Method for monitoring labor time of industrial robot, and system thereof

Abstract

Disclosed are a method for monitoring the labor time of an industrial robot, and a system thereof. The method for monitoring the labor time of an industrial robot may comprise a step for monitoring the labor time of a robot by means of an edge-cloud hybrid structure composed of an edge layer for collecting work data of the robot in a manufacturing environment and a cloud layer for analyzing the work data.

Description

Method and System for Monitoring Manufacturing Robot Labor Hours

[0001] The following description concerns technology for corporate work management and production systems.

[0002] The manufacturing industry is an industry that produces products using raw materials, undergoing a continuous process in which various processes are performed sequentially, and the outputs of each process are mixed with one another or the state of the output of a specific process changes before being supplied to subsequent processes.

[0003] With the recent advancement of IT technology, Smart Factory technology is emerging. This technology utilizes artificial intelligence and big data to analyze desired products, plan and design customized goods, and automatically produce them through optimized processes using IoT and automated robots, thereby controlling product receipts and orders in real time. A Smart Factory is an intelligent production plant that applies ICT-based digital automation solutions to production processes such as design and development, manufacturing, and distribution and logistics, encompassing various technologies capable of improving productivity, quality, and customer satisfaction.

[0004] As an example of smart factory technology, Korean Published Patent Application No. 10-2018-0104919 discloses a technology that supports global and automatic interoperability between information systems (MES, ERP) and field systems (Sensors, Actuators, Field Devices) used in smart factories.

[0005] Recently, automation robots are being used in corporate manufacturing environments, but the following problems exist.

[0006] 1) Limitations of data standardization and integration

[0007] Although different data formats and protocols exist depending on the robot manufacturer, the lack of data compatibility between existing ERP (Enterprise Resource Planning) / MES (Manufacturing Execution System) and robot control systems makes integrated processing of real-time and batch data difficult.

[0008] 2) Limitations of the accuracy of real-time performance measurement

[0009] Not only is it difficult to accurately measure the working time of complex robot movements, but in the case of articulated robots, it is also difficult to aggregate the movement time for each joint, and the distinction between waiting time and actual working time is ambiguous.

[0010] 3) Limitations of abnormal situation detection and response

[0011] It is difficult to determine abnormal situations caused by complex factors, detection of unexpected robot motion patterns is limited, and decision-making delays occur for real-time response.

[0012] 4) Limitations of Security and Data Integrity Guarantees

[0013] Implementing end-to-end security in edge-cloud environments is complex, perfect verification to prevent data tampering is difficult, and the integration of security policies across multiple systems is limited.

[0014] 5) Limitations of scalability and flexibility

[0015] Adding new robot types requires complex system modifications, makes flexible response to diverse manufacturing environments difficult, and results in performance degradation when scaling up the system.

[0016] We provide a method and system capable of monitoring the working hours of manufacturing robots in real time through an edge-cloud hybrid structure.

[0017] A method for monitoring the labor time of a manufacturing robot, performed by a computer device comprising at least one processor, wherein the labor time of the robot is monitored through an edge-cloud hybrid structure composed of an edge layer that collects work data of the robot within a manufacturing environment and a cloud layer that analyzes said work data, comprising: an edge layer step that collects real-time work data from the robot through an interface for an industrial standard communication protocol and performs protocol conversion and normalization on the collected data; a cloud layer step that integrates and stores data transmitted from the edge layer step and calculates the labor time for each operating state of the robot through pattern recognition based on time-series data analysis; and a real-time synchronization step that performs bidirectional data synchronization between the edge layer and the cloud layer, detects a collision situation resulting from simultaneous updates, and resolves the collision according to a predefined rule.

[0018] According to one aspect, the edge layer step may include the step of collecting real-time data related to the robot from a plurality of data sources and performing validation of the collected data.

[0019] According to another aspect, the step of performing the above may include the step of performing format verification of data collected in relation to the robot and filtering of abnormal data.

[0020] According to another aspect, the edge layer step may include a step of preprocessing and refining raw data collected in relation to the robot into an analyzable form.

[0021] According to another aspect, the cleaning step may include the step of performing noise removal and outlier correction, missing value processing and interpolation, and data format standardization of the raw data; and the step of performing time series data compression and redundant data removal of the raw data.

[0022] According to another aspect, the edge layer step may include a step of temporarily storing the task data in an edge buffer through priority-based queue operation and transmission scheduling.

[0023] According to another aspect, the cloud layer step may include a step of managing data from all sources by integrating and storing it through a central repository.

[0024] According to another aspect, the managing step may include a step of managing a data classification system by performing metadata indexing; and a step of performing storage management according to the data lifecycle and storage tiering based on data access patterns.

[0025] According to another aspect, the cloud layer step may include a step of visualizing analysis results by deriving insights into data collected in relation to the robot.

[0026] According to another aspect, the visualization step may include: a step of performing statistical analysis and pattern recognition of data collected in relation to the robot; and a step of generating a dashboard for the results of the data analysis based on the statistical analysis and the pattern recognition.

[0027] According to another aspect, the real-time synchronization step may include a step of monitoring the synchronization status between the edge layer and the cloud layer to detect a conflict situation resulting from the occurrence of simultaneous updates.

[0028] According to another aspect, the real-time synchronization step may include: a step of performing mutual authentication between the edge layer and the cloud layer; a step of encrypting and decrypting transmitted data between the edge layer and the cloud layer; and a step of verifying the validity of a certificate between the edge layer and the cloud layer.

[0029] The present invention provides a computer device comprising at least one processor implemented to execute a readable command on a computer device, wherein the at least one processor monitors the working time of the robot through an edge-cloud hybrid structure composed of an edge layer that collects work data of the robot in a manufacturing environment and a cloud layer that analyzes the work data, and comprises: an edge layer process that collects real-time work data from the robot through an interface for an industrial standard communication protocol and performs protocol conversion and normalization on the collected data; a cloud layer process that integrates and stores data transmitted from the edge layer stage and calculates the working time for each operating state of the robot through pattern recognition based on time-series data analysis; and a real-time synchronization process that performs bidirectional data synchronization between the edge layer and the cloud layer, detects a collision situation resulting from simultaneous updates, and resolves the collision according to a predefined rule.

[0030] According to embodiments of the present invention, it is possible to ensure cost reduction as well as optimize productivity and improve operational efficiency. Efficient resource utilization can be realized through real-time monitoring of robot operation time, productivity can be improved by minimizing work waiting time and idle time, and Overall Equipment Effectiveness (OEE) can be maximized. Furthermore, operating costs can be reduced through the optimization of energy consumption, and repair costs through preventive maintenance and labor costs through the efficiency of personnel management can be reduced.

[0031] According to embodiments of the present invention, transparency of tax standards can be secured through the calculation of accurate tax data and the guarantee of data reliability. Accurate measurement and verification of robot labor time can be performed, and accurate labor replacement effects by work type can be calculated, thereby providing objective tax calculation standards. In addition, a system to prevent data falsification can be established, audit trail can be secured, and regulatory compliance can be monitored in real time.

[0032] According to embodiments of the present invention, advanced smart factory capabilities can be achieved through the establishment of an intelligent production system and the implementation of an integrated operation platform. Predictive production management based on AI / ML can be realized, a real-time decision support system can be established, and autonomous production optimization is possible. Furthermore, perfect integration with enterprise systems such as ERP, MES, and PLM can be achieved, integrated monitoring based on real-time data can be provided, and centralized control and management are possible.

[0033] According to embodiments of the present invention, improved operational safety and system stability can be ensured. A real-time safety monitoring system can be established, which enables the early detection and prevention of hazardous situations and allows for the automatic verification of compliance with safety regulations. Additionally, a 24 / 7 uninterrupted operation system can be established, automatic failure detection and recovery are possible, and the data backup and recovery system can be strengthened.

[0034] According to embodiments of the present invention, a foundation for sustainable growth can be established by minimizing environmental impact and strengthening technological competitiveness. It is possible to ensure the optimization of energy usage efficiency and the minimization of resource waste, and to establish an eco-friendly production system. Furthermore, it is possible to secure a competitive advantage through the application of the latest technologies, lead global technological standards, and strengthen continuous innovation capabilities.

[0035] FIG. 1 is a block diagram illustrating an example of the internal configuration of a computer device in an embodiment of the present invention.

[0036] FIGS. 2 to 4 illustrate examples of manufacturing system architectures applying a manufacturing robot labor time monitoring system according to the present invention.

[0037] FIG. 5 illustrates the overall architecture of a manufacturing robot labor time monitoring system in one embodiment of the present invention.

[0038] FIGS. 6 to 8 illustrate the detailed configuration of a manufacturing robot labor time monitoring system in an embodiment of the present invention.

[0039] FIG. 9 illustrates the entire process of a manufacturing robot labor time monitoring system in one embodiment of the present invention.

[0040] Figures 10 and 11 illustrate examples of processes utilizing a manufacturing robot labor time monitoring system.

[0041] Hereinafter, embodiments of the present invention will be described in detail with reference to the attached drawings.

[0042]

[0043] Embodiments of the present invention relate to technology for work management and production systems of enterprises.

[0044] Embodiments including those specifically disclosed in this specification can improve work management in a company's manufacturing environment and establish a better production system by monitoring the labor time of manufacturing robots in real time through an edge-cloud hybrid structure.

[0045] A manufacturing robot labor time monitoring system according to embodiments of the present invention may be implemented by at least one computer device, and a manufacturing robot labor time monitoring method according to embodiments of the present invention may be performed through at least one computer device included in the manufacturing robot labor time monitoring system. At this time, a computer program according to one embodiment of the present invention may be installed and run on the computer device, and the computer device may perform the manufacturing robot labor time monitoring method according to embodiments of the present invention under the control of the run computer program. The above-described computer program may be stored on a computer-readable recording medium to be combined with the computer device to execute the manufacturing robot labor time monitoring method on the computer.

[0046] FIG. 1 is a block diagram illustrating an example of a computer device according to an embodiment of the present invention. For example, a manufacturing robot labor time monitoring system according to embodiments of the present invention can be implemented by a computer device (100) illustrated in FIG. 1.

[0047] As illustrated in FIG. 1, the computer device (100) may include a memory (110), a processor (120), a communication interface (130), and an input / output interface (140) as components for implementing a manufacturing robot labor time monitoring method according to embodiments of the present invention.

[0048] Memory (110) is a computer-readable recording medium and may include a non-perishable mass storage device such as RAM (random access memory), ROM (read only memory), and a disk drive. Here, a non-perishable mass storage device such as a ROM and a disk drive may be included in the computer device (100) as a separate permanent storage device distinct from memory (110). Additionally, an operating system and at least one program code may be stored in memory (110). These software components may be loaded into memory (110) from a computer-readable recording medium separate from memory (110). This separate computer-readable recording medium may include a computer-readable recording medium such as a floppy drive, disk, tape, DVD / CD-ROM drive, or memory card. In another embodiment, software components may be loaded into memory (110) through a communication interface (130) rather than a computer-readable recording medium. For example, software components can be loaded into the memory (110) of the computer device (100) based on a computer program installed by files received through the network (160).

[0049] The processor (120) may be configured to process instructions of a computer program by performing basic arithmetic, logic, and input / output operations. Instructions may be provided to the processor (120) via memory (110) or a communication interface (130). For example, the processor (120) may be configured to execute instructions received according to program code stored in a recording device such as memory (110).

[0050] The communication interface (130) may provide a function for the computer device (100) to communicate with other devices through a network (160). For example, requests, commands, data, files, etc. generated by the processor (120) of the computer device (100) according to program code stored in a recording device such as memory (110) may be transmitted to other devices through the network (160) under the control of the communication interface (130). Conversely, signals, commands, data, files, etc. from other devices may be received by the computer device (100) through the communication interface (130) of the computer device (100) via the network (160). Signals, commands, data, etc. received through the communication interface (130) may be transmitted to the processor (120) or memory (110), and files, etc. may be stored in a storage medium (the permanent storage device described above) that the computer device (100) may further include.

[0051] The communication method is not limited and may include not only communication methods utilizing communication networks (e.g., mobile communication networks, wired internet, wireless internet, broadcasting networks) that the network (160) may include, but also short-range wired / wireless communication between devices. For example, the network (160) may include any one or more networks such as a PAN (personal area network), LAN (local area network), CAN (campus area network), MAN (metropolitan area network), WAN (wide area network), BBN (broadband network), and the Internet. Additionally, the network (160) may include any one or more network topologies such as a bus network, star network, ring network, mesh network, star-bus network, tree or hierarchical network, but is not limited thereto.

[0052] The input / output interface (140) may be a means for interfacing with an input / output device (150). For example, the input device may include a device such as a microphone, keyboard, camera, or mouse, and the output device may include a device such as a display or speaker. As another example, the input / output interface (140) may be a means for interfacing with a device in which the functions for input and output are integrated into one, such as a touchscreen. The input / output device (150) may be composed of a computer device (100) and a single device.

[0053] Additionally, in other embodiments, the computer device (100) may include fewer or more components than the components of FIG. 1. However, it is not necessary to clearly illustrate most of the prior art components. For example, the computer device (100) may be implemented to include at least some of the input / output devices (150) described above, or may include other components such as a transceiver, a camera, various sensors, a database, etc.

[0054] The following describes specific embodiments of manufacturing robot labor time monitoring technology.

[0055] A robot tax refers to a tax imposed when companies replace employees with automated robots, and it can be utilized for retraining or re-employment programs for laid-off workers. Methods for applying the robot tax include imposing income tax based on the salaries of the replaced human employees and increasing the corporate tax rate for companies using robots.

[0056] In addition to economic benefits such as compensating for tax revenue losses caused by automation and preventing tax evasion by multinational corporations, there are social benefits including the ability to control the pace of job losses, secure time for workers' retraining, and secure funding for Universal Basic Income (UBI).

[0057] However, there may be practical difficulties due to the ambiguous definition of "robot" and regulatory challenges arising from differing national standards, and there are also economic disadvantages, such as concerns about reduced productivity, the persistence of low-wage jobs, and the potential to hinder innovation.

[0058] The introduction of a robot tax is being discussed in the UK, the US, Japan, Canada, and other countries, and while the EU (European Union) supports automation regulation, it takes a cautious stance on taxation.

[0059] In conclusion, while a robot tax is one way to mitigate the social impact of automation, there are many challenges to address, such as the ambiguity of the definition and concerns about hindering innovation.

[0060] The present invention can innovate work management in a company's manufacturing environment by monitoring the working hours of robots in real time in a manufacturing environment where robots are used.

[0061] The computer device (100) according to the present embodiment can provide a manufacturing robot labor time monitoring service to a client by accessing a dedicated application installed on the client or a web / mobile site related to the computer device (100). A manufacturing robot labor time monitoring system implemented on a computer may be configured in the computer device (100). For example, the manufacturing robot labor time monitoring system may be implemented in the form of a program that operates independently, or configured in the form of an in-app of a specific application so that it can operate on said specific application.

[0062] The processor (120) of the computer device (100) may be implemented as a component for performing the following manufacturing robot labor time monitoring method. Depending on the embodiment, the components of the processor (120) may be optionally included in or excluded from the processor (120). Additionally, depending on the embodiment, the components of the processor (120) may be separated or merged to represent the function of the processor (120).

[0063] These processors (120) and components of the processor (120) can control a computer device (100) to perform steps included in the following manufacturing robot labor time monitoring method. For example, the processor (120) and components of the processor (120) may be implemented to execute instructions according to the code of an operating system included in memory (110) and the code of at least one program.

[0064] Here, the components of the processor (120) may be representations of different functions performed by the processor (120) according to instructions provided by program code stored in the computer device (100).

[0065] The processor (120) can read necessary instructions from memory (110) in which instructions related to the control of the computer device (100) are loaded. In this case, the read instructions may include instructions for controlling the processor (120) to execute the steps to be described later.

[0066] The steps included in the manufacturing robot labor time monitoring method described below may be performed in a different order than the illustrated order, and some of the steps may be omitted or additional processes may be included.

[0067] The manufacturing robot labor time monitoring system according to the present invention can be applied to various manufacturing environments.

[0068] FIGS. 2 to 4 illustrate examples of manufacturing system architectures. FIG. 2 shows a manufacturing enterprise environment system architecture composed of an edge layer, an integration layer, and a cloud layer. FIG. 3 shows a hybrid cloud manufacturing system architecture composed of a factory site, an edge layer, a private cloud, a public cloud, and a cloud management layer. FIG. 4 shows a cloud-based manufacturing system architecture composed of a data source layer, a data collection layer, a storage layer, and a processing and service layer.

[0069] FIG. 5 illustrates the overall architecture of a manufacturing robot labor time monitoring system in one embodiment of the present invention.

[0070] Referring to FIG. 5, the manufacturing robot labor time monitoring system according to the present invention may include an edge layer module (510), a cloud layer module (520), and an integrated layer module (530).

[0071] The edge layer module (510) may include a data collection module, an edge preprocessing engine, an edge buffer, etc.

[0072] The data collection module of the edge layer module (510) can collect real-time data from various data sources and perform initial conversion. It can implement an adapter that supports various industrial protocols such as OPC-UA, Modbus, and MQTT. It can maintain a stable connection by managing a connector pool for each data source and perform initial validation of the collected data.

[0073] The data collection module of the edge layer module (510) may include a protocol adapter and a data validator. In this case, the protocol adapter provides an interface for industry standard communication protocols, can perform real-time data stream processing, and can handle protocol conversion and normalization. Additionally, the data validator performs format verification of the collected data, can verify basic data consistency, and can perform abnormal data filtering.

[0074] The edge preprocessing engine of the edge layer module (510) can preprocess and refine collected raw data into an analyzable form. It can perform real-time data filtering and normalization, improve transmission efficiency through data compression and optimization, and be responsible for generating and managing metadata.

[0075] The edge preprocessing engine of the edge layer module (510) may include a data cleaner and a data compressor. In this case, the data cleaner performs noise removal and outlier correction, can perform missing value processing and interpolation, and can perform data format standardization. Additionally, the data compressor performs time-series data compression, can perform duplicate data removal, and can monitor compression efficiency.

[0076] The edge buffer of the edge layer module (510) can prevent data loss in network instability situations and be responsible for temporary storage. It can perform local storage management and optimization, implement priority-based data queuing, and provide an automatic recovery mechanism.

[0077] The edge buffer of the edge layer module (510) may include a storage manager and a queue manager. In this case, the storage manager performs local storage allocation and management, can execute data retention policies, and can be responsible for storage optimization. Additionally, the queue manager performs priority-based queue operations, can manage memory-disk caching, and can be responsible for data transfer scheduling.

[0078] The cloud layer module (520) may include a data lake, an analysis platform, etc.

[0079] The data lake of the cloud layer module (520) can provide a central repository that integrates and stores data from all sources. It can perform data catalog and metadata management, is responsible for data version management and history tracking, and can provide large-scale data processing and search functions.

[0080] The data lake of the cloud layer module (520) may include a catalog manager and a storage optimizer. In this case, the catalog manager performs metadata indexing, can manage the data classification system, and can provide search functions. Additionally, the storage optimizer performs data partitioning, is responsible for storage efficiency, and can manage backup and recovery.

[0081] The analysis platform of the cloud layer module (520) can perform advanced analysis and derive insights on collected data. It can support real-time / batch hybrid processing, provide a machine learning model operating environment, and provide a visualization function for analysis results.

[0082] The analysis platform of the cloud layer module (520) may include an analysis engine and a visualization engine. In this case, the analysis engine performs statistical analysis, can execute pattern recognition, and can operate a prediction model. Additionally, the visualization engine is responsible for creating dashboards, can generate charts and graphs, and can provide interactive visualizations.

[0083] The integrated layer module (530) may include a real-time synchronization engine.

[0084] The real-time synchronization engine of the integrated layer module (530) can ensure data synchronization and consistency between the edge and the cloud. It can perform bidirectional data synchronization, handle collision detection and resolution, and implement network optimization.

[0085] The real-time synchronization engine of the integrated layer module (530) may include a synchronization manager and a network optimizer. In this case, the synchronization manager manages synchronization policies, can perform conflict resolution, and can monitor the synchronization status. Additionally, the network optimizer manages bandwidth, can be responsible for transmission efficiency, and can monitor connection stability.

[0086] FIG. 6 illustrates the detailed configuration of an edge layer module (510) in one embodiment of the present invention.

[0087] Referring to FIG. 6, the edge layer module (510) may include a data collection module (601), an edge preprocessing engine (602), an edge buffer (603), and an edge analysis engine (604).

[0088] The data collection module (601) may include a protocol adapter and a data verifier. In this case, the protocol adapter performs various industrial protocol support and conversion functions, is responsible for real-time data stream processing, and can perform connection management and monitoring for each data source. Additionally, the data verifier performs format verification of the collected data, can verify basic data consistency, and can perform initial data filtering.

[0089] The edge preprocessing engine (602) may include a data cleaner, a data compressor, and a noise filter. In this case, the data cleaner performs normalization of raw data, handles missing values ​​and outliers, and can perform preprocessing to improve data quality. Additionally, the data compressor performs compression for efficient data transmission, handles the removal of duplicate data, and can support network bandwidth optimization. Additionally, the noise filter performs noise removal of sensor data, handles signal quality improvement, and can support data reliability improvement.

[0090] The edge buffer (603) may include a storage manager and a queue manager. In this case, the storage manager manages the local data storage and can execute data retention policies and perform storage space optimization. Additionally, the queue manager performs priority-based data processing, is responsible for data transfer scheduling, and can manage buffer overflow prevention.

[0091] The edge analysis engine (604) may include a real-time analyzer, an anomaly detector, and a settings manager. In this case, the real-time analyzer performs immediate data analysis, is responsible for pattern recognition and trend analysis, and can provide real-time decision support. Additionally, the anomaly detector performs real-time anomaly detection, is responsible for threshold-based monitoring, and can support the generation of immediate alerts. Additionally, the settings manager performs analysis parameter management, is responsible for threshold settings, and can manage analysis model settings.

[0092] FIG. 7 illustrates the detailed configuration of a cloud layer module (520) in one embodiment of the present invention.

[0093] Referring to FIG. 7, the cloud layer module (520) may include a data lake module (701), an analysis platform module (702), and a data pipeline module (703).

[0094] The data lake module (701) can perform catalog manager functions, storage optimization functions, data governance functions, etc. At this time, the catalog manager function may include functions for performing systematic management and classification of metadata, functions for defining and managing characteristic information by data source, functions for generating and tracking data quality metadata, and functions for configuring and maintaining data lineage information. Additionally, the storage optimizer function may include functions for performing storage management according to the data lifecycle, functions for executing storage tiering based on data access patterns, functions for removing duplicate data and optimizing compression, and functions for managing storage capacity prediction and expansion. Additionally, the data governance function may include functions for managing data access rights, functions for executing and managing data retention policies, functions for monitoring regulatory compliance, and functions for providing data audit trails.

[0095] The analysis platform module (702) can perform analysis engine functions, ML model manager functions, visualization engine functions, etc. At this time, the analysis engine function may include functions for performing real-time / batch hybrid analysis, functions for executing advanced statistical analysis, functions for processing time series data analysis, and functions for performing predictive modeling. In addition, the ML model manager function may include functions for performing model training and evaluation, functions for managing model versions, functions for executing model performance monitoring, and functions for managing model retraining schedules. In addition, the visualization engine function may include functions for creating real-time dashboards, functions for creating customized reports, functions for providing interactive visualization tools, and functions for supporting multidimensional data visualization.

[0096] The data pipeline module (703) can perform ETL processor functions, data quality management functions, data lineage functions, etc. At this time, the ETL processor function may include functions for performing data extraction and transformation, functions for performing data cleaning and enrichment, functions for optimizing data loading, and functions for providing pipeline monitoring. Additionally, the data quality management function may include functions for performing data validation, functions for measuring quality metrics, functions for detecting quality issues and handling notifications, and functions for automating quality improvement measures. Additionally, the data lineage function may include functions for performing data flow tracking, functions for providing impact analysis, functions for managing change history, and functions for supporting root cause analysis when a problem occurs.

[0097] FIG. 8 illustrates the detailed configuration of an integrated layer module (530) in one embodiment of the present invention.

[0098] Referring to FIG. 8, the integrated layer module (530) may include a real-time synchronization engine (801), a secure communication module (802), and an integrated monitoring module (803).

[0099] The real-time synchronization engine (801) may include a synchronization manager, a collision resolver, a network manager, etc. In this case, the synchronization manager monitors the data synchronization status between the edge and the cloud in real time, manages and applies priority-based synchronization policies, and establishes dynamic synchronization strategies based on network status. Additionally, the collision resolver detects collision situations when simultaneous updates occur, automatically resolves collisions according to predefined rules, and manages and tracks the history of collision resolution. Additionally, the network manager monitors the network status in real time, optimizes and controls bandwidth usage, and executes a recovery process in the event of a network failure.

[0100] The secure communication module (802) may include an authentication manager, an encryption engine, a certificate manager, etc. In this case, the authentication manager performs mutual authentication between systems, can verify and control access rights, and can manage user and system authentication policies. Additionally, the encryption engine performs encryption / decryption of transmitted data, can manage and update encryption keys, and can apply secure communication protocols. Additionally, the certificate manager manages the lifecycle of digital certificates, can handle certificate issuance and revocation, and can verify the validity of certificates.

[0101] The integrated monitoring module (803) may include a metric collector, an alarm manager, a log manager, etc. In this case, the metric collector collects performance indicators across the entire system, can aggregate operational status data, and monitor resource usage status. Additionally, the alarm manager detects abnormal situations and generates alerts, can manage alert priorities, and handle alert delivery and escalation. Additionally, the log manager collects and stores system logs, can analyze logs and detect patterns, and manage logs for audit trails.

[0102] The entire process of the core modules (edge ​​layer module (510), cloud layer module (520), and integration layer module (530), etc.) of the manufacturing robot labor time monitoring system according to the present invention is summarized as shown in FIG. 9.

[0103] Figures 10 and 11 illustrate examples of processes applying a manufacturing robot labor time monitoring system.

[0104] Referring to FIG. 10, the manufacturing robot labor time monitoring system according to the present invention can perform production planning linkage processes, real-time work monitoring processes, tax data processing processes, abnormal situation processing processes, performance analysis and reporting processes, system synchronization processes, feedback and optimization processes, etc. through linkage with an enterprise's ERP (Enterprise Resource Planning), MES (Manufacturing Execution System), robot controller, tax system, monitoring system, etc.

[0105] Referring to FIG. 11, the manufacturing robot labor time monitoring system according to the present invention can perform a process of cross-verifying robot operations in the enterprise's ERP and MES (plan data verification step, real-time operation verification step, cross-check step, anomaly detection and response step, etc.).

[0106] As a specific example of robot work data, the work data example for an automobile door welding robot is shown in Table 1.

[0107] Planning Data (ERP) Job ID: WLD-2024-0123 Production Item: Front Left Door Daily Planned Quantity: 500 units Planned Work Time: 16 hours (2 shifts) Standard CT (Cycle Time): 115 seconds / unit Planned Utilization Rate: 95% Execution Data (MES) Actual CT: 112 seconds / unit Actual Production: 485 units Actual Operating Time: 15.2 hours Stoppages: 3 times Welding Quality Yield Rate: 98.2% Robot Detail Data json{"robot_id": "WR-052","timestamp": "2024-10-26 14:30:25.123","operation_data": {"joint_angles": [{"J1": 45.3, "torque": 2.1},{"J2": 90.1, "torque": 1.8},{"J3": 30.5, "torque": 2.3}],"welding_parameters": {"current": 180.5,"voltage": 22.3,"speed": 12.5},"temperature": {"motor1": 48.2,"motor2": 52.1,"motor3": 47.8}},"quality_metrics": {"weld_penetration": 2.8,"weld_width": 4.2,"defect_count": 0}}

[0108] An example of work data for a PCB assembly robot is shown in Table 2.

[0109] Planning Data (ERP) Job ID: SMT-2024-0456 Production Item: Mainboard Type A Daily Planned Quantity: 2,000 units Planned Work Time: 24 hours (3 shifts) Standard CT: 42 seconds / unit Planned Utilization Rate: 98% Execution Data (MES) Actual CT: 40.5 seconds / unit Actual Production Volume: 2,015 units Actual Operating Time: 23.5 hours Component Mounting Accuracy: 99.99% Number of Defects: 3 Robot Detail Data json{"robot_id": "SMT-123","timestamp": "2024-10-26 15:45:12.456","operation_data": {"pick_place_data": {"pickup_position": {"x": 156.234, "y": 89.567, "z": 2.123},"place_position": {"x": 234.567, "y": 178.901, "z": 0.789},"component_type": "IC-BGA-256","placement_force": 2.3,"alignment_offset": 0.02},"vision_data": {"recognition_score": 0.998,"alignment_accuracy": 0.015,"inspection_result": "PASS"}},"performance_metrics": {"cycle_time_ms": 40500,"accuracy_mm": 0.01,"vacuum_pressure": -0.8}}

[0110] An example of work data for a logistics warehouse picking robot is shown in Table 3.

[0111] Planning Data (ERP) Job ID: WHS-2024-0789 Job Type: Order Picking Daily Planned Throughput: 1,200 boxes Planned Work Time: 8 hours Standard CT: 24 seconds / box Planned Travel Distance: 15 km Execution Data (MES) Actual CT: 22.8 seconds / box Actual Throughput: 1,235 boxes Actual Uptime: 7.8 hours Actual Travel Distance: 14.2 km Picking Accuracy: 99.8% Robot Detail Data json{"robot_id": "AGV-789","timestamp": "2024-10-26 16:20:45.789","operation_data": {"navigation": {"current_position": {"x": 23.45, "y": 12.34},"speed": 1.2,"battery_level": 82.5,"path_efficiency": 94.8},"picking_data": {"grip_force": 35.6,"item_weight": 2.8,"success_rate": 99.8,"barcode_read_accuracy": 100}},"environment_data": {"obstacle_count": 2,"path_congestion": "LOW","area_traffic": "MEDIUM"}}

[0112] An example of work data for a semiconductor wafer handling robot is shown in Table 4.

[0113] Planning Data (ERP) Job ID: WFR-2024-1011 Production Item: 12-inch Wafer Daily Planned Quantity: 800 wafers Planned Job Time: 24 hours Standard CT: 108 seconds / wafer Planned Cleanroom Class: Class 1 Execution Data (MES) Actual CT: 106.5 seconds / wafer Actual Production Volume: 805 wafers Actual Uptime: 23.8 hours Cleanroom Maintenance Status: Class 1 Contamination Incidents: 0 Robot Detail Data json{"robot_id": "WHR-456","timestamp": "2024-10-26 17:15:33.234","operation_data": {"handling_parameters": {"acceleration": 0.1,"speed": 0.25,"vacuum_level": -0.95},"position_data": {"pickup": {"accuracy": 0.001,"repeatability": 0.0005,"vibration": 0.0002}},"environment_control": {"particle_count": 0.5,"humidity": 45.2,"temperature": 21.3}},"quality_check": {"wafer_condition": "PERFECT","alignment_error": 0.0008,"vacuum_stability": 99.999}}

[0114] An example of work data for a food packaging robot is shown in Table 5.

[0115] Planning Data (ERP) Job ID: PKG-2024-1314 Production Item: Ready-to-eat Food A Daily Planned Quantity: 15,000 units Planned Work Time: 16 hours Standard CT: 3.8 seconds / unit Planned Packaging Accuracy: 99.9% Execution Data (MES) Actual CT: 3.75 seconds / unit Actual Production Volume: 15,200 units Actual Uptime: 15.8 hours Packaging Quality Acceptance Rate: 99.95% Packaging Loss Rate: 0.15% Robot Detail Data json{"robot_id": "PKR-789","timestamp": "2024-10-26 18:30:15.567","operation_data": {"packaging_parameters": {"sealing_temperature": 165.5,"sealing_pressure": 2.8,"sealing_time": 0.35},"quality_inspection": {"weight_check": {"target": 250.0,"actual": 249.8,"tolerance": ±0.5},"seal_integrity": {"strength": 98.5,"uniformity": 99.2}},"environmental_monitoring": {"zone_temperature": 18.5,"humidity": 55.0,"air_quality": "NORMAL"}},"production_metrics": {"throughput": 960,"defect_rate": 0.05,"material_efficiency": 99.85}}

[0116] Accordingly, according to embodiments of the present invention, data accuracy can be guaranteed through an edge-cloud hybrid structure in terms of real-time accuracy and reliability. Real-time data verification at the edge can be performed, double verification through deep analysis in the cloud can be carried out, and a systematic process for ensuring data integrity can be established. Furthermore, by supporting a data loss prevention and recovery mechanism, data preservation through edge buffering is possible in the event of network disconnection, automatic recovery and re-synchronization functions can be provided, and continuous data collection can be guaranteed even in the event of a failure. Additionally, by supporting transparent tax data management, accurate measurement and verification of robot work time can be achieved, a security system to prevent data falsification can be established, and the traceability of tax-related data can be guaranteed. According to embodiments of the present invention, network load distribution through edge computing can be achieved through resource management optimized in terms of system efficiency and scalability, storage space can be optimized through data compression and filtering, and efficient computing resource allocation is possible. Furthermore, due to its flexible scalability and modular structure, it is easy to add new functions, enables the integration of various robot systems and sensors, and supports the flexible expansion of processing capacity. Additionally, by providing standardized interfaces, it is possible to support various industry standard protocols, achieve seamless integration with existing systems, and enable flexible API-based interoperability. According to embodiments of the present invention, in terms of operational efficiency and usability, intuitive monitoring using a real-time dashboard is possible through integrated monitoring and management, a centralized management system can be provided, and an automated notification and reporting system can be established. Moreover, for predictive maintenance, AI / ML-based anomaly detection is possible, proactive maintenance plans can be established, and equipment lifespan and performance optimization can be realized.In addition, intuitive dashboards and reporting can be provided as a user-friendly interface, customizable screen configurations are possible, and support for various access devices can be achieved.

[0117] According to embodiments of the present invention, encryption-based data security can be guaranteed through an end-to-end security system in terms of security and stability, user authentication and authorization management can be systematized, and security auditing and logging can be automated. Furthermore, a failure response and recovery system can be established through system stability effects, availability can be guaranteed through a redundancy structure, and continuous performance monitoring can be performed. In addition, compliance with relevant laws and regulations can be ensured through regulatory compliance, data protection policies can be applied, and audit traceability can be secured.

[0118] According to embodiments of the present invention, operating costs can be reduced through the efficient use of resources via cost optimization in terms of economic feasibility and investment efficiency; failure costs can be reduced through preventive maintenance; and labor costs can be reduced through automated management. Furthermore, investment risk can be minimized through phased implementation due to the return on investment effect; investment efficiency can be increased by utilizing existing systems; and early effects can be generated through rapid system stabilization. Additionally, a structure that facilitates the introduction of new technologies based on future scalability can be provided, enabling not only flexible response to business changes but also continuous value creation.

[0119] The manufacturing robot labor time monitoring technology according to the present invention can be applied to various manufacturing industrial fields.

[0120] In the automotive manufacturing industry, the working time of welding robots on the body assembly line can be monitored and analyzed in real time. In addition, optimal operational plans can be derived by analyzing robot work patterns in the painting process, and robot utilization in the parts assembly process can be measured and tax base data can be calculated.

[0121] Accordingly, the overall robot operation efficiency of the production line can be improved, tax-related administrative costs can be reduced through accurate measurement of robot working time, and robot downtime can be reduced through predictive maintenance.

[0122] In the electronics manufacturing industry, precision robot operation data from the PCB assembly process can be collected and analyzed. Additionally, the operational status of vision inspection robots in the product inspection process can be monitored, and the operational efficiency of component picking robots can be measured and optimized.

[0123] Accordingly, robot operation accuracy can be improved, leading to a reduction in the defect rate; quality management efficiency can be enhanced through real-time monitoring; and productivity can be increased by optimizing robot operation time.

[0124] In the food manufacturing industry, the operating time of food packaging robots can be precisely measured and managed. Additionally, robot work patterns in the raw material processing process can be analyzed and optimized, and the operational status of washing robots for hygiene management can be monitored.

[0125] Accordingly, compliance with hygiene standards for robot operations can be achieved, packaging process efficiency is improved to increase throughput, and raw material loss rates are reduced, thereby lowering production costs.

[0126] In the semiconductor manufacturing industry, the working time of wafer transport robots can be tracked and analyzed in real time. In addition, precision robot operation data for the inspection process can be collected and monitored, and robot operation efficiency in a cleanroom environment can be measured and optimized.

[0127] Accordingly, robot-based logistics operational efficiency can be improved, the processing capacity of precision inspection processes can be increased, and cleanroom operating costs can be reduced.

[0128] In the logistics automation industry, the status of picking robot operations in logistics centers can be monitored in real time. In addition, the operating patterns of automated loading robots can be analyzed and optimized, and the operational efficiency of AGV (Automated Guided Vehicle) systems can be measured and improved.

[0129] Accordingly, logistics processing speed can be improved to increase throughput, collision accidents can be reduced through robot operation coordination, and operating costs can be reduced by improving energy efficiency.

[0130] The device described above may be implemented as a hardware component, a software component, and / or a combination of a hardware component and a software component. For example, the device and components described in the embodiments may be implemented using one or more general-purpose or special-purpose computers, such as a processor, a controller, an arithmetic logic unit (ALU), a digital signal processor, a microcomputer, a field programmable gate array (FPGA), a programmable logic unit (PLU), a microprocessor, or any other device capable of executing and responding to instructions. The processing unit may execute an operating system (OS) and one or more software applications executed on said operating system. Additionally, the processing unit may access, store, manipulate, process, and generate data in response to the execution of the software. For ease of understanding, the processing unit may be described as being used as a single unit, but those skilled in the art will understand that the processing unit may include multiple processing elements and / or multiple types of processing elements. For example, the processing unit may include multiple processors or one processor and one controller. In addition, other processing configurations, such as parallel processors, are also possible.

[0131] Software may include computer programs, code, instructions, or a combination of one or more of these, and may configure a processing unit to operate as desired or instruct the processing unit independently or collectively. Software and / or data may be embodied in any type of machine, component, physical device, computer storage medium, or device so as to be interpreted by the processing unit or to provide instructions or data to the processing unit. Software may be distributed over networked computer systems and may be stored or executed in a distributed manner. Software and data may be stored on one or more computer-readable recording media.

[0132] The method according to the embodiment may be implemented in the form of program instructions that can be executed through various computer means and recorded on a computer-readable medium. In this case, 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 several hardware 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. Additionally, 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.

[0133] Although the embodiments have been described above with reference to limited examples and drawings, those skilled in the art can make various modifications and variations from the description above. For example, suitable results can be achieved even if the described techniques are performed in a different order than described, and / or the components of the described system, structure, device, circuit, etc. are combined or assembled in a form different from described, or replaced or substituted by other components or equivalents.

[0134] Therefore, other implementations, other embodiments, and equivalents to the claims also fall within the scope of the claims set forth below.

Claims

1. A method for monitoring manufacturing robot labor time performed by a computer device comprising at least one processor, wherein By monitoring the labor time of the robot through an edge-cloud hybrid structure composed of an edge layer that collects work data of the robot within a manufacturing environment and a cloud layer that analyzes the said work data, An edge layer step that collects real-time work data from the robot through an interface specific to industrial standard communication protocols and performs protocol conversion and normalization on the collected data; A cloud layer step that integrates and stores data transmitted from the edge layer step and calculates labor time for each operating state of the robot through pattern recognition based on time-series data analysis; and A real-time synchronization step that performs bidirectional data synchronization between the edge layer and the cloud layer, detects conflict situations resulting from simultaneous updates, and resolves conflicts according to predefined rules. A manufacturing robot labor time monitoring method including 2. In Paragraph 1, The above edge layer step is, A step of collecting real-time data related to the robot from multiple data sources and performing validation of the collected data A manufacturing robot labor time monitoring method including 3. In Paragraph 2, The steps performed above are, A step of performing format verification of collected data and filtering of abnormal data in relation to the above robot A manufacturing robot labor time monitoring method including 4. In Paragraph 1, The above edge layer step is, A step of preprocessing and refining raw data collected in relation to the above robot into an analyzable form. A manufacturing robot labor time monitoring method including 5. In Paragraph 4, The above purification step is, A step of performing noise removal and outlier correction, missing value processing and interpolation, and data format standardization of the above raw data; and Step of performing time series data compression and duplicate data removal of the above raw data A manufacturing robot labor time monitoring method including 6. In Paragraph 1, The above edge layer step is, Step of temporarily storing the above-mentioned work data in an edge buffer through priority-based queue operation and transmission scheduling. A manufacturing robot labor time monitoring method including 7. In Paragraph 1, The above cloud layer step is, The step of integrating and storing data from all sources through a central repository for management. A manufacturing robot labor time monitoring method including 8. In Paragraph 7, The above-mentioned management step is, A step of managing a data classification system by performing metadata indexing; and Steps to perform storage management based on the data lifecycle and storage tiering based on data access patterns A manufacturing robot labor time monitoring method including 9. In Paragraph 1, The above cloud layer step is, A step of visualizing analysis results by deriving insights from data collected in relation to the above-mentioned robot. A manufacturing robot labor time monitoring method including 10. In Paragraph 9, The above visualization step is, A step of performing statistical analysis and pattern recognition of data collected in relation to the above robot; and Step of creating a dashboard for the data analysis results based on the above statistical analysis and the above pattern recognition A manufacturing robot labor time monitoring method including 11. In Paragraph 1, The above real-time synchronization step is, A step of monitoring the synchronization status between the edge layer and the cloud layer to detect a conflict situation resulting from the occurrence of simultaneous updates A manufacturing robot labor time monitoring method including 12. In Paragraph 1, The above real-time synchronization step is, A step of performing mutual authentication between the edge layer and the cloud layer; A step of encrypting and decrypting transmission data between the edge layer and the cloud layer; and A step of verifying the validity of the certificate between the edge layer and the cloud layer. A manufacturing robot labor time monitoring method including 13. At least one processor implemented to execute readable instructions on a computer device Includes, The above at least one processor is, By monitoring the labor time of the robot through an edge-cloud hybrid structure composed of an edge layer that collects work data of the robot within a manufacturing environment and a cloud layer that analyzes the said work data, An edge layer process that collects real-time work data from the robot through an interface for industrial standard communication protocols and performs protocol conversion and normalization on the collected data; A cloud layer process that integrates and stores data transmitted from the edge layer stage and calculates labor time for each operating state of the robot through pattern recognition based on time-series data analysis; and A real-time synchronization process that performs bidirectional data synchronization between the edge layer and the cloud layer, detects conflict situations resulting from simultaneous updates, and resolves conflicts according to predefined rules. A computer device that processes.

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