Work assistance method and work assistance system

Abstract

Provided are a work assistance method and a work assistance system with which it is possible for an operator who works in a work area to accurately recognize an unmanned transport vehicle. Recording data in various environments in a working area (41) is collected, and a sound model is constructed by mechanical learning using sound feature amounts extracted from the recording data as learning data. When an operator wearing smart glasses (10) equipped with a sound model works in a work area (41), an unmanned transport vehicle (90) travels in the work area (41) while emitting a warning sound. The smart glasses (10) detect that the unmanned transport vehicle (90) emits a warning sound and issues a warning by inputting a sound feature amount extracted from the collected sound data into a sound model. Even if the operator does not notice the warning sound emitted by the unmanned transport vehicle (90), the operator can accurately recognize the unmanned transport vehicle (90) through the warning of the smart glasses (10).

Description

Technical Field

[0001] The present invention relates to a method and system for assisting operators in performing work in a work area where an automated transport vehicle (AGV) travels and where industrial equipment, such as a substrate processing apparatus, is located to perform predetermined processes on substrates. Substrates processed by the substrate processing apparatus include, for example, semiconductor substrates, substrates for liquid crystal displays (LCDs), substrates for flat panel displays (FPDs), substrates for optical disks, substrates for magnetic disks, and substrates for solar cells. Background Art

[0002] Conventionally, substrate processing equipment has been used in the manufacturing process of semiconductor devices to perform various processes on substrates such as semiconductor substrates. Examples of such equipment include substrate cleaning equipment, heat treatment equipment, and inspection equipment. Typically, multiple substrate processing equipment are neatly arranged in a spacious clean room. Maintenance of these substrate processing equipment is performed at appropriate intervals. Patent Document 1 describes a method of arranging multiple substrate processing equipment at a relatively high density within a clean room and performing maintenance on these equipment.

[0003] Within a clean room equipped with multiple substrate processing equipment, automated guided vehicles (AGVs) transport containers containing substrates. Patent Document 2 describes an AGV carrying containers containing substrates that autonomously travels along a predetermined path within the clean room. The AGV delivers unprocessed substrates to the substrate processing equipment and retrieves processed substrates from the equipment. Operators also operate and maintain the substrate processing equipment within the clean room. In other words, these operators perform their work while the AGV is operating within the clean room.

[0004] [Background Art Literature]

[0005] [Patent Document]

[0006] [Patent Document 1] Japanese Patent Application Laid-Open No. 2020-4866

[0007] [Patent Document 2] Japanese Patent Application Laid-Open No. 2020-189732 Summary of the Invention

[0008] [Problems to be solved by the invention]

[0009] Cleanrooms are visually complex environments filled with multiple substrate processing equipment, tools, and components. Consequently, workers working in cleanrooms experience visual fatigue, making it difficult to notice surrounding conditions or the movement of objects, potentially colliding with automated guided vehicles (AGVs).

[0010] To avoid such dangers, automated conveyance vehicles (AGVs) emit warning sounds while in operation. However, the robots and pumps in substrate processing equipment located within the cleanroom also generate noise. If an AGV continuously emits warning sounds in these situations, operators, focused on their work, may become accustomed to the warning sounds and fail to recognize the AGV. Furthermore, if an operator is working very close to a robot or pump, the noise from the robot or pump itself may prevent the operator from hearing the warning sound.

[0011] The present invention has been made in view of the above-mentioned problems, and an object of the present invention is to provide a work assisting method and a work assisting system that enable an operator working in a work area to accurately identify an automated guided vehicle.

[0012] [Technical means to solve the problem]

[0013] In order to solve the above-mentioned problem, the first form of the present invention is a work assistance method for an operator when working in a work area where industrial equipment is configured and an unmanned guided vehicle is used to travel, and the method comprises: a model construction process, constructing a sound model for identifying the warning sound emitted when the unmanned guided vehicle moves; a sound collection process, when an operator wearing a mobile terminal equipped with a display unit, a communication unit and a sound collection unit works in the above-mentioned work area, the sound collection unit collects the sound; a judgment process, determining whether the unmanned guided vehicle emits a warning sound by inputting the sound collected by the sound collection unit into the sound model; and a release process, when it is determined in the judgment process that the unmanned guided vehicle emits a warning sound, the mobile terminal releases a warning.

[0014] In addition, the second form is a work assistance method according to the first form, wherein the model construction process includes: a recording process, performing recording in the state where the unmanned guided vehicle emits a warning sound and recording in the state where the unmanned guided vehicle does not emit a warning sound in the working area; an analysis process, obtaining sound feature quantities from the recording data collected in the recording process; and a learning process, constructing the sound model through machine learning based on the labels assigned to the recording data collected in the recording process and the sound feature quantities.

[0015] In addition, a third aspect is the work assisting method according to the second aspect, wherein the sound feature is a mel-frequency cepstral coefficient, and in the determination step, the mel-frequency cepstral coefficient extracted from the sound collected by the sound collection unit is input into the sound model.

[0016] In addition, the fourth form is a work assistance method according to any one of the first to third forms, which further includes the following process, namely, when it is determined in the judgment process that the unmanned transport vehicle emits a warning sound, the sound collection unit analyzes the direction information of the warning sound, and in the issuance process, the mobile terminal issues a warning and the direction information.

[0017] In addition, a fifth aspect is the work support method according to any one of the first to fourth aspects, wherein in the issuing step, the mobile terminal displays a warning screen and emits a warning sound.

[0018] In addition, the sixth aspect is a work assist method according to any one of the first to fifth aspects, wherein the industrial equipment is a substrate processing device that performs prescribed processing on a substrate, and the mobile terminal is smart glasses.

[0019] In addition, the seventh form is a work assistance method for an operator when working in a work area where industrial equipment is configured and an unmanned guided vehicle is used to travel, and it comprises: a registration process for registering the travel path of the unmanned guided vehicle in the work area; and a display process for displaying the travel path on the mobile terminal when an operator wearing a mobile terminal equipped with a display unit, a communication unit, and a sound collection unit works in the work area.

[0020] Furthermore, an eighth aspect is the work support method according to the seventh aspect, further comprising an issuing step of causing the mobile terminal to issue a warning when the operator approaches within a predetermined range from the travel route.

[0021] In addition, a ninth aspect is the work assisting method according to the seventh or eighth aspect, wherein in the issuing step, the mobile terminal displays a warning screen and emits a warning sound.

[0022] In addition, the 10th form is a work assist method according to any one of the 7th to 9th forms, wherein the industrial equipment is a substrate processing device that performs prescribed processing on a substrate, and the mobile terminal is smart glasses.

[0023] In addition, the 11th form is a work assistance system for an operator when working in a work area where industrial equipment is configured and an unmanned guided vehicle is used to travel, and comprises: a mobile terminal having a display unit, a communication unit and a sound collection unit; a storage unit storing a sound model for identifying a warning sound emitted when the unmanned guided vehicle moves; a judgment unit for judging whether the unmanned guided vehicle emits a warning sound by inputting the sound collected by the sound collection unit when the operator wearing the mobile terminal works in the work area into the sound model; and a warning issuing unit for issuing a warning when the judgment unit determines that the unmanned guided vehicle emits a warning sound.

[0024] In addition, the 12th form is a work assistance system based on the 11th form, wherein the sound model is constructed by machine learning based on sound feature quantities obtained from recording data collected from recordings of the unmanned guided vehicle in the work area with a warning sound and recordings of the unmanned guided vehicle without a warning sound, and labels assigned to the recording data.

[0025] In addition, a thirteenth aspect is the work support system according to the twelfth aspect, wherein the voice feature is a mel-frequency cepstral coefficient, and the determination unit inputs the mel-frequency cepstral coefficient extracted from the voice collected by the sound collection unit into the voice model.

[0026] In addition, the 14th form is a work assistance system according to any one of the 11th to 13th forms, wherein when the judgment unit determines that the unmanned guided vehicle emits a warning sound, the sound collecting unit analyzes the direction information of the warning sound, and the warning issuing unit issues a warning and the direction information.

[0027] Furthermore, a fifteenth aspect is the work support system according to any one of the eleventh to fourteenth aspects, wherein the warning issuing unit displays a warning screen and emits a warning sound.

[0028] In addition, a 16th aspect is a work support system according to any one of the 11th to 15th aspects, wherein the industrial equipment is a substrate processing device that performs a predetermined process on a substrate, and the mobile terminal is smart glasses.

[0029] In addition, the 17th form is a work assistance system for an operator when working in a work area where industrial equipment is configured and an unmanned guided vehicle is used to travel, and comprises: a mobile terminal having a display unit, a communication unit and a sound collection unit; and a storage unit that stores the travel path of the unmanned guided vehicle in the work area; when an operator wearing the mobile terminal works in the work area, the mobile terminal displays the travel path.

[0030] Furthermore, an eighteenth aspect is the work support system according to the seventeenth aspect, further comprising a warning issuing unit configured to issue a warning when the operator approaches within a predetermined range from the travel path.

[0031] Furthermore, a nineteenth aspect is the work support system according to the seventeenth or eighteenth aspect, wherein the warning issuing unit displays a warning screen and emits a warning sound.

[0032] In addition, the 20th aspect is a work support system according to any one of the 17th to 19th aspects, wherein the industrial equipment is a substrate processing device that performs a predetermined process on a substrate, and the mobile terminal is a pair of smart glasses.

[0033] [Effects of the Invention]

[0034] According to the work assistance method of the first to sixth forms, the sound collected by the sound collection unit is input into the sound model to determine whether the unmanned guided vehicle has issued a warning sound. When it is determined that a warning sound has been issued, the mobile terminal issues a warning. Therefore, even if the operator does not notice the warning sound itself emitted by the unmanned guided vehicle, the operator working in the work area can accurately identify the unmanned guided vehicle through the warning of the mobile terminal.

[0035] In particular, according to the fourth aspect of the work support method, the direction information of the warning sound is analyzed, and the mobile terminal issues the warning and direction information, so the operator can also recognize the positional relationship with the automated guided vehicle.

[0036] According to the work assisting methods of the seventh to tenth aspects, when the operator is working in the work area, the mobile terminal displays the driving path of the automated guided vehicle, so the operator working in the work area can accurately identify the automated guided vehicle.

[0037] According to the work assistance system of the 11th to 16th forms, the sound collected by the sound collection unit is input into the sound model to determine whether the unmanned guided vehicle has issued a warning sound, and a warning is issued when it is determined that a warning sound has been issued. Therefore, even if the operator does not notice the warning sound itself emitted by the unmanned guided vehicle, the operator working in the work area can accurately identify the unmanned guided vehicle through the warning of the mobile terminal.

[0038] In particular, according to the work support system of the fourteenth aspect, the direction information of the warning sound is analyzed and the warning and the direction information are issued, so the operator can also recognize the positional relationship with the automated guided vehicle.

[0039] According to the work support system of the seventeenth to twentieth aspects, when the operator is working in the work area, the mobile terminal displays the travel path of the automated guided vehicle, so the operator working in the work area can accurately identify the automated guided vehicle. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1 It is a diagram schematically showing the general configuration of the work support system of the present invention.

[0041] Figure 2 It is a plan view showing an example of the layout of a plurality of substrate processing apparatuses.

[0042] Figure 3 It is a top view of the substrate processing apparatus.

[0043] Figure 4 This is a diagram showing a schematic configuration of a processing unit.

[0044] Figure 5This is a three-dimensional diagram showing the appearance of smart glasses.

[0045] Figure 6 This is a block diagram showing the functional structure of smart glasses, a server, a work support terminal, and a control unit of a substrate processing device.

[0046] Figure 7 This is a flowchart showing the procedure for constructing a sound model.

[0047] Figure 8 Schematically shows a recording of a sample sound in a working area.

[0048] Figure 9 This is a diagram schematically showing the model construction.

[0049] Figure 10 This is a flowchart showing the procedure for detecting the warning sound of an automated guided vehicle using smart glasses equipped with a sound model.

[0050] Figure 11 This is a diagram schematically showing an example of warning sound detection of an automated guided vehicle.

[0051] Figure 12 This is a diagram showing an example of a warning message according to the first embodiment.

[0052] Figure 13 This is a flowchart showing the procedure for displaying the travel route of an automated guided vehicle and calling attention to the situation.

[0053] Figure 14 This is a diagram showing an example of a travel route of an automated guided vehicle within a work area.

[0054] Figure 15 This is a diagram showing an example of a warning message according to the second embodiment. DETAILED DESCRIPTION

[0055] Hereinafter, the embodiments of the present invention will be described in detail with reference to the accompanying drawings. Hereinafter, the expressions (e.g., "in one direction," "along one direction," "parallel," "orthogonal," "center," "concentric," "coaxial," etc.) representing relative or absolute positional relationships, as long as not otherwise specified, not only strictly represent the positional relationships, but also represent the state of relative angle or distance displacement within the scope of obtaining tolerances or the same degree of function. In addition, the expressions (e.g., "same," "equal," "homogeneous," etc.) representing equal states, as long as not otherwise specified, not only strictly represent the equal states quantitatively, but also represent the state of the difference in obtaining tolerances or the same degree of function. In addition, the expressions (e.g., "circular," "quadrilateral," "cylindrical," etc.) representing shapes, as long as not otherwise specified, not only strictly represent the shapes geometrically, but also represent the shape of the scope of obtaining the same degree of effect, such as concave-convex or chamfered corners, etc. In addition, the expressions of "equipped," "equipped," "including," "containing," and "having" constituent elements are not exclusive expressions that exclude the existence of other constituent elements. In addition, the expression "at least one of A, B, and C" includes "only A", "only B", "only C", "any two of A, B, and C", and "all of A, B, and C".

[0056] <First embodiment>

[0057] Figure 1 It is a diagram schematically showing the general structure of the work assistance system of the present invention. The work assistance system of the present invention includes a plurality of substrate processing devices 50, smart glasses 10, a server 70, and a work assistance terminal 80. The controllers of the smart glasses 10 and the substrate processing devices 50 are connected to the information communication network 5 (for example, the Internet) by wireless communication. In addition, the work assistance terminal 80 and the server 70 are connected to the information communication network 5 by wire. Devices connected to the information communication network 5 can send and receive information to each other, for example, information can be provided or received between the smart glasses 10 and the work assistance terminal 80. In addition, whether each device is connected to the information communication network 5 by wireless or by wire is not limited to the example described above, and can be set to an appropriate form (for example, the work assistance terminal 80 can also be connected to the information communication network 5 by wireless).

[0058] Figure 2 FIG. 1 is a top view showing an example of the layout of a plurality of substrate processing apparatuses 50. Figure 2As shown, multiple substrate processing apparatuses 50 are regularly arranged at regular intervals within a clean room 40. The clean room 40 is, for example, a room located in a semiconductor device manufacturing plant where air cleanliness is maintained at a constant level and temperature and humidity are controlled. Operators perform operations and maintenance on the substrate processing apparatuses 50 within the clean room 40. In other words, in the first embodiment, the clean room 40 housing the multiple substrate processing apparatuses 50 serves as a work area.

[0059] An automated guided vehicle (AGV) 90 travels within the clean room 40, serving as a work area. The AGV 90 is controlled by preprogrammed software and travels on the floor of the clean room 40. The AGV 90 is guided, for example, by reading a magnetic tape or markings placed on the floor of the clean room 40. The AGV 90 travels based on route data provided by, for example, a host computer that manages the plurality of substrate processing apparatuses 50. In other words, the AGV 90 travels along a predetermined, predefined route. Furthermore, the AGV 90 may travel autonomously without guidance.

[0060] Figure 3 FIG2 is a top view of a substrate processing apparatus 50. The substrate processing apparatus 50 is, for example, a single-wafer type substrate cleaning apparatus that cleans substrates one by one. The substrate processing apparatus 50 includes an indexer 51, a plurality of processing units 52, a transfer robot 56, and a main transport robot 57.

[0061] The indexer 51 carries a carrier C that holds a plurality of substrates W. The indexer 51 can hold, for example, three carriers C. The unmanned transport vehicle 90 transports the carrier C that holds a plurality of substrates W within the clean room 40 and delivers it to the indexer 51 of the substrate processing apparatus 50. More specifically, the unmanned transport vehicle 90 loads and transports a carrier C that holds unprocessed substrates W and hands it over to the indexer 51 of the substrate processing apparatus 50. In addition, the unmanned transport vehicle 90 receives and transports a carrier C that holds a substrate W that has been processed by the substrate processing apparatus 50 from the indexer 51. The carrier C is, for example, a FOUP (frontopening unified pod) that stores the substrates W in a sealed space.

[0062] The transfer robot 56 is configured to be capable of sliding movement, lifting and lowering, rotating movement, and hand forward and backward movement along the arrangement direction of the plurality of carriers C. The transfer robot 56 removes unprocessed substrates W from the carriers C placed on the indexer 51. The transfer robot 56 also stores processed substrates W on the carriers C placed on the indexer 51.

[0063] In the first embodiment, for example, three processing units 52 are stacked to form one stacked body. Then, for example, four stacked bodies are arranged around the main transfer robot 57 of the substrate processing apparatus 50. That is, one substrate processing apparatus 50 includes, for example, 12 (=3×4) processing units 52. Figure 3 , a processing unit 52 of one layer included in the same horizontal plane of four layered bodies is shown.

[0064] The main transfer robot 57, positioned at the center of the four stacks, is capable of lifting, rotating, and advancing and retracting its transfer arm AM. The main transfer robot 57 can transfer substrates W to and from all twelve processing units 52. The main transfer robot 57 receives unprocessed substrates W from the transfer robot 56 and transfers them to any of the twelve processing units 52. Furthermore, the main transfer robot 57 removes processed substrates W from the processing units 52 and hands them over to the transfer robot 56.

[0065] In addition, the substrate processing apparatus 50 is provided with a control unit 55. The control unit 55 is a general computer and controls the operation of the transfer robot 56, the main transport robot 57 and each processing unit 52 provided in the apparatus. The control unit 55 has a touch panel as an input / output interface provided on the wall of the apparatus and a communication unit for communicating with the outside of the apparatus. Figure 3 In FIG. 5 , for convenience of illustration, the control unit 55 is depicted in the indexer 51 . However, the present invention is not limited thereto, and the control unit 55 is provided at an appropriate position in the substrate processing apparatus 50 .

[0066] Figure 4 This diagram schematically illustrates the structure of the processing unit 52. The processing unit 52 includes a processing chamber 60, a rotating holding unit 61, and a spray nozzle 65. The processing chamber 60 is a hollow housing. The rotating holding unit 61 and the spray nozzle 65 are located inside the processing chamber 60. Furthermore, a carry-in / out port (not shown) is provided in the processing chamber 60. This carry-in / out port is opened and closed by a shutter. When the carry-in / out port is open, the main transfer robot 57 carries substrates W into and out of the processing chamber 60. The carry-in / out port is closed during processing of the substrate W. Furthermore, a gas supply mechanism and an exhaust mechanism (not shown) are provided in the processing chamber 60.

[0067] The rotation and holding unit 61 includes a rotation chuck 62 and a rotation motor 63. The rotation chuck 62 is a substrate holding unit that holds the substrate W in a horizontal position (with the normal to the main surface of the substrate W aligned in the vertical direction). The rotation chuck 62 is, for example, a vacuum suction-type chuck. The rotation chuck 62 has a circular plate shape with a diameter smaller than that of the substrate W. The rotation chuck 62 suction-holds the center portion of the lower surface of the substrate W. When the lower surface of the substrate W is suction-held by the rotation chuck 62, the peripheral edge of the substrate W protrudes outward from the outer edge of the rotation chuck 62. Alternatively, the rotation chuck 62 may be another type of chuck, such as a clamping-type mechanical chuck.

[0068] The spin chuck 62 is coupled to the rotation motor 63 via a motor shaft. Specifically, the upper end of the motor shaft of the rotation motor 63 is connected to the center portion of the lower surface of the spin chuck 62. When the rotation motor 63 rotates the motor shaft while the substrate W is being held by the spin chuck 62 by suction, the substrate W and the spin chuck 62 rotate in a horizontal plane about a rotation axis extending in the vertical direction.

[0069] A shield 64 is provided to surround the spin chuck 62. The shield 64 can be raised and lowered by a lifting mechanism (not shown). The shield 64 has a cylindrical shape, and the upper portion of the shield 64 is inclined so that it approaches the spin chuck 62 as it moves upward. However, the inner diameter of the upper end portion of the shield 64 is larger than the diameter of the substrate W. When processing the substrate W, the upper end of the shield 64 is higher than the height position of the substrate W held by the spin chuck 62. Therefore, liquid scattered from the substrate W rotated by the rotation motor 63 due to centrifugal force is caught by the shield 64 and recovered. The liquid recovered by the shield 64 is discharged from a drain pipe provided at the bottom of the shield 64. Alternatively, the shield 64 may have a multi-layer structure with a plurality of recovery ports provided according to the purpose.

[0070] The spray nozzle 65 sprays a processing liquid onto the substrate W held by the rotary chuck 62. The so-called processing liquid is a term that includes the concepts of various chemical liquids and pure water. As the chemical liquid, for example, a liquid used for etching treatment or a liquid used for removing particles is included. Specifically, SC-1 liquid (a mixed solution of ammonium hydroxide, hydrogen peroxide solution and pure water), SC-2 liquid (a mixed solution of hydrochloric acid, hydrogen peroxide solution and pure water) or hydrofluoric acid is used. The spray nozzle 65 moves between a processing position above the rotary chuck 62 and a standby position outside the shield 64 by omitting a driving mechanism shown in the figure. By spraying a chemical liquid onto the substrate W held by the rotary chuck 62 at the processing position by the spray nozzle 65, for example, etching treatment of the substrate W is promoted. In addition, by spraying pure water onto the substrate W by the spray nozzle 65, pure water rinsing treatment of the substrate W is promoted.

[0071] Operators performing operations such as operating or maintaining the substrate processing apparatus 50 wear smart glasses 10. Smart glasses 10 are a type of head-mounted display (HMD) wearable terminal. Smart glasses 10 are also components used to implement AR (Augmented Reality) or MR (Mixed Reality). For example, "HoloLens" (registered trademark) manufactured by Microsoft can be used as smart glasses 10.

[0072] Figure 5 This is a perspective view showing the appearance of smart glasses 10. Smart glasses 10 include a sun visor 11 and a headband 12. The user puts on smart glasses 10 by strapping headband 12 onto their head. The user can adjust the length of headband 12 according to their head size. Headband 12 also includes a power button, brightness button, and volume button.

[0073] The sun visor 11 includes various sensors and a display. The display is a see-through holographic lens. In other words, the display can display a 3D image in the operator's field of view using a hologram, while also transmitting light from real objects in the same manner as conventional eyeglass lenses. Therefore, the operator wearing the smart glasses 10 can also recognize real objects through the display and see the displayed 3D image.

[0074] The sensors on the sun visor 11 primarily include, for example, multiple visible light cameras that capture images of the front of the sun visor 11, an infrared camera that tracks the operator's line of sight, a depth sensor that measures the distance to an object, and inertial measurement sensors. The infrared camera measures the wearer's eye movement and tracks their line of sight. The depth sensor measures the distance to an object using, for example, a ToF (Time of Flight) method. Inertial measurement sensors include accelerometers, gyroscopes, and magnetometers.

[0075] The smart glasses 10 also include a built-in computer equipped with a CPU and memory. They are also equipped with a wireless communication mechanism, which the computer in the smart glasses 10 uses to connect to the information communication network 5. Furthermore, the smart glasses 10 also include a microphone array, a speaker, and a battery. The microphone array is composed of multiple microphones spatially arranged (for example, six microphones spaced 60 degrees apart). By analyzing the differences (time differences, phase differences, etc.) in the sounds measured by each microphone, the location and direction of the sound can be estimated.

[0076] Figure 6This is a block diagram illustrating the functional configuration of the smart glasses 10, the server 70, the work support terminal 80, and the control unit 55 of the substrate processing apparatus 50. The smart glasses 10 include an imaging unit 21, a communication unit 22, a display unit 23, a sound collection unit 24, and a storage unit 25. The imaging unit 21 includes a visible light camera mounted on the sun visor 11. For example, the imaging unit 21 includes four visible light cameras that capture images of the front and diagonally forward directions, and can capture the field of view of the operator wearing the smart glasses 10.

[0077] The communication unit 22 comprises the wireless communication mechanism of the smart glasses 10. The communication unit 22 transmits and receives data with the work support terminal 80 and the server 70 via the information communication network 5. Furthermore, the communication unit 22 can also transmit and receive data directly with the control unit 55 of the substrate processing apparatus 50 if the communication unit 22 is in close proximity. In other words, the communication unit 22 can transmit data or commands to the control unit 55 of the substrate processing apparatus 50 directly or via the information communication network 5.

[0078] The display unit 23 includes the display of the sun visor 11. The display unit 23 includes a holographic processing device that uses holographic technology to display a 3D image at a predetermined spatial location. Furthermore, the 3D image displayed by the display unit 23 is not limited to a three-dimensional shape and can also be a two-dimensional image such as a document.

[0079] The sound collecting unit 24 includes the microphone array. The sound collecting unit 24 collects sounds around the smart glasses 10. Furthermore, the sound collecting unit 24 can analyze and identify the direction of the sound, that is, the direction from which the sound is propagating, by using analysis by the microphone array.

[0080] The storage unit 25 includes memory and storage devices installed in the smart glasses 10. Examples of memory and storage devices included in the smart glasses 10 include DRAM (Dynamic Random Access Memory) and UFS (Universal Flash Memory). The storage unit 25 stores applications and data used by the computer in the smart glasses 10.

[0081] The smart glasses 10 also include a determination unit 31 and a warning issuing unit 36. These determination unit 31 and warning issuing unit 36 are functional processing units implemented by the CPU of the smart glasses 10 executing a predetermined processing program. The processing details of the determination unit 31 and warning issuing unit 36 will be further described below.

[0082] The control unit 55 of the substrate processing apparatus 50 controls the operation of the mechanisms provided in the processing unit 52, such as the discharge nozzle 65. The control unit 55 of the substrate processing apparatus 50 can communicate with the communication unit 22 of the smart glasses 10 and can control the operation of various mechanisms provided in the processing unit 52 based on operation instructions transmitted from the smart glasses 10.

[0083] The work support terminal 80 and the server 70 are, for example, installed in a factory of a supplier that manufactures and maintains the substrate processing apparatus 50. The work support terminal 80 and the server 70 can communicate with the smart glasses 10 via the information communication network 5. Furthermore, the work support terminal 80 and the server 70 can communicate with each other via the information communication network 5.

[0084] The work support terminal 80 and the server 70 are general computer systems. Specifically, the work support terminal 80 and the server 70 include a CPU (a circuit that performs various calculations), a ROM (a read-only memory that stores basic programs), a RAM (a freely readable and writable memory that stores various information), a storage unit (e.g., a magnetic disk or SSD) that stores control software and data, and a communication unit that communicates with the information communication network 5.

[0085] The work support terminal 80 is, for example, a computer used by a work support staff of a supplier to support the work of an operator in the clean room 40. The work support staff can transmit various information from the work support terminal 80 to the smart glasses 10 worn by the operator in the clean room 40.

[0086] In the work support system of the present invention, the server 70 is a computer that executes predetermined processing in response to requests from the smart glasses 10 and the work support terminal 80. The server 70 includes a relatively large-capacity storage unit 74. Even large-scale data generated by the smart glasses 10 and the work support terminal 80 can be stored in the storage unit 74.

[0087] Next, a work assistance method using a work assistance system having the above-described structure is described. In the first embodiment, a sound model for detecting the warning sound emitted by the unmanned conveyor 90 is constructed, and the sound model is mounted on the smart glasses 10 to detect the warning sound of the unmanned conveyor 90 in the work area. When the unmanned conveyor 90 is driving in the work area, it emits a warning sound to avoid danger. The unmanned conveyor 90 drives in the work area while playing music, for example. The warning sound is not limited to music, and can also be, for example, a sound or a buzzer. Typically, the volume of the warning sound emitted by the unmanned conveyor 90 is greater than the level that can be heard by the operator in an environment where the robot and pump of the substrate processing device 50 in the work area make noise.

[0088] Figure 7 This is a flowchart showing the procedure for constructing a sound model. Constructing a sound model is a preparatory step for the work support method of the present invention. First, a sample sound is recorded in the work area and the recorded data is collected (step S11). Figure 8 This figure schematically illustrates recording of sample sounds within a work area. In the first embodiment, the work area 41 is within the clean room 40. Multiple substrate processing devices 50 are located within the work area 41, and an automated transport vehicle 90 operates there. In step S11, sample sounds within the work area 41 are recorded at multiple recording locations R1. Recording is performed using a predetermined recorder (e.g., an IC recorder).

[0089] Furthermore, at each of the multiple recording locations R1, recording is performed both when the AGV 90 is emitting a warning sound within the work area 41 and when it is not. The reason for performing recordings at multiple recording locations R1, not only when the AGV 90 is emitting a warning sound but also when it is not, is to collect recording data under a wide variety of environments. In other words, by recording both when the warning sound is emitted and when it is not, at different locations within the work area 41, recording data under a wide variety of environments can be collected. By enriching the recording data, the learning data used in the machine learning described below can be enriched, which can improve the accuracy of the constructed sound model.

[0090] Next, labels are created for each of the multiple recorded data collected (step S12). The so-called label in this embodiment refers to information indicating the correct answer assigned to each recorded data. For example, the recorded data in the state where the unmanned guided vehicle 90 emits a warning sound is labeled "with warning sound". In addition, the recorded data in the state where the unmanned guided vehicle 90 does not emit a warning sound is labeled "without warning sound". The creation of labels can be performed, for example, using the work auxiliary terminal 80 or the server 70. The created labels become teaching data in subsequent machine learning.

[0091] Next, each of the collected multiple recordings is converted into Mel-Frequency Cepstrum Coefficients (MFCCs) (step S13). MFCCs are one type of sound feature. The collected recordings are time-series signals, and directly inputting them into the model is inappropriate. Therefore, sound features, which are important information from the recordings, are extracted and applied to the model. In the first embodiment, MFCCs are used as sound features.

[0092] The conversion from the recorded data as a time series signal to the MFCC is performed, for example, by the following process. First, the recorded data as a time series signal is Fourier transformed to obtain a spectrum. A mel filter bank is applied to the spectrum to obtain a mel spectrum. The so-called mel spectrum refers to a spectrum in which the frequency axis is set to the mel scale. The so-called mel scale refers to a scale based on human hearing, which is sensitive to low-frequency sounds and insensitive to high-frequency sounds. Then, the component obtained by applying discrete cosine transform to the mel spectrum is MFCC. Since MFCC is a sound feature quantity that takes human hearing into consideration, it is widely used in the field of sound recognition. In addition, the conversion process from recorded data to MFCC can be performed, for example, using the work auxiliary terminal 80 or the server 70.

[0093] Next, a sound model is constructed by machine learning based on the obtained labels and MFCC (step S14 ). Figure 9 It is a diagram schematically showing the construction of a model. The sound model is constructed by machine learning in which the MFCC obtained by converting the recorded data and the label assigned to the recorded data are set as learning data. The label is information indicating the correct answer assigned to the recorded data, and becomes the teaching data for machine learning. That is, in the first embodiment, teaching learning is performed in which example questions (MFCC) and correct answers (labels) are paired. In addition, in the first embodiment, the accuracy of the sound model is improved by collecting recorded data in various environments. In addition, machine learning can be performed, for example, using the work auxiliary terminal 80 or the server 70, or by other computer systems. In addition, part of the MFCC and the label can also be used as verification data and test data for the constructed sound model.

[0094] In the first embodiment, the sound model constructed by machine learning is installed in the smart glasses 10 (step S15). For example, the sound model constructed by the work support terminal 80 is stored in the storage unit 25 of the smart glasses 10. The sound model can be constructed, for example, when the substrate processing apparatus 50 is installed in the clean room 40.

[0095] After the sound model is constructed and installed on the smart glasses 10, when an operator wearing the smart glasses 10 performs work in the work area 41 at appropriate times, the smart glasses 10 detect the warning sounds emitted by the automated guided vehicle 90 while it is in motion. For example, when an operator wearing the smart glasses 10 performs maintenance on or operates the substrate processing apparatus 50 in the work area 41, the smart glasses 10 equipped with the sound model detect the warning sounds emitted by the automated guided vehicle 90.

[0096] Figure 10This flowchart illustrates the process of detecting warning sounds from an automated guided vehicle (AGV) 90 using smart glasses 10 equipped with a sound model. First, while an operator wearing smart glasses 10 is working in a work area 41, the sound collection unit 24 of the smart glasses 10 collects sounds within the work area 41 (step S21). The sounds generated within the work area 41 include the operating sounds of the robot and pumps of the substrate processing apparatus 50, the operator's voice, and warning sounds emitted by the AGV 90 during operation. The sound data collected by the sound collection unit 24 is a time-series signal whose intensity changes over time.

[0097] Next, the sound data collected by the smart glasses 10 is converted into Mel-Frequency Cepstral Coefficients (MFCC) (step S22). As mentioned above, MFCC is one of the sound feature quantities, especially the feature quantity considering human hearing. The method of converting to MFCC and the process of constructing the sound model ( Figure 7 That is, the collected sound data is Fourier transformed to obtain a spectrum, a Mel filter bank is applied to the spectrum to obtain a Mel spectrum, and then a discrete cosine transform is applied to the Mel spectrum to obtain MFCC.

[0098] Next, the MFCC obtained by the determination unit 31 of the smart glasses 10 is input into the sound model mounted on the smart glasses 10 (step S23). By inputting the sound feature extracted from the sound data collected in the working area 41, that is, the MFCC, into the fully learned sound model, the sound model outputs whether the input data contains the warning sound of the unmanned guided vehicle 90. The determination unit 31 determines whether the unmanned guided vehicle 90 emits a warning sound in the working area 41 based on the output result of the sound model (step S24). That is, when the sound model outputs the result that the input data contains a warning sound, the determination unit 31 determines that the unmanned guided vehicle 90 emits a warning sound in the working area 41. On the other hand, when the sound model outputs the result that the input data does not contain a warning sound, the determination unit 31 determines that the unmanned guided vehicle 90 does not emit a warning sound.

[0099] When the determination unit 31 determines that the AGV 90 is emitting a warning sound within the work area 41, the sound collection unit 24 of the smart glasses 10 analyzes the direction information of the warning sound (step S25). The sound collection unit 24 includes a microphone array composed of multiple microphones arranged in a spatial arrangement, and is capable of analyzing the direction from which the warning sound is propagating.

[0100] Furthermore, the sound collection unit 24 detects whether the warning sound gradually increases or decreases. Based on this result, the smart glasses 10 determine whether the automated guided vehicle 90 is approaching or moving away. Specifically, if the warning sound collected by the sound collection unit 24 gradually increases in volume, the smart glasses 10 determine that the automated guided vehicle 90 is approaching. Conversely, if the warning sound gradually decreases in volume, the smart glasses 10 determine that the automated guided vehicle 90 is moving away.

[0101] Figure 11 Schematically shows an example of warning sound detection by an automated guided vehicle 90. An operator wearing smart glasses 10 equipped with a sound model works at position P1 within the work area 41. Figure 11 The AGV 90 travels in the direction indicated by arrow AR11. To avoid danger, the AGV 90 emits a warning sound while traveling within the work area 41. The operator's smart glasses 10 detect the AGV 90 emitting a warning sound by inputting MFCCs extracted from the collected sound data into a sound model. Furthermore, the smart glasses 10 detect that the AGV 90's warning sound is emanating from the operator's right front and also detect that the warning sound is gradually increasing in volume. As a result, the smart glasses 10 detect that the AGV 90 is approaching from the operator's right front.

[0102] Then, the warning issuing unit 36 of the smart glasses 10 issues a warning (step S26 ). Specifically, for example, the warning issuing unit 36 displays a warning screen as a stereoscopic image on the display unit 23 . Figure 12 36. As shown in the figure, the warning screen displays a warning about the presence of the unmanned guided vehicle 90 and the position information of the unmanned guided vehicle 90. The position information includes the direction of the unmanned guided vehicle 90 relative to the operator and information on whether the unmanned guided vehicle 90 is approaching or moving away. In other words, if Figure 11 For example, the warning message will be displayed as an unmanned guided vehicle 90 approaching from the right front. Figure 12 The operator can recognize that the automated guided vehicle 90 is approaching the right front by displaying a warning screen with a message, and will be alert to the automated guided vehicle 90. As a result, the operator's safety can be ensured. Furthermore, the warning issued by the warning issuing unit 36 is not limited to a warning screen; instead of or in addition to this, an audible warning may be issued, or a colored annotation may be displayed in the direction of the approaching automated guided vehicle 90.

[0103] In the first embodiment, a sound model for detecting warning sounds from automated guided vehicles (AGVs) 90 is pre-built and installed in the smart glasses 10. To build the sound model, recordings of the AGVs 90 both emitting and not emitting warning sounds within the work area 41 are performed, collecting recording data from various environments. MFCCs are extracted from these recordings as sound features and used as learning data for machine learning. This machine learning based on this rich learning data improves the accuracy of the sound model.

[0104] In the first embodiment, the smart glasses 10 worn by the operator input the MFCCs extracted from the collected sounds into the sound model to determine whether the automated guided vehicle 90 is issuing a warning sound. If the automated guided vehicle 90 is determined to be issuing a warning sound, the warning issuing unit 36 of the smart glasses 10 issues a warning.

[0105] The original purpose of the Automated Vehicle 90 emitting a warning sound is to notify nearby operators of its presence and prevent danger. However, if the Automated Vehicle 90 continues to emit a warning sound in an environment where noise is also emitted by the substrate processing equipment 50, etc., an operator, focused on their work, may become accustomed to the warning sound and fail to recognize the approach of the Automated Vehicle 90. As in this embodiment, even if an operator is accustomed to the warning sound emitted by the Automated Vehicle 90, the smart glasses 10 will issue a new warning, which will stimulate the operator to accurately recognize the presence of the Automated Vehicle 90 again.

[0106] Furthermore, in the first embodiment, when the smart glasses 10 determine that the AGV 90 has issued a warning sound, they analyze the direction information of the warning sound. The direction information obtained as a result of the analysis is included and displayed on the warning screen. The direction information includes the direction of the AGV 90 as seen by the operator and whether the AGV 90 is approaching or moving away. This allows the operator to more accurately identify their positional relationship with the AGV 90.

[0107] <Second embodiment>

[0108] Next, the second embodiment of the present invention will be described. The configuration of the work support system in the second embodiment is the same as that in the first embodiment. While the first embodiment detects the warning sound of the automated guided vehicle 90, the second embodiment displays the pre-registered travel path of the automated guided vehicle 90 and alerts the operator.

[0109] Figure 13This flowchart shows the procedure for displaying the travel route of the automated guided vehicle 90 and providing a warning. The travel route of the automated guided vehicle 90 within the clean room 40, which serves as the work area 41, is predetermined based on, for example, the processing schedules of the plurality of substrate processing apparatuses 50 disposed within the clean room 40. Data on the travel route of the automated guided vehicle 90 within the work area 41 is pre-registered in, for example, the storage unit 74 of the server 70 (step S31). Figure 14 1 is a diagram showing an example of a travel route of the automated guided vehicle 90 within the work area 41 .

[0110] When the operator wearing the smart glasses 10 is working in the work area 41, the display unit 23 of the smart glasses 10 displays the driving path of the automated guided vehicle 90 (step S32). Specifically, for example, when the operator wearing the smart glasses 10 enters the work area 41, the smart glasses 10 reads the driving path of the automated guided vehicle 90 registered in the storage unit 74 of the server 70, and the display unit 23 displays the driving path of the automated guided vehicle 90 as shown in FIG. Figure 14 The travel path shown is displayed as a three-dimensional image. This allows the operator to recognize the travel path of the automated guided vehicle 90 and pay attention to the automated guided vehicle 90. As a result, the operator's safety can be ensured.

[0111] The smart glasses 10 monitor whether the operator is not approaching the driving path of the unmanned guided vehicle 90 (step S33). Specifically, it is determined whether the position of the operator wearing the smart glasses 10 is within the specified range from the driving path of the unmanned guided vehicle 90. As a method of specifying the position of the operator, for example, the smart glasses 10 creates a spatial network by scanning the working area 41, and specifies the position of the operator by comparing the spatial network with the spatial network created in the past. As an operation of the smart glasses 10 for creating a spatial network, it is sufficient to enable the operator wearing the smart glasses 10 to turn on the scanning mode. The created spatial network is represented by a plurality of triangular networks. Various shapes including curved surfaces or planes are represented by a collection of multiple triangles connected together. Complex shapes including concave and convex are represented by high-density triangles, and conversely, flat shapes are represented by relatively low-density triangles.

[0112] If the location of the operator identified by the spatial network comparison is within a predetermined range from the automated guided vehicle 90's travel path, the operator is too close to the travel path, and the warning issuing unit 36 of the smart glasses 10 issues a warning (step S34). Specifically, for example, the warning issuing unit 36 displays a warning screen as a 3D image on the display unit 23. Figure 15 1 is a diagram showing an example of a warning screen of the second embodiment displayed by the warning issuing unit 36. As shown in the diagram, the warning screen displays a message indicating that the operator is too close to the driving path of the automated guided vehicle 90 as a warning message. Figure 15The operator can recognize that they are approaching the path of the automated guided vehicle 90 too closely and pay attention to the automated guided vehicle 90 by displaying a warning screen with a message. This ensures the operator's safety. Furthermore, as in the first embodiment, the warning issued by the warning issuing unit 36 is not limited to a warning screen. Instead of or in addition to this, for example, an audible warning may be issued or a colored annotation may be displayed on the path of the automated guided vehicle 90.

[0113] In the second embodiment, when an operator wearing the smart glasses 10 works in the work area 41 , the smart glasses 10 display the pre-registered travel path of the automated guided vehicle 90 . This allows the operator working in the work area 41 to recognize the automated guided vehicle 90 .

[0114] Furthermore, when the operator wearing the smart glasses 10 approaches within a predetermined range of the path of the automated guided vehicle 90, the smart glasses 10 issues a warning. This allows the operator to accurately recognize that the path of the automated guided vehicle 90 is too close.

[0115] <Example of variation>

[0116] The embodiments of the present invention have been described above, but the present invention can be modified in various ways other than the above as long as it does not deviate from the main purpose. For example, in the first embodiment, MFCC is extracted from the recorded data as a sound feature and set as learning data for machine learning, but this is not limited to this. As the sound feature, the spectrum obtained by Fourier transforming the recorded data can also be used. In this case, in step S14, a sound model is constructed by machine learning using the spectrum as learning data, and in step S23, the spectrum obtained from the collected sound data is input to the sound model. Alternatively, as the sound feature, a Mel filter bank can be applied to the spectrum to adopt a Mel spectrum. Alternatively, instead of extracting the sound feature, the sound data as a time series signal can be used directly and the sound model can be constructed.

[0117] Furthermore, in the first embodiment, the AGV 90 approaches the operator. However, even if the AGV 90 moves away from the operator, the smart glasses 10 will issue a warning upon detecting the warning sound of the AGV 90. In other words, the smart glasses 10 will issue a warning even if they detect that the warning sound of the AGV 90 is gradually decreasing. This allows the operator to accurately recognize the presence of the AGV 90.

[0118] In the first embodiment, the position information includes the direction of the automated guided vehicle 90 relative to the operator and information on whether the automated guided vehicle 90 is approaching or moving away. However, the position information may also include information on the distance from the operator to the automated guided vehicle 90. The distance from the operator to the automated guided vehicle 90 can be estimated based on the volume of the warning sound collected by the sound collection unit 24.

[0119] In the first embodiment, the constructed sound model is installed in the smart glasses 10, but this is not limiting. The sound model may be stored in the server 70 or the work support terminal 80 and used by the smart glasses 10. In the second embodiment, the data on the driving route of the automated guided vehicle 90 may be directly registered in the smart glasses 10.

[0120] Alternatively, both the first and second embodiments may be implemented. Specifically, the smart glasses 10 may display the path of the automated guided vehicle 90 while detecting the warning sound of the automated guided vehicle 90. This allows the operator to more accurately identify the automated guided vehicle 90.

[0121] In the above embodiments, the operator uses smart glasses 10, but this is not limiting. Instead of smart glasses 10, a mobile device such as a tablet or smartphone can be used. In other words, any mobile device equipped with a display, communication, and sound collection unit will suffice. However, using a tablet, etc., occupies the operator's hands while holding the tablet, so a wearable device such as smart glasses 10 is preferable.

[0122] Furthermore, the substrate processing apparatus 50 located in the general area of the clean room 40 is not limited to a substrate cleaning apparatus; it may be any apparatus that performs a predetermined process on a substrate, such as a thermal treatment apparatus, an exposure apparatus, a coating and developing apparatus, a measuring apparatus, or an inspection apparatus. If the substrate processing apparatus 50 is a substrate cleaning apparatus, it may be a single-wafer cleaning apparatus that cleans substrates one by one, or a batch cleaning apparatus that cleans multiple substrates at once.

[0123] Furthermore, the work support technology of the present invention is not limited to substrate processing equipment, but can be applied to any industrial equipment that performs certain processing. Examples of such industrial equipment include printing processing equipment, film forming equipment, medical equipment, and appearance inspection equipment.

[0124] [Explanation of Symbols]

[0125] 5: Information and Communication Network

[0126] 10: Smart glasses

[0127] 21: Photography Department

[0128] 22: Ministry of Communications

[0129] 23: Display unit

[0130] 24: Sound Collection

[0131] 25, 74: Storage

[0132] 31: Judgment Department

[0133] 36: Warning Release Department

[0134] 40: Clean Room

[0135] 41: Working area

[0136] 50: Substrate processing device

[0137] 70: Server

[0138] 80: Work auxiliary terminal

[0139] 90: Unmanned transport vehicle

[0140] W: substrate.

Claims

1. A work assistance method for an operator working in a work area where industrial equipment is located and where an automated guided vehicle is used for driving, and comprising: a model building step of building a sound model for recognizing a warning sound emitted when the automated guided vehicle moves; a sound collecting step of collecting sound by the sound collecting unit when an operator wearing a mobile terminal equipped with a display unit, a communication unit, and a sound collecting unit works in the work area; a determining step of determining whether the automated guided vehicle emits a warning sound by inputting the sound collected by the sound collecting unit into the sound model; and An issuing step, when it is determined in the determining step that the automated guided vehicle emits a warning sound, the mobile terminal issuing a warning.

2. The work assistance method according to claim 1, wherein The model building process includes: a recording step for recording a state in which the unmanned guided vehicle emits a warning sound and a state in which the unmanned guided vehicle does not emit a warning sound within the working area; an analysis step of obtaining a sound feature value from the recording data collected in the recording step; and The learning step constructs the voice model by machine learning based on the labels assigned to the audio data collected in the recording step and the audio feature values.

3. The work assistance method according to claim 2, wherein The sound feature is the Mel frequency cepstral coefficient. In the determination step, mel-frequency cepstral coefficients extracted from the sound collected by the sound collection unit are input into the sound model.

4. The work assisting method according to claim 1 further comprises the following steps: When it is determined in the determination step that the automated guided vehicle emits a warning sound, the sound collecting unit analyzes the direction information of the warning sound. In the issuing step, the mobile terminal issues a warning and the position information.

5. The work assistance method according to claim 1, wherein In the issuing step, the mobile terminal displays a warning screen and emits a warning sound.

6. The work assisting method according to any one of claims 1 to 5, wherein The industrial equipment is a substrate processing device that performs prescribed processing on a substrate. The mobile terminal is smart glasses.

7. A work assistance method for an operator working in a work area where industrial equipment is located and where an automated guided vehicle is used, and comprising: a registration step of registering a travel path of the AGV in the work area; and In the display step, when an operator wearing a mobile terminal equipped with a display unit, a communication unit, and a sound collection unit is working in the work area, the mobile terminal displays the travel route. 8 . The work support method according to claim 7 , further comprising an issuing step of causing the mobile terminal to issue a warning when the operator approaches within a predetermined range from the travel path.

9. The work assistance method according to claim 8, wherein In the issuing step, the mobile terminal displays a warning screen and emits a warning sound.

10. The work assisting method according to any one of claims 7 to 9, wherein The industrial equipment is a substrate processing device that performs prescribed processing on a substrate. The mobile terminal is smart glasses.

11. A work assistance system for an operator working in a work area where industrial equipment is located and where an automated guided vehicle (AGV) is used, and comprising: A mobile terminal having a display unit, a communication unit, and a sound collection unit; a storage unit storing a sound model for identifying a warning sound emitted when the automated guided vehicle moves; a determination unit that determines whether the automated guided vehicle emits a warning sound by inputting the sound collected by the sound collection unit into the sound model when the operator wearing the mobile terminal is working in the work area; and The warning issuing unit issues a warning when the determination unit determines that the unmanned guided vehicle emits a warning sound.

12. The work assist system according to claim 11, wherein The sound model is constructed by machine learning based on sound features obtained from recording data collected from recordings of the automated guided vehicle emitting a warning sound and recordings of the automated guided vehicle not emitting a warning sound within the work area, and labels assigned to the recording data.

13. The work assist system according to claim 12, wherein The sound feature is the Mel frequency cepstral coefficient. The determination unit inputs the mel-frequency cepstral coefficients extracted from the sound collected by the sound collection unit into the sound model.

14. The work assist system according to claim 11, wherein When the determination unit determines that the automated guided vehicle emits a warning sound, the sound collecting unit analyzes the direction information of the warning sound. The warning issuing unit issues a warning and the direction information.

15. The work assist system according to claim 11, wherein The warning issuing unit displays a warning screen and emits a warning sound.

16. The work assist system according to any one of claims 11 to 15, wherein The industrial equipment is a substrate processing device that performs prescribed processing on a substrate. The mobile terminal is smart glasses.

17. A work assistance system for an operator working in a work area where industrial equipment is located and where an automated guided vehicle (AGV) is used, and comprising: A mobile terminal comprising a display unit, a communication unit, and a sound collection unit; and a storage unit for storing a travel path of the automated guided vehicle in the work area; When the operator wearing the mobile terminal works in the work area, the mobile terminal displays the driving route. 18 . The work support system according to claim 17 , further comprising a warning issuing unit configured to issue a warning when the operator approaches within a predetermined range from the travel path.

19. The work assist system according to claim 18, wherein The warning issuing unit displays a warning screen and emits a warning sound.

20. The work assist system according to any one of claims 17 to 19, wherein The industrial equipment is a substrate processing device that performs prescribed processing on a substrate. The mobile terminal is smart glasses.

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