Method, apparatus, and computer program for providing forklift safety solution at work site

Abstract

If an object is identified based on sensor data collected for a work site where a forklift is deployed, an object area for the identified object may be set and the size of the object area may be adjusted on the basis of the number of events generated by the identified object during a predetermined period of time. In addition, provided are a method and apparatus for providing a forklift safety solution, which can adjust the size of an event detection area on the basis of the number of events generated in response to a forklift during a predetermined period of time, determine the degree of risk for a plurality of zones obtained by dividing a work site, adjust the size of the set event detection area on the basis of the determined degree of risk, and prevent an accident such as a collision between the forklift and a worker by providing a notification after detecting an event.

Description

Method, device, and computer program for providing forklift safety solutions at the work site

[0001] Various embodiments of the present disclosure relate to a method, apparatus, and computer program for providing a forklift safety solution at a work site.

[0002] Work sites are environments where various heavy equipment and workers coexist, making them places with a high potential for safety accidents. In particular, at sites such as construction sites or large-scale logistics warehouses, heavy equipment moves frequently, posing a risk of collisions between workers and equipment, as well as accidents caused by falling objects during work. Therefore, various safety management measures are being implemented to prevent such accidents.

[0003] Typically, safety managers at work sites conduct preventive activities to prevent accidents, such as patrolling the site to monitor for potential hazards and guiding workers on safety rules. However, as the scale of the workplace increases, the scope of management expands, presenting a problem that requires the deployment of multiple safety managers to prevent accidents. Furthermore, since it is difficult to predict the likelihood of accidents in real time, it is realistically challenging to prevent all accidents in advance, even if multiple managers patrol the site.

[0004] To address these issues, safety management systems utilizing various sensors are being introduced recently. A commonly used approach involves installing cameras, radar, and LiDAR sensors at work sites to monitor worker movements and the working environment in real time, and to provide warning alerts when hazards are detected. Since the use of such technology enables real-time monitoring of the work site, it can help prevent accidents by detecting risk factors early.

[0005] However, existing sensor-based safety management systems can only collect data within a limited range, which creates the possibility of blind spots. Furthermore, high-performance data processing technology and computational capabilities are required to analyze sensor-detected data in real time and execute appropriate responses. Consequently, depending on the environment of the work site, there may be cases where it is difficult to ensure sufficient safety using existing technologies alone.

[0006] Accordingly, various technological attempts are being made to manage workplace safety more effectively, and smart safety management systems combining sensors and artificial intelligence (AI) are gradually being introduced. In particular, technologies that track workers' locations in real time and analyze the potential for hazards to respond proactively are being researched, and efforts to create a safer working environment through these measures are continuing.

[0007] The aforementioned background technology is one that the inventor possessed or acquired in the process of deriving the content of the present disclosure, and it cannot be considered as prior art disclosed to the general public prior to the filing of this application.

[0008] The problem that the present disclosure aims to solve is to provide a method, device, and computer program for providing a forklift safety solution at a work site that can effectively prevent accidents such as collisions between a forklift and a worker within the work site by detecting the occurrence of an event based on sensor data collected about a work site where a forklift is deployed and providing a notification in response thereto, for the purpose of solving the aforementioned conventional problems.

[0009] The problems that this disclosure aims to solve are not limited to those mentioned above, and other unmentioned problems will be clearly understood by a person skilled in the art from the description below.

[0010] A method for providing a forklift safety solution at a work site according to one embodiment of the present disclosure for solving the above-mentioned problem may include, in a method performed by a computing device, a step of acquiring sensor data generated by scanning the work site through a sensor installed at the work site where the forklift is deployed, and a step of providing a safety solution that provides a notification corresponding to the generated event when it is determined that an event regarding the forklift has occurred based on the acquired sensor data.

[0011] In various embodiments, the step of providing the safety solution may include the step of setting an event detection area for the forklift and the step of determining whether an event occurs for the forklift based on whether an object enters the set event detection area.

[0012] In various embodiments, the step of setting the event detection area may include setting an event detection area having a predetermined radius centered on the forklift, and adjusting the size of the set event detection area based on the number of events that occurred in response to the forklift during a predetermined period.

[0013] In various embodiments, the step of setting the event detection area may include dividing the work site into a plurality of zones, determining the risk level for the plurality of divided zones based on the number of events that occurred in each of the plurality of divided zones during a predetermined period, and adjusting the size of the set event detection area based on the risk level determined for any one of the zones when the forklift enters any one of the plurality of divided zones.

[0014] In various embodiments, the step of setting the event detection area may include: creating a virtual forklift and one or more virtual objects within the work site based on past sensor data acquired during a past predetermined period; when it is determined that a virtual event has occurred by simulating the movement of the created virtual forklift and the created one or more virtual objects, setting a predetermined range centered on the point where the virtual event occurred as an area of ​​interest; and when the forklift enters the set area of ​​interest, increasing the size of the set event detection area.

[0015] In various embodiments, the step of determining whether the event has occurred may include, when an object is identified based on the acquired sensor data, the step of setting an object area for the identified object, and when at least a portion of the set event detection area and the set object area overlap, the step of determining that an event has occurred for the forklift.

[0016] In various embodiments, the step of setting the object area may include setting the object area for the identified object based on the size and shape of the identified object, and adjusting the size of the set object area based on the number of events generated by the identified object during a predetermined period.

[0017] In various embodiments, the step of setting the object area may include setting the object area for the identified object based on the type of the identified object, and, if the identified object is a worker, adjusting the size of the set object area based on the type of work being performed by the worker.

[0018] In various embodiments, the step of setting the object area may include, when the identified object is a worker, identifying the worker's behavioral pattern based on the acquired sensor data, and if the identified behavioral pattern is determined to be an abnormal behavioral pattern based on the type of work being performed by the worker, increasing the size of the set object area.

[0019] In various embodiments, the set event detection area includes a danger area having a radius of a first size based on the center of the forklift and a warning area having a radius of a second size larger than the first size, and the step of providing the safety solution may include providing a notification of a first attribute when it is determined that an object has entered the warning area and providing a notification of a second attribute when it is determined that an object has entered the danger area.

[0020] In various embodiments, the step of providing the safety solution may include, when it is determined that a specific event has occurred for the forklift based on the acquired sensor data, providing a notification corresponding to the specific event that has occurred, and changing the attributes of the provided notification according to the duration of the specific event that has occurred.

[0021] A device for providing a forklift safety solution at a work site according to another embodiment of the present disclosure for solving the above-described problem comprises a processor, a network interface, a memory, and a computer program loaded into the memory and executed by the processor, wherein the computer program may include an instruction for acquiring sensor data generated by scanning the work site through a sensor installed at the work site where the forklift is deployed, and an instruction for providing a safety solution that provides a notification corresponding to the generated event when it is determined that an event regarding the forklift has occurred based on the acquired sensor data.

[0022] A computer program according to another embodiment of the present disclosure for solving the above-described problem may be stored on a recording medium readable by a computing device to execute a method for providing a forklift safety solution at a work site, comprising the steps of: acquiring sensor data generated by scanning a work site where a forklift is deployed through a sensor installed at the work site; and providing a safety solution that provides a notification corresponding to the generated event when it is determined that an event regarding the forklift has occurred based on the acquired sensor data.

[0023] Other specific details of the present disclosure are included in the detailed description and drawings.

[0024] According to various embodiments of the present disclosure, there is an advantage in that accidents such as collisions between a forklift and a worker can be effectively prevented by detecting the occurrence of an event based on sensor data collected for a work site where a forklift is deployed and providing a notification in response thereto.

[0025] The effects of the present disclosure are not limited to those mentioned above, and other unmentioned effects will be clearly understood by a person skilled in the art from the description below.

[0026] The following drawings attached to this specification illustrate preferred embodiments of the present disclosure and serve to further enhance understanding of the technical concept of the present disclosure together with the detailed description of the invention; therefore, the present disclosure should not be interpreted as being limited only to the matters described in such drawings.

[0027] FIG. 1 is a drawing illustrating a system for providing a forklift safety solution at a work site according to one embodiment of the present disclosure.

[0028] FIG. 2 is a drawing illustrating an on-device AI-based forklift safety solution provision system at a work site according to various embodiments of the present disclosure.

[0029] FIG. 3 is a diagram illustrating the hardware configuration of a forklift safety solution providing device at a work site according to another embodiment of the present disclosure.

[0030] FIG. 4 is a flowchart of a method for providing a forklift safety solution at a work site according to another embodiment of the present disclosure.

[0031] FIG. 5 is a flowchart of a method for providing a safety solution that provides notifications depending on whether an event occurs in various embodiments.

[0032] FIG. 6 is a diagram illustrating, exemplarily, warning areas and danger areas set in correspondence with a forklift in various embodiments.

[0033] FIGS. 7 and 8 are drawings illustrating, in various embodiments, an exemplary user interface (UI) providing a forklift safety solution provision service.

[0034] The advantages and features of the present disclosure and the methods for achieving them will become clear by referring to the embodiments described below in detail together with the accompanying drawings. However, the present disclosure is not limited to the embodiments disclosed below but may be implemented in various different forms. These embodiments are provided merely to ensure that the disclosure is complete and to fully inform those skilled in the art of the scope of the present disclosure, and the present disclosure is defined only by the scope of the claims.

[0035] The terms used herein are for describing the embodiments and are not intended to limit the disclosure. In this specification, the singular form includes the plural form unless specifically stated otherwise in the text. As used herein, "comprises" and / or "comprising" do not exclude the presence or addition of one or more other components in addition to the components mentioned.

[0036] Throughout this specification, the same reference numerals refer to the same components, and "and / or" includes each of the mentioned components and all combinations of one or more thereof. Although terms such as "first," "second," etc., are used to describe various components, they are not limited by these terms. These terms are used merely to distinguish one component from another. Accordingly, the first component mentioned below may be the second component within the technical scope of this disclosure.

[0037] As used herein, the terms “part” or “module” refer to hardware components such as software, FPGAs, or ASICs, and the “part” or “module” performs certain roles. However, the “part” or “module” is not limited to software or hardware. The “part” or “module” may be configured to reside in an addressable storage medium or configured to run on one or more processors. Thus, by example, the “part” or “module” includes components such as software components, object-oriented software components, class components, and task components, as well as processes, functions, attributes, procedures, subroutines, segments of program code, drivers, firmware, microcode, circuits, data, databases, data structures, tables, arrays, and variables. The functions provided within the components and “parts” or “modules” may be combined into a smaller number of components and “parts” or “modules,” or further separated into additional components and “parts” or “modules.”

[0038] Spatially relative terms such as "below," "beneath," "lower," "above," and "upper" may be used to facilitate the description of the relationship between one component and other components as illustrated in the drawings. Spatially relative terms should be understood as encompassing different orientations of components during use or operation, in addition to the orientations depicted in the drawings. For example, if a component depicted in a drawing is inverted, a component described as "below" or "beneath" of another component may be placed "above" of that component. Therefore, the exemplary term "below" may encompass both the lower and upper directions. Components may also be oriented in other directions, and accordingly, spatially relative terms may be interpreted according to the orientation.

[0039] Expressions such as "first," "second," or "first," "second" as used in this specification are used to distinguish one object from another when referring to a plurality of objects of the same kind, unless otherwise indicated in the context, and do not limit the order or importance of said objects.

[0040] Expressions used herein such as “A, B, and C,” “A, B, or C,” “A, B, and / or C,” or “at least one of A, B, and C,” “at least one of A, B, or C,” “at least one of A, B, and / or C,” “at least one selected from A, B, and C,” “at least one selected from A, B, or C,” “at least one selected from A, B, and / or C,” etc., may mean each of the listed items or all possible combinations of the listed items. For example, “at least one selected from A and B” may refer to (1) A, (2) at least one of A, (3) B, (4) at least one of B, (5) at least one of A and at least one of B, (6) at least one of A and B, (7) at least one of B and A, and (8) all of A and B.

[0041] As used herein, the expression “based on” is used to describe one or more factors affecting an act or action of a decision or judgment described in the phrase or sentence containing such expression, and such expression does not exclude additional factors affecting said act or action of a decision or judgment.

[0042] As used in this specification, the expression that a certain component (e.g., a first component) is "connected" or "connected" to another component (e.g., a second component) may mean that the said certain component is not only directly connected or connected to the said other component, but is also connected or connected through a new other component (e.g., a third component).

[0043] As used herein, the expression "configured to" may have meanings such as "set to," "capable of," "modified to," "made to," or "capable of." Such expression is not limited to the meaning of "specifically designed in hardware," and, for example, a processor configured to perform a specific operation may mean a generic-purpose processor capable of performing that specific operation by executing software.

[0044] Unless otherwise defined, all terms used herein (including technical and scientific terms) may be used in a meaning commonly understood by those skilled in the art to which this disclosure pertains. Additionally, terms defined in commonly used dictionaries are not to be interpreted ideally or excessively unless explicitly and specifically defined otherwise.

[0045] In this specification, the term "computer" refers to any type of hardware device comprising at least one processor, and may be understood to include software configurations operating on said hardware device according to the embodiments. For example, the term "computer" may be understood to include smartphones, tablet PCs, desktops, laptops, and user clients and applications running on each of these devices, but is not limited thereto.

[0046] [Explanation of the symbol]

[0047] 100 : Forklift Safety Solution Provider

[0048] 200 : User terminal

[0049] 300 : External server

[0050] 400 : Network

[0051] Hereinafter, embodiments of the present disclosure will be described in detail with reference to the attached drawings.

[0052] Each step described in this specification is described as being performed by a computer, but the subject of each step is not limited thereto, and depending on the embodiment, at least some of each step may be performed on different devices.

[0053]

[0054] FIG. 1 is a drawing illustrating a system for providing a forklift safety solution at a work site according to one embodiment of the present disclosure.

[0055] Referring to FIG. 1, a forklift safety solution providing system at a work site according to one embodiment of the present disclosure may include a forklift safety solution providing device (100), a user terminal (200), an external server (300), and a network (400).

[0056] Here, the forklift safety solution providing system at a work site illustrated in FIG. 1 is according to one embodiment, and its components are not limited to the embodiment illustrated in FIG. 1 and may be added, changed, or deleted as needed.

[0057] In one embodiment, the forklift safety solution providing device (100) can provide a forklift safety solution providing service at a work site.

[0058] In various embodiments, the forklift safety solution providing device (100) can acquire sensor data generated by scanning the work site through a sensor installed at the work site where the forklift (10) is deployed, and can monitor whether there is an event for the forklift (10) based on the sensor data.

[0059] In addition, the forklift safety solution providing device (100) can provide a notification corresponding to the event when it is determined that an event has occurred for the forklift (10) based on sensor data.

[0060] In various embodiments, the forklift safety solution providing device (100) may be connected to a user terminal (200) via a network (400) and may provide a forklift safety solution to the user terminal (200). For example, the forklift safety solution providing device (100) may provide notifications to the user terminal (200) of a worker, a driver of the forklift (10), and a manager of the work site.

[0061] Additionally, the forklift safety solution providing device (100) can provide a user interface (UI) (e.g., FIG. 7 and FIG. 8) to a user terminal (200) of a manager at a work site, and can provide monitoring information about the work site through the user interface (UI). Here, the monitoring information about the work site may include, but is not limited to, sensor data collected in response to the work site, information about objects identified by analyzing the sensor data, and information related to the occurrence of events.

[0062] Here, the user terminal (200) may refer to any form of entity(s) in a system having a mechanism for communicating with a computing device (100). For example, such a user terminal (200) may include a PC (personal computer), a notebook, a mobile terminal, a smartphone, a tablet PC, and a wearable device, and may include any type of terminal capable of connecting to a wired or wireless network. Additionally, the user terminal (200) may include any computing device implemented by at least one of an agent, an API (Application Programming Interface), and a plug-in. Additionally, the user terminal (200) may include an application source and / or a client application.

[0063] Additionally, the network (400) may refer to a connection structure capable of exchanging information between each node, such as multiple terminals and servers. For example, the network (400) may include a Local Area Network (LAN), a Wide Area Network (WAN), the World Wide Web (WWW), a wired / wireless data network, a telephone network, a wired / wireless television network, a Controller Area Network (CAN), and Ethernet.

[0064] Wireless data communication networks may include, but are not limited to, 3G, 4G, 5G, 3GPP (3rd Generation Partnership Project), 5GPP (5th Generation Partnership Project), LTE (Long Term Evolution), WIMAX (World Interoperability for Microwave Access), Wi-Fi, Internet, LAN (Local Area Network), Wireless LAN (Wireless Local Area Network), WAN (Wide Area Network), PAN (Personal Area Network), RF (Radio Frequency), Bluetooth network, NFC (Near-Field Communication) network, satellite broadcasting network, analog broadcasting network, DMB (Digital Multimedia Broadcasting) network, etc.

[0065] In one embodiment, an external server (300) may be connected to a forklift safety solution providing device (100) via a network (400) and may store and manage various information and data necessary for the forklift safety solution providing device (100) to perform a forklift safety solution providing method at a work site. Additionally, the external server (300) may collect, store, and manage various information and data derived as the forklift safety solution providing device (100) performs a forklift safety solution providing method at a work site. For example, the external server (300) may be a storage server separately provided outside the forklift safety solution providing device (100), but is not limited thereto.

[0066]

[0067] FIG. 2 is a drawing illustrating an on-device AI-based forklift safety solution provision system at a work site according to various embodiments of the present disclosure.

[0068] Referring to FIG. 2, an on-device AI-based forklift safety solution provision system at a work site (hereinafter referred to as the "on-device AI system") according to various embodiments of the present disclosure can provide a forklift safety solution provision service at a work site based on on-device AI.

[0069] In various embodiments, the on-device AI system can perform various functions (e.g., object detection, distance estimation, hazard warning, event data upload and analysis, AI model update, etc.) to provide forklift safety solution provision services at the work site based on on-device AI.

[0070] In the on-device AI system, the on-device AI can detect surrounding objects, such as workers, other vehicles, and obstacles in real time by using an object detection and distance measurement AI model mounted on the forklift (10) and / or the user terminal (200) of the forklift (10) operator. If the detected object is located within a predefined warning zone or danger zone, the on-device AI can send a real-time warning to the operator and the worker. Additionally, the on-device AI can upload the detected event to a cloud portal, allowing a safety officer to review it and use it as training data for the AI ​​model.

[0071] On-device AI can provide immediate notifications to drivers and workers regarding detected dangers. Here, the notification provided to the driver may be a notification warning of a dangerous situation through an internal display, warning light, buzzer, and speaker of the forklift (10), and the notification provided to the worker may be a notification warning of a dangerous situation through a wearable device (RTLS-based) or a mobile device.

[0072] On-device AI uploads detected event data to the SAC cloud portal, enabling safety personnel to review and analyze the work site based on the uploaded data. The event data may include video, snapshots, object detection information (bounding boxes), distance information, vehicle GPS information, time of event occurrence, and other metadata. Based on this data, safety personnel can determine whether an event is a true positive or a false positive, and utilize the data for worker training and safety policy improvement if necessary.

[0073] In addition, safety managers can evaluate the validity of events based on government safety regulations (such as OSHA) and internal corporate safety standards, utilize the evaluation results for worker training and safety issue analysis, and thereby prevent accidents in the work environment.

[0074] An on-device AI system according to various embodiments of the present disclosure can improve object detection and distance measurement performance by continuously updating an AI model. Event data reviewed in a cloud portal is utilized as training data for the AI ​​model, and the on-device AI system can improve object detection and risk warning algorithms based on this. In particular, the on-device AI system can continuously improve model performance by applying a learning technique that identifies false positive data and removes it from the AI ​​model. Here, the AI ​​model update is distributed to the on-device AI through a service provider server, enabling the on-device AI to apply the latest model to perform more precise object detection and distance measurement.

[0075] On-device AI systems can be designed to go beyond simple risk detection and warning systems to analyze the root causes of safety issues and develop long-term solutions. More specifically, cloud portals can perform functions such as analyzing worker behavior patterns, evaluating response methods, and providing data to improve the work environment. Furthermore, they can support the implementation of practical measures to enhance safety awareness and reduce accident risks through worker education and training. They can also provide fundamental solutions, such as inducing proactive safety responses from operators, improving work processes, and recommending the installation of additional safety equipment.

[0076]

[0077] FIG. 3 is a diagram illustrating the hardware configuration of a forklift safety solution providing device at a work site according to another embodiment of the present disclosure.

[0078] Referring to FIG. 3, a forklift safety solution providing device (100) (hereinafter referred to as "computing device (100)") at a work site according to another embodiment of the present disclosure may include one or more processors (110), a memory (120) for loading a computer program (151) executed by the processor (110), a bus (130), a communication interface (140), and a storage (150) for storing the computer program (151). Here, FIG. 3 only illustrates components related to the embodiment of the present disclosure. Therefore, a person skilled in the art to which the present disclosure belongs will understand that other general-purpose components may be included in addition to the components illustrated in FIG. 3.

[0079] The processor (110) controls the overall operation of each component of the computing device (100). The processor (110) may be configured to include a CPU (Central Processing Unit), an MPU (Micro Processor Unit), an MCU (Micro Controller Unit), a GPU (Graphic Processing Unit), or any form of processor well known in the art of the present disclosure.

[0080] Additionally, the processor (110) may perform operations for at least one application or program for executing the method according to the embodiments of the present disclosure, and the computing device (100) may have one or more processors.

[0081] In various embodiments, the processor (110) may further include Random Access Memory (RAM) (not shown) and Read-Only Memory (ROM) (not shown) for temporarily and / or permanently storing signals (or data) processed within the processor (110). Additionally, the processor (110) may be implemented in the form of a System on Chip (SoC) comprising at least one of a graphics processing unit, RAM, and ROM.

[0082] Memory (120) stores various data, instructions and / or information. Memory (120) may load a computer program (151) from storage (150) to execute a method / operation according to various embodiments of the present disclosure. When the computer program (151) is loaded into memory (120), the processor (110) may perform the method / operation by executing one or more instructions constituting the computer program (151). Memory (120) may be implemented as a volatile memory such as RAM, but the technical scope of the present disclosure is not limited thereto.

[0083] The bus (130) provides communication functions between components of the computing device (100). The bus (130) can be implemented as various types of buses, such as an address bus, a data bus, and a control bus.

[0084] The communication interface (140) supports wired and wireless internet communication of the computing device (100). Additionally, the communication interface (140) may support various communication methods other than internet communication. To this end, the communication interface (140) may be configured to include a communication module well known in the art of the present disclosure. In some embodiments, the communication interface (140) may be omitted.

[0085] Storage (150) can store computer programs (151) non-temporarily. When performing a process of providing a forklift safety solution at a work site through a computing device (100), storage (150) can store various information necessary to provide a process of providing a forklift safety solution at a work site.

[0086] Storage (150) may be configured to include non-volatile memory such as ROM (Read Only Memory), EPROM (Erasable Programmable ROM), EEPROM (Electrically Erasable Programmable ROM), flash memory, a hard disk, a removable disk, or any form of computer-readable recording medium well known in the art to which this disclosure belongs.

[0087] A computer program (151) may include one or more instructions that cause a processor (110) to perform a method / operation according to various embodiments of the present disclosure when loaded into memory (120). That is, the processor (110) may perform the method / operation according to various embodiments of the present disclosure by executing the one or more instructions.

[0088] In one embodiment, a computer program (151) may include one or more instructions for performing a method of providing a forklift safety solution at a work site, the method comprising the steps of acquiring sensor data generated by scanning the work site through a sensor installed at the work site where the forklift is deployed, and providing a safety solution that provides a notification corresponding to the generated event when it is determined that an event regarding the forklift has occurred based on the acquired sensor data. Hereinafter, with reference to FIGS. 4 to 6, a method of providing a forklift safety solution at a work site performed by a computing device (100) will be described.

[0089]

[0090] Referring to FIG. 4, in step S110, the computing device (100) can acquire sensor data corresponding to the work site.

[0091] In various embodiments, the computing device (100) can acquire sensor data generated as it scans the work site through a sensor installed at the work site where the forklift (10) is deployed.

[0092] Here, sensor data may be image data acquired through a camera and / or point cloud data acquired through a LiDAR, but is not limited thereto, and various types and forms of sensor data may be acquired.

[0093] Also, here, the sensor may be installed at the work site, but in some cases, the sensor may be installed on a forklift (10), but is not limited thereto.

[0094] In various embodiments, the computing device (100) can acquire multiple sensor data from each of the multiple sensors deployed at the work site.

[0095] In various embodiments, the computing device (100) can acquire a plurality of sensor data at predetermined intervals. At this time, the collection period of the sensor data may be determined based on the risk level of the work site.

[0096] Here, the process of determining the risk level of the work site and determining the collection cycle accordingly can be performed as follows.

[0097] First, the computing device (100) can divide the work site into multiple zones.

[0098] Subsequently, the computing device (100) can determine the risk level for the multiple zones based on the number of events that occurred in each of the multiple zones. For example, the computing device (100) can calculate a score for each of the multiple zones based on the number of events that occurred in each of the multiple zones based on score data based on a pre-set number of events (or a score conversion ratio based on a pre-set number of events), and can classify the risk level for each of the multiple zones into one of the multiple grades based on the score for each of the multiple zones.

[0099] Here, the multiple grades are grades that classify risk levels stepwise; for example, the multiple grades may include, but are not limited to, safety grades, caution grades, warning grades, and danger grades.

[0100] Subsequently, the computing device (100) can determine a collection cycle for each of the multiple zones according to the risk level determined for each of the multiple zones. For example, the computing device (100) can determine the collection cycle to the longest first cycle when the risk level of a specific zone is a safety level, determine the collection cycle to a second cycle shorter than the first cycle when it is a caution level, determine the collection cycle to a third cycle shorter than the second cycle when it is a warning level, and determine the collection cycle to a fourth cycle shorter than the third cycle when it is a danger level.

[0101] That is, the computing device (100) can collect sensor data relatively more frequently than other areas for dangerous zones where events occur frequently, thereby monitoring event occurrences more frequently.

[0102] In step S120, the computing device (100) can provide a safety solution for the forklift (10) based on the sensor data obtained through step S110.

[0103] In various embodiments, the computing device (100) can identify the forklift (10) and objects by analyzing sensor data acquired for the work site, determine the occurrence of an event for the forklift (10), and, when the occurrence of an event for the forklift (10) is determined, provide a notification corresponding to the event.

[0104] Here, an event for the forklift (10) may refer to a situation where there is a possibility of a collision between the forklift (10) and an object (e.g., a situation where the probability of a collision is greater than or equal to a threshold probability), but is not limited thereto, and may include not only a collision but also all abnormal situations that may occur to the forklift (10) (e.g., breakdown, overturning, rollover, falling, fire, etc.).

[0105] In various embodiments, the computing device (100) can identify a forklift (10) and an object by analyzing sensor data based on a pre-trained artificial intelligence model. Here, the pre-trained artificial intelligence model may be a model trained using training data in which sensor data is used as input data and information regarding the forklift (10) and the object is used as correct data.

[0106] An artificial intelligence model (e.g., a neural network) consists of one or more network functions, and one or more network functions may consist of a set of interconnected computational units that can generally be referred to as 'nodes'. These 'nodes' may also be referred to as 'neurons'. One or more network functions are composed of at least one node. The nodes (or neurons) constituting one or more network functions may be interconnected by one or more 'links'.

[0107] In an artificial intelligence model, one or more nodes connected via links can form a relative relationship between an input node and an output node. The concepts of input and output nodes are relative; any node in an output node relationship with respect to one node may be in an input node relationship with respect to another node, and vice versa. As previously mentioned, the input node versus output node relationship can be generated based on links. One or more output nodes may be connected to a single input node via links, and vice versa.

[0108] In a relationship between input and output nodes connected via a single link, the value of the output node can be determined based on data input into the input node. Here, the nodes interconnecting the input and output nodes may have weights. These weights can be variable and may be varied by a user or an algorithm to enable the artificial intelligence model to perform desired functions. For example, if one or more input nodes are interconnected to a single output node via respective links, the output node value can be determined based on the values ​​input into the input nodes connected to the output node and the weights set on the links corresponding to each input node.

[0109] As described above, an artificial intelligence model consists of one or more nodes interconnected through one or more links, forming input and output node relationships within the model. The characteristics of an artificial intelligence model can be determined by the number of nodes and links within the model, the relationships between the nodes and links, and the weight values ​​assigned to each link. For example, if two artificial intelligence models exist with the same number of nodes and links but different weight values ​​between the links, the two models may be perceived as different from each other.

[0110] Some of the nodes constituting an artificial intelligence model may form a layer based on their distances from the initial input node. For example, a set of nodes with a distance of n from the initial input node may form n layers. The distance from the initial input node can be defined by the minimum number of links that must be traversed to reach that node from the initial input node. However, this definition of a layer is arbitrary for illustrative purposes, and the degree of a layer within the artificial intelligence model may be defined in a way different from that described above. For example, the layer of nodes may be defined by their distance from the final output node.

[0111] The initial input node may refer to one or more nodes within the artificial intelligence model to which data is directly input without passing through links in relation to other nodes. Alternatively, within the artificial intelligence model network, in terms of relationships between nodes based on links, it may refer to nodes that do not have other input nodes connected by links. Similarly, the final output node may refer to one or more nodes within the artificial intelligence model that do not have output nodes in relation to other nodes. Additionally, the hidden node may refer to nodes constituting the artificial intelligence model that are neither the initial input node nor the final output node. An artificial intelligence model according to one embodiment of the present disclosure may have more nodes in the input layer than nodes in the hidden layer that are close to the output layer, and may be an artificial intelligence model in which the number of nodes decreases as it progresses from the input layer to the hidden layer.

[0112] An artificial intelligence model may include one or more hidden layers. The hidden nodes of a hidden layer can take the output of the previous layer and the output of neighboring hidden nodes as input. The number of hidden nodes for each hidden layer may be the same or different. The number of nodes in the input layer may be determined based on the number of data fields in the input data and may be the same or different from the number of hidden nodes. The input data fed into the input layer can be processed by the hidden nodes of the hidden layer and output by the fully connected layer (FCL), which is the output layer.

[0113] In various embodiments, the artificial intelligence model may be a deep learning model (e.g., FIG. 4).

[0114] A deep learning model (e.g., a deep neural network (DNN)) can refer to an artificial intelligence model that includes multiple hidden layers in addition to input and output layers. Using a deep neural network, one can identify the latent structures of data. That is, one can identify the latent structures of photos, text, videos, voice, and music (e.g., what objects are in a photo, what the content and emotions of the text are, what the content and emotions of the voice are, etc.).

[0115] Deep neural networks may include, but are not limited to, convolutional neural networks (CNN), recurrent neural networks (RNN), autoencoders, Generative Adversarial Networks (GAN), restricted Boltzmann machines (RBM), deep belief networks (DBN), Q networks, U networks, and Siamese networks.

[0116] In various embodiments, the network function may include an autoencoder. Here, the autoencoder may be a type of artificial neural network for outputting output data similar to the input data.

[0117] An autoencoder may include at least one hidden layer, and an odd number of hidden layers may be placed between the input and output layers. The number of nodes in each layer may be reduced from the number of nodes in the input layer to an intermediate layer called the bottleneck layer (encoding), and then expanded symmetrically from the bottleneck layer to the output layer (symmetrical to the input layer). The nodes of the dimensionality reduction layer and the dimensionality restoration layer may or may not be symmetrical. Additionally, the autoencoder can perform non-linear dimensionality reduction. The number of input and output layers may correspond to the number of sensors remaining after the preprocessing of the input data. In the autoencoder structure, the number of nodes in the hidden layers included in the encoder may have a structure where it decreases as it moves away from the input layer. Since the number of nodes in the bottleneck layer (the layer with the fewest nodes located between the encoder and the decoder) may not transmit a sufficient amount of information if it is too small, it may be maintained at a certain number or higher (e.g., more than half the number of the input layer). Hereinafter, with reference to FIGS. 5 and FIGS. 6, the method by which the computing device (100) provides a safety solution will be described in more detail.

[0118]

[0119] FIG. 5 is a flowchart of a method for providing a safety solution that provides notifications depending on whether an event occurs in various embodiments.

[0120] FIG. 5 is a flowchart of a method for providing a safety solution that provides notifications depending on whether an event occurs in various embodiments.

[0121] Referring to FIG. 5, in step S210, the computing device (100) can set an event detection area for the forklift (10) when the forklift (10) is identified based on sensor data acquired for the work site.

[0122] In various embodiments, the computing device (100) may set an event detection area having a predetermined radius centered on the forklift (10). However, it is not limited thereto.

[0123] In various embodiments, the computing device (100) may set an event detection area for the forklift (10), as shown in FIG. 6, a danger area having a radius of a first size (R1) centered on the forklift (10) and a warning area having a radius of a second size (R2) larger than the first size.

[0124] In various embodiments, the computing device (100) sets an event detection area having a predetermined radius centered on the forklift (10), and can adjust the size of the event detection area according to the direction of movement and speed of movement of the forklift (10). For example, the computing device (100) can increase the size of the event detection area in proportion to a predetermined ratio as the speed of the forklift (10) increases. Additionally, the computing device (100) can set an event detection area in the shape of an elongated ellipse in the direction of movement by expanding the event detection area in the direction of movement of the forklift (10).

[0125] In various embodiments, the computing device (100) may adjust the size of the event detection area based on the number of events that occurred in response to the forklift (10) during a predetermined period.

[0126] For example, when the radius of the event detection area set for the forklift (10) is a first size, the computing device (100) can increase the radius of the event detection area to a second size larger than the first size if the number of events generated corresponding to the forklift (10) during a predetermined period is greater than or equal to a first reference number and less than a second reference number. Additionally, when the number of events generated corresponding to the forklift (10) during a predetermined period is greater than or equal to a second reference number, the computing device (100) can increase the radius of the event detection area to a third size larger than the second size.

[0127] In various embodiments, the computing device (100) can adjust the size of the event detection area based on the risk level determined for one of the areas when a forklift (10) enters one of the areas.

[0128] For example, when the radius of the event detection area within the safe zone is a first size, the computing device (100) can increase the radius of the event detection area to a second size larger than the first size when the forklift (10) enters a zone of caution level. Additionally, when the forklift (10) enters a zone of warning level, the computing device (100) can increase the radius of the event detection area to a third size larger than the second size. Additionally, when the forklift (10) enters a zone of danger level, the computing device (100) can increase the radius of the event detection area to a fourth size larger than the third size.

[0129] In various embodiments, the computing device (100) can adjust the size of the event detection area based on past sensor data acquired over a predetermined period of time for the work site.

[0130] More specifically, first, the computing device (100) can create a virtual forklift (10) and one or more virtual objects within the work site by analyzing past sensor data acquired during a predetermined period in the past. For example, the computing device (100) can calculate the probability that a forklift and an object are located in each of a plurality of zones corresponding to the work site based on the history of identifying forklifts and objects based on past sensor data, and create a virtual forklift and a virtual object in each of the plurality of zones according to the calculated probability, but is not limited thereto.

[0131] Subsequently, the computing device (100) can determine an expected movement plan (movement path and speed) of a virtual forklift and one or more virtual objects based on past sensor data-based forklift and object tracking history, and can simulate the movement of the virtual forklift and one or more virtual objects within the work site according to the determined expected movement plan.

[0132] At this time, when the computing device (100) determines that a virtual event has occurred between the virtual forklift and the virtual object as it simulates the movement of the virtual forklift and one or more virtual objects, it may set a predetermined range centered on the point where the virtual event occurred as a region of interest.

[0133] Afterwards, the computing device (100) can increase the radius of the event detection area to a second size larger than the first size when a forklift with an event detection area having a radius of a first size enters the area of ​​interest.

[0134] In step S220, the computing device (100) can analyze sensor data to identify objects within the work site.

[0135] Here, the object may include, but is not limited to, fixed objects other than the forklift (10), such as materials placed in the work site, work equipment, etc., and dynamic objects such as workers working in the work site, forklifts other than the forklift (10) that are the target of event detection.

[0136] In step S230, the computing device (100) can continuously perform the operation of identifying an object by analyzing sensor data, i.e., the operation of monitoring the work site, when no object is identified within the work site through step S220.

[0137] In step S240, if an object is identified within the work site through step S220, the computing device (100) can set an object area corresponding to the identified object.

[0138] In various embodiments, the computing device (100) can set an object area for an object based on the size and shape of the object.

[0139] In various embodiments, the computing device (100) sets an object area for an object, and can adjust the size of the set object area based on the number of events generated by the object during a predetermined period. For example, the computing device sets an object area having a radius of a first size based on the size and shape of a specific object, and if the number of events generated by the specific object is greater than or equal to a preset number, the radius of the object area corresponding to the specific object can be increased to a second size larger than the first size.

[0140] In various embodiments, the computing device (100) sets an object area for an object, and if the object is a worker, the size of the object area can be adjusted based on the type of work the worker is performing.

[0141] For example, the computing device (100) sets an object area for an object, and if the object is a worker, determines the type of work performed by the worker, and if the type of work is a work classified as a dangerous work in advance, the size of the object area set corresponding to the worker can be increased.

[0142] Here, the method for determining the type of work performed by a worker may, for example, extract the behavioral pattern of an object based on sensor data collected over a predetermined period and determine the type of work performed by the worker based on the extracted behavioral pattern, but is not limited thereto. Work schedule information for each worker may be stored in advance, and if a worker is identified by extracting the worker's attributes (e.g., face, height, body shape, etc.) based on sensor data, the type of work performed by the worker may be determined according to the schedule information stored in advance for the identified worker.

[0143] In various embodiments, the computing device (100) sets an object area for an object, and if the object is a worker, it identifies the worker's behavior pattern based on sensor data, and if the behavior pattern is determined to be an abnormal behavior pattern based on the type of work being performed by the worker, it can increase the size of the object area. For example, the computing device (100) may predefine behavior patterns for each type of work, and if it is determined that a specific worker's behavior pattern is not a behavior pattern corresponding to the type of work being performed by the specific worker, it can determine that the specific worker's behavior pattern is an abnormal behavior pattern and increase the object area for the specific worker.

[0144] In various embodiments, the computing device (100) can set an object area based on the direction of movement and speed of movement of the object when the object identified based on sensor data is a dynamic object.

[0145] In step S250, the computing device (100) can determine whether an event has occurred for the forklift (10) based on whether an object enters an event detection area set for the forklift (10).

[0146] In various embodiments, the computing device (100) can calculate the distance between the forklift (10) and the object identified from the sensor data, and can determine whether the object enters the event detection area based on the calculated distance.

[0147] In various embodiments, the computing device (100) can determine whether at least a portion of the event detection area set for the forklift (10) and the object area set for the object overlap, and can determine whether an event occurs based on whether the areas overlap.

[0148] For example, if the sum of the length of the event detection area in the direction of the object and the length of the object area in the direction of the forklift (10) is less than the distance between the objects of the forklift (10), the computing device (100) may determine that at least a part of the event detection area and the object area overlap, and thus determine that an event has occurred.

[0149] In step S260, if the computing device (100) determines through step S250 that no event has occurred for the forklift (10), it can track and detect the object until an event has occurred for the forklift (10).

[0150] In step S270, if the computing device (100) determines that an event has occurred for the forklift (10) through step S250, it may provide a notification corresponding to the event.

[0151] For example, when the computing device (100) determines that an event has occurred as the event detection area corresponding to the forklift (10) and at least a part of the object area corresponding to a specific worker overlap, it can provide a notification to the driver of the forklift (10) (e.g., notification via the screen of the forklift (10), notification via a warning light, notification via a buzzer, notification via the driver's user terminal (200), etc.).

[0152] Additionally, when the computing device (100) determines that an event has occurred as the event detection area corresponding to the forklift (10) and at least a part of the object area corresponding to the specific worker overlap, it can provide a notification of the event (e.g., output of a warning sound and / or guidance of a warning message, vibration and voice notification via a wearable device, SMS, etc.) to the specific worker's user terminal (200).

[0153] Additionally, when the computing device (100) detects the occurrence of an event within the work site, it can provide information about the event and a notification about the event (e.g., SMS, email, etc.) to the user terminal (200) of the manager of the work site and / or the management server of the work site.

[0154] In various embodiments, the computing device (100) may provide a notification of a first attribute (e.g., a voice notification and warning sound output of a first volume) when it is determined that an object has entered a warning area among the event detection areas corresponding to the forklift (10). Meanwhile, the computing device (100) may provide a notification of a second attribute (e.g., a voice notification and warning sound output of a second volume louder than the first volume) when it is determined that an object has entered a danger area among the event detection areas corresponding to the forklift (10).

[0155] In various embodiments, when the computing device (100) determines that an event has occurred regarding the forklift (10), it provides a notification corresponding to the event, and may change the attributes of the notification according to the duration of the event. For example, the computing device (100) may provide a notification of a first attribute in response to the initial detection of a specific event occurring regarding the forklift (10). At this time, if the specific event is detected continuously for a predetermined period, the computing device (100) may change the first attribute to a second attribute and provide a notification of a second attribute.

[0156]

[0157] The method for providing a forklift safety solution at a work site described above has been explained with reference to the flowchart illustrated in the drawings. For the sake of simplicity, the method for providing a forklift safety solution at a work site has been illustrated and described using a series of blocks; however, the present disclosure is not limited to the order of the blocks, and some blocks may be performed in a different order or simultaneously than those illustrated and described in this specification. Additionally, new blocks not described in this specification and drawings may be added, or some blocks may be deleted or modified.

[0158]

[0159] The method described above may be provided as a computer program stored on a computer-readable recording medium for execution on a computer. The medium may continuously store a program executable by a computer, or temporarily store it for execution or download. Additionally, the medium may be various recording or storage means in the form of a single or multiple hardware components combined, and may not be limited to a medium directly connected to a computer system but may exist distributed over a network. Examples of media may include magnetic media such as hard disks, floppy disks, and magnetic tapes; optical recording media such as CD-ROMs and DVDs; magneto-optical media such as floptical disks; and media configured to store program instructions, including ROM, RAM, and flash memory. Furthermore, other examples of media may include recording or storage media managed by app stores that distribute applications or sites and servers that supply or distribute various other software.

[0160] The methods, operations, or techniques of the present disclosure may be implemented by various means. For example, these techniques may be implemented in hardware, firmware, software, or a combination thereof. Those skilled in the art will understand that the various exemplary logical blocks, modules, circuits, and algorithmic steps described in connection with the disclosure herein may be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate such interchangeability between hardware and software, various exemplary components, blocks, modules, circuits, and steps have been generally described above in terms of their functional aspects. Whether such functions are implemented in hardware or in software depends on the design requirements imposed on the specific application and the overall system. Those skilled in the art may implement the functions described in various ways for each specific application, but such implementations should not be construed as departing from the scope of the present disclosure.

[0161] In a hardware implementation, the processing units used to perform the techniques may be implemented in one or more ASICs, DSPs, digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), processors, controllers, microcontrollers, microprocessors, electronic devices, other electronic units designed to perform the functions described in this disclosure, computers, or a combination thereof.

[0162] Accordingly, the various exemplary logic blocks, modules, and circuits described in connection with the present disclosure may be implemented or performed by any combination of general-purpose processors, DSPs, ASICs, FPGAs or other programmable logic devices, discrete gate or transistor logic, discrete hardware components, or those designed to perform the functions described herein. A general-purpose processor may be a microprocessor, but alternatively, the processor may be any conventional processor, controller, microcontroller, or state machine. The processor may also be implemented as a combination of computing devices, for example, a DSP and a microprocessor, a plurality of microprocessors, one or more microprocessors coupled with a DSP core, or any other combination of configurations.

[0163] In firmware and / or software implementations, techniques may be implemented as instructions stored on a computer-readable medium such as random access memory (RAM), read-only memory (ROM), non-volatile random access memory (NVRAM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable PROM (EEPROM), flash memory, compact disc (CD), magnetic or optical data storage devices, etc. The instructions may be executable by one or more processors, and may cause the processor(s) to perform specific aspects of the functions described in this disclosure.

[0164] When implemented in software, the techniques described above may be stored on a computer-readable medium as one or more instructions or code, or transmitted through a computer-readable medium. Computer-readable media include both computer storage media and communication media, including any medium that facilitates the transmission of a computer program from one place to another. Storage media may be any available media accessible by a computer. As a non-limiting example, such computer-readable media may include RAM, ROM, EEPROM, CD-ROM or other optical disk storage, magnetic disk storage or other magnetic storage devices, or any other medium accessible by a computer that can be used to transfer or store desired program code in the form of instructions or data structures. Additionally, any connection is appropriately referred to as a computer-readable medium.

[0165] For example, if software is transmitted from a website, server, or other remote source using coaxial cable, fiber optic cable, twisted pair cable, digital subscriber line (DSL), or wireless technologies such as infrared, radio, and microwave, coaxial cable, fiber optic cable, twisted pair cable, digital subscriber line, or wireless technologies such as infrared, radio, and microwave are included within the definition of a medium. As used herein, disk and disc include CD, laser disc, optical disc, DVD (digital versatile disc), floppy disk, and Blu-ray disc, wherein disks usually play data magnetically, whereas discs play data optically using a laser. The above combinations should also be included within the scope of computer-readable media.

[0166] The software module may reside in RAM memory, flash memory, ROM memory, EPROM memory, EEPROM memory, registers, a hard disk, a removable disk, a CD-ROM, or any other known form of storage medium. An exemplary storage medium may be connected to a processor so that the processor can read information from the storage medium or write information to the storage medium. Alternatively, the storage medium may be integrated into the processor. The processor and the storage medium may exist within an ASIC. The ASIC may exist within a user terminal. Alternatively, the processor and the storage medium may exist as separate components within the user terminal.

[0167] Although the embodiments described above have been described as utilizing aspects of the subject matter disclosed herein in one or more standalone computer systems, the present disclosure is not limited thereto and may be implemented in conjunction with any computing environment, such as a network or a distributed computing environment. Furthermore, aspects of the subject matter in the present disclosure may be implemented in a plurality of processing chips or devices, and storage may be similarly affected across a plurality of devices. Such devices may include PCs, network servers, and portable devices.

[0168] Although the present disclosure has been described in relation to some embodiments, various modifications and changes may be made without departing from the scope of the present disclosure as understood by a person skilled in the art to which the invention of the present disclosure pertains. Furthermore, such modifications and changes should be considered to fall within the scope of the claims appended to this specification.

Claims

1. In a method performed by a computing device, A step of acquiring sensor data generated by scanning the work site through a sensor installed at the work site where a forklift is deployed; and A safety solution comprising the step of providing a notification corresponding to the generated event when it is determined that an event regarding the forklift has occurred based on the sensor data acquired above. Method for providing forklift safety solutions at the work site.

2. In Paragraph 1, The step of providing the above safety solution is, Step of setting an event detection area for the above forklift; and A step comprising determining whether an event for the forklift occurs based on whether an object enters the event detection area set above. Method for providing forklift safety solutions at the work site.

3. In Paragraph 2, The step of setting the above event detection area is, A method comprising the step of setting an event detection area having a predetermined radius centered on the forklift, and adjusting the size of the set event detection area based on the number of events generated in response to the forklift during a predetermined period. Method for providing forklift safety solutions at the work site.

4. In Paragraph 2, The step of setting the above event detection area is, A step of dividing the above work site into multiple zones; A step of determining the risk level for the plurality of divided zones based on the number of events that occurred in each of the plurality of divided zones during a predetermined period; and When the forklift enters any one of the divided multiple zones, the method includes the step of adjusting the size of the set event detection area based on a risk level determined for any one of the zones. Method for providing forklift safety solutions at the work site.

5. In Paragraph 2, The step of setting the above event detection area is, A step of creating a virtual forklift and one or more virtual objects within the work site based on past sensor data acquired during a predetermined period; When it is determined that a virtual event has occurred as a result of simulating the movement of the generated virtual forklift and one or more generated virtual objects, a step of setting a predetermined range centered on the point where the virtual event occurred as a region of interest; and The method includes the step of increasing the size of the set event detection area when the forklift enters the set area of ​​interest. Method for providing forklift safety solutions at the work site.

6. In Paragraph 2, The step of determining whether the above event has occurred is, When an object is identified based on the sensor data acquired above, a step of setting an object area for the identified object; and A step of determining that an event for the forklift has occurred when at least a portion of the set event detection area and the set object area overlap. Method for providing forklift safety solutions at the work site.

7. In Paragraph 6, The step of setting the object area above is, A method comprising the step of setting an object area for the identified object based on the size and shape of the identified object, and adjusting the size of the set object area based on the number of events generated by the identified object during a predetermined period. Method for providing forklift safety solutions at the work site.

8. In Paragraph 6, The step of setting the object area above is, A method comprising the step of setting an object area for an identified object based on the type of the identified object, wherein, if the identified object is a worker, the size of the set object area is adjusted based on the type of work being performed by the worker. Method for providing forklift safety solutions at the work site.

9. In Paragraph 6, The step of setting the object area above is, If the identified object is a worker, the method includes the step of identifying the worker's behavioral pattern based on the acquired sensor data, and if the identified behavioral pattern is determined to be an abnormal behavioral pattern based on the type of work being performed by the worker, increasing the size of the set object area. Method for providing forklift safety solutions at the work site.

10. In Paragraph 2, The event detection area set above is, It includes a danger area having a radius of a first size based on the center of the forklift and a warning area having a radius of a second size larger than the first size, The step of providing the above safety solution is, A method comprising the step of providing a notification of a first attribute when it is determined that an object has entered the warning area, and providing a notification of a second attribute when it is determined that an object has entered the danger area. Method for providing forklift safety solutions at the work site.

11. In Paragraph 1, The step of providing the above safety solution is, If it is determined that a specific event regarding the forklift has occurred based on the acquired sensor data, the method comprises the step of providing a notification corresponding to the specific event that occurred, wherein the attributes of the provided notification are changed according to the duration of the specific event that occurred. Method for providing forklift safety solutions at the work site.

12. Processor; Network interface; Memory; and It includes a computer program that is loaded into the memory and executed by the processor, The above computer program is, An instruction for acquiring sensor data generated by scanning the said work site through a sensor installed at the work site where a forklift is deployed; and Including an instruction that provides a safety solution providing a notification corresponding to the generated event when it is determined that an event regarding the forklift has occurred based on the acquired sensor data. Forklift safety solution provider at the work site.

13. Combined with a computing device, A step of acquiring sensor data generated by scanning the work site through a sensor installed at the work site where a forklift is deployed; and A safety solution comprising the step of providing a notification corresponding to the generated event when it is determined that an event regarding the forklift has occurred based on the sensor data acquired above. A computer program stored on a recording medium readable by a computing device to implement a method for providing a forklift safety solution at a work site.

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