the operating status, work time reduction and average work amount in cargo handling work can be grasped on a monthly basis. Therefore, it can be used for setting target values for the next month and creating shifts.

FIG. 30 is an example of a time series analysis dashboard. Dashboard 409 shown in FIG. 30 includes graphs 4091, 4092, and map 4093. Dashboard 409 includes areas 4094 and 4095 for setting conditions for analysis. An area 4094 is an area for specifying the target installed beacon ID, and an area 4095 is an area for specifying which time data is to be displayed on the map 4093.

Graph 4091 is a line graph showing changes in average sensor values (absolute values) by time zone. On the vertical axis, the upper row shows the G sensor X-axis value, the middle row shows the G sensor Y-axis value, and the lower row shows the gyro sensor yaw angle value, and the horizontal axis shows time. In each row of graphs, solid lines indicate data for reach vehicles and dotted lines indicate data for counter vehicles.

Graph 4092 shows the average working time per cargo handling by area. The horizontal axis shows time, and the vertical axis shows the average working time per cargo handling. Graph 4092 shows the average working time in the area specified in area 4094.

The map 4093 is a map showing the warehouse layout with circular symbols showing the average acceleration in each area superimposed on the map. The larger the average acceleration, the larger the diameter of the circle displayed. The symbols are displayed in different colors depending on the vehicle.

According to the dashboard 409, sudden acceleration/sudden turning areas can be confirmed. Therefore, it can be used for guidance on work improvement, driving guidance for crew members, changing the internal layout of the warehouse, and measuring the effects after changing the layout.

According to the dashboard 409, when looking at the trends in each sensor value and the working time per cargo handling in the area R3 where the beacon B3 shown in Fig. 21 is installed, the working time is shorter for the counter vehicle than for the reach vehicle. Sensor value is low. Therefore, counter vehicles are mainly in this area to load cargo into the elevator 104, but reach vehicles are thought to pass by more often loaded with cargo from other areas than to load cargo into the elevator 104. It will be done. Overall, it can be seen that the sensor value is higher for the reach vehicle than for the counter vehicle in all areas, but the value in area R3 is particularly high.

FIG. 31 is an example of a time series analysis dashboard. Dashboard 410 shown in FIG. 31 includes graphs 4101, 4102, and 4103. Dashboard 410 includes areas 4104, 4105, 4106, and 4107 for setting conditions for analysis. Area 4104 is an area for specifying the target vehicle code, area 4105 is the target business office code, area 4106 is the target date, and area 4107 is the target time.

A graph 4101 is a bar graph showing working hours by time zone at the target business office. The top shows data for reach vehicles, and the bottom shows data for counter vehicles. The vertical axis shows the average work time (in seconds) for cargo handling work by time zone, and the horizontal axis shows the time zone.

Graph 4102 is a bar graph showing the number of cargo operations by time zone at the target business office. The top shows data for reach vehicles, and the bottom shows data for counter vehicles. The vertical axis shows the average number of cargo handling, and the horizontal axis shows the time period.

A graph 4103 shows the time-series operating status at the specified date and office. A graph 4103 shows the operating time of each vehicle, with the upper row showing the data for the reach vehicle and the lower row showing the data for the counter vehicle. In the graph 4103, the horizontal axis indicates time, and the presence or absence of cargo is displayed by color.

According to the dashboard 410, it is possible to grasp the operation status by time zone, work time reduction in cargo handling work, and average work amount.

Note that the present invention is not limited to the embodiments described above, and can be modified, improved, etc. as appropriate. In addition, the material, shape, size, numerical value, form, number, arrangement location, etc. of each component in the embodiments described above are arbitrary as long as they can achieve the present invention, and are not limited. For example, the mark to be displayed superimposed on the map is not limited to a circle, but may have any shape such as a square or a star, and the display mode is not limited. Further, marks may be superimposed on the map depending on the number of cargo handled.

Here, the features of the cargo handling vehicle management system, server, cargo handling vehicle management method, and program according to the embodiments of the present invention described above are briefly summarized and listed below in [1] to [9].

[1] A cargo handling vehicle management system (1) comprising an onboard device (10) mounted on a cargo handling vehicle (forklift 90), and a server (80) that analyzes data collected by the onboard device,
The on-vehicle device (10) is
A receiving unit (beacon receiving unit 15) that receives radio waves including identification information (beacon ID) of the transmitters, which are emitted from transmitters (beacons B) installed in each of the plurality of areas (R) in the facility;
a time acquisition unit (RTC unit 21, CPU 11) that acquires time information indicating an entry time when the cargo handling vehicle entered any one of the plurality of areas based on the intensity of the received radio waves;
an extraction unit (CPU 11) that extracts the identification information from the received radio waves;
a recording unit (17) that records the acquired time information and the extracted identification information in chronological order;
The server (80)
a calculation unit that specifies a travel route between the plurality of areas based on the plurality of time information and the identification information recorded by the recording unit, and calculates the number of times the cargo handling vehicle has traveled along the travel route; CPU81) and
An output unit (communication unit 82) that outputs the calculated result,
Cargo handling vehicle management system.

According to the cargo handling vehicle management system configured as described in [1] above, it is possible to grasp frequently used travel routes. Therefore, this cargo handling vehicle management system can be used to change the layout within a facility, improve work efficiency, and measure the effects after changing the layout.

[2] The calculation unit generates at least one of a table, a graph, and a map based on the number of times,
the output unit outputs at least one of the generated table, graph, and map;
The cargo handling vehicle management system according to [1] above.

[3] The calculation unit generates a dashboard (402, 404) including at least one of the plurality of tables, graphs, and maps;
the output unit outputs the generated dashboard;
The cargo handling vehicle management system according to [2] above.

[4] The dashboard (402) includes a table (route count tables 4022, 4023) showing the number of times for each time period, and a map (route count map 4024) showing the travel route.
The cargo handling vehicle management system according to [3] above.

According to the cargo handling vehicle management system configured as described in [4] above, for example, it is possible to check on a map the routes that are frequently traveled by time zone.

[5] In the on-vehicle device,
The recording unit records cargo presence information indicating whether the cargo handling vehicle is loaded or unloaded, in association with the time information;
In the server,
The calculation unit generates a table or graph (pie graph 4043, graph 4044) regarding the presence or absence of cargo based on the time information and the cargo presence information recorded by the recording unit,
The output unit outputs the dashboard (404) including at least one of a table, a graph, and a map regarding the presence or absence of cargo;
The cargo handling vehicle management system according to [3] or [4] above.

According to the cargo handling vehicle management system configured as described in [5] above, it is possible to simultaneously check the operating status of cargo handling vehicles, for example, for routes that are frequently traveled.

[6] The table or graph (circle graph 4043) regarding the presence or absence of cargo indicates the proportion of time in the cargo-free state or the cargo-carrying state in the operating time of the cargo handling vehicle during the designated time period;
The cargo handling vehicle management system according to [5] above.

According to the cargo handling vehicle management system configured as described in [6] above, it is possible to simultaneously check the operating status of cargo handling vehicles, for example, for routes that have a large number of movements in each time period.

[7] A server (80) that analyzes data collected by an onboard device mounted on a cargo handling vehicle,
The onboard device is
a receiving unit that receives radio waves including identification information of the transmitters, which are emitted from transmitters installed in a plurality of areas within the facility;
a time acquisition unit that acquires time information indicating an entry time when the cargo handling vehicle entered any one of the plurality of areas based on the intensity of the received radio waves;
an extraction unit that extracts the identification information from the received radio waves;
a recording unit that records the acquired time information and the extracted identification information in chronological order;
The server is
a calculation unit that specifies a travel route between the plurality of areas based on the plurality of time information and the identification information recorded by the recording unit and calculates the number of times the cargo handling vehicle has traveled along the travel route; ,
an output unit that outputs the calculated result;
server.

[8] A cargo handling vehicle management method in a cargo handling vehicle management system (1) comprising an onboard device (10) mounted on a cargo handling vehicle and a server (80) that analyzes data collected by the onboard device,
Receive radio waves containing identification information of the transmitters emitted from transmitters installed in multiple areas within the facility,
Obtaining time information indicating an entry time when the cargo handling vehicle entered any one of the plurality of areas based on the intensity of the received radio waves;
Extracting the identification information from the received radio waves,
Recording the acquired time information and the extracted identification information in chronological order,
Based on the plurality of recorded time information and the identification information, specifying a travel route between the plurality of areas, calculating the number of times the cargo handling vehicle has traveled along the travel route,
output the calculated results,
Cargo handling vehicle management method.

[9] A program for causing a computer to execute the cargo handling vehicle management method.

1 Cargo handling vehicle management system 10 Onboard equipment 11, 31, 81 Control unit (CPU)
15 Beacon receiving section 17 Recording section 23A, 23B Cameras 24, 32, 82 Communication section 25 Power supply section 26A Nonvolatile memory 26B Volatile memory 28 G sensor 30 Office PC
51 Vehicle speed sensor 52 Gyro sensor 53 Battery voltmeter 65 Memory card 71 Base station 80 Server 85 Database 90 Forklift 91 Claw 92 Backrest 93 Mast 94 Head guard 100 Luggage 110 Pallet 112 Temporary storage area 116 Shelf B, BA, BB, BC, BD , BE, BE, BF, BG Beacon B1, B2, B3, B4, B5 Beacon R, RA, RB, RC, RD, RE, RE, RF, RG Area R1, R2, R3, R4, R5 Area 401, 402 , 403, 404, 405, 406, 407, 408, 409, 410 Dashboard

Claims (9)

A cargo handling vehicle management system comprising an onboard device mounted on a cargo handling vehicle and a server that analyzes data collected by the onboard device,
The onboard device is
a receiving unit that receives radio waves including identification information of the transmitters, which are emitted from transmitters installed in a plurality of areas within the facility;
a time acquisition unit that acquires time information indicating an entry time when the cargo handling vehicle entered any one of the plurality of areas based on the intensity of the received radio waves;
an extraction unit that extracts the identification information from the received radio waves;
a recording unit that records the acquired time information and the extracted identification information in chronological order;
The server is
a calculation unit that specifies a travel route between the plurality of areas based on the plurality of time information and the identification information recorded by the recording unit and calculates the number of times the cargo handling vehicle has traveled along the travel route; ,
an output unit that outputs the calculated result;
Cargo handling vehicle management system. The calculation unit generates a table or a graph based on the number of times,
The output unit outputs the generated table or graph,
The cargo handling vehicle management system according to claim 1. The calculation unit generates a dashboard including a plurality of the tables or graphs,
the output unit outputs the generated dashboard;
The cargo handling vehicle management system according to claim 2. The dashboard includes a table showing the number of times by time zone, and a map showing the travel route.
The cargo handling vehicle management system according to claim 3. In the on-vehicle device,
The recording unit records cargo presence information indicating whether the cargo handling vehicle is loaded or unloaded, in association with the time information;
In the server,
The calculation unit generates a table or a graph regarding the presence or absence of cargo based on the time information and the cargo presence information recorded by the recording unit,
The output unit outputs the dashboard including a table or graph regarding the presence or absence of cargo;
The cargo handling vehicle management system according to claim 3 or 4. The table or graph regarding the presence or absence of cargo shows the proportion of the time in the cargo-free state or the cargo-carrying state in the operating time of the cargo handling vehicle during a specified time period.
The cargo handling vehicle management system according to claim 5. A server that analyzes data collected by onboard equipment installed on a cargo handling vehicle,
The onboard device is
a receiving unit that receives radio waves including identification information of the transmitters, which are emitted from transmitters installed in a plurality of areas within the facility;
a time acquisition unit that acquires time information indicating an entry time when the cargo handling vehicle entered any one of the plurality of areas based on the intensity of the received radio waves;
an extraction unit that extracts the identification information from the received radio waves;
a recording unit that records the acquired time information and the extracted identification information in chronological order;
The server is
a calculation unit that specifies a travel route between the plurality of areas based on the plurality of time information and the identification information recorded by the recording unit and calculates the number of times the cargo handling vehicle has traveled along the travel route; ,
an output unit that outputs the calculated result;
server. A cargo handling vehicle management method in a cargo handling vehicle management system comprising an onboard device mounted on a cargo handling vehicle and a server that analyzes data collected by the onboard device, the method comprising:
Receive radio waves containing identification information of the transmitters emitted from transmitters installed in multiple areas within the facility,
Obtaining time information indicating an entry time when the cargo handling vehicle entered any one of the plurality of areas based on the intensity of the received radio waves;
Extracting the identification information from the received radio waves,
Recording the acquired time information and the extracted identification information in chronological order,
Based on the plurality of recorded time information and the identification information, specifying a travel route between the plurality of areas, calculating the number of times the cargo handling vehicle has traveled along the travel route,
output the calculated results,
Cargo handling vehicle management method. A program for causing a computer to execute the cargo handling vehicle management method according to claim 8.

Images